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import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'files', [
['full:README.md', 'dataset_infos.json'],
['empty:README.md', 'dataset_infos.json'],
['dataset_infos.json'],
['full:README.md'],
], )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =tmp_path_factory.mktemp('dset_infos_dir' )
if "full:README.md" in files:
with open(dataset_infos_dir / 'README.md', 'w' ) as f:
f.write('---\ndataset_info:\n dataset_size: 42\n---' )
if "empty:README.md" in files:
with open(dataset_infos_dir / 'README.md', 'w' ) as f:
f.write('' )
# we want to support dataset_infos.json for backward compatibility
if "dataset_infos.json" in files:
with open(dataset_infos_dir / 'dataset_infos.json', 'w' ) as f:
f.write('{"default": {"dataset_size": 42}}' )
SCREAMING_SNAKE_CASE =DatasetInfosDict.from_directory(lowerCAmelCase_ )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 42
@pytest.mark.parametrize(
'dataset_info', [
DatasetInfo(),
DatasetInfo(
description='foo', features=Features({'a': Value('int32' )} ), builder_name='builder', config_name='config', version='1.0.0', splits=[{'name': 'train'}], download_size=42, ),
], )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =str(lowerCAmelCase_ )
dataset_info.write_to_directory(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =DatasetInfo.from_directory(lowerCAmelCase_ )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(lowerCAmelCase_, 'dataset_info.json' ) )
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =DatasetInfo(
description='foo', citation='bar', homepage='https://foo.bar', license='CC0', features=Features({'a': Value('int32' )} ), post_processed={}, supervised_keys=(), task_templates=[], builder_name='builder', config_name='config', version='1.0.0', splits=[{'name': 'train', 'num_examples': 42}], download_checksums={}, download_size=1337, post_processing_size=442, dataset_size=1234, size_in_bytes=1337 + 442 + 1234, )
SCREAMING_SNAKE_CASE =dataset_info._to_yaml_dict()
assert sorted(lowerCAmelCase_ ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
assert key in dataset_info_yaml_dict
assert isinstance(dataset_info_yaml_dict[key], (list, dict, int, str) )
SCREAMING_SNAKE_CASE =yaml.safe_dump(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =yaml.safe_load(lowerCAmelCase_ )
assert dataset_info_yaml_dict == reloaded
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =DatasetInfo()
SCREAMING_SNAKE_CASE =dataset_info._to_yaml_dict()
assert dataset_info_yaml_dict == {}
@pytest.mark.parametrize(
'dataset_infos_dict', [
DatasetInfosDict(),
DatasetInfosDict({'default': DatasetInfo()} ),
DatasetInfosDict({'my_config_name': DatasetInfo()} ),
DatasetInfosDict(
{
'default': DatasetInfo(
description='foo', features=Features({'a': Value('int32' )} ), builder_name='builder', config_name='config', version='1.0.0', splits=[{'name': 'train'}], download_size=42, )
} ),
DatasetInfosDict(
{
'v1': DatasetInfo(dataset_size=42 ),
'v2': DatasetInfo(dataset_size=1337 ),
} ),
], )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =str(lowerCAmelCase_ )
dataset_infos_dict.write_to_directory(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =DatasetInfosDict.from_directory(lowerCAmelCase_ )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
SCREAMING_SNAKE_CASE =config_name
# the yaml representation doesn't include fields like description or citation
# so we just test that we can recover what we can from the yaml
SCREAMING_SNAKE_CASE =DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
assert dataset_infos_dict == reloaded
if dataset_infos_dict:
assert os.path.exists(os.path.join(lowerCAmelCase_, 'README.md' ) )
| 334 |
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError
from .utils import require_pil
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([1, 2, 3] ) )
self.assertEqual(arr.type ,pa.intaa() )
def _lowerCAmelCase ( self : Any ):
with self.assertRaises(snake_case ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([1, 2, 3] ) ,type=pa.intaa() )
def _lowerCAmelCase ( self : Union[str, Any] ):
with self.assertRaises(snake_case ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([1, 2, 3] ,try_type=Value('bool' ) ,type=Value('int64' ) ) )
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([1, 2, 3] ,type=Value('int32' ) ) )
self.assertEqual(arr.type ,pa.intaa() )
def _lowerCAmelCase ( self : int ):
with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence(['foo', 'bar'] ,type=Value('int64' ) ) )
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([1, 2, 3] ,try_type=Value('int32' ) ) )
self.assertEqual(arr.type ,pa.intaa() )
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence(['foo', 'bar'] ,try_type=Value('int64' ) ) )
self.assertEqual(arr.type ,pa.string() )
def _lowerCAmelCase ( self : Dict ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([[[1, 2, 3]]] ,type=ArrayaD((1, 3) ,'int64' ) ) )
self.assertEqual(arr.type ,ArrayaDExtensionType((1, 3) ,'int64' ) )
def _lowerCAmelCase ( self : Dict ):
with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence(['foo', 'bar'] ,type=ArrayaD((1, 3) ,'int64' ) ) )
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([[[1, 2, 3]]] ,try_type=ArrayaD((1, 3) ,'int64' ) ) )
self.assertEqual(arr.type ,ArrayaDExtensionType((1, 3) ,'int64' ) )
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence(['foo', 'bar'] ,try_type=ArrayaD((1, 3) ,'int64' ) ) )
self.assertEqual(arr.type ,pa.string() )
@require_pil
def _lowerCAmelCase ( self : int ):
import PIL.Image
SCREAMING_SNAKE_CASE =PIL.Image.fromarray(np.arange(10 ,dtype=np.uinta ).reshape(2 ,5 ) )
with patch(
'datasets.arrow_writer.cast_to_python_objects' ,side_effect=snake_case ) as mock_cast_to_python_objects:
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([{'path': None, 'bytes': B'image_bytes'}, pil_image] ,type=Image() ) )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =mock_cast_to_python_objects.call_args_list[-1]
self.assertIn('optimize_list_casting' ,snake_case )
self.assertFalse(kwargs['optimize_list_casting'] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferReader(lowerCAmelCase_ ) if isinstance(lowerCAmelCase_, pa.Buffer ) else pa.memory_map(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =pa.ipc.open_stream(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =f.read_all()
assert len(pa_table.to_batches() ) == expected_num_chunks
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
del pa_table
@pytest.mark.parametrize('writer_batch_size', [None, 1, 10] )
@pytest.mark.parametrize(
'fields', [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
SCREAMING_SNAKE_CASE =pa.schema(lowerCAmelCase_ ) if fields else None
with ArrowWriter(stream=lowerCAmelCase_, schema=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_ ) as writer:
writer.write({'col_1': 'foo', 'col_2': 1} )
writer.write({'col_1': 'bar', 'col_2': 2} )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
SCREAMING_SNAKE_CASE ={'col_1': pa.string(), 'col_2': pa.intaa()}
assert writer._schema == pa.schema(lowerCAmelCase_, metadata=writer._schema.metadata )
_check_output(output.getvalue(), expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
SCREAMING_SNAKE_CASE =Features({'labels': ClassLabel(names=['neg', 'pos'] )} )
with ArrowWriter(stream=lowerCAmelCase_, features=lowerCAmelCase_ ) as writer:
writer.write({'labels': 0} )
writer.write({'labels': 1} )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert writer._schema == features.arrow_schema
assert writer._schema.metadata == features.arrow_schema.metadata
SCREAMING_SNAKE_CASE =pa.BufferReader(output.getvalue() )
SCREAMING_SNAKE_CASE =pa.ipc.open_stream(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =f.read_all()
SCREAMING_SNAKE_CASE =pa_table.schema
assert pa_table.num_rows == 2
assert schema == features.arrow_schema
assert schema.metadata == features.arrow_schema.metadata
assert features == Features.from_arrow_schema(lowerCAmelCase_ )
@pytest.mark.parametrize('writer_batch_size', [None, 1, 10] )
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
with ArrowWriter(
stream=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_, hash_salt='split_name', check_duplicates=lowerCAmelCase_, ) as writer:
with pytest.raises(lowerCAmelCase_ ):
writer.write({'col_1': 'foo', 'col_2': 1}, key=[1, 2] )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
@pytest.mark.parametrize('writer_batch_size', [None, 2, 10] )
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
with ArrowWriter(
stream=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_, hash_salt='split_name', check_duplicates=lowerCAmelCase_, ) as writer:
with pytest.raises(lowerCAmelCase_ ):
writer.write({'col_1': 'foo', 'col_2': 1}, key=10 )
writer.write({'col_1': 'bar', 'col_2': 2}, key=10 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
@pytest.mark.parametrize('writer_batch_size', [None, 2, 10] )
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
with ArrowWriter(
stream=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_, hash_salt='split_name', check_duplicates=lowerCAmelCase_, ) as writer:
writer.write({'col_1': 'foo', 'col_2': 1}, key=1 )
writer.write({'col_1': 'bar', 'col_2': 2}, key=2 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
_check_output(output.getvalue(), expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize('writer_batch_size', [None, 1, 10] )
@pytest.mark.parametrize(
'fields', [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
SCREAMING_SNAKE_CASE =pa.schema(lowerCAmelCase_ ) if fields else None
with ArrowWriter(stream=lowerCAmelCase_, schema=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_ ) as writer:
writer.write_batch({'col_1': ['foo', 'bar'], 'col_2': [1, 2]} )
writer.write_batch({'col_1': [], 'col_2': []} )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
SCREAMING_SNAKE_CASE ={'col_1': pa.string(), 'col_2': pa.intaa()}
assert writer._schema == pa.schema(lowerCAmelCase_, metadata=writer._schema.metadata )
_check_output(output.getvalue(), expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize('writer_batch_size', [None, 1, 10] )
@pytest.mark.parametrize(
'fields', [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
SCREAMING_SNAKE_CASE =pa.schema(lowerCAmelCase_ ) if fields else None
with ArrowWriter(stream=lowerCAmelCase_, schema=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_ ) as writer:
writer.write_table(pa.Table.from_pydict({'col_1': ['foo', 'bar'], 'col_2': [1, 2]} ) )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
SCREAMING_SNAKE_CASE ={'col_1': pa.string(), 'col_2': pa.intaa()}
assert writer._schema == pa.schema(lowerCAmelCase_, metadata=writer._schema.metadata )
_check_output(output.getvalue(), expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize('writer_batch_size', [None, 1, 10] )
@pytest.mark.parametrize(
'fields', [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
SCREAMING_SNAKE_CASE =pa.schema(lowerCAmelCase_ ) if fields else None
with ArrowWriter(stream=lowerCAmelCase_, schema=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_ ) as writer:
writer.write_row(pa.Table.from_pydict({'col_1': ['foo'], 'col_2': [1]} ) )
writer.write_row(pa.Table.from_pydict({'col_1': ['bar'], 'col_2': [2]} ) )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
SCREAMING_SNAKE_CASE ={'col_1': pa.string(), 'col_2': pa.intaa()}
assert writer._schema == pa.schema(lowerCAmelCase_, metadata=writer._schema.metadata )
_check_output(output.getvalue(), expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
def snake_case__ ( ):
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE ={'col_1': pa.string(), 'col_2': pa.intaa()}
SCREAMING_SNAKE_CASE =os.path.join(lowerCAmelCase_, 'test.arrow' )
with ArrowWriter(path=lowerCAmelCase_, schema=pa.schema(lowerCAmelCase_ ) ) as writer:
writer.write_batch({'col_1': ['foo', 'bar'], 'col_2': [1, 2]} )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert writer._schema == pa.schema(lowerCAmelCase_, metadata=writer._schema.metadata )
_check_output(lowerCAmelCase_, 1 )
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
if pa.types.is_list(lowerCAmelCase_ ):
return get_base_dtype(arr_type.value_type )
else:
return arr_type
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
if isinstance(lst[0], lowerCAmelCase_ ):
change_first_primitive_element_in_list(lst[0], lowerCAmelCase_ )
else:
SCREAMING_SNAKE_CASE =value
@pytest.mark.parametrize('optimized_int_type, expected_dtype', [(None, pa.intaa()), (Value('int32' ), pa.intaa())] )
@pytest.mark.parametrize('sequence', [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.array(TypedSequence(lowerCAmelCase_, optimized_int_type=lowerCAmelCase_ ) )
assert get_base_dtype(arr.type ) == expected_dtype
@pytest.mark.parametrize(
'col, expected_dtype', [
('attention_mask', pa.inta()),
('special_tokens_mask', pa.inta()),
('token_type_ids', pa.inta()),
('input_ids', pa.intaa()),
('other', pa.intaa()),
], )
@pytest.mark.parametrize('sequence', [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.array(OptimizedTypedSequence(lowerCAmelCase_, col=lowerCAmelCase_ ) )
assert get_base_dtype(arr.type ) == expected_dtype
# not in range
if col != "other":
# avoids errors due to in-place modifications
SCREAMING_SNAKE_CASE =copy.deepcopy(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =np.iinfo(expected_dtype.to_pandas_dtype() ).max + 1
change_first_primitive_element_in_list(lowerCAmelCase_, lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =pa.array(OptimizedTypedSequence(lowerCAmelCase_, col=lowerCAmelCase_ ) )
assert get_base_dtype(arr.type ) == pa.intaa()
@pytest.mark.parametrize('raise_exception', [False, True] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =str(tmp_path / 'dataset-train.arrow' )
try:
with ArrowWriter(path=lowerCAmelCase_ ) as writer:
if raise_exception:
raise pa.lib.ArrowInvalid()
else:
writer.stream.close()
except pa.lib.ArrowInvalid:
pass
finally:
assert writer.stream.closed
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ='mock://dataset-train.arrow'
with ArrowWriter(path=lowerCAmelCase_, storage_options=mockfs.storage_options ) as writer:
assert isinstance(writer._fs, type(lowerCAmelCase_ ) )
assert writer._fs.storage_options == mockfs.storage_options
writer.write({'col_1': 'foo', 'col_2': 1} )
writer.write({'col_1': 'bar', 'col_2': 2} )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert mockfs.exists(lowerCAmelCase_ )
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
with ParquetWriter(stream=lowerCAmelCase_ ) as writer:
writer.write({'col_1': 'foo', 'col_2': 1} )
writer.write({'col_1': 'bar', 'col_2': 2} )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
SCREAMING_SNAKE_CASE =pa.BufferReader(output.getvalue() )
SCREAMING_SNAKE_CASE =pq.read_table(lowerCAmelCase_ )
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
@require_pil
@pytest.mark.parametrize('embed_local_files', [False, True] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
import PIL.Image
SCREAMING_SNAKE_CASE =str(tmp_path / 'test_image_rgb.jpg' )
PIL.Image.fromarray(np.zeros((5, 5), dtype=np.uinta ) ).save(lowerCAmelCase_, format='png' )
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
with ParquetWriter(
stream=lowerCAmelCase_, features=Features({'image': Image()} ), embed_local_files=lowerCAmelCase_ ) as writer:
writer.write({'image': image_path} )
writer.finalize()
SCREAMING_SNAKE_CASE =pa.BufferReader(output.getvalue() )
SCREAMING_SNAKE_CASE =pq.read_table(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =pa_table.to_pydict()
if embed_local_files:
assert isinstance(out['image'][0]['path'], lowerCAmelCase_ )
with open(lowerCAmelCase_, 'rb' ) as f:
assert out["image"][0]["bytes"] == f.read()
else:
assert out["image"][0]["path"] == image_path
assert out["image"][0]["bytes"] is None
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.schema([pa.field('col_1', pa.string(), nullable=lowerCAmelCase_ )] )
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
with ArrowWriter(stream=lowerCAmelCase_ ) as writer:
writer._build_writer(inferred_schema=lowerCAmelCase_ )
assert writer._schema == pa.schema([pa.field('col_1', pa.string() )] )
| 334 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase ={
"configuration_trajectory_transformer": [
"TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TrajectoryTransformerConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase =[
"TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"TrajectoryTransformerModel",
"TrajectoryTransformerPreTrainedModel",
"load_tf_weights_in_trajectory_transformer",
]
if TYPE_CHECKING:
from .configuration_trajectory_transformer import (
TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
TrajectoryTransformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trajectory_transformer import (
TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TrajectoryTransformerModel,
TrajectoryTransformerPreTrainedModel,
load_tf_weights_in_trajectory_transformer,
)
else:
import sys
_lowerCamelCase =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 334 |
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def snake_case__ ( ):
"""simple docstring"""
assert or_gate(0, 0 ) == 0
assert or_gate(0, 1 ) == 1
assert or_gate(1, 0 ) == 1
assert or_gate(1, 1 ) == 1
if __name__ == "__main__":
print(or_gate(0, 1))
print(or_gate(1, 0))
print(or_gate(0, 0))
print(or_gate(1, 1))
| 334 | 1 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir
sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
_lowerCamelCase =get_tests_dir("fixtures")
_lowerCamelCase =get_tests_dir("fixtures/dummy_feature_extractor_config.json")
_lowerCamelCase =get_tests_dir("fixtures/dummy-config.json")
class a_ ( unittest.TestCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE =0
def _lowerCAmelCase ( self : str ):
SCREAMING_SNAKE_CASE =AutoFeatureExtractor.from_pretrained('facebook/wav2vec2-base-960h' )
self.assertIsInstance(snake_case ,snake_case )
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =AutoFeatureExtractor.from_pretrained(snake_case )
self.assertIsInstance(snake_case ,snake_case )
def _lowerCAmelCase ( self : Optional[int] ):
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE =WavaVecaConfig()
# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
SCREAMING_SNAKE_CASE =AutoFeatureExtractor.from_pretrained(snake_case ).to_dict()
config_dict.pop('feature_extractor_type' )
SCREAMING_SNAKE_CASE =WavaVecaFeatureExtractor(**snake_case )
# save in new folder
model_config.save_pretrained(snake_case )
config.save_pretrained(snake_case )
SCREAMING_SNAKE_CASE =AutoFeatureExtractor.from_pretrained(snake_case )
# make sure private variable is not incorrectly saved
SCREAMING_SNAKE_CASE =json.loads(config.to_json_string() )
self.assertTrue('_processor_class' not in dict_as_saved )
self.assertIsInstance(snake_case ,snake_case )
def _lowerCAmelCase ( self : Dict ):
SCREAMING_SNAKE_CASE =AutoFeatureExtractor.from_pretrained(snake_case )
self.assertIsInstance(snake_case ,snake_case )
def _lowerCAmelCase ( self : Any ):
with self.assertRaisesRegex(
snake_case ,'bert-base is not a local folder and is not a valid model identifier' ):
SCREAMING_SNAKE_CASE =AutoFeatureExtractor.from_pretrained('bert-base' )
def _lowerCAmelCase ( self : int ):
with self.assertRaisesRegex(
snake_case ,r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)' ):
SCREAMING_SNAKE_CASE =AutoFeatureExtractor.from_pretrained(snake_case ,revision='aaaaaa' )
def _lowerCAmelCase ( self : Dict ):
with self.assertRaisesRegex(
snake_case ,'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.' ,):
SCREAMING_SNAKE_CASE =AutoFeatureExtractor.from_pretrained('hf-internal-testing/config-no-model' )
def _lowerCAmelCase ( self : int ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(snake_case ):
SCREAMING_SNAKE_CASE =AutoFeatureExtractor.from_pretrained(
'hf-internal-testing/test_dynamic_feature_extractor' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(snake_case ):
SCREAMING_SNAKE_CASE =AutoFeatureExtractor.from_pretrained(
'hf-internal-testing/test_dynamic_feature_extractor' ,trust_remote_code=snake_case )
SCREAMING_SNAKE_CASE =AutoFeatureExtractor.from_pretrained(
'hf-internal-testing/test_dynamic_feature_extractor' ,trust_remote_code=snake_case )
self.assertEqual(feature_extractor.__class__.__name__ ,'NewFeatureExtractor' )
# Test feature extractor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(snake_case )
SCREAMING_SNAKE_CASE =AutoFeatureExtractor.from_pretrained(snake_case ,trust_remote_code=snake_case )
self.assertEqual(reloaded_feature_extractor.__class__.__name__ ,'NewFeatureExtractor' )
def _lowerCAmelCase ( self : Union[str, Any] ):
try:
AutoConfig.register('custom' ,snake_case )
AutoFeatureExtractor.register(snake_case ,snake_case )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(snake_case ):
AutoFeatureExtractor.register(snake_case ,snake_case )
# Now that the config is registered, it can be used as any other config with the auto-API
SCREAMING_SNAKE_CASE =CustomFeatureExtractor.from_pretrained(snake_case )
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(snake_case )
SCREAMING_SNAKE_CASE =AutoFeatureExtractor.from_pretrained(snake_case )
self.assertIsInstance(snake_case ,snake_case )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
def _lowerCAmelCase ( self : Tuple ):
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = True
try:
AutoConfig.register('custom' ,snake_case )
AutoFeatureExtractor.register(snake_case ,snake_case )
# If remote code is not set, the default is to use local
SCREAMING_SNAKE_CASE =AutoFeatureExtractor.from_pretrained(
'hf-internal-testing/test_dynamic_feature_extractor' )
self.assertEqual(feature_extractor.__class__.__name__ ,'NewFeatureExtractor' )
self.assertTrue(feature_extractor.is_local )
# If remote code is disabled, we load the local one.
SCREAMING_SNAKE_CASE =AutoFeatureExtractor.from_pretrained(
'hf-internal-testing/test_dynamic_feature_extractor' ,trust_remote_code=snake_case )
self.assertEqual(feature_extractor.__class__.__name__ ,'NewFeatureExtractor' )
self.assertTrue(feature_extractor.is_local )
# If remote is enabled, we load from the Hub
SCREAMING_SNAKE_CASE =AutoFeatureExtractor.from_pretrained(
'hf-internal-testing/test_dynamic_feature_extractor' ,trust_remote_code=snake_case )
self.assertEqual(feature_extractor.__class__.__name__ ,'NewFeatureExtractor' )
self.assertTrue(not hasattr(snake_case ,'is_local' ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
| 334 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={"vocab_file": "vocab.txt"}
_lowerCamelCase ={
"vocab_file": {
"facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt",
"facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt",
},
}
_lowerCamelCase ={
"facebook/esm2_t6_8M_UR50D": 10_24,
"facebook/esm2_t12_35M_UR50D": 10_24,
}
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
with open(lowerCAmelCase_, 'r' ) as f:
SCREAMING_SNAKE_CASE =f.read().splitlines()
return [l.strip() for l in lines]
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = VOCAB_FILES_NAMES
__UpperCAmelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase = ['input_ids', 'attention_mask']
def __init__( self : int ,snake_case : Dict ,snake_case : Dict="<unk>" ,snake_case : Optional[int]="<cls>" ,snake_case : Optional[int]="<pad>" ,snake_case : int="<mask>" ,snake_case : Optional[int]="<eos>" ,**snake_case : List[str] ,):
super().__init__(**snake_case )
SCREAMING_SNAKE_CASE =load_vocab_file(snake_case )
SCREAMING_SNAKE_CASE =dict(enumerate(self.all_tokens ) )
SCREAMING_SNAKE_CASE ={tok: ind for ind, tok in enumerate(self.all_tokens )}
SCREAMING_SNAKE_CASE =unk_token
SCREAMING_SNAKE_CASE =cls_token
SCREAMING_SNAKE_CASE =pad_token
SCREAMING_SNAKE_CASE =mask_token
SCREAMING_SNAKE_CASE =eos_token
SCREAMING_SNAKE_CASE =self.all_tokens
self._create_trie(self.unique_no_split_tokens )
def _lowerCAmelCase ( self : Optional[Any] ,snake_case : int ):
return self._id_to_token.get(snake_case ,self.unk_token )
def _lowerCAmelCase ( self : Dict ,snake_case : str ):
return self._token_to_id.get(snake_case ,self._token_to_id.get(self.unk_token ) )
def _lowerCAmelCase ( self : Tuple ,snake_case : List[str] ,**snake_case : Any ):
return text.split()
def _lowerCAmelCase ( self : Optional[int] ,snake_case : str=False ):
return len(self._id_to_token )
def _lowerCAmelCase ( self : List[str] ):
return {token: i for i, token in enumerate(self.all_tokens )}
def _lowerCAmelCase ( self : List[Any] ,snake_case : str ):
return self._token_to_id.get(snake_case ,self._token_to_id.get(self.unk_token ) )
def _lowerCAmelCase ( self : Any ,snake_case : int ):
return self._id_to_token.get(snake_case ,self.unk_token )
def _lowerCAmelCase ( self : List[str] ,snake_case : List[int] ,snake_case : Optional[List[int]] = None ):
SCREAMING_SNAKE_CASE =[self.cls_token_id]
SCREAMING_SNAKE_CASE =[self.eos_token_id] # No sep token in ESM vocabulary
if token_ids_a is None:
if self.eos_token_id is None:
return cls + token_ids_a
else:
return cls + token_ids_a + sep
elif self.eos_token_id is None:
raise ValueError('Cannot tokenize multiple sequences when EOS token is not set!' )
return cls + token_ids_a + sep + token_ids_a + sep # Multiple inputs always have an EOS token
def _lowerCAmelCase ( self : Optional[int] ,snake_case : List ,snake_case : Optional[List] = None ,snake_case : bool = False ):
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'You should not supply a second sequence if the provided sequence of '
'ids is already formatted with special tokens for the model.' )
return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
SCREAMING_SNAKE_CASE =[1] + ([0] * len(snake_case )) + [1]
if token_ids_a is not None:
mask += [0] * len(snake_case ) + [1]
return mask
def _lowerCAmelCase ( self : Optional[int] ,snake_case : Dict ,snake_case : Any ):
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,(filename_prefix + '-' if filename_prefix else '') + 'vocab.txt' )
with open(snake_case ,'w' ) as f:
f.write('\n'.join(self.all_tokens ) )
return (vocab_file,)
@property
def _lowerCAmelCase ( self : int ):
return self.get_vocab_size(with_added_tokens=snake_case )
def _lowerCAmelCase ( self : str ,snake_case : Union[List[str], List[AddedToken]] ,snake_case : bool = False ):
return super()._add_tokens(snake_case ,special_tokens=snake_case )
| 334 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
_lowerCamelCase ={"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase =["HerbertTokenizerFast"]
if TYPE_CHECKING:
from .tokenization_herbert import HerbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_herbert_fast import HerbertTokenizerFast
else:
import sys
_lowerCamelCase =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 334 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase =logging.get_logger()
@dataclass
class a_ :
"""simple docstring"""
__UpperCAmelCase = 42
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
def _lowerCAmelCase ( self : Any ,snake_case : Any ,snake_case : Tensor ,snake_case : Tensor ):
SCREAMING_SNAKE_CASE =len(list(m.modules() ) ) == 1 or isinstance(snake_case ,nn.Convad ) or isinstance(snake_case ,nn.BatchNormad )
if has_not_submodules:
self.traced.append(snake_case )
def __call__( self : int ,snake_case : Tensor ):
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(snake_case )
[x.remove() for x in self.handles]
return self
@property
def _lowerCAmelCase ( self : Tuple ):
# check the len of the state_dict keys to see if we have learnable params
return list(filter(lambda snake_case : len(list(x.state_dict().keys() ) ) > 0 ,self.traced ) )
@dataclass
class a_ :
"""simple docstring"""
__UpperCAmelCase = 42
__UpperCAmelCase = 42
__UpperCAmelCase = 0
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
def __call__( self : int ,snake_case : Tensor ):
SCREAMING_SNAKE_CASE =Tracker(self.dest )(snake_case ).parametrized
SCREAMING_SNAKE_CASE =Tracker(self.src )(snake_case ).parametrized
SCREAMING_SNAKE_CASE =list(filter(lambda snake_case : type(snake_case ) not in self.src_skip ,snake_case ) )
SCREAMING_SNAKE_CASE =list(filter(lambda snake_case : type(snake_case ) not in self.dest_skip ,snake_case ) )
if len(snake_case ) != len(snake_case ):
raise Exception(
f'Numbers of operations are different. Source module has {len(snake_case )} operations while'
f' destination module has {len(snake_case )}.' )
for dest_m, src_m in zip(snake_case ,snake_case ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(f'Transfered from={src_m} to={dest_m}' )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ = True ):
"""simple docstring"""
print(F'Converting {name}...' )
with torch.no_grad():
SCREAMING_SNAKE_CASE =timm.create_model(lowerCAmelCase_, pretrained=lowerCAmelCase_ ).eval()
SCREAMING_SNAKE_CASE =ResNetForImageClassification(lowerCAmelCase_ ).eval()
SCREAMING_SNAKE_CASE =ModuleTransfer(src=lowerCAmelCase_, dest=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =torch.randn((1, 3, 224, 224) )
module_transfer(lowerCAmelCase_ )
assert torch.allclose(from_model(lowerCAmelCase_ ), our_model(lowerCAmelCase_ ).logits ), "The model logits don't match the original one."
SCREAMING_SNAKE_CASE =F'resnet{"-".join(name.split("resnet" ) )}'
print(lowerCAmelCase_ )
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name, commit_message='Add model', use_temp_dir=lowerCAmelCase_, )
# we can use the convnext one
SCREAMING_SNAKE_CASE =AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' )
image_processor.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name, commit_message='Add image processor', use_temp_dir=lowerCAmelCase_, )
print(F'Pushed {checkpoint_name}' )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ = None, lowerCAmelCase_ = True ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ='imagenet-1k-id2label.json'
SCREAMING_SNAKE_CASE =1000
SCREAMING_SNAKE_CASE =(1, num_labels)
SCREAMING_SNAKE_CASE ='huggingface/label-files'
SCREAMING_SNAKE_CASE =num_labels
SCREAMING_SNAKE_CASE =json.load(open(hf_hub_download(lowerCAmelCase_, lowerCAmelCase_, repo_type='dataset' ), 'r' ) )
SCREAMING_SNAKE_CASE ={int(lowerCAmelCase_ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE =idalabel
SCREAMING_SNAKE_CASE ={v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE =partial(lowerCAmelCase_, num_labels=lowerCAmelCase_, idalabel=lowerCAmelCase_, labelaid=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE ={
'resnet18': ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2], hidden_sizes=[64, 128, 256, 512], layer_type='basic' ),
'resnet26': ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2], hidden_sizes=[256, 512, 1024, 2048], layer_type='bottleneck' ),
'resnet34': ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3], hidden_sizes=[64, 128, 256, 512], layer_type='basic' ),
'resnet50': ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3], hidden_sizes=[256, 512, 1024, 2048], layer_type='bottleneck' ),
'resnet101': ImageNetPreTrainedConfig(
depths=[3, 4, 23, 3], hidden_sizes=[256, 512, 1024, 2048], layer_type='bottleneck' ),
'resnet152': ImageNetPreTrainedConfig(
depths=[3, 8, 36, 3], hidden_sizes=[256, 512, 1024, 2048], layer_type='bottleneck' ),
}
if model_name:
convert_weight_and_push(lowerCAmelCase_, names_to_config[model_name], lowerCAmelCase_, lowerCAmelCase_ )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ )
return config, expected_shape
if __name__ == "__main__":
_lowerCamelCase =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default=None,
type=str,
help=(
"The name of the model you wish to convert, it must be one of the supported resnet* architecture,"
" currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."
),
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=Path,
required=True,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
default=True,
type=bool,
required=False,
help="If True, push model and image processor to the hub.",
)
_lowerCamelCase =parser.parse_args()
_lowerCamelCase =args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 334 | 1 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase =[
("bert.bert", "visual_bert"),
("bert.cls", "cls"),
("bert.classifier", "cls"),
("token_type_embeddings_visual", "visual_token_type_embeddings"),
("position_embeddings_visual", "visual_position_embeddings"),
("projection", "visual_projection"),
]
_lowerCamelCase =[
"nlvr2_coco_pre_trained.th",
"nlvr2_fine_tuned.th",
"nlvr2_pre_trained.th",
"vcr_coco_pre_train.th",
"vcr_fine_tune.th",
"vcr_pre_train.th",
"vqa_coco_pre_trained.th",
"vqa_fine_tuned.th",
"vqa_pre_trained.th",
]
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =torch.load(lowerCAmelCase_, map_location='cpu' )
return sd
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_=rename_keys_prefix ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =OrderedDict()
SCREAMING_SNAKE_CASE =torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
SCREAMING_SNAKE_CASE =key
for name_pair in rename_keys_prefix:
SCREAMING_SNAKE_CASE =new_key.replace(name_pair[0], name_pair[1] )
SCREAMING_SNAKE_CASE =d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
SCREAMING_SNAKE_CASE =new_d['cls.predictions.bias']
return new_d
@torch.no_grad()
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
assert (
checkpoint_path.split('/' )[-1] in ACCEPTABLE_CHECKPOINTS
), F'The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'
# Get Config
if "pre" in checkpoint_path:
SCREAMING_SNAKE_CASE ='pretraining'
if "vcr" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 512}
elif "vqa_advanced" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 2048}
elif "vqa" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 2048}
elif "nlvr" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 1024}
else:
raise NotImplementedError(F'No implementation found for `{checkpoint_path}`.' )
else:
if "vcr" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 512}
SCREAMING_SNAKE_CASE ='multichoice'
elif "vqa_advanced" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 2048}
SCREAMING_SNAKE_CASE ='vqa_advanced'
elif "vqa" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 2048, 'num_labels': 3129}
SCREAMING_SNAKE_CASE ='vqa'
elif "nlvr" in checkpoint_path:
SCREAMING_SNAKE_CASE ={
'visual_embedding_dim': 1024,
'num_labels': 2,
}
SCREAMING_SNAKE_CASE ='nlvr'
SCREAMING_SNAKE_CASE =VisualBertConfig(**lowerCAmelCase_ )
# Load State Dict
SCREAMING_SNAKE_CASE =load_state_dict(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =get_new_dict(lowerCAmelCase_, lowerCAmelCase_ )
if model_type == "pretraining":
SCREAMING_SNAKE_CASE =VisualBertForPreTraining(lowerCAmelCase_ )
elif model_type == "vqa":
SCREAMING_SNAKE_CASE =VisualBertForQuestionAnswering(lowerCAmelCase_ )
elif model_type == "nlvr":
SCREAMING_SNAKE_CASE =VisualBertForVisualReasoning(lowerCAmelCase_ )
elif model_type == "multichoice":
SCREAMING_SNAKE_CASE =VisualBertForMultipleChoice(lowerCAmelCase_ )
model.load_state_dict(lowerCAmelCase_ )
# Save Checkpoints
Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ )
model.save_pretrained(lowerCAmelCase_ )
if __name__ == "__main__":
_lowerCamelCase =argparse.ArgumentParser()
# Required parameters
parser.add_argument("orig_checkpoint_path", type=str, help="A path to .th on local filesystem.")
parser.add_argument("pytorch_dump_folder_path", type=str, help="Path to the output PyTorch model.")
_lowerCamelCase =parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 334 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_=7 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =None
if token is not None:
SCREAMING_SNAKE_CASE ={'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}
# The id of a workflow (not of a workflow run)
SCREAMING_SNAKE_CASE ='636036'
SCREAMING_SNAKE_CASE =F'https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs'
# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
url += F'?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}'
SCREAMING_SNAKE_CASE =requests.get(lowerCAmelCase_, headers=lowerCAmelCase_ ).json()
return result["workflow_runs"]
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =get_daily_ci_runs(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =None
for workflow_run in workflow_runs:
if workflow_run["status"] == "completed":
SCREAMING_SNAKE_CASE =workflow_run['id']
break
return workflow_run_id
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =get_last_daily_ci_runs(lowerCAmelCase_ )
if workflow_run_id is not None:
SCREAMING_SNAKE_CASE =get_artifacts_links(worflow_run_id=lowerCAmelCase_, token=lowerCAmelCase_ )
for artifact_name in artifact_names:
if artifact_name in artifacts_links:
SCREAMING_SNAKE_CASE =artifacts_links[artifact_name]
download_artifact(
artifact_name=lowerCAmelCase_, artifact_url=lowerCAmelCase_, output_dir=lowerCAmelCase_, token=lowerCAmelCase_ )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
get_last_daily_ci_artifacts(lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ )
SCREAMING_SNAKE_CASE ={}
for artifact_name in artifact_names:
SCREAMING_SNAKE_CASE =os.path.join(lowerCAmelCase_, F'{artifact_name}.zip' )
if os.path.isfile(lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE ={}
with zipfile.ZipFile(lowerCAmelCase_ ) as z:
for filename in z.namelist():
if not os.path.isdir(lowerCAmelCase_ ):
# read the file
with z.open(lowerCAmelCase_ ) as f:
SCREAMING_SNAKE_CASE =f.read().decode('UTF-8' )
return results
| 334 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
RandomHorizontalFlip,
RandomResizedCrop,
Resize,
ToTensor,
)
import transformers
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
AutoConfig,
AutoImageProcessor,
AutoModelForImageClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
_lowerCamelCase =logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-classification/requirements.txt")
_lowerCamelCase =list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
_lowerCamelCase =tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
with open(lowerCAmelCase_, 'rb' ) as f:
SCREAMING_SNAKE_CASE =Image.open(lowerCAmelCase_ )
return im.convert('RGB' )
@dataclass
class a_ :
"""simple docstring"""
__UpperCAmelCase = field(
default=lowerCamelCase_ , metadata={
'help': 'Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'
} , )
__UpperCAmelCase = field(
default=lowerCamelCase_ , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} )
__UpperCAmelCase = field(default=lowerCamelCase_ , metadata={'help': 'A folder containing the training data.'} )
__UpperCAmelCase = field(default=lowerCamelCase_ , metadata={'help': 'A folder containing the validation data.'} )
__UpperCAmelCase = field(
default=0.15 , metadata={'help': 'Percent to split off of train for validation.'} )
__UpperCAmelCase = field(
default=lowerCamelCase_ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
__UpperCAmelCase = field(
default=lowerCamelCase_ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
'value if set.'
)
} , )
def _lowerCAmelCase ( self : Dict ):
if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
raise ValueError(
'You must specify either a dataset name from the hub or a train and/or validation directory.' )
@dataclass
class a_ :
"""simple docstring"""
__UpperCAmelCase = field(
default='google/vit-base-patch16-224-in21k' , metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} , )
__UpperCAmelCase = field(
default=lowerCamelCase_ , metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(lowerCamelCase_ )} , )
__UpperCAmelCase = field(
default=lowerCamelCase_ , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
__UpperCAmelCase = field(
default=lowerCamelCase_ , metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'} )
__UpperCAmelCase = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
__UpperCAmelCase = field(default=lowerCamelCase_ , metadata={'help': 'Name or path of preprocessor config.'} )
__UpperCAmelCase = field(
default=lowerCamelCase_ , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
__UpperCAmelCase = field(
default=lowerCamelCase_ , metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'} , )
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =torch.stack([example['pixel_values'] for example in examples] )
SCREAMING_SNAKE_CASE =torch.tensor([example['labels'] for example in examples] )
return {"pixel_values": pixel_values, "labels": labels}
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('run_image_classification', lowerCAmelCase_, lowerCAmelCase_ )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', handlers=[logging.StreamHandler(sys.stdout )], )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
SCREAMING_SNAKE_CASE =training_args.get_process_log_level()
logger.setLevel(lowerCAmelCase_ )
transformers.utils.logging.set_verbosity(lowerCAmelCase_ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
+ F'distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}' )
logger.info(F'Training/evaluation parameters {training_args}' )
# Detecting last checkpoint.
SCREAMING_SNAKE_CASE =None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
SCREAMING_SNAKE_CASE =get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'Output directory ({training_args.output_dir}) already exists and is not empty. '
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Initialize our dataset and prepare it for the 'image-classification' task.
if data_args.dataset_name is not None:
SCREAMING_SNAKE_CASE =load_dataset(
data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir, task='image-classification', use_auth_token=True if model_args.use_auth_token else None, )
else:
SCREAMING_SNAKE_CASE ={}
if data_args.train_dir is not None:
SCREAMING_SNAKE_CASE =os.path.join(data_args.train_dir, '**' )
if data_args.validation_dir is not None:
SCREAMING_SNAKE_CASE =os.path.join(data_args.validation_dir, '**' )
SCREAMING_SNAKE_CASE =load_dataset(
'imagefolder', data_files=lowerCAmelCase_, cache_dir=model_args.cache_dir, task='image-classification', )
# If we don't have a validation split, split off a percentage of train as validation.
SCREAMING_SNAKE_CASE =None if 'validation' in dataset.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split, lowerCAmelCase_ ) and data_args.train_val_split > 0.0:
SCREAMING_SNAKE_CASE =dataset['train'].train_test_split(data_args.train_val_split )
SCREAMING_SNAKE_CASE =split['train']
SCREAMING_SNAKE_CASE =split['test']
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
SCREAMING_SNAKE_CASE =dataset['train'].features['labels'].names
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ={}, {}
for i, label in enumerate(lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE =str(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =label
# Load the accuracy metric from the datasets package
SCREAMING_SNAKE_CASE =evaluate.load('accuracy' )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(lowerCAmelCase_ ):
return metric.compute(predictions=np.argmax(p.predictions, axis=1 ), references=p.label_ids )
SCREAMING_SNAKE_CASE =AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path, num_labels=len(lowerCAmelCase_ ), labelaid=lowerCAmelCase_, idalabel=lowerCAmelCase_, finetuning_task='image-classification', cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, )
SCREAMING_SNAKE_CASE =AutoModelForImageClassification.from_pretrained(
model_args.model_name_or_path, from_tf=bool('.ckpt' in model_args.model_name_or_path ), config=lowerCAmelCase_, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, ignore_mismatched_sizes=model_args.ignore_mismatched_sizes, )
SCREAMING_SNAKE_CASE =AutoImageProcessor.from_pretrained(
model_args.image_processor_name or model_args.model_name_or_path, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, )
# Define torchvision transforms to be applied to each image.
if "shortest_edge" in image_processor.size:
SCREAMING_SNAKE_CASE =image_processor.size['shortest_edge']
else:
SCREAMING_SNAKE_CASE =(image_processor.size['height'], image_processor.size['width'])
SCREAMING_SNAKE_CASE =Normalize(mean=image_processor.image_mean, std=image_processor.image_std )
SCREAMING_SNAKE_CASE =Compose(
[
RandomResizedCrop(lowerCAmelCase_ ),
RandomHorizontalFlip(),
ToTensor(),
normalize,
] )
SCREAMING_SNAKE_CASE =Compose(
[
Resize(lowerCAmelCase_ ),
CenterCrop(lowerCAmelCase_ ),
ToTensor(),
normalize,
] )
def train_transforms(lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE =[
_train_transforms(pil_img.convert('RGB' ) ) for pil_img in example_batch['image']
]
return example_batch
def val_transforms(lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE =[_val_transforms(pil_img.convert('RGB' ) ) for pil_img in example_batch['image']]
return example_batch
if training_args.do_train:
if "train" not in dataset:
raise ValueError('--do_train requires a train dataset' )
if data_args.max_train_samples is not None:
SCREAMING_SNAKE_CASE =(
dataset['train'].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
dataset["train"].set_transform(lowerCAmelCase_ )
if training_args.do_eval:
if "validation" not in dataset:
raise ValueError('--do_eval requires a validation dataset' )
if data_args.max_eval_samples is not None:
SCREAMING_SNAKE_CASE =(
dataset['validation'].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
dataset["validation"].set_transform(lowerCAmelCase_ )
# Initalize our trainer
SCREAMING_SNAKE_CASE =Trainer(
model=lowerCAmelCase_, args=lowerCAmelCase_, train_dataset=dataset['train'] if training_args.do_train else None, eval_dataset=dataset['validation'] if training_args.do_eval else None, compute_metrics=lowerCAmelCase_, tokenizer=lowerCAmelCase_, data_collator=lowerCAmelCase_, )
# Training
if training_args.do_train:
SCREAMING_SNAKE_CASE =None
if training_args.resume_from_checkpoint is not None:
SCREAMING_SNAKE_CASE =training_args.resume_from_checkpoint
elif last_checkpoint is not None:
SCREAMING_SNAKE_CASE =last_checkpoint
SCREAMING_SNAKE_CASE =trainer.train(resume_from_checkpoint=lowerCAmelCase_ )
trainer.save_model()
trainer.log_metrics('train', train_result.metrics )
trainer.save_metrics('train', train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
SCREAMING_SNAKE_CASE =trainer.evaluate()
trainer.log_metrics('eval', lowerCAmelCase_ )
trainer.save_metrics('eval', lowerCAmelCase_ )
# Write model card and (optionally) push to hub
SCREAMING_SNAKE_CASE ={
'finetuned_from': model_args.model_name_or_path,
'tasks': 'image-classification',
'dataset': data_args.dataset_name,
'tags': ['image-classification', 'vision'],
}
if training_args.push_to_hub:
trainer.push_to_hub(**lowerCAmelCase_ )
else:
trainer.create_model_card(**lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 334 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
)
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
def __init__( self : Tuple ,snake_case : Optional[int] ,snake_case : Dict=13 ,snake_case : str=7 ,snake_case : Dict=True ,snake_case : List[Any]=True ,snake_case : Dict=False ,snake_case : int=True ,snake_case : Dict=99 ,snake_case : int=32 ,snake_case : List[str]=5 ,snake_case : Optional[Any]=4 ,snake_case : Tuple=64 ,snake_case : List[Any]="gelu" ,snake_case : str=0.1 ,snake_case : str=0.1 ,snake_case : List[str]=512 ,snake_case : List[str]=16 ,snake_case : str=2 ,snake_case : Dict=0.02 ,snake_case : Optional[int]=3 ,snake_case : int=4 ,snake_case : Any=None ,snake_case : Union[str, Any]=2 ,snake_case : List[Any]=2 ,snake_case : Optional[int]=2 ,snake_case : Dict=2 ,snake_case : List[str]=4 ,snake_case : int=1 ,):
SCREAMING_SNAKE_CASE =parent
SCREAMING_SNAKE_CASE =batch_size
SCREAMING_SNAKE_CASE =seq_length
SCREAMING_SNAKE_CASE =is_training
SCREAMING_SNAKE_CASE =use_input_mask
SCREAMING_SNAKE_CASE =use_token_type_ids
SCREAMING_SNAKE_CASE =use_labels
SCREAMING_SNAKE_CASE =vocab_size
SCREAMING_SNAKE_CASE =hidden_size
SCREAMING_SNAKE_CASE =num_hidden_layers
SCREAMING_SNAKE_CASE =num_attention_heads
SCREAMING_SNAKE_CASE =intermediate_size
SCREAMING_SNAKE_CASE =hidden_act
SCREAMING_SNAKE_CASE =hidden_dropout_prob
SCREAMING_SNAKE_CASE =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE =max_position_embeddings
SCREAMING_SNAKE_CASE =type_vocab_size
SCREAMING_SNAKE_CASE =type_sequence_label_size
SCREAMING_SNAKE_CASE =initializer_range
SCREAMING_SNAKE_CASE =num_labels
SCREAMING_SNAKE_CASE =num_choices
SCREAMING_SNAKE_CASE =scope
SCREAMING_SNAKE_CASE =q_groups
SCREAMING_SNAKE_CASE =k_groups
SCREAMING_SNAKE_CASE =v_groups
SCREAMING_SNAKE_CASE =post_attention_groups
SCREAMING_SNAKE_CASE =intermediate_groups
SCREAMING_SNAKE_CASE =output_groups
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
SCREAMING_SNAKE_CASE =None
if self.use_input_mask:
SCREAMING_SNAKE_CASE =random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE =None
SCREAMING_SNAKE_CASE =None
SCREAMING_SNAKE_CASE =None
if self.use_labels:
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size] ,self.type_sequence_label_size )
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size] ,self.num_choices )
SCREAMING_SNAKE_CASE =self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowerCAmelCase ( self : Optional[int] ):
return SqueezeBertConfig(
embedding_size=self.hidden_size ,vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,attention_probs_dropout_prob=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,initializer_range=self.initializer_range ,q_groups=self.q_groups ,k_groups=self.k_groups ,v_groups=self.v_groups ,post_attention_groups=self.post_attention_groups ,intermediate_groups=self.intermediate_groups ,output_groups=self.output_groups ,)
def _lowerCAmelCase ( self : Dict ,snake_case : List[str] ,snake_case : Optional[Any] ,snake_case : List[str] ,snake_case : List[Any] ,snake_case : str ,snake_case : Union[str, Any] ):
SCREAMING_SNAKE_CASE =SqueezeBertModel(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case ,snake_case )
SCREAMING_SNAKE_CASE =model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _lowerCAmelCase ( self : Optional[int] ,snake_case : Optional[int] ,snake_case : Union[str, Any] ,snake_case : List[Any] ,snake_case : int ,snake_case : Any ,snake_case : Tuple ):
SCREAMING_SNAKE_CASE =SqueezeBertForMaskedLM(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case ,attention_mask=snake_case ,labels=snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def _lowerCAmelCase ( self : Tuple ,snake_case : Union[str, Any] ,snake_case : Any ,snake_case : List[str] ,snake_case : List[Any] ,snake_case : Dict ,snake_case : Optional[Any] ):
SCREAMING_SNAKE_CASE =SqueezeBertForQuestionAnswering(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(
snake_case ,attention_mask=snake_case ,start_positions=snake_case ,end_positions=snake_case )
self.parent.assertEqual(result.start_logits.shape ,(self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape ,(self.batch_size, self.seq_length) )
def _lowerCAmelCase ( self : Optional[int] ,snake_case : Tuple ,snake_case : List[str] ,snake_case : List[str] ,snake_case : Any ,snake_case : Tuple ,snake_case : str ):
SCREAMING_SNAKE_CASE =self.num_labels
SCREAMING_SNAKE_CASE =SqueezeBertForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case ,attention_mask=snake_case ,labels=snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def _lowerCAmelCase ( self : Optional[Any] ,snake_case : List[str] ,snake_case : List[str] ,snake_case : Tuple ,snake_case : Dict ,snake_case : str ,snake_case : Tuple ):
SCREAMING_SNAKE_CASE =self.num_labels
SCREAMING_SNAKE_CASE =SqueezeBertForTokenClassification(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case ,attention_mask=snake_case ,labels=snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def _lowerCAmelCase ( self : List[str] ,snake_case : Dict ,snake_case : str ,snake_case : Union[str, Any] ,snake_case : Union[str, Any] ,snake_case : Any ,snake_case : Union[str, Any] ):
SCREAMING_SNAKE_CASE =self.num_choices
SCREAMING_SNAKE_CASE =SqueezeBertForMultipleChoice(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =input_ids.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
SCREAMING_SNAKE_CASE =input_mask.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
SCREAMING_SNAKE_CASE =model(
snake_case ,attention_mask=snake_case ,labels=snake_case ,)
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_choices) )
def _lowerCAmelCase ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE =self.prepare_config_and_inputs()
((SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE)) =config_and_inputs
SCREAMING_SNAKE_CASE ={'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class a_ ( lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase = (
(
SqueezeBertModel,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
)
if is_torch_available()
else None
)
__UpperCAmelCase = (
{
'feature-extraction': SqueezeBertModel,
'fill-mask': SqueezeBertForMaskedLM,
'question-answering': SqueezeBertForQuestionAnswering,
'text-classification': SqueezeBertForSequenceClassification,
'token-classification': SqueezeBertForTokenClassification,
'zero-shot': SqueezeBertForSequenceClassification,
}
if is_torch_available()
else {}
)
__UpperCAmelCase = False
__UpperCAmelCase = True
__UpperCAmelCase = False
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =SqueezeBertModelTester(self )
SCREAMING_SNAKE_CASE =ConfigTester(self ,config_class=snake_case ,dim=37 )
def _lowerCAmelCase ( self : List[str] ):
self.config_tester.run_common_tests()
def _lowerCAmelCase ( self : Optional[int] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_model(*snake_case )
def _lowerCAmelCase ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_masked_lm(*snake_case )
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_question_answering(*snake_case )
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_sequence_classification(*snake_case )
def _lowerCAmelCase ( self : int ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_token_classification(*snake_case )
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_multiple_choice(*snake_case )
@slow
def _lowerCAmelCase ( self : str ):
for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE =SqueezeBertModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
@require_sentencepiece
@require_tokenizers
@require_torch
class a_ ( unittest.TestCase ):
"""simple docstring"""
@slow
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =SqueezeBertForSequenceClassification.from_pretrained('squeezebert/squeezebert-mnli' )
SCREAMING_SNAKE_CASE =torch.tensor([[1, 29414, 232, 328, 740, 1140, 12695, 69, 13, 1588, 2]] )
SCREAMING_SNAKE_CASE =model(snake_case )[0]
SCREAMING_SNAKE_CASE =torch.Size((1, 3) )
self.assertEqual(output.shape ,snake_case )
SCREAMING_SNAKE_CASE =torch.tensor([[0.6_401, -0.0_349, -0.6_041]] )
self.assertTrue(torch.allclose(snake_case ,snake_case ,atol=1e-4 ) )
| 334 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
# See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'vit_mae'
def __init__( self : Union[str, Any] ,snake_case : Any=768 ,snake_case : List[str]=12 ,snake_case : Optional[int]=12 ,snake_case : int=3072 ,snake_case : List[Any]="gelu" ,snake_case : str=0.0 ,snake_case : str=0.0 ,snake_case : Optional[Any]=0.02 ,snake_case : Dict=1e-12 ,snake_case : List[str]=224 ,snake_case : Any=16 ,snake_case : Any=3 ,snake_case : Tuple=True ,snake_case : List[Any]=16 ,snake_case : List[str]=512 ,snake_case : List[Any]=8 ,snake_case : Dict=2048 ,snake_case : Union[str, Any]=0.75 ,snake_case : Union[str, Any]=False ,**snake_case : Optional[int] ,):
super().__init__(**snake_case )
SCREAMING_SNAKE_CASE =hidden_size
SCREAMING_SNAKE_CASE =num_hidden_layers
SCREAMING_SNAKE_CASE =num_attention_heads
SCREAMING_SNAKE_CASE =intermediate_size
SCREAMING_SNAKE_CASE =hidden_act
SCREAMING_SNAKE_CASE =hidden_dropout_prob
SCREAMING_SNAKE_CASE =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE =initializer_range
SCREAMING_SNAKE_CASE =layer_norm_eps
SCREAMING_SNAKE_CASE =image_size
SCREAMING_SNAKE_CASE =patch_size
SCREAMING_SNAKE_CASE =num_channels
SCREAMING_SNAKE_CASE =qkv_bias
SCREAMING_SNAKE_CASE =decoder_num_attention_heads
SCREAMING_SNAKE_CASE =decoder_hidden_size
SCREAMING_SNAKE_CASE =decoder_num_hidden_layers
SCREAMING_SNAKE_CASE =decoder_intermediate_size
SCREAMING_SNAKE_CASE =mask_ratio
SCREAMING_SNAKE_CASE =norm_pix_loss
| 334 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = None
__UpperCAmelCase = None
@property
def _lowerCAmelCase ( self : List[Any] ):
return self.feat_extract_tester.prepare_feat_extract_dict()
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(snake_case ,'feature_size' ) )
self.assertTrue(hasattr(snake_case ,'sampling_rate' ) )
self.assertTrue(hasattr(snake_case ,'padding_value' ) )
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common()
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(snake_case ) == len(snake_case ) for x, y in zip(snake_case ,processed_features[input_name] ) ) )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common(equal_length=snake_case )
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} ,tensor_type='np' )
SCREAMING_SNAKE_CASE =processed_features[input_name]
if len(batch_features_input.shape ) < 3:
SCREAMING_SNAKE_CASE =batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_torch
def _lowerCAmelCase ( self : Optional[int] ):
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common(equal_length=snake_case )
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} ,tensor_type='pt' )
SCREAMING_SNAKE_CASE =processed_features[input_name]
if len(batch_features_input.shape ) < 3:
SCREAMING_SNAKE_CASE =batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_tf
def _lowerCAmelCase ( self : str ):
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common(equal_length=snake_case )
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} ,tensor_type='tf' )
SCREAMING_SNAKE_CASE =processed_features[input_name]
if len(batch_features_input.shape ) < 3:
SCREAMING_SNAKE_CASE =batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
def _lowerCAmelCase ( self : List[Any] ,snake_case : Optional[Any]=False ):
def _inputs_have_equal_length(snake_case : Dict ):
SCREAMING_SNAKE_CASE =len(input[0] )
for input_slice in input[1:]:
if len(snake_case ) != length:
return False
return True
def _inputs_are_equal(snake_case : str ,snake_case : Dict ):
if len(snake_case ) != len(snake_case ):
return False
for input_slice_a, input_slice_a in zip(snake_case ,snake_case ):
if not np.allclose(np.asarray(snake_case ) ,np.asarray(snake_case ) ,atol=1e-3 ):
return False
return True
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common(numpify=snake_case )
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.seq_length_diff
SCREAMING_SNAKE_CASE =self.feat_extract_tester.max_seq_length + pad_diff
SCREAMING_SNAKE_CASE =self.feat_extract_tester.min_seq_length
SCREAMING_SNAKE_CASE =self.feat_extract_tester.batch_size
SCREAMING_SNAKE_CASE =self.feat_extract_tester.feature_size
# test padding for List[int] + numpy
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding=snake_case )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='max_length' ,max_length=len(speech_inputs[-1] ) )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='np' )
SCREAMING_SNAKE_CASE =input_a[input_name]
# max_length parameter has to be provided when setting `padding="max_length"`
with self.assertRaises(snake_case ):
feat_extract.pad(snake_case ,padding='max_length' )[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=snake_case ,return_tensors='np' )
SCREAMING_SNAKE_CASE =input_a[input_name]
self.assertFalse(_inputs_have_equal_length(snake_case ) )
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertTrue(_inputs_are_equal(snake_case ,snake_case ) )
self.assertTrue(len(input_a[0] ) == pad_min_length )
self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )
# test padding for `pad_to_multiple_of` for List[int] + numpy
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,pad_to_multiple_of=10 )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,pad_to_multiple_of=10 )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,pad_to_multiple_of=10 ,max_length=snake_case )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,pad_to_multiple_of=10 ,max_length=snake_case ,return_tensors='np' ,)
SCREAMING_SNAKE_CASE =input_a[input_name]
self.assertTrue(all(len(snake_case ) % 10 == 0 for x in input_a ) )
self.assertTrue(_inputs_are_equal(snake_case ,snake_case ) )
SCREAMING_SNAKE_CASE =pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
self.assertTrue(all(len(snake_case ) == expected_mult_pad_length for x in input_a ) )
self.assertEqual(input_a.shape[:2] ,(batch_size, expected_mult_pad_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == feature_size )
# Check padding value is correct
SCREAMING_SNAKE_CASE =(np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
self.assertTrue(
abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
< 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
< 1e-3 )
def _lowerCAmelCase ( self : Union[str, Any] ,snake_case : Optional[int]=False ):
def _inputs_have_equal_length(snake_case : str ):
SCREAMING_SNAKE_CASE =len(input[0] )
for input_slice in input[1:]:
if len(snake_case ) != length:
return False
return True
def _inputs_are_equal(snake_case : Tuple ,snake_case : Optional[Any] ):
if len(snake_case ) != len(snake_case ):
return False
for input_slice_a, input_slice_a in zip(snake_case ,snake_case ):
if not np.allclose(np.asarray(snake_case ) ,np.asarray(snake_case ) ,atol=1e-3 ):
return False
return True
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common(numpify=snake_case )
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
# truncate to smallest
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) ,truncation=snake_case )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) )
SCREAMING_SNAKE_CASE =input_a[input_name]
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertFalse(_inputs_have_equal_length(snake_case ) )
# truncate to smallest with np
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) ,return_tensors='np' ,truncation=snake_case ,)
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) ,return_tensors='np' )
SCREAMING_SNAKE_CASE =input_a[input_name]
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(snake_case ) )
# truncate to middle
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[1] ) ,truncation=snake_case ,return_tensors='np' ,)
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[1] ) ,truncation=snake_case )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[1] ) ,return_tensors='np' )
SCREAMING_SNAKE_CASE =input_a[input_name]
self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertTrue(_inputs_are_equal(snake_case ,snake_case ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(snake_case ) )
self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(snake_case ):
feat_extract.pad(snake_case ,truncation=snake_case )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(snake_case ):
feat_extract.pad(snake_case ,padding='longest' ,truncation=snake_case )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(snake_case ):
feat_extract.pad(snake_case ,padding='longest' ,truncation=snake_case )[input_name]
# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
with self.assertRaises(snake_case ):
feat_extract.pad(snake_case ,padding='max_length' ,truncation=snake_case )[input_name]
# test truncation for `pad_to_multiple_of` for List[int] + numpy
SCREAMING_SNAKE_CASE =12
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) ,pad_to_multiple_of=snake_case ,truncation=snake_case ,)
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) ,pad_to_multiple_of=snake_case ,)
SCREAMING_SNAKE_CASE =input_a[input_name]
# retrieve expected_length as multiple of pad_to_multiple_of
SCREAMING_SNAKE_CASE =len(speech_inputs[0] )
if expected_length % pad_to_multiple_of != 0:
SCREAMING_SNAKE_CASE =((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of
self.assertTrue(len(input_a[0] ) == expected_length )
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertFalse(_inputs_have_equal_length(snake_case ) )
def _lowerCAmelCase ( self : Optional[int] ):
self._check_padding(numpify=snake_case )
def _lowerCAmelCase ( self : Tuple ):
self._check_padding(numpify=snake_case )
def _lowerCAmelCase ( self : List[str] ):
self._check_truncation(numpify=snake_case )
def _lowerCAmelCase ( self : int ):
self._check_truncation(numpify=snake_case )
@require_torch
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common()
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='np' )[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='pt' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
@require_tf
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common()
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='np' )[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='tf' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE =self.feat_extract_dict
SCREAMING_SNAKE_CASE =True
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**snake_case )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common()
SCREAMING_SNAKE_CASE =[len(snake_case ) for x in speech_inputs]
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='np' )
self.assertIn('attention_mask' ,snake_case )
self.assertListEqual(list(processed.attention_mask.shape ) ,list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() ,snake_case )
def _lowerCAmelCase ( self : Dict ):
SCREAMING_SNAKE_CASE =self.feat_extract_dict
SCREAMING_SNAKE_CASE =True
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**snake_case )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common()
SCREAMING_SNAKE_CASE =[len(snake_case ) for x in speech_inputs]
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE =min(snake_case )
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=snake_case ,truncation=snake_case ,return_tensors='np' )
self.assertIn('attention_mask' ,snake_case )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) ,[processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() ,[max_length for x in speech_inputs] )
| 334 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
ViltForQuestionAnswering,
ViltImageProcessor,
ViltProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase =logging.get_logger(__name__)
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_=False, lowerCAmelCase_=False, lowerCAmelCase_=False ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =[]
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'transformer.blocks.{i}.norm1.weight', F'vilt.encoder.layer.{i}.layernorm_before.weight') )
rename_keys.append((F'transformer.blocks.{i}.norm1.bias', F'vilt.encoder.layer.{i}.layernorm_before.bias') )
rename_keys.append(
(F'transformer.blocks.{i}.attn.proj.weight', F'vilt.encoder.layer.{i}.attention.output.dense.weight') )
rename_keys.append(
(F'transformer.blocks.{i}.attn.proj.bias', F'vilt.encoder.layer.{i}.attention.output.dense.bias') )
rename_keys.append((F'transformer.blocks.{i}.norm2.weight', F'vilt.encoder.layer.{i}.layernorm_after.weight') )
rename_keys.append((F'transformer.blocks.{i}.norm2.bias', F'vilt.encoder.layer.{i}.layernorm_after.bias') )
rename_keys.append(
(F'transformer.blocks.{i}.mlp.fc1.weight', F'vilt.encoder.layer.{i}.intermediate.dense.weight') )
rename_keys.append((F'transformer.blocks.{i}.mlp.fc1.bias', F'vilt.encoder.layer.{i}.intermediate.dense.bias') )
rename_keys.append((F'transformer.blocks.{i}.mlp.fc2.weight', F'vilt.encoder.layer.{i}.output.dense.weight') )
rename_keys.append((F'transformer.blocks.{i}.mlp.fc2.bias', F'vilt.encoder.layer.{i}.output.dense.bias') )
# embeddings
rename_keys.extend(
[
# text embeddings
('text_embeddings.word_embeddings.weight', 'vilt.embeddings.text_embeddings.word_embeddings.weight'),
(
'text_embeddings.position_embeddings.weight',
'vilt.embeddings.text_embeddings.position_embeddings.weight',
),
('text_embeddings.position_ids', 'vilt.embeddings.text_embeddings.position_ids'),
(
'text_embeddings.token_type_embeddings.weight',
'vilt.embeddings.text_embeddings.token_type_embeddings.weight',
),
('text_embeddings.LayerNorm.weight', 'vilt.embeddings.text_embeddings.LayerNorm.weight'),
('text_embeddings.LayerNorm.bias', 'vilt.embeddings.text_embeddings.LayerNorm.bias'),
# patch embeddings
('transformer.cls_token', 'vilt.embeddings.cls_token'),
('transformer.patch_embed.proj.weight', 'vilt.embeddings.patch_embeddings.projection.weight'),
('transformer.patch_embed.proj.bias', 'vilt.embeddings.patch_embeddings.projection.bias'),
('transformer.pos_embed', 'vilt.embeddings.position_embeddings'),
# token type embeddings
('token_type_embeddings.weight', 'vilt.embeddings.token_type_embeddings.weight'),
] )
# final layernorm + pooler
rename_keys.extend(
[
('transformer.norm.weight', 'vilt.layernorm.weight'),
('transformer.norm.bias', 'vilt.layernorm.bias'),
('pooler.dense.weight', 'vilt.pooler.dense.weight'),
('pooler.dense.bias', 'vilt.pooler.dense.bias'),
] )
# classifier head(s)
if vqa_model:
# classification head
rename_keys.extend(
[
('vqa_classifier.0.weight', 'classifier.0.weight'),
('vqa_classifier.0.bias', 'classifier.0.bias'),
('vqa_classifier.1.weight', 'classifier.1.weight'),
('vqa_classifier.1.bias', 'classifier.1.bias'),
('vqa_classifier.3.weight', 'classifier.3.weight'),
('vqa_classifier.3.bias', 'classifier.3.bias'),
] )
elif nlvr_model:
# classification head
rename_keys.extend(
[
('nlvr2_classifier.0.weight', 'classifier.0.weight'),
('nlvr2_classifier.0.bias', 'classifier.0.bias'),
('nlvr2_classifier.1.weight', 'classifier.1.weight'),
('nlvr2_classifier.1.bias', 'classifier.1.bias'),
('nlvr2_classifier.3.weight', 'classifier.3.weight'),
('nlvr2_classifier.3.bias', 'classifier.3.bias'),
] )
else:
pass
return rename_keys
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
for i in range(config.num_hidden_layers ):
SCREAMING_SNAKE_CASE ='vilt.'
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
SCREAMING_SNAKE_CASE =state_dict.pop(F'transformer.blocks.{i}.attn.qkv.weight' )
SCREAMING_SNAKE_CASE =state_dict.pop(F'transformer.blocks.{i}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
SCREAMING_SNAKE_CASE =in_proj_weight[
: config.hidden_size, :
]
SCREAMING_SNAKE_CASE =in_proj_bias[: config.hidden_size]
SCREAMING_SNAKE_CASE =in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
SCREAMING_SNAKE_CASE =in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
SCREAMING_SNAKE_CASE =in_proj_weight[
-config.hidden_size :, :
]
SCREAMING_SNAKE_CASE =in_proj_bias[-config.hidden_size :]
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =['head.weight', 'head.bias']
for k in ignore_keys:
state_dict.pop(lowerCAmelCase_, lowerCAmelCase_ )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =dct.pop(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =val
@torch.no_grad()
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =ViltConfig(image_size=384, patch_size=32, tie_word_embeddings=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =False
SCREAMING_SNAKE_CASE =False
SCREAMING_SNAKE_CASE =False
SCREAMING_SNAKE_CASE =False
if "vqa" in checkpoint_url:
SCREAMING_SNAKE_CASE =True
SCREAMING_SNAKE_CASE =3129
SCREAMING_SNAKE_CASE ='huggingface/label-files'
SCREAMING_SNAKE_CASE ='vqa2-id2label.json'
SCREAMING_SNAKE_CASE =json.load(open(hf_hub_download(lowerCAmelCase_, lowerCAmelCase_, repo_type='dataset' ), 'r' ) )
SCREAMING_SNAKE_CASE ={int(lowerCAmelCase_ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE =idalabel
SCREAMING_SNAKE_CASE ={v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE =ViltForQuestionAnswering(lowerCAmelCase_ )
elif "nlvr" in checkpoint_url:
SCREAMING_SNAKE_CASE =True
SCREAMING_SNAKE_CASE =2
SCREAMING_SNAKE_CASE ={0: 'False', 1: 'True'}
SCREAMING_SNAKE_CASE ={v: k for k, v in config.idalabel.items()}
SCREAMING_SNAKE_CASE =3
SCREAMING_SNAKE_CASE =ViltForImagesAndTextClassification(lowerCAmelCase_ )
elif "irtr" in checkpoint_url:
SCREAMING_SNAKE_CASE =True
SCREAMING_SNAKE_CASE =ViltForImageAndTextRetrieval(lowerCAmelCase_ )
elif "mlm_itm" in checkpoint_url:
SCREAMING_SNAKE_CASE =True
SCREAMING_SNAKE_CASE =ViltForMaskedLM(lowerCAmelCase_ )
else:
raise ValueError('Unknown model type' )
# load state_dict of original model, remove and rename some keys
SCREAMING_SNAKE_CASE =torch.hub.load_state_dict_from_url(lowerCAmelCase_, map_location='cpu' )['state_dict']
SCREAMING_SNAKE_CASE =create_rename_keys(lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ )
for src, dest in rename_keys:
rename_key(lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ )
read_in_q_k_v(lowerCAmelCase_, lowerCAmelCase_ )
if mlm_model or irtr_model:
SCREAMING_SNAKE_CASE =['itm_score.fc.weight', 'itm_score.fc.bias']
for k in ignore_keys:
state_dict.pop(lowerCAmelCase_, lowerCAmelCase_ )
# load state dict into HuggingFace model
model.eval()
if mlm_model:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =model.load_state_dict(lowerCAmelCase_, strict=lowerCAmelCase_ )
assert missing_keys == ["mlm_score.decoder.bias"]
else:
model.load_state_dict(lowerCAmelCase_ )
# Define processor
SCREAMING_SNAKE_CASE =ViltImageProcessor(size=384 )
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained('bert-base-uncased' )
SCREAMING_SNAKE_CASE =ViltProcessor(lowerCAmelCase_, lowerCAmelCase_ )
# Forward pass on example inputs (image + text)
if nlvr_model:
SCREAMING_SNAKE_CASE =Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg', stream=lowerCAmelCase_ ).raw )
SCREAMING_SNAKE_CASE =Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg', stream=lowerCAmelCase_ ).raw )
SCREAMING_SNAKE_CASE =(
'The left image contains twice the number of dogs as the right image, and at least two dogs in total are'
' standing.'
)
SCREAMING_SNAKE_CASE =processor(lowerCAmelCase_, lowerCAmelCase_, return_tensors='pt' )
SCREAMING_SNAKE_CASE =processor(lowerCAmelCase_, lowerCAmelCase_, return_tensors='pt' )
SCREAMING_SNAKE_CASE =model(
input_ids=encoding_a.input_ids, pixel_values=encoding_a.pixel_values, pixel_values_a=encoding_a.pixel_values, )
else:
SCREAMING_SNAKE_CASE =Image.open(requests.get('http://images.cocodataset.org/val2017/000000039769.jpg', stream=lowerCAmelCase_ ).raw )
if mlm_model:
SCREAMING_SNAKE_CASE ='a bunch of [MASK] laying on a [MASK].'
else:
SCREAMING_SNAKE_CASE ='How many cats are there?'
SCREAMING_SNAKE_CASE =processor(lowerCAmelCase_, lowerCAmelCase_, return_tensors='pt' )
SCREAMING_SNAKE_CASE =model(**lowerCAmelCase_ )
# Verify outputs
if mlm_model:
SCREAMING_SNAKE_CASE =torch.Size([1, 11, 30522] )
SCREAMING_SNAKE_CASE =torch.tensor([-12.5061, -12.5123, -12.5174] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3], lowerCAmelCase_, atol=1e-4 )
# verify masked token prediction equals "cats"
SCREAMING_SNAKE_CASE =outputs.logits[0, 4, :].argmax(-1 ).item()
assert tokenizer.decode([predicted_id] ) == "cats"
elif vqa_model:
SCREAMING_SNAKE_CASE =torch.Size([1, 3129] )
SCREAMING_SNAKE_CASE =torch.tensor([-15.9495, -18.1472, -10.3041] )
assert torch.allclose(outputs.logits[0, :3], lowerCAmelCase_, atol=1e-4 )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3], lowerCAmelCase_, atol=1e-4 )
# verify vqa prediction equals "2"
SCREAMING_SNAKE_CASE =outputs.logits.argmax(-1 ).item()
assert model.config.idalabel[predicted_idx] == "2"
elif nlvr_model:
SCREAMING_SNAKE_CASE =torch.Size([1, 2] )
SCREAMING_SNAKE_CASE =torch.tensor([-2.8721, 2.1291] )
assert torch.allclose(outputs.logits[0, :3], lowerCAmelCase_, atol=1e-4 )
assert outputs.logits.shape == expected_shape
Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ )
print(F'Saving model and processor to {pytorch_dump_folder_path}' )
model.save_pretrained(lowerCAmelCase_ )
processor.save_pretrained(lowerCAmelCase_ )
if __name__ == "__main__":
_lowerCamelCase =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt",
type=str,
help="URL of the checkpoint you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
_lowerCamelCase =parser.parse_args()
convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 334 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_lowerCamelCase =2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
_lowerCamelCase =50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
_lowerCamelCase =0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 10_00))
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =len([g for position, g in enumerate(lowerCAmelCase_ ) if g == main_target[position]] )
return (item, float(lowerCAmelCase_ ))
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =random.randint(0, len(lowerCAmelCase_ ) - 1 )
SCREAMING_SNAKE_CASE =parent_a[:random_slice] + parent_a[random_slice:]
SCREAMING_SNAKE_CASE =parent_a[:random_slice] + parent_a[random_slice:]
return (child_a, child_a)
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =list(lowerCAmelCase_ )
if random.uniform(0, 1 ) < MUTATION_PROBABILITY:
SCREAMING_SNAKE_CASE =random.choice(lowerCAmelCase_ )
return "".join(lowerCAmelCase_ )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =[]
# Generate more children proportionally to the fitness score.
SCREAMING_SNAKE_CASE =int(parent_a[1] * 100 ) + 1
SCREAMING_SNAKE_CASE =10 if child_n >= 10 else child_n
for _ in range(lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE =population_score[random.randint(0, lowerCAmelCase_ )][0]
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =crossover(parent_a[0], lowerCAmelCase_ )
# Append new string to the population list.
pop.append(mutate(lowerCAmelCase_, lowerCAmelCase_ ) )
pop.append(mutate(lowerCAmelCase_, lowerCAmelCase_ ) )
return pop
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ = True ):
"""simple docstring"""
if N_POPULATION < N_SELECTED:
SCREAMING_SNAKE_CASE =F'{N_POPULATION} must be bigger than {N_SELECTED}'
raise ValueError(lowerCAmelCase_ )
# Verify that the target contains no genes besides the ones inside genes variable.
SCREAMING_SNAKE_CASE =sorted({c for c in target if c not in genes} )
if not_in_genes_list:
SCREAMING_SNAKE_CASE =F'{not_in_genes_list} is not in genes list, evolution cannot converge'
raise ValueError(lowerCAmelCase_ )
# Generate random starting population.
SCREAMING_SNAKE_CASE =[]
for _ in range(lowerCAmelCase_ ):
population.append(''.join([random.choice(lowerCAmelCase_ ) for i in range(len(lowerCAmelCase_ ) )] ) )
# Just some logs to know what the algorithms is doing.
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =0, 0
# This loop will end when we find a perfect match for our target.
while True:
generation += 1
total_population += len(lowerCAmelCase_ )
# Random population created. Now it's time to evaluate.
# Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this, it will probably be slower.
# We just need to call evaluate for every item inside the population.
SCREAMING_SNAKE_CASE =[evaluate(lowerCAmelCase_, lowerCAmelCase_ ) for item in population]
# Check if there is a matching evolution.
SCREAMING_SNAKE_CASE =sorted(lowerCAmelCase_, key=lambda lowerCAmelCase_ : x[1], reverse=lowerCAmelCase_ )
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the best result every 10 generation.
# Just to know that the algorithm is working.
if debug and generation % 10 == 0:
print(
F'\nGeneration: {generation}'
F'\nTotal Population:{total_population}'
F'\nBest score: {population_score[0][1]}'
F'\nBest string: {population_score[0][0]}' )
# Flush the old population, keeping some of the best evolutions.
# Keeping this avoid regression of evolution.
SCREAMING_SNAKE_CASE =population[: int(N_POPULATION / 3 )]
population.clear()
population.extend(lowerCAmelCase_ )
# Normalize population score to be between 0 and 1.
SCREAMING_SNAKE_CASE =[
(item, score / len(lowerCAmelCase_ )) for item, score in population_score
]
# This is selection
for i in range(lowerCAmelCase_ ):
population.extend(select(population_score[int(lowerCAmelCase_ )], lowerCAmelCase_, lowerCAmelCase_ ) )
# Check if the population has already reached the maximum value and if so,
# break the cycle. If this check is disabled, the algorithm will take
# forever to compute large strings, but will also calculate small strings in
# a far fewer generations.
if len(lowerCAmelCase_ ) > N_POPULATION:
break
if __name__ == "__main__":
_lowerCamelCase =(
"This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
)
_lowerCamelCase =list(
" ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
"nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
)
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase =basic(target_str, genes_list)
print(
f'\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'
)
| 334 | 1 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class a_ ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : List[str] ,snake_case : Dict[str, int] ,snake_case : List[str] ,snake_case : int = None ,snake_case : int = None ):
super().__init__()
SCREAMING_SNAKE_CASE =pad_token_id
SCREAMING_SNAKE_CASE =max_length
SCREAMING_SNAKE_CASE =vocab
SCREAMING_SNAKE_CASE =merges
SCREAMING_SNAKE_CASE =BytePairTokenizer(snake_case ,snake_case ,sequence_length=snake_case )
@classmethod
def _lowerCAmelCase ( cls : List[Any] ,snake_case : GPTaTokenizer ,*snake_case : List[str] ,**snake_case : Optional[Any] ):
SCREAMING_SNAKE_CASE =[' '.join(snake_case ) for m in tokenizer.bpe_ranks.keys()]
SCREAMING_SNAKE_CASE =tokenizer.get_vocab()
return cls(snake_case ,snake_case ,*snake_case ,**snake_case )
@classmethod
def _lowerCAmelCase ( cls : Optional[Any] ,snake_case : Union[str, os.PathLike] ,*snake_case : Optional[int] ,**snake_case : Union[str, Any] ):
SCREAMING_SNAKE_CASE =GPTaTokenizer.from_pretrained(snake_case ,*snake_case ,**snake_case )
return cls.from_tokenizer(snake_case ,*snake_case ,**snake_case )
@classmethod
def _lowerCAmelCase ( cls : Any ,snake_case : List[Any] ):
return cls(**snake_case )
def _lowerCAmelCase ( self : Any ):
return {
"vocab": self.vocab,
"merges": self.merges,
"max_length": self.max_length,
"pad_token_id": self.pad_token_id,
}
def _lowerCAmelCase ( self : Dict ,snake_case : Any ,snake_case : int = None ):
SCREAMING_SNAKE_CASE =self.tf_tokenizer(snake_case )
SCREAMING_SNAKE_CASE =tf.ones_like(snake_case )
if self.pad_token_id is not None:
# pad the tokens up to max length
SCREAMING_SNAKE_CASE =max_length if max_length is not None else self.max_length
if max_length is not None:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =pad_model_inputs(
snake_case ,max_seq_length=snake_case ,pad_value=self.pad_token_id )
return {"attention_mask": attention_mask, "input_ids": input_ids}
| 334 |
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer,
BertTokenizer,
BertTokenizerFast,
GPTaTokenizerFast,
is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_tokenization import CustomTokenizer # noqa E402
if is_tokenizers_available():
from test_module.custom_tokenization_fast import CustomTokenizerFast
class a_ ( unittest.TestCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : Dict ):
# A mock response for an HTTP head request to emulate server down
SCREAMING_SNAKE_CASE =mock.Mock()
SCREAMING_SNAKE_CASE =500
SCREAMING_SNAKE_CASE ={}
SCREAMING_SNAKE_CASE =HTTPError
SCREAMING_SNAKE_CASE ={}
# Download this model to make sure it's in the cache.
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert' )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch('requests.Session.request' ,return_value=snake_case ) as mock_head:
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert' )
# This check we did call the fake head request
mock_head.assert_called()
@require_tokenizers
def _lowerCAmelCase ( self : Optional[Any] ):
# A mock response for an HTTP head request to emulate server down
SCREAMING_SNAKE_CASE =mock.Mock()
SCREAMING_SNAKE_CASE =500
SCREAMING_SNAKE_CASE ={}
SCREAMING_SNAKE_CASE =HTTPError
SCREAMING_SNAKE_CASE ={}
# Download this model to make sure it's in the cache.
SCREAMING_SNAKE_CASE =GPTaTokenizerFast.from_pretrained('gpt2' )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch('requests.Session.request' ,return_value=snake_case ) as mock_head:
SCREAMING_SNAKE_CASE =GPTaTokenizerFast.from_pretrained('gpt2' )
# This check we did call the fake head request
mock_head.assert_called()
def _lowerCAmelCase ( self : Union[str, Any] ):
# This test is for deprecated behavior and can be removed in v5
try:
SCREAMING_SNAKE_CASE =tempfile.mktemp()
with open(snake_case ,'wb' ) as f:
http_get('https://huggingface.co/albert-base-v1/resolve/main/spiece.model' ,snake_case )
SCREAMING_SNAKE_CASE =AlbertTokenizer.from_pretrained(snake_case )
finally:
os.remove(snake_case )
# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
# the current folder and have the right name.
if os.path.isfile('tokenizer.json' ):
# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
return
try:
with open('tokenizer.json' ,'wb' ) as f:
http_get('https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json' ,snake_case )
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
self.assertEqual(tokenizer.vocab_size ,1000 )
# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.
finally:
os.remove('tokenizer.json' )
def _lowerCAmelCase ( self : int ):
# This test is for deprecated behavior and can be removed in v5
SCREAMING_SNAKE_CASE =AlbertTokenizer.from_pretrained('https://huggingface.co/albert-base-v1/resolve/main/spiece.model' )
@is_staging_test
class a_ ( unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase = ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']
@classmethod
def _lowerCAmelCase ( cls : List[Any] ):
SCREAMING_SNAKE_CASE =TOKEN
HfFolder.save_token(snake_case )
@classmethod
def _lowerCAmelCase ( cls : Tuple ):
try:
delete_repo(token=cls._token ,repo_id='test-tokenizer' )
except HTTPError:
pass
try:
delete_repo(token=cls._token ,repo_id='valid_org/test-tokenizer-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token ,repo_id='test-dynamic-tokenizer' )
except HTTPError:
pass
def _lowerCAmelCase ( self : Any ):
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,'vocab.txt' )
with open(snake_case ,'w' ,encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
SCREAMING_SNAKE_CASE =BertTokenizer(snake_case )
tokenizer.push_to_hub('test-tokenizer' ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained(f'{USER}/test-tokenizer' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
# Reset repo
delete_repo(token=self._token ,repo_id='test-tokenizer' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(snake_case ,repo_id='test-tokenizer' ,push_to_hub=snake_case ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained(f'{USER}/test-tokenizer' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
def _lowerCAmelCase ( self : Optional[Any] ):
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,'vocab.txt' )
with open(snake_case ,'w' ,encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
SCREAMING_SNAKE_CASE =BertTokenizer(snake_case )
tokenizer.push_to_hub('valid_org/test-tokenizer-org' ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained('valid_org/test-tokenizer-org' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
# Reset repo
delete_repo(token=self._token ,repo_id='valid_org/test-tokenizer-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(
snake_case ,repo_id='valid_org/test-tokenizer-org' ,push_to_hub=snake_case ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained('valid_org/test-tokenizer-org' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
@require_tokenizers
def _lowerCAmelCase ( self : str ):
CustomTokenizer.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,'vocab.txt' )
with open(snake_case ,'w' ,encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
SCREAMING_SNAKE_CASE =CustomTokenizer(snake_case )
# No fast custom tokenizer
tokenizer.push_to_hub('test-dynamic-tokenizer' ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer' ,trust_remote_code=snake_case )
# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,'CustomTokenizer' )
# Fast and slow custom tokenizer
CustomTokenizerFast.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,'vocab.txt' )
with open(snake_case ,'w' ,encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
SCREAMING_SNAKE_CASE =BertTokenizerFast.from_pretrained(snake_case )
bert_tokenizer.save_pretrained(snake_case )
SCREAMING_SNAKE_CASE =CustomTokenizerFast.from_pretrained(snake_case )
tokenizer.push_to_hub('test-dynamic-tokenizer' ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer' ,trust_remote_code=snake_case )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,'CustomTokenizerFast' )
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained(
f'{USER}/test-dynamic-tokenizer' ,use_fast=snake_case ,trust_remote_code=snake_case )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,'CustomTokenizer' )
class a_ ( unittest.TestCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('Hello 友達' )
self.assertEqual(trie.data ,{'H': {'e': {'l': {'l': {'o': {' ': {'友': {'達': {'': 1}}}}}}}}} )
trie.add('Hello' )
trie.data
self.assertEqual(trie.data ,{'H': {'e': {'l': {'l': {'o': {'': 1, ' ': {'友': {'達': {'': 1}}}}}}}}} )
def _lowerCAmelCase ( self : Optional[int] ):
SCREAMING_SNAKE_CASE =Trie()
self.assertEqual(trie.split('[CLS] This is a extra_id_100' ) ,['[CLS] This is a extra_id_100'] )
trie.add('[CLS]' )
trie.add('extra_id_1' )
trie.add('extra_id_100' )
self.assertEqual(trie.split('[CLS] This is a extra_id_100' ) ,['[CLS]', ' This is a ', 'extra_id_100'] )
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('A' )
self.assertEqual(trie.split('ABC' ) ,['A', 'BC'] )
self.assertEqual(trie.split('BCA' ) ,['BC', 'A'] )
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('TOKEN]' )
trie.add('[SPECIAL_TOKEN]' )
self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]' ) ,['This is something ', '[SPECIAL_TOKEN]'] )
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('A' )
trie.add('P' )
trie.add('[SPECIAL_TOKEN]' )
self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]' ) ,['This is something ', '[SPECIAL_TOKEN]'] )
def _lowerCAmelCase ( self : Dict ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('AB' )
trie.add('B' )
trie.add('C' )
self.assertEqual(trie.split('ABC' ) ,['AB', 'C'] )
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('ABC' )
trie.add('B' )
trie.add('CD' )
self.assertEqual(trie.split('ABCD' ) ,['ABC', 'D'] )
def _lowerCAmelCase ( self : Optional[Any] ):
# Even if the offsets are wrong, we necessarily output correct string
# parts.
SCREAMING_SNAKE_CASE =Trie()
SCREAMING_SNAKE_CASE =trie.cut_text('ABC' ,[0, 0, 2, 1, 2, 3] )
self.assertEqual(snake_case ,['AB', 'C'] )
| 334 | 1 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel
class a_ :
"""simple docstring"""
def __init__( self : List[Any] ,snake_case : str ,snake_case : Union[str, Any]=13 ,snake_case : Optional[int]=7 ,snake_case : Dict=True ,snake_case : str=True ,snake_case : str=False ,snake_case : List[str]=True ,snake_case : str=99 ,snake_case : str=32 ,snake_case : Union[str, Any]=5 ,snake_case : Dict=4 ,snake_case : Dict=37 ,snake_case : Tuple="gelu" ,snake_case : List[str]=0.1 ,snake_case : Optional[int]=0.1 ,snake_case : List[str]=512 ,snake_case : Tuple=16 ,snake_case : str=2 ,snake_case : List[Any]=0.02 ,snake_case : Dict=3 ,snake_case : Dict=4 ,snake_case : int=None ,):
SCREAMING_SNAKE_CASE =parent
SCREAMING_SNAKE_CASE =batch_size
SCREAMING_SNAKE_CASE =seq_length
SCREAMING_SNAKE_CASE =is_training
SCREAMING_SNAKE_CASE =use_input_mask
SCREAMING_SNAKE_CASE =use_token_type_ids
SCREAMING_SNAKE_CASE =use_labels
SCREAMING_SNAKE_CASE =vocab_size
SCREAMING_SNAKE_CASE =hidden_size
SCREAMING_SNAKE_CASE =num_hidden_layers
SCREAMING_SNAKE_CASE =num_attention_heads
SCREAMING_SNAKE_CASE =intermediate_size
SCREAMING_SNAKE_CASE =hidden_act
SCREAMING_SNAKE_CASE =hidden_dropout_prob
SCREAMING_SNAKE_CASE =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE =max_position_embeddings
SCREAMING_SNAKE_CASE =type_vocab_size
SCREAMING_SNAKE_CASE =type_sequence_label_size
SCREAMING_SNAKE_CASE =initializer_range
SCREAMING_SNAKE_CASE =num_labels
SCREAMING_SNAKE_CASE =num_choices
SCREAMING_SNAKE_CASE =scope
def _lowerCAmelCase ( self : Optional[int] ):
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
SCREAMING_SNAKE_CASE =None
if self.use_input_mask:
SCREAMING_SNAKE_CASE =random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE =None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
SCREAMING_SNAKE_CASE =None
SCREAMING_SNAKE_CASE =None
SCREAMING_SNAKE_CASE =None
if self.use_labels:
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size] ,self.type_sequence_label_size )
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size] ,self.num_choices )
SCREAMING_SNAKE_CASE =self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowerCAmelCase ( self : List[Any] ):
return OpenLlamaConfig(
vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,type_vocab_size=self.type_vocab_size ,is_decoder=snake_case ,initializer_range=self.initializer_range ,use_stable_embedding=snake_case ,)
def _lowerCAmelCase ( self : int ,snake_case : str ,snake_case : List[Any] ,snake_case : Union[str, Any] ,snake_case : Any ,snake_case : Tuple ,snake_case : Union[str, Any] ,snake_case : Optional[int] ):
SCREAMING_SNAKE_CASE =OpenLlamaModel(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case ,attention_mask=snake_case )
SCREAMING_SNAKE_CASE =model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _lowerCAmelCase ( self : int ,snake_case : List[str] ,snake_case : Union[str, Any] ,snake_case : List[Any] ,snake_case : Any ,snake_case : str ,snake_case : List[str] ,snake_case : Tuple ,snake_case : int ,snake_case : Dict ,):
SCREAMING_SNAKE_CASE =True
SCREAMING_SNAKE_CASE =OpenLlamaModel(snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(
snake_case ,attention_mask=snake_case ,encoder_hidden_states=snake_case ,encoder_attention_mask=snake_case ,)
SCREAMING_SNAKE_CASE =model(
snake_case ,attention_mask=snake_case ,encoder_hidden_states=snake_case ,)
SCREAMING_SNAKE_CASE =model(snake_case ,attention_mask=snake_case )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _lowerCAmelCase ( self : Tuple ,snake_case : str ,snake_case : List[Any] ,snake_case : Optional[int] ,snake_case : Optional[Any] ,snake_case : Optional[int] ,snake_case : Union[str, Any] ,snake_case : List[str] ,snake_case : Optional[Any] ,snake_case : List[str] ,):
SCREAMING_SNAKE_CASE =OpenLlamaForCausalLM(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case ,attention_mask=snake_case ,labels=snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def _lowerCAmelCase ( self : Any ,snake_case : Optional[Any] ,snake_case : int ,snake_case : str ,snake_case : Dict ,snake_case : Dict ,snake_case : Dict ,snake_case : str ,snake_case : List[Any] ,snake_case : List[str] ,):
SCREAMING_SNAKE_CASE =True
SCREAMING_SNAKE_CASE =True
SCREAMING_SNAKE_CASE =OpenLlamaForCausalLM(config=snake_case )
model.to(snake_case )
model.eval()
# first forward pass
SCREAMING_SNAKE_CASE =model(
snake_case ,attention_mask=snake_case ,encoder_hidden_states=snake_case ,encoder_attention_mask=snake_case ,use_cache=snake_case ,)
SCREAMING_SNAKE_CASE =outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
SCREAMING_SNAKE_CASE =ids_tensor((self.batch_size, 3) ,config.vocab_size )
SCREAMING_SNAKE_CASE =ids_tensor((self.batch_size, 3) ,vocab_size=2 )
# append to next input_ids and
SCREAMING_SNAKE_CASE =torch.cat([input_ids, next_tokens] ,dim=-1 )
SCREAMING_SNAKE_CASE =torch.cat([input_mask, next_mask] ,dim=-1 )
SCREAMING_SNAKE_CASE =model(
snake_case ,attention_mask=snake_case ,encoder_hidden_states=snake_case ,encoder_attention_mask=snake_case ,output_hidden_states=snake_case ,)['hidden_states'][0]
SCREAMING_SNAKE_CASE =model(
snake_case ,attention_mask=snake_case ,encoder_hidden_states=snake_case ,encoder_attention_mask=snake_case ,past_key_values=snake_case ,output_hidden_states=snake_case ,)['hidden_states'][0]
# select random slice
SCREAMING_SNAKE_CASE =ids_tensor((1,) ,output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE =output_from_no_past[:, -3:, random_slice_idx].detach()
SCREAMING_SNAKE_CASE =output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(snake_case ,snake_case ,atol=1e-3 ) )
def _lowerCAmelCase ( self : Dict ):
SCREAMING_SNAKE_CASE =self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) ,
) =config_and_inputs
SCREAMING_SNAKE_CASE ={'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class a_ ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase = (
(OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
)
__UpperCAmelCase = (OpenLlamaForCausalLM,) if is_torch_available() else ()
__UpperCAmelCase = (
{
'feature-extraction': OpenLlamaModel,
'text-classification': OpenLlamaForSequenceClassification,
'text-generation': OpenLlamaForCausalLM,
'zero-shot': OpenLlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
__UpperCAmelCase = False
__UpperCAmelCase = False
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =OpenLlamaModelTester(self )
SCREAMING_SNAKE_CASE =ConfigTester(self ,config_class=snake_case ,hidden_size=37 )
def _lowerCAmelCase ( self : Dict ):
self.config_tester.run_common_tests()
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def _lowerCAmelCase ( self : str ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
SCREAMING_SNAKE_CASE =type
self.model_tester.create_and_check_model(*snake_case )
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE =3
SCREAMING_SNAKE_CASE =input_dict['input_ids']
SCREAMING_SNAKE_CASE =input_ids.ne(1 ).to(snake_case )
SCREAMING_SNAKE_CASE =ids_tensor([self.model_tester.batch_size] ,self.model_tester.type_sequence_label_size )
SCREAMING_SNAKE_CASE =OpenLlamaForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case ,attention_mask=snake_case ,labels=snake_case )
self.assertEqual(result.logits.shape ,(self.model_tester.batch_size, self.model_tester.num_labels) )
def _lowerCAmelCase ( self : Optional[int] ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE =3
SCREAMING_SNAKE_CASE ='single_label_classification'
SCREAMING_SNAKE_CASE =input_dict['input_ids']
SCREAMING_SNAKE_CASE =input_ids.ne(1 ).to(snake_case )
SCREAMING_SNAKE_CASE =ids_tensor([self.model_tester.batch_size] ,self.model_tester.type_sequence_label_size )
SCREAMING_SNAKE_CASE =OpenLlamaForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case ,attention_mask=snake_case ,labels=snake_case )
self.assertEqual(result.logits.shape ,(self.model_tester.batch_size, self.model_tester.num_labels) )
def _lowerCAmelCase ( self : str ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE =3
SCREAMING_SNAKE_CASE ='multi_label_classification'
SCREAMING_SNAKE_CASE =input_dict['input_ids']
SCREAMING_SNAKE_CASE =input_ids.ne(1 ).to(snake_case )
SCREAMING_SNAKE_CASE =ids_tensor(
[self.model_tester.batch_size, config.num_labels] ,self.model_tester.type_sequence_label_size ).to(torch.float )
SCREAMING_SNAKE_CASE =OpenLlamaForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case ,attention_mask=snake_case ,labels=snake_case )
self.assertEqual(result.logits.shape ,(self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip('Open-Llama buffers include complex numbers, which breaks this test' )
def _lowerCAmelCase ( self : Union[str, Any] ):
pass
@parameterized.expand([('linear',), ('dynamic',)] )
def _lowerCAmelCase ( self : str ,snake_case : Union[str, Any] ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE =ids_tensor([1, 10] ,config.vocab_size )
SCREAMING_SNAKE_CASE =ids_tensor([1, int(config.max_position_embeddings * 1.5 )] ,config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
SCREAMING_SNAKE_CASE =OpenLlamaModel(snake_case )
original_model.to(snake_case )
original_model.eval()
SCREAMING_SNAKE_CASE =original_model(snake_case ).last_hidden_state
SCREAMING_SNAKE_CASE =original_model(snake_case ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
SCREAMING_SNAKE_CASE ={'type': scaling_type, 'factor': 10.0}
SCREAMING_SNAKE_CASE =OpenLlamaModel(snake_case )
scaled_model.to(snake_case )
scaled_model.eval()
SCREAMING_SNAKE_CASE =scaled_model(snake_case ).last_hidden_state
SCREAMING_SNAKE_CASE =scaled_model(snake_case ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(snake_case ,snake_case ,atol=1e-5 ) )
else:
self.assertFalse(torch.allclose(snake_case ,snake_case ,atol=1e-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(snake_case ,snake_case ,atol=1e-5 ) )
| 334 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase =[
("bert.bert", "visual_bert"),
("bert.cls", "cls"),
("bert.classifier", "cls"),
("token_type_embeddings_visual", "visual_token_type_embeddings"),
("position_embeddings_visual", "visual_position_embeddings"),
("projection", "visual_projection"),
]
_lowerCamelCase =[
"nlvr2_coco_pre_trained.th",
"nlvr2_fine_tuned.th",
"nlvr2_pre_trained.th",
"vcr_coco_pre_train.th",
"vcr_fine_tune.th",
"vcr_pre_train.th",
"vqa_coco_pre_trained.th",
"vqa_fine_tuned.th",
"vqa_pre_trained.th",
]
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =torch.load(lowerCAmelCase_, map_location='cpu' )
return sd
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_=rename_keys_prefix ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =OrderedDict()
SCREAMING_SNAKE_CASE =torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
SCREAMING_SNAKE_CASE =key
for name_pair in rename_keys_prefix:
SCREAMING_SNAKE_CASE =new_key.replace(name_pair[0], name_pair[1] )
SCREAMING_SNAKE_CASE =d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
SCREAMING_SNAKE_CASE =new_d['cls.predictions.bias']
return new_d
@torch.no_grad()
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
assert (
checkpoint_path.split('/' )[-1] in ACCEPTABLE_CHECKPOINTS
), F'The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'
# Get Config
if "pre" in checkpoint_path:
SCREAMING_SNAKE_CASE ='pretraining'
if "vcr" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 512}
elif "vqa_advanced" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 2048}
elif "vqa" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 2048}
elif "nlvr" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 1024}
else:
raise NotImplementedError(F'No implementation found for `{checkpoint_path}`.' )
else:
if "vcr" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 512}
SCREAMING_SNAKE_CASE ='multichoice'
elif "vqa_advanced" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 2048}
SCREAMING_SNAKE_CASE ='vqa_advanced'
elif "vqa" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 2048, 'num_labels': 3129}
SCREAMING_SNAKE_CASE ='vqa'
elif "nlvr" in checkpoint_path:
SCREAMING_SNAKE_CASE ={
'visual_embedding_dim': 1024,
'num_labels': 2,
}
SCREAMING_SNAKE_CASE ='nlvr'
SCREAMING_SNAKE_CASE =VisualBertConfig(**lowerCAmelCase_ )
# Load State Dict
SCREAMING_SNAKE_CASE =load_state_dict(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =get_new_dict(lowerCAmelCase_, lowerCAmelCase_ )
if model_type == "pretraining":
SCREAMING_SNAKE_CASE =VisualBertForPreTraining(lowerCAmelCase_ )
elif model_type == "vqa":
SCREAMING_SNAKE_CASE =VisualBertForQuestionAnswering(lowerCAmelCase_ )
elif model_type == "nlvr":
SCREAMING_SNAKE_CASE =VisualBertForVisualReasoning(lowerCAmelCase_ )
elif model_type == "multichoice":
SCREAMING_SNAKE_CASE =VisualBertForMultipleChoice(lowerCAmelCase_ )
model.load_state_dict(lowerCAmelCase_ )
# Save Checkpoints
Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ )
model.save_pretrained(lowerCAmelCase_ )
if __name__ == "__main__":
_lowerCamelCase =argparse.ArgumentParser()
# Required parameters
parser.add_argument("orig_checkpoint_path", type=str, help="A path to .th on local filesystem.")
parser.add_argument("pytorch_dump_folder_path", type=str, help="Path to the output PyTorch model.")
_lowerCamelCase =parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 334 | 1 |
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class a_ ( unittest.TestCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : Union[str, Any] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE =load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/sd2-inpaint/init_image.png' )
SCREAMING_SNAKE_CASE =load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png' )
SCREAMING_SNAKE_CASE ='xvjiarui/stable-diffusion-2-inpainting'
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =FlaxStableDiffusionInpaintPipeline.from_pretrained(snake_case ,safety_checker=snake_case )
SCREAMING_SNAKE_CASE ='Face of a yellow cat, high resolution, sitting on a park bench'
SCREAMING_SNAKE_CASE =jax.random.PRNGKey(0 )
SCREAMING_SNAKE_CASE =50
SCREAMING_SNAKE_CASE =jax.device_count()
SCREAMING_SNAKE_CASE =num_samples * [prompt]
SCREAMING_SNAKE_CASE =num_samples * [init_image]
SCREAMING_SNAKE_CASE =num_samples * [mask_image]
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =pipeline.prepare_inputs(snake_case ,snake_case ,snake_case )
# shard inputs and rng
SCREAMING_SNAKE_CASE =replicate(snake_case )
SCREAMING_SNAKE_CASE =jax.random.split(snake_case ,jax.device_count() )
SCREAMING_SNAKE_CASE =shard(snake_case )
SCREAMING_SNAKE_CASE =shard(snake_case )
SCREAMING_SNAKE_CASE =shard(snake_case )
SCREAMING_SNAKE_CASE =pipeline(
snake_case ,snake_case ,snake_case ,snake_case ,snake_case ,snake_case ,jit=snake_case )
SCREAMING_SNAKE_CASE =output.images.reshape(snake_case ,512 ,512 ,3 )
SCREAMING_SNAKE_CASE =images[0, 253:256, 253:256, -1]
SCREAMING_SNAKE_CASE =jnp.asarray(jax.device_get(image_slice.flatten() ) )
SCREAMING_SNAKE_CASE =jnp.array(
[0.3_611_307, 0.37_649_736, 0.3_757_408, 0.38_213_953, 0.39_295_167, 0.3_841_631, 0.41_554_978, 0.4_137_475, 0.4_217_084] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 334 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"facebook/nllb-moe-54B": "https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json",
}
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'nllb-moe'
__UpperCAmelCase = ['past_key_values']
__UpperCAmelCase = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self : str ,snake_case : Optional[int]=128112 ,snake_case : Any=1024 ,snake_case : List[str]=12 ,snake_case : Optional[int]=4096 ,snake_case : List[str]=16 ,snake_case : Optional[Any]=12 ,snake_case : Optional[Any]=4096 ,snake_case : List[Any]=16 ,snake_case : Optional[Any]=0.05 ,snake_case : str=0.05 ,snake_case : Optional[int]=True ,snake_case : Tuple=True ,snake_case : Optional[Any]="relu" ,snake_case : Any=1024 ,snake_case : List[Any]=0.1 ,snake_case : List[Any]=0.1 ,snake_case : Optional[Any]=0.0 ,snake_case : List[Any]=0.02 ,snake_case : Any=2 ,snake_case : Dict=True ,snake_case : Tuple=False ,snake_case : Any="float32" ,snake_case : Tuple=False ,snake_case : List[Any]=128 ,snake_case : Tuple=64 ,snake_case : List[Any]=4 ,snake_case : List[Any]=4 ,snake_case : List[Any]=0.001 ,snake_case : int=0.001 ,snake_case : Tuple="all" ,snake_case : Union[str, Any]=False ,snake_case : Union[str, Any]=False ,snake_case : Optional[int]=1.0 ,snake_case : Optional[Any]=0.2 ,snake_case : Optional[int]=1 ,snake_case : Union[str, Any]=0 ,snake_case : Tuple=2 ,snake_case : List[Any]=False ,**snake_case : List[Any] ,):
SCREAMING_SNAKE_CASE =vocab_size
SCREAMING_SNAKE_CASE =max_position_embeddings
SCREAMING_SNAKE_CASE =d_model
SCREAMING_SNAKE_CASE =encoder_ffn_dim
SCREAMING_SNAKE_CASE =encoder_layers
SCREAMING_SNAKE_CASE =encoder_attention_heads
SCREAMING_SNAKE_CASE =decoder_ffn_dim
SCREAMING_SNAKE_CASE =decoder_layers
SCREAMING_SNAKE_CASE =decoder_attention_heads
SCREAMING_SNAKE_CASE =dropout
SCREAMING_SNAKE_CASE =attention_dropout
SCREAMING_SNAKE_CASE =activation_dropout
SCREAMING_SNAKE_CASE =activation_function
SCREAMING_SNAKE_CASE =init_std
SCREAMING_SNAKE_CASE =encoder_layerdrop
SCREAMING_SNAKE_CASE =decoder_layerdrop
SCREAMING_SNAKE_CASE =use_cache
SCREAMING_SNAKE_CASE =encoder_layers
SCREAMING_SNAKE_CASE =scale_embedding # scale factor will be sqrt(d_model) if True
SCREAMING_SNAKE_CASE =router_z_loss_coef
SCREAMING_SNAKE_CASE =router_aux_loss_coef
SCREAMING_SNAKE_CASE =decoder_sparse_step
SCREAMING_SNAKE_CASE =encoder_sparse_step
SCREAMING_SNAKE_CASE =num_experts
SCREAMING_SNAKE_CASE =expert_capacity
SCREAMING_SNAKE_CASE =router_bias
if router_dtype not in ["float32", "float16", "bfloat16"]:
raise ValueError(f'`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}' )
SCREAMING_SNAKE_CASE =router_dtype
SCREAMING_SNAKE_CASE =router_ignore_padding_tokens
SCREAMING_SNAKE_CASE =batch_prioritized_routing
SCREAMING_SNAKE_CASE =second_expert_policy
SCREAMING_SNAKE_CASE =normalize_router_prob_before_dropping
SCREAMING_SNAKE_CASE =moe_eval_capacity_token_fraction
SCREAMING_SNAKE_CASE =moe_token_dropout
SCREAMING_SNAKE_CASE =output_router_logits
super().__init__(
pad_token_id=snake_case ,bos_token_id=snake_case ,eos_token_id=snake_case ,is_encoder_decoder=snake_case ,decoder_start_token_id=snake_case ,**snake_case ,)
| 334 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase ={
"configuration_convnext": ["CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvNextConfig", "ConvNextOnnxConfig"]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase =["ConvNextFeatureExtractor"]
_lowerCamelCase =["ConvNextImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase =[
"CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
"ConvNextForImageClassification",
"ConvNextModel",
"ConvNextPreTrainedModel",
"ConvNextBackbone",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase =[
"TFConvNextForImageClassification",
"TFConvNextModel",
"TFConvNextPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_convnext import ConvNextFeatureExtractor
from .image_processing_convnext import ConvNextImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convnext import (
CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvNextBackbone,
ConvNextForImageClassification,
ConvNextModel,
ConvNextPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel
else:
import sys
_lowerCamelCase =_LazyModule(__name__, globals()["__file__"], _import_structure)
| 334 |
from __future__ import annotations
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =sorted(numsa + numsa )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =divmod(len(lowerCAmelCase_ ), 2 )
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowerCamelCase =[float(x) for x in input("Enter the elements of first array: ").split()]
_lowerCamelCase =[float(x) for x in input("Enter the elements of second array: ").split()]
print(f'The median of two arrays is: {median_of_two_arrays(array_a, array_a)}')
| 334 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
HfArgumentParser,
MBartTokenizer,
MBartTokenizerFast,
set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
SeqaSeqDataCollator,
SeqaSeqDataset,
assert_all_frozen,
build_compute_metrics_fn,
check_output_dir,
freeze_embeds,
freeze_params,
lmap,
save_json,
use_task_specific_params,
write_txt_file,
)
_lowerCamelCase =logging.getLogger(__name__)
@dataclass
class a_ :
"""simple docstring"""
__UpperCAmelCase = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
__UpperCAmelCase = field(
default=lowerCamelCase_ , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
__UpperCAmelCase = field(
default=lowerCamelCase_ , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
__UpperCAmelCase = field(
default=lowerCamelCase_ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
__UpperCAmelCase = field(default=lowerCamelCase_ , metadata={'help': 'Whether tp freeze the encoder.'} )
__UpperCAmelCase = field(default=lowerCamelCase_ , metadata={'help': 'Whether to freeze the embeddings.'} )
@dataclass
class a_ :
"""simple docstring"""
__UpperCAmelCase = field(
metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'} )
__UpperCAmelCase = field(
default='summarization' , metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'} , )
__UpperCAmelCase = field(
default=1_024 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
__UpperCAmelCase = field(
default=128 , metadata={
'help': (
'The maximum total sequence length for target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
__UpperCAmelCase = field(
default=142 , metadata={
'help': (
'The maximum total sequence length for validation target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded. '
'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
'during ``evaluate`` and ``predict``.'
)
} , )
__UpperCAmelCase = field(
default=142 , metadata={
'help': (
'The maximum total sequence length for test target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
__UpperCAmelCase = field(default=-1 , metadata={'help': '# training examples. -1 means use all.'} )
__UpperCAmelCase = field(default=-1 , metadata={'help': '# validation examples. -1 means use all.'} )
__UpperCAmelCase = field(default=-1 , metadata={'help': '# test examples. -1 means use all.'} )
__UpperCAmelCase = field(default=lowerCamelCase_ , metadata={'help': 'Source language id for translation.'} )
__UpperCAmelCase = field(default=lowerCamelCase_ , metadata={'help': 'Target language id for translation.'} )
__UpperCAmelCase = field(default=lowerCamelCase_ , metadata={'help': '# num_beams to use for evaluation.'} )
__UpperCAmelCase = field(
default=lowerCamelCase_ , metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'} , )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
logger.info(F'***** {split} metrics *****' )
for key in sorted(metrics.keys() ):
logger.info(F' {key} = {metrics[key]}' )
save_json(lowerCAmelCase_, os.path.join(lowerCAmelCase_, F'{split}_results.json' ) )
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =parser.parse_args_into_dataclasses()
check_output_dir(lowerCAmelCase_ )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN, )
logger.warning(
'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s', training_args.local_rank, training_args.device, training_args.n_gpu, bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED ), training_args.fpaa, )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info('Training/evaluation parameters %s', lowerCAmelCase_ )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
SCREAMING_SNAKE_CASE =AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, )
SCREAMING_SNAKE_CASE =('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
for p in extra_model_params:
if getattr(lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
assert hasattr(lowerCAmelCase_, lowerCAmelCase_ ), F'({config.__class__.__name__}) doesn\'t have a `{p}` attribute'
setattr(lowerCAmelCase_, lowerCAmelCase_, getattr(lowerCAmelCase_, lowerCAmelCase_ ) )
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, )
SCREAMING_SNAKE_CASE =AutoModelForSeqaSeqLM.from_pretrained(
model_args.model_name_or_path, from_tf='.ckpt' in model_args.model_name_or_path, config=lowerCAmelCase_, cache_dir=model_args.cache_dir, )
# use task specific params
use_task_specific_params(lowerCAmelCase_, data_args.task )
# set num_beams for evaluation
if data_args.eval_beams is None:
SCREAMING_SNAKE_CASE =model.config.num_beams
# set decoder_start_token_id for MBart
if model.config.decoder_start_token_id is None and isinstance(lowerCAmelCase_, (MBartTokenizer, MBartTokenizerFast) ):
assert (
data_args.tgt_lang is not None and data_args.src_lang is not None
), "mBart requires --tgt_lang and --src_lang"
if isinstance(lowerCAmelCase_, lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE =tokenizer.lang_code_to_id[data_args.tgt_lang]
else:
SCREAMING_SNAKE_CASE =tokenizer.convert_tokens_to_ids(data_args.tgt_lang )
if model_args.freeze_embeds:
freeze_embeds(lowerCAmelCase_ )
if model_args.freeze_encoder:
freeze_params(model.get_encoder() )
assert_all_frozen(model.get_encoder() )
SCREAMING_SNAKE_CASE =SeqaSeqDataset
# Get datasets
SCREAMING_SNAKE_CASE =(
dataset_class(
lowerCAmelCase_, type_path='train', data_dir=data_args.data_dir, n_obs=data_args.n_train, max_target_length=data_args.max_target_length, max_source_length=data_args.max_source_length, prefix=model.config.prefix or '', )
if training_args.do_train
else None
)
SCREAMING_SNAKE_CASE =(
dataset_class(
lowerCAmelCase_, type_path='val', data_dir=data_args.data_dir, n_obs=data_args.n_val, max_target_length=data_args.val_max_target_length, max_source_length=data_args.max_source_length, prefix=model.config.prefix or '', )
if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
else None
)
SCREAMING_SNAKE_CASE =(
dataset_class(
lowerCAmelCase_, type_path='test', data_dir=data_args.data_dir, n_obs=data_args.n_test, max_target_length=data_args.test_max_target_length, max_source_length=data_args.max_source_length, prefix=model.config.prefix or '', )
if training_args.do_predict
else None
)
# Initialize our Trainer
SCREAMING_SNAKE_CASE =(
build_compute_metrics_fn(data_args.task, lowerCAmelCase_ ) if training_args.predict_with_generate else None
)
SCREAMING_SNAKE_CASE =SeqaSeqTrainer(
model=lowerCAmelCase_, args=lowerCAmelCase_, data_args=lowerCAmelCase_, train_dataset=lowerCAmelCase_, eval_dataset=lowerCAmelCase_, data_collator=SeqaSeqDataCollator(
lowerCAmelCase_, lowerCAmelCase_, model.config.decoder_start_token_id, training_args.tpu_num_cores ), compute_metrics=lowerCAmelCase_, tokenizer=lowerCAmelCase_, )
SCREAMING_SNAKE_CASE ={}
# Training
if training_args.do_train:
logger.info('*** Train ***' )
SCREAMING_SNAKE_CASE =trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
SCREAMING_SNAKE_CASE =train_result.metrics
SCREAMING_SNAKE_CASE =data_args.n_train
trainer.save_model() # this also saves the tokenizer
if trainer.is_world_process_zero():
handle_metrics('train', lowerCAmelCase_, training_args.output_dir )
all_metrics.update(lowerCAmelCase_ )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir, 'trainer_state.json' ) )
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
SCREAMING_SNAKE_CASE =trainer.evaluate(metric_key_prefix='val' )
SCREAMING_SNAKE_CASE =data_args.n_val
SCREAMING_SNAKE_CASE =round(metrics['val_loss'], 4 )
if trainer.is_world_process_zero():
handle_metrics('val', lowerCAmelCase_, training_args.output_dir )
all_metrics.update(lowerCAmelCase_ )
if training_args.do_predict:
logger.info('*** Predict ***' )
SCREAMING_SNAKE_CASE =trainer.predict(test_dataset=lowerCAmelCase_, metric_key_prefix='test' )
SCREAMING_SNAKE_CASE =test_output.metrics
SCREAMING_SNAKE_CASE =data_args.n_test
if trainer.is_world_process_zero():
SCREAMING_SNAKE_CASE =round(metrics['test_loss'], 4 )
handle_metrics('test', lowerCAmelCase_, training_args.output_dir )
all_metrics.update(lowerCAmelCase_ )
if training_args.predict_with_generate:
SCREAMING_SNAKE_CASE =tokenizer.batch_decode(
test_output.predictions, skip_special_tokens=lowerCAmelCase_, clean_up_tokenization_spaces=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =lmap(str.strip, lowerCAmelCase_ )
write_txt_file(lowerCAmelCase_, os.path.join(training_args.output_dir, 'test_generations.txt' ) )
if trainer.is_world_process_zero():
save_json(lowerCAmelCase_, os.path.join(training_args.output_dir, 'all_results.json' ) )
return all_metrics
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 334 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'transfo-xl'
__UpperCAmelCase = ['mems']
__UpperCAmelCase = {
'n_token': 'vocab_size',
'hidden_size': 'd_model',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self : Union[str, Any] ,snake_case : List[Any]=267735 ,snake_case : Optional[int]=[20000, 40000, 200000] ,snake_case : int=1024 ,snake_case : Optional[Any]=1024 ,snake_case : Tuple=16 ,snake_case : int=64 ,snake_case : Union[str, Any]=4096 ,snake_case : List[str]=4 ,snake_case : int=False ,snake_case : int=18 ,snake_case : Tuple=1600 ,snake_case : List[str]=1000 ,snake_case : Optional[Any]=True ,snake_case : List[str]=True ,snake_case : Optional[Any]=0 ,snake_case : Optional[Any]=-1 ,snake_case : List[Any]=True ,snake_case : Optional[Any]=0.1 ,snake_case : Union[str, Any]=0.0 ,snake_case : int=True ,snake_case : Any="normal" ,snake_case : int=0.01 ,snake_case : int=0.01 ,snake_case : str=0.02 ,snake_case : Any=1e-5 ,snake_case : Optional[int]=0 ,**snake_case : List[Any] ,):
SCREAMING_SNAKE_CASE =vocab_size
SCREAMING_SNAKE_CASE =[]
self.cutoffs.extend(snake_case )
if proj_share_all_but_first:
SCREAMING_SNAKE_CASE =[False] + [True] * len(self.cutoffs )
else:
SCREAMING_SNAKE_CASE =[False] + [False] * len(self.cutoffs )
SCREAMING_SNAKE_CASE =d_model
SCREAMING_SNAKE_CASE =d_embed
SCREAMING_SNAKE_CASE =d_head
SCREAMING_SNAKE_CASE =d_inner
SCREAMING_SNAKE_CASE =div_val
SCREAMING_SNAKE_CASE =pre_lnorm
SCREAMING_SNAKE_CASE =n_layer
SCREAMING_SNAKE_CASE =n_head
SCREAMING_SNAKE_CASE =mem_len
SCREAMING_SNAKE_CASE =same_length
SCREAMING_SNAKE_CASE =attn_type
SCREAMING_SNAKE_CASE =clamp_len
SCREAMING_SNAKE_CASE =sample_softmax
SCREAMING_SNAKE_CASE =adaptive
SCREAMING_SNAKE_CASE =dropout
SCREAMING_SNAKE_CASE =dropatt
SCREAMING_SNAKE_CASE =untie_r
SCREAMING_SNAKE_CASE =init
SCREAMING_SNAKE_CASE =init_range
SCREAMING_SNAKE_CASE =proj_init_std
SCREAMING_SNAKE_CASE =init_std
SCREAMING_SNAKE_CASE =layer_norm_epsilon
super().__init__(eos_token_id=snake_case ,**snake_case )
@property
def _lowerCAmelCase ( self : str ):
# Message copied from Transformer-XL documentation
logger.info(f'The model {self.model_type} is one of the few models that has no sequence length limit.' )
return -1
@max_position_embeddings.setter
def _lowerCAmelCase ( self : Union[str, Any] ,snake_case : Dict ):
# Message copied from Transformer-XL documentation
raise NotImplementedError(
f'The model {self.model_type} is one of the few models that has no sequence length limit.' )
| 334 | 1 |
class a_ :
"""simple docstring"""
def __init__( self : Optional[int] ,snake_case : Optional[Any] ,snake_case : Any ,snake_case : Dict ):
SCREAMING_SNAKE_CASE =None
SCREAMING_SNAKE_CASE =None
SCREAMING_SNAKE_CASE =graph
self._normalize_graph(snake_case ,snake_case )
SCREAMING_SNAKE_CASE =len(snake_case )
SCREAMING_SNAKE_CASE =None
def _lowerCAmelCase ( self : List[str] ,snake_case : List[str] ,snake_case : Union[str, Any] ):
if sources is int:
SCREAMING_SNAKE_CASE =[sources]
if sinks is int:
SCREAMING_SNAKE_CASE =[sinks]
if len(snake_case ) == 0 or len(snake_case ) == 0:
return
SCREAMING_SNAKE_CASE =sources[0]
SCREAMING_SNAKE_CASE =sinks[0]
# make fake vertex if there are more
# than one source or sink
if len(snake_case ) > 1 or len(snake_case ) > 1:
SCREAMING_SNAKE_CASE =0
for i in sources:
max_input_flow += sum(self.graph[i] )
SCREAMING_SNAKE_CASE =len(self.graph ) + 1
for room in self.graph:
room.insert(0 ,0 )
self.graph.insert(0 ,[0] * size )
for i in sources:
SCREAMING_SNAKE_CASE =max_input_flow
SCREAMING_SNAKE_CASE =0
SCREAMING_SNAKE_CASE =len(self.graph ) + 1
for room in self.graph:
room.append(0 )
self.graph.append([0] * size )
for i in sinks:
SCREAMING_SNAKE_CASE =max_input_flow
SCREAMING_SNAKE_CASE =size - 1
def _lowerCAmelCase ( self : Optional[int] ):
if self.maximum_flow_algorithm is None:
raise Exception('You need to set maximum flow algorithm before.' )
if self.source_index is None or self.sink_index is None:
return 0
self.maximum_flow_algorithm.execute()
return self.maximum_flow_algorithm.getMaximumFlow()
def _lowerCAmelCase ( self : Dict ,snake_case : Optional[Any] ):
SCREAMING_SNAKE_CASE =algorithm(self )
class a_ :
"""simple docstring"""
def __init__( self : Optional[int] ,snake_case : Optional[int] ):
SCREAMING_SNAKE_CASE =flow_network
SCREAMING_SNAKE_CASE =flow_network.verticesCount
SCREAMING_SNAKE_CASE =flow_network.sourceIndex
SCREAMING_SNAKE_CASE =flow_network.sinkIndex
# it's just a reference, so you shouldn't change
# it in your algorithms, use deep copy before doing that
SCREAMING_SNAKE_CASE =flow_network.graph
SCREAMING_SNAKE_CASE =False
def _lowerCAmelCase ( self : str ):
if not self.executed:
self._algorithm()
SCREAMING_SNAKE_CASE =True
def _lowerCAmelCase ( self : str ):
pass
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
def __init__( self : Union[str, Any] ,snake_case : List[Any] ):
super().__init__(snake_case )
# use this to save your result
SCREAMING_SNAKE_CASE =-1
def _lowerCAmelCase ( self : Dict ):
if not self.executed:
raise Exception('You should execute algorithm before using its result!' )
return self.maximum_flow
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
def __init__( self : List[Any] ,snake_case : Dict ):
super().__init__(snake_case )
SCREAMING_SNAKE_CASE =[[0] * self.verticies_count for i in range(self.verticies_count )]
SCREAMING_SNAKE_CASE =[0] * self.verticies_count
SCREAMING_SNAKE_CASE =[0] * self.verticies_count
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =self.verticies_count
# push some substance to graph
for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index] ):
self.preflow[self.source_index][nextvertex_index] += bandwidth
self.preflow[nextvertex_index][self.source_index] -= bandwidth
self.excesses[nextvertex_index] += bandwidth
# Relabel-to-front selection rule
SCREAMING_SNAKE_CASE =[
i
for i in range(self.verticies_count )
if i != self.source_index and i != self.sink_index
]
# move through list
SCREAMING_SNAKE_CASE =0
while i < len(snake_case ):
SCREAMING_SNAKE_CASE =vertices_list[i]
SCREAMING_SNAKE_CASE =self.heights[vertex_index]
self.process_vertex(snake_case )
if self.heights[vertex_index] > previous_height:
# if it was relabeled, swap elements
# and start from 0 index
vertices_list.insert(0 ,vertices_list.pop(snake_case ) )
SCREAMING_SNAKE_CASE =0
else:
i += 1
SCREAMING_SNAKE_CASE =sum(self.preflow[self.source_index] )
def _lowerCAmelCase ( self : Dict ,snake_case : Union[str, Any] ):
while self.excesses[vertex_index] > 0:
for neighbour_index in range(self.verticies_count ):
# if it's neighbour and current vertex is higher
if (
self.graph[vertex_index][neighbour_index]
- self.preflow[vertex_index][neighbour_index]
> 0
and self.heights[vertex_index] > self.heights[neighbour_index]
):
self.push(snake_case ,snake_case )
self.relabel(snake_case )
def _lowerCAmelCase ( self : Optional[Any] ,snake_case : Any ,snake_case : Dict ):
SCREAMING_SNAKE_CASE =min(
self.excesses[from_index] ,self.graph[from_index][to_index] - self.preflow[from_index][to_index] ,)
self.preflow[from_index][to_index] += preflow_delta
self.preflow[to_index][from_index] -= preflow_delta
self.excesses[from_index] -= preflow_delta
self.excesses[to_index] += preflow_delta
def _lowerCAmelCase ( self : List[Any] ,snake_case : Any ):
SCREAMING_SNAKE_CASE =None
for to_index in range(self.verticies_count ):
if (
self.graph[vertex_index][to_index]
- self.preflow[vertex_index][to_index]
> 0
) and (min_height is None or self.heights[to_index] < min_height):
SCREAMING_SNAKE_CASE =self.heights[to_index]
if min_height is not None:
SCREAMING_SNAKE_CASE =min_height + 1
if __name__ == "__main__":
_lowerCamelCase =[0]
_lowerCamelCase =[3]
# graph = [
# [0, 0, 4, 6, 0, 0],
# [0, 0, 5, 2, 0, 0],
# [0, 0, 0, 0, 4, 4],
# [0, 0, 0, 0, 6, 6],
# [0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0],
# ]
_lowerCamelCase =[[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]
# prepare our network
_lowerCamelCase =FlowNetwork(graph, entrances, exits)
# set algorithm
flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
# and calculate
_lowerCamelCase =flow_network.find_maximum_flow()
print(f'maximum flow is {maximum_flow}')
| 334 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_MAPPING,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
)
from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
import PIL
from PIL import Image
from transformers import BeitImageProcessor
class a_ :
"""simple docstring"""
def __init__( self : Optional[int] ,snake_case : Any ,snake_case : Dict=100 ,snake_case : List[Any]=13 ,snake_case : str=30 ,snake_case : List[str]=2 ,snake_case : List[Any]=3 ,snake_case : Tuple=True ,snake_case : Optional[Any]=True ,snake_case : int=32 ,snake_case : Tuple=4 ,snake_case : List[Any]=4 ,snake_case : Optional[Any]=37 ,snake_case : Optional[Any]="gelu" ,snake_case : Tuple=0.1 ,snake_case : Union[str, Any]=0.1 ,snake_case : List[Any]=10 ,snake_case : Tuple=0.02 ,snake_case : List[str]=3 ,snake_case : Any=None ,snake_case : int=[0, 1, 2, 3] ,):
SCREAMING_SNAKE_CASE =parent
SCREAMING_SNAKE_CASE =100
SCREAMING_SNAKE_CASE =batch_size
SCREAMING_SNAKE_CASE =image_size
SCREAMING_SNAKE_CASE =patch_size
SCREAMING_SNAKE_CASE =num_channels
SCREAMING_SNAKE_CASE =is_training
SCREAMING_SNAKE_CASE =use_labels
SCREAMING_SNAKE_CASE =hidden_size
SCREAMING_SNAKE_CASE =num_hidden_layers
SCREAMING_SNAKE_CASE =num_attention_heads
SCREAMING_SNAKE_CASE =intermediate_size
SCREAMING_SNAKE_CASE =hidden_act
SCREAMING_SNAKE_CASE =hidden_dropout_prob
SCREAMING_SNAKE_CASE =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE =type_sequence_label_size
SCREAMING_SNAKE_CASE =initializer_range
SCREAMING_SNAKE_CASE =scope
SCREAMING_SNAKE_CASE =out_indices
SCREAMING_SNAKE_CASE =num_labels
# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
SCREAMING_SNAKE_CASE =(image_size // patch_size) ** 2
SCREAMING_SNAKE_CASE =num_patches + 1
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE =None
SCREAMING_SNAKE_CASE =None
if self.use_labels:
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size] ,self.type_sequence_label_size )
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size, self.image_size, self.image_size] ,self.num_labels )
SCREAMING_SNAKE_CASE =self.get_config()
return config, pixel_values, labels, pixel_labels
def _lowerCAmelCase ( self : Dict ):
return BeitConfig(
vocab_size=self.vocab_size ,image_size=self.image_size ,patch_size=self.patch_size ,num_channels=self.num_channels ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,is_decoder=snake_case ,initializer_range=self.initializer_range ,out_indices=self.out_indices ,)
def _lowerCAmelCase ( self : Union[str, Any] ,snake_case : Tuple ,snake_case : Optional[Any] ,snake_case : Union[str, Any] ,snake_case : Optional[int] ):
SCREAMING_SNAKE_CASE =BeitModel(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _lowerCAmelCase ( self : Union[str, Any] ,snake_case : Optional[int] ,snake_case : Dict ,snake_case : Any ,snake_case : List[str] ):
SCREAMING_SNAKE_CASE =BeitForMaskedImageModeling(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length - 1, self.vocab_size) )
def _lowerCAmelCase ( self : Optional[Any] ,snake_case : Any ,snake_case : str ,snake_case : Any ,snake_case : str ):
SCREAMING_SNAKE_CASE =self.type_sequence_label_size
SCREAMING_SNAKE_CASE =BeitForImageClassification(snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case ,labels=snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
# test greyscale images
SCREAMING_SNAKE_CASE =1
SCREAMING_SNAKE_CASE =BeitForImageClassification(snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE =model(snake_case ,labels=snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
def _lowerCAmelCase ( self : List[str] ,snake_case : Tuple ,snake_case : str ,snake_case : Optional[int] ,snake_case : int ):
SCREAMING_SNAKE_CASE =self.num_labels
SCREAMING_SNAKE_CASE =BeitForSemanticSegmentation(snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case )
self.parent.assertEqual(
result.logits.shape ,(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
SCREAMING_SNAKE_CASE =model(snake_case ,labels=snake_case )
self.parent.assertEqual(
result.logits.shape ,(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
def _lowerCAmelCase ( self : str ):
SCREAMING_SNAKE_CASE =self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =config_and_inputs
SCREAMING_SNAKE_CASE ={'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class a_ ( lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase = (
(BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
if is_torch_available()
else ()
)
__UpperCAmelCase = (
{
'feature-extraction': BeitModel,
'image-classification': BeitForImageClassification,
'image-segmentation': BeitForSemanticSegmentation,
}
if is_torch_available()
else {}
)
__UpperCAmelCase = False
__UpperCAmelCase = False
__UpperCAmelCase = False
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =BeitModelTester(self )
SCREAMING_SNAKE_CASE =ConfigTester(self ,config_class=snake_case ,has_text_modality=snake_case ,hidden_size=37 )
def _lowerCAmelCase ( self : List[str] ):
self.config_tester.run_common_tests()
@unittest.skip(reason='BEiT does not use inputs_embeds' )
def _lowerCAmelCase ( self : List[Any] ):
pass
@require_torch_multi_gpu
@unittest.skip(reason='BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`' )
def _lowerCAmelCase ( self : Union[str, Any] ):
pass
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE =model_class(snake_case )
self.assertIsInstance(model.get_input_embeddings() ,(nn.Module) )
SCREAMING_SNAKE_CASE =model.get_output_embeddings()
self.assertTrue(x is None or isinstance(snake_case ,nn.Linear ) )
def _lowerCAmelCase ( self : int ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE =model_class(snake_case )
SCREAMING_SNAKE_CASE =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE =[*signature.parameters.keys()]
SCREAMING_SNAKE_CASE =['pixel_values']
self.assertListEqual(arg_names[:1] ,snake_case )
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def _lowerCAmelCase ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*snake_case )
def _lowerCAmelCase ( self : Dict ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case )
def _lowerCAmelCase ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*snake_case )
def _lowerCAmelCase ( self : Any ):
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE =True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if model_class in [*get_values(snake_case ), BeitForMaskedImageModeling]:
continue
SCREAMING_SNAKE_CASE =model_class(snake_case )
model.to(snake_case )
model.train()
SCREAMING_SNAKE_CASE =self._prepare_for_class(snake_case ,snake_case ,return_labels=snake_case )
SCREAMING_SNAKE_CASE =model(**snake_case ).loss
loss.backward()
def _lowerCAmelCase ( self : int ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE =False
SCREAMING_SNAKE_CASE =True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if (
model_class in [*get_values(snake_case ), BeitForMaskedImageModeling]
or not model_class.supports_gradient_checkpointing
):
continue
SCREAMING_SNAKE_CASE =model_class(snake_case )
model.gradient_checkpointing_enable()
model.to(snake_case )
model.train()
SCREAMING_SNAKE_CASE =self._prepare_for_class(snake_case ,snake_case ,return_labels=snake_case )
SCREAMING_SNAKE_CASE =model(**snake_case ).loss
loss.backward()
def _lowerCAmelCase ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE =_config_zero_init(snake_case )
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE =model_class(config=snake_case )
for name, param in model.named_parameters():
# we skip lambda parameters as these require special initial values
# determined by config.layer_scale_init_value
if "lambda" in name:
continue
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() ,[0.0, 1.0] ,msg=f'Parameter {name} of model {model_class} seems not properly initialized' ,)
@slow
def _lowerCAmelCase ( self : List[str] ):
for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE =BeitModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class a_ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def _lowerCAmelCase ( self : Tuple ):
return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224' ) if is_vision_available() else None
@slow
def _lowerCAmelCase ( self : int ):
SCREAMING_SNAKE_CASE =BeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k' ).to(snake_case )
SCREAMING_SNAKE_CASE =self.default_image_processor
SCREAMING_SNAKE_CASE =prepare_img()
SCREAMING_SNAKE_CASE =image_processor(images=snake_case ,return_tensors='pt' ).pixel_values.to(snake_case )
# prepare bool_masked_pos
SCREAMING_SNAKE_CASE =torch.ones((1, 196) ,dtype=torch.bool ).to(snake_case )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE =model(pixel_values=snake_case ,bool_masked_pos=snake_case )
SCREAMING_SNAKE_CASE =outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE =torch.Size((1, 196, 8192) )
self.assertEqual(logits.shape ,snake_case )
SCREAMING_SNAKE_CASE =torch.tensor(
[[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]] ).to(snake_case )
self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3] ,snake_case ,atol=1e-2 ) )
@slow
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =BeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224' ).to(snake_case )
SCREAMING_SNAKE_CASE =self.default_image_processor
SCREAMING_SNAKE_CASE =prepare_img()
SCREAMING_SNAKE_CASE =image_processor(images=snake_case ,return_tensors='pt' ).to(snake_case )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE =model(**snake_case )
SCREAMING_SNAKE_CASE =outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE =torch.Size((1, 1000) )
self.assertEqual(logits.shape ,snake_case )
SCREAMING_SNAKE_CASE =torch.tensor([-1.2_385, -1.0_987, -1.0_108] ).to(snake_case )
self.assertTrue(torch.allclose(logits[0, :3] ,snake_case ,atol=1e-4 ) )
SCREAMING_SNAKE_CASE =281
self.assertEqual(logits.argmax(-1 ).item() ,snake_case )
@slow
def _lowerCAmelCase ( self : int ):
SCREAMING_SNAKE_CASE =BeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k' ).to(
snake_case )
SCREAMING_SNAKE_CASE =self.default_image_processor
SCREAMING_SNAKE_CASE =prepare_img()
SCREAMING_SNAKE_CASE =image_processor(images=snake_case ,return_tensors='pt' ).to(snake_case )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE =model(**snake_case )
SCREAMING_SNAKE_CASE =outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE =torch.Size((1, 21841) )
self.assertEqual(logits.shape ,snake_case )
SCREAMING_SNAKE_CASE =torch.tensor([1.6_881, -0.2_787, 0.5_901] ).to(snake_case )
self.assertTrue(torch.allclose(logits[0, :3] ,snake_case ,atol=1e-4 ) )
SCREAMING_SNAKE_CASE =2396
self.assertEqual(logits.argmax(-1 ).item() ,snake_case )
@slow
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE =BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640' )
SCREAMING_SNAKE_CASE =model.to(snake_case )
SCREAMING_SNAKE_CASE =BeitImageProcessor(do_resize=snake_case ,size=640 ,do_center_crop=snake_case )
SCREAMING_SNAKE_CASE =load_dataset('hf-internal-testing/fixtures_ade20k' ,split='test' )
SCREAMING_SNAKE_CASE =Image.open(ds[0]['file'] )
SCREAMING_SNAKE_CASE =image_processor(images=snake_case ,return_tensors='pt' ).to(snake_case )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE =model(**snake_case )
SCREAMING_SNAKE_CASE =outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE =torch.Size((1, 150, 160, 160) )
self.assertEqual(logits.shape ,snake_case )
SCREAMING_SNAKE_CASE =version.parse(PIL.__version__ ) < version.parse('9.0.0' )
if is_pillow_less_than_a:
SCREAMING_SNAKE_CASE =torch.tensor(
[
[[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]],
[[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]],
[[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]],
] ,device=snake_case ,)
else:
SCREAMING_SNAKE_CASE =torch.tensor(
[
[[-4.8_960, -2.3_688, -3.0_355], [-2.8_478, -0.9_836, -1.7_418], [-2.9_449, -1.3_332, -2.1_456]],
[[-5.8_081, -3.4_124, -4.1_006], [-3.8_561, -2.2_081, -3.0_323], [-3.8_365, -2.4_601, -3.3_669]],
[[-0.0_309, 3.9_868, 4.0_540], [2.9_640, 4.6_877, 4.9_976], [3.2_081, 4.7_690, 4.9_942]],
] ,device=snake_case ,)
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] ,snake_case ,atol=1e-4 ) )
@slow
def _lowerCAmelCase ( self : int ):
SCREAMING_SNAKE_CASE =BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640' )
SCREAMING_SNAKE_CASE =model.to(snake_case )
SCREAMING_SNAKE_CASE =BeitImageProcessor(do_resize=snake_case ,size=640 ,do_center_crop=snake_case )
SCREAMING_SNAKE_CASE =load_dataset('hf-internal-testing/fixtures_ade20k' ,split='test' )
SCREAMING_SNAKE_CASE =Image.open(ds[0]['file'] )
SCREAMING_SNAKE_CASE =image_processor(images=snake_case ,return_tensors='pt' ).to(snake_case )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE =model(**snake_case )
SCREAMING_SNAKE_CASE =outputs.logits.detach().cpu()
SCREAMING_SNAKE_CASE =image_processor.post_process_semantic_segmentation(outputs=snake_case ,target_sizes=[(500, 300)] )
SCREAMING_SNAKE_CASE =torch.Size((500, 300) )
self.assertEqual(segmentation[0].shape ,snake_case )
SCREAMING_SNAKE_CASE =image_processor.post_process_semantic_segmentation(outputs=snake_case )
SCREAMING_SNAKE_CASE =torch.Size((160, 160) )
self.assertEqual(segmentation[0].shape ,snake_case )
| 334 | 1 |
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput
_lowerCamelCase =8
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_=BITS ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =x.device
SCREAMING_SNAKE_CASE =(x * 255).int().clamp(0, 255 )
SCREAMING_SNAKE_CASE =2 ** torch.arange(bits - 1, -1, -1, device=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =rearrange(lowerCAmelCase_, 'd -> d 1 1' )
SCREAMING_SNAKE_CASE =rearrange(lowerCAmelCase_, 'b c h w -> b c 1 h w' )
SCREAMING_SNAKE_CASE =((x & mask) != 0).float()
SCREAMING_SNAKE_CASE =rearrange(lowerCAmelCase_, 'b c d h w -> b (c d) h w' )
SCREAMING_SNAKE_CASE =bits * 2 - 1
return bits
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_=BITS ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =x.device
SCREAMING_SNAKE_CASE =(x > 0).int()
SCREAMING_SNAKE_CASE =2 ** torch.arange(bits - 1, -1, -1, device=lowerCAmelCase_, dtype=torch.intaa )
SCREAMING_SNAKE_CASE =rearrange(lowerCAmelCase_, 'd -> d 1 1' )
SCREAMING_SNAKE_CASE =rearrange(lowerCAmelCase_, 'b (c d) h w -> b c d h w', d=8 )
SCREAMING_SNAKE_CASE =reduce(x * mask, 'b c d h w -> b c h w', 'sum' )
return (dec / 255).clamp(0.0, 1.0 )
def snake_case__ ( self, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ = 0.0, lowerCAmelCase_ = True, lowerCAmelCase_=None, lowerCAmelCase_ = True, ):
"""simple docstring"""
if self.num_inference_steps is None:
raise ValueError(
'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler' )
# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
# Ideally, read DDIM paper in-detail understanding
# Notation (<variable name> -> <name in paper>
# - pred_noise_t -> e_theta(x_t, t)
# - pred_original_sample -> f_theta(x_t, t) or x_0
# - std_dev_t -> sigma_t
# - eta -> η
# - pred_sample_direction -> "direction pointing to x_t"
# - pred_prev_sample -> "x_t-1"
# 1. get previous step value (=t-1)
SCREAMING_SNAKE_CASE =timestep - self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
SCREAMING_SNAKE_CASE =self.alphas_cumprod[timestep]
SCREAMING_SNAKE_CASE =self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
SCREAMING_SNAKE_CASE =1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
SCREAMING_SNAKE_CASE =(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
# 4. Clip "predicted x_0"
SCREAMING_SNAKE_CASE =self.bit_scale
if self.config.clip_sample:
SCREAMING_SNAKE_CASE =torch.clamp(lowerCAmelCase_, -scale, lowerCAmelCase_ )
# 5. compute variance: "sigma_t(η)" -> see formula (16)
# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
SCREAMING_SNAKE_CASE =self._get_variance(lowerCAmelCase_, lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =eta * variance ** 0.5
if use_clipped_model_output:
# the model_output is always re-derived from the clipped x_0 in Glide
SCREAMING_SNAKE_CASE =(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
SCREAMING_SNAKE_CASE =(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output
# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
SCREAMING_SNAKE_CASE =alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
if eta > 0:
# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
SCREAMING_SNAKE_CASE =model_output.device if torch.is_tensor(lowerCAmelCase_ ) else 'cpu'
SCREAMING_SNAKE_CASE =torch.randn(model_output.shape, dtype=model_output.dtype, generator=lowerCAmelCase_ ).to(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =self._get_variance(lowerCAmelCase_, lowerCAmelCase_ ) ** 0.5 * eta * noise
SCREAMING_SNAKE_CASE =prev_sample + variance
if not return_dict:
return (prev_sample,)
return DDIMSchedulerOutput(prev_sample=lowerCAmelCase_, pred_original_sample=lowerCAmelCase_ )
def snake_case__ ( self, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_="epsilon", lowerCAmelCase_=None, lowerCAmelCase_ = True, ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =torch.split(lowerCAmelCase_, sample.shape[1], dim=1 )
else:
SCREAMING_SNAKE_CASE =None
# 1. compute alphas, betas
SCREAMING_SNAKE_CASE =self.alphas_cumprod[t]
SCREAMING_SNAKE_CASE =self.alphas_cumprod[t - 1] if t > 0 else self.one
SCREAMING_SNAKE_CASE =1 - alpha_prod_t
SCREAMING_SNAKE_CASE =1 - alpha_prod_t_prev
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if prediction_type == "epsilon":
SCREAMING_SNAKE_CASE =(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif prediction_type == "sample":
SCREAMING_SNAKE_CASE =model_output
else:
raise ValueError(F'Unsupported prediction_type {prediction_type}.' )
# 3. Clip "predicted x_0"
SCREAMING_SNAKE_CASE =self.bit_scale
if self.config.clip_sample:
SCREAMING_SNAKE_CASE =torch.clamp(lowerCAmelCase_, -scale, lowerCAmelCase_ )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
SCREAMING_SNAKE_CASE =(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
SCREAMING_SNAKE_CASE =self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
SCREAMING_SNAKE_CASE =pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
SCREAMING_SNAKE_CASE =0
if t > 0:
SCREAMING_SNAKE_CASE =torch.randn(
model_output.size(), dtype=model_output.dtype, layout=model_output.layout, generator=lowerCAmelCase_ ).to(model_output.device )
SCREAMING_SNAKE_CASE =(self._get_variance(lowerCAmelCase_, predicted_variance=lowerCAmelCase_ ) ** 0.5) * noise
SCREAMING_SNAKE_CASE =pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return DDPMSchedulerOutput(prev_sample=lowerCAmelCase_, pred_original_sample=lowerCAmelCase_ )
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
def __init__( self : int ,snake_case : UNetaDConditionModel ,snake_case : Union[DDIMScheduler, DDPMScheduler] ,snake_case : Optional[float] = 1.0 ,):
super().__init__()
SCREAMING_SNAKE_CASE =bit_scale
SCREAMING_SNAKE_CASE =(
ddim_bit_scheduler_step if isinstance(snake_case ,snake_case ) else ddpm_bit_scheduler_step
)
self.register_modules(unet=snake_case ,scheduler=snake_case )
@torch.no_grad()
def __call__( self : List[Any] ,snake_case : Optional[int] = 256 ,snake_case : Optional[int] = 256 ,snake_case : Optional[int] = 50 ,snake_case : Optional[torch.Generator] = None ,snake_case : Optional[int] = 1 ,snake_case : Optional[str] = "pil" ,snake_case : bool = True ,**snake_case : str ,):
SCREAMING_SNAKE_CASE =torch.randn(
(batch_size, self.unet.config.in_channels, height, width) ,generator=snake_case ,)
SCREAMING_SNAKE_CASE =decimal_to_bits(snake_case ) * self.bit_scale
SCREAMING_SNAKE_CASE =latents.to(self.device )
self.scheduler.set_timesteps(snake_case )
for t in self.progress_bar(self.scheduler.timesteps ):
# predict the noise residual
SCREAMING_SNAKE_CASE =self.unet(snake_case ,snake_case ).sample
# compute the previous noisy sample x_t -> x_t-1
SCREAMING_SNAKE_CASE =self.scheduler.step(snake_case ,snake_case ,snake_case ).prev_sample
SCREAMING_SNAKE_CASE =bits_to_decimal(snake_case )
if output_type == "pil":
SCREAMING_SNAKE_CASE =self.numpy_to_pil(snake_case )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=snake_case )
| 334 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs
from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase =logging.get_logger()
@dataclass
class a_ :
"""simple docstring"""
__UpperCAmelCase = 42
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
def _lowerCAmelCase ( self : List[Any] ,snake_case : Dict ,snake_case : Tensor ,snake_case : Tensor ):
SCREAMING_SNAKE_CASE =len(list(m.modules() ) ) == 1 or isinstance(snake_case ,nn.Convad ) or isinstance(snake_case ,nn.BatchNormad )
if has_not_submodules:
self.traced.append(snake_case )
def __call__( self : List[str] ,snake_case : Tensor ):
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(snake_case )
[x.remove() for x in self.handles]
return self
@property
def _lowerCAmelCase ( self : Optional[Any] ):
# check the len of the state_dict keys to see if we have learnable params
return list(filter(lambda snake_case : len(list(x.state_dict().keys() ) ) > 0 ,self.traced ) )
@dataclass
class a_ :
"""simple docstring"""
__UpperCAmelCase = 42
__UpperCAmelCase = 42
__UpperCAmelCase = 1
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
__UpperCAmelCase = True
def __call__( self : str ,snake_case : Tensor ):
SCREAMING_SNAKE_CASE =Tracker(self.dest )(snake_case ).parametrized
SCREAMING_SNAKE_CASE =Tracker(self.src )(snake_case ).parametrized
SCREAMING_SNAKE_CASE =list(filter(lambda snake_case : type(snake_case ) not in self.src_skip ,snake_case ) )
SCREAMING_SNAKE_CASE =list(filter(lambda snake_case : type(snake_case ) not in self.dest_skip ,snake_case ) )
if len(snake_case ) != len(snake_case ) and self.raise_if_mismatch:
raise Exception(
f'Numbers of operations are different. Source module has {len(snake_case )} operations while'
f' destination module has {len(snake_case )}.' )
for dest_m, src_m in zip(snake_case ,snake_case ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(f'Transfered from={src_m} to={dest_m}' )
class a_ ( nn.Module ):
"""simple docstring"""
def __init__( self : Any ,snake_case : nn.Module ):
super().__init__()
SCREAMING_SNAKE_CASE =[]
# - get the stem
feature_blocks.append(('conv1', model.stem) )
# - get all the feature blocks
for k, v in model.trunk_output.named_children():
assert k.startswith('block' ), f'Unexpected layer name {k}'
SCREAMING_SNAKE_CASE =len(snake_case ) + 1
feature_blocks.append((f'res{block_index}', v) )
SCREAMING_SNAKE_CASE =nn.ModuleDict(snake_case )
def _lowerCAmelCase ( self : Dict ,snake_case : Tensor ):
return get_trunk_forward_outputs(
snake_case ,out_feat_keys=snake_case ,feature_blocks=self._feature_blocks ,)
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
def _lowerCAmelCase ( self : Optional[int] ,snake_case : str ):
SCREAMING_SNAKE_CASE =x.split('-' )
return x_split[0] + x_split[1] + "_" + "".join(x_split[2:] )
def __getitem__( self : Optional[Any] ,snake_case : str ):
# default to timm!
if x not in self:
SCREAMING_SNAKE_CASE =self.convert_name_to_timm(snake_case )
SCREAMING_SNAKE_CASE =partial(lambda: (timm.create_model(snake_case ,pretrained=snake_case ).eval(), None) )
else:
SCREAMING_SNAKE_CASE =super().__getitem__(snake_case )
return val
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
def __getitem__( self : int ,snake_case : str ):
if "seer" in x and "in1k" not in x:
SCREAMING_SNAKE_CASE =RegNetModel
else:
SCREAMING_SNAKE_CASE =RegNetForImageClassification
return val
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
for from_key, to_key in keys:
SCREAMING_SNAKE_CASE =from_state_dict[from_key].clone()
print(F'Copied key={from_key} to={to_key}' )
return to_state_dict
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ = True, ):
"""simple docstring"""
print(F'Converting {name}...' )
with torch.no_grad():
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =from_model_func()
SCREAMING_SNAKE_CASE =our_model_func(lowerCAmelCase_ ).eval()
SCREAMING_SNAKE_CASE =ModuleTransfer(src=lowerCAmelCase_, dest=lowerCAmelCase_, raise_if_mismatch=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =torch.randn((1, 3, 224, 224) )
module_transfer(lowerCAmelCase_ )
if from_state_dict is not None:
SCREAMING_SNAKE_CASE =[]
# for seer - in1k finetuned we have to manually copy the head
if "seer" in name and "in1k" in name:
SCREAMING_SNAKE_CASE =[('0.clf.0.weight', 'classifier.1.weight'), ('0.clf.0.bias', 'classifier.1.bias')]
SCREAMING_SNAKE_CASE =manually_copy_vissl_head(lowerCAmelCase_, our_model.state_dict(), lowerCAmelCase_ )
our_model.load_state_dict(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =our_model(lowerCAmelCase_, output_hidden_states=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =(
our_outputs.logits if isinstance(lowerCAmelCase_, lowerCAmelCase_ ) else our_outputs.last_hidden_state
)
SCREAMING_SNAKE_CASE =from_model(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =from_output[-1] if type(lowerCAmelCase_ ) is list else from_output
# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
if "seer" in name and "in1k" in name:
SCREAMING_SNAKE_CASE =our_outputs.hidden_states[-1]
assert torch.allclose(lowerCAmelCase_, lowerCAmelCase_ ), "The model logits don't match the original one."
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / name, commit_message='Add model', use_temp_dir=lowerCAmelCase_, )
SCREAMING_SNAKE_CASE =224 if 'seer' not in name else 384
# we can use the convnext one
SCREAMING_SNAKE_CASE =AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k', size=lowerCAmelCase_ )
image_processor.push_to_hub(
repo_path_or_name=save_directory / name, commit_message='Add image processor', use_temp_dir=lowerCAmelCase_, )
print(F'Pushed {name}' )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ = None, lowerCAmelCase_ = True ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ='imagenet-1k-id2label.json'
SCREAMING_SNAKE_CASE =1000
SCREAMING_SNAKE_CASE =(1, num_labels)
SCREAMING_SNAKE_CASE ='huggingface/label-files'
SCREAMING_SNAKE_CASE =num_labels
SCREAMING_SNAKE_CASE =json.load(open(cached_download(hf_hub_url(lowerCAmelCase_, lowerCAmelCase_, repo_type='dataset' ) ), 'r' ) )
SCREAMING_SNAKE_CASE ={int(lowerCAmelCase_ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE =idalabel
SCREAMING_SNAKE_CASE ={v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE =partial(lowerCAmelCase_, num_labels=lowerCAmelCase_, idalabel=lowerCAmelCase_, labelaid=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE ={
'regnet-x-002': ImageNetPreTrainedConfig(
depths=[1, 1, 4, 7], hidden_sizes=[24, 56, 152, 368], groups_width=8, layer_type='x' ),
'regnet-x-004': ImageNetPreTrainedConfig(
depths=[1, 2, 7, 12], hidden_sizes=[32, 64, 160, 384], groups_width=16, layer_type='x' ),
'regnet-x-006': ImageNetPreTrainedConfig(
depths=[1, 3, 5, 7], hidden_sizes=[48, 96, 240, 528], groups_width=24, layer_type='x' ),
'regnet-x-008': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 5], hidden_sizes=[64, 128, 288, 672], groups_width=16, layer_type='x' ),
'regnet-x-016': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 2], hidden_sizes=[72, 168, 408, 912], groups_width=24, layer_type='x' ),
'regnet-x-032': ImageNetPreTrainedConfig(
depths=[2, 6, 15, 2], hidden_sizes=[96, 192, 432, 1008], groups_width=48, layer_type='x' ),
'regnet-x-040': ImageNetPreTrainedConfig(
depths=[2, 5, 14, 2], hidden_sizes=[80, 240, 560, 1360], groups_width=40, layer_type='x' ),
'regnet-x-064': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1], hidden_sizes=[168, 392, 784, 1624], groups_width=56, layer_type='x' ),
'regnet-x-080': ImageNetPreTrainedConfig(
depths=[2, 5, 15, 1], hidden_sizes=[80, 240, 720, 1920], groups_width=120, layer_type='x' ),
'regnet-x-120': ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1], hidden_sizes=[224, 448, 896, 2240], groups_width=112, layer_type='x' ),
'regnet-x-160': ImageNetPreTrainedConfig(
depths=[2, 6, 13, 1], hidden_sizes=[256, 512, 896, 2048], groups_width=128, layer_type='x' ),
'regnet-x-320': ImageNetPreTrainedConfig(
depths=[2, 7, 13, 1], hidden_sizes=[336, 672, 1344, 2520], groups_width=168, layer_type='x' ),
# y variant
'regnet-y-002': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7], hidden_sizes=[24, 56, 152, 368], groups_width=8 ),
'regnet-y-004': ImageNetPreTrainedConfig(
depths=[1, 3, 6, 6], hidden_sizes=[48, 104, 208, 440], groups_width=8 ),
'regnet-y-006': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 4], hidden_sizes=[48, 112, 256, 608], groups_width=16 ),
'regnet-y-008': ImageNetPreTrainedConfig(
depths=[1, 3, 8, 2], hidden_sizes=[64, 128, 320, 768], groups_width=16 ),
'regnet-y-016': ImageNetPreTrainedConfig(
depths=[2, 6, 17, 2], hidden_sizes=[48, 120, 336, 888], groups_width=24 ),
'regnet-y-032': ImageNetPreTrainedConfig(
depths=[2, 5, 13, 1], hidden_sizes=[72, 216, 576, 1512], groups_width=24 ),
'regnet-y-040': ImageNetPreTrainedConfig(
depths=[2, 6, 12, 2], hidden_sizes=[128, 192, 512, 1088], groups_width=64 ),
'regnet-y-064': ImageNetPreTrainedConfig(
depths=[2, 7, 14, 2], hidden_sizes=[144, 288, 576, 1296], groups_width=72 ),
'regnet-y-080': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1], hidden_sizes=[168, 448, 896, 2016], groups_width=56 ),
'regnet-y-120': ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1], hidden_sizes=[224, 448, 896, 2240], groups_width=112 ),
'regnet-y-160': ImageNetPreTrainedConfig(
depths=[2, 4, 11, 1], hidden_sizes=[224, 448, 1232, 3024], groups_width=112 ),
'regnet-y-320': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1], hidden_sizes=[232, 696, 1392, 3712], groups_width=232 ),
# models created by SEER -> https://arxiv.org/abs/2202.08360
'regnet-y-320-seer': RegNetConfig(depths=[2, 5, 12, 1], hidden_sizes=[232, 696, 1392, 3712], groups_width=232 ),
'regnet-y-640-seer': RegNetConfig(depths=[2, 5, 12, 1], hidden_sizes=[328, 984, 1968, 4920], groups_width=328 ),
'regnet-y-1280-seer': RegNetConfig(
depths=[2, 7, 17, 1], hidden_sizes=[528, 1056, 2904, 7392], groups_width=264 ),
'regnet-y-2560-seer': RegNetConfig(
depths=[3, 7, 16, 1], hidden_sizes=[640, 1696, 2544, 5088], groups_width=640 ),
'regnet-y-10b-seer': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1], hidden_sizes=[2020, 4040, 11110, 28280], groups_width=1010 ),
# finetuned on imagenet
'regnet-y-320-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1], hidden_sizes=[232, 696, 1392, 3712], groups_width=232 ),
'regnet-y-640-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1], hidden_sizes=[328, 984, 1968, 4920], groups_width=328 ),
'regnet-y-1280-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1], hidden_sizes=[528, 1056, 2904, 7392], groups_width=264 ),
'regnet-y-2560-seer-in1k': ImageNetPreTrainedConfig(
depths=[3, 7, 16, 1], hidden_sizes=[640, 1696, 2544, 5088], groups_width=640 ),
'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1], hidden_sizes=[2020, 4040, 11110, 28280], groups_width=1010 ),
}
SCREAMING_SNAKE_CASE =NameToOurModelFuncMap()
SCREAMING_SNAKE_CASE =NameToFromModelFuncMap()
# add seer weights logic
def load_using_classy_vision(lowerCAmelCase_, lowerCAmelCase_ ) -> Tuple[nn.Module, Dict]:
SCREAMING_SNAKE_CASE =torch.hub.load_state_dict_from_url(lowerCAmelCase_, model_dir=str(lowerCAmelCase_ ), map_location='cpu' )
SCREAMING_SNAKE_CASE =model_func()
# check if we have a head, if yes add it
SCREAMING_SNAKE_CASE =files['classy_state_dict']['base_model']['model']
SCREAMING_SNAKE_CASE =model_state_dict['trunk']
model.load_state_dict(lowerCAmelCase_ )
return model.eval(), model_state_dict["heads"]
# pretrained
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch', lambda: FakeRegNetVisslWrapper(RegNetYaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch', lambda: FakeRegNetVisslWrapper(RegNetYaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch', lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch', lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27, group_width=1010, w_a=1744, w_a=620.83, w_m=2.52 ) ) ), )
# IN1K finetuned
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch', lambda: FakeRegNetVisslWrapper(RegNetYaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch', lambda: FakeRegNetVisslWrapper(RegNetYaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch', lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch', lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27, group_width=1010, w_a=1744, w_a=620.83, w_m=2.52 ) ) ), )
if model_name:
convert_weight_and_push(
lowerCAmelCase_, names_to_from_model_map[model_name], names_to_ours_model_map[model_name], names_to_config[model_name], lowerCAmelCase_, lowerCAmelCase_, )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(
lowerCAmelCase_, names_to_from_model_map[model_name], names_to_ours_model_map[model_name], lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, )
return config, expected_shape
if __name__ == "__main__":
_lowerCamelCase =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default=None,
type=str,
help=(
"The name of the model you wish to convert, it must be one of the supported regnet* architecture,"
" currently: regnetx-*, regnety-*. If `None`, all of them will the converted."
),
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=Path,
required=True,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
default=True,
type=bool,
required=False,
help="If True, push model and image processor to the hub.",
)
_lowerCamelCase =parser.parse_args()
_lowerCamelCase =args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 334 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCamelCase ={
"configuration_vivit": ["VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VivitConfig"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase =["VivitImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase =[
"VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"VivitModel",
"VivitPreTrainedModel",
"VivitForVideoClassification",
]
if TYPE_CHECKING:
from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_vivit import VivitImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vivit import (
VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
VivitForVideoClassification,
VivitModel,
VivitPreTrainedModel,
)
else:
import sys
_lowerCamelCase =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 334 |
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
_lowerCamelCase =16
_lowerCamelCase =32
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ = 16 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained('bert-base-cased' )
SCREAMING_SNAKE_CASE =load_dataset('glue', 'mrpc' )
def tokenize_function(lowerCAmelCase_ ):
# max_length=None => use the model max length (it's actually the default)
SCREAMING_SNAKE_CASE =tokenizer(examples['sentence1'], examples['sentence2'], truncation=lowerCAmelCase_, max_length=lowerCAmelCase_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
SCREAMING_SNAKE_CASE =datasets.map(
lowerCAmelCase_, batched=lowerCAmelCase_, remove_columns=['idx', 'sentence1', 'sentence2'], )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
SCREAMING_SNAKE_CASE =tokenized_datasets.rename_column('label', 'labels' )
def collate_fn(lowerCAmelCase_ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
SCREAMING_SNAKE_CASE =128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
SCREAMING_SNAKE_CASE =16
elif accelerator.mixed_precision != "no":
SCREAMING_SNAKE_CASE =8
else:
SCREAMING_SNAKE_CASE =None
return tokenizer.pad(
lowerCAmelCase_, padding='longest', max_length=lowerCAmelCase_, pad_to_multiple_of=lowerCAmelCase_, return_tensors='pt', )
# Instantiate dataloaders.
SCREAMING_SNAKE_CASE =DataLoader(
tokenized_datasets['train'], shuffle=lowerCAmelCase_, collate_fn=lowerCAmelCase_, batch_size=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =DataLoader(
tokenized_datasets['validation'], shuffle=lowerCAmelCase_, collate_fn=lowerCAmelCase_, batch_size=lowerCAmelCase_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
_lowerCamelCase =mocked_dataloaders # noqa: F811
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
if os.environ.get('TESTING_MOCKED_DATALOADERS', lowerCAmelCase_ ) == "1":
SCREAMING_SNAKE_CASE =2
# Initialize accelerator
SCREAMING_SNAKE_CASE =Accelerator(cpu=args.cpu, mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
SCREAMING_SNAKE_CASE =config['lr']
SCREAMING_SNAKE_CASE =int(config['num_epochs'] )
SCREAMING_SNAKE_CASE =int(config['seed'] )
SCREAMING_SNAKE_CASE =int(config['batch_size'] )
SCREAMING_SNAKE_CASE =evaluate.load('glue', 'mrpc' )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=lowerCAmelCase_ )
def inner_training_loop(lowerCAmelCase_ ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(lowerCAmelCase_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
SCREAMING_SNAKE_CASE =AutoModelForSequenceClassification.from_pretrained('bert-base-cased', return_dict=lowerCAmelCase_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
SCREAMING_SNAKE_CASE =model.to(accelerator.device )
# Instantiate optimizer
SCREAMING_SNAKE_CASE =AdamW(params=model.parameters(), lr=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =get_dataloaders(lowerCAmelCase_, lowerCAmelCase_ )
# Instantiate scheduler
SCREAMING_SNAKE_CASE =get_linear_schedule_with_warmup(
optimizer=lowerCAmelCase_, num_warmup_steps=100, num_training_steps=(len(lowerCAmelCase_ ) * num_epochs), )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =accelerator.prepare(
lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ )
# Now we train the model
for epoch in range(lowerCAmelCase_ ):
model.train()
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
SCREAMING_SNAKE_CASE =model(**lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =outputs.loss
accelerator.backward(lowerCAmelCase_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
SCREAMING_SNAKE_CASE =model(**lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =outputs.logits.argmax(dim=-1 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =accelerator.gather_for_metrics((predictions, batch['labels']) )
metric.add_batch(
predictions=lowerCAmelCase_, references=lowerCAmelCase_, )
SCREAMING_SNAKE_CASE =metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'epoch {epoch}:', lowerCAmelCase_ )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =argparse.ArgumentParser(description='Simple example of training script.' )
parser.add_argument(
'--mixed_precision', type=lowerCAmelCase_, default=lowerCAmelCase_, choices=['no', 'fp16', 'bf16', 'fp8'], help='Whether to use mixed precision. Choose'
'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
'and an Nvidia Ampere GPU.', )
parser.add_argument('--cpu', action='store_true', help='If passed, will train on the CPU.' )
SCREAMING_SNAKE_CASE =parser.parse_args()
SCREAMING_SNAKE_CASE ={'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
training_function(lowerCAmelCase_, lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 334 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import GLPNImageProcessor
class a_ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Any ,snake_case : Optional[int] ,snake_case : List[str]=7 ,snake_case : Any=3 ,snake_case : Tuple=18 ,snake_case : List[str]=30 ,snake_case : int=400 ,snake_case : Tuple=True ,snake_case : List[Any]=32 ,snake_case : Tuple=True ,):
SCREAMING_SNAKE_CASE =parent
SCREAMING_SNAKE_CASE =batch_size
SCREAMING_SNAKE_CASE =num_channels
SCREAMING_SNAKE_CASE =image_size
SCREAMING_SNAKE_CASE =min_resolution
SCREAMING_SNAKE_CASE =max_resolution
SCREAMING_SNAKE_CASE =do_resize
SCREAMING_SNAKE_CASE =size_divisor
SCREAMING_SNAKE_CASE =do_rescale
def _lowerCAmelCase ( self : Optional[int] ):
return {
"do_resize": self.do_resize,
"size_divisor": self.size_divisor,
"do_rescale": self.do_rescale,
}
@require_torch
@require_vision
class a_ ( lowerCamelCase_ , unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase = GLPNImageProcessor if is_vision_available() else None
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =GLPNImageProcessingTester(self )
@property
def _lowerCAmelCase ( self : str ):
return self.image_processor_tester.prepare_image_processor_dict()
def _lowerCAmelCase ( self : int ):
SCREAMING_SNAKE_CASE =self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(snake_case ,'do_resize' ) )
self.assertTrue(hasattr(snake_case ,'size_divisor' ) )
self.assertTrue(hasattr(snake_case ,'resample' ) )
self.assertTrue(hasattr(snake_case ,'do_rescale' ) )
def _lowerCAmelCase ( self : Dict ):
pass
def _lowerCAmelCase ( self : Optional[int] ):
# Initialize image_processing
SCREAMING_SNAKE_CASE =self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE =prepare_image_inputs(self.image_processor_tester ,equal_resolution=snake_case )
for image in image_inputs:
self.assertIsInstance(snake_case ,Image.Image )
# Test not batched input (GLPNImageProcessor doesn't support batching)
SCREAMING_SNAKE_CASE =image_processing(image_inputs[0] ,return_tensors='pt' ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
def _lowerCAmelCase ( self : Union[str, Any] ):
# Initialize image_processing
SCREAMING_SNAKE_CASE =self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
SCREAMING_SNAKE_CASE =prepare_image_inputs(self.image_processor_tester ,equal_resolution=snake_case ,numpify=snake_case )
for image in image_inputs:
self.assertIsInstance(snake_case ,np.ndarray )
# Test not batched input (GLPNImageProcessor doesn't support batching)
SCREAMING_SNAKE_CASE =image_processing(image_inputs[0] ,return_tensors='pt' ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
def _lowerCAmelCase ( self : Union[str, Any] ):
# Initialize image_processing
SCREAMING_SNAKE_CASE =self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
SCREAMING_SNAKE_CASE =prepare_image_inputs(self.image_processor_tester ,equal_resolution=snake_case ,torchify=snake_case )
for image in image_inputs:
self.assertIsInstance(snake_case ,torch.Tensor )
# Test not batched input (GLPNImageProcessor doesn't support batching)
SCREAMING_SNAKE_CASE =image_processing(image_inputs[0] ,return_tensors='pt' ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
| 334 |
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
return " ".join(
''.join(word[::-1] ) if len(lowerCAmelCase_ ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words("Hey wollef sroirraw"))
| 334 | 1 |
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class a_ ( lowerCamelCase_ , unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase = FlaxAutoencoderKL
@property
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =4
SCREAMING_SNAKE_CASE =3
SCREAMING_SNAKE_CASE =(32, 32)
SCREAMING_SNAKE_CASE =jax.random.PRNGKey(0 )
SCREAMING_SNAKE_CASE =jax.random.uniform(snake_case ,((batch_size, num_channels) + sizes) )
return {"sample": image, "prng_key": prng_key}
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE ={
'block_out_channels': [32, 64],
'in_channels': 3,
'out_channels': 3,
'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
'latent_channels': 4,
}
SCREAMING_SNAKE_CASE =self.dummy_input
return init_dict, inputs_dict
| 334 |
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json
_lowerCamelCase ="sshleifer/mar_enro_6_3_student"
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
def _lowerCAmelCase ( self : Union[str, Any] ):
super().setUp()
SCREAMING_SNAKE_CASE =cached_path(
'https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz' ,extract_compressed_file=snake_case ,)
SCREAMING_SNAKE_CASE =f'{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'
@slow
@require_torch_gpu
def _lowerCAmelCase ( self : Optional[int] ):
MarianMTModel.from_pretrained(snake_case )
@slow
@require_torch_gpu
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE ={
'$MAX_LEN': 64,
'$BS': 64,
'$GAS': 1,
'$ENRO_DIR': self.data_dir,
'facebook/mbart-large-cc25': MARIAN_MODEL,
# "val_check_interval=0.25": "val_check_interval=1.0",
'--learning_rate=3e-5': '--learning_rate 3e-4',
'--num_train_epochs 6': '--num_train_epochs 1',
}
# Clean up bash script
SCREAMING_SNAKE_CASE =(self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py' )[1].strip()
SCREAMING_SNAKE_CASE =bash_script.replace('\\\n' ,'' ).strip().replace('"$@"' ,'' )
for k, v in env_vars_to_replace.items():
SCREAMING_SNAKE_CASE =bash_script.replace(snake_case ,str(snake_case ) )
SCREAMING_SNAKE_CASE =self.get_auto_remove_tmp_dir()
# bash_script = bash_script.replace("--fp16 ", "")
SCREAMING_SNAKE_CASE =f'\n --output_dir {output_dir}\n --tokenizer_name Helsinki-NLP/opus-mt-en-ro\n --sortish_sampler\n --do_predict\n --gpus 1\n --freeze_encoder\n --n_train 40000\n --n_val 500\n --n_test 500\n --fp16_opt_level O1\n --num_sanity_val_steps 0\n --eval_beams 2\n '.split()
# XXX: args.gpus > 1 : handle multi_gpu in the future
SCREAMING_SNAKE_CASE =['finetune.py'] + bash_script.split() + args
with patch.object(snake_case ,'argv' ,snake_case ):
SCREAMING_SNAKE_CASE =argparse.ArgumentParser()
SCREAMING_SNAKE_CASE =pl.Trainer.add_argparse_args(snake_case )
SCREAMING_SNAKE_CASE =SummarizationModule.add_model_specific_args(snake_case ,os.getcwd() )
SCREAMING_SNAKE_CASE =parser.parse_args()
SCREAMING_SNAKE_CASE =main(snake_case )
# Check metrics
SCREAMING_SNAKE_CASE =load_json(model.metrics_save_path )
SCREAMING_SNAKE_CASE =metrics['val'][0]
SCREAMING_SNAKE_CASE =metrics['val'][-1]
self.assertEqual(len(metrics['val'] ) ,(args.max_epochs / args.val_check_interval) )
assert isinstance(last_step_stats[f'val_avg_{model.val_metric}'] ,snake_case )
self.assertGreater(last_step_stats['val_avg_gen_time'] ,0.01 )
# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
self.assertLessEqual(last_step_stats['val_avg_gen_time'] ,1.0 )
# test learning requirements:
# 1. BLEU improves over the course of training by more than 2 pts
self.assertGreater(last_step_stats['val_avg_bleu'] - first_step_stats['val_avg_bleu'] ,2 )
# 2. BLEU finishes above 17
self.assertGreater(last_step_stats['val_avg_bleu'] ,17 )
# 3. test BLEU and val BLEU within ~1.1 pt.
self.assertLess(abs(metrics['val'][-1]['val_avg_bleu'] - metrics['test'][-1]['test_avg_bleu'] ) ,1.1 )
# check lightning ckpt can be loaded and has a reasonable statedict
SCREAMING_SNAKE_CASE =os.listdir(snake_case )
SCREAMING_SNAKE_CASE =[x for x in contents if x.endswith('.ckpt' )][0]
SCREAMING_SNAKE_CASE =os.path.join(args.output_dir ,snake_case )
SCREAMING_SNAKE_CASE =torch.load(snake_case ,map_location='cpu' )
SCREAMING_SNAKE_CASE ='model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
SCREAMING_SNAKE_CASE ={os.path.basename(snake_case ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics['test'] ) == 1
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
@timeout_decorator.timeout(600 )
@slow
@require_torch_gpu
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =f'{self.test_file_dir_str}/test_data/wmt_en_ro'
SCREAMING_SNAKE_CASE ={
'--fp16_opt_level=O1': '',
'$MAX_LEN': 128,
'$BS': 16,
'$GAS': 1,
'$ENRO_DIR': data_dir,
'$m': 'sshleifer/student_marian_en_ro_6_1',
'val_check_interval=0.25': 'val_check_interval=1.0',
}
# Clean up bash script
SCREAMING_SNAKE_CASE =(
(self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py' )[1].strip()
)
SCREAMING_SNAKE_CASE =bash_script.replace('\\\n' ,'' ).strip().replace('"$@"' ,'' )
SCREAMING_SNAKE_CASE =bash_script.replace('--fp16 ' ,' ' )
for k, v in env_vars_to_replace.items():
SCREAMING_SNAKE_CASE =bash_script.replace(snake_case ,str(snake_case ) )
SCREAMING_SNAKE_CASE =self.get_auto_remove_tmp_dir()
SCREAMING_SNAKE_CASE =bash_script.replace('--fp16' ,'' )
SCREAMING_SNAKE_CASE =6
SCREAMING_SNAKE_CASE =(
['distillation.py']
+ bash_script.split()
+ [
f'--output_dir={output_dir}',
'--gpus=1',
'--learning_rate=1e-3',
f'--num_train_epochs={epochs}',
'--warmup_steps=10',
'--val_check_interval=1.0',
'--do_predict',
]
)
with patch.object(snake_case ,'argv' ,snake_case ):
SCREAMING_SNAKE_CASE =argparse.ArgumentParser()
SCREAMING_SNAKE_CASE =pl.Trainer.add_argparse_args(snake_case )
SCREAMING_SNAKE_CASE =SummarizationDistiller.add_model_specific_args(snake_case ,os.getcwd() )
SCREAMING_SNAKE_CASE =parser.parse_args()
# assert args.gpus == gpus THIS BREAKS for multi_gpu
SCREAMING_SNAKE_CASE =distill_main(snake_case )
# Check metrics
SCREAMING_SNAKE_CASE =load_json(model.metrics_save_path )
SCREAMING_SNAKE_CASE =metrics['val'][0]
SCREAMING_SNAKE_CASE =metrics['val'][-1]
assert len(metrics['val'] ) >= (args.max_epochs / args.val_check_interval) # +1 accounts for val_sanity_check
assert last_step_stats["val_avg_gen_time"] >= 0.01
assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"] # model learned nothing
assert 1.0 >= last_step_stats["val_avg_gen_time"] # model hanging on generate. Maybe bad config was saved.
assert isinstance(last_step_stats[f'val_avg_{model.val_metric}'] ,snake_case )
# check lightning ckpt can be loaded and has a reasonable statedict
SCREAMING_SNAKE_CASE =os.listdir(snake_case )
SCREAMING_SNAKE_CASE =[x for x in contents if x.endswith('.ckpt' )][0]
SCREAMING_SNAKE_CASE =os.path.join(args.output_dir ,snake_case )
SCREAMING_SNAKE_CASE =torch.load(snake_case ,map_location='cpu' )
SCREAMING_SNAKE_CASE ='model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
SCREAMING_SNAKE_CASE ={os.path.basename(snake_case ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics['test'] ) == 1
| 334 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"SCUT-DLVCLab/lilt-roberta-en-base": (
"https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
),
}
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'lilt'
def __init__( self : Union[str, Any] ,snake_case : Optional[Any]=30522 ,snake_case : Optional[int]=768 ,snake_case : int=12 ,snake_case : Tuple=12 ,snake_case : Union[str, Any]=3072 ,snake_case : Optional[int]="gelu" ,snake_case : Tuple=0.1 ,snake_case : Union[str, Any]=0.1 ,snake_case : Optional[Any]=512 ,snake_case : Union[str, Any]=2 ,snake_case : List[str]=0.02 ,snake_case : Optional[int]=1e-12 ,snake_case : int=0 ,snake_case : Optional[Any]="absolute" ,snake_case : str=None ,snake_case : List[Any]=4 ,snake_case : Dict=1024 ,**snake_case : Optional[int] ,):
super().__init__(pad_token_id=snake_case ,**snake_case )
SCREAMING_SNAKE_CASE =vocab_size
SCREAMING_SNAKE_CASE =hidden_size
SCREAMING_SNAKE_CASE =num_hidden_layers
SCREAMING_SNAKE_CASE =num_attention_heads
SCREAMING_SNAKE_CASE =hidden_act
SCREAMING_SNAKE_CASE =intermediate_size
SCREAMING_SNAKE_CASE =hidden_dropout_prob
SCREAMING_SNAKE_CASE =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE =max_position_embeddings
SCREAMING_SNAKE_CASE =type_vocab_size
SCREAMING_SNAKE_CASE =initializer_range
SCREAMING_SNAKE_CASE =layer_norm_eps
SCREAMING_SNAKE_CASE =position_embedding_type
SCREAMING_SNAKE_CASE =classifier_dropout
SCREAMING_SNAKE_CASE =channel_shrink_ratio
SCREAMING_SNAKE_CASE =max_ad_position_embeddings
| 334 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"salesforce/blip2-opt-2.7b": "https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json",
}
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'blip_2_vision_model'
def __init__( self : List[Any] ,snake_case : List[Any]=1408 ,snake_case : Optional[Any]=6144 ,snake_case : Optional[int]=39 ,snake_case : Optional[int]=16 ,snake_case : Optional[Any]=224 ,snake_case : Tuple=14 ,snake_case : Optional[Any]="gelu" ,snake_case : Union[str, Any]=0.00_001 ,snake_case : Dict=0.0 ,snake_case : Union[str, Any]=1e-10 ,snake_case : int=True ,**snake_case : str ,):
super().__init__(**snake_case )
SCREAMING_SNAKE_CASE =hidden_size
SCREAMING_SNAKE_CASE =intermediate_size
SCREAMING_SNAKE_CASE =num_hidden_layers
SCREAMING_SNAKE_CASE =num_attention_heads
SCREAMING_SNAKE_CASE =patch_size
SCREAMING_SNAKE_CASE =image_size
SCREAMING_SNAKE_CASE =initializer_range
SCREAMING_SNAKE_CASE =attention_dropout
SCREAMING_SNAKE_CASE =layer_norm_eps
SCREAMING_SNAKE_CASE =hidden_act
SCREAMING_SNAKE_CASE =qkv_bias
@classmethod
def _lowerCAmelCase ( cls : Dict ,snake_case : Union[str, os.PathLike] ,**snake_case : str ):
cls._set_token_in_kwargs(snake_case )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =cls.get_config_dict(snake_case ,**snake_case )
# get the vision config dict if we are loading from Blip2Config
if config_dict.get('model_type' ) == "blip-2":
SCREAMING_SNAKE_CASE =config_dict['vision_config']
if "model_type" in config_dict and hasattr(cls ,'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(snake_case ,**snake_case )
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'blip_2_qformer'
def __init__( self : Any ,snake_case : Dict=30522 ,snake_case : int=768 ,snake_case : List[Any]=12 ,snake_case : List[str]=12 ,snake_case : Optional[Any]=3072 ,snake_case : str="gelu" ,snake_case : Optional[Any]=0.1 ,snake_case : Union[str, Any]=0.1 ,snake_case : Optional[Any]=512 ,snake_case : List[Any]=0.02 ,snake_case : List[str]=1e-12 ,snake_case : Tuple=0 ,snake_case : Union[str, Any]="absolute" ,snake_case : List[Any]=2 ,snake_case : List[str]=1408 ,**snake_case : Optional[Any] ,):
super().__init__(pad_token_id=snake_case ,**snake_case )
SCREAMING_SNAKE_CASE =vocab_size
SCREAMING_SNAKE_CASE =hidden_size
SCREAMING_SNAKE_CASE =num_hidden_layers
SCREAMING_SNAKE_CASE =num_attention_heads
SCREAMING_SNAKE_CASE =hidden_act
SCREAMING_SNAKE_CASE =intermediate_size
SCREAMING_SNAKE_CASE =hidden_dropout_prob
SCREAMING_SNAKE_CASE =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE =max_position_embeddings
SCREAMING_SNAKE_CASE =initializer_range
SCREAMING_SNAKE_CASE =layer_norm_eps
SCREAMING_SNAKE_CASE =position_embedding_type
SCREAMING_SNAKE_CASE =cross_attention_frequency
SCREAMING_SNAKE_CASE =encoder_hidden_size
@classmethod
def _lowerCAmelCase ( cls : List[Any] ,snake_case : Union[str, os.PathLike] ,**snake_case : Dict ):
cls._set_token_in_kwargs(snake_case )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =cls.get_config_dict(snake_case ,**snake_case )
# get the qformer config dict if we are loading from Blip2Config
if config_dict.get('model_type' ) == "blip-2":
SCREAMING_SNAKE_CASE =config_dict['qformer_config']
if "model_type" in config_dict and hasattr(cls ,'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(snake_case ,**snake_case )
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'blip-2'
__UpperCAmelCase = True
def __init__( self : int ,snake_case : Dict=None ,snake_case : Tuple=None ,snake_case : str=None ,snake_case : Union[str, Any]=32 ,**snake_case : int ):
super().__init__(**snake_case )
if vision_config is None:
SCREAMING_SNAKE_CASE ={}
logger.info('vision_config is None. initializing the Blip2VisionConfig with default values.' )
if qformer_config is None:
SCREAMING_SNAKE_CASE ={}
logger.info('qformer_config is None. Initializing the Blip2QFormerConfig with default values.' )
if text_config is None:
SCREAMING_SNAKE_CASE ={}
logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).' )
SCREAMING_SNAKE_CASE =BlipaVisionConfig(**snake_case )
SCREAMING_SNAKE_CASE =BlipaQFormerConfig(**snake_case )
SCREAMING_SNAKE_CASE =text_config['model_type'] if 'model_type' in text_config else 'opt'
SCREAMING_SNAKE_CASE =CONFIG_MAPPING[text_model_type](**snake_case )
SCREAMING_SNAKE_CASE =self.text_config.tie_word_embeddings
SCREAMING_SNAKE_CASE =self.text_config.is_encoder_decoder
SCREAMING_SNAKE_CASE =num_query_tokens
SCREAMING_SNAKE_CASE =self.vision_config.hidden_size
SCREAMING_SNAKE_CASE =self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
SCREAMING_SNAKE_CASE =1.0
SCREAMING_SNAKE_CASE =0.02
@classmethod
def _lowerCAmelCase ( cls : Union[str, Any] ,snake_case : BlipaVisionConfig ,snake_case : BlipaQFormerConfig ,snake_case : PretrainedConfig ,**snake_case : Any ,):
return cls(
vision_config=vision_config.to_dict() ,qformer_config=qformer_config.to_dict() ,text_config=text_config.to_dict() ,**snake_case ,)
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE =self.vision_config.to_dict()
SCREAMING_SNAKE_CASE =self.qformer_config.to_dict()
SCREAMING_SNAKE_CASE =self.text_config.to_dict()
SCREAMING_SNAKE_CASE =self.__class__.model_type
return output
| 334 | 1 |
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =0
SCREAMING_SNAKE_CASE =len(lowerCAmelCase_ )
for i in range(n - 1 ):
for j in range(i + 1, lowerCAmelCase_ ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
if len(lowerCAmelCase_ ) <= 1:
return arr, 0
SCREAMING_SNAKE_CASE =len(lowerCAmelCase_ ) // 2
SCREAMING_SNAKE_CASE =arr[0:mid]
SCREAMING_SNAKE_CASE =arr[mid:]
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =count_inversions_recursive(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =count_inversions_recursive(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =_count_cross_inversions(lowerCAmelCase_, lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =inversion_p + inversions_q + cross_inversions
return c, num_inversions
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =[]
SCREAMING_SNAKE_CASE =SCREAMING_SNAKE_CASE =SCREAMING_SNAKE_CASE =0
while i < len(lowerCAmelCase_ ) and j < len(lowerCAmelCase_ ):
if p[i] > q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(lowerCAmelCase_ ) - i
r.append(q[j] )
j += 1
else:
r.append(p[i] )
i += 1
if i < len(lowerCAmelCase_ ):
r.extend(p[i:] )
else:
r.extend(q[j:] )
return r, num_inversion
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =[10, 2, 1, 5, 5, 2, 11]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
SCREAMING_SNAKE_CASE =count_inversions_bf(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =count_inversions_recursive(lowerCAmelCase_ )
assert num_inversions_bf == num_inversions_recursive == 8
print('number of inversions = ', lowerCAmelCase_ )
# testing an array with zero inversion (a sorted arr_1)
arr_a.sort()
SCREAMING_SNAKE_CASE =count_inversions_bf(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =count_inversions_recursive(lowerCAmelCase_ )
assert num_inversions_bf == num_inversions_recursive == 0
print('number of inversions = ', lowerCAmelCase_ )
# an empty list should also have zero inversions
SCREAMING_SNAKE_CASE =[]
SCREAMING_SNAKE_CASE =count_inversions_bf(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =count_inversions_recursive(lowerCAmelCase_ )
assert num_inversions_bf == num_inversions_recursive == 0
print('number of inversions = ', lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 334 |
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve # From: mauve-text
import datasets
_lowerCamelCase ="\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n booktitle = {NeurIPS},\n year = {2021}\n}\n\n"
_lowerCamelCase ="\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n"
_lowerCamelCase ="\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n predictions: list of generated text to score. Each predictions\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\nOptional Args:\n num_buckets: the size of the histogram to quantize P and Q. Options: 'auto' (default) or an integer\n pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n kmeans_max_iter: maximum number of k-means iterations. Default 500\n featurize_model_name: name of the model from which features are obtained. Default 'gpt2-large' Use one of ['gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'].\n device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU\n max_text_length: maximum number of tokens to consider. Default 1024\n divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n mauve_scaling_factor: \"c\" from the paper. Default 5.\n verbose: If True (default), print running time updates\n seed: random seed to initialize k-means cluster assignments.\nReturns:\n mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n q_hist: same as above, but with q_text.\nExamples:\n\n >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n >>> import datasets\n >>> mauve = datasets.load_metric('mauve')\n >>> predictions = [\"hello there\", \"general kenobi\"]\n >>> references = [\"hello there\", \"general kenobi\"]\n >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n >>> print(out.mauve) # doctest: +SKIP\n 1.0\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a_ ( datasets.Metric ):
"""simple docstring"""
def _lowerCAmelCase ( self : Tuple ):
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,homepage='https://github.com/krishnap25/mauve' ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
'predictions': datasets.Value('string' ,id='sequence' ),
'references': datasets.Value('string' ,id='sequence' ),
} ) ,codebase_urls=['https://github.com/krishnap25/mauve'] ,reference_urls=[
'https://arxiv.org/abs/2102.01454',
'https://github.com/krishnap25/mauve',
] ,)
def _lowerCAmelCase ( self : Union[str, Any] ,snake_case : Optional[int] ,snake_case : str ,snake_case : List[str]=None ,snake_case : str=None ,snake_case : int=None ,snake_case : Union[str, Any]=None ,snake_case : Optional[int]="auto" ,snake_case : List[str]=-1 ,snake_case : Union[str, Any]=0.9 ,snake_case : Tuple=5 ,snake_case : Union[str, Any]=500 ,snake_case : Union[str, Any]="gpt2-large" ,snake_case : Union[str, Any]=-1 ,snake_case : Optional[Any]=1024 ,snake_case : Optional[Any]=25 ,snake_case : List[str]=5 ,snake_case : List[str]=True ,snake_case : Optional[Any]=25 ,):
SCREAMING_SNAKE_CASE =compute_mauve(
p_text=snake_case ,q_text=snake_case ,p_features=snake_case ,q_features=snake_case ,p_tokens=snake_case ,q_tokens=snake_case ,num_buckets=snake_case ,pca_max_data=snake_case ,kmeans_explained_var=snake_case ,kmeans_num_redo=snake_case ,kmeans_max_iter=snake_case ,featurize_model_name=snake_case ,device_id=snake_case ,max_text_length=snake_case ,divergence_curve_discretization_size=snake_case ,mauve_scaling_factor=snake_case ,verbose=snake_case ,seed=snake_case ,)
return out
| 334 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block
@dataclass
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 42
class a_ ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
@register_to_config
def __init__( self : int ,snake_case : int = 65536 ,snake_case : Optional[int] = None ,snake_case : int = 2 ,snake_case : int = 2 ,snake_case : int = 0 ,snake_case : str = "fourier" ,snake_case : bool = True ,snake_case : bool = False ,snake_case : float = 0.0 ,snake_case : Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") ,snake_case : Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") ,snake_case : Tuple[str] = "UNetMidBlock1D" ,snake_case : str = None ,snake_case : Tuple[int] = (32, 32, 64) ,snake_case : str = None ,snake_case : int = 8 ,snake_case : int = 1 ,snake_case : bool = False ,):
super().__init__()
SCREAMING_SNAKE_CASE =sample_size
# time
if time_embedding_type == "fourier":
SCREAMING_SNAKE_CASE =GaussianFourierProjection(
embedding_size=8 ,set_W_to_weight=snake_case ,log=snake_case ,flip_sin_to_cos=snake_case )
SCREAMING_SNAKE_CASE =2 * block_out_channels[0]
elif time_embedding_type == "positional":
SCREAMING_SNAKE_CASE =Timesteps(
block_out_channels[0] ,flip_sin_to_cos=snake_case ,downscale_freq_shift=snake_case )
SCREAMING_SNAKE_CASE =block_out_channels[0]
if use_timestep_embedding:
SCREAMING_SNAKE_CASE =block_out_channels[0] * 4
SCREAMING_SNAKE_CASE =TimestepEmbedding(
in_channels=snake_case ,time_embed_dim=snake_case ,act_fn=snake_case ,out_dim=block_out_channels[0] ,)
SCREAMING_SNAKE_CASE =nn.ModuleList([] )
SCREAMING_SNAKE_CASE =None
SCREAMING_SNAKE_CASE =nn.ModuleList([] )
SCREAMING_SNAKE_CASE =None
# down
SCREAMING_SNAKE_CASE =in_channels
for i, down_block_type in enumerate(snake_case ):
SCREAMING_SNAKE_CASE =output_channel
SCREAMING_SNAKE_CASE =block_out_channels[i]
if i == 0:
input_channel += extra_in_channels
SCREAMING_SNAKE_CASE =i == len(snake_case ) - 1
SCREAMING_SNAKE_CASE =get_down_block(
snake_case ,num_layers=snake_case ,in_channels=snake_case ,out_channels=snake_case ,temb_channels=block_out_channels[0] ,add_downsample=not is_final_block or downsample_each_block ,)
self.down_blocks.append(snake_case )
# mid
SCREAMING_SNAKE_CASE =get_mid_block(
snake_case ,in_channels=block_out_channels[-1] ,mid_channels=block_out_channels[-1] ,out_channels=block_out_channels[-1] ,embed_dim=block_out_channels[0] ,num_layers=snake_case ,add_downsample=snake_case ,)
# up
SCREAMING_SNAKE_CASE =list(reversed(snake_case ) )
SCREAMING_SNAKE_CASE =reversed_block_out_channels[0]
if out_block_type is None:
SCREAMING_SNAKE_CASE =out_channels
else:
SCREAMING_SNAKE_CASE =block_out_channels[0]
for i, up_block_type in enumerate(snake_case ):
SCREAMING_SNAKE_CASE =output_channel
SCREAMING_SNAKE_CASE =(
reversed_block_out_channels[i + 1] if i < len(snake_case ) - 1 else final_upsample_channels
)
SCREAMING_SNAKE_CASE =i == len(snake_case ) - 1
SCREAMING_SNAKE_CASE =get_up_block(
snake_case ,num_layers=snake_case ,in_channels=snake_case ,out_channels=snake_case ,temb_channels=block_out_channels[0] ,add_upsample=not is_final_block ,)
self.up_blocks.append(snake_case )
SCREAMING_SNAKE_CASE =output_channel
# out
SCREAMING_SNAKE_CASE =norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 ,32 )
SCREAMING_SNAKE_CASE =get_out_block(
out_block_type=snake_case ,num_groups_out=snake_case ,embed_dim=block_out_channels[0] ,out_channels=snake_case ,act_fn=snake_case ,fc_dim=block_out_channels[-1] // 4 ,)
def _lowerCAmelCase ( self : Any ,snake_case : torch.FloatTensor ,snake_case : Union[torch.Tensor, float, int] ,snake_case : bool = True ,):
SCREAMING_SNAKE_CASE =timestep
if not torch.is_tensor(snake_case ):
SCREAMING_SNAKE_CASE =torch.tensor([timesteps] ,dtype=torch.long ,device=sample.device )
elif torch.is_tensor(snake_case ) and len(timesteps.shape ) == 0:
SCREAMING_SNAKE_CASE =timesteps[None].to(sample.device )
SCREAMING_SNAKE_CASE =self.time_proj(snake_case )
if self.config.use_timestep_embedding:
SCREAMING_SNAKE_CASE =self.time_mlp(snake_case )
else:
SCREAMING_SNAKE_CASE =timestep_embed[..., None]
SCREAMING_SNAKE_CASE =timestep_embed.repeat([1, 1, sample.shape[2]] ).to(sample.dtype )
SCREAMING_SNAKE_CASE =timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]) )
# 2. down
SCREAMING_SNAKE_CASE =()
for downsample_block in self.down_blocks:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =downsample_block(hidden_states=snake_case ,temb=snake_case )
down_block_res_samples += res_samples
# 3. mid
if self.mid_block:
SCREAMING_SNAKE_CASE =self.mid_block(snake_case ,snake_case )
# 4. up
for i, upsample_block in enumerate(self.up_blocks ):
SCREAMING_SNAKE_CASE =down_block_res_samples[-1:]
SCREAMING_SNAKE_CASE =down_block_res_samples[:-1]
SCREAMING_SNAKE_CASE =upsample_block(snake_case ,res_hidden_states_tuple=snake_case ,temb=snake_case )
# 5. post-process
if self.out_block:
SCREAMING_SNAKE_CASE =self.out_block(snake_case ,snake_case )
if not return_dict:
return (sample,)
return UNetaDOutput(sample=snake_case )
| 334 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
# See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'vit_mae'
def __init__( self : Union[str, Any] ,snake_case : Any=768 ,snake_case : List[str]=12 ,snake_case : Optional[int]=12 ,snake_case : int=3072 ,snake_case : List[Any]="gelu" ,snake_case : str=0.0 ,snake_case : str=0.0 ,snake_case : Optional[Any]=0.02 ,snake_case : Dict=1e-12 ,snake_case : List[str]=224 ,snake_case : Any=16 ,snake_case : Any=3 ,snake_case : Tuple=True ,snake_case : List[Any]=16 ,snake_case : List[str]=512 ,snake_case : List[Any]=8 ,snake_case : Dict=2048 ,snake_case : Union[str, Any]=0.75 ,snake_case : Union[str, Any]=False ,**snake_case : Optional[int] ,):
super().__init__(**snake_case )
SCREAMING_SNAKE_CASE =hidden_size
SCREAMING_SNAKE_CASE =num_hidden_layers
SCREAMING_SNAKE_CASE =num_attention_heads
SCREAMING_SNAKE_CASE =intermediate_size
SCREAMING_SNAKE_CASE =hidden_act
SCREAMING_SNAKE_CASE =hidden_dropout_prob
SCREAMING_SNAKE_CASE =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE =initializer_range
SCREAMING_SNAKE_CASE =layer_norm_eps
SCREAMING_SNAKE_CASE =image_size
SCREAMING_SNAKE_CASE =patch_size
SCREAMING_SNAKE_CASE =num_channels
SCREAMING_SNAKE_CASE =qkv_bias
SCREAMING_SNAKE_CASE =decoder_num_attention_heads
SCREAMING_SNAKE_CASE =decoder_hidden_size
SCREAMING_SNAKE_CASE =decoder_num_hidden_layers
SCREAMING_SNAKE_CASE =decoder_intermediate_size
SCREAMING_SNAKE_CASE =mask_ratio
SCREAMING_SNAKE_CASE =norm_pix_loss
| 334 | 1 |
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
if bit_count < 0:
raise ValueError('The given input must be positive' )
# get the generated string sequence
SCREAMING_SNAKE_CASE =gray_code_sequence_string(lowerCAmelCase_ )
#
# convert them to integers
for i in range(len(lowerCAmelCase_ ) ):
SCREAMING_SNAKE_CASE =int(sequence[i], 2 )
return sequence
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
if bit_count == 0:
return ["0"]
if bit_count == 1:
return ["0", "1"]
SCREAMING_SNAKE_CASE =1 << bit_count # defines the length of the sequence
# 1<< n is equivalent to 2^n
# recursive answer will generate answer for n-1 bits
SCREAMING_SNAKE_CASE =gray_code_sequence_string(bit_count - 1 )
SCREAMING_SNAKE_CASE =[]
# append 0 to first half of the smaller sequence generated
for i in range(seq_len // 2 ):
SCREAMING_SNAKE_CASE ='0' + smaller_sequence[i]
sequence.append(lowerCAmelCase_ )
# append 1 to second half ... start from the end of the list
for i in reversed(range(seq_len // 2 ) ):
SCREAMING_SNAKE_CASE ='1' + smaller_sequence[i]
sequence.append(lowerCAmelCase_ )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
| 334 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
_lowerCamelCase ={
"configuration_ernie": ["ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", "ErnieOnnxConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase =[
"ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST",
"ErnieForCausalLM",
"ErnieForMaskedLM",
"ErnieForMultipleChoice",
"ErnieForNextSentencePrediction",
"ErnieForPreTraining",
"ErnieForQuestionAnswering",
"ErnieForSequenceClassification",
"ErnieForTokenClassification",
"ErnieModel",
"ErniePreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ernie import (
ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
ErnieForCausalLM,
ErnieForMaskedLM,
ErnieForMultipleChoice,
ErnieForNextSentencePrediction,
ErnieForPreTraining,
ErnieForQuestionAnswering,
ErnieForSequenceClassification,
ErnieForTokenClassification,
ErnieModel,
ErniePreTrainedModel,
)
else:
import sys
_lowerCamelCase =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 334 | 1 |
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =SwinConfig(image_size=192 )
if "base" in model_name:
SCREAMING_SNAKE_CASE =6
SCREAMING_SNAKE_CASE =128
SCREAMING_SNAKE_CASE =(2, 2, 18, 2)
SCREAMING_SNAKE_CASE =(4, 8, 16, 32)
elif "large" in model_name:
SCREAMING_SNAKE_CASE =12
SCREAMING_SNAKE_CASE =192
SCREAMING_SNAKE_CASE =(2, 2, 18, 2)
SCREAMING_SNAKE_CASE =(6, 12, 24, 48)
else:
raise ValueError('Model not supported, only supports base and large variants' )
SCREAMING_SNAKE_CASE =window_size
SCREAMING_SNAKE_CASE =embed_dim
SCREAMING_SNAKE_CASE =depths
SCREAMING_SNAKE_CASE =num_heads
return config
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
if "encoder.mask_token" in name:
SCREAMING_SNAKE_CASE =name.replace('encoder.mask_token', 'embeddings.mask_token' )
if "encoder.patch_embed.proj" in name:
SCREAMING_SNAKE_CASE =name.replace('encoder.patch_embed.proj', 'embeddings.patch_embeddings.projection' )
if "encoder.patch_embed.norm" in name:
SCREAMING_SNAKE_CASE =name.replace('encoder.patch_embed.norm', 'embeddings.norm' )
if "attn.proj" in name:
SCREAMING_SNAKE_CASE =name.replace('attn.proj', 'attention.output.dense' )
if "attn" in name:
SCREAMING_SNAKE_CASE =name.replace('attn', 'attention.self' )
if "norm1" in name:
SCREAMING_SNAKE_CASE =name.replace('norm1', 'layernorm_before' )
if "norm2" in name:
SCREAMING_SNAKE_CASE =name.replace('norm2', 'layernorm_after' )
if "mlp.fc1" in name:
SCREAMING_SNAKE_CASE =name.replace('mlp.fc1', 'intermediate.dense' )
if "mlp.fc2" in name:
SCREAMING_SNAKE_CASE =name.replace('mlp.fc2', 'output.dense' )
if name == "encoder.norm.weight":
SCREAMING_SNAKE_CASE ='layernorm.weight'
if name == "encoder.norm.bias":
SCREAMING_SNAKE_CASE ='layernorm.bias'
if "decoder" in name:
pass
else:
SCREAMING_SNAKE_CASE ='swin.' + name
return name
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
for key in orig_state_dict.copy().keys():
SCREAMING_SNAKE_CASE =orig_state_dict.pop(lowerCAmelCase_ )
if "attn_mask" in key:
pass
elif "qkv" in key:
SCREAMING_SNAKE_CASE =key.split('.' )
SCREAMING_SNAKE_CASE =int(key_split[2] )
SCREAMING_SNAKE_CASE =int(key_split[4] )
SCREAMING_SNAKE_CASE =model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
SCREAMING_SNAKE_CASE =val[:dim, :]
SCREAMING_SNAKE_CASE =val[
dim : dim * 2, :
]
SCREAMING_SNAKE_CASE =val[-dim:, :]
else:
SCREAMING_SNAKE_CASE =val[
:dim
]
SCREAMING_SNAKE_CASE =val[
dim : dim * 2
]
SCREAMING_SNAKE_CASE =val[
-dim:
]
else:
SCREAMING_SNAKE_CASE =val
return orig_state_dict
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =torch.load(lowerCAmelCase_, map_location='cpu' )['model']
SCREAMING_SNAKE_CASE =get_swin_config(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =SwinForMaskedImageModeling(lowerCAmelCase_ )
model.eval()
SCREAMING_SNAKE_CASE =convert_state_dict(lowerCAmelCase_, lowerCAmelCase_ )
model.load_state_dict(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE ='http://images.cocodataset.org/val2017/000000039769.jpg'
SCREAMING_SNAKE_CASE =ViTImageProcessor(size={'height': 192, 'width': 192} )
SCREAMING_SNAKE_CASE =Image.open(requests.get(lowerCAmelCase_, stream=lowerCAmelCase_ ).raw )
SCREAMING_SNAKE_CASE =image_processor(images=lowerCAmelCase_, return_tensors='pt' )
with torch.no_grad():
SCREAMING_SNAKE_CASE =model(**lowerCAmelCase_ ).logits
print(outputs.keys() )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
print(F'Saving model {model_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(lowerCAmelCase_ )
print(F'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(lowerCAmelCase_ )
if push_to_hub:
print(F'Pushing model and image processor for {model_name} to hub' )
model.push_to_hub(F'microsoft/{model_name}' )
image_processor.push_to_hub(F'microsoft/{model_name}' )
if __name__ == "__main__":
_lowerCamelCase =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="swin-base-simmim-window6-192",
type=str,
choices=["swin-base-simmim-window6-192", "swin-large-simmim-window12-192"],
help="Name of the Swin SimMIM model you'd like to convert.",
)
parser.add_argument(
"--checkpoint_path",
default="/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth",
type=str,
help="Path to the original PyTorch checkpoint (.pth file).",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
_lowerCamelCase =parser.parse_args()
convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
| 334 |
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError
from .utils import require_pil
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([1, 2, 3] ) )
self.assertEqual(arr.type ,pa.intaa() )
def _lowerCAmelCase ( self : Any ):
with self.assertRaises(snake_case ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([1, 2, 3] ) ,type=pa.intaa() )
def _lowerCAmelCase ( self : Union[str, Any] ):
with self.assertRaises(snake_case ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([1, 2, 3] ,try_type=Value('bool' ) ,type=Value('int64' ) ) )
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([1, 2, 3] ,type=Value('int32' ) ) )
self.assertEqual(arr.type ,pa.intaa() )
def _lowerCAmelCase ( self : int ):
with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence(['foo', 'bar'] ,type=Value('int64' ) ) )
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([1, 2, 3] ,try_type=Value('int32' ) ) )
self.assertEqual(arr.type ,pa.intaa() )
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence(['foo', 'bar'] ,try_type=Value('int64' ) ) )
self.assertEqual(arr.type ,pa.string() )
def _lowerCAmelCase ( self : Dict ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([[[1, 2, 3]]] ,type=ArrayaD((1, 3) ,'int64' ) ) )
self.assertEqual(arr.type ,ArrayaDExtensionType((1, 3) ,'int64' ) )
def _lowerCAmelCase ( self : Dict ):
with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence(['foo', 'bar'] ,type=ArrayaD((1, 3) ,'int64' ) ) )
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([[[1, 2, 3]]] ,try_type=ArrayaD((1, 3) ,'int64' ) ) )
self.assertEqual(arr.type ,ArrayaDExtensionType((1, 3) ,'int64' ) )
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence(['foo', 'bar'] ,try_type=ArrayaD((1, 3) ,'int64' ) ) )
self.assertEqual(arr.type ,pa.string() )
@require_pil
def _lowerCAmelCase ( self : int ):
import PIL.Image
SCREAMING_SNAKE_CASE =PIL.Image.fromarray(np.arange(10 ,dtype=np.uinta ).reshape(2 ,5 ) )
with patch(
'datasets.arrow_writer.cast_to_python_objects' ,side_effect=snake_case ) as mock_cast_to_python_objects:
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([{'path': None, 'bytes': B'image_bytes'}, pil_image] ,type=Image() ) )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =mock_cast_to_python_objects.call_args_list[-1]
self.assertIn('optimize_list_casting' ,snake_case )
self.assertFalse(kwargs['optimize_list_casting'] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferReader(lowerCAmelCase_ ) if isinstance(lowerCAmelCase_, pa.Buffer ) else pa.memory_map(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =pa.ipc.open_stream(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =f.read_all()
assert len(pa_table.to_batches() ) == expected_num_chunks
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
del pa_table
@pytest.mark.parametrize('writer_batch_size', [None, 1, 10] )
@pytest.mark.parametrize(
'fields', [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
SCREAMING_SNAKE_CASE =pa.schema(lowerCAmelCase_ ) if fields else None
with ArrowWriter(stream=lowerCAmelCase_, schema=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_ ) as writer:
writer.write({'col_1': 'foo', 'col_2': 1} )
writer.write({'col_1': 'bar', 'col_2': 2} )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
SCREAMING_SNAKE_CASE ={'col_1': pa.string(), 'col_2': pa.intaa()}
assert writer._schema == pa.schema(lowerCAmelCase_, metadata=writer._schema.metadata )
_check_output(output.getvalue(), expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
SCREAMING_SNAKE_CASE =Features({'labels': ClassLabel(names=['neg', 'pos'] )} )
with ArrowWriter(stream=lowerCAmelCase_, features=lowerCAmelCase_ ) as writer:
writer.write({'labels': 0} )
writer.write({'labels': 1} )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert writer._schema == features.arrow_schema
assert writer._schema.metadata == features.arrow_schema.metadata
SCREAMING_SNAKE_CASE =pa.BufferReader(output.getvalue() )
SCREAMING_SNAKE_CASE =pa.ipc.open_stream(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =f.read_all()
SCREAMING_SNAKE_CASE =pa_table.schema
assert pa_table.num_rows == 2
assert schema == features.arrow_schema
assert schema.metadata == features.arrow_schema.metadata
assert features == Features.from_arrow_schema(lowerCAmelCase_ )
@pytest.mark.parametrize('writer_batch_size', [None, 1, 10] )
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
with ArrowWriter(
stream=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_, hash_salt='split_name', check_duplicates=lowerCAmelCase_, ) as writer:
with pytest.raises(lowerCAmelCase_ ):
writer.write({'col_1': 'foo', 'col_2': 1}, key=[1, 2] )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
@pytest.mark.parametrize('writer_batch_size', [None, 2, 10] )
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
with ArrowWriter(
stream=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_, hash_salt='split_name', check_duplicates=lowerCAmelCase_, ) as writer:
with pytest.raises(lowerCAmelCase_ ):
writer.write({'col_1': 'foo', 'col_2': 1}, key=10 )
writer.write({'col_1': 'bar', 'col_2': 2}, key=10 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
@pytest.mark.parametrize('writer_batch_size', [None, 2, 10] )
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
with ArrowWriter(
stream=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_, hash_salt='split_name', check_duplicates=lowerCAmelCase_, ) as writer:
writer.write({'col_1': 'foo', 'col_2': 1}, key=1 )
writer.write({'col_1': 'bar', 'col_2': 2}, key=2 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
_check_output(output.getvalue(), expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize('writer_batch_size', [None, 1, 10] )
@pytest.mark.parametrize(
'fields', [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
SCREAMING_SNAKE_CASE =pa.schema(lowerCAmelCase_ ) if fields else None
with ArrowWriter(stream=lowerCAmelCase_, schema=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_ ) as writer:
writer.write_batch({'col_1': ['foo', 'bar'], 'col_2': [1, 2]} )
writer.write_batch({'col_1': [], 'col_2': []} )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
SCREAMING_SNAKE_CASE ={'col_1': pa.string(), 'col_2': pa.intaa()}
assert writer._schema == pa.schema(lowerCAmelCase_, metadata=writer._schema.metadata )
_check_output(output.getvalue(), expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize('writer_batch_size', [None, 1, 10] )
@pytest.mark.parametrize(
'fields', [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
SCREAMING_SNAKE_CASE =pa.schema(lowerCAmelCase_ ) if fields else None
with ArrowWriter(stream=lowerCAmelCase_, schema=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_ ) as writer:
writer.write_table(pa.Table.from_pydict({'col_1': ['foo', 'bar'], 'col_2': [1, 2]} ) )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
SCREAMING_SNAKE_CASE ={'col_1': pa.string(), 'col_2': pa.intaa()}
assert writer._schema == pa.schema(lowerCAmelCase_, metadata=writer._schema.metadata )
_check_output(output.getvalue(), expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize('writer_batch_size', [None, 1, 10] )
@pytest.mark.parametrize(
'fields', [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
SCREAMING_SNAKE_CASE =pa.schema(lowerCAmelCase_ ) if fields else None
with ArrowWriter(stream=lowerCAmelCase_, schema=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_ ) as writer:
writer.write_row(pa.Table.from_pydict({'col_1': ['foo'], 'col_2': [1]} ) )
writer.write_row(pa.Table.from_pydict({'col_1': ['bar'], 'col_2': [2]} ) )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
SCREAMING_SNAKE_CASE ={'col_1': pa.string(), 'col_2': pa.intaa()}
assert writer._schema == pa.schema(lowerCAmelCase_, metadata=writer._schema.metadata )
_check_output(output.getvalue(), expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
def snake_case__ ( ):
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE ={'col_1': pa.string(), 'col_2': pa.intaa()}
SCREAMING_SNAKE_CASE =os.path.join(lowerCAmelCase_, 'test.arrow' )
with ArrowWriter(path=lowerCAmelCase_, schema=pa.schema(lowerCAmelCase_ ) ) as writer:
writer.write_batch({'col_1': ['foo', 'bar'], 'col_2': [1, 2]} )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert writer._schema == pa.schema(lowerCAmelCase_, metadata=writer._schema.metadata )
_check_output(lowerCAmelCase_, 1 )
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
if pa.types.is_list(lowerCAmelCase_ ):
return get_base_dtype(arr_type.value_type )
else:
return arr_type
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
if isinstance(lst[0], lowerCAmelCase_ ):
change_first_primitive_element_in_list(lst[0], lowerCAmelCase_ )
else:
SCREAMING_SNAKE_CASE =value
@pytest.mark.parametrize('optimized_int_type, expected_dtype', [(None, pa.intaa()), (Value('int32' ), pa.intaa())] )
@pytest.mark.parametrize('sequence', [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.array(TypedSequence(lowerCAmelCase_, optimized_int_type=lowerCAmelCase_ ) )
assert get_base_dtype(arr.type ) == expected_dtype
@pytest.mark.parametrize(
'col, expected_dtype', [
('attention_mask', pa.inta()),
('special_tokens_mask', pa.inta()),
('token_type_ids', pa.inta()),
('input_ids', pa.intaa()),
('other', pa.intaa()),
], )
@pytest.mark.parametrize('sequence', [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.array(OptimizedTypedSequence(lowerCAmelCase_, col=lowerCAmelCase_ ) )
assert get_base_dtype(arr.type ) == expected_dtype
# not in range
if col != "other":
# avoids errors due to in-place modifications
SCREAMING_SNAKE_CASE =copy.deepcopy(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =np.iinfo(expected_dtype.to_pandas_dtype() ).max + 1
change_first_primitive_element_in_list(lowerCAmelCase_, lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =pa.array(OptimizedTypedSequence(lowerCAmelCase_, col=lowerCAmelCase_ ) )
assert get_base_dtype(arr.type ) == pa.intaa()
@pytest.mark.parametrize('raise_exception', [False, True] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =str(tmp_path / 'dataset-train.arrow' )
try:
with ArrowWriter(path=lowerCAmelCase_ ) as writer:
if raise_exception:
raise pa.lib.ArrowInvalid()
else:
writer.stream.close()
except pa.lib.ArrowInvalid:
pass
finally:
assert writer.stream.closed
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ='mock://dataset-train.arrow'
with ArrowWriter(path=lowerCAmelCase_, storage_options=mockfs.storage_options ) as writer:
assert isinstance(writer._fs, type(lowerCAmelCase_ ) )
assert writer._fs.storage_options == mockfs.storage_options
writer.write({'col_1': 'foo', 'col_2': 1} )
writer.write({'col_1': 'bar', 'col_2': 2} )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert mockfs.exists(lowerCAmelCase_ )
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
with ParquetWriter(stream=lowerCAmelCase_ ) as writer:
writer.write({'col_1': 'foo', 'col_2': 1} )
writer.write({'col_1': 'bar', 'col_2': 2} )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
SCREAMING_SNAKE_CASE =pa.BufferReader(output.getvalue() )
SCREAMING_SNAKE_CASE =pq.read_table(lowerCAmelCase_ )
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
@require_pil
@pytest.mark.parametrize('embed_local_files', [False, True] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
import PIL.Image
SCREAMING_SNAKE_CASE =str(tmp_path / 'test_image_rgb.jpg' )
PIL.Image.fromarray(np.zeros((5, 5), dtype=np.uinta ) ).save(lowerCAmelCase_, format='png' )
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
with ParquetWriter(
stream=lowerCAmelCase_, features=Features({'image': Image()} ), embed_local_files=lowerCAmelCase_ ) as writer:
writer.write({'image': image_path} )
writer.finalize()
SCREAMING_SNAKE_CASE =pa.BufferReader(output.getvalue() )
SCREAMING_SNAKE_CASE =pq.read_table(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =pa_table.to_pydict()
if embed_local_files:
assert isinstance(out['image'][0]['path'], lowerCAmelCase_ )
with open(lowerCAmelCase_, 'rb' ) as f:
assert out["image"][0]["bytes"] == f.read()
else:
assert out["image"][0]["path"] == image_path
assert out["image"][0]["bytes"] is None
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.schema([pa.field('col_1', pa.string(), nullable=lowerCAmelCase_ )] )
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
with ArrowWriter(stream=lowerCAmelCase_ ) as writer:
writer._build_writer(inferred_schema=lowerCAmelCase_ )
assert writer._schema == pa.schema([pa.field('col_1', pa.string() )] )
| 334 | 1 |
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =tempfile.mkdtemp()
SCREAMING_SNAKE_CASE =5
# Realm tok
SCREAMING_SNAKE_CASE =[
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'test',
'question',
'this',
'is',
'the',
'first',
'second',
'third',
'fourth',
'fifth',
'record',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
SCREAMING_SNAKE_CASE =os.path.join(self.tmpdirname ,'realm_tokenizer' )
os.makedirs(snake_case ,exist_ok=snake_case )
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file ,'w' ,encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
SCREAMING_SNAKE_CASE =os.path.join(self.tmpdirname ,'realm_block_records' )
os.makedirs(snake_case ,exist_ok=snake_case )
def _lowerCAmelCase ( self : str ):
return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname ,'realm_tokenizer' ) )
def _lowerCAmelCase ( self : List[Any] ):
shutil.rmtree(self.tmpdirname )
def _lowerCAmelCase ( self : str ):
SCREAMING_SNAKE_CASE =RealmConfig(num_block_records=self.num_block_records )
return config
def _lowerCAmelCase ( self : int ):
SCREAMING_SNAKE_CASE =Dataset.from_dict(
{
'id': ['0', '1'],
'question': ['foo', 'bar'],
'answers': [['Foo', 'Bar'], ['Bar']],
} )
return dataset
def _lowerCAmelCase ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE =np.array(
[
B'This is the first record',
B'This is the second record',
B'This is the third record',
B'This is the fourth record',
B'This is the fifth record',
B'This is a longer longer longer record',
] ,dtype=snake_case ,)
return block_records
def _lowerCAmelCase ( self : Optional[int] ):
SCREAMING_SNAKE_CASE =RealmRetriever(
block_records=self.get_dummy_block_records() ,tokenizer=self.get_tokenizer() ,)
return retriever
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =self.get_config()
SCREAMING_SNAKE_CASE =self.get_dummy_retriever()
SCREAMING_SNAKE_CASE =retriever.tokenizer
SCREAMING_SNAKE_CASE =np.array([0, 3] ,dtype='long' )
SCREAMING_SNAKE_CASE =tokenizer(['Test question'] ).input_ids
SCREAMING_SNAKE_CASE =tokenizer(
['the fourth'] ,add_special_tokens=snake_case ,return_token_type_ids=snake_case ,return_attention_mask=snake_case ,).input_ids
SCREAMING_SNAKE_CASE =config.reader_seq_len
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =retriever(
snake_case ,snake_case ,answer_ids=snake_case ,max_length=snake_case ,return_tensors='np' )
self.assertEqual(len(snake_case ) ,2 )
self.assertEqual(len(snake_case ) ,2 )
self.assertEqual(len(snake_case ) ,2 )
self.assertEqual(concat_inputs.input_ids.shape ,(2, 10) )
self.assertEqual(concat_inputs.attention_mask.shape ,(2, 10) )
self.assertEqual(concat_inputs.token_type_ids.shape ,(2, 10) )
self.assertEqual(concat_inputs.special_tokens_mask.shape ,(2, 10) )
self.assertEqual(
tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0] ) ,['[CLS]', 'test', 'question', '[SEP]', 'this', 'is', 'the', 'first', 'record', '[SEP]'] ,)
self.assertEqual(
tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1] ) ,['[CLS]', 'test', 'question', '[SEP]', 'this', 'is', 'the', 'fourth', 'record', '[SEP]'] ,)
def _lowerCAmelCase ( self : Optional[int] ):
SCREAMING_SNAKE_CASE =self.get_config()
SCREAMING_SNAKE_CASE =self.get_dummy_retriever()
SCREAMING_SNAKE_CASE =retriever.tokenizer
SCREAMING_SNAKE_CASE =np.array([0, 3, 5] ,dtype='long' )
SCREAMING_SNAKE_CASE =tokenizer(['Test question'] ).input_ids
SCREAMING_SNAKE_CASE =tokenizer(
['the fourth', 'longer longer'] ,add_special_tokens=snake_case ,return_token_type_ids=snake_case ,return_attention_mask=snake_case ,).input_ids
SCREAMING_SNAKE_CASE =config.reader_seq_len
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =retriever(
snake_case ,snake_case ,answer_ids=snake_case ,max_length=snake_case ,return_tensors='np' )
self.assertEqual([False, True, True] ,snake_case )
self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]] ,snake_case )
self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]] ,snake_case )
def _lowerCAmelCase ( self : Optional[int] ):
SCREAMING_SNAKE_CASE =self.get_dummy_retriever()
retriever.save_pretrained(os.path.join(self.tmpdirname ,'realm_block_records' ) )
# Test local path
SCREAMING_SNAKE_CASE =retriever.from_pretrained(os.path.join(self.tmpdirname ,'realm_block_records' ) )
self.assertEqual(retriever.block_records[0] ,B'This is the first record' )
# Test mocked remote path
with patch('transformers.models.realm.retrieval_realm.hf_hub_download' ) as mock_hf_hub_download:
SCREAMING_SNAKE_CASE =os.path.join(
os.path.join(self.tmpdirname ,'realm_block_records' ) ,_REALM_BLOCK_RECORDS_FILENAME )
SCREAMING_SNAKE_CASE =RealmRetriever.from_pretrained('google/realm-cc-news-pretrained-openqa' )
self.assertEqual(retriever.block_records[0] ,B'This is the first record' )
| 334 |
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def snake_case__ ( ):
"""simple docstring"""
assert or_gate(0, 0 ) == 0
assert or_gate(0, 1 ) == 1
assert or_gate(1, 0 ) == 1
assert or_gate(1, 1 ) == 1
if __name__ == "__main__":
print(or_gate(0, 1))
print(or_gate(1, 0))
print(or_gate(0, 0))
print(or_gate(1, 1))
| 334 | 1 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
_lowerCamelCase =data_utils.TransfoXLTokenizer
_lowerCamelCase =data_utils.TransfoXLCorpus
_lowerCamelCase =data_utils
_lowerCamelCase =data_utils
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
if transfo_xl_dataset_file:
# Convert a pre-processed corpus (see original TensorFlow repo)
with open(lowerCAmelCase_, 'rb' ) as fp:
SCREAMING_SNAKE_CASE =pickle.load(lowerCAmelCase_, encoding='latin1' )
# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
SCREAMING_SNAKE_CASE =pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
print(F'Save vocabulary to {pytorch_vocab_dump_path}' )
SCREAMING_SNAKE_CASE =corpus.vocab.__dict__
torch.save(lowerCAmelCase_, lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =corpus.__dict__
corpus_dict_no_vocab.pop('vocab', lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =pytorch_dump_folder_path + '/' + CORPUS_NAME
print(F'Save dataset to {pytorch_dataset_dump_path}' )
torch.save(lowerCAmelCase_, lowerCAmelCase_ )
if tf_checkpoint_path:
# Convert a pre-trained TensorFlow model
SCREAMING_SNAKE_CASE =os.path.abspath(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =os.path.abspath(lowerCAmelCase_ )
print(F'Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.' )
# Initialise PyTorch model
if transfo_xl_config_file == "":
SCREAMING_SNAKE_CASE =TransfoXLConfig()
else:
SCREAMING_SNAKE_CASE =TransfoXLConfig.from_json_file(lowerCAmelCase_ )
print(F'Building PyTorch model from configuration: {config}' )
SCREAMING_SNAKE_CASE =TransfoXLLMHeadModel(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =load_tf_weights_in_transfo_xl(lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ )
# Save pytorch-model
SCREAMING_SNAKE_CASE =os.path.join(lowerCAmelCase_, lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =os.path.join(lowerCAmelCase_, lowerCAmelCase_ )
print(F'Save PyTorch model to {os.path.abspath(lowerCAmelCase_ )}' )
torch.save(model.state_dict(), lowerCAmelCase_ )
print(F'Save configuration file to {os.path.abspath(lowerCAmelCase_ )}' )
with open(lowerCAmelCase_, 'w', encoding='utf-8' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
_lowerCamelCase =argparse.ArgumentParser()
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=str,
required=True,
help="Path to the folder to store the PyTorch model or dataset/vocab.",
)
parser.add_argument(
"--tf_checkpoint_path",
default="",
type=str,
help="An optional path to a TensorFlow checkpoint path to be converted.",
)
parser.add_argument(
"--transfo_xl_config_file",
default="",
type=str,
help=(
"An optional config json file corresponding to the pre-trained BERT model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--transfo_xl_dataset_file",
default="",
type=str,
help="An optional dataset file to be converted in a vocabulary.",
)
_lowerCamelCase =parser.parse_args()
convert_transfo_xl_checkpoint_to_pytorch(
args.tf_checkpoint_path,
args.transfo_xl_config_file,
args.pytorch_dump_folder_path,
args.transfo_xl_dataset_file,
)
| 334 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={"vocab_file": "vocab.txt"}
_lowerCamelCase ={
"vocab_file": {
"facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt",
"facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt",
},
}
_lowerCamelCase ={
"facebook/esm2_t6_8M_UR50D": 10_24,
"facebook/esm2_t12_35M_UR50D": 10_24,
}
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
with open(lowerCAmelCase_, 'r' ) as f:
SCREAMING_SNAKE_CASE =f.read().splitlines()
return [l.strip() for l in lines]
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = VOCAB_FILES_NAMES
__UpperCAmelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase = ['input_ids', 'attention_mask']
def __init__( self : int ,snake_case : Dict ,snake_case : Dict="<unk>" ,snake_case : Optional[int]="<cls>" ,snake_case : Optional[int]="<pad>" ,snake_case : int="<mask>" ,snake_case : Optional[int]="<eos>" ,**snake_case : List[str] ,):
super().__init__(**snake_case )
SCREAMING_SNAKE_CASE =load_vocab_file(snake_case )
SCREAMING_SNAKE_CASE =dict(enumerate(self.all_tokens ) )
SCREAMING_SNAKE_CASE ={tok: ind for ind, tok in enumerate(self.all_tokens )}
SCREAMING_SNAKE_CASE =unk_token
SCREAMING_SNAKE_CASE =cls_token
SCREAMING_SNAKE_CASE =pad_token
SCREAMING_SNAKE_CASE =mask_token
SCREAMING_SNAKE_CASE =eos_token
SCREAMING_SNAKE_CASE =self.all_tokens
self._create_trie(self.unique_no_split_tokens )
def _lowerCAmelCase ( self : Optional[Any] ,snake_case : int ):
return self._id_to_token.get(snake_case ,self.unk_token )
def _lowerCAmelCase ( self : Dict ,snake_case : str ):
return self._token_to_id.get(snake_case ,self._token_to_id.get(self.unk_token ) )
def _lowerCAmelCase ( self : Tuple ,snake_case : List[str] ,**snake_case : Any ):
return text.split()
def _lowerCAmelCase ( self : Optional[int] ,snake_case : str=False ):
return len(self._id_to_token )
def _lowerCAmelCase ( self : List[str] ):
return {token: i for i, token in enumerate(self.all_tokens )}
def _lowerCAmelCase ( self : List[Any] ,snake_case : str ):
return self._token_to_id.get(snake_case ,self._token_to_id.get(self.unk_token ) )
def _lowerCAmelCase ( self : Any ,snake_case : int ):
return self._id_to_token.get(snake_case ,self.unk_token )
def _lowerCAmelCase ( self : List[str] ,snake_case : List[int] ,snake_case : Optional[List[int]] = None ):
SCREAMING_SNAKE_CASE =[self.cls_token_id]
SCREAMING_SNAKE_CASE =[self.eos_token_id] # No sep token in ESM vocabulary
if token_ids_a is None:
if self.eos_token_id is None:
return cls + token_ids_a
else:
return cls + token_ids_a + sep
elif self.eos_token_id is None:
raise ValueError('Cannot tokenize multiple sequences when EOS token is not set!' )
return cls + token_ids_a + sep + token_ids_a + sep # Multiple inputs always have an EOS token
def _lowerCAmelCase ( self : Optional[int] ,snake_case : List ,snake_case : Optional[List] = None ,snake_case : bool = False ):
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'You should not supply a second sequence if the provided sequence of '
'ids is already formatted with special tokens for the model.' )
return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
SCREAMING_SNAKE_CASE =[1] + ([0] * len(snake_case )) + [1]
if token_ids_a is not None:
mask += [0] * len(snake_case ) + [1]
return mask
def _lowerCAmelCase ( self : Optional[int] ,snake_case : Dict ,snake_case : Any ):
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,(filename_prefix + '-' if filename_prefix else '') + 'vocab.txt' )
with open(snake_case ,'w' ) as f:
f.write('\n'.join(self.all_tokens ) )
return (vocab_file,)
@property
def _lowerCAmelCase ( self : int ):
return self.get_vocab_size(with_added_tokens=snake_case )
def _lowerCAmelCase ( self : str ,snake_case : Union[List[str], List[AddedToken]] ,snake_case : bool = False ):
return super()._add_tokens(snake_case ,special_tokens=snake_case )
| 334 | 1 |
import math
import sys
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
if number != int(lowerCAmelCase_ ):
raise ValueError('the value of input must be a natural number' )
if number < 0:
raise ValueError('the value of input must not be a negative number' )
if number == 0:
return 1
SCREAMING_SNAKE_CASE =[-1] * (number + 1)
SCREAMING_SNAKE_CASE =0
for i in range(1, number + 1 ):
SCREAMING_SNAKE_CASE =sys.maxsize
SCREAMING_SNAKE_CASE =int(math.sqrt(lowerCAmelCase_ ) )
for j in range(1, root + 1 ):
SCREAMING_SNAKE_CASE =1 + answers[i - (j**2)]
SCREAMING_SNAKE_CASE =min(lowerCAmelCase_, lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =answer
return answers[number]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 334 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase =logging.get_logger()
@dataclass
class a_ :
"""simple docstring"""
__UpperCAmelCase = 42
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
def _lowerCAmelCase ( self : Any ,snake_case : Any ,snake_case : Tensor ,snake_case : Tensor ):
SCREAMING_SNAKE_CASE =len(list(m.modules() ) ) == 1 or isinstance(snake_case ,nn.Convad ) or isinstance(snake_case ,nn.BatchNormad )
if has_not_submodules:
self.traced.append(snake_case )
def __call__( self : int ,snake_case : Tensor ):
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(snake_case )
[x.remove() for x in self.handles]
return self
@property
def _lowerCAmelCase ( self : Tuple ):
# check the len of the state_dict keys to see if we have learnable params
return list(filter(lambda snake_case : len(list(x.state_dict().keys() ) ) > 0 ,self.traced ) )
@dataclass
class a_ :
"""simple docstring"""
__UpperCAmelCase = 42
__UpperCAmelCase = 42
__UpperCAmelCase = 0
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
def __call__( self : int ,snake_case : Tensor ):
SCREAMING_SNAKE_CASE =Tracker(self.dest )(snake_case ).parametrized
SCREAMING_SNAKE_CASE =Tracker(self.src )(snake_case ).parametrized
SCREAMING_SNAKE_CASE =list(filter(lambda snake_case : type(snake_case ) not in self.src_skip ,snake_case ) )
SCREAMING_SNAKE_CASE =list(filter(lambda snake_case : type(snake_case ) not in self.dest_skip ,snake_case ) )
if len(snake_case ) != len(snake_case ):
raise Exception(
f'Numbers of operations are different. Source module has {len(snake_case )} operations while'
f' destination module has {len(snake_case )}.' )
for dest_m, src_m in zip(snake_case ,snake_case ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(f'Transfered from={src_m} to={dest_m}' )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ = True ):
"""simple docstring"""
print(F'Converting {name}...' )
with torch.no_grad():
SCREAMING_SNAKE_CASE =timm.create_model(lowerCAmelCase_, pretrained=lowerCAmelCase_ ).eval()
SCREAMING_SNAKE_CASE =ResNetForImageClassification(lowerCAmelCase_ ).eval()
SCREAMING_SNAKE_CASE =ModuleTransfer(src=lowerCAmelCase_, dest=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =torch.randn((1, 3, 224, 224) )
module_transfer(lowerCAmelCase_ )
assert torch.allclose(from_model(lowerCAmelCase_ ), our_model(lowerCAmelCase_ ).logits ), "The model logits don't match the original one."
SCREAMING_SNAKE_CASE =F'resnet{"-".join(name.split("resnet" ) )}'
print(lowerCAmelCase_ )
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name, commit_message='Add model', use_temp_dir=lowerCAmelCase_, )
# we can use the convnext one
SCREAMING_SNAKE_CASE =AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' )
image_processor.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name, commit_message='Add image processor', use_temp_dir=lowerCAmelCase_, )
print(F'Pushed {checkpoint_name}' )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ = None, lowerCAmelCase_ = True ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ='imagenet-1k-id2label.json'
SCREAMING_SNAKE_CASE =1000
SCREAMING_SNAKE_CASE =(1, num_labels)
SCREAMING_SNAKE_CASE ='huggingface/label-files'
SCREAMING_SNAKE_CASE =num_labels
SCREAMING_SNAKE_CASE =json.load(open(hf_hub_download(lowerCAmelCase_, lowerCAmelCase_, repo_type='dataset' ), 'r' ) )
SCREAMING_SNAKE_CASE ={int(lowerCAmelCase_ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE =idalabel
SCREAMING_SNAKE_CASE ={v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE =partial(lowerCAmelCase_, num_labels=lowerCAmelCase_, idalabel=lowerCAmelCase_, labelaid=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE ={
'resnet18': ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2], hidden_sizes=[64, 128, 256, 512], layer_type='basic' ),
'resnet26': ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2], hidden_sizes=[256, 512, 1024, 2048], layer_type='bottleneck' ),
'resnet34': ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3], hidden_sizes=[64, 128, 256, 512], layer_type='basic' ),
'resnet50': ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3], hidden_sizes=[256, 512, 1024, 2048], layer_type='bottleneck' ),
'resnet101': ImageNetPreTrainedConfig(
depths=[3, 4, 23, 3], hidden_sizes=[256, 512, 1024, 2048], layer_type='bottleneck' ),
'resnet152': ImageNetPreTrainedConfig(
depths=[3, 8, 36, 3], hidden_sizes=[256, 512, 1024, 2048], layer_type='bottleneck' ),
}
if model_name:
convert_weight_and_push(lowerCAmelCase_, names_to_config[model_name], lowerCAmelCase_, lowerCAmelCase_ )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ )
return config, expected_shape
if __name__ == "__main__":
_lowerCamelCase =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default=None,
type=str,
help=(
"The name of the model you wish to convert, it must be one of the supported resnet* architecture,"
" currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."
),
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=Path,
required=True,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
default=True,
type=bool,
required=False,
help="If True, push model and image processor to the hub.",
)
_lowerCamelCase =parser.parse_args()
_lowerCamelCase =args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 334 | 1 |
import requests
_lowerCamelCase ="https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page['articles'], 1 ):
print(F'{i}.) {article["title"]}' )
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")
| 334 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_=7 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =None
if token is not None:
SCREAMING_SNAKE_CASE ={'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}
# The id of a workflow (not of a workflow run)
SCREAMING_SNAKE_CASE ='636036'
SCREAMING_SNAKE_CASE =F'https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs'
# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
url += F'?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}'
SCREAMING_SNAKE_CASE =requests.get(lowerCAmelCase_, headers=lowerCAmelCase_ ).json()
return result["workflow_runs"]
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =get_daily_ci_runs(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =None
for workflow_run in workflow_runs:
if workflow_run["status"] == "completed":
SCREAMING_SNAKE_CASE =workflow_run['id']
break
return workflow_run_id
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =get_last_daily_ci_runs(lowerCAmelCase_ )
if workflow_run_id is not None:
SCREAMING_SNAKE_CASE =get_artifacts_links(worflow_run_id=lowerCAmelCase_, token=lowerCAmelCase_ )
for artifact_name in artifact_names:
if artifact_name in artifacts_links:
SCREAMING_SNAKE_CASE =artifacts_links[artifact_name]
download_artifact(
artifact_name=lowerCAmelCase_, artifact_url=lowerCAmelCase_, output_dir=lowerCAmelCase_, token=lowerCAmelCase_ )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
get_last_daily_ci_artifacts(lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ )
SCREAMING_SNAKE_CASE ={}
for artifact_name in artifact_names:
SCREAMING_SNAKE_CASE =os.path.join(lowerCAmelCase_, F'{artifact_name}.zip' )
if os.path.isfile(lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE ={}
with zipfile.ZipFile(lowerCAmelCase_ ) as z:
for filename in z.namelist():
if not os.path.isdir(lowerCAmelCase_ ):
# read the file
with z.open(lowerCAmelCase_ ) as f:
SCREAMING_SNAKE_CASE =f.read().decode('UTF-8' )
return results
| 334 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_pytesseract_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class a_ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Tuple ,snake_case : Dict ,snake_case : Union[str, Any]=7 ,snake_case : List[Any]=3 ,snake_case : Tuple=18 ,snake_case : int=30 ,snake_case : Optional[int]=400 ,snake_case : Tuple=True ,snake_case : Optional[int]=None ,snake_case : Optional[int]=True ,):
SCREAMING_SNAKE_CASE =size if size is not None else {'height': 18, 'width': 18}
SCREAMING_SNAKE_CASE =parent
SCREAMING_SNAKE_CASE =batch_size
SCREAMING_SNAKE_CASE =num_channels
SCREAMING_SNAKE_CASE =image_size
SCREAMING_SNAKE_CASE =min_resolution
SCREAMING_SNAKE_CASE =max_resolution
SCREAMING_SNAKE_CASE =do_resize
SCREAMING_SNAKE_CASE =size
SCREAMING_SNAKE_CASE =apply_ocr
def _lowerCAmelCase ( self : str ):
return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
@require_torch
@require_pytesseract
class a_ ( lowerCamelCase_ , unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase = LayoutLMvaImageProcessor if is_pytesseract_available() else None
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE =LayoutLMvaImageProcessingTester(self )
@property
def _lowerCAmelCase ( self : int ):
return self.image_processor_tester.prepare_image_processor_dict()
def _lowerCAmelCase ( self : int ):
SCREAMING_SNAKE_CASE =self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(snake_case ,'do_resize' ) )
self.assertTrue(hasattr(snake_case ,'size' ) )
self.assertTrue(hasattr(snake_case ,'apply_ocr' ) )
def _lowerCAmelCase ( self : Dict ):
SCREAMING_SNAKE_CASE =self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size ,{'height': 18, 'width': 18} )
SCREAMING_SNAKE_CASE =self.image_processing_class.from_dict(self.image_processor_dict ,size=42 )
self.assertEqual(image_processor.size ,{'height': 42, 'width': 42} )
def _lowerCAmelCase ( self : Tuple ):
pass
def _lowerCAmelCase ( self : List[Any] ):
# Initialize image_processing
SCREAMING_SNAKE_CASE =self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE =prepare_image_inputs(self.image_processor_tester ,equal_resolution=snake_case )
for image in image_inputs:
self.assertIsInstance(snake_case ,Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE =image_processing(image_inputs[0] ,return_tensors='pt' )
self.assertEqual(
encoding.pixel_values.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) ,)
self.assertIsInstance(encoding.words ,snake_case )
self.assertIsInstance(encoding.boxes ,snake_case )
# Test batched
SCREAMING_SNAKE_CASE =image_processing(snake_case ,return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) ,)
def _lowerCAmelCase ( self : Union[str, Any] ):
# Initialize image_processing
SCREAMING_SNAKE_CASE =self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
SCREAMING_SNAKE_CASE =prepare_image_inputs(self.image_processor_tester ,equal_resolution=snake_case ,numpify=snake_case )
for image in image_inputs:
self.assertIsInstance(snake_case ,np.ndarray )
# Test not batched input
SCREAMING_SNAKE_CASE =image_processing(image_inputs[0] ,return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) ,)
# Test batched
SCREAMING_SNAKE_CASE =image_processing(snake_case ,return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) ,)
def _lowerCAmelCase ( self : Tuple ):
# Initialize image_processing
SCREAMING_SNAKE_CASE =self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
SCREAMING_SNAKE_CASE =prepare_image_inputs(self.image_processor_tester ,equal_resolution=snake_case ,torchify=snake_case )
for image in image_inputs:
self.assertIsInstance(snake_case ,torch.Tensor )
# Test not batched input
SCREAMING_SNAKE_CASE =image_processing(image_inputs[0] ,return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) ,)
# Test batched
SCREAMING_SNAKE_CASE =image_processing(snake_case ,return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) ,)
def _lowerCAmelCase ( self : Dict ):
# with apply_OCR = True
SCREAMING_SNAKE_CASE =LayoutLMvaImageProcessor()
from datasets import load_dataset
SCREAMING_SNAKE_CASE =load_dataset('hf-internal-testing/fixtures_docvqa' ,split='test' )
SCREAMING_SNAKE_CASE =Image.open(ds[0]['file'] ).convert('RGB' )
SCREAMING_SNAKE_CASE =image_processing(snake_case ,return_tensors='pt' )
self.assertEqual(encoding.pixel_values.shape ,(1, 3, 224, 224) )
self.assertEqual(len(encoding.words ) ,len(encoding.boxes ) )
# fmt: off
# the words and boxes were obtained with Tesseract 4.1.1
SCREAMING_SNAKE_CASE =[['11:14', 'to', '11:39', 'a.m', '11:39', 'to', '11:44', 'a.m.', '11:44', 'a.m.', 'to', '12:25', 'p.m.', '12:25', 'to', '12:58', 'p.m.', '12:58', 'to', '4:00', 'p.m.', '2:00', 'to', '5:00', 'p.m.', 'Coffee', 'Break', 'Coffee', 'will', 'be', 'served', 'for', 'men', 'and', 'women', 'in', 'the', 'lobby', 'adjacent', 'to', 'exhibit', 'area.', 'Please', 'move', 'into', 'exhibit', 'area.', '(Exhibits', 'Open)', 'TRRF', 'GENERAL', 'SESSION', '(PART', '|)', 'Presiding:', 'Lee', 'A.', 'Waller', 'TRRF', 'Vice', 'President', '“Introductory', 'Remarks”', 'Lee', 'A.', 'Waller,', 'TRRF', 'Vice', 'Presi-', 'dent', 'Individual', 'Interviews', 'with', 'TRRF', 'Public', 'Board', 'Members', 'and', 'Sci-', 'entific', 'Advisory', 'Council', 'Mem-', 'bers', 'Conducted', 'by', 'TRRF', 'Treasurer', 'Philip', 'G.', 'Kuehn', 'to', 'get', 'answers', 'which', 'the', 'public', 'refrigerated', 'warehousing', 'industry', 'is', 'looking', 'for.', 'Plus', 'questions', 'from', 'the', 'floor.', 'Dr.', 'Emil', 'M.', 'Mrak,', 'University', 'of', 'Cal-', 'ifornia,', 'Chairman,', 'TRRF', 'Board;', 'Sam', 'R.', 'Cecil,', 'University', 'of', 'Georgia', 'College', 'of', 'Agriculture;', 'Dr.', 'Stanley', 'Charm,', 'Tufts', 'University', 'School', 'of', 'Medicine;', 'Dr.', 'Robert', 'H.', 'Cotton,', 'ITT', 'Continental', 'Baking', 'Company;', 'Dr.', 'Owen', 'Fennema,', 'University', 'of', 'Wis-', 'consin;', 'Dr.', 'Robert', 'E.', 'Hardenburg,', 'USDA.', 'Questions', 'and', 'Answers', 'Exhibits', 'Open', 'Capt.', 'Jack', 'Stoney', 'Room', 'TRRF', 'Scientific', 'Advisory', 'Council', 'Meeting', 'Ballroom', 'Foyer']] # noqa: E231
SCREAMING_SNAKE_CASE =[[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]] # noqa: E231
# fmt: on
self.assertListEqual(encoding.words ,snake_case )
self.assertListEqual(encoding.boxes ,snake_case )
# with apply_OCR = False
SCREAMING_SNAKE_CASE =LayoutLMvaImageProcessor(apply_ocr=snake_case )
SCREAMING_SNAKE_CASE =image_processing(snake_case ,return_tensors='pt' )
self.assertEqual(encoding.pixel_values.shape ,(1, 3, 224, 224) )
| 334 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
)
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
def __init__( self : Tuple ,snake_case : Optional[int] ,snake_case : Dict=13 ,snake_case : str=7 ,snake_case : Dict=True ,snake_case : List[Any]=True ,snake_case : Dict=False ,snake_case : int=True ,snake_case : Dict=99 ,snake_case : int=32 ,snake_case : List[str]=5 ,snake_case : Optional[Any]=4 ,snake_case : Tuple=64 ,snake_case : List[Any]="gelu" ,snake_case : str=0.1 ,snake_case : str=0.1 ,snake_case : List[str]=512 ,snake_case : List[str]=16 ,snake_case : str=2 ,snake_case : Dict=0.02 ,snake_case : Optional[int]=3 ,snake_case : int=4 ,snake_case : Any=None ,snake_case : Union[str, Any]=2 ,snake_case : List[Any]=2 ,snake_case : Optional[int]=2 ,snake_case : Dict=2 ,snake_case : List[str]=4 ,snake_case : int=1 ,):
SCREAMING_SNAKE_CASE =parent
SCREAMING_SNAKE_CASE =batch_size
SCREAMING_SNAKE_CASE =seq_length
SCREAMING_SNAKE_CASE =is_training
SCREAMING_SNAKE_CASE =use_input_mask
SCREAMING_SNAKE_CASE =use_token_type_ids
SCREAMING_SNAKE_CASE =use_labels
SCREAMING_SNAKE_CASE =vocab_size
SCREAMING_SNAKE_CASE =hidden_size
SCREAMING_SNAKE_CASE =num_hidden_layers
SCREAMING_SNAKE_CASE =num_attention_heads
SCREAMING_SNAKE_CASE =intermediate_size
SCREAMING_SNAKE_CASE =hidden_act
SCREAMING_SNAKE_CASE =hidden_dropout_prob
SCREAMING_SNAKE_CASE =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE =max_position_embeddings
SCREAMING_SNAKE_CASE =type_vocab_size
SCREAMING_SNAKE_CASE =type_sequence_label_size
SCREAMING_SNAKE_CASE =initializer_range
SCREAMING_SNAKE_CASE =num_labels
SCREAMING_SNAKE_CASE =num_choices
SCREAMING_SNAKE_CASE =scope
SCREAMING_SNAKE_CASE =q_groups
SCREAMING_SNAKE_CASE =k_groups
SCREAMING_SNAKE_CASE =v_groups
SCREAMING_SNAKE_CASE =post_attention_groups
SCREAMING_SNAKE_CASE =intermediate_groups
SCREAMING_SNAKE_CASE =output_groups
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
SCREAMING_SNAKE_CASE =None
if self.use_input_mask:
SCREAMING_SNAKE_CASE =random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE =None
SCREAMING_SNAKE_CASE =None
SCREAMING_SNAKE_CASE =None
if self.use_labels:
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size] ,self.type_sequence_label_size )
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size] ,self.num_choices )
SCREAMING_SNAKE_CASE =self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowerCAmelCase ( self : Optional[int] ):
return SqueezeBertConfig(
embedding_size=self.hidden_size ,vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,attention_probs_dropout_prob=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,initializer_range=self.initializer_range ,q_groups=self.q_groups ,k_groups=self.k_groups ,v_groups=self.v_groups ,post_attention_groups=self.post_attention_groups ,intermediate_groups=self.intermediate_groups ,output_groups=self.output_groups ,)
def _lowerCAmelCase ( self : Dict ,snake_case : List[str] ,snake_case : Optional[Any] ,snake_case : List[str] ,snake_case : List[Any] ,snake_case : str ,snake_case : Union[str, Any] ):
SCREAMING_SNAKE_CASE =SqueezeBertModel(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case ,snake_case )
SCREAMING_SNAKE_CASE =model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _lowerCAmelCase ( self : Optional[int] ,snake_case : Optional[int] ,snake_case : Union[str, Any] ,snake_case : List[Any] ,snake_case : int ,snake_case : Any ,snake_case : Tuple ):
SCREAMING_SNAKE_CASE =SqueezeBertForMaskedLM(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case ,attention_mask=snake_case ,labels=snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def _lowerCAmelCase ( self : Tuple ,snake_case : Union[str, Any] ,snake_case : Any ,snake_case : List[str] ,snake_case : List[Any] ,snake_case : Dict ,snake_case : Optional[Any] ):
SCREAMING_SNAKE_CASE =SqueezeBertForQuestionAnswering(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(
snake_case ,attention_mask=snake_case ,start_positions=snake_case ,end_positions=snake_case )
self.parent.assertEqual(result.start_logits.shape ,(self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape ,(self.batch_size, self.seq_length) )
def _lowerCAmelCase ( self : Optional[int] ,snake_case : Tuple ,snake_case : List[str] ,snake_case : List[str] ,snake_case : Any ,snake_case : Tuple ,snake_case : str ):
SCREAMING_SNAKE_CASE =self.num_labels
SCREAMING_SNAKE_CASE =SqueezeBertForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case ,attention_mask=snake_case ,labels=snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def _lowerCAmelCase ( self : Optional[Any] ,snake_case : List[str] ,snake_case : List[str] ,snake_case : Tuple ,snake_case : Dict ,snake_case : str ,snake_case : Tuple ):
SCREAMING_SNAKE_CASE =self.num_labels
SCREAMING_SNAKE_CASE =SqueezeBertForTokenClassification(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case ,attention_mask=snake_case ,labels=snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def _lowerCAmelCase ( self : List[str] ,snake_case : Dict ,snake_case : str ,snake_case : Union[str, Any] ,snake_case : Union[str, Any] ,snake_case : Any ,snake_case : Union[str, Any] ):
SCREAMING_SNAKE_CASE =self.num_choices
SCREAMING_SNAKE_CASE =SqueezeBertForMultipleChoice(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =input_ids.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
SCREAMING_SNAKE_CASE =input_mask.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
SCREAMING_SNAKE_CASE =model(
snake_case ,attention_mask=snake_case ,labels=snake_case ,)
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_choices) )
def _lowerCAmelCase ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE =self.prepare_config_and_inputs()
((SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE)) =config_and_inputs
SCREAMING_SNAKE_CASE ={'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class a_ ( lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase = (
(
SqueezeBertModel,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
)
if is_torch_available()
else None
)
__UpperCAmelCase = (
{
'feature-extraction': SqueezeBertModel,
'fill-mask': SqueezeBertForMaskedLM,
'question-answering': SqueezeBertForQuestionAnswering,
'text-classification': SqueezeBertForSequenceClassification,
'token-classification': SqueezeBertForTokenClassification,
'zero-shot': SqueezeBertForSequenceClassification,
}
if is_torch_available()
else {}
)
__UpperCAmelCase = False
__UpperCAmelCase = True
__UpperCAmelCase = False
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =SqueezeBertModelTester(self )
SCREAMING_SNAKE_CASE =ConfigTester(self ,config_class=snake_case ,dim=37 )
def _lowerCAmelCase ( self : List[str] ):
self.config_tester.run_common_tests()
def _lowerCAmelCase ( self : Optional[int] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_model(*snake_case )
def _lowerCAmelCase ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_masked_lm(*snake_case )
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_question_answering(*snake_case )
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_sequence_classification(*snake_case )
def _lowerCAmelCase ( self : int ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_token_classification(*snake_case )
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_multiple_choice(*snake_case )
@slow
def _lowerCAmelCase ( self : str ):
for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE =SqueezeBertModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
@require_sentencepiece
@require_tokenizers
@require_torch
class a_ ( unittest.TestCase ):
"""simple docstring"""
@slow
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =SqueezeBertForSequenceClassification.from_pretrained('squeezebert/squeezebert-mnli' )
SCREAMING_SNAKE_CASE =torch.tensor([[1, 29414, 232, 328, 740, 1140, 12695, 69, 13, 1588, 2]] )
SCREAMING_SNAKE_CASE =model(snake_case )[0]
SCREAMING_SNAKE_CASE =torch.Size((1, 3) )
self.assertEqual(output.shape ,snake_case )
SCREAMING_SNAKE_CASE =torch.tensor([[0.6_401, -0.0_349, -0.6_041]] )
self.assertTrue(torch.allclose(snake_case ,snake_case ,atol=1e-4 ) )
| 334 | 1 |
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
from transformers.models.bert.modeling_flax_bert import FlaxBertModel
from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel
@require_flax
class a_ ( unittest.TestCase ):
"""simple docstring"""
@slow
def _lowerCAmelCase ( self : Dict ):
for model_name in ["bert-base-cased", "bert-large-uncased"]:
with self.subTest(snake_case ):
SCREAMING_SNAKE_CASE =AutoConfig.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
self.assertIsInstance(snake_case ,snake_case )
SCREAMING_SNAKE_CASE =FlaxAutoModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
self.assertIsInstance(snake_case ,snake_case )
@slow
def _lowerCAmelCase ( self : Tuple ):
for model_name in ["roberta-base", "roberta-large"]:
with self.subTest(snake_case ):
SCREAMING_SNAKE_CASE =AutoConfig.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
self.assertIsInstance(snake_case ,snake_case )
SCREAMING_SNAKE_CASE =FlaxAutoModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
self.assertIsInstance(snake_case ,snake_case )
@slow
def _lowerCAmelCase ( self : int ):
for model_name in ["bert-base-cased", "bert-large-uncased"]:
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained(snake_case )
SCREAMING_SNAKE_CASE =FlaxBertModel.from_pretrained(snake_case )
SCREAMING_SNAKE_CASE =tokenizer('Do you support jax jitted function?' ,return_tensors=TensorType.JAX )
@jax.jit
def eval(**snake_case : List[str] ):
return model(**snake_case )
eval(**snake_case ).block_until_ready()
@slow
def _lowerCAmelCase ( self : Optional[Any] ):
for model_name in ["roberta-base", "roberta-large"]:
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained(snake_case )
SCREAMING_SNAKE_CASE =FlaxRobertaModel.from_pretrained(snake_case )
SCREAMING_SNAKE_CASE =tokenizer('Do you support jax jitted function?' ,return_tensors=TensorType.JAX )
@jax.jit
def eval(**snake_case : Tuple ):
return model(**snake_case )
eval(**snake_case ).block_until_ready()
def _lowerCAmelCase ( self : Union[str, Any] ):
with self.assertRaisesRegex(
snake_case ,'bert-base is not a local folder and is not a valid model identifier' ):
SCREAMING_SNAKE_CASE =FlaxAutoModel.from_pretrained('bert-base' )
def _lowerCAmelCase ( self : str ):
with self.assertRaisesRegex(
snake_case ,r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)' ):
SCREAMING_SNAKE_CASE =FlaxAutoModel.from_pretrained(snake_case ,revision='aaaaaa' )
def _lowerCAmelCase ( self : Union[str, Any] ):
with self.assertRaisesRegex(
snake_case ,'hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack' ,):
SCREAMING_SNAKE_CASE =FlaxAutoModel.from_pretrained('hf-internal-testing/config-no-model' )
def _lowerCAmelCase ( self : Tuple ):
with self.assertRaisesRegex(snake_case ,'Use `from_pt=True` to load this model' ):
SCREAMING_SNAKE_CASE =FlaxAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only' )
| 334 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = None
__UpperCAmelCase = None
@property
def _lowerCAmelCase ( self : List[Any] ):
return self.feat_extract_tester.prepare_feat_extract_dict()
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(snake_case ,'feature_size' ) )
self.assertTrue(hasattr(snake_case ,'sampling_rate' ) )
self.assertTrue(hasattr(snake_case ,'padding_value' ) )
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common()
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(snake_case ) == len(snake_case ) for x, y in zip(snake_case ,processed_features[input_name] ) ) )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common(equal_length=snake_case )
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} ,tensor_type='np' )
SCREAMING_SNAKE_CASE =processed_features[input_name]
if len(batch_features_input.shape ) < 3:
SCREAMING_SNAKE_CASE =batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_torch
def _lowerCAmelCase ( self : Optional[int] ):
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common(equal_length=snake_case )
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} ,tensor_type='pt' )
SCREAMING_SNAKE_CASE =processed_features[input_name]
if len(batch_features_input.shape ) < 3:
SCREAMING_SNAKE_CASE =batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_tf
def _lowerCAmelCase ( self : str ):
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common(equal_length=snake_case )
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} ,tensor_type='tf' )
SCREAMING_SNAKE_CASE =processed_features[input_name]
if len(batch_features_input.shape ) < 3:
SCREAMING_SNAKE_CASE =batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
def _lowerCAmelCase ( self : List[Any] ,snake_case : Optional[Any]=False ):
def _inputs_have_equal_length(snake_case : Dict ):
SCREAMING_SNAKE_CASE =len(input[0] )
for input_slice in input[1:]:
if len(snake_case ) != length:
return False
return True
def _inputs_are_equal(snake_case : str ,snake_case : Dict ):
if len(snake_case ) != len(snake_case ):
return False
for input_slice_a, input_slice_a in zip(snake_case ,snake_case ):
if not np.allclose(np.asarray(snake_case ) ,np.asarray(snake_case ) ,atol=1e-3 ):
return False
return True
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common(numpify=snake_case )
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.seq_length_diff
SCREAMING_SNAKE_CASE =self.feat_extract_tester.max_seq_length + pad_diff
SCREAMING_SNAKE_CASE =self.feat_extract_tester.min_seq_length
SCREAMING_SNAKE_CASE =self.feat_extract_tester.batch_size
SCREAMING_SNAKE_CASE =self.feat_extract_tester.feature_size
# test padding for List[int] + numpy
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding=snake_case )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='max_length' ,max_length=len(speech_inputs[-1] ) )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='np' )
SCREAMING_SNAKE_CASE =input_a[input_name]
# max_length parameter has to be provided when setting `padding="max_length"`
with self.assertRaises(snake_case ):
feat_extract.pad(snake_case ,padding='max_length' )[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=snake_case ,return_tensors='np' )
SCREAMING_SNAKE_CASE =input_a[input_name]
self.assertFalse(_inputs_have_equal_length(snake_case ) )
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertTrue(_inputs_are_equal(snake_case ,snake_case ) )
self.assertTrue(len(input_a[0] ) == pad_min_length )
self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )
# test padding for `pad_to_multiple_of` for List[int] + numpy
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,pad_to_multiple_of=10 )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,pad_to_multiple_of=10 )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,pad_to_multiple_of=10 ,max_length=snake_case )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,pad_to_multiple_of=10 ,max_length=snake_case ,return_tensors='np' ,)
SCREAMING_SNAKE_CASE =input_a[input_name]
self.assertTrue(all(len(snake_case ) % 10 == 0 for x in input_a ) )
self.assertTrue(_inputs_are_equal(snake_case ,snake_case ) )
SCREAMING_SNAKE_CASE =pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
self.assertTrue(all(len(snake_case ) == expected_mult_pad_length for x in input_a ) )
self.assertEqual(input_a.shape[:2] ,(batch_size, expected_mult_pad_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == feature_size )
# Check padding value is correct
SCREAMING_SNAKE_CASE =(np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
self.assertTrue(
abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
< 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
< 1e-3 )
def _lowerCAmelCase ( self : Union[str, Any] ,snake_case : Optional[int]=False ):
def _inputs_have_equal_length(snake_case : str ):
SCREAMING_SNAKE_CASE =len(input[0] )
for input_slice in input[1:]:
if len(snake_case ) != length:
return False
return True
def _inputs_are_equal(snake_case : Tuple ,snake_case : Optional[Any] ):
if len(snake_case ) != len(snake_case ):
return False
for input_slice_a, input_slice_a in zip(snake_case ,snake_case ):
if not np.allclose(np.asarray(snake_case ) ,np.asarray(snake_case ) ,atol=1e-3 ):
return False
return True
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common(numpify=snake_case )
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
# truncate to smallest
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) ,truncation=snake_case )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) )
SCREAMING_SNAKE_CASE =input_a[input_name]
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertFalse(_inputs_have_equal_length(snake_case ) )
# truncate to smallest with np
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) ,return_tensors='np' ,truncation=snake_case ,)
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) ,return_tensors='np' )
SCREAMING_SNAKE_CASE =input_a[input_name]
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(snake_case ) )
# truncate to middle
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[1] ) ,truncation=snake_case ,return_tensors='np' ,)
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[1] ) ,truncation=snake_case )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[1] ) ,return_tensors='np' )
SCREAMING_SNAKE_CASE =input_a[input_name]
self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertTrue(_inputs_are_equal(snake_case ,snake_case ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(snake_case ) )
self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(snake_case ):
feat_extract.pad(snake_case ,truncation=snake_case )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(snake_case ):
feat_extract.pad(snake_case ,padding='longest' ,truncation=snake_case )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(snake_case ):
feat_extract.pad(snake_case ,padding='longest' ,truncation=snake_case )[input_name]
# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
with self.assertRaises(snake_case ):
feat_extract.pad(snake_case ,padding='max_length' ,truncation=snake_case )[input_name]
# test truncation for `pad_to_multiple_of` for List[int] + numpy
SCREAMING_SNAKE_CASE =12
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) ,pad_to_multiple_of=snake_case ,truncation=snake_case ,)
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) ,pad_to_multiple_of=snake_case ,)
SCREAMING_SNAKE_CASE =input_a[input_name]
# retrieve expected_length as multiple of pad_to_multiple_of
SCREAMING_SNAKE_CASE =len(speech_inputs[0] )
if expected_length % pad_to_multiple_of != 0:
SCREAMING_SNAKE_CASE =((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of
self.assertTrue(len(input_a[0] ) == expected_length )
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertFalse(_inputs_have_equal_length(snake_case ) )
def _lowerCAmelCase ( self : Optional[int] ):
self._check_padding(numpify=snake_case )
def _lowerCAmelCase ( self : Tuple ):
self._check_padding(numpify=snake_case )
def _lowerCAmelCase ( self : List[str] ):
self._check_truncation(numpify=snake_case )
def _lowerCAmelCase ( self : int ):
self._check_truncation(numpify=snake_case )
@require_torch
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common()
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='np' )[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='pt' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
@require_tf
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common()
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='np' )[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='tf' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE =self.feat_extract_dict
SCREAMING_SNAKE_CASE =True
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**snake_case )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common()
SCREAMING_SNAKE_CASE =[len(snake_case ) for x in speech_inputs]
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='np' )
self.assertIn('attention_mask' ,snake_case )
self.assertListEqual(list(processed.attention_mask.shape ) ,list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() ,snake_case )
def _lowerCAmelCase ( self : Dict ):
SCREAMING_SNAKE_CASE =self.feat_extract_dict
SCREAMING_SNAKE_CASE =True
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**snake_case )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common()
SCREAMING_SNAKE_CASE =[len(snake_case ) for x in speech_inputs]
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE =min(snake_case )
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=snake_case ,truncation=snake_case ,return_tensors='np' )
self.assertIn('attention_mask' ,snake_case )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) ,[processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() ,[max_length for x in speech_inputs] )
| 334 | 1 |
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends
if is_vision_available():
import PIL
# soft dependency
if is_pytesseract_available():
import pytesseract
_lowerCamelCase =logging.get_logger(__name__)
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
return [
int(1000 * (box[0] / width) ),
int(1000 * (box[1] / height) ),
int(1000 * (box[2] / width) ),
int(1000 * (box[3] / height) ),
]
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =to_pil_image(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =pil_image.size
SCREAMING_SNAKE_CASE =pytesseract.image_to_data(lowerCAmelCase_, lang=lowerCAmelCase_, output_type='dict', config=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =data['text'], data['left'], data['top'], data['width'], data['height']
# filter empty words and corresponding coordinates
SCREAMING_SNAKE_CASE =[idx for idx, word in enumerate(lowerCAmelCase_ ) if not word.strip()]
SCREAMING_SNAKE_CASE =[word for idx, word in enumerate(lowerCAmelCase_ ) if idx not in irrelevant_indices]
SCREAMING_SNAKE_CASE =[coord for idx, coord in enumerate(lowerCAmelCase_ ) if idx not in irrelevant_indices]
SCREAMING_SNAKE_CASE =[coord for idx, coord in enumerate(lowerCAmelCase_ ) if idx not in irrelevant_indices]
SCREAMING_SNAKE_CASE =[coord for idx, coord in enumerate(lowerCAmelCase_ ) if idx not in irrelevant_indices]
SCREAMING_SNAKE_CASE =[coord for idx, coord in enumerate(lowerCAmelCase_ ) if idx not in irrelevant_indices]
# turn coordinates into (left, top, left+width, top+height) format
SCREAMING_SNAKE_CASE =[]
for x, y, w, h in zip(lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE =[x, y, x + w, y + h]
actual_boxes.append(lowerCAmelCase_ )
# finally, normalize the bounding boxes
SCREAMING_SNAKE_CASE =[]
for box in actual_boxes:
normalized_boxes.append(normalize_box(lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ) )
assert len(lowerCAmelCase_ ) == len(lowerCAmelCase_ ), "Not as many words as there are bounding boxes"
return words, normalized_boxes
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = ['pixel_values']
def __init__( self : int ,snake_case : bool = True ,snake_case : Dict[str, int] = None ,snake_case : PILImageResampling = PILImageResampling.BILINEAR ,snake_case : bool = True ,snake_case : float = 1 / 255 ,snake_case : bool = True ,snake_case : Union[float, Iterable[float]] = None ,snake_case : Union[float, Iterable[float]] = None ,snake_case : bool = True ,snake_case : Optional[str] = None ,snake_case : Optional[str] = "" ,**snake_case : Any ,):
super().__init__(**snake_case )
SCREAMING_SNAKE_CASE =size if size is not None else {'height': 224, 'width': 224}
SCREAMING_SNAKE_CASE =get_size_dict(snake_case )
SCREAMING_SNAKE_CASE =do_resize
SCREAMING_SNAKE_CASE =size
SCREAMING_SNAKE_CASE =resample
SCREAMING_SNAKE_CASE =do_rescale
SCREAMING_SNAKE_CASE =rescale_value
SCREAMING_SNAKE_CASE =do_normalize
SCREAMING_SNAKE_CASE =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
SCREAMING_SNAKE_CASE =image_std if image_std is not None else IMAGENET_STANDARD_STD
SCREAMING_SNAKE_CASE =apply_ocr
SCREAMING_SNAKE_CASE =ocr_lang
SCREAMING_SNAKE_CASE =tesseract_config
def _lowerCAmelCase ( self : Optional[int] ,snake_case : np.ndarray ,snake_case : Dict[str, int] ,snake_case : PILImageResampling = PILImageResampling.BILINEAR ,snake_case : Optional[Union[str, ChannelDimension]] = None ,**snake_case : Union[str, Any] ,):
SCREAMING_SNAKE_CASE =get_size_dict(snake_case )
if "height" not in size or "width" not in size:
raise ValueError(f'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}' )
SCREAMING_SNAKE_CASE =(size['height'], size['width'])
return resize(snake_case ,size=snake_case ,resample=snake_case ,data_format=snake_case ,**snake_case )
def _lowerCAmelCase ( self : Tuple ,snake_case : np.ndarray ,snake_case : Union[int, float] ,snake_case : Optional[Union[str, ChannelDimension]] = None ,**snake_case : Optional[int] ,):
return rescale(snake_case ,scale=snake_case ,data_format=snake_case ,**snake_case )
def _lowerCAmelCase ( self : Any ,snake_case : np.ndarray ,snake_case : Union[float, Iterable[float]] ,snake_case : Union[float, Iterable[float]] ,snake_case : Optional[Union[str, ChannelDimension]] = None ,**snake_case : Union[str, Any] ,):
return normalize(snake_case ,mean=snake_case ,std=snake_case ,data_format=snake_case ,**snake_case )
def _lowerCAmelCase ( self : Optional[int] ,snake_case : ImageInput ,snake_case : bool = None ,snake_case : Dict[str, int] = None ,snake_case : str=None ,snake_case : bool = None ,snake_case : float = None ,snake_case : bool = None ,snake_case : Union[float, Iterable[float]] = None ,snake_case : Union[float, Iterable[float]] = None ,snake_case : bool = None ,snake_case : Optional[str] = None ,snake_case : Optional[str] = None ,snake_case : Optional[Union[str, TensorType]] = None ,snake_case : ChannelDimension = ChannelDimension.FIRST ,**snake_case : Optional[Any] ,):
SCREAMING_SNAKE_CASE =do_resize if do_resize is not None else self.do_resize
SCREAMING_SNAKE_CASE =size if size is not None else self.size
SCREAMING_SNAKE_CASE =get_size_dict(snake_case )
SCREAMING_SNAKE_CASE =resample if resample is not None else self.resample
SCREAMING_SNAKE_CASE =do_rescale if do_rescale is not None else self.do_rescale
SCREAMING_SNAKE_CASE =rescale_factor if rescale_factor is not None else self.rescale_factor
SCREAMING_SNAKE_CASE =do_normalize if do_normalize is not None else self.do_normalize
SCREAMING_SNAKE_CASE =image_mean if image_mean is not None else self.image_mean
SCREAMING_SNAKE_CASE =image_std if image_std is not None else self.image_std
SCREAMING_SNAKE_CASE =apply_ocr if apply_ocr is not None else self.apply_ocr
SCREAMING_SNAKE_CASE =ocr_lang if ocr_lang is not None else self.ocr_lang
SCREAMING_SNAKE_CASE =tesseract_config if tesseract_config is not None else self.tesseract_config
SCREAMING_SNAKE_CASE =make_list_of_images(snake_case )
if not valid_images(snake_case ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None:
raise ValueError('Size must be specified if do_resize is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('If do_normalize is True, image_mean and image_std must be specified.' )
# All transformations expect numpy arrays.
SCREAMING_SNAKE_CASE =[to_numpy_array(snake_case ) for image in images]
# Tesseract OCR to get words + normalized bounding boxes
if apply_ocr:
requires_backends(self ,'pytesseract' )
SCREAMING_SNAKE_CASE =[]
SCREAMING_SNAKE_CASE =[]
for image in images:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =apply_tesseract(snake_case ,snake_case ,snake_case )
words_batch.append(snake_case )
boxes_batch.append(snake_case )
if do_resize:
SCREAMING_SNAKE_CASE =[self.resize(image=snake_case ,size=snake_case ,resample=snake_case ) for image in images]
if do_rescale:
SCREAMING_SNAKE_CASE =[self.rescale(image=snake_case ,scale=snake_case ) for image in images]
if do_normalize:
SCREAMING_SNAKE_CASE =[self.normalize(image=snake_case ,mean=snake_case ,std=snake_case ) for image in images]
SCREAMING_SNAKE_CASE =[to_channel_dimension_format(snake_case ,snake_case ) for image in images]
SCREAMING_SNAKE_CASE =BatchFeature(data={'pixel_values': images} ,tensor_type=snake_case )
if apply_ocr:
SCREAMING_SNAKE_CASE =words_batch
SCREAMING_SNAKE_CASE =boxes_batch
return data
| 334 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_lowerCamelCase =2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
_lowerCamelCase =50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
_lowerCamelCase =0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 10_00))
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =len([g for position, g in enumerate(lowerCAmelCase_ ) if g == main_target[position]] )
return (item, float(lowerCAmelCase_ ))
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =random.randint(0, len(lowerCAmelCase_ ) - 1 )
SCREAMING_SNAKE_CASE =parent_a[:random_slice] + parent_a[random_slice:]
SCREAMING_SNAKE_CASE =parent_a[:random_slice] + parent_a[random_slice:]
return (child_a, child_a)
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =list(lowerCAmelCase_ )
if random.uniform(0, 1 ) < MUTATION_PROBABILITY:
SCREAMING_SNAKE_CASE =random.choice(lowerCAmelCase_ )
return "".join(lowerCAmelCase_ )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =[]
# Generate more children proportionally to the fitness score.
SCREAMING_SNAKE_CASE =int(parent_a[1] * 100 ) + 1
SCREAMING_SNAKE_CASE =10 if child_n >= 10 else child_n
for _ in range(lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE =population_score[random.randint(0, lowerCAmelCase_ )][0]
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =crossover(parent_a[0], lowerCAmelCase_ )
# Append new string to the population list.
pop.append(mutate(lowerCAmelCase_, lowerCAmelCase_ ) )
pop.append(mutate(lowerCAmelCase_, lowerCAmelCase_ ) )
return pop
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ = True ):
"""simple docstring"""
if N_POPULATION < N_SELECTED:
SCREAMING_SNAKE_CASE =F'{N_POPULATION} must be bigger than {N_SELECTED}'
raise ValueError(lowerCAmelCase_ )
# Verify that the target contains no genes besides the ones inside genes variable.
SCREAMING_SNAKE_CASE =sorted({c for c in target if c not in genes} )
if not_in_genes_list:
SCREAMING_SNAKE_CASE =F'{not_in_genes_list} is not in genes list, evolution cannot converge'
raise ValueError(lowerCAmelCase_ )
# Generate random starting population.
SCREAMING_SNAKE_CASE =[]
for _ in range(lowerCAmelCase_ ):
population.append(''.join([random.choice(lowerCAmelCase_ ) for i in range(len(lowerCAmelCase_ ) )] ) )
# Just some logs to know what the algorithms is doing.
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =0, 0
# This loop will end when we find a perfect match for our target.
while True:
generation += 1
total_population += len(lowerCAmelCase_ )
# Random population created. Now it's time to evaluate.
# Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this, it will probably be slower.
# We just need to call evaluate for every item inside the population.
SCREAMING_SNAKE_CASE =[evaluate(lowerCAmelCase_, lowerCAmelCase_ ) for item in population]
# Check if there is a matching evolution.
SCREAMING_SNAKE_CASE =sorted(lowerCAmelCase_, key=lambda lowerCAmelCase_ : x[1], reverse=lowerCAmelCase_ )
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the best result every 10 generation.
# Just to know that the algorithm is working.
if debug and generation % 10 == 0:
print(
F'\nGeneration: {generation}'
F'\nTotal Population:{total_population}'
F'\nBest score: {population_score[0][1]}'
F'\nBest string: {population_score[0][0]}' )
# Flush the old population, keeping some of the best evolutions.
# Keeping this avoid regression of evolution.
SCREAMING_SNAKE_CASE =population[: int(N_POPULATION / 3 )]
population.clear()
population.extend(lowerCAmelCase_ )
# Normalize population score to be between 0 and 1.
SCREAMING_SNAKE_CASE =[
(item, score / len(lowerCAmelCase_ )) for item, score in population_score
]
# This is selection
for i in range(lowerCAmelCase_ ):
population.extend(select(population_score[int(lowerCAmelCase_ )], lowerCAmelCase_, lowerCAmelCase_ ) )
# Check if the population has already reached the maximum value and if so,
# break the cycle. If this check is disabled, the algorithm will take
# forever to compute large strings, but will also calculate small strings in
# a far fewer generations.
if len(lowerCAmelCase_ ) > N_POPULATION:
break
if __name__ == "__main__":
_lowerCamelCase =(
"This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
)
_lowerCamelCase =list(
" ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
"nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
)
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase =basic(target_str, genes_list)
print(
f'\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'
)
| 334 | 1 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =TapasConfig.from_json_file(lowerCAmelCase_ )
# set absolute/relative position embeddings parameter
SCREAMING_SNAKE_CASE =reset_position_index_per_cell
# set remaining parameters of TapasConfig as well as the model based on the task
if task == "SQA":
SCREAMING_SNAKE_CASE =TapasForQuestionAnswering(config=lowerCAmelCase_ )
elif task == "WTQ":
# run_task_main.py hparams
SCREAMING_SNAKE_CASE =4
SCREAMING_SNAKE_CASE =True
# hparam_utils.py hparams
SCREAMING_SNAKE_CASE =0.66_4694
SCREAMING_SNAKE_CASE =0.20_7951
SCREAMING_SNAKE_CASE =0.12_1194
SCREAMING_SNAKE_CASE =True
SCREAMING_SNAKE_CASE =True
SCREAMING_SNAKE_CASE =False
SCREAMING_SNAKE_CASE =0.035_2513
SCREAMING_SNAKE_CASE =TapasForQuestionAnswering(config=lowerCAmelCase_ )
elif task == "WIKISQL_SUPERVISED":
# run_task_main.py hparams
SCREAMING_SNAKE_CASE =4
SCREAMING_SNAKE_CASE =False
# hparam_utils.py hparams
SCREAMING_SNAKE_CASE =36.4519
SCREAMING_SNAKE_CASE =0.90_3421
SCREAMING_SNAKE_CASE =222.088
SCREAMING_SNAKE_CASE =True
SCREAMING_SNAKE_CASE =True
SCREAMING_SNAKE_CASE =True
SCREAMING_SNAKE_CASE =0.76_3141
SCREAMING_SNAKE_CASE =TapasForQuestionAnswering(config=lowerCAmelCase_ )
elif task == "TABFACT":
SCREAMING_SNAKE_CASE =TapasForSequenceClassification(config=lowerCAmelCase_ )
elif task == "MLM":
SCREAMING_SNAKE_CASE =TapasForMaskedLM(config=lowerCAmelCase_ )
elif task == "INTERMEDIATE_PRETRAINING":
SCREAMING_SNAKE_CASE =TapasModel(config=lowerCAmelCase_ )
else:
raise ValueError(F'Task {task} not supported.' )
print(F'Building PyTorch model from configuration: {config}' )
# Load weights from tf checkpoint
load_tf_weights_in_tapas(lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ )
# Save pytorch-model (weights and configuration)
print(F'Save PyTorch model to {pytorch_dump_path}' )
model.save_pretrained(lowerCAmelCase_ )
# Save tokenizer files
print(F'Save tokenizer files to {pytorch_dump_path}' )
SCREAMING_SNAKE_CASE =TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt', model_max_length=512 )
tokenizer.save_pretrained(lowerCAmelCase_ )
print('Used relative position embeddings:', model.config.reset_position_index_per_cell )
if __name__ == "__main__":
_lowerCamelCase =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--task", default="SQA", type=str, help="Model task for which to convert a checkpoint. Defaults to SQA."
)
parser.add_argument(
"--reset_position_index_per_cell",
default=False,
action="store_true",
help="Whether to use relative position embeddings or not. Defaults to True.",
)
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--tapas_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained TAPAS model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
_lowerCamelCase =parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.task,
args.reset_position_index_per_cell,
args.tf_checkpoint_path,
args.tapas_config_file,
args.pytorch_dump_path,
)
| 334 |
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer,
BertTokenizer,
BertTokenizerFast,
GPTaTokenizerFast,
is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_tokenization import CustomTokenizer # noqa E402
if is_tokenizers_available():
from test_module.custom_tokenization_fast import CustomTokenizerFast
class a_ ( unittest.TestCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : Dict ):
# A mock response for an HTTP head request to emulate server down
SCREAMING_SNAKE_CASE =mock.Mock()
SCREAMING_SNAKE_CASE =500
SCREAMING_SNAKE_CASE ={}
SCREAMING_SNAKE_CASE =HTTPError
SCREAMING_SNAKE_CASE ={}
# Download this model to make sure it's in the cache.
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert' )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch('requests.Session.request' ,return_value=snake_case ) as mock_head:
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert' )
# This check we did call the fake head request
mock_head.assert_called()
@require_tokenizers
def _lowerCAmelCase ( self : Optional[Any] ):
# A mock response for an HTTP head request to emulate server down
SCREAMING_SNAKE_CASE =mock.Mock()
SCREAMING_SNAKE_CASE =500
SCREAMING_SNAKE_CASE ={}
SCREAMING_SNAKE_CASE =HTTPError
SCREAMING_SNAKE_CASE ={}
# Download this model to make sure it's in the cache.
SCREAMING_SNAKE_CASE =GPTaTokenizerFast.from_pretrained('gpt2' )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch('requests.Session.request' ,return_value=snake_case ) as mock_head:
SCREAMING_SNAKE_CASE =GPTaTokenizerFast.from_pretrained('gpt2' )
# This check we did call the fake head request
mock_head.assert_called()
def _lowerCAmelCase ( self : Union[str, Any] ):
# This test is for deprecated behavior and can be removed in v5
try:
SCREAMING_SNAKE_CASE =tempfile.mktemp()
with open(snake_case ,'wb' ) as f:
http_get('https://huggingface.co/albert-base-v1/resolve/main/spiece.model' ,snake_case )
SCREAMING_SNAKE_CASE =AlbertTokenizer.from_pretrained(snake_case )
finally:
os.remove(snake_case )
# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
# the current folder and have the right name.
if os.path.isfile('tokenizer.json' ):
# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
return
try:
with open('tokenizer.json' ,'wb' ) as f:
http_get('https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json' ,snake_case )
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
self.assertEqual(tokenizer.vocab_size ,1000 )
# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.
finally:
os.remove('tokenizer.json' )
def _lowerCAmelCase ( self : int ):
# This test is for deprecated behavior and can be removed in v5
SCREAMING_SNAKE_CASE =AlbertTokenizer.from_pretrained('https://huggingface.co/albert-base-v1/resolve/main/spiece.model' )
@is_staging_test
class a_ ( unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase = ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']
@classmethod
def _lowerCAmelCase ( cls : List[Any] ):
SCREAMING_SNAKE_CASE =TOKEN
HfFolder.save_token(snake_case )
@classmethod
def _lowerCAmelCase ( cls : Tuple ):
try:
delete_repo(token=cls._token ,repo_id='test-tokenizer' )
except HTTPError:
pass
try:
delete_repo(token=cls._token ,repo_id='valid_org/test-tokenizer-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token ,repo_id='test-dynamic-tokenizer' )
except HTTPError:
pass
def _lowerCAmelCase ( self : Any ):
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,'vocab.txt' )
with open(snake_case ,'w' ,encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
SCREAMING_SNAKE_CASE =BertTokenizer(snake_case )
tokenizer.push_to_hub('test-tokenizer' ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained(f'{USER}/test-tokenizer' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
# Reset repo
delete_repo(token=self._token ,repo_id='test-tokenizer' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(snake_case ,repo_id='test-tokenizer' ,push_to_hub=snake_case ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained(f'{USER}/test-tokenizer' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
def _lowerCAmelCase ( self : Optional[Any] ):
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,'vocab.txt' )
with open(snake_case ,'w' ,encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
SCREAMING_SNAKE_CASE =BertTokenizer(snake_case )
tokenizer.push_to_hub('valid_org/test-tokenizer-org' ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained('valid_org/test-tokenizer-org' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
# Reset repo
delete_repo(token=self._token ,repo_id='valid_org/test-tokenizer-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(
snake_case ,repo_id='valid_org/test-tokenizer-org' ,push_to_hub=snake_case ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained('valid_org/test-tokenizer-org' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
@require_tokenizers
def _lowerCAmelCase ( self : str ):
CustomTokenizer.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,'vocab.txt' )
with open(snake_case ,'w' ,encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
SCREAMING_SNAKE_CASE =CustomTokenizer(snake_case )
# No fast custom tokenizer
tokenizer.push_to_hub('test-dynamic-tokenizer' ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer' ,trust_remote_code=snake_case )
# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,'CustomTokenizer' )
# Fast and slow custom tokenizer
CustomTokenizerFast.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,'vocab.txt' )
with open(snake_case ,'w' ,encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
SCREAMING_SNAKE_CASE =BertTokenizerFast.from_pretrained(snake_case )
bert_tokenizer.save_pretrained(snake_case )
SCREAMING_SNAKE_CASE =CustomTokenizerFast.from_pretrained(snake_case )
tokenizer.push_to_hub('test-dynamic-tokenizer' ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer' ,trust_remote_code=snake_case )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,'CustomTokenizerFast' )
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained(
f'{USER}/test-dynamic-tokenizer' ,use_fast=snake_case ,trust_remote_code=snake_case )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,'CustomTokenizer' )
class a_ ( unittest.TestCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('Hello 友達' )
self.assertEqual(trie.data ,{'H': {'e': {'l': {'l': {'o': {' ': {'友': {'達': {'': 1}}}}}}}}} )
trie.add('Hello' )
trie.data
self.assertEqual(trie.data ,{'H': {'e': {'l': {'l': {'o': {'': 1, ' ': {'友': {'達': {'': 1}}}}}}}}} )
def _lowerCAmelCase ( self : Optional[int] ):
SCREAMING_SNAKE_CASE =Trie()
self.assertEqual(trie.split('[CLS] This is a extra_id_100' ) ,['[CLS] This is a extra_id_100'] )
trie.add('[CLS]' )
trie.add('extra_id_1' )
trie.add('extra_id_100' )
self.assertEqual(trie.split('[CLS] This is a extra_id_100' ) ,['[CLS]', ' This is a ', 'extra_id_100'] )
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('A' )
self.assertEqual(trie.split('ABC' ) ,['A', 'BC'] )
self.assertEqual(trie.split('BCA' ) ,['BC', 'A'] )
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('TOKEN]' )
trie.add('[SPECIAL_TOKEN]' )
self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]' ) ,['This is something ', '[SPECIAL_TOKEN]'] )
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('A' )
trie.add('P' )
trie.add('[SPECIAL_TOKEN]' )
self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]' ) ,['This is something ', '[SPECIAL_TOKEN]'] )
def _lowerCAmelCase ( self : Dict ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('AB' )
trie.add('B' )
trie.add('C' )
self.assertEqual(trie.split('ABC' ) ,['AB', 'C'] )
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('ABC' )
trie.add('B' )
trie.add('CD' )
self.assertEqual(trie.split('ABCD' ) ,['ABC', 'D'] )
def _lowerCAmelCase ( self : Optional[Any] ):
# Even if the offsets are wrong, we necessarily output correct string
# parts.
SCREAMING_SNAKE_CASE =Trie()
SCREAMING_SNAKE_CASE =trie.cut_text('ABC' ,[0, 0, 2, 1, 2, 3] )
self.assertEqual(snake_case ,['AB', 'C'] )
| 334 | 1 |
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ = 0, lowerCAmelCase_ = 0 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =right or len(lowerCAmelCase_ ) - 1
if left > right:
return -1
elif list_data[left] == key:
return left
elif list_data[right] == key:
return right
else:
return search(lowerCAmelCase_, lowerCAmelCase_, left + 1, right - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 334 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase =[
("bert.bert", "visual_bert"),
("bert.cls", "cls"),
("bert.classifier", "cls"),
("token_type_embeddings_visual", "visual_token_type_embeddings"),
("position_embeddings_visual", "visual_position_embeddings"),
("projection", "visual_projection"),
]
_lowerCamelCase =[
"nlvr2_coco_pre_trained.th",
"nlvr2_fine_tuned.th",
"nlvr2_pre_trained.th",
"vcr_coco_pre_train.th",
"vcr_fine_tune.th",
"vcr_pre_train.th",
"vqa_coco_pre_trained.th",
"vqa_fine_tuned.th",
"vqa_pre_trained.th",
]
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =torch.load(lowerCAmelCase_, map_location='cpu' )
return sd
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_=rename_keys_prefix ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =OrderedDict()
SCREAMING_SNAKE_CASE =torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
SCREAMING_SNAKE_CASE =key
for name_pair in rename_keys_prefix:
SCREAMING_SNAKE_CASE =new_key.replace(name_pair[0], name_pair[1] )
SCREAMING_SNAKE_CASE =d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
SCREAMING_SNAKE_CASE =new_d['cls.predictions.bias']
return new_d
@torch.no_grad()
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
assert (
checkpoint_path.split('/' )[-1] in ACCEPTABLE_CHECKPOINTS
), F'The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'
# Get Config
if "pre" in checkpoint_path:
SCREAMING_SNAKE_CASE ='pretraining'
if "vcr" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 512}
elif "vqa_advanced" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 2048}
elif "vqa" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 2048}
elif "nlvr" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 1024}
else:
raise NotImplementedError(F'No implementation found for `{checkpoint_path}`.' )
else:
if "vcr" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 512}
SCREAMING_SNAKE_CASE ='multichoice'
elif "vqa_advanced" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 2048}
SCREAMING_SNAKE_CASE ='vqa_advanced'
elif "vqa" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 2048, 'num_labels': 3129}
SCREAMING_SNAKE_CASE ='vqa'
elif "nlvr" in checkpoint_path:
SCREAMING_SNAKE_CASE ={
'visual_embedding_dim': 1024,
'num_labels': 2,
}
SCREAMING_SNAKE_CASE ='nlvr'
SCREAMING_SNAKE_CASE =VisualBertConfig(**lowerCAmelCase_ )
# Load State Dict
SCREAMING_SNAKE_CASE =load_state_dict(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =get_new_dict(lowerCAmelCase_, lowerCAmelCase_ )
if model_type == "pretraining":
SCREAMING_SNAKE_CASE =VisualBertForPreTraining(lowerCAmelCase_ )
elif model_type == "vqa":
SCREAMING_SNAKE_CASE =VisualBertForQuestionAnswering(lowerCAmelCase_ )
elif model_type == "nlvr":
SCREAMING_SNAKE_CASE =VisualBertForVisualReasoning(lowerCAmelCase_ )
elif model_type == "multichoice":
SCREAMING_SNAKE_CASE =VisualBertForMultipleChoice(lowerCAmelCase_ )
model.load_state_dict(lowerCAmelCase_ )
# Save Checkpoints
Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ )
model.save_pretrained(lowerCAmelCase_ )
if __name__ == "__main__":
_lowerCamelCase =argparse.ArgumentParser()
# Required parameters
parser.add_argument("orig_checkpoint_path", type=str, help="A path to .th on local filesystem.")
parser.add_argument("pytorch_dump_folder_path", type=str, help="Path to the output PyTorch model.")
_lowerCamelCase =parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 334 | 1 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = ['image_processor', 'tokenizer']
__UpperCAmelCase = 'CLIPImageProcessor'
__UpperCAmelCase = ('CLIPTokenizer', 'CLIPTokenizerFast')
def __init__( self : Any ,snake_case : Tuple=None ,snake_case : Optional[Any]=None ,**snake_case : str ):
SCREAMING_SNAKE_CASE =None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' ,snake_case ,)
SCREAMING_SNAKE_CASE =kwargs.pop('feature_extractor' )
SCREAMING_SNAKE_CASE =image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.' )
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.' )
super().__init__(snake_case ,snake_case )
def __call__( self : int ,snake_case : Tuple=None ,snake_case : Optional[Any]=None ,snake_case : Union[str, Any]=None ,**snake_case : Optional[int] ):
if text is None and images is None:
raise ValueError('You have to specify either text or images. Both cannot be none.' )
if text is not None:
SCREAMING_SNAKE_CASE =self.tokenizer(snake_case ,return_tensors=snake_case ,**snake_case )
if images is not None:
SCREAMING_SNAKE_CASE =self.image_processor(snake_case ,return_tensors=snake_case ,**snake_case )
if text is not None and images is not None:
SCREAMING_SNAKE_CASE =image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**snake_case ) ,tensor_type=snake_case )
def _lowerCAmelCase ( self : str ,*snake_case : List[Any] ,**snake_case : str ):
return self.tokenizer.batch_decode(*snake_case ,**snake_case )
def _lowerCAmelCase ( self : Optional[Any] ,*snake_case : Dict ,**snake_case : Tuple ):
return self.tokenizer.decode(*snake_case ,**snake_case )
@property
def _lowerCAmelCase ( self : int ):
SCREAMING_SNAKE_CASE =self.tokenizer.model_input_names
SCREAMING_SNAKE_CASE =self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def _lowerCAmelCase ( self : Any ):
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' ,snake_case ,)
return self.image_processor_class
@property
def _lowerCAmelCase ( self : Dict ):
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' ,snake_case ,)
return self.image_processor
| 334 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"facebook/nllb-moe-54B": "https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json",
}
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'nllb-moe'
__UpperCAmelCase = ['past_key_values']
__UpperCAmelCase = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self : str ,snake_case : Optional[int]=128112 ,snake_case : Any=1024 ,snake_case : List[str]=12 ,snake_case : Optional[int]=4096 ,snake_case : List[str]=16 ,snake_case : Optional[Any]=12 ,snake_case : Optional[Any]=4096 ,snake_case : List[Any]=16 ,snake_case : Optional[Any]=0.05 ,snake_case : str=0.05 ,snake_case : Optional[int]=True ,snake_case : Tuple=True ,snake_case : Optional[Any]="relu" ,snake_case : Any=1024 ,snake_case : List[Any]=0.1 ,snake_case : List[Any]=0.1 ,snake_case : Optional[Any]=0.0 ,snake_case : List[Any]=0.02 ,snake_case : Any=2 ,snake_case : Dict=True ,snake_case : Tuple=False ,snake_case : Any="float32" ,snake_case : Tuple=False ,snake_case : List[Any]=128 ,snake_case : Tuple=64 ,snake_case : List[Any]=4 ,snake_case : List[Any]=4 ,snake_case : List[Any]=0.001 ,snake_case : int=0.001 ,snake_case : Tuple="all" ,snake_case : Union[str, Any]=False ,snake_case : Union[str, Any]=False ,snake_case : Optional[int]=1.0 ,snake_case : Optional[Any]=0.2 ,snake_case : Optional[int]=1 ,snake_case : Union[str, Any]=0 ,snake_case : Tuple=2 ,snake_case : List[Any]=False ,**snake_case : List[Any] ,):
SCREAMING_SNAKE_CASE =vocab_size
SCREAMING_SNAKE_CASE =max_position_embeddings
SCREAMING_SNAKE_CASE =d_model
SCREAMING_SNAKE_CASE =encoder_ffn_dim
SCREAMING_SNAKE_CASE =encoder_layers
SCREAMING_SNAKE_CASE =encoder_attention_heads
SCREAMING_SNAKE_CASE =decoder_ffn_dim
SCREAMING_SNAKE_CASE =decoder_layers
SCREAMING_SNAKE_CASE =decoder_attention_heads
SCREAMING_SNAKE_CASE =dropout
SCREAMING_SNAKE_CASE =attention_dropout
SCREAMING_SNAKE_CASE =activation_dropout
SCREAMING_SNAKE_CASE =activation_function
SCREAMING_SNAKE_CASE =init_std
SCREAMING_SNAKE_CASE =encoder_layerdrop
SCREAMING_SNAKE_CASE =decoder_layerdrop
SCREAMING_SNAKE_CASE =use_cache
SCREAMING_SNAKE_CASE =encoder_layers
SCREAMING_SNAKE_CASE =scale_embedding # scale factor will be sqrt(d_model) if True
SCREAMING_SNAKE_CASE =router_z_loss_coef
SCREAMING_SNAKE_CASE =router_aux_loss_coef
SCREAMING_SNAKE_CASE =decoder_sparse_step
SCREAMING_SNAKE_CASE =encoder_sparse_step
SCREAMING_SNAKE_CASE =num_experts
SCREAMING_SNAKE_CASE =expert_capacity
SCREAMING_SNAKE_CASE =router_bias
if router_dtype not in ["float32", "float16", "bfloat16"]:
raise ValueError(f'`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}' )
SCREAMING_SNAKE_CASE =router_dtype
SCREAMING_SNAKE_CASE =router_ignore_padding_tokens
SCREAMING_SNAKE_CASE =batch_prioritized_routing
SCREAMING_SNAKE_CASE =second_expert_policy
SCREAMING_SNAKE_CASE =normalize_router_prob_before_dropping
SCREAMING_SNAKE_CASE =moe_eval_capacity_token_fraction
SCREAMING_SNAKE_CASE =moe_token_dropout
SCREAMING_SNAKE_CASE =output_router_logits
super().__init__(
pad_token_id=snake_case ,bos_token_id=snake_case ,eos_token_id=snake_case ,is_encoder_decoder=snake_case ,decoder_start_token_id=snake_case ,**snake_case ,)
| 334 | 1 |
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
if len(lowerCAmelCase_ ) <= 1:
return [tuple(lowerCAmelCase_ )]
SCREAMING_SNAKE_CASE =[]
def generate(lowerCAmelCase_, lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE =[0] * n
res.append(tuple(lowerCAmelCase_ ) )
SCREAMING_SNAKE_CASE =0
while i < n:
if c[i] < i:
if i % 2 == 0:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =arr[i], arr[0]
else:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =arr[i], arr[c[i]]
res.append(tuple(lowerCAmelCase_ ) )
c[i] += 1
SCREAMING_SNAKE_CASE =0
else:
SCREAMING_SNAKE_CASE =0
i += 1
generate(len(lowerCAmelCase_ ), lowerCAmelCase_ )
return res
if __name__ == "__main__":
_lowerCamelCase =input("Enter numbers separated by a comma:\n").strip()
_lowerCamelCase =[int(item) for item in user_input.split(",")]
print(heaps(arr))
| 334 |
from __future__ import annotations
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =sorted(numsa + numsa )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =divmod(len(lowerCAmelCase_ ), 2 )
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowerCamelCase =[float(x) for x in input("Enter the elements of first array: ").split()]
_lowerCamelCase =[float(x) for x in input("Enter the elements of second array: ").split()]
print(f'The median of two arrays is: {median_of_two_arrays(array_a, array_a)}')
| 334 | 1 |
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =[0] * len(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =[]
SCREAMING_SNAKE_CASE =[1] * len(lowerCAmelCase_ )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(lowerCAmelCase_ ) ):
if indegree[i] == 0:
queue.append(lowerCAmelCase_ )
while queue:
SCREAMING_SNAKE_CASE =queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
SCREAMING_SNAKE_CASE =long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(lowerCAmelCase_ )
print(max(lowerCAmelCase_ ) )
# Adjacency list of Graph
_lowerCamelCase ={0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
| 334 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'transfo-xl'
__UpperCAmelCase = ['mems']
__UpperCAmelCase = {
'n_token': 'vocab_size',
'hidden_size': 'd_model',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self : Union[str, Any] ,snake_case : List[Any]=267735 ,snake_case : Optional[int]=[20000, 40000, 200000] ,snake_case : int=1024 ,snake_case : Optional[Any]=1024 ,snake_case : Tuple=16 ,snake_case : int=64 ,snake_case : Union[str, Any]=4096 ,snake_case : List[str]=4 ,snake_case : int=False ,snake_case : int=18 ,snake_case : Tuple=1600 ,snake_case : List[str]=1000 ,snake_case : Optional[Any]=True ,snake_case : List[str]=True ,snake_case : Optional[Any]=0 ,snake_case : Optional[Any]=-1 ,snake_case : List[Any]=True ,snake_case : Optional[Any]=0.1 ,snake_case : Union[str, Any]=0.0 ,snake_case : int=True ,snake_case : Any="normal" ,snake_case : int=0.01 ,snake_case : int=0.01 ,snake_case : str=0.02 ,snake_case : Any=1e-5 ,snake_case : Optional[int]=0 ,**snake_case : List[Any] ,):
SCREAMING_SNAKE_CASE =vocab_size
SCREAMING_SNAKE_CASE =[]
self.cutoffs.extend(snake_case )
if proj_share_all_but_first:
SCREAMING_SNAKE_CASE =[False] + [True] * len(self.cutoffs )
else:
SCREAMING_SNAKE_CASE =[False] + [False] * len(self.cutoffs )
SCREAMING_SNAKE_CASE =d_model
SCREAMING_SNAKE_CASE =d_embed
SCREAMING_SNAKE_CASE =d_head
SCREAMING_SNAKE_CASE =d_inner
SCREAMING_SNAKE_CASE =div_val
SCREAMING_SNAKE_CASE =pre_lnorm
SCREAMING_SNAKE_CASE =n_layer
SCREAMING_SNAKE_CASE =n_head
SCREAMING_SNAKE_CASE =mem_len
SCREAMING_SNAKE_CASE =same_length
SCREAMING_SNAKE_CASE =attn_type
SCREAMING_SNAKE_CASE =clamp_len
SCREAMING_SNAKE_CASE =sample_softmax
SCREAMING_SNAKE_CASE =adaptive
SCREAMING_SNAKE_CASE =dropout
SCREAMING_SNAKE_CASE =dropatt
SCREAMING_SNAKE_CASE =untie_r
SCREAMING_SNAKE_CASE =init
SCREAMING_SNAKE_CASE =init_range
SCREAMING_SNAKE_CASE =proj_init_std
SCREAMING_SNAKE_CASE =init_std
SCREAMING_SNAKE_CASE =layer_norm_epsilon
super().__init__(eos_token_id=snake_case ,**snake_case )
@property
def _lowerCAmelCase ( self : str ):
# Message copied from Transformer-XL documentation
logger.info(f'The model {self.model_type} is one of the few models that has no sequence length limit.' )
return -1
@max_position_embeddings.setter
def _lowerCAmelCase ( self : Union[str, Any] ,snake_case : Dict ):
# Message copied from Transformer-XL documentation
raise NotImplementedError(
f'The model {self.model_type} is one of the few models that has no sequence length limit.' )
| 334 | 1 |
import logging
import os
from .state import PartialState
class a_ ( logging.LoggerAdapter ):
"""simple docstring"""
@staticmethod
def _lowerCAmelCase ( snake_case : Dict ):
SCREAMING_SNAKE_CASE =PartialState()
return not main_process_only or (main_process_only and state.is_main_process)
def _lowerCAmelCase ( self : int ,snake_case : Dict ,snake_case : int ,*snake_case : str ,**snake_case : List[str] ):
if PartialState._shared_state == {}:
raise RuntimeError(
'You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.' )
SCREAMING_SNAKE_CASE =kwargs.pop('main_process_only' ,snake_case )
SCREAMING_SNAKE_CASE =kwargs.pop('in_order' ,snake_case )
if self.isEnabledFor(snake_case ):
if self._should_log(snake_case ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.process(snake_case ,snake_case )
self.logger.log(snake_case ,snake_case ,*snake_case ,**snake_case )
elif in_order:
SCREAMING_SNAKE_CASE =PartialState()
for i in range(state.num_processes ):
if i == state.process_index:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.process(snake_case ,snake_case )
self.logger.log(snake_case ,snake_case ,*snake_case ,**snake_case )
state.wait_for_everyone()
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ = None ):
"""simple docstring"""
if log_level is None:
SCREAMING_SNAKE_CASE =os.environ.get('ACCELERATE_LOG_LEVEL', lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =logging.getLogger(lowerCAmelCase_ )
if log_level is not None:
logger.setLevel(log_level.upper() )
logger.root.setLevel(log_level.upper() )
return MultiProcessAdapter(lowerCAmelCase_, {} )
| 334 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_MAPPING,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
)
from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
import PIL
from PIL import Image
from transformers import BeitImageProcessor
class a_ :
"""simple docstring"""
def __init__( self : Optional[int] ,snake_case : Any ,snake_case : Dict=100 ,snake_case : List[Any]=13 ,snake_case : str=30 ,snake_case : List[str]=2 ,snake_case : List[Any]=3 ,snake_case : Tuple=True ,snake_case : Optional[Any]=True ,snake_case : int=32 ,snake_case : Tuple=4 ,snake_case : List[Any]=4 ,snake_case : Optional[Any]=37 ,snake_case : Optional[Any]="gelu" ,snake_case : Tuple=0.1 ,snake_case : Union[str, Any]=0.1 ,snake_case : List[Any]=10 ,snake_case : Tuple=0.02 ,snake_case : List[str]=3 ,snake_case : Any=None ,snake_case : int=[0, 1, 2, 3] ,):
SCREAMING_SNAKE_CASE =parent
SCREAMING_SNAKE_CASE =100
SCREAMING_SNAKE_CASE =batch_size
SCREAMING_SNAKE_CASE =image_size
SCREAMING_SNAKE_CASE =patch_size
SCREAMING_SNAKE_CASE =num_channels
SCREAMING_SNAKE_CASE =is_training
SCREAMING_SNAKE_CASE =use_labels
SCREAMING_SNAKE_CASE =hidden_size
SCREAMING_SNAKE_CASE =num_hidden_layers
SCREAMING_SNAKE_CASE =num_attention_heads
SCREAMING_SNAKE_CASE =intermediate_size
SCREAMING_SNAKE_CASE =hidden_act
SCREAMING_SNAKE_CASE =hidden_dropout_prob
SCREAMING_SNAKE_CASE =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE =type_sequence_label_size
SCREAMING_SNAKE_CASE =initializer_range
SCREAMING_SNAKE_CASE =scope
SCREAMING_SNAKE_CASE =out_indices
SCREAMING_SNAKE_CASE =num_labels
# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
SCREAMING_SNAKE_CASE =(image_size // patch_size) ** 2
SCREAMING_SNAKE_CASE =num_patches + 1
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE =None
SCREAMING_SNAKE_CASE =None
if self.use_labels:
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size] ,self.type_sequence_label_size )
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size, self.image_size, self.image_size] ,self.num_labels )
SCREAMING_SNAKE_CASE =self.get_config()
return config, pixel_values, labels, pixel_labels
def _lowerCAmelCase ( self : Dict ):
return BeitConfig(
vocab_size=self.vocab_size ,image_size=self.image_size ,patch_size=self.patch_size ,num_channels=self.num_channels ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,is_decoder=snake_case ,initializer_range=self.initializer_range ,out_indices=self.out_indices ,)
def _lowerCAmelCase ( self : Union[str, Any] ,snake_case : Tuple ,snake_case : Optional[Any] ,snake_case : Union[str, Any] ,snake_case : Optional[int] ):
SCREAMING_SNAKE_CASE =BeitModel(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _lowerCAmelCase ( self : Union[str, Any] ,snake_case : Optional[int] ,snake_case : Dict ,snake_case : Any ,snake_case : List[str] ):
SCREAMING_SNAKE_CASE =BeitForMaskedImageModeling(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length - 1, self.vocab_size) )
def _lowerCAmelCase ( self : Optional[Any] ,snake_case : Any ,snake_case : str ,snake_case : Any ,snake_case : str ):
SCREAMING_SNAKE_CASE =self.type_sequence_label_size
SCREAMING_SNAKE_CASE =BeitForImageClassification(snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case ,labels=snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
# test greyscale images
SCREAMING_SNAKE_CASE =1
SCREAMING_SNAKE_CASE =BeitForImageClassification(snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE =model(snake_case ,labels=snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
def _lowerCAmelCase ( self : List[str] ,snake_case : Tuple ,snake_case : str ,snake_case : Optional[int] ,snake_case : int ):
SCREAMING_SNAKE_CASE =self.num_labels
SCREAMING_SNAKE_CASE =BeitForSemanticSegmentation(snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case )
self.parent.assertEqual(
result.logits.shape ,(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
SCREAMING_SNAKE_CASE =model(snake_case ,labels=snake_case )
self.parent.assertEqual(
result.logits.shape ,(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
def _lowerCAmelCase ( self : str ):
SCREAMING_SNAKE_CASE =self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =config_and_inputs
SCREAMING_SNAKE_CASE ={'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class a_ ( lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase = (
(BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
if is_torch_available()
else ()
)
__UpperCAmelCase = (
{
'feature-extraction': BeitModel,
'image-classification': BeitForImageClassification,
'image-segmentation': BeitForSemanticSegmentation,
}
if is_torch_available()
else {}
)
__UpperCAmelCase = False
__UpperCAmelCase = False
__UpperCAmelCase = False
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =BeitModelTester(self )
SCREAMING_SNAKE_CASE =ConfigTester(self ,config_class=snake_case ,has_text_modality=snake_case ,hidden_size=37 )
def _lowerCAmelCase ( self : List[str] ):
self.config_tester.run_common_tests()
@unittest.skip(reason='BEiT does not use inputs_embeds' )
def _lowerCAmelCase ( self : List[Any] ):
pass
@require_torch_multi_gpu
@unittest.skip(reason='BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`' )
def _lowerCAmelCase ( self : Union[str, Any] ):
pass
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE =model_class(snake_case )
self.assertIsInstance(model.get_input_embeddings() ,(nn.Module) )
SCREAMING_SNAKE_CASE =model.get_output_embeddings()
self.assertTrue(x is None or isinstance(snake_case ,nn.Linear ) )
def _lowerCAmelCase ( self : int ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE =model_class(snake_case )
SCREAMING_SNAKE_CASE =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE =[*signature.parameters.keys()]
SCREAMING_SNAKE_CASE =['pixel_values']
self.assertListEqual(arg_names[:1] ,snake_case )
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def _lowerCAmelCase ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*snake_case )
def _lowerCAmelCase ( self : Dict ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case )
def _lowerCAmelCase ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*snake_case )
def _lowerCAmelCase ( self : Any ):
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE =True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if model_class in [*get_values(snake_case ), BeitForMaskedImageModeling]:
continue
SCREAMING_SNAKE_CASE =model_class(snake_case )
model.to(snake_case )
model.train()
SCREAMING_SNAKE_CASE =self._prepare_for_class(snake_case ,snake_case ,return_labels=snake_case )
SCREAMING_SNAKE_CASE =model(**snake_case ).loss
loss.backward()
def _lowerCAmelCase ( self : int ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE =False
SCREAMING_SNAKE_CASE =True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if (
model_class in [*get_values(snake_case ), BeitForMaskedImageModeling]
or not model_class.supports_gradient_checkpointing
):
continue
SCREAMING_SNAKE_CASE =model_class(snake_case )
model.gradient_checkpointing_enable()
model.to(snake_case )
model.train()
SCREAMING_SNAKE_CASE =self._prepare_for_class(snake_case ,snake_case ,return_labels=snake_case )
SCREAMING_SNAKE_CASE =model(**snake_case ).loss
loss.backward()
def _lowerCAmelCase ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE =_config_zero_init(snake_case )
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE =model_class(config=snake_case )
for name, param in model.named_parameters():
# we skip lambda parameters as these require special initial values
# determined by config.layer_scale_init_value
if "lambda" in name:
continue
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() ,[0.0, 1.0] ,msg=f'Parameter {name} of model {model_class} seems not properly initialized' ,)
@slow
def _lowerCAmelCase ( self : List[str] ):
for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE =BeitModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class a_ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def _lowerCAmelCase ( self : Tuple ):
return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224' ) if is_vision_available() else None
@slow
def _lowerCAmelCase ( self : int ):
SCREAMING_SNAKE_CASE =BeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k' ).to(snake_case )
SCREAMING_SNAKE_CASE =self.default_image_processor
SCREAMING_SNAKE_CASE =prepare_img()
SCREAMING_SNAKE_CASE =image_processor(images=snake_case ,return_tensors='pt' ).pixel_values.to(snake_case )
# prepare bool_masked_pos
SCREAMING_SNAKE_CASE =torch.ones((1, 196) ,dtype=torch.bool ).to(snake_case )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE =model(pixel_values=snake_case ,bool_masked_pos=snake_case )
SCREAMING_SNAKE_CASE =outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE =torch.Size((1, 196, 8192) )
self.assertEqual(logits.shape ,snake_case )
SCREAMING_SNAKE_CASE =torch.tensor(
[[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]] ).to(snake_case )
self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3] ,snake_case ,atol=1e-2 ) )
@slow
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =BeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224' ).to(snake_case )
SCREAMING_SNAKE_CASE =self.default_image_processor
SCREAMING_SNAKE_CASE =prepare_img()
SCREAMING_SNAKE_CASE =image_processor(images=snake_case ,return_tensors='pt' ).to(snake_case )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE =model(**snake_case )
SCREAMING_SNAKE_CASE =outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE =torch.Size((1, 1000) )
self.assertEqual(logits.shape ,snake_case )
SCREAMING_SNAKE_CASE =torch.tensor([-1.2_385, -1.0_987, -1.0_108] ).to(snake_case )
self.assertTrue(torch.allclose(logits[0, :3] ,snake_case ,atol=1e-4 ) )
SCREAMING_SNAKE_CASE =281
self.assertEqual(logits.argmax(-1 ).item() ,snake_case )
@slow
def _lowerCAmelCase ( self : int ):
SCREAMING_SNAKE_CASE =BeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k' ).to(
snake_case )
SCREAMING_SNAKE_CASE =self.default_image_processor
SCREAMING_SNAKE_CASE =prepare_img()
SCREAMING_SNAKE_CASE =image_processor(images=snake_case ,return_tensors='pt' ).to(snake_case )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE =model(**snake_case )
SCREAMING_SNAKE_CASE =outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE =torch.Size((1, 21841) )
self.assertEqual(logits.shape ,snake_case )
SCREAMING_SNAKE_CASE =torch.tensor([1.6_881, -0.2_787, 0.5_901] ).to(snake_case )
self.assertTrue(torch.allclose(logits[0, :3] ,snake_case ,atol=1e-4 ) )
SCREAMING_SNAKE_CASE =2396
self.assertEqual(logits.argmax(-1 ).item() ,snake_case )
@slow
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE =BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640' )
SCREAMING_SNAKE_CASE =model.to(snake_case )
SCREAMING_SNAKE_CASE =BeitImageProcessor(do_resize=snake_case ,size=640 ,do_center_crop=snake_case )
SCREAMING_SNAKE_CASE =load_dataset('hf-internal-testing/fixtures_ade20k' ,split='test' )
SCREAMING_SNAKE_CASE =Image.open(ds[0]['file'] )
SCREAMING_SNAKE_CASE =image_processor(images=snake_case ,return_tensors='pt' ).to(snake_case )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE =model(**snake_case )
SCREAMING_SNAKE_CASE =outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE =torch.Size((1, 150, 160, 160) )
self.assertEqual(logits.shape ,snake_case )
SCREAMING_SNAKE_CASE =version.parse(PIL.__version__ ) < version.parse('9.0.0' )
if is_pillow_less_than_a:
SCREAMING_SNAKE_CASE =torch.tensor(
[
[[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]],
[[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]],
[[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]],
] ,device=snake_case ,)
else:
SCREAMING_SNAKE_CASE =torch.tensor(
[
[[-4.8_960, -2.3_688, -3.0_355], [-2.8_478, -0.9_836, -1.7_418], [-2.9_449, -1.3_332, -2.1_456]],
[[-5.8_081, -3.4_124, -4.1_006], [-3.8_561, -2.2_081, -3.0_323], [-3.8_365, -2.4_601, -3.3_669]],
[[-0.0_309, 3.9_868, 4.0_540], [2.9_640, 4.6_877, 4.9_976], [3.2_081, 4.7_690, 4.9_942]],
] ,device=snake_case ,)
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] ,snake_case ,atol=1e-4 ) )
@slow
def _lowerCAmelCase ( self : int ):
SCREAMING_SNAKE_CASE =BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640' )
SCREAMING_SNAKE_CASE =model.to(snake_case )
SCREAMING_SNAKE_CASE =BeitImageProcessor(do_resize=snake_case ,size=640 ,do_center_crop=snake_case )
SCREAMING_SNAKE_CASE =load_dataset('hf-internal-testing/fixtures_ade20k' ,split='test' )
SCREAMING_SNAKE_CASE =Image.open(ds[0]['file'] )
SCREAMING_SNAKE_CASE =image_processor(images=snake_case ,return_tensors='pt' ).to(snake_case )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE =model(**snake_case )
SCREAMING_SNAKE_CASE =outputs.logits.detach().cpu()
SCREAMING_SNAKE_CASE =image_processor.post_process_semantic_segmentation(outputs=snake_case ,target_sizes=[(500, 300)] )
SCREAMING_SNAKE_CASE =torch.Size((500, 300) )
self.assertEqual(segmentation[0].shape ,snake_case )
SCREAMING_SNAKE_CASE =image_processor.post_process_semantic_segmentation(outputs=snake_case )
SCREAMING_SNAKE_CASE =torch.Size((160, 160) )
self.assertEqual(segmentation[0].shape ,snake_case )
| 334 | 1 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
_lowerCamelCase =models.Sequential()
# Step 1 - Convolution
# Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
# (3,3) is the kernel size (filter matrix)
classifier.add(
layers.ConvaD(32, (3, 3), input_shape=(64, 64, 3), activation="relu")
)
# Step 2 - Pooling
classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))
# Adding a second convolutional layer
classifier.add(layers.ConvaD(32, (3, 3), activation="relu"))
classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))
# Step 3 - Flattening
classifier.add(layers.Flatten())
# Step 4 - Full connection
classifier.add(layers.Dense(units=1_28, activation="relu"))
classifier.add(layers.Dense(units=1, activation="sigmoid"))
# Compiling the CNN
classifier.compile(
optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"]
)
# Part 2 - Fitting the CNN to the images
# Load Trained model weights
# from keras.models import load_model
# regressor=load_model('cnn.h5')
_lowerCamelCase =tf.keras.preprocessing.image.ImageDataGenerator(
rescale=1.0 / 2_55, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
)
_lowerCamelCase =tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_55)
_lowerCamelCase =train_datagen.flow_from_directory(
"dataset/training_set", target_size=(64, 64), batch_size=32, class_mode="binary"
)
_lowerCamelCase =test_datagen.flow_from_directory(
"dataset/test_set", target_size=(64, 64), batch_size=32, class_mode="binary"
)
classifier.fit_generator(
training_set, steps_per_epoch=5, epochs=30, validation_data=test_set
)
classifier.save("cnn.h5")
# Part 3 - Making new predictions
_lowerCamelCase =tf.keras.preprocessing.image.load_img(
"dataset/single_prediction/image.png", target_size=(64, 64)
)
_lowerCamelCase =tf.keras.preprocessing.image.img_to_array(test_image)
_lowerCamelCase =np.expand_dims(test_image, axis=0)
_lowerCamelCase =classifier.predict(test_image)
# training_set.class_indices
if result[0][0] == 0:
_lowerCamelCase ="Normal"
if result[0][0] == 1:
_lowerCamelCase ="Abnormality detected"
| 334 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs
from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase =logging.get_logger()
@dataclass
class a_ :
"""simple docstring"""
__UpperCAmelCase = 42
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
def _lowerCAmelCase ( self : List[Any] ,snake_case : Dict ,snake_case : Tensor ,snake_case : Tensor ):
SCREAMING_SNAKE_CASE =len(list(m.modules() ) ) == 1 or isinstance(snake_case ,nn.Convad ) or isinstance(snake_case ,nn.BatchNormad )
if has_not_submodules:
self.traced.append(snake_case )
def __call__( self : List[str] ,snake_case : Tensor ):
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(snake_case )
[x.remove() for x in self.handles]
return self
@property
def _lowerCAmelCase ( self : Optional[Any] ):
# check the len of the state_dict keys to see if we have learnable params
return list(filter(lambda snake_case : len(list(x.state_dict().keys() ) ) > 0 ,self.traced ) )
@dataclass
class a_ :
"""simple docstring"""
__UpperCAmelCase = 42
__UpperCAmelCase = 42
__UpperCAmelCase = 1
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
__UpperCAmelCase = True
def __call__( self : str ,snake_case : Tensor ):
SCREAMING_SNAKE_CASE =Tracker(self.dest )(snake_case ).parametrized
SCREAMING_SNAKE_CASE =Tracker(self.src )(snake_case ).parametrized
SCREAMING_SNAKE_CASE =list(filter(lambda snake_case : type(snake_case ) not in self.src_skip ,snake_case ) )
SCREAMING_SNAKE_CASE =list(filter(lambda snake_case : type(snake_case ) not in self.dest_skip ,snake_case ) )
if len(snake_case ) != len(snake_case ) and self.raise_if_mismatch:
raise Exception(
f'Numbers of operations are different. Source module has {len(snake_case )} operations while'
f' destination module has {len(snake_case )}.' )
for dest_m, src_m in zip(snake_case ,snake_case ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(f'Transfered from={src_m} to={dest_m}' )
class a_ ( nn.Module ):
"""simple docstring"""
def __init__( self : Any ,snake_case : nn.Module ):
super().__init__()
SCREAMING_SNAKE_CASE =[]
# - get the stem
feature_blocks.append(('conv1', model.stem) )
# - get all the feature blocks
for k, v in model.trunk_output.named_children():
assert k.startswith('block' ), f'Unexpected layer name {k}'
SCREAMING_SNAKE_CASE =len(snake_case ) + 1
feature_blocks.append((f'res{block_index}', v) )
SCREAMING_SNAKE_CASE =nn.ModuleDict(snake_case )
def _lowerCAmelCase ( self : Dict ,snake_case : Tensor ):
return get_trunk_forward_outputs(
snake_case ,out_feat_keys=snake_case ,feature_blocks=self._feature_blocks ,)
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
def _lowerCAmelCase ( self : Optional[int] ,snake_case : str ):
SCREAMING_SNAKE_CASE =x.split('-' )
return x_split[0] + x_split[1] + "_" + "".join(x_split[2:] )
def __getitem__( self : Optional[Any] ,snake_case : str ):
# default to timm!
if x not in self:
SCREAMING_SNAKE_CASE =self.convert_name_to_timm(snake_case )
SCREAMING_SNAKE_CASE =partial(lambda: (timm.create_model(snake_case ,pretrained=snake_case ).eval(), None) )
else:
SCREAMING_SNAKE_CASE =super().__getitem__(snake_case )
return val
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
def __getitem__( self : int ,snake_case : str ):
if "seer" in x and "in1k" not in x:
SCREAMING_SNAKE_CASE =RegNetModel
else:
SCREAMING_SNAKE_CASE =RegNetForImageClassification
return val
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
for from_key, to_key in keys:
SCREAMING_SNAKE_CASE =from_state_dict[from_key].clone()
print(F'Copied key={from_key} to={to_key}' )
return to_state_dict
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ = True, ):
"""simple docstring"""
print(F'Converting {name}...' )
with torch.no_grad():
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =from_model_func()
SCREAMING_SNAKE_CASE =our_model_func(lowerCAmelCase_ ).eval()
SCREAMING_SNAKE_CASE =ModuleTransfer(src=lowerCAmelCase_, dest=lowerCAmelCase_, raise_if_mismatch=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =torch.randn((1, 3, 224, 224) )
module_transfer(lowerCAmelCase_ )
if from_state_dict is not None:
SCREAMING_SNAKE_CASE =[]
# for seer - in1k finetuned we have to manually copy the head
if "seer" in name and "in1k" in name:
SCREAMING_SNAKE_CASE =[('0.clf.0.weight', 'classifier.1.weight'), ('0.clf.0.bias', 'classifier.1.bias')]
SCREAMING_SNAKE_CASE =manually_copy_vissl_head(lowerCAmelCase_, our_model.state_dict(), lowerCAmelCase_ )
our_model.load_state_dict(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =our_model(lowerCAmelCase_, output_hidden_states=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =(
our_outputs.logits if isinstance(lowerCAmelCase_, lowerCAmelCase_ ) else our_outputs.last_hidden_state
)
SCREAMING_SNAKE_CASE =from_model(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =from_output[-1] if type(lowerCAmelCase_ ) is list else from_output
# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
if "seer" in name and "in1k" in name:
SCREAMING_SNAKE_CASE =our_outputs.hidden_states[-1]
assert torch.allclose(lowerCAmelCase_, lowerCAmelCase_ ), "The model logits don't match the original one."
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / name, commit_message='Add model', use_temp_dir=lowerCAmelCase_, )
SCREAMING_SNAKE_CASE =224 if 'seer' not in name else 384
# we can use the convnext one
SCREAMING_SNAKE_CASE =AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k', size=lowerCAmelCase_ )
image_processor.push_to_hub(
repo_path_or_name=save_directory / name, commit_message='Add image processor', use_temp_dir=lowerCAmelCase_, )
print(F'Pushed {name}' )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ = None, lowerCAmelCase_ = True ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ='imagenet-1k-id2label.json'
SCREAMING_SNAKE_CASE =1000
SCREAMING_SNAKE_CASE =(1, num_labels)
SCREAMING_SNAKE_CASE ='huggingface/label-files'
SCREAMING_SNAKE_CASE =num_labels
SCREAMING_SNAKE_CASE =json.load(open(cached_download(hf_hub_url(lowerCAmelCase_, lowerCAmelCase_, repo_type='dataset' ) ), 'r' ) )
SCREAMING_SNAKE_CASE ={int(lowerCAmelCase_ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE =idalabel
SCREAMING_SNAKE_CASE ={v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE =partial(lowerCAmelCase_, num_labels=lowerCAmelCase_, idalabel=lowerCAmelCase_, labelaid=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE ={
'regnet-x-002': ImageNetPreTrainedConfig(
depths=[1, 1, 4, 7], hidden_sizes=[24, 56, 152, 368], groups_width=8, layer_type='x' ),
'regnet-x-004': ImageNetPreTrainedConfig(
depths=[1, 2, 7, 12], hidden_sizes=[32, 64, 160, 384], groups_width=16, layer_type='x' ),
'regnet-x-006': ImageNetPreTrainedConfig(
depths=[1, 3, 5, 7], hidden_sizes=[48, 96, 240, 528], groups_width=24, layer_type='x' ),
'regnet-x-008': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 5], hidden_sizes=[64, 128, 288, 672], groups_width=16, layer_type='x' ),
'regnet-x-016': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 2], hidden_sizes=[72, 168, 408, 912], groups_width=24, layer_type='x' ),
'regnet-x-032': ImageNetPreTrainedConfig(
depths=[2, 6, 15, 2], hidden_sizes=[96, 192, 432, 1008], groups_width=48, layer_type='x' ),
'regnet-x-040': ImageNetPreTrainedConfig(
depths=[2, 5, 14, 2], hidden_sizes=[80, 240, 560, 1360], groups_width=40, layer_type='x' ),
'regnet-x-064': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1], hidden_sizes=[168, 392, 784, 1624], groups_width=56, layer_type='x' ),
'regnet-x-080': ImageNetPreTrainedConfig(
depths=[2, 5, 15, 1], hidden_sizes=[80, 240, 720, 1920], groups_width=120, layer_type='x' ),
'regnet-x-120': ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1], hidden_sizes=[224, 448, 896, 2240], groups_width=112, layer_type='x' ),
'regnet-x-160': ImageNetPreTrainedConfig(
depths=[2, 6, 13, 1], hidden_sizes=[256, 512, 896, 2048], groups_width=128, layer_type='x' ),
'regnet-x-320': ImageNetPreTrainedConfig(
depths=[2, 7, 13, 1], hidden_sizes=[336, 672, 1344, 2520], groups_width=168, layer_type='x' ),
# y variant
'regnet-y-002': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7], hidden_sizes=[24, 56, 152, 368], groups_width=8 ),
'regnet-y-004': ImageNetPreTrainedConfig(
depths=[1, 3, 6, 6], hidden_sizes=[48, 104, 208, 440], groups_width=8 ),
'regnet-y-006': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 4], hidden_sizes=[48, 112, 256, 608], groups_width=16 ),
'regnet-y-008': ImageNetPreTrainedConfig(
depths=[1, 3, 8, 2], hidden_sizes=[64, 128, 320, 768], groups_width=16 ),
'regnet-y-016': ImageNetPreTrainedConfig(
depths=[2, 6, 17, 2], hidden_sizes=[48, 120, 336, 888], groups_width=24 ),
'regnet-y-032': ImageNetPreTrainedConfig(
depths=[2, 5, 13, 1], hidden_sizes=[72, 216, 576, 1512], groups_width=24 ),
'regnet-y-040': ImageNetPreTrainedConfig(
depths=[2, 6, 12, 2], hidden_sizes=[128, 192, 512, 1088], groups_width=64 ),
'regnet-y-064': ImageNetPreTrainedConfig(
depths=[2, 7, 14, 2], hidden_sizes=[144, 288, 576, 1296], groups_width=72 ),
'regnet-y-080': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1], hidden_sizes=[168, 448, 896, 2016], groups_width=56 ),
'regnet-y-120': ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1], hidden_sizes=[224, 448, 896, 2240], groups_width=112 ),
'regnet-y-160': ImageNetPreTrainedConfig(
depths=[2, 4, 11, 1], hidden_sizes=[224, 448, 1232, 3024], groups_width=112 ),
'regnet-y-320': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1], hidden_sizes=[232, 696, 1392, 3712], groups_width=232 ),
# models created by SEER -> https://arxiv.org/abs/2202.08360
'regnet-y-320-seer': RegNetConfig(depths=[2, 5, 12, 1], hidden_sizes=[232, 696, 1392, 3712], groups_width=232 ),
'regnet-y-640-seer': RegNetConfig(depths=[2, 5, 12, 1], hidden_sizes=[328, 984, 1968, 4920], groups_width=328 ),
'regnet-y-1280-seer': RegNetConfig(
depths=[2, 7, 17, 1], hidden_sizes=[528, 1056, 2904, 7392], groups_width=264 ),
'regnet-y-2560-seer': RegNetConfig(
depths=[3, 7, 16, 1], hidden_sizes=[640, 1696, 2544, 5088], groups_width=640 ),
'regnet-y-10b-seer': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1], hidden_sizes=[2020, 4040, 11110, 28280], groups_width=1010 ),
# finetuned on imagenet
'regnet-y-320-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1], hidden_sizes=[232, 696, 1392, 3712], groups_width=232 ),
'regnet-y-640-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1], hidden_sizes=[328, 984, 1968, 4920], groups_width=328 ),
'regnet-y-1280-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1], hidden_sizes=[528, 1056, 2904, 7392], groups_width=264 ),
'regnet-y-2560-seer-in1k': ImageNetPreTrainedConfig(
depths=[3, 7, 16, 1], hidden_sizes=[640, 1696, 2544, 5088], groups_width=640 ),
'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1], hidden_sizes=[2020, 4040, 11110, 28280], groups_width=1010 ),
}
SCREAMING_SNAKE_CASE =NameToOurModelFuncMap()
SCREAMING_SNAKE_CASE =NameToFromModelFuncMap()
# add seer weights logic
def load_using_classy_vision(lowerCAmelCase_, lowerCAmelCase_ ) -> Tuple[nn.Module, Dict]:
SCREAMING_SNAKE_CASE =torch.hub.load_state_dict_from_url(lowerCAmelCase_, model_dir=str(lowerCAmelCase_ ), map_location='cpu' )
SCREAMING_SNAKE_CASE =model_func()
# check if we have a head, if yes add it
SCREAMING_SNAKE_CASE =files['classy_state_dict']['base_model']['model']
SCREAMING_SNAKE_CASE =model_state_dict['trunk']
model.load_state_dict(lowerCAmelCase_ )
return model.eval(), model_state_dict["heads"]
# pretrained
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch', lambda: FakeRegNetVisslWrapper(RegNetYaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch', lambda: FakeRegNetVisslWrapper(RegNetYaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch', lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch', lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27, group_width=1010, w_a=1744, w_a=620.83, w_m=2.52 ) ) ), )
# IN1K finetuned
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch', lambda: FakeRegNetVisslWrapper(RegNetYaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch', lambda: FakeRegNetVisslWrapper(RegNetYaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch', lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch', lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27, group_width=1010, w_a=1744, w_a=620.83, w_m=2.52 ) ) ), )
if model_name:
convert_weight_and_push(
lowerCAmelCase_, names_to_from_model_map[model_name], names_to_ours_model_map[model_name], names_to_config[model_name], lowerCAmelCase_, lowerCAmelCase_, )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(
lowerCAmelCase_, names_to_from_model_map[model_name], names_to_ours_model_map[model_name], lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, )
return config, expected_shape
if __name__ == "__main__":
_lowerCamelCase =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default=None,
type=str,
help=(
"The name of the model you wish to convert, it must be one of the supported regnet* architecture,"
" currently: regnetx-*, regnety-*. If `None`, all of them will the converted."
),
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=Path,
required=True,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
default=True,
type=bool,
required=False,
help="If True, push model and image processor to the hub.",
)
_lowerCamelCase =parser.parse_args()
_lowerCamelCase =args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 334 | 1 |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class a_ :
"""simple docstring"""
__UpperCAmelCase = field(
default='codeparrot/codeparrot' , metadata={'help': 'Model name or path of model to be trained.'} )
__UpperCAmelCase = field(
default='./' , metadata={'help': 'Save dir where model repo is cloned and models updates are saved to.'} )
__UpperCAmelCase = field(
default='codeparrot/codeparrot-clean-train' , metadata={'help': 'Name or path of training dataset.'} )
__UpperCAmelCase = field(
default='codeparrot/codeparrot-clean-valid' , metadata={'help': 'Name or path of validation dataset.'} )
__UpperCAmelCase = field(default=2 , metadata={'help': 'Batch size for training.'} )
__UpperCAmelCase = field(default=2 , metadata={'help': 'Batch size for evaluation.'} )
__UpperCAmelCase = field(default=0.1 , metadata={'help': 'Value of weight decay.'} )
__UpperCAmelCase = field(
default=10_000 , metadata={'help': 'Size of buffer used to shuffle streaming dataset.'} )
__UpperCAmelCase = field(default=2e-4 , metadata={'help': 'Learning rate fo training.'} )
__UpperCAmelCase = field(default='cosine' , metadata={'help': 'Learning rate.'} )
__UpperCAmelCase = field(
default=750 , metadata={'help': 'Number of warmup steps in the learning rate schedule.'} )
__UpperCAmelCase = field(
default=16 , metadata={'help': 'Number of gradient accumulation steps.'} )
__UpperCAmelCase = field(
default=lowerCamelCase_ , metadata={'help': 'Use gradient checkpointing to reduce memory footprint.'} )
__UpperCAmelCase = field(default=50_000 , metadata={'help': 'Maximum number of training steps.'} )
__UpperCAmelCase = field(
default=-1 , metadata={'help': 'Maximum number of evaluation steps. If -1 the full dataset is evaluated.'} )
__UpperCAmelCase = field(default=1_024 , metadata={'help': 'Sequence lengths used for training.'} )
__UpperCAmelCase = field(default=1 , metadata={'help': 'Training seed.'} )
__UpperCAmelCase = field(
default=1_024 , metadata={'help': 'Interval to save checkpoints. Measured as number of forward passes not training steps.'} , )
__UpperCAmelCase = field(
default=lowerCamelCase_ , metadata={'help': 'States path if the training should continue from a checkpoint folder.'} )
__UpperCAmelCase = field(default=lowerCamelCase_ , metadata={'help': 'If True the data is pretokenized.'} )
@dataclass
class a_ :
"""simple docstring"""
__UpperCAmelCase = field(
default='codeparrot/codeparrot' , metadata={'help': 'Model name or path of model to be evaluated.'} )
__UpperCAmelCase = field(
default='codeparrot/codeparrot-clean-valid' , metadata={'help': 'Name or path of validation dataset.'} )
__UpperCAmelCase = field(default=2 , metadata={'help': 'Batch size used for evaluation.'} )
__UpperCAmelCase = field(
default=-1 , metadata={'help': 'Maximum number of evaluation steps. If -1 the full dataset is evaluated.'} )
__UpperCAmelCase = field(default=1_024 , metadata={'help': 'Length of sequences to be evaluated.'} )
__UpperCAmelCase = field(default=1 , metadata={'help': 'Random seed used for evaluation.'} )
@dataclass
class a_ :
"""simple docstring"""
__UpperCAmelCase = field(
default='codeparrot/codeparrot' , metadata={'help': 'Model name or path of model to be evaluated.'} )
__UpperCAmelCase = field(default=lowerCamelCase_ , metadata={'help': 'Number of workers used for code evaluation.'} )
__UpperCAmelCase = field(
default=lowerCamelCase_ , metadata={'help': 'The number of human-eval tasks to run. If not included all tasks are evaluated.'} , )
__UpperCAmelCase = field(
default=lowerCamelCase_ , metadata={'help': 'Sample from the language model\'s output distribution.'} )
__UpperCAmelCase = field(default=0.2 , metadata={'help': 'Sampling temperature used for generation.'} )
__UpperCAmelCase = field(default=256 , metadata={'help': 'Maximum number of newly generated tokens.'} )
__UpperCAmelCase = field(default=0 , metadata={'help': 'Top-k parameter used for generation.'} )
__UpperCAmelCase = field(default=0.95 , metadata={'help': 'Top-p parameter used for nucleus sampling.'} )
__UpperCAmelCase = field(default=10 , metadata={'help': 'Number of generations to run in parallel.'} )
__UpperCAmelCase = field(
default=200 , metadata={'help': 'Number of completions to generate for each sample.'} )
__UpperCAmelCase = field(default=1 , metadata={'help': 'Random seed used for evaluation.'} )
__UpperCAmelCase = field(
default='eval_results.json' , metadata={'help': 'Random seed used for evaluation.'} )
__UpperCAmelCase = field(
default='0' , metadata={'help': 'Allow `code_eval` to execute Python code on machine'} )
__UpperCAmelCase = field(
default=-1 , metadata={
'help': (
'Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive'
' number corresponds to which GPU device id to run on.'
)
} , )
@dataclass
class a_ :
"""simple docstring"""
__UpperCAmelCase = field(
default=lowerCamelCase_ , metadata={
'help': 'The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'
} , )
__UpperCAmelCase = field(
default='transformersbook/codeparrot' , metadata={'help': 'Folder or name of dataset to process.'} )
__UpperCAmelCase = field(
default='codeparrot-clean' , metadata={'help': 'Folder to save processed processed dataset.'} )
__UpperCAmelCase = field(
default=100_000 , metadata={'help': 'Number of files to save per JSON output file.'} )
__UpperCAmelCase = field(default='content' , metadata={'help': 'Column containing text data to process.'} )
__UpperCAmelCase = field(
default=1_000 , metadata={'help': 'Maximum line length in file, otherwise file is filtered.'} )
__UpperCAmelCase = field(
default=100 , metadata={'help': 'Maximum mean line length in file, otherwise file is filtered.'} )
__UpperCAmelCase = field(
default=0.25 , metadata={'help': 'Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'} )
__UpperCAmelCase = field(
default=1.5 , metadata={'help': 'Minimum character token ratio for the file, otherwise file is filtered.'} )
__UpperCAmelCase = field(
default=0.7 , metadata={'help': 'Probability for filtering config, test and uncommon files.'} )
__UpperCAmelCase = field(
default='codeparrot/codeparrot' , metadata={'help': 'Name or path to the tokenizer.'} , )
__UpperCAmelCase = field(
default=lowerCamelCase_ , metadata={'help': 'If True, near-duplicate samples are removed.'} )
__UpperCAmelCase = field(
default=0.85 , metadata={'help': 'Jaccard threshold for near-duplicate samples.'} )
@dataclass
class a_ :
"""simple docstring"""
__UpperCAmelCase = field(
default='gpt2' , metadata={'help': 'Base tokenizer to build new tokenizer from.'} )
__UpperCAmelCase = field(
default='transformersbook/codeparrot-train' , metadata={'help': 'Dataset to train tokenizer on.'} )
__UpperCAmelCase = field(default='content' , metadata={'help': 'Column containing text data to process.'} )
__UpperCAmelCase = field(default=200_000 , metadata={'help': 'Number of examples to train tokenizer on.'} )
__UpperCAmelCase = field(
default=32_768 , metadata={'help': 'Number of examples to train the tokenizer on.'} )
__UpperCAmelCase = field(default='codeparrot' , metadata={'help': 'Name of new tokenizer.'} )
__UpperCAmelCase = field(default=lowerCamelCase_ , metadata={'help': 'Push saved tokenizer to the hub.'} )
@dataclass
class a_ :
"""simple docstring"""
__UpperCAmelCase = field(
default='codeparrot/codeparrot' , metadata={'help': 'Name or path to the tokenizer.'} )
__UpperCAmelCase = field(
default='codeparrot/codeparrot-clean-train' , metadata={'help': 'Name or path to the dataset to pretokenize.'} )
__UpperCAmelCase = field(
default='tokenized-codeparrot-train' , metadata={'help': 'Repo name of the pretokenized data.'} )
__UpperCAmelCase = field(default=lowerCamelCase_ , metadata={'help': 'Number of workers used for code evaluation.'} )
@dataclass
class a_ :
"""simple docstring"""
__UpperCAmelCase = field(
default='gpt2-large' , metadata={'help': 'Configuration to use for model initialization.'} )
__UpperCAmelCase = field(
default='codeparrot/codeparrot' , metadata={'help': 'Tokenizer attached to model.'} )
__UpperCAmelCase = field(default='codeparrot' , metadata={'help': 'Name of the created model.'} )
__UpperCAmelCase = field(default=lowerCamelCase_ , metadata={'help': 'Push saved tokenizer to the hub.'} )
| 334 |
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
_lowerCamelCase =16
_lowerCamelCase =32
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ = 16 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained('bert-base-cased' )
SCREAMING_SNAKE_CASE =load_dataset('glue', 'mrpc' )
def tokenize_function(lowerCAmelCase_ ):
# max_length=None => use the model max length (it's actually the default)
SCREAMING_SNAKE_CASE =tokenizer(examples['sentence1'], examples['sentence2'], truncation=lowerCAmelCase_, max_length=lowerCAmelCase_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
SCREAMING_SNAKE_CASE =datasets.map(
lowerCAmelCase_, batched=lowerCAmelCase_, remove_columns=['idx', 'sentence1', 'sentence2'], )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
SCREAMING_SNAKE_CASE =tokenized_datasets.rename_column('label', 'labels' )
def collate_fn(lowerCAmelCase_ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
SCREAMING_SNAKE_CASE =128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
SCREAMING_SNAKE_CASE =16
elif accelerator.mixed_precision != "no":
SCREAMING_SNAKE_CASE =8
else:
SCREAMING_SNAKE_CASE =None
return tokenizer.pad(
lowerCAmelCase_, padding='longest', max_length=lowerCAmelCase_, pad_to_multiple_of=lowerCAmelCase_, return_tensors='pt', )
# Instantiate dataloaders.
SCREAMING_SNAKE_CASE =DataLoader(
tokenized_datasets['train'], shuffle=lowerCAmelCase_, collate_fn=lowerCAmelCase_, batch_size=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =DataLoader(
tokenized_datasets['validation'], shuffle=lowerCAmelCase_, collate_fn=lowerCAmelCase_, batch_size=lowerCAmelCase_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
_lowerCamelCase =mocked_dataloaders # noqa: F811
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
if os.environ.get('TESTING_MOCKED_DATALOADERS', lowerCAmelCase_ ) == "1":
SCREAMING_SNAKE_CASE =2
# Initialize accelerator
SCREAMING_SNAKE_CASE =Accelerator(cpu=args.cpu, mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
SCREAMING_SNAKE_CASE =config['lr']
SCREAMING_SNAKE_CASE =int(config['num_epochs'] )
SCREAMING_SNAKE_CASE =int(config['seed'] )
SCREAMING_SNAKE_CASE =int(config['batch_size'] )
SCREAMING_SNAKE_CASE =evaluate.load('glue', 'mrpc' )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=lowerCAmelCase_ )
def inner_training_loop(lowerCAmelCase_ ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(lowerCAmelCase_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
SCREAMING_SNAKE_CASE =AutoModelForSequenceClassification.from_pretrained('bert-base-cased', return_dict=lowerCAmelCase_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
SCREAMING_SNAKE_CASE =model.to(accelerator.device )
# Instantiate optimizer
SCREAMING_SNAKE_CASE =AdamW(params=model.parameters(), lr=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =get_dataloaders(lowerCAmelCase_, lowerCAmelCase_ )
# Instantiate scheduler
SCREAMING_SNAKE_CASE =get_linear_schedule_with_warmup(
optimizer=lowerCAmelCase_, num_warmup_steps=100, num_training_steps=(len(lowerCAmelCase_ ) * num_epochs), )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =accelerator.prepare(
lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ )
# Now we train the model
for epoch in range(lowerCAmelCase_ ):
model.train()
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
SCREAMING_SNAKE_CASE =model(**lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =outputs.loss
accelerator.backward(lowerCAmelCase_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
SCREAMING_SNAKE_CASE =model(**lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =outputs.logits.argmax(dim=-1 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =accelerator.gather_for_metrics((predictions, batch['labels']) )
metric.add_batch(
predictions=lowerCAmelCase_, references=lowerCAmelCase_, )
SCREAMING_SNAKE_CASE =metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'epoch {epoch}:', lowerCAmelCase_ )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =argparse.ArgumentParser(description='Simple example of training script.' )
parser.add_argument(
'--mixed_precision', type=lowerCAmelCase_, default=lowerCAmelCase_, choices=['no', 'fp16', 'bf16', 'fp8'], help='Whether to use mixed precision. Choose'
'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
'and an Nvidia Ampere GPU.', )
parser.add_argument('--cpu', action='store_true', help='If passed, will train on the CPU.' )
SCREAMING_SNAKE_CASE =parser.parse_args()
SCREAMING_SNAKE_CASE ={'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
training_function(lowerCAmelCase_, lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 334 | 1 |
import numpy as np
import datasets
_lowerCamelCase ="\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P. C. Mahalanobis in 1936\nand has been used in various statistical applications ever since\n[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]\n"
_lowerCamelCase ="\\n@article{de2000mahalanobis,\n title={The mahalanobis distance},\n author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},\n journal={Chemometrics and intelligent laboratory systems},\n volume={50},\n number={1},\n pages={1--18},\n year={2000},\n publisher={Elsevier}\n}\n"
_lowerCamelCase ="\nArgs:\n X: List of datapoints to be compared with the `reference_distribution`.\n reference_distribution: List of datapoints from the reference distribution we want to compare to.\nReturns:\n mahalanobis: The Mahalonobis distance for each datapoint in `X`.\nExamples:\n\n >>> mahalanobis_metric = datasets.load_metric(\"mahalanobis\")\n >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])\n >>> print(results)\n {'mahalanobis': array([0.5])}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a_ ( datasets.Metric ):
"""simple docstring"""
def _lowerCAmelCase ( self : List[str] ):
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
'X': datasets.Sequence(datasets.Value('float' ,id='sequence' ) ,id='X' ),
} ) ,)
def _lowerCAmelCase ( self : List[str] ,snake_case : Optional[Any] ,snake_case : List[Any] ):
# convert to numpy arrays
SCREAMING_SNAKE_CASE =np.array(snake_case )
SCREAMING_SNAKE_CASE =np.array(snake_case )
# Assert that arrays are 2D
if len(X.shape ) != 2:
raise ValueError('Expected `X` to be a 2D vector' )
if len(reference_distribution.shape ) != 2:
raise ValueError('Expected `reference_distribution` to be a 2D vector' )
if reference_distribution.shape[0] < 2:
raise ValueError(
'Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension' )
# Get mahalanobis distance for each prediction
SCREAMING_SNAKE_CASE =X - np.mean(snake_case )
SCREAMING_SNAKE_CASE =np.cov(reference_distribution.T )
try:
SCREAMING_SNAKE_CASE =np.linalg.inv(snake_case )
except np.linalg.LinAlgError:
SCREAMING_SNAKE_CASE =np.linalg.pinv(snake_case )
SCREAMING_SNAKE_CASE =np.dot(snake_case ,snake_case )
SCREAMING_SNAKE_CASE =np.dot(snake_case ,X_minus_mu.T ).diagonal()
return {"mahalanobis": mahal_dist}
| 334 |
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
return " ".join(
''.join(word[::-1] ) if len(lowerCAmelCase_ ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words("Hey wollef sroirraw"))
| 334 | 1 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_from_cache,
http_get,
http_head,
)
_lowerCamelCase ="\\n Text data.\n Second line of data."
_lowerCamelCase ="file"
@pytest.fixture(scope='session' )
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =tmp_path_factory.mktemp('data' ) / (FILE_PATH + '.zstd')
SCREAMING_SNAKE_CASE =bytes(lowerCAmelCase_, 'utf-8' )
with zstd.open(lowerCAmelCase_, 'wb' ) as f:
f.write(lowerCAmelCase_ )
return path
@pytest.fixture
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
with open(os.path.join(tmpfs.local_root_dir, lowerCAmelCase_ ), 'w' ) as f:
f.write(lowerCAmelCase_ )
return FILE_PATH
@pytest.mark.parametrize('compression_format', ['gzip', 'xz', 'zstd'] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ={'gzip': gz_file, 'xz': xz_file, 'zstd': zstd_path}
SCREAMING_SNAKE_CASE =input_paths[compression_format]
SCREAMING_SNAKE_CASE =tmp_path / 'cache'
SCREAMING_SNAKE_CASE =DownloadConfig(cache_dir=lowerCAmelCase_, extract_compressed_file=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =cached_path(lowerCAmelCase_, download_config=lowerCAmelCase_ )
with open(lowerCAmelCase_ ) as f:
SCREAMING_SNAKE_CASE =f.read()
with open(lowerCAmelCase_ ) as f:
SCREAMING_SNAKE_CASE =f.read()
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize('default_extracted', [True, False] )
@pytest.mark.parametrize('default_cache_dir', [True, False] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ='custom_cache'
SCREAMING_SNAKE_CASE ='custom_extracted_dir'
SCREAMING_SNAKE_CASE =tmp_path / 'custom_extracted_path'
if default_extracted:
SCREAMING_SNAKE_CASE =('downloads' if default_cache_dir else custom_cache_dir, 'extracted')
else:
monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_DIR', lowerCAmelCase_ )
monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH', str(lowerCAmelCase_ ) )
SCREAMING_SNAKE_CASE =custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
SCREAMING_SNAKE_CASE =xz_file
SCREAMING_SNAKE_CASE =(
DownloadConfig(extract_compressed_file=lowerCAmelCase_ )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir, extract_compressed_file=lowerCAmelCase_ )
)
SCREAMING_SNAKE_CASE =cached_path(lowerCAmelCase_, download_config=lowerCAmelCase_ )
assert Path(lowerCAmelCase_ ).parent.parts[-2:] == expected
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =str(Path(lowerCAmelCase_ ).resolve() )
assert cached_path(lowerCAmelCase_ ) == text_file
# relative path
SCREAMING_SNAKE_CASE =str(Path(lowerCAmelCase_ ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(lowerCAmelCase_ ) == text_file
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =str(tmp_path.resolve() / '__missing_file__.txt' )
with pytest.raises(lowerCAmelCase_ ):
cached_path(lowerCAmelCase_ )
# relative path
SCREAMING_SNAKE_CASE ='./__missing_file__.txt'
with pytest.raises(lowerCAmelCase_ ):
cached_path(lowerCAmelCase_ )
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =get_from_cache(F'tmp://{tmpfs_file}' )
with open(lowerCAmelCase_ ) as f:
SCREAMING_SNAKE_CASE =f.read()
assert output_file_content == FILE_CONTENT
@patch('datasets.config.HF_DATASETS_OFFLINE', lowerCAmelCase_ )
def snake_case__ ( ):
"""simple docstring"""
with pytest.raises(lowerCAmelCase_ ):
cached_path('https://huggingface.co' )
@patch('datasets.config.HF_DATASETS_OFFLINE', lowerCAmelCase_ )
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =tmp_path_factory.mktemp('data' ) / 'file.html'
with pytest.raises(lowerCAmelCase_ ):
http_get('https://huggingface.co', temp_file=lowerCAmelCase_ )
with pytest.raises(lowerCAmelCase_ ):
http_head('https://huggingface.co' )
@patch('datasets.config.HF_DATASETS_OFFLINE', lowerCAmelCase_ )
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =tmp_path_factory.mktemp('data' ) / 'file.html'
with pytest.raises(lowerCAmelCase_ ):
ftp_get('ftp://huggingface.co', temp_file=lowerCAmelCase_ )
with pytest.raises(lowerCAmelCase_ ):
ftp_head('ftp://huggingface.co' )
@patch('datasets.config.HF_DATASETS_OFFLINE', lowerCAmelCase_ )
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =tmp_path_factory.mktemp('data' ) / 'file.html'
with pytest.raises(lowerCAmelCase_ ):
fsspec_get('s3://huggingface.co', temp_file=lowerCAmelCase_ )
with pytest.raises(lowerCAmelCase_ ):
fsspec_head('s3://huggingface.co' )
| 334 |
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json
_lowerCamelCase ="sshleifer/mar_enro_6_3_student"
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
def _lowerCAmelCase ( self : Union[str, Any] ):
super().setUp()
SCREAMING_SNAKE_CASE =cached_path(
'https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz' ,extract_compressed_file=snake_case ,)
SCREAMING_SNAKE_CASE =f'{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'
@slow
@require_torch_gpu
def _lowerCAmelCase ( self : Optional[int] ):
MarianMTModel.from_pretrained(snake_case )
@slow
@require_torch_gpu
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE ={
'$MAX_LEN': 64,
'$BS': 64,
'$GAS': 1,
'$ENRO_DIR': self.data_dir,
'facebook/mbart-large-cc25': MARIAN_MODEL,
# "val_check_interval=0.25": "val_check_interval=1.0",
'--learning_rate=3e-5': '--learning_rate 3e-4',
'--num_train_epochs 6': '--num_train_epochs 1',
}
# Clean up bash script
SCREAMING_SNAKE_CASE =(self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py' )[1].strip()
SCREAMING_SNAKE_CASE =bash_script.replace('\\\n' ,'' ).strip().replace('"$@"' ,'' )
for k, v in env_vars_to_replace.items():
SCREAMING_SNAKE_CASE =bash_script.replace(snake_case ,str(snake_case ) )
SCREAMING_SNAKE_CASE =self.get_auto_remove_tmp_dir()
# bash_script = bash_script.replace("--fp16 ", "")
SCREAMING_SNAKE_CASE =f'\n --output_dir {output_dir}\n --tokenizer_name Helsinki-NLP/opus-mt-en-ro\n --sortish_sampler\n --do_predict\n --gpus 1\n --freeze_encoder\n --n_train 40000\n --n_val 500\n --n_test 500\n --fp16_opt_level O1\n --num_sanity_val_steps 0\n --eval_beams 2\n '.split()
# XXX: args.gpus > 1 : handle multi_gpu in the future
SCREAMING_SNAKE_CASE =['finetune.py'] + bash_script.split() + args
with patch.object(snake_case ,'argv' ,snake_case ):
SCREAMING_SNAKE_CASE =argparse.ArgumentParser()
SCREAMING_SNAKE_CASE =pl.Trainer.add_argparse_args(snake_case )
SCREAMING_SNAKE_CASE =SummarizationModule.add_model_specific_args(snake_case ,os.getcwd() )
SCREAMING_SNAKE_CASE =parser.parse_args()
SCREAMING_SNAKE_CASE =main(snake_case )
# Check metrics
SCREAMING_SNAKE_CASE =load_json(model.metrics_save_path )
SCREAMING_SNAKE_CASE =metrics['val'][0]
SCREAMING_SNAKE_CASE =metrics['val'][-1]
self.assertEqual(len(metrics['val'] ) ,(args.max_epochs / args.val_check_interval) )
assert isinstance(last_step_stats[f'val_avg_{model.val_metric}'] ,snake_case )
self.assertGreater(last_step_stats['val_avg_gen_time'] ,0.01 )
# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
self.assertLessEqual(last_step_stats['val_avg_gen_time'] ,1.0 )
# test learning requirements:
# 1. BLEU improves over the course of training by more than 2 pts
self.assertGreater(last_step_stats['val_avg_bleu'] - first_step_stats['val_avg_bleu'] ,2 )
# 2. BLEU finishes above 17
self.assertGreater(last_step_stats['val_avg_bleu'] ,17 )
# 3. test BLEU and val BLEU within ~1.1 pt.
self.assertLess(abs(metrics['val'][-1]['val_avg_bleu'] - metrics['test'][-1]['test_avg_bleu'] ) ,1.1 )
# check lightning ckpt can be loaded and has a reasonable statedict
SCREAMING_SNAKE_CASE =os.listdir(snake_case )
SCREAMING_SNAKE_CASE =[x for x in contents if x.endswith('.ckpt' )][0]
SCREAMING_SNAKE_CASE =os.path.join(args.output_dir ,snake_case )
SCREAMING_SNAKE_CASE =torch.load(snake_case ,map_location='cpu' )
SCREAMING_SNAKE_CASE ='model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
SCREAMING_SNAKE_CASE ={os.path.basename(snake_case ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics['test'] ) == 1
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
@timeout_decorator.timeout(600 )
@slow
@require_torch_gpu
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =f'{self.test_file_dir_str}/test_data/wmt_en_ro'
SCREAMING_SNAKE_CASE ={
'--fp16_opt_level=O1': '',
'$MAX_LEN': 128,
'$BS': 16,
'$GAS': 1,
'$ENRO_DIR': data_dir,
'$m': 'sshleifer/student_marian_en_ro_6_1',
'val_check_interval=0.25': 'val_check_interval=1.0',
}
# Clean up bash script
SCREAMING_SNAKE_CASE =(
(self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py' )[1].strip()
)
SCREAMING_SNAKE_CASE =bash_script.replace('\\\n' ,'' ).strip().replace('"$@"' ,'' )
SCREAMING_SNAKE_CASE =bash_script.replace('--fp16 ' ,' ' )
for k, v in env_vars_to_replace.items():
SCREAMING_SNAKE_CASE =bash_script.replace(snake_case ,str(snake_case ) )
SCREAMING_SNAKE_CASE =self.get_auto_remove_tmp_dir()
SCREAMING_SNAKE_CASE =bash_script.replace('--fp16' ,'' )
SCREAMING_SNAKE_CASE =6
SCREAMING_SNAKE_CASE =(
['distillation.py']
+ bash_script.split()
+ [
f'--output_dir={output_dir}',
'--gpus=1',
'--learning_rate=1e-3',
f'--num_train_epochs={epochs}',
'--warmup_steps=10',
'--val_check_interval=1.0',
'--do_predict',
]
)
with patch.object(snake_case ,'argv' ,snake_case ):
SCREAMING_SNAKE_CASE =argparse.ArgumentParser()
SCREAMING_SNAKE_CASE =pl.Trainer.add_argparse_args(snake_case )
SCREAMING_SNAKE_CASE =SummarizationDistiller.add_model_specific_args(snake_case ,os.getcwd() )
SCREAMING_SNAKE_CASE =parser.parse_args()
# assert args.gpus == gpus THIS BREAKS for multi_gpu
SCREAMING_SNAKE_CASE =distill_main(snake_case )
# Check metrics
SCREAMING_SNAKE_CASE =load_json(model.metrics_save_path )
SCREAMING_SNAKE_CASE =metrics['val'][0]
SCREAMING_SNAKE_CASE =metrics['val'][-1]
assert len(metrics['val'] ) >= (args.max_epochs / args.val_check_interval) # +1 accounts for val_sanity_check
assert last_step_stats["val_avg_gen_time"] >= 0.01
assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"] # model learned nothing
assert 1.0 >= last_step_stats["val_avg_gen_time"] # model hanging on generate. Maybe bad config was saved.
assert isinstance(last_step_stats[f'val_avg_{model.val_metric}'] ,snake_case )
# check lightning ckpt can be loaded and has a reasonable statedict
SCREAMING_SNAKE_CASE =os.listdir(snake_case )
SCREAMING_SNAKE_CASE =[x for x in contents if x.endswith('.ckpt' )][0]
SCREAMING_SNAKE_CASE =os.path.join(args.output_dir ,snake_case )
SCREAMING_SNAKE_CASE =torch.load(snake_case ,map_location='cpu' )
SCREAMING_SNAKE_CASE ='model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
SCREAMING_SNAKE_CASE ={os.path.basename(snake_case ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics['test'] ) == 1
| 334 | 1 |
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase =logging.get_logger()
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ = True ):
"""simple docstring"""
print(F'Converting {name}...' )
with torch.no_grad():
if hidden_sizes == 128:
if name[-1] == "S":
SCREAMING_SNAKE_CASE =timm.create_model('levit_128s', pretrained=lowerCAmelCase_ )
else:
SCREAMING_SNAKE_CASE =timm.create_model('levit_128', pretrained=lowerCAmelCase_ )
if hidden_sizes == 192:
SCREAMING_SNAKE_CASE =timm.create_model('levit_192', pretrained=lowerCAmelCase_ )
if hidden_sizes == 256:
SCREAMING_SNAKE_CASE =timm.create_model('levit_256', pretrained=lowerCAmelCase_ )
if hidden_sizes == 384:
SCREAMING_SNAKE_CASE =timm.create_model('levit_384', pretrained=lowerCAmelCase_ )
from_model.eval()
SCREAMING_SNAKE_CASE =LevitForImageClassificationWithTeacher(lowerCAmelCase_ ).eval()
SCREAMING_SNAKE_CASE =OrderedDict()
SCREAMING_SNAKE_CASE =from_model.state_dict()
SCREAMING_SNAKE_CASE =list(from_model.state_dict().keys() )
SCREAMING_SNAKE_CASE =list(our_model.state_dict().keys() )
print(len(lowerCAmelCase_ ), len(lowerCAmelCase_ ) )
for i in range(len(lowerCAmelCase_ ) ):
SCREAMING_SNAKE_CASE =weights[og_keys[i]]
our_model.load_state_dict(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =torch.randn((2, 3, 224, 224) )
SCREAMING_SNAKE_CASE =from_model(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =our_model(lowerCAmelCase_ ).logits
assert torch.allclose(lowerCAmelCase_, lowerCAmelCase_ ), "The model logits don't match the original one."
SCREAMING_SNAKE_CASE =name
print(lowerCAmelCase_ )
if push_to_hub:
our_model.save_pretrained(save_directory / checkpoint_name )
SCREAMING_SNAKE_CASE =LevitImageProcessor()
image_processor.save_pretrained(save_directory / checkpoint_name )
print(F'Pushed {checkpoint_name}' )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ = None, lowerCAmelCase_ = True ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ='imagenet-1k-id2label.json'
SCREAMING_SNAKE_CASE =1000
SCREAMING_SNAKE_CASE =(1, num_labels)
SCREAMING_SNAKE_CASE ='huggingface/label-files'
SCREAMING_SNAKE_CASE =num_labels
SCREAMING_SNAKE_CASE =json.load(open(hf_hub_download(lowerCAmelCase_, lowerCAmelCase_, repo_type='dataset' ), 'r' ) )
SCREAMING_SNAKE_CASE ={int(lowerCAmelCase_ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE =idalabel
SCREAMING_SNAKE_CASE ={v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE =partial(lowerCAmelCase_, num_labels=lowerCAmelCase_, idalabel=lowerCAmelCase_, labelaid=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE ={
'levit-128S': 128,
'levit-128': 128,
'levit-192': 192,
'levit-256': 256,
'levit-384': 384,
}
SCREAMING_SNAKE_CASE ={
'levit-128S': ImageNetPreTrainedConfig(
hidden_sizes=[128, 256, 384], num_attention_heads=[4, 6, 8], depths=[2, 3, 4], key_dim=[16, 16, 16], drop_path_rate=0, ),
'levit-128': ImageNetPreTrainedConfig(
hidden_sizes=[128, 256, 384], num_attention_heads=[4, 8, 12], depths=[4, 4, 4], key_dim=[16, 16, 16], drop_path_rate=0, ),
'levit-192': ImageNetPreTrainedConfig(
hidden_sizes=[192, 288, 384], num_attention_heads=[3, 5, 6], depths=[4, 4, 4], key_dim=[32, 32, 32], drop_path_rate=0, ),
'levit-256': ImageNetPreTrainedConfig(
hidden_sizes=[256, 384, 512], num_attention_heads=[4, 6, 8], depths=[4, 4, 4], key_dim=[32, 32, 32], drop_path_rate=0, ),
'levit-384': ImageNetPreTrainedConfig(
hidden_sizes=[384, 512, 768], num_attention_heads=[6, 9, 12], depths=[4, 4, 4], key_dim=[32, 32, 32], drop_path_rate=0.1, ),
}
if model_name:
convert_weight_and_push(
names_to_hidden_sizes[model_name], lowerCAmelCase_, names_to_config[model_name], lowerCAmelCase_, lowerCAmelCase_ )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(names_to_hidden_sizes[model_name], lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ )
return config, expected_shape
if __name__ == "__main__":
_lowerCamelCase =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default=None,
type=str,
help="The name of the model you wish to convert, it must be one of the supported Levit* architecture,",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default="levit-dump-folder/",
type=Path,
required=False,
help="Path to the output PyTorch model directory.",
)
parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
parser.add_argument(
"--no-push_to_hub",
dest="push_to_hub",
action="store_false",
help="Do not push model and image processor to the hub",
)
_lowerCamelCase =parser.parse_args()
_lowerCamelCase =args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 334 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"salesforce/blip2-opt-2.7b": "https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json",
}
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'blip_2_vision_model'
def __init__( self : List[Any] ,snake_case : List[Any]=1408 ,snake_case : Optional[Any]=6144 ,snake_case : Optional[int]=39 ,snake_case : Optional[int]=16 ,snake_case : Optional[Any]=224 ,snake_case : Tuple=14 ,snake_case : Optional[Any]="gelu" ,snake_case : Union[str, Any]=0.00_001 ,snake_case : Dict=0.0 ,snake_case : Union[str, Any]=1e-10 ,snake_case : int=True ,**snake_case : str ,):
super().__init__(**snake_case )
SCREAMING_SNAKE_CASE =hidden_size
SCREAMING_SNAKE_CASE =intermediate_size
SCREAMING_SNAKE_CASE =num_hidden_layers
SCREAMING_SNAKE_CASE =num_attention_heads
SCREAMING_SNAKE_CASE =patch_size
SCREAMING_SNAKE_CASE =image_size
SCREAMING_SNAKE_CASE =initializer_range
SCREAMING_SNAKE_CASE =attention_dropout
SCREAMING_SNAKE_CASE =layer_norm_eps
SCREAMING_SNAKE_CASE =hidden_act
SCREAMING_SNAKE_CASE =qkv_bias
@classmethod
def _lowerCAmelCase ( cls : Dict ,snake_case : Union[str, os.PathLike] ,**snake_case : str ):
cls._set_token_in_kwargs(snake_case )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =cls.get_config_dict(snake_case ,**snake_case )
# get the vision config dict if we are loading from Blip2Config
if config_dict.get('model_type' ) == "blip-2":
SCREAMING_SNAKE_CASE =config_dict['vision_config']
if "model_type" in config_dict and hasattr(cls ,'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(snake_case ,**snake_case )
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'blip_2_qformer'
def __init__( self : Any ,snake_case : Dict=30522 ,snake_case : int=768 ,snake_case : List[Any]=12 ,snake_case : List[str]=12 ,snake_case : Optional[Any]=3072 ,snake_case : str="gelu" ,snake_case : Optional[Any]=0.1 ,snake_case : Union[str, Any]=0.1 ,snake_case : Optional[Any]=512 ,snake_case : List[Any]=0.02 ,snake_case : List[str]=1e-12 ,snake_case : Tuple=0 ,snake_case : Union[str, Any]="absolute" ,snake_case : List[Any]=2 ,snake_case : List[str]=1408 ,**snake_case : Optional[Any] ,):
super().__init__(pad_token_id=snake_case ,**snake_case )
SCREAMING_SNAKE_CASE =vocab_size
SCREAMING_SNAKE_CASE =hidden_size
SCREAMING_SNAKE_CASE =num_hidden_layers
SCREAMING_SNAKE_CASE =num_attention_heads
SCREAMING_SNAKE_CASE =hidden_act
SCREAMING_SNAKE_CASE =intermediate_size
SCREAMING_SNAKE_CASE =hidden_dropout_prob
SCREAMING_SNAKE_CASE =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE =max_position_embeddings
SCREAMING_SNAKE_CASE =initializer_range
SCREAMING_SNAKE_CASE =layer_norm_eps
SCREAMING_SNAKE_CASE =position_embedding_type
SCREAMING_SNAKE_CASE =cross_attention_frequency
SCREAMING_SNAKE_CASE =encoder_hidden_size
@classmethod
def _lowerCAmelCase ( cls : List[Any] ,snake_case : Union[str, os.PathLike] ,**snake_case : Dict ):
cls._set_token_in_kwargs(snake_case )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =cls.get_config_dict(snake_case ,**snake_case )
# get the qformer config dict if we are loading from Blip2Config
if config_dict.get('model_type' ) == "blip-2":
SCREAMING_SNAKE_CASE =config_dict['qformer_config']
if "model_type" in config_dict and hasattr(cls ,'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(snake_case ,**snake_case )
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'blip-2'
__UpperCAmelCase = True
def __init__( self : int ,snake_case : Dict=None ,snake_case : Tuple=None ,snake_case : str=None ,snake_case : Union[str, Any]=32 ,**snake_case : int ):
super().__init__(**snake_case )
if vision_config is None:
SCREAMING_SNAKE_CASE ={}
logger.info('vision_config is None. initializing the Blip2VisionConfig with default values.' )
if qformer_config is None:
SCREAMING_SNAKE_CASE ={}
logger.info('qformer_config is None. Initializing the Blip2QFormerConfig with default values.' )
if text_config is None:
SCREAMING_SNAKE_CASE ={}
logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).' )
SCREAMING_SNAKE_CASE =BlipaVisionConfig(**snake_case )
SCREAMING_SNAKE_CASE =BlipaQFormerConfig(**snake_case )
SCREAMING_SNAKE_CASE =text_config['model_type'] if 'model_type' in text_config else 'opt'
SCREAMING_SNAKE_CASE =CONFIG_MAPPING[text_model_type](**snake_case )
SCREAMING_SNAKE_CASE =self.text_config.tie_word_embeddings
SCREAMING_SNAKE_CASE =self.text_config.is_encoder_decoder
SCREAMING_SNAKE_CASE =num_query_tokens
SCREAMING_SNAKE_CASE =self.vision_config.hidden_size
SCREAMING_SNAKE_CASE =self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
SCREAMING_SNAKE_CASE =1.0
SCREAMING_SNAKE_CASE =0.02
@classmethod
def _lowerCAmelCase ( cls : Union[str, Any] ,snake_case : BlipaVisionConfig ,snake_case : BlipaQFormerConfig ,snake_case : PretrainedConfig ,**snake_case : Any ,):
return cls(
vision_config=vision_config.to_dict() ,qformer_config=qformer_config.to_dict() ,text_config=text_config.to_dict() ,**snake_case ,)
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE =self.vision_config.to_dict()
SCREAMING_SNAKE_CASE =self.qformer_config.to_dict()
SCREAMING_SNAKE_CASE =self.text_config.to_dict()
SCREAMING_SNAKE_CASE =self.__class__.model_type
return output
| 334 | 1 |
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th
logging.set_verbosity_info()
_lowerCamelCase =logging.get_logger("transformers.models.encodec")
_lowerCamelCase ={
"quantizer.vq.layers.*._codebook.inited": "quantizer.layers.*.codebook.inited",
"quantizer.vq.layers.*._codebook.cluster_size": "quantizer.layers.*.codebook.cluster_size",
"quantizer.vq.layers.*._codebook.embed": "quantizer.layers.*.codebook.embed",
"quantizer.vq.layers.*._codebook.embed_avg": "quantizer.layers.*.codebook.embed_avg",
}
_lowerCamelCase ={
"encoder.model.0.conv.conv": "encoder.layers.0.conv",
"encoder.model.1.block.1.conv.conv": "encoder.layers.1.block.1.conv",
"encoder.model.1.block.3.conv.conv": "encoder.layers.1.block.3.conv",
"encoder.model.1.shortcut.conv.conv": "encoder.layers.1.shortcut.conv",
"encoder.model.3.conv.conv": "encoder.layers.3.conv",
"encoder.model.4.block.1.conv.conv": "encoder.layers.4.block.1.conv",
"encoder.model.4.block.3.conv.conv": "encoder.layers.4.block.3.conv",
"encoder.model.4.shortcut.conv.conv": "encoder.layers.4.shortcut.conv",
"encoder.model.6.conv.conv": "encoder.layers.6.conv",
"encoder.model.7.block.1.conv.conv": "encoder.layers.7.block.1.conv",
"encoder.model.7.block.3.conv.conv": "encoder.layers.7.block.3.conv",
"encoder.model.7.shortcut.conv.conv": "encoder.layers.7.shortcut.conv",
"encoder.model.9.conv.conv": "encoder.layers.9.conv",
"encoder.model.10.block.1.conv.conv": "encoder.layers.10.block.1.conv",
"encoder.model.10.block.3.conv.conv": "encoder.layers.10.block.3.conv",
"encoder.model.10.shortcut.conv.conv": "encoder.layers.10.shortcut.conv",
"encoder.model.12.conv.conv": "encoder.layers.12.conv",
"encoder.model.13.lstm": "encoder.layers.13.lstm",
"encoder.model.15.conv.conv": "encoder.layers.15.conv",
}
_lowerCamelCase ={
"encoder.model.0.conv.norm": "encoder.layers.0.norm",
"encoder.model.1.block.1.conv.norm": "encoder.layers.1.block.1.norm",
"encoder.model.1.block.3.conv.norm": "encoder.layers.1.block.3.norm",
"encoder.model.1.shortcut.conv.norm": "encoder.layers.1.shortcut.norm",
"encoder.model.3.conv.norm": "encoder.layers.3.norm",
"encoder.model.4.block.1.conv.norm": "encoder.layers.4.block.1.norm",
"encoder.model.4.block.3.conv.norm": "encoder.layers.4.block.3.norm",
"encoder.model.4.shortcut.conv.norm": "encoder.layers.4.shortcut.norm",
"encoder.model.6.conv.norm": "encoder.layers.6.norm",
"encoder.model.7.block.1.conv.norm": "encoder.layers.7.block.1.norm",
"encoder.model.7.block.3.conv.norm": "encoder.layers.7.block.3.norm",
"encoder.model.7.shortcut.conv.norm": "encoder.layers.7.shortcut.norm",
"encoder.model.9.conv.norm": "encoder.layers.9.norm",
"encoder.model.10.block.1.conv.norm": "encoder.layers.10.block.1.norm",
"encoder.model.10.block.3.conv.norm": "encoder.layers.10.block.3.norm",
"encoder.model.10.shortcut.conv.norm": "encoder.layers.10.shortcut.norm",
"encoder.model.12.conv.norm": "encoder.layers.12.norm",
"encoder.model.15.conv.norm": "encoder.layers.15.norm",
}
_lowerCamelCase ={
"decoder.model.0.conv.conv": "decoder.layers.0.conv",
"decoder.model.1.lstm": "decoder.layers.1.lstm",
"decoder.model.3.convtr.convtr": "decoder.layers.3.conv",
"decoder.model.4.block.1.conv.conv": "decoder.layers.4.block.1.conv",
"decoder.model.4.block.3.conv.conv": "decoder.layers.4.block.3.conv",
"decoder.model.4.shortcut.conv.conv": "decoder.layers.4.shortcut.conv",
"decoder.model.6.convtr.convtr": "decoder.layers.6.conv",
"decoder.model.7.block.1.conv.conv": "decoder.layers.7.block.1.conv",
"decoder.model.7.block.3.conv.conv": "decoder.layers.7.block.3.conv",
"decoder.model.7.shortcut.conv.conv": "decoder.layers.7.shortcut.conv",
"decoder.model.9.convtr.convtr": "decoder.layers.9.conv",
"decoder.model.10.block.1.conv.conv": "decoder.layers.10.block.1.conv",
"decoder.model.10.block.3.conv.conv": "decoder.layers.10.block.3.conv",
"decoder.model.10.shortcut.conv.conv": "decoder.layers.10.shortcut.conv",
"decoder.model.12.convtr.convtr": "decoder.layers.12.conv",
"decoder.model.13.block.1.conv.conv": "decoder.layers.13.block.1.conv",
"decoder.model.13.block.3.conv.conv": "decoder.layers.13.block.3.conv",
"decoder.model.13.shortcut.conv.conv": "decoder.layers.13.shortcut.conv",
"decoder.model.15.conv.conv": "decoder.layers.15.conv",
}
_lowerCamelCase ={
"decoder.model.0.conv.norm": "decoder.layers.0.norm",
"decoder.model.3.convtr.norm": "decoder.layers.3.norm",
"decoder.model.4.block.1.conv.norm": "decoder.layers.4.block.1.norm",
"decoder.model.4.block.3.conv.norm": "decoder.layers.4.block.3.norm",
"decoder.model.4.shortcut.conv.norm": "decoder.layers.4.shortcut.norm",
"decoder.model.6.convtr.norm": "decoder.layers.6.norm",
"decoder.model.7.block.1.conv.norm": "decoder.layers.7.block.1.norm",
"decoder.model.7.block.3.conv.norm": "decoder.layers.7.block.3.norm",
"decoder.model.7.shortcut.conv.norm": "decoder.layers.7.shortcut.norm",
"decoder.model.9.convtr.norm": "decoder.layers.9.norm",
"decoder.model.10.block.1.conv.norm": "decoder.layers.10.block.1.norm",
"decoder.model.10.block.3.conv.norm": "decoder.layers.10.block.3.norm",
"decoder.model.10.shortcut.conv.norm": "decoder.layers.10.shortcut.norm",
"decoder.model.12.convtr.norm": "decoder.layers.12.norm",
"decoder.model.13.block.1.conv.norm": "decoder.layers.13.block.1.norm",
"decoder.model.13.block.3.conv.norm": "decoder.layers.13.block.3.norm",
"decoder.model.13.shortcut.conv.norm": "decoder.layers.13.shortcut.norm",
"decoder.model.15.conv.norm": "decoder.layers.15.norm",
}
_lowerCamelCase ={
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_DECODER,
}
_lowerCamelCase ={
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_ENCODER_48K,
**MAPPING_DECODER,
**MAPPING_DECODER_48K,
}
_lowerCamelCase =[]
_lowerCamelCase =[]
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
for attribute in key.split('.' ):
SCREAMING_SNAKE_CASE =getattr(lowerCAmelCase_, lowerCAmelCase_ )
if weight_type is not None:
SCREAMING_SNAKE_CASE =getattr(lowerCAmelCase_, lowerCAmelCase_ ).shape
else:
SCREAMING_SNAKE_CASE =hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
F' {value.shape} for {full_name}' )
if weight_type == "weight":
SCREAMING_SNAKE_CASE =value
elif weight_type == "weight_g":
SCREAMING_SNAKE_CASE =value
elif weight_type == "weight_v":
SCREAMING_SNAKE_CASE =value
elif weight_type == "bias":
SCREAMING_SNAKE_CASE =value
elif weight_type == "running_mean":
SCREAMING_SNAKE_CASE =value
elif weight_type == "running_var":
SCREAMING_SNAKE_CASE =value
elif weight_type == "num_batches_tracked":
SCREAMING_SNAKE_CASE =value
elif weight_type == "weight_ih_l0":
SCREAMING_SNAKE_CASE =value
elif weight_type == "weight_hh_l0":
SCREAMING_SNAKE_CASE =value
elif weight_type == "bias_ih_l0":
SCREAMING_SNAKE_CASE =value
elif weight_type == "bias_hh_l0":
SCREAMING_SNAKE_CASE =value
elif weight_type == "weight_ih_l1":
SCREAMING_SNAKE_CASE =value
elif weight_type == "weight_hh_l1":
SCREAMING_SNAKE_CASE =value
elif weight_type == "bias_ih_l1":
SCREAMING_SNAKE_CASE =value
elif weight_type == "bias_hh_l1":
SCREAMING_SNAKE_CASE =value
else:
SCREAMING_SNAKE_CASE =value
logger.info(F'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.' )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
for key in ignore_keys:
if key.endswith('.*' ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =key.split('.*.' )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =[]
if model_name == "encodec_24khz" or "encodec_32khz":
SCREAMING_SNAKE_CASE =MAPPING_24K
elif model_name == "encodec_48khz":
SCREAMING_SNAKE_CASE =MAPPING_48K
else:
raise ValueError(F'Unsupported model: {model_name}' )
for name, value in orig_dict.items():
if should_ignore(lowerCAmelCase_, lowerCAmelCase_ ):
logger.info(F'{name} was ignored' )
continue
SCREAMING_SNAKE_CASE =False
for key, mapped_key in MAPPING.items():
if "*" in key:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =key.split('.*.' )
if prefix in name and suffix in name:
SCREAMING_SNAKE_CASE =suffix
if key in name:
# HACK otherwise .embed gets initialized with .embed_avg too
if key.endswith('embed' ) and name.endswith('embed_avg' ):
continue
SCREAMING_SNAKE_CASE =True
if "*" in mapped_key:
SCREAMING_SNAKE_CASE =name.split(lowerCAmelCase_ )[0].split('.' )[-2]
SCREAMING_SNAKE_CASE =mapped_key.replace('*', lowerCAmelCase_ )
if "weight_g" in name:
SCREAMING_SNAKE_CASE ='weight_g'
elif "weight_v" in name:
SCREAMING_SNAKE_CASE ='weight_v'
elif "weight_ih_l0" in name:
SCREAMING_SNAKE_CASE ='weight_ih_l0'
elif "weight_hh_l0" in name:
SCREAMING_SNAKE_CASE ='weight_hh_l0'
elif "bias_ih_l0" in name:
SCREAMING_SNAKE_CASE ='bias_ih_l0'
elif "bias_hh_l0" in name:
SCREAMING_SNAKE_CASE ='bias_hh_l0'
elif "weight_ih_l1" in name:
SCREAMING_SNAKE_CASE ='weight_ih_l1'
elif "weight_hh_l1" in name:
SCREAMING_SNAKE_CASE ='weight_hh_l1'
elif "bias_ih_l1" in name:
SCREAMING_SNAKE_CASE ='bias_ih_l1'
elif "bias_hh_l1" in name:
SCREAMING_SNAKE_CASE ='bias_hh_l1'
elif "bias" in name:
SCREAMING_SNAKE_CASE ='bias'
elif "weight" in name:
SCREAMING_SNAKE_CASE ='weight'
elif "running_mean" in name:
SCREAMING_SNAKE_CASE ='running_mean'
elif "running_var" in name:
SCREAMING_SNAKE_CASE ='running_var'
elif "num_batches_tracked" in name:
SCREAMING_SNAKE_CASE ='num_batches_tracked'
else:
SCREAMING_SNAKE_CASE =None
set_recursively(lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ )
continue
if not is_used:
unused_weights.append(lowerCAmelCase_ )
logger.warning(F'Unused weights: {unused_weights}' )
@torch.no_grad()
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_=None, lowerCAmelCase_=None, ):
"""simple docstring"""
if config_path is not None:
SCREAMING_SNAKE_CASE =EncodecConfig.from_pretrained(lowerCAmelCase_ )
else:
SCREAMING_SNAKE_CASE =EncodecConfig()
if model_name == "encodec_24khz":
pass # config is already correct
elif model_name == "encodec_32khz":
SCREAMING_SNAKE_CASE =[8, 5, 4, 4]
SCREAMING_SNAKE_CASE =[2.2]
SCREAMING_SNAKE_CASE =64
SCREAMING_SNAKE_CASE =32000
SCREAMING_SNAKE_CASE =2048
SCREAMING_SNAKE_CASE =False
SCREAMING_SNAKE_CASE =False
SCREAMING_SNAKE_CASE =False
elif model_name == "encodec_48khz":
SCREAMING_SNAKE_CASE =[8, 5, 4, 2]
SCREAMING_SNAKE_CASE =[3.0, 6.0, 12.0, 24.0]
SCREAMING_SNAKE_CASE =48000
SCREAMING_SNAKE_CASE =2
SCREAMING_SNAKE_CASE =False
SCREAMING_SNAKE_CASE ='time_group_norm'
SCREAMING_SNAKE_CASE =True
SCREAMING_SNAKE_CASE =1.0
SCREAMING_SNAKE_CASE =0.01
else:
raise ValueError(F'Unknown model name: {model_name}' )
SCREAMING_SNAKE_CASE =EncodecModel(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =EncodecFeatureExtractor(
feature_size=config.audio_channels, sampling_rate=config.sampling_rate, chunk_length_s=config.chunk_length_s, overlap=config.overlap, )
feature_extractor.save_pretrained(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =torch.load(lowerCAmelCase_ )
if "best_state" in original_checkpoint:
# we might have a training state saved, in which case discard the yaml results and just retain the weights
SCREAMING_SNAKE_CASE =original_checkpoint['best_state']
recursively_load_weights(lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ )
model.save_pretrained(lowerCAmelCase_ )
if repo_id:
print('Pushing to the hub...' )
feature_extractor.push_to_hub(lowerCAmelCase_ )
model.push_to_hub(lowerCAmelCase_ )
if __name__ == "__main__":
_lowerCamelCase =argparse.ArgumentParser()
parser.add_argument(
"--model",
default="encodec_24khz",
type=str,
help="The model to convert. Should be one of 'encodec_24khz', 'encodec_32khz', 'encodec_48khz'.",
)
parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
)
parser.add_argument(
"--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
)
_lowerCamelCase =parser.parse_args()
convert_checkpoint(
args.model,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 334 |
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve # From: mauve-text
import datasets
_lowerCamelCase ="\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n booktitle = {NeurIPS},\n year = {2021}\n}\n\n"
_lowerCamelCase ="\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n"
_lowerCamelCase ="\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n predictions: list of generated text to score. Each predictions\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\nOptional Args:\n num_buckets: the size of the histogram to quantize P and Q. Options: 'auto' (default) or an integer\n pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n kmeans_max_iter: maximum number of k-means iterations. Default 500\n featurize_model_name: name of the model from which features are obtained. Default 'gpt2-large' Use one of ['gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'].\n device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU\n max_text_length: maximum number of tokens to consider. Default 1024\n divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n mauve_scaling_factor: \"c\" from the paper. Default 5.\n verbose: If True (default), print running time updates\n seed: random seed to initialize k-means cluster assignments.\nReturns:\n mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n q_hist: same as above, but with q_text.\nExamples:\n\n >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n >>> import datasets\n >>> mauve = datasets.load_metric('mauve')\n >>> predictions = [\"hello there\", \"general kenobi\"]\n >>> references = [\"hello there\", \"general kenobi\"]\n >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n >>> print(out.mauve) # doctest: +SKIP\n 1.0\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a_ ( datasets.Metric ):
"""simple docstring"""
def _lowerCAmelCase ( self : Tuple ):
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,homepage='https://github.com/krishnap25/mauve' ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
'predictions': datasets.Value('string' ,id='sequence' ),
'references': datasets.Value('string' ,id='sequence' ),
} ) ,codebase_urls=['https://github.com/krishnap25/mauve'] ,reference_urls=[
'https://arxiv.org/abs/2102.01454',
'https://github.com/krishnap25/mauve',
] ,)
def _lowerCAmelCase ( self : Union[str, Any] ,snake_case : Optional[int] ,snake_case : str ,snake_case : List[str]=None ,snake_case : str=None ,snake_case : int=None ,snake_case : Union[str, Any]=None ,snake_case : Optional[int]="auto" ,snake_case : List[str]=-1 ,snake_case : Union[str, Any]=0.9 ,snake_case : Tuple=5 ,snake_case : Union[str, Any]=500 ,snake_case : Union[str, Any]="gpt2-large" ,snake_case : Union[str, Any]=-1 ,snake_case : Optional[Any]=1024 ,snake_case : Optional[Any]=25 ,snake_case : List[str]=5 ,snake_case : List[str]=True ,snake_case : Optional[Any]=25 ,):
SCREAMING_SNAKE_CASE =compute_mauve(
p_text=snake_case ,q_text=snake_case ,p_features=snake_case ,q_features=snake_case ,p_tokens=snake_case ,q_tokens=snake_case ,num_buckets=snake_case ,pca_max_data=snake_case ,kmeans_explained_var=snake_case ,kmeans_num_redo=snake_case ,kmeans_max_iter=snake_case ,featurize_model_name=snake_case ,device_id=snake_case ,max_text_length=snake_case ,divergence_curve_discretization_size=snake_case ,mauve_scaling_factor=snake_case ,verbose=snake_case ,seed=snake_case ,)
return out
| 334 | 1 |
class a_ :
"""simple docstring"""
def __init__( self : Optional[Any] ):
SCREAMING_SNAKE_CASE ={} # Mapping from char to TrieNode
SCREAMING_SNAKE_CASE =False
def _lowerCAmelCase ( self : Optional[Any] ,snake_case : list[str] ):
for word in words:
self.insert(snake_case )
def _lowerCAmelCase ( self : Union[str, Any] ,snake_case : str ):
SCREAMING_SNAKE_CASE =self
for char in word:
if char not in curr.nodes:
SCREAMING_SNAKE_CASE =TrieNode()
SCREAMING_SNAKE_CASE =curr.nodes[char]
SCREAMING_SNAKE_CASE =True
def _lowerCAmelCase ( self : List[Any] ,snake_case : str ):
SCREAMING_SNAKE_CASE =self
for char in word:
if char not in curr.nodes:
return False
SCREAMING_SNAKE_CASE =curr.nodes[char]
return curr.is_leaf
def _lowerCAmelCase ( self : List[str] ,snake_case : str ):
def _delete(snake_case : TrieNode ,snake_case : str ,snake_case : int ) -> bool:
if index == len(snake_case ):
# If word does not exist
if not curr.is_leaf:
return False
SCREAMING_SNAKE_CASE =False
return len(curr.nodes ) == 0
SCREAMING_SNAKE_CASE =word[index]
SCREAMING_SNAKE_CASE =curr.nodes.get(snake_case )
# If char not in current trie node
if not char_node:
return False
# Flag to check if node can be deleted
SCREAMING_SNAKE_CASE =_delete(snake_case ,snake_case ,index + 1 )
if delete_curr:
del curr.nodes[char]
return len(curr.nodes ) == 0
return delete_curr
_delete(self ,snake_case ,0 )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
if node.is_leaf:
print(lowerCAmelCase_, end=' ' )
for key, value in node.nodes.items():
print_words(lowerCAmelCase_, word + key )
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ='banana bananas bandana band apple all beast'.split()
SCREAMING_SNAKE_CASE =TrieNode()
root.insert_many(lowerCAmelCase_ )
# print_words(root, "")
assert all(root.find(lowerCAmelCase_ ) for word in words )
assert root.find('banana' )
assert not root.find('bandanas' )
assert not root.find('apps' )
assert root.find('apple' )
assert root.find('all' )
root.delete('all' )
assert not root.find('all' )
root.delete('banana' )
assert not root.find('banana' )
assert root.find('bananas' )
return True
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
print(str(lowerCAmelCase_ ), 'works!' if passes else 'doesn\'t work :(' )
def snake_case__ ( ):
"""simple docstring"""
assert test_trie()
def snake_case__ ( ):
"""simple docstring"""
print_results('Testing trie functionality', test_trie() )
if __name__ == "__main__":
main()
| 334 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
# See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'vit_mae'
def __init__( self : Union[str, Any] ,snake_case : Any=768 ,snake_case : List[str]=12 ,snake_case : Optional[int]=12 ,snake_case : int=3072 ,snake_case : List[Any]="gelu" ,snake_case : str=0.0 ,snake_case : str=0.0 ,snake_case : Optional[Any]=0.02 ,snake_case : Dict=1e-12 ,snake_case : List[str]=224 ,snake_case : Any=16 ,snake_case : Any=3 ,snake_case : Tuple=True ,snake_case : List[Any]=16 ,snake_case : List[str]=512 ,snake_case : List[Any]=8 ,snake_case : Dict=2048 ,snake_case : Union[str, Any]=0.75 ,snake_case : Union[str, Any]=False ,**snake_case : Optional[int] ,):
super().__init__(**snake_case )
SCREAMING_SNAKE_CASE =hidden_size
SCREAMING_SNAKE_CASE =num_hidden_layers
SCREAMING_SNAKE_CASE =num_attention_heads
SCREAMING_SNAKE_CASE =intermediate_size
SCREAMING_SNAKE_CASE =hidden_act
SCREAMING_SNAKE_CASE =hidden_dropout_prob
SCREAMING_SNAKE_CASE =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE =initializer_range
SCREAMING_SNAKE_CASE =layer_norm_eps
SCREAMING_SNAKE_CASE =image_size
SCREAMING_SNAKE_CASE =patch_size
SCREAMING_SNAKE_CASE =num_channels
SCREAMING_SNAKE_CASE =qkv_bias
SCREAMING_SNAKE_CASE =decoder_num_attention_heads
SCREAMING_SNAKE_CASE =decoder_hidden_size
SCREAMING_SNAKE_CASE =decoder_num_hidden_layers
SCREAMING_SNAKE_CASE =decoder_intermediate_size
SCREAMING_SNAKE_CASE =mask_ratio
SCREAMING_SNAKE_CASE =norm_pix_loss
| 334 | 1 |
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerModel,
)
from transformers.models.roformer.modeling_tf_roformer import (
TFRoFormerSelfAttention,
TFRoFormerSinusoidalPositionalEmbedding,
)
class a_ :
"""simple docstring"""
def __init__( self : Optional[int] ,snake_case : Optional[int] ,snake_case : List[str]=13 ,snake_case : Any=7 ,snake_case : Any=True ,snake_case : Any=True ,snake_case : Union[str, Any]=True ,snake_case : List[str]=True ,snake_case : Union[str, Any]=99 ,snake_case : List[str]=32 ,snake_case : Union[str, Any]=2 ,snake_case : Tuple=4 ,snake_case : List[Any]=37 ,snake_case : Any="gelu" ,snake_case : Tuple=0.1 ,snake_case : List[str]=0.1 ,snake_case : Dict=512 ,snake_case : Optional[int]=16 ,snake_case : List[Any]=2 ,snake_case : Optional[int]=0.02 ,snake_case : Dict=3 ,snake_case : Optional[int]=4 ,snake_case : Optional[int]=None ,):
SCREAMING_SNAKE_CASE =parent
SCREAMING_SNAKE_CASE =13
SCREAMING_SNAKE_CASE =7
SCREAMING_SNAKE_CASE =True
SCREAMING_SNAKE_CASE =True
SCREAMING_SNAKE_CASE =True
SCREAMING_SNAKE_CASE =True
SCREAMING_SNAKE_CASE =99
SCREAMING_SNAKE_CASE =32
SCREAMING_SNAKE_CASE =2
SCREAMING_SNAKE_CASE =4
SCREAMING_SNAKE_CASE =37
SCREAMING_SNAKE_CASE ='gelu'
SCREAMING_SNAKE_CASE =0.1
SCREAMING_SNAKE_CASE =0.1
SCREAMING_SNAKE_CASE =512
SCREAMING_SNAKE_CASE =16
SCREAMING_SNAKE_CASE =2
SCREAMING_SNAKE_CASE =0.02
SCREAMING_SNAKE_CASE =3
SCREAMING_SNAKE_CASE =4
SCREAMING_SNAKE_CASE =None
def _lowerCAmelCase ( self : Dict ):
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
SCREAMING_SNAKE_CASE =None
if self.use_input_mask:
SCREAMING_SNAKE_CASE =random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE =None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
SCREAMING_SNAKE_CASE =None
SCREAMING_SNAKE_CASE =None
SCREAMING_SNAKE_CASE =None
if self.use_labels:
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size] ,self.type_sequence_label_size )
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size] ,self.num_choices )
SCREAMING_SNAKE_CASE =RoFormerConfig(
vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,type_vocab_size=self.type_vocab_size ,initializer_range=self.initializer_range ,return_dict=snake_case ,)
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowerCAmelCase ( self : Dict ,snake_case : Any ,snake_case : Dict ,snake_case : Optional[Any] ,snake_case : Optional[int] ,snake_case : Optional[Any] ,snake_case : Optional[Any] ,snake_case : int ):
SCREAMING_SNAKE_CASE =TFRoFormerModel(config=snake_case )
SCREAMING_SNAKE_CASE ={'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
SCREAMING_SNAKE_CASE =[input_ids, input_mask]
SCREAMING_SNAKE_CASE =model(snake_case )
SCREAMING_SNAKE_CASE =model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _lowerCAmelCase ( self : Tuple ,snake_case : Union[str, Any] ,snake_case : Tuple ,snake_case : Tuple ,snake_case : Union[str, Any] ,snake_case : List[str] ,snake_case : Optional[Any] ,snake_case : Any ):
SCREAMING_SNAKE_CASE =True
SCREAMING_SNAKE_CASE =TFRoFormerForCausalLM(config=snake_case )
SCREAMING_SNAKE_CASE ={
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
SCREAMING_SNAKE_CASE =model(snake_case )['logits']
self.parent.assertListEqual(
list(prediction_scores.numpy().shape ) ,[self.batch_size, self.seq_length, self.vocab_size] )
def _lowerCAmelCase ( self : Any ,snake_case : Optional[int] ,snake_case : int ,snake_case : str ,snake_case : Any ,snake_case : Any ,snake_case : Optional[int] ,snake_case : Optional[Any] ):
SCREAMING_SNAKE_CASE =TFRoFormerForMaskedLM(config=snake_case )
SCREAMING_SNAKE_CASE ={
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
SCREAMING_SNAKE_CASE =model(snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def _lowerCAmelCase ( self : Tuple ,snake_case : Tuple ,snake_case : Any ,snake_case : str ,snake_case : int ,snake_case : Tuple ,snake_case : Union[str, Any] ,snake_case : str ):
SCREAMING_SNAKE_CASE =self.num_labels
SCREAMING_SNAKE_CASE =TFRoFormerForSequenceClassification(config=snake_case )
SCREAMING_SNAKE_CASE ={
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
SCREAMING_SNAKE_CASE =model(snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def _lowerCAmelCase ( self : List[str] ,snake_case : List[str] ,snake_case : Optional[int] ,snake_case : str ,snake_case : int ,snake_case : Tuple ,snake_case : Optional[int] ,snake_case : str ):
SCREAMING_SNAKE_CASE =self.num_choices
SCREAMING_SNAKE_CASE =TFRoFormerForMultipleChoice(config=snake_case )
SCREAMING_SNAKE_CASE =tf.tile(tf.expand_dims(snake_case ,1 ) ,(1, self.num_choices, 1) )
SCREAMING_SNAKE_CASE =tf.tile(tf.expand_dims(snake_case ,1 ) ,(1, self.num_choices, 1) )
SCREAMING_SNAKE_CASE =tf.tile(tf.expand_dims(snake_case ,1 ) ,(1, self.num_choices, 1) )
SCREAMING_SNAKE_CASE ={
'input_ids': multiple_choice_inputs_ids,
'attention_mask': multiple_choice_input_mask,
'token_type_ids': multiple_choice_token_type_ids,
}
SCREAMING_SNAKE_CASE =model(snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_choices) )
def _lowerCAmelCase ( self : Dict ,snake_case : Optional[Any] ,snake_case : Optional[int] ,snake_case : Optional[Any] ,snake_case : Optional[int] ,snake_case : int ,snake_case : List[Any] ,snake_case : Dict ):
SCREAMING_SNAKE_CASE =self.num_labels
SCREAMING_SNAKE_CASE =TFRoFormerForTokenClassification(config=snake_case )
SCREAMING_SNAKE_CASE ={
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
SCREAMING_SNAKE_CASE =model(snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def _lowerCAmelCase ( self : List[Any] ,snake_case : Optional[int] ,snake_case : Optional[Any] ,snake_case : Optional[int] ,snake_case : Tuple ,snake_case : Dict ,snake_case : Dict ,snake_case : Any ):
SCREAMING_SNAKE_CASE =TFRoFormerForQuestionAnswering(config=snake_case )
SCREAMING_SNAKE_CASE ={
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
SCREAMING_SNAKE_CASE =model(snake_case )
self.parent.assertEqual(result.start_logits.shape ,(self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape ,(self.batch_size, self.seq_length) )
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) ,
) =config_and_inputs
SCREAMING_SNAKE_CASE ={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_tf
class a_ ( lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase = (
(
TFRoFormerModel,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerForMultipleChoice,
)
if is_tf_available()
else ()
)
__UpperCAmelCase = (
{
'feature-extraction': TFRoFormerModel,
'fill-mask': TFRoFormerForMaskedLM,
'question-answering': TFRoFormerForQuestionAnswering,
'text-classification': TFRoFormerForSequenceClassification,
'text-generation': TFRoFormerForCausalLM,
'token-classification': TFRoFormerForTokenClassification,
'zero-shot': TFRoFormerForSequenceClassification,
}
if is_tf_available()
else {}
)
__UpperCAmelCase = False
__UpperCAmelCase = False
def _lowerCAmelCase ( self : List[str] ,snake_case : Tuple ,snake_case : Tuple ,snake_case : Tuple ,snake_case : List[Any] ,snake_case : Optional[Any] ):
if pipeline_test_casse_name == "TextGenerationPipelineTests":
return True
return False
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =TFRoFormerModelTester(self )
SCREAMING_SNAKE_CASE =ConfigTester(self ,config_class=snake_case ,hidden_size=37 )
def _lowerCAmelCase ( self : Optional[int] ):
self.config_tester.run_common_tests()
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def _lowerCAmelCase ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*snake_case )
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head(*snake_case )
def _lowerCAmelCase ( self : Dict ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*snake_case )
def _lowerCAmelCase ( self : int ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*snake_case )
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*snake_case )
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*snake_case )
@slow
def _lowerCAmelCase ( self : Dict ):
SCREAMING_SNAKE_CASE =TFRoFormerModel.from_pretrained('junnyu/roformer_chinese_base' )
self.assertIsNotNone(snake_case )
@require_tf
class a_ ( unittest.TestCase ):
"""simple docstring"""
@slow
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =TFRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base' )
SCREAMING_SNAKE_CASE =tf.constant([[0, 1, 2, 3, 4, 5]] )
SCREAMING_SNAKE_CASE =model(snake_case )[0]
# TODO Replace vocab size
SCREAMING_SNAKE_CASE =50000
SCREAMING_SNAKE_CASE =[1, 6, vocab_size]
self.assertEqual(output.shape ,snake_case )
print(output[:, :3, :3] )
# TODO Replace values below with what was printed above.
SCREAMING_SNAKE_CASE =tf.constant(
[
[
[-0.12_053_341, -1.0_264_901, 0.29_221_946],
[-1.5_133_783, 0.197_433, 0.15_190_607],
[-5.0_135_403, -3.900_256, -0.84_038_764],
]
] )
tf.debugging.assert_near(output[:, :3, :3] ,snake_case ,atol=1e-4 )
@require_tf
class a_ ( unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase = 1e-4
def _lowerCAmelCase ( self : int ):
SCREAMING_SNAKE_CASE =tf.constant([[4, 10]] )
SCREAMING_SNAKE_CASE =TFRoFormerSinusoidalPositionalEmbedding(num_positions=6 ,embedding_dim=6 )
SCREAMING_SNAKE_CASE =emba(input_ids.shape )
SCREAMING_SNAKE_CASE =tf.constant(
[[0.0_000, 0.0_000, 0.0_000, 1.0_000, 1.0_000, 1.0_000], [0.8_415, 0.0_464, 0.0_022, 0.5_403, 0.9_989, 1.0_000]] )
tf.debugging.assert_near(snake_case ,snake_case ,atol=self.tolerance )
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =tf.constant(
[
[0.0_000, 0.0_000, 0.0_000, 0.0_000, 0.0_000],
[0.8_415, 0.8_219, 0.8_020, 0.7_819, 0.7_617],
[0.9_093, 0.9_364, 0.9_581, 0.9_749, 0.9_870],
] )
SCREAMING_SNAKE_CASE =TFRoFormerSinusoidalPositionalEmbedding(num_positions=512 ,embedding_dim=512 )
emba([2, 16, 512] )
SCREAMING_SNAKE_CASE =emba.weight[:3, :5]
tf.debugging.assert_near(snake_case ,snake_case ,atol=self.tolerance )
@require_tf
class a_ ( unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase = 1e-4
def _lowerCAmelCase ( self : str ):
# 2,12,16,64
SCREAMING_SNAKE_CASE =tf.reshape(tf.range(2 * 12 * 16 * 64 ,dtype=tf.floataa ) ,shape=(2, 12, 16, 64) ) / 100
SCREAMING_SNAKE_CASE =-tf.reshape(tf.range(2 * 12 * 16 * 64 ,dtype=tf.floataa ) ,shape=(2, 12, 16, 64) ) / 100
SCREAMING_SNAKE_CASE =TFRoFormerSinusoidalPositionalEmbedding(num_positions=32 ,embedding_dim=64 )
SCREAMING_SNAKE_CASE =embed_positions([2, 16, 768] )[None, None, :, :]
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =TFRoFormerSelfAttention.apply_rotary_position_embeddings(
snake_case ,snake_case ,snake_case )
SCREAMING_SNAKE_CASE =tf.constant(
[
[0.0_000, 0.0_100, 0.0_200, 0.0_300, 0.0_400, 0.0_500, 0.0_600, 0.0_700],
[-0.2_012, 0.8_897, 0.0_263, 0.9_401, 0.2_074, 0.9_463, 0.3_481, 0.9_343],
[-1.7_057, 0.6_271, -1.2_145, 1.3_897, -0.6_303, 1.7_647, -0.1_173, 1.8_985],
[-2.1_731, -1.6_397, -2.7_358, 0.2_854, -2.1_840, 1.7_183, -1.3_018, 2.4_871],
[0.2_717, -3.6_173, -2.9_206, -2.1_988, -3.6_638, 0.3_858, -2.9_155, 2.2_980],
[3.9_859, -2.1_580, -0.7_984, -4.4_904, -4.1_181, -2.0_252, -4.4_782, 1.1_253],
] )
SCREAMING_SNAKE_CASE =tf.constant(
[
[0.0_000, -0.0_100, -0.0_200, -0.0_300, -0.0_400, -0.0_500, -0.0_600, -0.0_700],
[0.2_012, -0.8_897, -0.0_263, -0.9_401, -0.2_074, -0.9_463, -0.3_481, -0.9_343],
[1.7_057, -0.6_271, 1.2_145, -1.3_897, 0.6_303, -1.7_647, 0.1_173, -1.8_985],
[2.1_731, 1.6_397, 2.7_358, -0.2_854, 2.1_840, -1.7_183, 1.3_018, -2.4_871],
[-0.2_717, 3.6_173, 2.9_206, 2.1_988, 3.6_638, -0.3_858, 2.9_155, -2.2_980],
[-3.9_859, 2.1_580, 0.7_984, 4.4_904, 4.1_181, 2.0_252, 4.4_782, -1.1_253],
] )
tf.debugging.assert_near(query_layer[0, 0, :6, :8] ,snake_case ,atol=self.tolerance )
tf.debugging.assert_near(key_layer[0, 0, :6, :8] ,snake_case ,atol=self.tolerance )
| 334 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
_lowerCamelCase ={
"configuration_ernie": ["ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", "ErnieOnnxConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase =[
"ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST",
"ErnieForCausalLM",
"ErnieForMaskedLM",
"ErnieForMultipleChoice",
"ErnieForNextSentencePrediction",
"ErnieForPreTraining",
"ErnieForQuestionAnswering",
"ErnieForSequenceClassification",
"ErnieForTokenClassification",
"ErnieModel",
"ErniePreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ernie import (
ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
ErnieForCausalLM,
ErnieForMaskedLM,
ErnieForMultipleChoice,
ErnieForNextSentencePrediction,
ErnieForPreTraining,
ErnieForQuestionAnswering,
ErnieForSequenceClassification,
ErnieForTokenClassification,
ErnieModel,
ErniePreTrainedModel,
)
else:
import sys
_lowerCamelCase =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 334 | 1 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase =logging.get_logger(__name__)
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_=False ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =[]
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'blocks.{i}.norm1.weight', F'vit.encoder.layer.{i}.layernorm_before.weight') )
rename_keys.append((F'blocks.{i}.norm1.bias', F'vit.encoder.layer.{i}.layernorm_before.bias') )
rename_keys.append((F'blocks.{i}.attn.proj.weight', F'vit.encoder.layer.{i}.attention.output.dense.weight') )
rename_keys.append((F'blocks.{i}.attn.proj.bias', F'vit.encoder.layer.{i}.attention.output.dense.bias') )
rename_keys.append((F'blocks.{i}.norm2.weight', F'vit.encoder.layer.{i}.layernorm_after.weight') )
rename_keys.append((F'blocks.{i}.norm2.bias', F'vit.encoder.layer.{i}.layernorm_after.bias') )
rename_keys.append((F'blocks.{i}.mlp.fc1.weight', F'vit.encoder.layer.{i}.intermediate.dense.weight') )
rename_keys.append((F'blocks.{i}.mlp.fc1.bias', F'vit.encoder.layer.{i}.intermediate.dense.bias') )
rename_keys.append((F'blocks.{i}.mlp.fc2.weight', F'vit.encoder.layer.{i}.output.dense.weight') )
rename_keys.append((F'blocks.{i}.mlp.fc2.bias', F'vit.encoder.layer.{i}.output.dense.bias') )
# projection layer + position embeddings
rename_keys.extend(
[
('cls_token', 'vit.embeddings.cls_token'),
('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
('pos_embed', 'vit.embeddings.position_embeddings'),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('norm.weight', 'layernorm.weight'),
('norm.bias', 'layernorm.bias'),
('pre_logits.fc.weight', 'pooler.dense.weight'),
('pre_logits.fc.bias', 'pooler.dense.bias'),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
SCREAMING_SNAKE_CASE =[(pair[0], pair[1][4:]) if pair[1].startswith('vit' ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
('norm.weight', 'vit.layernorm.weight'),
('norm.bias', 'vit.layernorm.bias'),
('head.weight', 'classifier.weight'),
('head.bias', 'classifier.bias'),
] )
return rename_keys
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_=False ):
"""simple docstring"""
for i in range(config.num_hidden_layers ):
if base_model:
SCREAMING_SNAKE_CASE =''
else:
SCREAMING_SNAKE_CASE ='vit.'
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
SCREAMING_SNAKE_CASE =state_dict.pop(F'blocks.{i}.attn.qkv.weight' )
SCREAMING_SNAKE_CASE =state_dict.pop(F'blocks.{i}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
SCREAMING_SNAKE_CASE =in_proj_weight[
: config.hidden_size, :
]
SCREAMING_SNAKE_CASE =in_proj_bias[: config.hidden_size]
SCREAMING_SNAKE_CASE =in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
SCREAMING_SNAKE_CASE =in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
SCREAMING_SNAKE_CASE =in_proj_weight[
-config.hidden_size :, :
]
SCREAMING_SNAKE_CASE =in_proj_bias[-config.hidden_size :]
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =['head.weight', 'head.bias']
for k in ignore_keys:
state_dict.pop(lowerCAmelCase_, lowerCAmelCase_ )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =dct.pop(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =val
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ='http://images.cocodataset.org/val2017/000000039769.jpg'
SCREAMING_SNAKE_CASE =Image.open(requests.get(lowerCAmelCase_, stream=lowerCAmelCase_ ).raw )
return im
@torch.no_grad()
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =ViTConfig()
SCREAMING_SNAKE_CASE =False
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
if vit_name[-5:] == "in21k":
SCREAMING_SNAKE_CASE =True
SCREAMING_SNAKE_CASE =int(vit_name[-12:-10] )
SCREAMING_SNAKE_CASE =int(vit_name[-9:-6] )
else:
SCREAMING_SNAKE_CASE =1000
SCREAMING_SNAKE_CASE ='huggingface/label-files'
SCREAMING_SNAKE_CASE ='imagenet-1k-id2label.json'
SCREAMING_SNAKE_CASE =json.load(open(hf_hub_download(lowerCAmelCase_, lowerCAmelCase_, repo_type='dataset' ), 'r' ) )
SCREAMING_SNAKE_CASE ={int(lowerCAmelCase_ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE =idalabel
SCREAMING_SNAKE_CASE ={v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE =int(vit_name[-6:-4] )
SCREAMING_SNAKE_CASE =int(vit_name[-3:] )
# size of the architecture
if "deit" in vit_name:
if vit_name[9:].startswith('tiny' ):
SCREAMING_SNAKE_CASE =192
SCREAMING_SNAKE_CASE =768
SCREAMING_SNAKE_CASE =12
SCREAMING_SNAKE_CASE =3
elif vit_name[9:].startswith('small' ):
SCREAMING_SNAKE_CASE =384
SCREAMING_SNAKE_CASE =1536
SCREAMING_SNAKE_CASE =12
SCREAMING_SNAKE_CASE =6
else:
pass
else:
if vit_name[4:].startswith('small' ):
SCREAMING_SNAKE_CASE =768
SCREAMING_SNAKE_CASE =2304
SCREAMING_SNAKE_CASE =8
SCREAMING_SNAKE_CASE =8
elif vit_name[4:].startswith('base' ):
pass
elif vit_name[4:].startswith('large' ):
SCREAMING_SNAKE_CASE =1024
SCREAMING_SNAKE_CASE =4096
SCREAMING_SNAKE_CASE =24
SCREAMING_SNAKE_CASE =16
elif vit_name[4:].startswith('huge' ):
SCREAMING_SNAKE_CASE =1280
SCREAMING_SNAKE_CASE =5120
SCREAMING_SNAKE_CASE =32
SCREAMING_SNAKE_CASE =16
# load original model from timm
SCREAMING_SNAKE_CASE =timm.create_model(lowerCAmelCase_, pretrained=lowerCAmelCase_ )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
SCREAMING_SNAKE_CASE =timm_model.state_dict()
if base_model:
remove_classification_head_(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =create_rename_keys(lowerCAmelCase_, lowerCAmelCase_ )
for src, dest in rename_keys:
rename_key(lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ )
read_in_q_k_v(lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ )
# load HuggingFace model
if vit_name[-5:] == "in21k":
SCREAMING_SNAKE_CASE =ViTModel(lowerCAmelCase_ ).eval()
else:
SCREAMING_SNAKE_CASE =ViTForImageClassification(lowerCAmelCase_ ).eval()
model.load_state_dict(lowerCAmelCase_ )
# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
if "deit" in vit_name:
SCREAMING_SNAKE_CASE =DeiTImageProcessor(size=config.image_size )
else:
SCREAMING_SNAKE_CASE =ViTImageProcessor(size=config.image_size )
SCREAMING_SNAKE_CASE =image_processor(images=prepare_img(), return_tensors='pt' )
SCREAMING_SNAKE_CASE =encoding['pixel_values']
SCREAMING_SNAKE_CASE =model(lowerCAmelCase_ )
if base_model:
SCREAMING_SNAKE_CASE =timm_model.forward_features(lowerCAmelCase_ )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(lowerCAmelCase_, outputs.pooler_output, atol=1e-3 )
else:
SCREAMING_SNAKE_CASE =timm_model(lowerCAmelCase_ )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(lowerCAmelCase_, outputs.logits, atol=1e-3 )
Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ )
print(F'Saving model {vit_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(lowerCAmelCase_ )
print(F'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(lowerCAmelCase_ )
if __name__ == "__main__":
_lowerCamelCase =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--vit_name",
default="vit_base_patch16_224",
type=str,
help="Name of the ViT timm model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
_lowerCamelCase =parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)
| 334 |
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError
from .utils import require_pil
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([1, 2, 3] ) )
self.assertEqual(arr.type ,pa.intaa() )
def _lowerCAmelCase ( self : Any ):
with self.assertRaises(snake_case ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([1, 2, 3] ) ,type=pa.intaa() )
def _lowerCAmelCase ( self : Union[str, Any] ):
with self.assertRaises(snake_case ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([1, 2, 3] ,try_type=Value('bool' ) ,type=Value('int64' ) ) )
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([1, 2, 3] ,type=Value('int32' ) ) )
self.assertEqual(arr.type ,pa.intaa() )
def _lowerCAmelCase ( self : int ):
with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence(['foo', 'bar'] ,type=Value('int64' ) ) )
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([1, 2, 3] ,try_type=Value('int32' ) ) )
self.assertEqual(arr.type ,pa.intaa() )
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence(['foo', 'bar'] ,try_type=Value('int64' ) ) )
self.assertEqual(arr.type ,pa.string() )
def _lowerCAmelCase ( self : Dict ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([[[1, 2, 3]]] ,type=ArrayaD((1, 3) ,'int64' ) ) )
self.assertEqual(arr.type ,ArrayaDExtensionType((1, 3) ,'int64' ) )
def _lowerCAmelCase ( self : Dict ):
with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence(['foo', 'bar'] ,type=ArrayaD((1, 3) ,'int64' ) ) )
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([[[1, 2, 3]]] ,try_type=ArrayaD((1, 3) ,'int64' ) ) )
self.assertEqual(arr.type ,ArrayaDExtensionType((1, 3) ,'int64' ) )
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence(['foo', 'bar'] ,try_type=ArrayaD((1, 3) ,'int64' ) ) )
self.assertEqual(arr.type ,pa.string() )
@require_pil
def _lowerCAmelCase ( self : int ):
import PIL.Image
SCREAMING_SNAKE_CASE =PIL.Image.fromarray(np.arange(10 ,dtype=np.uinta ).reshape(2 ,5 ) )
with patch(
'datasets.arrow_writer.cast_to_python_objects' ,side_effect=snake_case ) as mock_cast_to_python_objects:
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([{'path': None, 'bytes': B'image_bytes'}, pil_image] ,type=Image() ) )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =mock_cast_to_python_objects.call_args_list[-1]
self.assertIn('optimize_list_casting' ,snake_case )
self.assertFalse(kwargs['optimize_list_casting'] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferReader(lowerCAmelCase_ ) if isinstance(lowerCAmelCase_, pa.Buffer ) else pa.memory_map(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =pa.ipc.open_stream(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =f.read_all()
assert len(pa_table.to_batches() ) == expected_num_chunks
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
del pa_table
@pytest.mark.parametrize('writer_batch_size', [None, 1, 10] )
@pytest.mark.parametrize(
'fields', [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
SCREAMING_SNAKE_CASE =pa.schema(lowerCAmelCase_ ) if fields else None
with ArrowWriter(stream=lowerCAmelCase_, schema=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_ ) as writer:
writer.write({'col_1': 'foo', 'col_2': 1} )
writer.write({'col_1': 'bar', 'col_2': 2} )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
SCREAMING_SNAKE_CASE ={'col_1': pa.string(), 'col_2': pa.intaa()}
assert writer._schema == pa.schema(lowerCAmelCase_, metadata=writer._schema.metadata )
_check_output(output.getvalue(), expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
SCREAMING_SNAKE_CASE =Features({'labels': ClassLabel(names=['neg', 'pos'] )} )
with ArrowWriter(stream=lowerCAmelCase_, features=lowerCAmelCase_ ) as writer:
writer.write({'labels': 0} )
writer.write({'labels': 1} )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert writer._schema == features.arrow_schema
assert writer._schema.metadata == features.arrow_schema.metadata
SCREAMING_SNAKE_CASE =pa.BufferReader(output.getvalue() )
SCREAMING_SNAKE_CASE =pa.ipc.open_stream(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =f.read_all()
SCREAMING_SNAKE_CASE =pa_table.schema
assert pa_table.num_rows == 2
assert schema == features.arrow_schema
assert schema.metadata == features.arrow_schema.metadata
assert features == Features.from_arrow_schema(lowerCAmelCase_ )
@pytest.mark.parametrize('writer_batch_size', [None, 1, 10] )
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
with ArrowWriter(
stream=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_, hash_salt='split_name', check_duplicates=lowerCAmelCase_, ) as writer:
with pytest.raises(lowerCAmelCase_ ):
writer.write({'col_1': 'foo', 'col_2': 1}, key=[1, 2] )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
@pytest.mark.parametrize('writer_batch_size', [None, 2, 10] )
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
with ArrowWriter(
stream=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_, hash_salt='split_name', check_duplicates=lowerCAmelCase_, ) as writer:
with pytest.raises(lowerCAmelCase_ ):
writer.write({'col_1': 'foo', 'col_2': 1}, key=10 )
writer.write({'col_1': 'bar', 'col_2': 2}, key=10 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
@pytest.mark.parametrize('writer_batch_size', [None, 2, 10] )
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
with ArrowWriter(
stream=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_, hash_salt='split_name', check_duplicates=lowerCAmelCase_, ) as writer:
writer.write({'col_1': 'foo', 'col_2': 1}, key=1 )
writer.write({'col_1': 'bar', 'col_2': 2}, key=2 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
_check_output(output.getvalue(), expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize('writer_batch_size', [None, 1, 10] )
@pytest.mark.parametrize(
'fields', [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
SCREAMING_SNAKE_CASE =pa.schema(lowerCAmelCase_ ) if fields else None
with ArrowWriter(stream=lowerCAmelCase_, schema=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_ ) as writer:
writer.write_batch({'col_1': ['foo', 'bar'], 'col_2': [1, 2]} )
writer.write_batch({'col_1': [], 'col_2': []} )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
SCREAMING_SNAKE_CASE ={'col_1': pa.string(), 'col_2': pa.intaa()}
assert writer._schema == pa.schema(lowerCAmelCase_, metadata=writer._schema.metadata )
_check_output(output.getvalue(), expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize('writer_batch_size', [None, 1, 10] )
@pytest.mark.parametrize(
'fields', [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
SCREAMING_SNAKE_CASE =pa.schema(lowerCAmelCase_ ) if fields else None
with ArrowWriter(stream=lowerCAmelCase_, schema=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_ ) as writer:
writer.write_table(pa.Table.from_pydict({'col_1': ['foo', 'bar'], 'col_2': [1, 2]} ) )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
SCREAMING_SNAKE_CASE ={'col_1': pa.string(), 'col_2': pa.intaa()}
assert writer._schema == pa.schema(lowerCAmelCase_, metadata=writer._schema.metadata )
_check_output(output.getvalue(), expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize('writer_batch_size', [None, 1, 10] )
@pytest.mark.parametrize(
'fields', [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
SCREAMING_SNAKE_CASE =pa.schema(lowerCAmelCase_ ) if fields else None
with ArrowWriter(stream=lowerCAmelCase_, schema=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_ ) as writer:
writer.write_row(pa.Table.from_pydict({'col_1': ['foo'], 'col_2': [1]} ) )
writer.write_row(pa.Table.from_pydict({'col_1': ['bar'], 'col_2': [2]} ) )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
SCREAMING_SNAKE_CASE ={'col_1': pa.string(), 'col_2': pa.intaa()}
assert writer._schema == pa.schema(lowerCAmelCase_, metadata=writer._schema.metadata )
_check_output(output.getvalue(), expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
def snake_case__ ( ):
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE ={'col_1': pa.string(), 'col_2': pa.intaa()}
SCREAMING_SNAKE_CASE =os.path.join(lowerCAmelCase_, 'test.arrow' )
with ArrowWriter(path=lowerCAmelCase_, schema=pa.schema(lowerCAmelCase_ ) ) as writer:
writer.write_batch({'col_1': ['foo', 'bar'], 'col_2': [1, 2]} )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert writer._schema == pa.schema(lowerCAmelCase_, metadata=writer._schema.metadata )
_check_output(lowerCAmelCase_, 1 )
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
if pa.types.is_list(lowerCAmelCase_ ):
return get_base_dtype(arr_type.value_type )
else:
return arr_type
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
if isinstance(lst[0], lowerCAmelCase_ ):
change_first_primitive_element_in_list(lst[0], lowerCAmelCase_ )
else:
SCREAMING_SNAKE_CASE =value
@pytest.mark.parametrize('optimized_int_type, expected_dtype', [(None, pa.intaa()), (Value('int32' ), pa.intaa())] )
@pytest.mark.parametrize('sequence', [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.array(TypedSequence(lowerCAmelCase_, optimized_int_type=lowerCAmelCase_ ) )
assert get_base_dtype(arr.type ) == expected_dtype
@pytest.mark.parametrize(
'col, expected_dtype', [
('attention_mask', pa.inta()),
('special_tokens_mask', pa.inta()),
('token_type_ids', pa.inta()),
('input_ids', pa.intaa()),
('other', pa.intaa()),
], )
@pytest.mark.parametrize('sequence', [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.array(OptimizedTypedSequence(lowerCAmelCase_, col=lowerCAmelCase_ ) )
assert get_base_dtype(arr.type ) == expected_dtype
# not in range
if col != "other":
# avoids errors due to in-place modifications
SCREAMING_SNAKE_CASE =copy.deepcopy(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =np.iinfo(expected_dtype.to_pandas_dtype() ).max + 1
change_first_primitive_element_in_list(lowerCAmelCase_, lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =pa.array(OptimizedTypedSequence(lowerCAmelCase_, col=lowerCAmelCase_ ) )
assert get_base_dtype(arr.type ) == pa.intaa()
@pytest.mark.parametrize('raise_exception', [False, True] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =str(tmp_path / 'dataset-train.arrow' )
try:
with ArrowWriter(path=lowerCAmelCase_ ) as writer:
if raise_exception:
raise pa.lib.ArrowInvalid()
else:
writer.stream.close()
except pa.lib.ArrowInvalid:
pass
finally:
assert writer.stream.closed
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ='mock://dataset-train.arrow'
with ArrowWriter(path=lowerCAmelCase_, storage_options=mockfs.storage_options ) as writer:
assert isinstance(writer._fs, type(lowerCAmelCase_ ) )
assert writer._fs.storage_options == mockfs.storage_options
writer.write({'col_1': 'foo', 'col_2': 1} )
writer.write({'col_1': 'bar', 'col_2': 2} )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert mockfs.exists(lowerCAmelCase_ )
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
with ParquetWriter(stream=lowerCAmelCase_ ) as writer:
writer.write({'col_1': 'foo', 'col_2': 1} )
writer.write({'col_1': 'bar', 'col_2': 2} )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
SCREAMING_SNAKE_CASE =pa.BufferReader(output.getvalue() )
SCREAMING_SNAKE_CASE =pq.read_table(lowerCAmelCase_ )
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
@require_pil
@pytest.mark.parametrize('embed_local_files', [False, True] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
import PIL.Image
SCREAMING_SNAKE_CASE =str(tmp_path / 'test_image_rgb.jpg' )
PIL.Image.fromarray(np.zeros((5, 5), dtype=np.uinta ) ).save(lowerCAmelCase_, format='png' )
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
with ParquetWriter(
stream=lowerCAmelCase_, features=Features({'image': Image()} ), embed_local_files=lowerCAmelCase_ ) as writer:
writer.write({'image': image_path} )
writer.finalize()
SCREAMING_SNAKE_CASE =pa.BufferReader(output.getvalue() )
SCREAMING_SNAKE_CASE =pq.read_table(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =pa_table.to_pydict()
if embed_local_files:
assert isinstance(out['image'][0]['path'], lowerCAmelCase_ )
with open(lowerCAmelCase_, 'rb' ) as f:
assert out["image"][0]["bytes"] == f.read()
else:
assert out["image"][0]["path"] == image_path
assert out["image"][0]["bytes"] is None
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.schema([pa.field('col_1', pa.string(), nullable=lowerCAmelCase_ )] )
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
with ArrowWriter(stream=lowerCAmelCase_ ) as writer:
writer._build_writer(inferred_schema=lowerCAmelCase_ )
assert writer._schema == pa.schema([pa.field('col_1', pa.string() )] )
| 334 | 1 |
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
def __init__( self : Any ,snake_case : UNetaDModel ,snake_case : UNetaDModel ,snake_case : DDPMScheduler ,snake_case : str ,):
super().__init__()
SCREAMING_SNAKE_CASE =value_function
SCREAMING_SNAKE_CASE =unet
SCREAMING_SNAKE_CASE =scheduler
SCREAMING_SNAKE_CASE =env
SCREAMING_SNAKE_CASE =env.get_dataset()
SCREAMING_SNAKE_CASE ={}
for key in self.data.keys():
try:
SCREAMING_SNAKE_CASE =self.data[key].mean()
except: # noqa: E722
pass
SCREAMING_SNAKE_CASE ={}
for key in self.data.keys():
try:
SCREAMING_SNAKE_CASE =self.data[key].std()
except: # noqa: E722
pass
SCREAMING_SNAKE_CASE =env.observation_space.shape[0]
SCREAMING_SNAKE_CASE =env.action_space.shape[0]
def _lowerCAmelCase ( self : List[str] ,snake_case : str ,snake_case : List[str] ):
return (x_in - self.means[key]) / self.stds[key]
def _lowerCAmelCase ( self : List[Any] ,snake_case : Optional[int] ,snake_case : Any ):
return x_in * self.stds[key] + self.means[key]
def _lowerCAmelCase ( self : str ,snake_case : Optional[Any] ):
if type(snake_case ) is dict:
return {k: self.to_torch(snake_case ) for k, v in x_in.items()}
elif torch.is_tensor(snake_case ):
return x_in.to(self.unet.device )
return torch.tensor(snake_case ,device=self.unet.device )
def _lowerCAmelCase ( self : Dict ,snake_case : List[Any] ,snake_case : Optional[int] ,snake_case : Optional[int] ):
for key, val in cond.items():
SCREAMING_SNAKE_CASE =val.clone()
return x_in
def _lowerCAmelCase ( self : str ,snake_case : str ,snake_case : str ,snake_case : Tuple ,snake_case : Tuple ):
SCREAMING_SNAKE_CASE =x.shape[0]
SCREAMING_SNAKE_CASE =None
for i in tqdm.tqdm(self.scheduler.timesteps ):
# create batch of timesteps to pass into model
SCREAMING_SNAKE_CASE =torch.full((batch_size,) ,snake_case ,device=self.unet.device ,dtype=torch.long )
for _ in range(snake_case ):
with torch.enable_grad():
x.requires_grad_()
# permute to match dimension for pre-trained models
SCREAMING_SNAKE_CASE =self.value_function(x.permute(0 ,2 ,1 ) ,snake_case ).sample
SCREAMING_SNAKE_CASE =torch.autograd.grad([y.sum()] ,[x] )[0]
SCREAMING_SNAKE_CASE =self.scheduler._get_variance(snake_case )
SCREAMING_SNAKE_CASE =torch.exp(0.5 * posterior_variance )
SCREAMING_SNAKE_CASE =model_std * grad
SCREAMING_SNAKE_CASE =0
SCREAMING_SNAKE_CASE =x.detach()
SCREAMING_SNAKE_CASE =x + scale * grad
SCREAMING_SNAKE_CASE =self.reset_xa(snake_case ,snake_case ,self.action_dim )
SCREAMING_SNAKE_CASE =self.unet(x.permute(0 ,2 ,1 ) ,snake_case ).sample.permute(0 ,2 ,1 )
# TODO: verify deprecation of this kwarg
SCREAMING_SNAKE_CASE =self.scheduler.step(snake_case ,snake_case ,snake_case ,predict_epsilon=snake_case )['prev_sample']
# apply conditions to the trajectory (set the initial state)
SCREAMING_SNAKE_CASE =self.reset_xa(snake_case ,snake_case ,self.action_dim )
SCREAMING_SNAKE_CASE =self.to_torch(snake_case )
return x, y
def __call__( self : Dict ,snake_case : Tuple ,snake_case : List[Any]=64 ,snake_case : Any=32 ,snake_case : List[str]=2 ,snake_case : Union[str, Any]=0.1 ):
# normalize the observations and create batch dimension
SCREAMING_SNAKE_CASE =self.normalize(snake_case ,'observations' )
SCREAMING_SNAKE_CASE =obs[None].repeat(snake_case ,axis=0 )
SCREAMING_SNAKE_CASE ={0: self.to_torch(snake_case )}
SCREAMING_SNAKE_CASE =(batch_size, planning_horizon, self.state_dim + self.action_dim)
# generate initial noise and apply our conditions (to make the trajectories start at current state)
SCREAMING_SNAKE_CASE =randn_tensor(snake_case ,device=self.unet.device )
SCREAMING_SNAKE_CASE =self.reset_xa(snake_case ,snake_case ,self.action_dim )
SCREAMING_SNAKE_CASE =self.to_torch(snake_case )
# run the diffusion process
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.run_diffusion(snake_case ,snake_case ,snake_case ,snake_case )
# sort output trajectories by value
SCREAMING_SNAKE_CASE =y.argsort(0 ,descending=snake_case ).squeeze()
SCREAMING_SNAKE_CASE =x[sorted_idx]
SCREAMING_SNAKE_CASE =sorted_values[:, :, : self.action_dim]
SCREAMING_SNAKE_CASE =actions.detach().cpu().numpy()
SCREAMING_SNAKE_CASE =self.de_normalize(snake_case ,key='actions' )
# select the action with the highest value
if y is not None:
SCREAMING_SNAKE_CASE =0
else:
# if we didn't run value guiding, select a random action
SCREAMING_SNAKE_CASE =np.random.randint(0 ,snake_case )
SCREAMING_SNAKE_CASE =denorm_actions[selected_index, 0]
return denorm_actions
| 334 |
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def snake_case__ ( ):
"""simple docstring"""
assert or_gate(0, 0 ) == 0
assert or_gate(0, 1 ) == 1
assert or_gate(1, 0 ) == 1
assert or_gate(1, 1 ) == 1
if __name__ == "__main__":
print(or_gate(0, 1))
print(or_gate(1, 0))
print(or_gate(0, 0))
print(or_gate(1, 1))
| 334 | 1 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
_lowerCamelCase =object()
# For specifying empty leaf dict `{}`
_lowerCamelCase =object()
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =tuple((re.compile(x + '$' ) for x in qs) )
for i in range(len(lowerCAmelCase_ ) - len(lowerCAmelCase_ ) + 1 ):
SCREAMING_SNAKE_CASE =[x.match(lowerCAmelCase_ ) for x, y in zip(lowerCAmelCase_, ks[i:] )]
if matches and all(lowerCAmelCase_ ):
return True
return False
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
def replace(lowerCAmelCase_, lowerCAmelCase_ ):
for rule, replacement in rules:
if _match(lowerCAmelCase_, lowerCAmelCase_ ):
return replacement
return val
return replace
def snake_case__ ( ):
"""simple docstring"""
return [
# embeddings
(("transformer", "wpe", "embedding"), P('mp', lowerCAmelCase_ )),
(("transformer", "wte", "embedding"), P('mp', lowerCAmelCase_ )),
# atention
(("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(lowerCAmelCase_, 'mp' )),
(("attention", "out_proj", "kernel"), P('mp', lowerCAmelCase_ )),
(("attention", "out_proj", "bias"), None),
# mlp
(("mlp", "c_fc", "kernel"), P(lowerCAmelCase_, 'mp' )),
(("mlp", "c_fc", "bias"), P('mp' )),
(("mlp", "c_proj", "kernel"), P('mp', lowerCAmelCase_ )),
(("mlp", "c_proj", "bias"), None),
# layer norms
((r"ln_\d+", "bias"), None),
((r"\d+", r"ln_\d+", "scale"), None),
(("ln_f", "bias"), None),
(("ln_f", "scale"), None),
]
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =_get_partition_rules()
SCREAMING_SNAKE_CASE =_replacement_rules(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE ={k: _unmatched for k in flatten_dict(lowerCAmelCase_ )}
SCREAMING_SNAKE_CASE ={k: replace(lowerCAmelCase_, lowerCAmelCase_ ) for k, v in initd.items()}
assert _unmatched not in result.values(), "Incomplete partition spec."
return freeze(unflatten_dict(lowerCAmelCase_ ) )
| 334 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={"vocab_file": "vocab.txt"}
_lowerCamelCase ={
"vocab_file": {
"facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt",
"facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt",
},
}
_lowerCamelCase ={
"facebook/esm2_t6_8M_UR50D": 10_24,
"facebook/esm2_t12_35M_UR50D": 10_24,
}
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
with open(lowerCAmelCase_, 'r' ) as f:
SCREAMING_SNAKE_CASE =f.read().splitlines()
return [l.strip() for l in lines]
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = VOCAB_FILES_NAMES
__UpperCAmelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase = ['input_ids', 'attention_mask']
def __init__( self : int ,snake_case : Dict ,snake_case : Dict="<unk>" ,snake_case : Optional[int]="<cls>" ,snake_case : Optional[int]="<pad>" ,snake_case : int="<mask>" ,snake_case : Optional[int]="<eos>" ,**snake_case : List[str] ,):
super().__init__(**snake_case )
SCREAMING_SNAKE_CASE =load_vocab_file(snake_case )
SCREAMING_SNAKE_CASE =dict(enumerate(self.all_tokens ) )
SCREAMING_SNAKE_CASE ={tok: ind for ind, tok in enumerate(self.all_tokens )}
SCREAMING_SNAKE_CASE =unk_token
SCREAMING_SNAKE_CASE =cls_token
SCREAMING_SNAKE_CASE =pad_token
SCREAMING_SNAKE_CASE =mask_token
SCREAMING_SNAKE_CASE =eos_token
SCREAMING_SNAKE_CASE =self.all_tokens
self._create_trie(self.unique_no_split_tokens )
def _lowerCAmelCase ( self : Optional[Any] ,snake_case : int ):
return self._id_to_token.get(snake_case ,self.unk_token )
def _lowerCAmelCase ( self : Dict ,snake_case : str ):
return self._token_to_id.get(snake_case ,self._token_to_id.get(self.unk_token ) )
def _lowerCAmelCase ( self : Tuple ,snake_case : List[str] ,**snake_case : Any ):
return text.split()
def _lowerCAmelCase ( self : Optional[int] ,snake_case : str=False ):
return len(self._id_to_token )
def _lowerCAmelCase ( self : List[str] ):
return {token: i for i, token in enumerate(self.all_tokens )}
def _lowerCAmelCase ( self : List[Any] ,snake_case : str ):
return self._token_to_id.get(snake_case ,self._token_to_id.get(self.unk_token ) )
def _lowerCAmelCase ( self : Any ,snake_case : int ):
return self._id_to_token.get(snake_case ,self.unk_token )
def _lowerCAmelCase ( self : List[str] ,snake_case : List[int] ,snake_case : Optional[List[int]] = None ):
SCREAMING_SNAKE_CASE =[self.cls_token_id]
SCREAMING_SNAKE_CASE =[self.eos_token_id] # No sep token in ESM vocabulary
if token_ids_a is None:
if self.eos_token_id is None:
return cls + token_ids_a
else:
return cls + token_ids_a + sep
elif self.eos_token_id is None:
raise ValueError('Cannot tokenize multiple sequences when EOS token is not set!' )
return cls + token_ids_a + sep + token_ids_a + sep # Multiple inputs always have an EOS token
def _lowerCAmelCase ( self : Optional[int] ,snake_case : List ,snake_case : Optional[List] = None ,snake_case : bool = False ):
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'You should not supply a second sequence if the provided sequence of '
'ids is already formatted with special tokens for the model.' )
return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
SCREAMING_SNAKE_CASE =[1] + ([0] * len(snake_case )) + [1]
if token_ids_a is not None:
mask += [0] * len(snake_case ) + [1]
return mask
def _lowerCAmelCase ( self : Optional[int] ,snake_case : Dict ,snake_case : Any ):
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,(filename_prefix + '-' if filename_prefix else '') + 'vocab.txt' )
with open(snake_case ,'w' ) as f:
f.write('\n'.join(self.all_tokens ) )
return (vocab_file,)
@property
def _lowerCAmelCase ( self : int ):
return self.get_vocab_size(with_added_tokens=snake_case )
def _lowerCAmelCase ( self : str ,snake_case : Union[List[str], List[AddedToken]] ,snake_case : bool = False ):
return super()._add_tokens(snake_case ,special_tokens=snake_case )
| 334 | 1 |
import warnings
from ..trainer import Trainer
from ..utils import logging
_lowerCamelCase =logging.get_logger(__name__)
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
def __init__( self : List[str] ,snake_case : str=None ,**snake_case : Dict ):
warnings.warn(
'`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '
'instead.' ,snake_case ,)
super().__init__(args=snake_case ,**snake_case )
| 334 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase =logging.get_logger()
@dataclass
class a_ :
"""simple docstring"""
__UpperCAmelCase = 42
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
def _lowerCAmelCase ( self : Any ,snake_case : Any ,snake_case : Tensor ,snake_case : Tensor ):
SCREAMING_SNAKE_CASE =len(list(m.modules() ) ) == 1 or isinstance(snake_case ,nn.Convad ) or isinstance(snake_case ,nn.BatchNormad )
if has_not_submodules:
self.traced.append(snake_case )
def __call__( self : int ,snake_case : Tensor ):
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(snake_case )
[x.remove() for x in self.handles]
return self
@property
def _lowerCAmelCase ( self : Tuple ):
# check the len of the state_dict keys to see if we have learnable params
return list(filter(lambda snake_case : len(list(x.state_dict().keys() ) ) > 0 ,self.traced ) )
@dataclass
class a_ :
"""simple docstring"""
__UpperCAmelCase = 42
__UpperCAmelCase = 42
__UpperCAmelCase = 0
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
def __call__( self : int ,snake_case : Tensor ):
SCREAMING_SNAKE_CASE =Tracker(self.dest )(snake_case ).parametrized
SCREAMING_SNAKE_CASE =Tracker(self.src )(snake_case ).parametrized
SCREAMING_SNAKE_CASE =list(filter(lambda snake_case : type(snake_case ) not in self.src_skip ,snake_case ) )
SCREAMING_SNAKE_CASE =list(filter(lambda snake_case : type(snake_case ) not in self.dest_skip ,snake_case ) )
if len(snake_case ) != len(snake_case ):
raise Exception(
f'Numbers of operations are different. Source module has {len(snake_case )} operations while'
f' destination module has {len(snake_case )}.' )
for dest_m, src_m in zip(snake_case ,snake_case ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(f'Transfered from={src_m} to={dest_m}' )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ = True ):
"""simple docstring"""
print(F'Converting {name}...' )
with torch.no_grad():
SCREAMING_SNAKE_CASE =timm.create_model(lowerCAmelCase_, pretrained=lowerCAmelCase_ ).eval()
SCREAMING_SNAKE_CASE =ResNetForImageClassification(lowerCAmelCase_ ).eval()
SCREAMING_SNAKE_CASE =ModuleTransfer(src=lowerCAmelCase_, dest=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =torch.randn((1, 3, 224, 224) )
module_transfer(lowerCAmelCase_ )
assert torch.allclose(from_model(lowerCAmelCase_ ), our_model(lowerCAmelCase_ ).logits ), "The model logits don't match the original one."
SCREAMING_SNAKE_CASE =F'resnet{"-".join(name.split("resnet" ) )}'
print(lowerCAmelCase_ )
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name, commit_message='Add model', use_temp_dir=lowerCAmelCase_, )
# we can use the convnext one
SCREAMING_SNAKE_CASE =AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' )
image_processor.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name, commit_message='Add image processor', use_temp_dir=lowerCAmelCase_, )
print(F'Pushed {checkpoint_name}' )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ = None, lowerCAmelCase_ = True ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ='imagenet-1k-id2label.json'
SCREAMING_SNAKE_CASE =1000
SCREAMING_SNAKE_CASE =(1, num_labels)
SCREAMING_SNAKE_CASE ='huggingface/label-files'
SCREAMING_SNAKE_CASE =num_labels
SCREAMING_SNAKE_CASE =json.load(open(hf_hub_download(lowerCAmelCase_, lowerCAmelCase_, repo_type='dataset' ), 'r' ) )
SCREAMING_SNAKE_CASE ={int(lowerCAmelCase_ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE =idalabel
SCREAMING_SNAKE_CASE ={v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE =partial(lowerCAmelCase_, num_labels=lowerCAmelCase_, idalabel=lowerCAmelCase_, labelaid=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE ={
'resnet18': ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2], hidden_sizes=[64, 128, 256, 512], layer_type='basic' ),
'resnet26': ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2], hidden_sizes=[256, 512, 1024, 2048], layer_type='bottleneck' ),
'resnet34': ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3], hidden_sizes=[64, 128, 256, 512], layer_type='basic' ),
'resnet50': ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3], hidden_sizes=[256, 512, 1024, 2048], layer_type='bottleneck' ),
'resnet101': ImageNetPreTrainedConfig(
depths=[3, 4, 23, 3], hidden_sizes=[256, 512, 1024, 2048], layer_type='bottleneck' ),
'resnet152': ImageNetPreTrainedConfig(
depths=[3, 8, 36, 3], hidden_sizes=[256, 512, 1024, 2048], layer_type='bottleneck' ),
}
if model_name:
convert_weight_and_push(lowerCAmelCase_, names_to_config[model_name], lowerCAmelCase_, lowerCAmelCase_ )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ )
return config, expected_shape
if __name__ == "__main__":
_lowerCamelCase =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default=None,
type=str,
help=(
"The name of the model you wish to convert, it must be one of the supported resnet* architecture,"
" currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."
),
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=Path,
required=True,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
default=True,
type=bool,
required=False,
help="If True, push model and image processor to the hub.",
)
_lowerCamelCase =parser.parse_args()
_lowerCamelCase =args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 334 | 1 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
_lowerCamelCase =[
# replace left string with right string to get the relevant state_dict key (identical state dict to bart)
["memory_attention", "encoder_attn"],
["attention", "attn"],
["/", "."],
[".LayerNorm.gamma", "_layer_norm.weight"],
[".LayerNorm.beta", "_layer_norm.bias"],
["r.layer_", "r.layers."],
["output_proj", "out_proj"],
["ffn.dense_1.", "fc2."],
["ffn.dense.", "fc1."],
["ffn_layer_norm", "final_layer_norm"],
["kernel", "weight"],
["encoder_layer_norm.", "encoder.layer_norm."],
["decoder_layer_norm.", "decoder.layer_norm."],
["embeddings.weights", "shared.weight"],
]
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
for pegasus_name, hf_name in PATTERNS:
SCREAMING_SNAKE_CASE =k.replace(lowerCAmelCase_, lowerCAmelCase_ )
return k
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =DEFAULTS.copy()
cfg_kwargs.update(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =PegasusConfig(**lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =PegasusForConditionalGeneration(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =torch_model.model.state_dict()
SCREAMING_SNAKE_CASE ={}
for k, v in tf_weights.items():
SCREAMING_SNAKE_CASE =rename_state_dict_key(lowerCAmelCase_ )
if new_k not in sd:
raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})' )
if "dense" in k or "proj" in new_k:
SCREAMING_SNAKE_CASE =v.T
SCREAMING_SNAKE_CASE =torch.tensor(lowerCAmelCase_, dtype=sd[new_k].dtype )
assert v.shape == sd[new_k].shape, F'{new_k}, {k}, {v.shape}, {sd[new_k].shape}'
# make sure embedding.padding_idx is respected
SCREAMING_SNAKE_CASE =torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1] )
SCREAMING_SNAKE_CASE =mapping['shared.weight']
SCREAMING_SNAKE_CASE =mapping['shared.weight']
SCREAMING_SNAKE_CASE ={k: torch.zeros_like(lowerCAmelCase_ ) for k, v in sd.items() if k.endswith('bias' ) and k not in mapping}
mapping.update(**lowerCAmelCase_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =torch_model.model.load_state_dict(lowerCAmelCase_, strict=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =[
k for k in missing if k not in ['encoder.embed_positions.weight', 'decoder.embed_positions.weight']
]
assert unexpected_missing == [], F'no matches found for the following torch keys {unexpected_missing}'
assert extra == [], F'no matches found for the following tf keys {extra}'
return torch_model
def snake_case__ ( lowerCAmelCase_="./ckpt/aeslc/model.ckpt-32000" ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =tf.train.list_variables(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE ={}
SCREAMING_SNAKE_CASE =['Adafactor', 'global_step']
for name, shape in tqdm(lowerCAmelCase_, desc='converting tf checkpoint to dict' ):
SCREAMING_SNAKE_CASE =any(pat in name for pat in ignore_name )
if skip_key:
continue
SCREAMING_SNAKE_CASE =tf.train.load_variable(lowerCAmelCase_, lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =array
return tf_weights
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =Path(lowerCAmelCase_ ).parent.name
SCREAMING_SNAKE_CASE =task_specific_params[F'summarization_{dataset}']['max_position_embeddings']
SCREAMING_SNAKE_CASE =PegasusTokenizer.from_pretrained('sshleifer/pegasus', model_max_length=lowerCAmelCase_ )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(lowerCAmelCase_ )
# convert model
SCREAMING_SNAKE_CASE =get_tf_weights_as_numpy(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =task_specific_params[F'summarization_{dataset}']
if dataset == "large":
SCREAMING_SNAKE_CASE =task_specific_params
SCREAMING_SNAKE_CASE =convert_pegasus(lowerCAmelCase_, lowerCAmelCase_ )
torch_model.save_pretrained(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =torch_model.state_dict()
sd.pop('model.decoder.embed_positions.weight' )
sd.pop('model.encoder.embed_positions.weight' )
torch.save(lowerCAmelCase_, Path(lowerCAmelCase_ ) / 'pytorch_model.bin' )
if __name__ == "__main__":
_lowerCamelCase =argparse.ArgumentParser()
# Required parameters
parser.add_argument("tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
parser.add_argument("save_dir", default=None, type=str, help="Path to the output PyTorch model.")
_lowerCamelCase =parser.parse_args()
if args.save_dir is None:
_lowerCamelCase =Path(args.tf_ckpt_path).parent.name
_lowerCamelCase =os.path.join("pegasus", dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 334 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_=7 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =None
if token is not None:
SCREAMING_SNAKE_CASE ={'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}
# The id of a workflow (not of a workflow run)
SCREAMING_SNAKE_CASE ='636036'
SCREAMING_SNAKE_CASE =F'https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs'
# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
url += F'?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}'
SCREAMING_SNAKE_CASE =requests.get(lowerCAmelCase_, headers=lowerCAmelCase_ ).json()
return result["workflow_runs"]
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =get_daily_ci_runs(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =None
for workflow_run in workflow_runs:
if workflow_run["status"] == "completed":
SCREAMING_SNAKE_CASE =workflow_run['id']
break
return workflow_run_id
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =get_last_daily_ci_runs(lowerCAmelCase_ )
if workflow_run_id is not None:
SCREAMING_SNAKE_CASE =get_artifacts_links(worflow_run_id=lowerCAmelCase_, token=lowerCAmelCase_ )
for artifact_name in artifact_names:
if artifact_name in artifacts_links:
SCREAMING_SNAKE_CASE =artifacts_links[artifact_name]
download_artifact(
artifact_name=lowerCAmelCase_, artifact_url=lowerCAmelCase_, output_dir=lowerCAmelCase_, token=lowerCAmelCase_ )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
get_last_daily_ci_artifacts(lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ )
SCREAMING_SNAKE_CASE ={}
for artifact_name in artifact_names:
SCREAMING_SNAKE_CASE =os.path.join(lowerCAmelCase_, F'{artifact_name}.zip' )
if os.path.isfile(lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE ={}
with zipfile.ZipFile(lowerCAmelCase_ ) as z:
for filename in z.namelist():
if not os.path.isdir(lowerCAmelCase_ ):
# read the file
with z.open(lowerCAmelCase_ ) as f:
SCREAMING_SNAKE_CASE =f.read().decode('UTF-8' )
return results
| 334 | 1 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
_lowerCamelCase =get_tests_dir("fixtures")
class a_ ( unittest.TestCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : Optional[int] ):
# A mock response for an HTTP head request to emulate server down
SCREAMING_SNAKE_CASE =mock.Mock()
SCREAMING_SNAKE_CASE =500
SCREAMING_SNAKE_CASE ={}
SCREAMING_SNAKE_CASE =HTTPError
SCREAMING_SNAKE_CASE ={}
# Download this model to make sure it's in the cache.
SCREAMING_SNAKE_CASE =WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('requests.Session.request' ,return_value=snake_case ) as mock_head:
SCREAMING_SNAKE_CASE =WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2' )
# This check we did call the fake head request
mock_head.assert_called()
def _lowerCAmelCase ( self : str ):
# This test is for deprecated behavior and can be removed in v5
SCREAMING_SNAKE_CASE =WavaVecaFeatureExtractor.from_pretrained(
'https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json' )
@is_staging_test
class a_ ( unittest.TestCase ):
"""simple docstring"""
@classmethod
def _lowerCAmelCase ( cls : Optional[int] ):
SCREAMING_SNAKE_CASE =TOKEN
HfFolder.save_token(snake_case )
@classmethod
def _lowerCAmelCase ( cls : List[Any] ):
try:
delete_repo(token=cls._token ,repo_id='test-feature-extractor' )
except HTTPError:
pass
try:
delete_repo(token=cls._token ,repo_id='valid_org/test-feature-extractor-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token ,repo_id='test-dynamic-feature-extractor' )
except HTTPError:
pass
def _lowerCAmelCase ( self : str ):
SCREAMING_SNAKE_CASE =WavaVecaFeatureExtractor.from_pretrained(snake_case )
feature_extractor.push_to_hub('test-feature-extractor' ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =WavaVecaFeatureExtractor.from_pretrained(f'{USER}/test-feature-extractor' )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(snake_case ,getattr(snake_case ,snake_case ) )
# Reset repo
delete_repo(token=self._token ,repo_id='test-feature-extractor' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
snake_case ,repo_id='test-feature-extractor' ,push_to_hub=snake_case ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =WavaVecaFeatureExtractor.from_pretrained(f'{USER}/test-feature-extractor' )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(snake_case ,getattr(snake_case ,snake_case ) )
def _lowerCAmelCase ( self : Dict ):
SCREAMING_SNAKE_CASE =WavaVecaFeatureExtractor.from_pretrained(snake_case )
feature_extractor.push_to_hub('valid_org/test-feature-extractor' ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor' )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(snake_case ,getattr(snake_case ,snake_case ) )
# Reset repo
delete_repo(token=self._token ,repo_id='valid_org/test-feature-extractor' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
snake_case ,repo_id='valid_org/test-feature-extractor-org' ,push_to_hub=snake_case ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor-org' )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(snake_case ,getattr(snake_case ,snake_case ) )
def _lowerCAmelCase ( self : Dict ):
CustomFeatureExtractor.register_for_auto_class()
SCREAMING_SNAKE_CASE =CustomFeatureExtractor.from_pretrained(snake_case )
feature_extractor.push_to_hub('test-dynamic-feature-extractor' ,use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
feature_extractor.auto_map ,{'AutoFeatureExtractor': 'custom_feature_extraction.CustomFeatureExtractor'} ,)
SCREAMING_SNAKE_CASE =AutoFeatureExtractor.from_pretrained(
f'{USER}/test-dynamic-feature-extractor' ,trust_remote_code=snake_case )
# Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
self.assertEqual(new_feature_extractor.__class__.__name__ ,'CustomFeatureExtractor' )
| 334 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
)
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
def __init__( self : Tuple ,snake_case : Optional[int] ,snake_case : Dict=13 ,snake_case : str=7 ,snake_case : Dict=True ,snake_case : List[Any]=True ,snake_case : Dict=False ,snake_case : int=True ,snake_case : Dict=99 ,snake_case : int=32 ,snake_case : List[str]=5 ,snake_case : Optional[Any]=4 ,snake_case : Tuple=64 ,snake_case : List[Any]="gelu" ,snake_case : str=0.1 ,snake_case : str=0.1 ,snake_case : List[str]=512 ,snake_case : List[str]=16 ,snake_case : str=2 ,snake_case : Dict=0.02 ,snake_case : Optional[int]=3 ,snake_case : int=4 ,snake_case : Any=None ,snake_case : Union[str, Any]=2 ,snake_case : List[Any]=2 ,snake_case : Optional[int]=2 ,snake_case : Dict=2 ,snake_case : List[str]=4 ,snake_case : int=1 ,):
SCREAMING_SNAKE_CASE =parent
SCREAMING_SNAKE_CASE =batch_size
SCREAMING_SNAKE_CASE =seq_length
SCREAMING_SNAKE_CASE =is_training
SCREAMING_SNAKE_CASE =use_input_mask
SCREAMING_SNAKE_CASE =use_token_type_ids
SCREAMING_SNAKE_CASE =use_labels
SCREAMING_SNAKE_CASE =vocab_size
SCREAMING_SNAKE_CASE =hidden_size
SCREAMING_SNAKE_CASE =num_hidden_layers
SCREAMING_SNAKE_CASE =num_attention_heads
SCREAMING_SNAKE_CASE =intermediate_size
SCREAMING_SNAKE_CASE =hidden_act
SCREAMING_SNAKE_CASE =hidden_dropout_prob
SCREAMING_SNAKE_CASE =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE =max_position_embeddings
SCREAMING_SNAKE_CASE =type_vocab_size
SCREAMING_SNAKE_CASE =type_sequence_label_size
SCREAMING_SNAKE_CASE =initializer_range
SCREAMING_SNAKE_CASE =num_labels
SCREAMING_SNAKE_CASE =num_choices
SCREAMING_SNAKE_CASE =scope
SCREAMING_SNAKE_CASE =q_groups
SCREAMING_SNAKE_CASE =k_groups
SCREAMING_SNAKE_CASE =v_groups
SCREAMING_SNAKE_CASE =post_attention_groups
SCREAMING_SNAKE_CASE =intermediate_groups
SCREAMING_SNAKE_CASE =output_groups
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
SCREAMING_SNAKE_CASE =None
if self.use_input_mask:
SCREAMING_SNAKE_CASE =random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE =None
SCREAMING_SNAKE_CASE =None
SCREAMING_SNAKE_CASE =None
if self.use_labels:
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size] ,self.type_sequence_label_size )
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size] ,self.num_choices )
SCREAMING_SNAKE_CASE =self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowerCAmelCase ( self : Optional[int] ):
return SqueezeBertConfig(
embedding_size=self.hidden_size ,vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,attention_probs_dropout_prob=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,initializer_range=self.initializer_range ,q_groups=self.q_groups ,k_groups=self.k_groups ,v_groups=self.v_groups ,post_attention_groups=self.post_attention_groups ,intermediate_groups=self.intermediate_groups ,output_groups=self.output_groups ,)
def _lowerCAmelCase ( self : Dict ,snake_case : List[str] ,snake_case : Optional[Any] ,snake_case : List[str] ,snake_case : List[Any] ,snake_case : str ,snake_case : Union[str, Any] ):
SCREAMING_SNAKE_CASE =SqueezeBertModel(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case ,snake_case )
SCREAMING_SNAKE_CASE =model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _lowerCAmelCase ( self : Optional[int] ,snake_case : Optional[int] ,snake_case : Union[str, Any] ,snake_case : List[Any] ,snake_case : int ,snake_case : Any ,snake_case : Tuple ):
SCREAMING_SNAKE_CASE =SqueezeBertForMaskedLM(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case ,attention_mask=snake_case ,labels=snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def _lowerCAmelCase ( self : Tuple ,snake_case : Union[str, Any] ,snake_case : Any ,snake_case : List[str] ,snake_case : List[Any] ,snake_case : Dict ,snake_case : Optional[Any] ):
SCREAMING_SNAKE_CASE =SqueezeBertForQuestionAnswering(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(
snake_case ,attention_mask=snake_case ,start_positions=snake_case ,end_positions=snake_case )
self.parent.assertEqual(result.start_logits.shape ,(self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape ,(self.batch_size, self.seq_length) )
def _lowerCAmelCase ( self : Optional[int] ,snake_case : Tuple ,snake_case : List[str] ,snake_case : List[str] ,snake_case : Any ,snake_case : Tuple ,snake_case : str ):
SCREAMING_SNAKE_CASE =self.num_labels
SCREAMING_SNAKE_CASE =SqueezeBertForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case ,attention_mask=snake_case ,labels=snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def _lowerCAmelCase ( self : Optional[Any] ,snake_case : List[str] ,snake_case : List[str] ,snake_case : Tuple ,snake_case : Dict ,snake_case : str ,snake_case : Tuple ):
SCREAMING_SNAKE_CASE =self.num_labels
SCREAMING_SNAKE_CASE =SqueezeBertForTokenClassification(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case ,attention_mask=snake_case ,labels=snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def _lowerCAmelCase ( self : List[str] ,snake_case : Dict ,snake_case : str ,snake_case : Union[str, Any] ,snake_case : Union[str, Any] ,snake_case : Any ,snake_case : Union[str, Any] ):
SCREAMING_SNAKE_CASE =self.num_choices
SCREAMING_SNAKE_CASE =SqueezeBertForMultipleChoice(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =input_ids.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
SCREAMING_SNAKE_CASE =input_mask.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
SCREAMING_SNAKE_CASE =model(
snake_case ,attention_mask=snake_case ,labels=snake_case ,)
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_choices) )
def _lowerCAmelCase ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE =self.prepare_config_and_inputs()
((SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE)) =config_and_inputs
SCREAMING_SNAKE_CASE ={'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class a_ ( lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase = (
(
SqueezeBertModel,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
)
if is_torch_available()
else None
)
__UpperCAmelCase = (
{
'feature-extraction': SqueezeBertModel,
'fill-mask': SqueezeBertForMaskedLM,
'question-answering': SqueezeBertForQuestionAnswering,
'text-classification': SqueezeBertForSequenceClassification,
'token-classification': SqueezeBertForTokenClassification,
'zero-shot': SqueezeBertForSequenceClassification,
}
if is_torch_available()
else {}
)
__UpperCAmelCase = False
__UpperCAmelCase = True
__UpperCAmelCase = False
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =SqueezeBertModelTester(self )
SCREAMING_SNAKE_CASE =ConfigTester(self ,config_class=snake_case ,dim=37 )
def _lowerCAmelCase ( self : List[str] ):
self.config_tester.run_common_tests()
def _lowerCAmelCase ( self : Optional[int] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_model(*snake_case )
def _lowerCAmelCase ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_masked_lm(*snake_case )
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_question_answering(*snake_case )
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_sequence_classification(*snake_case )
def _lowerCAmelCase ( self : int ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_token_classification(*snake_case )
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_multiple_choice(*snake_case )
@slow
def _lowerCAmelCase ( self : str ):
for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE =SqueezeBertModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
@require_sentencepiece
@require_tokenizers
@require_torch
class a_ ( unittest.TestCase ):
"""simple docstring"""
@slow
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =SqueezeBertForSequenceClassification.from_pretrained('squeezebert/squeezebert-mnli' )
SCREAMING_SNAKE_CASE =torch.tensor([[1, 29414, 232, 328, 740, 1140, 12695, 69, 13, 1588, 2]] )
SCREAMING_SNAKE_CASE =model(snake_case )[0]
SCREAMING_SNAKE_CASE =torch.Size((1, 3) )
self.assertEqual(output.shape ,snake_case )
SCREAMING_SNAKE_CASE =torch.tensor([[0.6_401, -0.0_349, -0.6_041]] )
self.assertTrue(torch.allclose(snake_case ,snake_case ,atol=1e-4 ) )
| 334 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_lowerCamelCase ={
"configuration_data2vec_audio": ["DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig"],
"configuration_data2vec_text": [
"DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Data2VecTextConfig",
"Data2VecTextOnnxConfig",
],
"configuration_data2vec_vision": [
"DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Data2VecVisionConfig",
"Data2VecVisionOnnxConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase =[
"DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST",
"Data2VecAudioForAudioFrameClassification",
"Data2VecAudioForCTC",
"Data2VecAudioForSequenceClassification",
"Data2VecAudioForXVector",
"Data2VecAudioModel",
"Data2VecAudioPreTrainedModel",
]
_lowerCamelCase =[
"DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
"Data2VecTextForCausalLM",
"Data2VecTextForMaskedLM",
"Data2VecTextForMultipleChoice",
"Data2VecTextForQuestionAnswering",
"Data2VecTextForSequenceClassification",
"Data2VecTextForTokenClassification",
"Data2VecTextModel",
"Data2VecTextPreTrainedModel",
]
_lowerCamelCase =[
"DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST",
"Data2VecVisionForImageClassification",
"Data2VecVisionForMaskedImageModeling",
"Data2VecVisionForSemanticSegmentation",
"Data2VecVisionModel",
"Data2VecVisionPreTrainedModel",
]
if is_tf_available():
_lowerCamelCase =[
"TFData2VecVisionForImageClassification",
"TFData2VecVisionForSemanticSegmentation",
"TFData2VecVisionModel",
"TFData2VecVisionPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
from .configuration_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecTextConfig,
DataaVecTextOnnxConfig,
)
from .configuration_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecVisionConfig,
DataaVecVisionOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dataavec_audio import (
DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecAudioForAudioFrameClassification,
DataaVecAudioForCTC,
DataaVecAudioForSequenceClassification,
DataaVecAudioForXVector,
DataaVecAudioModel,
DataaVecAudioPreTrainedModel,
)
from .modeling_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecTextForCausalLM,
DataaVecTextForMaskedLM,
DataaVecTextForMultipleChoice,
DataaVecTextForQuestionAnswering,
DataaVecTextForSequenceClassification,
DataaVecTextForTokenClassification,
DataaVecTextModel,
DataaVecTextPreTrainedModel,
)
from .modeling_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecVisionForImageClassification,
DataaVecVisionForMaskedImageModeling,
DataaVecVisionForSemanticSegmentation,
DataaVecVisionModel,
DataaVecVisionPreTrainedModel,
)
if is_tf_available():
from .modeling_tf_dataavec_vision import (
TFDataaVecVisionForImageClassification,
TFDataaVecVisionForSemanticSegmentation,
TFDataaVecVisionModel,
TFDataaVecVisionPreTrainedModel,
)
else:
import sys
_lowerCamelCase =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 334 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = None
__UpperCAmelCase = None
@property
def _lowerCAmelCase ( self : List[Any] ):
return self.feat_extract_tester.prepare_feat_extract_dict()
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(snake_case ,'feature_size' ) )
self.assertTrue(hasattr(snake_case ,'sampling_rate' ) )
self.assertTrue(hasattr(snake_case ,'padding_value' ) )
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common()
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(snake_case ) == len(snake_case ) for x, y in zip(snake_case ,processed_features[input_name] ) ) )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common(equal_length=snake_case )
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} ,tensor_type='np' )
SCREAMING_SNAKE_CASE =processed_features[input_name]
if len(batch_features_input.shape ) < 3:
SCREAMING_SNAKE_CASE =batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_torch
def _lowerCAmelCase ( self : Optional[int] ):
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common(equal_length=snake_case )
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} ,tensor_type='pt' )
SCREAMING_SNAKE_CASE =processed_features[input_name]
if len(batch_features_input.shape ) < 3:
SCREAMING_SNAKE_CASE =batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_tf
def _lowerCAmelCase ( self : str ):
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common(equal_length=snake_case )
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} ,tensor_type='tf' )
SCREAMING_SNAKE_CASE =processed_features[input_name]
if len(batch_features_input.shape ) < 3:
SCREAMING_SNAKE_CASE =batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
def _lowerCAmelCase ( self : List[Any] ,snake_case : Optional[Any]=False ):
def _inputs_have_equal_length(snake_case : Dict ):
SCREAMING_SNAKE_CASE =len(input[0] )
for input_slice in input[1:]:
if len(snake_case ) != length:
return False
return True
def _inputs_are_equal(snake_case : str ,snake_case : Dict ):
if len(snake_case ) != len(snake_case ):
return False
for input_slice_a, input_slice_a in zip(snake_case ,snake_case ):
if not np.allclose(np.asarray(snake_case ) ,np.asarray(snake_case ) ,atol=1e-3 ):
return False
return True
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common(numpify=snake_case )
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.seq_length_diff
SCREAMING_SNAKE_CASE =self.feat_extract_tester.max_seq_length + pad_diff
SCREAMING_SNAKE_CASE =self.feat_extract_tester.min_seq_length
SCREAMING_SNAKE_CASE =self.feat_extract_tester.batch_size
SCREAMING_SNAKE_CASE =self.feat_extract_tester.feature_size
# test padding for List[int] + numpy
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding=snake_case )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='max_length' ,max_length=len(speech_inputs[-1] ) )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='np' )
SCREAMING_SNAKE_CASE =input_a[input_name]
# max_length parameter has to be provided when setting `padding="max_length"`
with self.assertRaises(snake_case ):
feat_extract.pad(snake_case ,padding='max_length' )[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=snake_case ,return_tensors='np' )
SCREAMING_SNAKE_CASE =input_a[input_name]
self.assertFalse(_inputs_have_equal_length(snake_case ) )
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertTrue(_inputs_are_equal(snake_case ,snake_case ) )
self.assertTrue(len(input_a[0] ) == pad_min_length )
self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )
# test padding for `pad_to_multiple_of` for List[int] + numpy
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,pad_to_multiple_of=10 )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,pad_to_multiple_of=10 )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,pad_to_multiple_of=10 ,max_length=snake_case )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,pad_to_multiple_of=10 ,max_length=snake_case ,return_tensors='np' ,)
SCREAMING_SNAKE_CASE =input_a[input_name]
self.assertTrue(all(len(snake_case ) % 10 == 0 for x in input_a ) )
self.assertTrue(_inputs_are_equal(snake_case ,snake_case ) )
SCREAMING_SNAKE_CASE =pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
self.assertTrue(all(len(snake_case ) == expected_mult_pad_length for x in input_a ) )
self.assertEqual(input_a.shape[:2] ,(batch_size, expected_mult_pad_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == feature_size )
# Check padding value is correct
SCREAMING_SNAKE_CASE =(np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
self.assertTrue(
abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
< 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
< 1e-3 )
def _lowerCAmelCase ( self : Union[str, Any] ,snake_case : Optional[int]=False ):
def _inputs_have_equal_length(snake_case : str ):
SCREAMING_SNAKE_CASE =len(input[0] )
for input_slice in input[1:]:
if len(snake_case ) != length:
return False
return True
def _inputs_are_equal(snake_case : Tuple ,snake_case : Optional[Any] ):
if len(snake_case ) != len(snake_case ):
return False
for input_slice_a, input_slice_a in zip(snake_case ,snake_case ):
if not np.allclose(np.asarray(snake_case ) ,np.asarray(snake_case ) ,atol=1e-3 ):
return False
return True
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common(numpify=snake_case )
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
# truncate to smallest
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) ,truncation=snake_case )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) )
SCREAMING_SNAKE_CASE =input_a[input_name]
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertFalse(_inputs_have_equal_length(snake_case ) )
# truncate to smallest with np
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) ,return_tensors='np' ,truncation=snake_case ,)
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) ,return_tensors='np' )
SCREAMING_SNAKE_CASE =input_a[input_name]
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(snake_case ) )
# truncate to middle
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[1] ) ,truncation=snake_case ,return_tensors='np' ,)
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[1] ) ,truncation=snake_case )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[1] ) ,return_tensors='np' )
SCREAMING_SNAKE_CASE =input_a[input_name]
self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertTrue(_inputs_are_equal(snake_case ,snake_case ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(snake_case ) )
self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(snake_case ):
feat_extract.pad(snake_case ,truncation=snake_case )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(snake_case ):
feat_extract.pad(snake_case ,padding='longest' ,truncation=snake_case )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(snake_case ):
feat_extract.pad(snake_case ,padding='longest' ,truncation=snake_case )[input_name]
# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
with self.assertRaises(snake_case ):
feat_extract.pad(snake_case ,padding='max_length' ,truncation=snake_case )[input_name]
# test truncation for `pad_to_multiple_of` for List[int] + numpy
SCREAMING_SNAKE_CASE =12
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) ,pad_to_multiple_of=snake_case ,truncation=snake_case ,)
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) ,pad_to_multiple_of=snake_case ,)
SCREAMING_SNAKE_CASE =input_a[input_name]
# retrieve expected_length as multiple of pad_to_multiple_of
SCREAMING_SNAKE_CASE =len(speech_inputs[0] )
if expected_length % pad_to_multiple_of != 0:
SCREAMING_SNAKE_CASE =((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of
self.assertTrue(len(input_a[0] ) == expected_length )
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertFalse(_inputs_have_equal_length(snake_case ) )
def _lowerCAmelCase ( self : Optional[int] ):
self._check_padding(numpify=snake_case )
def _lowerCAmelCase ( self : Tuple ):
self._check_padding(numpify=snake_case )
def _lowerCAmelCase ( self : List[str] ):
self._check_truncation(numpify=snake_case )
def _lowerCAmelCase ( self : int ):
self._check_truncation(numpify=snake_case )
@require_torch
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common()
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='np' )[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='pt' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
@require_tf
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common()
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='np' )[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='tf' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE =self.feat_extract_dict
SCREAMING_SNAKE_CASE =True
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**snake_case )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common()
SCREAMING_SNAKE_CASE =[len(snake_case ) for x in speech_inputs]
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='np' )
self.assertIn('attention_mask' ,snake_case )
self.assertListEqual(list(processed.attention_mask.shape ) ,list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() ,snake_case )
def _lowerCAmelCase ( self : Dict ):
SCREAMING_SNAKE_CASE =self.feat_extract_dict
SCREAMING_SNAKE_CASE =True
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**snake_case )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common()
SCREAMING_SNAKE_CASE =[len(snake_case ) for x in speech_inputs]
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE =min(snake_case )
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=snake_case ,truncation=snake_case ,return_tensors='np' )
self.assertIn('attention_mask' ,snake_case )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) ,[processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() ,[max_length for x in speech_inputs] )
| 334 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
_lowerCamelCase ={
"configuration_ernie": ["ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", "ErnieOnnxConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase =[
"ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST",
"ErnieForCausalLM",
"ErnieForMaskedLM",
"ErnieForMultipleChoice",
"ErnieForNextSentencePrediction",
"ErnieForPreTraining",
"ErnieForQuestionAnswering",
"ErnieForSequenceClassification",
"ErnieForTokenClassification",
"ErnieModel",
"ErniePreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ernie import (
ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
ErnieForCausalLM,
ErnieForMaskedLM,
ErnieForMultipleChoice,
ErnieForNextSentencePrediction,
ErnieForPreTraining,
ErnieForQuestionAnswering,
ErnieForSequenceClassification,
ErnieForTokenClassification,
ErnieModel,
ErniePreTrainedModel,
)
else:
import sys
_lowerCamelCase =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 334 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_lowerCamelCase =2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
_lowerCamelCase =50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
_lowerCamelCase =0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 10_00))
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =len([g for position, g in enumerate(lowerCAmelCase_ ) if g == main_target[position]] )
return (item, float(lowerCAmelCase_ ))
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =random.randint(0, len(lowerCAmelCase_ ) - 1 )
SCREAMING_SNAKE_CASE =parent_a[:random_slice] + parent_a[random_slice:]
SCREAMING_SNAKE_CASE =parent_a[:random_slice] + parent_a[random_slice:]
return (child_a, child_a)
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =list(lowerCAmelCase_ )
if random.uniform(0, 1 ) < MUTATION_PROBABILITY:
SCREAMING_SNAKE_CASE =random.choice(lowerCAmelCase_ )
return "".join(lowerCAmelCase_ )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =[]
# Generate more children proportionally to the fitness score.
SCREAMING_SNAKE_CASE =int(parent_a[1] * 100 ) + 1
SCREAMING_SNAKE_CASE =10 if child_n >= 10 else child_n
for _ in range(lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE =population_score[random.randint(0, lowerCAmelCase_ )][0]
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =crossover(parent_a[0], lowerCAmelCase_ )
# Append new string to the population list.
pop.append(mutate(lowerCAmelCase_, lowerCAmelCase_ ) )
pop.append(mutate(lowerCAmelCase_, lowerCAmelCase_ ) )
return pop
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ = True ):
"""simple docstring"""
if N_POPULATION < N_SELECTED:
SCREAMING_SNAKE_CASE =F'{N_POPULATION} must be bigger than {N_SELECTED}'
raise ValueError(lowerCAmelCase_ )
# Verify that the target contains no genes besides the ones inside genes variable.
SCREAMING_SNAKE_CASE =sorted({c for c in target if c not in genes} )
if not_in_genes_list:
SCREAMING_SNAKE_CASE =F'{not_in_genes_list} is not in genes list, evolution cannot converge'
raise ValueError(lowerCAmelCase_ )
# Generate random starting population.
SCREAMING_SNAKE_CASE =[]
for _ in range(lowerCAmelCase_ ):
population.append(''.join([random.choice(lowerCAmelCase_ ) for i in range(len(lowerCAmelCase_ ) )] ) )
# Just some logs to know what the algorithms is doing.
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =0, 0
# This loop will end when we find a perfect match for our target.
while True:
generation += 1
total_population += len(lowerCAmelCase_ )
# Random population created. Now it's time to evaluate.
# Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this, it will probably be slower.
# We just need to call evaluate for every item inside the population.
SCREAMING_SNAKE_CASE =[evaluate(lowerCAmelCase_, lowerCAmelCase_ ) for item in population]
# Check if there is a matching evolution.
SCREAMING_SNAKE_CASE =sorted(lowerCAmelCase_, key=lambda lowerCAmelCase_ : x[1], reverse=lowerCAmelCase_ )
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the best result every 10 generation.
# Just to know that the algorithm is working.
if debug and generation % 10 == 0:
print(
F'\nGeneration: {generation}'
F'\nTotal Population:{total_population}'
F'\nBest score: {population_score[0][1]}'
F'\nBest string: {population_score[0][0]}' )
# Flush the old population, keeping some of the best evolutions.
# Keeping this avoid regression of evolution.
SCREAMING_SNAKE_CASE =population[: int(N_POPULATION / 3 )]
population.clear()
population.extend(lowerCAmelCase_ )
# Normalize population score to be between 0 and 1.
SCREAMING_SNAKE_CASE =[
(item, score / len(lowerCAmelCase_ )) for item, score in population_score
]
# This is selection
for i in range(lowerCAmelCase_ ):
population.extend(select(population_score[int(lowerCAmelCase_ )], lowerCAmelCase_, lowerCAmelCase_ ) )
# Check if the population has already reached the maximum value and if so,
# break the cycle. If this check is disabled, the algorithm will take
# forever to compute large strings, but will also calculate small strings in
# a far fewer generations.
if len(lowerCAmelCase_ ) > N_POPULATION:
break
if __name__ == "__main__":
_lowerCamelCase =(
"This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
)
_lowerCamelCase =list(
" ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
"nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
)
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase =basic(target_str, genes_list)
print(
f'\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'
)
| 334 | 1 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
_lowerCamelCase ={
"vocab_file": {
"allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json"
},
"merges_file": {
"allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt"
},
}
_lowerCamelCase ={"allegro/herbert-base-cased": 5_14}
_lowerCamelCase ={}
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = VOCAB_FILES_NAMES
__UpperCAmelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase = PRETRAINED_INIT_CONFIGURATION
__UpperCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase = HerbertTokenizer
def __init__( self : Optional[int] ,snake_case : Any=None ,snake_case : str=None ,snake_case : Optional[Any]=None ,snake_case : List[str]="<s>" ,snake_case : Union[str, Any]="<unk>" ,snake_case : Dict="<pad>" ,snake_case : List[Any]="<mask>" ,snake_case : Any="</s>" ,**snake_case : Dict ,):
super().__init__(
snake_case ,snake_case ,tokenizer_file=snake_case ,cls_token=snake_case ,unk_token=snake_case ,pad_token=snake_case ,mask_token=snake_case ,sep_token=snake_case ,**snake_case ,)
def _lowerCAmelCase ( self : Union[str, Any] ,snake_case : List[int] ,snake_case : Optional[List[int]] = None ):
SCREAMING_SNAKE_CASE =[self.cls_token_id]
SCREAMING_SNAKE_CASE =[self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def _lowerCAmelCase ( self : List[Any] ,snake_case : List[int] ,snake_case : Optional[List[int]] = None ,snake_case : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=snake_case ,token_ids_a=snake_case ,already_has_special_tokens=snake_case )
if token_ids_a is None:
return [1] + ([0] * len(snake_case )) + [1]
return [1] + ([0] * len(snake_case )) + [1] + ([0] * len(snake_case )) + [1]
def _lowerCAmelCase ( self : str ,snake_case : List[int] ,snake_case : Optional[List[int]] = None ):
SCREAMING_SNAKE_CASE =[self.sep_token_id]
SCREAMING_SNAKE_CASE =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def _lowerCAmelCase ( self : int ,snake_case : str ,snake_case : Optional[str] = None ):
SCREAMING_SNAKE_CASE =self._tokenizer.model.save(snake_case ,name=snake_case )
return tuple(snake_case )
| 334 |
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer,
BertTokenizer,
BertTokenizerFast,
GPTaTokenizerFast,
is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_tokenization import CustomTokenizer # noqa E402
if is_tokenizers_available():
from test_module.custom_tokenization_fast import CustomTokenizerFast
class a_ ( unittest.TestCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : Dict ):
# A mock response for an HTTP head request to emulate server down
SCREAMING_SNAKE_CASE =mock.Mock()
SCREAMING_SNAKE_CASE =500
SCREAMING_SNAKE_CASE ={}
SCREAMING_SNAKE_CASE =HTTPError
SCREAMING_SNAKE_CASE ={}
# Download this model to make sure it's in the cache.
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert' )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch('requests.Session.request' ,return_value=snake_case ) as mock_head:
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert' )
# This check we did call the fake head request
mock_head.assert_called()
@require_tokenizers
def _lowerCAmelCase ( self : Optional[Any] ):
# A mock response for an HTTP head request to emulate server down
SCREAMING_SNAKE_CASE =mock.Mock()
SCREAMING_SNAKE_CASE =500
SCREAMING_SNAKE_CASE ={}
SCREAMING_SNAKE_CASE =HTTPError
SCREAMING_SNAKE_CASE ={}
# Download this model to make sure it's in the cache.
SCREAMING_SNAKE_CASE =GPTaTokenizerFast.from_pretrained('gpt2' )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch('requests.Session.request' ,return_value=snake_case ) as mock_head:
SCREAMING_SNAKE_CASE =GPTaTokenizerFast.from_pretrained('gpt2' )
# This check we did call the fake head request
mock_head.assert_called()
def _lowerCAmelCase ( self : Union[str, Any] ):
# This test is for deprecated behavior and can be removed in v5
try:
SCREAMING_SNAKE_CASE =tempfile.mktemp()
with open(snake_case ,'wb' ) as f:
http_get('https://huggingface.co/albert-base-v1/resolve/main/spiece.model' ,snake_case )
SCREAMING_SNAKE_CASE =AlbertTokenizer.from_pretrained(snake_case )
finally:
os.remove(snake_case )
# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
# the current folder and have the right name.
if os.path.isfile('tokenizer.json' ):
# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
return
try:
with open('tokenizer.json' ,'wb' ) as f:
http_get('https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json' ,snake_case )
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
self.assertEqual(tokenizer.vocab_size ,1000 )
# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.
finally:
os.remove('tokenizer.json' )
def _lowerCAmelCase ( self : int ):
# This test is for deprecated behavior and can be removed in v5
SCREAMING_SNAKE_CASE =AlbertTokenizer.from_pretrained('https://huggingface.co/albert-base-v1/resolve/main/spiece.model' )
@is_staging_test
class a_ ( unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase = ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']
@classmethod
def _lowerCAmelCase ( cls : List[Any] ):
SCREAMING_SNAKE_CASE =TOKEN
HfFolder.save_token(snake_case )
@classmethod
def _lowerCAmelCase ( cls : Tuple ):
try:
delete_repo(token=cls._token ,repo_id='test-tokenizer' )
except HTTPError:
pass
try:
delete_repo(token=cls._token ,repo_id='valid_org/test-tokenizer-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token ,repo_id='test-dynamic-tokenizer' )
except HTTPError:
pass
def _lowerCAmelCase ( self : Any ):
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,'vocab.txt' )
with open(snake_case ,'w' ,encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
SCREAMING_SNAKE_CASE =BertTokenizer(snake_case )
tokenizer.push_to_hub('test-tokenizer' ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained(f'{USER}/test-tokenizer' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
# Reset repo
delete_repo(token=self._token ,repo_id='test-tokenizer' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(snake_case ,repo_id='test-tokenizer' ,push_to_hub=snake_case ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained(f'{USER}/test-tokenizer' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
def _lowerCAmelCase ( self : Optional[Any] ):
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,'vocab.txt' )
with open(snake_case ,'w' ,encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
SCREAMING_SNAKE_CASE =BertTokenizer(snake_case )
tokenizer.push_to_hub('valid_org/test-tokenizer-org' ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained('valid_org/test-tokenizer-org' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
# Reset repo
delete_repo(token=self._token ,repo_id='valid_org/test-tokenizer-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(
snake_case ,repo_id='valid_org/test-tokenizer-org' ,push_to_hub=snake_case ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained('valid_org/test-tokenizer-org' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
@require_tokenizers
def _lowerCAmelCase ( self : str ):
CustomTokenizer.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,'vocab.txt' )
with open(snake_case ,'w' ,encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
SCREAMING_SNAKE_CASE =CustomTokenizer(snake_case )
# No fast custom tokenizer
tokenizer.push_to_hub('test-dynamic-tokenizer' ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer' ,trust_remote_code=snake_case )
# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,'CustomTokenizer' )
# Fast and slow custom tokenizer
CustomTokenizerFast.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,'vocab.txt' )
with open(snake_case ,'w' ,encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
SCREAMING_SNAKE_CASE =BertTokenizerFast.from_pretrained(snake_case )
bert_tokenizer.save_pretrained(snake_case )
SCREAMING_SNAKE_CASE =CustomTokenizerFast.from_pretrained(snake_case )
tokenizer.push_to_hub('test-dynamic-tokenizer' ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer' ,trust_remote_code=snake_case )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,'CustomTokenizerFast' )
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained(
f'{USER}/test-dynamic-tokenizer' ,use_fast=snake_case ,trust_remote_code=snake_case )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,'CustomTokenizer' )
class a_ ( unittest.TestCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('Hello 友達' )
self.assertEqual(trie.data ,{'H': {'e': {'l': {'l': {'o': {' ': {'友': {'達': {'': 1}}}}}}}}} )
trie.add('Hello' )
trie.data
self.assertEqual(trie.data ,{'H': {'e': {'l': {'l': {'o': {'': 1, ' ': {'友': {'達': {'': 1}}}}}}}}} )
def _lowerCAmelCase ( self : Optional[int] ):
SCREAMING_SNAKE_CASE =Trie()
self.assertEqual(trie.split('[CLS] This is a extra_id_100' ) ,['[CLS] This is a extra_id_100'] )
trie.add('[CLS]' )
trie.add('extra_id_1' )
trie.add('extra_id_100' )
self.assertEqual(trie.split('[CLS] This is a extra_id_100' ) ,['[CLS]', ' This is a ', 'extra_id_100'] )
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('A' )
self.assertEqual(trie.split('ABC' ) ,['A', 'BC'] )
self.assertEqual(trie.split('BCA' ) ,['BC', 'A'] )
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('TOKEN]' )
trie.add('[SPECIAL_TOKEN]' )
self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]' ) ,['This is something ', '[SPECIAL_TOKEN]'] )
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('A' )
trie.add('P' )
trie.add('[SPECIAL_TOKEN]' )
self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]' ) ,['This is something ', '[SPECIAL_TOKEN]'] )
def _lowerCAmelCase ( self : Dict ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('AB' )
trie.add('B' )
trie.add('C' )
self.assertEqual(trie.split('ABC' ) ,['AB', 'C'] )
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('ABC' )
trie.add('B' )
trie.add('CD' )
self.assertEqual(trie.split('ABCD' ) ,['ABC', 'D'] )
def _lowerCAmelCase ( self : Optional[Any] ):
# Even if the offsets are wrong, we necessarily output correct string
# parts.
SCREAMING_SNAKE_CASE =Trie()
SCREAMING_SNAKE_CASE =trie.cut_text('ABC' ,[0, 0, 2, 1, 2, 3] )
self.assertEqual(snake_case ,['AB', 'C'] )
| 334 | 1 |
from __future__ import annotations
import math
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
if len(lowerCAmelCase_ ) != 2 or len(a[0] ) != 2 or len(lowerCAmelCase_ ) != 2 or len(b[0] ) != 2:
raise Exception('Matrices are not 2x2' )
SCREAMING_SNAKE_CASE =[
[a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
[a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
]
return new_matrix
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
return [
[matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row] ) )]
for row in range(len(lowerCAmelCase_ ) )
]
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
return [
[matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row] ) )]
for row in range(len(lowerCAmelCase_ ) )
]
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
if len(lowerCAmelCase_ ) % 2 != 0 or len(a[0] ) % 2 != 0:
raise Exception('Odd matrices are not supported!' )
SCREAMING_SNAKE_CASE =len(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =matrix_length // 2
SCREAMING_SNAKE_CASE =[[a[i][j] for j in range(lowerCAmelCase_, lowerCAmelCase_ )] for i in range(lowerCAmelCase_ )]
SCREAMING_SNAKE_CASE =[
[a[i][j] for j in range(lowerCAmelCase_, lowerCAmelCase_ )] for i in range(lowerCAmelCase_, lowerCAmelCase_ )
]
SCREAMING_SNAKE_CASE =[[a[i][j] for j in range(lowerCAmelCase_ )] for i in range(lowerCAmelCase_ )]
SCREAMING_SNAKE_CASE =[[a[i][j] for j in range(lowerCAmelCase_ )] for i in range(lowerCAmelCase_, lowerCAmelCase_ )]
return top_left, top_right, bot_left, bot_right
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
return len(lowerCAmelCase_ ), len(matrix[0] )
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
print('\n'.join(str(lowerCAmelCase_ ) for line in matrix ) )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
if matrix_dimensions(lowerCAmelCase_ ) == (2, 2):
return default_matrix_multiplication(lowerCAmelCase_, lowerCAmelCase_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =split_matrix(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =split_matrix(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =actual_strassen(lowerCAmelCase_, matrix_subtraction(lowerCAmelCase_, lowerCAmelCase_ ) )
SCREAMING_SNAKE_CASE =actual_strassen(matrix_addition(lowerCAmelCase_, lowerCAmelCase_ ), lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =actual_strassen(matrix_addition(lowerCAmelCase_, lowerCAmelCase_ ), lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =actual_strassen(lowerCAmelCase_, matrix_subtraction(lowerCAmelCase_, lowerCAmelCase_ ) )
SCREAMING_SNAKE_CASE =actual_strassen(matrix_addition(lowerCAmelCase_, lowerCAmelCase_ ), matrix_addition(lowerCAmelCase_, lowerCAmelCase_ ) )
SCREAMING_SNAKE_CASE =actual_strassen(matrix_subtraction(lowerCAmelCase_, lowerCAmelCase_ ), matrix_addition(lowerCAmelCase_, lowerCAmelCase_ ) )
SCREAMING_SNAKE_CASE =actual_strassen(matrix_subtraction(lowerCAmelCase_, lowerCAmelCase_ ), matrix_addition(lowerCAmelCase_, lowerCAmelCase_ ) )
SCREAMING_SNAKE_CASE =matrix_addition(matrix_subtraction(matrix_addition(lowerCAmelCase_, lowerCAmelCase_ ), lowerCAmelCase_ ), lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =matrix_addition(lowerCAmelCase_, lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =matrix_addition(lowerCAmelCase_, lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =matrix_subtraction(matrix_subtraction(matrix_addition(lowerCAmelCase_, lowerCAmelCase_ ), lowerCAmelCase_ ), lowerCAmelCase_ )
# construct the new matrix from our 4 quadrants
SCREAMING_SNAKE_CASE =[]
for i in range(len(lowerCAmelCase_ ) ):
new_matrix.append(top_left[i] + top_right[i] )
for i in range(len(lowerCAmelCase_ ) ):
new_matrix.append(bot_left[i] + bot_right[i] )
return new_matrix
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
if matrix_dimensions(lowerCAmelCase_ )[1] != matrix_dimensions(lowerCAmelCase_ )[0]:
SCREAMING_SNAKE_CASE =(
'Unable to multiply these matrices, please check the dimensions.\n'
F'Matrix A: {matrixa}\n'
F'Matrix B: {matrixa}'
)
raise Exception(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =matrix_dimensions(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =matrix_dimensions(lowerCAmelCase_ )
if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
return [matrixa, matrixa]
SCREAMING_SNAKE_CASE =max(*lowerCAmelCase_, *lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =int(math.pow(2, math.ceil(math.loga(lowerCAmelCase_ ) ) ) )
SCREAMING_SNAKE_CASE =matrixa
SCREAMING_SNAKE_CASE =matrixa
# Adding zeros to the matrices so that the arrays dimensions are the same and also
# power of 2
for i in range(0, lowerCAmelCase_ ):
if i < dimensiona[0]:
for _ in range(dimensiona[1], lowerCAmelCase_ ):
new_matrixa[i].append(0 )
else:
new_matrixa.append([0] * maxim )
if i < dimensiona[0]:
for _ in range(dimensiona[1], lowerCAmelCase_ ):
new_matrixa[i].append(0 )
else:
new_matrixa.append([0] * maxim )
SCREAMING_SNAKE_CASE =actual_strassen(lowerCAmelCase_, lowerCAmelCase_ )
# Removing the additional zeros
for i in range(0, lowerCAmelCase_ ):
if i < dimensiona[0]:
for _ in range(dimensiona[1], lowerCAmelCase_ ):
final_matrix[i].pop()
else:
final_matrix.pop()
return final_matrix
if __name__ == "__main__":
_lowerCamelCase =[
[2, 3, 4, 5],
[6, 4, 3, 1],
[2, 3, 6, 7],
[3, 1, 2, 4],
[2, 3, 4, 5],
[6, 4, 3, 1],
[2, 3, 6, 7],
[3, 1, 2, 4],
[2, 3, 4, 5],
[6, 2, 3, 1],
]
_lowerCamelCase =[[0, 2, 1, 1], [16, 2, 3, 3], [2, 2, 7, 7], [13, 11, 22, 4]]
print(strassen(matrixa, matrixa))
| 334 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase =[
("bert.bert", "visual_bert"),
("bert.cls", "cls"),
("bert.classifier", "cls"),
("token_type_embeddings_visual", "visual_token_type_embeddings"),
("position_embeddings_visual", "visual_position_embeddings"),
("projection", "visual_projection"),
]
_lowerCamelCase =[
"nlvr2_coco_pre_trained.th",
"nlvr2_fine_tuned.th",
"nlvr2_pre_trained.th",
"vcr_coco_pre_train.th",
"vcr_fine_tune.th",
"vcr_pre_train.th",
"vqa_coco_pre_trained.th",
"vqa_fine_tuned.th",
"vqa_pre_trained.th",
]
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =torch.load(lowerCAmelCase_, map_location='cpu' )
return sd
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_=rename_keys_prefix ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =OrderedDict()
SCREAMING_SNAKE_CASE =torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
SCREAMING_SNAKE_CASE =key
for name_pair in rename_keys_prefix:
SCREAMING_SNAKE_CASE =new_key.replace(name_pair[0], name_pair[1] )
SCREAMING_SNAKE_CASE =d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
SCREAMING_SNAKE_CASE =new_d['cls.predictions.bias']
return new_d
@torch.no_grad()
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
assert (
checkpoint_path.split('/' )[-1] in ACCEPTABLE_CHECKPOINTS
), F'The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'
# Get Config
if "pre" in checkpoint_path:
SCREAMING_SNAKE_CASE ='pretraining'
if "vcr" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 512}
elif "vqa_advanced" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 2048}
elif "vqa" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 2048}
elif "nlvr" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 1024}
else:
raise NotImplementedError(F'No implementation found for `{checkpoint_path}`.' )
else:
if "vcr" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 512}
SCREAMING_SNAKE_CASE ='multichoice'
elif "vqa_advanced" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 2048}
SCREAMING_SNAKE_CASE ='vqa_advanced'
elif "vqa" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 2048, 'num_labels': 3129}
SCREAMING_SNAKE_CASE ='vqa'
elif "nlvr" in checkpoint_path:
SCREAMING_SNAKE_CASE ={
'visual_embedding_dim': 1024,
'num_labels': 2,
}
SCREAMING_SNAKE_CASE ='nlvr'
SCREAMING_SNAKE_CASE =VisualBertConfig(**lowerCAmelCase_ )
# Load State Dict
SCREAMING_SNAKE_CASE =load_state_dict(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =get_new_dict(lowerCAmelCase_, lowerCAmelCase_ )
if model_type == "pretraining":
SCREAMING_SNAKE_CASE =VisualBertForPreTraining(lowerCAmelCase_ )
elif model_type == "vqa":
SCREAMING_SNAKE_CASE =VisualBertForQuestionAnswering(lowerCAmelCase_ )
elif model_type == "nlvr":
SCREAMING_SNAKE_CASE =VisualBertForVisualReasoning(lowerCAmelCase_ )
elif model_type == "multichoice":
SCREAMING_SNAKE_CASE =VisualBertForMultipleChoice(lowerCAmelCase_ )
model.load_state_dict(lowerCAmelCase_ )
# Save Checkpoints
Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ )
model.save_pretrained(lowerCAmelCase_ )
if __name__ == "__main__":
_lowerCamelCase =argparse.ArgumentParser()
# Required parameters
parser.add_argument("orig_checkpoint_path", type=str, help="A path to .th on local filesystem.")
parser.add_argument("pytorch_dump_folder_path", type=str, help="Path to the output PyTorch model.")
_lowerCamelCase =parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 334 | 1 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs
from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase =logging.get_logger()
@dataclass
class a_ :
"""simple docstring"""
__UpperCAmelCase = 42
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
def _lowerCAmelCase ( self : List[Any] ,snake_case : Dict ,snake_case : Tensor ,snake_case : Tensor ):
SCREAMING_SNAKE_CASE =len(list(m.modules() ) ) == 1 or isinstance(snake_case ,nn.Convad ) or isinstance(snake_case ,nn.BatchNormad )
if has_not_submodules:
self.traced.append(snake_case )
def __call__( self : List[str] ,snake_case : Tensor ):
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(snake_case )
[x.remove() for x in self.handles]
return self
@property
def _lowerCAmelCase ( self : Optional[Any] ):
# check the len of the state_dict keys to see if we have learnable params
return list(filter(lambda snake_case : len(list(x.state_dict().keys() ) ) > 0 ,self.traced ) )
@dataclass
class a_ :
"""simple docstring"""
__UpperCAmelCase = 42
__UpperCAmelCase = 42
__UpperCAmelCase = 1
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
__UpperCAmelCase = True
def __call__( self : str ,snake_case : Tensor ):
SCREAMING_SNAKE_CASE =Tracker(self.dest )(snake_case ).parametrized
SCREAMING_SNAKE_CASE =Tracker(self.src )(snake_case ).parametrized
SCREAMING_SNAKE_CASE =list(filter(lambda snake_case : type(snake_case ) not in self.src_skip ,snake_case ) )
SCREAMING_SNAKE_CASE =list(filter(lambda snake_case : type(snake_case ) not in self.dest_skip ,snake_case ) )
if len(snake_case ) != len(snake_case ) and self.raise_if_mismatch:
raise Exception(
f'Numbers of operations are different. Source module has {len(snake_case )} operations while'
f' destination module has {len(snake_case )}.' )
for dest_m, src_m in zip(snake_case ,snake_case ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(f'Transfered from={src_m} to={dest_m}' )
class a_ ( nn.Module ):
"""simple docstring"""
def __init__( self : Any ,snake_case : nn.Module ):
super().__init__()
SCREAMING_SNAKE_CASE =[]
# - get the stem
feature_blocks.append(('conv1', model.stem) )
# - get all the feature blocks
for k, v in model.trunk_output.named_children():
assert k.startswith('block' ), f'Unexpected layer name {k}'
SCREAMING_SNAKE_CASE =len(snake_case ) + 1
feature_blocks.append((f'res{block_index}', v) )
SCREAMING_SNAKE_CASE =nn.ModuleDict(snake_case )
def _lowerCAmelCase ( self : Dict ,snake_case : Tensor ):
return get_trunk_forward_outputs(
snake_case ,out_feat_keys=snake_case ,feature_blocks=self._feature_blocks ,)
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
def _lowerCAmelCase ( self : Optional[int] ,snake_case : str ):
SCREAMING_SNAKE_CASE =x.split('-' )
return x_split[0] + x_split[1] + "_" + "".join(x_split[2:] )
def __getitem__( self : Optional[Any] ,snake_case : str ):
# default to timm!
if x not in self:
SCREAMING_SNAKE_CASE =self.convert_name_to_timm(snake_case )
SCREAMING_SNAKE_CASE =partial(lambda: (timm.create_model(snake_case ,pretrained=snake_case ).eval(), None) )
else:
SCREAMING_SNAKE_CASE =super().__getitem__(snake_case )
return val
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
def __getitem__( self : int ,snake_case : str ):
if "seer" in x and "in1k" not in x:
SCREAMING_SNAKE_CASE =RegNetModel
else:
SCREAMING_SNAKE_CASE =RegNetForImageClassification
return val
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
for from_key, to_key in keys:
SCREAMING_SNAKE_CASE =from_state_dict[from_key].clone()
print(F'Copied key={from_key} to={to_key}' )
return to_state_dict
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ = True, ):
"""simple docstring"""
print(F'Converting {name}...' )
with torch.no_grad():
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =from_model_func()
SCREAMING_SNAKE_CASE =our_model_func(lowerCAmelCase_ ).eval()
SCREAMING_SNAKE_CASE =ModuleTransfer(src=lowerCAmelCase_, dest=lowerCAmelCase_, raise_if_mismatch=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =torch.randn((1, 3, 224, 224) )
module_transfer(lowerCAmelCase_ )
if from_state_dict is not None:
SCREAMING_SNAKE_CASE =[]
# for seer - in1k finetuned we have to manually copy the head
if "seer" in name and "in1k" in name:
SCREAMING_SNAKE_CASE =[('0.clf.0.weight', 'classifier.1.weight'), ('0.clf.0.bias', 'classifier.1.bias')]
SCREAMING_SNAKE_CASE =manually_copy_vissl_head(lowerCAmelCase_, our_model.state_dict(), lowerCAmelCase_ )
our_model.load_state_dict(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =our_model(lowerCAmelCase_, output_hidden_states=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =(
our_outputs.logits if isinstance(lowerCAmelCase_, lowerCAmelCase_ ) else our_outputs.last_hidden_state
)
SCREAMING_SNAKE_CASE =from_model(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =from_output[-1] if type(lowerCAmelCase_ ) is list else from_output
# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
if "seer" in name and "in1k" in name:
SCREAMING_SNAKE_CASE =our_outputs.hidden_states[-1]
assert torch.allclose(lowerCAmelCase_, lowerCAmelCase_ ), "The model logits don't match the original one."
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / name, commit_message='Add model', use_temp_dir=lowerCAmelCase_, )
SCREAMING_SNAKE_CASE =224 if 'seer' not in name else 384
# we can use the convnext one
SCREAMING_SNAKE_CASE =AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k', size=lowerCAmelCase_ )
image_processor.push_to_hub(
repo_path_or_name=save_directory / name, commit_message='Add image processor', use_temp_dir=lowerCAmelCase_, )
print(F'Pushed {name}' )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ = None, lowerCAmelCase_ = True ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ='imagenet-1k-id2label.json'
SCREAMING_SNAKE_CASE =1000
SCREAMING_SNAKE_CASE =(1, num_labels)
SCREAMING_SNAKE_CASE ='huggingface/label-files'
SCREAMING_SNAKE_CASE =num_labels
SCREAMING_SNAKE_CASE =json.load(open(cached_download(hf_hub_url(lowerCAmelCase_, lowerCAmelCase_, repo_type='dataset' ) ), 'r' ) )
SCREAMING_SNAKE_CASE ={int(lowerCAmelCase_ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE =idalabel
SCREAMING_SNAKE_CASE ={v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE =partial(lowerCAmelCase_, num_labels=lowerCAmelCase_, idalabel=lowerCAmelCase_, labelaid=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE ={
'regnet-x-002': ImageNetPreTrainedConfig(
depths=[1, 1, 4, 7], hidden_sizes=[24, 56, 152, 368], groups_width=8, layer_type='x' ),
'regnet-x-004': ImageNetPreTrainedConfig(
depths=[1, 2, 7, 12], hidden_sizes=[32, 64, 160, 384], groups_width=16, layer_type='x' ),
'regnet-x-006': ImageNetPreTrainedConfig(
depths=[1, 3, 5, 7], hidden_sizes=[48, 96, 240, 528], groups_width=24, layer_type='x' ),
'regnet-x-008': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 5], hidden_sizes=[64, 128, 288, 672], groups_width=16, layer_type='x' ),
'regnet-x-016': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 2], hidden_sizes=[72, 168, 408, 912], groups_width=24, layer_type='x' ),
'regnet-x-032': ImageNetPreTrainedConfig(
depths=[2, 6, 15, 2], hidden_sizes=[96, 192, 432, 1008], groups_width=48, layer_type='x' ),
'regnet-x-040': ImageNetPreTrainedConfig(
depths=[2, 5, 14, 2], hidden_sizes=[80, 240, 560, 1360], groups_width=40, layer_type='x' ),
'regnet-x-064': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1], hidden_sizes=[168, 392, 784, 1624], groups_width=56, layer_type='x' ),
'regnet-x-080': ImageNetPreTrainedConfig(
depths=[2, 5, 15, 1], hidden_sizes=[80, 240, 720, 1920], groups_width=120, layer_type='x' ),
'regnet-x-120': ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1], hidden_sizes=[224, 448, 896, 2240], groups_width=112, layer_type='x' ),
'regnet-x-160': ImageNetPreTrainedConfig(
depths=[2, 6, 13, 1], hidden_sizes=[256, 512, 896, 2048], groups_width=128, layer_type='x' ),
'regnet-x-320': ImageNetPreTrainedConfig(
depths=[2, 7, 13, 1], hidden_sizes=[336, 672, 1344, 2520], groups_width=168, layer_type='x' ),
# y variant
'regnet-y-002': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7], hidden_sizes=[24, 56, 152, 368], groups_width=8 ),
'regnet-y-004': ImageNetPreTrainedConfig(
depths=[1, 3, 6, 6], hidden_sizes=[48, 104, 208, 440], groups_width=8 ),
'regnet-y-006': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 4], hidden_sizes=[48, 112, 256, 608], groups_width=16 ),
'regnet-y-008': ImageNetPreTrainedConfig(
depths=[1, 3, 8, 2], hidden_sizes=[64, 128, 320, 768], groups_width=16 ),
'regnet-y-016': ImageNetPreTrainedConfig(
depths=[2, 6, 17, 2], hidden_sizes=[48, 120, 336, 888], groups_width=24 ),
'regnet-y-032': ImageNetPreTrainedConfig(
depths=[2, 5, 13, 1], hidden_sizes=[72, 216, 576, 1512], groups_width=24 ),
'regnet-y-040': ImageNetPreTrainedConfig(
depths=[2, 6, 12, 2], hidden_sizes=[128, 192, 512, 1088], groups_width=64 ),
'regnet-y-064': ImageNetPreTrainedConfig(
depths=[2, 7, 14, 2], hidden_sizes=[144, 288, 576, 1296], groups_width=72 ),
'regnet-y-080': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1], hidden_sizes=[168, 448, 896, 2016], groups_width=56 ),
'regnet-y-120': ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1], hidden_sizes=[224, 448, 896, 2240], groups_width=112 ),
'regnet-y-160': ImageNetPreTrainedConfig(
depths=[2, 4, 11, 1], hidden_sizes=[224, 448, 1232, 3024], groups_width=112 ),
'regnet-y-320': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1], hidden_sizes=[232, 696, 1392, 3712], groups_width=232 ),
# models created by SEER -> https://arxiv.org/abs/2202.08360
'regnet-y-320-seer': RegNetConfig(depths=[2, 5, 12, 1], hidden_sizes=[232, 696, 1392, 3712], groups_width=232 ),
'regnet-y-640-seer': RegNetConfig(depths=[2, 5, 12, 1], hidden_sizes=[328, 984, 1968, 4920], groups_width=328 ),
'regnet-y-1280-seer': RegNetConfig(
depths=[2, 7, 17, 1], hidden_sizes=[528, 1056, 2904, 7392], groups_width=264 ),
'regnet-y-2560-seer': RegNetConfig(
depths=[3, 7, 16, 1], hidden_sizes=[640, 1696, 2544, 5088], groups_width=640 ),
'regnet-y-10b-seer': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1], hidden_sizes=[2020, 4040, 11110, 28280], groups_width=1010 ),
# finetuned on imagenet
'regnet-y-320-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1], hidden_sizes=[232, 696, 1392, 3712], groups_width=232 ),
'regnet-y-640-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1], hidden_sizes=[328, 984, 1968, 4920], groups_width=328 ),
'regnet-y-1280-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1], hidden_sizes=[528, 1056, 2904, 7392], groups_width=264 ),
'regnet-y-2560-seer-in1k': ImageNetPreTrainedConfig(
depths=[3, 7, 16, 1], hidden_sizes=[640, 1696, 2544, 5088], groups_width=640 ),
'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1], hidden_sizes=[2020, 4040, 11110, 28280], groups_width=1010 ),
}
SCREAMING_SNAKE_CASE =NameToOurModelFuncMap()
SCREAMING_SNAKE_CASE =NameToFromModelFuncMap()
# add seer weights logic
def load_using_classy_vision(lowerCAmelCase_, lowerCAmelCase_ ) -> Tuple[nn.Module, Dict]:
SCREAMING_SNAKE_CASE =torch.hub.load_state_dict_from_url(lowerCAmelCase_, model_dir=str(lowerCAmelCase_ ), map_location='cpu' )
SCREAMING_SNAKE_CASE =model_func()
# check if we have a head, if yes add it
SCREAMING_SNAKE_CASE =files['classy_state_dict']['base_model']['model']
SCREAMING_SNAKE_CASE =model_state_dict['trunk']
model.load_state_dict(lowerCAmelCase_ )
return model.eval(), model_state_dict["heads"]
# pretrained
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch', lambda: FakeRegNetVisslWrapper(RegNetYaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch', lambda: FakeRegNetVisslWrapper(RegNetYaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch', lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch', lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27, group_width=1010, w_a=1744, w_a=620.83, w_m=2.52 ) ) ), )
# IN1K finetuned
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch', lambda: FakeRegNetVisslWrapper(RegNetYaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch', lambda: FakeRegNetVisslWrapper(RegNetYaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch', lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch', lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27, group_width=1010, w_a=1744, w_a=620.83, w_m=2.52 ) ) ), )
if model_name:
convert_weight_and_push(
lowerCAmelCase_, names_to_from_model_map[model_name], names_to_ours_model_map[model_name], names_to_config[model_name], lowerCAmelCase_, lowerCAmelCase_, )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(
lowerCAmelCase_, names_to_from_model_map[model_name], names_to_ours_model_map[model_name], lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, )
return config, expected_shape
if __name__ == "__main__":
_lowerCamelCase =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default=None,
type=str,
help=(
"The name of the model you wish to convert, it must be one of the supported regnet* architecture,"
" currently: regnetx-*, regnety-*. If `None`, all of them will the converted."
),
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=Path,
required=True,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
default=True,
type=bool,
required=False,
help="If True, push model and image processor to the hub.",
)
_lowerCamelCase =parser.parse_args()
_lowerCamelCase =args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 334 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"facebook/nllb-moe-54B": "https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json",
}
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'nllb-moe'
__UpperCAmelCase = ['past_key_values']
__UpperCAmelCase = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self : str ,snake_case : Optional[int]=128112 ,snake_case : Any=1024 ,snake_case : List[str]=12 ,snake_case : Optional[int]=4096 ,snake_case : List[str]=16 ,snake_case : Optional[Any]=12 ,snake_case : Optional[Any]=4096 ,snake_case : List[Any]=16 ,snake_case : Optional[Any]=0.05 ,snake_case : str=0.05 ,snake_case : Optional[int]=True ,snake_case : Tuple=True ,snake_case : Optional[Any]="relu" ,snake_case : Any=1024 ,snake_case : List[Any]=0.1 ,snake_case : List[Any]=0.1 ,snake_case : Optional[Any]=0.0 ,snake_case : List[Any]=0.02 ,snake_case : Any=2 ,snake_case : Dict=True ,snake_case : Tuple=False ,snake_case : Any="float32" ,snake_case : Tuple=False ,snake_case : List[Any]=128 ,snake_case : Tuple=64 ,snake_case : List[Any]=4 ,snake_case : List[Any]=4 ,snake_case : List[Any]=0.001 ,snake_case : int=0.001 ,snake_case : Tuple="all" ,snake_case : Union[str, Any]=False ,snake_case : Union[str, Any]=False ,snake_case : Optional[int]=1.0 ,snake_case : Optional[Any]=0.2 ,snake_case : Optional[int]=1 ,snake_case : Union[str, Any]=0 ,snake_case : Tuple=2 ,snake_case : List[Any]=False ,**snake_case : List[Any] ,):
SCREAMING_SNAKE_CASE =vocab_size
SCREAMING_SNAKE_CASE =max_position_embeddings
SCREAMING_SNAKE_CASE =d_model
SCREAMING_SNAKE_CASE =encoder_ffn_dim
SCREAMING_SNAKE_CASE =encoder_layers
SCREAMING_SNAKE_CASE =encoder_attention_heads
SCREAMING_SNAKE_CASE =decoder_ffn_dim
SCREAMING_SNAKE_CASE =decoder_layers
SCREAMING_SNAKE_CASE =decoder_attention_heads
SCREAMING_SNAKE_CASE =dropout
SCREAMING_SNAKE_CASE =attention_dropout
SCREAMING_SNAKE_CASE =activation_dropout
SCREAMING_SNAKE_CASE =activation_function
SCREAMING_SNAKE_CASE =init_std
SCREAMING_SNAKE_CASE =encoder_layerdrop
SCREAMING_SNAKE_CASE =decoder_layerdrop
SCREAMING_SNAKE_CASE =use_cache
SCREAMING_SNAKE_CASE =encoder_layers
SCREAMING_SNAKE_CASE =scale_embedding # scale factor will be sqrt(d_model) if True
SCREAMING_SNAKE_CASE =router_z_loss_coef
SCREAMING_SNAKE_CASE =router_aux_loss_coef
SCREAMING_SNAKE_CASE =decoder_sparse_step
SCREAMING_SNAKE_CASE =encoder_sparse_step
SCREAMING_SNAKE_CASE =num_experts
SCREAMING_SNAKE_CASE =expert_capacity
SCREAMING_SNAKE_CASE =router_bias
if router_dtype not in ["float32", "float16", "bfloat16"]:
raise ValueError(f'`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}' )
SCREAMING_SNAKE_CASE =router_dtype
SCREAMING_SNAKE_CASE =router_ignore_padding_tokens
SCREAMING_SNAKE_CASE =batch_prioritized_routing
SCREAMING_SNAKE_CASE =second_expert_policy
SCREAMING_SNAKE_CASE =normalize_router_prob_before_dropping
SCREAMING_SNAKE_CASE =moe_eval_capacity_token_fraction
SCREAMING_SNAKE_CASE =moe_token_dropout
SCREAMING_SNAKE_CASE =output_router_logits
super().__init__(
pad_token_id=snake_case ,bos_token_id=snake_case ,eos_token_id=snake_case ,is_encoder_decoder=snake_case ,decoder_start_token_id=snake_case ,**snake_case ,)
| 334 | 1 |
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer,
BertTokenizer,
BertTokenizerFast,
GPTaTokenizerFast,
is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_tokenization import CustomTokenizer # noqa E402
if is_tokenizers_available():
from test_module.custom_tokenization_fast import CustomTokenizerFast
class a_ ( unittest.TestCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : Dict ):
# A mock response for an HTTP head request to emulate server down
SCREAMING_SNAKE_CASE =mock.Mock()
SCREAMING_SNAKE_CASE =500
SCREAMING_SNAKE_CASE ={}
SCREAMING_SNAKE_CASE =HTTPError
SCREAMING_SNAKE_CASE ={}
# Download this model to make sure it's in the cache.
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert' )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch('requests.Session.request' ,return_value=snake_case ) as mock_head:
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert' )
# This check we did call the fake head request
mock_head.assert_called()
@require_tokenizers
def _lowerCAmelCase ( self : Optional[Any] ):
# A mock response for an HTTP head request to emulate server down
SCREAMING_SNAKE_CASE =mock.Mock()
SCREAMING_SNAKE_CASE =500
SCREAMING_SNAKE_CASE ={}
SCREAMING_SNAKE_CASE =HTTPError
SCREAMING_SNAKE_CASE ={}
# Download this model to make sure it's in the cache.
SCREAMING_SNAKE_CASE =GPTaTokenizerFast.from_pretrained('gpt2' )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch('requests.Session.request' ,return_value=snake_case ) as mock_head:
SCREAMING_SNAKE_CASE =GPTaTokenizerFast.from_pretrained('gpt2' )
# This check we did call the fake head request
mock_head.assert_called()
def _lowerCAmelCase ( self : Union[str, Any] ):
# This test is for deprecated behavior and can be removed in v5
try:
SCREAMING_SNAKE_CASE =tempfile.mktemp()
with open(snake_case ,'wb' ) as f:
http_get('https://huggingface.co/albert-base-v1/resolve/main/spiece.model' ,snake_case )
SCREAMING_SNAKE_CASE =AlbertTokenizer.from_pretrained(snake_case )
finally:
os.remove(snake_case )
# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
# the current folder and have the right name.
if os.path.isfile('tokenizer.json' ):
# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
return
try:
with open('tokenizer.json' ,'wb' ) as f:
http_get('https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json' ,snake_case )
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
self.assertEqual(tokenizer.vocab_size ,1000 )
# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.
finally:
os.remove('tokenizer.json' )
def _lowerCAmelCase ( self : int ):
# This test is for deprecated behavior and can be removed in v5
SCREAMING_SNAKE_CASE =AlbertTokenizer.from_pretrained('https://huggingface.co/albert-base-v1/resolve/main/spiece.model' )
@is_staging_test
class a_ ( unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase = ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']
@classmethod
def _lowerCAmelCase ( cls : List[Any] ):
SCREAMING_SNAKE_CASE =TOKEN
HfFolder.save_token(snake_case )
@classmethod
def _lowerCAmelCase ( cls : Tuple ):
try:
delete_repo(token=cls._token ,repo_id='test-tokenizer' )
except HTTPError:
pass
try:
delete_repo(token=cls._token ,repo_id='valid_org/test-tokenizer-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token ,repo_id='test-dynamic-tokenizer' )
except HTTPError:
pass
def _lowerCAmelCase ( self : Any ):
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,'vocab.txt' )
with open(snake_case ,'w' ,encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
SCREAMING_SNAKE_CASE =BertTokenizer(snake_case )
tokenizer.push_to_hub('test-tokenizer' ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained(f'{USER}/test-tokenizer' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
# Reset repo
delete_repo(token=self._token ,repo_id='test-tokenizer' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(snake_case ,repo_id='test-tokenizer' ,push_to_hub=snake_case ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained(f'{USER}/test-tokenizer' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
def _lowerCAmelCase ( self : Optional[Any] ):
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,'vocab.txt' )
with open(snake_case ,'w' ,encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
SCREAMING_SNAKE_CASE =BertTokenizer(snake_case )
tokenizer.push_to_hub('valid_org/test-tokenizer-org' ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained('valid_org/test-tokenizer-org' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
# Reset repo
delete_repo(token=self._token ,repo_id='valid_org/test-tokenizer-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(
snake_case ,repo_id='valid_org/test-tokenizer-org' ,push_to_hub=snake_case ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained('valid_org/test-tokenizer-org' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
@require_tokenizers
def _lowerCAmelCase ( self : str ):
CustomTokenizer.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,'vocab.txt' )
with open(snake_case ,'w' ,encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
SCREAMING_SNAKE_CASE =CustomTokenizer(snake_case )
# No fast custom tokenizer
tokenizer.push_to_hub('test-dynamic-tokenizer' ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer' ,trust_remote_code=snake_case )
# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,'CustomTokenizer' )
# Fast and slow custom tokenizer
CustomTokenizerFast.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,'vocab.txt' )
with open(snake_case ,'w' ,encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
SCREAMING_SNAKE_CASE =BertTokenizerFast.from_pretrained(snake_case )
bert_tokenizer.save_pretrained(snake_case )
SCREAMING_SNAKE_CASE =CustomTokenizerFast.from_pretrained(snake_case )
tokenizer.push_to_hub('test-dynamic-tokenizer' ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer' ,trust_remote_code=snake_case )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,'CustomTokenizerFast' )
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained(
f'{USER}/test-dynamic-tokenizer' ,use_fast=snake_case ,trust_remote_code=snake_case )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,'CustomTokenizer' )
class a_ ( unittest.TestCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('Hello 友達' )
self.assertEqual(trie.data ,{'H': {'e': {'l': {'l': {'o': {' ': {'友': {'達': {'': 1}}}}}}}}} )
trie.add('Hello' )
trie.data
self.assertEqual(trie.data ,{'H': {'e': {'l': {'l': {'o': {'': 1, ' ': {'友': {'達': {'': 1}}}}}}}}} )
def _lowerCAmelCase ( self : Optional[int] ):
SCREAMING_SNAKE_CASE =Trie()
self.assertEqual(trie.split('[CLS] This is a extra_id_100' ) ,['[CLS] This is a extra_id_100'] )
trie.add('[CLS]' )
trie.add('extra_id_1' )
trie.add('extra_id_100' )
self.assertEqual(trie.split('[CLS] This is a extra_id_100' ) ,['[CLS]', ' This is a ', 'extra_id_100'] )
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('A' )
self.assertEqual(trie.split('ABC' ) ,['A', 'BC'] )
self.assertEqual(trie.split('BCA' ) ,['BC', 'A'] )
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('TOKEN]' )
trie.add('[SPECIAL_TOKEN]' )
self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]' ) ,['This is something ', '[SPECIAL_TOKEN]'] )
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('A' )
trie.add('P' )
trie.add('[SPECIAL_TOKEN]' )
self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]' ) ,['This is something ', '[SPECIAL_TOKEN]'] )
def _lowerCAmelCase ( self : Dict ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('AB' )
trie.add('B' )
trie.add('C' )
self.assertEqual(trie.split('ABC' ) ,['AB', 'C'] )
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('ABC' )
trie.add('B' )
trie.add('CD' )
self.assertEqual(trie.split('ABCD' ) ,['ABC', 'D'] )
def _lowerCAmelCase ( self : Optional[Any] ):
# Even if the offsets are wrong, we necessarily output correct string
# parts.
SCREAMING_SNAKE_CASE =Trie()
SCREAMING_SNAKE_CASE =trie.cut_text('ABC' ,[0, 0, 2, 1, 2, 3] )
self.assertEqual(snake_case ,['AB', 'C'] )
| 334 |
from __future__ import annotations
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =sorted(numsa + numsa )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =divmod(len(lowerCAmelCase_ ), 2 )
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowerCamelCase =[float(x) for x in input("Enter the elements of first array: ").split()]
_lowerCamelCase =[float(x) for x in input("Enter the elements of second array: ").split()]
print(f'The median of two arrays is: {median_of_two_arrays(array_a, array_a)}')
| 334 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
from .pipeline_kandinsky import KandinskyPipeline
from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
from .text_encoder import MultilingualCLIP
| 334 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'transfo-xl'
__UpperCAmelCase = ['mems']
__UpperCAmelCase = {
'n_token': 'vocab_size',
'hidden_size': 'd_model',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self : Union[str, Any] ,snake_case : List[Any]=267735 ,snake_case : Optional[int]=[20000, 40000, 200000] ,snake_case : int=1024 ,snake_case : Optional[Any]=1024 ,snake_case : Tuple=16 ,snake_case : int=64 ,snake_case : Union[str, Any]=4096 ,snake_case : List[str]=4 ,snake_case : int=False ,snake_case : int=18 ,snake_case : Tuple=1600 ,snake_case : List[str]=1000 ,snake_case : Optional[Any]=True ,snake_case : List[str]=True ,snake_case : Optional[Any]=0 ,snake_case : Optional[Any]=-1 ,snake_case : List[Any]=True ,snake_case : Optional[Any]=0.1 ,snake_case : Union[str, Any]=0.0 ,snake_case : int=True ,snake_case : Any="normal" ,snake_case : int=0.01 ,snake_case : int=0.01 ,snake_case : str=0.02 ,snake_case : Any=1e-5 ,snake_case : Optional[int]=0 ,**snake_case : List[Any] ,):
SCREAMING_SNAKE_CASE =vocab_size
SCREAMING_SNAKE_CASE =[]
self.cutoffs.extend(snake_case )
if proj_share_all_but_first:
SCREAMING_SNAKE_CASE =[False] + [True] * len(self.cutoffs )
else:
SCREAMING_SNAKE_CASE =[False] + [False] * len(self.cutoffs )
SCREAMING_SNAKE_CASE =d_model
SCREAMING_SNAKE_CASE =d_embed
SCREAMING_SNAKE_CASE =d_head
SCREAMING_SNAKE_CASE =d_inner
SCREAMING_SNAKE_CASE =div_val
SCREAMING_SNAKE_CASE =pre_lnorm
SCREAMING_SNAKE_CASE =n_layer
SCREAMING_SNAKE_CASE =n_head
SCREAMING_SNAKE_CASE =mem_len
SCREAMING_SNAKE_CASE =same_length
SCREAMING_SNAKE_CASE =attn_type
SCREAMING_SNAKE_CASE =clamp_len
SCREAMING_SNAKE_CASE =sample_softmax
SCREAMING_SNAKE_CASE =adaptive
SCREAMING_SNAKE_CASE =dropout
SCREAMING_SNAKE_CASE =dropatt
SCREAMING_SNAKE_CASE =untie_r
SCREAMING_SNAKE_CASE =init
SCREAMING_SNAKE_CASE =init_range
SCREAMING_SNAKE_CASE =proj_init_std
SCREAMING_SNAKE_CASE =init_std
SCREAMING_SNAKE_CASE =layer_norm_epsilon
super().__init__(eos_token_id=snake_case ,**snake_case )
@property
def _lowerCAmelCase ( self : str ):
# Message copied from Transformer-XL documentation
logger.info(f'The model {self.model_type} is one of the few models that has no sequence length limit.' )
return -1
@max_position_embeddings.setter
def _lowerCAmelCase ( self : Union[str, Any] ,snake_case : Dict ):
# Message copied from Transformer-XL documentation
raise NotImplementedError(
f'The model {self.model_type} is one of the few models that has no sequence length limit.' )
| 334 | 1 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
_lowerCamelCase =logging.get_logger(__name__)
class a_ :
"""simple docstring"""
__UpperCAmelCase = 42
__UpperCAmelCase = None
@staticmethod
def _lowerCAmelCase ( ):
raise NotImplementedError
def _lowerCAmelCase ( self : Dict ,snake_case : Any ,snake_case : int ,snake_case : str ,**snake_case : Optional[Any] ):
raise NotImplementedError
def _lowerCAmelCase ( self : str ,snake_case : List[Any] ):
raise NotImplementedError
def _lowerCAmelCase ( self : List[Any] ):
if not self.is_available():
raise RuntimeError(
f'You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.' )
@classmethod
def _lowerCAmelCase ( cls : Optional[int] ):
return f'`pip install {cls.pip_package or cls.name}`'
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'optuna'
@staticmethod
def _lowerCAmelCase ( ):
return is_optuna_available()
def _lowerCAmelCase ( self : int ,snake_case : str ,snake_case : int ,snake_case : str ,**snake_case : List[str] ):
return run_hp_search_optuna(snake_case ,snake_case ,snake_case ,**snake_case )
def _lowerCAmelCase ( self : str ,snake_case : Optional[int] ):
return default_hp_space_optuna(snake_case )
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'ray'
__UpperCAmelCase = '\'ray[tune]\''
@staticmethod
def _lowerCAmelCase ( ):
return is_ray_available()
def _lowerCAmelCase ( self : int ,snake_case : Union[str, Any] ,snake_case : int ,snake_case : str ,**snake_case : List[str] ):
return run_hp_search_ray(snake_case ,snake_case ,snake_case ,**snake_case )
def _lowerCAmelCase ( self : str ,snake_case : int ):
return default_hp_space_ray(snake_case )
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'sigopt'
@staticmethod
def _lowerCAmelCase ( ):
return is_sigopt_available()
def _lowerCAmelCase ( self : int ,snake_case : Optional[Any] ,snake_case : int ,snake_case : str ,**snake_case : Union[str, Any] ):
return run_hp_search_sigopt(snake_case ,snake_case ,snake_case ,**snake_case )
def _lowerCAmelCase ( self : Tuple ,snake_case : List[Any] ):
return default_hp_space_sigopt(snake_case )
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'wandb'
@staticmethod
def _lowerCAmelCase ( ):
return is_wandb_available()
def _lowerCAmelCase ( self : List[Any] ,snake_case : Tuple ,snake_case : int ,snake_case : str ,**snake_case : List[str] ):
return run_hp_search_wandb(snake_case ,snake_case ,snake_case ,**snake_case )
def _lowerCAmelCase ( self : Tuple ,snake_case : Any ):
return default_hp_space_wandb(snake_case )
_lowerCamelCase ={
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =[backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(lowerCAmelCase_ ) > 0:
SCREAMING_SNAKE_CASE =available_backends[0].name
if len(lowerCAmelCase_ ) > 1:
logger.info(
F'{len(lowerCAmelCase_ )} hyperparameter search backends available. Using {name} as the default.' )
return name
raise RuntimeError(
'No hyperparameter search backend available.\n'
+ '\n'.join(
F' - To install {backend.name} run {backend.pip_install()}'
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 334 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_MAPPING,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
)
from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
import PIL
from PIL import Image
from transformers import BeitImageProcessor
class a_ :
"""simple docstring"""
def __init__( self : Optional[int] ,snake_case : Any ,snake_case : Dict=100 ,snake_case : List[Any]=13 ,snake_case : str=30 ,snake_case : List[str]=2 ,snake_case : List[Any]=3 ,snake_case : Tuple=True ,snake_case : Optional[Any]=True ,snake_case : int=32 ,snake_case : Tuple=4 ,snake_case : List[Any]=4 ,snake_case : Optional[Any]=37 ,snake_case : Optional[Any]="gelu" ,snake_case : Tuple=0.1 ,snake_case : Union[str, Any]=0.1 ,snake_case : List[Any]=10 ,snake_case : Tuple=0.02 ,snake_case : List[str]=3 ,snake_case : Any=None ,snake_case : int=[0, 1, 2, 3] ,):
SCREAMING_SNAKE_CASE =parent
SCREAMING_SNAKE_CASE =100
SCREAMING_SNAKE_CASE =batch_size
SCREAMING_SNAKE_CASE =image_size
SCREAMING_SNAKE_CASE =patch_size
SCREAMING_SNAKE_CASE =num_channels
SCREAMING_SNAKE_CASE =is_training
SCREAMING_SNAKE_CASE =use_labels
SCREAMING_SNAKE_CASE =hidden_size
SCREAMING_SNAKE_CASE =num_hidden_layers
SCREAMING_SNAKE_CASE =num_attention_heads
SCREAMING_SNAKE_CASE =intermediate_size
SCREAMING_SNAKE_CASE =hidden_act
SCREAMING_SNAKE_CASE =hidden_dropout_prob
SCREAMING_SNAKE_CASE =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE =type_sequence_label_size
SCREAMING_SNAKE_CASE =initializer_range
SCREAMING_SNAKE_CASE =scope
SCREAMING_SNAKE_CASE =out_indices
SCREAMING_SNAKE_CASE =num_labels
# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
SCREAMING_SNAKE_CASE =(image_size // patch_size) ** 2
SCREAMING_SNAKE_CASE =num_patches + 1
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE =None
SCREAMING_SNAKE_CASE =None
if self.use_labels:
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size] ,self.type_sequence_label_size )
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size, self.image_size, self.image_size] ,self.num_labels )
SCREAMING_SNAKE_CASE =self.get_config()
return config, pixel_values, labels, pixel_labels
def _lowerCAmelCase ( self : Dict ):
return BeitConfig(
vocab_size=self.vocab_size ,image_size=self.image_size ,patch_size=self.patch_size ,num_channels=self.num_channels ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,is_decoder=snake_case ,initializer_range=self.initializer_range ,out_indices=self.out_indices ,)
def _lowerCAmelCase ( self : Union[str, Any] ,snake_case : Tuple ,snake_case : Optional[Any] ,snake_case : Union[str, Any] ,snake_case : Optional[int] ):
SCREAMING_SNAKE_CASE =BeitModel(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _lowerCAmelCase ( self : Union[str, Any] ,snake_case : Optional[int] ,snake_case : Dict ,snake_case : Any ,snake_case : List[str] ):
SCREAMING_SNAKE_CASE =BeitForMaskedImageModeling(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length - 1, self.vocab_size) )
def _lowerCAmelCase ( self : Optional[Any] ,snake_case : Any ,snake_case : str ,snake_case : Any ,snake_case : str ):
SCREAMING_SNAKE_CASE =self.type_sequence_label_size
SCREAMING_SNAKE_CASE =BeitForImageClassification(snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case ,labels=snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
# test greyscale images
SCREAMING_SNAKE_CASE =1
SCREAMING_SNAKE_CASE =BeitForImageClassification(snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE =model(snake_case ,labels=snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
def _lowerCAmelCase ( self : List[str] ,snake_case : Tuple ,snake_case : str ,snake_case : Optional[int] ,snake_case : int ):
SCREAMING_SNAKE_CASE =self.num_labels
SCREAMING_SNAKE_CASE =BeitForSemanticSegmentation(snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case )
self.parent.assertEqual(
result.logits.shape ,(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
SCREAMING_SNAKE_CASE =model(snake_case ,labels=snake_case )
self.parent.assertEqual(
result.logits.shape ,(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
def _lowerCAmelCase ( self : str ):
SCREAMING_SNAKE_CASE =self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =config_and_inputs
SCREAMING_SNAKE_CASE ={'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class a_ ( lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase = (
(BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
if is_torch_available()
else ()
)
__UpperCAmelCase = (
{
'feature-extraction': BeitModel,
'image-classification': BeitForImageClassification,
'image-segmentation': BeitForSemanticSegmentation,
}
if is_torch_available()
else {}
)
__UpperCAmelCase = False
__UpperCAmelCase = False
__UpperCAmelCase = False
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =BeitModelTester(self )
SCREAMING_SNAKE_CASE =ConfigTester(self ,config_class=snake_case ,has_text_modality=snake_case ,hidden_size=37 )
def _lowerCAmelCase ( self : List[str] ):
self.config_tester.run_common_tests()
@unittest.skip(reason='BEiT does not use inputs_embeds' )
def _lowerCAmelCase ( self : List[Any] ):
pass
@require_torch_multi_gpu
@unittest.skip(reason='BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`' )
def _lowerCAmelCase ( self : Union[str, Any] ):
pass
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE =model_class(snake_case )
self.assertIsInstance(model.get_input_embeddings() ,(nn.Module) )
SCREAMING_SNAKE_CASE =model.get_output_embeddings()
self.assertTrue(x is None or isinstance(snake_case ,nn.Linear ) )
def _lowerCAmelCase ( self : int ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE =model_class(snake_case )
SCREAMING_SNAKE_CASE =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE =[*signature.parameters.keys()]
SCREAMING_SNAKE_CASE =['pixel_values']
self.assertListEqual(arg_names[:1] ,snake_case )
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def _lowerCAmelCase ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*snake_case )
def _lowerCAmelCase ( self : Dict ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case )
def _lowerCAmelCase ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*snake_case )
def _lowerCAmelCase ( self : Any ):
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE =True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if model_class in [*get_values(snake_case ), BeitForMaskedImageModeling]:
continue
SCREAMING_SNAKE_CASE =model_class(snake_case )
model.to(snake_case )
model.train()
SCREAMING_SNAKE_CASE =self._prepare_for_class(snake_case ,snake_case ,return_labels=snake_case )
SCREAMING_SNAKE_CASE =model(**snake_case ).loss
loss.backward()
def _lowerCAmelCase ( self : int ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE =False
SCREAMING_SNAKE_CASE =True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if (
model_class in [*get_values(snake_case ), BeitForMaskedImageModeling]
or not model_class.supports_gradient_checkpointing
):
continue
SCREAMING_SNAKE_CASE =model_class(snake_case )
model.gradient_checkpointing_enable()
model.to(snake_case )
model.train()
SCREAMING_SNAKE_CASE =self._prepare_for_class(snake_case ,snake_case ,return_labels=snake_case )
SCREAMING_SNAKE_CASE =model(**snake_case ).loss
loss.backward()
def _lowerCAmelCase ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE =_config_zero_init(snake_case )
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE =model_class(config=snake_case )
for name, param in model.named_parameters():
# we skip lambda parameters as these require special initial values
# determined by config.layer_scale_init_value
if "lambda" in name:
continue
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() ,[0.0, 1.0] ,msg=f'Parameter {name} of model {model_class} seems not properly initialized' ,)
@slow
def _lowerCAmelCase ( self : List[str] ):
for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE =BeitModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class a_ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def _lowerCAmelCase ( self : Tuple ):
return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224' ) if is_vision_available() else None
@slow
def _lowerCAmelCase ( self : int ):
SCREAMING_SNAKE_CASE =BeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k' ).to(snake_case )
SCREAMING_SNAKE_CASE =self.default_image_processor
SCREAMING_SNAKE_CASE =prepare_img()
SCREAMING_SNAKE_CASE =image_processor(images=snake_case ,return_tensors='pt' ).pixel_values.to(snake_case )
# prepare bool_masked_pos
SCREAMING_SNAKE_CASE =torch.ones((1, 196) ,dtype=torch.bool ).to(snake_case )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE =model(pixel_values=snake_case ,bool_masked_pos=snake_case )
SCREAMING_SNAKE_CASE =outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE =torch.Size((1, 196, 8192) )
self.assertEqual(logits.shape ,snake_case )
SCREAMING_SNAKE_CASE =torch.tensor(
[[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]] ).to(snake_case )
self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3] ,snake_case ,atol=1e-2 ) )
@slow
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =BeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224' ).to(snake_case )
SCREAMING_SNAKE_CASE =self.default_image_processor
SCREAMING_SNAKE_CASE =prepare_img()
SCREAMING_SNAKE_CASE =image_processor(images=snake_case ,return_tensors='pt' ).to(snake_case )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE =model(**snake_case )
SCREAMING_SNAKE_CASE =outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE =torch.Size((1, 1000) )
self.assertEqual(logits.shape ,snake_case )
SCREAMING_SNAKE_CASE =torch.tensor([-1.2_385, -1.0_987, -1.0_108] ).to(snake_case )
self.assertTrue(torch.allclose(logits[0, :3] ,snake_case ,atol=1e-4 ) )
SCREAMING_SNAKE_CASE =281
self.assertEqual(logits.argmax(-1 ).item() ,snake_case )
@slow
def _lowerCAmelCase ( self : int ):
SCREAMING_SNAKE_CASE =BeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k' ).to(
snake_case )
SCREAMING_SNAKE_CASE =self.default_image_processor
SCREAMING_SNAKE_CASE =prepare_img()
SCREAMING_SNAKE_CASE =image_processor(images=snake_case ,return_tensors='pt' ).to(snake_case )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE =model(**snake_case )
SCREAMING_SNAKE_CASE =outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE =torch.Size((1, 21841) )
self.assertEqual(logits.shape ,snake_case )
SCREAMING_SNAKE_CASE =torch.tensor([1.6_881, -0.2_787, 0.5_901] ).to(snake_case )
self.assertTrue(torch.allclose(logits[0, :3] ,snake_case ,atol=1e-4 ) )
SCREAMING_SNAKE_CASE =2396
self.assertEqual(logits.argmax(-1 ).item() ,snake_case )
@slow
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE =BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640' )
SCREAMING_SNAKE_CASE =model.to(snake_case )
SCREAMING_SNAKE_CASE =BeitImageProcessor(do_resize=snake_case ,size=640 ,do_center_crop=snake_case )
SCREAMING_SNAKE_CASE =load_dataset('hf-internal-testing/fixtures_ade20k' ,split='test' )
SCREAMING_SNAKE_CASE =Image.open(ds[0]['file'] )
SCREAMING_SNAKE_CASE =image_processor(images=snake_case ,return_tensors='pt' ).to(snake_case )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE =model(**snake_case )
SCREAMING_SNAKE_CASE =outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE =torch.Size((1, 150, 160, 160) )
self.assertEqual(logits.shape ,snake_case )
SCREAMING_SNAKE_CASE =version.parse(PIL.__version__ ) < version.parse('9.0.0' )
if is_pillow_less_than_a:
SCREAMING_SNAKE_CASE =torch.tensor(
[
[[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]],
[[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]],
[[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]],
] ,device=snake_case ,)
else:
SCREAMING_SNAKE_CASE =torch.tensor(
[
[[-4.8_960, -2.3_688, -3.0_355], [-2.8_478, -0.9_836, -1.7_418], [-2.9_449, -1.3_332, -2.1_456]],
[[-5.8_081, -3.4_124, -4.1_006], [-3.8_561, -2.2_081, -3.0_323], [-3.8_365, -2.4_601, -3.3_669]],
[[-0.0_309, 3.9_868, 4.0_540], [2.9_640, 4.6_877, 4.9_976], [3.2_081, 4.7_690, 4.9_942]],
] ,device=snake_case ,)
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] ,snake_case ,atol=1e-4 ) )
@slow
def _lowerCAmelCase ( self : int ):
SCREAMING_SNAKE_CASE =BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640' )
SCREAMING_SNAKE_CASE =model.to(snake_case )
SCREAMING_SNAKE_CASE =BeitImageProcessor(do_resize=snake_case ,size=640 ,do_center_crop=snake_case )
SCREAMING_SNAKE_CASE =load_dataset('hf-internal-testing/fixtures_ade20k' ,split='test' )
SCREAMING_SNAKE_CASE =Image.open(ds[0]['file'] )
SCREAMING_SNAKE_CASE =image_processor(images=snake_case ,return_tensors='pt' ).to(snake_case )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE =model(**snake_case )
SCREAMING_SNAKE_CASE =outputs.logits.detach().cpu()
SCREAMING_SNAKE_CASE =image_processor.post_process_semantic_segmentation(outputs=snake_case ,target_sizes=[(500, 300)] )
SCREAMING_SNAKE_CASE =torch.Size((500, 300) )
self.assertEqual(segmentation[0].shape ,snake_case )
SCREAMING_SNAKE_CASE =image_processor.post_process_semantic_segmentation(outputs=snake_case )
SCREAMING_SNAKE_CASE =torch.Size((160, 160) )
self.assertEqual(segmentation[0].shape ,snake_case )
| 334 | 1 |
from __future__ import annotations
from typing import Any
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
pass
class a_ :
"""simple docstring"""
def __init__( self : Union[str, Any] ,snake_case : Any ):
SCREAMING_SNAKE_CASE =data
SCREAMING_SNAKE_CASE =None
def __iter__( self : List[Any] ):
SCREAMING_SNAKE_CASE =self
SCREAMING_SNAKE_CASE =[]
while node:
if node in visited:
raise ContainsLoopError
visited.append(snake_case )
yield node.data
SCREAMING_SNAKE_CASE =node.next_node
@property
def _lowerCAmelCase ( self : Union[str, Any] ):
try:
list(self )
return False
except ContainsLoopError:
return True
if __name__ == "__main__":
_lowerCamelCase =Node(1)
_lowerCamelCase =Node(2)
_lowerCamelCase =Node(3)
_lowerCamelCase =Node(4)
print(root_node.has_loop) # False
_lowerCamelCase =root_node.next_node
print(root_node.has_loop) # True
_lowerCamelCase =Node(5)
_lowerCamelCase =Node(6)
_lowerCamelCase =Node(5)
_lowerCamelCase =Node(6)
print(root_node.has_loop) # False
_lowerCamelCase =Node(1)
print(root_node.has_loop) # False
| 334 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs
from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase =logging.get_logger()
@dataclass
class a_ :
"""simple docstring"""
__UpperCAmelCase = 42
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
def _lowerCAmelCase ( self : List[Any] ,snake_case : Dict ,snake_case : Tensor ,snake_case : Tensor ):
SCREAMING_SNAKE_CASE =len(list(m.modules() ) ) == 1 or isinstance(snake_case ,nn.Convad ) or isinstance(snake_case ,nn.BatchNormad )
if has_not_submodules:
self.traced.append(snake_case )
def __call__( self : List[str] ,snake_case : Tensor ):
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(snake_case )
[x.remove() for x in self.handles]
return self
@property
def _lowerCAmelCase ( self : Optional[Any] ):
# check the len of the state_dict keys to see if we have learnable params
return list(filter(lambda snake_case : len(list(x.state_dict().keys() ) ) > 0 ,self.traced ) )
@dataclass
class a_ :
"""simple docstring"""
__UpperCAmelCase = 42
__UpperCAmelCase = 42
__UpperCAmelCase = 1
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
__UpperCAmelCase = True
def __call__( self : str ,snake_case : Tensor ):
SCREAMING_SNAKE_CASE =Tracker(self.dest )(snake_case ).parametrized
SCREAMING_SNAKE_CASE =Tracker(self.src )(snake_case ).parametrized
SCREAMING_SNAKE_CASE =list(filter(lambda snake_case : type(snake_case ) not in self.src_skip ,snake_case ) )
SCREAMING_SNAKE_CASE =list(filter(lambda snake_case : type(snake_case ) not in self.dest_skip ,snake_case ) )
if len(snake_case ) != len(snake_case ) and self.raise_if_mismatch:
raise Exception(
f'Numbers of operations are different. Source module has {len(snake_case )} operations while'
f' destination module has {len(snake_case )}.' )
for dest_m, src_m in zip(snake_case ,snake_case ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(f'Transfered from={src_m} to={dest_m}' )
class a_ ( nn.Module ):
"""simple docstring"""
def __init__( self : Any ,snake_case : nn.Module ):
super().__init__()
SCREAMING_SNAKE_CASE =[]
# - get the stem
feature_blocks.append(('conv1', model.stem) )
# - get all the feature blocks
for k, v in model.trunk_output.named_children():
assert k.startswith('block' ), f'Unexpected layer name {k}'
SCREAMING_SNAKE_CASE =len(snake_case ) + 1
feature_blocks.append((f'res{block_index}', v) )
SCREAMING_SNAKE_CASE =nn.ModuleDict(snake_case )
def _lowerCAmelCase ( self : Dict ,snake_case : Tensor ):
return get_trunk_forward_outputs(
snake_case ,out_feat_keys=snake_case ,feature_blocks=self._feature_blocks ,)
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
def _lowerCAmelCase ( self : Optional[int] ,snake_case : str ):
SCREAMING_SNAKE_CASE =x.split('-' )
return x_split[0] + x_split[1] + "_" + "".join(x_split[2:] )
def __getitem__( self : Optional[Any] ,snake_case : str ):
# default to timm!
if x not in self:
SCREAMING_SNAKE_CASE =self.convert_name_to_timm(snake_case )
SCREAMING_SNAKE_CASE =partial(lambda: (timm.create_model(snake_case ,pretrained=snake_case ).eval(), None) )
else:
SCREAMING_SNAKE_CASE =super().__getitem__(snake_case )
return val
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
def __getitem__( self : int ,snake_case : str ):
if "seer" in x and "in1k" not in x:
SCREAMING_SNAKE_CASE =RegNetModel
else:
SCREAMING_SNAKE_CASE =RegNetForImageClassification
return val
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
for from_key, to_key in keys:
SCREAMING_SNAKE_CASE =from_state_dict[from_key].clone()
print(F'Copied key={from_key} to={to_key}' )
return to_state_dict
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ = True, ):
"""simple docstring"""
print(F'Converting {name}...' )
with torch.no_grad():
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =from_model_func()
SCREAMING_SNAKE_CASE =our_model_func(lowerCAmelCase_ ).eval()
SCREAMING_SNAKE_CASE =ModuleTransfer(src=lowerCAmelCase_, dest=lowerCAmelCase_, raise_if_mismatch=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =torch.randn((1, 3, 224, 224) )
module_transfer(lowerCAmelCase_ )
if from_state_dict is not None:
SCREAMING_SNAKE_CASE =[]
# for seer - in1k finetuned we have to manually copy the head
if "seer" in name and "in1k" in name:
SCREAMING_SNAKE_CASE =[('0.clf.0.weight', 'classifier.1.weight'), ('0.clf.0.bias', 'classifier.1.bias')]
SCREAMING_SNAKE_CASE =manually_copy_vissl_head(lowerCAmelCase_, our_model.state_dict(), lowerCAmelCase_ )
our_model.load_state_dict(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =our_model(lowerCAmelCase_, output_hidden_states=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =(
our_outputs.logits if isinstance(lowerCAmelCase_, lowerCAmelCase_ ) else our_outputs.last_hidden_state
)
SCREAMING_SNAKE_CASE =from_model(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =from_output[-1] if type(lowerCAmelCase_ ) is list else from_output
# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
if "seer" in name and "in1k" in name:
SCREAMING_SNAKE_CASE =our_outputs.hidden_states[-1]
assert torch.allclose(lowerCAmelCase_, lowerCAmelCase_ ), "The model logits don't match the original one."
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / name, commit_message='Add model', use_temp_dir=lowerCAmelCase_, )
SCREAMING_SNAKE_CASE =224 if 'seer' not in name else 384
# we can use the convnext one
SCREAMING_SNAKE_CASE =AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k', size=lowerCAmelCase_ )
image_processor.push_to_hub(
repo_path_or_name=save_directory / name, commit_message='Add image processor', use_temp_dir=lowerCAmelCase_, )
print(F'Pushed {name}' )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ = None, lowerCAmelCase_ = True ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ='imagenet-1k-id2label.json'
SCREAMING_SNAKE_CASE =1000
SCREAMING_SNAKE_CASE =(1, num_labels)
SCREAMING_SNAKE_CASE ='huggingface/label-files'
SCREAMING_SNAKE_CASE =num_labels
SCREAMING_SNAKE_CASE =json.load(open(cached_download(hf_hub_url(lowerCAmelCase_, lowerCAmelCase_, repo_type='dataset' ) ), 'r' ) )
SCREAMING_SNAKE_CASE ={int(lowerCAmelCase_ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE =idalabel
SCREAMING_SNAKE_CASE ={v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE =partial(lowerCAmelCase_, num_labels=lowerCAmelCase_, idalabel=lowerCAmelCase_, labelaid=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE ={
'regnet-x-002': ImageNetPreTrainedConfig(
depths=[1, 1, 4, 7], hidden_sizes=[24, 56, 152, 368], groups_width=8, layer_type='x' ),
'regnet-x-004': ImageNetPreTrainedConfig(
depths=[1, 2, 7, 12], hidden_sizes=[32, 64, 160, 384], groups_width=16, layer_type='x' ),
'regnet-x-006': ImageNetPreTrainedConfig(
depths=[1, 3, 5, 7], hidden_sizes=[48, 96, 240, 528], groups_width=24, layer_type='x' ),
'regnet-x-008': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 5], hidden_sizes=[64, 128, 288, 672], groups_width=16, layer_type='x' ),
'regnet-x-016': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 2], hidden_sizes=[72, 168, 408, 912], groups_width=24, layer_type='x' ),
'regnet-x-032': ImageNetPreTrainedConfig(
depths=[2, 6, 15, 2], hidden_sizes=[96, 192, 432, 1008], groups_width=48, layer_type='x' ),
'regnet-x-040': ImageNetPreTrainedConfig(
depths=[2, 5, 14, 2], hidden_sizes=[80, 240, 560, 1360], groups_width=40, layer_type='x' ),
'regnet-x-064': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1], hidden_sizes=[168, 392, 784, 1624], groups_width=56, layer_type='x' ),
'regnet-x-080': ImageNetPreTrainedConfig(
depths=[2, 5, 15, 1], hidden_sizes=[80, 240, 720, 1920], groups_width=120, layer_type='x' ),
'regnet-x-120': ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1], hidden_sizes=[224, 448, 896, 2240], groups_width=112, layer_type='x' ),
'regnet-x-160': ImageNetPreTrainedConfig(
depths=[2, 6, 13, 1], hidden_sizes=[256, 512, 896, 2048], groups_width=128, layer_type='x' ),
'regnet-x-320': ImageNetPreTrainedConfig(
depths=[2, 7, 13, 1], hidden_sizes=[336, 672, 1344, 2520], groups_width=168, layer_type='x' ),
# y variant
'regnet-y-002': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7], hidden_sizes=[24, 56, 152, 368], groups_width=8 ),
'regnet-y-004': ImageNetPreTrainedConfig(
depths=[1, 3, 6, 6], hidden_sizes=[48, 104, 208, 440], groups_width=8 ),
'regnet-y-006': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 4], hidden_sizes=[48, 112, 256, 608], groups_width=16 ),
'regnet-y-008': ImageNetPreTrainedConfig(
depths=[1, 3, 8, 2], hidden_sizes=[64, 128, 320, 768], groups_width=16 ),
'regnet-y-016': ImageNetPreTrainedConfig(
depths=[2, 6, 17, 2], hidden_sizes=[48, 120, 336, 888], groups_width=24 ),
'regnet-y-032': ImageNetPreTrainedConfig(
depths=[2, 5, 13, 1], hidden_sizes=[72, 216, 576, 1512], groups_width=24 ),
'regnet-y-040': ImageNetPreTrainedConfig(
depths=[2, 6, 12, 2], hidden_sizes=[128, 192, 512, 1088], groups_width=64 ),
'regnet-y-064': ImageNetPreTrainedConfig(
depths=[2, 7, 14, 2], hidden_sizes=[144, 288, 576, 1296], groups_width=72 ),
'regnet-y-080': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1], hidden_sizes=[168, 448, 896, 2016], groups_width=56 ),
'regnet-y-120': ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1], hidden_sizes=[224, 448, 896, 2240], groups_width=112 ),
'regnet-y-160': ImageNetPreTrainedConfig(
depths=[2, 4, 11, 1], hidden_sizes=[224, 448, 1232, 3024], groups_width=112 ),
'regnet-y-320': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1], hidden_sizes=[232, 696, 1392, 3712], groups_width=232 ),
# models created by SEER -> https://arxiv.org/abs/2202.08360
'regnet-y-320-seer': RegNetConfig(depths=[2, 5, 12, 1], hidden_sizes=[232, 696, 1392, 3712], groups_width=232 ),
'regnet-y-640-seer': RegNetConfig(depths=[2, 5, 12, 1], hidden_sizes=[328, 984, 1968, 4920], groups_width=328 ),
'regnet-y-1280-seer': RegNetConfig(
depths=[2, 7, 17, 1], hidden_sizes=[528, 1056, 2904, 7392], groups_width=264 ),
'regnet-y-2560-seer': RegNetConfig(
depths=[3, 7, 16, 1], hidden_sizes=[640, 1696, 2544, 5088], groups_width=640 ),
'regnet-y-10b-seer': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1], hidden_sizes=[2020, 4040, 11110, 28280], groups_width=1010 ),
# finetuned on imagenet
'regnet-y-320-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1], hidden_sizes=[232, 696, 1392, 3712], groups_width=232 ),
'regnet-y-640-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1], hidden_sizes=[328, 984, 1968, 4920], groups_width=328 ),
'regnet-y-1280-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1], hidden_sizes=[528, 1056, 2904, 7392], groups_width=264 ),
'regnet-y-2560-seer-in1k': ImageNetPreTrainedConfig(
depths=[3, 7, 16, 1], hidden_sizes=[640, 1696, 2544, 5088], groups_width=640 ),
'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1], hidden_sizes=[2020, 4040, 11110, 28280], groups_width=1010 ),
}
SCREAMING_SNAKE_CASE =NameToOurModelFuncMap()
SCREAMING_SNAKE_CASE =NameToFromModelFuncMap()
# add seer weights logic
def load_using_classy_vision(lowerCAmelCase_, lowerCAmelCase_ ) -> Tuple[nn.Module, Dict]:
SCREAMING_SNAKE_CASE =torch.hub.load_state_dict_from_url(lowerCAmelCase_, model_dir=str(lowerCAmelCase_ ), map_location='cpu' )
SCREAMING_SNAKE_CASE =model_func()
# check if we have a head, if yes add it
SCREAMING_SNAKE_CASE =files['classy_state_dict']['base_model']['model']
SCREAMING_SNAKE_CASE =model_state_dict['trunk']
model.load_state_dict(lowerCAmelCase_ )
return model.eval(), model_state_dict["heads"]
# pretrained
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch', lambda: FakeRegNetVisslWrapper(RegNetYaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch', lambda: FakeRegNetVisslWrapper(RegNetYaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch', lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch', lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27, group_width=1010, w_a=1744, w_a=620.83, w_m=2.52 ) ) ), )
# IN1K finetuned
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch', lambda: FakeRegNetVisslWrapper(RegNetYaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch', lambda: FakeRegNetVisslWrapper(RegNetYaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch', lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch', lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27, group_width=1010, w_a=1744, w_a=620.83, w_m=2.52 ) ) ), )
if model_name:
convert_weight_and_push(
lowerCAmelCase_, names_to_from_model_map[model_name], names_to_ours_model_map[model_name], names_to_config[model_name], lowerCAmelCase_, lowerCAmelCase_, )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(
lowerCAmelCase_, names_to_from_model_map[model_name], names_to_ours_model_map[model_name], lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, )
return config, expected_shape
if __name__ == "__main__":
_lowerCamelCase =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default=None,
type=str,
help=(
"The name of the model you wish to convert, it must be one of the supported regnet* architecture,"
" currently: regnetx-*, regnety-*. If `None`, all of them will the converted."
),
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=Path,
required=True,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
default=True,
type=bool,
required=False,
help="If True, push model and image processor to the hub.",
)
_lowerCamelCase =parser.parse_args()
_lowerCamelCase =args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 334 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowerCamelCase ={
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase =[
"MEGA_PRETRAINED_MODEL_ARCHIVE_LIST",
"MegaForCausalLM",
"MegaForMaskedLM",
"MegaForMultipleChoice",
"MegaForQuestionAnswering",
"MegaForSequenceClassification",
"MegaForTokenClassification",
"MegaModel",
"MegaPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mega import (
MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
MegaForCausalLM,
MegaForMaskedLM,
MegaForMultipleChoice,
MegaForQuestionAnswering,
MegaForSequenceClassification,
MegaForTokenClassification,
MegaModel,
MegaPreTrainedModel,
)
else:
import sys
_lowerCamelCase =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 334 |
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
_lowerCamelCase =16
_lowerCamelCase =32
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ = 16 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained('bert-base-cased' )
SCREAMING_SNAKE_CASE =load_dataset('glue', 'mrpc' )
def tokenize_function(lowerCAmelCase_ ):
# max_length=None => use the model max length (it's actually the default)
SCREAMING_SNAKE_CASE =tokenizer(examples['sentence1'], examples['sentence2'], truncation=lowerCAmelCase_, max_length=lowerCAmelCase_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
SCREAMING_SNAKE_CASE =datasets.map(
lowerCAmelCase_, batched=lowerCAmelCase_, remove_columns=['idx', 'sentence1', 'sentence2'], )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
SCREAMING_SNAKE_CASE =tokenized_datasets.rename_column('label', 'labels' )
def collate_fn(lowerCAmelCase_ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
SCREAMING_SNAKE_CASE =128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
SCREAMING_SNAKE_CASE =16
elif accelerator.mixed_precision != "no":
SCREAMING_SNAKE_CASE =8
else:
SCREAMING_SNAKE_CASE =None
return tokenizer.pad(
lowerCAmelCase_, padding='longest', max_length=lowerCAmelCase_, pad_to_multiple_of=lowerCAmelCase_, return_tensors='pt', )
# Instantiate dataloaders.
SCREAMING_SNAKE_CASE =DataLoader(
tokenized_datasets['train'], shuffle=lowerCAmelCase_, collate_fn=lowerCAmelCase_, batch_size=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =DataLoader(
tokenized_datasets['validation'], shuffle=lowerCAmelCase_, collate_fn=lowerCAmelCase_, batch_size=lowerCAmelCase_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
_lowerCamelCase =mocked_dataloaders # noqa: F811
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
if os.environ.get('TESTING_MOCKED_DATALOADERS', lowerCAmelCase_ ) == "1":
SCREAMING_SNAKE_CASE =2
# Initialize accelerator
SCREAMING_SNAKE_CASE =Accelerator(cpu=args.cpu, mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
SCREAMING_SNAKE_CASE =config['lr']
SCREAMING_SNAKE_CASE =int(config['num_epochs'] )
SCREAMING_SNAKE_CASE =int(config['seed'] )
SCREAMING_SNAKE_CASE =int(config['batch_size'] )
SCREAMING_SNAKE_CASE =evaluate.load('glue', 'mrpc' )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=lowerCAmelCase_ )
def inner_training_loop(lowerCAmelCase_ ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(lowerCAmelCase_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
SCREAMING_SNAKE_CASE =AutoModelForSequenceClassification.from_pretrained('bert-base-cased', return_dict=lowerCAmelCase_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
SCREAMING_SNAKE_CASE =model.to(accelerator.device )
# Instantiate optimizer
SCREAMING_SNAKE_CASE =AdamW(params=model.parameters(), lr=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =get_dataloaders(lowerCAmelCase_, lowerCAmelCase_ )
# Instantiate scheduler
SCREAMING_SNAKE_CASE =get_linear_schedule_with_warmup(
optimizer=lowerCAmelCase_, num_warmup_steps=100, num_training_steps=(len(lowerCAmelCase_ ) * num_epochs), )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =accelerator.prepare(
lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ )
# Now we train the model
for epoch in range(lowerCAmelCase_ ):
model.train()
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
SCREAMING_SNAKE_CASE =model(**lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =outputs.loss
accelerator.backward(lowerCAmelCase_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
SCREAMING_SNAKE_CASE =model(**lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =outputs.logits.argmax(dim=-1 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =accelerator.gather_for_metrics((predictions, batch['labels']) )
metric.add_batch(
predictions=lowerCAmelCase_, references=lowerCAmelCase_, )
SCREAMING_SNAKE_CASE =metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'epoch {epoch}:', lowerCAmelCase_ )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =argparse.ArgumentParser(description='Simple example of training script.' )
parser.add_argument(
'--mixed_precision', type=lowerCAmelCase_, default=lowerCAmelCase_, choices=['no', 'fp16', 'bf16', 'fp8'], help='Whether to use mixed precision. Choose'
'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
'and an Nvidia Ampere GPU.', )
parser.add_argument('--cpu', action='store_true', help='If passed, will train on the CPU.' )
SCREAMING_SNAKE_CASE =parser.parse_args()
SCREAMING_SNAKE_CASE ={'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
training_function(lowerCAmelCase_, lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 334 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"xlm-mlm-en-2048": "https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json",
"xlm-mlm-ende-1024": "https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json",
"xlm-mlm-enfr-1024": "https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json",
"xlm-mlm-enro-1024": "https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json",
"xlm-mlm-tlm-xnli15-1024": "https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json",
"xlm-mlm-xnli15-1024": "https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json",
"xlm-clm-enfr-1024": "https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json",
"xlm-clm-ende-1024": "https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json",
"xlm-mlm-17-1280": "https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json",
"xlm-mlm-100-1280": "https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json",
}
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'xlm'
__UpperCAmelCase = {
'hidden_size': 'emb_dim',
'num_attention_heads': 'n_heads',
'num_hidden_layers': 'n_layers',
'n_words': 'vocab_size', # For backward compatibility
}
def __init__( self : Optional[Any] ,snake_case : Optional[Any]=30145 ,snake_case : Optional[int]=2048 ,snake_case : List[Any]=12 ,snake_case : List[str]=16 ,snake_case : Dict=0.1 ,snake_case : int=0.1 ,snake_case : Union[str, Any]=True ,snake_case : Optional[int]=False ,snake_case : Any=False ,snake_case : Tuple=False ,snake_case : Union[str, Any]=1 ,snake_case : Any=True ,snake_case : Tuple=512 ,snake_case : Dict=2048**-0.5 ,snake_case : Union[str, Any]=1e-12 ,snake_case : List[Any]=0.02 ,snake_case : List[str]=0 ,snake_case : int=1 ,snake_case : Optional[Any]=2 ,snake_case : Optional[int]=3 ,snake_case : Union[str, Any]=5 ,snake_case : List[str]=True ,snake_case : Union[str, Any]="first" ,snake_case : Optional[int]=True ,snake_case : int=None ,snake_case : List[Any]=True ,snake_case : str=0.1 ,snake_case : List[Any]=5 ,snake_case : Optional[Any]=5 ,snake_case : Optional[Any]=0 ,snake_case : List[Any]=0 ,snake_case : List[str]=2 ,snake_case : Optional[Any]=0 ,**snake_case : Any ,):
SCREAMING_SNAKE_CASE =vocab_size
SCREAMING_SNAKE_CASE =emb_dim
SCREAMING_SNAKE_CASE =n_layers
SCREAMING_SNAKE_CASE =n_heads
SCREAMING_SNAKE_CASE =dropout
SCREAMING_SNAKE_CASE =attention_dropout
SCREAMING_SNAKE_CASE =gelu_activation
SCREAMING_SNAKE_CASE =sinusoidal_embeddings
SCREAMING_SNAKE_CASE =causal
SCREAMING_SNAKE_CASE =asm
SCREAMING_SNAKE_CASE =n_langs
SCREAMING_SNAKE_CASE =use_lang_emb
SCREAMING_SNAKE_CASE =layer_norm_eps
SCREAMING_SNAKE_CASE =bos_index
SCREAMING_SNAKE_CASE =eos_index
SCREAMING_SNAKE_CASE =pad_index
SCREAMING_SNAKE_CASE =unk_index
SCREAMING_SNAKE_CASE =mask_index
SCREAMING_SNAKE_CASE =is_encoder
SCREAMING_SNAKE_CASE =max_position_embeddings
SCREAMING_SNAKE_CASE =embed_init_std
SCREAMING_SNAKE_CASE =init_std
SCREAMING_SNAKE_CASE =summary_type
SCREAMING_SNAKE_CASE =summary_use_proj
SCREAMING_SNAKE_CASE =summary_activation
SCREAMING_SNAKE_CASE =summary_proj_to_labels
SCREAMING_SNAKE_CASE =summary_first_dropout
SCREAMING_SNAKE_CASE =start_n_top
SCREAMING_SNAKE_CASE =end_n_top
SCREAMING_SNAKE_CASE =mask_token_id
SCREAMING_SNAKE_CASE =lang_id
if "n_words" in kwargs:
SCREAMING_SNAKE_CASE =kwargs['n_words']
super().__init__(pad_token_id=snake_case ,bos_token_id=snake_case ,**snake_case )
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
@property
def _lowerCAmelCase ( self : Dict ):
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE ={0: 'batch', 1: 'choice', 2: 'sequence'}
else:
SCREAMING_SNAKE_CASE ={0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
('token_type_ids', dynamic_axis),
] )
| 334 |
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
return " ".join(
''.join(word[::-1] ) if len(lowerCAmelCase_ ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words("Hey wollef sroirraw"))
| 334 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import YolosImageProcessor
class a_ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : int ,snake_case : str ,snake_case : Union[str, Any]=7 ,snake_case : int=3 ,snake_case : int=30 ,snake_case : Tuple=400 ,snake_case : str=True ,snake_case : Optional[Any]=None ,snake_case : Optional[int]=True ,snake_case : Dict=[0.5, 0.5, 0.5] ,snake_case : List[str]=[0.5, 0.5, 0.5] ,snake_case : Dict=True ,snake_case : int=1 / 255 ,snake_case : List[str]=True ,):
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
SCREAMING_SNAKE_CASE =size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
SCREAMING_SNAKE_CASE =parent
SCREAMING_SNAKE_CASE =batch_size
SCREAMING_SNAKE_CASE =num_channels
SCREAMING_SNAKE_CASE =min_resolution
SCREAMING_SNAKE_CASE =max_resolution
SCREAMING_SNAKE_CASE =do_resize
SCREAMING_SNAKE_CASE =size
SCREAMING_SNAKE_CASE =do_normalize
SCREAMING_SNAKE_CASE =image_mean
SCREAMING_SNAKE_CASE =image_std
SCREAMING_SNAKE_CASE =do_rescale
SCREAMING_SNAKE_CASE =rescale_factor
SCREAMING_SNAKE_CASE =do_pad
def _lowerCAmelCase ( self : Any ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def _lowerCAmelCase ( self : Optional[int] ,snake_case : Dict ,snake_case : List[str]=False ):
if not batched:
SCREAMING_SNAKE_CASE =image_inputs[0]
if isinstance(snake_case ,Image.Image ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =image.size
else:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =image.shape[1], image.shape[2]
if w < h:
SCREAMING_SNAKE_CASE =int(self.size['shortest_edge'] * h / w )
SCREAMING_SNAKE_CASE =self.size['shortest_edge']
elif w > h:
SCREAMING_SNAKE_CASE =self.size['shortest_edge']
SCREAMING_SNAKE_CASE =int(self.size['shortest_edge'] * w / h )
else:
SCREAMING_SNAKE_CASE =self.size['shortest_edge']
SCREAMING_SNAKE_CASE =self.size['shortest_edge']
else:
SCREAMING_SNAKE_CASE =[]
for image in image_inputs:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
SCREAMING_SNAKE_CASE =max(snake_case ,key=lambda snake_case : item[0] )[0]
SCREAMING_SNAKE_CASE =max(snake_case ,key=lambda snake_case : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class a_ ( lowerCamelCase_ , unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase = YolosImageProcessor if is_vision_available() else None
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =YolosImageProcessingTester(self )
@property
def _lowerCAmelCase ( self : Dict ):
return self.image_processor_tester.prepare_image_processor_dict()
def _lowerCAmelCase ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE =self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(snake_case ,'image_mean' ) )
self.assertTrue(hasattr(snake_case ,'image_std' ) )
self.assertTrue(hasattr(snake_case ,'do_normalize' ) )
self.assertTrue(hasattr(snake_case ,'do_resize' ) )
self.assertTrue(hasattr(snake_case ,'size' ) )
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size ,{'shortest_edge': 18, 'longest_edge': 1333} )
self.assertEqual(image_processor.do_pad ,snake_case )
SCREAMING_SNAKE_CASE =self.image_processing_class.from_dict(
self.image_processor_dict ,size=42 ,max_size=84 ,pad_and_return_pixel_mask=snake_case )
self.assertEqual(image_processor.size ,{'shortest_edge': 42, 'longest_edge': 84} )
self.assertEqual(image_processor.do_pad ,snake_case )
def _lowerCAmelCase ( self : Optional[Any] ):
pass
def _lowerCAmelCase ( self : Any ):
# Initialize image_processing
SCREAMING_SNAKE_CASE =self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE =prepare_image_inputs(self.image_processor_tester ,equal_resolution=snake_case )
for image in image_inputs:
self.assertIsInstance(snake_case ,Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE =image_processing(image_inputs[0] ,return_tensors='pt' ).pixel_values
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.image_processor_tester.get_expected_values(snake_case )
self.assertEqual(
encoded_images.shape ,(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,)
# Test batched
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.image_processor_tester.get_expected_values(snake_case ,batched=snake_case )
SCREAMING_SNAKE_CASE =image_processing(snake_case ,return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) ,)
def _lowerCAmelCase ( self : int ):
# Initialize image_processing
SCREAMING_SNAKE_CASE =self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
SCREAMING_SNAKE_CASE =prepare_image_inputs(self.image_processor_tester ,equal_resolution=snake_case ,numpify=snake_case )
for image in image_inputs:
self.assertIsInstance(snake_case ,np.ndarray )
# Test not batched input
SCREAMING_SNAKE_CASE =image_processing(image_inputs[0] ,return_tensors='pt' ).pixel_values
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.image_processor_tester.get_expected_values(snake_case )
self.assertEqual(
encoded_images.shape ,(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,)
# Test batched
SCREAMING_SNAKE_CASE =image_processing(snake_case ,return_tensors='pt' ).pixel_values
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.image_processor_tester.get_expected_values(snake_case ,batched=snake_case )
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) ,)
def _lowerCAmelCase ( self : Optional[Any] ):
# Initialize image_processing
SCREAMING_SNAKE_CASE =self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
SCREAMING_SNAKE_CASE =prepare_image_inputs(self.image_processor_tester ,equal_resolution=snake_case ,torchify=snake_case )
for image in image_inputs:
self.assertIsInstance(snake_case ,torch.Tensor )
# Test not batched input
SCREAMING_SNAKE_CASE =image_processing(image_inputs[0] ,return_tensors='pt' ).pixel_values
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.image_processor_tester.get_expected_values(snake_case )
self.assertEqual(
encoded_images.shape ,(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,)
# Test batched
SCREAMING_SNAKE_CASE =image_processing(snake_case ,return_tensors='pt' ).pixel_values
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.image_processor_tester.get_expected_values(snake_case ,batched=snake_case )
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) ,)
def _lowerCAmelCase ( self : Optional[int] ):
# Initialize image_processings
SCREAMING_SNAKE_CASE =self.image_processing_class(**self.image_processor_dict )
SCREAMING_SNAKE_CASE =self.image_processing_class(do_resize=snake_case ,do_normalize=snake_case ,do_rescale=snake_case )
# create random PyTorch tensors
SCREAMING_SNAKE_CASE =prepare_image_inputs(self.image_processor_tester ,equal_resolution=snake_case ,torchify=snake_case )
for image in image_inputs:
self.assertIsInstance(snake_case ,torch.Tensor )
# Test whether the method "pad" and calling the image processor return the same tensors
SCREAMING_SNAKE_CASE =image_processing_a.pad(snake_case ,return_tensors='pt' )
SCREAMING_SNAKE_CASE =image_processing_a(snake_case ,return_tensors='pt' )
self.assertTrue(
torch.allclose(encoded_images_with_method['pixel_values'] ,encoded_images['pixel_values'] ,atol=1e-4 ) )
@slow
def _lowerCAmelCase ( self : Optional[int] ):
# prepare image and target
SCREAMING_SNAKE_CASE =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt' ,'r' ) as f:
SCREAMING_SNAKE_CASE =json.loads(f.read() )
SCREAMING_SNAKE_CASE ={'image_id': 39769, 'annotations': target}
# encode them
SCREAMING_SNAKE_CASE =YolosImageProcessor.from_pretrained('hustvl/yolos-small' )
SCREAMING_SNAKE_CASE =image_processing(images=snake_case ,annotations=snake_case ,return_tensors='pt' )
# verify pixel values
SCREAMING_SNAKE_CASE =torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['pixel_values'].shape ,snake_case )
SCREAMING_SNAKE_CASE =torch.tensor([0.2_796, 0.3_138, 0.3_481] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] ,snake_case ,atol=1e-4 ) )
# verify area
SCREAMING_SNAKE_CASE =torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] ,snake_case ) )
# verify boxes
SCREAMING_SNAKE_CASE =torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape ,snake_case )
SCREAMING_SNAKE_CASE =torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] ,snake_case ,atol=1e-3 ) )
# verify image_id
SCREAMING_SNAKE_CASE =torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] ,snake_case ) )
# verify is_crowd
SCREAMING_SNAKE_CASE =torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] ,snake_case ) )
# verify class_labels
SCREAMING_SNAKE_CASE =torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] ,snake_case ) )
# verify orig_size
SCREAMING_SNAKE_CASE =torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] ,snake_case ) )
# verify size
SCREAMING_SNAKE_CASE =torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] ,snake_case ) )
@slow
def _lowerCAmelCase ( self : Optional[int] ):
# prepare image, target and masks_path
SCREAMING_SNAKE_CASE =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt' ,'r' ) as f:
SCREAMING_SNAKE_CASE =json.loads(f.read() )
SCREAMING_SNAKE_CASE ={'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}
SCREAMING_SNAKE_CASE =pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic' )
# encode them
SCREAMING_SNAKE_CASE =YolosImageProcessor(format='coco_panoptic' )
SCREAMING_SNAKE_CASE =image_processing(images=snake_case ,annotations=snake_case ,masks_path=snake_case ,return_tensors='pt' )
# verify pixel values
SCREAMING_SNAKE_CASE =torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['pixel_values'].shape ,snake_case )
SCREAMING_SNAKE_CASE =torch.tensor([0.2_796, 0.3_138, 0.3_481] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] ,snake_case ,atol=1e-4 ) )
# verify area
SCREAMING_SNAKE_CASE =torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] ,snake_case ) )
# verify boxes
SCREAMING_SNAKE_CASE =torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape ,snake_case )
SCREAMING_SNAKE_CASE =torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] ,snake_case ,atol=1e-3 ) )
# verify image_id
SCREAMING_SNAKE_CASE =torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] ,snake_case ) )
# verify is_crowd
SCREAMING_SNAKE_CASE =torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] ,snake_case ) )
# verify class_labels
SCREAMING_SNAKE_CASE =torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] ,snake_case ) )
# verify masks
SCREAMING_SNAKE_CASE =822873
self.assertEqual(encoding['labels'][0]['masks'].sum().item() ,snake_case )
# verify orig_size
SCREAMING_SNAKE_CASE =torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] ,snake_case ) )
# verify size
SCREAMING_SNAKE_CASE =torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] ,snake_case ) )
| 334 |
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json
_lowerCamelCase ="sshleifer/mar_enro_6_3_student"
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
def _lowerCAmelCase ( self : Union[str, Any] ):
super().setUp()
SCREAMING_SNAKE_CASE =cached_path(
'https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz' ,extract_compressed_file=snake_case ,)
SCREAMING_SNAKE_CASE =f'{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'
@slow
@require_torch_gpu
def _lowerCAmelCase ( self : Optional[int] ):
MarianMTModel.from_pretrained(snake_case )
@slow
@require_torch_gpu
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE ={
'$MAX_LEN': 64,
'$BS': 64,
'$GAS': 1,
'$ENRO_DIR': self.data_dir,
'facebook/mbart-large-cc25': MARIAN_MODEL,
# "val_check_interval=0.25": "val_check_interval=1.0",
'--learning_rate=3e-5': '--learning_rate 3e-4',
'--num_train_epochs 6': '--num_train_epochs 1',
}
# Clean up bash script
SCREAMING_SNAKE_CASE =(self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py' )[1].strip()
SCREAMING_SNAKE_CASE =bash_script.replace('\\\n' ,'' ).strip().replace('"$@"' ,'' )
for k, v in env_vars_to_replace.items():
SCREAMING_SNAKE_CASE =bash_script.replace(snake_case ,str(snake_case ) )
SCREAMING_SNAKE_CASE =self.get_auto_remove_tmp_dir()
# bash_script = bash_script.replace("--fp16 ", "")
SCREAMING_SNAKE_CASE =f'\n --output_dir {output_dir}\n --tokenizer_name Helsinki-NLP/opus-mt-en-ro\n --sortish_sampler\n --do_predict\n --gpus 1\n --freeze_encoder\n --n_train 40000\n --n_val 500\n --n_test 500\n --fp16_opt_level O1\n --num_sanity_val_steps 0\n --eval_beams 2\n '.split()
# XXX: args.gpus > 1 : handle multi_gpu in the future
SCREAMING_SNAKE_CASE =['finetune.py'] + bash_script.split() + args
with patch.object(snake_case ,'argv' ,snake_case ):
SCREAMING_SNAKE_CASE =argparse.ArgumentParser()
SCREAMING_SNAKE_CASE =pl.Trainer.add_argparse_args(snake_case )
SCREAMING_SNAKE_CASE =SummarizationModule.add_model_specific_args(snake_case ,os.getcwd() )
SCREAMING_SNAKE_CASE =parser.parse_args()
SCREAMING_SNAKE_CASE =main(snake_case )
# Check metrics
SCREAMING_SNAKE_CASE =load_json(model.metrics_save_path )
SCREAMING_SNAKE_CASE =metrics['val'][0]
SCREAMING_SNAKE_CASE =metrics['val'][-1]
self.assertEqual(len(metrics['val'] ) ,(args.max_epochs / args.val_check_interval) )
assert isinstance(last_step_stats[f'val_avg_{model.val_metric}'] ,snake_case )
self.assertGreater(last_step_stats['val_avg_gen_time'] ,0.01 )
# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
self.assertLessEqual(last_step_stats['val_avg_gen_time'] ,1.0 )
# test learning requirements:
# 1. BLEU improves over the course of training by more than 2 pts
self.assertGreater(last_step_stats['val_avg_bleu'] - first_step_stats['val_avg_bleu'] ,2 )
# 2. BLEU finishes above 17
self.assertGreater(last_step_stats['val_avg_bleu'] ,17 )
# 3. test BLEU and val BLEU within ~1.1 pt.
self.assertLess(abs(metrics['val'][-1]['val_avg_bleu'] - metrics['test'][-1]['test_avg_bleu'] ) ,1.1 )
# check lightning ckpt can be loaded and has a reasonable statedict
SCREAMING_SNAKE_CASE =os.listdir(snake_case )
SCREAMING_SNAKE_CASE =[x for x in contents if x.endswith('.ckpt' )][0]
SCREAMING_SNAKE_CASE =os.path.join(args.output_dir ,snake_case )
SCREAMING_SNAKE_CASE =torch.load(snake_case ,map_location='cpu' )
SCREAMING_SNAKE_CASE ='model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
SCREAMING_SNAKE_CASE ={os.path.basename(snake_case ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics['test'] ) == 1
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
@timeout_decorator.timeout(600 )
@slow
@require_torch_gpu
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =f'{self.test_file_dir_str}/test_data/wmt_en_ro'
SCREAMING_SNAKE_CASE ={
'--fp16_opt_level=O1': '',
'$MAX_LEN': 128,
'$BS': 16,
'$GAS': 1,
'$ENRO_DIR': data_dir,
'$m': 'sshleifer/student_marian_en_ro_6_1',
'val_check_interval=0.25': 'val_check_interval=1.0',
}
# Clean up bash script
SCREAMING_SNAKE_CASE =(
(self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py' )[1].strip()
)
SCREAMING_SNAKE_CASE =bash_script.replace('\\\n' ,'' ).strip().replace('"$@"' ,'' )
SCREAMING_SNAKE_CASE =bash_script.replace('--fp16 ' ,' ' )
for k, v in env_vars_to_replace.items():
SCREAMING_SNAKE_CASE =bash_script.replace(snake_case ,str(snake_case ) )
SCREAMING_SNAKE_CASE =self.get_auto_remove_tmp_dir()
SCREAMING_SNAKE_CASE =bash_script.replace('--fp16' ,'' )
SCREAMING_SNAKE_CASE =6
SCREAMING_SNAKE_CASE =(
['distillation.py']
+ bash_script.split()
+ [
f'--output_dir={output_dir}',
'--gpus=1',
'--learning_rate=1e-3',
f'--num_train_epochs={epochs}',
'--warmup_steps=10',
'--val_check_interval=1.0',
'--do_predict',
]
)
with patch.object(snake_case ,'argv' ,snake_case ):
SCREAMING_SNAKE_CASE =argparse.ArgumentParser()
SCREAMING_SNAKE_CASE =pl.Trainer.add_argparse_args(snake_case )
SCREAMING_SNAKE_CASE =SummarizationDistiller.add_model_specific_args(snake_case ,os.getcwd() )
SCREAMING_SNAKE_CASE =parser.parse_args()
# assert args.gpus == gpus THIS BREAKS for multi_gpu
SCREAMING_SNAKE_CASE =distill_main(snake_case )
# Check metrics
SCREAMING_SNAKE_CASE =load_json(model.metrics_save_path )
SCREAMING_SNAKE_CASE =metrics['val'][0]
SCREAMING_SNAKE_CASE =metrics['val'][-1]
assert len(metrics['val'] ) >= (args.max_epochs / args.val_check_interval) # +1 accounts for val_sanity_check
assert last_step_stats["val_avg_gen_time"] >= 0.01
assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"] # model learned nothing
assert 1.0 >= last_step_stats["val_avg_gen_time"] # model hanging on generate. Maybe bad config was saved.
assert isinstance(last_step_stats[f'val_avg_{model.val_metric}'] ,snake_case )
# check lightning ckpt can be loaded and has a reasonable statedict
SCREAMING_SNAKE_CASE =os.listdir(snake_case )
SCREAMING_SNAKE_CASE =[x for x in contents if x.endswith('.ckpt' )][0]
SCREAMING_SNAKE_CASE =os.path.join(args.output_dir ,snake_case )
SCREAMING_SNAKE_CASE =torch.load(snake_case ,map_location='cpu' )
SCREAMING_SNAKE_CASE ='model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
SCREAMING_SNAKE_CASE ={os.path.basename(snake_case ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics['test'] ) == 1
| 334 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert import CamembertTokenizer
else:
_lowerCamelCase =None
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
_lowerCamelCase ={
"vocab_file": {
"camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
},
"tokenizer_file": {
"camembert-base": "https://huggingface.co/camembert-base/resolve/main/tokenizer.json",
},
}
_lowerCamelCase ={
"camembert-base": 5_12,
}
_lowerCamelCase ="▁"
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = VOCAB_FILES_NAMES
__UpperCAmelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase = ['input_ids', 'attention_mask']
__UpperCAmelCase = CamembertTokenizer
def __init__( self : List[str] ,snake_case : str=None ,snake_case : Dict=None ,snake_case : int="<s>" ,snake_case : Optional[int]="</s>" ,snake_case : Optional[Any]="</s>" ,snake_case : Union[str, Any]="<s>" ,snake_case : Optional[int]="<unk>" ,snake_case : Optional[Any]="<pad>" ,snake_case : str="<mask>" ,snake_case : Tuple=["<s>NOTUSED", "</s>NOTUSED"] ,**snake_case : Any ,):
# Mask token behave like a normal word, i.e. include the space before it
SCREAMING_SNAKE_CASE =AddedToken(snake_case ,lstrip=snake_case ,rstrip=snake_case ) if isinstance(snake_case ,snake_case ) else mask_token
super().__init__(
snake_case ,tokenizer_file=snake_case ,bos_token=snake_case ,eos_token=snake_case ,sep_token=snake_case ,cls_token=snake_case ,unk_token=snake_case ,pad_token=snake_case ,mask_token=snake_case ,additional_special_tokens=snake_case ,**snake_case ,)
SCREAMING_SNAKE_CASE =vocab_file
SCREAMING_SNAKE_CASE =False if not self.vocab_file else True
def _lowerCAmelCase ( self : Dict ,snake_case : List[int] ,snake_case : Optional[List[int]] = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
SCREAMING_SNAKE_CASE =[self.cls_token_id]
SCREAMING_SNAKE_CASE =[self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _lowerCAmelCase ( self : int ,snake_case : List[int] ,snake_case : Optional[List[int]] = None ):
SCREAMING_SNAKE_CASE =[self.sep_token_id]
SCREAMING_SNAKE_CASE =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _lowerCAmelCase ( self : str ,snake_case : str ,snake_case : Optional[str] = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
'tokenizer.' )
if not os.path.isdir(snake_case ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
SCREAMING_SNAKE_CASE =os.path.join(
snake_case ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case ):
copyfile(self.vocab_file ,snake_case )
return (out_vocab_file,)
| 334 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"salesforce/blip2-opt-2.7b": "https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json",
}
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'blip_2_vision_model'
def __init__( self : List[Any] ,snake_case : List[Any]=1408 ,snake_case : Optional[Any]=6144 ,snake_case : Optional[int]=39 ,snake_case : Optional[int]=16 ,snake_case : Optional[Any]=224 ,snake_case : Tuple=14 ,snake_case : Optional[Any]="gelu" ,snake_case : Union[str, Any]=0.00_001 ,snake_case : Dict=0.0 ,snake_case : Union[str, Any]=1e-10 ,snake_case : int=True ,**snake_case : str ,):
super().__init__(**snake_case )
SCREAMING_SNAKE_CASE =hidden_size
SCREAMING_SNAKE_CASE =intermediate_size
SCREAMING_SNAKE_CASE =num_hidden_layers
SCREAMING_SNAKE_CASE =num_attention_heads
SCREAMING_SNAKE_CASE =patch_size
SCREAMING_SNAKE_CASE =image_size
SCREAMING_SNAKE_CASE =initializer_range
SCREAMING_SNAKE_CASE =attention_dropout
SCREAMING_SNAKE_CASE =layer_norm_eps
SCREAMING_SNAKE_CASE =hidden_act
SCREAMING_SNAKE_CASE =qkv_bias
@classmethod
def _lowerCAmelCase ( cls : Dict ,snake_case : Union[str, os.PathLike] ,**snake_case : str ):
cls._set_token_in_kwargs(snake_case )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =cls.get_config_dict(snake_case ,**snake_case )
# get the vision config dict if we are loading from Blip2Config
if config_dict.get('model_type' ) == "blip-2":
SCREAMING_SNAKE_CASE =config_dict['vision_config']
if "model_type" in config_dict and hasattr(cls ,'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(snake_case ,**snake_case )
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'blip_2_qformer'
def __init__( self : Any ,snake_case : Dict=30522 ,snake_case : int=768 ,snake_case : List[Any]=12 ,snake_case : List[str]=12 ,snake_case : Optional[Any]=3072 ,snake_case : str="gelu" ,snake_case : Optional[Any]=0.1 ,snake_case : Union[str, Any]=0.1 ,snake_case : Optional[Any]=512 ,snake_case : List[Any]=0.02 ,snake_case : List[str]=1e-12 ,snake_case : Tuple=0 ,snake_case : Union[str, Any]="absolute" ,snake_case : List[Any]=2 ,snake_case : List[str]=1408 ,**snake_case : Optional[Any] ,):
super().__init__(pad_token_id=snake_case ,**snake_case )
SCREAMING_SNAKE_CASE =vocab_size
SCREAMING_SNAKE_CASE =hidden_size
SCREAMING_SNAKE_CASE =num_hidden_layers
SCREAMING_SNAKE_CASE =num_attention_heads
SCREAMING_SNAKE_CASE =hidden_act
SCREAMING_SNAKE_CASE =intermediate_size
SCREAMING_SNAKE_CASE =hidden_dropout_prob
SCREAMING_SNAKE_CASE =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE =max_position_embeddings
SCREAMING_SNAKE_CASE =initializer_range
SCREAMING_SNAKE_CASE =layer_norm_eps
SCREAMING_SNAKE_CASE =position_embedding_type
SCREAMING_SNAKE_CASE =cross_attention_frequency
SCREAMING_SNAKE_CASE =encoder_hidden_size
@classmethod
def _lowerCAmelCase ( cls : List[Any] ,snake_case : Union[str, os.PathLike] ,**snake_case : Dict ):
cls._set_token_in_kwargs(snake_case )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =cls.get_config_dict(snake_case ,**snake_case )
# get the qformer config dict if we are loading from Blip2Config
if config_dict.get('model_type' ) == "blip-2":
SCREAMING_SNAKE_CASE =config_dict['qformer_config']
if "model_type" in config_dict and hasattr(cls ,'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(snake_case ,**snake_case )
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'blip-2'
__UpperCAmelCase = True
def __init__( self : int ,snake_case : Dict=None ,snake_case : Tuple=None ,snake_case : str=None ,snake_case : Union[str, Any]=32 ,**snake_case : int ):
super().__init__(**snake_case )
if vision_config is None:
SCREAMING_SNAKE_CASE ={}
logger.info('vision_config is None. initializing the Blip2VisionConfig with default values.' )
if qformer_config is None:
SCREAMING_SNAKE_CASE ={}
logger.info('qformer_config is None. Initializing the Blip2QFormerConfig with default values.' )
if text_config is None:
SCREAMING_SNAKE_CASE ={}
logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).' )
SCREAMING_SNAKE_CASE =BlipaVisionConfig(**snake_case )
SCREAMING_SNAKE_CASE =BlipaQFormerConfig(**snake_case )
SCREAMING_SNAKE_CASE =text_config['model_type'] if 'model_type' in text_config else 'opt'
SCREAMING_SNAKE_CASE =CONFIG_MAPPING[text_model_type](**snake_case )
SCREAMING_SNAKE_CASE =self.text_config.tie_word_embeddings
SCREAMING_SNAKE_CASE =self.text_config.is_encoder_decoder
SCREAMING_SNAKE_CASE =num_query_tokens
SCREAMING_SNAKE_CASE =self.vision_config.hidden_size
SCREAMING_SNAKE_CASE =self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
SCREAMING_SNAKE_CASE =1.0
SCREAMING_SNAKE_CASE =0.02
@classmethod
def _lowerCAmelCase ( cls : Union[str, Any] ,snake_case : BlipaVisionConfig ,snake_case : BlipaQFormerConfig ,snake_case : PretrainedConfig ,**snake_case : Any ,):
return cls(
vision_config=vision_config.to_dict() ,qformer_config=qformer_config.to_dict() ,text_config=text_config.to_dict() ,**snake_case ,)
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE =self.vision_config.to_dict()
SCREAMING_SNAKE_CASE =self.qformer_config.to_dict()
SCREAMING_SNAKE_CASE =self.text_config.to_dict()
SCREAMING_SNAKE_CASE =self.__class__.model_type
return output
| 334 | 1 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = None
__UpperCAmelCase = None
@property
def _lowerCAmelCase ( self : List[Any] ):
return self.feat_extract_tester.prepare_feat_extract_dict()
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(snake_case ,'feature_size' ) )
self.assertTrue(hasattr(snake_case ,'sampling_rate' ) )
self.assertTrue(hasattr(snake_case ,'padding_value' ) )
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common()
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(snake_case ) == len(snake_case ) for x, y in zip(snake_case ,processed_features[input_name] ) ) )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common(equal_length=snake_case )
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} ,tensor_type='np' )
SCREAMING_SNAKE_CASE =processed_features[input_name]
if len(batch_features_input.shape ) < 3:
SCREAMING_SNAKE_CASE =batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_torch
def _lowerCAmelCase ( self : Optional[int] ):
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common(equal_length=snake_case )
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} ,tensor_type='pt' )
SCREAMING_SNAKE_CASE =processed_features[input_name]
if len(batch_features_input.shape ) < 3:
SCREAMING_SNAKE_CASE =batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_tf
def _lowerCAmelCase ( self : str ):
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common(equal_length=snake_case )
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} ,tensor_type='tf' )
SCREAMING_SNAKE_CASE =processed_features[input_name]
if len(batch_features_input.shape ) < 3:
SCREAMING_SNAKE_CASE =batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
def _lowerCAmelCase ( self : List[Any] ,snake_case : Optional[Any]=False ):
def _inputs_have_equal_length(snake_case : Dict ):
SCREAMING_SNAKE_CASE =len(input[0] )
for input_slice in input[1:]:
if len(snake_case ) != length:
return False
return True
def _inputs_are_equal(snake_case : str ,snake_case : Dict ):
if len(snake_case ) != len(snake_case ):
return False
for input_slice_a, input_slice_a in zip(snake_case ,snake_case ):
if not np.allclose(np.asarray(snake_case ) ,np.asarray(snake_case ) ,atol=1e-3 ):
return False
return True
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common(numpify=snake_case )
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.seq_length_diff
SCREAMING_SNAKE_CASE =self.feat_extract_tester.max_seq_length + pad_diff
SCREAMING_SNAKE_CASE =self.feat_extract_tester.min_seq_length
SCREAMING_SNAKE_CASE =self.feat_extract_tester.batch_size
SCREAMING_SNAKE_CASE =self.feat_extract_tester.feature_size
# test padding for List[int] + numpy
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding=snake_case )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='max_length' ,max_length=len(speech_inputs[-1] ) )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='np' )
SCREAMING_SNAKE_CASE =input_a[input_name]
# max_length parameter has to be provided when setting `padding="max_length"`
with self.assertRaises(snake_case ):
feat_extract.pad(snake_case ,padding='max_length' )[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=snake_case ,return_tensors='np' )
SCREAMING_SNAKE_CASE =input_a[input_name]
self.assertFalse(_inputs_have_equal_length(snake_case ) )
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertTrue(_inputs_are_equal(snake_case ,snake_case ) )
self.assertTrue(len(input_a[0] ) == pad_min_length )
self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )
# test padding for `pad_to_multiple_of` for List[int] + numpy
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,pad_to_multiple_of=10 )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,pad_to_multiple_of=10 )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,pad_to_multiple_of=10 ,max_length=snake_case )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,pad_to_multiple_of=10 ,max_length=snake_case ,return_tensors='np' ,)
SCREAMING_SNAKE_CASE =input_a[input_name]
self.assertTrue(all(len(snake_case ) % 10 == 0 for x in input_a ) )
self.assertTrue(_inputs_are_equal(snake_case ,snake_case ) )
SCREAMING_SNAKE_CASE =pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
self.assertTrue(all(len(snake_case ) == expected_mult_pad_length for x in input_a ) )
self.assertEqual(input_a.shape[:2] ,(batch_size, expected_mult_pad_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == feature_size )
# Check padding value is correct
SCREAMING_SNAKE_CASE =(np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
self.assertTrue(
abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
< 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
< 1e-3 )
def _lowerCAmelCase ( self : Union[str, Any] ,snake_case : Optional[int]=False ):
def _inputs_have_equal_length(snake_case : str ):
SCREAMING_SNAKE_CASE =len(input[0] )
for input_slice in input[1:]:
if len(snake_case ) != length:
return False
return True
def _inputs_are_equal(snake_case : Tuple ,snake_case : Optional[Any] ):
if len(snake_case ) != len(snake_case ):
return False
for input_slice_a, input_slice_a in zip(snake_case ,snake_case ):
if not np.allclose(np.asarray(snake_case ) ,np.asarray(snake_case ) ,atol=1e-3 ):
return False
return True
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common(numpify=snake_case )
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
# truncate to smallest
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) ,truncation=snake_case )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) )
SCREAMING_SNAKE_CASE =input_a[input_name]
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertFalse(_inputs_have_equal_length(snake_case ) )
# truncate to smallest with np
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) ,return_tensors='np' ,truncation=snake_case ,)
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) ,return_tensors='np' )
SCREAMING_SNAKE_CASE =input_a[input_name]
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(snake_case ) )
# truncate to middle
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[1] ) ,truncation=snake_case ,return_tensors='np' ,)
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[1] ) ,truncation=snake_case )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[1] ) ,return_tensors='np' )
SCREAMING_SNAKE_CASE =input_a[input_name]
self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertTrue(_inputs_are_equal(snake_case ,snake_case ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(snake_case ) )
self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(snake_case ):
feat_extract.pad(snake_case ,truncation=snake_case )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(snake_case ):
feat_extract.pad(snake_case ,padding='longest' ,truncation=snake_case )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(snake_case ):
feat_extract.pad(snake_case ,padding='longest' ,truncation=snake_case )[input_name]
# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
with self.assertRaises(snake_case ):
feat_extract.pad(snake_case ,padding='max_length' ,truncation=snake_case )[input_name]
# test truncation for `pad_to_multiple_of` for List[int] + numpy
SCREAMING_SNAKE_CASE =12
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) ,pad_to_multiple_of=snake_case ,truncation=snake_case ,)
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) ,pad_to_multiple_of=snake_case ,)
SCREAMING_SNAKE_CASE =input_a[input_name]
# retrieve expected_length as multiple of pad_to_multiple_of
SCREAMING_SNAKE_CASE =len(speech_inputs[0] )
if expected_length % pad_to_multiple_of != 0:
SCREAMING_SNAKE_CASE =((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of
self.assertTrue(len(input_a[0] ) == expected_length )
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertFalse(_inputs_have_equal_length(snake_case ) )
def _lowerCAmelCase ( self : Optional[int] ):
self._check_padding(numpify=snake_case )
def _lowerCAmelCase ( self : Tuple ):
self._check_padding(numpify=snake_case )
def _lowerCAmelCase ( self : List[str] ):
self._check_truncation(numpify=snake_case )
def _lowerCAmelCase ( self : int ):
self._check_truncation(numpify=snake_case )
@require_torch
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common()
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='np' )[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='pt' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
@require_tf
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common()
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='np' )[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='tf' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE =self.feat_extract_dict
SCREAMING_SNAKE_CASE =True
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**snake_case )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common()
SCREAMING_SNAKE_CASE =[len(snake_case ) for x in speech_inputs]
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='np' )
self.assertIn('attention_mask' ,snake_case )
self.assertListEqual(list(processed.attention_mask.shape ) ,list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() ,snake_case )
def _lowerCAmelCase ( self : Dict ):
SCREAMING_SNAKE_CASE =self.feat_extract_dict
SCREAMING_SNAKE_CASE =True
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**snake_case )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common()
SCREAMING_SNAKE_CASE =[len(snake_case ) for x in speech_inputs]
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE =min(snake_case )
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=snake_case ,truncation=snake_case ,return_tensors='np' )
self.assertIn('attention_mask' ,snake_case )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) ,[processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() ,[max_length for x in speech_inputs] )
| 334 |
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve # From: mauve-text
import datasets
_lowerCamelCase ="\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n booktitle = {NeurIPS},\n year = {2021}\n}\n\n"
_lowerCamelCase ="\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n"
_lowerCamelCase ="\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n predictions: list of generated text to score. Each predictions\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\nOptional Args:\n num_buckets: the size of the histogram to quantize P and Q. Options: 'auto' (default) or an integer\n pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n kmeans_max_iter: maximum number of k-means iterations. Default 500\n featurize_model_name: name of the model from which features are obtained. Default 'gpt2-large' Use one of ['gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'].\n device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU\n max_text_length: maximum number of tokens to consider. Default 1024\n divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n mauve_scaling_factor: \"c\" from the paper. Default 5.\n verbose: If True (default), print running time updates\n seed: random seed to initialize k-means cluster assignments.\nReturns:\n mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n q_hist: same as above, but with q_text.\nExamples:\n\n >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n >>> import datasets\n >>> mauve = datasets.load_metric('mauve')\n >>> predictions = [\"hello there\", \"general kenobi\"]\n >>> references = [\"hello there\", \"general kenobi\"]\n >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n >>> print(out.mauve) # doctest: +SKIP\n 1.0\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a_ ( datasets.Metric ):
"""simple docstring"""
def _lowerCAmelCase ( self : Tuple ):
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,homepage='https://github.com/krishnap25/mauve' ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
'predictions': datasets.Value('string' ,id='sequence' ),
'references': datasets.Value('string' ,id='sequence' ),
} ) ,codebase_urls=['https://github.com/krishnap25/mauve'] ,reference_urls=[
'https://arxiv.org/abs/2102.01454',
'https://github.com/krishnap25/mauve',
] ,)
def _lowerCAmelCase ( self : Union[str, Any] ,snake_case : Optional[int] ,snake_case : str ,snake_case : List[str]=None ,snake_case : str=None ,snake_case : int=None ,snake_case : Union[str, Any]=None ,snake_case : Optional[int]="auto" ,snake_case : List[str]=-1 ,snake_case : Union[str, Any]=0.9 ,snake_case : Tuple=5 ,snake_case : Union[str, Any]=500 ,snake_case : Union[str, Any]="gpt2-large" ,snake_case : Union[str, Any]=-1 ,snake_case : Optional[Any]=1024 ,snake_case : Optional[Any]=25 ,snake_case : List[str]=5 ,snake_case : List[str]=True ,snake_case : Optional[Any]=25 ,):
SCREAMING_SNAKE_CASE =compute_mauve(
p_text=snake_case ,q_text=snake_case ,p_features=snake_case ,q_features=snake_case ,p_tokens=snake_case ,q_tokens=snake_case ,num_buckets=snake_case ,pca_max_data=snake_case ,kmeans_explained_var=snake_case ,kmeans_num_redo=snake_case ,kmeans_max_iter=snake_case ,featurize_model_name=snake_case ,device_id=snake_case ,max_text_length=snake_case ,divergence_curve_discretization_size=snake_case ,mauve_scaling_factor=snake_case ,verbose=snake_case ,seed=snake_case ,)
return out
| 334 | 1 |
from __future__ import annotations
import numpy as np
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
return np.maximum(0, lowerCAmelCase_ )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 334 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
# See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'vit_mae'
def __init__( self : Union[str, Any] ,snake_case : Any=768 ,snake_case : List[str]=12 ,snake_case : Optional[int]=12 ,snake_case : int=3072 ,snake_case : List[Any]="gelu" ,snake_case : str=0.0 ,snake_case : str=0.0 ,snake_case : Optional[Any]=0.02 ,snake_case : Dict=1e-12 ,snake_case : List[str]=224 ,snake_case : Any=16 ,snake_case : Any=3 ,snake_case : Tuple=True ,snake_case : List[Any]=16 ,snake_case : List[str]=512 ,snake_case : List[Any]=8 ,snake_case : Dict=2048 ,snake_case : Union[str, Any]=0.75 ,snake_case : Union[str, Any]=False ,**snake_case : Optional[int] ,):
super().__init__(**snake_case )
SCREAMING_SNAKE_CASE =hidden_size
SCREAMING_SNAKE_CASE =num_hidden_layers
SCREAMING_SNAKE_CASE =num_attention_heads
SCREAMING_SNAKE_CASE =intermediate_size
SCREAMING_SNAKE_CASE =hidden_act
SCREAMING_SNAKE_CASE =hidden_dropout_prob
SCREAMING_SNAKE_CASE =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE =initializer_range
SCREAMING_SNAKE_CASE =layer_norm_eps
SCREAMING_SNAKE_CASE =image_size
SCREAMING_SNAKE_CASE =patch_size
SCREAMING_SNAKE_CASE =num_channels
SCREAMING_SNAKE_CASE =qkv_bias
SCREAMING_SNAKE_CASE =decoder_num_attention_heads
SCREAMING_SNAKE_CASE =decoder_hidden_size
SCREAMING_SNAKE_CASE =decoder_num_hidden_layers
SCREAMING_SNAKE_CASE =decoder_intermediate_size
SCREAMING_SNAKE_CASE =mask_ratio
SCREAMING_SNAKE_CASE =norm_pix_loss
| 334 | 1 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
_lowerCamelCase =1_00
_lowerCamelCase =set(range(3, NUM_PRIMES, 2))
primes.add(2)
_lowerCamelCase =42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=100 )
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
if number_to_partition < 0:
return set()
elif number_to_partition == 0:
return {1}
SCREAMING_SNAKE_CASE =set()
SCREAMING_SNAKE_CASE =42
SCREAMING_SNAKE_CASE =42
for prime in primes:
if prime > number_to_partition:
continue
for sub in partition(number_to_partition - prime ):
ret.add(sub * prime )
return ret
def snake_case__ ( lowerCAmelCase_ = 5000 ):
"""simple docstring"""
for number_to_partition in range(1, lowerCAmelCase_ ):
if len(partition(lowerCAmelCase_ ) ) > number_unique_partitions:
return number_to_partition
return None
if __name__ == "__main__":
print(f'{solution() = }')
| 334 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
_lowerCamelCase ={
"configuration_ernie": ["ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", "ErnieOnnxConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase =[
"ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST",
"ErnieForCausalLM",
"ErnieForMaskedLM",
"ErnieForMultipleChoice",
"ErnieForNextSentencePrediction",
"ErnieForPreTraining",
"ErnieForQuestionAnswering",
"ErnieForSequenceClassification",
"ErnieForTokenClassification",
"ErnieModel",
"ErniePreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ernie import (
ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
ErnieForCausalLM,
ErnieForMaskedLM,
ErnieForMultipleChoice,
ErnieForNextSentencePrediction,
ErnieForPreTraining,
ErnieForQuestionAnswering,
ErnieForSequenceClassification,
ErnieForTokenClassification,
ErnieModel,
ErniePreTrainedModel,
)
else:
import sys
_lowerCamelCase =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 334 | 1 |
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
_lowerCamelCase =16
_lowerCamelCase =32
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ = 16 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained('bert-base-cased' )
SCREAMING_SNAKE_CASE =load_dataset('glue', 'mrpc' )
def tokenize_function(lowerCAmelCase_ ):
# max_length=None => use the model max length (it's actually the default)
SCREAMING_SNAKE_CASE =tokenizer(examples['sentence1'], examples['sentence2'], truncation=lowerCAmelCase_, max_length=lowerCAmelCase_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
SCREAMING_SNAKE_CASE =datasets.map(
lowerCAmelCase_, batched=lowerCAmelCase_, remove_columns=['idx', 'sentence1', 'sentence2'], )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
SCREAMING_SNAKE_CASE =tokenized_datasets.rename_column('label', 'labels' )
def collate_fn(lowerCAmelCase_ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
SCREAMING_SNAKE_CASE =128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
SCREAMING_SNAKE_CASE =16
elif accelerator.mixed_precision != "no":
SCREAMING_SNAKE_CASE =8
else:
SCREAMING_SNAKE_CASE =None
return tokenizer.pad(
lowerCAmelCase_, padding='longest', max_length=lowerCAmelCase_, pad_to_multiple_of=lowerCAmelCase_, return_tensors='pt', )
# Instantiate dataloaders.
SCREAMING_SNAKE_CASE =DataLoader(
tokenized_datasets['train'], shuffle=lowerCAmelCase_, collate_fn=lowerCAmelCase_, batch_size=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =DataLoader(
tokenized_datasets['validation'], shuffle=lowerCAmelCase_, collate_fn=lowerCAmelCase_, batch_size=lowerCAmelCase_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
_lowerCamelCase =mocked_dataloaders # noqa: F811
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
if os.environ.get('TESTING_MOCKED_DATALOADERS', lowerCAmelCase_ ) == "1":
SCREAMING_SNAKE_CASE =2
# Initialize accelerator
SCREAMING_SNAKE_CASE =Accelerator(cpu=args.cpu, mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
SCREAMING_SNAKE_CASE =config['lr']
SCREAMING_SNAKE_CASE =int(config['num_epochs'] )
SCREAMING_SNAKE_CASE =int(config['seed'] )
SCREAMING_SNAKE_CASE =int(config['batch_size'] )
SCREAMING_SNAKE_CASE =evaluate.load('glue', 'mrpc' )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=lowerCAmelCase_ )
def inner_training_loop(lowerCAmelCase_ ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(lowerCAmelCase_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
SCREAMING_SNAKE_CASE =AutoModelForSequenceClassification.from_pretrained('bert-base-cased', return_dict=lowerCAmelCase_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
SCREAMING_SNAKE_CASE =model.to(accelerator.device )
# Instantiate optimizer
SCREAMING_SNAKE_CASE =AdamW(params=model.parameters(), lr=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =get_dataloaders(lowerCAmelCase_, lowerCAmelCase_ )
# Instantiate scheduler
SCREAMING_SNAKE_CASE =get_linear_schedule_with_warmup(
optimizer=lowerCAmelCase_, num_warmup_steps=100, num_training_steps=(len(lowerCAmelCase_ ) * num_epochs), )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =accelerator.prepare(
lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ )
# Now we train the model
for epoch in range(lowerCAmelCase_ ):
model.train()
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
SCREAMING_SNAKE_CASE =model(**lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =outputs.loss
accelerator.backward(lowerCAmelCase_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
SCREAMING_SNAKE_CASE =model(**lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =outputs.logits.argmax(dim=-1 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =accelerator.gather_for_metrics((predictions, batch['labels']) )
metric.add_batch(
predictions=lowerCAmelCase_, references=lowerCAmelCase_, )
SCREAMING_SNAKE_CASE =metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'epoch {epoch}:', lowerCAmelCase_ )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =argparse.ArgumentParser(description='Simple example of training script.' )
parser.add_argument(
'--mixed_precision', type=lowerCAmelCase_, default=lowerCAmelCase_, choices=['no', 'fp16', 'bf16', 'fp8'], help='Whether to use mixed precision. Choose'
'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
'and an Nvidia Ampere GPU.', )
parser.add_argument('--cpu', action='store_true', help='If passed, will train on the CPU.' )
SCREAMING_SNAKE_CASE =parser.parse_args()
SCREAMING_SNAKE_CASE ={'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
training_function(lowerCAmelCase_, lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 334 |
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError
from .utils import require_pil
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([1, 2, 3] ) )
self.assertEqual(arr.type ,pa.intaa() )
def _lowerCAmelCase ( self : Any ):
with self.assertRaises(snake_case ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([1, 2, 3] ) ,type=pa.intaa() )
def _lowerCAmelCase ( self : Union[str, Any] ):
with self.assertRaises(snake_case ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([1, 2, 3] ,try_type=Value('bool' ) ,type=Value('int64' ) ) )
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([1, 2, 3] ,type=Value('int32' ) ) )
self.assertEqual(arr.type ,pa.intaa() )
def _lowerCAmelCase ( self : int ):
with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence(['foo', 'bar'] ,type=Value('int64' ) ) )
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([1, 2, 3] ,try_type=Value('int32' ) ) )
self.assertEqual(arr.type ,pa.intaa() )
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence(['foo', 'bar'] ,try_type=Value('int64' ) ) )
self.assertEqual(arr.type ,pa.string() )
def _lowerCAmelCase ( self : Dict ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([[[1, 2, 3]]] ,type=ArrayaD((1, 3) ,'int64' ) ) )
self.assertEqual(arr.type ,ArrayaDExtensionType((1, 3) ,'int64' ) )
def _lowerCAmelCase ( self : Dict ):
with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence(['foo', 'bar'] ,type=ArrayaD((1, 3) ,'int64' ) ) )
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([[[1, 2, 3]]] ,try_type=ArrayaD((1, 3) ,'int64' ) ) )
self.assertEqual(arr.type ,ArrayaDExtensionType((1, 3) ,'int64' ) )
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence(['foo', 'bar'] ,try_type=ArrayaD((1, 3) ,'int64' ) ) )
self.assertEqual(arr.type ,pa.string() )
@require_pil
def _lowerCAmelCase ( self : int ):
import PIL.Image
SCREAMING_SNAKE_CASE =PIL.Image.fromarray(np.arange(10 ,dtype=np.uinta ).reshape(2 ,5 ) )
with patch(
'datasets.arrow_writer.cast_to_python_objects' ,side_effect=snake_case ) as mock_cast_to_python_objects:
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([{'path': None, 'bytes': B'image_bytes'}, pil_image] ,type=Image() ) )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =mock_cast_to_python_objects.call_args_list[-1]
self.assertIn('optimize_list_casting' ,snake_case )
self.assertFalse(kwargs['optimize_list_casting'] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferReader(lowerCAmelCase_ ) if isinstance(lowerCAmelCase_, pa.Buffer ) else pa.memory_map(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =pa.ipc.open_stream(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =f.read_all()
assert len(pa_table.to_batches() ) == expected_num_chunks
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
del pa_table
@pytest.mark.parametrize('writer_batch_size', [None, 1, 10] )
@pytest.mark.parametrize(
'fields', [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
SCREAMING_SNAKE_CASE =pa.schema(lowerCAmelCase_ ) if fields else None
with ArrowWriter(stream=lowerCAmelCase_, schema=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_ ) as writer:
writer.write({'col_1': 'foo', 'col_2': 1} )
writer.write({'col_1': 'bar', 'col_2': 2} )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
SCREAMING_SNAKE_CASE ={'col_1': pa.string(), 'col_2': pa.intaa()}
assert writer._schema == pa.schema(lowerCAmelCase_, metadata=writer._schema.metadata )
_check_output(output.getvalue(), expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
SCREAMING_SNAKE_CASE =Features({'labels': ClassLabel(names=['neg', 'pos'] )} )
with ArrowWriter(stream=lowerCAmelCase_, features=lowerCAmelCase_ ) as writer:
writer.write({'labels': 0} )
writer.write({'labels': 1} )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert writer._schema == features.arrow_schema
assert writer._schema.metadata == features.arrow_schema.metadata
SCREAMING_SNAKE_CASE =pa.BufferReader(output.getvalue() )
SCREAMING_SNAKE_CASE =pa.ipc.open_stream(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =f.read_all()
SCREAMING_SNAKE_CASE =pa_table.schema
assert pa_table.num_rows == 2
assert schema == features.arrow_schema
assert schema.metadata == features.arrow_schema.metadata
assert features == Features.from_arrow_schema(lowerCAmelCase_ )
@pytest.mark.parametrize('writer_batch_size', [None, 1, 10] )
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
with ArrowWriter(
stream=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_, hash_salt='split_name', check_duplicates=lowerCAmelCase_, ) as writer:
with pytest.raises(lowerCAmelCase_ ):
writer.write({'col_1': 'foo', 'col_2': 1}, key=[1, 2] )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
@pytest.mark.parametrize('writer_batch_size', [None, 2, 10] )
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
with ArrowWriter(
stream=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_, hash_salt='split_name', check_duplicates=lowerCAmelCase_, ) as writer:
with pytest.raises(lowerCAmelCase_ ):
writer.write({'col_1': 'foo', 'col_2': 1}, key=10 )
writer.write({'col_1': 'bar', 'col_2': 2}, key=10 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
@pytest.mark.parametrize('writer_batch_size', [None, 2, 10] )
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
with ArrowWriter(
stream=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_, hash_salt='split_name', check_duplicates=lowerCAmelCase_, ) as writer:
writer.write({'col_1': 'foo', 'col_2': 1}, key=1 )
writer.write({'col_1': 'bar', 'col_2': 2}, key=2 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
_check_output(output.getvalue(), expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize('writer_batch_size', [None, 1, 10] )
@pytest.mark.parametrize(
'fields', [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
SCREAMING_SNAKE_CASE =pa.schema(lowerCAmelCase_ ) if fields else None
with ArrowWriter(stream=lowerCAmelCase_, schema=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_ ) as writer:
writer.write_batch({'col_1': ['foo', 'bar'], 'col_2': [1, 2]} )
writer.write_batch({'col_1': [], 'col_2': []} )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
SCREAMING_SNAKE_CASE ={'col_1': pa.string(), 'col_2': pa.intaa()}
assert writer._schema == pa.schema(lowerCAmelCase_, metadata=writer._schema.metadata )
_check_output(output.getvalue(), expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize('writer_batch_size', [None, 1, 10] )
@pytest.mark.parametrize(
'fields', [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
SCREAMING_SNAKE_CASE =pa.schema(lowerCAmelCase_ ) if fields else None
with ArrowWriter(stream=lowerCAmelCase_, schema=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_ ) as writer:
writer.write_table(pa.Table.from_pydict({'col_1': ['foo', 'bar'], 'col_2': [1, 2]} ) )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
SCREAMING_SNAKE_CASE ={'col_1': pa.string(), 'col_2': pa.intaa()}
assert writer._schema == pa.schema(lowerCAmelCase_, metadata=writer._schema.metadata )
_check_output(output.getvalue(), expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize('writer_batch_size', [None, 1, 10] )
@pytest.mark.parametrize(
'fields', [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
SCREAMING_SNAKE_CASE =pa.schema(lowerCAmelCase_ ) if fields else None
with ArrowWriter(stream=lowerCAmelCase_, schema=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_ ) as writer:
writer.write_row(pa.Table.from_pydict({'col_1': ['foo'], 'col_2': [1]} ) )
writer.write_row(pa.Table.from_pydict({'col_1': ['bar'], 'col_2': [2]} ) )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
SCREAMING_SNAKE_CASE ={'col_1': pa.string(), 'col_2': pa.intaa()}
assert writer._schema == pa.schema(lowerCAmelCase_, metadata=writer._schema.metadata )
_check_output(output.getvalue(), expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
def snake_case__ ( ):
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE ={'col_1': pa.string(), 'col_2': pa.intaa()}
SCREAMING_SNAKE_CASE =os.path.join(lowerCAmelCase_, 'test.arrow' )
with ArrowWriter(path=lowerCAmelCase_, schema=pa.schema(lowerCAmelCase_ ) ) as writer:
writer.write_batch({'col_1': ['foo', 'bar'], 'col_2': [1, 2]} )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert writer._schema == pa.schema(lowerCAmelCase_, metadata=writer._schema.metadata )
_check_output(lowerCAmelCase_, 1 )
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
if pa.types.is_list(lowerCAmelCase_ ):
return get_base_dtype(arr_type.value_type )
else:
return arr_type
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
if isinstance(lst[0], lowerCAmelCase_ ):
change_first_primitive_element_in_list(lst[0], lowerCAmelCase_ )
else:
SCREAMING_SNAKE_CASE =value
@pytest.mark.parametrize('optimized_int_type, expected_dtype', [(None, pa.intaa()), (Value('int32' ), pa.intaa())] )
@pytest.mark.parametrize('sequence', [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.array(TypedSequence(lowerCAmelCase_, optimized_int_type=lowerCAmelCase_ ) )
assert get_base_dtype(arr.type ) == expected_dtype
@pytest.mark.parametrize(
'col, expected_dtype', [
('attention_mask', pa.inta()),
('special_tokens_mask', pa.inta()),
('token_type_ids', pa.inta()),
('input_ids', pa.intaa()),
('other', pa.intaa()),
], )
@pytest.mark.parametrize('sequence', [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.array(OptimizedTypedSequence(lowerCAmelCase_, col=lowerCAmelCase_ ) )
assert get_base_dtype(arr.type ) == expected_dtype
# not in range
if col != "other":
# avoids errors due to in-place modifications
SCREAMING_SNAKE_CASE =copy.deepcopy(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =np.iinfo(expected_dtype.to_pandas_dtype() ).max + 1
change_first_primitive_element_in_list(lowerCAmelCase_, lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =pa.array(OptimizedTypedSequence(lowerCAmelCase_, col=lowerCAmelCase_ ) )
assert get_base_dtype(arr.type ) == pa.intaa()
@pytest.mark.parametrize('raise_exception', [False, True] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =str(tmp_path / 'dataset-train.arrow' )
try:
with ArrowWriter(path=lowerCAmelCase_ ) as writer:
if raise_exception:
raise pa.lib.ArrowInvalid()
else:
writer.stream.close()
except pa.lib.ArrowInvalid:
pass
finally:
assert writer.stream.closed
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ='mock://dataset-train.arrow'
with ArrowWriter(path=lowerCAmelCase_, storage_options=mockfs.storage_options ) as writer:
assert isinstance(writer._fs, type(lowerCAmelCase_ ) )
assert writer._fs.storage_options == mockfs.storage_options
writer.write({'col_1': 'foo', 'col_2': 1} )
writer.write({'col_1': 'bar', 'col_2': 2} )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert mockfs.exists(lowerCAmelCase_ )
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
with ParquetWriter(stream=lowerCAmelCase_ ) as writer:
writer.write({'col_1': 'foo', 'col_2': 1} )
writer.write({'col_1': 'bar', 'col_2': 2} )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
SCREAMING_SNAKE_CASE =pa.BufferReader(output.getvalue() )
SCREAMING_SNAKE_CASE =pq.read_table(lowerCAmelCase_ )
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
@require_pil
@pytest.mark.parametrize('embed_local_files', [False, True] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
import PIL.Image
SCREAMING_SNAKE_CASE =str(tmp_path / 'test_image_rgb.jpg' )
PIL.Image.fromarray(np.zeros((5, 5), dtype=np.uinta ) ).save(lowerCAmelCase_, format='png' )
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
with ParquetWriter(
stream=lowerCAmelCase_, features=Features({'image': Image()} ), embed_local_files=lowerCAmelCase_ ) as writer:
writer.write({'image': image_path} )
writer.finalize()
SCREAMING_SNAKE_CASE =pa.BufferReader(output.getvalue() )
SCREAMING_SNAKE_CASE =pq.read_table(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =pa_table.to_pydict()
if embed_local_files:
assert isinstance(out['image'][0]['path'], lowerCAmelCase_ )
with open(lowerCAmelCase_, 'rb' ) as f:
assert out["image"][0]["bytes"] == f.read()
else:
assert out["image"][0]["path"] == image_path
assert out["image"][0]["bytes"] is None
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.schema([pa.field('col_1', pa.string(), nullable=lowerCAmelCase_ )] )
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
with ArrowWriter(stream=lowerCAmelCase_ ) as writer:
writer._build_writer(inferred_schema=lowerCAmelCase_ )
assert writer._schema == pa.schema([pa.field('col_1', pa.string() )] )
| 334 | 1 |
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class a_ ( unittest.TestCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : str ):
SCREAMING_SNAKE_CASE =inspect.getfile(accelerate.test_utils )
SCREAMING_SNAKE_CASE =os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['scripts', 'test_script.py'] )
SCREAMING_SNAKE_CASE =os.path.sep.join(inspect.getfile(self.__class__ ).split(os.path.sep )[:-1] )
@require_tpu
def _lowerCAmelCase ( self : Optional[int] ):
SCREAMING_SNAKE_CASE =f'\n {self.test_dir}/xla_spawn.py\n --num_cores 8\n {self.test_file_path}\n '.split()
SCREAMING_SNAKE_CASE =[sys.executable] + distributed_args
execute_subprocess_async(snake_case ,env=os.environ.copy() )
| 334 |
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def snake_case__ ( ):
"""simple docstring"""
assert or_gate(0, 0 ) == 0
assert or_gate(0, 1 ) == 1
assert or_gate(1, 0 ) == 1
assert or_gate(1, 1 ) == 1
if __name__ == "__main__":
print(or_gate(0, 1))
print(or_gate(1, 0))
print(or_gate(0, 0))
print(or_gate(1, 1))
| 334 | 1 |
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
if digit_amount > 0:
return round(number - int(lowerCAmelCase_ ), lowerCAmelCase_ )
return number - int(lowerCAmelCase_ )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.345, 1))
print(decimal_isolate(35.345, 2))
print(decimal_isolate(35.345, 3))
print(decimal_isolate(-14.789, 3))
print(decimal_isolate(0, 2))
print(decimal_isolate(-14.123, 1))
print(decimal_isolate(-14.123, 2))
print(decimal_isolate(-14.123, 3))
| 334 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={"vocab_file": "vocab.txt"}
_lowerCamelCase ={
"vocab_file": {
"facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt",
"facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt",
},
}
_lowerCamelCase ={
"facebook/esm2_t6_8M_UR50D": 10_24,
"facebook/esm2_t12_35M_UR50D": 10_24,
}
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
with open(lowerCAmelCase_, 'r' ) as f:
SCREAMING_SNAKE_CASE =f.read().splitlines()
return [l.strip() for l in lines]
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = VOCAB_FILES_NAMES
__UpperCAmelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase = ['input_ids', 'attention_mask']
def __init__( self : int ,snake_case : Dict ,snake_case : Dict="<unk>" ,snake_case : Optional[int]="<cls>" ,snake_case : Optional[int]="<pad>" ,snake_case : int="<mask>" ,snake_case : Optional[int]="<eos>" ,**snake_case : List[str] ,):
super().__init__(**snake_case )
SCREAMING_SNAKE_CASE =load_vocab_file(snake_case )
SCREAMING_SNAKE_CASE =dict(enumerate(self.all_tokens ) )
SCREAMING_SNAKE_CASE ={tok: ind for ind, tok in enumerate(self.all_tokens )}
SCREAMING_SNAKE_CASE =unk_token
SCREAMING_SNAKE_CASE =cls_token
SCREAMING_SNAKE_CASE =pad_token
SCREAMING_SNAKE_CASE =mask_token
SCREAMING_SNAKE_CASE =eos_token
SCREAMING_SNAKE_CASE =self.all_tokens
self._create_trie(self.unique_no_split_tokens )
def _lowerCAmelCase ( self : Optional[Any] ,snake_case : int ):
return self._id_to_token.get(snake_case ,self.unk_token )
def _lowerCAmelCase ( self : Dict ,snake_case : str ):
return self._token_to_id.get(snake_case ,self._token_to_id.get(self.unk_token ) )
def _lowerCAmelCase ( self : Tuple ,snake_case : List[str] ,**snake_case : Any ):
return text.split()
def _lowerCAmelCase ( self : Optional[int] ,snake_case : str=False ):
return len(self._id_to_token )
def _lowerCAmelCase ( self : List[str] ):
return {token: i for i, token in enumerate(self.all_tokens )}
def _lowerCAmelCase ( self : List[Any] ,snake_case : str ):
return self._token_to_id.get(snake_case ,self._token_to_id.get(self.unk_token ) )
def _lowerCAmelCase ( self : Any ,snake_case : int ):
return self._id_to_token.get(snake_case ,self.unk_token )
def _lowerCAmelCase ( self : List[str] ,snake_case : List[int] ,snake_case : Optional[List[int]] = None ):
SCREAMING_SNAKE_CASE =[self.cls_token_id]
SCREAMING_SNAKE_CASE =[self.eos_token_id] # No sep token in ESM vocabulary
if token_ids_a is None:
if self.eos_token_id is None:
return cls + token_ids_a
else:
return cls + token_ids_a + sep
elif self.eos_token_id is None:
raise ValueError('Cannot tokenize multiple sequences when EOS token is not set!' )
return cls + token_ids_a + sep + token_ids_a + sep # Multiple inputs always have an EOS token
def _lowerCAmelCase ( self : Optional[int] ,snake_case : List ,snake_case : Optional[List] = None ,snake_case : bool = False ):
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'You should not supply a second sequence if the provided sequence of '
'ids is already formatted with special tokens for the model.' )
return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
SCREAMING_SNAKE_CASE =[1] + ([0] * len(snake_case )) + [1]
if token_ids_a is not None:
mask += [0] * len(snake_case ) + [1]
return mask
def _lowerCAmelCase ( self : Optional[int] ,snake_case : Dict ,snake_case : Any ):
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,(filename_prefix + '-' if filename_prefix else '') + 'vocab.txt' )
with open(snake_case ,'w' ) as f:
f.write('\n'.join(self.all_tokens ) )
return (vocab_file,)
@property
def _lowerCAmelCase ( self : int ):
return self.get_vocab_size(with_added_tokens=snake_case )
def _lowerCAmelCase ( self : str ,snake_case : Union[List[str], List[AddedToken]] ,snake_case : bool = False ):
return super()._add_tokens(snake_case ,special_tokens=snake_case )
| 334 | 1 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class a_ :
"""simple docstring"""
@staticmethod
def _lowerCAmelCase ( *snake_case : Dict ,**snake_case : str ):
pass
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
_lowerCamelCase =(
"https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png"
)
@is_pipeline_test
@require_torch
@require_vision
class a_ ( unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def _lowerCAmelCase ( self : Any ,snake_case : Dict ,snake_case : List[Any] ,snake_case : str ):
SCREAMING_SNAKE_CASE =pipeline(
'document-question-answering' ,model=snake_case ,tokenizer=snake_case ,image_processor=snake_case )
SCREAMING_SNAKE_CASE =INVOICE_URL
SCREAMING_SNAKE_CASE =list(zip(*apply_tesseract(load_image(snake_case ) ,snake_case ,'' ) ) )
SCREAMING_SNAKE_CASE ='What is the placebo?'
SCREAMING_SNAKE_CASE =[
{
'image': load_image(snake_case ),
'question': question,
},
{
'image': image,
'question': question,
},
{
'image': image,
'question': question,
'word_boxes': word_boxes,
},
]
return dqa_pipeline, examples
def _lowerCAmelCase ( self : Optional[Any] ,snake_case : int ,snake_case : int ):
SCREAMING_SNAKE_CASE =dqa_pipeline(snake_case ,top_k=2 )
self.assertEqual(
snake_case ,[
[
{'score': ANY(snake_case ), 'answer': ANY(snake_case ), 'start': ANY(snake_case ), 'end': ANY(snake_case )},
{'score': ANY(snake_case ), 'answer': ANY(snake_case ), 'start': ANY(snake_case ), 'end': ANY(snake_case )},
]
]
* 3 ,)
@require_torch
@require_detectrona
@require_pytesseract
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =pipeline('document-question-answering' ,model='hf-internal-testing/tiny-random-layoutlmv2' )
SCREAMING_SNAKE_CASE =INVOICE_URL
SCREAMING_SNAKE_CASE ='How many cats are there?'
SCREAMING_SNAKE_CASE =[
{'score': 0.0_001, 'answer': 'oy 2312/2019', 'start': 38, 'end': 39},
{'score': 0.0_001, 'answer': 'oy 2312/2019 DUE', 'start': 38, 'end': 40},
]
SCREAMING_SNAKE_CASE =dqa_pipeline(image=snake_case ,question=snake_case ,top_k=2 )
self.assertEqual(nested_simplify(snake_case ,decimals=4 ) ,snake_case )
SCREAMING_SNAKE_CASE =dqa_pipeline({'image': image, 'question': question} ,top_k=2 )
self.assertEqual(nested_simplify(snake_case ,decimals=4 ) ,snake_case )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
SCREAMING_SNAKE_CASE ='./tests/fixtures/tests_samples/COCO/000000039769.png'
SCREAMING_SNAKE_CASE =dqa_pipeline(image=snake_case ,question=snake_case ,top_k=2 )
self.assertEqual(snake_case ,[] )
# We can optionnally pass directly the words and bounding boxes
SCREAMING_SNAKE_CASE ='./tests/fixtures/tests_samples/COCO/000000039769.png'
SCREAMING_SNAKE_CASE =[]
SCREAMING_SNAKE_CASE =[]
SCREAMING_SNAKE_CASE =dqa_pipeline(image=snake_case ,question=snake_case ,words=snake_case ,boxes=snake_case ,top_k=2 )
self.assertEqual(snake_case ,[] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def _lowerCAmelCase ( self : Dict ):
SCREAMING_SNAKE_CASE =pipeline(
'document-question-answering' ,model='tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa' ,revision='9977165' ,)
SCREAMING_SNAKE_CASE =INVOICE_URL
SCREAMING_SNAKE_CASE ='What is the invoice number?'
SCREAMING_SNAKE_CASE =dqa_pipeline(image=snake_case ,question=snake_case ,top_k=2 )
self.assertEqual(
nested_simplify(snake_case ,decimals=4 ) ,[
{'score': 0.9_944, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.0_009, 'answer': 'us-001', 'start': 16, 'end': 16},
] ,)
SCREAMING_SNAKE_CASE =dqa_pipeline({'image': image, 'question': question} ,top_k=2 )
self.assertEqual(
nested_simplify(snake_case ,decimals=4 ) ,[
{'score': 0.9_944, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.0_009, 'answer': 'us-001', 'start': 16, 'end': 16},
] ,)
SCREAMING_SNAKE_CASE =dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] ,top_k=2 )
self.assertEqual(
nested_simplify(snake_case ,decimals=4 ) ,[
[
{'score': 0.9_944, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.0_009, 'answer': 'us-001', 'start': 16, 'end': 16},
],
]
* 2 ,)
@slow
@require_torch
@require_detectrona
@require_pytesseract
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE =pipeline(
'document-question-answering' ,model='tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa' ,revision='9977165' ,max_seq_len=50 ,)
SCREAMING_SNAKE_CASE =INVOICE_URL
SCREAMING_SNAKE_CASE ='What is the invoice number?'
SCREAMING_SNAKE_CASE =dqa_pipeline(image=snake_case ,question=snake_case ,top_k=2 )
self.assertEqual(
nested_simplify(snake_case ,decimals=4 ) ,[
{'score': 0.9_974, 'answer': '1110212019', 'start': 23, 'end': 23},
{'score': 0.9_948, 'answer': 'us-001', 'start': 16, 'end': 16},
] ,)
SCREAMING_SNAKE_CASE =dqa_pipeline({'image': image, 'question': question} ,top_k=2 )
self.assertEqual(
nested_simplify(snake_case ,decimals=4 ) ,[
{'score': 0.9_974, 'answer': '1110212019', 'start': 23, 'end': 23},
{'score': 0.9_948, 'answer': 'us-001', 'start': 16, 'end': 16},
] ,)
SCREAMING_SNAKE_CASE =dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] ,top_k=2 )
self.assertEqual(
nested_simplify(snake_case ,decimals=4 ) ,[
[
{'score': 0.9_974, 'answer': '1110212019', 'start': 23, 'end': 23},
{'score': 0.9_948, 'answer': 'us-001', 'start': 16, 'end': 16},
]
]
* 2 ,)
@slow
@require_torch
@require_pytesseract
@require_vision
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained(
'impira/layoutlm-document-qa' ,revision='3dc6de3' ,add_prefix_space=snake_case )
SCREAMING_SNAKE_CASE =pipeline(
'document-question-answering' ,model='impira/layoutlm-document-qa' ,tokenizer=snake_case ,revision='3dc6de3' ,)
SCREAMING_SNAKE_CASE =INVOICE_URL
SCREAMING_SNAKE_CASE ='What is the invoice number?'
SCREAMING_SNAKE_CASE =dqa_pipeline(image=snake_case ,question=snake_case ,top_k=2 )
self.assertEqual(
nested_simplify(snake_case ,decimals=4 ) ,[
{'score': 0.4_251, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.0_819, 'answer': '1110212019', 'start': 23, 'end': 23},
] ,)
SCREAMING_SNAKE_CASE =dqa_pipeline({'image': image, 'question': question} ,top_k=2 )
self.assertEqual(
nested_simplify(snake_case ,decimals=4 ) ,[
{'score': 0.4_251, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.0_819, 'answer': '1110212019', 'start': 23, 'end': 23},
] ,)
SCREAMING_SNAKE_CASE =dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] ,top_k=2 )
self.assertEqual(
nested_simplify(snake_case ,decimals=4 ) ,[
[
{'score': 0.4_251, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.0_819, 'answer': '1110212019', 'start': 23, 'end': 23},
]
]
* 2 ,)
SCREAMING_SNAKE_CASE =list(zip(*apply_tesseract(load_image(snake_case ) ,snake_case ,'' ) ) )
# This model should also work if `image` is set to None
SCREAMING_SNAKE_CASE =dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question} ,top_k=2 )
self.assertEqual(
nested_simplify(snake_case ,decimals=4 ) ,[
{'score': 0.4_251, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.0_819, 'answer': '1110212019', 'start': 23, 'end': 23},
] ,)
@slow
@require_torch
@require_pytesseract
@require_vision
def _lowerCAmelCase ( self : str ):
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained(
'impira/layoutlm-document-qa' ,revision='3dc6de3' ,add_prefix_space=snake_case )
SCREAMING_SNAKE_CASE =pipeline(
'document-question-answering' ,model='impira/layoutlm-document-qa' ,tokenizer=snake_case ,revision='3dc6de3' ,max_seq_len=50 ,)
SCREAMING_SNAKE_CASE =INVOICE_URL
SCREAMING_SNAKE_CASE ='What is the invoice number?'
SCREAMING_SNAKE_CASE =dqa_pipeline(image=snake_case ,question=snake_case ,top_k=2 )
self.assertEqual(
nested_simplify(snake_case ,decimals=4 ) ,[
{'score': 0.9_999, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.9_998, 'answer': 'us-001', 'start': 16, 'end': 16},
] ,)
SCREAMING_SNAKE_CASE =dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] ,top_k=2 )
self.assertEqual(
nested_simplify(snake_case ,decimals=4 ) ,[
[
{'score': 0.9_999, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.9_998, 'answer': 'us-001', 'start': 16, 'end': 16},
]
]
* 2 ,)
SCREAMING_SNAKE_CASE =list(zip(*apply_tesseract(load_image(snake_case ) ,snake_case ,'' ) ) )
# This model should also work if `image` is set to None
SCREAMING_SNAKE_CASE =dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question} ,top_k=2 )
self.assertEqual(
nested_simplify(snake_case ,decimals=4 ) ,[
{'score': 0.9_999, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.9_998, 'answer': 'us-001', 'start': 16, 'end': 16},
] ,)
@slow
@require_torch
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =pipeline(
'document-question-answering' ,model='naver-clova-ix/donut-base-finetuned-docvqa' ,tokenizer=AutoTokenizer.from_pretrained('naver-clova-ix/donut-base-finetuned-docvqa' ) ,feature_extractor='naver-clova-ix/donut-base-finetuned-docvqa' ,)
SCREAMING_SNAKE_CASE =INVOICE_URL
SCREAMING_SNAKE_CASE ='What is the invoice number?'
SCREAMING_SNAKE_CASE =dqa_pipeline(image=snake_case ,question=snake_case ,top_k=2 )
self.assertEqual(nested_simplify(snake_case ,decimals=4 ) ,[{'answer': 'us-001'}] )
@require_tf
@unittest.skip('Document question answering not implemented in TF' )
def _lowerCAmelCase ( self : Union[str, Any] ):
pass
| 334 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase =logging.get_logger()
@dataclass
class a_ :
"""simple docstring"""
__UpperCAmelCase = 42
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
def _lowerCAmelCase ( self : Any ,snake_case : Any ,snake_case : Tensor ,snake_case : Tensor ):
SCREAMING_SNAKE_CASE =len(list(m.modules() ) ) == 1 or isinstance(snake_case ,nn.Convad ) or isinstance(snake_case ,nn.BatchNormad )
if has_not_submodules:
self.traced.append(snake_case )
def __call__( self : int ,snake_case : Tensor ):
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(snake_case )
[x.remove() for x in self.handles]
return self
@property
def _lowerCAmelCase ( self : Tuple ):
# check the len of the state_dict keys to see if we have learnable params
return list(filter(lambda snake_case : len(list(x.state_dict().keys() ) ) > 0 ,self.traced ) )
@dataclass
class a_ :
"""simple docstring"""
__UpperCAmelCase = 42
__UpperCAmelCase = 42
__UpperCAmelCase = 0
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
def __call__( self : int ,snake_case : Tensor ):
SCREAMING_SNAKE_CASE =Tracker(self.dest )(snake_case ).parametrized
SCREAMING_SNAKE_CASE =Tracker(self.src )(snake_case ).parametrized
SCREAMING_SNAKE_CASE =list(filter(lambda snake_case : type(snake_case ) not in self.src_skip ,snake_case ) )
SCREAMING_SNAKE_CASE =list(filter(lambda snake_case : type(snake_case ) not in self.dest_skip ,snake_case ) )
if len(snake_case ) != len(snake_case ):
raise Exception(
f'Numbers of operations are different. Source module has {len(snake_case )} operations while'
f' destination module has {len(snake_case )}.' )
for dest_m, src_m in zip(snake_case ,snake_case ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(f'Transfered from={src_m} to={dest_m}' )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ = True ):
"""simple docstring"""
print(F'Converting {name}...' )
with torch.no_grad():
SCREAMING_SNAKE_CASE =timm.create_model(lowerCAmelCase_, pretrained=lowerCAmelCase_ ).eval()
SCREAMING_SNAKE_CASE =ResNetForImageClassification(lowerCAmelCase_ ).eval()
SCREAMING_SNAKE_CASE =ModuleTransfer(src=lowerCAmelCase_, dest=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =torch.randn((1, 3, 224, 224) )
module_transfer(lowerCAmelCase_ )
assert torch.allclose(from_model(lowerCAmelCase_ ), our_model(lowerCAmelCase_ ).logits ), "The model logits don't match the original one."
SCREAMING_SNAKE_CASE =F'resnet{"-".join(name.split("resnet" ) )}'
print(lowerCAmelCase_ )
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name, commit_message='Add model', use_temp_dir=lowerCAmelCase_, )
# we can use the convnext one
SCREAMING_SNAKE_CASE =AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' )
image_processor.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name, commit_message='Add image processor', use_temp_dir=lowerCAmelCase_, )
print(F'Pushed {checkpoint_name}' )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ = None, lowerCAmelCase_ = True ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ='imagenet-1k-id2label.json'
SCREAMING_SNAKE_CASE =1000
SCREAMING_SNAKE_CASE =(1, num_labels)
SCREAMING_SNAKE_CASE ='huggingface/label-files'
SCREAMING_SNAKE_CASE =num_labels
SCREAMING_SNAKE_CASE =json.load(open(hf_hub_download(lowerCAmelCase_, lowerCAmelCase_, repo_type='dataset' ), 'r' ) )
SCREAMING_SNAKE_CASE ={int(lowerCAmelCase_ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE =idalabel
SCREAMING_SNAKE_CASE ={v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE =partial(lowerCAmelCase_, num_labels=lowerCAmelCase_, idalabel=lowerCAmelCase_, labelaid=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE ={
'resnet18': ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2], hidden_sizes=[64, 128, 256, 512], layer_type='basic' ),
'resnet26': ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2], hidden_sizes=[256, 512, 1024, 2048], layer_type='bottleneck' ),
'resnet34': ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3], hidden_sizes=[64, 128, 256, 512], layer_type='basic' ),
'resnet50': ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3], hidden_sizes=[256, 512, 1024, 2048], layer_type='bottleneck' ),
'resnet101': ImageNetPreTrainedConfig(
depths=[3, 4, 23, 3], hidden_sizes=[256, 512, 1024, 2048], layer_type='bottleneck' ),
'resnet152': ImageNetPreTrainedConfig(
depths=[3, 8, 36, 3], hidden_sizes=[256, 512, 1024, 2048], layer_type='bottleneck' ),
}
if model_name:
convert_weight_and_push(lowerCAmelCase_, names_to_config[model_name], lowerCAmelCase_, lowerCAmelCase_ )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ )
return config, expected_shape
if __name__ == "__main__":
_lowerCamelCase =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default=None,
type=str,
help=(
"The name of the model you wish to convert, it must be one of the supported resnet* architecture,"
" currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."
),
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=Path,
required=True,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
default=True,
type=bool,
required=False,
help="If True, push model and image processor to the hub.",
)
_lowerCamelCase =parser.parse_args()
_lowerCamelCase =args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 334 | 1 |
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
def __init__( self : Tuple ,snake_case : str ,snake_case : Union[str, Any] ):
super().__init__()
self.register_modules(unet=snake_case ,scheduler=snake_case )
@torch.no_grad()
def __call__( self : List[Any] ,snake_case : int = 1 ,snake_case : Optional[torch.Generator] = None ,snake_case : int = 50 ,snake_case : Optional[str] = "pil" ,snake_case : bool = True ,**snake_case : Tuple ,):
SCREAMING_SNAKE_CASE =torch.randn(
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,generator=snake_case ,)
SCREAMING_SNAKE_CASE =image.to(self.device )
# set step values
self.scheduler.set_timesteps(snake_case )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
SCREAMING_SNAKE_CASE =self.unet(snake_case ,snake_case ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
SCREAMING_SNAKE_CASE =self.scheduler.step(snake_case ,snake_case ,snake_case ).prev_sample
SCREAMING_SNAKE_CASE =(image / 2 + 0.5).clamp(0 ,1 )
SCREAMING_SNAKE_CASE =image.cpu().permute(0 ,2 ,3 ,1 ).numpy()
if output_type == "pil":
SCREAMING_SNAKE_CASE =self.numpy_to_pil(snake_case )
if not return_dict:
return (image,), "This is a local test"
return ImagePipelineOutput(images=snake_case ), "This is a local test"
| 334 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_=7 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =None
if token is not None:
SCREAMING_SNAKE_CASE ={'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}
# The id of a workflow (not of a workflow run)
SCREAMING_SNAKE_CASE ='636036'
SCREAMING_SNAKE_CASE =F'https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs'
# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
url += F'?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}'
SCREAMING_SNAKE_CASE =requests.get(lowerCAmelCase_, headers=lowerCAmelCase_ ).json()
return result["workflow_runs"]
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =get_daily_ci_runs(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =None
for workflow_run in workflow_runs:
if workflow_run["status"] == "completed":
SCREAMING_SNAKE_CASE =workflow_run['id']
break
return workflow_run_id
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =get_last_daily_ci_runs(lowerCAmelCase_ )
if workflow_run_id is not None:
SCREAMING_SNAKE_CASE =get_artifacts_links(worflow_run_id=lowerCAmelCase_, token=lowerCAmelCase_ )
for artifact_name in artifact_names:
if artifact_name in artifacts_links:
SCREAMING_SNAKE_CASE =artifacts_links[artifact_name]
download_artifact(
artifact_name=lowerCAmelCase_, artifact_url=lowerCAmelCase_, output_dir=lowerCAmelCase_, token=lowerCAmelCase_ )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
get_last_daily_ci_artifacts(lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ )
SCREAMING_SNAKE_CASE ={}
for artifact_name in artifact_names:
SCREAMING_SNAKE_CASE =os.path.join(lowerCAmelCase_, F'{artifact_name}.zip' )
if os.path.isfile(lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE ={}
with zipfile.ZipFile(lowerCAmelCase_ ) as z:
for filename in z.namelist():
if not os.path.isdir(lowerCAmelCase_ ):
# read the file
with z.open(lowerCAmelCase_ ) as f:
SCREAMING_SNAKE_CASE =f.read().decode('UTF-8' )
return results
| 334 | 1 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =list(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =list(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =0
for i in range(len(lowerCAmelCase_ ) ):
if lista[i] != lista[i]:
count += 1
SCREAMING_SNAKE_CASE ='_'
if count > 1:
return False
else:
return "".join(lowerCAmelCase_ )
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =[]
while True:
SCREAMING_SNAKE_CASE =['$'] * len(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =[]
for i in range(len(lowerCAmelCase_ ) ):
for j in range(i + 1, len(lowerCAmelCase_ ) ):
SCREAMING_SNAKE_CASE =compare_string(binary[i], binary[j] )
if k is False:
SCREAMING_SNAKE_CASE ='*'
SCREAMING_SNAKE_CASE ='*'
temp.append('X' )
for i in range(len(lowerCAmelCase_ ) ):
if checka[i] == "$":
pi.append(binary[i] )
if len(lowerCAmelCase_ ) == 0:
return pi
SCREAMING_SNAKE_CASE =list(set(lowerCAmelCase_ ) )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =[]
for minterm in minterms:
SCREAMING_SNAKE_CASE =''
for _ in range(lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE =str(minterm % 2 ) + string
minterm //= 2
temp.append(lowerCAmelCase_ )
return temp
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =list(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =list(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =0
for i in range(len(lowerCAmelCase_ ) ):
if lista[i] != lista[i]:
count_n += 1
return count_n == count
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =[]
SCREAMING_SNAKE_CASE =[0] * len(lowerCAmelCase_ )
for i in range(len(chart[0] ) ):
SCREAMING_SNAKE_CASE =0
SCREAMING_SNAKE_CASE =-1
for j in range(len(lowerCAmelCase_ ) ):
if chart[j][i] == 1:
count += 1
SCREAMING_SNAKE_CASE =j
if count == 1:
SCREAMING_SNAKE_CASE =1
for i in range(len(lowerCAmelCase_ ) ):
if select[i] == 1:
for j in range(len(chart[0] ) ):
if chart[i][j] == 1:
for k in range(len(lowerCAmelCase_ ) ):
SCREAMING_SNAKE_CASE =0
temp.append(prime_implicants[i] )
while True:
SCREAMING_SNAKE_CASE =0
SCREAMING_SNAKE_CASE =-1
SCREAMING_SNAKE_CASE =0
for i in range(len(lowerCAmelCase_ ) ):
SCREAMING_SNAKE_CASE =chart[i].count(1 )
if count_n > max_n:
SCREAMING_SNAKE_CASE =count_n
SCREAMING_SNAKE_CASE =i
if max_n == 0:
return temp
temp.append(prime_implicants[rem] )
for i in range(len(chart[0] ) ):
if chart[rem][i] == 1:
for j in range(len(lowerCAmelCase_ ) ):
SCREAMING_SNAKE_CASE =0
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =[[0 for x in range(len(lowerCAmelCase_ ) )] for x in range(len(lowerCAmelCase_ ) )]
for i in range(len(lowerCAmelCase_ ) ):
SCREAMING_SNAKE_CASE =prime_implicants[i].count('_' )
for j in range(len(lowerCAmelCase_ ) ):
if is_for_table(prime_implicants[i], binary[j], lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE =1
return chart
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =int(input('Enter the no. of variables\n' ) )
SCREAMING_SNAKE_CASE =[
float(lowerCAmelCase_ )
for x in input(
'Enter the decimal representation of Minterms \'Spaces Separated\'\n' ).split()
]
SCREAMING_SNAKE_CASE =decimal_to_binary(lowerCAmelCase_, lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =check(lowerCAmelCase_ )
print('Prime Implicants are:' )
print(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =prime_implicant_chart(lowerCAmelCase_, lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =selection(lowerCAmelCase_, lowerCAmelCase_ )
print('Essential Prime Implicants are:' )
print(lowerCAmelCase_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 334 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
)
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
def __init__( self : Tuple ,snake_case : Optional[int] ,snake_case : Dict=13 ,snake_case : str=7 ,snake_case : Dict=True ,snake_case : List[Any]=True ,snake_case : Dict=False ,snake_case : int=True ,snake_case : Dict=99 ,snake_case : int=32 ,snake_case : List[str]=5 ,snake_case : Optional[Any]=4 ,snake_case : Tuple=64 ,snake_case : List[Any]="gelu" ,snake_case : str=0.1 ,snake_case : str=0.1 ,snake_case : List[str]=512 ,snake_case : List[str]=16 ,snake_case : str=2 ,snake_case : Dict=0.02 ,snake_case : Optional[int]=3 ,snake_case : int=4 ,snake_case : Any=None ,snake_case : Union[str, Any]=2 ,snake_case : List[Any]=2 ,snake_case : Optional[int]=2 ,snake_case : Dict=2 ,snake_case : List[str]=4 ,snake_case : int=1 ,):
SCREAMING_SNAKE_CASE =parent
SCREAMING_SNAKE_CASE =batch_size
SCREAMING_SNAKE_CASE =seq_length
SCREAMING_SNAKE_CASE =is_training
SCREAMING_SNAKE_CASE =use_input_mask
SCREAMING_SNAKE_CASE =use_token_type_ids
SCREAMING_SNAKE_CASE =use_labels
SCREAMING_SNAKE_CASE =vocab_size
SCREAMING_SNAKE_CASE =hidden_size
SCREAMING_SNAKE_CASE =num_hidden_layers
SCREAMING_SNAKE_CASE =num_attention_heads
SCREAMING_SNAKE_CASE =intermediate_size
SCREAMING_SNAKE_CASE =hidden_act
SCREAMING_SNAKE_CASE =hidden_dropout_prob
SCREAMING_SNAKE_CASE =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE =max_position_embeddings
SCREAMING_SNAKE_CASE =type_vocab_size
SCREAMING_SNAKE_CASE =type_sequence_label_size
SCREAMING_SNAKE_CASE =initializer_range
SCREAMING_SNAKE_CASE =num_labels
SCREAMING_SNAKE_CASE =num_choices
SCREAMING_SNAKE_CASE =scope
SCREAMING_SNAKE_CASE =q_groups
SCREAMING_SNAKE_CASE =k_groups
SCREAMING_SNAKE_CASE =v_groups
SCREAMING_SNAKE_CASE =post_attention_groups
SCREAMING_SNAKE_CASE =intermediate_groups
SCREAMING_SNAKE_CASE =output_groups
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
SCREAMING_SNAKE_CASE =None
if self.use_input_mask:
SCREAMING_SNAKE_CASE =random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE =None
SCREAMING_SNAKE_CASE =None
SCREAMING_SNAKE_CASE =None
if self.use_labels:
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size] ,self.type_sequence_label_size )
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size] ,self.num_choices )
SCREAMING_SNAKE_CASE =self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowerCAmelCase ( self : Optional[int] ):
return SqueezeBertConfig(
embedding_size=self.hidden_size ,vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,attention_probs_dropout_prob=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,initializer_range=self.initializer_range ,q_groups=self.q_groups ,k_groups=self.k_groups ,v_groups=self.v_groups ,post_attention_groups=self.post_attention_groups ,intermediate_groups=self.intermediate_groups ,output_groups=self.output_groups ,)
def _lowerCAmelCase ( self : Dict ,snake_case : List[str] ,snake_case : Optional[Any] ,snake_case : List[str] ,snake_case : List[Any] ,snake_case : str ,snake_case : Union[str, Any] ):
SCREAMING_SNAKE_CASE =SqueezeBertModel(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case ,snake_case )
SCREAMING_SNAKE_CASE =model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _lowerCAmelCase ( self : Optional[int] ,snake_case : Optional[int] ,snake_case : Union[str, Any] ,snake_case : List[Any] ,snake_case : int ,snake_case : Any ,snake_case : Tuple ):
SCREAMING_SNAKE_CASE =SqueezeBertForMaskedLM(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case ,attention_mask=snake_case ,labels=snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def _lowerCAmelCase ( self : Tuple ,snake_case : Union[str, Any] ,snake_case : Any ,snake_case : List[str] ,snake_case : List[Any] ,snake_case : Dict ,snake_case : Optional[Any] ):
SCREAMING_SNAKE_CASE =SqueezeBertForQuestionAnswering(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(
snake_case ,attention_mask=snake_case ,start_positions=snake_case ,end_positions=snake_case )
self.parent.assertEqual(result.start_logits.shape ,(self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape ,(self.batch_size, self.seq_length) )
def _lowerCAmelCase ( self : Optional[int] ,snake_case : Tuple ,snake_case : List[str] ,snake_case : List[str] ,snake_case : Any ,snake_case : Tuple ,snake_case : str ):
SCREAMING_SNAKE_CASE =self.num_labels
SCREAMING_SNAKE_CASE =SqueezeBertForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case ,attention_mask=snake_case ,labels=snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def _lowerCAmelCase ( self : Optional[Any] ,snake_case : List[str] ,snake_case : List[str] ,snake_case : Tuple ,snake_case : Dict ,snake_case : str ,snake_case : Tuple ):
SCREAMING_SNAKE_CASE =self.num_labels
SCREAMING_SNAKE_CASE =SqueezeBertForTokenClassification(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case ,attention_mask=snake_case ,labels=snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def _lowerCAmelCase ( self : List[str] ,snake_case : Dict ,snake_case : str ,snake_case : Union[str, Any] ,snake_case : Union[str, Any] ,snake_case : Any ,snake_case : Union[str, Any] ):
SCREAMING_SNAKE_CASE =self.num_choices
SCREAMING_SNAKE_CASE =SqueezeBertForMultipleChoice(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =input_ids.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
SCREAMING_SNAKE_CASE =input_mask.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
SCREAMING_SNAKE_CASE =model(
snake_case ,attention_mask=snake_case ,labels=snake_case ,)
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_choices) )
def _lowerCAmelCase ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE =self.prepare_config_and_inputs()
((SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE)) =config_and_inputs
SCREAMING_SNAKE_CASE ={'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class a_ ( lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase = (
(
SqueezeBertModel,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
)
if is_torch_available()
else None
)
__UpperCAmelCase = (
{
'feature-extraction': SqueezeBertModel,
'fill-mask': SqueezeBertForMaskedLM,
'question-answering': SqueezeBertForQuestionAnswering,
'text-classification': SqueezeBertForSequenceClassification,
'token-classification': SqueezeBertForTokenClassification,
'zero-shot': SqueezeBertForSequenceClassification,
}
if is_torch_available()
else {}
)
__UpperCAmelCase = False
__UpperCAmelCase = True
__UpperCAmelCase = False
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =SqueezeBertModelTester(self )
SCREAMING_SNAKE_CASE =ConfigTester(self ,config_class=snake_case ,dim=37 )
def _lowerCAmelCase ( self : List[str] ):
self.config_tester.run_common_tests()
def _lowerCAmelCase ( self : Optional[int] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_model(*snake_case )
def _lowerCAmelCase ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_masked_lm(*snake_case )
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_question_answering(*snake_case )
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_sequence_classification(*snake_case )
def _lowerCAmelCase ( self : int ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_token_classification(*snake_case )
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_multiple_choice(*snake_case )
@slow
def _lowerCAmelCase ( self : str ):
for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE =SqueezeBertModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
@require_sentencepiece
@require_tokenizers
@require_torch
class a_ ( unittest.TestCase ):
"""simple docstring"""
@slow
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =SqueezeBertForSequenceClassification.from_pretrained('squeezebert/squeezebert-mnli' )
SCREAMING_SNAKE_CASE =torch.tensor([[1, 29414, 232, 328, 740, 1140, 12695, 69, 13, 1588, 2]] )
SCREAMING_SNAKE_CASE =model(snake_case )[0]
SCREAMING_SNAKE_CASE =torch.Size((1, 3) )
self.assertEqual(output.shape ,snake_case )
SCREAMING_SNAKE_CASE =torch.tensor([[0.6_401, -0.0_349, -0.6_041]] )
self.assertTrue(torch.allclose(snake_case ,snake_case ,atol=1e-4 ) )
| 334 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase ={"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase =["XGLMTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase =["XGLMTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase =[
"XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"XGLMForCausalLM",
"XGLMModel",
"XGLMPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase =[
"FlaxXGLMForCausalLM",
"FlaxXGLMModel",
"FlaxXGLMPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase =[
"TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFXGLMForCausalLM",
"TFXGLMModel",
"TFXGLMPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm import XGLMTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm_fast import XGLMTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
TFXGLMPreTrainedModel,
)
else:
import sys
_lowerCamelCase =_LazyModule(__name__, globals()["__file__"], _import_structure)
| 334 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = None
__UpperCAmelCase = None
@property
def _lowerCAmelCase ( self : List[Any] ):
return self.feat_extract_tester.prepare_feat_extract_dict()
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(snake_case ,'feature_size' ) )
self.assertTrue(hasattr(snake_case ,'sampling_rate' ) )
self.assertTrue(hasattr(snake_case ,'padding_value' ) )
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common()
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(snake_case ) == len(snake_case ) for x, y in zip(snake_case ,processed_features[input_name] ) ) )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common(equal_length=snake_case )
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} ,tensor_type='np' )
SCREAMING_SNAKE_CASE =processed_features[input_name]
if len(batch_features_input.shape ) < 3:
SCREAMING_SNAKE_CASE =batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_torch
def _lowerCAmelCase ( self : Optional[int] ):
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common(equal_length=snake_case )
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} ,tensor_type='pt' )
SCREAMING_SNAKE_CASE =processed_features[input_name]
if len(batch_features_input.shape ) < 3:
SCREAMING_SNAKE_CASE =batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_tf
def _lowerCAmelCase ( self : str ):
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common(equal_length=snake_case )
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} ,tensor_type='tf' )
SCREAMING_SNAKE_CASE =processed_features[input_name]
if len(batch_features_input.shape ) < 3:
SCREAMING_SNAKE_CASE =batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
def _lowerCAmelCase ( self : List[Any] ,snake_case : Optional[Any]=False ):
def _inputs_have_equal_length(snake_case : Dict ):
SCREAMING_SNAKE_CASE =len(input[0] )
for input_slice in input[1:]:
if len(snake_case ) != length:
return False
return True
def _inputs_are_equal(snake_case : str ,snake_case : Dict ):
if len(snake_case ) != len(snake_case ):
return False
for input_slice_a, input_slice_a in zip(snake_case ,snake_case ):
if not np.allclose(np.asarray(snake_case ) ,np.asarray(snake_case ) ,atol=1e-3 ):
return False
return True
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common(numpify=snake_case )
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.seq_length_diff
SCREAMING_SNAKE_CASE =self.feat_extract_tester.max_seq_length + pad_diff
SCREAMING_SNAKE_CASE =self.feat_extract_tester.min_seq_length
SCREAMING_SNAKE_CASE =self.feat_extract_tester.batch_size
SCREAMING_SNAKE_CASE =self.feat_extract_tester.feature_size
# test padding for List[int] + numpy
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding=snake_case )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='max_length' ,max_length=len(speech_inputs[-1] ) )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='np' )
SCREAMING_SNAKE_CASE =input_a[input_name]
# max_length parameter has to be provided when setting `padding="max_length"`
with self.assertRaises(snake_case ):
feat_extract.pad(snake_case ,padding='max_length' )[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=snake_case ,return_tensors='np' )
SCREAMING_SNAKE_CASE =input_a[input_name]
self.assertFalse(_inputs_have_equal_length(snake_case ) )
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertTrue(_inputs_are_equal(snake_case ,snake_case ) )
self.assertTrue(len(input_a[0] ) == pad_min_length )
self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )
# test padding for `pad_to_multiple_of` for List[int] + numpy
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,pad_to_multiple_of=10 )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,pad_to_multiple_of=10 )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,pad_to_multiple_of=10 ,max_length=snake_case )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,pad_to_multiple_of=10 ,max_length=snake_case ,return_tensors='np' ,)
SCREAMING_SNAKE_CASE =input_a[input_name]
self.assertTrue(all(len(snake_case ) % 10 == 0 for x in input_a ) )
self.assertTrue(_inputs_are_equal(snake_case ,snake_case ) )
SCREAMING_SNAKE_CASE =pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
self.assertTrue(all(len(snake_case ) == expected_mult_pad_length for x in input_a ) )
self.assertEqual(input_a.shape[:2] ,(batch_size, expected_mult_pad_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == feature_size )
# Check padding value is correct
SCREAMING_SNAKE_CASE =(np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
self.assertTrue(
abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
< 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
< 1e-3 )
def _lowerCAmelCase ( self : Union[str, Any] ,snake_case : Optional[int]=False ):
def _inputs_have_equal_length(snake_case : str ):
SCREAMING_SNAKE_CASE =len(input[0] )
for input_slice in input[1:]:
if len(snake_case ) != length:
return False
return True
def _inputs_are_equal(snake_case : Tuple ,snake_case : Optional[Any] ):
if len(snake_case ) != len(snake_case ):
return False
for input_slice_a, input_slice_a in zip(snake_case ,snake_case ):
if not np.allclose(np.asarray(snake_case ) ,np.asarray(snake_case ) ,atol=1e-3 ):
return False
return True
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common(numpify=snake_case )
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
# truncate to smallest
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) ,truncation=snake_case )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) )
SCREAMING_SNAKE_CASE =input_a[input_name]
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertFalse(_inputs_have_equal_length(snake_case ) )
# truncate to smallest with np
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) ,return_tensors='np' ,truncation=snake_case ,)
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) ,return_tensors='np' )
SCREAMING_SNAKE_CASE =input_a[input_name]
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(snake_case ) )
# truncate to middle
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[1] ) ,truncation=snake_case ,return_tensors='np' ,)
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[1] ) ,truncation=snake_case )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[1] ) ,return_tensors='np' )
SCREAMING_SNAKE_CASE =input_a[input_name]
self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertTrue(_inputs_are_equal(snake_case ,snake_case ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(snake_case ) )
self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(snake_case ):
feat_extract.pad(snake_case ,truncation=snake_case )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(snake_case ):
feat_extract.pad(snake_case ,padding='longest' ,truncation=snake_case )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(snake_case ):
feat_extract.pad(snake_case ,padding='longest' ,truncation=snake_case )[input_name]
# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
with self.assertRaises(snake_case ):
feat_extract.pad(snake_case ,padding='max_length' ,truncation=snake_case )[input_name]
# test truncation for `pad_to_multiple_of` for List[int] + numpy
SCREAMING_SNAKE_CASE =12
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) ,pad_to_multiple_of=snake_case ,truncation=snake_case ,)
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) ,pad_to_multiple_of=snake_case ,)
SCREAMING_SNAKE_CASE =input_a[input_name]
# retrieve expected_length as multiple of pad_to_multiple_of
SCREAMING_SNAKE_CASE =len(speech_inputs[0] )
if expected_length % pad_to_multiple_of != 0:
SCREAMING_SNAKE_CASE =((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of
self.assertTrue(len(input_a[0] ) == expected_length )
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertFalse(_inputs_have_equal_length(snake_case ) )
def _lowerCAmelCase ( self : Optional[int] ):
self._check_padding(numpify=snake_case )
def _lowerCAmelCase ( self : Tuple ):
self._check_padding(numpify=snake_case )
def _lowerCAmelCase ( self : List[str] ):
self._check_truncation(numpify=snake_case )
def _lowerCAmelCase ( self : int ):
self._check_truncation(numpify=snake_case )
@require_torch
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common()
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='np' )[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='pt' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
@require_tf
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common()
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='np' )[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='tf' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE =self.feat_extract_dict
SCREAMING_SNAKE_CASE =True
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**snake_case )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common()
SCREAMING_SNAKE_CASE =[len(snake_case ) for x in speech_inputs]
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='np' )
self.assertIn('attention_mask' ,snake_case )
self.assertListEqual(list(processed.attention_mask.shape ) ,list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() ,snake_case )
def _lowerCAmelCase ( self : Dict ):
SCREAMING_SNAKE_CASE =self.feat_extract_dict
SCREAMING_SNAKE_CASE =True
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**snake_case )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common()
SCREAMING_SNAKE_CASE =[len(snake_case ) for x in speech_inputs]
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE =min(snake_case )
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=snake_case ,truncation=snake_case ,return_tensors='np' )
self.assertIn('attention_mask' ,snake_case )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) ,[processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() ,[max_length for x in speech_inputs] )
| 334 | 1 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
_lowerCamelCase ={
"vocab_file": {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"
),
"facebook/dpr-ctx_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"
),
"facebook/dpr-ctx_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"
),
},
}
_lowerCamelCase ={
"vocab_file": {
"facebook/dpr-question_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"
),
"facebook/dpr-question_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"facebook/dpr-question_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"
),
"facebook/dpr-question_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"
),
},
}
_lowerCamelCase ={
"vocab_file": {
"facebook/dpr-reader-single-nq-base": (
"https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"
),
"facebook/dpr-reader-multiset-base": (
"https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"facebook/dpr-reader-single-nq-base": (
"https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"
),
"facebook/dpr-reader-multiset-base": (
"https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"
),
},
}
_lowerCamelCase ={
"facebook/dpr-ctx_encoder-single-nq-base": 5_12,
"facebook/dpr-ctx_encoder-multiset-base": 5_12,
}
_lowerCamelCase ={
"facebook/dpr-question_encoder-single-nq-base": 5_12,
"facebook/dpr-question_encoder-multiset-base": 5_12,
}
_lowerCamelCase ={
"facebook/dpr-reader-single-nq-base": 5_12,
"facebook/dpr-reader-multiset-base": 5_12,
}
_lowerCamelCase ={
"facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
"facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
_lowerCamelCase ={
"facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
"facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
_lowerCamelCase ={
"facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
"facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = VOCAB_FILES_NAMES
__UpperCAmelCase = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
__UpperCAmelCase = DPRContextEncoderTokenizer
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = VOCAB_FILES_NAMES
__UpperCAmelCase = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
__UpperCAmelCase = DPRQuestionEncoderTokenizer
_lowerCamelCase =collections.namedtuple(
"DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)
_lowerCamelCase =collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])
_lowerCamelCase =R"\n Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n with the format:\n\n [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n Args:\n questions (`str` or `List[str]`):\n The questions to be encoded. You can specify one question for many passages. In this case, the question\n will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n `titles` or `texts`.\n titles (`str` or `List[str]`):\n The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n texts (`str` or `List[str]`):\n The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n Activates and controls padding. Accepts the following values:\n\n - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n if provided).\n - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided.\n - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n lengths).\n truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n Activates and controls truncation. Accepts the following values:\n\n - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n the maximum acceptable input length for the model if that argument is not provided. This will truncate\n token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n of pairs) is provided.\n - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the first\n sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the\n second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n greater than the model maximum admissible input size).\n max_length (`int`, *optional*):\n Controls the maximum length to use by one of the truncation/padding parameters.\n\n If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n is required by one of the truncation/padding parameters. If the model has no specific maximum input\n length (like XLNet) truncation/padding to a maximum length will be deactivated.\n return_tensors (`str` or [`~utils.TensorType`], *optional*):\n If set, will return tensors instead of list of python integers. Acceptable values are:\n\n - `'tf'`: Return TensorFlow `tf.constant` objects.\n - `'pt'`: Return PyTorch `torch.Tensor` objects.\n - `'np'`: Return Numpy `np.ndarray` objects.\n return_attention_mask (`bool`, *optional*):\n Whether or not to return the attention mask. If not set, will return the attention mask according to the\n specific tokenizer's default, defined by the `return_outputs` attribute.\n\n [What are attention masks?](../glossary#attention-mask)\n\n Return:\n `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n - `input_ids`: List of token ids to be fed to a model.\n - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n "
@add_start_docstrings(lowerCamelCase_ )
class a_ :
"""simple docstring"""
def __call__( self : Any ,snake_case : str ,snake_case : Optional[str] = None ,snake_case : Optional[str] = None ,snake_case : Union[bool, str] = False ,snake_case : Union[bool, str] = False ,snake_case : Optional[int] = None ,snake_case : Optional[Union[str, TensorType]] = None ,snake_case : Optional[bool] = None ,**snake_case : int ,):
if titles is None and texts is None:
return super().__call__(
snake_case ,padding=snake_case ,truncation=snake_case ,max_length=snake_case ,return_tensors=snake_case ,return_attention_mask=snake_case ,**snake_case ,)
elif titles is None or texts is None:
SCREAMING_SNAKE_CASE =titles if texts is None else texts
return super().__call__(
snake_case ,snake_case ,padding=snake_case ,truncation=snake_case ,max_length=snake_case ,return_tensors=snake_case ,return_attention_mask=snake_case ,**snake_case ,)
SCREAMING_SNAKE_CASE =titles if not isinstance(snake_case ,snake_case ) else [titles]
SCREAMING_SNAKE_CASE =texts if not isinstance(snake_case ,snake_case ) else [texts]
SCREAMING_SNAKE_CASE =len(snake_case )
SCREAMING_SNAKE_CASE =questions if not isinstance(snake_case ,snake_case ) else [questions] * n_passages
assert len(snake_case ) == len(
snake_case ), f'There should be as many titles than texts but got {len(snake_case )} titles and {len(snake_case )} texts.'
SCREAMING_SNAKE_CASE =super().__call__(snake_case ,snake_case ,padding=snake_case ,truncation=snake_case )['input_ids']
SCREAMING_SNAKE_CASE =super().__call__(snake_case ,add_special_tokens=snake_case ,padding=snake_case ,truncation=snake_case )['input_ids']
SCREAMING_SNAKE_CASE ={
'input_ids': [
(encoded_question_and_title + encoded_text)[:max_length]
if max_length is not None and truncation
else encoded_question_and_title + encoded_text
for encoded_question_and_title, encoded_text in zip(snake_case ,snake_case )
]
}
if return_attention_mask is not False:
SCREAMING_SNAKE_CASE =[]
for input_ids in encoded_inputs["input_ids"]:
attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
SCREAMING_SNAKE_CASE =attention_mask
return self.pad(snake_case ,padding=snake_case ,max_length=snake_case ,return_tensors=snake_case )
def _lowerCAmelCase ( self : Dict ,snake_case : BatchEncoding ,snake_case : DPRReaderOutput ,snake_case : int = 16 ,snake_case : int = 64 ,snake_case : int = 4 ,):
SCREAMING_SNAKE_CASE =reader_input['input_ids']
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =reader_output[:3]
SCREAMING_SNAKE_CASE =len(snake_case )
SCREAMING_SNAKE_CASE =sorted(range(snake_case ) ,reverse=snake_case ,key=relevance_logits.__getitem__ )
SCREAMING_SNAKE_CASE =[]
for doc_id in sorted_docs:
SCREAMING_SNAKE_CASE =list(input_ids[doc_id] )
# assuming question & title information is at the beginning of the sequence
SCREAMING_SNAKE_CASE =sequence_ids.index(self.sep_token_id ,2 ) + 1 # second sep id
if sequence_ids[-1] == self.pad_token_id:
SCREAMING_SNAKE_CASE =sequence_ids.index(self.pad_token_id )
else:
SCREAMING_SNAKE_CASE =len(snake_case )
SCREAMING_SNAKE_CASE =self._get_best_spans(
start_logits=start_logits[doc_id][passage_offset:sequence_len] ,end_logits=end_logits[doc_id][passage_offset:sequence_len] ,max_answer_length=snake_case ,top_spans=snake_case ,)
for start_index, end_index in best_spans:
start_index += passage_offset
end_index += passage_offset
nbest_spans_predictions.append(
DPRSpanPrediction(
span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index] ,relevance_score=relevance_logits[doc_id] ,doc_id=snake_case ,start_index=snake_case ,end_index=snake_case ,text=self.decode(sequence_ids[start_index : end_index + 1] ) ,) )
if len(snake_case ) >= num_spans:
break
return nbest_spans_predictions[:num_spans]
def _lowerCAmelCase ( self : Optional[int] ,snake_case : List[int] ,snake_case : List[int] ,snake_case : int ,snake_case : int ,):
SCREAMING_SNAKE_CASE =[]
for start_index, start_score in enumerate(snake_case ):
for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length] ):
scores.append(((start_index, start_index + answer_length), start_score + end_score) )
SCREAMING_SNAKE_CASE =sorted(snake_case ,key=lambda snake_case : x[1] ,reverse=snake_case )
SCREAMING_SNAKE_CASE =[]
for (start_index, end_index), score in scores:
assert start_index <= end_index, f'Wrong span indices: [{start_index}:{end_index}]'
SCREAMING_SNAKE_CASE =end_index - start_index + 1
assert length <= max_answer_length, f'Span is too long: {length} > {max_answer_length}'
if any(
start_index <= prev_start_index <= prev_end_index <= end_index
or prev_start_index <= start_index <= end_index <= prev_end_index
for (prev_start_index, prev_end_index) in chosen_span_intervals ):
continue
chosen_span_intervals.append((start_index, end_index) )
if len(snake_case ) == top_spans:
break
return chosen_span_intervals
@add_end_docstrings(lowerCamelCase_ )
class a_ ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = VOCAB_FILES_NAMES
__UpperCAmelCase = READER_PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase = READER_PRETRAINED_INIT_CONFIGURATION
__UpperCAmelCase = ['input_ids', 'attention_mask']
__UpperCAmelCase = DPRReaderTokenizer
| 334 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_lowerCamelCase =2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
_lowerCamelCase =50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
_lowerCamelCase =0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 10_00))
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =len([g for position, g in enumerate(lowerCAmelCase_ ) if g == main_target[position]] )
return (item, float(lowerCAmelCase_ ))
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =random.randint(0, len(lowerCAmelCase_ ) - 1 )
SCREAMING_SNAKE_CASE =parent_a[:random_slice] + parent_a[random_slice:]
SCREAMING_SNAKE_CASE =parent_a[:random_slice] + parent_a[random_slice:]
return (child_a, child_a)
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =list(lowerCAmelCase_ )
if random.uniform(0, 1 ) < MUTATION_PROBABILITY:
SCREAMING_SNAKE_CASE =random.choice(lowerCAmelCase_ )
return "".join(lowerCAmelCase_ )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =[]
# Generate more children proportionally to the fitness score.
SCREAMING_SNAKE_CASE =int(parent_a[1] * 100 ) + 1
SCREAMING_SNAKE_CASE =10 if child_n >= 10 else child_n
for _ in range(lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE =population_score[random.randint(0, lowerCAmelCase_ )][0]
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =crossover(parent_a[0], lowerCAmelCase_ )
# Append new string to the population list.
pop.append(mutate(lowerCAmelCase_, lowerCAmelCase_ ) )
pop.append(mutate(lowerCAmelCase_, lowerCAmelCase_ ) )
return pop
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ = True ):
"""simple docstring"""
if N_POPULATION < N_SELECTED:
SCREAMING_SNAKE_CASE =F'{N_POPULATION} must be bigger than {N_SELECTED}'
raise ValueError(lowerCAmelCase_ )
# Verify that the target contains no genes besides the ones inside genes variable.
SCREAMING_SNAKE_CASE =sorted({c for c in target if c not in genes} )
if not_in_genes_list:
SCREAMING_SNAKE_CASE =F'{not_in_genes_list} is not in genes list, evolution cannot converge'
raise ValueError(lowerCAmelCase_ )
# Generate random starting population.
SCREAMING_SNAKE_CASE =[]
for _ in range(lowerCAmelCase_ ):
population.append(''.join([random.choice(lowerCAmelCase_ ) for i in range(len(lowerCAmelCase_ ) )] ) )
# Just some logs to know what the algorithms is doing.
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =0, 0
# This loop will end when we find a perfect match for our target.
while True:
generation += 1
total_population += len(lowerCAmelCase_ )
# Random population created. Now it's time to evaluate.
# Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this, it will probably be slower.
# We just need to call evaluate for every item inside the population.
SCREAMING_SNAKE_CASE =[evaluate(lowerCAmelCase_, lowerCAmelCase_ ) for item in population]
# Check if there is a matching evolution.
SCREAMING_SNAKE_CASE =sorted(lowerCAmelCase_, key=lambda lowerCAmelCase_ : x[1], reverse=lowerCAmelCase_ )
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the best result every 10 generation.
# Just to know that the algorithm is working.
if debug and generation % 10 == 0:
print(
F'\nGeneration: {generation}'
F'\nTotal Population:{total_population}'
F'\nBest score: {population_score[0][1]}'
F'\nBest string: {population_score[0][0]}' )
# Flush the old population, keeping some of the best evolutions.
# Keeping this avoid regression of evolution.
SCREAMING_SNAKE_CASE =population[: int(N_POPULATION / 3 )]
population.clear()
population.extend(lowerCAmelCase_ )
# Normalize population score to be between 0 and 1.
SCREAMING_SNAKE_CASE =[
(item, score / len(lowerCAmelCase_ )) for item, score in population_score
]
# This is selection
for i in range(lowerCAmelCase_ ):
population.extend(select(population_score[int(lowerCAmelCase_ )], lowerCAmelCase_, lowerCAmelCase_ ) )
# Check if the population has already reached the maximum value and if so,
# break the cycle. If this check is disabled, the algorithm will take
# forever to compute large strings, but will also calculate small strings in
# a far fewer generations.
if len(lowerCAmelCase_ ) > N_POPULATION:
break
if __name__ == "__main__":
_lowerCamelCase =(
"This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
)
_lowerCamelCase =list(
" ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
"nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
)
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase =basic(target_str, genes_list)
print(
f'\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'
)
| 334 | 1 |
def snake_case__ ( lowerCAmelCase_ = 1000 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =-1
SCREAMING_SNAKE_CASE =0
for a in range(1, n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
SCREAMING_SNAKE_CASE =(n * n - 2 * a * n) // (2 * n - 2 * a)
SCREAMING_SNAKE_CASE =n - a - b
if c * c == (a * a + b * b):
SCREAMING_SNAKE_CASE =a * b * c
if candidate >= product:
SCREAMING_SNAKE_CASE =candidate
return product
if __name__ == "__main__":
print(f'{solution() = }')
| 334 |
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer,
BertTokenizer,
BertTokenizerFast,
GPTaTokenizerFast,
is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_tokenization import CustomTokenizer # noqa E402
if is_tokenizers_available():
from test_module.custom_tokenization_fast import CustomTokenizerFast
class a_ ( unittest.TestCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : Dict ):
# A mock response for an HTTP head request to emulate server down
SCREAMING_SNAKE_CASE =mock.Mock()
SCREAMING_SNAKE_CASE =500
SCREAMING_SNAKE_CASE ={}
SCREAMING_SNAKE_CASE =HTTPError
SCREAMING_SNAKE_CASE ={}
# Download this model to make sure it's in the cache.
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert' )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch('requests.Session.request' ,return_value=snake_case ) as mock_head:
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert' )
# This check we did call the fake head request
mock_head.assert_called()
@require_tokenizers
def _lowerCAmelCase ( self : Optional[Any] ):
# A mock response for an HTTP head request to emulate server down
SCREAMING_SNAKE_CASE =mock.Mock()
SCREAMING_SNAKE_CASE =500
SCREAMING_SNAKE_CASE ={}
SCREAMING_SNAKE_CASE =HTTPError
SCREAMING_SNAKE_CASE ={}
# Download this model to make sure it's in the cache.
SCREAMING_SNAKE_CASE =GPTaTokenizerFast.from_pretrained('gpt2' )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch('requests.Session.request' ,return_value=snake_case ) as mock_head:
SCREAMING_SNAKE_CASE =GPTaTokenizerFast.from_pretrained('gpt2' )
# This check we did call the fake head request
mock_head.assert_called()
def _lowerCAmelCase ( self : Union[str, Any] ):
# This test is for deprecated behavior and can be removed in v5
try:
SCREAMING_SNAKE_CASE =tempfile.mktemp()
with open(snake_case ,'wb' ) as f:
http_get('https://huggingface.co/albert-base-v1/resolve/main/spiece.model' ,snake_case )
SCREAMING_SNAKE_CASE =AlbertTokenizer.from_pretrained(snake_case )
finally:
os.remove(snake_case )
# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
# the current folder and have the right name.
if os.path.isfile('tokenizer.json' ):
# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
return
try:
with open('tokenizer.json' ,'wb' ) as f:
http_get('https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json' ,snake_case )
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
self.assertEqual(tokenizer.vocab_size ,1000 )
# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.
finally:
os.remove('tokenizer.json' )
def _lowerCAmelCase ( self : int ):
# This test is for deprecated behavior and can be removed in v5
SCREAMING_SNAKE_CASE =AlbertTokenizer.from_pretrained('https://huggingface.co/albert-base-v1/resolve/main/spiece.model' )
@is_staging_test
class a_ ( unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase = ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']
@classmethod
def _lowerCAmelCase ( cls : List[Any] ):
SCREAMING_SNAKE_CASE =TOKEN
HfFolder.save_token(snake_case )
@classmethod
def _lowerCAmelCase ( cls : Tuple ):
try:
delete_repo(token=cls._token ,repo_id='test-tokenizer' )
except HTTPError:
pass
try:
delete_repo(token=cls._token ,repo_id='valid_org/test-tokenizer-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token ,repo_id='test-dynamic-tokenizer' )
except HTTPError:
pass
def _lowerCAmelCase ( self : Any ):
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,'vocab.txt' )
with open(snake_case ,'w' ,encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
SCREAMING_SNAKE_CASE =BertTokenizer(snake_case )
tokenizer.push_to_hub('test-tokenizer' ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained(f'{USER}/test-tokenizer' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
# Reset repo
delete_repo(token=self._token ,repo_id='test-tokenizer' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(snake_case ,repo_id='test-tokenizer' ,push_to_hub=snake_case ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained(f'{USER}/test-tokenizer' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
def _lowerCAmelCase ( self : Optional[Any] ):
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,'vocab.txt' )
with open(snake_case ,'w' ,encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
SCREAMING_SNAKE_CASE =BertTokenizer(snake_case )
tokenizer.push_to_hub('valid_org/test-tokenizer-org' ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained('valid_org/test-tokenizer-org' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
# Reset repo
delete_repo(token=self._token ,repo_id='valid_org/test-tokenizer-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(
snake_case ,repo_id='valid_org/test-tokenizer-org' ,push_to_hub=snake_case ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained('valid_org/test-tokenizer-org' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
@require_tokenizers
def _lowerCAmelCase ( self : str ):
CustomTokenizer.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,'vocab.txt' )
with open(snake_case ,'w' ,encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
SCREAMING_SNAKE_CASE =CustomTokenizer(snake_case )
# No fast custom tokenizer
tokenizer.push_to_hub('test-dynamic-tokenizer' ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer' ,trust_remote_code=snake_case )
# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,'CustomTokenizer' )
# Fast and slow custom tokenizer
CustomTokenizerFast.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,'vocab.txt' )
with open(snake_case ,'w' ,encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
SCREAMING_SNAKE_CASE =BertTokenizerFast.from_pretrained(snake_case )
bert_tokenizer.save_pretrained(snake_case )
SCREAMING_SNAKE_CASE =CustomTokenizerFast.from_pretrained(snake_case )
tokenizer.push_to_hub('test-dynamic-tokenizer' ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer' ,trust_remote_code=snake_case )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,'CustomTokenizerFast' )
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained(
f'{USER}/test-dynamic-tokenizer' ,use_fast=snake_case ,trust_remote_code=snake_case )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,'CustomTokenizer' )
class a_ ( unittest.TestCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('Hello 友達' )
self.assertEqual(trie.data ,{'H': {'e': {'l': {'l': {'o': {' ': {'友': {'達': {'': 1}}}}}}}}} )
trie.add('Hello' )
trie.data
self.assertEqual(trie.data ,{'H': {'e': {'l': {'l': {'o': {'': 1, ' ': {'友': {'達': {'': 1}}}}}}}}} )
def _lowerCAmelCase ( self : Optional[int] ):
SCREAMING_SNAKE_CASE =Trie()
self.assertEqual(trie.split('[CLS] This is a extra_id_100' ) ,['[CLS] This is a extra_id_100'] )
trie.add('[CLS]' )
trie.add('extra_id_1' )
trie.add('extra_id_100' )
self.assertEqual(trie.split('[CLS] This is a extra_id_100' ) ,['[CLS]', ' This is a ', 'extra_id_100'] )
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('A' )
self.assertEqual(trie.split('ABC' ) ,['A', 'BC'] )
self.assertEqual(trie.split('BCA' ) ,['BC', 'A'] )
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('TOKEN]' )
trie.add('[SPECIAL_TOKEN]' )
self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]' ) ,['This is something ', '[SPECIAL_TOKEN]'] )
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('A' )
trie.add('P' )
trie.add('[SPECIAL_TOKEN]' )
self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]' ) ,['This is something ', '[SPECIAL_TOKEN]'] )
def _lowerCAmelCase ( self : Dict ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('AB' )
trie.add('B' )
trie.add('C' )
self.assertEqual(trie.split('ABC' ) ,['AB', 'C'] )
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('ABC' )
trie.add('B' )
trie.add('CD' )
self.assertEqual(trie.split('ABCD' ) ,['ABC', 'D'] )
def _lowerCAmelCase ( self : Optional[Any] ):
# Even if the offsets are wrong, we necessarily output correct string
# parts.
SCREAMING_SNAKE_CASE =Trie()
SCREAMING_SNAKE_CASE =trie.cut_text('ABC' ,[0, 0, 2, 1, 2, 3] )
self.assertEqual(snake_case ,['AB', 'C'] )
| 334 | 1 |
class a_ :
"""simple docstring"""
def __init__( self : Union[str, Any] ,snake_case : list ):
SCREAMING_SNAKE_CASE =set_counts
SCREAMING_SNAKE_CASE =max(snake_case )
SCREAMING_SNAKE_CASE =len(snake_case )
SCREAMING_SNAKE_CASE =[1] * num_sets
SCREAMING_SNAKE_CASE =list(range(snake_case ) )
def _lowerCAmelCase ( self : int ,snake_case : int ,snake_case : int ):
SCREAMING_SNAKE_CASE =self.get_parent(snake_case )
SCREAMING_SNAKE_CASE =self.get_parent(snake_case )
if src_parent == dst_parent:
return False
if self.ranks[dst_parent] >= self.ranks[src_parent]:
self.set_counts[dst_parent] += self.set_counts[src_parent]
SCREAMING_SNAKE_CASE =0
SCREAMING_SNAKE_CASE =dst_parent
if self.ranks[dst_parent] == self.ranks[src_parent]:
self.ranks[dst_parent] += 1
SCREAMING_SNAKE_CASE =self.set_counts[dst_parent]
else:
self.set_counts[src_parent] += self.set_counts[dst_parent]
SCREAMING_SNAKE_CASE =0
SCREAMING_SNAKE_CASE =src_parent
SCREAMING_SNAKE_CASE =self.set_counts[src_parent]
SCREAMING_SNAKE_CASE =max(self.max_set ,snake_case )
return True
def _lowerCAmelCase ( self : Optional[int] ,snake_case : int ):
if self.parents[disj_set] == disj_set:
return disj_set
SCREAMING_SNAKE_CASE =self.get_parent(self.parents[disj_set] )
return self.parents[disj_set]
| 334 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase =[
("bert.bert", "visual_bert"),
("bert.cls", "cls"),
("bert.classifier", "cls"),
("token_type_embeddings_visual", "visual_token_type_embeddings"),
("position_embeddings_visual", "visual_position_embeddings"),
("projection", "visual_projection"),
]
_lowerCamelCase =[
"nlvr2_coco_pre_trained.th",
"nlvr2_fine_tuned.th",
"nlvr2_pre_trained.th",
"vcr_coco_pre_train.th",
"vcr_fine_tune.th",
"vcr_pre_train.th",
"vqa_coco_pre_trained.th",
"vqa_fine_tuned.th",
"vqa_pre_trained.th",
]
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =torch.load(lowerCAmelCase_, map_location='cpu' )
return sd
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_=rename_keys_prefix ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =OrderedDict()
SCREAMING_SNAKE_CASE =torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
SCREAMING_SNAKE_CASE =key
for name_pair in rename_keys_prefix:
SCREAMING_SNAKE_CASE =new_key.replace(name_pair[0], name_pair[1] )
SCREAMING_SNAKE_CASE =d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
SCREAMING_SNAKE_CASE =new_d['cls.predictions.bias']
return new_d
@torch.no_grad()
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
assert (
checkpoint_path.split('/' )[-1] in ACCEPTABLE_CHECKPOINTS
), F'The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'
# Get Config
if "pre" in checkpoint_path:
SCREAMING_SNAKE_CASE ='pretraining'
if "vcr" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 512}
elif "vqa_advanced" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 2048}
elif "vqa" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 2048}
elif "nlvr" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 1024}
else:
raise NotImplementedError(F'No implementation found for `{checkpoint_path}`.' )
else:
if "vcr" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 512}
SCREAMING_SNAKE_CASE ='multichoice'
elif "vqa_advanced" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 2048}
SCREAMING_SNAKE_CASE ='vqa_advanced'
elif "vqa" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 2048, 'num_labels': 3129}
SCREAMING_SNAKE_CASE ='vqa'
elif "nlvr" in checkpoint_path:
SCREAMING_SNAKE_CASE ={
'visual_embedding_dim': 1024,
'num_labels': 2,
}
SCREAMING_SNAKE_CASE ='nlvr'
SCREAMING_SNAKE_CASE =VisualBertConfig(**lowerCAmelCase_ )
# Load State Dict
SCREAMING_SNAKE_CASE =load_state_dict(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =get_new_dict(lowerCAmelCase_, lowerCAmelCase_ )
if model_type == "pretraining":
SCREAMING_SNAKE_CASE =VisualBertForPreTraining(lowerCAmelCase_ )
elif model_type == "vqa":
SCREAMING_SNAKE_CASE =VisualBertForQuestionAnswering(lowerCAmelCase_ )
elif model_type == "nlvr":
SCREAMING_SNAKE_CASE =VisualBertForVisualReasoning(lowerCAmelCase_ )
elif model_type == "multichoice":
SCREAMING_SNAKE_CASE =VisualBertForMultipleChoice(lowerCAmelCase_ )
model.load_state_dict(lowerCAmelCase_ )
# Save Checkpoints
Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ )
model.save_pretrained(lowerCAmelCase_ )
if __name__ == "__main__":
_lowerCamelCase =argparse.ArgumentParser()
# Required parameters
parser.add_argument("orig_checkpoint_path", type=str, help="A path to .th on local filesystem.")
parser.add_argument("pytorch_dump_folder_path", type=str, help="Path to the output PyTorch model.")
_lowerCamelCase =parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 334 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"microsoft/table-transformer-detection": (
"https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
),
}
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'table-transformer'
__UpperCAmelCase = ['past_key_values']
__UpperCAmelCase = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self : Union[str, Any] ,snake_case : List[Any]=True ,snake_case : Dict=None ,snake_case : Union[str, Any]=3 ,snake_case : Optional[int]=100 ,snake_case : List[Any]=6 ,snake_case : Dict=2048 ,snake_case : int=8 ,snake_case : Tuple=6 ,snake_case : Union[str, Any]=2048 ,snake_case : Union[str, Any]=8 ,snake_case : int=0.0 ,snake_case : Dict=0.0 ,snake_case : Tuple=True ,snake_case : int="relu" ,snake_case : Any=256 ,snake_case : Tuple=0.1 ,snake_case : int=0.0 ,snake_case : List[str]=0.0 ,snake_case : str=0.02 ,snake_case : List[str]=1.0 ,snake_case : Any=False ,snake_case : Tuple="sine" ,snake_case : int="resnet50" ,snake_case : Optional[int]=True ,snake_case : Optional[Any]=False ,snake_case : str=1 ,snake_case : List[Any]=5 ,snake_case : Optional[Any]=2 ,snake_case : int=1 ,snake_case : List[str]=1 ,snake_case : Union[str, Any]=5 ,snake_case : str=2 ,snake_case : int=0.1 ,**snake_case : Dict ,):
if backbone_config is not None and use_timm_backbone:
raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.' )
if not use_timm_backbone:
if backbone_config is None:
logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.' )
SCREAMING_SNAKE_CASE =CONFIG_MAPPING['resnet'](out_features=['stage4'] )
elif isinstance(snake_case ,snake_case ):
SCREAMING_SNAKE_CASE =backbone_config.get('model_type' )
SCREAMING_SNAKE_CASE =CONFIG_MAPPING[backbone_model_type]
SCREAMING_SNAKE_CASE =config_class.from_dict(snake_case )
# set timm attributes to None
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =None, None, None
SCREAMING_SNAKE_CASE =use_timm_backbone
SCREAMING_SNAKE_CASE =backbone_config
SCREAMING_SNAKE_CASE =num_channels
SCREAMING_SNAKE_CASE =num_queries
SCREAMING_SNAKE_CASE =d_model
SCREAMING_SNAKE_CASE =encoder_ffn_dim
SCREAMING_SNAKE_CASE =encoder_layers
SCREAMING_SNAKE_CASE =encoder_attention_heads
SCREAMING_SNAKE_CASE =decoder_ffn_dim
SCREAMING_SNAKE_CASE =decoder_layers
SCREAMING_SNAKE_CASE =decoder_attention_heads
SCREAMING_SNAKE_CASE =dropout
SCREAMING_SNAKE_CASE =attention_dropout
SCREAMING_SNAKE_CASE =activation_dropout
SCREAMING_SNAKE_CASE =activation_function
SCREAMING_SNAKE_CASE =init_std
SCREAMING_SNAKE_CASE =init_xavier_std
SCREAMING_SNAKE_CASE =encoder_layerdrop
SCREAMING_SNAKE_CASE =decoder_layerdrop
SCREAMING_SNAKE_CASE =encoder_layers
SCREAMING_SNAKE_CASE =auxiliary_loss
SCREAMING_SNAKE_CASE =position_embedding_type
SCREAMING_SNAKE_CASE =backbone
SCREAMING_SNAKE_CASE =use_pretrained_backbone
SCREAMING_SNAKE_CASE =dilation
# Hungarian matcher
SCREAMING_SNAKE_CASE =class_cost
SCREAMING_SNAKE_CASE =bbox_cost
SCREAMING_SNAKE_CASE =giou_cost
# Loss coefficients
SCREAMING_SNAKE_CASE =mask_loss_coefficient
SCREAMING_SNAKE_CASE =dice_loss_coefficient
SCREAMING_SNAKE_CASE =bbox_loss_coefficient
SCREAMING_SNAKE_CASE =giou_loss_coefficient
SCREAMING_SNAKE_CASE =eos_coefficient
super().__init__(is_encoder_decoder=snake_case ,**snake_case )
@property
def _lowerCAmelCase ( self : int ):
return self.encoder_attention_heads
@property
def _lowerCAmelCase ( self : Any ):
return self.d_model
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = version.parse('1.11' )
@property
def _lowerCAmelCase ( self : Dict ):
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
('pixel_mask', {0: 'batch'}),
] )
@property
def _lowerCAmelCase ( self : Union[str, Any] ):
return 1e-5
@property
def _lowerCAmelCase ( self : Union[str, Any] ):
return 12
| 334 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"facebook/nllb-moe-54B": "https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json",
}
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'nllb-moe'
__UpperCAmelCase = ['past_key_values']
__UpperCAmelCase = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self : str ,snake_case : Optional[int]=128112 ,snake_case : Any=1024 ,snake_case : List[str]=12 ,snake_case : Optional[int]=4096 ,snake_case : List[str]=16 ,snake_case : Optional[Any]=12 ,snake_case : Optional[Any]=4096 ,snake_case : List[Any]=16 ,snake_case : Optional[Any]=0.05 ,snake_case : str=0.05 ,snake_case : Optional[int]=True ,snake_case : Tuple=True ,snake_case : Optional[Any]="relu" ,snake_case : Any=1024 ,snake_case : List[Any]=0.1 ,snake_case : List[Any]=0.1 ,snake_case : Optional[Any]=0.0 ,snake_case : List[Any]=0.02 ,snake_case : Any=2 ,snake_case : Dict=True ,snake_case : Tuple=False ,snake_case : Any="float32" ,snake_case : Tuple=False ,snake_case : List[Any]=128 ,snake_case : Tuple=64 ,snake_case : List[Any]=4 ,snake_case : List[Any]=4 ,snake_case : List[Any]=0.001 ,snake_case : int=0.001 ,snake_case : Tuple="all" ,snake_case : Union[str, Any]=False ,snake_case : Union[str, Any]=False ,snake_case : Optional[int]=1.0 ,snake_case : Optional[Any]=0.2 ,snake_case : Optional[int]=1 ,snake_case : Union[str, Any]=0 ,snake_case : Tuple=2 ,snake_case : List[Any]=False ,**snake_case : List[Any] ,):
SCREAMING_SNAKE_CASE =vocab_size
SCREAMING_SNAKE_CASE =max_position_embeddings
SCREAMING_SNAKE_CASE =d_model
SCREAMING_SNAKE_CASE =encoder_ffn_dim
SCREAMING_SNAKE_CASE =encoder_layers
SCREAMING_SNAKE_CASE =encoder_attention_heads
SCREAMING_SNAKE_CASE =decoder_ffn_dim
SCREAMING_SNAKE_CASE =decoder_layers
SCREAMING_SNAKE_CASE =decoder_attention_heads
SCREAMING_SNAKE_CASE =dropout
SCREAMING_SNAKE_CASE =attention_dropout
SCREAMING_SNAKE_CASE =activation_dropout
SCREAMING_SNAKE_CASE =activation_function
SCREAMING_SNAKE_CASE =init_std
SCREAMING_SNAKE_CASE =encoder_layerdrop
SCREAMING_SNAKE_CASE =decoder_layerdrop
SCREAMING_SNAKE_CASE =use_cache
SCREAMING_SNAKE_CASE =encoder_layers
SCREAMING_SNAKE_CASE =scale_embedding # scale factor will be sqrt(d_model) if True
SCREAMING_SNAKE_CASE =router_z_loss_coef
SCREAMING_SNAKE_CASE =router_aux_loss_coef
SCREAMING_SNAKE_CASE =decoder_sparse_step
SCREAMING_SNAKE_CASE =encoder_sparse_step
SCREAMING_SNAKE_CASE =num_experts
SCREAMING_SNAKE_CASE =expert_capacity
SCREAMING_SNAKE_CASE =router_bias
if router_dtype not in ["float32", "float16", "bfloat16"]:
raise ValueError(f'`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}' )
SCREAMING_SNAKE_CASE =router_dtype
SCREAMING_SNAKE_CASE =router_ignore_padding_tokens
SCREAMING_SNAKE_CASE =batch_prioritized_routing
SCREAMING_SNAKE_CASE =second_expert_policy
SCREAMING_SNAKE_CASE =normalize_router_prob_before_dropping
SCREAMING_SNAKE_CASE =moe_eval_capacity_token_fraction
SCREAMING_SNAKE_CASE =moe_token_dropout
SCREAMING_SNAKE_CASE =output_router_logits
super().__init__(
pad_token_id=snake_case ,bos_token_id=snake_case ,eos_token_id=snake_case ,is_encoder_decoder=snake_case ,decoder_start_token_id=snake_case ,**snake_case ,)
| 334 | 1 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCamelCase_ )
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = field(default='summarization' , metadata={'include_in_asdict_even_if_is_default': True} )
__UpperCAmelCase = Features({'text': Value('string' )} )
__UpperCAmelCase = Features({'summary': Value('string' )} )
__UpperCAmelCase = "text"
__UpperCAmelCase = "summary"
@property
def _lowerCAmelCase ( self : Any ):
return {self.text_column: "text", self.summary_column: "summary"}
| 334 |
from __future__ import annotations
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =sorted(numsa + numsa )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =divmod(len(lowerCAmelCase_ ), 2 )
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowerCamelCase =[float(x) for x in input("Enter the elements of first array: ").split()]
_lowerCamelCase =[float(x) for x in input("Enter the elements of second array: ").split()]
print(f'The median of two arrays is: {median_of_two_arrays(array_a, array_a)}')
| 334 | 1 |
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
_lowerCamelCase =logging.get_logger(__name__)
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = ['input_features', 'attention_mask']
def __init__( self : List[str] ,snake_case : Any=80 ,snake_case : Optional[Any]=16000 ,snake_case : str=80 ,snake_case : Tuple=0.0 ,snake_case : List[Any]=True ,snake_case : str=True ,snake_case : Optional[int]=True ,**snake_case : Optional[Any] ,):
super().__init__(feature_size=snake_case ,sampling_rate=snake_case ,padding_value=snake_case ,**snake_case )
SCREAMING_SNAKE_CASE =num_mel_bins
SCREAMING_SNAKE_CASE =do_ceptral_normalize
SCREAMING_SNAKE_CASE =normalize_means
SCREAMING_SNAKE_CASE =normalize_vars
SCREAMING_SNAKE_CASE =True
def _lowerCAmelCase ( self : Dict ,snake_case : np.ndarray ,):
SCREAMING_SNAKE_CASE =waveform * (2**15) # Kaldi compliance: 16-bit signed integers
SCREAMING_SNAKE_CASE =torch.from_numpy(snake_case ).unsqueeze(0 )
SCREAMING_SNAKE_CASE =ta_kaldi.fbank(snake_case ,num_mel_bins=self.num_mel_bins ,sample_frequency=self.sampling_rate )
return features.numpy()
@staticmethod
def _lowerCAmelCase ( snake_case : np.ndarray ,snake_case : int ,snake_case : Optional[bool] = True ,snake_case : Optional[bool] = True ,snake_case : float = 0.0 ,):
# make sure we normalize float32 arrays
if normalize_means:
SCREAMING_SNAKE_CASE =x[:input_length].mean(axis=0 )
SCREAMING_SNAKE_CASE =np.subtract(snake_case ,snake_case )
if normalize_vars:
SCREAMING_SNAKE_CASE =x[:input_length].std(axis=0 )
SCREAMING_SNAKE_CASE =np.divide(snake_case ,snake_case )
if input_length < x.shape[0]:
SCREAMING_SNAKE_CASE =padding_value
# make sure array is in float32
SCREAMING_SNAKE_CASE =x.astype(np.floataa )
return x
def _lowerCAmelCase ( self : List[Any] ,snake_case : List[np.ndarray] ,snake_case : Optional[np.ndarray] = None ):
SCREAMING_SNAKE_CASE =attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [
self.utterance_cmvn(snake_case ,snake_case ,self.normalize_means ,self.normalize_vars ,self.padding_value )
for x, n in zip(snake_case ,snake_case )
]
def __call__( self : str ,snake_case : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,snake_case : Union[bool, str, PaddingStrategy] = False ,snake_case : Optional[int] = None ,snake_case : bool = False ,snake_case : Optional[int] = None ,snake_case : Optional[Union[str, TensorType]] = None ,snake_case : Optional[int] = None ,snake_case : Optional[bool] = None ,**snake_case : Optional[Any] ,):
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
f' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
f' {self.sampling_rate} and not {sampling_rate}.' )
else:
logger.warning(
'It is strongly recommended to pass the `sampling_rate` argument to this function. '
'Failing to do so can result in silent errors that might be hard to debug.' )
SCREAMING_SNAKE_CASE =isinstance(snake_case ,np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f'Only mono-channel audio is supported for input to {self}' )
SCREAMING_SNAKE_CASE =is_batched_numpy or (
isinstance(snake_case ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) ))
)
if is_batched:
SCREAMING_SNAKE_CASE =[np.asarray(snake_case ,dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(snake_case ,np.ndarray ):
SCREAMING_SNAKE_CASE =np.asarray(snake_case ,dtype=np.floataa )
elif isinstance(snake_case ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
SCREAMING_SNAKE_CASE =raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
SCREAMING_SNAKE_CASE =[raw_speech]
# extract fbank features
SCREAMING_SNAKE_CASE =[self._extract_fbank_features(snake_case ) for waveform in raw_speech]
# convert into correct format for padding
SCREAMING_SNAKE_CASE =BatchFeature({'input_features': features} )
SCREAMING_SNAKE_CASE =self.pad(
snake_case ,padding=snake_case ,max_length=snake_case ,truncation=snake_case ,pad_to_multiple_of=snake_case ,return_attention_mask=snake_case ,**snake_case ,)
# make sure list is in array format
SCREAMING_SNAKE_CASE =padded_inputs.get('input_features' )
if isinstance(input_features[0] ,snake_case ):
SCREAMING_SNAKE_CASE =[np.asarray(snake_case ,dtype=np.floataa ) for feature in input_features]
SCREAMING_SNAKE_CASE =padded_inputs.get('attention_mask' )
if attention_mask is not None:
SCREAMING_SNAKE_CASE =[np.asarray(snake_case ,dtype=np.intaa ) for array in attention_mask]
# Utterance-level cepstral mean and variance normalization
if self.do_ceptral_normalize:
SCREAMING_SNAKE_CASE =(
np.array(snake_case ,dtype=np.intaa )
if self._get_padding_strategies(snake_case ,max_length=snake_case ) is not PaddingStrategy.DO_NOT_PAD
else None
)
SCREAMING_SNAKE_CASE =self.normalize(
padded_inputs['input_features'] ,attention_mask=snake_case )
if return_tensors is not None:
SCREAMING_SNAKE_CASE =padded_inputs.convert_to_tensors(snake_case )
return padded_inputs
| 334 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'transfo-xl'
__UpperCAmelCase = ['mems']
__UpperCAmelCase = {
'n_token': 'vocab_size',
'hidden_size': 'd_model',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self : Union[str, Any] ,snake_case : List[Any]=267735 ,snake_case : Optional[int]=[20000, 40000, 200000] ,snake_case : int=1024 ,snake_case : Optional[Any]=1024 ,snake_case : Tuple=16 ,snake_case : int=64 ,snake_case : Union[str, Any]=4096 ,snake_case : List[str]=4 ,snake_case : int=False ,snake_case : int=18 ,snake_case : Tuple=1600 ,snake_case : List[str]=1000 ,snake_case : Optional[Any]=True ,snake_case : List[str]=True ,snake_case : Optional[Any]=0 ,snake_case : Optional[Any]=-1 ,snake_case : List[Any]=True ,snake_case : Optional[Any]=0.1 ,snake_case : Union[str, Any]=0.0 ,snake_case : int=True ,snake_case : Any="normal" ,snake_case : int=0.01 ,snake_case : int=0.01 ,snake_case : str=0.02 ,snake_case : Any=1e-5 ,snake_case : Optional[int]=0 ,**snake_case : List[Any] ,):
SCREAMING_SNAKE_CASE =vocab_size
SCREAMING_SNAKE_CASE =[]
self.cutoffs.extend(snake_case )
if proj_share_all_but_first:
SCREAMING_SNAKE_CASE =[False] + [True] * len(self.cutoffs )
else:
SCREAMING_SNAKE_CASE =[False] + [False] * len(self.cutoffs )
SCREAMING_SNAKE_CASE =d_model
SCREAMING_SNAKE_CASE =d_embed
SCREAMING_SNAKE_CASE =d_head
SCREAMING_SNAKE_CASE =d_inner
SCREAMING_SNAKE_CASE =div_val
SCREAMING_SNAKE_CASE =pre_lnorm
SCREAMING_SNAKE_CASE =n_layer
SCREAMING_SNAKE_CASE =n_head
SCREAMING_SNAKE_CASE =mem_len
SCREAMING_SNAKE_CASE =same_length
SCREAMING_SNAKE_CASE =attn_type
SCREAMING_SNAKE_CASE =clamp_len
SCREAMING_SNAKE_CASE =sample_softmax
SCREAMING_SNAKE_CASE =adaptive
SCREAMING_SNAKE_CASE =dropout
SCREAMING_SNAKE_CASE =dropatt
SCREAMING_SNAKE_CASE =untie_r
SCREAMING_SNAKE_CASE =init
SCREAMING_SNAKE_CASE =init_range
SCREAMING_SNAKE_CASE =proj_init_std
SCREAMING_SNAKE_CASE =init_std
SCREAMING_SNAKE_CASE =layer_norm_epsilon
super().__init__(eos_token_id=snake_case ,**snake_case )
@property
def _lowerCAmelCase ( self : str ):
# Message copied from Transformer-XL documentation
logger.info(f'The model {self.model_type} is one of the few models that has no sequence length limit.' )
return -1
@max_position_embeddings.setter
def _lowerCAmelCase ( self : Union[str, Any] ,snake_case : Dict ):
# Message copied from Transformer-XL documentation
raise NotImplementedError(
f'The model {self.model_type} is one of the few models that has no sequence length limit.' )
| 334 | 1 |
import fire
from utils import calculate_rouge, save_json
def _a ( a :Optional[int] , a :Tuple , a :Tuple=None , **a :Union[str, Any] ) -> Any:
a = [x.strip() for x in open(a ).readlines()]
a = [x.strip() for x in open(a ).readlines()][: len(a )]
a = calculate_rouge(a , a , **a )
if save_path is not None:
save_json(a , a , indent=a )
return metrics # these print nicely
if __name__ == "__main__":
fire.Fire(calculate_rouge_path)
| 0 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_MAPPING,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
)
from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
import PIL
from PIL import Image
from transformers import BeitImageProcessor
class a_ :
"""simple docstring"""
def __init__( self : Optional[int] ,snake_case : Any ,snake_case : Dict=100 ,snake_case : List[Any]=13 ,snake_case : str=30 ,snake_case : List[str]=2 ,snake_case : List[Any]=3 ,snake_case : Tuple=True ,snake_case : Optional[Any]=True ,snake_case : int=32 ,snake_case : Tuple=4 ,snake_case : List[Any]=4 ,snake_case : Optional[Any]=37 ,snake_case : Optional[Any]="gelu" ,snake_case : Tuple=0.1 ,snake_case : Union[str, Any]=0.1 ,snake_case : List[Any]=10 ,snake_case : Tuple=0.02 ,snake_case : List[str]=3 ,snake_case : Any=None ,snake_case : int=[0, 1, 2, 3] ,):
SCREAMING_SNAKE_CASE =parent
SCREAMING_SNAKE_CASE =100
SCREAMING_SNAKE_CASE =batch_size
SCREAMING_SNAKE_CASE =image_size
SCREAMING_SNAKE_CASE =patch_size
SCREAMING_SNAKE_CASE =num_channels
SCREAMING_SNAKE_CASE =is_training
SCREAMING_SNAKE_CASE =use_labels
SCREAMING_SNAKE_CASE =hidden_size
SCREAMING_SNAKE_CASE =num_hidden_layers
SCREAMING_SNAKE_CASE =num_attention_heads
SCREAMING_SNAKE_CASE =intermediate_size
SCREAMING_SNAKE_CASE =hidden_act
SCREAMING_SNAKE_CASE =hidden_dropout_prob
SCREAMING_SNAKE_CASE =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE =type_sequence_label_size
SCREAMING_SNAKE_CASE =initializer_range
SCREAMING_SNAKE_CASE =scope
SCREAMING_SNAKE_CASE =out_indices
SCREAMING_SNAKE_CASE =num_labels
# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
SCREAMING_SNAKE_CASE =(image_size // patch_size) ** 2
SCREAMING_SNAKE_CASE =num_patches + 1
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE =None
SCREAMING_SNAKE_CASE =None
if self.use_labels:
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size] ,self.type_sequence_label_size )
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size, self.image_size, self.image_size] ,self.num_labels )
SCREAMING_SNAKE_CASE =self.get_config()
return config, pixel_values, labels, pixel_labels
def _lowerCAmelCase ( self : Dict ):
return BeitConfig(
vocab_size=self.vocab_size ,image_size=self.image_size ,patch_size=self.patch_size ,num_channels=self.num_channels ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,is_decoder=snake_case ,initializer_range=self.initializer_range ,out_indices=self.out_indices ,)
def _lowerCAmelCase ( self : Union[str, Any] ,snake_case : Tuple ,snake_case : Optional[Any] ,snake_case : Union[str, Any] ,snake_case : Optional[int] ):
SCREAMING_SNAKE_CASE =BeitModel(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _lowerCAmelCase ( self : Union[str, Any] ,snake_case : Optional[int] ,snake_case : Dict ,snake_case : Any ,snake_case : List[str] ):
SCREAMING_SNAKE_CASE =BeitForMaskedImageModeling(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length - 1, self.vocab_size) )
def _lowerCAmelCase ( self : Optional[Any] ,snake_case : Any ,snake_case : str ,snake_case : Any ,snake_case : str ):
SCREAMING_SNAKE_CASE =self.type_sequence_label_size
SCREAMING_SNAKE_CASE =BeitForImageClassification(snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case ,labels=snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
# test greyscale images
SCREAMING_SNAKE_CASE =1
SCREAMING_SNAKE_CASE =BeitForImageClassification(snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE =model(snake_case ,labels=snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
def _lowerCAmelCase ( self : List[str] ,snake_case : Tuple ,snake_case : str ,snake_case : Optional[int] ,snake_case : int ):
SCREAMING_SNAKE_CASE =self.num_labels
SCREAMING_SNAKE_CASE =BeitForSemanticSegmentation(snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case )
self.parent.assertEqual(
result.logits.shape ,(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
SCREAMING_SNAKE_CASE =model(snake_case ,labels=snake_case )
self.parent.assertEqual(
result.logits.shape ,(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
def _lowerCAmelCase ( self : str ):
SCREAMING_SNAKE_CASE =self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =config_and_inputs
SCREAMING_SNAKE_CASE ={'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class a_ ( lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase = (
(BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
if is_torch_available()
else ()
)
__UpperCAmelCase = (
{
'feature-extraction': BeitModel,
'image-classification': BeitForImageClassification,
'image-segmentation': BeitForSemanticSegmentation,
}
if is_torch_available()
else {}
)
__UpperCAmelCase = False
__UpperCAmelCase = False
__UpperCAmelCase = False
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =BeitModelTester(self )
SCREAMING_SNAKE_CASE =ConfigTester(self ,config_class=snake_case ,has_text_modality=snake_case ,hidden_size=37 )
def _lowerCAmelCase ( self : List[str] ):
self.config_tester.run_common_tests()
@unittest.skip(reason='BEiT does not use inputs_embeds' )
def _lowerCAmelCase ( self : List[Any] ):
pass
@require_torch_multi_gpu
@unittest.skip(reason='BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`' )
def _lowerCAmelCase ( self : Union[str, Any] ):
pass
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE =model_class(snake_case )
self.assertIsInstance(model.get_input_embeddings() ,(nn.Module) )
SCREAMING_SNAKE_CASE =model.get_output_embeddings()
self.assertTrue(x is None or isinstance(snake_case ,nn.Linear ) )
def _lowerCAmelCase ( self : int ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE =model_class(snake_case )
SCREAMING_SNAKE_CASE =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE =[*signature.parameters.keys()]
SCREAMING_SNAKE_CASE =['pixel_values']
self.assertListEqual(arg_names[:1] ,snake_case )
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def _lowerCAmelCase ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*snake_case )
def _lowerCAmelCase ( self : Dict ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case )
def _lowerCAmelCase ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*snake_case )
def _lowerCAmelCase ( self : Any ):
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE =True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if model_class in [*get_values(snake_case ), BeitForMaskedImageModeling]:
continue
SCREAMING_SNAKE_CASE =model_class(snake_case )
model.to(snake_case )
model.train()
SCREAMING_SNAKE_CASE =self._prepare_for_class(snake_case ,snake_case ,return_labels=snake_case )
SCREAMING_SNAKE_CASE =model(**snake_case ).loss
loss.backward()
def _lowerCAmelCase ( self : int ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE =False
SCREAMING_SNAKE_CASE =True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if (
model_class in [*get_values(snake_case ), BeitForMaskedImageModeling]
or not model_class.supports_gradient_checkpointing
):
continue
SCREAMING_SNAKE_CASE =model_class(snake_case )
model.gradient_checkpointing_enable()
model.to(snake_case )
model.train()
SCREAMING_SNAKE_CASE =self._prepare_for_class(snake_case ,snake_case ,return_labels=snake_case )
SCREAMING_SNAKE_CASE =model(**snake_case ).loss
loss.backward()
def _lowerCAmelCase ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE =_config_zero_init(snake_case )
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE =model_class(config=snake_case )
for name, param in model.named_parameters():
# we skip lambda parameters as these require special initial values
# determined by config.layer_scale_init_value
if "lambda" in name:
continue
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() ,[0.0, 1.0] ,msg=f'Parameter {name} of model {model_class} seems not properly initialized' ,)
@slow
def _lowerCAmelCase ( self : List[str] ):
for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE =BeitModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class a_ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def _lowerCAmelCase ( self : Tuple ):
return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224' ) if is_vision_available() else None
@slow
def _lowerCAmelCase ( self : int ):
SCREAMING_SNAKE_CASE =BeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k' ).to(snake_case )
SCREAMING_SNAKE_CASE =self.default_image_processor
SCREAMING_SNAKE_CASE =prepare_img()
SCREAMING_SNAKE_CASE =image_processor(images=snake_case ,return_tensors='pt' ).pixel_values.to(snake_case )
# prepare bool_masked_pos
SCREAMING_SNAKE_CASE =torch.ones((1, 196) ,dtype=torch.bool ).to(snake_case )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE =model(pixel_values=snake_case ,bool_masked_pos=snake_case )
SCREAMING_SNAKE_CASE =outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE =torch.Size((1, 196, 8192) )
self.assertEqual(logits.shape ,snake_case )
SCREAMING_SNAKE_CASE =torch.tensor(
[[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]] ).to(snake_case )
self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3] ,snake_case ,atol=1e-2 ) )
@slow
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =BeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224' ).to(snake_case )
SCREAMING_SNAKE_CASE =self.default_image_processor
SCREAMING_SNAKE_CASE =prepare_img()
SCREAMING_SNAKE_CASE =image_processor(images=snake_case ,return_tensors='pt' ).to(snake_case )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE =model(**snake_case )
SCREAMING_SNAKE_CASE =outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE =torch.Size((1, 1000) )
self.assertEqual(logits.shape ,snake_case )
SCREAMING_SNAKE_CASE =torch.tensor([-1.2_385, -1.0_987, -1.0_108] ).to(snake_case )
self.assertTrue(torch.allclose(logits[0, :3] ,snake_case ,atol=1e-4 ) )
SCREAMING_SNAKE_CASE =281
self.assertEqual(logits.argmax(-1 ).item() ,snake_case )
@slow
def _lowerCAmelCase ( self : int ):
SCREAMING_SNAKE_CASE =BeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k' ).to(
snake_case )
SCREAMING_SNAKE_CASE =self.default_image_processor
SCREAMING_SNAKE_CASE =prepare_img()
SCREAMING_SNAKE_CASE =image_processor(images=snake_case ,return_tensors='pt' ).to(snake_case )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE =model(**snake_case )
SCREAMING_SNAKE_CASE =outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE =torch.Size((1, 21841) )
self.assertEqual(logits.shape ,snake_case )
SCREAMING_SNAKE_CASE =torch.tensor([1.6_881, -0.2_787, 0.5_901] ).to(snake_case )
self.assertTrue(torch.allclose(logits[0, :3] ,snake_case ,atol=1e-4 ) )
SCREAMING_SNAKE_CASE =2396
self.assertEqual(logits.argmax(-1 ).item() ,snake_case )
@slow
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE =BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640' )
SCREAMING_SNAKE_CASE =model.to(snake_case )
SCREAMING_SNAKE_CASE =BeitImageProcessor(do_resize=snake_case ,size=640 ,do_center_crop=snake_case )
SCREAMING_SNAKE_CASE =load_dataset('hf-internal-testing/fixtures_ade20k' ,split='test' )
SCREAMING_SNAKE_CASE =Image.open(ds[0]['file'] )
SCREAMING_SNAKE_CASE =image_processor(images=snake_case ,return_tensors='pt' ).to(snake_case )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE =model(**snake_case )
SCREAMING_SNAKE_CASE =outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE =torch.Size((1, 150, 160, 160) )
self.assertEqual(logits.shape ,snake_case )
SCREAMING_SNAKE_CASE =version.parse(PIL.__version__ ) < version.parse('9.0.0' )
if is_pillow_less_than_a:
SCREAMING_SNAKE_CASE =torch.tensor(
[
[[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]],
[[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]],
[[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]],
] ,device=snake_case ,)
else:
SCREAMING_SNAKE_CASE =torch.tensor(
[
[[-4.8_960, -2.3_688, -3.0_355], [-2.8_478, -0.9_836, -1.7_418], [-2.9_449, -1.3_332, -2.1_456]],
[[-5.8_081, -3.4_124, -4.1_006], [-3.8_561, -2.2_081, -3.0_323], [-3.8_365, -2.4_601, -3.3_669]],
[[-0.0_309, 3.9_868, 4.0_540], [2.9_640, 4.6_877, 4.9_976], [3.2_081, 4.7_690, 4.9_942]],
] ,device=snake_case ,)
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] ,snake_case ,atol=1e-4 ) )
@slow
def _lowerCAmelCase ( self : int ):
SCREAMING_SNAKE_CASE =BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640' )
SCREAMING_SNAKE_CASE =model.to(snake_case )
SCREAMING_SNAKE_CASE =BeitImageProcessor(do_resize=snake_case ,size=640 ,do_center_crop=snake_case )
SCREAMING_SNAKE_CASE =load_dataset('hf-internal-testing/fixtures_ade20k' ,split='test' )
SCREAMING_SNAKE_CASE =Image.open(ds[0]['file'] )
SCREAMING_SNAKE_CASE =image_processor(images=snake_case ,return_tensors='pt' ).to(snake_case )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE =model(**snake_case )
SCREAMING_SNAKE_CASE =outputs.logits.detach().cpu()
SCREAMING_SNAKE_CASE =image_processor.post_process_semantic_segmentation(outputs=snake_case ,target_sizes=[(500, 300)] )
SCREAMING_SNAKE_CASE =torch.Size((500, 300) )
self.assertEqual(segmentation[0].shape ,snake_case )
SCREAMING_SNAKE_CASE =image_processor.post_process_semantic_segmentation(outputs=snake_case )
SCREAMING_SNAKE_CASE =torch.Size((160, 160) )
self.assertEqual(segmentation[0].shape ,snake_case )
| 334 | 0 |
'''simple docstring'''
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
def lowerCAmelCase_ ( snake_case_ : Optional[int] ) -> str:
'''simple docstring'''
return 1.0 / (1.0 + np.exp(-_outputs ))
def lowerCAmelCase_ ( snake_case_ : Optional[int] ) -> Dict:
'''simple docstring'''
UpperCAmelCase_ = np.max(_outputs , axis=-1 , keepdims=snake_case_ )
UpperCAmelCase_ = np.exp(_outputs - maxes )
return shifted_exp / shifted_exp.sum(axis=-1 , keepdims=snake_case_ )
class __A ( UpperCamelCase__ ):
a__ : List[str] = """sigmoid"""
a__ : Tuple = """softmax"""
a__ : Union[str, Any] = """none"""
@add_end_docstrings(
UpperCamelCase__ , r"""
return_all_scores (`bool`, *optional*, defaults to `False`):
Whether to return all prediction scores or just the one of the predicted class.
function_to_apply (`str`, *optional*, defaults to `\"default\"`):
The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:
- `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model
has several labels, will apply the softmax function on the output.
- `\"sigmoid\"`: Applies the sigmoid function on the output.
- `\"softmax\"`: Applies the softmax function on the output.
- `\"none\"`: Does not apply any function on the output.
""" , )
class __A ( UpperCamelCase__ ):
a__ : Optional[Any] = False
a__ : List[str] = ClassificationFunction.NONE
def __init__(self : Dict , **__a : Any ):
super().__init__(**__a )
self.check_model_type(
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if self.framework == "tf"
else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING )
def _lowercase (self : Dict , __a : Dict=None , __a : Any=None , __a : int="" , **__a : Dict ):
# Using "" as default argument because we're going to use `top_k=None` in user code to declare
# "No top_k"
UpperCAmelCase_ = tokenizer_kwargs
UpperCAmelCase_ = {}
if hasattr(self.model.config , "return_all_scores" ) and return_all_scores is None:
UpperCAmelCase_ = self.model.config.return_all_scores
if isinstance(__a , __a ) or top_k is None:
UpperCAmelCase_ = top_k
UpperCAmelCase_ = False
elif return_all_scores is not None:
warnings.warn(
"`return_all_scores` is now deprecated, if want a similar functionality use `top_k=None` instead of"
" `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`." , __a , )
if return_all_scores:
UpperCAmelCase_ = None
else:
UpperCAmelCase_ = 1
if isinstance(__a , __a ):
UpperCAmelCase_ = ClassificationFunction[function_to_apply.upper()]
if function_to_apply is not None:
UpperCAmelCase_ = function_to_apply
return preprocess_params, {}, postprocess_params
def __call__(self : Optional[int] , *__a : List[Any] , **__a : Tuple ):
UpperCAmelCase_ = super().__call__(*__a , **__a )
# TODO try and retrieve it in a nicer way from _sanitize_parameters.
UpperCAmelCase_ = "top_k" not in kwargs
if isinstance(args[0] , __a ) and _legacy:
# This pipeline is odd, and return a list when single item is run
return [result]
else:
return result
def _lowercase (self : Optional[Any] , __a : Dict , **__a : List[str] ):
UpperCAmelCase_ = self.framework
if isinstance(__a , __a ):
return self.tokenizer(**__a , return_tensors=__a , **__a )
elif isinstance(__a , __a ) and len(__a ) == 1 and isinstance(inputs[0] , __a ) and len(inputs[0] ) == 2:
# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
return self.tokenizer(
text=inputs[0][0] , text_pair=inputs[0][1] , return_tensors=__a , **__a )
elif isinstance(__a , __a ):
# This is likely an invalid usage of the pipeline attempting to pass text pairs.
raise ValueError(
"The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a"
" dictionary `{\"text\": \"My text\", \"text_pair\": \"My pair\"}` in order to send a text pair." )
return self.tokenizer(__a , return_tensors=__a , **__a )
def _lowercase (self : Dict , __a : Optional[Any] ):
return self.model(**__a )
def _lowercase (self : Dict , __a : int , __a : int=None , __a : List[str]=1 , __a : List[str]=True ):
# `_legacy` is used to determine if we're running the naked pipeline and in backward
# compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
# the more natural result containing the list.
# Default value before `set_parameters`
if function_to_apply is None:
if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
UpperCAmelCase_ = ClassificationFunction.SIGMOID
elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
UpperCAmelCase_ = ClassificationFunction.SOFTMAX
elif hasattr(self.model.config , "function_to_apply" ) and function_to_apply is None:
UpperCAmelCase_ = self.model.config.function_to_apply
else:
UpperCAmelCase_ = ClassificationFunction.NONE
UpperCAmelCase_ = model_outputs["logits"][0]
UpperCAmelCase_ = outputs.numpy()
if function_to_apply == ClassificationFunction.SIGMOID:
UpperCAmelCase_ = sigmoid(__a )
elif function_to_apply == ClassificationFunction.SOFTMAX:
UpperCAmelCase_ = softmax(__a )
elif function_to_apply == ClassificationFunction.NONE:
UpperCAmelCase_ = outputs
else:
raise ValueError(f"""Unrecognized `function_to_apply` argument: {function_to_apply}""" )
if top_k == 1 and _legacy:
return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}
UpperCAmelCase_ = [
{"label": self.model.config.idalabel[i], "score": score.item()} for i, score in enumerate(__a )
]
if not _legacy:
dict_scores.sort(key=lambda __a : x["score"] , reverse=__a )
if top_k is not None:
UpperCAmelCase_ = dict_scores[:top_k]
return dict_scores
| 1 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs
from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase =logging.get_logger()
@dataclass
class a_ :
"""simple docstring"""
__UpperCAmelCase = 42
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
def _lowerCAmelCase ( self : List[Any] ,snake_case : Dict ,snake_case : Tensor ,snake_case : Tensor ):
SCREAMING_SNAKE_CASE =len(list(m.modules() ) ) == 1 or isinstance(snake_case ,nn.Convad ) or isinstance(snake_case ,nn.BatchNormad )
if has_not_submodules:
self.traced.append(snake_case )
def __call__( self : List[str] ,snake_case : Tensor ):
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(snake_case )
[x.remove() for x in self.handles]
return self
@property
def _lowerCAmelCase ( self : Optional[Any] ):
# check the len of the state_dict keys to see if we have learnable params
return list(filter(lambda snake_case : len(list(x.state_dict().keys() ) ) > 0 ,self.traced ) )
@dataclass
class a_ :
"""simple docstring"""
__UpperCAmelCase = 42
__UpperCAmelCase = 42
__UpperCAmelCase = 1
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
__UpperCAmelCase = True
def __call__( self : str ,snake_case : Tensor ):
SCREAMING_SNAKE_CASE =Tracker(self.dest )(snake_case ).parametrized
SCREAMING_SNAKE_CASE =Tracker(self.src )(snake_case ).parametrized
SCREAMING_SNAKE_CASE =list(filter(lambda snake_case : type(snake_case ) not in self.src_skip ,snake_case ) )
SCREAMING_SNAKE_CASE =list(filter(lambda snake_case : type(snake_case ) not in self.dest_skip ,snake_case ) )
if len(snake_case ) != len(snake_case ) and self.raise_if_mismatch:
raise Exception(
f'Numbers of operations are different. Source module has {len(snake_case )} operations while'
f' destination module has {len(snake_case )}.' )
for dest_m, src_m in zip(snake_case ,snake_case ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(f'Transfered from={src_m} to={dest_m}' )
class a_ ( nn.Module ):
"""simple docstring"""
def __init__( self : Any ,snake_case : nn.Module ):
super().__init__()
SCREAMING_SNAKE_CASE =[]
# - get the stem
feature_blocks.append(('conv1', model.stem) )
# - get all the feature blocks
for k, v in model.trunk_output.named_children():
assert k.startswith('block' ), f'Unexpected layer name {k}'
SCREAMING_SNAKE_CASE =len(snake_case ) + 1
feature_blocks.append((f'res{block_index}', v) )
SCREAMING_SNAKE_CASE =nn.ModuleDict(snake_case )
def _lowerCAmelCase ( self : Dict ,snake_case : Tensor ):
return get_trunk_forward_outputs(
snake_case ,out_feat_keys=snake_case ,feature_blocks=self._feature_blocks ,)
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
def _lowerCAmelCase ( self : Optional[int] ,snake_case : str ):
SCREAMING_SNAKE_CASE =x.split('-' )
return x_split[0] + x_split[1] + "_" + "".join(x_split[2:] )
def __getitem__( self : Optional[Any] ,snake_case : str ):
# default to timm!
if x not in self:
SCREAMING_SNAKE_CASE =self.convert_name_to_timm(snake_case )
SCREAMING_SNAKE_CASE =partial(lambda: (timm.create_model(snake_case ,pretrained=snake_case ).eval(), None) )
else:
SCREAMING_SNAKE_CASE =super().__getitem__(snake_case )
return val
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
def __getitem__( self : int ,snake_case : str ):
if "seer" in x and "in1k" not in x:
SCREAMING_SNAKE_CASE =RegNetModel
else:
SCREAMING_SNAKE_CASE =RegNetForImageClassification
return val
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
for from_key, to_key in keys:
SCREAMING_SNAKE_CASE =from_state_dict[from_key].clone()
print(F'Copied key={from_key} to={to_key}' )
return to_state_dict
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ = True, ):
"""simple docstring"""
print(F'Converting {name}...' )
with torch.no_grad():
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =from_model_func()
SCREAMING_SNAKE_CASE =our_model_func(lowerCAmelCase_ ).eval()
SCREAMING_SNAKE_CASE =ModuleTransfer(src=lowerCAmelCase_, dest=lowerCAmelCase_, raise_if_mismatch=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =torch.randn((1, 3, 224, 224) )
module_transfer(lowerCAmelCase_ )
if from_state_dict is not None:
SCREAMING_SNAKE_CASE =[]
# for seer - in1k finetuned we have to manually copy the head
if "seer" in name and "in1k" in name:
SCREAMING_SNAKE_CASE =[('0.clf.0.weight', 'classifier.1.weight'), ('0.clf.0.bias', 'classifier.1.bias')]
SCREAMING_SNAKE_CASE =manually_copy_vissl_head(lowerCAmelCase_, our_model.state_dict(), lowerCAmelCase_ )
our_model.load_state_dict(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =our_model(lowerCAmelCase_, output_hidden_states=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =(
our_outputs.logits if isinstance(lowerCAmelCase_, lowerCAmelCase_ ) else our_outputs.last_hidden_state
)
SCREAMING_SNAKE_CASE =from_model(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =from_output[-1] if type(lowerCAmelCase_ ) is list else from_output
# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
if "seer" in name and "in1k" in name:
SCREAMING_SNAKE_CASE =our_outputs.hidden_states[-1]
assert torch.allclose(lowerCAmelCase_, lowerCAmelCase_ ), "The model logits don't match the original one."
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / name, commit_message='Add model', use_temp_dir=lowerCAmelCase_, )
SCREAMING_SNAKE_CASE =224 if 'seer' not in name else 384
# we can use the convnext one
SCREAMING_SNAKE_CASE =AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k', size=lowerCAmelCase_ )
image_processor.push_to_hub(
repo_path_or_name=save_directory / name, commit_message='Add image processor', use_temp_dir=lowerCAmelCase_, )
print(F'Pushed {name}' )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ = None, lowerCAmelCase_ = True ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ='imagenet-1k-id2label.json'
SCREAMING_SNAKE_CASE =1000
SCREAMING_SNAKE_CASE =(1, num_labels)
SCREAMING_SNAKE_CASE ='huggingface/label-files'
SCREAMING_SNAKE_CASE =num_labels
SCREAMING_SNAKE_CASE =json.load(open(cached_download(hf_hub_url(lowerCAmelCase_, lowerCAmelCase_, repo_type='dataset' ) ), 'r' ) )
SCREAMING_SNAKE_CASE ={int(lowerCAmelCase_ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE =idalabel
SCREAMING_SNAKE_CASE ={v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE =partial(lowerCAmelCase_, num_labels=lowerCAmelCase_, idalabel=lowerCAmelCase_, labelaid=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE ={
'regnet-x-002': ImageNetPreTrainedConfig(
depths=[1, 1, 4, 7], hidden_sizes=[24, 56, 152, 368], groups_width=8, layer_type='x' ),
'regnet-x-004': ImageNetPreTrainedConfig(
depths=[1, 2, 7, 12], hidden_sizes=[32, 64, 160, 384], groups_width=16, layer_type='x' ),
'regnet-x-006': ImageNetPreTrainedConfig(
depths=[1, 3, 5, 7], hidden_sizes=[48, 96, 240, 528], groups_width=24, layer_type='x' ),
'regnet-x-008': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 5], hidden_sizes=[64, 128, 288, 672], groups_width=16, layer_type='x' ),
'regnet-x-016': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 2], hidden_sizes=[72, 168, 408, 912], groups_width=24, layer_type='x' ),
'regnet-x-032': ImageNetPreTrainedConfig(
depths=[2, 6, 15, 2], hidden_sizes=[96, 192, 432, 1008], groups_width=48, layer_type='x' ),
'regnet-x-040': ImageNetPreTrainedConfig(
depths=[2, 5, 14, 2], hidden_sizes=[80, 240, 560, 1360], groups_width=40, layer_type='x' ),
'regnet-x-064': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1], hidden_sizes=[168, 392, 784, 1624], groups_width=56, layer_type='x' ),
'regnet-x-080': ImageNetPreTrainedConfig(
depths=[2, 5, 15, 1], hidden_sizes=[80, 240, 720, 1920], groups_width=120, layer_type='x' ),
'regnet-x-120': ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1], hidden_sizes=[224, 448, 896, 2240], groups_width=112, layer_type='x' ),
'regnet-x-160': ImageNetPreTrainedConfig(
depths=[2, 6, 13, 1], hidden_sizes=[256, 512, 896, 2048], groups_width=128, layer_type='x' ),
'regnet-x-320': ImageNetPreTrainedConfig(
depths=[2, 7, 13, 1], hidden_sizes=[336, 672, 1344, 2520], groups_width=168, layer_type='x' ),
# y variant
'regnet-y-002': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7], hidden_sizes=[24, 56, 152, 368], groups_width=8 ),
'regnet-y-004': ImageNetPreTrainedConfig(
depths=[1, 3, 6, 6], hidden_sizes=[48, 104, 208, 440], groups_width=8 ),
'regnet-y-006': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 4], hidden_sizes=[48, 112, 256, 608], groups_width=16 ),
'regnet-y-008': ImageNetPreTrainedConfig(
depths=[1, 3, 8, 2], hidden_sizes=[64, 128, 320, 768], groups_width=16 ),
'regnet-y-016': ImageNetPreTrainedConfig(
depths=[2, 6, 17, 2], hidden_sizes=[48, 120, 336, 888], groups_width=24 ),
'regnet-y-032': ImageNetPreTrainedConfig(
depths=[2, 5, 13, 1], hidden_sizes=[72, 216, 576, 1512], groups_width=24 ),
'regnet-y-040': ImageNetPreTrainedConfig(
depths=[2, 6, 12, 2], hidden_sizes=[128, 192, 512, 1088], groups_width=64 ),
'regnet-y-064': ImageNetPreTrainedConfig(
depths=[2, 7, 14, 2], hidden_sizes=[144, 288, 576, 1296], groups_width=72 ),
'regnet-y-080': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1], hidden_sizes=[168, 448, 896, 2016], groups_width=56 ),
'regnet-y-120': ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1], hidden_sizes=[224, 448, 896, 2240], groups_width=112 ),
'regnet-y-160': ImageNetPreTrainedConfig(
depths=[2, 4, 11, 1], hidden_sizes=[224, 448, 1232, 3024], groups_width=112 ),
'regnet-y-320': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1], hidden_sizes=[232, 696, 1392, 3712], groups_width=232 ),
# models created by SEER -> https://arxiv.org/abs/2202.08360
'regnet-y-320-seer': RegNetConfig(depths=[2, 5, 12, 1], hidden_sizes=[232, 696, 1392, 3712], groups_width=232 ),
'regnet-y-640-seer': RegNetConfig(depths=[2, 5, 12, 1], hidden_sizes=[328, 984, 1968, 4920], groups_width=328 ),
'regnet-y-1280-seer': RegNetConfig(
depths=[2, 7, 17, 1], hidden_sizes=[528, 1056, 2904, 7392], groups_width=264 ),
'regnet-y-2560-seer': RegNetConfig(
depths=[3, 7, 16, 1], hidden_sizes=[640, 1696, 2544, 5088], groups_width=640 ),
'regnet-y-10b-seer': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1], hidden_sizes=[2020, 4040, 11110, 28280], groups_width=1010 ),
# finetuned on imagenet
'regnet-y-320-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1], hidden_sizes=[232, 696, 1392, 3712], groups_width=232 ),
'regnet-y-640-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1], hidden_sizes=[328, 984, 1968, 4920], groups_width=328 ),
'regnet-y-1280-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1], hidden_sizes=[528, 1056, 2904, 7392], groups_width=264 ),
'regnet-y-2560-seer-in1k': ImageNetPreTrainedConfig(
depths=[3, 7, 16, 1], hidden_sizes=[640, 1696, 2544, 5088], groups_width=640 ),
'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1], hidden_sizes=[2020, 4040, 11110, 28280], groups_width=1010 ),
}
SCREAMING_SNAKE_CASE =NameToOurModelFuncMap()
SCREAMING_SNAKE_CASE =NameToFromModelFuncMap()
# add seer weights logic
def load_using_classy_vision(lowerCAmelCase_, lowerCAmelCase_ ) -> Tuple[nn.Module, Dict]:
SCREAMING_SNAKE_CASE =torch.hub.load_state_dict_from_url(lowerCAmelCase_, model_dir=str(lowerCAmelCase_ ), map_location='cpu' )
SCREAMING_SNAKE_CASE =model_func()
# check if we have a head, if yes add it
SCREAMING_SNAKE_CASE =files['classy_state_dict']['base_model']['model']
SCREAMING_SNAKE_CASE =model_state_dict['trunk']
model.load_state_dict(lowerCAmelCase_ )
return model.eval(), model_state_dict["heads"]
# pretrained
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch', lambda: FakeRegNetVisslWrapper(RegNetYaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch', lambda: FakeRegNetVisslWrapper(RegNetYaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch', lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch', lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27, group_width=1010, w_a=1744, w_a=620.83, w_m=2.52 ) ) ), )
# IN1K finetuned
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch', lambda: FakeRegNetVisslWrapper(RegNetYaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch', lambda: FakeRegNetVisslWrapper(RegNetYaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch', lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ), )
SCREAMING_SNAKE_CASE =partial(
lowerCAmelCase_, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch', lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27, group_width=1010, w_a=1744, w_a=620.83, w_m=2.52 ) ) ), )
if model_name:
convert_weight_and_push(
lowerCAmelCase_, names_to_from_model_map[model_name], names_to_ours_model_map[model_name], names_to_config[model_name], lowerCAmelCase_, lowerCAmelCase_, )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(
lowerCAmelCase_, names_to_from_model_map[model_name], names_to_ours_model_map[model_name], lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, )
return config, expected_shape
if __name__ == "__main__":
_lowerCamelCase =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default=None,
type=str,
help=(
"The name of the model you wish to convert, it must be one of the supported regnet* architecture,"
" currently: regnetx-*, regnety-*. If `None`, all of them will the converted."
),
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=Path,
required=True,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
default=True,
type=bool,
required=False,
help="If True, push model and image processor to the hub.",
)
_lowerCamelCase =parser.parse_args()
_lowerCamelCase =args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 334 | 0 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser
lowerCamelCase : Dict = logging.getLogger(__name__)
torch.set_grad_enabled(False)
lowerCamelCase : List[Any] = 'cuda' if torch.cuda.is_available() else 'cpu'
def _SCREAMING_SNAKE_CASE (A , A=100 , A=" " ) -> List[str]:
"""simple docstring"""
lowercase__ = text.split(A )
return [character.join(text[i : i + n] ).strip() for i in range(0 , len(A ) , A )]
def _SCREAMING_SNAKE_CASE (A ) -> dict:
"""simple docstring"""
lowercase__ ,lowercase__ = [], []
for title, text in zip(documents['''title'''] , documents['''text'''] ):
if text is not None:
for passage in split_text(A ):
titles.append(title if title is not None else '''''' )
texts.append(A )
return {"title": titles, "text": texts}
def _SCREAMING_SNAKE_CASE (A , A , A ) -> dict:
"""simple docstring"""
lowercase__ = ctx_tokenizer(
documents['''title'''] , documents['''text'''] , truncation=A , padding='''longest''' , return_tensors='''pt''' )['''input_ids''']
lowercase__ = ctx_encoder(input_ids.to(device=A ) , return_dict=A ).pooler_output
return {"embeddings": embeddings.detach().cpu().numpy()}
def _SCREAMING_SNAKE_CASE (A , A , A , ) -> List[str]:
"""simple docstring"""
logger.info('''Step 1 - Create the dataset''' )
######################################
# The dataset needed for RAG must have three columns:
# - title (string): title of the document
# - text (string): text of a passage of the document
# - embeddings (array of dimension d): DPR representation of the passage
# Let's say you have documents in tab-separated csv files with columns "title" and "text"
assert os.path.isfile(rag_example_args.csv_path ), "Please provide a valid path to a csv file"
# You can load a Dataset object this way
lowercase__ = load_dataset(
'''csv''' , data_files=[rag_example_args.csv_path] , split='''train''' , delimiter='''\t''' , column_names=['''title''', '''text'''] )
# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files
# Then split the documents into passages of 100 words
lowercase__ = dataset.map(A , batched=A , num_proc=processing_args.num_proc )
# And compute the embeddings
lowercase__ = DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name ).to(device=A )
lowercase__ = DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name )
lowercase__ = Features(
{'''text''': Value('''string''' ), '''title''': Value('''string''' ), '''embeddings''': Sequence(Value('''float32''' ) )} ) # optional, save as float32 instead of float64 to save space
lowercase__ = dataset.map(
partial(A , ctx_encoder=A , ctx_tokenizer=A ) , batched=A , batch_size=processing_args.batch_size , features=A , )
# And finally save your dataset
lowercase__ = os.path.join(rag_example_args.output_dir , '''my_knowledge_dataset''' )
dataset.save_to_disk(A )
# from datasets import load_from_disk
# dataset = load_from_disk(passages_path) # to reload the dataset
######################################
logger.info('''Step 2 - Index the dataset''' )
######################################
# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
lowercase__ = faiss.IndexHNSWFlat(index_hnsw_args.d , index_hnsw_args.m , faiss.METRIC_INNER_PRODUCT )
dataset.add_faiss_index('''embeddings''' , custom_index=A )
# And save the index
lowercase__ = os.path.join(rag_example_args.output_dir , '''my_knowledge_dataset_hnsw_index.faiss''' )
dataset.get_index('''embeddings''' ).save(A )
# dataset.load_faiss_index("embeddings", index_path) # to reload the index
@dataclass
class __lowerCAmelCase :
'''simple docstring'''
lowerCAmelCase__ : str = field(
default=str(Path(lowercase_ ).parent / """test_run""" / """dummy-kb""" / """my_knowledge_dataset.csv""" ) , metadata={"""help""": """Path to a tab-separated csv file with columns 'title' and 'text'"""} , )
lowerCAmelCase__ : Optional[str] = field(
default=lowercase_ , metadata={"""help""": """Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."""} , )
lowerCAmelCase__ : str = field(
default="""facebook/rag-sequence-nq""" , metadata={"""help""": """The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"""} , )
lowerCAmelCase__ : str = field(
default="""facebook/dpr-ctx_encoder-multiset-base""" , metadata={
"""help""": (
"""The DPR context encoder model to use. Either 'facebook/dpr-ctx_encoder-single-nq-base' or"""
""" 'facebook/dpr-ctx_encoder-multiset-base'"""
)
} , )
lowerCAmelCase__ : Optional[str] = field(
default=str(Path(lowercase_ ).parent / """test_run""" / """dummy-kb""" ) , metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""} , )
@dataclass
class __lowerCAmelCase :
'''simple docstring'''
lowerCAmelCase__ : Optional[int] = field(
default=lowercase_ , metadata={
"""help""": """The number of processes to use to split the documents into passages. Default is single process."""
} , )
lowerCAmelCase__ : int = field(
default=16 , metadata={
"""help""": """The batch size to use when computing the passages embeddings using the DPR context encoder."""
} , )
@dataclass
class __lowerCAmelCase :
'''simple docstring'''
lowerCAmelCase__ : int = field(
default=768 , metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""} , )
lowerCAmelCase__ : int = field(
default=128 , metadata={
"""help""": (
"""The number of bi-directional links created for every new element during the HNSW index construction."""
)
} , )
if __name__ == "__main__":
logging.basicConfig(level=logging.WARNING)
logger.setLevel(logging.INFO)
lowerCamelCase : Optional[Any] = HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
lowerCamelCase , lowerCamelCase , lowerCamelCase : Union[str, Any] = parser.parse_args_into_dataclasses()
with TemporaryDirectory() as tmp_dir:
lowerCamelCase : Optional[int] = rag_example_args.output_dir or tmp_dir
main(rag_example_args, processing_args, index_hnsw_args)
| 2 |
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
_lowerCamelCase =16
_lowerCamelCase =32
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ = 16 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained('bert-base-cased' )
SCREAMING_SNAKE_CASE =load_dataset('glue', 'mrpc' )
def tokenize_function(lowerCAmelCase_ ):
# max_length=None => use the model max length (it's actually the default)
SCREAMING_SNAKE_CASE =tokenizer(examples['sentence1'], examples['sentence2'], truncation=lowerCAmelCase_, max_length=lowerCAmelCase_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
SCREAMING_SNAKE_CASE =datasets.map(
lowerCAmelCase_, batched=lowerCAmelCase_, remove_columns=['idx', 'sentence1', 'sentence2'], )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
SCREAMING_SNAKE_CASE =tokenized_datasets.rename_column('label', 'labels' )
def collate_fn(lowerCAmelCase_ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
SCREAMING_SNAKE_CASE =128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
SCREAMING_SNAKE_CASE =16
elif accelerator.mixed_precision != "no":
SCREAMING_SNAKE_CASE =8
else:
SCREAMING_SNAKE_CASE =None
return tokenizer.pad(
lowerCAmelCase_, padding='longest', max_length=lowerCAmelCase_, pad_to_multiple_of=lowerCAmelCase_, return_tensors='pt', )
# Instantiate dataloaders.
SCREAMING_SNAKE_CASE =DataLoader(
tokenized_datasets['train'], shuffle=lowerCAmelCase_, collate_fn=lowerCAmelCase_, batch_size=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =DataLoader(
tokenized_datasets['validation'], shuffle=lowerCAmelCase_, collate_fn=lowerCAmelCase_, batch_size=lowerCAmelCase_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
_lowerCamelCase =mocked_dataloaders # noqa: F811
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
if os.environ.get('TESTING_MOCKED_DATALOADERS', lowerCAmelCase_ ) == "1":
SCREAMING_SNAKE_CASE =2
# Initialize accelerator
SCREAMING_SNAKE_CASE =Accelerator(cpu=args.cpu, mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
SCREAMING_SNAKE_CASE =config['lr']
SCREAMING_SNAKE_CASE =int(config['num_epochs'] )
SCREAMING_SNAKE_CASE =int(config['seed'] )
SCREAMING_SNAKE_CASE =int(config['batch_size'] )
SCREAMING_SNAKE_CASE =evaluate.load('glue', 'mrpc' )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=lowerCAmelCase_ )
def inner_training_loop(lowerCAmelCase_ ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(lowerCAmelCase_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
SCREAMING_SNAKE_CASE =AutoModelForSequenceClassification.from_pretrained('bert-base-cased', return_dict=lowerCAmelCase_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
SCREAMING_SNAKE_CASE =model.to(accelerator.device )
# Instantiate optimizer
SCREAMING_SNAKE_CASE =AdamW(params=model.parameters(), lr=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =get_dataloaders(lowerCAmelCase_, lowerCAmelCase_ )
# Instantiate scheduler
SCREAMING_SNAKE_CASE =get_linear_schedule_with_warmup(
optimizer=lowerCAmelCase_, num_warmup_steps=100, num_training_steps=(len(lowerCAmelCase_ ) * num_epochs), )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =accelerator.prepare(
lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ )
# Now we train the model
for epoch in range(lowerCAmelCase_ ):
model.train()
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
SCREAMING_SNAKE_CASE =model(**lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =outputs.loss
accelerator.backward(lowerCAmelCase_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowerCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
SCREAMING_SNAKE_CASE =model(**lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =outputs.logits.argmax(dim=-1 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =accelerator.gather_for_metrics((predictions, batch['labels']) )
metric.add_batch(
predictions=lowerCAmelCase_, references=lowerCAmelCase_, )
SCREAMING_SNAKE_CASE =metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'epoch {epoch}:', lowerCAmelCase_ )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =argparse.ArgumentParser(description='Simple example of training script.' )
parser.add_argument(
'--mixed_precision', type=lowerCAmelCase_, default=lowerCAmelCase_, choices=['no', 'fp16', 'bf16', 'fp8'], help='Whether to use mixed precision. Choose'
'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
'and an Nvidia Ampere GPU.', )
parser.add_argument('--cpu', action='store_true', help='If passed, will train on the CPU.' )
SCREAMING_SNAKE_CASE =parser.parse_args()
SCREAMING_SNAKE_CASE ={'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
training_function(lowerCAmelCase_, lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 334 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Tuple = logging.get_logger(__name__)
lowercase : Union[str, Any] = {
'huggingface/informer-tourism-monthly': (
'https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json'
),
# See all Informer models at https://huggingface.co/models?filter=informer
}
class A ( __snake_case ):
__magic_name__ = '''informer'''
__magic_name__ = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
'''num_hidden_layers''': '''encoder_layers''',
}
def __init__( self , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = "student_t" , SCREAMING_SNAKE_CASE = "nll" , SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = "mean" , SCREAMING_SNAKE_CASE = 0 , SCREAMING_SNAKE_CASE = 0 , SCREAMING_SNAKE_CASE = 0 , SCREAMING_SNAKE_CASE = 0 , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = 64 , SCREAMING_SNAKE_CASE = 32 , SCREAMING_SNAKE_CASE = 32 , SCREAMING_SNAKE_CASE = 2 , SCREAMING_SNAKE_CASE = 2 , SCREAMING_SNAKE_CASE = 2 , SCREAMING_SNAKE_CASE = 2 , SCREAMING_SNAKE_CASE = True , SCREAMING_SNAKE_CASE = "gelu" , SCREAMING_SNAKE_CASE = 0.05 , SCREAMING_SNAKE_CASE = 0.1 , SCREAMING_SNAKE_CASE = 0.1 , SCREAMING_SNAKE_CASE = 0.1 , SCREAMING_SNAKE_CASE = 0.1 , SCREAMING_SNAKE_CASE = 100 , SCREAMING_SNAKE_CASE = 0.02 , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE = "prob" , SCREAMING_SNAKE_CASE = 5 , SCREAMING_SNAKE_CASE = True , **SCREAMING_SNAKE_CASE , ) -> Any:
"""simple docstring"""
A : Any = prediction_length
A : Dict = context_length or prediction_length
A : List[Any] = distribution_output
A : List[str] = loss
A : int = input_size
A : List[Any] = num_time_features
A : str = lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
A : List[str] = scaling
A : Any = num_dynamic_real_features
A : str = num_static_real_features
A : Optional[int] = num_static_categorical_features
# set cardinality
if cardinality and num_static_categorical_features > 0:
if len(SCREAMING_SNAKE_CASE ) != num_static_categorical_features:
raise ValueError(
'''The cardinality should be a list of the same length as `num_static_categorical_features`''' )
A : str = cardinality
else:
A : List[Any] = [0]
# set embedding_dimension
if embedding_dimension and num_static_categorical_features > 0:
if len(SCREAMING_SNAKE_CASE ) != num_static_categorical_features:
raise ValueError(
'''The embedding dimension should be a list of the same length as `num_static_categorical_features`''' )
A : int = embedding_dimension
else:
A : List[str] = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
A : int = num_parallel_samples
# Transformer architecture configuration
A : str = input_size * len(self.lags_sequence ) + self._number_of_features
A : Dict = d_model
A : int = encoder_attention_heads
A : Optional[Any] = decoder_attention_heads
A : Union[str, Any] = encoder_ffn_dim
A : int = decoder_ffn_dim
A : Tuple = encoder_layers
A : List[str] = decoder_layers
A : Optional[int] = dropout
A : List[Any] = attention_dropout
A : List[Any] = activation_dropout
A : int = encoder_layerdrop
A : str = decoder_layerdrop
A : Optional[Any] = activation_function
A : List[str] = init_std
A : List[str] = use_cache
# Informer
A : List[Any] = attention_type
A : Tuple = sampling_factor
A : Dict = distil
super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
@property
def __lowerCAmelCase ( self ) -> int:
"""simple docstring"""
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 3 |
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
return " ".join(
''.join(word[::-1] ) if len(lowerCAmelCase_ ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words("Hey wollef sroirraw"))
| 334 | 0 |
'''simple docstring'''
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
__snake_case =logging.get_logger(__name__)
class UpperCAmelCase_ ( enum.Enum ):
lowerCamelCase : Optional[int] = 0
lowerCamelCase : str = 1
@add_end_docstrings(__lowercase )
class UpperCAmelCase_ ( __lowercase ):
lowerCamelCase : Tuple = '''generated'''
def __init__( self : Optional[int] , *UpperCAmelCase__ : Optional[Any] , **UpperCAmelCase__ : Optional[int] ) -> Tuple:
super().__init__(*UpperCAmelCase__ , **UpperCAmelCase__ )
self.check_model_type(
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
if self.framework == 'tf'
else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING )
def __UpperCAmelCase ( self : Dict , UpperCAmelCase__ : List[str]=None , UpperCAmelCase__ : Dict=None , UpperCAmelCase__ : List[str]=None , UpperCAmelCase__ : Tuple=None , UpperCAmelCase__ : Any=None , UpperCAmelCase__ : Dict=None , **UpperCAmelCase__ : List[str] , ) -> Union[str, Any]:
lowerCAmelCase = {}
if truncation is not None:
lowerCAmelCase = truncation
lowerCAmelCase = generate_kwargs
lowerCAmelCase = {}
if return_tensors is not None and return_type is None:
lowerCAmelCase = ReturnType.TENSORS if return_tensors else ReturnType.TEXT
if return_type is not None:
lowerCAmelCase = return_type
if clean_up_tokenization_spaces is not None:
lowerCAmelCase = clean_up_tokenization_spaces
if stop_sequence is not None:
lowerCAmelCase = self.tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
if len(UpperCAmelCase__ ) > 1:
warnings.warn(
'Stopping on a multiple token sequence is not yet supported on transformers. The first token of'
' the stop sequence will be used as the stop sequence string in the interim.' )
lowerCAmelCase = stop_sequence_ids[0]
return preprocess_params, forward_params, postprocess_params
def __UpperCAmelCase ( self : List[Any] , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int ) -> List[Any]:
return True
def __UpperCAmelCase ( self : Optional[int] , *UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : List[Any] ) -> Optional[int]:
lowerCAmelCase = self.model.config.prefix if self.model.config.prefix is not None else ''
if isinstance(args[0] , UpperCAmelCase__ ):
if self.tokenizer.pad_token_id is None:
raise ValueError('Please make sure that the tokenizer has a pad_token_id when using a batch input' )
lowerCAmelCase = ([prefix + arg for arg in args[0]],)
lowerCAmelCase = True
elif isinstance(args[0] , UpperCAmelCase__ ):
lowerCAmelCase = (prefix + args[0],)
lowerCAmelCase = False
else:
raise ValueError(
F''' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`''' )
lowerCAmelCase = self.tokenizer(*UpperCAmelCase__ , padding=UpperCAmelCase__ , truncation=UpperCAmelCase__ , return_tensors=self.framework )
# This is produced by tokenizers but is an invalid generate kwargs
if "token_type_ids" in inputs:
del inputs["token_type_ids"]
return inputs
def __call__( self : Optional[Any] , *UpperCAmelCase__ : List[Any] , **UpperCAmelCase__ : str ) -> List[Any]:
lowerCAmelCase = super().__call__(*UpperCAmelCase__ , **UpperCAmelCase__ )
if (
isinstance(args[0] , UpperCAmelCase__ )
and all(isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) for el in args[0] )
and all(len(UpperCAmelCase__ ) == 1 for res in result )
):
return [res[0] for res in result]
return result
def __UpperCAmelCase ( self : Optional[Any] , UpperCAmelCase__ : Any , UpperCAmelCase__ : Optional[Any]=TruncationStrategy.DO_NOT_TRUNCATE , **UpperCAmelCase__ : int ) -> int:
lowerCAmelCase = self._parse_and_tokenize(UpperCAmelCase__ , truncation=UpperCAmelCase__ , **UpperCAmelCase__ )
return inputs
def __UpperCAmelCase ( self : int , UpperCAmelCase__ : Union[str, Any] , **UpperCAmelCase__ : str ) -> Tuple:
if self.framework == "pt":
lowerCAmelCase , lowerCAmelCase = model_inputs['input_ids'].shape
elif self.framework == "tf":
lowerCAmelCase , lowerCAmelCase = tf.shape(model_inputs['input_ids'] ).numpy()
lowerCAmelCase = generate_kwargs.get('min_length' , self.model.config.min_length )
lowerCAmelCase = generate_kwargs.get('max_length' , self.model.config.max_length )
self.check_inputs(UpperCAmelCase__ , generate_kwargs['min_length'] , generate_kwargs['max_length'] )
lowerCAmelCase = self.model.generate(**UpperCAmelCase__ , **UpperCAmelCase__ )
lowerCAmelCase = output_ids.shape[0]
if self.framework == "pt":
lowerCAmelCase = output_ids.reshape(UpperCAmelCase__ , out_b // in_b , *output_ids.shape[1:] )
elif self.framework == "tf":
lowerCAmelCase = tf.reshape(UpperCAmelCase__ , (in_b, out_b // in_b, *output_ids.shape[1:]) )
return {"output_ids": output_ids}
def __UpperCAmelCase ( self : List[str] , UpperCAmelCase__ : int , UpperCAmelCase__ : List[Any]=ReturnType.TEXT , UpperCAmelCase__ : str=False ) -> int:
lowerCAmelCase = []
for output_ids in model_outputs["output_ids"][0]:
if return_type == ReturnType.TENSORS:
lowerCAmelCase = {F'''{self.return_name}_token_ids''': output_ids}
elif return_type == ReturnType.TEXT:
lowerCAmelCase = {
F'''{self.return_name}_text''': self.tokenizer.decode(
UpperCAmelCase__ , skip_special_tokens=UpperCAmelCase__ , clean_up_tokenization_spaces=UpperCAmelCase__ , )
}
records.append(UpperCAmelCase__ )
return records
@add_end_docstrings(__lowercase )
class UpperCAmelCase_ ( __lowercase ):
lowerCamelCase : Union[str, Any] = '''summary'''
def __call__( self : Any , *UpperCAmelCase__ : int , **UpperCAmelCase__ : str ) -> Union[str, Any]:
return super().__call__(*UpperCAmelCase__ , **UpperCAmelCase__ )
def __UpperCAmelCase ( self : Optional[int] , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int ) -> bool:
if max_length < min_length:
logger.warning(F'''Your min_length={min_length} must be inferior than your max_length={max_length}.''' )
if input_length < max_length:
logger.warning(
F'''Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is '''
'a summarization task, where outputs shorter than the input are typically wanted, you might '
F'''consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})''' )
@add_end_docstrings(__lowercase )
class UpperCAmelCase_ ( __lowercase ):
lowerCamelCase : Union[str, Any] = '''translation'''
def __UpperCAmelCase ( self : str , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int ) -> Union[str, Any]:
if input_length > 0.9 * max_length:
logger.warning(
F'''Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider '''
'increasing your max_length manually, e.g. translator(\'...\', max_length=400)' )
return True
def __UpperCAmelCase ( self : Union[str, Any] , *UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Optional[int]=TruncationStrategy.DO_NOT_TRUNCATE , UpperCAmelCase__ : str=None , UpperCAmelCase__ : Optional[int]=None ) -> str:
if getattr(self.tokenizer , '_build_translation_inputs' , UpperCAmelCase__ ):
return self.tokenizer._build_translation_inputs(
*UpperCAmelCase__ , return_tensors=self.framework , truncation=UpperCAmelCase__ , src_lang=UpperCAmelCase__ , tgt_lang=UpperCAmelCase__ )
else:
return super()._parse_and_tokenize(*UpperCAmelCase__ , truncation=UpperCAmelCase__ )
def __UpperCAmelCase ( self : Optional[int] , UpperCAmelCase__ : List[Any]=None , UpperCAmelCase__ : Union[str, Any]=None , **UpperCAmelCase__ : int ) -> Tuple:
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = super()._sanitize_parameters(**UpperCAmelCase__ )
if src_lang is not None:
lowerCAmelCase = src_lang
if tgt_lang is not None:
lowerCAmelCase = tgt_lang
if src_lang is None and tgt_lang is None:
# Backward compatibility, direct arguments use is preferred.
lowerCAmelCase = kwargs.get('task' , self.task )
lowerCAmelCase = task.split('_' )
if task and len(UpperCAmelCase__ ) == 4:
# translation, XX, to YY
lowerCAmelCase = items[1]
lowerCAmelCase = items[3]
return preprocess_params, forward_params, postprocess_params
def __call__( self : Optional[int] , *UpperCAmelCase__ : int , **UpperCAmelCase__ : Any ) -> List[Any]:
return super().__call__(*UpperCAmelCase__ , **UpperCAmelCase__ )
| 4 |
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json
_lowerCamelCase ="sshleifer/mar_enro_6_3_student"
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
def _lowerCAmelCase ( self : Union[str, Any] ):
super().setUp()
SCREAMING_SNAKE_CASE =cached_path(
'https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz' ,extract_compressed_file=snake_case ,)
SCREAMING_SNAKE_CASE =f'{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'
@slow
@require_torch_gpu
def _lowerCAmelCase ( self : Optional[int] ):
MarianMTModel.from_pretrained(snake_case )
@slow
@require_torch_gpu
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE ={
'$MAX_LEN': 64,
'$BS': 64,
'$GAS': 1,
'$ENRO_DIR': self.data_dir,
'facebook/mbart-large-cc25': MARIAN_MODEL,
# "val_check_interval=0.25": "val_check_interval=1.0",
'--learning_rate=3e-5': '--learning_rate 3e-4',
'--num_train_epochs 6': '--num_train_epochs 1',
}
# Clean up bash script
SCREAMING_SNAKE_CASE =(self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py' )[1].strip()
SCREAMING_SNAKE_CASE =bash_script.replace('\\\n' ,'' ).strip().replace('"$@"' ,'' )
for k, v in env_vars_to_replace.items():
SCREAMING_SNAKE_CASE =bash_script.replace(snake_case ,str(snake_case ) )
SCREAMING_SNAKE_CASE =self.get_auto_remove_tmp_dir()
# bash_script = bash_script.replace("--fp16 ", "")
SCREAMING_SNAKE_CASE =f'\n --output_dir {output_dir}\n --tokenizer_name Helsinki-NLP/opus-mt-en-ro\n --sortish_sampler\n --do_predict\n --gpus 1\n --freeze_encoder\n --n_train 40000\n --n_val 500\n --n_test 500\n --fp16_opt_level O1\n --num_sanity_val_steps 0\n --eval_beams 2\n '.split()
# XXX: args.gpus > 1 : handle multi_gpu in the future
SCREAMING_SNAKE_CASE =['finetune.py'] + bash_script.split() + args
with patch.object(snake_case ,'argv' ,snake_case ):
SCREAMING_SNAKE_CASE =argparse.ArgumentParser()
SCREAMING_SNAKE_CASE =pl.Trainer.add_argparse_args(snake_case )
SCREAMING_SNAKE_CASE =SummarizationModule.add_model_specific_args(snake_case ,os.getcwd() )
SCREAMING_SNAKE_CASE =parser.parse_args()
SCREAMING_SNAKE_CASE =main(snake_case )
# Check metrics
SCREAMING_SNAKE_CASE =load_json(model.metrics_save_path )
SCREAMING_SNAKE_CASE =metrics['val'][0]
SCREAMING_SNAKE_CASE =metrics['val'][-1]
self.assertEqual(len(metrics['val'] ) ,(args.max_epochs / args.val_check_interval) )
assert isinstance(last_step_stats[f'val_avg_{model.val_metric}'] ,snake_case )
self.assertGreater(last_step_stats['val_avg_gen_time'] ,0.01 )
# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
self.assertLessEqual(last_step_stats['val_avg_gen_time'] ,1.0 )
# test learning requirements:
# 1. BLEU improves over the course of training by more than 2 pts
self.assertGreater(last_step_stats['val_avg_bleu'] - first_step_stats['val_avg_bleu'] ,2 )
# 2. BLEU finishes above 17
self.assertGreater(last_step_stats['val_avg_bleu'] ,17 )
# 3. test BLEU and val BLEU within ~1.1 pt.
self.assertLess(abs(metrics['val'][-1]['val_avg_bleu'] - metrics['test'][-1]['test_avg_bleu'] ) ,1.1 )
# check lightning ckpt can be loaded and has a reasonable statedict
SCREAMING_SNAKE_CASE =os.listdir(snake_case )
SCREAMING_SNAKE_CASE =[x for x in contents if x.endswith('.ckpt' )][0]
SCREAMING_SNAKE_CASE =os.path.join(args.output_dir ,snake_case )
SCREAMING_SNAKE_CASE =torch.load(snake_case ,map_location='cpu' )
SCREAMING_SNAKE_CASE ='model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
SCREAMING_SNAKE_CASE ={os.path.basename(snake_case ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics['test'] ) == 1
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
@timeout_decorator.timeout(600 )
@slow
@require_torch_gpu
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =f'{self.test_file_dir_str}/test_data/wmt_en_ro'
SCREAMING_SNAKE_CASE ={
'--fp16_opt_level=O1': '',
'$MAX_LEN': 128,
'$BS': 16,
'$GAS': 1,
'$ENRO_DIR': data_dir,
'$m': 'sshleifer/student_marian_en_ro_6_1',
'val_check_interval=0.25': 'val_check_interval=1.0',
}
# Clean up bash script
SCREAMING_SNAKE_CASE =(
(self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py' )[1].strip()
)
SCREAMING_SNAKE_CASE =bash_script.replace('\\\n' ,'' ).strip().replace('"$@"' ,'' )
SCREAMING_SNAKE_CASE =bash_script.replace('--fp16 ' ,' ' )
for k, v in env_vars_to_replace.items():
SCREAMING_SNAKE_CASE =bash_script.replace(snake_case ,str(snake_case ) )
SCREAMING_SNAKE_CASE =self.get_auto_remove_tmp_dir()
SCREAMING_SNAKE_CASE =bash_script.replace('--fp16' ,'' )
SCREAMING_SNAKE_CASE =6
SCREAMING_SNAKE_CASE =(
['distillation.py']
+ bash_script.split()
+ [
f'--output_dir={output_dir}',
'--gpus=1',
'--learning_rate=1e-3',
f'--num_train_epochs={epochs}',
'--warmup_steps=10',
'--val_check_interval=1.0',
'--do_predict',
]
)
with patch.object(snake_case ,'argv' ,snake_case ):
SCREAMING_SNAKE_CASE =argparse.ArgumentParser()
SCREAMING_SNAKE_CASE =pl.Trainer.add_argparse_args(snake_case )
SCREAMING_SNAKE_CASE =SummarizationDistiller.add_model_specific_args(snake_case ,os.getcwd() )
SCREAMING_SNAKE_CASE =parser.parse_args()
# assert args.gpus == gpus THIS BREAKS for multi_gpu
SCREAMING_SNAKE_CASE =distill_main(snake_case )
# Check metrics
SCREAMING_SNAKE_CASE =load_json(model.metrics_save_path )
SCREAMING_SNAKE_CASE =metrics['val'][0]
SCREAMING_SNAKE_CASE =metrics['val'][-1]
assert len(metrics['val'] ) >= (args.max_epochs / args.val_check_interval) # +1 accounts for val_sanity_check
assert last_step_stats["val_avg_gen_time"] >= 0.01
assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"] # model learned nothing
assert 1.0 >= last_step_stats["val_avg_gen_time"] # model hanging on generate. Maybe bad config was saved.
assert isinstance(last_step_stats[f'val_avg_{model.val_metric}'] ,snake_case )
# check lightning ckpt can be loaded and has a reasonable statedict
SCREAMING_SNAKE_CASE =os.listdir(snake_case )
SCREAMING_SNAKE_CASE =[x for x in contents if x.endswith('.ckpt' )][0]
SCREAMING_SNAKE_CASE =os.path.join(args.output_dir ,snake_case )
SCREAMING_SNAKE_CASE =torch.load(snake_case ,map_location='cpu' )
SCREAMING_SNAKE_CASE ='model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
SCREAMING_SNAKE_CASE ={os.path.basename(snake_case ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics['test'] ) == 1
| 334 | 0 |
import os
# Precomputes a list of the 100 first triangular numbers
UpperCAmelCase__ = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def UpperCAmelCase_ ( ) -> Tuple:
"""simple docstring"""
_lowercase =os.path.dirname(os.path.realpath(__snake_case ) )
_lowercase =os.path.join(__snake_case , '''words.txt''' )
_lowercase =''''''
with open(__snake_case ) as f:
_lowercase =f.readline()
_lowercase =[word.strip('''"''' ) for word in words.strip('''\r\n''' ).split(''',''' )]
_lowercase =[
word
for word in [sum(ord(__snake_case ) - 64 for x in word ) for word in words]
if word in TRIANGULAR_NUMBERS
]
return len(__snake_case )
if __name__ == "__main__":
print(solution())
| 5 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"salesforce/blip2-opt-2.7b": "https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json",
}
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'blip_2_vision_model'
def __init__( self : List[Any] ,snake_case : List[Any]=1408 ,snake_case : Optional[Any]=6144 ,snake_case : Optional[int]=39 ,snake_case : Optional[int]=16 ,snake_case : Optional[Any]=224 ,snake_case : Tuple=14 ,snake_case : Optional[Any]="gelu" ,snake_case : Union[str, Any]=0.00_001 ,snake_case : Dict=0.0 ,snake_case : Union[str, Any]=1e-10 ,snake_case : int=True ,**snake_case : str ,):
super().__init__(**snake_case )
SCREAMING_SNAKE_CASE =hidden_size
SCREAMING_SNAKE_CASE =intermediate_size
SCREAMING_SNAKE_CASE =num_hidden_layers
SCREAMING_SNAKE_CASE =num_attention_heads
SCREAMING_SNAKE_CASE =patch_size
SCREAMING_SNAKE_CASE =image_size
SCREAMING_SNAKE_CASE =initializer_range
SCREAMING_SNAKE_CASE =attention_dropout
SCREAMING_SNAKE_CASE =layer_norm_eps
SCREAMING_SNAKE_CASE =hidden_act
SCREAMING_SNAKE_CASE =qkv_bias
@classmethod
def _lowerCAmelCase ( cls : Dict ,snake_case : Union[str, os.PathLike] ,**snake_case : str ):
cls._set_token_in_kwargs(snake_case )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =cls.get_config_dict(snake_case ,**snake_case )
# get the vision config dict if we are loading from Blip2Config
if config_dict.get('model_type' ) == "blip-2":
SCREAMING_SNAKE_CASE =config_dict['vision_config']
if "model_type" in config_dict and hasattr(cls ,'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(snake_case ,**snake_case )
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'blip_2_qformer'
def __init__( self : Any ,snake_case : Dict=30522 ,snake_case : int=768 ,snake_case : List[Any]=12 ,snake_case : List[str]=12 ,snake_case : Optional[Any]=3072 ,snake_case : str="gelu" ,snake_case : Optional[Any]=0.1 ,snake_case : Union[str, Any]=0.1 ,snake_case : Optional[Any]=512 ,snake_case : List[Any]=0.02 ,snake_case : List[str]=1e-12 ,snake_case : Tuple=0 ,snake_case : Union[str, Any]="absolute" ,snake_case : List[Any]=2 ,snake_case : List[str]=1408 ,**snake_case : Optional[Any] ,):
super().__init__(pad_token_id=snake_case ,**snake_case )
SCREAMING_SNAKE_CASE =vocab_size
SCREAMING_SNAKE_CASE =hidden_size
SCREAMING_SNAKE_CASE =num_hidden_layers
SCREAMING_SNAKE_CASE =num_attention_heads
SCREAMING_SNAKE_CASE =hidden_act
SCREAMING_SNAKE_CASE =intermediate_size
SCREAMING_SNAKE_CASE =hidden_dropout_prob
SCREAMING_SNAKE_CASE =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE =max_position_embeddings
SCREAMING_SNAKE_CASE =initializer_range
SCREAMING_SNAKE_CASE =layer_norm_eps
SCREAMING_SNAKE_CASE =position_embedding_type
SCREAMING_SNAKE_CASE =cross_attention_frequency
SCREAMING_SNAKE_CASE =encoder_hidden_size
@classmethod
def _lowerCAmelCase ( cls : List[Any] ,snake_case : Union[str, os.PathLike] ,**snake_case : Dict ):
cls._set_token_in_kwargs(snake_case )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =cls.get_config_dict(snake_case ,**snake_case )
# get the qformer config dict if we are loading from Blip2Config
if config_dict.get('model_type' ) == "blip-2":
SCREAMING_SNAKE_CASE =config_dict['qformer_config']
if "model_type" in config_dict and hasattr(cls ,'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(snake_case ,**snake_case )
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'blip-2'
__UpperCAmelCase = True
def __init__( self : int ,snake_case : Dict=None ,snake_case : Tuple=None ,snake_case : str=None ,snake_case : Union[str, Any]=32 ,**snake_case : int ):
super().__init__(**snake_case )
if vision_config is None:
SCREAMING_SNAKE_CASE ={}
logger.info('vision_config is None. initializing the Blip2VisionConfig with default values.' )
if qformer_config is None:
SCREAMING_SNAKE_CASE ={}
logger.info('qformer_config is None. Initializing the Blip2QFormerConfig with default values.' )
if text_config is None:
SCREAMING_SNAKE_CASE ={}
logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).' )
SCREAMING_SNAKE_CASE =BlipaVisionConfig(**snake_case )
SCREAMING_SNAKE_CASE =BlipaQFormerConfig(**snake_case )
SCREAMING_SNAKE_CASE =text_config['model_type'] if 'model_type' in text_config else 'opt'
SCREAMING_SNAKE_CASE =CONFIG_MAPPING[text_model_type](**snake_case )
SCREAMING_SNAKE_CASE =self.text_config.tie_word_embeddings
SCREAMING_SNAKE_CASE =self.text_config.is_encoder_decoder
SCREAMING_SNAKE_CASE =num_query_tokens
SCREAMING_SNAKE_CASE =self.vision_config.hidden_size
SCREAMING_SNAKE_CASE =self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
SCREAMING_SNAKE_CASE =1.0
SCREAMING_SNAKE_CASE =0.02
@classmethod
def _lowerCAmelCase ( cls : Union[str, Any] ,snake_case : BlipaVisionConfig ,snake_case : BlipaQFormerConfig ,snake_case : PretrainedConfig ,**snake_case : Any ,):
return cls(
vision_config=vision_config.to_dict() ,qformer_config=qformer_config.to_dict() ,text_config=text_config.to_dict() ,**snake_case ,)
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE =self.vision_config.to_dict()
SCREAMING_SNAKE_CASE =self.qformer_config.to_dict()
SCREAMING_SNAKE_CASE =self.text_config.to_dict()
SCREAMING_SNAKE_CASE =self.__class__.model_type
return output
| 334 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
A : Optional[int] = logging.get_logger(__name__)
A : List[Any] = {
'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}
class __A( a ):
snake_case_ = '''instructblip_vision_model'''
def __init__( self , _snake_case=1_408 , _snake_case=6_144 , _snake_case=39 , _snake_case=16 , _snake_case=224 , _snake_case=14 , _snake_case="gelu" , _snake_case=1E-6 , _snake_case=0.0 , _snake_case=1E-10 , _snake_case=True , **_snake_case , ) -> List[str]:
'''simple docstring'''
super().__init__(**_snake_case )
__a = hidden_size
__a = intermediate_size
__a = num_hidden_layers
__a = num_attention_heads
__a = patch_size
__a = image_size
__a = initializer_range
__a = attention_dropout
__a = layer_norm_eps
__a = hidden_act
__a = qkv_bias
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls , _snake_case , **_snake_case ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(_snake_case )
__a , __a = cls.get_config_dict(_snake_case , **_snake_case )
# get the vision config dict if we are loading from InstructBlipConfig
if config_dict.get('''model_type''' ) == "instructblip":
__a = config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(_snake_case , **_snake_case )
class __A( a ):
snake_case_ = '''instructblip_qformer'''
def __init__( self , _snake_case=30_522 , _snake_case=768 , _snake_case=12 , _snake_case=12 , _snake_case=3_072 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=512 , _snake_case=0.02 , _snake_case=1E-12 , _snake_case=0 , _snake_case="absolute" , _snake_case=2 , _snake_case=1_408 , **_snake_case , ) -> Dict:
'''simple docstring'''
super().__init__(pad_token_id=_snake_case , **_snake_case )
__a = vocab_size
__a = hidden_size
__a = num_hidden_layers
__a = num_attention_heads
__a = hidden_act
__a = intermediate_size
__a = hidden_dropout_prob
__a = attention_probs_dropout_prob
__a = max_position_embeddings
__a = initializer_range
__a = layer_norm_eps
__a = position_embedding_type
__a = cross_attention_frequency
__a = encoder_hidden_size
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls , _snake_case , **_snake_case ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(_snake_case )
__a , __a = cls.get_config_dict(_snake_case , **_snake_case )
# get the qformer config dict if we are loading from InstructBlipConfig
if config_dict.get('''model_type''' ) == "instructblip":
__a = config_dict['''qformer_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(_snake_case , **_snake_case )
class __A( a ):
snake_case_ = '''instructblip'''
snake_case_ = True
def __init__( self , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=32 , **_snake_case ) -> Optional[Any]:
'''simple docstring'''
super().__init__(**_snake_case )
if vision_config is None:
__a = {}
logger.info('''vision_config is None. initializing the InstructBlipVisionConfig with default values.''' )
if qformer_config is None:
__a = {}
logger.info('''qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.''' )
if text_config is None:
__a = {}
logger.info('''text_config is None. Initializing the text config with default values (`OPTConfig`).''' )
__a = InstructBlipVisionConfig(**_snake_case )
__a = InstructBlipQFormerConfig(**_snake_case )
__a = text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
__a = CONFIG_MAPPING[text_model_type](**_snake_case )
__a = self.text_config.tie_word_embeddings
__a = self.text_config.is_encoder_decoder
__a = num_query_tokens
__a = self.vision_config.hidden_size
__a = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
__a = 1.0
__a = 0.02
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls , _snake_case , _snake_case , _snake_case , **_snake_case , ) -> Optional[int]:
'''simple docstring'''
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **_snake_case , )
def SCREAMING_SNAKE_CASE_ ( self ) -> str:
'''simple docstring'''
__a = copy.deepcopy(self.__dict__ )
__a = self.vision_config.to_dict()
__a = self.qformer_config.to_dict()
__a = self.text_config.to_dict()
__a = self.__class__.model_type
return output | 6 |
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve # From: mauve-text
import datasets
_lowerCamelCase ="\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n booktitle = {NeurIPS},\n year = {2021}\n}\n\n"
_lowerCamelCase ="\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n"
_lowerCamelCase ="\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n predictions: list of generated text to score. Each predictions\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\nOptional Args:\n num_buckets: the size of the histogram to quantize P and Q. Options: 'auto' (default) or an integer\n pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n kmeans_max_iter: maximum number of k-means iterations. Default 500\n featurize_model_name: name of the model from which features are obtained. Default 'gpt2-large' Use one of ['gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'].\n device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU\n max_text_length: maximum number of tokens to consider. Default 1024\n divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n mauve_scaling_factor: \"c\" from the paper. Default 5.\n verbose: If True (default), print running time updates\n seed: random seed to initialize k-means cluster assignments.\nReturns:\n mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n q_hist: same as above, but with q_text.\nExamples:\n\n >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n >>> import datasets\n >>> mauve = datasets.load_metric('mauve')\n >>> predictions = [\"hello there\", \"general kenobi\"]\n >>> references = [\"hello there\", \"general kenobi\"]\n >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n >>> print(out.mauve) # doctest: +SKIP\n 1.0\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a_ ( datasets.Metric ):
"""simple docstring"""
def _lowerCAmelCase ( self : Tuple ):
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,homepage='https://github.com/krishnap25/mauve' ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
'predictions': datasets.Value('string' ,id='sequence' ),
'references': datasets.Value('string' ,id='sequence' ),
} ) ,codebase_urls=['https://github.com/krishnap25/mauve'] ,reference_urls=[
'https://arxiv.org/abs/2102.01454',
'https://github.com/krishnap25/mauve',
] ,)
def _lowerCAmelCase ( self : Union[str, Any] ,snake_case : Optional[int] ,snake_case : str ,snake_case : List[str]=None ,snake_case : str=None ,snake_case : int=None ,snake_case : Union[str, Any]=None ,snake_case : Optional[int]="auto" ,snake_case : List[str]=-1 ,snake_case : Union[str, Any]=0.9 ,snake_case : Tuple=5 ,snake_case : Union[str, Any]=500 ,snake_case : Union[str, Any]="gpt2-large" ,snake_case : Union[str, Any]=-1 ,snake_case : Optional[Any]=1024 ,snake_case : Optional[Any]=25 ,snake_case : List[str]=5 ,snake_case : List[str]=True ,snake_case : Optional[Any]=25 ,):
SCREAMING_SNAKE_CASE =compute_mauve(
p_text=snake_case ,q_text=snake_case ,p_features=snake_case ,q_features=snake_case ,p_tokens=snake_case ,q_tokens=snake_case ,num_buckets=snake_case ,pca_max_data=snake_case ,kmeans_explained_var=snake_case ,kmeans_num_redo=snake_case ,kmeans_max_iter=snake_case ,featurize_model_name=snake_case ,device_id=snake_case ,max_text_length=snake_case ,divergence_curve_discretization_size=snake_case ,mauve_scaling_factor=snake_case ,verbose=snake_case ,seed=snake_case ,)
return out
| 334 | 0 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging
logging.set_verbosity_info()
lowercase_ = logging.get_logger()
@dataclass
class A :
"""simple docstring"""
lowerCamelCase = 42
lowerCamelCase = field(default_factory=_UpperCAmelCase )
lowerCamelCase = field(default_factory=_UpperCAmelCase )
def snake_case__ ( self : Union[str, Any],lowercase_ : Any,lowercase_ : Tensor,lowercase_ : Tensor )-> List[Any]:
'''simple docstring'''
A__ = len(list(m.modules() ) ) == 1 or isinstance(lowercase_,nn.Convad ) or isinstance(lowercase_,nn.BatchNormad )
if has_not_submodules:
self.traced.append(lowercase_ )
def __call__( self : Dict,lowercase_ : Tensor )-> Tuple:
'''simple docstring'''
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(lowercase_ )
[x.remove() for x in self.handles]
return self
@property
def snake_case__ ( self : str )-> Optional[Any]:
'''simple docstring'''
return list(filter(lambda lowercase_ : len(list(x.state_dict().keys() ) ) > 0,self.traced ) )
@dataclass
class A :
"""simple docstring"""
lowerCamelCase = 42
lowerCamelCase = 42
lowerCamelCase = 0
lowerCamelCase = field(default_factory=_UpperCAmelCase )
lowerCamelCase = field(default_factory=_UpperCAmelCase )
def __call__( self : List[str],lowercase_ : Tensor )-> Union[str, Any]:
'''simple docstring'''
A__ = Tracker(self.dest )(lowercase_ ).parametrized
A__ = Tracker(self.src )(lowercase_ ).parametrized
A__ = list(filter(lambda lowercase_ : type(lowercase_ ) not in self.src_skip,lowercase_ ) )
A__ = list(filter(lambda lowercase_ : type(lowercase_ ) not in self.dest_skip,lowercase_ ) )
if len(lowercase_ ) != len(lowercase_ ):
raise Exception(
F'Numbers of operations are different. Source module has {len(lowercase_ )} operations while'
F' destination module has {len(lowercase_ )}.' )
for dest_m, src_m in zip(lowercase_,lowercase_ ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(F'Transfered from={src_m} to={dest_m}' )
def _snake_case( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : ResNetConfig , SCREAMING_SNAKE_CASE__ : Path , SCREAMING_SNAKE_CASE__ : bool = True ) -> int:
'''simple docstring'''
print(f'Converting {name}...' )
with torch.no_grad():
A__ = timm.create_model(SCREAMING_SNAKE_CASE__ , pretrained=SCREAMING_SNAKE_CASE__ ).eval()
A__ = ResNetForImageClassification(SCREAMING_SNAKE_CASE__ ).eval()
A__ = ModuleTransfer(src=SCREAMING_SNAKE_CASE__ , dest=SCREAMING_SNAKE_CASE__ )
A__ = torch.randn((1, 3, 224, 224) )
module_transfer(SCREAMING_SNAKE_CASE__ )
assert torch.allclose(from_model(SCREAMING_SNAKE_CASE__ ) , our_model(SCREAMING_SNAKE_CASE__ ).logits ), "The model logits don't match the original one."
A__ = f'resnet{"-".join(name.split("resnet" ) )}'
print(SCREAMING_SNAKE_CASE__ )
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message='Add model' , use_temp_dir=SCREAMING_SNAKE_CASE__ , )
# we can use the convnext one
A__ = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' )
image_processor.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message='Add image processor' , use_temp_dir=SCREAMING_SNAKE_CASE__ , )
print(f'Pushed {checkpoint_name}' )
def _snake_case( SCREAMING_SNAKE_CASE__ : Path , SCREAMING_SNAKE_CASE__ : str = None , SCREAMING_SNAKE_CASE__ : bool = True ) -> Dict:
'''simple docstring'''
A__ = 'imagenet-1k-id2label.json'
A__ = 1000
A__ = (1, num_labels)
A__ = 'huggingface/label-files'
A__ = num_labels
A__ = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , repo_type='dataset' ) , 'r' ) )
A__ = {int(SCREAMING_SNAKE_CASE__ ): v for k, v in idalabel.items()}
A__ = idalabel
A__ = {v: k for k, v in idalabel.items()}
A__ = partial(SCREAMING_SNAKE_CASE__ , num_labels=SCREAMING_SNAKE_CASE__ , idalabel=SCREAMING_SNAKE_CASE__ , labelaid=SCREAMING_SNAKE_CASE__ )
A__ = {
'resnet18': ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[64, 128, 256, 512] , layer_type='basic' ),
'resnet26': ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[256, 512, 1024, 2048] , layer_type='bottleneck' ),
'resnet34': ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[64, 128, 256, 512] , layer_type='basic' ),
'resnet50': ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[256, 512, 1024, 2048] , layer_type='bottleneck' ),
'resnet101': ImageNetPreTrainedConfig(
depths=[3, 4, 23, 3] , hidden_sizes=[256, 512, 1024, 2048] , layer_type='bottleneck' ),
'resnet152': ImageNetPreTrainedConfig(
depths=[3, 8, 36, 3] , hidden_sizes=[256, 512, 1024, 2048] , layer_type='bottleneck' ),
}
if model_name:
convert_weight_and_push(SCREAMING_SNAKE_CASE__ , names_to_config[model_name] , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
return config, expected_shape
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default=None,
type=str,
help=(
"The name of the model you wish to convert, it must be one of the supported resnet* architecture,"
" currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."
),
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=Path,
required=True,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
default=True,
type=bool,
required=False,
help="If True, push model and image processor to the hub.",
)
lowercase_ = parser.parse_args()
lowercase_ = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 7 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
# See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'vit_mae'
def __init__( self : Union[str, Any] ,snake_case : Any=768 ,snake_case : List[str]=12 ,snake_case : Optional[int]=12 ,snake_case : int=3072 ,snake_case : List[Any]="gelu" ,snake_case : str=0.0 ,snake_case : str=0.0 ,snake_case : Optional[Any]=0.02 ,snake_case : Dict=1e-12 ,snake_case : List[str]=224 ,snake_case : Any=16 ,snake_case : Any=3 ,snake_case : Tuple=True ,snake_case : List[Any]=16 ,snake_case : List[str]=512 ,snake_case : List[Any]=8 ,snake_case : Dict=2048 ,snake_case : Union[str, Any]=0.75 ,snake_case : Union[str, Any]=False ,**snake_case : Optional[int] ,):
super().__init__(**snake_case )
SCREAMING_SNAKE_CASE =hidden_size
SCREAMING_SNAKE_CASE =num_hidden_layers
SCREAMING_SNAKE_CASE =num_attention_heads
SCREAMING_SNAKE_CASE =intermediate_size
SCREAMING_SNAKE_CASE =hidden_act
SCREAMING_SNAKE_CASE =hidden_dropout_prob
SCREAMING_SNAKE_CASE =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE =initializer_range
SCREAMING_SNAKE_CASE =layer_norm_eps
SCREAMING_SNAKE_CASE =image_size
SCREAMING_SNAKE_CASE =patch_size
SCREAMING_SNAKE_CASE =num_channels
SCREAMING_SNAKE_CASE =qkv_bias
SCREAMING_SNAKE_CASE =decoder_num_attention_heads
SCREAMING_SNAKE_CASE =decoder_hidden_size
SCREAMING_SNAKE_CASE =decoder_num_hidden_layers
SCREAMING_SNAKE_CASE =decoder_intermediate_size
SCREAMING_SNAKE_CASE =mask_ratio
SCREAMING_SNAKE_CASE =norm_pix_loss
| 334 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class snake_case_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def snake_case__( self : int ) ->str:
snake_case_ = TFCamembertModel.from_pretrained('''jplu/tf-camembert-base''' )
snake_case_ = tf.convert_to_tensor(
[[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
snake_case_ = model(_UpperCamelCase )['''last_hidden_state''']
snake_case_ = tf.TensorShape((1, 1_0, 7_6_8) )
self.assertEqual(output.shape , _UpperCamelCase )
# compare the actual values for a slice.
snake_case_ = tf.convert_to_tensor(
[[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-4 ) ) | 8 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
_lowerCamelCase ={
"configuration_ernie": ["ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", "ErnieOnnxConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase =[
"ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST",
"ErnieForCausalLM",
"ErnieForMaskedLM",
"ErnieForMultipleChoice",
"ErnieForNextSentencePrediction",
"ErnieForPreTraining",
"ErnieForQuestionAnswering",
"ErnieForSequenceClassification",
"ErnieForTokenClassification",
"ErnieModel",
"ErniePreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ernie import (
ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
ErnieForCausalLM,
ErnieForMaskedLM,
ErnieForMultipleChoice,
ErnieForNextSentencePrediction,
ErnieForPreTraining,
ErnieForQuestionAnswering,
ErnieForSequenceClassification,
ErnieForTokenClassification,
ErnieModel,
ErniePreTrainedModel,
)
else:
import sys
_lowerCamelCase =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 334 | 0 |
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends
if is_vision_available():
import PIL
# soft dependency
if is_pytesseract_available():
import pytesseract
__lowerCAmelCase : int =logging.get_logger(__name__)
def _UpperCamelCase ( lowercase__ , lowercase__ , lowercase__ ):
return [
int(1000 * (box[0] / width) ),
int(1000 * (box[1] / height) ),
int(1000 * (box[2] / width) ),
int(1000 * (box[3] / height) ),
]
def _UpperCamelCase ( lowercase__ , lowercase__ , lowercase__ = None ):
__SCREAMING_SNAKE_CASE : int = tesseract_config if tesseract_config is not None else ''''''
# apply OCR
__SCREAMING_SNAKE_CASE : List[Any] = to_pil_image(lowercase__ )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = pil_image.size
__SCREAMING_SNAKE_CASE : Tuple = pytesseract.image_to_data(lowercase__ , lang=lowercase__ , output_type='''dict''' , config=lowercase__ )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']
# filter empty words and corresponding coordinates
__SCREAMING_SNAKE_CASE : Union[str, Any] = [idx for idx, word in enumerate(lowercase__ ) if not word.strip()]
__SCREAMING_SNAKE_CASE : List[str] = [word for idx, word in enumerate(lowercase__ ) if idx not in irrelevant_indices]
__SCREAMING_SNAKE_CASE : Dict = [coord for idx, coord in enumerate(lowercase__ ) if idx not in irrelevant_indices]
__SCREAMING_SNAKE_CASE : Union[str, Any] = [coord for idx, coord in enumerate(lowercase__ ) if idx not in irrelevant_indices]
__SCREAMING_SNAKE_CASE : Dict = [coord for idx, coord in enumerate(lowercase__ ) if idx not in irrelevant_indices]
__SCREAMING_SNAKE_CASE : Union[str, Any] = [coord for idx, coord in enumerate(lowercase__ ) if idx not in irrelevant_indices]
# turn coordinates into (left, top, left+width, top+height) format
__SCREAMING_SNAKE_CASE : List[Any] = []
for x, y, w, h in zip(lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = [x, y, x + w, y + h]
actual_boxes.append(lowercase__ )
# finally, normalize the bounding boxes
__SCREAMING_SNAKE_CASE : Dict = []
for box in actual_boxes:
normalized_boxes.append(normalize_box(lowercase__ , lowercase__ , lowercase__ ) )
assert len(lowercase__ ) == len(lowercase__ ), "Not as many words as there are bounding boxes"
return words, normalized_boxes
class _lowercase ( A__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = ['''pixel_values''']
def __init__( self :Optional[int] , lowerCAmelCase__ :bool = True , lowerCAmelCase__ :Dict[str, int] = None , lowerCAmelCase__ :PILImageResampling = PILImageResampling.BILINEAR , lowerCAmelCase__ :bool = True , lowerCAmelCase__ :Optional[str] = None , lowerCAmelCase__ :Optional[str] = "" , **lowerCAmelCase__ :str , ) -> None:
super().__init__(**lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : List[str] = size if size is not None else {'''height''': 224, '''width''': 224}
__SCREAMING_SNAKE_CASE : Union[str, Any] = get_size_dict(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : int = do_resize
__SCREAMING_SNAKE_CASE : Union[str, Any] = size
__SCREAMING_SNAKE_CASE : int = resample
__SCREAMING_SNAKE_CASE : int = apply_ocr
__SCREAMING_SNAKE_CASE : Tuple = ocr_lang
__SCREAMING_SNAKE_CASE : List[Any] = tesseract_config
def __magic_name__( self :Optional[int] , lowerCAmelCase__ :np.ndarray , lowerCAmelCase__ :Dict[str, int] , lowerCAmelCase__ :PILImageResampling = PILImageResampling.BILINEAR , lowerCAmelCase__ :Optional[Union[str, ChannelDimension]] = None , **lowerCAmelCase__ :Tuple , ) -> np.ndarray:
__SCREAMING_SNAKE_CASE : List[Any] = get_size_dict(lowerCAmelCase__ )
if "height" not in size or "width" not in size:
raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}''' )
__SCREAMING_SNAKE_CASE : Optional[int] = (size['''height'''], size['''width'''])
return resize(lowerCAmelCase__ , size=lowerCAmelCase__ , resample=lowerCAmelCase__ , data_format=lowerCAmelCase__ , **lowerCAmelCase__ )
def __magic_name__( self :Optional[Any] , lowerCAmelCase__ :ImageInput , lowerCAmelCase__ :bool = None , lowerCAmelCase__ :Dict[str, int] = None , lowerCAmelCase__ :PILImageResampling = None , lowerCAmelCase__ :bool = None , lowerCAmelCase__ :Optional[str] = None , lowerCAmelCase__ :Optional[str] = None , lowerCAmelCase__ :Optional[Union[str, TensorType]] = None , lowerCAmelCase__ :ChannelDimension = ChannelDimension.FIRST , **lowerCAmelCase__ :Dict , ) -> PIL.Image.Image:
__SCREAMING_SNAKE_CASE : Any = do_resize if do_resize is not None else self.do_resize
__SCREAMING_SNAKE_CASE : Optional[Any] = size if size is not None else self.size
__SCREAMING_SNAKE_CASE : Optional[int] = get_size_dict(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : int = resample if resample is not None else self.resample
__SCREAMING_SNAKE_CASE : List[Any] = apply_ocr if apply_ocr is not None else self.apply_ocr
__SCREAMING_SNAKE_CASE : Union[str, Any] = ocr_lang if ocr_lang is not None else self.ocr_lang
__SCREAMING_SNAKE_CASE : List[str] = tesseract_config if tesseract_config is not None else self.tesseract_config
__SCREAMING_SNAKE_CASE : Any = make_list_of_images(lowerCAmelCase__ )
if not valid_images(lowerCAmelCase__ ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
# All transformations expect numpy arrays.
__SCREAMING_SNAKE_CASE : Any = [to_numpy_array(lowerCAmelCase__ ) for image in images]
if apply_ocr:
requires_backends(self , '''pytesseract''' )
__SCREAMING_SNAKE_CASE : Tuple = []
__SCREAMING_SNAKE_CASE : Any = []
for image in images:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[int] = apply_tesseract(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
words_batch.append(lowerCAmelCase__ )
boxes_batch.append(lowerCAmelCase__ )
if do_resize:
__SCREAMING_SNAKE_CASE : Optional[Any] = [self.resize(image=lowerCAmelCase__ , size=lowerCAmelCase__ , resample=lowerCAmelCase__ ) for image in images]
# flip color channels from RGB to BGR (as Detectron2 requires this)
__SCREAMING_SNAKE_CASE : Any = [flip_channel_order(lowerCAmelCase__ ) for image in images]
__SCREAMING_SNAKE_CASE : List[Any] = [to_channel_dimension_format(lowerCAmelCase__ , lowerCAmelCase__ ) for image in images]
__SCREAMING_SNAKE_CASE : Optional[Any] = BatchFeature(data={'''pixel_values''': images} , tensor_type=lowerCAmelCase__ )
if apply_ocr:
__SCREAMING_SNAKE_CASE : Any = words_batch
__SCREAMING_SNAKE_CASE : Optional[Any] = boxes_batch
return data
| 9 |
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError
from .utils import require_pil
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([1, 2, 3] ) )
self.assertEqual(arr.type ,pa.intaa() )
def _lowerCAmelCase ( self : Any ):
with self.assertRaises(snake_case ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([1, 2, 3] ) ,type=pa.intaa() )
def _lowerCAmelCase ( self : Union[str, Any] ):
with self.assertRaises(snake_case ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([1, 2, 3] ,try_type=Value('bool' ) ,type=Value('int64' ) ) )
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([1, 2, 3] ,type=Value('int32' ) ) )
self.assertEqual(arr.type ,pa.intaa() )
def _lowerCAmelCase ( self : int ):
with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence(['foo', 'bar'] ,type=Value('int64' ) ) )
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([1, 2, 3] ,try_type=Value('int32' ) ) )
self.assertEqual(arr.type ,pa.intaa() )
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence(['foo', 'bar'] ,try_type=Value('int64' ) ) )
self.assertEqual(arr.type ,pa.string() )
def _lowerCAmelCase ( self : Dict ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([[[1, 2, 3]]] ,type=ArrayaD((1, 3) ,'int64' ) ) )
self.assertEqual(arr.type ,ArrayaDExtensionType((1, 3) ,'int64' ) )
def _lowerCAmelCase ( self : Dict ):
with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence(['foo', 'bar'] ,type=ArrayaD((1, 3) ,'int64' ) ) )
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([[[1, 2, 3]]] ,try_type=ArrayaD((1, 3) ,'int64' ) ) )
self.assertEqual(arr.type ,ArrayaDExtensionType((1, 3) ,'int64' ) )
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =pa.array(TypedSequence(['foo', 'bar'] ,try_type=ArrayaD((1, 3) ,'int64' ) ) )
self.assertEqual(arr.type ,pa.string() )
@require_pil
def _lowerCAmelCase ( self : int ):
import PIL.Image
SCREAMING_SNAKE_CASE =PIL.Image.fromarray(np.arange(10 ,dtype=np.uinta ).reshape(2 ,5 ) )
with patch(
'datasets.arrow_writer.cast_to_python_objects' ,side_effect=snake_case ) as mock_cast_to_python_objects:
SCREAMING_SNAKE_CASE =pa.array(TypedSequence([{'path': None, 'bytes': B'image_bytes'}, pil_image] ,type=Image() ) )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =mock_cast_to_python_objects.call_args_list[-1]
self.assertIn('optimize_list_casting' ,snake_case )
self.assertFalse(kwargs['optimize_list_casting'] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferReader(lowerCAmelCase_ ) if isinstance(lowerCAmelCase_, pa.Buffer ) else pa.memory_map(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =pa.ipc.open_stream(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =f.read_all()
assert len(pa_table.to_batches() ) == expected_num_chunks
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
del pa_table
@pytest.mark.parametrize('writer_batch_size', [None, 1, 10] )
@pytest.mark.parametrize(
'fields', [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
SCREAMING_SNAKE_CASE =pa.schema(lowerCAmelCase_ ) if fields else None
with ArrowWriter(stream=lowerCAmelCase_, schema=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_ ) as writer:
writer.write({'col_1': 'foo', 'col_2': 1} )
writer.write({'col_1': 'bar', 'col_2': 2} )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
SCREAMING_SNAKE_CASE ={'col_1': pa.string(), 'col_2': pa.intaa()}
assert writer._schema == pa.schema(lowerCAmelCase_, metadata=writer._schema.metadata )
_check_output(output.getvalue(), expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
SCREAMING_SNAKE_CASE =Features({'labels': ClassLabel(names=['neg', 'pos'] )} )
with ArrowWriter(stream=lowerCAmelCase_, features=lowerCAmelCase_ ) as writer:
writer.write({'labels': 0} )
writer.write({'labels': 1} )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert writer._schema == features.arrow_schema
assert writer._schema.metadata == features.arrow_schema.metadata
SCREAMING_SNAKE_CASE =pa.BufferReader(output.getvalue() )
SCREAMING_SNAKE_CASE =pa.ipc.open_stream(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =f.read_all()
SCREAMING_SNAKE_CASE =pa_table.schema
assert pa_table.num_rows == 2
assert schema == features.arrow_schema
assert schema.metadata == features.arrow_schema.metadata
assert features == Features.from_arrow_schema(lowerCAmelCase_ )
@pytest.mark.parametrize('writer_batch_size', [None, 1, 10] )
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
with ArrowWriter(
stream=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_, hash_salt='split_name', check_duplicates=lowerCAmelCase_, ) as writer:
with pytest.raises(lowerCAmelCase_ ):
writer.write({'col_1': 'foo', 'col_2': 1}, key=[1, 2] )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
@pytest.mark.parametrize('writer_batch_size', [None, 2, 10] )
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
with ArrowWriter(
stream=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_, hash_salt='split_name', check_duplicates=lowerCAmelCase_, ) as writer:
with pytest.raises(lowerCAmelCase_ ):
writer.write({'col_1': 'foo', 'col_2': 1}, key=10 )
writer.write({'col_1': 'bar', 'col_2': 2}, key=10 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
@pytest.mark.parametrize('writer_batch_size', [None, 2, 10] )
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
with ArrowWriter(
stream=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_, hash_salt='split_name', check_duplicates=lowerCAmelCase_, ) as writer:
writer.write({'col_1': 'foo', 'col_2': 1}, key=1 )
writer.write({'col_1': 'bar', 'col_2': 2}, key=2 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
_check_output(output.getvalue(), expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize('writer_batch_size', [None, 1, 10] )
@pytest.mark.parametrize(
'fields', [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
SCREAMING_SNAKE_CASE =pa.schema(lowerCAmelCase_ ) if fields else None
with ArrowWriter(stream=lowerCAmelCase_, schema=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_ ) as writer:
writer.write_batch({'col_1': ['foo', 'bar'], 'col_2': [1, 2]} )
writer.write_batch({'col_1': [], 'col_2': []} )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
SCREAMING_SNAKE_CASE ={'col_1': pa.string(), 'col_2': pa.intaa()}
assert writer._schema == pa.schema(lowerCAmelCase_, metadata=writer._schema.metadata )
_check_output(output.getvalue(), expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize('writer_batch_size', [None, 1, 10] )
@pytest.mark.parametrize(
'fields', [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
SCREAMING_SNAKE_CASE =pa.schema(lowerCAmelCase_ ) if fields else None
with ArrowWriter(stream=lowerCAmelCase_, schema=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_ ) as writer:
writer.write_table(pa.Table.from_pydict({'col_1': ['foo', 'bar'], 'col_2': [1, 2]} ) )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
SCREAMING_SNAKE_CASE ={'col_1': pa.string(), 'col_2': pa.intaa()}
assert writer._schema == pa.schema(lowerCAmelCase_, metadata=writer._schema.metadata )
_check_output(output.getvalue(), expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize('writer_batch_size', [None, 1, 10] )
@pytest.mark.parametrize(
'fields', [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
SCREAMING_SNAKE_CASE =pa.schema(lowerCAmelCase_ ) if fields else None
with ArrowWriter(stream=lowerCAmelCase_, schema=lowerCAmelCase_, writer_batch_size=lowerCAmelCase_ ) as writer:
writer.write_row(pa.Table.from_pydict({'col_1': ['foo'], 'col_2': [1]} ) )
writer.write_row(pa.Table.from_pydict({'col_1': ['bar'], 'col_2': [2]} ) )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
SCREAMING_SNAKE_CASE ={'col_1': pa.string(), 'col_2': pa.intaa()}
assert writer._schema == pa.schema(lowerCAmelCase_, metadata=writer._schema.metadata )
_check_output(output.getvalue(), expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
def snake_case__ ( ):
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE ={'col_1': pa.string(), 'col_2': pa.intaa()}
SCREAMING_SNAKE_CASE =os.path.join(lowerCAmelCase_, 'test.arrow' )
with ArrowWriter(path=lowerCAmelCase_, schema=pa.schema(lowerCAmelCase_ ) ) as writer:
writer.write_batch({'col_1': ['foo', 'bar'], 'col_2': [1, 2]} )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert writer._schema == pa.schema(lowerCAmelCase_, metadata=writer._schema.metadata )
_check_output(lowerCAmelCase_, 1 )
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
if pa.types.is_list(lowerCAmelCase_ ):
return get_base_dtype(arr_type.value_type )
else:
return arr_type
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
if isinstance(lst[0], lowerCAmelCase_ ):
change_first_primitive_element_in_list(lst[0], lowerCAmelCase_ )
else:
SCREAMING_SNAKE_CASE =value
@pytest.mark.parametrize('optimized_int_type, expected_dtype', [(None, pa.intaa()), (Value('int32' ), pa.intaa())] )
@pytest.mark.parametrize('sequence', [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.array(TypedSequence(lowerCAmelCase_, optimized_int_type=lowerCAmelCase_ ) )
assert get_base_dtype(arr.type ) == expected_dtype
@pytest.mark.parametrize(
'col, expected_dtype', [
('attention_mask', pa.inta()),
('special_tokens_mask', pa.inta()),
('token_type_ids', pa.inta()),
('input_ids', pa.intaa()),
('other', pa.intaa()),
], )
@pytest.mark.parametrize('sequence', [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.array(OptimizedTypedSequence(lowerCAmelCase_, col=lowerCAmelCase_ ) )
assert get_base_dtype(arr.type ) == expected_dtype
# not in range
if col != "other":
# avoids errors due to in-place modifications
SCREAMING_SNAKE_CASE =copy.deepcopy(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =np.iinfo(expected_dtype.to_pandas_dtype() ).max + 1
change_first_primitive_element_in_list(lowerCAmelCase_, lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =pa.array(OptimizedTypedSequence(lowerCAmelCase_, col=lowerCAmelCase_ ) )
assert get_base_dtype(arr.type ) == pa.intaa()
@pytest.mark.parametrize('raise_exception', [False, True] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =str(tmp_path / 'dataset-train.arrow' )
try:
with ArrowWriter(path=lowerCAmelCase_ ) as writer:
if raise_exception:
raise pa.lib.ArrowInvalid()
else:
writer.stream.close()
except pa.lib.ArrowInvalid:
pass
finally:
assert writer.stream.closed
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ='mock://dataset-train.arrow'
with ArrowWriter(path=lowerCAmelCase_, storage_options=mockfs.storage_options ) as writer:
assert isinstance(writer._fs, type(lowerCAmelCase_ ) )
assert writer._fs.storage_options == mockfs.storage_options
writer.write({'col_1': 'foo', 'col_2': 1} )
writer.write({'col_1': 'bar', 'col_2': 2} )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert mockfs.exists(lowerCAmelCase_ )
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
with ParquetWriter(stream=lowerCAmelCase_ ) as writer:
writer.write({'col_1': 'foo', 'col_2': 1} )
writer.write({'col_1': 'bar', 'col_2': 2} )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =writer.finalize()
assert num_examples == 2
assert num_bytes > 0
SCREAMING_SNAKE_CASE =pa.BufferReader(output.getvalue() )
SCREAMING_SNAKE_CASE =pq.read_table(lowerCAmelCase_ )
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
@require_pil
@pytest.mark.parametrize('embed_local_files', [False, True] )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
import PIL.Image
SCREAMING_SNAKE_CASE =str(tmp_path / 'test_image_rgb.jpg' )
PIL.Image.fromarray(np.zeros((5, 5), dtype=np.uinta ) ).save(lowerCAmelCase_, format='png' )
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
with ParquetWriter(
stream=lowerCAmelCase_, features=Features({'image': Image()} ), embed_local_files=lowerCAmelCase_ ) as writer:
writer.write({'image': image_path} )
writer.finalize()
SCREAMING_SNAKE_CASE =pa.BufferReader(output.getvalue() )
SCREAMING_SNAKE_CASE =pq.read_table(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =pa_table.to_pydict()
if embed_local_files:
assert isinstance(out['image'][0]['path'], lowerCAmelCase_ )
with open(lowerCAmelCase_, 'rb' ) as f:
assert out["image"][0]["bytes"] == f.read()
else:
assert out["image"][0]["path"] == image_path
assert out["image"][0]["bytes"] is None
def snake_case__ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =pa.schema([pa.field('col_1', pa.string(), nullable=lowerCAmelCase_ )] )
SCREAMING_SNAKE_CASE =pa.BufferOutputStream()
with ArrowWriter(stream=lowerCAmelCase_ ) as writer:
writer._build_writer(inferred_schema=lowerCAmelCase_ )
assert writer._schema == pa.schema([pa.field('col_1', pa.string() )] )
| 334 | 0 |
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available
if is_torch_available():
from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification
if is_tf_available():
from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification
if is_flax_available():
from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification
__A = DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co
__A = "main"
# Default branch name
__A = "f2c752cfc5c0ab6f4bdec59acea69eefbee381c2"
# One particular commit (not the top of `main`)
__A = "aaaaaaa"
# This commit does not exist, so we should 404.
__A = "d9e9f15bc825e4b2c9249e9578f884bbcb5e3684"
# Sha-1 of config.json on the top of `main`, for checking purposes
__A = "4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3"
@contextlib.contextmanager
def lowerCAmelCase_ ( ) -> List[Any]:
"""simple docstring"""
print("Welcome!" )
yield
print("Bye!" )
@contextlib.contextmanager
def lowerCAmelCase_ ( ) -> int:
"""simple docstring"""
print("Bonjour!" )
yield
print("Au revoir!" )
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ (self : int) ->Optional[int]:
'''simple docstring'''
assert transformers.__spec__ is not None
assert importlib.util.find_spec("transformers") is not None
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
@unittest.mock.patch("sys.stdout" , new_callable=io.StringIO)
def SCREAMING_SNAKE_CASE_ (self : Union[str, Any] , UpperCAmelCase_ : List[Any]) ->Dict:
'''simple docstring'''
with ContextManagers([]):
print("Transformers are awesome!")
# The print statement adds a new line at the end of the output
self.assertEqual(mock_stdout.getvalue() , "Transformers are awesome!\n")
@unittest.mock.patch("sys.stdout" , new_callable=io.StringIO)
def SCREAMING_SNAKE_CASE_ (self : List[str] , UpperCAmelCase_ : List[str]) ->Any:
'''simple docstring'''
with ContextManagers([context_en()]):
print("Transformers are awesome!")
# The output should be wrapped with an English welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , "Welcome!\nTransformers are awesome!\nBye!\n")
@unittest.mock.patch("sys.stdout" , new_callable=io.StringIO)
def SCREAMING_SNAKE_CASE_ (self : List[str] , UpperCAmelCase_ : Dict) ->List[Any]:
'''simple docstring'''
with ContextManagers([context_fr(), context_en()]):
print("Transformers are awesome!")
# The output should be wrapped with an English and French welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , "Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n")
@require_torch
def SCREAMING_SNAKE_CASE_ (self : Optional[Any]) ->Union[str, Any]:
'''simple docstring'''
self.assertEqual(find_labels(UpperCAmelCase_) , ["labels"])
self.assertEqual(find_labels(UpperCAmelCase_) , ["labels", "next_sentence_label"])
self.assertEqual(find_labels(UpperCAmelCase_) , ["start_positions", "end_positions"])
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
pass
self.assertEqual(find_labels(UpperCAmelCase_) , ["labels"])
@require_tf
def SCREAMING_SNAKE_CASE_ (self : Dict) ->Optional[Any]:
'''simple docstring'''
self.assertEqual(find_labels(UpperCAmelCase_) , ["labels"])
self.assertEqual(find_labels(UpperCAmelCase_) , ["labels", "next_sentence_label"])
self.assertEqual(find_labels(UpperCAmelCase_) , ["start_positions", "end_positions"])
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
pass
self.assertEqual(find_labels(UpperCAmelCase_) , ["labels"])
@require_flax
def SCREAMING_SNAKE_CASE_ (self : Tuple) ->Any:
'''simple docstring'''
self.assertEqual(find_labels(UpperCAmelCase_) , [])
self.assertEqual(find_labels(UpperCAmelCase_) , [])
self.assertEqual(find_labels(UpperCAmelCase_) , [])
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
pass
self.assertEqual(find_labels(UpperCAmelCase_) , [])
| 10 |
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def snake_case__ ( ):
"""simple docstring"""
assert or_gate(0, 0 ) == 0
assert or_gate(0, 1 ) == 1
assert or_gate(1, 0 ) == 1
assert or_gate(1, 1 ) == 1
if __name__ == "__main__":
print(or_gate(0, 1))
print(or_gate(1, 0))
print(or_gate(0, 0))
print(or_gate(1, 1))
| 334 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase__ = {'configuration_beit': ['BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BeitConfig', 'BeitOnnxConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = ['BeitFeatureExtractor']
lowerCAmelCase__ = ['BeitImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'BEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'BeitForImageClassification',
'BeitForMaskedImageModeling',
'BeitForSemanticSegmentation',
'BeitModel',
'BeitPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'FlaxBeitForImageClassification',
'FlaxBeitForMaskedImageModeling',
'FlaxBeitModel',
'FlaxBeitPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_beit import BeitFeatureExtractor
from .image_processing_beit import BeitImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_beit import (
BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
BeitPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_beit import (
FlaxBeitForImageClassification,
FlaxBeitForMaskedImageModeling,
FlaxBeitModel,
FlaxBeitPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 11 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={"vocab_file": "vocab.txt"}
_lowerCamelCase ={
"vocab_file": {
"facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt",
"facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt",
},
}
_lowerCamelCase ={
"facebook/esm2_t6_8M_UR50D": 10_24,
"facebook/esm2_t12_35M_UR50D": 10_24,
}
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
with open(lowerCAmelCase_, 'r' ) as f:
SCREAMING_SNAKE_CASE =f.read().splitlines()
return [l.strip() for l in lines]
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = VOCAB_FILES_NAMES
__UpperCAmelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase = ['input_ids', 'attention_mask']
def __init__( self : int ,snake_case : Dict ,snake_case : Dict="<unk>" ,snake_case : Optional[int]="<cls>" ,snake_case : Optional[int]="<pad>" ,snake_case : int="<mask>" ,snake_case : Optional[int]="<eos>" ,**snake_case : List[str] ,):
super().__init__(**snake_case )
SCREAMING_SNAKE_CASE =load_vocab_file(snake_case )
SCREAMING_SNAKE_CASE =dict(enumerate(self.all_tokens ) )
SCREAMING_SNAKE_CASE ={tok: ind for ind, tok in enumerate(self.all_tokens )}
SCREAMING_SNAKE_CASE =unk_token
SCREAMING_SNAKE_CASE =cls_token
SCREAMING_SNAKE_CASE =pad_token
SCREAMING_SNAKE_CASE =mask_token
SCREAMING_SNAKE_CASE =eos_token
SCREAMING_SNAKE_CASE =self.all_tokens
self._create_trie(self.unique_no_split_tokens )
def _lowerCAmelCase ( self : Optional[Any] ,snake_case : int ):
return self._id_to_token.get(snake_case ,self.unk_token )
def _lowerCAmelCase ( self : Dict ,snake_case : str ):
return self._token_to_id.get(snake_case ,self._token_to_id.get(self.unk_token ) )
def _lowerCAmelCase ( self : Tuple ,snake_case : List[str] ,**snake_case : Any ):
return text.split()
def _lowerCAmelCase ( self : Optional[int] ,snake_case : str=False ):
return len(self._id_to_token )
def _lowerCAmelCase ( self : List[str] ):
return {token: i for i, token in enumerate(self.all_tokens )}
def _lowerCAmelCase ( self : List[Any] ,snake_case : str ):
return self._token_to_id.get(snake_case ,self._token_to_id.get(self.unk_token ) )
def _lowerCAmelCase ( self : Any ,snake_case : int ):
return self._id_to_token.get(snake_case ,self.unk_token )
def _lowerCAmelCase ( self : List[str] ,snake_case : List[int] ,snake_case : Optional[List[int]] = None ):
SCREAMING_SNAKE_CASE =[self.cls_token_id]
SCREAMING_SNAKE_CASE =[self.eos_token_id] # No sep token in ESM vocabulary
if token_ids_a is None:
if self.eos_token_id is None:
return cls + token_ids_a
else:
return cls + token_ids_a + sep
elif self.eos_token_id is None:
raise ValueError('Cannot tokenize multiple sequences when EOS token is not set!' )
return cls + token_ids_a + sep + token_ids_a + sep # Multiple inputs always have an EOS token
def _lowerCAmelCase ( self : Optional[int] ,snake_case : List ,snake_case : Optional[List] = None ,snake_case : bool = False ):
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'You should not supply a second sequence if the provided sequence of '
'ids is already formatted with special tokens for the model.' )
return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
SCREAMING_SNAKE_CASE =[1] + ([0] * len(snake_case )) + [1]
if token_ids_a is not None:
mask += [0] * len(snake_case ) + [1]
return mask
def _lowerCAmelCase ( self : Optional[int] ,snake_case : Dict ,snake_case : Any ):
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,(filename_prefix + '-' if filename_prefix else '') + 'vocab.txt' )
with open(snake_case ,'w' ) as f:
f.write('\n'.join(self.all_tokens ) )
return (vocab_file,)
@property
def _lowerCAmelCase ( self : int ):
return self.get_vocab_size(with_added_tokens=snake_case )
def _lowerCAmelCase ( self : str ,snake_case : Union[List[str], List[AddedToken]] ,snake_case : bool = False ):
return super()._add_tokens(snake_case ,special_tokens=snake_case )
| 334 | 0 |
class lowerCamelCase__:
def __init__( self: Dict , UpperCamelCase_: int ):
__lowerCamelCase = n
__lowerCamelCase = [None] * self.n
__lowerCamelCase = 0 # index of the first element
__lowerCamelCase = 0
__lowerCamelCase = 0
def __len__( self: Optional[int] ):
return self.size
def lowerCAmelCase__ ( self: str ):
return self.size == 0
def lowerCAmelCase__ ( self: Union[str, Any] ):
return False if self.is_empty() else self.array[self.front]
def lowerCAmelCase__ ( self: List[str] , UpperCamelCase_: List[Any] ):
if self.size >= self.n:
raise Exception("""QUEUE IS FULL""" )
__lowerCamelCase = data
__lowerCamelCase = (self.rear + 1) % self.n
self.size += 1
return self
def lowerCAmelCase__ ( self: Union[str, Any] ):
if self.size == 0:
raise Exception("""UNDERFLOW""" )
__lowerCamelCase = self.array[self.front]
__lowerCamelCase = None
__lowerCamelCase = (self.front + 1) % self.n
self.size -= 1
return temp
| 12 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase =logging.get_logger()
@dataclass
class a_ :
"""simple docstring"""
__UpperCAmelCase = 42
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
def _lowerCAmelCase ( self : Any ,snake_case : Any ,snake_case : Tensor ,snake_case : Tensor ):
SCREAMING_SNAKE_CASE =len(list(m.modules() ) ) == 1 or isinstance(snake_case ,nn.Convad ) or isinstance(snake_case ,nn.BatchNormad )
if has_not_submodules:
self.traced.append(snake_case )
def __call__( self : int ,snake_case : Tensor ):
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(snake_case )
[x.remove() for x in self.handles]
return self
@property
def _lowerCAmelCase ( self : Tuple ):
# check the len of the state_dict keys to see if we have learnable params
return list(filter(lambda snake_case : len(list(x.state_dict().keys() ) ) > 0 ,self.traced ) )
@dataclass
class a_ :
"""simple docstring"""
__UpperCAmelCase = 42
__UpperCAmelCase = 42
__UpperCAmelCase = 0
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
__UpperCAmelCase = field(default_factory=lowerCamelCase_ )
def __call__( self : int ,snake_case : Tensor ):
SCREAMING_SNAKE_CASE =Tracker(self.dest )(snake_case ).parametrized
SCREAMING_SNAKE_CASE =Tracker(self.src )(snake_case ).parametrized
SCREAMING_SNAKE_CASE =list(filter(lambda snake_case : type(snake_case ) not in self.src_skip ,snake_case ) )
SCREAMING_SNAKE_CASE =list(filter(lambda snake_case : type(snake_case ) not in self.dest_skip ,snake_case ) )
if len(snake_case ) != len(snake_case ):
raise Exception(
f'Numbers of operations are different. Source module has {len(snake_case )} operations while'
f' destination module has {len(snake_case )}.' )
for dest_m, src_m in zip(snake_case ,snake_case ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(f'Transfered from={src_m} to={dest_m}' )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ = True ):
"""simple docstring"""
print(F'Converting {name}...' )
with torch.no_grad():
SCREAMING_SNAKE_CASE =timm.create_model(lowerCAmelCase_, pretrained=lowerCAmelCase_ ).eval()
SCREAMING_SNAKE_CASE =ResNetForImageClassification(lowerCAmelCase_ ).eval()
SCREAMING_SNAKE_CASE =ModuleTransfer(src=lowerCAmelCase_, dest=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =torch.randn((1, 3, 224, 224) )
module_transfer(lowerCAmelCase_ )
assert torch.allclose(from_model(lowerCAmelCase_ ), our_model(lowerCAmelCase_ ).logits ), "The model logits don't match the original one."
SCREAMING_SNAKE_CASE =F'resnet{"-".join(name.split("resnet" ) )}'
print(lowerCAmelCase_ )
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name, commit_message='Add model', use_temp_dir=lowerCAmelCase_, )
# we can use the convnext one
SCREAMING_SNAKE_CASE =AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' )
image_processor.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name, commit_message='Add image processor', use_temp_dir=lowerCAmelCase_, )
print(F'Pushed {checkpoint_name}' )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ = None, lowerCAmelCase_ = True ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ='imagenet-1k-id2label.json'
SCREAMING_SNAKE_CASE =1000
SCREAMING_SNAKE_CASE =(1, num_labels)
SCREAMING_SNAKE_CASE ='huggingface/label-files'
SCREAMING_SNAKE_CASE =num_labels
SCREAMING_SNAKE_CASE =json.load(open(hf_hub_download(lowerCAmelCase_, lowerCAmelCase_, repo_type='dataset' ), 'r' ) )
SCREAMING_SNAKE_CASE ={int(lowerCAmelCase_ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE =idalabel
SCREAMING_SNAKE_CASE ={v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE =partial(lowerCAmelCase_, num_labels=lowerCAmelCase_, idalabel=lowerCAmelCase_, labelaid=lowerCAmelCase_ )
SCREAMING_SNAKE_CASE ={
'resnet18': ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2], hidden_sizes=[64, 128, 256, 512], layer_type='basic' ),
'resnet26': ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2], hidden_sizes=[256, 512, 1024, 2048], layer_type='bottleneck' ),
'resnet34': ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3], hidden_sizes=[64, 128, 256, 512], layer_type='basic' ),
'resnet50': ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3], hidden_sizes=[256, 512, 1024, 2048], layer_type='bottleneck' ),
'resnet101': ImageNetPreTrainedConfig(
depths=[3, 4, 23, 3], hidden_sizes=[256, 512, 1024, 2048], layer_type='bottleneck' ),
'resnet152': ImageNetPreTrainedConfig(
depths=[3, 8, 36, 3], hidden_sizes=[256, 512, 1024, 2048], layer_type='bottleneck' ),
}
if model_name:
convert_weight_and_push(lowerCAmelCase_, names_to_config[model_name], lowerCAmelCase_, lowerCAmelCase_ )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ )
return config, expected_shape
if __name__ == "__main__":
_lowerCamelCase =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default=None,
type=str,
help=(
"The name of the model you wish to convert, it must be one of the supported resnet* architecture,"
" currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."
),
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=Path,
required=True,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
default=True,
type=bool,
required=False,
help="If True, push model and image processor to the hub.",
)
_lowerCamelCase =parser.parse_args()
_lowerCamelCase =args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 334 | 0 |
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class __lowercase ( UpperCAmelCase_ , UpperCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
_UpperCAmelCase : Tuple = IFInpaintingSuperResolutionPipeline
_UpperCAmelCase : List[Any] = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
_UpperCAmelCase : Optional[Any] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'''original_image'''} )
_UpperCAmelCase : List[Any] = PipelineTesterMixin.required_optional_params - {'''latents'''}
def _SCREAMING_SNAKE_CASE ( self : str):
return self._get_superresolution_dummy_components()
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : str=0):
if str(lowerCAmelCase__).startswith("mps"):
SCREAMING_SNAKE_CASE_: Tuple = torch.manual_seed(lowerCAmelCase__)
else:
SCREAMING_SNAKE_CASE_: str = torch.Generator(device=lowerCAmelCase__).manual_seed(lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Union[str, Any] = floats_tensor((1, 3, 16, 16) , rng=random.Random(lowerCAmelCase__)).to(lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Dict = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowerCAmelCase__)).to(lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Optional[int] = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowerCAmelCase__)).to(lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Any = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"original_image": original_image,
"mask_image": mask_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def _SCREAMING_SNAKE_CASE ( self : Dict):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)
def _SCREAMING_SNAKE_CASE ( self : List[Any]):
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != "cuda" , reason="float16 requires CUDA")
def _SCREAMING_SNAKE_CASE ( self : Any):
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1E-1)
def _SCREAMING_SNAKE_CASE ( self : int):
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)
def _SCREAMING_SNAKE_CASE ( self : Optional[Any]):
self._test_save_load_local()
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any]):
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
| 13 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_=7 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =None
if token is not None:
SCREAMING_SNAKE_CASE ={'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}
# The id of a workflow (not of a workflow run)
SCREAMING_SNAKE_CASE ='636036'
SCREAMING_SNAKE_CASE =F'https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs'
# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
url += F'?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}'
SCREAMING_SNAKE_CASE =requests.get(lowerCAmelCase_, headers=lowerCAmelCase_ ).json()
return result["workflow_runs"]
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =get_daily_ci_runs(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =None
for workflow_run in workflow_runs:
if workflow_run["status"] == "completed":
SCREAMING_SNAKE_CASE =workflow_run['id']
break
return workflow_run_id
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =get_last_daily_ci_runs(lowerCAmelCase_ )
if workflow_run_id is not None:
SCREAMING_SNAKE_CASE =get_artifacts_links(worflow_run_id=lowerCAmelCase_, token=lowerCAmelCase_ )
for artifact_name in artifact_names:
if artifact_name in artifacts_links:
SCREAMING_SNAKE_CASE =artifacts_links[artifact_name]
download_artifact(
artifact_name=lowerCAmelCase_, artifact_url=lowerCAmelCase_, output_dir=lowerCAmelCase_, token=lowerCAmelCase_ )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
get_last_daily_ci_artifacts(lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ )
SCREAMING_SNAKE_CASE ={}
for artifact_name in artifact_names:
SCREAMING_SNAKE_CASE =os.path.join(lowerCAmelCase_, F'{artifact_name}.zip' )
if os.path.isfile(lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE ={}
with zipfile.ZipFile(lowerCAmelCase_ ) as z:
for filename in z.namelist():
if not os.path.isdir(lowerCAmelCase_ ):
# read the file
with z.open(lowerCAmelCase_ ) as f:
SCREAMING_SNAKE_CASE =f.read().decode('UTF-8' )
return results
| 334 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : int = {
"""configuration_blip""": [
"""BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BlipConfig""",
"""BlipTextConfig""",
"""BlipVisionConfig""",
],
"""processing_blip""": ["""BlipProcessor"""],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Tuple = ["""BlipImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : List[Any] = [
"""BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""BlipModel""",
"""BlipPreTrainedModel""",
"""BlipForConditionalGeneration""",
"""BlipForQuestionAnswering""",
"""BlipVisionModel""",
"""BlipTextModel""",
"""BlipForImageTextRetrieval""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Optional[Any] = [
"""TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFBlipModel""",
"""TFBlipPreTrainedModel""",
"""TFBlipForConditionalGeneration""",
"""TFBlipForQuestionAnswering""",
"""TFBlipVisionModel""",
"""TFBlipTextModel""",
"""TFBlipForImageTextRetrieval""",
]
if TYPE_CHECKING:
from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
from .processing_blip import BlipProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_blip import BlipImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip import (
BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
BlipModel,
BlipPreTrainedModel,
BlipTextModel,
BlipVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blip import (
TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBlipForConditionalGeneration,
TFBlipForImageTextRetrieval,
TFBlipForQuestionAnswering,
TFBlipModel,
TFBlipPreTrainedModel,
TFBlipTextModel,
TFBlipVisionModel,
)
else:
import sys
_lowerCamelCase : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 14 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
)
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
def __init__( self : Tuple ,snake_case : Optional[int] ,snake_case : Dict=13 ,snake_case : str=7 ,snake_case : Dict=True ,snake_case : List[Any]=True ,snake_case : Dict=False ,snake_case : int=True ,snake_case : Dict=99 ,snake_case : int=32 ,snake_case : List[str]=5 ,snake_case : Optional[Any]=4 ,snake_case : Tuple=64 ,snake_case : List[Any]="gelu" ,snake_case : str=0.1 ,snake_case : str=0.1 ,snake_case : List[str]=512 ,snake_case : List[str]=16 ,snake_case : str=2 ,snake_case : Dict=0.02 ,snake_case : Optional[int]=3 ,snake_case : int=4 ,snake_case : Any=None ,snake_case : Union[str, Any]=2 ,snake_case : List[Any]=2 ,snake_case : Optional[int]=2 ,snake_case : Dict=2 ,snake_case : List[str]=4 ,snake_case : int=1 ,):
SCREAMING_SNAKE_CASE =parent
SCREAMING_SNAKE_CASE =batch_size
SCREAMING_SNAKE_CASE =seq_length
SCREAMING_SNAKE_CASE =is_training
SCREAMING_SNAKE_CASE =use_input_mask
SCREAMING_SNAKE_CASE =use_token_type_ids
SCREAMING_SNAKE_CASE =use_labels
SCREAMING_SNAKE_CASE =vocab_size
SCREAMING_SNAKE_CASE =hidden_size
SCREAMING_SNAKE_CASE =num_hidden_layers
SCREAMING_SNAKE_CASE =num_attention_heads
SCREAMING_SNAKE_CASE =intermediate_size
SCREAMING_SNAKE_CASE =hidden_act
SCREAMING_SNAKE_CASE =hidden_dropout_prob
SCREAMING_SNAKE_CASE =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE =max_position_embeddings
SCREAMING_SNAKE_CASE =type_vocab_size
SCREAMING_SNAKE_CASE =type_sequence_label_size
SCREAMING_SNAKE_CASE =initializer_range
SCREAMING_SNAKE_CASE =num_labels
SCREAMING_SNAKE_CASE =num_choices
SCREAMING_SNAKE_CASE =scope
SCREAMING_SNAKE_CASE =q_groups
SCREAMING_SNAKE_CASE =k_groups
SCREAMING_SNAKE_CASE =v_groups
SCREAMING_SNAKE_CASE =post_attention_groups
SCREAMING_SNAKE_CASE =intermediate_groups
SCREAMING_SNAKE_CASE =output_groups
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
SCREAMING_SNAKE_CASE =None
if self.use_input_mask:
SCREAMING_SNAKE_CASE =random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE =None
SCREAMING_SNAKE_CASE =None
SCREAMING_SNAKE_CASE =None
if self.use_labels:
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size] ,self.type_sequence_label_size )
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size] ,self.num_choices )
SCREAMING_SNAKE_CASE =self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowerCAmelCase ( self : Optional[int] ):
return SqueezeBertConfig(
embedding_size=self.hidden_size ,vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,attention_probs_dropout_prob=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,initializer_range=self.initializer_range ,q_groups=self.q_groups ,k_groups=self.k_groups ,v_groups=self.v_groups ,post_attention_groups=self.post_attention_groups ,intermediate_groups=self.intermediate_groups ,output_groups=self.output_groups ,)
def _lowerCAmelCase ( self : Dict ,snake_case : List[str] ,snake_case : Optional[Any] ,snake_case : List[str] ,snake_case : List[Any] ,snake_case : str ,snake_case : Union[str, Any] ):
SCREAMING_SNAKE_CASE =SqueezeBertModel(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case ,snake_case )
SCREAMING_SNAKE_CASE =model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _lowerCAmelCase ( self : Optional[int] ,snake_case : Optional[int] ,snake_case : Union[str, Any] ,snake_case : List[Any] ,snake_case : int ,snake_case : Any ,snake_case : Tuple ):
SCREAMING_SNAKE_CASE =SqueezeBertForMaskedLM(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case ,attention_mask=snake_case ,labels=snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def _lowerCAmelCase ( self : Tuple ,snake_case : Union[str, Any] ,snake_case : Any ,snake_case : List[str] ,snake_case : List[Any] ,snake_case : Dict ,snake_case : Optional[Any] ):
SCREAMING_SNAKE_CASE =SqueezeBertForQuestionAnswering(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(
snake_case ,attention_mask=snake_case ,start_positions=snake_case ,end_positions=snake_case )
self.parent.assertEqual(result.start_logits.shape ,(self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape ,(self.batch_size, self.seq_length) )
def _lowerCAmelCase ( self : Optional[int] ,snake_case : Tuple ,snake_case : List[str] ,snake_case : List[str] ,snake_case : Any ,snake_case : Tuple ,snake_case : str ):
SCREAMING_SNAKE_CASE =self.num_labels
SCREAMING_SNAKE_CASE =SqueezeBertForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case ,attention_mask=snake_case ,labels=snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def _lowerCAmelCase ( self : Optional[Any] ,snake_case : List[str] ,snake_case : List[str] ,snake_case : Tuple ,snake_case : Dict ,snake_case : str ,snake_case : Tuple ):
SCREAMING_SNAKE_CASE =self.num_labels
SCREAMING_SNAKE_CASE =SqueezeBertForTokenClassification(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =model(snake_case ,attention_mask=snake_case ,labels=snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def _lowerCAmelCase ( self : List[str] ,snake_case : Dict ,snake_case : str ,snake_case : Union[str, Any] ,snake_case : Union[str, Any] ,snake_case : Any ,snake_case : Union[str, Any] ):
SCREAMING_SNAKE_CASE =self.num_choices
SCREAMING_SNAKE_CASE =SqueezeBertForMultipleChoice(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE =input_ids.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
SCREAMING_SNAKE_CASE =input_mask.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
SCREAMING_SNAKE_CASE =model(
snake_case ,attention_mask=snake_case ,labels=snake_case ,)
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_choices) )
def _lowerCAmelCase ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE =self.prepare_config_and_inputs()
((SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE) , (SCREAMING_SNAKE_CASE)) =config_and_inputs
SCREAMING_SNAKE_CASE ={'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class a_ ( lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase = (
(
SqueezeBertModel,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
)
if is_torch_available()
else None
)
__UpperCAmelCase = (
{
'feature-extraction': SqueezeBertModel,
'fill-mask': SqueezeBertForMaskedLM,
'question-answering': SqueezeBertForQuestionAnswering,
'text-classification': SqueezeBertForSequenceClassification,
'token-classification': SqueezeBertForTokenClassification,
'zero-shot': SqueezeBertForSequenceClassification,
}
if is_torch_available()
else {}
)
__UpperCAmelCase = False
__UpperCAmelCase = True
__UpperCAmelCase = False
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =SqueezeBertModelTester(self )
SCREAMING_SNAKE_CASE =ConfigTester(self ,config_class=snake_case ,dim=37 )
def _lowerCAmelCase ( self : List[str] ):
self.config_tester.run_common_tests()
def _lowerCAmelCase ( self : Optional[int] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_model(*snake_case )
def _lowerCAmelCase ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_masked_lm(*snake_case )
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_question_answering(*snake_case )
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_sequence_classification(*snake_case )
def _lowerCAmelCase ( self : int ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_token_classification(*snake_case )
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_multiple_choice(*snake_case )
@slow
def _lowerCAmelCase ( self : str ):
for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE =SqueezeBertModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
@require_sentencepiece
@require_tokenizers
@require_torch
class a_ ( unittest.TestCase ):
"""simple docstring"""
@slow
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =SqueezeBertForSequenceClassification.from_pretrained('squeezebert/squeezebert-mnli' )
SCREAMING_SNAKE_CASE =torch.tensor([[1, 29414, 232, 328, 740, 1140, 12695, 69, 13, 1588, 2]] )
SCREAMING_SNAKE_CASE =model(snake_case )[0]
SCREAMING_SNAKE_CASE =torch.Size((1, 3) )
self.assertEqual(output.shape ,snake_case )
SCREAMING_SNAKE_CASE =torch.tensor([[0.6_401, -0.0_349, -0.6_041]] )
self.assertTrue(torch.allclose(snake_case ,snake_case ,atol=1e-4 ) )
| 334 | 0 |
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
from transformers.models.mobilevitva.modeling_mobilevitva import (
MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
make_divisible,
)
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def UpperCamelCase_ ( self : Optional[Any] ):
__A = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(A ,"width_multiplier" ) )
class UpperCAmelCase :
'''simple docstring'''
def __init__( self : List[str] ,A : List[Any] ,A : Optional[int]=13 ,A : Dict=64 ,A : Optional[Any]=2 ,A : Optional[int]=3 ,A : int="swish" ,A : Tuple=3 ,A : Tuple=32 ,A : int=0.1 ,A : Any=0.02 ,A : Any=True ,A : Optional[int]=True ,A : Tuple=10 ,A : Any=None ,A : Any=0.25 ,A : Tuple=0.0 ,A : Optional[int]=0.0 ,):
__A = parent
__A = batch_size
__A = image_size
__A = patch_size
__A = num_channels
__A = make_divisible(5_12 * width_multiplier ,divisor=8 )
__A = hidden_act
__A = conv_kernel_size
__A = output_stride
__A = classifier_dropout_prob
__A = use_labels
__A = is_training
__A = num_labels
__A = initializer_range
__A = scope
__A = width_multiplier
__A = ffn_dropout
__A = attn_dropout
def UpperCamelCase_ ( self : Tuple ):
__A = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__A = None
__A = None
if self.use_labels:
__A = ids_tensor([self.batch_size] ,self.num_labels )
__A = ids_tensor([self.batch_size, self.image_size, self.image_size] ,self.num_labels )
__A = self.get_config()
return config, pixel_values, labels, pixel_labels
def UpperCamelCase_ ( self : List[Any] ):
return MobileViTVaConfig(
image_size=self.image_size ,patch_size=self.patch_size ,num_channels=self.num_channels ,hidden_act=self.hidden_act ,conv_kernel_size=self.conv_kernel_size ,output_stride=self.output_stride ,classifier_dropout_prob=self.classifier_dropout_prob ,initializer_range=self.initializer_range ,width_multiplier=self.width_multiplier ,ffn_dropout=self.ffn_dropout_prob ,attn_dropout=self.attn_dropout_prob ,)
def UpperCamelCase_ ( self : int ,A : Any ,A : Any ,A : Union[str, Any] ,A : Optional[int] ):
__A = MobileViTVaModel(config=A )
model.to(A )
model.eval()
__A = model(A )
self.parent.assertEqual(
result.last_hidden_state.shape ,(
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) ,)
def UpperCamelCase_ ( self : List[Any] ,A : Optional[Any] ,A : Optional[Any] ,A : int ,A : Tuple ):
__A = self.num_labels
__A = MobileViTVaForImageClassification(A )
model.to(A )
model.eval()
__A = model(A ,labels=A )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def UpperCamelCase_ ( self : List[Any] ,A : Dict ,A : List[str] ,A : Union[str, Any] ,A : int ):
__A = self.num_labels
__A = MobileViTVaForSemanticSegmentation(A )
model.to(A )
model.eval()
__A = model(A )
self.parent.assertEqual(
result.logits.shape ,(
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) ,)
__A = model(A ,labels=A )
self.parent.assertEqual(
result.logits.shape ,(
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) ,)
def UpperCamelCase_ ( self : Dict ):
__A = self.prepare_config_and_inputs()
__A , __A , __A , __A = config_and_inputs
__A = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
snake_case_ = (
(MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
if is_torch_available()
else ()
)
snake_case_ = (
{
"feature-extraction": MobileViTVaModel,
"image-classification": MobileViTVaForImageClassification,
"image-segmentation": MobileViTVaForSemanticSegmentation,
}
if is_torch_available()
else {}
)
snake_case_ = False
snake_case_ = False
snake_case_ = False
snake_case_ = False
def UpperCamelCase_ ( self : Union[str, Any] ):
__A = MobileViTVaModelTester(self )
__A = MobileViTVaConfigTester(self ,config_class=A ,has_text_modality=A )
def UpperCamelCase_ ( self : Optional[Any] ):
self.config_tester.run_common_tests()
@unittest.skip(reason="MobileViTV2 does not use inputs_embeds" )
def UpperCamelCase_ ( self : List[str] ):
pass
@unittest.skip(reason="MobileViTV2 does not support input and output embeddings" )
def UpperCamelCase_ ( self : List[Any] ):
pass
@unittest.skip(reason="MobileViTV2 does not output attentions" )
def UpperCamelCase_ ( self : List[Any] ):
pass
@require_torch_multi_gpu
@unittest.skip(reason="Got `CUDA error: misaligned address` for tests after this one being run." )
def UpperCamelCase_ ( self : int ):
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def UpperCamelCase_ ( self : Optional[int] ):
pass
def UpperCamelCase_ ( self : Dict ):
__A , __A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__A = model_class(A )
__A = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__A = [*signature.parameters.keys()]
__A = ["pixel_values"]
self.assertListEqual(arg_names[:1] ,A )
def UpperCamelCase_ ( self : Any ):
__A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def UpperCamelCase_ ( self : Optional[Any] ):
def check_hidden_states_output(A : Dict ,A : Optional[int] ,A : Any ):
__A = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
__A = model(**self._prepare_for_class(A ,A ) )
__A = outputs.hidden_states
__A = 5
self.assertEqual(len(A ) ,A )
# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
# with the width and height being successively divided by 2.
__A = 2
for i in range(len(A ) ):
self.assertListEqual(
list(hidden_states[i].shape[-2:] ) ,[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor] ,)
divisor *= 2
self.assertEqual(self.model_tester.output_stride ,divisor // 2 )
__A , __A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__A = True
check_hidden_states_output(A ,A ,A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__A = True
check_hidden_states_output(A ,A ,A )
def UpperCamelCase_ ( self : Any ):
__A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A )
def UpperCamelCase_ ( self : Optional[int] ):
__A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*A )
@slow
def UpperCamelCase_ ( self : Optional[Any] ):
for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__A = MobileViTVaModel.from_pretrained(A )
self.assertIsNotNone(A )
def UpperCAmelCase ( ) -> Union[str, Any]:
"""simple docstring"""
__A = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def UpperCamelCase_ ( self : Any ):
return (
MobileViTImageProcessor.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256" )
if is_vision_available()
else None
)
@slow
def UpperCamelCase_ ( self : str ):
__A = MobileViTVaForImageClassification.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256" ).to(
A )
__A = self.default_image_processor
__A = prepare_img()
__A = image_processor(images=A ,return_tensors="pt" ).to(A )
# forward pass
with torch.no_grad():
__A = model(**A )
# verify the logits
__A = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape ,A )
__A = torch.tensor([-1.6336E00, -7.3204E-02, -5.1883E-01] ).to(A )
self.assertTrue(torch.allclose(outputs.logits[0, :3] ,A ,atol=1E-4 ) )
@slow
def UpperCamelCase_ ( self : int ):
__A = MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3" )
__A = model.to(A )
__A = MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3" )
__A = prepare_img()
__A = image_processor(images=A ,return_tensors="pt" ).to(A )
# forward pass
with torch.no_grad():
__A = model(**A )
__A = outputs.logits
# verify the logits
__A = torch.Size((1, 21, 32, 32) )
self.assertEqual(logits.shape ,A )
__A = torch.tensor(
[
[[7.08_63, 7.15_25, 6.82_01], [6.69_31, 6.87_70, 6.89_33], [6.29_78, 7.03_66, 6.96_36]],
[[-3.71_34, -3.67_12, -3.66_75], [-3.58_25, -3.35_49, -3.47_77], [-3.34_35, -3.39_79, -3.28_57]],
[[-2.93_29, -2.80_03, -2.73_69], [-3.05_64, -2.47_80, -2.02_07], [-2.68_89, -1.92_98, -1.76_40]],
] ,device=A ,)
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] ,A ,atol=1E-4 ) )
@slow
def UpperCamelCase_ ( self : List[Any] ):
__A = MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3" )
__A = model.to(A )
__A = MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3" )
__A = prepare_img()
__A = image_processor(images=A ,return_tensors="pt" ).to(A )
# forward pass
with torch.no_grad():
__A = model(**A )
__A = outputs.logits.detach().cpu()
__A = image_processor.post_process_semantic_segmentation(outputs=A ,target_sizes=[(50, 60)] )
__A = torch.Size((50, 60) )
self.assertEqual(segmentation[0].shape ,A )
__A = image_processor.post_process_semantic_segmentation(outputs=A )
__A = torch.Size((32, 32) )
self.assertEqual(segmentation[0].shape ,A )
| 15 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = None
__UpperCAmelCase = None
@property
def _lowerCAmelCase ( self : List[Any] ):
return self.feat_extract_tester.prepare_feat_extract_dict()
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(snake_case ,'feature_size' ) )
self.assertTrue(hasattr(snake_case ,'sampling_rate' ) )
self.assertTrue(hasattr(snake_case ,'padding_value' ) )
def _lowerCAmelCase ( self : Any ):
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common()
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(snake_case ) == len(snake_case ) for x, y in zip(snake_case ,processed_features[input_name] ) ) )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common(equal_length=snake_case )
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} ,tensor_type='np' )
SCREAMING_SNAKE_CASE =processed_features[input_name]
if len(batch_features_input.shape ) < 3:
SCREAMING_SNAKE_CASE =batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_torch
def _lowerCAmelCase ( self : Optional[int] ):
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common(equal_length=snake_case )
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} ,tensor_type='pt' )
SCREAMING_SNAKE_CASE =processed_features[input_name]
if len(batch_features_input.shape ) < 3:
SCREAMING_SNAKE_CASE =batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_tf
def _lowerCAmelCase ( self : str ):
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common(equal_length=snake_case )
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} ,tensor_type='tf' )
SCREAMING_SNAKE_CASE =processed_features[input_name]
if len(batch_features_input.shape ) < 3:
SCREAMING_SNAKE_CASE =batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
def _lowerCAmelCase ( self : List[Any] ,snake_case : Optional[Any]=False ):
def _inputs_have_equal_length(snake_case : Dict ):
SCREAMING_SNAKE_CASE =len(input[0] )
for input_slice in input[1:]:
if len(snake_case ) != length:
return False
return True
def _inputs_are_equal(snake_case : str ,snake_case : Dict ):
if len(snake_case ) != len(snake_case ):
return False
for input_slice_a, input_slice_a in zip(snake_case ,snake_case ):
if not np.allclose(np.asarray(snake_case ) ,np.asarray(snake_case ) ,atol=1e-3 ):
return False
return True
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common(numpify=snake_case )
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.seq_length_diff
SCREAMING_SNAKE_CASE =self.feat_extract_tester.max_seq_length + pad_diff
SCREAMING_SNAKE_CASE =self.feat_extract_tester.min_seq_length
SCREAMING_SNAKE_CASE =self.feat_extract_tester.batch_size
SCREAMING_SNAKE_CASE =self.feat_extract_tester.feature_size
# test padding for List[int] + numpy
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding=snake_case )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='max_length' ,max_length=len(speech_inputs[-1] ) )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='np' )
SCREAMING_SNAKE_CASE =input_a[input_name]
# max_length parameter has to be provided when setting `padding="max_length"`
with self.assertRaises(snake_case ):
feat_extract.pad(snake_case ,padding='max_length' )[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=snake_case ,return_tensors='np' )
SCREAMING_SNAKE_CASE =input_a[input_name]
self.assertFalse(_inputs_have_equal_length(snake_case ) )
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertTrue(_inputs_are_equal(snake_case ,snake_case ) )
self.assertTrue(len(input_a[0] ) == pad_min_length )
self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )
# test padding for `pad_to_multiple_of` for List[int] + numpy
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,pad_to_multiple_of=10 )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,pad_to_multiple_of=10 )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,pad_to_multiple_of=10 ,max_length=snake_case )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,pad_to_multiple_of=10 ,max_length=snake_case ,return_tensors='np' ,)
SCREAMING_SNAKE_CASE =input_a[input_name]
self.assertTrue(all(len(snake_case ) % 10 == 0 for x in input_a ) )
self.assertTrue(_inputs_are_equal(snake_case ,snake_case ) )
SCREAMING_SNAKE_CASE =pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
self.assertTrue(all(len(snake_case ) == expected_mult_pad_length for x in input_a ) )
self.assertEqual(input_a.shape[:2] ,(batch_size, expected_mult_pad_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == feature_size )
# Check padding value is correct
SCREAMING_SNAKE_CASE =(np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
self.assertTrue(
abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
< 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
< 1e-3 )
def _lowerCAmelCase ( self : Union[str, Any] ,snake_case : Optional[int]=False ):
def _inputs_have_equal_length(snake_case : str ):
SCREAMING_SNAKE_CASE =len(input[0] )
for input_slice in input[1:]:
if len(snake_case ) != length:
return False
return True
def _inputs_are_equal(snake_case : Tuple ,snake_case : Optional[Any] ):
if len(snake_case ) != len(snake_case ):
return False
for input_slice_a, input_slice_a in zip(snake_case ,snake_case ):
if not np.allclose(np.asarray(snake_case ) ,np.asarray(snake_case ) ,atol=1e-3 ):
return False
return True
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common(numpify=snake_case )
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
# truncate to smallest
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) ,truncation=snake_case )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) )
SCREAMING_SNAKE_CASE =input_a[input_name]
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertFalse(_inputs_have_equal_length(snake_case ) )
# truncate to smallest with np
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) ,return_tensors='np' ,truncation=snake_case ,)
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) ,return_tensors='np' )
SCREAMING_SNAKE_CASE =input_a[input_name]
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(snake_case ) )
# truncate to middle
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[1] ) ,truncation=snake_case ,return_tensors='np' ,)
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[1] ) ,truncation=snake_case )
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[1] ) ,return_tensors='np' )
SCREAMING_SNAKE_CASE =input_a[input_name]
self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertTrue(_inputs_are_equal(snake_case ,snake_case ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(snake_case ) )
self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(snake_case ):
feat_extract.pad(snake_case ,truncation=snake_case )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(snake_case ):
feat_extract.pad(snake_case ,padding='longest' ,truncation=snake_case )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(snake_case ):
feat_extract.pad(snake_case ,padding='longest' ,truncation=snake_case )[input_name]
# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
with self.assertRaises(snake_case ):
feat_extract.pad(snake_case ,padding='max_length' ,truncation=snake_case )[input_name]
# test truncation for `pad_to_multiple_of` for List[int] + numpy
SCREAMING_SNAKE_CASE =12
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) ,pad_to_multiple_of=snake_case ,truncation=snake_case ,)
SCREAMING_SNAKE_CASE =input_a[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=len(speech_inputs[0] ) ,pad_to_multiple_of=snake_case ,)
SCREAMING_SNAKE_CASE =input_a[input_name]
# retrieve expected_length as multiple of pad_to_multiple_of
SCREAMING_SNAKE_CASE =len(speech_inputs[0] )
if expected_length % pad_to_multiple_of != 0:
SCREAMING_SNAKE_CASE =((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of
self.assertTrue(len(input_a[0] ) == expected_length )
self.assertTrue(_inputs_have_equal_length(snake_case ) )
self.assertFalse(_inputs_have_equal_length(snake_case ) )
def _lowerCAmelCase ( self : Optional[int] ):
self._check_padding(numpify=snake_case )
def _lowerCAmelCase ( self : Tuple ):
self._check_padding(numpify=snake_case )
def _lowerCAmelCase ( self : List[str] ):
self._check_truncation(numpify=snake_case )
def _lowerCAmelCase ( self : int ):
self._check_truncation(numpify=snake_case )
@require_torch
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common()
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='np' )[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='pt' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
@require_tf
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common()
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='np' )[input_name]
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='tf' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def _lowerCAmelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE =self.feat_extract_dict
SCREAMING_SNAKE_CASE =True
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**snake_case )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common()
SCREAMING_SNAKE_CASE =[len(snake_case ) for x in speech_inputs]
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE =feat_extract.pad(snake_case ,padding='longest' ,return_tensors='np' )
self.assertIn('attention_mask' ,snake_case )
self.assertListEqual(list(processed.attention_mask.shape ) ,list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() ,snake_case )
def _lowerCAmelCase ( self : Dict ):
SCREAMING_SNAKE_CASE =self.feat_extract_dict
SCREAMING_SNAKE_CASE =True
SCREAMING_SNAKE_CASE =self.feature_extraction_class(**snake_case )
SCREAMING_SNAKE_CASE =self.feat_extract_tester.prepare_inputs_for_common()
SCREAMING_SNAKE_CASE =[len(snake_case ) for x in speech_inputs]
SCREAMING_SNAKE_CASE =feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE =BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE =min(snake_case )
SCREAMING_SNAKE_CASE =feat_extract.pad(
snake_case ,padding='max_length' ,max_length=snake_case ,truncation=snake_case ,return_tensors='np' )
self.assertIn('attention_mask' ,snake_case )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) ,[processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() ,[max_length for x in speech_inputs] )
| 334 | 0 |
"""simple docstring"""
import os
def __UpperCAmelCase ( ) -> int:
with open(os.path.dirname(__lowerCamelCase ) + '''/p022_names.txt''' ) as file:
lowercase__ : List[Any] = str(file.readlines()[0] )
lowercase__ : Dict = names.replace('''"''' , '''''' ).split(''',''' )
names.sort()
lowercase__ : int = 0
lowercase__ : Optional[Any] = 0
for i, name in enumerate(__lowerCamelCase ):
for letter in name:
name_score += ord(__lowerCamelCase ) - 64
total_score += (i + 1) * name_score
lowercase__ : List[str] = 0
return total_score
if __name__ == "__main__":
print(solution())
| 16 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_lowerCamelCase =2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
_lowerCamelCase =50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
_lowerCamelCase =0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 10_00))
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =len([g for position, g in enumerate(lowerCAmelCase_ ) if g == main_target[position]] )
return (item, float(lowerCAmelCase_ ))
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =random.randint(0, len(lowerCAmelCase_ ) - 1 )
SCREAMING_SNAKE_CASE =parent_a[:random_slice] + parent_a[random_slice:]
SCREAMING_SNAKE_CASE =parent_a[:random_slice] + parent_a[random_slice:]
return (child_a, child_a)
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =list(lowerCAmelCase_ )
if random.uniform(0, 1 ) < MUTATION_PROBABILITY:
SCREAMING_SNAKE_CASE =random.choice(lowerCAmelCase_ )
return "".join(lowerCAmelCase_ )
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =[]
# Generate more children proportionally to the fitness score.
SCREAMING_SNAKE_CASE =int(parent_a[1] * 100 ) + 1
SCREAMING_SNAKE_CASE =10 if child_n >= 10 else child_n
for _ in range(lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE =population_score[random.randint(0, lowerCAmelCase_ )][0]
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =crossover(parent_a[0], lowerCAmelCase_ )
# Append new string to the population list.
pop.append(mutate(lowerCAmelCase_, lowerCAmelCase_ ) )
pop.append(mutate(lowerCAmelCase_, lowerCAmelCase_ ) )
return pop
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ = True ):
"""simple docstring"""
if N_POPULATION < N_SELECTED:
SCREAMING_SNAKE_CASE =F'{N_POPULATION} must be bigger than {N_SELECTED}'
raise ValueError(lowerCAmelCase_ )
# Verify that the target contains no genes besides the ones inside genes variable.
SCREAMING_SNAKE_CASE =sorted({c for c in target if c not in genes} )
if not_in_genes_list:
SCREAMING_SNAKE_CASE =F'{not_in_genes_list} is not in genes list, evolution cannot converge'
raise ValueError(lowerCAmelCase_ )
# Generate random starting population.
SCREAMING_SNAKE_CASE =[]
for _ in range(lowerCAmelCase_ ):
population.append(''.join([random.choice(lowerCAmelCase_ ) for i in range(len(lowerCAmelCase_ ) )] ) )
# Just some logs to know what the algorithms is doing.
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =0, 0
# This loop will end when we find a perfect match for our target.
while True:
generation += 1
total_population += len(lowerCAmelCase_ )
# Random population created. Now it's time to evaluate.
# Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this, it will probably be slower.
# We just need to call evaluate for every item inside the population.
SCREAMING_SNAKE_CASE =[evaluate(lowerCAmelCase_, lowerCAmelCase_ ) for item in population]
# Check if there is a matching evolution.
SCREAMING_SNAKE_CASE =sorted(lowerCAmelCase_, key=lambda lowerCAmelCase_ : x[1], reverse=lowerCAmelCase_ )
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the best result every 10 generation.
# Just to know that the algorithm is working.
if debug and generation % 10 == 0:
print(
F'\nGeneration: {generation}'
F'\nTotal Population:{total_population}'
F'\nBest score: {population_score[0][1]}'
F'\nBest string: {population_score[0][0]}' )
# Flush the old population, keeping some of the best evolutions.
# Keeping this avoid regression of evolution.
SCREAMING_SNAKE_CASE =population[: int(N_POPULATION / 3 )]
population.clear()
population.extend(lowerCAmelCase_ )
# Normalize population score to be between 0 and 1.
SCREAMING_SNAKE_CASE =[
(item, score / len(lowerCAmelCase_ )) for item, score in population_score
]
# This is selection
for i in range(lowerCAmelCase_ ):
population.extend(select(population_score[int(lowerCAmelCase_ )], lowerCAmelCase_, lowerCAmelCase_ ) )
# Check if the population has already reached the maximum value and if so,
# break the cycle. If this check is disabled, the algorithm will take
# forever to compute large strings, but will also calculate small strings in
# a far fewer generations.
if len(lowerCAmelCase_ ) > N_POPULATION:
break
if __name__ == "__main__":
_lowerCamelCase =(
"This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
)
_lowerCamelCase =list(
" ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
"nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
)
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase =basic(target_str, genes_list)
print(
f'\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'
)
| 334 | 0 |
"""simple docstring"""
from math import pi, sqrt
def _A ( UpperCamelCase_ : float) -> float:
'''simple docstring'''
if num <= 0:
raise ValueError("math domain error")
if num > 171.5:
raise OverflowError("math range error")
elif num - int(UpperCamelCase_) not in (0, 0.5):
raise NotImplementedError("num must be an integer or a half-integer")
elif num == 0.5:
return sqrt(UpperCamelCase_)
else:
return 1.0 if num == 1 else (num - 1) * gamma(num - 1)
def _A ( ) -> None:
'''simple docstring'''
assert gamma(0.5) == sqrt(UpperCamelCase_)
assert gamma(1) == 1.0
assert gamma(2) == 1.0
if __name__ == "__main__":
from doctest import testmod
testmod()
_a = 1.0
while num:
_a = float(input('Gamma of: '))
print(F"gamma({num}) = {gamma(num)}")
print('\nEnter 0 to exit...')
| 17 |
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer,
BertTokenizer,
BertTokenizerFast,
GPTaTokenizerFast,
is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_tokenization import CustomTokenizer # noqa E402
if is_tokenizers_available():
from test_module.custom_tokenization_fast import CustomTokenizerFast
class a_ ( unittest.TestCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : Dict ):
# A mock response for an HTTP head request to emulate server down
SCREAMING_SNAKE_CASE =mock.Mock()
SCREAMING_SNAKE_CASE =500
SCREAMING_SNAKE_CASE ={}
SCREAMING_SNAKE_CASE =HTTPError
SCREAMING_SNAKE_CASE ={}
# Download this model to make sure it's in the cache.
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert' )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch('requests.Session.request' ,return_value=snake_case ) as mock_head:
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert' )
# This check we did call the fake head request
mock_head.assert_called()
@require_tokenizers
def _lowerCAmelCase ( self : Optional[Any] ):
# A mock response for an HTTP head request to emulate server down
SCREAMING_SNAKE_CASE =mock.Mock()
SCREAMING_SNAKE_CASE =500
SCREAMING_SNAKE_CASE ={}
SCREAMING_SNAKE_CASE =HTTPError
SCREAMING_SNAKE_CASE ={}
# Download this model to make sure it's in the cache.
SCREAMING_SNAKE_CASE =GPTaTokenizerFast.from_pretrained('gpt2' )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch('requests.Session.request' ,return_value=snake_case ) as mock_head:
SCREAMING_SNAKE_CASE =GPTaTokenizerFast.from_pretrained('gpt2' )
# This check we did call the fake head request
mock_head.assert_called()
def _lowerCAmelCase ( self : Union[str, Any] ):
# This test is for deprecated behavior and can be removed in v5
try:
SCREAMING_SNAKE_CASE =tempfile.mktemp()
with open(snake_case ,'wb' ) as f:
http_get('https://huggingface.co/albert-base-v1/resolve/main/spiece.model' ,snake_case )
SCREAMING_SNAKE_CASE =AlbertTokenizer.from_pretrained(snake_case )
finally:
os.remove(snake_case )
# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
# the current folder and have the right name.
if os.path.isfile('tokenizer.json' ):
# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
return
try:
with open('tokenizer.json' ,'wb' ) as f:
http_get('https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json' ,snake_case )
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
self.assertEqual(tokenizer.vocab_size ,1000 )
# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.
finally:
os.remove('tokenizer.json' )
def _lowerCAmelCase ( self : int ):
# This test is for deprecated behavior and can be removed in v5
SCREAMING_SNAKE_CASE =AlbertTokenizer.from_pretrained('https://huggingface.co/albert-base-v1/resolve/main/spiece.model' )
@is_staging_test
class a_ ( unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase = ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']
@classmethod
def _lowerCAmelCase ( cls : List[Any] ):
SCREAMING_SNAKE_CASE =TOKEN
HfFolder.save_token(snake_case )
@classmethod
def _lowerCAmelCase ( cls : Tuple ):
try:
delete_repo(token=cls._token ,repo_id='test-tokenizer' )
except HTTPError:
pass
try:
delete_repo(token=cls._token ,repo_id='valid_org/test-tokenizer-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token ,repo_id='test-dynamic-tokenizer' )
except HTTPError:
pass
def _lowerCAmelCase ( self : Any ):
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,'vocab.txt' )
with open(snake_case ,'w' ,encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
SCREAMING_SNAKE_CASE =BertTokenizer(snake_case )
tokenizer.push_to_hub('test-tokenizer' ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained(f'{USER}/test-tokenizer' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
# Reset repo
delete_repo(token=self._token ,repo_id='test-tokenizer' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(snake_case ,repo_id='test-tokenizer' ,push_to_hub=snake_case ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained(f'{USER}/test-tokenizer' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
def _lowerCAmelCase ( self : Optional[Any] ):
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,'vocab.txt' )
with open(snake_case ,'w' ,encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
SCREAMING_SNAKE_CASE =BertTokenizer(snake_case )
tokenizer.push_to_hub('valid_org/test-tokenizer-org' ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained('valid_org/test-tokenizer-org' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
# Reset repo
delete_repo(token=self._token ,repo_id='valid_org/test-tokenizer-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(
snake_case ,repo_id='valid_org/test-tokenizer-org' ,push_to_hub=snake_case ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained('valid_org/test-tokenizer-org' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
@require_tokenizers
def _lowerCAmelCase ( self : str ):
CustomTokenizer.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,'vocab.txt' )
with open(snake_case ,'w' ,encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
SCREAMING_SNAKE_CASE =CustomTokenizer(snake_case )
# No fast custom tokenizer
tokenizer.push_to_hub('test-dynamic-tokenizer' ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer' ,trust_remote_code=snake_case )
# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,'CustomTokenizer' )
# Fast and slow custom tokenizer
CustomTokenizerFast.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE =os.path.join(snake_case ,'vocab.txt' )
with open(snake_case ,'w' ,encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
SCREAMING_SNAKE_CASE =BertTokenizerFast.from_pretrained(snake_case )
bert_tokenizer.save_pretrained(snake_case )
SCREAMING_SNAKE_CASE =CustomTokenizerFast.from_pretrained(snake_case )
tokenizer.push_to_hub('test-dynamic-tokenizer' ,use_auth_token=self._token )
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer' ,trust_remote_code=snake_case )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,'CustomTokenizerFast' )
SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained(
f'{USER}/test-dynamic-tokenizer' ,use_fast=snake_case ,trust_remote_code=snake_case )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,'CustomTokenizer' )
class a_ ( unittest.TestCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : List[Any] ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('Hello 友達' )
self.assertEqual(trie.data ,{'H': {'e': {'l': {'l': {'o': {' ': {'友': {'達': {'': 1}}}}}}}}} )
trie.add('Hello' )
trie.data
self.assertEqual(trie.data ,{'H': {'e': {'l': {'l': {'o': {'': 1, ' ': {'友': {'達': {'': 1}}}}}}}}} )
def _lowerCAmelCase ( self : Optional[int] ):
SCREAMING_SNAKE_CASE =Trie()
self.assertEqual(trie.split('[CLS] This is a extra_id_100' ) ,['[CLS] This is a extra_id_100'] )
trie.add('[CLS]' )
trie.add('extra_id_1' )
trie.add('extra_id_100' )
self.assertEqual(trie.split('[CLS] This is a extra_id_100' ) ,['[CLS]', ' This is a ', 'extra_id_100'] )
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('A' )
self.assertEqual(trie.split('ABC' ) ,['A', 'BC'] )
self.assertEqual(trie.split('BCA' ) ,['BC', 'A'] )
def _lowerCAmelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('TOKEN]' )
trie.add('[SPECIAL_TOKEN]' )
self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]' ) ,['This is something ', '[SPECIAL_TOKEN]'] )
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('A' )
trie.add('P' )
trie.add('[SPECIAL_TOKEN]' )
self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]' ) ,['This is something ', '[SPECIAL_TOKEN]'] )
def _lowerCAmelCase ( self : Dict ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('AB' )
trie.add('B' )
trie.add('C' )
self.assertEqual(trie.split('ABC' ) ,['AB', 'C'] )
def _lowerCAmelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE =Trie()
trie.add('ABC' )
trie.add('B' )
trie.add('CD' )
self.assertEqual(trie.split('ABCD' ) ,['ABC', 'D'] )
def _lowerCAmelCase ( self : Optional[Any] ):
# Even if the offsets are wrong, we necessarily output correct string
# parts.
SCREAMING_SNAKE_CASE =Trie()
SCREAMING_SNAKE_CASE =trie.cut_text('ABC' ,[0, 0, 2, 1, 2, 3] )
self.assertEqual(snake_case ,['AB', 'C'] )
| 334 | 0 |
from math import factorial
class a__ :
def __init__( self : List[str],_A : Tuple,_A : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = real
if isinstance(_A,_A ):
SCREAMING_SNAKE_CASE_ : Tuple = [1] * rank
else:
SCREAMING_SNAKE_CASE_ : Optional[Any] = rank
def __repr__( self : List[Any] ):
"""simple docstring"""
return (
F'{self.real}+'
F'{"+".join(str(_A )+"E"+str(n+1 )for n,dual in enumerate(self.duals ) )}'
)
def __UpperCamelCase ( self : Optional[int] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = self.duals.copy()
while cur[-1] == 0:
cur.pop(-1 )
return Dual(self.real,_A )
def __add__( self : Any,_A : List[Any] ):
"""simple docstring"""
if not isinstance(_A,_A ):
return Dual(self.real + other,self.duals )
SCREAMING_SNAKE_CASE_ : str = self.duals.copy()
SCREAMING_SNAKE_CASE_ : Union[str, Any] = other.duals.copy()
if len(_A ) > len(_A ):
o_dual.extend([1] * (len(_A ) - len(_A )) )
elif len(_A ) < len(_A ):
s_dual.extend([1] * (len(_A ) - len(_A )) )
SCREAMING_SNAKE_CASE_ : int = []
for i in range(len(_A ) ):
new_duals.append(s_dual[i] + o_dual[i] )
return Dual(self.real + other.real,_A )
A = __add__
def __sub__( self : Any,_A : Union[str, Any] ):
"""simple docstring"""
return self + other * -1
def __mul__( self : Tuple,_A : Any ):
"""simple docstring"""
if not isinstance(_A,_A ):
SCREAMING_SNAKE_CASE_ : int = []
for i in self.duals:
new_duals.append(i * other )
return Dual(self.real * other,_A )
SCREAMING_SNAKE_CASE_ : str = [0] * (len(self.duals ) + len(other.duals ) + 1)
for i, item in enumerate(self.duals ):
for j, jtem in enumerate(other.duals ):
new_duals[i + j + 1] += item * jtem
for k in range(len(self.duals ) ):
new_duals[k] += self.duals[k] * other.real
for index in range(len(other.duals ) ):
new_duals[index] += other.duals[index] * self.real
return Dual(self.real * other.real,_A )
A = __mul__
def __truediv__( self : Optional[Any],_A : Dict ):
"""simple docstring"""
if not isinstance(_A,_A ):
SCREAMING_SNAKE_CASE_ : Dict = []
for i in self.duals:
new_duals.append(i / other )
return Dual(self.real / other,_A )
raise ValueError
def __floordiv__( self : List[str],_A : Any ):
"""simple docstring"""
if not isinstance(_A,_A ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = []
for i in self.duals:
new_duals.append(i // other )
return Dual(self.real // other,_A )
raise ValueError
def __pow__( self : Optional[Any],_A : Any ):
"""simple docstring"""
if n < 0 or isinstance(_A,_A ):
raise ValueError("power must be a positive integer" )
if n == 0:
return 1
if n == 1:
return self
SCREAMING_SNAKE_CASE_ : Union[str, Any] = self
for _ in range(n - 1 ):
x *= self
return x
def _snake_case ( lowerCAmelCase : Tuple , lowerCAmelCase : List[str] , lowerCAmelCase : Any ):
"""simple docstring"""
if not callable(lowerCAmelCase ):
raise ValueError("differentiate() requires a function as input for func" )
if not isinstance(lowerCAmelCase , (float, int) ):
raise ValueError("differentiate() requires a float as input for position" )
if not isinstance(lowerCAmelCase , lowerCAmelCase ):
raise ValueError("differentiate() requires an int as input for order" )
SCREAMING_SNAKE_CASE_ : Tuple = Dual(lowerCAmelCase , 1 )
SCREAMING_SNAKE_CASE_ : Dict = func(lowerCAmelCase )
if order == 0:
return result.real
return result.duals[order - 1] * factorial(lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
def _snake_case ( lowerCAmelCase : Any ):
"""simple docstring"""
return y**2 * y**4
print(differentiate(f, 9, 2))
| 18 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase =[
("bert.bert", "visual_bert"),
("bert.cls", "cls"),
("bert.classifier", "cls"),
("token_type_embeddings_visual", "visual_token_type_embeddings"),
("position_embeddings_visual", "visual_position_embeddings"),
("projection", "visual_projection"),
]
_lowerCamelCase =[
"nlvr2_coco_pre_trained.th",
"nlvr2_fine_tuned.th",
"nlvr2_pre_trained.th",
"vcr_coco_pre_train.th",
"vcr_fine_tune.th",
"vcr_pre_train.th",
"vqa_coco_pre_trained.th",
"vqa_fine_tuned.th",
"vqa_pre_trained.th",
]
def snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =torch.load(lowerCAmelCase_, map_location='cpu' )
return sd
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_=rename_keys_prefix ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =OrderedDict()
SCREAMING_SNAKE_CASE =torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
SCREAMING_SNAKE_CASE =key
for name_pair in rename_keys_prefix:
SCREAMING_SNAKE_CASE =new_key.replace(name_pair[0], name_pair[1] )
SCREAMING_SNAKE_CASE =d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
SCREAMING_SNAKE_CASE =new_d['cls.predictions.bias']
return new_d
@torch.no_grad()
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
assert (
checkpoint_path.split('/' )[-1] in ACCEPTABLE_CHECKPOINTS
), F'The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'
# Get Config
if "pre" in checkpoint_path:
SCREAMING_SNAKE_CASE ='pretraining'
if "vcr" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 512}
elif "vqa_advanced" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 2048}
elif "vqa" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 2048}
elif "nlvr" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 1024}
else:
raise NotImplementedError(F'No implementation found for `{checkpoint_path}`.' )
else:
if "vcr" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 512}
SCREAMING_SNAKE_CASE ='multichoice'
elif "vqa_advanced" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 2048}
SCREAMING_SNAKE_CASE ='vqa_advanced'
elif "vqa" in checkpoint_path:
SCREAMING_SNAKE_CASE ={'visual_embedding_dim': 2048, 'num_labels': 3129}
SCREAMING_SNAKE_CASE ='vqa'
elif "nlvr" in checkpoint_path:
SCREAMING_SNAKE_CASE ={
'visual_embedding_dim': 1024,
'num_labels': 2,
}
SCREAMING_SNAKE_CASE ='nlvr'
SCREAMING_SNAKE_CASE =VisualBertConfig(**lowerCAmelCase_ )
# Load State Dict
SCREAMING_SNAKE_CASE =load_state_dict(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE =get_new_dict(lowerCAmelCase_, lowerCAmelCase_ )
if model_type == "pretraining":
SCREAMING_SNAKE_CASE =VisualBertForPreTraining(lowerCAmelCase_ )
elif model_type == "vqa":
SCREAMING_SNAKE_CASE =VisualBertForQuestionAnswering(lowerCAmelCase_ )
elif model_type == "nlvr":
SCREAMING_SNAKE_CASE =VisualBertForVisualReasoning(lowerCAmelCase_ )
elif model_type == "multichoice":
SCREAMING_SNAKE_CASE =VisualBertForMultipleChoice(lowerCAmelCase_ )
model.load_state_dict(lowerCAmelCase_ )
# Save Checkpoints
Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ )
model.save_pretrained(lowerCAmelCase_ )
if __name__ == "__main__":
_lowerCamelCase =argparse.ArgumentParser()
# Required parameters
parser.add_argument("orig_checkpoint_path", type=str, help="A path to .th on local filesystem.")
parser.add_argument("pytorch_dump_folder_path", type=str, help="Path to the output PyTorch model.")
_lowerCamelCase =parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 334 | 0 |
import math
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ = 0 , lowerCamelCase__ = 0 ):
lowerCamelCase_ = end or len(lowerCamelCase__ )
for i in range(lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = i
lowerCamelCase_ = array[i]
while temp_index != start and temp_index_value < array[temp_index - 1]:
lowerCamelCase_ = array[temp_index - 1]
temp_index -= 1
lowerCamelCase_ = temp_index_value
return array
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ): # Max Heap
lowerCamelCase_ = index
lowerCamelCase_ = 2 * index + 1 # Left Node
lowerCamelCase_ = 2 * index + 2 # Right Node
if left_index < heap_size and array[largest] < array[left_index]:
lowerCamelCase_ = left_index
if right_index < heap_size and array[largest] < array[right_index]:
lowerCamelCase_ = right_index
if largest != index:
lowerCamelCase_ , lowerCamelCase_ = array[largest], array[index]
heapify(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = len(lowerCamelCase__ )
for i in range(n // 2 , -1 , -1 ):
heapify(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
for i in range(n - 1 , 0 , -1 ):
lowerCamelCase_ , lowerCamelCase_ = array[0], array[i]
heapify(lowerCamelCase__ , 0 , lowerCamelCase__ )
return array
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
if (array[first_index] > array[middle_index]) != (
array[first_index] > array[last_index]
):
return array[first_index]
elif (array[middle_index] > array[first_index]) != (
array[middle_index] > array[last_index]
):
return array[middle_index]
else:
return array[last_index]
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = low
lowerCamelCase_ = high
while True:
while array[i] < pivot:
i += 1
j -= 1
while pivot < array[j]:
j -= 1
if i >= j:
return i
lowerCamelCase_ , lowerCamelCase_ = array[j], array[i]
i += 1
def lowerCamelCase_ ( lowerCamelCase__ ):
if len(lowerCamelCase__ ) == 0:
return array
lowerCamelCase_ = 2 * math.ceil(math.loga(len(lowerCamelCase__ ) ) )
lowerCamelCase_ = 1_6
return intro_sort(lowerCamelCase__ , 0 , len(lowerCamelCase__ ) , lowerCamelCase__ , lowerCamelCase__ )
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
while end - start > size_threshold:
if max_depth == 0:
return heap_sort(lowerCamelCase__ )
max_depth -= 1
lowerCamelCase_ = median_of_a(lowerCamelCase__ , lowerCamelCase__ , start + ((end - start) // 2) + 1 , end - 1 )
lowerCamelCase_ = partition(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
intro_sort(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
lowerCamelCase_ = p
return insertion_sort(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
__A =input('''Enter numbers separated by a comma : ''').strip()
__A =[float(item) for item in user_input.split(''',''')]
print(sort(unsorted))
| 19 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"facebook/nllb-moe-54B": "https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json",
}
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'nllb-moe'
__UpperCAmelCase = ['past_key_values']
__UpperCAmelCase = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self : str ,snake_case : Optional[int]=128112 ,snake_case : Any=1024 ,snake_case : List[str]=12 ,snake_case : Optional[int]=4096 ,snake_case : List[str]=16 ,snake_case : Optional[Any]=12 ,snake_case : Optional[Any]=4096 ,snake_case : List[Any]=16 ,snake_case : Optional[Any]=0.05 ,snake_case : str=0.05 ,snake_case : Optional[int]=True ,snake_case : Tuple=True ,snake_case : Optional[Any]="relu" ,snake_case : Any=1024 ,snake_case : List[Any]=0.1 ,snake_case : List[Any]=0.1 ,snake_case : Optional[Any]=0.0 ,snake_case : List[Any]=0.02 ,snake_case : Any=2 ,snake_case : Dict=True ,snake_case : Tuple=False ,snake_case : Any="float32" ,snake_case : Tuple=False ,snake_case : List[Any]=128 ,snake_case : Tuple=64 ,snake_case : List[Any]=4 ,snake_case : List[Any]=4 ,snake_case : List[Any]=0.001 ,snake_case : int=0.001 ,snake_case : Tuple="all" ,snake_case : Union[str, Any]=False ,snake_case : Union[str, Any]=False ,snake_case : Optional[int]=1.0 ,snake_case : Optional[Any]=0.2 ,snake_case : Optional[int]=1 ,snake_case : Union[str, Any]=0 ,snake_case : Tuple=2 ,snake_case : List[Any]=False ,**snake_case : List[Any] ,):
SCREAMING_SNAKE_CASE =vocab_size
SCREAMING_SNAKE_CASE =max_position_embeddings
SCREAMING_SNAKE_CASE =d_model
SCREAMING_SNAKE_CASE =encoder_ffn_dim
SCREAMING_SNAKE_CASE =encoder_layers
SCREAMING_SNAKE_CASE =encoder_attention_heads
SCREAMING_SNAKE_CASE =decoder_ffn_dim
SCREAMING_SNAKE_CASE =decoder_layers
SCREAMING_SNAKE_CASE =decoder_attention_heads
SCREAMING_SNAKE_CASE =dropout
SCREAMING_SNAKE_CASE =attention_dropout
SCREAMING_SNAKE_CASE =activation_dropout
SCREAMING_SNAKE_CASE =activation_function
SCREAMING_SNAKE_CASE =init_std
SCREAMING_SNAKE_CASE =encoder_layerdrop
SCREAMING_SNAKE_CASE =decoder_layerdrop
SCREAMING_SNAKE_CASE =use_cache
SCREAMING_SNAKE_CASE =encoder_layers
SCREAMING_SNAKE_CASE =scale_embedding # scale factor will be sqrt(d_model) if True
SCREAMING_SNAKE_CASE =router_z_loss_coef
SCREAMING_SNAKE_CASE =router_aux_loss_coef
SCREAMING_SNAKE_CASE =decoder_sparse_step
SCREAMING_SNAKE_CASE =encoder_sparse_step
SCREAMING_SNAKE_CASE =num_experts
SCREAMING_SNAKE_CASE =expert_capacity
SCREAMING_SNAKE_CASE =router_bias
if router_dtype not in ["float32", "float16", "bfloat16"]:
raise ValueError(f'`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}' )
SCREAMING_SNAKE_CASE =router_dtype
SCREAMING_SNAKE_CASE =router_ignore_padding_tokens
SCREAMING_SNAKE_CASE =batch_prioritized_routing
SCREAMING_SNAKE_CASE =second_expert_policy
SCREAMING_SNAKE_CASE =normalize_router_prob_before_dropping
SCREAMING_SNAKE_CASE =moe_eval_capacity_token_fraction
SCREAMING_SNAKE_CASE =moe_token_dropout
SCREAMING_SNAKE_CASE =output_router_logits
super().__init__(
pad_token_id=snake_case ,bos_token_id=snake_case ,eos_token_id=snake_case ,is_encoder_decoder=snake_case ,decoder_start_token_id=snake_case ,**snake_case ,)
| 334 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowercase : int = logging.get_logger(__name__)
lowercase : Tuple = {
"""Salesforce/instruct-blip-flan-t5""": """https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json""",
}
class __snake_case ( lowerCAmelCase ):
_a : str= "instructblip_vision_model"
def __init__( self ,snake_case=1408 ,snake_case=6144 ,snake_case=39 ,snake_case=16 ,snake_case=224 ,snake_case=14 ,snake_case="gelu" ,snake_case=1e-6 ,snake_case=0.0 ,snake_case=1e-10 ,snake_case=True ,**snake_case ,):
'''simple docstring'''
super().__init__(**snake_case )
lowercase : Any = hidden_size
lowercase : Optional[int] = intermediate_size
lowercase : Optional[int] = num_hidden_layers
lowercase : str = num_attention_heads
lowercase : List[Any] = patch_size
lowercase : Any = image_size
lowercase : List[Any] = initializer_range
lowercase : Optional[Any] = attention_dropout
lowercase : str = layer_norm_eps
lowercase : Optional[int] = hidden_act
lowercase : Union[str, Any] = qkv_bias
@classmethod
def _SCREAMING_SNAKE_CASE ( cls ,snake_case ,**snake_case ):
'''simple docstring'''
cls._set_token_in_kwargs(snake_case )
lowercase , lowercase : Optional[int] = cls.get_config_dict(snake_case ,**snake_case )
# get the vision config dict if we are loading from InstructBlipConfig
if config_dict.get("""model_type""" ) == "instructblip":
lowercase : Tuple = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls ,"""model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." )
return cls.from_dict(snake_case ,**snake_case )
class __snake_case ( lowerCAmelCase ):
_a : List[str]= "instructblip_qformer"
def __init__( self ,snake_case=30522 ,snake_case=768 ,snake_case=12 ,snake_case=12 ,snake_case=3072 ,snake_case="gelu" ,snake_case=0.1 ,snake_case=0.1 ,snake_case=512 ,snake_case=0.02 ,snake_case=1e-12 ,snake_case=0 ,snake_case="absolute" ,snake_case=2 ,snake_case=1408 ,**snake_case ,):
'''simple docstring'''
super().__init__(pad_token_id=snake_case ,**snake_case )
lowercase : Tuple = vocab_size
lowercase : str = hidden_size
lowercase : Optional[Any] = num_hidden_layers
lowercase : List[str] = num_attention_heads
lowercase : List[str] = hidden_act
lowercase : Optional[int] = intermediate_size
lowercase : str = hidden_dropout_prob
lowercase : Dict = attention_probs_dropout_prob
lowercase : Optional[Any] = max_position_embeddings
lowercase : Any = initializer_range
lowercase : List[Any] = layer_norm_eps
lowercase : Optional[Any] = position_embedding_type
lowercase : Dict = cross_attention_frequency
lowercase : int = encoder_hidden_size
@classmethod
def _SCREAMING_SNAKE_CASE ( cls ,snake_case ,**snake_case ):
'''simple docstring'''
cls._set_token_in_kwargs(snake_case )
lowercase , lowercase : Tuple = cls.get_config_dict(snake_case ,**snake_case )
# get the qformer config dict if we are loading from InstructBlipConfig
if config_dict.get("""model_type""" ) == "instructblip":
lowercase : Any = config_dict["""qformer_config"""]
if "model_type" in config_dict and hasattr(cls ,"""model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." )
return cls.from_dict(snake_case ,**snake_case )
class __snake_case ( lowerCAmelCase ):
_a : List[str]= "instructblip"
_a : List[Any]= True
def __init__( self ,snake_case=None ,snake_case=None ,snake_case=None ,snake_case=32 ,**snake_case ):
'''simple docstring'''
super().__init__(**snake_case )
if vision_config is None:
lowercase : int = {}
logger.info("""vision_config is None. initializing the InstructBlipVisionConfig with default values.""" )
if qformer_config is None:
lowercase : Tuple = {}
logger.info("""qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.""" )
if text_config is None:
lowercase : Tuple = {}
logger.info("""text_config is None. Initializing the text config with default values (`OPTConfig`).""" )
lowercase : Dict = InstructBlipVisionConfig(**snake_case )
lowercase : Optional[Any] = InstructBlipQFormerConfig(**snake_case )
lowercase : Optional[int] = text_config["""model_type"""] if """model_type""" in text_config else """opt"""
lowercase : Optional[Any] = CONFIG_MAPPING[text_model_type](**snake_case )
lowercase : Union[str, Any] = self.text_config.tie_word_embeddings
lowercase : Optional[int] = self.text_config.is_encoder_decoder
lowercase : Tuple = num_query_tokens
lowercase : int = self.vision_config.hidden_size
lowercase : Optional[Any] = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
lowercase : Tuple = 1.0
lowercase : int = 0.02
@classmethod
def _SCREAMING_SNAKE_CASE ( cls ,snake_case ,snake_case ,snake_case ,**snake_case ,):
'''simple docstring'''
return cls(
vision_config=vision_config.to_dict() ,qformer_config=qformer_config.to_dict() ,text_config=text_config.to_dict() ,**snake_case ,)
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
lowercase : Optional[int] = copy.deepcopy(self.__dict__ )
lowercase : str = self.vision_config.to_dict()
lowercase : int = self.qformer_config.to_dict()
lowercase : List[Any] = self.text_config.to_dict()
lowercase : Optional[int] = self.__class__.model_type
return output
| 20 |
from __future__ import annotations
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =sorted(numsa + numsa )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =divmod(len(lowerCAmelCase_ ), 2 )
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowerCamelCase =[float(x) for x in input("Enter the elements of first array: ").split()]
_lowerCamelCase =[float(x) for x in input("Enter the elements of second array: ").split()]
print(f'The median of two arrays is: {median_of_two_arrays(array_a, array_a)}')
| 334 | 0 |
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