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'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase : Optional[int] = {
'configuration_blenderbot': [
'BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BlenderbotConfig',
'BlenderbotOnnxConfig',
],
'tokenization_blenderbot': ['BlenderbotTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Tuple = ['BlenderbotTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : List[str] = [
'BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST',
'BlenderbotForCausalLM',
'BlenderbotForConditionalGeneration',
'BlenderbotModel',
'BlenderbotPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Tuple = [
'TFBlenderbotForConditionalGeneration',
'TFBlenderbotModel',
'TFBlenderbotPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Optional[int] = [
'FlaxBlenderbotForConditionalGeneration',
'FlaxBlenderbotModel',
'FlaxBlenderbotPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_blenderbot import (
BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotConfig,
BlenderbotOnnxConfig,
)
from .tokenization_blenderbot import BlenderbotTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_fast import BlenderbotTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot import (
BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotForCausalLM,
BlenderbotForConditionalGeneration,
BlenderbotModel,
BlenderbotPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot import (
TFBlenderbotForConditionalGeneration,
TFBlenderbotModel,
TFBlenderbotPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
FlaxBlenderbotPreTrainedModel,
)
else:
import sys
UpperCamelCase : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 50 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase : Tuple = {
'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
'tokenization_mvp': ['MvpTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : str = ['MvpTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Optional[int] = [
'MVP_PRETRAINED_MODEL_ARCHIVE_LIST',
'MvpForCausalLM',
'MvpForConditionalGeneration',
'MvpForQuestionAnswering',
'MvpForSequenceClassification',
'MvpModel',
'MvpPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
UpperCamelCase : Optional[int] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 50 | 1 |
'''simple docstring'''
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAccumulator, create_optimizer
@require_tf
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
self.assertEqual(len(_lowerCAmelCase ) ,len(_lowerCAmelCase ) )
for a, b in zip(_lowerCAmelCase ,_lowerCAmelCase ):
self.assertAlmostEqual(_lowerCAmelCase ,_lowerCAmelCase ,delta=_lowerCAmelCase )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = GradientAccumulator()
accumulator([tf.constant([1.0, 2.0] )] )
accumulator([tf.constant([-2.0, 1.0] )] )
accumulator([tf.constant([-1.0, 2.0] )] )
with self.assertRaises(_lowerCAmelCase ):
accumulator([tf.constant([1.0, 1.0] ), tf.constant([2.0, 2.0] )] )
self.assertEqual(accumulator.step ,3 )
self.assertEqual(len(accumulator.gradients ) ,1 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() ,[-2.0, 5.0] ,tol=1E-2 )
accumulator.reset()
self.assertEqual(accumulator.step ,0 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() ,[0.0, 0.0] ,tol=1E-2 )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = None
ops.enable_eager_execution_internal()
lowerCamelCase__ = tf.config.list_physical_devices("""CPU""" )
if len(_lowerCAmelCase ) == 1:
tf.config.set_logical_device_configuration(
physical_devices[0] ,[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()] )
lowerCamelCase__ = tf.config.list_logical_devices(device_type="""CPU""" )
lowerCamelCase__ = tf.distribute.MirroredStrategy(devices=devices[:2] )
with strategy.scope():
lowerCamelCase__ = GradientAccumulator()
lowerCamelCase__ = tf.Variable([4.0, 3.0] )
lowerCamelCase__ , lowerCamelCase__ = create_optimizer(5E-5 ,10 ,5 )
lowerCamelCase__ = tf.Variable([0.0, 0.0] ,trainable=_lowerCAmelCase )
def accumulate_on_replica(_lowerCAmelCase ):
accumulator([gradient] )
def apply_on_replica():
optimizer.apply_gradients(list(zip(accumulator.gradients ,[variable] ) ) )
@tf.function
def accumulate(_lowerCAmelCase ,_lowerCAmelCase ):
with strategy.scope():
lowerCamelCase__ = strategy.experimental_local_results(_lowerCAmelCase )
local_variables[0].assign(_lowerCAmelCase )
local_variables[1].assign(_lowerCAmelCase )
strategy.run(_lowerCAmelCase ,args=(gradient_placeholder,) )
@tf.function
def apply_grad():
with strategy.scope():
strategy.run(_lowerCAmelCase )
def _check_local_values(_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = strategy.experimental_local_results(accumulator._gradients[0] )
self.assertListAlmostEqual(values[0].value() ,_lowerCAmelCase ,tol=1E-2 )
self.assertListAlmostEqual(values[1].value() ,_lowerCAmelCase ,tol=1E-2 )
accumulate([1.0, 2.0] ,[-1.0, 1.0] )
accumulate([3.0, -1.0] ,[-1.0, -1.0] )
accumulate([-2.0, 2.0] ,[3.0, -2.0] )
self.assertEqual(accumulator.step ,3 )
_check_local_values([2.0, 3.0] ,[1.0, -2.0] )
apply_grad()
self.assertListAlmostEqual(variable.value() ,[4.0, 3.0] ,tol=1E-2 )
accumulator.reset()
self.assertEqual(accumulator.step ,0 )
_check_local_values([0.0, 0.0] ,[0.0, 0.0] )
| 50 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
UpperCamelCase : Tuple = logging.get_logger(__name__)
UpperCamelCase : Dict = {
'Salesforce/codegen-350M-nl': 'https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json',
'Salesforce/codegen-350M-multi': 'https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json',
'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json',
'Salesforce/codegen-2B-nl': 'https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json',
'Salesforce/codegen-2B-multi': 'https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json',
'Salesforce/codegen-2B-mono': 'https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json',
'Salesforce/codegen-6B-nl': 'https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json',
'Salesforce/codegen-6B-multi': 'https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json',
'Salesforce/codegen-6B-mono': 'https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json',
'Salesforce/codegen-16B-nl': 'https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json',
'Salesforce/codegen-16B-multi': 'https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json',
'Salesforce/codegen-16B-mono': 'https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json',
}
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = 'codegen'
_UpperCamelCase = {
'max_position_embeddings': 'n_positions',
'hidden_size': 'n_embd',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self ,_lowerCAmelCase=5_04_00 ,_lowerCAmelCase=20_48 ,_lowerCAmelCase=20_48 ,_lowerCAmelCase=40_96 ,_lowerCAmelCase=28 ,_lowerCAmelCase=16 ,_lowerCAmelCase=64 ,_lowerCAmelCase=None ,_lowerCAmelCase="gelu_new" ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=1E-5 ,_lowerCAmelCase=0.02 ,_lowerCAmelCase=True ,_lowerCAmelCase=5_02_56 ,_lowerCAmelCase=5_02_56 ,_lowerCAmelCase=False ,**_lowerCAmelCase ,):
lowerCamelCase__ = vocab_size
lowerCamelCase__ = n_ctx
lowerCamelCase__ = n_positions
lowerCamelCase__ = n_embd
lowerCamelCase__ = n_layer
lowerCamelCase__ = n_head
lowerCamelCase__ = n_inner
lowerCamelCase__ = rotary_dim
lowerCamelCase__ = activation_function
lowerCamelCase__ = resid_pdrop
lowerCamelCase__ = embd_pdrop
lowerCamelCase__ = attn_pdrop
lowerCamelCase__ = layer_norm_epsilon
lowerCamelCase__ = initializer_range
lowerCamelCase__ = use_cache
lowerCamelCase__ = bos_token_id
lowerCamelCase__ = eos_token_id
super().__init__(
bos_token_id=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase ,tie_word_embeddings=_lowerCAmelCase ,**_lowerCAmelCase )
class UpperCamelCase__ (a ):
'''simple docstring'''
def __init__( self ,_lowerCAmelCase ,_lowerCAmelCase = "default" ,_lowerCAmelCase = None ,_lowerCAmelCase = False ,):
super().__init__(_lowerCAmelCase ,task=_lowerCAmelCase ,patching_specs=_lowerCAmelCase ,use_past=_lowerCAmelCase )
if not getattr(self._config ,"""pad_token_id""" ,_lowerCAmelCase ):
# TODO: how to do that better?
lowerCamelCase__ = 0
@property
def UpperCamelCase_ ( self ):
lowerCamelCase__ = OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}} )
if self.use_past:
self.fill_with_past_key_values_(_lowerCAmelCase ,direction="""inputs""" )
lowerCamelCase__ = {0: """batch""", 1: """past_sequence + sequence"""}
else:
lowerCamelCase__ = {0: """batch""", 1: """sequence"""}
return common_inputs
@property
def UpperCamelCase_ ( self ):
return self._config.n_layer
@property
def UpperCamelCase_ ( self ):
return self._config.n_head
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase = -1 ,_lowerCAmelCase = -1 ,_lowerCAmelCase = False ,_lowerCAmelCase = None ,):
lowerCamelCase__ = super(_lowerCAmelCase ,self ).generate_dummy_inputs(
_lowerCAmelCase ,batch_size=_lowerCAmelCase ,seq_length=_lowerCAmelCase ,is_pair=_lowerCAmelCase ,framework=_lowerCAmelCase )
# We need to order the input in the way they appears in the forward()
lowerCamelCase__ = OrderedDict({"""input_ids""": common_inputs["""input_ids"""]} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
lowerCamelCase__ , lowerCamelCase__ = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
lowerCamelCase__ = seqlen + 2
lowerCamelCase__ = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
lowerCamelCase__ = [
(torch.zeros(_lowerCAmelCase ), torch.zeros(_lowerCAmelCase )) for _ in range(self.num_layers )
]
lowerCamelCase__ = common_inputs["""attention_mask"""]
if self.use_past:
lowerCamelCase__ = ordered_inputs["""attention_mask"""].dtype
lowerCamelCase__ = torch.cat(
[ordered_inputs["""attention_mask"""], torch.ones(_lowerCAmelCase ,_lowerCAmelCase ,dtype=_lowerCAmelCase )] ,dim=1 )
return ordered_inputs
@property
def UpperCamelCase_ ( self ):
return 13
| 50 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCamelCase : Tuple = {
'configuration_gpt_bigcode': ['GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTBigCodeConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Any = [
'GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST',
'GPTBigCodeForSequenceClassification',
'GPTBigCodeForTokenClassification',
'GPTBigCodeForCausalLM',
'GPTBigCodeModel',
'GPTBigCodePreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_bigcode import (
GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTBigCodeForCausalLM,
GPTBigCodeForSequenceClassification,
GPTBigCodeForTokenClassification,
GPTBigCodeModel,
GPTBigCodePreTrainedModel,
)
else:
import sys
UpperCamelCase : int = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 50 |
'''simple docstring'''
# 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
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase : int = {
'configuration_xmod': [
'XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XmodConfig',
'XmodOnnxConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Tuple = [
'XMOD_PRETRAINED_MODEL_ARCHIVE_LIST',
'XmodForCausalLM',
'XmodForMaskedLM',
'XmodForMultipleChoice',
'XmodForQuestionAnswering',
'XmodForSequenceClassification',
'XmodForTokenClassification',
'XmodModel',
'XmodPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xmod import (
XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
XmodForCausalLM,
XmodForMaskedLM,
XmodForMultipleChoice,
XmodForQuestionAnswering,
XmodForSequenceClassification,
XmodForTokenClassification,
XmodModel,
XmodPreTrainedModel,
)
else:
import sys
UpperCamelCase : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 50 | 1 |
'''simple docstring'''
def A__ ( ):
for n in range(1 , 100_0000 ):
yield n * (n + 1) // 2
def A__ ( __lowerCAmelCase : Union[str, Any] ):
lowerCamelCase__ = 1
lowerCamelCase__ = 2
while i * i <= n:
lowerCamelCase__ = 0
while n % i == 0:
n //= i
multiplicity += 1
divisors_count *= multiplicity + 1
i += 1
if n > 1:
divisors_count *= 2
return divisors_count
def A__ ( ):
return next(i for i in triangle_number_generator() if count_divisors(__lowerCAmelCase ) > 500 )
if __name__ == "__main__":
print(solution())
| 50 |
'''simple docstring'''
from typing import Union
import fire
import torch
from tqdm import tqdm
def A__ ( __lowerCAmelCase : str , __lowerCAmelCase : str = "cpu" , __lowerCAmelCase : Union[str, None] = None ):
lowerCamelCase__ = torch.load(__lowerCAmelCase , map_location=__lowerCAmelCase )
for k, v in tqdm(state_dict.items() ):
if not isinstance(__lowerCAmelCase , torch.Tensor ):
raise TypeError("""FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin""" )
lowerCamelCase__ = v.half()
if save_path is None: # overwrite src_path
lowerCamelCase__ = src_path
torch.save(__lowerCAmelCase , __lowerCAmelCase )
if __name__ == "__main__":
fire.Fire(convert)
| 50 | 1 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
UpperCamelCase : List[Any] = 'examples/'
UpperCamelCase : int = {
'examples': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
'init': (re.compile(r'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'),
'setup': (re.compile(r'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), r'\1version="VERSION",'),
'doc': (re.compile(r'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), 'release = "VERSION"\n'),
}
UpperCamelCase : Any = {
'init': 'src/transformers/__init__.py',
'setup': 'setup.py',
}
UpperCamelCase : Any = 'README.md'
def A__ ( __lowerCAmelCase : Any , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[int] ):
with open(__lowerCAmelCase , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
lowerCamelCase__ = f.read()
lowerCamelCase__ , lowerCamelCase__ = REPLACE_PATTERNS[pattern]
lowerCamelCase__ = replace.replace("""VERSION""" , __lowerCAmelCase )
lowerCamelCase__ = re_pattern.sub(__lowerCAmelCase , __lowerCAmelCase )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.write(__lowerCAmelCase )
def A__ ( __lowerCAmelCase : str ):
for folder, directories, fnames in os.walk(__lowerCAmelCase ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove("""research_projects""" )
if "legacy" in directories:
directories.remove("""legacy""" )
for fname in fnames:
if fname.endswith(""".py""" ):
update_version_in_file(os.path.join(__lowerCAmelCase , __lowerCAmelCase ) , __lowerCAmelCase , pattern="""examples""" )
def A__ ( __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[Any]=False ):
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
if not patch:
update_version_in_examples(__lowerCAmelCase )
def A__ ( ):
lowerCamelCase__ = """🤗 Transformers currently provides the following architectures"""
lowerCamelCase__ = """1. Want to contribute a new model?"""
with open(__lowerCAmelCase , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
lowerCamelCase__ = f.readlines()
# Find the start of the list.
lowerCamelCase__ = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
lowerCamelCase__ = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith("""1.""" ):
lowerCamelCase__ = lines[index].replace(
"""https://huggingface.co/docs/transformers/main/model_doc""" , """https://huggingface.co/docs/transformers/model_doc""" , )
index += 1
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.writelines(__lowerCAmelCase )
def A__ ( ):
with open(REPLACE_FILES["""init"""] , """r""" ) as f:
lowerCamelCase__ = f.read()
lowerCamelCase__ = REPLACE_PATTERNS["""init"""][0].search(__lowerCAmelCase ).groups()[0]
return packaging.version.parse(__lowerCAmelCase )
def A__ ( __lowerCAmelCase : Union[str, Any]=False ):
lowerCamelCase__ = get_version()
if patch and default_version.is_devrelease:
raise ValueError("""Can't create a patch version from the dev branch, checkout a released version!""" )
if default_version.is_devrelease:
lowerCamelCase__ = default_version.base_version
elif patch:
lowerCamelCase__ = F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
else:
lowerCamelCase__ = F'''{default_version.major}.{default_version.minor + 1}.0'''
# Now let's ask nicely if that's the right one.
lowerCamelCase__ = input(F'''Which version are you releasing? [{default_version}]''' )
if len(__lowerCAmelCase ) == 0:
lowerCamelCase__ = default_version
print(F'''Updating version to {version}.''' )
global_version_update(__lowerCAmelCase , patch=__lowerCAmelCase )
if not patch:
print("""Cleaning main README, don't forget to run `make fix-copies`.""" )
clean_main_ref_in_model_list()
def A__ ( ):
lowerCamelCase__ = get_version()
lowerCamelCase__ = F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
lowerCamelCase__ = current_version.base_version
# Check with the user we got that right.
lowerCamelCase__ = input(F'''Which version are we developing now? [{dev_version}]''' )
if len(__lowerCAmelCase ) == 0:
lowerCamelCase__ = dev_version
print(F'''Updating version to {version}.''' )
global_version_update(__lowerCAmelCase )
print("""Cleaning main README, don't forget to run `make fix-copies`.""" )
clean_main_ref_in_model_list()
if __name__ == "__main__":
UpperCamelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')
parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')
UpperCamelCase : Any = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print('Nothing to do after a patch :-)')
else:
post_release_work()
| 50 |
'''simple docstring'''
import os
from pathlib import Path
def A__ ( ):
from torch.utils.cpp_extension import load
lowerCamelCase__ = Path(__lowerCAmelCase ).resolve().parent.parent.parent / """kernels""" / """deformable_detr"""
lowerCamelCase__ = [
root / filename
for filename in [
"""vision.cpp""",
os.path.join("""cpu""" , """ms_deform_attn_cpu.cpp""" ),
os.path.join("""cuda""" , """ms_deform_attn_cuda.cu""" ),
]
]
load(
"""MultiScaleDeformableAttention""" , __lowerCAmelCase , with_cuda=__lowerCAmelCase , extra_include_paths=[str(__lowerCAmelCase )] , extra_cflags=["""-DWITH_CUDA=1"""] , extra_cuda_cflags=[
"""-DCUDA_HAS_FP16=1""",
"""-D__CUDA_NO_HALF_OPERATORS__""",
"""-D__CUDA_NO_HALF_CONVERSIONS__""",
"""-D__CUDA_NO_HALF2_OPERATORS__""",
] , )
import MultiScaleDeformableAttention as MSDA
return MSDA
| 50 | 1 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase : int = logging.get_logger(__name__)
# TODO Update this
UpperCamelCase : Tuple = {
'facebook/esm-1b': 'https://huggingface.co/facebook/esm-1b/resolve/main/config.json',
# See all ESM models at https://huggingface.co/models?filter=esm
}
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = 'esm'
def __init__( self ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=7_68 ,_lowerCAmelCase=12 ,_lowerCAmelCase=12 ,_lowerCAmelCase=30_72 ,_lowerCAmelCase=0.1 ,_lowerCAmelCase=0.1 ,_lowerCAmelCase=10_26 ,_lowerCAmelCase=0.02 ,_lowerCAmelCase=1E-12 ,_lowerCAmelCase="absolute" ,_lowerCAmelCase=True ,_lowerCAmelCase=None ,_lowerCAmelCase=False ,_lowerCAmelCase=False ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,**_lowerCAmelCase ,):
super().__init__(pad_token_id=_lowerCAmelCase ,mask_token_id=_lowerCAmelCase ,**_lowerCAmelCase )
lowerCamelCase__ = vocab_size
lowerCamelCase__ = hidden_size
lowerCamelCase__ = num_hidden_layers
lowerCamelCase__ = num_attention_heads
lowerCamelCase__ = intermediate_size
lowerCamelCase__ = hidden_dropout_prob
lowerCamelCase__ = attention_probs_dropout_prob
lowerCamelCase__ = max_position_embeddings
lowerCamelCase__ = initializer_range
lowerCamelCase__ = layer_norm_eps
lowerCamelCase__ = position_embedding_type
lowerCamelCase__ = use_cache
lowerCamelCase__ = emb_layer_norm_before
lowerCamelCase__ = token_dropout
lowerCamelCase__ = is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info("""No esmfold_config supplied for folding model, using default values.""" )
lowerCamelCase__ = EsmFoldConfig()
elif isinstance(_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = EsmFoldConfig(**_lowerCAmelCase )
lowerCamelCase__ = esmfold_config
if vocab_list is None:
logger.warning("""No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!""" )
lowerCamelCase__ = get_default_vocab_list()
else:
lowerCamelCase__ = vocab_list
else:
lowerCamelCase__ = None
lowerCamelCase__ = None
if self.esmfold_config is not None and getattr(self.esmfold_config ,"""use_esm_attn_map""" ,_lowerCAmelCase ):
raise ValueError("""The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!""" )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = super().to_dict()
if isinstance(self.esmfold_config ,_lowerCAmelCase ):
lowerCamelCase__ = self.esmfold_config.to_dict()
return output
@dataclass
class UpperCamelCase__ :
'''simple docstring'''
_UpperCamelCase = None
_UpperCamelCase = True
_UpperCamelCase = False
_UpperCamelCase = False
_UpperCamelCase = False
_UpperCamelCase = 0
_UpperCamelCase = True
_UpperCamelCase = False
_UpperCamelCase = 128
_UpperCamelCase = None
def UpperCamelCase_ ( self ):
if self.trunk is None:
lowerCamelCase__ = TrunkConfig()
elif isinstance(self.trunk ,_lowerCAmelCase ):
lowerCamelCase__ = TrunkConfig(**self.trunk )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = asdict(self )
lowerCamelCase__ = self.trunk.to_dict()
return output
@dataclass
class UpperCamelCase__ :
'''simple docstring'''
_UpperCamelCase = 48
_UpperCamelCase = 1024
_UpperCamelCase = 128
_UpperCamelCase = 32
_UpperCamelCase = 32
_UpperCamelCase = 32
_UpperCamelCase = 0
_UpperCamelCase = 0
_UpperCamelCase = False
_UpperCamelCase = 4
_UpperCamelCase = 128
_UpperCamelCase = None
def UpperCamelCase_ ( self ):
if self.structure_module is None:
lowerCamelCase__ = StructureModuleConfig()
elif isinstance(self.structure_module ,_lowerCAmelCase ):
lowerCamelCase__ = StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(F'''`max_recycles` should be positive, got {self.max_recycles}.''' )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
"""`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"""
F''' {self.sequence_state_dim} and {self.sequence_state_dim}.''' )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
"""`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"""
F''' {self.pairwise_state_dim} and {self.pairwise_state_dim}.''' )
lowerCamelCase__ = self.sequence_state_dim // self.sequence_head_width
lowerCamelCase__ = self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
"""`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"""
F''' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.''' )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
"""`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"""
F''' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.''' )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(F'''`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.''' )
if self.dropout >= 0.4:
raise ValueError(F'''`dropout` should not be greater than 0.4, got {self.dropout}.''' )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = asdict(self )
lowerCamelCase__ = self.structure_module.to_dict()
return output
@dataclass
class UpperCamelCase__ :
'''simple docstring'''
_UpperCamelCase = 384
_UpperCamelCase = 128
_UpperCamelCase = 16
_UpperCamelCase = 128
_UpperCamelCase = 12
_UpperCamelCase = 4
_UpperCamelCase = 8
_UpperCamelCase = 0.1
_UpperCamelCase = 8
_UpperCamelCase = 1
_UpperCamelCase = 2
_UpperCamelCase = 7
_UpperCamelCase = 10
_UpperCamelCase = 1e-8
_UpperCamelCase = 1e5
def UpperCamelCase_ ( self ):
return asdict(self )
def A__ ( ):
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 50 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : list[int] , __lowerCAmelCase : list[int] ):
lowerCamelCase__ = len(__lowerCAmelCase )
print("""The following activities are selected:""" )
# The first activity is always selected
lowerCamelCase__ = 0
print(__lowerCAmelCase , end=""",""" )
# Consider rest of the activities
for j in range(__lowerCAmelCase ):
# If this activity has start time greater than
# or equal to the finish time of previously
# selected activity, then select it
if start[j] >= finish[i]:
print(__lowerCAmelCase , end=""",""" )
lowerCamelCase__ = j
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCamelCase : Union[str, Any] = [1, 3, 0, 5, 8, 5]
UpperCamelCase : int = [2, 4, 6, 7, 9, 9]
print_max_activities(start, finish)
| 50 | 1 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : List[Any] ):
lowerCamelCase__ = 1
lowerCamelCase__ = 2
while i * i <= n:
lowerCamelCase__ = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *= multiplicity + 1
i += 1
if n > 1:
n_divisors *= 2
return n_divisors
def A__ ( ):
lowerCamelCase__ = 1
lowerCamelCase__ = 1
while True:
i += 1
t_num += i
if count_divisors(__lowerCAmelCase ) > 500:
break
return t_num
if __name__ == "__main__":
print(solution())
| 50 |
'''simple docstring'''
import warnings
from ..trainer import Trainer
from ..utils import logging
UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
class UpperCamelCase__ (a ):
'''simple docstring'''
def __init__( self ,_lowerCAmelCase=None ,**_lowerCAmelCase ):
warnings.warn(
"""`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` """
"""instead.""" ,_lowerCAmelCase ,)
super().__init__(args=_lowerCAmelCase ,**_lowerCAmelCase )
| 50 | 1 |
'''simple docstring'''
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ):
lowerCamelCase__ = """hf-internal-testing/tiny-random-t5"""
lowerCamelCase__ = AutoTokenizer.from_pretrained(_lowerCAmelCase )
lowerCamelCase__ = AutoModelForSeqaSeqLM.from_pretrained(_lowerCAmelCase )
lowerCamelCase__ = tokenizer("""This is me""" ,return_tensors="""pt""" )
lowerCamelCase__ = model.to_bettertransformer()
self.assertTrue(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules() ) )
lowerCamelCase__ = model.generate(**_lowerCAmelCase )
lowerCamelCase__ = model.reverse_bettertransformer()
self.assertFalse(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules() ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_lowerCAmelCase )
lowerCamelCase__ = AutoModelForSeqaSeqLM.from_pretrained(_lowerCAmelCase )
self.assertFalse(
any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules() ) )
lowerCamelCase__ = model_reloaded.generate(**_lowerCAmelCase )
self.assertTrue(torch.allclose(_lowerCAmelCase ,_lowerCAmelCase ) )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = """hf-internal-testing/tiny-random-t5"""
lowerCamelCase__ = AutoModelForSeqaSeqLM.from_pretrained(_lowerCAmelCase )
lowerCamelCase__ = model.to_bettertransformer()
with tempfile.TemporaryDirectory() as tmpdirname:
with self.assertRaises(_lowerCAmelCase ):
model.save_pretrained(_lowerCAmelCase )
lowerCamelCase__ = model.reverse_bettertransformer()
model.save_pretrained(_lowerCAmelCase )
| 50 |
'''simple docstring'''
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def A__ ( __lowerCAmelCase : List[str] ):
lowerCamelCase__ = []
for line in lines:
lowerCamelCase__ = re.sub(R"""#.*""" , """""" , __lowerCAmelCase ) # remove comments
if line:
filtered_lines.append(__lowerCAmelCase )
lowerCamelCase__ = """\n""".join(__lowerCAmelCase )
# Make a hash from all this code
lowerCamelCase__ = full_str.encode("""utf-8""" )
return shaaaa(__lowerCAmelCase ).hexdigest()
# get importable module names and hash for caching
UpperCamelCase : Dict = {
'csv': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
'json': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
'pandas': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
'parquet': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
'arrow': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
'text': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
'imagefolder': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
'audiofolder': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
UpperCamelCase : str = {
'.csv': ('csv', {}),
'.tsv': ('csv', {'sep': '\t'}),
'.json': ('json', {}),
'.jsonl': ('json', {}),
'.parquet': ('parquet', {}),
'.arrow': ('arrow', {}),
'.txt': ('text', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
UpperCamelCase : List[Any] = {'imagefolder', 'audiofolder'}
# Used to filter data files based on extensions given a module name
UpperCamelCase : Dict[str, List[str]] = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append('.zip')
_MODULE_TO_EXTENSIONS["audiofolder"].append('.zip')
| 50 | 1 |
'''simple docstring'''
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 UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ):
# A mock response for an HTTP head request to emulate server down
lowerCamelCase__ = mock.Mock()
lowerCamelCase__ = 5_00
lowerCamelCase__ = {}
lowerCamelCase__ = HTTPError
lowerCamelCase__ = {}
# Download this model to make sure it's in the cache.
lowerCamelCase__ = 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=_lowerCAmelCase ) as mock_head:
lowerCamelCase__ = 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 UpperCamelCase_ ( self ):
# A mock response for an HTTP head request to emulate server down
lowerCamelCase__ = mock.Mock()
lowerCamelCase__ = 5_00
lowerCamelCase__ = {}
lowerCamelCase__ = HTTPError
lowerCamelCase__ = {}
# Download this model to make sure it's in the cache.
lowerCamelCase__ = 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=_lowerCAmelCase ) as mock_head:
lowerCamelCase__ = GPTaTokenizerFast.from_pretrained("""gpt2""" )
# This check we did call the fake head request
mock_head.assert_called()
def UpperCamelCase_ ( self ):
# This test is for deprecated behavior and can be removed in v5
try:
lowerCamelCase__ = tempfile.mktemp()
with open(_lowerCAmelCase ,"""wb""" ) as f:
http_get("""https://huggingface.co/albert-base-v1/resolve/main/spiece.model""" ,_lowerCAmelCase )
lowerCamelCase__ = AlbertTokenizer.from_pretrained(_lowerCAmelCase )
finally:
os.remove(_lowerCAmelCase )
# 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""" ,_lowerCAmelCase )
lowerCamelCase__ = 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 ,10_00 )
# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.
finally:
os.remove("""tokenizer.json""" )
def UpperCamelCase_ ( self ):
# This test is for deprecated behavior and can be removed in v5
lowerCamelCase__ = AlbertTokenizer.from_pretrained("""https://huggingface.co/albert-base-v1/resolve/main/spiece.model""" )
@is_staging_test
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase = ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']
@classmethod
def UpperCamelCase_ ( cls ):
lowerCamelCase__ = TOKEN
HfFolder.save_token(_lowerCAmelCase )
@classmethod
def UpperCamelCase_ ( cls ):
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 UpperCamelCase_ ( self ):
with tempfile.TemporaryDirectory() as tmp_dir:
lowerCamelCase__ = os.path.join(_lowerCAmelCase ,"""vocab.txt""" )
with open(_lowerCAmelCase ,"""w""" ,encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens] ) )
lowerCamelCase__ = BertTokenizer(_lowerCAmelCase )
tokenizer.push_to_hub("""test-tokenizer""" ,use_auth_token=self._token )
lowerCamelCase__ = 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(_lowerCAmelCase ,repo_id="""test-tokenizer""" ,push_to_hub=_lowerCAmelCase ,use_auth_token=self._token )
lowerCamelCase__ = BertTokenizer.from_pretrained(F'''{USER}/test-tokenizer''' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
def UpperCamelCase_ ( self ):
with tempfile.TemporaryDirectory() as tmp_dir:
lowerCamelCase__ = os.path.join(_lowerCAmelCase ,"""vocab.txt""" )
with open(_lowerCAmelCase ,"""w""" ,encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens] ) )
lowerCamelCase__ = BertTokenizer(_lowerCAmelCase )
tokenizer.push_to_hub("""valid_org/test-tokenizer-org""" ,use_auth_token=self._token )
lowerCamelCase__ = 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(
_lowerCAmelCase ,repo_id="""valid_org/test-tokenizer-org""" ,push_to_hub=_lowerCAmelCase ,use_auth_token=self._token )
lowerCamelCase__ = BertTokenizer.from_pretrained("""valid_org/test-tokenizer-org""" )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
@require_tokenizers
def UpperCamelCase_ ( self ):
CustomTokenizer.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
lowerCamelCase__ = os.path.join(_lowerCAmelCase ,"""vocab.txt""" )
with open(_lowerCAmelCase ,"""w""" ,encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens] ) )
lowerCamelCase__ = CustomTokenizer(_lowerCAmelCase )
# No fast custom tokenizer
tokenizer.push_to_hub("""test-dynamic-tokenizer""" ,use_auth_token=self._token )
lowerCamelCase__ = AutoTokenizer.from_pretrained(F'''{USER}/test-dynamic-tokenizer''' ,trust_remote_code=_lowerCAmelCase )
# 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:
lowerCamelCase__ = os.path.join(_lowerCAmelCase ,"""vocab.txt""" )
with open(_lowerCAmelCase ,"""w""" ,encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens] ) )
lowerCamelCase__ = BertTokenizerFast.from_pretrained(_lowerCAmelCase )
bert_tokenizer.save_pretrained(_lowerCAmelCase )
lowerCamelCase__ = CustomTokenizerFast.from_pretrained(_lowerCAmelCase )
tokenizer.push_to_hub("""test-dynamic-tokenizer""" ,use_auth_token=self._token )
lowerCamelCase__ = AutoTokenizer.from_pretrained(F'''{USER}/test-dynamic-tokenizer''' ,trust_remote_code=_lowerCAmelCase )
# 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""" )
lowerCamelCase__ = AutoTokenizer.from_pretrained(
F'''{USER}/test-dynamic-tokenizer''' ,use_fast=_lowerCAmelCase ,trust_remote_code=_lowerCAmelCase )
# 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 UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 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 UpperCamelCase_ ( self ):
lowerCamelCase__ = 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 UpperCamelCase_ ( self ):
lowerCamelCase__ = Trie()
trie.add("""A""" )
self.assertEqual(trie.split("""ABC""" ) ,["""A""", """BC"""] )
self.assertEqual(trie.split("""BCA""" ) ,["""BC""", """A"""] )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = Trie()
trie.add("""TOKEN]""" )
trie.add("""[SPECIAL_TOKEN]""" )
self.assertEqual(trie.split("""This is something [SPECIAL_TOKEN]""" ) ,["""This is something """, """[SPECIAL_TOKEN]"""] )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 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 UpperCamelCase_ ( self ):
lowerCamelCase__ = Trie()
trie.add("""AB""" )
trie.add("""B""" )
trie.add("""C""" )
self.assertEqual(trie.split("""ABC""" ) ,["""AB""", """C"""] )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = Trie()
trie.add("""ABC""" )
trie.add("""B""" )
trie.add("""CD""" )
self.assertEqual(trie.split("""ABCD""" ) ,["""ABC""", """D"""] )
def UpperCamelCase_ ( self ):
# Even if the offsets are wrong, we necessarily output correct string
# parts.
lowerCamelCase__ = Trie()
lowerCamelCase__ = trie.cut_text("""ABC""" ,[0, 0, 2, 1, 2, 3] )
self.assertEqual(_lowerCAmelCase ,["""AB""", """C"""] )
| 50 |
'''simple docstring'''
import operator
def A__ ( __lowerCAmelCase : list , __lowerCAmelCase : bool = False , __lowerCAmelCase : list | None = None ):
lowerCamelCase__ = operator.lt if reverse else operator.gt
lowerCamelCase__ = solution or []
if not arr:
return solution
lowerCamelCase__ = [arr.pop(0 )]
for i, item in enumerate(__lowerCAmelCase ):
if _operator(__lowerCAmelCase , sublist[-1] ):
sublist.append(__lowerCAmelCase )
arr.pop(__lowerCAmelCase )
# merging sublist into solution list
if not solution:
solution.extend(__lowerCAmelCase )
else:
while sublist:
lowerCamelCase__ = sublist.pop(0 )
for i, xx in enumerate(__lowerCAmelCase ):
if not _operator(__lowerCAmelCase , __lowerCAmelCase ):
solution.insert(__lowerCAmelCase , __lowerCAmelCase )
break
else:
solution.append(__lowerCAmelCase )
strand_sort(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
return solution
if __name__ == "__main__":
assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]
| 50 | 1 |
'''simple docstring'''
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 1
lowerCamelCase__ = 3
lowerCamelCase__ = (32, 32)
lowerCamelCase__ = floats_tensor((batch_size, num_channels) + sizes ,rng=random.Random(0 ) ).to(_lowerCAmelCase )
return image
@property
def UpperCamelCase_ ( self ):
torch.manual_seed(0 )
lowerCamelCase__ = UNetaDConditionModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=4 ,out_channels=4 ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") ,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") ,cross_attention_dim=32 ,)
return model
@property
def UpperCamelCase_ ( self ):
torch.manual_seed(0 )
lowerCamelCase__ = AutoencoderKL(
block_out_channels=[32, 64] ,in_channels=3 ,out_channels=3 ,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] ,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] ,latent_channels=4 ,)
return model
@property
def UpperCamelCase_ ( self ):
torch.manual_seed(0 )
lowerCamelCase__ = CLIPTextConfig(
bos_token_id=0 ,eos_token_id=2 ,hidden_size=32 ,intermediate_size=37 ,layer_norm_eps=1E-05 ,num_attention_heads=4 ,num_hidden_layers=5 ,pad_token_id=1 ,vocab_size=10_00 ,)
return CLIPTextModel(_lowerCAmelCase )
@property
def UpperCamelCase_ ( self ):
def extract(*_lowerCAmelCase ,**_lowerCAmelCase ):
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self ):
lowerCamelCase__ = torch.ones([0] )
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
self.pixel_values.to(_lowerCAmelCase )
return self
return Out()
return extract
def UpperCamelCase_ ( self ):
lowerCamelCase__ = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowerCamelCase__ = self.dummy_cond_unet
lowerCamelCase__ = DDIMScheduler(
beta_start=0.0_0085 ,beta_end=0.012 ,beta_schedule="""scaled_linear""" ,clip_sample=_lowerCAmelCase ,set_alpha_to_one=_lowerCAmelCase ,)
lowerCamelCase__ = self.dummy_vae
lowerCamelCase__ = self.dummy_text_encoder
lowerCamelCase__ = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
# make sure here that pndm scheduler skips prk
lowerCamelCase__ = StableDiffusionPipeline(
unet=_lowerCAmelCase ,scheduler=_lowerCAmelCase ,vae=_lowerCAmelCase ,text_encoder=_lowerCAmelCase ,tokenizer=_lowerCAmelCase ,safety_checker=_lowerCAmelCase ,feature_extractor=self.dummy_extractor ,)
lowerCamelCase__ = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowerCamelCase__ = """A painting of a squirrel eating a burger"""
lowerCamelCase__ = torch.Generator(device=_lowerCAmelCase ).manual_seed(0 )
lowerCamelCase__ = sd_pipe([prompt] ,generator=_lowerCAmelCase ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type="""np""" )
lowerCamelCase__ = output.images
lowerCamelCase__ = torch.Generator(device=_lowerCAmelCase ).manual_seed(0 )
lowerCamelCase__ = sd_pipe(
[prompt] ,generator=_lowerCAmelCase ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type="""np""" ,return_dict=_lowerCAmelCase ,)[0]
lowerCamelCase__ = image[0, -3:, -3:, -1]
lowerCamelCase__ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowerCamelCase__ = np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def UpperCamelCase_ ( self ):
lowerCamelCase__ = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowerCamelCase__ = self.dummy_cond_unet
lowerCamelCase__ = PNDMScheduler(skip_prk_steps=_lowerCAmelCase )
lowerCamelCase__ = self.dummy_vae
lowerCamelCase__ = self.dummy_text_encoder
lowerCamelCase__ = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
# make sure here that pndm scheduler skips prk
lowerCamelCase__ = StableDiffusionPipeline(
unet=_lowerCAmelCase ,scheduler=_lowerCAmelCase ,vae=_lowerCAmelCase ,text_encoder=_lowerCAmelCase ,tokenizer=_lowerCAmelCase ,safety_checker=_lowerCAmelCase ,feature_extractor=self.dummy_extractor ,)
lowerCamelCase__ = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowerCamelCase__ = """A painting of a squirrel eating a burger"""
lowerCamelCase__ = torch.Generator(device=_lowerCAmelCase ).manual_seed(0 )
lowerCamelCase__ = sd_pipe([prompt] ,generator=_lowerCAmelCase ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type="""np""" )
lowerCamelCase__ = output.images
lowerCamelCase__ = torch.Generator(device=_lowerCAmelCase ).manual_seed(0 )
lowerCamelCase__ = sd_pipe(
[prompt] ,generator=_lowerCAmelCase ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type="""np""" ,return_dict=_lowerCAmelCase ,)[0]
lowerCamelCase__ = image[0, -3:, -3:, -1]
lowerCamelCase__ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowerCamelCase__ = np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def UpperCamelCase_ ( self ):
lowerCamelCase__ = StableDiffusionPipeline.from_pretrained(
"""hf-internal-testing/tiny-stable-diffusion-lms-pipe""" ,safety_checker=_lowerCAmelCase )
assert isinstance(_lowerCAmelCase ,_lowerCAmelCase )
assert isinstance(pipe.scheduler ,_lowerCAmelCase )
assert pipe.safety_checker is None
lowerCamelCase__ = pipe("""example prompt""" ,num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(_lowerCAmelCase )
lowerCamelCase__ = StableDiffusionPipeline.from_pretrained(_lowerCAmelCase )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
lowerCamelCase__ = pipe("""example prompt""" ,num_inference_steps=2 ).images[0]
assert image is not None
@unittest.skipIf(torch_device != """cuda""" ,"""This test requires a GPU""" )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.dummy_cond_unet
lowerCamelCase__ = PNDMScheduler(skip_prk_steps=_lowerCAmelCase )
lowerCamelCase__ = self.dummy_vae
lowerCamelCase__ = self.dummy_text_encoder
lowerCamelCase__ = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
# put models in fp16
lowerCamelCase__ = unet.half()
lowerCamelCase__ = vae.half()
lowerCamelCase__ = bert.half()
# make sure here that pndm scheduler skips prk
lowerCamelCase__ = StableDiffusionPipeline(
unet=_lowerCAmelCase ,scheduler=_lowerCAmelCase ,vae=_lowerCAmelCase ,text_encoder=_lowerCAmelCase ,tokenizer=_lowerCAmelCase ,safety_checker=_lowerCAmelCase ,feature_extractor=self.dummy_extractor ,)
lowerCamelCase__ = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowerCamelCase__ = """A painting of a squirrel eating a burger"""
lowerCamelCase__ = sd_pipe([prompt] ,num_inference_steps=2 ,output_type="""np""" ).images
assert image.shape == (1, 64, 64, 3)
@nightly
@require_torch_gpu
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase_ ( self ):
lowerCamelCase__ = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" ,safety_checker=_lowerCAmelCase )
lowerCamelCase__ = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
lowerCamelCase__ = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowerCamelCase__ = (
"""portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"""
""" coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"""
""" anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"""
""" children from bahnhof zoo, detailed """
)
lowerCamelCase__ = 40_03_66_03_46
lowerCamelCase__ = 7
# without safety guidance (sld_guidance_scale = 0)
lowerCamelCase__ = torch.manual_seed(_lowerCAmelCase )
lowerCamelCase__ = sd_pipe(
[prompt] ,generator=_lowerCAmelCase ,guidance_scale=_lowerCAmelCase ,num_inference_steps=50 ,output_type="""np""" ,width=5_12 ,height=5_12 ,sld_guidance_scale=0 ,)
lowerCamelCase__ = output.images
lowerCamelCase__ = image[0, -3:, -3:, -1]
lowerCamelCase__ = [0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]
assert image.shape == (1, 5_12, 5_12, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
# without safety guidance (strong configuration)
lowerCamelCase__ = torch.manual_seed(_lowerCAmelCase )
lowerCamelCase__ = sd_pipe(
[prompt] ,generator=_lowerCAmelCase ,guidance_scale=_lowerCAmelCase ,num_inference_steps=50 ,output_type="""np""" ,width=5_12 ,height=5_12 ,sld_guidance_scale=20_00 ,sld_warmup_steps=7 ,sld_threshold=0.025 ,sld_momentum_scale=0.5 ,sld_mom_beta=0.7 ,)
lowerCamelCase__ = output.images
lowerCamelCase__ = image[0, -3:, -3:, -1]
lowerCamelCase__ = [0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]
assert image.shape == (1, 5_12, 5_12, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def UpperCamelCase_ ( self ):
lowerCamelCase__ = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" ,safety_checker=_lowerCAmelCase )
lowerCamelCase__ = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
lowerCamelCase__ = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowerCamelCase__ = """padme amidala taking a bath artwork, safe for work, no nudity"""
lowerCamelCase__ = 27_34_97_17_55
lowerCamelCase__ = 7
lowerCamelCase__ = torch.manual_seed(_lowerCAmelCase )
lowerCamelCase__ = sd_pipe(
[prompt] ,generator=_lowerCAmelCase ,guidance_scale=_lowerCAmelCase ,num_inference_steps=50 ,output_type="""np""" ,width=5_12 ,height=5_12 ,sld_guidance_scale=0 ,)
lowerCamelCase__ = output.images
lowerCamelCase__ = image[0, -3:, -3:, -1]
lowerCamelCase__ = [0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]
assert image.shape == (1, 5_12, 5_12, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
lowerCamelCase__ = torch.manual_seed(_lowerCAmelCase )
lowerCamelCase__ = sd_pipe(
[prompt] ,generator=_lowerCAmelCase ,guidance_scale=_lowerCAmelCase ,num_inference_steps=50 ,output_type="""np""" ,width=5_12 ,height=5_12 ,sld_guidance_scale=20_00 ,sld_warmup_steps=7 ,sld_threshold=0.025 ,sld_momentum_scale=0.5 ,sld_mom_beta=0.7 ,)
lowerCamelCase__ = output.images
lowerCamelCase__ = image[0, -3:, -3:, -1]
lowerCamelCase__ = [0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]
assert image.shape == (1, 5_12, 5_12, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def UpperCamelCase_ ( self ):
lowerCamelCase__ = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" )
lowerCamelCase__ = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowerCamelCase__ = (
"""the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
""" leyendecker"""
)
lowerCamelCase__ = 10_44_35_52_34
lowerCamelCase__ = 12
lowerCamelCase__ = torch.manual_seed(_lowerCAmelCase )
lowerCamelCase__ = sd_pipe(
[prompt] ,generator=_lowerCAmelCase ,guidance_scale=_lowerCAmelCase ,num_inference_steps=50 ,output_type="""np""" ,width=5_12 ,height=5_12 ,sld_guidance_scale=0 ,)
lowerCamelCase__ = output.images
lowerCamelCase__ = image[0, -3:, -3:, -1]
lowerCamelCase__ = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] )
assert image.shape == (1, 5_12, 5_12, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-7
lowerCamelCase__ = torch.manual_seed(_lowerCAmelCase )
lowerCamelCase__ = sd_pipe(
[prompt] ,generator=_lowerCAmelCase ,guidance_scale=_lowerCAmelCase ,num_inference_steps=50 ,output_type="""np""" ,width=5_12 ,height=5_12 ,sld_guidance_scale=20_00 ,sld_warmup_steps=7 ,sld_threshold=0.025 ,sld_momentum_scale=0.5 ,sld_mom_beta=0.7 ,)
lowerCamelCase__ = output.images
lowerCamelCase__ = image[0, -3:, -3:, -1]
lowerCamelCase__ = np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561] )
assert image.shape == (1, 5_12, 5_12, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 50 |
'''simple docstring'''
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def A__ ( __lowerCAmelCase : dict ):
return (data["data"], data["target"])
def A__ ( __lowerCAmelCase : np.ndarray , __lowerCAmelCase : np.ndarray , __lowerCAmelCase : np.ndarray ):
lowerCamelCase__ = XGBRegressor(verbosity=0 , random_state=42 )
xgb.fit(__lowerCAmelCase , __lowerCAmelCase )
# Predict target for test data
lowerCamelCase__ = xgb.predict(__lowerCAmelCase )
lowerCamelCase__ = predictions.reshape(len(__lowerCAmelCase ) , 1 )
return predictions
def A__ ( ):
lowerCamelCase__ = fetch_california_housing()
lowerCamelCase__ , lowerCamelCase__ = data_handling(__lowerCAmelCase )
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = train_test_split(
__lowerCAmelCase , __lowerCAmelCase , test_size=0.25 , random_state=1 )
lowerCamelCase__ = xgboost(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# Error printing
print(F'''Mean Absolute Error : {mean_absolute_error(__lowerCAmelCase , __lowerCAmelCase )}''' )
print(F'''Mean Square Error : {mean_squared_error(__lowerCAmelCase , __lowerCAmelCase )}''' )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 50 | 1 |
'''simple docstring'''
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def A__ ( __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : int ):
lowerCamelCase__ = []
for part_id in partition_order:
lowerCamelCase__ = df.where(F'''SPARK_PARTITION_ID() = {part_id}''' ).collect()
for row_idx, row in enumerate(__lowerCAmelCase ):
expected_row_ids_and_row_dicts.append((F'''{part_id}_{row_idx}''', row.asDict()) )
return expected_row_ids_and_row_dicts
@require_not_windows
@require_dill_gt_0_3_2
def A__ ( ):
lowerCamelCase__ = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
lowerCamelCase__ = spark.range(100 ).repartition(1 )
lowerCamelCase__ = Spark(__lowerCAmelCase )
# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
# that each partition can hold 2 rows.
spark_builder._repartition_df_if_needed(max_shard_size=16 )
# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
assert spark_builder.df.rdd.getNumPartitions() == 50
@require_not_windows
@require_dill_gt_0_3_2
def A__ ( ):
lowerCamelCase__ = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
lowerCamelCase__ = spark.range(10 ).repartition(2 )
lowerCamelCase__ = [1, 0]
lowerCamelCase__ = _generate_iterable_examples(__lowerCAmelCase , __lowerCAmelCase ) # Reverse the partitions.
lowerCamelCase__ = _get_expected_row_ids_and_row_dicts_for_partition_order(__lowerCAmelCase , __lowerCAmelCase )
for i, (row_id, row_dict) in enumerate(generate_fn() ):
lowerCamelCase__ , lowerCamelCase__ = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def A__ ( ):
lowerCamelCase__ = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
lowerCamelCase__ = spark.range(10 ).repartition(1 )
lowerCamelCase__ = SparkExamplesIterable(__lowerCAmelCase )
assert it.n_shards == 1
for i, (row_id, row_dict) in enumerate(__lowerCAmelCase ):
assert row_id == F'''0_{i}'''
assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def A__ ( ):
lowerCamelCase__ = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
lowerCamelCase__ = spark.range(30 ).repartition(3 )
# Mock the generator so that shuffle reverses the partition indices.
with patch("""numpy.random.Generator""" ) as generator_mock:
lowerCamelCase__ = lambda __lowerCAmelCase : x.reverse()
lowerCamelCase__ = _get_expected_row_ids_and_row_dicts_for_partition_order(__lowerCAmelCase , [2, 1, 0] )
lowerCamelCase__ = SparkExamplesIterable(__lowerCAmelCase ).shuffle_data_sources(__lowerCAmelCase )
assert shuffled_it.n_shards == 3
for i, (row_id, row_dict) in enumerate(__lowerCAmelCase ):
lowerCamelCase__ , lowerCamelCase__ = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def A__ ( ):
lowerCamelCase__ = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
lowerCamelCase__ = spark.range(20 ).repartition(4 )
# Partitions 0 and 2
lowerCamelCase__ = SparkExamplesIterable(__lowerCAmelCase ).shard_data_sources(worker_id=0 , num_workers=2 )
assert shard_it_a.n_shards == 2
lowerCamelCase__ = _get_expected_row_ids_and_row_dicts_for_partition_order(__lowerCAmelCase , [0, 2] )
for i, (row_id, row_dict) in enumerate(__lowerCAmelCase ):
lowerCamelCase__ , lowerCamelCase__ = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
# Partitions 1 and 3
lowerCamelCase__ = SparkExamplesIterable(__lowerCAmelCase ).shard_data_sources(worker_id=1 , num_workers=2 )
assert shard_it_a.n_shards == 2
lowerCamelCase__ = _get_expected_row_ids_and_row_dicts_for_partition_order(__lowerCAmelCase , [1, 3] )
for i, (row_id, row_dict) in enumerate(__lowerCAmelCase ):
lowerCamelCase__ , lowerCamelCase__ = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def A__ ( ):
lowerCamelCase__ = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
lowerCamelCase__ = spark.range(100 ).repartition(1 )
lowerCamelCase__ = Spark(__lowerCAmelCase )
# Choose a small max_shard_size for maximum partitioning.
spark_builder._repartition_df_if_needed(max_shard_size=1 )
# The new number of partitions should not be greater than the number of rows.
assert spark_builder.df.rdd.getNumPartitions() == 100
| 50 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBOSTokenLogitsProcessor,
FlaxForcedEOSTokenLogitsProcessor,
FlaxLogitsProcessorList,
FlaxMinLengthLogitsProcessor,
FlaxTemperatureLogitsWarper,
FlaxTopKLogitsWarper,
FlaxTopPLogitsWarper,
)
@require_flax
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = jnp.ones((batch_size, length) ) / length
return scores
def UpperCamelCase_ ( self ):
lowerCamelCase__ = None
lowerCamelCase__ = 20
lowerCamelCase__ = self._get_uniform_logits(batch_size=2 ,length=_lowerCAmelCase )
# tweak scores to not be uniform anymore
lowerCamelCase__ = scores.at[1, 5].set((1 / length) + 0.1 ) # peak, 1st batch
lowerCamelCase__ = scores.at[1, 10].set((1 / length) - 0.4 ) # valley, 1st batch
# compute softmax
lowerCamelCase__ = jax.nn.softmax(_lowerCAmelCase ,axis=-1 )
lowerCamelCase__ = FlaxTemperatureLogitsWarper(temperature=0.5 )
lowerCamelCase__ = FlaxTemperatureLogitsWarper(temperature=1.3 )
lowerCamelCase__ = jax.nn.softmax(temp_dist_warper_sharper(_lowerCAmelCase ,scores.copy() ,cur_len=_lowerCAmelCase ) ,axis=-1 )
lowerCamelCase__ = jax.nn.softmax(temp_dist_warper_smoother(_lowerCAmelCase ,scores.copy() ,cur_len=_lowerCAmelCase ) ,axis=-1 )
# uniform distribution stays uniform
self.assertTrue(jnp.allclose(probs[0, :] ,warped_prob_sharp[0, :] ,atol=1E-3 ) )
self.assertTrue(jnp.allclose(probs[0, :] ,warped_prob_smooth[0, :] ,atol=1E-3 ) )
# sharp peaks get higher, valleys get lower
self.assertLess(probs[1, :].max() ,warped_prob_sharp[1, :].max() )
self.assertGreater(probs[1, :].min() ,warped_prob_sharp[1, :].min() )
# smooth peaks get lower, valleys get higher
self.assertGreater(probs[1, :].max() ,warped_prob_smooth[1, :].max() )
self.assertLess(probs[1, :].min() ,warped_prob_smooth[1, :].min() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = None
lowerCamelCase__ = 10
lowerCamelCase__ = 2
# create ramp distribution
lowerCamelCase__ = np.broadcast_to(np.arange(_lowerCAmelCase )[None, :] ,(batch_size, vocab_size) ).copy()
lowerCamelCase__ = ramp_logits[1:, : vocab_size // 2] + vocab_size
lowerCamelCase__ = FlaxTopKLogitsWarper(3 )
lowerCamelCase__ = top_k_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# check that correct tokens are filtered
self.assertListEqual(jnp.isinf(scores[0] ).tolist() ,7 * [True] + 3 * [False] )
self.assertListEqual(jnp.isinf(scores[1] ).tolist() ,2 * [True] + 3 * [False] + 5 * [True] )
# check special case
lowerCamelCase__ = 5
lowerCamelCase__ = FlaxTopKLogitsWarper(top_k=1 ,filter_value=0.0 ,min_tokens_to_keep=3 )
lowerCamelCase__ = np.broadcast_to(np.arange(_lowerCAmelCase )[None, :] ,(batch_size, length) ).copy()
lowerCamelCase__ = top_k_warp_safety_check(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
self.assertListEqual((scores == 0.0).sum(axis=-1 ).tolist() ,[2, 2] )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = None
lowerCamelCase__ = 10
lowerCamelCase__ = 2
# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
lowerCamelCase__ = np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]] ) )
lowerCamelCase__ = FlaxTopPLogitsWarper(0.8 )
lowerCamelCase__ = np.exp(top_p_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase ) )
# dist should be filtered to keep min num values so that sum is >= top_p
# exp (-inf) => 0
lowerCamelCase__ = np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]] )
self.assertTrue(np.allclose(_lowerCAmelCase ,_lowerCAmelCase ,atol=1E-3 ) )
# check edge cases with negative and extreme logits
lowerCamelCase__ = np.broadcast_to(np.arange(_lowerCAmelCase )[None, :] ,(batch_size, vocab_size) ).copy() - (
vocab_size // 2
)
# make ramp_logits more extreme
lowerCamelCase__ = ramp_logits[1] * 100.0
# make sure at least 2 tokens are kept
lowerCamelCase__ = FlaxTopPLogitsWarper(0.9 ,min_tokens_to_keep=2 ,filter_value=0.0 )
lowerCamelCase__ = top_p_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
self.assertListEqual((filtered_dist != 0.0).sum(axis=-1 ).tolist() ,[3, 2] )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 20
lowerCamelCase__ = 4
lowerCamelCase__ = 0
lowerCamelCase__ = FlaxMinLengthLogitsProcessor(min_length=10 ,eos_token_id=_lowerCAmelCase )
# check that min length is applied at length 5
lowerCamelCase__ = ids_tensor((batch_size, 20) ,vocab_size=20 )
lowerCamelCase__ = 5
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = min_dist_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist() ,4 * [-float("""inf""" )] )
# check that min length is not applied anymore at length 15
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = 15
lowerCamelCase__ = min_dist_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertFalse(jnp.isinf(_lowerCAmelCase ).any() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 20
lowerCamelCase__ = 4
lowerCamelCase__ = 0
lowerCamelCase__ = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase )
# check that all scores are -inf except the bos_token_id score
lowerCamelCase__ = ids_tensor((batch_size, 1) ,vocab_size=20 )
lowerCamelCase__ = 1
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = logits_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, bos_token_id].tolist() ,4 * [0] ) # score for bos_token_id shold be zero
# check that bos_token_id is not forced if current length is greater than 1
lowerCamelCase__ = 3
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = logits_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertFalse(jnp.isinf(_lowerCAmelCase ).any() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 20
lowerCamelCase__ = 4
lowerCamelCase__ = 0
lowerCamelCase__ = 5
lowerCamelCase__ = FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase )
# check that all scores are -inf except the eos_token_id when max_length is reached
lowerCamelCase__ = ids_tensor((batch_size, 4) ,vocab_size=20 )
lowerCamelCase__ = 4
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = logits_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, eos_token_id].tolist() ,4 * [0] ) # score for eos_token_id should be zero
# check that eos_token_id is not forced if max_length is not reached
lowerCamelCase__ = 3
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = logits_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertFalse(jnp.isinf(_lowerCAmelCase ).any() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 4
lowerCamelCase__ = 10
lowerCamelCase__ = 15
lowerCamelCase__ = 2
lowerCamelCase__ = 1
lowerCamelCase__ = 15
# dummy input_ids and scores
lowerCamelCase__ = ids_tensor((batch_size, sequence_length) ,_lowerCAmelCase )
lowerCamelCase__ = input_ids.copy()
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = scores.copy()
# instantiate all dist processors
lowerCamelCase__ = FlaxTemperatureLogitsWarper(temperature=0.5 )
lowerCamelCase__ = FlaxTopKLogitsWarper(3 )
lowerCamelCase__ = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
lowerCamelCase__ = FlaxMinLengthLogitsProcessor(min_length=10 ,eos_token_id=_lowerCAmelCase )
lowerCamelCase__ = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase )
lowerCamelCase__ = FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase )
lowerCamelCase__ = 10
# no processor list
lowerCamelCase__ = temp_dist_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = top_k_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = top_p_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = min_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = bos_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = eos_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# with processor list
lowerCamelCase__ = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
lowerCamelCase__ = processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# scores should be equal
self.assertTrue(jnp.allclose(_lowerCAmelCase ,_lowerCAmelCase ,atol=1E-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() ,input_ids_comp.tolist() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 4
lowerCamelCase__ = 10
lowerCamelCase__ = 15
lowerCamelCase__ = 2
lowerCamelCase__ = 1
lowerCamelCase__ = 15
# dummy input_ids and scores
lowerCamelCase__ = ids_tensor((batch_size, sequence_length) ,_lowerCAmelCase )
lowerCamelCase__ = input_ids.copy()
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = scores.copy()
# instantiate all dist processors
lowerCamelCase__ = FlaxTemperatureLogitsWarper(temperature=0.5 )
lowerCamelCase__ = FlaxTopKLogitsWarper(3 )
lowerCamelCase__ = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
lowerCamelCase__ = FlaxMinLengthLogitsProcessor(min_length=10 ,eos_token_id=_lowerCAmelCase )
lowerCamelCase__ = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase )
lowerCamelCase__ = FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase )
lowerCamelCase__ = 10
# no processor list
def run_no_processor_list(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = temp_dist_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = top_k_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = top_p_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = min_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = bos_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = eos_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
return scores
# with processor list
def run_processor_list(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
lowerCamelCase__ = processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
return scores
lowerCamelCase__ = jax.jit(_lowerCAmelCase )
lowerCamelCase__ = jax.jit(_lowerCAmelCase )
lowerCamelCase__ = jitted_run_no_processor_list(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = jitted_run_processor_list(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
# scores should be equal
self.assertTrue(jnp.allclose(_lowerCAmelCase ,_lowerCAmelCase ,atol=1E-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() ,input_ids_comp.tolist() )
| 50 | 1 |
'''simple docstring'''
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
PIL_INTERPOLATION,
randn_tensor,
)
def A__ ( __lowerCAmelCase : int , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] ):
if isinstance(__lowerCAmelCase , torch.Tensor ):
return image
elif isinstance(__lowerCAmelCase , PIL.Image.Image ):
lowerCamelCase__ = [image]
if isinstance(image[0] , PIL.Image.Image ):
lowerCamelCase__ = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION["""lanczos"""] ) )[None, :] for i in image]
lowerCamelCase__ = np.concatenate(__lowerCAmelCase , axis=0 )
lowerCamelCase__ = np.array(__lowerCAmelCase ).astype(np.floataa ) / 255.0
lowerCamelCase__ = image.transpose(0 , 3 , 1 , 2 )
lowerCamelCase__ = 2.0 * image - 1.0
lowerCamelCase__ = torch.from_numpy(__lowerCAmelCase )
elif isinstance(image[0] , torch.Tensor ):
lowerCamelCase__ = torch.cat(__lowerCAmelCase , dim=0 )
return image
def A__ ( __lowerCAmelCase : List[str] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : int , __lowerCAmelCase : Any=0.9995 ):
if not isinstance(__lowerCAmelCase , np.ndarray ):
lowerCamelCase__ = True
lowerCamelCase__ = va.device
lowerCamelCase__ = va.cpu().numpy()
lowerCamelCase__ = va.cpu().numpy()
lowerCamelCase__ = np.sum(va * va / (np.linalg.norm(__lowerCAmelCase ) * np.linalg.norm(__lowerCAmelCase )) )
if np.abs(__lowerCAmelCase ) > DOT_THRESHOLD:
lowerCamelCase__ = (1 - t) * va + t * va
else:
lowerCamelCase__ = np.arccos(__lowerCAmelCase )
lowerCamelCase__ = np.sin(__lowerCAmelCase )
lowerCamelCase__ = theta_a * t
lowerCamelCase__ = np.sin(__lowerCAmelCase )
lowerCamelCase__ = np.sin(theta_a - theta_t ) / sin_theta_a
lowerCamelCase__ = sin_theta_t / sin_theta_a
lowerCamelCase__ = sa * va + sa * va
if inputs_are_torch:
lowerCamelCase__ = torch.from_numpy(__lowerCAmelCase ).to(__lowerCAmelCase )
return va
def A__ ( __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[Any] ):
lowerCamelCase__ = F.normalize(__lowerCAmelCase , dim=-1 )
lowerCamelCase__ = F.normalize(__lowerCAmelCase , dim=-1 )
return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )
def A__ ( __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : str ):
for param in model.parameters():
lowerCamelCase__ = value
class UpperCamelCase__ (a ):
'''simple docstring'''
def __init__( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,):
super().__init__()
self.register_modules(
vae=_lowerCAmelCase ,text_encoder=_lowerCAmelCase ,clip_model=_lowerCAmelCase ,tokenizer=_lowerCAmelCase ,unet=_lowerCAmelCase ,scheduler=_lowerCAmelCase ,feature_extractor=_lowerCAmelCase ,coca_model=_lowerCAmelCase ,coca_tokenizer=_lowerCAmelCase ,coca_transform=_lowerCAmelCase ,)
lowerCamelCase__ = (
feature_extractor.size
if isinstance(feature_extractor.size ,_lowerCAmelCase )
else feature_extractor.size["""shortest_edge"""]
)
lowerCamelCase__ = transforms.Normalize(mean=feature_extractor.image_mean ,std=feature_extractor.image_std )
set_requires_grad(self.text_encoder ,_lowerCAmelCase )
set_requires_grad(self.clip_model ,_lowerCAmelCase )
def UpperCamelCase_ ( self ,_lowerCAmelCase = "auto" ):
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
lowerCamelCase__ = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(_lowerCAmelCase )
def UpperCamelCase_ ( self ):
self.enable_attention_slicing(_lowerCAmelCase )
def UpperCamelCase_ ( self ):
set_requires_grad(self.vae ,_lowerCAmelCase )
def UpperCamelCase_ ( self ):
set_requires_grad(self.vae ,_lowerCAmelCase )
def UpperCamelCase_ ( self ):
set_requires_grad(self.unet ,_lowerCAmelCase )
def UpperCamelCase_ ( self ):
set_requires_grad(self.unet ,_lowerCAmelCase )
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
# get the original timestep using init_timestep
lowerCamelCase__ = min(int(num_inference_steps * strength ) ,_lowerCAmelCase )
lowerCamelCase__ = max(num_inference_steps - init_timestep ,0 )
lowerCamelCase__ = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase=None ):
if not isinstance(_lowerCAmelCase ,torch.Tensor ):
raise ValueError(F'''`image` has to be of type `torch.Tensor` but is {type(_lowerCAmelCase )}''' )
lowerCamelCase__ = image.to(device=_lowerCAmelCase ,dtype=_lowerCAmelCase )
if isinstance(_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = [
self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(_lowerCAmelCase )
]
lowerCamelCase__ = torch.cat(_lowerCAmelCase ,dim=0 )
else:
lowerCamelCase__ = self.vae.encode(_lowerCAmelCase ).latent_dist.sample(_lowerCAmelCase )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
lowerCamelCase__ = 0.1_8215 * init_latents
lowerCamelCase__ = init_latents.repeat_interleave(_lowerCAmelCase ,dim=0 )
lowerCamelCase__ = randn_tensor(init_latents.shape ,generator=_lowerCAmelCase ,device=_lowerCAmelCase ,dtype=_lowerCAmelCase )
# get latents
lowerCamelCase__ = self.scheduler.add_noise(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = init_latents
return latents
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
lowerCamelCase__ = self.coca_transform(_lowerCAmelCase ).unsqueeze(0 )
with torch.no_grad(), torch.cuda.amp.autocast():
lowerCamelCase__ = self.coca_model.generate(transformed_image.to(device=self.device ,dtype=self.coca_model.dtype ) )
lowerCamelCase__ = self.coca_tokenizer.decode(generated[0].cpu().numpy() )
return generated.split("""<end_of_text>""" )[0].replace("""<start_of_text>""" ,"""""" ).rstrip(""" .,""" )
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = self.feature_extractor.preprocess(_lowerCAmelCase )
lowerCamelCase__ = torch.from_numpy(clip_image_input["""pixel_values"""][0] ).unsqueeze(0 ).to(self.device ).half()
lowerCamelCase__ = self.clip_model.get_image_features(_lowerCAmelCase )
lowerCamelCase__ = image_embeddings_clip / image_embeddings_clip.norm(p=2 ,dim=-1 ,keepdim=_lowerCAmelCase )
lowerCamelCase__ = image_embeddings_clip.repeat_interleave(_lowerCAmelCase ,dim=0 )
return image_embeddings_clip
@torch.enable_grad()
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,):
lowerCamelCase__ = latents.detach().requires_grad_()
lowerCamelCase__ = self.scheduler.scale_model_input(_lowerCAmelCase ,_lowerCAmelCase )
# predict the noise residual
lowerCamelCase__ = self.unet(_lowerCAmelCase ,_lowerCAmelCase ,encoder_hidden_states=_lowerCAmelCase ).sample
if isinstance(self.scheduler ,(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
lowerCamelCase__ = self.scheduler.alphas_cumprod[timestep]
lowerCamelCase__ = 1 - alpha_prod_t
# compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
lowerCamelCase__ = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
lowerCamelCase__ = torch.sqrt(_lowerCAmelCase )
lowerCamelCase__ = pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler ,_lowerCAmelCase ):
lowerCamelCase__ = self.scheduler.sigmas[index]
lowerCamelCase__ = latents - sigma * noise_pred
else:
raise ValueError(F'''scheduler type {type(self.scheduler )} not supported''' )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
lowerCamelCase__ = 1 / 0.1_8215 * sample
lowerCamelCase__ = self.vae.decode(_lowerCAmelCase ).sample
lowerCamelCase__ = (image / 2 + 0.5).clamp(0 ,1 )
lowerCamelCase__ = transforms.Resize(self.feature_extractor_size )(_lowerCAmelCase )
lowerCamelCase__ = self.normalize(_lowerCAmelCase ).to(latents.dtype )
lowerCamelCase__ = self.clip_model.get_image_features(_lowerCAmelCase )
lowerCamelCase__ = image_embeddings_clip / image_embeddings_clip.norm(p=2 ,dim=-1 ,keepdim=_lowerCAmelCase )
lowerCamelCase__ = spherical_dist_loss(_lowerCAmelCase ,_lowerCAmelCase ).mean() * clip_guidance_scale
lowerCamelCase__ = -torch.autograd.grad(_lowerCAmelCase ,_lowerCAmelCase )[0]
if isinstance(self.scheduler ,_lowerCAmelCase ):
lowerCamelCase__ = latents.detach() + grads * (sigma**2)
lowerCamelCase__ = noise_pred_original
else:
lowerCamelCase__ = noise_pred_original - torch.sqrt(_lowerCAmelCase ) * grads
return noise_pred, latents
@torch.no_grad()
def __call__( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase = None ,_lowerCAmelCase = None ,_lowerCAmelCase = 5_12 ,_lowerCAmelCase = 5_12 ,_lowerCAmelCase = 0.6 ,_lowerCAmelCase = 50 ,_lowerCAmelCase = 7.5 ,_lowerCAmelCase = 1 ,_lowerCAmelCase = 0.0 ,_lowerCAmelCase = 1_00 ,_lowerCAmelCase = None ,_lowerCAmelCase = "pil" ,_lowerCAmelCase = True ,_lowerCAmelCase = 0.8 ,_lowerCAmelCase = 0.1 ,_lowerCAmelCase = 0.1 ,):
if isinstance(_lowerCAmelCase ,_lowerCAmelCase ) and len(_lowerCAmelCase ) != batch_size:
raise ValueError(F'''You have passed {batch_size} batch_size, but only {len(_lowerCAmelCase )} generators.''' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''' )
if isinstance(_lowerCAmelCase ,torch.Generator ) and batch_size > 1:
lowerCamelCase__ = [generator] + [None] * (batch_size - 1)
lowerCamelCase__ = [
("""model""", self.coca_model is None),
("""tokenizer""", self.coca_tokenizer is None),
("""transform""", self.coca_transform is None),
]
lowerCamelCase__ = [x[0] for x in coca_is_none if x[1]]
lowerCamelCase__ = """, """.join(_lowerCAmelCase )
# generate prompts with coca model if prompt is None
if content_prompt is None:
if len(_lowerCAmelCase ):
raise ValueError(
F'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
F'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''' )
lowerCamelCase__ = self.get_image_description(_lowerCAmelCase )
if style_prompt is None:
if len(_lowerCAmelCase ):
raise ValueError(
F'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
F''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''' )
lowerCamelCase__ = self.get_image_description(_lowerCAmelCase )
# get prompt text embeddings for content and style
lowerCamelCase__ = self.tokenizer(
_lowerCAmelCase ,padding="""max_length""" ,max_length=self.tokenizer.model_max_length ,truncation=_lowerCAmelCase ,return_tensors="""pt""" ,)
lowerCamelCase__ = self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]
lowerCamelCase__ = self.tokenizer(
_lowerCAmelCase ,padding="""max_length""" ,max_length=self.tokenizer.model_max_length ,truncation=_lowerCAmelCase ,return_tensors="""pt""" ,)
lowerCamelCase__ = self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]
lowerCamelCase__ = slerp(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
# duplicate text embeddings for each generation per prompt
lowerCamelCase__ = text_embeddings.repeat_interleave(_lowerCAmelCase ,dim=0 )
# set timesteps
lowerCamelCase__ = """offset""" in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
lowerCamelCase__ = {}
if accepts_offset:
lowerCamelCase__ = 1
self.scheduler.set_timesteps(_lowerCAmelCase ,**_lowerCAmelCase )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
self.scheduler.timesteps.to(self.device )
lowerCamelCase__ , lowerCamelCase__ = self.get_timesteps(_lowerCAmelCase ,_lowerCAmelCase ,self.device )
lowerCamelCase__ = timesteps[:1].repeat(_lowerCAmelCase )
# Preprocess image
lowerCamelCase__ = preprocess(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = self.prepare_latents(
_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,text_embeddings.dtype ,self.device ,_lowerCAmelCase )
lowerCamelCase__ = preprocess(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = self.prepare_latents(
_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,text_embeddings.dtype ,self.device ,_lowerCAmelCase )
lowerCamelCase__ = slerp(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
if clip_guidance_scale > 0:
lowerCamelCase__ = self.get_clip_image_embeddings(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = self.get_clip_image_embeddings(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = slerp(
_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
lowerCamelCase__ = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
lowerCamelCase__ = content_text_input.input_ids.shape[-1]
lowerCamelCase__ = self.tokenizer([""""""] ,padding="""max_length""" ,max_length=_lowerCAmelCase ,return_tensors="""pt""" )
lowerCamelCase__ = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt
lowerCamelCase__ = uncond_embeddings.repeat_interleave(_lowerCAmelCase ,dim=0 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
lowerCamelCase__ = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
lowerCamelCase__ = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
lowerCamelCase__ = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
lowerCamelCase__ = torch.randn(_lowerCAmelCase ,generator=_lowerCAmelCase ,device="""cpu""" ,dtype=_lowerCAmelCase ).to(
self.device )
else:
lowerCamelCase__ = torch.randn(_lowerCAmelCase ,generator=_lowerCAmelCase ,device=self.device ,dtype=_lowerCAmelCase )
else:
if latents.shape != latents_shape:
raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
lowerCamelCase__ = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
lowerCamelCase__ = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
lowerCamelCase__ = """eta""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
lowerCamelCase__ = {}
if accepts_eta:
lowerCamelCase__ = eta
# check if the scheduler accepts generator
lowerCamelCase__ = """generator""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
if accepts_generator:
lowerCamelCase__ = generator
with self.progress_bar(total=_lowerCAmelCase ):
for i, t in enumerate(_lowerCAmelCase ):
# expand the latents if we are doing classifier free guidance
lowerCamelCase__ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
lowerCamelCase__ = self.scheduler.scale_model_input(_lowerCAmelCase ,_lowerCAmelCase )
# predict the noise residual
lowerCamelCase__ = self.unet(_lowerCAmelCase ,_lowerCAmelCase ,encoder_hidden_states=_lowerCAmelCase ).sample
# perform classifier free guidance
if do_classifier_free_guidance:
lowerCamelCase__ , lowerCamelCase__ = noise_pred.chunk(2 )
lowerCamelCase__ = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
lowerCamelCase__ = (
text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
)
lowerCamelCase__ , lowerCamelCase__ = self.cond_fn(
_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,)
# compute the previous noisy sample x_t -> x_t-1
lowerCamelCase__ = self.scheduler.step(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,**_lowerCAmelCase ).prev_sample
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
lowerCamelCase__ = 1 / 0.1_8215 * latents
lowerCamelCase__ = self.vae.decode(_lowerCAmelCase ).sample
lowerCamelCase__ = (image / 2 + 0.5).clamp(0 ,1 )
lowerCamelCase__ = image.cpu().permute(0 ,2 ,3 ,1 ).numpy()
if output_type == "pil":
lowerCamelCase__ = self.numpy_to_pil(_lowerCAmelCase )
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=_lowerCAmelCase ,nsfw_content_detected=_lowerCAmelCase )
| 50 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase : Any = {
'configuration_groupvit': [
'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'GroupViTConfig',
'GroupViTOnnxConfig',
'GroupViTTextConfig',
'GroupViTVisionConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : List[str] = [
'GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'GroupViTModel',
'GroupViTPreTrainedModel',
'GroupViTTextModel',
'GroupViTVisionModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : List[str] = [
'TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFGroupViTModel',
'TFGroupViTPreTrainedModel',
'TFGroupViTTextModel',
'TFGroupViTVisionModel',
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
UpperCamelCase : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 50 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase : Dict = {
'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOnnxConfig'],
'tokenization_perceiver': ['PerceiverTokenizer'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Tuple = ['PerceiverFeatureExtractor']
UpperCamelCase : Union[str, Any] = ['PerceiverImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Optional[int] = [
'PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST',
'PerceiverForImageClassificationConvProcessing',
'PerceiverForImageClassificationFourier',
'PerceiverForImageClassificationLearned',
'PerceiverForMaskedLM',
'PerceiverForMultimodalAutoencoding',
'PerceiverForOpticalFlow',
'PerceiverForSequenceClassification',
'PerceiverLayer',
'PerceiverModel',
'PerceiverPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
from .tokenization_perceiver import PerceiverTokenizer
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_perceiver import PerceiverFeatureExtractor
from .image_processing_perceiver import PerceiverImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_perceiver import (
PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
PerceiverForImageClassificationConvProcessing,
PerceiverForImageClassificationFourier,
PerceiverForImageClassificationLearned,
PerceiverForMaskedLM,
PerceiverForMultimodalAutoencoding,
PerceiverForOpticalFlow,
PerceiverForSequenceClassification,
PerceiverLayer,
PerceiverModel,
PerceiverPreTrainedModel,
)
else:
import sys
UpperCamelCase : Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 50 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ):
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 50 | 1 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[Any] ):
lowerCamelCase__ = [0 for i in range(r + 1 )]
# nc0 = 1
lowerCamelCase__ = 1
for i in range(1 , n + 1 ):
# to compute current row from previous row.
lowerCamelCase__ = min(__lowerCAmelCase , __lowerCAmelCase )
while j > 0:
c[j] += c[j - 1]
j -= 1
return c[r]
print(binomial_coefficient(n=10, r=5))
| 50 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase : Union[str, Any] = {
'configuration_canine': ['CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CanineConfig'],
'tokenization_canine': ['CanineTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Any = [
'CANINE_PRETRAINED_MODEL_ARCHIVE_LIST',
'CanineForMultipleChoice',
'CanineForQuestionAnswering',
'CanineForSequenceClassification',
'CanineForTokenClassification',
'CanineLayer',
'CanineModel',
'CaninePreTrainedModel',
'load_tf_weights_in_canine',
]
if TYPE_CHECKING:
from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
from .tokenization_canine import CanineTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_canine import (
CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
CanineForMultipleChoice,
CanineForQuestionAnswering,
CanineForSequenceClassification,
CanineForTokenClassification,
CanineLayer,
CanineModel,
CaninePreTrainedModel,
load_tf_weights_in_canine,
)
else:
import sys
UpperCamelCase : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 50 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase : Any = {
'configuration_groupvit': [
'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'GroupViTConfig',
'GroupViTOnnxConfig',
'GroupViTTextConfig',
'GroupViTVisionConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : List[str] = [
'GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'GroupViTModel',
'GroupViTPreTrainedModel',
'GroupViTTextModel',
'GroupViTVisionModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : List[str] = [
'TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFGroupViTModel',
'TFGroupViTPreTrainedModel',
'TFGroupViTTextModel',
'TFGroupViTVisionModel',
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
UpperCamelCase : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 50 |
'''simple docstring'''
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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.
# this script dumps information about the environment
import os
import sys
import transformers
UpperCamelCase : int = '3'
print('Python version:', sys.version)
print('transformers version:', transformers.__version__)
try:
import torch
print('Torch version:', torch.__version__)
print('Cuda available:', torch.cuda.is_available())
print('Cuda version:', torch.version.cuda)
print('CuDNN version:', torch.backends.cudnn.version())
print('Number of GPUs available:', torch.cuda.device_count())
print('NCCL version:', torch.cuda.nccl.version())
except ImportError:
print('Torch version:', None)
try:
import deepspeed
print('DeepSpeed version:', deepspeed.__version__)
except ImportError:
print('DeepSpeed version:', None)
try:
import tensorflow as tf
print('TensorFlow version:', tf.__version__)
print('TF GPUs available:', bool(tf.config.list_physical_devices('GPU')))
print('Number of TF GPUs available:', len(tf.config.list_physical_devices('GPU')))
except ImportError:
print('TensorFlow version:', None)
| 50 | 1 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
UpperCamelCase : Union[str, Any] = 'docs/source/en/_toctree.yml'
def A__ ( __lowerCAmelCase : int ):
lowerCamelCase__ = defaultdict(__lowerCAmelCase )
lowerCamelCase__ = []
lowerCamelCase__ = []
for doc in doc_list:
if "local" in doc:
counts[doc["local"]] += 1
if doc["title"].lower() == "overview":
overview_doc.append({"""local""": doc["""local"""], """title""": doc["""title"""]} )
else:
new_doc_list.append(__lowerCAmelCase )
lowerCamelCase__ = new_doc_list
lowerCamelCase__ = [key for key, value in counts.items() if value > 1]
lowerCamelCase__ = []
for duplicate_key in duplicates:
lowerCamelCase__ = list({doc["""title"""] for doc in doc_list if doc["""local"""] == duplicate_key} )
if len(__lowerCAmelCase ) > 1:
raise ValueError(
F'''{duplicate_key} is present several times in the documentation table of content at '''
"""`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
"""others.""" )
# Only add this once
new_doc.append({"""local""": duplicate_key, """title""": titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in doc_list if """local""" not in counts or counts[doc["""local"""]] == 1] )
lowerCamelCase__ = sorted(__lowerCAmelCase , key=lambda __lowerCAmelCase : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(__lowerCAmelCase ) > 1:
raise ValueError("""{doc_list} has two 'overview' docs which is not allowed.""" )
overview_doc.extend(__lowerCAmelCase )
# Sort
return overview_doc
def A__ ( __lowerCAmelCase : Optional[Any]=False ):
with open(__lowerCAmelCase , encoding="""utf-8""" ) as f:
lowerCamelCase__ = yaml.safe_load(f.read() )
# Get to the API doc
lowerCamelCase__ = 0
while content[api_idx]["title"] != "API":
api_idx += 1
lowerCamelCase__ = content[api_idx]["""sections"""]
# Then to the model doc
lowerCamelCase__ = 0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
lowerCamelCase__ = api_doc[scheduler_idx]["""sections"""]
lowerCamelCase__ = clean_doc_toc(__lowerCAmelCase )
lowerCamelCase__ = False
if new_scheduler_doc != scheduler_doc:
lowerCamelCase__ = True
if overwrite:
lowerCamelCase__ = new_scheduler_doc
if diff:
if overwrite:
lowerCamelCase__ = api_doc
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(__lowerCAmelCase , allow_unicode=__lowerCAmelCase ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
def A__ ( __lowerCAmelCase : List[str]=False ):
with open(__lowerCAmelCase , encoding="""utf-8""" ) as f:
lowerCamelCase__ = yaml.safe_load(f.read() )
# Get to the API doc
lowerCamelCase__ = 0
while content[api_idx]["title"] != "API":
api_idx += 1
lowerCamelCase__ = content[api_idx]["""sections"""]
# Then to the model doc
lowerCamelCase__ = 0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
lowerCamelCase__ = False
lowerCamelCase__ = api_doc[pipeline_idx]["""sections"""]
lowerCamelCase__ = []
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
lowerCamelCase__ = pipeline_doc["""section"""]
lowerCamelCase__ = clean_doc_toc(__lowerCAmelCase )
if overwrite:
lowerCamelCase__ = new_sub_pipeline_doc
new_pipeline_docs.append(__lowerCAmelCase )
# sort overall pipeline doc
lowerCamelCase__ = clean_doc_toc(__lowerCAmelCase )
if new_pipeline_docs != pipeline_docs:
lowerCamelCase__ = True
if overwrite:
lowerCamelCase__ = new_pipeline_docs
if diff:
if overwrite:
lowerCamelCase__ = api_doc
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(__lowerCAmelCase , allow_unicode=__lowerCAmelCase ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
if __name__ == "__main__":
UpperCamelCase : Tuple = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
UpperCamelCase : Any = parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite)
| 50 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase : Tuple = logging.get_logger(__name__)
UpperCamelCase : Union[str, Any] = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = 'gpt_bigcode'
_UpperCamelCase = ['past_key_values']
_UpperCamelCase = {
'hidden_size': 'n_embd',
'max_position_embeddings': 'n_positions',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self ,_lowerCAmelCase=5_02_57 ,_lowerCAmelCase=10_24 ,_lowerCAmelCase=7_68 ,_lowerCAmelCase=12 ,_lowerCAmelCase=12 ,_lowerCAmelCase=None ,_lowerCAmelCase="gelu_pytorch_tanh" ,_lowerCAmelCase=0.1 ,_lowerCAmelCase=0.1 ,_lowerCAmelCase=0.1 ,_lowerCAmelCase=1E-5 ,_lowerCAmelCase=0.02 ,_lowerCAmelCase=True ,_lowerCAmelCase=True ,_lowerCAmelCase=5_02_56 ,_lowerCAmelCase=5_02_56 ,_lowerCAmelCase=True ,_lowerCAmelCase=True ,_lowerCAmelCase=True ,**_lowerCAmelCase ,):
lowerCamelCase__ = vocab_size
lowerCamelCase__ = n_positions
lowerCamelCase__ = n_embd
lowerCamelCase__ = n_layer
lowerCamelCase__ = n_head
lowerCamelCase__ = n_inner
lowerCamelCase__ = activation_function
lowerCamelCase__ = resid_pdrop
lowerCamelCase__ = embd_pdrop
lowerCamelCase__ = attn_pdrop
lowerCamelCase__ = layer_norm_epsilon
lowerCamelCase__ = initializer_range
lowerCamelCase__ = scale_attn_weights
lowerCamelCase__ = use_cache
lowerCamelCase__ = attention_softmax_in_fpaa
lowerCamelCase__ = scale_attention_softmax_in_fpaa
lowerCamelCase__ = multi_query
lowerCamelCase__ = bos_token_id
lowerCamelCase__ = eos_token_id
super().__init__(bos_token_id=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase ,**_lowerCAmelCase )
| 50 | 1 |
'''simple docstring'''
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def A__ ( __lowerCAmelCase : dict ):
return (data["data"], data["target"])
def A__ ( __lowerCAmelCase : np.ndarray , __lowerCAmelCase : np.ndarray , __lowerCAmelCase : np.ndarray ):
lowerCamelCase__ = XGBRegressor(verbosity=0 , random_state=42 )
xgb.fit(__lowerCAmelCase , __lowerCAmelCase )
# Predict target for test data
lowerCamelCase__ = xgb.predict(__lowerCAmelCase )
lowerCamelCase__ = predictions.reshape(len(__lowerCAmelCase ) , 1 )
return predictions
def A__ ( ):
lowerCamelCase__ = fetch_california_housing()
lowerCamelCase__ , lowerCamelCase__ = data_handling(__lowerCAmelCase )
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = train_test_split(
__lowerCAmelCase , __lowerCAmelCase , test_size=0.25 , random_state=1 )
lowerCamelCase__ = xgboost(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# Error printing
print(F'''Mean Absolute Error : {mean_absolute_error(__lowerCAmelCase , __lowerCAmelCase )}''' )
print(F'''Mean Square Error : {mean_squared_error(__lowerCAmelCase , __lowerCAmelCase )}''' )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 50 |
'''simple docstring'''
from PIL import Image
def A__ ( __lowerCAmelCase : Image , __lowerCAmelCase : float ):
def brightness(__lowerCAmelCase : int ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError("""level must be between -255.0 (black) and 255.0 (white)""" )
return img.point(__lowerCAmelCase )
if __name__ == "__main__":
# Load image
with Image.open('image_data/lena.jpg') as img:
# Change brightness to 100
UpperCamelCase : Union[str, Any] = change_brightness(img, 1_00)
brigt_img.save('image_data/lena_brightness.png', format='png')
| 50 | 1 |
'''simple docstring'''
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
UpperCamelCase : Optional[int] = '\\n@misc{wu2016googles,\n title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n and Jeffrey Dean},\n year={2016},\n eprint={1609.08144},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n'
UpperCamelCase : List[str] = '\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe \'GLEU score\'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore\'s range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n'
UpperCamelCase : Any = '\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n predictions (list of str): list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references (list of list of str): list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n \'google_bleu\': google_bleu score\n\nExamples:\n Example 1:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results["google_bleu"], 2))\n 0.44\n\n Example 2:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results["google_bleu"], 2))\n 0.61\n\n Example 3:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n >>> print(round(results["google_bleu"], 2))\n 0.53\n\n Example 4:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n >>> print(round(results["google_bleu"], 2))\n 0.4\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION )
class UpperCamelCase__ (datasets.Metric ):
'''simple docstring'''
def UpperCamelCase_ ( self ):
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
"""predictions""": datasets.Sequence(datasets.Value("""string""" ,id="""token""" ) ,id="""sequence""" ),
"""references""": datasets.Sequence(
datasets.Sequence(datasets.Value("""string""" ,id="""token""" ) ,id="""sequence""" ) ,id="""references""" ),
} ) ,)
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase = 1 ,_lowerCAmelCase = 4 ,):
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=_lowerCAmelCase ,hypotheses=_lowerCAmelCase ,min_len=_lowerCAmelCase ,max_len=_lowerCAmelCase )
}
| 50 |
'''simple docstring'''
def A__ ( ):
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
UpperCamelCase : Dict = generate_large_matrix()
UpperCamelCase : Any = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7, 7, 6], [-1, -2, -3]],
grid,
)
def A__ ( __lowerCAmelCase : list[list[int]] ):
assert all(row == sorted(__lowerCAmelCase , reverse=__lowerCAmelCase ) for row in grid )
assert all(list(__lowerCAmelCase ) == sorted(__lowerCAmelCase , reverse=__lowerCAmelCase ) for col in zip(*__lowerCAmelCase ) )
def A__ ( __lowerCAmelCase : list[int] ):
lowerCamelCase__ = 0
lowerCamelCase__ = len(__lowerCAmelCase ) - 1
# Edge cases such as no values or all numbers are negative.
if not array or array[0] < 0:
return 0
while right + 1 > left:
lowerCamelCase__ = (left + right) // 2
lowerCamelCase__ = array[mid]
# Num must be negative and the index must be greater than or equal to 0.
if num < 0 and array[mid - 1] >= 0:
return mid
if num >= 0:
lowerCamelCase__ = mid + 1
else:
lowerCamelCase__ = mid - 1
# No negative numbers so return the last index of the array + 1 which is the length.
return len(__lowerCAmelCase )
def A__ ( __lowerCAmelCase : list[list[int]] ):
lowerCamelCase__ = 0
lowerCamelCase__ = len(grid[0] )
for i in range(len(__lowerCAmelCase ) ):
lowerCamelCase__ = find_negative_index(grid[i][:bound] )
total += bound
return (len(__lowerCAmelCase ) * len(grid[0] )) - total
def A__ ( __lowerCAmelCase : list[list[int]] ):
return len([number for row in grid for number in row if number < 0] )
def A__ ( __lowerCAmelCase : list[list[int]] ):
lowerCamelCase__ = 0
for row in grid:
for i, number in enumerate(__lowerCAmelCase ):
if number < 0:
total += len(__lowerCAmelCase ) - i
break
return total
def A__ ( ):
from timeit import timeit
print("""Running benchmarks""" )
lowerCamelCase__ = (
"""from __main__ import count_negatives_binary_search, """
"""count_negatives_brute_force, count_negatives_brute_force_with_break, grid"""
)
for func in (
"count_negatives_binary_search", # took 0.7727 seconds
"count_negatives_brute_force_with_break", # took 4.6505 seconds
"count_negatives_brute_force", # took 12.8160 seconds
):
lowerCamelCase__ = timeit(F'''{func}(grid=grid)''' , setup=__lowerCAmelCase , number=500 )
print(F'''{func}() took {time:0.4f} seconds''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 50 | 1 |
'''simple docstring'''
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('Googling.....')
UpperCamelCase : int = 'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
UpperCamelCase : List[str] = requests.get(url, headers={'UserAgent': UserAgent().random})
# res.raise_for_status()
with open('project1a.html', 'wb') as out_file: # only for knowing the class
for data in res.iter_content(1_00_00):
out_file.write(data)
UpperCamelCase : int = BeautifulSoup(res.text, 'html.parser')
UpperCamelCase : Optional[Any] = list(soup.select('.eZt8xd'))[:5]
print(len(links))
for link in links:
if link.text == "Maps":
webbrowser.open(link.get('href'))
else:
webbrowser.open(F'https://google.com{link.get("href")}')
| 50 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
UpperCamelCase : List[Any] = 'examples/'
UpperCamelCase : int = {
'examples': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
'init': (re.compile(r'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'),
'setup': (re.compile(r'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), r'\1version="VERSION",'),
'doc': (re.compile(r'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), 'release = "VERSION"\n'),
}
UpperCamelCase : Any = {
'init': 'src/transformers/__init__.py',
'setup': 'setup.py',
}
UpperCamelCase : Any = 'README.md'
def A__ ( __lowerCAmelCase : Any , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[int] ):
with open(__lowerCAmelCase , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
lowerCamelCase__ = f.read()
lowerCamelCase__ , lowerCamelCase__ = REPLACE_PATTERNS[pattern]
lowerCamelCase__ = replace.replace("""VERSION""" , __lowerCAmelCase )
lowerCamelCase__ = re_pattern.sub(__lowerCAmelCase , __lowerCAmelCase )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.write(__lowerCAmelCase )
def A__ ( __lowerCAmelCase : str ):
for folder, directories, fnames in os.walk(__lowerCAmelCase ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove("""research_projects""" )
if "legacy" in directories:
directories.remove("""legacy""" )
for fname in fnames:
if fname.endswith(""".py""" ):
update_version_in_file(os.path.join(__lowerCAmelCase , __lowerCAmelCase ) , __lowerCAmelCase , pattern="""examples""" )
def A__ ( __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[Any]=False ):
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
if not patch:
update_version_in_examples(__lowerCAmelCase )
def A__ ( ):
lowerCamelCase__ = """🤗 Transformers currently provides the following architectures"""
lowerCamelCase__ = """1. Want to contribute a new model?"""
with open(__lowerCAmelCase , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
lowerCamelCase__ = f.readlines()
# Find the start of the list.
lowerCamelCase__ = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
lowerCamelCase__ = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith("""1.""" ):
lowerCamelCase__ = lines[index].replace(
"""https://huggingface.co/docs/transformers/main/model_doc""" , """https://huggingface.co/docs/transformers/model_doc""" , )
index += 1
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.writelines(__lowerCAmelCase )
def A__ ( ):
with open(REPLACE_FILES["""init"""] , """r""" ) as f:
lowerCamelCase__ = f.read()
lowerCamelCase__ = REPLACE_PATTERNS["""init"""][0].search(__lowerCAmelCase ).groups()[0]
return packaging.version.parse(__lowerCAmelCase )
def A__ ( __lowerCAmelCase : Union[str, Any]=False ):
lowerCamelCase__ = get_version()
if patch and default_version.is_devrelease:
raise ValueError("""Can't create a patch version from the dev branch, checkout a released version!""" )
if default_version.is_devrelease:
lowerCamelCase__ = default_version.base_version
elif patch:
lowerCamelCase__ = F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
else:
lowerCamelCase__ = F'''{default_version.major}.{default_version.minor + 1}.0'''
# Now let's ask nicely if that's the right one.
lowerCamelCase__ = input(F'''Which version are you releasing? [{default_version}]''' )
if len(__lowerCAmelCase ) == 0:
lowerCamelCase__ = default_version
print(F'''Updating version to {version}.''' )
global_version_update(__lowerCAmelCase , patch=__lowerCAmelCase )
if not patch:
print("""Cleaning main README, don't forget to run `make fix-copies`.""" )
clean_main_ref_in_model_list()
def A__ ( ):
lowerCamelCase__ = get_version()
lowerCamelCase__ = F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
lowerCamelCase__ = current_version.base_version
# Check with the user we got that right.
lowerCamelCase__ = input(F'''Which version are we developing now? [{dev_version}]''' )
if len(__lowerCAmelCase ) == 0:
lowerCamelCase__ = dev_version
print(F'''Updating version to {version}.''' )
global_version_update(__lowerCAmelCase )
print("""Cleaning main README, don't forget to run `make fix-copies`.""" )
clean_main_ref_in_model_list()
if __name__ == "__main__":
UpperCamelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')
parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')
UpperCamelCase : Any = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print('Nothing to do after a patch :-)')
else:
post_release_work()
| 50 | 1 |
'''simple docstring'''
import math
def A__ ( __lowerCAmelCase : int ):
lowerCamelCase__ = [True] * n
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
lowerCamelCase__ = i * 2
while index < n:
lowerCamelCase__ = False
lowerCamelCase__ = index + i
lowerCamelCase__ = [2]
for i in range(3 , __lowerCAmelCase , 2 ):
if is_prime[i]:
primes.append(__lowerCAmelCase )
return primes
def A__ ( __lowerCAmelCase : int = 9999_6666_3333 ):
lowerCamelCase__ = math.floor(math.sqrt(__lowerCAmelCase ) ) + 100
lowerCamelCase__ = prime_sieve(__lowerCAmelCase )
lowerCamelCase__ = 0
lowerCamelCase__ = 0
lowerCamelCase__ = primes[prime_index]
while (last_prime**2) <= limit:
lowerCamelCase__ = primes[prime_index + 1]
lowerCamelCase__ = last_prime**2
lowerCamelCase__ = next_prime**2
# Get numbers divisible by lps(current)
lowerCamelCase__ = lower_bound + last_prime
while upper_bound > current <= limit:
matches_sum += current
current += last_prime
# Reset the upper_bound
while (upper_bound - next_prime) > limit:
upper_bound -= next_prime
# Add the numbers divisible by ups(current)
lowerCamelCase__ = upper_bound - next_prime
while current > lower_bound:
matches_sum += current
current -= next_prime
# Remove the numbers divisible by both ups and lps
lowerCamelCase__ = 0
while upper_bound > current <= limit:
if current <= lower_bound:
# Increment the current number
current += last_prime * next_prime
continue
if current > limit:
break
# Remove twice since it was added by both ups and lps
matches_sum -= current * 2
# Increment the current number
current += last_prime * next_prime
# Setup for next pair
lowerCamelCase__ = next_prime
prime_index += 1
return matches_sum
if __name__ == "__main__":
print(solution())
| 50 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
UpperCamelCase : List[str] = logging.get_logger(__name__)
UpperCamelCase : Dict = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
UpperCamelCase : int = {
'vocab_file': {
'squeezebert/squeezebert-uncased': (
'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'
),
'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt',
'squeezebert/squeezebert-mnli-headless': (
'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'squeezebert/squeezebert-uncased': (
'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'
),
'squeezebert/squeezebert-mnli': (
'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'
),
'squeezebert/squeezebert-mnli-headless': (
'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'
),
},
}
UpperCamelCase : Tuple = {
'squeezebert/squeezebert-uncased': 5_12,
'squeezebert/squeezebert-mnli': 5_12,
'squeezebert/squeezebert-mnli-headless': 5_12,
}
UpperCamelCase : Dict = {
'squeezebert/squeezebert-uncased': {'do_lower_case': True},
'squeezebert/squeezebert-mnli': {'do_lower_case': True},
'squeezebert/squeezebert-mnli-headless': {'do_lower_case': True},
}
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = VOCAB_FILES_NAMES
_UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
_UpperCamelCase = PRETRAINED_INIT_CONFIGURATION
_UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCamelCase = SqueezeBertTokenizer
def __init__( self ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=True ,_lowerCAmelCase="[UNK]" ,_lowerCAmelCase="[SEP]" ,_lowerCAmelCase="[PAD]" ,_lowerCAmelCase="[CLS]" ,_lowerCAmelCase="[MASK]" ,_lowerCAmelCase=True ,_lowerCAmelCase=None ,**_lowerCAmelCase ,):
super().__init__(
_lowerCAmelCase ,tokenizer_file=_lowerCAmelCase ,do_lower_case=_lowerCAmelCase ,unk_token=_lowerCAmelCase ,sep_token=_lowerCAmelCase ,pad_token=_lowerCAmelCase ,cls_token=_lowerCAmelCase ,mask_token=_lowerCAmelCase ,tokenize_chinese_chars=_lowerCAmelCase ,strip_accents=_lowerCAmelCase ,**_lowerCAmelCase ,)
lowerCamelCase__ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("""lowercase""" ,_lowerCAmelCase ) != do_lower_case
or normalizer_state.get("""strip_accents""" ,_lowerCAmelCase ) != strip_accents
or normalizer_state.get("""handle_chinese_chars""" ,_lowerCAmelCase ) != tokenize_chinese_chars
):
lowerCamelCase__ = getattr(_lowerCAmelCase ,normalizer_state.pop("""type""" ) )
lowerCamelCase__ = do_lower_case
lowerCamelCase__ = strip_accents
lowerCamelCase__ = tokenize_chinese_chars
lowerCamelCase__ = normalizer_class(**_lowerCAmelCase )
lowerCamelCase__ = do_lower_case
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase=None ):
lowerCamelCase__ = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase = None ):
lowerCamelCase__ = [self.sep_token_id]
lowerCamelCase__ = [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 UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase = None ):
lowerCamelCase__ = self._tokenizer.model.save(_lowerCAmelCase ,name=_lowerCAmelCase )
return tuple(_lowerCAmelCase )
| 50 | 1 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def A__ ( __lowerCAmelCase : int ):
if num < 0:
raise ValueError("""Number should not be negative.""" )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 50 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def A__ ( __lowerCAmelCase : Any ):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0x4_e_0_0 and cp <= 0x9_f_f_f)
or (cp >= 0x3_4_0_0 and cp <= 0x4_d_b_f) #
or (cp >= 0x2_0_0_0_0 and cp <= 0x2_a_6_d_f) #
or (cp >= 0x2_a_7_0_0 and cp <= 0x2_b_7_3_f) #
or (cp >= 0x2_b_7_4_0 and cp <= 0x2_b_8_1_f) #
or (cp >= 0x2_b_8_2_0 and cp <= 0x2_c_e_a_f) #
or (cp >= 0xf_9_0_0 and cp <= 0xf_a_f_f)
or (cp >= 0x2_f_8_0_0 and cp <= 0x2_f_a_1_f) #
): #
return True
return False
def A__ ( __lowerCAmelCase : str ):
# word like '180' or '身高' or '神'
for char in word:
lowerCamelCase__ = ord(__lowerCAmelCase )
if not _is_chinese_char(__lowerCAmelCase ):
return 0
return 1
def A__ ( __lowerCAmelCase : List[str] ):
lowerCamelCase__ = set()
for token in tokens:
lowerCamelCase__ = len(__lowerCAmelCase ) > 1 and is_chinese(__lowerCAmelCase )
if chinese_word:
word_set.add(__lowerCAmelCase )
lowerCamelCase__ = list(__lowerCAmelCase )
return word_list
def A__ ( __lowerCAmelCase : List[str] , __lowerCAmelCase : set() ):
if not chinese_word_set:
return bert_tokens
lowerCamelCase__ = max([len(__lowerCAmelCase ) for w in chinese_word_set] )
lowerCamelCase__ = bert_tokens
lowerCamelCase__ , lowerCamelCase__ = 0, len(__lowerCAmelCase )
while start < end:
lowerCamelCase__ = True
if is_chinese(bert_word[start] ):
lowerCamelCase__ = min(end - start , __lowerCAmelCase )
for i in range(__lowerCAmelCase , 1 , -1 ):
lowerCamelCase__ = """""".join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1 , start + i ):
lowerCamelCase__ = """##""" + bert_word[j]
lowerCamelCase__ = start + i
lowerCamelCase__ = False
break
if single_word:
start += 1
return bert_word
def A__ ( __lowerCAmelCase : List[str] , __lowerCAmelCase : LTP , __lowerCAmelCase : BertTokenizer ):
lowerCamelCase__ = []
for i in range(0 , len(__lowerCAmelCase ) , 100 ):
lowerCamelCase__ = ltp_tokenizer.pipeline(lines[i : i + 100] , tasks=["""cws"""] ).cws
lowerCamelCase__ = [get_chinese_word(__lowerCAmelCase ) for r in res]
ltp_res.extend(__lowerCAmelCase )
assert len(__lowerCAmelCase ) == len(__lowerCAmelCase )
lowerCamelCase__ = []
for i in range(0 , len(__lowerCAmelCase ) , 100 ):
lowerCamelCase__ = bert_tokenizer(lines[i : i + 100] , add_special_tokens=__lowerCAmelCase , truncation=__lowerCAmelCase , max_length=512 )
bert_res.extend(res["""input_ids"""] )
assert len(__lowerCAmelCase ) == len(__lowerCAmelCase )
lowerCamelCase__ = []
for input_ids, chinese_word in zip(__lowerCAmelCase , __lowerCAmelCase ):
lowerCamelCase__ = []
for id in input_ids:
lowerCamelCase__ = bert_tokenizer._convert_id_to_token(__lowerCAmelCase )
input_tokens.append(__lowerCAmelCase )
lowerCamelCase__ = add_sub_symbol(__lowerCAmelCase , __lowerCAmelCase )
lowerCamelCase__ = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(__lowerCAmelCase ):
if token[:2] == "##":
lowerCamelCase__ = token[2:]
# save chinese tokens' pos
if len(__lowerCAmelCase ) == 1 and _is_chinese_char(ord(__lowerCAmelCase ) ):
ref_id.append(__lowerCAmelCase )
ref_ids.append(__lowerCAmelCase )
assert len(__lowerCAmelCase ) == len(__lowerCAmelCase )
return ref_ids
def A__ ( __lowerCAmelCase : Optional[int] ):
# For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
# If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
with open(args.file_name , """r""" , encoding="""utf-8""" ) as f:
lowerCamelCase__ = f.readlines()
lowerCamelCase__ = [line.strip() for line in data if len(__lowerCAmelCase ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
lowerCamelCase__ = LTP(args.ltp ) # faster in GPU device
lowerCamelCase__ = BertTokenizer.from_pretrained(args.bert )
lowerCamelCase__ = prepare_ref(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
with open(args.save_path , """w""" , encoding="""utf-8""" ) as f:
lowerCamelCase__ = [json.dumps(__lowerCAmelCase ) + """\n""" for ref in ref_ids]
f.writelines(__lowerCAmelCase )
if __name__ == "__main__":
UpperCamelCase : Optional[int] = argparse.ArgumentParser(description='prepare_chinese_ref')
parser.add_argument(
'--file_name',
required=False,
type=str,
default='./resources/chinese-demo.txt',
help='file need process, same as training data in lm',
)
parser.add_argument(
'--ltp',
required=False,
type=str,
default='./resources/ltp',
help='resources for LTP tokenizer, usually a path',
)
parser.add_argument(
'--bert',
required=False,
type=str,
default='./resources/robert',
help='resources for Bert tokenizer',
)
parser.add_argument(
'--save_path',
required=False,
type=str,
default='./resources/ref.txt',
help='path to save res',
)
UpperCamelCase : Any = parser.parse_args()
main(args)
| 50 | 1 |
'''simple docstring'''
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
MaxNewTokensCriteria,
MaxTimeCriteria,
StoppingCriteriaList,
validate_stopping_criteria,
)
@require_torch
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
lowerCamelCase__ = 3
lowerCamelCase__ = 2_50
lowerCamelCase__ = ids_tensor((batch_size, length) ,_lowerCAmelCase )
lowerCamelCase__ = torch.ones((batch_size, length) ,device=_lowerCAmelCase ,dtype=torch.float ) / length
return input_ids, scores
def UpperCamelCase_ ( self ):
lowerCamelCase__ , lowerCamelCase__ = self._get_tensors(5 )
lowerCamelCase__ = StoppingCriteriaList(
[
MaxLengthCriteria(max_length=10 ),
MaxTimeCriteria(max_time=0.1 ),
] )
self.assertFalse(criteria(_lowerCAmelCase ,_lowerCAmelCase ) )
lowerCamelCase__ , lowerCamelCase__ = self._get_tensors(9 )
self.assertFalse(criteria(_lowerCAmelCase ,_lowerCAmelCase ) )
lowerCamelCase__ , lowerCamelCase__ = self._get_tensors(10 )
self.assertTrue(criteria(_lowerCAmelCase ,_lowerCAmelCase ) )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = MaxLengthCriteria(max_length=10 )
lowerCamelCase__ , lowerCamelCase__ = self._get_tensors(5 )
self.assertFalse(criteria(_lowerCAmelCase ,_lowerCAmelCase ) )
lowerCamelCase__ , lowerCamelCase__ = self._get_tensors(9 )
self.assertFalse(criteria(_lowerCAmelCase ,_lowerCAmelCase ) )
lowerCamelCase__ , lowerCamelCase__ = self._get_tensors(10 )
self.assertTrue(criteria(_lowerCAmelCase ,_lowerCAmelCase ) )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = MaxNewTokensCriteria(start_length=5 ,max_new_tokens=5 )
lowerCamelCase__ , lowerCamelCase__ = self._get_tensors(5 )
self.assertFalse(criteria(_lowerCAmelCase ,_lowerCAmelCase ) )
lowerCamelCase__ , lowerCamelCase__ = self._get_tensors(9 )
self.assertFalse(criteria(_lowerCAmelCase ,_lowerCAmelCase ) )
lowerCamelCase__ , lowerCamelCase__ = self._get_tensors(10 )
self.assertTrue(criteria(_lowerCAmelCase ,_lowerCAmelCase ) )
lowerCamelCase__ = StoppingCriteriaList([criteria] )
self.assertEqual(criteria_list.max_length ,10 )
def UpperCamelCase_ ( self ):
lowerCamelCase__ , lowerCamelCase__ = self._get_tensors(5 )
lowerCamelCase__ = MaxTimeCriteria(max_time=0.1 )
self.assertFalse(criteria(_lowerCAmelCase ,_lowerCAmelCase ) )
lowerCamelCase__ = MaxTimeCriteria(max_time=0.1 ,initial_timestamp=time.time() - 0.2 )
self.assertTrue(criteria(_lowerCAmelCase ,_lowerCAmelCase ) )
def UpperCamelCase_ ( self ):
validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10 )] ) ,10 )
with self.assertWarns(_lowerCAmelCase ):
validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10 )] ) ,11 )
lowerCamelCase__ = validate_stopping_criteria(StoppingCriteriaList() ,11 )
self.assertEqual(len(_lowerCAmelCase ) ,1 )
| 50 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase : Tuple = logging.get_logger(__name__)
def A__ ( __lowerCAmelCase : int ):
lowerCamelCase__ = DPTConfig(embedding_type="""hybrid""" )
if "large" in checkpoint_url:
lowerCamelCase__ = 1024
lowerCamelCase__ = 4096
lowerCamelCase__ = 24
lowerCamelCase__ = 16
lowerCamelCase__ = [5, 11, 17, 23]
lowerCamelCase__ = [256, 512, 1024, 1024]
lowerCamelCase__ = (1, 384, 384)
if "nyu" or "midas" in checkpoint_url:
lowerCamelCase__ = 768
lowerCamelCase__ = [1, 1, 1, 0.5]
lowerCamelCase__ = [256, 512, 768, 768]
lowerCamelCase__ = 150
lowerCamelCase__ = 16
lowerCamelCase__ = (1, 384, 384)
lowerCamelCase__ = False
lowerCamelCase__ = """project"""
if "ade" in checkpoint_url:
lowerCamelCase__ = True
lowerCamelCase__ = 768
lowerCamelCase__ = [1, 1, 1, 0.5]
lowerCamelCase__ = 150
lowerCamelCase__ = 16
lowerCamelCase__ = """huggingface/label-files"""
lowerCamelCase__ = """ade20k-id2label.json"""
lowerCamelCase__ = json.load(open(cached_download(hf_hub_url(__lowerCAmelCase , __lowerCAmelCase , repo_type="""dataset""" ) ) , """r""" ) )
lowerCamelCase__ = {int(__lowerCAmelCase ): v for k, v in idalabel.items()}
lowerCamelCase__ = idalabel
lowerCamelCase__ = {v: k for k, v in idalabel.items()}
lowerCamelCase__ = [1, 150, 480, 480]
return config, expected_shape
def A__ ( __lowerCAmelCase : Optional[int] ):
lowerCamelCase__ = ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
for k in ignore_keys:
state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
def A__ ( __lowerCAmelCase : List[Any] ):
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
lowerCamelCase__ = name.replace("""pretrained.model""" , """dpt.encoder""" )
if "pretrained.model" in name:
lowerCamelCase__ = name.replace("""pretrained.model""" , """dpt.embeddings""" )
if "patch_embed" in name:
lowerCamelCase__ = name.replace("""patch_embed""" , """""" )
if "pos_embed" in name:
lowerCamelCase__ = name.replace("""pos_embed""" , """position_embeddings""" )
if "attn.proj" in name:
lowerCamelCase__ = name.replace("""attn.proj""" , """attention.output.dense""" )
if "proj" in name and "project" not in name:
lowerCamelCase__ = name.replace("""proj""" , """projection""" )
if "blocks" in name:
lowerCamelCase__ = name.replace("""blocks""" , """layer""" )
if "mlp.fc1" in name:
lowerCamelCase__ = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
lowerCamelCase__ = name.replace("""mlp.fc2""" , """output.dense""" )
if "norm1" in name and "backbone" not in name:
lowerCamelCase__ = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name and "backbone" not in name:
lowerCamelCase__ = name.replace("""norm2""" , """layernorm_after""" )
if "scratch.output_conv" in name:
lowerCamelCase__ = name.replace("""scratch.output_conv""" , """head""" )
if "scratch" in name:
lowerCamelCase__ = name.replace("""scratch""" , """neck""" )
if "layer1_rn" in name:
lowerCamelCase__ = name.replace("""layer1_rn""" , """convs.0""" )
if "layer2_rn" in name:
lowerCamelCase__ = name.replace("""layer2_rn""" , """convs.1""" )
if "layer3_rn" in name:
lowerCamelCase__ = name.replace("""layer3_rn""" , """convs.2""" )
if "layer4_rn" in name:
lowerCamelCase__ = name.replace("""layer4_rn""" , """convs.3""" )
if "refinenet" in name:
lowerCamelCase__ = int(name[len("""neck.refinenet""" ) : len("""neck.refinenet""" ) + 1] )
# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
lowerCamelCase__ = name.replace(F'''refinenet{layer_idx}''' , F'''fusion_stage.layers.{abs(layer_idx-4 )}''' )
if "out_conv" in name:
lowerCamelCase__ = name.replace("""out_conv""" , """projection""" )
if "resConfUnit1" in name:
lowerCamelCase__ = name.replace("""resConfUnit1""" , """residual_layer1""" )
if "resConfUnit2" in name:
lowerCamelCase__ = name.replace("""resConfUnit2""" , """residual_layer2""" )
if "conv1" in name:
lowerCamelCase__ = name.replace("""conv1""" , """convolution1""" )
if "conv2" in name:
lowerCamelCase__ = name.replace("""conv2""" , """convolution2""" )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess1.0.project.0""" , """neck.reassemble_stage.readout_projects.0.0""" )
if "pretrained.act_postprocess2.0.project.0" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess2.0.project.0""" , """neck.reassemble_stage.readout_projects.1.0""" )
if "pretrained.act_postprocess3.0.project.0" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess3.0.project.0""" , """neck.reassemble_stage.readout_projects.2.0""" )
if "pretrained.act_postprocess4.0.project.0" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess4.0.project.0""" , """neck.reassemble_stage.readout_projects.3.0""" )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess1.3""" , """neck.reassemble_stage.layers.0.projection""" )
if "pretrained.act_postprocess1.4" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess1.4""" , """neck.reassemble_stage.layers.0.resize""" )
if "pretrained.act_postprocess2.3" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess2.3""" , """neck.reassemble_stage.layers.1.projection""" )
if "pretrained.act_postprocess2.4" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess2.4""" , """neck.reassemble_stage.layers.1.resize""" )
if "pretrained.act_postprocess3.3" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess3.3""" , """neck.reassemble_stage.layers.2.projection""" )
if "pretrained.act_postprocess4.3" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess4.3""" , """neck.reassemble_stage.layers.3.projection""" )
if "pretrained.act_postprocess4.4" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess4.4""" , """neck.reassemble_stage.layers.3.resize""" )
if "pretrained" in name:
lowerCamelCase__ = name.replace("""pretrained""" , """dpt""" )
if "bn" in name:
lowerCamelCase__ = name.replace("""bn""" , """batch_norm""" )
if "head" in name:
lowerCamelCase__ = name.replace("""head""" , """head.head""" )
if "encoder.norm" in name:
lowerCamelCase__ = name.replace("""encoder.norm""" , """layernorm""" )
if "auxlayer" in name:
lowerCamelCase__ = name.replace("""auxlayer""" , """auxiliary_head.head""" )
if "backbone" in name:
lowerCamelCase__ = name.replace("""backbone""" , """backbone.bit.encoder""" )
if ".." in name:
lowerCamelCase__ = name.replace("""..""" , """.""" )
if "stem.conv" in name:
lowerCamelCase__ = name.replace("""stem.conv""" , """bit.embedder.convolution""" )
if "blocks" in name:
lowerCamelCase__ = name.replace("""blocks""" , """layers""" )
if "convolution" in name and "backbone" in name:
lowerCamelCase__ = name.replace("""convolution""" , """conv""" )
if "layer" in name and "backbone" in name:
lowerCamelCase__ = name.replace("""layer""" , """layers""" )
if "backbone.bit.encoder.bit" in name:
lowerCamelCase__ = name.replace("""backbone.bit.encoder.bit""" , """backbone.bit""" )
if "embedder.conv" in name:
lowerCamelCase__ = name.replace("""embedder.conv""" , """embedder.convolution""" )
if "backbone.bit.encoder.stem.norm" in name:
lowerCamelCase__ = name.replace("""backbone.bit.encoder.stem.norm""" , """backbone.bit.embedder.norm""" )
return name
def A__ ( __lowerCAmelCase : str , __lowerCAmelCase : int ):
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowerCamelCase__ = state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight''' )
lowerCamelCase__ = state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
lowerCamelCase__ = in_proj_weight[: config.hidden_size, :]
lowerCamelCase__ = in_proj_bias[: config.hidden_size]
lowerCamelCase__ = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowerCamelCase__ = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowerCamelCase__ = in_proj_weight[
-config.hidden_size :, :
]
lowerCamelCase__ = in_proj_bias[-config.hidden_size :]
def A__ ( ):
lowerCamelCase__ = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowerCamelCase__ = Image.open(requests.get(__lowerCAmelCase , stream=__lowerCAmelCase ).raw )
return im
@torch.no_grad()
def A__ ( __lowerCAmelCase : Dict , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Any ):
lowerCamelCase__ , lowerCamelCase__ = get_dpt_config(__lowerCAmelCase )
# load original state_dict from URL
# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
lowerCamelCase__ = torch.load(__lowerCAmelCase , map_location="""cpu""" )
# remove certain keys
remove_ignore_keys_(__lowerCAmelCase )
# rename keys
for key in state_dict.copy().keys():
lowerCamelCase__ = state_dict.pop(__lowerCAmelCase )
lowerCamelCase__ = val
# read in qkv matrices
read_in_q_k_v(__lowerCAmelCase , __lowerCAmelCase )
# load HuggingFace model
lowerCamelCase__ = DPTForSemanticSegmentation(__lowerCAmelCase ) if """ade""" in checkpoint_url else DPTForDepthEstimation(__lowerCAmelCase )
model.load_state_dict(__lowerCAmelCase )
model.eval()
# Check outputs on an image
lowerCamelCase__ = 480 if """ade""" in checkpoint_url else 384
lowerCamelCase__ = DPTImageProcessor(size=__lowerCAmelCase )
lowerCamelCase__ = prepare_img()
lowerCamelCase__ = image_processor(__lowerCAmelCase , return_tensors="""pt""" )
# forward pass
lowerCamelCase__ = model(**__lowerCAmelCase ).logits if """ade""" in checkpoint_url else model(**__lowerCAmelCase ).predicted_depth
if show_prediction:
lowerCamelCase__ = (
torch.nn.functional.interpolate(
outputs.unsqueeze(1 ) , size=(image.size[1], image.size[0]) , mode="""bicubic""" , align_corners=__lowerCAmelCase , )
.squeeze()
.cpu()
.numpy()
)
Image.fromarray((prediction / prediction.max()) * 255 ).show()
if pytorch_dump_folder_path is not None:
Path(__lowerCAmelCase ).mkdir(exist_ok=__lowerCAmelCase )
print(F'''Saving model 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:
model.push_to_hub("""ybelkada/dpt-hybrid-midas""" )
image_processor.push_to_hub("""ybelkada/dpt-hybrid-midas""" )
if __name__ == "__main__":
UpperCamelCase : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint_url',
default='https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt',
type=str,
help='URL of the original DPT checkpoint you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default=None,
type=str,
required=False,
help='Path to the output PyTorch model directory.',
)
parser.add_argument(
'--push_to_hub',
action='store_true',
)
parser.add_argument(
'--model_name',
default='dpt-large',
type=str,
help='Name of the model, in case you\'re pushing to the hub.',
)
parser.add_argument(
'--show_prediction',
action='store_true',
)
UpperCamelCase : List[str] = parser.parse_args()
convert_dpt_checkpoint(
args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
)
| 50 | 1 |
'''simple docstring'''
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def A__ ( __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : str ):
lowerCamelCase__ = multiprocessing.Manager()
lowerCamelCase__ = manager.list()
lowerCamelCase__ = multiprocessing.Process(target=__lowerCAmelCase , args=(check_program, result, timeout) )
p.start()
p.join(timeout=timeout + 1 )
if p.is_alive():
p.kill()
if not result:
result.append("""timed out""" )
return {
"task_id": task_id,
"passed": result[0] == "passed",
"result": result[0],
"completion_id": completion_id,
}
def A__ ( __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Tuple ):
with create_tempdir():
# These system calls are needed when cleaning up tempdir.
import os
import shutil
lowerCamelCase__ = shutil.rmtree
lowerCamelCase__ = os.rmdir
lowerCamelCase__ = os.chdir
# Disable functionalities that can make destructive changes to the test.
reliability_guard()
# Run program.
try:
lowerCamelCase__ = {}
with swallow_io():
with time_limit(__lowerCAmelCase ):
exec(__lowerCAmelCase , __lowerCAmelCase )
result.append("""passed""" )
except TimeoutException:
result.append("""timed out""" )
except BaseException as e:
result.append(F'''failed: {e}''' )
# Needed for cleaning up.
lowerCamelCase__ = rmtree
lowerCamelCase__ = rmdir
lowerCamelCase__ = chdir
@contextlib.contextmanager
def A__ ( __lowerCAmelCase : Tuple ):
def signal_handler(__lowerCAmelCase : str , __lowerCAmelCase : Tuple ):
raise TimeoutException("""Timed out!""" )
signal.setitimer(signal.ITIMER_REAL , __lowerCAmelCase )
signal.signal(signal.SIGALRM , __lowerCAmelCase )
try:
yield
finally:
signal.setitimer(signal.ITIMER_REAL , 0 )
@contextlib.contextmanager
def A__ ( ):
lowerCamelCase__ = WriteOnlyStringIO()
with contextlib.redirect_stdout(__lowerCAmelCase ):
with contextlib.redirect_stderr(__lowerCAmelCase ):
with redirect_stdin(__lowerCAmelCase ):
yield
@contextlib.contextmanager
def A__ ( ):
with tempfile.TemporaryDirectory() as dirname:
with chdir(__lowerCAmelCase ):
yield dirname
class UpperCamelCase__ (a ):
'''simple docstring'''
pass
class UpperCamelCase__ (io.StringIO ):
'''simple docstring'''
def UpperCamelCase_ ( self ,*_lowerCAmelCase ,**_lowerCAmelCase ):
raise OSError
def UpperCamelCase_ ( self ,*_lowerCAmelCase ,**_lowerCAmelCase ):
raise OSError
def UpperCamelCase_ ( self ,*_lowerCAmelCase ,**_lowerCAmelCase ):
raise OSError
def UpperCamelCase_ ( self ,*_lowerCAmelCase ,**_lowerCAmelCase ):
return False
class UpperCamelCase__ (contextlib._RedirectStream ): # type: ignore
'''simple docstring'''
_UpperCamelCase = 'stdin'
@contextlib.contextmanager
def A__ ( __lowerCAmelCase : Optional[int] ):
if root == ".":
yield
return
lowerCamelCase__ = os.getcwd()
os.chdir(__lowerCAmelCase )
try:
yield
except BaseException as exc:
raise exc
finally:
os.chdir(__lowerCAmelCase )
def A__ ( __lowerCAmelCase : List[str]=None ):
if maximum_memory_bytes is not None:
import resource
resource.setrlimit(resource.RLIMIT_AS , (maximum_memory_bytes, maximum_memory_bytes) )
resource.setrlimit(resource.RLIMIT_DATA , (maximum_memory_bytes, maximum_memory_bytes) )
if not platform.uname().system == "Darwin":
resource.setrlimit(resource.RLIMIT_STACK , (maximum_memory_bytes, maximum_memory_bytes) )
faulthandler.disable()
import builtins
lowerCamelCase__ = None
lowerCamelCase__ = None
import os
lowerCamelCase__ = """1"""
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
import shutil
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
import subprocess
lowerCamelCase__ = None # type: ignore
lowerCamelCase__ = None
import sys
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
| 50 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase : Tuple = {
'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
'tokenization_mvp': ['MvpTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : str = ['MvpTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Optional[int] = [
'MVP_PRETRAINED_MODEL_ARCHIVE_LIST',
'MvpForCausalLM',
'MvpForConditionalGeneration',
'MvpForQuestionAnswering',
'MvpForSequenceClassification',
'MvpModel',
'MvpPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
UpperCamelCase : Optional[int] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 50 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
UpperCamelCase : Union[str, Any] = transforms.Compose(
[
transforms.Resize((2_56, 2_56)),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def A__ ( __lowerCAmelCase : Optional[int] ):
if isinstance(__lowerCAmelCase , torch.Tensor ):
return image
elif isinstance(__lowerCAmelCase , PIL.Image.Image ):
lowerCamelCase__ = [image]
lowerCamelCase__ = [trans(img.convert("""RGB""" ) ) for img in image]
lowerCamelCase__ = torch.stack(__lowerCAmelCase )
return image
class UpperCamelCase__ (a ):
'''simple docstring'''
def __init__( self ,_lowerCAmelCase ,_lowerCAmelCase ):
super().__init__()
# make sure scheduler can always be converted to DDIM
lowerCamelCase__ = DDIMScheduler.from_config(scheduler.config )
self.register_modules(unet=_lowerCAmelCase ,scheduler=_lowerCAmelCase )
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
if strength < 0 or strength > 1:
raise ValueError(F'''The value of strength should in [0.0, 1.0] but is {strength}''' )
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
# get the original timestep using init_timestep
lowerCamelCase__ = min(int(num_inference_steps * strength ) ,_lowerCAmelCase )
lowerCamelCase__ = max(num_inference_steps - init_timestep ,0 )
lowerCamelCase__ = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase=None ):
if not isinstance(_lowerCAmelCase ,(torch.Tensor, PIL.Image.Image, list) ):
raise ValueError(
F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_lowerCAmelCase )}''' )
lowerCamelCase__ = image.to(device=_lowerCAmelCase ,dtype=_lowerCAmelCase )
if isinstance(_lowerCAmelCase ,_lowerCAmelCase ) and len(_lowerCAmelCase ) != batch_size:
raise ValueError(
F'''You have passed a list of generators of length {len(_lowerCAmelCase )}, but requested an effective batch'''
F''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )
lowerCamelCase__ = init_latents.shape
lowerCamelCase__ = randn_tensor(_lowerCAmelCase ,generator=_lowerCAmelCase ,device=_lowerCAmelCase ,dtype=_lowerCAmelCase )
# get latents
print("""add noise to latents at timestep""" ,_lowerCAmelCase )
lowerCamelCase__ = self.scheduler.add_noise(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = init_latents
return latents
@torch.no_grad()
def __call__( self ,_lowerCAmelCase = None ,_lowerCAmelCase = 0.8 ,_lowerCAmelCase = 1 ,_lowerCAmelCase = None ,_lowerCAmelCase = 0.0 ,_lowerCAmelCase = 50 ,_lowerCAmelCase = None ,_lowerCAmelCase = "pil" ,_lowerCAmelCase = True ,):
self.check_inputs(_lowerCAmelCase )
# 2. Preprocess image
lowerCamelCase__ = preprocess(_lowerCAmelCase )
# 3. set timesteps
self.scheduler.set_timesteps(_lowerCAmelCase ,device=self.device )
lowerCamelCase__ , lowerCamelCase__ = self.get_timesteps(_lowerCAmelCase ,_lowerCAmelCase ,self.device )
lowerCamelCase__ = timesteps[:1].repeat(_lowerCAmelCase )
# 4. Prepare latent variables
lowerCamelCase__ = self.prepare_latents(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,self.unet.dtype ,self.device ,_lowerCAmelCase )
lowerCamelCase__ = latents
# 5. Denoising loop
for t in self.progress_bar(_lowerCAmelCase ):
# 1. predict noise model_output
lowerCamelCase__ = self.unet(_lowerCAmelCase ,_lowerCAmelCase ).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
lowerCamelCase__ = self.scheduler.step(
_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,eta=_lowerCAmelCase ,use_clipped_model_output=_lowerCAmelCase ,generator=_lowerCAmelCase ,).prev_sample
lowerCamelCase__ = (image / 2 + 0.5).clamp(0 ,1 )
lowerCamelCase__ = image.cpu().permute(0 ,2 ,3 ,1 ).numpy()
if output_type == "pil":
lowerCamelCase__ = self.numpy_to_pil(_lowerCAmelCase )
if not return_dict:
return (image, latent_timestep.item())
return ImagePipelineOutput(images=_lowerCAmelCase )
| 50 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
UpperCamelCase : Tuple = logging.get_logger(__name__)
UpperCamelCase : Dict = {
'Salesforce/codegen-350M-nl': 'https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json',
'Salesforce/codegen-350M-multi': 'https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json',
'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json',
'Salesforce/codegen-2B-nl': 'https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json',
'Salesforce/codegen-2B-multi': 'https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json',
'Salesforce/codegen-2B-mono': 'https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json',
'Salesforce/codegen-6B-nl': 'https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json',
'Salesforce/codegen-6B-multi': 'https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json',
'Salesforce/codegen-6B-mono': 'https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json',
'Salesforce/codegen-16B-nl': 'https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json',
'Salesforce/codegen-16B-multi': 'https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json',
'Salesforce/codegen-16B-mono': 'https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json',
}
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = 'codegen'
_UpperCamelCase = {
'max_position_embeddings': 'n_positions',
'hidden_size': 'n_embd',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self ,_lowerCAmelCase=5_04_00 ,_lowerCAmelCase=20_48 ,_lowerCAmelCase=20_48 ,_lowerCAmelCase=40_96 ,_lowerCAmelCase=28 ,_lowerCAmelCase=16 ,_lowerCAmelCase=64 ,_lowerCAmelCase=None ,_lowerCAmelCase="gelu_new" ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=1E-5 ,_lowerCAmelCase=0.02 ,_lowerCAmelCase=True ,_lowerCAmelCase=5_02_56 ,_lowerCAmelCase=5_02_56 ,_lowerCAmelCase=False ,**_lowerCAmelCase ,):
lowerCamelCase__ = vocab_size
lowerCamelCase__ = n_ctx
lowerCamelCase__ = n_positions
lowerCamelCase__ = n_embd
lowerCamelCase__ = n_layer
lowerCamelCase__ = n_head
lowerCamelCase__ = n_inner
lowerCamelCase__ = rotary_dim
lowerCamelCase__ = activation_function
lowerCamelCase__ = resid_pdrop
lowerCamelCase__ = embd_pdrop
lowerCamelCase__ = attn_pdrop
lowerCamelCase__ = layer_norm_epsilon
lowerCamelCase__ = initializer_range
lowerCamelCase__ = use_cache
lowerCamelCase__ = bos_token_id
lowerCamelCase__ = eos_token_id
super().__init__(
bos_token_id=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase ,tie_word_embeddings=_lowerCAmelCase ,**_lowerCAmelCase )
class UpperCamelCase__ (a ):
'''simple docstring'''
def __init__( self ,_lowerCAmelCase ,_lowerCAmelCase = "default" ,_lowerCAmelCase = None ,_lowerCAmelCase = False ,):
super().__init__(_lowerCAmelCase ,task=_lowerCAmelCase ,patching_specs=_lowerCAmelCase ,use_past=_lowerCAmelCase )
if not getattr(self._config ,"""pad_token_id""" ,_lowerCAmelCase ):
# TODO: how to do that better?
lowerCamelCase__ = 0
@property
def UpperCamelCase_ ( self ):
lowerCamelCase__ = OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}} )
if self.use_past:
self.fill_with_past_key_values_(_lowerCAmelCase ,direction="""inputs""" )
lowerCamelCase__ = {0: """batch""", 1: """past_sequence + sequence"""}
else:
lowerCamelCase__ = {0: """batch""", 1: """sequence"""}
return common_inputs
@property
def UpperCamelCase_ ( self ):
return self._config.n_layer
@property
def UpperCamelCase_ ( self ):
return self._config.n_head
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase = -1 ,_lowerCAmelCase = -1 ,_lowerCAmelCase = False ,_lowerCAmelCase = None ,):
lowerCamelCase__ = super(_lowerCAmelCase ,self ).generate_dummy_inputs(
_lowerCAmelCase ,batch_size=_lowerCAmelCase ,seq_length=_lowerCAmelCase ,is_pair=_lowerCAmelCase ,framework=_lowerCAmelCase )
# We need to order the input in the way they appears in the forward()
lowerCamelCase__ = OrderedDict({"""input_ids""": common_inputs["""input_ids"""]} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
lowerCamelCase__ , lowerCamelCase__ = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
lowerCamelCase__ = seqlen + 2
lowerCamelCase__ = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
lowerCamelCase__ = [
(torch.zeros(_lowerCAmelCase ), torch.zeros(_lowerCAmelCase )) for _ in range(self.num_layers )
]
lowerCamelCase__ = common_inputs["""attention_mask"""]
if self.use_past:
lowerCamelCase__ = ordered_inputs["""attention_mask"""].dtype
lowerCamelCase__ = torch.cat(
[ordered_inputs["""attention_mask"""], torch.ones(_lowerCAmelCase ,_lowerCAmelCase ,dtype=_lowerCAmelCase )] ,dim=1 )
return ordered_inputs
@property
def UpperCamelCase_ ( self ):
return 13
| 50 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
UpperCamelCase : Optional[int] = {
'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = 'mgp-str'
def __init__( self ,_lowerCAmelCase=[32, 1_28] ,_lowerCAmelCase=4 ,_lowerCAmelCase=3 ,_lowerCAmelCase=27 ,_lowerCAmelCase=38 ,_lowerCAmelCase=5_02_57 ,_lowerCAmelCase=3_05_22 ,_lowerCAmelCase=7_68 ,_lowerCAmelCase=12 ,_lowerCAmelCase=12 ,_lowerCAmelCase=4.0 ,_lowerCAmelCase=True ,_lowerCAmelCase=False ,_lowerCAmelCase=1E-5 ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=False ,_lowerCAmelCase=0.02 ,**_lowerCAmelCase ,):
super().__init__(**_lowerCAmelCase )
lowerCamelCase__ = image_size
lowerCamelCase__ = patch_size
lowerCamelCase__ = num_channels
lowerCamelCase__ = max_token_length
lowerCamelCase__ = num_character_labels
lowerCamelCase__ = num_bpe_labels
lowerCamelCase__ = num_wordpiece_labels
lowerCamelCase__ = hidden_size
lowerCamelCase__ = num_hidden_layers
lowerCamelCase__ = num_attention_heads
lowerCamelCase__ = mlp_ratio
lowerCamelCase__ = distilled
lowerCamelCase__ = layer_norm_eps
lowerCamelCase__ = drop_rate
lowerCamelCase__ = qkv_bias
lowerCamelCase__ = attn_drop_rate
lowerCamelCase__ = drop_path_rate
lowerCamelCase__ = output_aa_attentions
lowerCamelCase__ = initializer_range
| 50 |
'''simple docstring'''
# 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
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase : int = {
'configuration_xmod': [
'XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XmodConfig',
'XmodOnnxConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Tuple = [
'XMOD_PRETRAINED_MODEL_ARCHIVE_LIST',
'XmodForCausalLM',
'XmodForMaskedLM',
'XmodForMultipleChoice',
'XmodForQuestionAnswering',
'XmodForSequenceClassification',
'XmodForTokenClassification',
'XmodModel',
'XmodPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xmod import (
XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
XmodForCausalLM,
XmodForMaskedLM,
XmodForMultipleChoice,
XmodForQuestionAnswering,
XmodForSequenceClassification,
XmodForTokenClassification,
XmodModel,
XmodPreTrainedModel,
)
else:
import sys
UpperCamelCase : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 50 | 1 |
'''simple docstring'''
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler
from transformers import GPTaLMHeadModel
def A__ ( __lowerCAmelCase : Tuple=32 , __lowerCAmelCase : int=10 , __lowerCAmelCase : Optional[Any]=100 , __lowerCAmelCase : Union[str, Any]=1026 , __lowerCAmelCase : List[str]=True , __lowerCAmelCase : List[Any]="data/tokenized_stories_train_wikitext103.jbl" , __lowerCAmelCase : Optional[int]="igf_context_pairs.jbl" , ):
set_seed(3 )
# generate train_data and objective_set
lowerCamelCase__ , lowerCamelCase__ = generate_datasets(
__lowerCAmelCase , __lowerCAmelCase , number=__lowerCAmelCase , min_len=1026 , trim=__lowerCAmelCase )
# keeps model same across runs
set_seed(4 )
# model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
# can we train on GPU?
lowerCamelCase__ = torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu""" )
# load pretrained model
lowerCamelCase__ = load_gpta("""gpt2""" ).to(__lowerCAmelCase )
print("""computing perplexity on objective set""" )
lowerCamelCase__ = compute_perplexity(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ).item()
print("""perplexity on objective set:""" , __lowerCAmelCase )
# collect igf pairs and save to file demo.jbl
collect_objective_set(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# clean up, delete model and data we don't need anymore
del model, train_data, objective_set
torch.cuda.empty_cache()
def A__ ( __lowerCAmelCase : Dict , __lowerCAmelCase : Any=15 , __lowerCAmelCase : List[str]=128 , __lowerCAmelCase : Any=100 , __lowerCAmelCase : Optional[int]="igf_model.pt" , ):
set_seed(42 )
# Load pre-trained model
lowerCamelCase__ = GPTaLMHeadModel.from_pretrained("""gpt2""" )
# Initialize secondary learner to use embedding weights of model
lowerCamelCase__ = SecondaryLearner(__lowerCAmelCase )
# Train secondary learner
lowerCamelCase__ = train_secondary_learner(
__lowerCAmelCase , __lowerCAmelCase , max_epochs=__lowerCAmelCase , batch_size=__lowerCAmelCase , eval_freq=100 , igf_model_path=__lowerCAmelCase , )
del model, secondary_learner_train_data
torch.cuda.empty_cache()
return secondary_learner
def A__ ( __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : str=32 , __lowerCAmelCase : Tuple=1000 , __lowerCAmelCase : str=16 , __lowerCAmelCase : Optional[int]=1.0 , __lowerCAmelCase : List[str]=recopy_gpta , __lowerCAmelCase : Optional[int]=None , __lowerCAmelCase : str=10 , __lowerCAmelCase : int="gpt2_finetuned.pt" , ):
lowerCamelCase__ = torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu""" )
lowerCamelCase__ = RandomSampler(__lowerCAmelCase )
lowerCamelCase__ = DataLoader(__lowerCAmelCase , sampler=__lowerCAmelCase )
lowerCamelCase__ = max_steps // (len(__lowerCAmelCase )) + 1
lowerCamelCase__ = 0
lowerCamelCase__ = torch.zeros((1, context_len) , dtype=torch.long , device=__lowerCAmelCase )
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = recopy_model(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
model.train()
if secondary_learner is not None:
secondary_learner.to(__lowerCAmelCase )
secondary_learner.eval()
lowerCamelCase__ = []
lowerCamelCase__ = 0
lowerCamelCase__ = []
lowerCamelCase__ = []
# Compute the performance of the transformer model at the beginning
lowerCamelCase__ = compute_perplexity(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
test_perps.append(__lowerCAmelCase )
print("""Test perplexity, step""" , __lowerCAmelCase , """:""" , __lowerCAmelCase )
for epoch in range(int(__lowerCAmelCase ) ):
for step, example in enumerate(__lowerCAmelCase ):
torch.cuda.empty_cache()
lowerCamelCase__ = random.randint(0 , example.size(2 ) - context_len - 1 )
lowerCamelCase__ = example[0, 0, start : start + context_len]
lm_optimizer.zero_grad()
lowerCamelCase__ = model(__lowerCAmelCase , labels=__lowerCAmelCase )
lowerCamelCase__ = True
if secondary_learner is not None:
lowerCamelCase__ = secondary_learner.forward(
torch.tensor(__lowerCAmelCase , dtype=torch.long , device=__lowerCAmelCase ).unsqueeze(0 ) )[0].item()
observed_qs.append(float(__lowerCAmelCase ) )
# Here we implement the simple non-constant threshold for the predicted IG(X) value
# We will decay the selectivity of our secondary learner filter from
# 1 standard deviation above average to 1 below average after 10 batches.
if global_step == 10:
lowerCamelCase__ = -1
if predicted_q < threshold:
lowerCamelCase__ = False
# If we passed the filter, add the context to the batch!
if do_backprop:
contexts.append(np.array(context.cpu() ) )
lowerCamelCase__ = outputs[0]
lm_loss.backward()
examples += 1
del outputs
# Once the batch is filled with enough contexts, backprop on the batch.
if examples == batch_size:
torch.cuda.empty_cache()
lowerCamelCase__ = 0
# Do LM backprop
torch.nn.utils.clip_grad_norm_(model.parameters() , 3.0 )
lm_optimizer.step()
lm_scheduler.step() # Update learning rate schedule
global_step += 1
# Compute the performance of the transformer model at this batch
if global_step % eval_interval == 0:
lowerCamelCase__ = compute_perplexity(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
test_perps.append(__lowerCAmelCase )
print("""Test perplexity, step""" , __lowerCAmelCase , """:""" , __lowerCAmelCase )
# Break out of the loop after 60 batches
if max_steps > 0 and global_step > 60:
break
if max_steps > 0 and global_step > 60:
break
# save finetuned transformer model
torch.save(model.state_dict() , __lowerCAmelCase )
torch.cuda.empty_cache()
# Do some cleaning up so we can reinitialize for the next run of this function
del lm_optimizer
del lm_scheduler
return model
def A__ ( ):
lowerCamelCase__ = argparse.ArgumentParser(description="""Fine-tune a transformer model with IGF on a language modeling task""" )
# Required parameters
parser.add_argument(
"""--data_dir""" , default=__lowerCAmelCase , type=__lowerCAmelCase , required=__lowerCAmelCase , help="""The input data dir. Should contain data files for WikiText.""" , )
parser.add_argument(
"""--model_name_or_path""" , default=__lowerCAmelCase , type=__lowerCAmelCase , required=__lowerCAmelCase , help="""Path to pretrained model or model identifier from huggingface.co/models""" , )
parser.add_argument(
"""--data_file""" , type=__lowerCAmelCase , default=__lowerCAmelCase , help=(
"""A jbl file containing tokenized data which can be split as objective dataset, """
"""train_dataset and test_dataset."""
) , )
parser.add_argument(
"""--igf_data_file""" , type=__lowerCAmelCase , default=__lowerCAmelCase , help="""A jbl file containing the context and information gain pairs to train secondary learner.""" , )
parser.add_argument(
"""--output_dir""" , default=__lowerCAmelCase , type=__lowerCAmelCase , required=__lowerCAmelCase , help="""The output directory where the final fine-tuned model is stored.""" , )
parser.add_argument(
"""--tokenizer_name""" , default=__lowerCAmelCase , type=__lowerCAmelCase , help="""Pretrained tokenizer name or path if not the same as model_name""" , )
parser.add_argument("""--seed""" , type=__lowerCAmelCase , default=__lowerCAmelCase , help="""A seed for reproducible training.""" )
parser.add_argument(
"""--context_len""" , default=32 , type=__lowerCAmelCase , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--size_objective_set""" , default=100 , type=__lowerCAmelCase , help="""number of articles that are long enough to be used as our objective set""" , )
parser.add_argument(
"""--eval_freq""" , default=100 , type=__lowerCAmelCase , help="""secondary model evaluation is triggered at eval_freq""" )
parser.add_argument("""--max_steps""" , default=1000 , type=__lowerCAmelCase , help="""To calculate training epochs""" )
parser.add_argument(
"""--secondary_learner_batch_size""" , default=128 , type=__lowerCAmelCase , help="""batch size of training data for secondary learner""" , )
parser.add_argument(
"""--batch_size""" , default=16 , type=__lowerCAmelCase , help="""batch size of training data of language model(gpt2) """ )
parser.add_argument(
"""--eval_interval""" , default=10 , type=__lowerCAmelCase , help=(
"""decay the selectivity of our secondary learner filter from"""
"""1 standard deviation above average to 1 below average after 10 batches"""
) , )
parser.add_argument(
"""--number""" , default=100 , type=__lowerCAmelCase , help="""The number of examples split to be used as objective_set/test_data""" )
parser.add_argument(
"""--min_len""" , default=1026 , type=__lowerCAmelCase , help="""The minimum length of the article to be used as objective set""" )
parser.add_argument(
"""--secondary_learner_max_epochs""" , default=15 , type=__lowerCAmelCase , help="""number of epochs to train secondary learner""" )
parser.add_argument("""--trim""" , default=__lowerCAmelCase , type=__lowerCAmelCase , help="""truncate the example if it exceeds context length""" )
parser.add_argument(
"""--threshold""" , default=1.0 , type=__lowerCAmelCase , help=(
"""The threshold value used by secondary learner to filter the train_data and allow only"""
""" informative data as input to the model"""
) , )
parser.add_argument("""--finetuned_model_name""" , default="""gpt2_finetuned.pt""" , type=__lowerCAmelCase , help="""finetuned_model_name""" )
parser.add_argument(
"""--recopy_model""" , default=__lowerCAmelCase , type=__lowerCAmelCase , help="""Reset the model to the original pretrained GPT-2 weights after each iteration""" , )
# function calls
# Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
generate_n_pairs(
context_len=32 , max_steps=10 , size_objective_set=100 , min_len=1026 , trim=__lowerCAmelCase , data_file="""data/tokenized_stories_train_wikitext103.jbl""" , igf_data_file="""igf_context_pairs.jbl""" , )
# Load train data for secondary learner
lowerCamelCase__ = joblib.load("""data/IGF_values.jbl""" )
# Train secondary learner
lowerCamelCase__ = training_secondary_learner(
__lowerCAmelCase , secondary_learner_max_epochs=15 , secondary_learner_batch_size=128 , eval_freq=100 , igf_model_path="""igf_model.pt""" , )
# load pretrained gpt2 model
lowerCamelCase__ = GPTaLMHeadModel.from_pretrained("""gpt2""" )
set_seed(42 )
# Generate train and test data to train and evaluate gpt2 model
lowerCamelCase__ , lowerCamelCase__ = generate_datasets(
context_len=32 , file="""data/tokenized_stories_train_wikitext103.jbl""" , number=100 , min_len=1026 , trim=__lowerCAmelCase )
# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
finetune(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , context_len=32 , max_steps=1000 , batch_size=16 , threshold=1.0 , recopy_model=__lowerCAmelCase , secondary_learner=__lowerCAmelCase , eval_interval=10 , finetuned_model_name="""gpt2_finetuned.pt""" , )
if __name__ == "__main__":
main()
| 50 |
'''simple docstring'''
from typing import Union
import fire
import torch
from tqdm import tqdm
def A__ ( __lowerCAmelCase : str , __lowerCAmelCase : str = "cpu" , __lowerCAmelCase : Union[str, None] = None ):
lowerCamelCase__ = torch.load(__lowerCAmelCase , map_location=__lowerCAmelCase )
for k, v in tqdm(state_dict.items() ):
if not isinstance(__lowerCAmelCase , torch.Tensor ):
raise TypeError("""FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin""" )
lowerCamelCase__ = v.half()
if save_path is None: # overwrite src_path
lowerCamelCase__ = src_path
torch.save(__lowerCAmelCase , __lowerCAmelCase )
if __name__ == "__main__":
fire.Fire(convert)
| 50 | 1 |
'''simple docstring'''
from collections.abc import Generator
def A__ ( ):
lowerCamelCase__ , lowerCamelCase__ = 0, 1
while True:
lowerCamelCase__ , lowerCamelCase__ = b, a + b
yield b
def A__ ( __lowerCAmelCase : int = 1000 ):
lowerCamelCase__ = 1
lowerCamelCase__ = fibonacci_generator()
while len(str(next(__lowerCAmelCase ) ) ) < n:
answer += 1
return answer + 1
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 50 |
'''simple docstring'''
import os
from pathlib import Path
def A__ ( ):
from torch.utils.cpp_extension import load
lowerCamelCase__ = Path(__lowerCAmelCase ).resolve().parent.parent.parent / """kernels""" / """deformable_detr"""
lowerCamelCase__ = [
root / filename
for filename in [
"""vision.cpp""",
os.path.join("""cpu""" , """ms_deform_attn_cpu.cpp""" ),
os.path.join("""cuda""" , """ms_deform_attn_cuda.cu""" ),
]
]
load(
"""MultiScaleDeformableAttention""" , __lowerCAmelCase , with_cuda=__lowerCAmelCase , extra_include_paths=[str(__lowerCAmelCase )] , extra_cflags=["""-DWITH_CUDA=1"""] , extra_cuda_cflags=[
"""-DCUDA_HAS_FP16=1""",
"""-D__CUDA_NO_HALF_OPERATORS__""",
"""-D__CUDA_NO_HALF_CONVERSIONS__""",
"""-D__CUDA_NO_HALF2_OPERATORS__""",
] , )
import MultiScaleDeformableAttention as MSDA
return MSDA
| 50 | 1 |
'''simple docstring'''
import re
import string
import numpy as np
import datasets
UpperCamelCase : str = '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'
UpperCamelCase : Optional[Any] = '\nArgs:\n predictions: List of predicted texts.\n references: List of reference texts.\n regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n ignore when calculating the exact matches. Note: these regexes are removed\n from the input data before the changes based on the options below (e.g. ignore_case,\n ignore_punctuation, ignore_numbers) are applied.\n ignore_case: Boolean, defaults to False. If true, turns everything\n to lowercase so that capitalization differences are ignored.\n ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\n ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\nReturns:\n exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n >>> preds = ["cat?", "theater", "yelling", "agent"]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results["exact_match"], 1))\n 25.0\n\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n >>> preds = ["cat?", "theater", "yelling", "agent"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results["exact_match"], 1))\n 50.0\n\n\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n >>> preds = ["cat?", "theater", "yelling", "agent"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results["exact_match"], 1))\n 75.0\n\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n >>> preds = ["cat?", "theater", "yelling", "agent"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n >>> print(round(results["exact_match"], 1))\n 100.0\n\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]\n >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results["exact_match"], 1))\n 33.3\n\n'
UpperCamelCase : Union[str, Any] = '\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION )
class UpperCamelCase__ (datasets.Metric ):
'''simple docstring'''
def UpperCamelCase_ ( self ):
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" ,id="""sequence""" ),
"""references""": datasets.Value("""string""" ,id="""sequence""" ),
} ) ,reference_urls=[] ,)
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase=None ,_lowerCAmelCase=False ,_lowerCAmelCase=False ,_lowerCAmelCase=False ,):
if regexes_to_ignore is not None:
for s in regexes_to_ignore:
lowerCamelCase__ = np.array([re.sub(_lowerCAmelCase ,"""""" ,_lowerCAmelCase ) for x in predictions] )
lowerCamelCase__ = np.array([re.sub(_lowerCAmelCase ,"""""" ,_lowerCAmelCase ) for x in references] )
else:
lowerCamelCase__ = np.asarray(_lowerCAmelCase )
lowerCamelCase__ = np.asarray(_lowerCAmelCase )
if ignore_case:
lowerCamelCase__ = np.char.lower(_lowerCAmelCase )
lowerCamelCase__ = np.char.lower(_lowerCAmelCase )
if ignore_punctuation:
lowerCamelCase__ = string.punctuation.maketrans("""""" ,"""""" ,string.punctuation )
lowerCamelCase__ = np.char.translate(_lowerCAmelCase ,table=_lowerCAmelCase )
lowerCamelCase__ = np.char.translate(_lowerCAmelCase ,table=_lowerCAmelCase )
if ignore_numbers:
lowerCamelCase__ = string.digits.maketrans("""""" ,"""""" ,string.digits )
lowerCamelCase__ = np.char.translate(_lowerCAmelCase ,table=_lowerCAmelCase )
lowerCamelCase__ = np.char.translate(_lowerCAmelCase ,table=_lowerCAmelCase )
lowerCamelCase__ = predictions == references
return {"exact_match": np.mean(_lowerCAmelCase ) * 1_00}
| 50 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : list[int] , __lowerCAmelCase : list[int] ):
lowerCamelCase__ = len(__lowerCAmelCase )
print("""The following activities are selected:""" )
# The first activity is always selected
lowerCamelCase__ = 0
print(__lowerCAmelCase , end=""",""" )
# Consider rest of the activities
for j in range(__lowerCAmelCase ):
# If this activity has start time greater than
# or equal to the finish time of previously
# selected activity, then select it
if start[j] >= finish[i]:
print(__lowerCAmelCase , end=""",""" )
lowerCamelCase__ = j
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCamelCase : Union[str, Any] = [1, 3, 0, 5, 8, 5]
UpperCamelCase : int = [2, 4, 6, 7, 9, 9]
print_max_activities(start, finish)
| 50 | 1 |
'''simple docstring'''
from __future__ import annotations
def A__ ( __lowerCAmelCase : int ):
lowerCamelCase__ = 2
lowerCamelCase__ = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__lowerCAmelCase )
if n > 1:
factors.append(__lowerCAmelCase )
return factors
if __name__ == "__main__":
import doctest
doctest.testmod()
| 50 |
'''simple docstring'''
import warnings
from ..trainer import Trainer
from ..utils import logging
UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
class UpperCamelCase__ (a ):
'''simple docstring'''
def __init__( self ,_lowerCAmelCase=None ,**_lowerCAmelCase ):
warnings.warn(
"""`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` """
"""instead.""" ,_lowerCAmelCase ,)
super().__init__(args=_lowerCAmelCase ,**_lowerCAmelCase )
| 50 | 1 |
'''simple docstring'''
from functools import reduce
UpperCamelCase : Dict = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648950445244523161731856403098711121722383113'
'62229893423380308135336276614282806444486645238749'
'30358907296290491560440772390713810515859307960866'
'70172427121883998797908792274921901699720888093776'
'65727333001053367881220235421809751254540594752243'
'52584907711670556013604839586446706324415722155397'
'53697817977846174064955149290862569321978468622482'
'83972241375657056057490261407972968652414535100474'
'82166370484403199890008895243450658541227588666881'
'16427171479924442928230863465674813919123162824586'
'17866458359124566529476545682848912883142607690042'
'24219022671055626321111109370544217506941658960408'
'07198403850962455444362981230987879927244284909188'
'84580156166097919133875499200524063689912560717606'
'05886116467109405077541002256983155200055935729725'
'71636269561882670428252483600823257530420752963450'
)
def A__ ( __lowerCAmelCase : str = N ):
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda __lowerCAmelCase , __lowerCAmelCase : str(int(__lowerCAmelCase ) * int(__lowerCAmelCase ) ) , n[i : i + 13] ) )
for i in range(len(__lowerCAmelCase ) - 12 ) )
if __name__ == "__main__":
print(F'{solution() = }')
| 50 |
'''simple docstring'''
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def A__ ( __lowerCAmelCase : List[str] ):
lowerCamelCase__ = []
for line in lines:
lowerCamelCase__ = re.sub(R"""#.*""" , """""" , __lowerCAmelCase ) # remove comments
if line:
filtered_lines.append(__lowerCAmelCase )
lowerCamelCase__ = """\n""".join(__lowerCAmelCase )
# Make a hash from all this code
lowerCamelCase__ = full_str.encode("""utf-8""" )
return shaaaa(__lowerCAmelCase ).hexdigest()
# get importable module names and hash for caching
UpperCamelCase : Dict = {
'csv': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
'json': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
'pandas': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
'parquet': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
'arrow': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
'text': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
'imagefolder': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
'audiofolder': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
UpperCamelCase : str = {
'.csv': ('csv', {}),
'.tsv': ('csv', {'sep': '\t'}),
'.json': ('json', {}),
'.jsonl': ('json', {}),
'.parquet': ('parquet', {}),
'.arrow': ('arrow', {}),
'.txt': ('text', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
UpperCamelCase : List[Any] = {'imagefolder', 'audiofolder'}
# Used to filter data files based on extensions given a module name
UpperCamelCase : Dict[str, List[str]] = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append('.zip')
_MODULE_TO_EXTENSIONS["audiofolder"].append('.zip')
| 50 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCamelCase__ (metaclass=a ):
'''simple docstring'''
_UpperCamelCase = ['torch', 'transformers', 'onnx']
def __init__( self ,*_lowerCAmelCase ,**_lowerCAmelCase ):
requires_backends(self ,["""torch""", """transformers""", """onnx"""] )
@classmethod
def UpperCamelCase_ ( cls ,*_lowerCAmelCase ,**_lowerCAmelCase ):
requires_backends(cls ,["""torch""", """transformers""", """onnx"""] )
@classmethod
def UpperCamelCase_ ( cls ,*_lowerCAmelCase ,**_lowerCAmelCase ):
requires_backends(cls ,["""torch""", """transformers""", """onnx"""] )
class UpperCamelCase__ (metaclass=a ):
'''simple docstring'''
_UpperCamelCase = ['torch', 'transformers', 'onnx']
def __init__( self ,*_lowerCAmelCase ,**_lowerCAmelCase ):
requires_backends(self ,["""torch""", """transformers""", """onnx"""] )
@classmethod
def UpperCamelCase_ ( cls ,*_lowerCAmelCase ,**_lowerCAmelCase ):
requires_backends(cls ,["""torch""", """transformers""", """onnx"""] )
@classmethod
def UpperCamelCase_ ( cls ,*_lowerCAmelCase ,**_lowerCAmelCase ):
requires_backends(cls ,["""torch""", """transformers""", """onnx"""] )
class UpperCamelCase__ (metaclass=a ):
'''simple docstring'''
_UpperCamelCase = ['torch', 'transformers', 'onnx']
def __init__( self ,*_lowerCAmelCase ,**_lowerCAmelCase ):
requires_backends(self ,["""torch""", """transformers""", """onnx"""] )
@classmethod
def UpperCamelCase_ ( cls ,*_lowerCAmelCase ,**_lowerCAmelCase ):
requires_backends(cls ,["""torch""", """transformers""", """onnx"""] )
@classmethod
def UpperCamelCase_ ( cls ,*_lowerCAmelCase ,**_lowerCAmelCase ):
requires_backends(cls ,["""torch""", """transformers""", """onnx"""] )
class UpperCamelCase__ (metaclass=a ):
'''simple docstring'''
_UpperCamelCase = ['torch', 'transformers', 'onnx']
def __init__( self ,*_lowerCAmelCase ,**_lowerCAmelCase ):
requires_backends(self ,["""torch""", """transformers""", """onnx"""] )
@classmethod
def UpperCamelCase_ ( cls ,*_lowerCAmelCase ,**_lowerCAmelCase ):
requires_backends(cls ,["""torch""", """transformers""", """onnx"""] )
@classmethod
def UpperCamelCase_ ( cls ,*_lowerCAmelCase ,**_lowerCAmelCase ):
requires_backends(cls ,["""torch""", """transformers""", """onnx"""] )
class UpperCamelCase__ (metaclass=a ):
'''simple docstring'''
_UpperCamelCase = ['torch', 'transformers', 'onnx']
def __init__( self ,*_lowerCAmelCase ,**_lowerCAmelCase ):
requires_backends(self ,["""torch""", """transformers""", """onnx"""] )
@classmethod
def UpperCamelCase_ ( cls ,*_lowerCAmelCase ,**_lowerCAmelCase ):
requires_backends(cls ,["""torch""", """transformers""", """onnx"""] )
@classmethod
def UpperCamelCase_ ( cls ,*_lowerCAmelCase ,**_lowerCAmelCase ):
requires_backends(cls ,["""torch""", """transformers""", """onnx"""] )
class UpperCamelCase__ (metaclass=a ):
'''simple docstring'''
_UpperCamelCase = ['torch', 'transformers', 'onnx']
def __init__( self ,*_lowerCAmelCase ,**_lowerCAmelCase ):
requires_backends(self ,["""torch""", """transformers""", """onnx"""] )
@classmethod
def UpperCamelCase_ ( cls ,*_lowerCAmelCase ,**_lowerCAmelCase ):
requires_backends(cls ,["""torch""", """transformers""", """onnx"""] )
@classmethod
def UpperCamelCase_ ( cls ,*_lowerCAmelCase ,**_lowerCAmelCase ):
requires_backends(cls ,["""torch""", """transformers""", """onnx"""] )
| 50 |
'''simple docstring'''
import operator
def A__ ( __lowerCAmelCase : list , __lowerCAmelCase : bool = False , __lowerCAmelCase : list | None = None ):
lowerCamelCase__ = operator.lt if reverse else operator.gt
lowerCamelCase__ = solution or []
if not arr:
return solution
lowerCamelCase__ = [arr.pop(0 )]
for i, item in enumerate(__lowerCAmelCase ):
if _operator(__lowerCAmelCase , sublist[-1] ):
sublist.append(__lowerCAmelCase )
arr.pop(__lowerCAmelCase )
# merging sublist into solution list
if not solution:
solution.extend(__lowerCAmelCase )
else:
while sublist:
lowerCamelCase__ = sublist.pop(0 )
for i, xx in enumerate(__lowerCAmelCase ):
if not _operator(__lowerCAmelCase , __lowerCAmelCase ):
solution.insert(__lowerCAmelCase , __lowerCAmelCase )
break
else:
solution.append(__lowerCAmelCase )
strand_sort(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
return solution
if __name__ == "__main__":
assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]
| 50 | 1 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : int ):
lowerCamelCase__ = int(__lowerCAmelCase )
if decimal in (0, 1): # Exit cases for the recursion
return str(__lowerCAmelCase )
lowerCamelCase__ , lowerCamelCase__ = divmod(__lowerCAmelCase , 2 )
return binary_recursive(__lowerCAmelCase ) + str(__lowerCAmelCase )
def A__ ( __lowerCAmelCase : str ):
lowerCamelCase__ = str(__lowerCAmelCase ).strip()
if not number:
raise ValueError("""No input value was provided""" )
lowerCamelCase__ = """-""" if number.startswith("""-""" ) else """"""
lowerCamelCase__ = number.lstrip("""-""" )
if not number.isnumeric():
raise ValueError("""Input value is not an integer""" )
return F'''{negative}0b{binary_recursive(int(__lowerCAmelCase ) )}'''
if __name__ == "__main__":
from doctest import testmod
testmod()
| 50 |
'''simple docstring'''
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def A__ ( __lowerCAmelCase : dict ):
return (data["data"], data["target"])
def A__ ( __lowerCAmelCase : np.ndarray , __lowerCAmelCase : np.ndarray , __lowerCAmelCase : np.ndarray ):
lowerCamelCase__ = XGBRegressor(verbosity=0 , random_state=42 )
xgb.fit(__lowerCAmelCase , __lowerCAmelCase )
# Predict target for test data
lowerCamelCase__ = xgb.predict(__lowerCAmelCase )
lowerCamelCase__ = predictions.reshape(len(__lowerCAmelCase ) , 1 )
return predictions
def A__ ( ):
lowerCamelCase__ = fetch_california_housing()
lowerCamelCase__ , lowerCamelCase__ = data_handling(__lowerCAmelCase )
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = train_test_split(
__lowerCAmelCase , __lowerCAmelCase , test_size=0.25 , random_state=1 )
lowerCamelCase__ = xgboost(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# Error printing
print(F'''Mean Absolute Error : {mean_absolute_error(__lowerCAmelCase , __lowerCAmelCase )}''' )
print(F'''Mean Square Error : {mean_squared_error(__lowerCAmelCase , __lowerCAmelCase )}''' )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 50 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
UpperCamelCase : List[Any] = {'tokenization_herbert': ['HerbertTokenizer']}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Tuple = ['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
UpperCamelCase : List[str] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 50 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBOSTokenLogitsProcessor,
FlaxForcedEOSTokenLogitsProcessor,
FlaxLogitsProcessorList,
FlaxMinLengthLogitsProcessor,
FlaxTemperatureLogitsWarper,
FlaxTopKLogitsWarper,
FlaxTopPLogitsWarper,
)
@require_flax
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = jnp.ones((batch_size, length) ) / length
return scores
def UpperCamelCase_ ( self ):
lowerCamelCase__ = None
lowerCamelCase__ = 20
lowerCamelCase__ = self._get_uniform_logits(batch_size=2 ,length=_lowerCAmelCase )
# tweak scores to not be uniform anymore
lowerCamelCase__ = scores.at[1, 5].set((1 / length) + 0.1 ) # peak, 1st batch
lowerCamelCase__ = scores.at[1, 10].set((1 / length) - 0.4 ) # valley, 1st batch
# compute softmax
lowerCamelCase__ = jax.nn.softmax(_lowerCAmelCase ,axis=-1 )
lowerCamelCase__ = FlaxTemperatureLogitsWarper(temperature=0.5 )
lowerCamelCase__ = FlaxTemperatureLogitsWarper(temperature=1.3 )
lowerCamelCase__ = jax.nn.softmax(temp_dist_warper_sharper(_lowerCAmelCase ,scores.copy() ,cur_len=_lowerCAmelCase ) ,axis=-1 )
lowerCamelCase__ = jax.nn.softmax(temp_dist_warper_smoother(_lowerCAmelCase ,scores.copy() ,cur_len=_lowerCAmelCase ) ,axis=-1 )
# uniform distribution stays uniform
self.assertTrue(jnp.allclose(probs[0, :] ,warped_prob_sharp[0, :] ,atol=1E-3 ) )
self.assertTrue(jnp.allclose(probs[0, :] ,warped_prob_smooth[0, :] ,atol=1E-3 ) )
# sharp peaks get higher, valleys get lower
self.assertLess(probs[1, :].max() ,warped_prob_sharp[1, :].max() )
self.assertGreater(probs[1, :].min() ,warped_prob_sharp[1, :].min() )
# smooth peaks get lower, valleys get higher
self.assertGreater(probs[1, :].max() ,warped_prob_smooth[1, :].max() )
self.assertLess(probs[1, :].min() ,warped_prob_smooth[1, :].min() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = None
lowerCamelCase__ = 10
lowerCamelCase__ = 2
# create ramp distribution
lowerCamelCase__ = np.broadcast_to(np.arange(_lowerCAmelCase )[None, :] ,(batch_size, vocab_size) ).copy()
lowerCamelCase__ = ramp_logits[1:, : vocab_size // 2] + vocab_size
lowerCamelCase__ = FlaxTopKLogitsWarper(3 )
lowerCamelCase__ = top_k_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# check that correct tokens are filtered
self.assertListEqual(jnp.isinf(scores[0] ).tolist() ,7 * [True] + 3 * [False] )
self.assertListEqual(jnp.isinf(scores[1] ).tolist() ,2 * [True] + 3 * [False] + 5 * [True] )
# check special case
lowerCamelCase__ = 5
lowerCamelCase__ = FlaxTopKLogitsWarper(top_k=1 ,filter_value=0.0 ,min_tokens_to_keep=3 )
lowerCamelCase__ = np.broadcast_to(np.arange(_lowerCAmelCase )[None, :] ,(batch_size, length) ).copy()
lowerCamelCase__ = top_k_warp_safety_check(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
self.assertListEqual((scores == 0.0).sum(axis=-1 ).tolist() ,[2, 2] )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = None
lowerCamelCase__ = 10
lowerCamelCase__ = 2
# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
lowerCamelCase__ = np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]] ) )
lowerCamelCase__ = FlaxTopPLogitsWarper(0.8 )
lowerCamelCase__ = np.exp(top_p_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase ) )
# dist should be filtered to keep min num values so that sum is >= top_p
# exp (-inf) => 0
lowerCamelCase__ = np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]] )
self.assertTrue(np.allclose(_lowerCAmelCase ,_lowerCAmelCase ,atol=1E-3 ) )
# check edge cases with negative and extreme logits
lowerCamelCase__ = np.broadcast_to(np.arange(_lowerCAmelCase )[None, :] ,(batch_size, vocab_size) ).copy() - (
vocab_size // 2
)
# make ramp_logits more extreme
lowerCamelCase__ = ramp_logits[1] * 100.0
# make sure at least 2 tokens are kept
lowerCamelCase__ = FlaxTopPLogitsWarper(0.9 ,min_tokens_to_keep=2 ,filter_value=0.0 )
lowerCamelCase__ = top_p_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
self.assertListEqual((filtered_dist != 0.0).sum(axis=-1 ).tolist() ,[3, 2] )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 20
lowerCamelCase__ = 4
lowerCamelCase__ = 0
lowerCamelCase__ = FlaxMinLengthLogitsProcessor(min_length=10 ,eos_token_id=_lowerCAmelCase )
# check that min length is applied at length 5
lowerCamelCase__ = ids_tensor((batch_size, 20) ,vocab_size=20 )
lowerCamelCase__ = 5
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = min_dist_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist() ,4 * [-float("""inf""" )] )
# check that min length is not applied anymore at length 15
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = 15
lowerCamelCase__ = min_dist_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertFalse(jnp.isinf(_lowerCAmelCase ).any() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 20
lowerCamelCase__ = 4
lowerCamelCase__ = 0
lowerCamelCase__ = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase )
# check that all scores are -inf except the bos_token_id score
lowerCamelCase__ = ids_tensor((batch_size, 1) ,vocab_size=20 )
lowerCamelCase__ = 1
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = logits_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, bos_token_id].tolist() ,4 * [0] ) # score for bos_token_id shold be zero
# check that bos_token_id is not forced if current length is greater than 1
lowerCamelCase__ = 3
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = logits_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertFalse(jnp.isinf(_lowerCAmelCase ).any() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 20
lowerCamelCase__ = 4
lowerCamelCase__ = 0
lowerCamelCase__ = 5
lowerCamelCase__ = FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase )
# check that all scores are -inf except the eos_token_id when max_length is reached
lowerCamelCase__ = ids_tensor((batch_size, 4) ,vocab_size=20 )
lowerCamelCase__ = 4
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = logits_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, eos_token_id].tolist() ,4 * [0] ) # score for eos_token_id should be zero
# check that eos_token_id is not forced if max_length is not reached
lowerCamelCase__ = 3
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = logits_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertFalse(jnp.isinf(_lowerCAmelCase ).any() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 4
lowerCamelCase__ = 10
lowerCamelCase__ = 15
lowerCamelCase__ = 2
lowerCamelCase__ = 1
lowerCamelCase__ = 15
# dummy input_ids and scores
lowerCamelCase__ = ids_tensor((batch_size, sequence_length) ,_lowerCAmelCase )
lowerCamelCase__ = input_ids.copy()
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = scores.copy()
# instantiate all dist processors
lowerCamelCase__ = FlaxTemperatureLogitsWarper(temperature=0.5 )
lowerCamelCase__ = FlaxTopKLogitsWarper(3 )
lowerCamelCase__ = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
lowerCamelCase__ = FlaxMinLengthLogitsProcessor(min_length=10 ,eos_token_id=_lowerCAmelCase )
lowerCamelCase__ = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase )
lowerCamelCase__ = FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase )
lowerCamelCase__ = 10
# no processor list
lowerCamelCase__ = temp_dist_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = top_k_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = top_p_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = min_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = bos_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = eos_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# with processor list
lowerCamelCase__ = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
lowerCamelCase__ = processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# scores should be equal
self.assertTrue(jnp.allclose(_lowerCAmelCase ,_lowerCAmelCase ,atol=1E-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() ,input_ids_comp.tolist() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 4
lowerCamelCase__ = 10
lowerCamelCase__ = 15
lowerCamelCase__ = 2
lowerCamelCase__ = 1
lowerCamelCase__ = 15
# dummy input_ids and scores
lowerCamelCase__ = ids_tensor((batch_size, sequence_length) ,_lowerCAmelCase )
lowerCamelCase__ = input_ids.copy()
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = scores.copy()
# instantiate all dist processors
lowerCamelCase__ = FlaxTemperatureLogitsWarper(temperature=0.5 )
lowerCamelCase__ = FlaxTopKLogitsWarper(3 )
lowerCamelCase__ = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
lowerCamelCase__ = FlaxMinLengthLogitsProcessor(min_length=10 ,eos_token_id=_lowerCAmelCase )
lowerCamelCase__ = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase )
lowerCamelCase__ = FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase )
lowerCamelCase__ = 10
# no processor list
def run_no_processor_list(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = temp_dist_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = top_k_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = top_p_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = min_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = bos_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = eos_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
return scores
# with processor list
def run_processor_list(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
lowerCamelCase__ = processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
return scores
lowerCamelCase__ = jax.jit(_lowerCAmelCase )
lowerCamelCase__ = jax.jit(_lowerCAmelCase )
lowerCamelCase__ = jitted_run_no_processor_list(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = jitted_run_processor_list(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
# scores should be equal
self.assertTrue(jnp.allclose(_lowerCAmelCase ,_lowerCAmelCase ,atol=1E-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() ,input_ids_comp.tolist() )
| 50 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 10
def UpperCamelCase_ ( self ):
lowerCamelCase__ = [1, 2, 3, 4]
lowerCamelCase__ = [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
self.assertEqual(truncate_or_pad(_lowerCAmelCase ,self.block_size ,0 ) ,_lowerCAmelCase )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
lowerCamelCase__ = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(_lowerCAmelCase ,self.block_size ,0 ) ,_lowerCAmelCase )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
lowerCamelCase__ = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(_lowerCAmelCase ,self.block_size ,0 ) ,_lowerCAmelCase )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = """It was the year of Our Lord one thousand seven hundred and
seventy-five.\n\nSpiritual revelations were conceded to England at that
favoured period, as at this."""
lowerCamelCase__ , lowerCamelCase__ = process_story(_lowerCAmelCase )
self.assertEqual(_lowerCAmelCase ,[] )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = """"""
lowerCamelCase__ , lowerCamelCase__ = process_story(_lowerCAmelCase )
self.assertEqual(_lowerCAmelCase ,[] )
self.assertEqual(_lowerCAmelCase ,[] )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = (
"""It was the year of Our Lord one thousand seven hundred and """
"""seventy-five\n\nSpiritual revelations were conceded to England """
"""at that favoured period, as at this.\n@highlight\n\nIt was the best of times"""
)
lowerCamelCase__ , lowerCamelCase__ = process_story(_lowerCAmelCase )
lowerCamelCase__ = [
"""It was the year of Our Lord one thousand seven hundred and seventy-five.""",
"""Spiritual revelations were conceded to England at that favoured period, as at this.""",
]
self.assertEqual(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = ["""It was the best of times."""]
self.assertEqual(_lowerCAmelCase ,_lowerCAmelCase )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = torch.tensor([1, 2, 3, 4] )
lowerCamelCase__ = torch.tensor([1, 1, 1, 1] )
np.testing.assert_array_equal(build_mask(_lowerCAmelCase ,0 ).numpy() ,expected.numpy() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = torch.tensor([1, 2, 3, 4, 23, 23, 23] )
lowerCamelCase__ = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(_lowerCAmelCase ,23 ).numpy() ,expected.numpy() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = torch.tensor([8, 2, 3, 4, 1, 1, 1] )
lowerCamelCase__ = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(_lowerCAmelCase ,1 ).numpy() ,expected.numpy() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 1_01
lowerCamelCase__ = torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 1_01, 5, 6], [1, 1_01, 3, 4, 1_01, 6]] )
lowerCamelCase__ = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]] )
lowerCamelCase__ = compute_token_type_ids(_lowerCAmelCase ,_lowerCAmelCase )
np.testing.assert_array_equal(_lowerCAmelCase ,_lowerCAmelCase )
| 50 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase : Any = {
'configuration_groupvit': [
'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'GroupViTConfig',
'GroupViTOnnxConfig',
'GroupViTTextConfig',
'GroupViTVisionConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : List[str] = [
'GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'GroupViTModel',
'GroupViTPreTrainedModel',
'GroupViTTextModel',
'GroupViTVisionModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : List[str] = [
'TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFGroupViTModel',
'TFGroupViTPreTrainedModel',
'TFGroupViTTextModel',
'TFGroupViTVisionModel',
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
UpperCamelCase : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 50 | 1 |
'''simple docstring'''
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase : str = [
'word_embeddings_layernorm.weight',
'word_embeddings_layernorm.bias',
'input_layernorm.weight',
'input_layernorm.bias',
'post_attention_layernorm.weight',
'post_attention_layernorm.bias',
'self_attention.dense.bias',
'mlp.dense_4h_to_h.bias',
'ln_f.weight',
'ln_f.bias',
]
UpperCamelCase : Tuple = [
'mlp.dense_4h_to_h.weight',
'self_attention.dense.weight',
]
def A__ ( __lowerCAmelCase : Any , __lowerCAmelCase : Any ):
lowerCamelCase__ = {
"""word_embeddings.weight""": """word_embeddings.weight""",
"""word_embeddings.norm.weight""": """word_embeddings_layernorm.weight""",
"""word_embeddings.norm.bias""": """word_embeddings_layernorm.bias""",
"""weight""": """ln_f.weight""",
"""bias""": """ln_f.bias""",
}
if key in layer_rename_map:
return layer_rename_map[key]
# Handle transformer blocks
lowerCamelCase__ = int(re.match(R""".*layer_(\d*).*""" , __lowerCAmelCase )[1] )
layer_number -= 3
return F'''h.{layer_number}.''' + key
def A__ ( __lowerCAmelCase : Tuple ):
if dtype == torch.bool:
return 1 / 8
lowerCamelCase__ = re.search(R"""[^\d](\d+)$""" , str(__lowerCAmelCase ) )
if bit_search is None:
raise ValueError(F'''`dtype` is not a valid dtype: {dtype}.''' )
lowerCamelCase__ = int(bit_search.groups()[0] )
return bit_size // 8
def A__ ( __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Optional[Any] ):
# Construct model
if bloom_config_file == "":
lowerCamelCase__ = BloomConfig()
else:
lowerCamelCase__ = BloomConfig.from_json_file(__lowerCAmelCase )
if shard_model:
lowerCamelCase__ = os.listdir(__lowerCAmelCase )
lowerCamelCase__ = sorted(filter(lambda __lowerCAmelCase : s.startswith("""layer""" ) and "model_00" in s , __lowerCAmelCase ) )
lowerCamelCase__ = {"""weight_map""": {}, """metadata""": {}}
lowerCamelCase__ = 0
lowerCamelCase__ = None
lowerCamelCase__ = BloomConfig()
for j, file in enumerate(__lowerCAmelCase ):
print("""Processing file: {}""".format(__lowerCAmelCase ) )
lowerCamelCase__ = None
for i in range(__lowerCAmelCase ):
# load all TP files
lowerCamelCase__ = file.replace("""model_00""" , F'''model_0{i}''' )
lowerCamelCase__ = torch.load(os.path.join(__lowerCAmelCase , __lowerCAmelCase ) , map_location="""cpu""" )
# Rename keys in the transformers names
lowerCamelCase__ = list(temp.keys() )
for key in keys:
lowerCamelCase__ = temp.pop(__lowerCAmelCase )
if tensors is None:
lowerCamelCase__ = temp
else:
for key in tensors.keys():
if any(key.endswith(__lowerCAmelCase ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
lowerCamelCase__ = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
lowerCamelCase__ = torch.cat([tensors[key], temp[key]] , dim=__lowerCAmelCase )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(__lowerCAmelCase ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
lowerCamelCase__ = tensors[key] / pretraining_tp
torch.save(
__lowerCAmelCase , os.path.join(
__lowerCAmelCase , """pytorch_model_{}-of-{}.bin""".format(str(j + 1 ).zfill(5 ) , str(len(__lowerCAmelCase ) ).zfill(5 ) ) , ) , )
for key in tensors.keys():
lowerCamelCase__ = tensors[key]
total_size += value.numel() * get_dtype_size(value.dtype )
if key not in index_dict["weight_map"]:
lowerCamelCase__ = """pytorch_model_{}-of-{}.bin""".format(
str(j + 1 ).zfill(5 ) , str(len(__lowerCAmelCase ) ).zfill(5 ) )
lowerCamelCase__ = BloomConfig()
lowerCamelCase__ = pytorch_dump_folder_path + """/""" + CONFIG_NAME
lowerCamelCase__ = total_size
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(config.to_json_string() )
with open(os.path.join(__lowerCAmelCase , WEIGHTS_NAME + """.index.json""" ) , """w""" , encoding="""utf-8""" ) as f:
lowerCamelCase__ = json.dumps(__lowerCAmelCase , indent=2 , sort_keys=__lowerCAmelCase ) + """\n"""
f.write(__lowerCAmelCase )
else:
lowerCamelCase__ = BloomModel(__lowerCAmelCase )
lowerCamelCase__ = os.listdir(__lowerCAmelCase )
lowerCamelCase__ = sorted(filter(lambda __lowerCAmelCase : s.startswith("""layer""" ) and "model_00" in s , __lowerCAmelCase ) )
lowerCamelCase__ = None
for i, file in enumerate(__lowerCAmelCase ):
lowerCamelCase__ = None
for i in range(__lowerCAmelCase ):
# load all TP files
lowerCamelCase__ = file.replace("""model_00""" , F'''model_0{i}''' )
lowerCamelCase__ = torch.load(os.path.join(__lowerCAmelCase , __lowerCAmelCase ) , map_location="""cpu""" )
# Rename keys in the transformers names
lowerCamelCase__ = list(temp.keys() )
for key in keys:
lowerCamelCase__ = temp.pop(__lowerCAmelCase )
if tensors is None:
lowerCamelCase__ = temp
else:
for key in tensors.keys():
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
if any(key.endswith(__lowerCAmelCase ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
lowerCamelCase__ = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
lowerCamelCase__ = torch.cat([tensors[key], temp[key]] , dim=__lowerCAmelCase )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(__lowerCAmelCase ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
lowerCamelCase__ = tensors[key] / pretraining_tp
lowerCamelCase__ = model.load_state_dict(__lowerCAmelCase , strict=__lowerCAmelCase )
assert not other_keys.unexpected_keys, F'''The keys {other_keys.unexpected_keys} are unexpected'''
if missing_keys is None:
lowerCamelCase__ = set(other_keys.missing_keys )
else:
lowerCamelCase__ = missing_keys.intersection(set(other_keys.missing_keys ) )
assert not missing_keys, F'''The keys {missing_keys} are missing'''
# Save pytorch-model
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
lowerCamelCase__ = pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
lowerCamelCase__ = pytorch_dump_folder_path + """/""" + CONFIG_NAME
print(F'''Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}''' )
if config.torch_dtype is not None:
lowerCamelCase__ = model.to(config.torch_dtype )
torch.save(model.state_dict() , __lowerCAmelCase )
print(F'''Save configuration file to {pytorch_config_dump_path}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
UpperCamelCase : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--bloom_checkpoint_path',
default=None,
type=str,
required=True,
help='Path to the Megatron-LM checkpoint path.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--bloom_config_file',
default='',
type=str,
help=(
'An optional config json file corresponding to the pre-trained model. \n'
'This specifies the model architecture.'
),
)
parser.add_argument(
'--shard_model',
action='store_true',
help='An optional setting to shard the output model \nThis enables sharding the converted checkpoint',
)
parser.add_argument(
'--pretraining_tp',
default=4,
type=int,
help='Pretraining TP rank that has been used when training the model in Megatron-LM \n',
)
UpperCamelCase : Optional[Any] = parser.parse_args()
convert_bloom_checkpoint_to_pytorch(
args.bloom_checkpoint_path,
args.bloom_config_file,
args.pytorch_dump_folder_path,
args.shard_model,
args.pretraining_tp,
)
| 50 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ):
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 50 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
UpperCamelCase : List[str] = r'\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n Args:\n title_sep (`str`, *optional*, defaults to `" / "`):\n Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n doc_sep (`str`, *optional*, defaults to `" // "`):\n Separator inserted between the text of the retrieved document and the original input when calling\n [`RagRetriever`].\n n_docs (`int`, *optional*, defaults to 5):\n Number of documents to retrieve.\n max_combined_length (`int`, *optional*, defaults to 300):\n Max length of contextualized input returned by [`~RagRetriever.__call__`].\n retrieval_vector_size (`int`, *optional*, defaults to 768):\n Dimensionality of the document embeddings indexed by [`RagRetriever`].\n retrieval_batch_size (`int`, *optional*, defaults to 8):\n Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n [`RagRetriever`].\n dataset (`str`, *optional*, defaults to `"wiki_dpr"`):\n A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n using `datasets.list_datasets()`).\n dataset_split (`str`, *optional*, defaults to `"train"`)\n Which split of the `dataset` to load.\n index_name (`str`, *optional*, defaults to `"compressed"`)\n The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and\n `"compressed"`.\n index_path (`str`, *optional*)\n The path to the serialized faiss index on disk.\n passages_path (`str`, *optional*):\n A path to text passages compatible with the faiss index. Required if using\n [`~models.rag.retrieval_rag.LegacyIndex`]\n use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n Whether to load a "dummy" variant of the dataset specified by `dataset`.\n label_smoothing (`float`, *optional*, defaults to 0.0):\n Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n in the loss calculation. If set to 0, no label smoothing is performed.\n do_marginalize (`bool`, *optional*, defaults to `False`):\n If `True`, the logits are marginalized over all documents by making use of\n `torch.nn.functional.log_softmax`.\n reduce_loss (`bool`, *optional*, defaults to `False`):\n Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n do_deduplication (`bool`, *optional*, defaults to `True`):\n Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n set to `False` if used while training with distributed backend.\n exclude_bos_score (`bool`, *optional*, defaults to `False`):\n Whether or not to disregard the BOS token when computing the loss.\n output_retrieved(`bool`, *optional*, defaults to `False`):\n If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n `context_attention_mask` are returned. See returned tensors for more detail.\n use_cache (`bool`, *optional*, defaults to `True`):\n Whether or not the model should return the last key/values attentions (not used by all models).\n forced_eos_token_id (`int`, *optional*):\n The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n `eos_token_id`.\n'
@add_start_docstrings(a )
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = 'rag'
_UpperCamelCase = True
def __init__( self ,_lowerCAmelCase=None ,_lowerCAmelCase=True ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=" / " ,_lowerCAmelCase=" // " ,_lowerCAmelCase=5 ,_lowerCAmelCase=3_00 ,_lowerCAmelCase=7_68 ,_lowerCAmelCase=8 ,_lowerCAmelCase="wiki_dpr" ,_lowerCAmelCase="train" ,_lowerCAmelCase="compressed" ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=False ,_lowerCAmelCase=False ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=True ,_lowerCAmelCase=False ,_lowerCAmelCase=False ,_lowerCAmelCase=False ,_lowerCAmelCase=True ,_lowerCAmelCase=None ,**_lowerCAmelCase ,):
super().__init__(
bos_token_id=_lowerCAmelCase ,pad_token_id=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase ,decoder_start_token_id=_lowerCAmelCase ,forced_eos_token_id=_lowerCAmelCase ,is_encoder_decoder=_lowerCAmelCase ,prefix=_lowerCAmelCase ,vocab_size=_lowerCAmelCase ,**_lowerCAmelCase ,)
assert (
"question_encoder" in kwargs and "generator" in kwargs
), "Config has to be initialized with question_encoder and generator config"
lowerCamelCase__ = kwargs.pop("""question_encoder""" )
lowerCamelCase__ = question_encoder_config.pop("""model_type""" )
lowerCamelCase__ = kwargs.pop("""generator""" )
lowerCamelCase__ = decoder_config.pop("""model_type""" )
from ..auto.configuration_auto import AutoConfig
lowerCamelCase__ = AutoConfig.for_model(_lowerCAmelCase ,**_lowerCAmelCase )
lowerCamelCase__ = AutoConfig.for_model(_lowerCAmelCase ,**_lowerCAmelCase )
lowerCamelCase__ = reduce_loss
lowerCamelCase__ = label_smoothing
lowerCamelCase__ = exclude_bos_score
lowerCamelCase__ = do_marginalize
lowerCamelCase__ = title_sep
lowerCamelCase__ = doc_sep
lowerCamelCase__ = n_docs
lowerCamelCase__ = max_combined_length
lowerCamelCase__ = dataset
lowerCamelCase__ = dataset_split
lowerCamelCase__ = index_name
lowerCamelCase__ = retrieval_vector_size
lowerCamelCase__ = retrieval_batch_size
lowerCamelCase__ = passages_path
lowerCamelCase__ = index_path
lowerCamelCase__ = use_dummy_dataset
lowerCamelCase__ = output_retrieved
lowerCamelCase__ = do_deduplication
lowerCamelCase__ = use_cache
if self.forced_eos_token_id is None:
lowerCamelCase__ = getattr(self.generator ,"""forced_eos_token_id""" ,_lowerCAmelCase )
@classmethod
def UpperCamelCase_ ( cls ,_lowerCAmelCase ,_lowerCAmelCase ,**_lowerCAmelCase ):
return cls(question_encoder=question_encoder_config.to_dict() ,generator=generator_config.to_dict() ,**_lowerCAmelCase )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = copy.deepcopy(self.__dict__ )
lowerCamelCase__ = self.question_encoder.to_dict()
lowerCamelCase__ = self.generator.to_dict()
lowerCamelCase__ = self.__class__.model_type
return output
| 50 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase : Union[str, Any] = {
'configuration_canine': ['CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CanineConfig'],
'tokenization_canine': ['CanineTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Any = [
'CANINE_PRETRAINED_MODEL_ARCHIVE_LIST',
'CanineForMultipleChoice',
'CanineForQuestionAnswering',
'CanineForSequenceClassification',
'CanineForTokenClassification',
'CanineLayer',
'CanineModel',
'CaninePreTrainedModel',
'load_tf_weights_in_canine',
]
if TYPE_CHECKING:
from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
from .tokenization_canine import CanineTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_canine import (
CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
CanineForMultipleChoice,
CanineForQuestionAnswering,
CanineForSequenceClassification,
CanineForTokenClassification,
CanineLayer,
CanineModel,
CaninePreTrainedModel,
load_tf_weights_in_canine,
)
else:
import sys
UpperCamelCase : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 50 | 1 |
'''simple docstring'''
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
UpperCamelCase : List[Any] = pytest.mark.integration
@pytest.mark.parametrize("""path""" , ["""paws""", """csv"""] )
def A__ ( __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[int] ):
inspect_dataset(__lowerCAmelCase , __lowerCAmelCase )
lowerCamelCase__ = path + """.py"""
assert script_name in os.listdir(__lowerCAmelCase )
assert "__pycache__" not in os.listdir(__lowerCAmelCase )
@pytest.mark.filterwarnings("""ignore:inspect_metric is deprecated:FutureWarning""" )
@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning""" )
@pytest.mark.parametrize("""path""" , ["""accuracy"""] )
def A__ ( __lowerCAmelCase : List[Any] , __lowerCAmelCase : int ):
inspect_metric(__lowerCAmelCase , __lowerCAmelCase )
lowerCamelCase__ = path + """.py"""
assert script_name in os.listdir(__lowerCAmelCase )
assert "__pycache__" not in os.listdir(__lowerCAmelCase )
@pytest.mark.parametrize(
"""path, config_name, expected_splits""" , [
("""squad""", """plain_text""", ["""train""", """validation"""]),
("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
] , )
def A__ ( __lowerCAmelCase : Any , __lowerCAmelCase : int , __lowerCAmelCase : Optional[int] ):
lowerCamelCase__ = get_dataset_config_info(__lowerCAmelCase , config_name=__lowerCAmelCase )
assert info.config_name == config_name
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
"""path, config_name, expected_exception""" , [
("""paws""", None, ValueError),
] , )
def A__ ( __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[int] ):
with pytest.raises(__lowerCAmelCase ):
get_dataset_config_info(__lowerCAmelCase , config_name=__lowerCAmelCase )
@pytest.mark.parametrize(
"""path, expected""" , [
("""squad""", """plain_text"""),
("""acronym_identification""", """default"""),
("""lhoestq/squad""", """plain_text"""),
("""lhoestq/test""", """default"""),
("""lhoestq/demo1""", """lhoestq--demo1"""),
("""dalle-mini/wit""", """dalle-mini--wit"""),
] , )
def A__ ( __lowerCAmelCase : Tuple , __lowerCAmelCase : Any ):
lowerCamelCase__ = get_dataset_config_names(__lowerCAmelCase )
assert expected in config_names
@pytest.mark.parametrize(
"""path, expected_configs, expected_splits_in_first_config""" , [
("""squad""", ["""plain_text"""], ["""train""", """validation"""]),
("""dalle-mini/wit""", ["""dalle-mini--wit"""], ["""train"""]),
("""paws""", ["""labeled_final""", """labeled_swap""", """unlabeled_final"""], ["""train""", """test""", """validation"""]),
] , )
def A__ ( __lowerCAmelCase : List[str] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Any ):
lowerCamelCase__ = get_dataset_infos(__lowerCAmelCase )
assert list(infos.keys() ) == expected_configs
lowerCamelCase__ = expected_configs[0]
assert expected_config in infos
lowerCamelCase__ = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits_in_first_config
@pytest.mark.parametrize(
"""path, expected_config, expected_splits""" , [
("""squad""", """plain_text""", ["""train""", """validation"""]),
("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
] , )
def A__ ( __lowerCAmelCase : int , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] ):
lowerCamelCase__ = get_dataset_infos(__lowerCAmelCase )
assert expected_config in infos
lowerCamelCase__ = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
"""path, config_name, expected_exception""" , [
("""paws""", None, ValueError),
] , )
def A__ ( __lowerCAmelCase : Any , __lowerCAmelCase : Dict , __lowerCAmelCase : List[Any] ):
with pytest.raises(__lowerCAmelCase ):
get_dataset_split_names(__lowerCAmelCase , config_name=__lowerCAmelCase )
| 50 |
'''simple docstring'''
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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.
# this script dumps information about the environment
import os
import sys
import transformers
UpperCamelCase : int = '3'
print('Python version:', sys.version)
print('transformers version:', transformers.__version__)
try:
import torch
print('Torch version:', torch.__version__)
print('Cuda available:', torch.cuda.is_available())
print('Cuda version:', torch.version.cuda)
print('CuDNN version:', torch.backends.cudnn.version())
print('Number of GPUs available:', torch.cuda.device_count())
print('NCCL version:', torch.cuda.nccl.version())
except ImportError:
print('Torch version:', None)
try:
import deepspeed
print('DeepSpeed version:', deepspeed.__version__)
except ImportError:
print('DeepSpeed version:', None)
try:
import tensorflow as tf
print('TensorFlow version:', tf.__version__)
print('TF GPUs available:', bool(tf.config.list_physical_devices('GPU')))
print('Number of TF GPUs available:', len(tf.config.list_physical_devices('GPU')))
except ImportError:
print('TensorFlow version:', None)
| 50 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase : Union[str, Any] = {
'configuration_canine': ['CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CanineConfig'],
'tokenization_canine': ['CanineTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Any = [
'CANINE_PRETRAINED_MODEL_ARCHIVE_LIST',
'CanineForMultipleChoice',
'CanineForQuestionAnswering',
'CanineForSequenceClassification',
'CanineForTokenClassification',
'CanineLayer',
'CanineModel',
'CaninePreTrainedModel',
'load_tf_weights_in_canine',
]
if TYPE_CHECKING:
from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
from .tokenization_canine import CanineTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_canine import (
CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
CanineForMultipleChoice,
CanineForQuestionAnswering,
CanineForSequenceClassification,
CanineForTokenClassification,
CanineLayer,
CanineModel,
CaninePreTrainedModel,
load_tf_weights_in_canine,
)
else:
import sys
UpperCamelCase : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 50 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase : Tuple = logging.get_logger(__name__)
UpperCamelCase : Union[str, Any] = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = 'gpt_bigcode'
_UpperCamelCase = ['past_key_values']
_UpperCamelCase = {
'hidden_size': 'n_embd',
'max_position_embeddings': 'n_positions',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self ,_lowerCAmelCase=5_02_57 ,_lowerCAmelCase=10_24 ,_lowerCAmelCase=7_68 ,_lowerCAmelCase=12 ,_lowerCAmelCase=12 ,_lowerCAmelCase=None ,_lowerCAmelCase="gelu_pytorch_tanh" ,_lowerCAmelCase=0.1 ,_lowerCAmelCase=0.1 ,_lowerCAmelCase=0.1 ,_lowerCAmelCase=1E-5 ,_lowerCAmelCase=0.02 ,_lowerCAmelCase=True ,_lowerCAmelCase=True ,_lowerCAmelCase=5_02_56 ,_lowerCAmelCase=5_02_56 ,_lowerCAmelCase=True ,_lowerCAmelCase=True ,_lowerCAmelCase=True ,**_lowerCAmelCase ,):
lowerCamelCase__ = vocab_size
lowerCamelCase__ = n_positions
lowerCamelCase__ = n_embd
lowerCamelCase__ = n_layer
lowerCamelCase__ = n_head
lowerCamelCase__ = n_inner
lowerCamelCase__ = activation_function
lowerCamelCase__ = resid_pdrop
lowerCamelCase__ = embd_pdrop
lowerCamelCase__ = attn_pdrop
lowerCamelCase__ = layer_norm_epsilon
lowerCamelCase__ = initializer_range
lowerCamelCase__ = scale_attn_weights
lowerCamelCase__ = use_cache
lowerCamelCase__ = attention_softmax_in_fpaa
lowerCamelCase__ = scale_attention_softmax_in_fpaa
lowerCamelCase__ = multi_query
lowerCamelCase__ = bos_token_id
lowerCamelCase__ = eos_token_id
super().__init__(bos_token_id=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase ,**_lowerCAmelCase )
| 50 | 1 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : str ):
if n_term == "":
return []
lowerCamelCase__ = []
for temp in range(int(__lowerCAmelCase ) ):
series.append(F'''1/{temp + 1}''' if series else """1""" )
return series
if __name__ == "__main__":
UpperCamelCase : Union[str, Any] = input('Enter the last number (nth term) of the Harmonic Series')
print('Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n')
print(harmonic_series(nth_term))
| 50 |
'''simple docstring'''
from PIL import Image
def A__ ( __lowerCAmelCase : Image , __lowerCAmelCase : float ):
def brightness(__lowerCAmelCase : int ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError("""level must be between -255.0 (black) and 255.0 (white)""" )
return img.point(__lowerCAmelCase )
if __name__ == "__main__":
# Load image
with Image.open('image_data/lena.jpg') as img:
# Change brightness to 100
UpperCamelCase : Union[str, Any] = change_brightness(img, 1_00)
brigt_img.save('image_data/lena_brightness.png', format='png')
| 50 | 1 |
'''simple docstring'''
from __future__ import annotations
from scipy.special import comb # type: ignore
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self ,_lowerCAmelCase ):
lowerCamelCase__ = list_of_points
# Degree determines the flexibility of the curve.
# Degree = 1 will produce a straight line.
lowerCamelCase__ = len(_lowerCAmelCase ) - 1
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
assert 0 <= t <= 1, "Time t must be between 0 and 1."
lowerCamelCase__ = []
for i in range(len(self.list_of_points ) ):
# basis function for each i
output_values.append(
comb(self.degree ,_lowerCAmelCase ) * ((1 - t) ** (self.degree - i)) * (t**i) )
# the basis must sum up to 1 for it to produce a valid Bezier curve.
assert round(sum(_lowerCAmelCase ) ,5 ) == 1
return output_values
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
assert 0 <= t <= 1, "Time t must be between 0 and 1."
lowerCamelCase__ = self.basis_function(_lowerCAmelCase )
lowerCamelCase__ = 0.0
lowerCamelCase__ = 0.0
for i in range(len(self.list_of_points ) ):
# For all points, sum up the product of i-th basis function and i-th point.
x += basis_function[i] * self.list_of_points[i][0]
y += basis_function[i] * self.list_of_points[i][1]
return (x, y)
def UpperCamelCase_ ( self ,_lowerCAmelCase = 0.01 ):
from matplotlib import pyplot as plt # type: ignore
lowerCamelCase__ = [] # x coordinates of points to plot
lowerCamelCase__ = [] # y coordinates of points to plot
lowerCamelCase__ = 0.0
while t <= 1:
lowerCamelCase__ = self.bezier_curve_function(_lowerCAmelCase )
to_plot_x.append(value[0] )
to_plot_y.append(value[1] )
t += step_size
lowerCamelCase__ = [i[0] for i in self.list_of_points]
lowerCamelCase__ = [i[1] for i in self.list_of_points]
plt.plot(
_lowerCAmelCase ,_lowerCAmelCase ,color="""blue""" ,label="""Curve of Degree """ + str(self.degree ) ,)
plt.scatter(_lowerCAmelCase ,_lowerCAmelCase ,color="""red""" ,label="""Control Points""" )
plt.legend()
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
BezierCurve([(1, 2), (3, 5)]).plot_curve() # degree 1
BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve() # degree 2
BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve() # degree 3
| 50 |
'''simple docstring'''
def A__ ( ):
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
UpperCamelCase : Dict = generate_large_matrix()
UpperCamelCase : Any = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7, 7, 6], [-1, -2, -3]],
grid,
)
def A__ ( __lowerCAmelCase : list[list[int]] ):
assert all(row == sorted(__lowerCAmelCase , reverse=__lowerCAmelCase ) for row in grid )
assert all(list(__lowerCAmelCase ) == sorted(__lowerCAmelCase , reverse=__lowerCAmelCase ) for col in zip(*__lowerCAmelCase ) )
def A__ ( __lowerCAmelCase : list[int] ):
lowerCamelCase__ = 0
lowerCamelCase__ = len(__lowerCAmelCase ) - 1
# Edge cases such as no values or all numbers are negative.
if not array or array[0] < 0:
return 0
while right + 1 > left:
lowerCamelCase__ = (left + right) // 2
lowerCamelCase__ = array[mid]
# Num must be negative and the index must be greater than or equal to 0.
if num < 0 and array[mid - 1] >= 0:
return mid
if num >= 0:
lowerCamelCase__ = mid + 1
else:
lowerCamelCase__ = mid - 1
# No negative numbers so return the last index of the array + 1 which is the length.
return len(__lowerCAmelCase )
def A__ ( __lowerCAmelCase : list[list[int]] ):
lowerCamelCase__ = 0
lowerCamelCase__ = len(grid[0] )
for i in range(len(__lowerCAmelCase ) ):
lowerCamelCase__ = find_negative_index(grid[i][:bound] )
total += bound
return (len(__lowerCAmelCase ) * len(grid[0] )) - total
def A__ ( __lowerCAmelCase : list[list[int]] ):
return len([number for row in grid for number in row if number < 0] )
def A__ ( __lowerCAmelCase : list[list[int]] ):
lowerCamelCase__ = 0
for row in grid:
for i, number in enumerate(__lowerCAmelCase ):
if number < 0:
total += len(__lowerCAmelCase ) - i
break
return total
def A__ ( ):
from timeit import timeit
print("""Running benchmarks""" )
lowerCamelCase__ = (
"""from __main__ import count_negatives_binary_search, """
"""count_negatives_brute_force, count_negatives_brute_force_with_break, grid"""
)
for func in (
"count_negatives_binary_search", # took 0.7727 seconds
"count_negatives_brute_force_with_break", # took 4.6505 seconds
"count_negatives_brute_force", # took 12.8160 seconds
):
lowerCamelCase__ = timeit(F'''{func}(grid=grid)''' , setup=__lowerCAmelCase , number=500 )
print(F'''{func}() took {time:0.4f} seconds''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 50 | 1 |
'''simple docstring'''
from statistics import mean
import numpy as np
def A__ ( __lowerCAmelCase : list , __lowerCAmelCase : list , __lowerCAmelCase : list , __lowerCAmelCase : int ):
lowerCamelCase__ = 0
# Number of processes finished
lowerCamelCase__ = 0
# Displays the finished process.
# If it is 0, the performance is completed if it is 1, before the performance.
lowerCamelCase__ = [0] * no_of_process
# List to include calculation results
lowerCamelCase__ = [0] * no_of_process
# Sort by arrival time.
lowerCamelCase__ = [burst_time[i] for i in np.argsort(__lowerCAmelCase )]
lowerCamelCase__ = [process_name[i] for i in np.argsort(__lowerCAmelCase )]
arrival_time.sort()
while no_of_process > finished_process_count:
lowerCamelCase__ = 0
while finished_process[i] == 1:
i += 1
if current_time < arrival_time[i]:
lowerCamelCase__ = arrival_time[i]
lowerCamelCase__ = 0
# Index showing the location of the process being performed
lowerCamelCase__ = 0
# Saves the current response ratio.
lowerCamelCase__ = 0
for i in range(0 , __lowerCAmelCase ):
if finished_process[i] == 0 and arrival_time[i] <= current_time:
lowerCamelCase__ = (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
i
]
if response_ratio < temp:
lowerCamelCase__ = temp
lowerCamelCase__ = i
# Calculate the turn around time
lowerCamelCase__ = current_time + burst_time[loc] - arrival_time[loc]
current_time += burst_time[loc]
# Indicates that the process has been performed.
lowerCamelCase__ = 1
# Increase finished_process_count by 1
finished_process_count += 1
return turn_around_time
def A__ ( __lowerCAmelCase : list , __lowerCAmelCase : list , __lowerCAmelCase : list , __lowerCAmelCase : int ):
lowerCamelCase__ = [0] * no_of_process
for i in range(0 , __lowerCAmelCase ):
lowerCamelCase__ = turn_around_time[i] - burst_time[i]
return waiting_time
if __name__ == "__main__":
UpperCamelCase : Optional[int] = 5
UpperCamelCase : str = ['A', 'B', 'C', 'D', 'E']
UpperCamelCase : Optional[int] = [1, 2, 3, 4, 5]
UpperCamelCase : Tuple = [1, 2, 3, 4, 5]
UpperCamelCase : str = calculate_turn_around_time(
process_name, arrival_time, burst_time, no_of_process
)
UpperCamelCase : Dict = calculate_waiting_time(
process_name, turn_around_time, burst_time, no_of_process
)
print('Process name \tArrival time \tBurst time \tTurn around time \tWaiting time')
for i in range(0, no_of_process):
print(
F'{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'
F'{turn_around_time[i]}\t\t\t{waiting_time[i]}'
)
print(F'average waiting time : {mean(waiting_time):.5f}')
print(F'average turn around time : {mean(turn_around_time):.5f}')
| 50 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
UpperCamelCase : List[Any] = 'examples/'
UpperCamelCase : int = {
'examples': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
'init': (re.compile(r'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'),
'setup': (re.compile(r'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), r'\1version="VERSION",'),
'doc': (re.compile(r'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), 'release = "VERSION"\n'),
}
UpperCamelCase : Any = {
'init': 'src/transformers/__init__.py',
'setup': 'setup.py',
}
UpperCamelCase : Any = 'README.md'
def A__ ( __lowerCAmelCase : Any , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[int] ):
with open(__lowerCAmelCase , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
lowerCamelCase__ = f.read()
lowerCamelCase__ , lowerCamelCase__ = REPLACE_PATTERNS[pattern]
lowerCamelCase__ = replace.replace("""VERSION""" , __lowerCAmelCase )
lowerCamelCase__ = re_pattern.sub(__lowerCAmelCase , __lowerCAmelCase )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.write(__lowerCAmelCase )
def A__ ( __lowerCAmelCase : str ):
for folder, directories, fnames in os.walk(__lowerCAmelCase ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove("""research_projects""" )
if "legacy" in directories:
directories.remove("""legacy""" )
for fname in fnames:
if fname.endswith(""".py""" ):
update_version_in_file(os.path.join(__lowerCAmelCase , __lowerCAmelCase ) , __lowerCAmelCase , pattern="""examples""" )
def A__ ( __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[Any]=False ):
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
if not patch:
update_version_in_examples(__lowerCAmelCase )
def A__ ( ):
lowerCamelCase__ = """🤗 Transformers currently provides the following architectures"""
lowerCamelCase__ = """1. Want to contribute a new model?"""
with open(__lowerCAmelCase , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
lowerCamelCase__ = f.readlines()
# Find the start of the list.
lowerCamelCase__ = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
lowerCamelCase__ = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith("""1.""" ):
lowerCamelCase__ = lines[index].replace(
"""https://huggingface.co/docs/transformers/main/model_doc""" , """https://huggingface.co/docs/transformers/model_doc""" , )
index += 1
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.writelines(__lowerCAmelCase )
def A__ ( ):
with open(REPLACE_FILES["""init"""] , """r""" ) as f:
lowerCamelCase__ = f.read()
lowerCamelCase__ = REPLACE_PATTERNS["""init"""][0].search(__lowerCAmelCase ).groups()[0]
return packaging.version.parse(__lowerCAmelCase )
def A__ ( __lowerCAmelCase : Union[str, Any]=False ):
lowerCamelCase__ = get_version()
if patch and default_version.is_devrelease:
raise ValueError("""Can't create a patch version from the dev branch, checkout a released version!""" )
if default_version.is_devrelease:
lowerCamelCase__ = default_version.base_version
elif patch:
lowerCamelCase__ = F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
else:
lowerCamelCase__ = F'''{default_version.major}.{default_version.minor + 1}.0'''
# Now let's ask nicely if that's the right one.
lowerCamelCase__ = input(F'''Which version are you releasing? [{default_version}]''' )
if len(__lowerCAmelCase ) == 0:
lowerCamelCase__ = default_version
print(F'''Updating version to {version}.''' )
global_version_update(__lowerCAmelCase , patch=__lowerCAmelCase )
if not patch:
print("""Cleaning main README, don't forget to run `make fix-copies`.""" )
clean_main_ref_in_model_list()
def A__ ( ):
lowerCamelCase__ = get_version()
lowerCamelCase__ = F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
lowerCamelCase__ = current_version.base_version
# Check with the user we got that right.
lowerCamelCase__ = input(F'''Which version are we developing now? [{dev_version}]''' )
if len(__lowerCAmelCase ) == 0:
lowerCamelCase__ = dev_version
print(F'''Updating version to {version}.''' )
global_version_update(__lowerCAmelCase )
print("""Cleaning main README, don't forget to run `make fix-copies`.""" )
clean_main_ref_in_model_list()
if __name__ == "__main__":
UpperCamelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')
parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')
UpperCamelCase : Any = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print('Nothing to do after a patch :-)')
else:
post_release_work()
| 50 | 1 |
'''simple docstring'''
import socket
def A__ ( ):
lowerCamelCase__ = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
lowerCamelCase__ = socket.gethostname()
lowerCamelCase__ = 1_2312
sock.connect((host, port) )
sock.send(b"""Hello server!""" )
with open("""Received_file""" , """wb""" ) as out_file:
print("""File opened""" )
print("""Receiving data...""" )
while True:
lowerCamelCase__ = sock.recv(1024 )
if not data:
break
out_file.write(__lowerCAmelCase )
print("""Successfully received the file""" )
sock.close()
print("""Connection closed""" )
if __name__ == "__main__":
main()
| 50 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
UpperCamelCase : List[str] = logging.get_logger(__name__)
UpperCamelCase : Dict = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
UpperCamelCase : int = {
'vocab_file': {
'squeezebert/squeezebert-uncased': (
'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'
),
'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt',
'squeezebert/squeezebert-mnli-headless': (
'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'squeezebert/squeezebert-uncased': (
'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'
),
'squeezebert/squeezebert-mnli': (
'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'
),
'squeezebert/squeezebert-mnli-headless': (
'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'
),
},
}
UpperCamelCase : Tuple = {
'squeezebert/squeezebert-uncased': 5_12,
'squeezebert/squeezebert-mnli': 5_12,
'squeezebert/squeezebert-mnli-headless': 5_12,
}
UpperCamelCase : Dict = {
'squeezebert/squeezebert-uncased': {'do_lower_case': True},
'squeezebert/squeezebert-mnli': {'do_lower_case': True},
'squeezebert/squeezebert-mnli-headless': {'do_lower_case': True},
}
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = VOCAB_FILES_NAMES
_UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
_UpperCamelCase = PRETRAINED_INIT_CONFIGURATION
_UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCamelCase = SqueezeBertTokenizer
def __init__( self ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=True ,_lowerCAmelCase="[UNK]" ,_lowerCAmelCase="[SEP]" ,_lowerCAmelCase="[PAD]" ,_lowerCAmelCase="[CLS]" ,_lowerCAmelCase="[MASK]" ,_lowerCAmelCase=True ,_lowerCAmelCase=None ,**_lowerCAmelCase ,):
super().__init__(
_lowerCAmelCase ,tokenizer_file=_lowerCAmelCase ,do_lower_case=_lowerCAmelCase ,unk_token=_lowerCAmelCase ,sep_token=_lowerCAmelCase ,pad_token=_lowerCAmelCase ,cls_token=_lowerCAmelCase ,mask_token=_lowerCAmelCase ,tokenize_chinese_chars=_lowerCAmelCase ,strip_accents=_lowerCAmelCase ,**_lowerCAmelCase ,)
lowerCamelCase__ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("""lowercase""" ,_lowerCAmelCase ) != do_lower_case
or normalizer_state.get("""strip_accents""" ,_lowerCAmelCase ) != strip_accents
or normalizer_state.get("""handle_chinese_chars""" ,_lowerCAmelCase ) != tokenize_chinese_chars
):
lowerCamelCase__ = getattr(_lowerCAmelCase ,normalizer_state.pop("""type""" ) )
lowerCamelCase__ = do_lower_case
lowerCamelCase__ = strip_accents
lowerCamelCase__ = tokenize_chinese_chars
lowerCamelCase__ = normalizer_class(**_lowerCAmelCase )
lowerCamelCase__ = do_lower_case
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase=None ):
lowerCamelCase__ = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase = None ):
lowerCamelCase__ = [self.sep_token_id]
lowerCamelCase__ = [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 UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase = None ):
lowerCamelCase__ = self._tokenizer.model.save(_lowerCAmelCase ,name=_lowerCAmelCase )
return tuple(_lowerCAmelCase )
| 50 | 1 |
'''simple docstring'''
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase : Optional[Any] = get_tests_dir('fixtures/test_sentencepiece.model')
UpperCamelCase : Any = get_tests_dir('fixtures/test_sentencepiece_bpe.model')
UpperCamelCase : int = 'pt' if is_torch_available() else 'tf'
@require_sentencepiece
@require_tokenizers
class UpperCamelCase__ (a ,unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase = CamembertTokenizer
_UpperCamelCase = CamembertTokenizerFast
_UpperCamelCase = True
_UpperCamelCase = True
def UpperCamelCase_ ( self ):
super().setUp()
# We have a SentencePiece fixture for testing
lowerCamelCase__ = CamembertTokenizer(_lowerCAmelCase )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = """<pad>"""
lowerCamelCase__ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCAmelCase ) ,_lowerCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCAmelCase ) ,_lowerCAmelCase )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] ,"""<s>NOTUSED""" )
self.assertEqual(vocab_keys[1] ,"""<pad>""" )
self.assertEqual(vocab_keys[-1] ,"""<mask>""" )
self.assertEqual(len(_lowerCAmelCase ) ,10_04 )
def UpperCamelCase_ ( self ):
self.assertEqual(self.get_tokenizer().vocab_size ,10_05 )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = CamembertTokenizer(_lowerCAmelCase )
tokenizer.save_pretrained(self.tmpdirname )
lowerCamelCase__ = CamembertTokenizerFast.from_pretrained(self.tmpdirname )
lowerCamelCase__ = """I was born in 92000, and this is falsé."""
lowerCamelCase__ = tokenizer.encode(_lowerCAmelCase )
lowerCamelCase__ = rust_tokenizer.encode(_lowerCAmelCase )
self.assertListEqual(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = tokenizer.encode(_lowerCAmelCase ,add_special_tokens=_lowerCAmelCase )
lowerCamelCase__ = rust_tokenizer.encode(_lowerCAmelCase ,add_special_tokens=_lowerCAmelCase )
self.assertListEqual(_lowerCAmelCase ,_lowerCAmelCase )
# <unk> tokens are not the same for `rust` than for `slow`.
# Because spm gives back raw token instead of `unk` in EncodeAsPieces
# tokens = tokenizer.tokenize(sequence)
lowerCamelCase__ = tokenizer.convert_ids_to_tokens(_lowerCAmelCase )
lowerCamelCase__ = rust_tokenizer.tokenize(_lowerCAmelCase )
self.assertListEqual(_lowerCAmelCase ,_lowerCAmelCase )
def UpperCamelCase_ ( self ):
if not self.test_rust_tokenizer:
return
lowerCamelCase__ = self.get_tokenizer()
lowerCamelCase__ = self.get_rust_tokenizer()
lowerCamelCase__ = """I was born in 92000, and this is falsé."""
lowerCamelCase__ = tokenizer.tokenize(_lowerCAmelCase )
lowerCamelCase__ = rust_tokenizer.tokenize(_lowerCAmelCase )
self.assertListEqual(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = tokenizer.encode(_lowerCAmelCase ,add_special_tokens=_lowerCAmelCase )
lowerCamelCase__ = rust_tokenizer.encode(_lowerCAmelCase ,add_special_tokens=_lowerCAmelCase )
self.assertListEqual(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = self.get_rust_tokenizer()
lowerCamelCase__ = tokenizer.encode(_lowerCAmelCase )
lowerCamelCase__ = rust_tokenizer.encode(_lowerCAmelCase )
self.assertListEqual(_lowerCAmelCase ,_lowerCAmelCase )
@slow
def UpperCamelCase_ ( self ):
# fmt: off
lowerCamelCase__ = {"""input_ids""": [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 9, 6]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# camembert is a french model. So we also use french texts.
lowerCamelCase__ = [
"""Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
"""utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
"""À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
"""pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
"""telles que la traduction et la synthèse de texte.""",
]
self.tokenizer_integration_test_util(
expected_encoding=_lowerCAmelCase ,model_name="""camembert-base""" ,revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf""" ,sequences=_lowerCAmelCase ,)
| 50 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def A__ ( __lowerCAmelCase : Any ):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0x4_e_0_0 and cp <= 0x9_f_f_f)
or (cp >= 0x3_4_0_0 and cp <= 0x4_d_b_f) #
or (cp >= 0x2_0_0_0_0 and cp <= 0x2_a_6_d_f) #
or (cp >= 0x2_a_7_0_0 and cp <= 0x2_b_7_3_f) #
or (cp >= 0x2_b_7_4_0 and cp <= 0x2_b_8_1_f) #
or (cp >= 0x2_b_8_2_0 and cp <= 0x2_c_e_a_f) #
or (cp >= 0xf_9_0_0 and cp <= 0xf_a_f_f)
or (cp >= 0x2_f_8_0_0 and cp <= 0x2_f_a_1_f) #
): #
return True
return False
def A__ ( __lowerCAmelCase : str ):
# word like '180' or '身高' or '神'
for char in word:
lowerCamelCase__ = ord(__lowerCAmelCase )
if not _is_chinese_char(__lowerCAmelCase ):
return 0
return 1
def A__ ( __lowerCAmelCase : List[str] ):
lowerCamelCase__ = set()
for token in tokens:
lowerCamelCase__ = len(__lowerCAmelCase ) > 1 and is_chinese(__lowerCAmelCase )
if chinese_word:
word_set.add(__lowerCAmelCase )
lowerCamelCase__ = list(__lowerCAmelCase )
return word_list
def A__ ( __lowerCAmelCase : List[str] , __lowerCAmelCase : set() ):
if not chinese_word_set:
return bert_tokens
lowerCamelCase__ = max([len(__lowerCAmelCase ) for w in chinese_word_set] )
lowerCamelCase__ = bert_tokens
lowerCamelCase__ , lowerCamelCase__ = 0, len(__lowerCAmelCase )
while start < end:
lowerCamelCase__ = True
if is_chinese(bert_word[start] ):
lowerCamelCase__ = min(end - start , __lowerCAmelCase )
for i in range(__lowerCAmelCase , 1 , -1 ):
lowerCamelCase__ = """""".join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1 , start + i ):
lowerCamelCase__ = """##""" + bert_word[j]
lowerCamelCase__ = start + i
lowerCamelCase__ = False
break
if single_word:
start += 1
return bert_word
def A__ ( __lowerCAmelCase : List[str] , __lowerCAmelCase : LTP , __lowerCAmelCase : BertTokenizer ):
lowerCamelCase__ = []
for i in range(0 , len(__lowerCAmelCase ) , 100 ):
lowerCamelCase__ = ltp_tokenizer.pipeline(lines[i : i + 100] , tasks=["""cws"""] ).cws
lowerCamelCase__ = [get_chinese_word(__lowerCAmelCase ) for r in res]
ltp_res.extend(__lowerCAmelCase )
assert len(__lowerCAmelCase ) == len(__lowerCAmelCase )
lowerCamelCase__ = []
for i in range(0 , len(__lowerCAmelCase ) , 100 ):
lowerCamelCase__ = bert_tokenizer(lines[i : i + 100] , add_special_tokens=__lowerCAmelCase , truncation=__lowerCAmelCase , max_length=512 )
bert_res.extend(res["""input_ids"""] )
assert len(__lowerCAmelCase ) == len(__lowerCAmelCase )
lowerCamelCase__ = []
for input_ids, chinese_word in zip(__lowerCAmelCase , __lowerCAmelCase ):
lowerCamelCase__ = []
for id in input_ids:
lowerCamelCase__ = bert_tokenizer._convert_id_to_token(__lowerCAmelCase )
input_tokens.append(__lowerCAmelCase )
lowerCamelCase__ = add_sub_symbol(__lowerCAmelCase , __lowerCAmelCase )
lowerCamelCase__ = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(__lowerCAmelCase ):
if token[:2] == "##":
lowerCamelCase__ = token[2:]
# save chinese tokens' pos
if len(__lowerCAmelCase ) == 1 and _is_chinese_char(ord(__lowerCAmelCase ) ):
ref_id.append(__lowerCAmelCase )
ref_ids.append(__lowerCAmelCase )
assert len(__lowerCAmelCase ) == len(__lowerCAmelCase )
return ref_ids
def A__ ( __lowerCAmelCase : Optional[int] ):
# For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
# If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
with open(args.file_name , """r""" , encoding="""utf-8""" ) as f:
lowerCamelCase__ = f.readlines()
lowerCamelCase__ = [line.strip() for line in data if len(__lowerCAmelCase ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
lowerCamelCase__ = LTP(args.ltp ) # faster in GPU device
lowerCamelCase__ = BertTokenizer.from_pretrained(args.bert )
lowerCamelCase__ = prepare_ref(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
with open(args.save_path , """w""" , encoding="""utf-8""" ) as f:
lowerCamelCase__ = [json.dumps(__lowerCAmelCase ) + """\n""" for ref in ref_ids]
f.writelines(__lowerCAmelCase )
if __name__ == "__main__":
UpperCamelCase : Optional[int] = argparse.ArgumentParser(description='prepare_chinese_ref')
parser.add_argument(
'--file_name',
required=False,
type=str,
default='./resources/chinese-demo.txt',
help='file need process, same as training data in lm',
)
parser.add_argument(
'--ltp',
required=False,
type=str,
default='./resources/ltp',
help='resources for LTP tokenizer, usually a path',
)
parser.add_argument(
'--bert',
required=False,
type=str,
default='./resources/robert',
help='resources for Bert tokenizer',
)
parser.add_argument(
'--save_path',
required=False,
type=str,
default='./resources/ref.txt',
help='path to save res',
)
UpperCamelCase : Any = parser.parse_args()
main(args)
| 50 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
def __init__( self ,_lowerCAmelCase ,_lowerCAmelCase=3 ,_lowerCAmelCase=32 ,_lowerCAmelCase=3 ,_lowerCAmelCase=10 ,_lowerCAmelCase=[10, 20, 30, 40] ,_lowerCAmelCase=[1, 1, 2, 1] ,_lowerCAmelCase=True ,_lowerCAmelCase=True ,_lowerCAmelCase="relu" ,_lowerCAmelCase=3 ,_lowerCAmelCase=None ,):
lowerCamelCase__ = parent
lowerCamelCase__ = batch_size
lowerCamelCase__ = image_size
lowerCamelCase__ = num_channels
lowerCamelCase__ = embeddings_size
lowerCamelCase__ = hidden_sizes
lowerCamelCase__ = depths
lowerCamelCase__ = is_training
lowerCamelCase__ = use_labels
lowerCamelCase__ = hidden_act
lowerCamelCase__ = num_labels
lowerCamelCase__ = scope
lowerCamelCase__ = len(_lowerCAmelCase )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCamelCase__ = self.get_config()
return config, pixel_values
def UpperCamelCase_ ( self ):
return RegNetConfig(
num_channels=self.num_channels ,embeddings_size=self.embeddings_size ,hidden_sizes=self.hidden_sizes ,depths=self.depths ,hidden_act=self.hidden_act ,num_labels=self.num_labels ,image_size=self.image_size ,)
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = FlaxRegNetModel(config=_lowerCAmelCase )
lowerCamelCase__ = model(_lowerCAmelCase )
# Output shape (b, c, h, w)
self.parent.assertEqual(
result.last_hidden_state.shape ,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) ,)
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = self.num_labels
lowerCamelCase__ = FlaxRegNetForImageClassification(config=_lowerCAmelCase )
lowerCamelCase__ = model(_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.prepare_config_and_inputs()
lowerCamelCase__ , lowerCamelCase__ = config_and_inputs
lowerCamelCase__ = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_flax
class UpperCamelCase__ (a ,unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase = (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()
_UpperCamelCase = False
_UpperCamelCase = False
_UpperCamelCase = False
def UpperCamelCase_ ( self ):
lowerCamelCase__ = FlaxRegNetModelTester(self )
lowerCamelCase__ = ConfigTester(self ,config_class=_lowerCAmelCase ,has_text_modality=_lowerCAmelCase )
def UpperCamelCase_ ( self ):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def UpperCamelCase_ ( self ):
return
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase )
@unittest.skip(reason="""RegNet does not use inputs_embeds""" )
def UpperCamelCase_ ( self ):
pass
@unittest.skip(reason="""RegNet does not support input and output embeddings""" )
def UpperCamelCase_ ( self ):
pass
def UpperCamelCase_ ( self ):
lowerCamelCase__ , lowerCamelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase__ = model_class(_lowerCAmelCase )
lowerCamelCase__ = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCamelCase__ = [*signature.parameters.keys()]
lowerCamelCase__ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] ,_lowerCAmelCase )
def UpperCamelCase_ ( self ):
def check_hidden_states_output(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = model_class(_lowerCAmelCase )
lowerCamelCase__ = model(**self._prepare_for_class(_lowerCAmelCase ,_lowerCAmelCase ) )
lowerCamelCase__ = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowerCamelCase__ = self.model_tester.num_stages
self.assertEqual(len(_lowerCAmelCase ) ,expected_num_stages + 1 )
lowerCamelCase__ , lowerCamelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase__ = True
check_hidden_states_output(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCamelCase__ = True
check_hidden_states_output(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
def UpperCamelCase_ ( self ):
lowerCamelCase__ , lowerCamelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
lowerCamelCase__ = self._prepare_for_class(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = model_class(_lowerCAmelCase )
@jax.jit
def model_jitted(_lowerCAmelCase ,**_lowerCAmelCase ):
return model(pixel_values=_lowerCAmelCase ,**_lowerCAmelCase )
with self.subTest("""JIT Enabled""" ):
lowerCamelCase__ = model_jitted(**_lowerCAmelCase ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
lowerCamelCase__ = model_jitted(**_lowerCAmelCase ).to_tuple()
self.assertEqual(len(_lowerCAmelCase ) ,len(_lowerCAmelCase ) )
for jitted_output, output in zip(_lowerCAmelCase ,_lowerCAmelCase ):
self.assertEqual(jitted_output.shape ,output.shape )
def A__ ( ):
lowerCamelCase__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_flax
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
@cached_property
def UpperCamelCase_ ( self ):
return AutoImageProcessor.from_pretrained("""facebook/regnet-y-040""" ) if is_vision_available() else None
@slow
def UpperCamelCase_ ( self ):
lowerCamelCase__ = FlaxRegNetForImageClassification.from_pretrained("""facebook/regnet-y-040""" )
lowerCamelCase__ = self.default_image_processor
lowerCamelCase__ = prepare_img()
lowerCamelCase__ = image_processor(images=_lowerCAmelCase ,return_tensors="""np""" )
lowerCamelCase__ = model(**_lowerCAmelCase )
# verify the logits
lowerCamelCase__ = (1, 10_00)
self.assertEqual(outputs.logits.shape ,_lowerCAmelCase )
lowerCamelCase__ = jnp.array([-0.4180, -1.5051, -3.4836] )
self.assertTrue(jnp.allclose(outputs.logits[0, :3] ,_lowerCAmelCase ,atol=1E-4 ) )
| 50 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase : Tuple = logging.get_logger(__name__)
def A__ ( __lowerCAmelCase : int ):
lowerCamelCase__ = DPTConfig(embedding_type="""hybrid""" )
if "large" in checkpoint_url:
lowerCamelCase__ = 1024
lowerCamelCase__ = 4096
lowerCamelCase__ = 24
lowerCamelCase__ = 16
lowerCamelCase__ = [5, 11, 17, 23]
lowerCamelCase__ = [256, 512, 1024, 1024]
lowerCamelCase__ = (1, 384, 384)
if "nyu" or "midas" in checkpoint_url:
lowerCamelCase__ = 768
lowerCamelCase__ = [1, 1, 1, 0.5]
lowerCamelCase__ = [256, 512, 768, 768]
lowerCamelCase__ = 150
lowerCamelCase__ = 16
lowerCamelCase__ = (1, 384, 384)
lowerCamelCase__ = False
lowerCamelCase__ = """project"""
if "ade" in checkpoint_url:
lowerCamelCase__ = True
lowerCamelCase__ = 768
lowerCamelCase__ = [1, 1, 1, 0.5]
lowerCamelCase__ = 150
lowerCamelCase__ = 16
lowerCamelCase__ = """huggingface/label-files"""
lowerCamelCase__ = """ade20k-id2label.json"""
lowerCamelCase__ = json.load(open(cached_download(hf_hub_url(__lowerCAmelCase , __lowerCAmelCase , repo_type="""dataset""" ) ) , """r""" ) )
lowerCamelCase__ = {int(__lowerCAmelCase ): v for k, v in idalabel.items()}
lowerCamelCase__ = idalabel
lowerCamelCase__ = {v: k for k, v in idalabel.items()}
lowerCamelCase__ = [1, 150, 480, 480]
return config, expected_shape
def A__ ( __lowerCAmelCase : Optional[int] ):
lowerCamelCase__ = ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
for k in ignore_keys:
state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
def A__ ( __lowerCAmelCase : List[Any] ):
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
lowerCamelCase__ = name.replace("""pretrained.model""" , """dpt.encoder""" )
if "pretrained.model" in name:
lowerCamelCase__ = name.replace("""pretrained.model""" , """dpt.embeddings""" )
if "patch_embed" in name:
lowerCamelCase__ = name.replace("""patch_embed""" , """""" )
if "pos_embed" in name:
lowerCamelCase__ = name.replace("""pos_embed""" , """position_embeddings""" )
if "attn.proj" in name:
lowerCamelCase__ = name.replace("""attn.proj""" , """attention.output.dense""" )
if "proj" in name and "project" not in name:
lowerCamelCase__ = name.replace("""proj""" , """projection""" )
if "blocks" in name:
lowerCamelCase__ = name.replace("""blocks""" , """layer""" )
if "mlp.fc1" in name:
lowerCamelCase__ = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
lowerCamelCase__ = name.replace("""mlp.fc2""" , """output.dense""" )
if "norm1" in name and "backbone" not in name:
lowerCamelCase__ = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name and "backbone" not in name:
lowerCamelCase__ = name.replace("""norm2""" , """layernorm_after""" )
if "scratch.output_conv" in name:
lowerCamelCase__ = name.replace("""scratch.output_conv""" , """head""" )
if "scratch" in name:
lowerCamelCase__ = name.replace("""scratch""" , """neck""" )
if "layer1_rn" in name:
lowerCamelCase__ = name.replace("""layer1_rn""" , """convs.0""" )
if "layer2_rn" in name:
lowerCamelCase__ = name.replace("""layer2_rn""" , """convs.1""" )
if "layer3_rn" in name:
lowerCamelCase__ = name.replace("""layer3_rn""" , """convs.2""" )
if "layer4_rn" in name:
lowerCamelCase__ = name.replace("""layer4_rn""" , """convs.3""" )
if "refinenet" in name:
lowerCamelCase__ = int(name[len("""neck.refinenet""" ) : len("""neck.refinenet""" ) + 1] )
# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
lowerCamelCase__ = name.replace(F'''refinenet{layer_idx}''' , F'''fusion_stage.layers.{abs(layer_idx-4 )}''' )
if "out_conv" in name:
lowerCamelCase__ = name.replace("""out_conv""" , """projection""" )
if "resConfUnit1" in name:
lowerCamelCase__ = name.replace("""resConfUnit1""" , """residual_layer1""" )
if "resConfUnit2" in name:
lowerCamelCase__ = name.replace("""resConfUnit2""" , """residual_layer2""" )
if "conv1" in name:
lowerCamelCase__ = name.replace("""conv1""" , """convolution1""" )
if "conv2" in name:
lowerCamelCase__ = name.replace("""conv2""" , """convolution2""" )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess1.0.project.0""" , """neck.reassemble_stage.readout_projects.0.0""" )
if "pretrained.act_postprocess2.0.project.0" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess2.0.project.0""" , """neck.reassemble_stage.readout_projects.1.0""" )
if "pretrained.act_postprocess3.0.project.0" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess3.0.project.0""" , """neck.reassemble_stage.readout_projects.2.0""" )
if "pretrained.act_postprocess4.0.project.0" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess4.0.project.0""" , """neck.reassemble_stage.readout_projects.3.0""" )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess1.3""" , """neck.reassemble_stage.layers.0.projection""" )
if "pretrained.act_postprocess1.4" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess1.4""" , """neck.reassemble_stage.layers.0.resize""" )
if "pretrained.act_postprocess2.3" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess2.3""" , """neck.reassemble_stage.layers.1.projection""" )
if "pretrained.act_postprocess2.4" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess2.4""" , """neck.reassemble_stage.layers.1.resize""" )
if "pretrained.act_postprocess3.3" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess3.3""" , """neck.reassemble_stage.layers.2.projection""" )
if "pretrained.act_postprocess4.3" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess4.3""" , """neck.reassemble_stage.layers.3.projection""" )
if "pretrained.act_postprocess4.4" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess4.4""" , """neck.reassemble_stage.layers.3.resize""" )
if "pretrained" in name:
lowerCamelCase__ = name.replace("""pretrained""" , """dpt""" )
if "bn" in name:
lowerCamelCase__ = name.replace("""bn""" , """batch_norm""" )
if "head" in name:
lowerCamelCase__ = name.replace("""head""" , """head.head""" )
if "encoder.norm" in name:
lowerCamelCase__ = name.replace("""encoder.norm""" , """layernorm""" )
if "auxlayer" in name:
lowerCamelCase__ = name.replace("""auxlayer""" , """auxiliary_head.head""" )
if "backbone" in name:
lowerCamelCase__ = name.replace("""backbone""" , """backbone.bit.encoder""" )
if ".." in name:
lowerCamelCase__ = name.replace("""..""" , """.""" )
if "stem.conv" in name:
lowerCamelCase__ = name.replace("""stem.conv""" , """bit.embedder.convolution""" )
if "blocks" in name:
lowerCamelCase__ = name.replace("""blocks""" , """layers""" )
if "convolution" in name and "backbone" in name:
lowerCamelCase__ = name.replace("""convolution""" , """conv""" )
if "layer" in name and "backbone" in name:
lowerCamelCase__ = name.replace("""layer""" , """layers""" )
if "backbone.bit.encoder.bit" in name:
lowerCamelCase__ = name.replace("""backbone.bit.encoder.bit""" , """backbone.bit""" )
if "embedder.conv" in name:
lowerCamelCase__ = name.replace("""embedder.conv""" , """embedder.convolution""" )
if "backbone.bit.encoder.stem.norm" in name:
lowerCamelCase__ = name.replace("""backbone.bit.encoder.stem.norm""" , """backbone.bit.embedder.norm""" )
return name
def A__ ( __lowerCAmelCase : str , __lowerCAmelCase : int ):
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowerCamelCase__ = state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight''' )
lowerCamelCase__ = state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
lowerCamelCase__ = in_proj_weight[: config.hidden_size, :]
lowerCamelCase__ = in_proj_bias[: config.hidden_size]
lowerCamelCase__ = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowerCamelCase__ = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowerCamelCase__ = in_proj_weight[
-config.hidden_size :, :
]
lowerCamelCase__ = in_proj_bias[-config.hidden_size :]
def A__ ( ):
lowerCamelCase__ = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowerCamelCase__ = Image.open(requests.get(__lowerCAmelCase , stream=__lowerCAmelCase ).raw )
return im
@torch.no_grad()
def A__ ( __lowerCAmelCase : Dict , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Any ):
lowerCamelCase__ , lowerCamelCase__ = get_dpt_config(__lowerCAmelCase )
# load original state_dict from URL
# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
lowerCamelCase__ = torch.load(__lowerCAmelCase , map_location="""cpu""" )
# remove certain keys
remove_ignore_keys_(__lowerCAmelCase )
# rename keys
for key in state_dict.copy().keys():
lowerCamelCase__ = state_dict.pop(__lowerCAmelCase )
lowerCamelCase__ = val
# read in qkv matrices
read_in_q_k_v(__lowerCAmelCase , __lowerCAmelCase )
# load HuggingFace model
lowerCamelCase__ = DPTForSemanticSegmentation(__lowerCAmelCase ) if """ade""" in checkpoint_url else DPTForDepthEstimation(__lowerCAmelCase )
model.load_state_dict(__lowerCAmelCase )
model.eval()
# Check outputs on an image
lowerCamelCase__ = 480 if """ade""" in checkpoint_url else 384
lowerCamelCase__ = DPTImageProcessor(size=__lowerCAmelCase )
lowerCamelCase__ = prepare_img()
lowerCamelCase__ = image_processor(__lowerCAmelCase , return_tensors="""pt""" )
# forward pass
lowerCamelCase__ = model(**__lowerCAmelCase ).logits if """ade""" in checkpoint_url else model(**__lowerCAmelCase ).predicted_depth
if show_prediction:
lowerCamelCase__ = (
torch.nn.functional.interpolate(
outputs.unsqueeze(1 ) , size=(image.size[1], image.size[0]) , mode="""bicubic""" , align_corners=__lowerCAmelCase , )
.squeeze()
.cpu()
.numpy()
)
Image.fromarray((prediction / prediction.max()) * 255 ).show()
if pytorch_dump_folder_path is not None:
Path(__lowerCAmelCase ).mkdir(exist_ok=__lowerCAmelCase )
print(F'''Saving model 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:
model.push_to_hub("""ybelkada/dpt-hybrid-midas""" )
image_processor.push_to_hub("""ybelkada/dpt-hybrid-midas""" )
if __name__ == "__main__":
UpperCamelCase : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint_url',
default='https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt',
type=str,
help='URL of the original DPT checkpoint you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default=None,
type=str,
required=False,
help='Path to the output PyTorch model directory.',
)
parser.add_argument(
'--push_to_hub',
action='store_true',
)
parser.add_argument(
'--model_name',
default='dpt-large',
type=str,
help='Name of the model, in case you\'re pushing to the hub.',
)
parser.add_argument(
'--show_prediction',
action='store_true',
)
UpperCamelCase : List[str] = parser.parse_args()
convert_dpt_checkpoint(
args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
)
| 50 | 1 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : list[int] ):
lowerCamelCase__ = []
if len(__lowerCAmelCase ) == 1:
return [nums.copy()]
for _ in range(len(__lowerCAmelCase ) ):
lowerCamelCase__ = nums.pop(0 )
lowerCamelCase__ = permute(__lowerCAmelCase )
for perm in permutations:
perm.append(__lowerCAmelCase )
result.extend(__lowerCAmelCase )
nums.append(__lowerCAmelCase )
return result
def A__ ( __lowerCAmelCase : List[Any] ):
def backtrack(__lowerCAmelCase : Optional[int] ):
if start == len(__lowerCAmelCase ) - 1:
output.append(nums[:] )
else:
for i in range(__lowerCAmelCase , len(__lowerCAmelCase ) ):
lowerCamelCase__ , lowerCamelCase__ = nums[i], nums[start]
backtrack(start + 1 )
lowerCamelCase__ , lowerCamelCase__ = nums[i], nums[start] # backtrack
lowerCamelCase__ = []
backtrack(0 )
return output
if __name__ == "__main__":
import doctest
# use res to print the data in permute2 function
UpperCamelCase : Dict = permutea([1, 2, 3])
print(res)
doctest.testmod()
| 50 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase : Tuple = {
'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
'tokenization_mvp': ['MvpTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : str = ['MvpTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Optional[int] = [
'MVP_PRETRAINED_MODEL_ARCHIVE_LIST',
'MvpForCausalLM',
'MvpForConditionalGeneration',
'MvpForQuestionAnswering',
'MvpForSequenceClassification',
'MvpModel',
'MvpPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
UpperCamelCase : Optional[int] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 50 | 1 |
'''simple docstring'''
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetModel,
DDIMScheduler,
StableDiffusionControlNetImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
)
enable_full_determinism()
class UpperCamelCase__ (a ,a ,a ,unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase = StableDiffusionControlNetImgaImgPipeline
_UpperCamelCase = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
_UpperCamelCase = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
_UpperCamelCase = IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'control_image'} )
_UpperCamelCase = IMAGE_TO_IMAGE_IMAGE_PARAMS
def UpperCamelCase_ ( self ):
torch.manual_seed(0 )
lowerCamelCase__ = UNetaDConditionModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=4 ,out_channels=4 ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") ,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") ,cross_attention_dim=32 ,)
torch.manual_seed(0 )
lowerCamelCase__ = ControlNetModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,in_channels=4 ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") ,cross_attention_dim=32 ,conditioning_embedding_out_channels=(16, 32) ,)
torch.manual_seed(0 )
lowerCamelCase__ = DDIMScheduler(
beta_start=0.0_0085 ,beta_end=0.012 ,beta_schedule="""scaled_linear""" ,clip_sample=_lowerCAmelCase ,set_alpha_to_one=_lowerCAmelCase ,)
torch.manual_seed(0 )
lowerCamelCase__ = AutoencoderKL(
block_out_channels=[32, 64] ,in_channels=3 ,out_channels=3 ,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] ,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] ,latent_channels=4 ,)
torch.manual_seed(0 )
lowerCamelCase__ = CLIPTextConfig(
bos_token_id=0 ,eos_token_id=2 ,hidden_size=32 ,intermediate_size=37 ,layer_norm_eps=1E-05 ,num_attention_heads=4 ,num_hidden_layers=5 ,pad_token_id=1 ,vocab_size=10_00 ,)
lowerCamelCase__ = CLIPTextModel(_lowerCAmelCase )
lowerCamelCase__ = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
lowerCamelCase__ = {
"""unet""": unet,
"""controlnet""": controlnet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase=0 ):
if str(_lowerCAmelCase ).startswith("""mps""" ):
lowerCamelCase__ = torch.manual_seed(_lowerCAmelCase )
else:
lowerCamelCase__ = torch.Generator(device=_lowerCAmelCase ).manual_seed(_lowerCAmelCase )
lowerCamelCase__ = 2
lowerCamelCase__ = randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,generator=_lowerCAmelCase ,device=torch.device(_lowerCAmelCase ) ,)
lowerCamelCase__ = floats_tensor(control_image.shape ,rng=random.Random(_lowerCAmelCase ) ).to(_lowerCAmelCase )
lowerCamelCase__ = image.cpu().permute(0 ,2 ,3 ,1 )[0]
lowerCamelCase__ = Image.fromarray(np.uinta(_lowerCAmelCase ) ).convert("""RGB""" ).resize((64, 64) )
lowerCamelCase__ = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
"""image""": image,
"""control_image""": control_image,
}
return inputs
def UpperCamelCase_ ( self ):
return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() ,reason="""XFormers attention is only available with CUDA and `xformers` installed""" ,)
def UpperCamelCase_ ( self ):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )
def UpperCamelCase_ ( self ):
self._test_inference_batch_single_identical(expected_max_diff=2E-3 )
class UpperCamelCase__ (a ,a ,unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase = StableDiffusionControlNetImgaImgPipeline
_UpperCamelCase = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
_UpperCamelCase = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
_UpperCamelCase = frozenset([] ) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
def UpperCamelCase_ ( self ):
torch.manual_seed(0 )
lowerCamelCase__ = UNetaDConditionModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=4 ,out_channels=4 ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") ,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") ,cross_attention_dim=32 ,)
torch.manual_seed(0 )
def init_weights(_lowerCAmelCase ):
if isinstance(_lowerCAmelCase ,torch.nn.Convad ):
torch.nn.init.normal(m.weight )
m.bias.data.fill_(1.0 )
lowerCamelCase__ = ControlNetModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,in_channels=4 ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") ,cross_attention_dim=32 ,conditioning_embedding_out_channels=(16, 32) ,)
controlneta.controlnet_down_blocks.apply(_lowerCAmelCase )
torch.manual_seed(0 )
lowerCamelCase__ = ControlNetModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,in_channels=4 ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") ,cross_attention_dim=32 ,conditioning_embedding_out_channels=(16, 32) ,)
controlneta.controlnet_down_blocks.apply(_lowerCAmelCase )
torch.manual_seed(0 )
lowerCamelCase__ = DDIMScheduler(
beta_start=0.0_0085 ,beta_end=0.012 ,beta_schedule="""scaled_linear""" ,clip_sample=_lowerCAmelCase ,set_alpha_to_one=_lowerCAmelCase ,)
torch.manual_seed(0 )
lowerCamelCase__ = AutoencoderKL(
block_out_channels=[32, 64] ,in_channels=3 ,out_channels=3 ,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] ,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] ,latent_channels=4 ,)
torch.manual_seed(0 )
lowerCamelCase__ = CLIPTextConfig(
bos_token_id=0 ,eos_token_id=2 ,hidden_size=32 ,intermediate_size=37 ,layer_norm_eps=1E-05 ,num_attention_heads=4 ,num_hidden_layers=5 ,pad_token_id=1 ,vocab_size=10_00 ,)
lowerCamelCase__ = CLIPTextModel(_lowerCAmelCase )
lowerCamelCase__ = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
lowerCamelCase__ = MultiControlNetModel([controlneta, controlneta] )
lowerCamelCase__ = {
"""unet""": unet,
"""controlnet""": controlnet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase=0 ):
if str(_lowerCAmelCase ).startswith("""mps""" ):
lowerCamelCase__ = torch.manual_seed(_lowerCAmelCase )
else:
lowerCamelCase__ = torch.Generator(device=_lowerCAmelCase ).manual_seed(_lowerCAmelCase )
lowerCamelCase__ = 2
lowerCamelCase__ = [
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,generator=_lowerCAmelCase ,device=torch.device(_lowerCAmelCase ) ,),
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,generator=_lowerCAmelCase ,device=torch.device(_lowerCAmelCase ) ,),
]
lowerCamelCase__ = floats_tensor(control_image[0].shape ,rng=random.Random(_lowerCAmelCase ) ).to(_lowerCAmelCase )
lowerCamelCase__ = image.cpu().permute(0 ,2 ,3 ,1 )[0]
lowerCamelCase__ = Image.fromarray(np.uinta(_lowerCAmelCase ) ).convert("""RGB""" ).resize((64, 64) )
lowerCamelCase__ = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
"""image""": image,
"""control_image""": control_image,
}
return inputs
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.get_dummy_components()
lowerCamelCase__ = self.pipeline_class(**_lowerCAmelCase )
pipe.to(_lowerCAmelCase )
lowerCamelCase__ = 10.0
lowerCamelCase__ = 4
lowerCamelCase__ = self.get_dummy_inputs(_lowerCAmelCase )
lowerCamelCase__ = steps
lowerCamelCase__ = scale
lowerCamelCase__ = pipe(**_lowerCAmelCase )[0]
lowerCamelCase__ = self.get_dummy_inputs(_lowerCAmelCase )
lowerCamelCase__ = steps
lowerCamelCase__ = scale
lowerCamelCase__ = pipe(**_lowerCAmelCase ,control_guidance_start=0.1 ,control_guidance_end=0.2 )[0]
lowerCamelCase__ = self.get_dummy_inputs(_lowerCAmelCase )
lowerCamelCase__ = steps
lowerCamelCase__ = scale
lowerCamelCase__ = pipe(**_lowerCAmelCase ,control_guidance_start=[0.1, 0.3] ,control_guidance_end=[0.2, 0.7] )[0]
lowerCamelCase__ = self.get_dummy_inputs(_lowerCAmelCase )
lowerCamelCase__ = steps
lowerCamelCase__ = scale
lowerCamelCase__ = pipe(**_lowerCAmelCase ,control_guidance_start=0.4 ,control_guidance_end=[0.5, 0.8] )[0]
# make sure that all outputs are different
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
def UpperCamelCase_ ( self ):
return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() ,reason="""XFormers attention is only available with CUDA and `xformers` installed""" ,)
def UpperCamelCase_ ( self ):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )
def UpperCamelCase_ ( self ):
self._test_inference_batch_single_identical(expected_max_diff=2E-3 )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.get_dummy_components()
lowerCamelCase__ = self.pipeline_class(**_lowerCAmelCase )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
with tempfile.TemporaryDirectory() as tmpdir:
try:
# save_pretrained is not implemented for Multi-ControlNet
pipe.save_pretrained(_lowerCAmelCase )
except NotImplementedError:
pass
@slow
@require_torch_gpu
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase_ ( self ):
lowerCamelCase__ = ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny""" )
lowerCamelCase__ = StableDiffusionControlNetImgaImgPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" ,safety_checker=_lowerCAmelCase ,controlnet=_lowerCAmelCase )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowerCamelCase__ = torch.Generator(device="""cpu""" ).manual_seed(0 )
lowerCamelCase__ = """evil space-punk bird"""
lowerCamelCase__ = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png""" ).resize((5_12, 5_12) )
lowerCamelCase__ = load_image(
"""https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png""" ).resize((5_12, 5_12) )
lowerCamelCase__ = pipe(
_lowerCAmelCase ,_lowerCAmelCase ,control_image=_lowerCAmelCase ,generator=_lowerCAmelCase ,output_type="""np""" ,num_inference_steps=50 ,strength=0.6 ,)
lowerCamelCase__ = output.images[0]
assert image.shape == (5_12, 5_12, 3)
lowerCamelCase__ = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy""" )
assert np.abs(expected_image - image ).max() < 9E-2
| 50 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
UpperCamelCase : Tuple = logging.get_logger(__name__)
UpperCamelCase : Dict = {
'Salesforce/codegen-350M-nl': 'https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json',
'Salesforce/codegen-350M-multi': 'https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json',
'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json',
'Salesforce/codegen-2B-nl': 'https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json',
'Salesforce/codegen-2B-multi': 'https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json',
'Salesforce/codegen-2B-mono': 'https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json',
'Salesforce/codegen-6B-nl': 'https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json',
'Salesforce/codegen-6B-multi': 'https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json',
'Salesforce/codegen-6B-mono': 'https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json',
'Salesforce/codegen-16B-nl': 'https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json',
'Salesforce/codegen-16B-multi': 'https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json',
'Salesforce/codegen-16B-mono': 'https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json',
}
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = 'codegen'
_UpperCamelCase = {
'max_position_embeddings': 'n_positions',
'hidden_size': 'n_embd',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self ,_lowerCAmelCase=5_04_00 ,_lowerCAmelCase=20_48 ,_lowerCAmelCase=20_48 ,_lowerCAmelCase=40_96 ,_lowerCAmelCase=28 ,_lowerCAmelCase=16 ,_lowerCAmelCase=64 ,_lowerCAmelCase=None ,_lowerCAmelCase="gelu_new" ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=1E-5 ,_lowerCAmelCase=0.02 ,_lowerCAmelCase=True ,_lowerCAmelCase=5_02_56 ,_lowerCAmelCase=5_02_56 ,_lowerCAmelCase=False ,**_lowerCAmelCase ,):
lowerCamelCase__ = vocab_size
lowerCamelCase__ = n_ctx
lowerCamelCase__ = n_positions
lowerCamelCase__ = n_embd
lowerCamelCase__ = n_layer
lowerCamelCase__ = n_head
lowerCamelCase__ = n_inner
lowerCamelCase__ = rotary_dim
lowerCamelCase__ = activation_function
lowerCamelCase__ = resid_pdrop
lowerCamelCase__ = embd_pdrop
lowerCamelCase__ = attn_pdrop
lowerCamelCase__ = layer_norm_epsilon
lowerCamelCase__ = initializer_range
lowerCamelCase__ = use_cache
lowerCamelCase__ = bos_token_id
lowerCamelCase__ = eos_token_id
super().__init__(
bos_token_id=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase ,tie_word_embeddings=_lowerCAmelCase ,**_lowerCAmelCase )
class UpperCamelCase__ (a ):
'''simple docstring'''
def __init__( self ,_lowerCAmelCase ,_lowerCAmelCase = "default" ,_lowerCAmelCase = None ,_lowerCAmelCase = False ,):
super().__init__(_lowerCAmelCase ,task=_lowerCAmelCase ,patching_specs=_lowerCAmelCase ,use_past=_lowerCAmelCase )
if not getattr(self._config ,"""pad_token_id""" ,_lowerCAmelCase ):
# TODO: how to do that better?
lowerCamelCase__ = 0
@property
def UpperCamelCase_ ( self ):
lowerCamelCase__ = OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}} )
if self.use_past:
self.fill_with_past_key_values_(_lowerCAmelCase ,direction="""inputs""" )
lowerCamelCase__ = {0: """batch""", 1: """past_sequence + sequence"""}
else:
lowerCamelCase__ = {0: """batch""", 1: """sequence"""}
return common_inputs
@property
def UpperCamelCase_ ( self ):
return self._config.n_layer
@property
def UpperCamelCase_ ( self ):
return self._config.n_head
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase = -1 ,_lowerCAmelCase = -1 ,_lowerCAmelCase = False ,_lowerCAmelCase = None ,):
lowerCamelCase__ = super(_lowerCAmelCase ,self ).generate_dummy_inputs(
_lowerCAmelCase ,batch_size=_lowerCAmelCase ,seq_length=_lowerCAmelCase ,is_pair=_lowerCAmelCase ,framework=_lowerCAmelCase )
# We need to order the input in the way they appears in the forward()
lowerCamelCase__ = OrderedDict({"""input_ids""": common_inputs["""input_ids"""]} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
lowerCamelCase__ , lowerCamelCase__ = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
lowerCamelCase__ = seqlen + 2
lowerCamelCase__ = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
lowerCamelCase__ = [
(torch.zeros(_lowerCAmelCase ), torch.zeros(_lowerCAmelCase )) for _ in range(self.num_layers )
]
lowerCamelCase__ = common_inputs["""attention_mask"""]
if self.use_past:
lowerCamelCase__ = ordered_inputs["""attention_mask"""].dtype
lowerCamelCase__ = torch.cat(
[ordered_inputs["""attention_mask"""], torch.ones(_lowerCAmelCase ,_lowerCAmelCase ,dtype=_lowerCAmelCase )] ,dim=1 )
return ordered_inputs
@property
def UpperCamelCase_ ( self ):
return 13
| 50 | 1 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : str ):
lowerCamelCase__ = 0
# if input_string is "aba" than new_input_string become "a|b|a"
lowerCamelCase__ = """"""
lowerCamelCase__ = """"""
# append each character + "|" in new_string for range(0, length-1)
for i in input_string[: len(__lowerCAmelCase ) - 1]:
new_input_string += i + "|"
# append last character
new_input_string += input_string[-1]
# we will store the starting and ending of previous furthest ending palindromic
# substring
lowerCamelCase__ , lowerCamelCase__ = 0, 0
# length[i] shows the length of palindromic substring with center i
lowerCamelCase__ = [1 for i in range(len(__lowerCAmelCase ) )]
# for each character in new_string find corresponding palindromic string
lowerCamelCase__ = 0
for j in range(len(__lowerCAmelCase ) ):
lowerCamelCase__ = 1 if j > r else min(length[l + r - j] // 2 , r - j + 1 )
while (
j - k >= 0
and j + k < len(__lowerCAmelCase )
and new_input_string[k + j] == new_input_string[j - k]
):
k += 1
lowerCamelCase__ = 2 * k - 1
# does this string is ending after the previously explored end (that is r) ?
# if yes the update the new r to the last index of this
if j + k - 1 > r:
lowerCamelCase__ = j - k + 1 # noqa: E741
lowerCamelCase__ = j + k - 1
# update max_length and start position
if max_length < length[j]:
lowerCamelCase__ = length[j]
lowerCamelCase__ = j
# create that string
lowerCamelCase__ = new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
for i in s:
if i != "|":
output_string += i
return output_string
if __name__ == "__main__":
import doctest
doctest.testmod()
| 50 |
'''simple docstring'''
# 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
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase : int = {
'configuration_xmod': [
'XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XmodConfig',
'XmodOnnxConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Tuple = [
'XMOD_PRETRAINED_MODEL_ARCHIVE_LIST',
'XmodForCausalLM',
'XmodForMaskedLM',
'XmodForMultipleChoice',
'XmodForQuestionAnswering',
'XmodForSequenceClassification',
'XmodForTokenClassification',
'XmodModel',
'XmodPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xmod import (
XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
XmodForCausalLM,
XmodForMaskedLM,
XmodForMultipleChoice,
XmodForQuestionAnswering,
XmodForSequenceClassification,
XmodForTokenClassification,
XmodModel,
XmodPreTrainedModel,
)
else:
import sys
UpperCamelCase : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 50 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
@property
def UpperCamelCase_ ( self ):
torch.manual_seed(0 )
lowerCamelCase__ = UNetaDModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=3 ,out_channels=3 ,down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") ,up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") ,)
return model
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.dummy_uncond_unet
lowerCamelCase__ = PNDMScheduler()
lowerCamelCase__ = PNDMPipeline(unet=_lowerCAmelCase ,scheduler=_lowerCAmelCase )
pndm.to(_lowerCAmelCase )
pndm.set_progress_bar_config(disable=_lowerCAmelCase )
lowerCamelCase__ = torch.manual_seed(0 )
lowerCamelCase__ = pndm(generator=_lowerCAmelCase ,num_inference_steps=20 ,output_type="""numpy""" ).images
lowerCamelCase__ = torch.manual_seed(0 )
lowerCamelCase__ = pndm(generator=_lowerCAmelCase ,num_inference_steps=20 ,output_type="""numpy""" ,return_dict=_lowerCAmelCase )[0]
lowerCamelCase__ = image[0, -3:, -3:, -1]
lowerCamelCase__ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowerCamelCase__ = np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ):
lowerCamelCase__ = """google/ddpm-cifar10-32"""
lowerCamelCase__ = UNetaDModel.from_pretrained(_lowerCAmelCase )
lowerCamelCase__ = PNDMScheduler()
lowerCamelCase__ = PNDMPipeline(unet=_lowerCAmelCase ,scheduler=_lowerCAmelCase )
pndm.to(_lowerCAmelCase )
pndm.set_progress_bar_config(disable=_lowerCAmelCase )
lowerCamelCase__ = torch.manual_seed(0 )
lowerCamelCase__ = pndm(generator=_lowerCAmelCase ,output_type="""numpy""" ).images
lowerCamelCase__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowerCamelCase__ = np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 50 |
'''simple docstring'''
from typing import Union
import fire
import torch
from tqdm import tqdm
def A__ ( __lowerCAmelCase : str , __lowerCAmelCase : str = "cpu" , __lowerCAmelCase : Union[str, None] = None ):
lowerCamelCase__ = torch.load(__lowerCAmelCase , map_location=__lowerCAmelCase )
for k, v in tqdm(state_dict.items() ):
if not isinstance(__lowerCAmelCase , torch.Tensor ):
raise TypeError("""FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin""" )
lowerCamelCase__ = v.half()
if save_path is None: # overwrite src_path
lowerCamelCase__ = src_path
torch.save(__lowerCAmelCase , __lowerCAmelCase )
if __name__ == "__main__":
fire.Fire(convert)
| 50 | 1 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ):
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 50 |
'''simple docstring'''
import os
from pathlib import Path
def A__ ( ):
from torch.utils.cpp_extension import load
lowerCamelCase__ = Path(__lowerCAmelCase ).resolve().parent.parent.parent / """kernels""" / """deformable_detr"""
lowerCamelCase__ = [
root / filename
for filename in [
"""vision.cpp""",
os.path.join("""cpu""" , """ms_deform_attn_cpu.cpp""" ),
os.path.join("""cuda""" , """ms_deform_attn_cuda.cu""" ),
]
]
load(
"""MultiScaleDeformableAttention""" , __lowerCAmelCase , with_cuda=__lowerCAmelCase , extra_include_paths=[str(__lowerCAmelCase )] , extra_cflags=["""-DWITH_CUDA=1"""] , extra_cuda_cflags=[
"""-DCUDA_HAS_FP16=1""",
"""-D__CUDA_NO_HALF_OPERATORS__""",
"""-D__CUDA_NO_HALF_CONVERSIONS__""",
"""-D__CUDA_NO_HALF2_OPERATORS__""",
] , )
import MultiScaleDeformableAttention as MSDA
return MSDA
| 50 | 1 |
'''simple docstring'''
from math import factorial
def A__ ( __lowerCAmelCase : int = 20 ):
lowerCamelCase__ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
lowerCamelCase__ = n // 2
return int(factorial(__lowerCAmelCase ) / (factorial(__lowerCAmelCase ) * factorial(n - k )) )
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution(20))
else:
try:
UpperCamelCase : Tuple = int(sys.argv[1])
print(solution(n))
except ValueError:
print('Invalid entry - please enter a number.')
| 50 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : list[int] , __lowerCAmelCase : list[int] ):
lowerCamelCase__ = len(__lowerCAmelCase )
print("""The following activities are selected:""" )
# The first activity is always selected
lowerCamelCase__ = 0
print(__lowerCAmelCase , end=""",""" )
# Consider rest of the activities
for j in range(__lowerCAmelCase ):
# If this activity has start time greater than
# or equal to the finish time of previously
# selected activity, then select it
if start[j] >= finish[i]:
print(__lowerCAmelCase , end=""",""" )
lowerCamelCase__ = j
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCamelCase : Union[str, Any] = [1, 3, 0, 5, 8, 5]
UpperCamelCase : int = [2, 4, 6, 7, 9, 9]
print_max_activities(start, finish)
| 50 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPImageProcessor, CLIPProcessor
@require_vision
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ):
lowerCamelCase__ = tempfile.mkdtemp()
# fmt: off
lowerCamelCase__ = ["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
# fmt: on
lowerCamelCase__ = dict(zip(_lowerCAmelCase ,range(len(_lowerCAmelCase ) ) ) )
lowerCamelCase__ = ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
lowerCamelCase__ = {"""unk_token""": """<unk>"""}
lowerCamelCase__ = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""vocab_file"""] )
lowerCamelCase__ = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write(json.dumps(_lowerCAmelCase ) + """\n""" )
with open(self.merges_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(_lowerCAmelCase ) )
lowerCamelCase__ = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.4814_5466, 0.457_8275, 0.4082_1073],
"""image_std""": [0.2686_2954, 0.2613_0258, 0.2757_7711],
}
lowerCamelCase__ = os.path.join(self.tmpdirname ,_lowerCAmelCase )
with open(self.image_processor_file ,"""w""" ,encoding="""utf-8""" ) as fp:
json.dump(_lowerCAmelCase ,_lowerCAmelCase )
def UpperCamelCase_ ( self ,**_lowerCAmelCase ):
return CLIPTokenizer.from_pretrained(self.tmpdirname ,**_lowerCAmelCase )
def UpperCamelCase_ ( self ,**_lowerCAmelCase ):
return CLIPTokenizerFast.from_pretrained(self.tmpdirname ,**_lowerCAmelCase )
def UpperCamelCase_ ( self ,**_lowerCAmelCase ):
return CLIPImageProcessor.from_pretrained(self.tmpdirname ,**_lowerCAmelCase )
def UpperCamelCase_ ( self ):
shutil.rmtree(self.tmpdirname )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = [np.random.randint(2_55 ,size=(3, 30, 4_00) ,dtype=np.uinta )]
lowerCamelCase__ = [Image.fromarray(np.moveaxis(_lowerCAmelCase ,0 ,-1 ) ) for x in image_inputs]
return image_inputs
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.get_tokenizer()
lowerCamelCase__ = self.get_rust_tokenizer()
lowerCamelCase__ = self.get_image_processor()
lowerCamelCase__ = CLIPProcessor(tokenizer=_lowerCAmelCase ,image_processor=_lowerCAmelCase )
processor_slow.save_pretrained(self.tmpdirname )
lowerCamelCase__ = CLIPProcessor.from_pretrained(self.tmpdirname ,use_fast=_lowerCAmelCase )
lowerCamelCase__ = CLIPProcessor(tokenizer=_lowerCAmelCase ,image_processor=_lowerCAmelCase )
processor_fast.save_pretrained(self.tmpdirname )
lowerCamelCase__ = CLIPProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() ,tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() ,tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() ,tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer ,_lowerCAmelCase )
self.assertIsInstance(processor_fast.tokenizer ,_lowerCAmelCase )
self.assertEqual(processor_slow.image_processor.to_json_string() ,image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() ,image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor ,_lowerCAmelCase )
self.assertIsInstance(processor_fast.image_processor ,_lowerCAmelCase )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = CLIPProcessor(tokenizer=self.get_tokenizer() ,image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
lowerCamelCase__ = self.get_tokenizer(bos_token="""(BOS)""" ,eos_token="""(EOS)""" )
lowerCamelCase__ = self.get_image_processor(do_normalize=_lowerCAmelCase ,padding_value=1.0 )
lowerCamelCase__ = CLIPProcessor.from_pretrained(
self.tmpdirname ,bos_token="""(BOS)""" ,eos_token="""(EOS)""" ,do_normalize=_lowerCAmelCase ,padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() ,tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer ,_lowerCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() ,image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor ,_lowerCAmelCase )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.get_image_processor()
lowerCamelCase__ = self.get_tokenizer()
lowerCamelCase__ = CLIPProcessor(tokenizer=_lowerCAmelCase ,image_processor=_lowerCAmelCase )
lowerCamelCase__ = self.prepare_image_inputs()
lowerCamelCase__ = image_processor(_lowerCAmelCase ,return_tensors="""np""" )
lowerCamelCase__ = processor(images=_lowerCAmelCase ,return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() ,input_processor[key].sum() ,delta=1E-2 )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.get_image_processor()
lowerCamelCase__ = self.get_tokenizer()
lowerCamelCase__ = CLIPProcessor(tokenizer=_lowerCAmelCase ,image_processor=_lowerCAmelCase )
lowerCamelCase__ = """lower newer"""
lowerCamelCase__ = processor(text=_lowerCAmelCase )
lowerCamelCase__ = tokenizer(_lowerCAmelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] ,encoded_processor[key] )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.get_image_processor()
lowerCamelCase__ = self.get_tokenizer()
lowerCamelCase__ = CLIPProcessor(tokenizer=_lowerCAmelCase ,image_processor=_lowerCAmelCase )
lowerCamelCase__ = """lower newer"""
lowerCamelCase__ = self.prepare_image_inputs()
lowerCamelCase__ = processor(text=_lowerCAmelCase ,images=_lowerCAmelCase )
self.assertListEqual(list(inputs.keys() ) ,["""input_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(_lowerCAmelCase ):
processor()
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.get_image_processor()
lowerCamelCase__ = self.get_tokenizer()
lowerCamelCase__ = CLIPProcessor(tokenizer=_lowerCAmelCase ,image_processor=_lowerCAmelCase )
lowerCamelCase__ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowerCamelCase__ = processor.batch_decode(_lowerCAmelCase )
lowerCamelCase__ = tokenizer.batch_decode(_lowerCAmelCase )
self.assertListEqual(_lowerCAmelCase ,_lowerCAmelCase )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.get_image_processor()
lowerCamelCase__ = self.get_tokenizer()
lowerCamelCase__ = CLIPProcessor(tokenizer=_lowerCAmelCase ,image_processor=_lowerCAmelCase )
lowerCamelCase__ = """lower newer"""
lowerCamelCase__ = self.prepare_image_inputs()
lowerCamelCase__ = processor(text=_lowerCAmelCase ,images=_lowerCAmelCase )
self.assertListEqual(list(inputs.keys() ) ,processor.model_input_names )
| 50 |
'''simple docstring'''
import warnings
from ..trainer import Trainer
from ..utils import logging
UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
class UpperCamelCase__ (a ):
'''simple docstring'''
def __init__( self ,_lowerCAmelCase=None ,**_lowerCAmelCase ):
warnings.warn(
"""`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` """
"""instead.""" ,_lowerCAmelCase ,)
super().__init__(args=_lowerCAmelCase ,**_lowerCAmelCase )
| 50 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import List, Optional
class UpperCamelCase__ (a ):
'''simple docstring'''
def __init__( self ):
# test for the above condition
self.test()
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 0
lowerCamelCase__ = False
while not completed:
if counter == 1:
self.reset()
lowerCamelCase__ = self.advance()
if not self.does_advance(_lowerCAmelCase ):
raise Exception(
"""Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.""" )
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = self.update(_lowerCAmelCase )
counter += 1
if counter > 1_00_00:
raise Exception("""update() does not fulfill the constraint.""" )
if self.remaining() != 0:
raise Exception("""Custom Constraint is not defined correctly.""" )
@abstractmethod
def UpperCamelCase_ ( self ):
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def UpperCamelCase_ ( self ):
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def UpperCamelCase_ ( self ):
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def UpperCamelCase_ ( self ,_lowerCAmelCase=False ):
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
class UpperCamelCase__ (a ):
'''simple docstring'''
def __init__( self ,_lowerCAmelCase ):
super(_lowerCAmelCase ,self ).__init__()
if not isinstance(_lowerCAmelCase ,_lowerCAmelCase ) or len(_lowerCAmelCase ) == 0:
raise ValueError(F'''`token_ids` has to be a non-empty list, but is {token_ids}.''' )
if any((not isinstance(_lowerCAmelCase ,_lowerCAmelCase ) or token_id < 0) for token_id in token_ids ):
raise ValueError(F'''Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.''' )
lowerCamelCase__ = token_ids
lowerCamelCase__ = len(self.token_ids )
lowerCamelCase__ = -1 # the index of the currently fulfilled step
lowerCamelCase__ = False
def UpperCamelCase_ ( self ):
if self.completed:
return None
return self.token_ids[self.fulfilled_idx + 1]
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
if not isinstance(_lowerCAmelCase ,_lowerCAmelCase ):
raise ValueError(F'''`token_id` has to be an `int`, but is {token_id} of type {type(_lowerCAmelCase )}''' )
if self.completed:
return False
return token_id == self.token_ids[self.fulfilled_idx + 1]
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
if not isinstance(_lowerCAmelCase ,_lowerCAmelCase ):
raise ValueError(F'''`token_id` has to be an `int`, but is {token_id} of type {type(_lowerCAmelCase )}''' )
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
if self.does_advance(_lowerCAmelCase ):
self.fulfilled_idx += 1
lowerCamelCase__ = True
if self.fulfilled_idx == (self.seqlen - 1):
lowerCamelCase__ = True
lowerCamelCase__ = completed
else:
# failed to make progress.
lowerCamelCase__ = True
self.reset()
return stepped, completed, reset
def UpperCamelCase_ ( self ):
lowerCamelCase__ = False
lowerCamelCase__ = 0
def UpperCamelCase_ ( self ):
return self.seqlen - (self.fulfilled_idx + 1)
def UpperCamelCase_ ( self ,_lowerCAmelCase=False ):
lowerCamelCase__ = PhrasalConstraint(self.token_ids )
if stateful:
lowerCamelCase__ = self.seqlen
lowerCamelCase__ = self.fulfilled_idx
lowerCamelCase__ = self.completed
return new_constraint
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self ,_lowerCAmelCase ,_lowerCAmelCase=True ):
lowerCamelCase__ = max([len(_lowerCAmelCase ) for one in nested_token_ids] )
lowerCamelCase__ = {}
for token_ids in nested_token_ids:
lowerCamelCase__ = root
for tidx, token_id in enumerate(_lowerCAmelCase ):
if token_id not in level:
lowerCamelCase__ = {}
lowerCamelCase__ = level[token_id]
if no_subsets and self.has_subsets(_lowerCAmelCase ,_lowerCAmelCase ):
raise ValueError(
"""Each list in `nested_token_ids` can't be a complete subset of another list, but is"""
F''' {nested_token_ids}.''' )
lowerCamelCase__ = root
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
lowerCamelCase__ = self.trie
for current_token in current_seq:
lowerCamelCase__ = start[current_token]
lowerCamelCase__ = list(start.keys() )
return next_tokens
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
lowerCamelCase__ = self.next_tokens(_lowerCAmelCase )
return len(_lowerCAmelCase ) == 0
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
lowerCamelCase__ = list(root.values() )
if len(_lowerCAmelCase ) == 0:
return 1
else:
return sum([self.count_leaves(_lowerCAmelCase ) for nn in next_nodes] )
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = self.count_leaves(_lowerCAmelCase )
return len(_lowerCAmelCase ) != leaf_count
class UpperCamelCase__ (a ):
'''simple docstring'''
def __init__( self ,_lowerCAmelCase ):
super(_lowerCAmelCase ,self ).__init__()
if not isinstance(_lowerCAmelCase ,_lowerCAmelCase ) or len(_lowerCAmelCase ) == 0:
raise ValueError(F'''`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.''' )
if any(not isinstance(_lowerCAmelCase ,_lowerCAmelCase ) for token_ids in nested_token_ids ):
raise ValueError(F'''`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.''' )
if any(
any((not isinstance(_lowerCAmelCase ,_lowerCAmelCase ) or token_id < 0) for token_id in token_ids )
for token_ids in nested_token_ids ):
raise ValueError(
F'''Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.''' )
lowerCamelCase__ = DisjunctiveTrie(_lowerCAmelCase )
lowerCamelCase__ = nested_token_ids
lowerCamelCase__ = self.trie.max_height
lowerCamelCase__ = []
lowerCamelCase__ = False
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.trie.next_tokens(self.current_seq )
if len(_lowerCAmelCase ) == 0:
return None
else:
return token_list
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
if not isinstance(_lowerCAmelCase ,_lowerCAmelCase ):
raise ValueError(F'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(_lowerCAmelCase )}''' )
lowerCamelCase__ = self.trie.next_tokens(self.current_seq )
return token_id in next_tokens
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
if not isinstance(_lowerCAmelCase ,_lowerCAmelCase ):
raise ValueError(F'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(_lowerCAmelCase )}''' )
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
if self.does_advance(_lowerCAmelCase ):
self.current_seq.append(_lowerCAmelCase )
lowerCamelCase__ = True
else:
lowerCamelCase__ = True
self.reset()
lowerCamelCase__ = self.trie.reached_leaf(self.current_seq )
lowerCamelCase__ = completed
return stepped, completed, reset
def UpperCamelCase_ ( self ):
lowerCamelCase__ = False
lowerCamelCase__ = []
def UpperCamelCase_ ( self ):
if self.completed:
# since this can be completed without reaching max height
return 0
else:
return self.seqlen - len(self.current_seq )
def UpperCamelCase_ ( self ,_lowerCAmelCase=False ):
lowerCamelCase__ = DisjunctiveConstraint(self.token_ids )
if stateful:
lowerCamelCase__ = self.seqlen
lowerCamelCase__ = self.current_seq
lowerCamelCase__ = self.completed
return new_constraint
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self ,_lowerCAmelCase ):
lowerCamelCase__ = constraints
# max # of steps required to fulfill a given constraint
lowerCamelCase__ = max([c.seqlen for c in constraints] )
lowerCamelCase__ = len(_lowerCAmelCase )
lowerCamelCase__ = False
self.init_state()
def UpperCamelCase_ ( self ):
lowerCamelCase__ = []
lowerCamelCase__ = None
lowerCamelCase__ = [constraint.copy(stateful=_lowerCAmelCase ) for constraint in self.constraints]
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 0
if self.inprogress_constraint:
# extra points for having a constraint mid-fulfilled
add += self.max_seqlen - self.inprogress_constraint.remaining()
return (len(self.complete_constraints ) * self.max_seqlen) + add
def UpperCamelCase_ ( self ):
lowerCamelCase__ = []
if self.inprogress_constraint is None:
for constraint in self.pending_constraints: # "pending" == "unfulfilled yet"
lowerCamelCase__ = constraint.advance()
if isinstance(_lowerCAmelCase ,_lowerCAmelCase ):
token_list.append(_lowerCAmelCase )
elif isinstance(_lowerCAmelCase ,_lowerCAmelCase ):
token_list.extend(_lowerCAmelCase )
else:
lowerCamelCase__ = self.inprogress_constraint.advance()
if isinstance(_lowerCAmelCase ,_lowerCAmelCase ):
token_list.append(_lowerCAmelCase )
elif isinstance(_lowerCAmelCase ,_lowerCAmelCase ):
token_list.extend(_lowerCAmelCase )
if len(_lowerCAmelCase ) == 0:
return None
else:
return token_list
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
self.init_state()
if token_ids is not None:
for token in token_ids:
# completes or steps **one** constraint
lowerCamelCase__ , lowerCamelCase__ = self.add(_lowerCAmelCase )
# the entire list of constraints are fulfilled
if self.completed:
break
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
if not isinstance(_lowerCAmelCase ,_lowerCAmelCase ):
raise ValueError(F'''`token_id` should be an `int`, but is `{token_id}`.''' )
lowerCamelCase__ , lowerCamelCase__ = False, False
if self.completed:
lowerCamelCase__ = True
lowerCamelCase__ = False
return complete, stepped
if self.inprogress_constraint is not None:
# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
# job, simply update the state
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = self.inprogress_constraint.update(_lowerCAmelCase )
if reset:
# 1. If the next token breaks the progress, then we must restart.
# e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".
# But that doesn't mean we self.init_state(), since we only reset the state for this particular
# constraint, not the full list of constraints.
self.pending_constraints.append(self.inprogress_constraint.copy(stateful=_lowerCAmelCase ) )
lowerCamelCase__ = None
if complete:
# 2. If the next token completes the constraint, move it to completed list, set
# inprogress to None. If there are no pending constraints either, then this full list of constraints
# is complete.
self.complete_constraints.append(self.inprogress_constraint )
lowerCamelCase__ = None
if len(self.pending_constraints ) == 0:
# we're done!
lowerCamelCase__ = True
else:
# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
# of constraints?
for cidx, pending_constraint in enumerate(self.pending_constraints ):
if pending_constraint.does_advance(_lowerCAmelCase ):
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = pending_constraint.update(_lowerCAmelCase )
if not stepped:
raise Exception(
"""`constraint.update(token_id)` is not yielding incremental progress, """
"""even though `constraint.does_advance(token_id)` is true.""" )
if complete:
self.complete_constraints.append(_lowerCAmelCase )
lowerCamelCase__ = None
if not complete and stepped:
lowerCamelCase__ = pending_constraint
if complete or stepped:
# If we made any progress at all, then it's at least not a "pending constraint".
lowerCamelCase__ = (
self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
)
if len(self.pending_constraints ) == 0 and self.inprogress_constraint is None:
# If there's no longer any pending after this and no inprogress either, then we must be
# complete.
lowerCamelCase__ = True
break # prevent accidentally stepping through multiple constraints with just one token.
return complete, stepped
def UpperCamelCase_ ( self ,_lowerCAmelCase=True ):
lowerCamelCase__ = ConstraintListState(self.constraints ) # we actually never though self.constraints objects
# throughout this process. So it's at initialization state.
if stateful:
lowerCamelCase__ = [
constraint.copy(stateful=_lowerCAmelCase ) for constraint in self.complete_constraints
]
if self.inprogress_constraint is not None:
lowerCamelCase__ = self.inprogress_constraint.copy(stateful=_lowerCAmelCase )
lowerCamelCase__ = [constraint.copy() for constraint in self.pending_constraints]
return new_state
| 50 |
'''simple docstring'''
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def A__ ( __lowerCAmelCase : List[str] ):
lowerCamelCase__ = []
for line in lines:
lowerCamelCase__ = re.sub(R"""#.*""" , """""" , __lowerCAmelCase ) # remove comments
if line:
filtered_lines.append(__lowerCAmelCase )
lowerCamelCase__ = """\n""".join(__lowerCAmelCase )
# Make a hash from all this code
lowerCamelCase__ = full_str.encode("""utf-8""" )
return shaaaa(__lowerCAmelCase ).hexdigest()
# get importable module names and hash for caching
UpperCamelCase : Dict = {
'csv': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
'json': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
'pandas': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
'parquet': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
'arrow': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
'text': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
'imagefolder': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
'audiofolder': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
UpperCamelCase : str = {
'.csv': ('csv', {}),
'.tsv': ('csv', {'sep': '\t'}),
'.json': ('json', {}),
'.jsonl': ('json', {}),
'.parquet': ('parquet', {}),
'.arrow': ('arrow', {}),
'.txt': ('text', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
UpperCamelCase : List[Any] = {'imagefolder', 'audiofolder'}
# Used to filter data files based on extensions given a module name
UpperCamelCase : Dict[str, List[str]] = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append('.zip')
_MODULE_TO_EXTENSIONS["audiofolder"].append('.zip')
| 50 | 1 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : str , __lowerCAmelCase : str ):
if len(__lowerCAmelCase ) != len(__lowerCAmelCase ):
raise ValueError("""String lengths must match!""" )
lowerCamelCase__ = 0
for chara, chara in zip(__lowerCAmelCase , __lowerCAmelCase ):
if chara != chara:
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 50 |
'''simple docstring'''
import operator
def A__ ( __lowerCAmelCase : list , __lowerCAmelCase : bool = False , __lowerCAmelCase : list | None = None ):
lowerCamelCase__ = operator.lt if reverse else operator.gt
lowerCamelCase__ = solution or []
if not arr:
return solution
lowerCamelCase__ = [arr.pop(0 )]
for i, item in enumerate(__lowerCAmelCase ):
if _operator(__lowerCAmelCase , sublist[-1] ):
sublist.append(__lowerCAmelCase )
arr.pop(__lowerCAmelCase )
# merging sublist into solution list
if not solution:
solution.extend(__lowerCAmelCase )
else:
while sublist:
lowerCamelCase__ = sublist.pop(0 )
for i, xx in enumerate(__lowerCAmelCase ):
if not _operator(__lowerCAmelCase , __lowerCAmelCase ):
solution.insert(__lowerCAmelCase , __lowerCAmelCase )
break
else:
solution.append(__lowerCAmelCase )
strand_sort(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
return solution
if __name__ == "__main__":
assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]
| 50 | 1 |
'''simple docstring'''
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 UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
@slow
def UpperCamelCase_ ( self ):
lowerCamelCase__ = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base""" )
lowerCamelCase__ = tf.convert_to_tensor(
[[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]] ,dtype=tf.intaa ,) # J'aime le camembert !"
lowerCamelCase__ = model(_lowerCAmelCase )["""last_hidden_state"""]
lowerCamelCase__ = tf.TensorShape((1, 10, 7_68) )
self.assertEqual(output.shape ,_lowerCAmelCase )
# compare the actual values for a slice.
lowerCamelCase__ = 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 ) )
| 50 |
'''simple docstring'''
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def A__ ( __lowerCAmelCase : dict ):
return (data["data"], data["target"])
def A__ ( __lowerCAmelCase : np.ndarray , __lowerCAmelCase : np.ndarray , __lowerCAmelCase : np.ndarray ):
lowerCamelCase__ = XGBRegressor(verbosity=0 , random_state=42 )
xgb.fit(__lowerCAmelCase , __lowerCAmelCase )
# Predict target for test data
lowerCamelCase__ = xgb.predict(__lowerCAmelCase )
lowerCamelCase__ = predictions.reshape(len(__lowerCAmelCase ) , 1 )
return predictions
def A__ ( ):
lowerCamelCase__ = fetch_california_housing()
lowerCamelCase__ , lowerCamelCase__ = data_handling(__lowerCAmelCase )
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = train_test_split(
__lowerCAmelCase , __lowerCAmelCase , test_size=0.25 , random_state=1 )
lowerCamelCase__ = xgboost(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# Error printing
print(F'''Mean Absolute Error : {mean_absolute_error(__lowerCAmelCase , __lowerCAmelCase )}''' )
print(F'''Mean Square Error : {mean_squared_error(__lowerCAmelCase , __lowerCAmelCase )}''' )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 50 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
UpperCamelCase : Tuple = logging.get_logger(__name__)
UpperCamelCase : Dict = {
'Salesforce/codegen-350M-nl': 'https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json',
'Salesforce/codegen-350M-multi': 'https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json',
'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json',
'Salesforce/codegen-2B-nl': 'https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json',
'Salesforce/codegen-2B-multi': 'https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json',
'Salesforce/codegen-2B-mono': 'https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json',
'Salesforce/codegen-6B-nl': 'https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json',
'Salesforce/codegen-6B-multi': 'https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json',
'Salesforce/codegen-6B-mono': 'https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json',
'Salesforce/codegen-16B-nl': 'https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json',
'Salesforce/codegen-16B-multi': 'https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json',
'Salesforce/codegen-16B-mono': 'https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json',
}
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = 'codegen'
_UpperCamelCase = {
'max_position_embeddings': 'n_positions',
'hidden_size': 'n_embd',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self ,_lowerCAmelCase=5_04_00 ,_lowerCAmelCase=20_48 ,_lowerCAmelCase=20_48 ,_lowerCAmelCase=40_96 ,_lowerCAmelCase=28 ,_lowerCAmelCase=16 ,_lowerCAmelCase=64 ,_lowerCAmelCase=None ,_lowerCAmelCase="gelu_new" ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=1E-5 ,_lowerCAmelCase=0.02 ,_lowerCAmelCase=True ,_lowerCAmelCase=5_02_56 ,_lowerCAmelCase=5_02_56 ,_lowerCAmelCase=False ,**_lowerCAmelCase ,):
lowerCamelCase__ = vocab_size
lowerCamelCase__ = n_ctx
lowerCamelCase__ = n_positions
lowerCamelCase__ = n_embd
lowerCamelCase__ = n_layer
lowerCamelCase__ = n_head
lowerCamelCase__ = n_inner
lowerCamelCase__ = rotary_dim
lowerCamelCase__ = activation_function
lowerCamelCase__ = resid_pdrop
lowerCamelCase__ = embd_pdrop
lowerCamelCase__ = attn_pdrop
lowerCamelCase__ = layer_norm_epsilon
lowerCamelCase__ = initializer_range
lowerCamelCase__ = use_cache
lowerCamelCase__ = bos_token_id
lowerCamelCase__ = eos_token_id
super().__init__(
bos_token_id=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase ,tie_word_embeddings=_lowerCAmelCase ,**_lowerCAmelCase )
class UpperCamelCase__ (a ):
'''simple docstring'''
def __init__( self ,_lowerCAmelCase ,_lowerCAmelCase = "default" ,_lowerCAmelCase = None ,_lowerCAmelCase = False ,):
super().__init__(_lowerCAmelCase ,task=_lowerCAmelCase ,patching_specs=_lowerCAmelCase ,use_past=_lowerCAmelCase )
if not getattr(self._config ,"""pad_token_id""" ,_lowerCAmelCase ):
# TODO: how to do that better?
lowerCamelCase__ = 0
@property
def UpperCamelCase_ ( self ):
lowerCamelCase__ = OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}} )
if self.use_past:
self.fill_with_past_key_values_(_lowerCAmelCase ,direction="""inputs""" )
lowerCamelCase__ = {0: """batch""", 1: """past_sequence + sequence"""}
else:
lowerCamelCase__ = {0: """batch""", 1: """sequence"""}
return common_inputs
@property
def UpperCamelCase_ ( self ):
return self._config.n_layer
@property
def UpperCamelCase_ ( self ):
return self._config.n_head
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase = -1 ,_lowerCAmelCase = -1 ,_lowerCAmelCase = False ,_lowerCAmelCase = None ,):
lowerCamelCase__ = super(_lowerCAmelCase ,self ).generate_dummy_inputs(
_lowerCAmelCase ,batch_size=_lowerCAmelCase ,seq_length=_lowerCAmelCase ,is_pair=_lowerCAmelCase ,framework=_lowerCAmelCase )
# We need to order the input in the way they appears in the forward()
lowerCamelCase__ = OrderedDict({"""input_ids""": common_inputs["""input_ids"""]} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
lowerCamelCase__ , lowerCamelCase__ = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
lowerCamelCase__ = seqlen + 2
lowerCamelCase__ = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
lowerCamelCase__ = [
(torch.zeros(_lowerCAmelCase ), torch.zeros(_lowerCAmelCase )) for _ in range(self.num_layers )
]
lowerCamelCase__ = common_inputs["""attention_mask"""]
if self.use_past:
lowerCamelCase__ = ordered_inputs["""attention_mask"""].dtype
lowerCamelCase__ = torch.cat(
[ordered_inputs["""attention_mask"""], torch.ones(_lowerCAmelCase ,_lowerCAmelCase ,dtype=_lowerCAmelCase )] ,dim=1 )
return ordered_inputs
@property
def UpperCamelCase_ ( self ):
return 13
| 50 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBOSTokenLogitsProcessor,
FlaxForcedEOSTokenLogitsProcessor,
FlaxLogitsProcessorList,
FlaxMinLengthLogitsProcessor,
FlaxTemperatureLogitsWarper,
FlaxTopKLogitsWarper,
FlaxTopPLogitsWarper,
)
@require_flax
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = jnp.ones((batch_size, length) ) / length
return scores
def UpperCamelCase_ ( self ):
lowerCamelCase__ = None
lowerCamelCase__ = 20
lowerCamelCase__ = self._get_uniform_logits(batch_size=2 ,length=_lowerCAmelCase )
# tweak scores to not be uniform anymore
lowerCamelCase__ = scores.at[1, 5].set((1 / length) + 0.1 ) # peak, 1st batch
lowerCamelCase__ = scores.at[1, 10].set((1 / length) - 0.4 ) # valley, 1st batch
# compute softmax
lowerCamelCase__ = jax.nn.softmax(_lowerCAmelCase ,axis=-1 )
lowerCamelCase__ = FlaxTemperatureLogitsWarper(temperature=0.5 )
lowerCamelCase__ = FlaxTemperatureLogitsWarper(temperature=1.3 )
lowerCamelCase__ = jax.nn.softmax(temp_dist_warper_sharper(_lowerCAmelCase ,scores.copy() ,cur_len=_lowerCAmelCase ) ,axis=-1 )
lowerCamelCase__ = jax.nn.softmax(temp_dist_warper_smoother(_lowerCAmelCase ,scores.copy() ,cur_len=_lowerCAmelCase ) ,axis=-1 )
# uniform distribution stays uniform
self.assertTrue(jnp.allclose(probs[0, :] ,warped_prob_sharp[0, :] ,atol=1E-3 ) )
self.assertTrue(jnp.allclose(probs[0, :] ,warped_prob_smooth[0, :] ,atol=1E-3 ) )
# sharp peaks get higher, valleys get lower
self.assertLess(probs[1, :].max() ,warped_prob_sharp[1, :].max() )
self.assertGreater(probs[1, :].min() ,warped_prob_sharp[1, :].min() )
# smooth peaks get lower, valleys get higher
self.assertGreater(probs[1, :].max() ,warped_prob_smooth[1, :].max() )
self.assertLess(probs[1, :].min() ,warped_prob_smooth[1, :].min() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = None
lowerCamelCase__ = 10
lowerCamelCase__ = 2
# create ramp distribution
lowerCamelCase__ = np.broadcast_to(np.arange(_lowerCAmelCase )[None, :] ,(batch_size, vocab_size) ).copy()
lowerCamelCase__ = ramp_logits[1:, : vocab_size // 2] + vocab_size
lowerCamelCase__ = FlaxTopKLogitsWarper(3 )
lowerCamelCase__ = top_k_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# check that correct tokens are filtered
self.assertListEqual(jnp.isinf(scores[0] ).tolist() ,7 * [True] + 3 * [False] )
self.assertListEqual(jnp.isinf(scores[1] ).tolist() ,2 * [True] + 3 * [False] + 5 * [True] )
# check special case
lowerCamelCase__ = 5
lowerCamelCase__ = FlaxTopKLogitsWarper(top_k=1 ,filter_value=0.0 ,min_tokens_to_keep=3 )
lowerCamelCase__ = np.broadcast_to(np.arange(_lowerCAmelCase )[None, :] ,(batch_size, length) ).copy()
lowerCamelCase__ = top_k_warp_safety_check(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
self.assertListEqual((scores == 0.0).sum(axis=-1 ).tolist() ,[2, 2] )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = None
lowerCamelCase__ = 10
lowerCamelCase__ = 2
# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
lowerCamelCase__ = np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]] ) )
lowerCamelCase__ = FlaxTopPLogitsWarper(0.8 )
lowerCamelCase__ = np.exp(top_p_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase ) )
# dist should be filtered to keep min num values so that sum is >= top_p
# exp (-inf) => 0
lowerCamelCase__ = np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]] )
self.assertTrue(np.allclose(_lowerCAmelCase ,_lowerCAmelCase ,atol=1E-3 ) )
# check edge cases with negative and extreme logits
lowerCamelCase__ = np.broadcast_to(np.arange(_lowerCAmelCase )[None, :] ,(batch_size, vocab_size) ).copy() - (
vocab_size // 2
)
# make ramp_logits more extreme
lowerCamelCase__ = ramp_logits[1] * 100.0
# make sure at least 2 tokens are kept
lowerCamelCase__ = FlaxTopPLogitsWarper(0.9 ,min_tokens_to_keep=2 ,filter_value=0.0 )
lowerCamelCase__ = top_p_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
self.assertListEqual((filtered_dist != 0.0).sum(axis=-1 ).tolist() ,[3, 2] )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 20
lowerCamelCase__ = 4
lowerCamelCase__ = 0
lowerCamelCase__ = FlaxMinLengthLogitsProcessor(min_length=10 ,eos_token_id=_lowerCAmelCase )
# check that min length is applied at length 5
lowerCamelCase__ = ids_tensor((batch_size, 20) ,vocab_size=20 )
lowerCamelCase__ = 5
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = min_dist_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist() ,4 * [-float("""inf""" )] )
# check that min length is not applied anymore at length 15
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = 15
lowerCamelCase__ = min_dist_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertFalse(jnp.isinf(_lowerCAmelCase ).any() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 20
lowerCamelCase__ = 4
lowerCamelCase__ = 0
lowerCamelCase__ = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase )
# check that all scores are -inf except the bos_token_id score
lowerCamelCase__ = ids_tensor((batch_size, 1) ,vocab_size=20 )
lowerCamelCase__ = 1
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = logits_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, bos_token_id].tolist() ,4 * [0] ) # score for bos_token_id shold be zero
# check that bos_token_id is not forced if current length is greater than 1
lowerCamelCase__ = 3
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = logits_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertFalse(jnp.isinf(_lowerCAmelCase ).any() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 20
lowerCamelCase__ = 4
lowerCamelCase__ = 0
lowerCamelCase__ = 5
lowerCamelCase__ = FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase )
# check that all scores are -inf except the eos_token_id when max_length is reached
lowerCamelCase__ = ids_tensor((batch_size, 4) ,vocab_size=20 )
lowerCamelCase__ = 4
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = logits_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, eos_token_id].tolist() ,4 * [0] ) # score for eos_token_id should be zero
# check that eos_token_id is not forced if max_length is not reached
lowerCamelCase__ = 3
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = logits_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertFalse(jnp.isinf(_lowerCAmelCase ).any() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 4
lowerCamelCase__ = 10
lowerCamelCase__ = 15
lowerCamelCase__ = 2
lowerCamelCase__ = 1
lowerCamelCase__ = 15
# dummy input_ids and scores
lowerCamelCase__ = ids_tensor((batch_size, sequence_length) ,_lowerCAmelCase )
lowerCamelCase__ = input_ids.copy()
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = scores.copy()
# instantiate all dist processors
lowerCamelCase__ = FlaxTemperatureLogitsWarper(temperature=0.5 )
lowerCamelCase__ = FlaxTopKLogitsWarper(3 )
lowerCamelCase__ = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
lowerCamelCase__ = FlaxMinLengthLogitsProcessor(min_length=10 ,eos_token_id=_lowerCAmelCase )
lowerCamelCase__ = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase )
lowerCamelCase__ = FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase )
lowerCamelCase__ = 10
# no processor list
lowerCamelCase__ = temp_dist_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = top_k_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = top_p_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = min_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = bos_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = eos_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# with processor list
lowerCamelCase__ = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
lowerCamelCase__ = processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# scores should be equal
self.assertTrue(jnp.allclose(_lowerCAmelCase ,_lowerCAmelCase ,atol=1E-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() ,input_ids_comp.tolist() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 4
lowerCamelCase__ = 10
lowerCamelCase__ = 15
lowerCamelCase__ = 2
lowerCamelCase__ = 1
lowerCamelCase__ = 15
# dummy input_ids and scores
lowerCamelCase__ = ids_tensor((batch_size, sequence_length) ,_lowerCAmelCase )
lowerCamelCase__ = input_ids.copy()
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = scores.copy()
# instantiate all dist processors
lowerCamelCase__ = FlaxTemperatureLogitsWarper(temperature=0.5 )
lowerCamelCase__ = FlaxTopKLogitsWarper(3 )
lowerCamelCase__ = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
lowerCamelCase__ = FlaxMinLengthLogitsProcessor(min_length=10 ,eos_token_id=_lowerCAmelCase )
lowerCamelCase__ = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase )
lowerCamelCase__ = FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase )
lowerCamelCase__ = 10
# no processor list
def run_no_processor_list(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = temp_dist_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = top_k_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = top_p_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = min_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = bos_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = eos_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
return scores
# with processor list
def run_processor_list(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
lowerCamelCase__ = processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
return scores
lowerCamelCase__ = jax.jit(_lowerCAmelCase )
lowerCamelCase__ = jax.jit(_lowerCAmelCase )
lowerCamelCase__ = jitted_run_no_processor_list(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = jitted_run_processor_list(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
# scores should be equal
self.assertTrue(jnp.allclose(_lowerCAmelCase ,_lowerCAmelCase ,atol=1E-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() ,input_ids_comp.tolist() )
| 50 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase : Tuple = logging.get_logger(__name__)
def A__ ( __lowerCAmelCase : int ):
lowerCamelCase__ = DPTConfig(embedding_type="""hybrid""" )
if "large" in checkpoint_url:
lowerCamelCase__ = 1024
lowerCamelCase__ = 4096
lowerCamelCase__ = 24
lowerCamelCase__ = 16
lowerCamelCase__ = [5, 11, 17, 23]
lowerCamelCase__ = [256, 512, 1024, 1024]
lowerCamelCase__ = (1, 384, 384)
if "nyu" or "midas" in checkpoint_url:
lowerCamelCase__ = 768
lowerCamelCase__ = [1, 1, 1, 0.5]
lowerCamelCase__ = [256, 512, 768, 768]
lowerCamelCase__ = 150
lowerCamelCase__ = 16
lowerCamelCase__ = (1, 384, 384)
lowerCamelCase__ = False
lowerCamelCase__ = """project"""
if "ade" in checkpoint_url:
lowerCamelCase__ = True
lowerCamelCase__ = 768
lowerCamelCase__ = [1, 1, 1, 0.5]
lowerCamelCase__ = 150
lowerCamelCase__ = 16
lowerCamelCase__ = """huggingface/label-files"""
lowerCamelCase__ = """ade20k-id2label.json"""
lowerCamelCase__ = json.load(open(cached_download(hf_hub_url(__lowerCAmelCase , __lowerCAmelCase , repo_type="""dataset""" ) ) , """r""" ) )
lowerCamelCase__ = {int(__lowerCAmelCase ): v for k, v in idalabel.items()}
lowerCamelCase__ = idalabel
lowerCamelCase__ = {v: k for k, v in idalabel.items()}
lowerCamelCase__ = [1, 150, 480, 480]
return config, expected_shape
def A__ ( __lowerCAmelCase : Optional[int] ):
lowerCamelCase__ = ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
for k in ignore_keys:
state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
def A__ ( __lowerCAmelCase : List[Any] ):
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
lowerCamelCase__ = name.replace("""pretrained.model""" , """dpt.encoder""" )
if "pretrained.model" in name:
lowerCamelCase__ = name.replace("""pretrained.model""" , """dpt.embeddings""" )
if "patch_embed" in name:
lowerCamelCase__ = name.replace("""patch_embed""" , """""" )
if "pos_embed" in name:
lowerCamelCase__ = name.replace("""pos_embed""" , """position_embeddings""" )
if "attn.proj" in name:
lowerCamelCase__ = name.replace("""attn.proj""" , """attention.output.dense""" )
if "proj" in name and "project" not in name:
lowerCamelCase__ = name.replace("""proj""" , """projection""" )
if "blocks" in name:
lowerCamelCase__ = name.replace("""blocks""" , """layer""" )
if "mlp.fc1" in name:
lowerCamelCase__ = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
lowerCamelCase__ = name.replace("""mlp.fc2""" , """output.dense""" )
if "norm1" in name and "backbone" not in name:
lowerCamelCase__ = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name and "backbone" not in name:
lowerCamelCase__ = name.replace("""norm2""" , """layernorm_after""" )
if "scratch.output_conv" in name:
lowerCamelCase__ = name.replace("""scratch.output_conv""" , """head""" )
if "scratch" in name:
lowerCamelCase__ = name.replace("""scratch""" , """neck""" )
if "layer1_rn" in name:
lowerCamelCase__ = name.replace("""layer1_rn""" , """convs.0""" )
if "layer2_rn" in name:
lowerCamelCase__ = name.replace("""layer2_rn""" , """convs.1""" )
if "layer3_rn" in name:
lowerCamelCase__ = name.replace("""layer3_rn""" , """convs.2""" )
if "layer4_rn" in name:
lowerCamelCase__ = name.replace("""layer4_rn""" , """convs.3""" )
if "refinenet" in name:
lowerCamelCase__ = int(name[len("""neck.refinenet""" ) : len("""neck.refinenet""" ) + 1] )
# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
lowerCamelCase__ = name.replace(F'''refinenet{layer_idx}''' , F'''fusion_stage.layers.{abs(layer_idx-4 )}''' )
if "out_conv" in name:
lowerCamelCase__ = name.replace("""out_conv""" , """projection""" )
if "resConfUnit1" in name:
lowerCamelCase__ = name.replace("""resConfUnit1""" , """residual_layer1""" )
if "resConfUnit2" in name:
lowerCamelCase__ = name.replace("""resConfUnit2""" , """residual_layer2""" )
if "conv1" in name:
lowerCamelCase__ = name.replace("""conv1""" , """convolution1""" )
if "conv2" in name:
lowerCamelCase__ = name.replace("""conv2""" , """convolution2""" )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess1.0.project.0""" , """neck.reassemble_stage.readout_projects.0.0""" )
if "pretrained.act_postprocess2.0.project.0" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess2.0.project.0""" , """neck.reassemble_stage.readout_projects.1.0""" )
if "pretrained.act_postprocess3.0.project.0" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess3.0.project.0""" , """neck.reassemble_stage.readout_projects.2.0""" )
if "pretrained.act_postprocess4.0.project.0" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess4.0.project.0""" , """neck.reassemble_stage.readout_projects.3.0""" )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess1.3""" , """neck.reassemble_stage.layers.0.projection""" )
if "pretrained.act_postprocess1.4" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess1.4""" , """neck.reassemble_stage.layers.0.resize""" )
if "pretrained.act_postprocess2.3" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess2.3""" , """neck.reassemble_stage.layers.1.projection""" )
if "pretrained.act_postprocess2.4" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess2.4""" , """neck.reassemble_stage.layers.1.resize""" )
if "pretrained.act_postprocess3.3" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess3.3""" , """neck.reassemble_stage.layers.2.projection""" )
if "pretrained.act_postprocess4.3" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess4.3""" , """neck.reassemble_stage.layers.3.projection""" )
if "pretrained.act_postprocess4.4" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess4.4""" , """neck.reassemble_stage.layers.3.resize""" )
if "pretrained" in name:
lowerCamelCase__ = name.replace("""pretrained""" , """dpt""" )
if "bn" in name:
lowerCamelCase__ = name.replace("""bn""" , """batch_norm""" )
if "head" in name:
lowerCamelCase__ = name.replace("""head""" , """head.head""" )
if "encoder.norm" in name:
lowerCamelCase__ = name.replace("""encoder.norm""" , """layernorm""" )
if "auxlayer" in name:
lowerCamelCase__ = name.replace("""auxlayer""" , """auxiliary_head.head""" )
if "backbone" in name:
lowerCamelCase__ = name.replace("""backbone""" , """backbone.bit.encoder""" )
if ".." in name:
lowerCamelCase__ = name.replace("""..""" , """.""" )
if "stem.conv" in name:
lowerCamelCase__ = name.replace("""stem.conv""" , """bit.embedder.convolution""" )
if "blocks" in name:
lowerCamelCase__ = name.replace("""blocks""" , """layers""" )
if "convolution" in name and "backbone" in name:
lowerCamelCase__ = name.replace("""convolution""" , """conv""" )
if "layer" in name and "backbone" in name:
lowerCamelCase__ = name.replace("""layer""" , """layers""" )
if "backbone.bit.encoder.bit" in name:
lowerCamelCase__ = name.replace("""backbone.bit.encoder.bit""" , """backbone.bit""" )
if "embedder.conv" in name:
lowerCamelCase__ = name.replace("""embedder.conv""" , """embedder.convolution""" )
if "backbone.bit.encoder.stem.norm" in name:
lowerCamelCase__ = name.replace("""backbone.bit.encoder.stem.norm""" , """backbone.bit.embedder.norm""" )
return name
def A__ ( __lowerCAmelCase : str , __lowerCAmelCase : int ):
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowerCamelCase__ = state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight''' )
lowerCamelCase__ = state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
lowerCamelCase__ = in_proj_weight[: config.hidden_size, :]
lowerCamelCase__ = in_proj_bias[: config.hidden_size]
lowerCamelCase__ = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowerCamelCase__ = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowerCamelCase__ = in_proj_weight[
-config.hidden_size :, :
]
lowerCamelCase__ = in_proj_bias[-config.hidden_size :]
def A__ ( ):
lowerCamelCase__ = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowerCamelCase__ = Image.open(requests.get(__lowerCAmelCase , stream=__lowerCAmelCase ).raw )
return im
@torch.no_grad()
def A__ ( __lowerCAmelCase : Dict , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Any ):
lowerCamelCase__ , lowerCamelCase__ = get_dpt_config(__lowerCAmelCase )
# load original state_dict from URL
# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
lowerCamelCase__ = torch.load(__lowerCAmelCase , map_location="""cpu""" )
# remove certain keys
remove_ignore_keys_(__lowerCAmelCase )
# rename keys
for key in state_dict.copy().keys():
lowerCamelCase__ = state_dict.pop(__lowerCAmelCase )
lowerCamelCase__ = val
# read in qkv matrices
read_in_q_k_v(__lowerCAmelCase , __lowerCAmelCase )
# load HuggingFace model
lowerCamelCase__ = DPTForSemanticSegmentation(__lowerCAmelCase ) if """ade""" in checkpoint_url else DPTForDepthEstimation(__lowerCAmelCase )
model.load_state_dict(__lowerCAmelCase )
model.eval()
# Check outputs on an image
lowerCamelCase__ = 480 if """ade""" in checkpoint_url else 384
lowerCamelCase__ = DPTImageProcessor(size=__lowerCAmelCase )
lowerCamelCase__ = prepare_img()
lowerCamelCase__ = image_processor(__lowerCAmelCase , return_tensors="""pt""" )
# forward pass
lowerCamelCase__ = model(**__lowerCAmelCase ).logits if """ade""" in checkpoint_url else model(**__lowerCAmelCase ).predicted_depth
if show_prediction:
lowerCamelCase__ = (
torch.nn.functional.interpolate(
outputs.unsqueeze(1 ) , size=(image.size[1], image.size[0]) , mode="""bicubic""" , align_corners=__lowerCAmelCase , )
.squeeze()
.cpu()
.numpy()
)
Image.fromarray((prediction / prediction.max()) * 255 ).show()
if pytorch_dump_folder_path is not None:
Path(__lowerCAmelCase ).mkdir(exist_ok=__lowerCAmelCase )
print(F'''Saving model 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:
model.push_to_hub("""ybelkada/dpt-hybrid-midas""" )
image_processor.push_to_hub("""ybelkada/dpt-hybrid-midas""" )
if __name__ == "__main__":
UpperCamelCase : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint_url',
default='https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt',
type=str,
help='URL of the original DPT checkpoint you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default=None,
type=str,
required=False,
help='Path to the output PyTorch model directory.',
)
parser.add_argument(
'--push_to_hub',
action='store_true',
)
parser.add_argument(
'--model_name',
default='dpt-large',
type=str,
help='Name of the model, in case you\'re pushing to the hub.',
)
parser.add_argument(
'--show_prediction',
action='store_true',
)
UpperCamelCase : List[str] = parser.parse_args()
convert_dpt_checkpoint(
args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
)
| 50 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase : Any = {
'configuration_groupvit': [
'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'GroupViTConfig',
'GroupViTOnnxConfig',
'GroupViTTextConfig',
'GroupViTVisionConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : List[str] = [
'GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'GroupViTModel',
'GroupViTPreTrainedModel',
'GroupViTTextModel',
'GroupViTVisionModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : List[str] = [
'TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFGroupViTModel',
'TFGroupViTPreTrainedModel',
'TFGroupViTTextModel',
'TFGroupViTVisionModel',
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
UpperCamelCase : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 50 | 1 |
'''simple docstring'''
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
UpperCamelCase : int = logging.get_logger(__name__)
def A__ ( __lowerCAmelCase : int , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any]=False ):
try:
import torch # noqa: F401
except ImportError:
logger.error(
"""Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"""
""" https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
""" instructions.""" )
raise
if not is_sharded:
lowerCamelCase__ = os.path.abspath(__lowerCAmelCase )
logger.info(F'''Loading PyTorch weights from {pt_path}''' )
lowerCamelCase__ = torch.load(__lowerCAmelCase , map_location="""cpu""" )
logger.info(F'''PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values() ):,} parameters.''' )
lowerCamelCase__ = convert_pytorch_state_dict_to_flax(__lowerCAmelCase , __lowerCAmelCase )
else:
# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
lowerCamelCase__ = convert_pytorch_sharded_state_dict_to_flax(__lowerCAmelCase , __lowerCAmelCase )
return flax_state_dict
def A__ ( __lowerCAmelCase : Tuple[str] , __lowerCAmelCase : np.ndarray , __lowerCAmelCase : Dict[str, jnp.ndarray] , __lowerCAmelCase : str , ):
def is_key_or_prefix_key_in_dict(__lowerCAmelCase : Tuple[str] ) -> bool:
return len(set(__lowerCAmelCase ) & {key, (model_prefix,) + key} ) > 0
# layer norm
lowerCamelCase__ = pt_tuple_key[:-1] + ("""scale""",)
if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(__lowerCAmelCase ):
return renamed_pt_tuple_key, pt_tensor
# batch norm layer mean
lowerCamelCase__ = pt_tuple_key[:-1] + ("""mean""",)
if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(__lowerCAmelCase ):
return renamed_pt_tuple_key, pt_tensor
# batch norm layer var
lowerCamelCase__ = pt_tuple_key[:-1] + ("""var""",)
if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(__lowerCAmelCase ):
return renamed_pt_tuple_key, pt_tensor
# embedding
lowerCamelCase__ = pt_tuple_key[:-1] + ("""embedding""",)
if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(__lowerCAmelCase ):
return renamed_pt_tuple_key, pt_tensor
# conv layer
lowerCamelCase__ = pt_tuple_key[:-1] + ("""kernel""",)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(__lowerCAmelCase ):
lowerCamelCase__ = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
lowerCamelCase__ = pt_tuple_key[:-1] + ("""kernel""",)
if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(__lowerCAmelCase ):
lowerCamelCase__ = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
lowerCamelCase__ = pt_tuple_key[:-1] + ("""weight""",)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
lowerCamelCase__ = pt_tuple_key[:-1] + ("""bias""",)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
lowerCamelCase__ = None
if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
lowerCamelCase__ = pt_tuple_key[-2] + """_g"""
elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
lowerCamelCase__ = pt_tuple_key[-2] + """_v"""
if name is not None:
lowerCamelCase__ = pt_tuple_key[:-3] + (name,)
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def A__ ( __lowerCAmelCase : Tuple , __lowerCAmelCase : Any ):
# convert pytorch tensor to numpy
lowerCamelCase__ = {k: v.numpy() for k, v in pt_state_dict.items()}
lowerCamelCase__ = flax_model.base_model_prefix
# use params dict if the model contains batch norm layers
if "params" in flax_model.params:
lowerCamelCase__ = flax_model.params["""params"""]
else:
lowerCamelCase__ = flax_model.params
lowerCamelCase__ = flatten_dict(__lowerCAmelCase )
# add batch_stats keys,values to dict
if "batch_stats" in flax_model.params:
lowerCamelCase__ = flatten_dict(flax_model.params["""batch_stats"""] )
random_flax_state_dict.update(__lowerCAmelCase )
lowerCamelCase__ = {}
lowerCamelCase__ = (model_prefix not in flax_model_params) and (
model_prefix in {k.split(""".""" )[0] for k in pt_state_dict.keys()}
)
lowerCamelCase__ = (model_prefix in flax_model_params) and (
model_prefix not in {k.split(""".""" )[0] for k in pt_state_dict.keys()}
)
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
lowerCamelCase__ = tuple(pt_key.split(""".""" ) )
# remove base model prefix if necessary
lowerCamelCase__ = pt_tuple_key[0] == model_prefix
if load_model_with_head_into_base_model and has_base_model_prefix:
lowerCamelCase__ = pt_tuple_key[1:]
# Correctly rename weight parameters
lowerCamelCase__ , lowerCamelCase__ = rename_key_and_reshape_tensor(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# add model prefix if necessary
lowerCamelCase__ = (model_prefix,) + flax_key in random_flax_state_dict
if load_base_model_into_model_with_head and require_base_model_prefix:
lowerCamelCase__ = (model_prefix,) + flax_key
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
F'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
F'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.''' )
# add batch stats if the model contains batchnorm layers
if "batch_stats" in flax_model.params:
if "mean" in flax_key[-1] or "var" in flax_key[-1]:
lowerCamelCase__ = jnp.asarray(__lowerCAmelCase )
continue
# remove num_batches_tracked key
if "num_batches_tracked" in flax_key[-1]:
flax_state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
continue
# also add unexpected weight so that warning is thrown
lowerCamelCase__ = jnp.asarray(__lowerCAmelCase )
else:
# also add unexpected weight so that warning is thrown
lowerCamelCase__ = jnp.asarray(__lowerCAmelCase )
return unflatten_dict(__lowerCAmelCase )
def A__ ( __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[str] ):
import torch
# Load the index
lowerCamelCase__ = {}
for shard_file in shard_filenames:
# load using msgpack utils
lowerCamelCase__ = torch.load(__lowerCAmelCase )
lowerCamelCase__ = {k: v.numpy() for k, v in pt_state_dict.items()}
lowerCamelCase__ = flax_model.base_model_prefix
# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
if "batch_stats" in flax_model.params:
lowerCamelCase__ = flax_model.params["""params"""]
lowerCamelCase__ = flatten_dict(__lowerCAmelCase )
random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""] ) )
else:
lowerCamelCase__ = flax_model.params
lowerCamelCase__ = flatten_dict(__lowerCAmelCase )
lowerCamelCase__ = (model_prefix not in flax_model_params) and (
model_prefix in {k.split(""".""" )[0] for k in pt_state_dict.keys()}
)
lowerCamelCase__ = (model_prefix in flax_model_params) and (
model_prefix not in {k.split(""".""" )[0] for k in pt_state_dict.keys()}
)
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
lowerCamelCase__ = tuple(pt_key.split(""".""" ) )
# remove base model prefix if necessary
lowerCamelCase__ = pt_tuple_key[0] == model_prefix
if load_model_with_head_into_base_model and has_base_model_prefix:
lowerCamelCase__ = pt_tuple_key[1:]
# Correctly rename weight parameters
lowerCamelCase__ , lowerCamelCase__ = rename_key_and_reshape_tensor(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# add model prefix if necessary
lowerCamelCase__ = (model_prefix,) + flax_key in random_flax_state_dict
if load_base_model_into_model_with_head and require_base_model_prefix:
lowerCamelCase__ = (model_prefix,) + flax_key
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
F'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
F'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.''' )
# add batch stats if the model contains batchnorm layers
if "batch_stats" in flax_model.params:
if "mean" in flax_key[-1]:
lowerCamelCase__ = jnp.asarray(__lowerCAmelCase )
continue
if "var" in flax_key[-1]:
lowerCamelCase__ = jnp.asarray(__lowerCAmelCase )
continue
# remove num_batches_tracked key
if "num_batches_tracked" in flax_key[-1]:
flax_state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
continue
# also add unexpected weight so that warning is thrown
lowerCamelCase__ = jnp.asarray(__lowerCAmelCase )
else:
# also add unexpected weight so that warning is thrown
lowerCamelCase__ = jnp.asarray(__lowerCAmelCase )
return unflatten_dict(__lowerCAmelCase )
def A__ ( __lowerCAmelCase : Any , __lowerCAmelCase : Tuple ):
lowerCamelCase__ = os.path.abspath(__lowerCAmelCase )
logger.info(F'''Loading Flax weights from {flax_checkpoint_path}''' )
# import correct flax class
lowerCamelCase__ = getattr(__lowerCAmelCase , """Flax""" + model.__class__.__name__ )
# load flax weight dict
with open(__lowerCAmelCase , """rb""" ) as state_f:
try:
lowerCamelCase__ = from_bytes(__lowerCAmelCase , state_f.read() )
except UnpicklingError:
raise EnvironmentError(F'''Unable to convert {flax_checkpoint_path} to Flax deserializable object. ''' )
return load_flax_weights_in_pytorch_model(__lowerCAmelCase , __lowerCAmelCase )
def A__ ( __lowerCAmelCase : str , __lowerCAmelCase : Tuple ):
try:
import torch # noqa: F401
except ImportError:
logger.error(
"""Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see"""
""" https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
""" instructions.""" )
raise
# check if we have bf16 weights
lowerCamelCase__ = flatten_dict(jax.tree_util.tree_map(lambda __lowerCAmelCase : x.dtype == jnp.bfloataa , __lowerCAmelCase ) ).values()
if any(__lowerCAmelCase ):
# convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
# and bf16 is not fully supported in PT yet.
logger.warning(
"""Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
"""before loading those in PyTorch model.""" )
lowerCamelCase__ = jax.tree_util.tree_map(
lambda __lowerCAmelCase : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params , __lowerCAmelCase )
lowerCamelCase__ = flatten_dict(__lowerCAmelCase )
lowerCamelCase__ = pt_model.state_dict()
lowerCamelCase__ = (pt_model.base_model_prefix in flax_state) and (
pt_model.base_model_prefix not in {k.split(""".""" )[0] for k in pt_model_dict.keys()}
)
lowerCamelCase__ = (pt_model.base_model_prefix not in flax_state) and (
pt_model.base_model_prefix in {k.split(""".""" )[0] for k in pt_model_dict.keys()}
)
# keep track of unexpected & missing keys
lowerCamelCase__ = []
lowerCamelCase__ = set(pt_model_dict.keys() )
for flax_key_tuple, flax_tensor in flax_state_dict.items():
lowerCamelCase__ = flax_key_tuple[0] == pt_model.base_model_prefix
lowerCamelCase__ = """.""".join((pt_model.base_model_prefix,) + flax_key_tuple ) in pt_model_dict
# adapt flax_key to prepare for loading from/to base model only
if load_model_with_head_into_base_model and has_base_model_prefix:
lowerCamelCase__ = flax_key_tuple[1:]
elif load_base_model_into_model_with_head and require_base_model_prefix:
lowerCamelCase__ = (pt_model.base_model_prefix,) + flax_key_tuple
# rename flax weights to PyTorch format
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(__lowerCAmelCase ) not in pt_model_dict:
# conv layer
lowerCamelCase__ = flax_key_tuple[:-1] + ("""weight""",)
lowerCamelCase__ = jnp.transpose(__lowerCAmelCase , (3, 2, 0, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(__lowerCAmelCase ) not in pt_model_dict:
# linear layer
lowerCamelCase__ = flax_key_tuple[:-1] + ("""weight""",)
lowerCamelCase__ = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
lowerCamelCase__ = flax_key_tuple[:-1] + ("""weight""",)
# adding batch stats from flax batch norm to pt
elif "mean" in flax_key_tuple[-1]:
lowerCamelCase__ = flax_key_tuple[:-1] + ("""running_mean""",)
elif "var" in flax_key_tuple[-1]:
lowerCamelCase__ = flax_key_tuple[:-1] + ("""running_var""",)
if "batch_stats" in flax_state:
lowerCamelCase__ = """.""".join(flax_key_tuple[1:] ) # Remove the params/batch_stats header
else:
lowerCamelCase__ = """.""".join(__lowerCAmelCase )
# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
lowerCamelCase__ = {}
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
for key in pt_model_dict:
lowerCamelCase__ = key.split(""".""" )
lowerCamelCase__ = None
if key_components[-3::2] == ["parametrizations", "original0"]:
lowerCamelCase__ = key_components[-2] + """_g"""
elif key_components[-3::2] == ["parametrizations", "original1"]:
lowerCamelCase__ = key_components[-2] + """_v"""
if name is not None:
lowerCamelCase__ = key_components[:-3] + [name]
lowerCamelCase__ = """.""".join(__lowerCAmelCase )
lowerCamelCase__ = key
if flax_key in special_pt_names:
lowerCamelCase__ = special_pt_names[flax_key]
if flax_key in pt_model_dict:
if flax_tensor.shape != pt_model_dict[flax_key].shape:
raise ValueError(
F'''Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '''
F'''to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.''' )
else:
# add weight to pytorch dict
lowerCamelCase__ = np.asarray(__lowerCAmelCase ) if not isinstance(__lowerCAmelCase , np.ndarray ) else flax_tensor
lowerCamelCase__ = torch.from_numpy(__lowerCAmelCase )
# remove from missing keys
missing_keys.remove(__lowerCAmelCase )
else:
# weight is not expected by PyTorch model
unexpected_keys.append(__lowerCAmelCase )
pt_model.load_state_dict(__lowerCAmelCase )
# re-transform missing_keys to list
lowerCamelCase__ = list(__lowerCAmelCase )
if len(__lowerCAmelCase ) > 0:
logger.warning(
"""Some weights of the Flax model were not used when initializing the PyTorch model"""
F''' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'''
F''' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'''
""" (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
F''' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'''
""" to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
""" FlaxBertForSequenceClassification model).""" )
else:
logger.warning(F'''All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n''' )
if len(__lowerCAmelCase ) > 0:
logger.warning(
F'''Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'''
F''' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'''
""" use it for predictions and inference.""" )
else:
logger.warning(
F'''All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'''
"""If your task is similar to the task the model of the checkpoint was trained on, """
F'''you can already use {pt_model.__class__.__name__} for predictions without further training.''' )
return pt_model
| 50 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ):
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 50 | 1 |
'''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
@add_end_docstrings(a )
class UpperCamelCase__ (a ):
'''simple docstring'''
def __init__( self ,*_lowerCAmelCase ,**_lowerCAmelCase ):
super().__init__(*_lowerCAmelCase ,**_lowerCAmelCase )
requires_backends(self ,"""vision""" )
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == """tf""" else MODEL_FOR_VISION_2_SEQ_MAPPING )
def UpperCamelCase_ ( self ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=None ):
lowerCamelCase__ = {}
lowerCamelCase__ = {}
if prompt is not None:
lowerCamelCase__ = prompt
if generate_kwargs is not None:
lowerCamelCase__ = generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
lowerCamelCase__ = {}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
"""'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"""
""" please use only one""" )
lowerCamelCase__ = max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__( self ,_lowerCAmelCase ,**_lowerCAmelCase ):
return super().__call__(_lowerCAmelCase ,**_lowerCAmelCase )
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase=None ):
lowerCamelCase__ = load_image(_lowerCAmelCase )
if prompt is not None:
if not isinstance(_lowerCAmelCase ,_lowerCAmelCase ):
raise ValueError(
F'''Received an invalid text input, got - {type(_lowerCAmelCase )} - but expected a single string. '''
"""Note also that one single text can be provided for conditional image to text generation.""" )
lowerCamelCase__ = self.model.config.model_type
if model_type == "git":
lowerCamelCase__ = self.image_processor(images=_lowerCAmelCase ,return_tensors=self.framework )
lowerCamelCase__ = self.tokenizer(text=_lowerCAmelCase ,add_special_tokens=_lowerCAmelCase ).input_ids
lowerCamelCase__ = [self.tokenizer.cls_token_id] + input_ids
lowerCamelCase__ = torch.tensor(_lowerCAmelCase ).unsqueeze(0 )
model_inputs.update({"""input_ids""": input_ids} )
elif model_type == "pix2struct":
lowerCamelCase__ = self.image_processor(images=_lowerCAmelCase ,header_text=_lowerCAmelCase ,return_tensors=self.framework )
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
lowerCamelCase__ = self.image_processor(images=_lowerCAmelCase ,return_tensors=self.framework )
lowerCamelCase__ = self.tokenizer(_lowerCAmelCase ,return_tensors=self.framework )
model_inputs.update(_lowerCAmelCase )
else:
raise ValueError(F'''Model type {model_type} does not support conditional text generation''' )
else:
lowerCamelCase__ = self.image_processor(images=_lowerCAmelCase ,return_tensors=self.framework )
if self.model.config.model_type == "git" and prompt is None:
lowerCamelCase__ = None
return model_inputs
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase=None ):
# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
if (
"input_ids" in model_inputs
and isinstance(model_inputs["""input_ids"""] ,_lowerCAmelCase )
and all(x is None for x in model_inputs["""input_ids"""] )
):
lowerCamelCase__ = None
if generate_kwargs is None:
lowerCamelCase__ = {}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
lowerCamelCase__ = model_inputs.pop(self.model.main_input_name )
lowerCamelCase__ = self.model.generate(_lowerCAmelCase ,**_lowerCAmelCase ,**_lowerCAmelCase )
return model_outputs
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
lowerCamelCase__ = []
for output_ids in model_outputs:
lowerCamelCase__ = {
"""generated_text""": self.tokenizer.decode(
_lowerCAmelCase ,skip_special_tokens=_lowerCAmelCase ,)
}
records.append(_lowerCAmelCase )
return records
| 50 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase : Union[str, Any] = {
'configuration_canine': ['CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CanineConfig'],
'tokenization_canine': ['CanineTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Any = [
'CANINE_PRETRAINED_MODEL_ARCHIVE_LIST',
'CanineForMultipleChoice',
'CanineForQuestionAnswering',
'CanineForSequenceClassification',
'CanineForTokenClassification',
'CanineLayer',
'CanineModel',
'CaninePreTrainedModel',
'load_tf_weights_in_canine',
]
if TYPE_CHECKING:
from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
from .tokenization_canine import CanineTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_canine import (
CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
CanineForMultipleChoice,
CanineForQuestionAnswering,
CanineForSequenceClassification,
CanineForTokenClassification,
CanineLayer,
CanineModel,
CaninePreTrainedModel,
load_tf_weights_in_canine,
)
else:
import sys
UpperCamelCase : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 50 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
UpperCamelCase : List[str] = logging.get_logger(__name__)
UpperCamelCase : Dict = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
UpperCamelCase : int = {
'vocab_file': {
'squeezebert/squeezebert-uncased': (
'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'
),
'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt',
'squeezebert/squeezebert-mnli-headless': (
'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'squeezebert/squeezebert-uncased': (
'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'
),
'squeezebert/squeezebert-mnli': (
'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'
),
'squeezebert/squeezebert-mnli-headless': (
'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'
),
},
}
UpperCamelCase : Tuple = {
'squeezebert/squeezebert-uncased': 5_12,
'squeezebert/squeezebert-mnli': 5_12,
'squeezebert/squeezebert-mnli-headless': 5_12,
}
UpperCamelCase : Dict = {
'squeezebert/squeezebert-uncased': {'do_lower_case': True},
'squeezebert/squeezebert-mnli': {'do_lower_case': True},
'squeezebert/squeezebert-mnli-headless': {'do_lower_case': True},
}
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = VOCAB_FILES_NAMES
_UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
_UpperCamelCase = PRETRAINED_INIT_CONFIGURATION
_UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCamelCase = SqueezeBertTokenizer
def __init__( self ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=True ,_lowerCAmelCase="[UNK]" ,_lowerCAmelCase="[SEP]" ,_lowerCAmelCase="[PAD]" ,_lowerCAmelCase="[CLS]" ,_lowerCAmelCase="[MASK]" ,_lowerCAmelCase=True ,_lowerCAmelCase=None ,**_lowerCAmelCase ,):
super().__init__(
_lowerCAmelCase ,tokenizer_file=_lowerCAmelCase ,do_lower_case=_lowerCAmelCase ,unk_token=_lowerCAmelCase ,sep_token=_lowerCAmelCase ,pad_token=_lowerCAmelCase ,cls_token=_lowerCAmelCase ,mask_token=_lowerCAmelCase ,tokenize_chinese_chars=_lowerCAmelCase ,strip_accents=_lowerCAmelCase ,**_lowerCAmelCase ,)
lowerCamelCase__ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("""lowercase""" ,_lowerCAmelCase ) != do_lower_case
or normalizer_state.get("""strip_accents""" ,_lowerCAmelCase ) != strip_accents
or normalizer_state.get("""handle_chinese_chars""" ,_lowerCAmelCase ) != tokenize_chinese_chars
):
lowerCamelCase__ = getattr(_lowerCAmelCase ,normalizer_state.pop("""type""" ) )
lowerCamelCase__ = do_lower_case
lowerCamelCase__ = strip_accents
lowerCamelCase__ = tokenize_chinese_chars
lowerCamelCase__ = normalizer_class(**_lowerCAmelCase )
lowerCamelCase__ = do_lower_case
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase=None ):
lowerCamelCase__ = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase = None ):
lowerCamelCase__ = [self.sep_token_id]
lowerCamelCase__ = [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 UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase = None ):
lowerCamelCase__ = self._tokenizer.model.save(_lowerCAmelCase ,name=_lowerCAmelCase )
return tuple(_lowerCAmelCase )
| 50 |
'''simple docstring'''
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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.
# this script dumps information about the environment
import os
import sys
import transformers
UpperCamelCase : int = '3'
print('Python version:', sys.version)
print('transformers version:', transformers.__version__)
try:
import torch
print('Torch version:', torch.__version__)
print('Cuda available:', torch.cuda.is_available())
print('Cuda version:', torch.version.cuda)
print('CuDNN version:', torch.backends.cudnn.version())
print('Number of GPUs available:', torch.cuda.device_count())
print('NCCL version:', torch.cuda.nccl.version())
except ImportError:
print('Torch version:', None)
try:
import deepspeed
print('DeepSpeed version:', deepspeed.__version__)
except ImportError:
print('DeepSpeed version:', None)
try:
import tensorflow as tf
print('TensorFlow version:', tf.__version__)
print('TF GPUs available:', bool(tf.config.list_physical_devices('GPU')))
print('Number of TF GPUs available:', len(tf.config.list_physical_devices('GPU')))
except ImportError:
print('TensorFlow version:', None)
| 50 | 1 |
'''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = (EulerDiscreteScheduler,)
_UpperCamelCase = 10
def UpperCamelCase_ ( self ,**_lowerCAmelCase ):
lowerCamelCase__ = {
"""num_train_timesteps""": 11_00,
"""beta_start""": 0.0001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
}
config.update(**_lowerCAmelCase )
return config
def UpperCamelCase_ ( self ):
for timesteps in [10, 50, 1_00, 10_00]:
self.check_over_configs(num_train_timesteps=_lowerCAmelCase )
def UpperCamelCase_ ( self ):
for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] ,[0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=_lowerCAmelCase ,beta_end=_lowerCAmelCase )
def UpperCamelCase_ ( self ):
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=_lowerCAmelCase )
def UpperCamelCase_ ( self ):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=_lowerCAmelCase )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.scheduler_classes[0]
lowerCamelCase__ = self.get_scheduler_config()
lowerCamelCase__ = scheduler_class(**_lowerCAmelCase )
scheduler.set_timesteps(self.num_inference_steps )
lowerCamelCase__ = torch.manual_seed(0 )
lowerCamelCase__ = self.dummy_model()
lowerCamelCase__ = self.dummy_sample_deter * scheduler.init_noise_sigma
lowerCamelCase__ = sample.to(_lowerCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
lowerCamelCase__ = scheduler.scale_model_input(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = model(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = scheduler.step(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,generator=_lowerCAmelCase )
lowerCamelCase__ = output.prev_sample
lowerCamelCase__ = torch.sum(torch.abs(_lowerCAmelCase ) )
lowerCamelCase__ = torch.mean(torch.abs(_lowerCAmelCase ) )
assert abs(result_sum.item() - 10.0807 ) < 1E-2
assert abs(result_mean.item() - 0.0131 ) < 1E-3
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.scheduler_classes[0]
lowerCamelCase__ = self.get_scheduler_config(prediction_type="""v_prediction""" )
lowerCamelCase__ = scheduler_class(**_lowerCAmelCase )
scheduler.set_timesteps(self.num_inference_steps )
lowerCamelCase__ = torch.manual_seed(0 )
lowerCamelCase__ = self.dummy_model()
lowerCamelCase__ = self.dummy_sample_deter * scheduler.init_noise_sigma
lowerCamelCase__ = sample.to(_lowerCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
lowerCamelCase__ = scheduler.scale_model_input(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = model(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = scheduler.step(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,generator=_lowerCAmelCase )
lowerCamelCase__ = output.prev_sample
lowerCamelCase__ = torch.sum(torch.abs(_lowerCAmelCase ) )
lowerCamelCase__ = torch.mean(torch.abs(_lowerCAmelCase ) )
assert abs(result_sum.item() - 0.0002 ) < 1E-2
assert abs(result_mean.item() - 2.2676E-06 ) < 1E-3
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.scheduler_classes[0]
lowerCamelCase__ = self.get_scheduler_config()
lowerCamelCase__ = scheduler_class(**_lowerCAmelCase )
scheduler.set_timesteps(self.num_inference_steps ,device=_lowerCAmelCase )
lowerCamelCase__ = torch.manual_seed(0 )
lowerCamelCase__ = self.dummy_model()
lowerCamelCase__ = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
lowerCamelCase__ = sample.to(_lowerCAmelCase )
for t in scheduler.timesteps:
lowerCamelCase__ = scheduler.scale_model_input(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = model(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = scheduler.step(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,generator=_lowerCAmelCase )
lowerCamelCase__ = output.prev_sample
lowerCamelCase__ = torch.sum(torch.abs(_lowerCAmelCase ) )
lowerCamelCase__ = torch.mean(torch.abs(_lowerCAmelCase ) )
assert abs(result_sum.item() - 10.0807 ) < 1E-2
assert abs(result_mean.item() - 0.0131 ) < 1E-3
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.scheduler_classes[0]
lowerCamelCase__ = self.get_scheduler_config()
lowerCamelCase__ = scheduler_class(**_lowerCAmelCase ,use_karras_sigmas=_lowerCAmelCase )
scheduler.set_timesteps(self.num_inference_steps ,device=_lowerCAmelCase )
lowerCamelCase__ = torch.manual_seed(0 )
lowerCamelCase__ = self.dummy_model()
lowerCamelCase__ = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
lowerCamelCase__ = sample.to(_lowerCAmelCase )
for t in scheduler.timesteps:
lowerCamelCase__ = scheduler.scale_model_input(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = model(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = scheduler.step(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,generator=_lowerCAmelCase )
lowerCamelCase__ = output.prev_sample
lowerCamelCase__ = torch.sum(torch.abs(_lowerCAmelCase ) )
lowerCamelCase__ = torch.mean(torch.abs(_lowerCAmelCase ) )
assert abs(result_sum.item() - 124.52_2994_9951_1719 ) < 1E-2
assert abs(result_mean.item() - 0.1_6213_9326_3339_9963 ) < 1E-3
| 50 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase : Tuple = logging.get_logger(__name__)
UpperCamelCase : Union[str, Any] = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = 'gpt_bigcode'
_UpperCamelCase = ['past_key_values']
_UpperCamelCase = {
'hidden_size': 'n_embd',
'max_position_embeddings': 'n_positions',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self ,_lowerCAmelCase=5_02_57 ,_lowerCAmelCase=10_24 ,_lowerCAmelCase=7_68 ,_lowerCAmelCase=12 ,_lowerCAmelCase=12 ,_lowerCAmelCase=None ,_lowerCAmelCase="gelu_pytorch_tanh" ,_lowerCAmelCase=0.1 ,_lowerCAmelCase=0.1 ,_lowerCAmelCase=0.1 ,_lowerCAmelCase=1E-5 ,_lowerCAmelCase=0.02 ,_lowerCAmelCase=True ,_lowerCAmelCase=True ,_lowerCAmelCase=5_02_56 ,_lowerCAmelCase=5_02_56 ,_lowerCAmelCase=True ,_lowerCAmelCase=True ,_lowerCAmelCase=True ,**_lowerCAmelCase ,):
lowerCamelCase__ = vocab_size
lowerCamelCase__ = n_positions
lowerCamelCase__ = n_embd
lowerCamelCase__ = n_layer
lowerCamelCase__ = n_head
lowerCamelCase__ = n_inner
lowerCamelCase__ = activation_function
lowerCamelCase__ = resid_pdrop
lowerCamelCase__ = embd_pdrop
lowerCamelCase__ = attn_pdrop
lowerCamelCase__ = layer_norm_epsilon
lowerCamelCase__ = initializer_range
lowerCamelCase__ = scale_attn_weights
lowerCamelCase__ = use_cache
lowerCamelCase__ = attention_softmax_in_fpaa
lowerCamelCase__ = scale_attention_softmax_in_fpaa
lowerCamelCase__ = multi_query
lowerCamelCase__ = bos_token_id
lowerCamelCase__ = eos_token_id
super().__init__(bos_token_id=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase ,**_lowerCAmelCase )
| 50 | 1 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : str , __lowerCAmelCase : bool = False ):
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
lowerCamelCase__ = F'''Expected string as input, found {type(__lowerCAmelCase )}'''
raise ValueError(__lowerCAmelCase )
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
lowerCamelCase__ = F'''Expected boolean as use_pascal parameter, found {type(__lowerCAmelCase )}'''
raise ValueError(__lowerCAmelCase )
lowerCamelCase__ = input_str.split("""_""" )
lowerCamelCase__ = 0 if use_pascal else 1
lowerCamelCase__ = words[start_index:]
lowerCamelCase__ = [word[0].upper() + word[1:] for word in words_to_capitalize]
lowerCamelCase__ = """""" if use_pascal else words[0]
return "".join([initial_word, *capitalized_words] )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 50 |
'''simple docstring'''
from PIL import Image
def A__ ( __lowerCAmelCase : Image , __lowerCAmelCase : float ):
def brightness(__lowerCAmelCase : int ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError("""level must be between -255.0 (black) and 255.0 (white)""" )
return img.point(__lowerCAmelCase )
if __name__ == "__main__":
# Load image
with Image.open('image_data/lena.jpg') as img:
# Change brightness to 100
UpperCamelCase : Union[str, Any] = change_brightness(img, 1_00)
brigt_img.save('image_data/lena_brightness.png', format='png')
| 50 | 1 |
'''simple docstring'''
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
UpperCamelCase : Dict = [
# tf -> hf
('/', '.'),
('layer_', 'layers.'),
('kernel', 'weight'),
('beta', 'bias'),
('gamma', 'weight'),
('pegasus', 'model'),
]
UpperCamelCase : Union[str, Any] = [
('.output.dense', '.fc2'),
('intermediate.LayerNorm', 'final_layer_norm'),
('intermediate.dense', 'fc1'),
]
UpperCamelCase : Optional[int] = (
INIT_COMMON
+ [
('attention.self.LayerNorm', 'self_attn_layer_norm'),
('attention.output.dense', 'self_attn.out_proj'),
('attention.self', 'self_attn'),
('attention.encdec.LayerNorm', 'encoder_attn_layer_norm'),
('attention.encdec_output.dense', 'encoder_attn.out_proj'),
('attention.encdec', 'encoder_attn'),
('key', 'k_proj'),
('value', 'v_proj'),
('query', 'q_proj'),
('decoder.LayerNorm', 'decoder.layernorm_embedding'),
]
+ END_COMMON
)
UpperCamelCase : Dict = (
INIT_COMMON
+ [
('embeddings.word_embeddings', 'shared.weight'),
('embeddings.position_embeddings', 'embed_positions.weight'),
('attention.self.LayerNorm', 'self_attn_layer_norm'),
('attention.output.dense', 'self_attn.output'),
('attention.self', 'self_attn.self'),
('encoder.LayerNorm', 'encoder.layernorm_embedding'),
]
+ END_COMMON
)
UpperCamelCase : List[str] = [
'encdec/key/bias',
'encdec/query/bias',
'encdec/value/bias',
'self/key/bias',
'self/query/bias',
'self/value/bias',
'encdec_output/dense/bias',
'attention/output/dense/bias',
]
def A__ ( __lowerCAmelCase : str , __lowerCAmelCase : str ):
for tf_name, hf_name in patterns:
lowerCamelCase__ = k.replace(__lowerCAmelCase , __lowerCAmelCase )
return k
def A__ ( __lowerCAmelCase : dict , __lowerCAmelCase : dict ):
lowerCamelCase__ = BigBirdPegasusConfig(**__lowerCAmelCase )
lowerCamelCase__ = BigBirdPegasusForConditionalGeneration(__lowerCAmelCase )
lowerCamelCase__ = torch_model.state_dict()
lowerCamelCase__ = {}
# separating decoder weights
lowerCamelCase__ = {k: tf_weights[k] for k in tf_weights if k.startswith("""pegasus/decoder""" )}
lowerCamelCase__ = {k: tf_weights[k] for k in tf_weights if not k.startswith("""pegasus/decoder""" )}
for k, v in tqdm(decoder_weights.items() , """tf -> hf conversion""" ):
lowerCamelCase__ = [k.endswith(__lowerCAmelCase ) for ending in KEYS_TO_IGNORE]
if any(__lowerCAmelCase ):
continue
lowerCamelCase__ = DECODER_PATTERNS
lowerCamelCase__ = rename_state_dict_key(__lowerCAmelCase , __lowerCAmelCase )
if new_k not in state_dict:
raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})''' )
if any(True if i in k else False for i in ["""dense""", """query""", """key""", """value"""] ):
lowerCamelCase__ = v.T
lowerCamelCase__ = torch.from_numpy(__lowerCAmelCase )
assert v.shape == state_dict[new_k].shape, F'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''
for k, v in tqdm(remaining_weights.items() , """tf -> hf conversion""" ):
lowerCamelCase__ = [k.endswith(__lowerCAmelCase ) for ending in KEYS_TO_IGNORE]
if any(__lowerCAmelCase ):
continue
lowerCamelCase__ = REMAINING_PATTERNS
lowerCamelCase__ = rename_state_dict_key(__lowerCAmelCase , __lowerCAmelCase )
if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})''' )
if any(True if i in k else False for i in ["""dense""", """query""", """key""", """value"""] ):
lowerCamelCase__ = v.T
lowerCamelCase__ = torch.from_numpy(__lowerCAmelCase )
if k != "pegasus/embeddings/position_embeddings":
assert v.shape == state_dict[new_k].shape, F'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''
lowerCamelCase__ = mapping["""model.embed_positions.weight"""]
lowerCamelCase__ = mapping.pop("""model.embed_positions.weight""" )
lowerCamelCase__ , lowerCamelCase__ = torch_model.load_state_dict(__lowerCAmelCase , strict=__lowerCAmelCase )
lowerCamelCase__ = [
k
for k in missing
if k
not in [
"""final_logits_bias""",
"""model.encoder.embed_tokens.weight""",
"""model.decoder.embed_tokens.weight""",
"""lm_head.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 A__ ( __lowerCAmelCase : List[Any] ):
lowerCamelCase__ = tf.train.list_variables(__lowerCAmelCase )
lowerCamelCase__ = {}
lowerCamelCase__ = ["""global_step"""]
for name, shape in tqdm(__lowerCAmelCase , desc="""converting tf checkpoint to dict""" ):
lowerCamelCase__ = any(pat in name for pat in ignore_name )
if skip_key:
continue
lowerCamelCase__ = tf.train.load_variable(__lowerCAmelCase , __lowerCAmelCase )
lowerCamelCase__ = array
return tf_weights
def A__ ( __lowerCAmelCase : str , __lowerCAmelCase : str , __lowerCAmelCase : dict ):
lowerCamelCase__ = get_tf_weights_as_numpy(__lowerCAmelCase )
lowerCamelCase__ = convert_bigbird_pegasus(__lowerCAmelCase , __lowerCAmelCase )
torch_model.save_pretrained(__lowerCAmelCase )
if __name__ == "__main__":
UpperCamelCase : Any = argparse.ArgumentParser()
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.')
UpperCamelCase : Union[str, Any] = parser.parse_args()
UpperCamelCase : List[Any] = {}
convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)
| 50 |
'''simple docstring'''
def A__ ( ):
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
UpperCamelCase : Dict = generate_large_matrix()
UpperCamelCase : Any = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7, 7, 6], [-1, -2, -3]],
grid,
)
def A__ ( __lowerCAmelCase : list[list[int]] ):
assert all(row == sorted(__lowerCAmelCase , reverse=__lowerCAmelCase ) for row in grid )
assert all(list(__lowerCAmelCase ) == sorted(__lowerCAmelCase , reverse=__lowerCAmelCase ) for col in zip(*__lowerCAmelCase ) )
def A__ ( __lowerCAmelCase : list[int] ):
lowerCamelCase__ = 0
lowerCamelCase__ = len(__lowerCAmelCase ) - 1
# Edge cases such as no values or all numbers are negative.
if not array or array[0] < 0:
return 0
while right + 1 > left:
lowerCamelCase__ = (left + right) // 2
lowerCamelCase__ = array[mid]
# Num must be negative and the index must be greater than or equal to 0.
if num < 0 and array[mid - 1] >= 0:
return mid
if num >= 0:
lowerCamelCase__ = mid + 1
else:
lowerCamelCase__ = mid - 1
# No negative numbers so return the last index of the array + 1 which is the length.
return len(__lowerCAmelCase )
def A__ ( __lowerCAmelCase : list[list[int]] ):
lowerCamelCase__ = 0
lowerCamelCase__ = len(grid[0] )
for i in range(len(__lowerCAmelCase ) ):
lowerCamelCase__ = find_negative_index(grid[i][:bound] )
total += bound
return (len(__lowerCAmelCase ) * len(grid[0] )) - total
def A__ ( __lowerCAmelCase : list[list[int]] ):
return len([number for row in grid for number in row if number < 0] )
def A__ ( __lowerCAmelCase : list[list[int]] ):
lowerCamelCase__ = 0
for row in grid:
for i, number in enumerate(__lowerCAmelCase ):
if number < 0:
total += len(__lowerCAmelCase ) - i
break
return total
def A__ ( ):
from timeit import timeit
print("""Running benchmarks""" )
lowerCamelCase__ = (
"""from __main__ import count_negatives_binary_search, """
"""count_negatives_brute_force, count_negatives_brute_force_with_break, grid"""
)
for func in (
"count_negatives_binary_search", # took 0.7727 seconds
"count_negatives_brute_force_with_break", # took 4.6505 seconds
"count_negatives_brute_force", # took 12.8160 seconds
):
lowerCamelCase__ = timeit(F'''{func}(grid=grid)''' , setup=__lowerCAmelCase , number=500 )
print(F'''{func}() took {time:0.4f} seconds''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 50 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase : List[str] = logging.get_logger(__name__)
UpperCamelCase : Optional[int] = {
'facebook/s2t-small-librispeech-asr': (
'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'
),
# See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = 'speech_to_text'
_UpperCamelCase = ['past_key_values']
_UpperCamelCase = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self ,_lowerCAmelCase=1_00_00 ,_lowerCAmelCase=12 ,_lowerCAmelCase=20_48 ,_lowerCAmelCase=4 ,_lowerCAmelCase=6 ,_lowerCAmelCase=20_48 ,_lowerCAmelCase=4 ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=True ,_lowerCAmelCase=True ,_lowerCAmelCase="relu" ,_lowerCAmelCase=2_56 ,_lowerCAmelCase=0.1 ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=0.02 ,_lowerCAmelCase=2 ,_lowerCAmelCase=True ,_lowerCAmelCase=1 ,_lowerCAmelCase=0 ,_lowerCAmelCase=2 ,_lowerCAmelCase=60_00 ,_lowerCAmelCase=10_24 ,_lowerCAmelCase=2 ,_lowerCAmelCase=(5, 5) ,_lowerCAmelCase=10_24 ,_lowerCAmelCase=80 ,_lowerCAmelCase=1 ,**_lowerCAmelCase ,):
lowerCamelCase__ = vocab_size
lowerCamelCase__ = d_model
lowerCamelCase__ = encoder_ffn_dim
lowerCamelCase__ = encoder_layers
lowerCamelCase__ = encoder_attention_heads
lowerCamelCase__ = decoder_ffn_dim
lowerCamelCase__ = decoder_layers
lowerCamelCase__ = decoder_attention_heads
lowerCamelCase__ = dropout
lowerCamelCase__ = attention_dropout
lowerCamelCase__ = activation_dropout
lowerCamelCase__ = activation_function
lowerCamelCase__ = init_std
lowerCamelCase__ = encoder_layerdrop
lowerCamelCase__ = decoder_layerdrop
lowerCamelCase__ = use_cache
lowerCamelCase__ = encoder_layers
lowerCamelCase__ = scale_embedding # scale factor will be sqrt(d_model) if True
lowerCamelCase__ = max_source_positions
lowerCamelCase__ = max_target_positions
lowerCamelCase__ = num_conv_layers
lowerCamelCase__ = list(_lowerCAmelCase )
lowerCamelCase__ = conv_channels
lowerCamelCase__ = input_feat_per_channel
lowerCamelCase__ = input_channels
if len(self.conv_kernel_sizes ) != self.num_conv_layers:
raise ValueError(
"""Configuration for convolutional module is incorrect. """
"""It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` """
F'''but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes )}`, '''
F'''`config.num_conv_layers = {self.num_conv_layers}`.''' )
super().__init__(
pad_token_id=_lowerCAmelCase ,bos_token_id=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase ,is_encoder_decoder=_lowerCAmelCase ,decoder_start_token_id=_lowerCAmelCase ,**_lowerCAmelCase ,)
| 50 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
UpperCamelCase : List[Any] = 'examples/'
UpperCamelCase : int = {
'examples': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
'init': (re.compile(r'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'),
'setup': (re.compile(r'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), r'\1version="VERSION",'),
'doc': (re.compile(r'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), 'release = "VERSION"\n'),
}
UpperCamelCase : Any = {
'init': 'src/transformers/__init__.py',
'setup': 'setup.py',
}
UpperCamelCase : Any = 'README.md'
def A__ ( __lowerCAmelCase : Any , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[int] ):
with open(__lowerCAmelCase , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
lowerCamelCase__ = f.read()
lowerCamelCase__ , lowerCamelCase__ = REPLACE_PATTERNS[pattern]
lowerCamelCase__ = replace.replace("""VERSION""" , __lowerCAmelCase )
lowerCamelCase__ = re_pattern.sub(__lowerCAmelCase , __lowerCAmelCase )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.write(__lowerCAmelCase )
def A__ ( __lowerCAmelCase : str ):
for folder, directories, fnames in os.walk(__lowerCAmelCase ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove("""research_projects""" )
if "legacy" in directories:
directories.remove("""legacy""" )
for fname in fnames:
if fname.endswith(""".py""" ):
update_version_in_file(os.path.join(__lowerCAmelCase , __lowerCAmelCase ) , __lowerCAmelCase , pattern="""examples""" )
def A__ ( __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[Any]=False ):
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
if not patch:
update_version_in_examples(__lowerCAmelCase )
def A__ ( ):
lowerCamelCase__ = """🤗 Transformers currently provides the following architectures"""
lowerCamelCase__ = """1. Want to contribute a new model?"""
with open(__lowerCAmelCase , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
lowerCamelCase__ = f.readlines()
# Find the start of the list.
lowerCamelCase__ = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
lowerCamelCase__ = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith("""1.""" ):
lowerCamelCase__ = lines[index].replace(
"""https://huggingface.co/docs/transformers/main/model_doc""" , """https://huggingface.co/docs/transformers/model_doc""" , )
index += 1
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.writelines(__lowerCAmelCase )
def A__ ( ):
with open(REPLACE_FILES["""init"""] , """r""" ) as f:
lowerCamelCase__ = f.read()
lowerCamelCase__ = REPLACE_PATTERNS["""init"""][0].search(__lowerCAmelCase ).groups()[0]
return packaging.version.parse(__lowerCAmelCase )
def A__ ( __lowerCAmelCase : Union[str, Any]=False ):
lowerCamelCase__ = get_version()
if patch and default_version.is_devrelease:
raise ValueError("""Can't create a patch version from the dev branch, checkout a released version!""" )
if default_version.is_devrelease:
lowerCamelCase__ = default_version.base_version
elif patch:
lowerCamelCase__ = F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
else:
lowerCamelCase__ = F'''{default_version.major}.{default_version.minor + 1}.0'''
# Now let's ask nicely if that's the right one.
lowerCamelCase__ = input(F'''Which version are you releasing? [{default_version}]''' )
if len(__lowerCAmelCase ) == 0:
lowerCamelCase__ = default_version
print(F'''Updating version to {version}.''' )
global_version_update(__lowerCAmelCase , patch=__lowerCAmelCase )
if not patch:
print("""Cleaning main README, don't forget to run `make fix-copies`.""" )
clean_main_ref_in_model_list()
def A__ ( ):
lowerCamelCase__ = get_version()
lowerCamelCase__ = F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
lowerCamelCase__ = current_version.base_version
# Check with the user we got that right.
lowerCamelCase__ = input(F'''Which version are we developing now? [{dev_version}]''' )
if len(__lowerCAmelCase ) == 0:
lowerCamelCase__ = dev_version
print(F'''Updating version to {version}.''' )
global_version_update(__lowerCAmelCase )
print("""Cleaning main README, don't forget to run `make fix-copies`.""" )
clean_main_ref_in_model_list()
if __name__ == "__main__":
UpperCamelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')
parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')
UpperCamelCase : Any = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print('Nothing to do after a patch :-)')
else:
post_release_work()
| 50 | 1 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = ['image_processor', 'tokenizer']
_UpperCamelCase = 'ViTImageProcessor'
_UpperCamelCase = ('CLIPTokenizer', 'CLIPTokenizerFast')
def __init__( self ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,**_lowerCAmelCase ):
lowerCamelCase__ = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" ,_lowerCAmelCase ,)
lowerCamelCase__ = kwargs.pop("""feature_extractor""" )
lowerCamelCase__ = 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__(_lowerCAmelCase ,_lowerCAmelCase )
def __call__( self ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,**_lowerCAmelCase ):
if text is None and visual_prompt is None and images is None:
raise ValueError("""You have to specify either text, visual prompt or images.""" )
if text is not None and visual_prompt is not None:
raise ValueError("""You have to specify exactly one type of prompt. Either text or visual prompt.""" )
if text is not None:
lowerCamelCase__ = self.tokenizer(_lowerCAmelCase ,return_tensors=_lowerCAmelCase ,**_lowerCAmelCase )
if visual_prompt is not None:
lowerCamelCase__ = self.image_processor(_lowerCAmelCase ,return_tensors=_lowerCAmelCase ,**_lowerCAmelCase )
if images is not None:
lowerCamelCase__ = self.image_processor(_lowerCAmelCase ,return_tensors=_lowerCAmelCase ,**_lowerCAmelCase )
if visual_prompt is not None and images is not None:
lowerCamelCase__ = {
"""pixel_values""": image_features.pixel_values,
"""conditional_pixel_values""": prompt_features.pixel_values,
}
return encoding
elif text is not None and images is not None:
lowerCamelCase__ = image_features.pixel_values
return encoding
elif text is not None:
return encoding
elif visual_prompt is not None:
lowerCamelCase__ = {
"""conditional_pixel_values""": prompt_features.pixel_values,
}
return encoding
else:
return BatchEncoding(data=dict(**_lowerCAmelCase ) ,tensor_type=_lowerCAmelCase )
def UpperCamelCase_ ( self ,*_lowerCAmelCase ,**_lowerCAmelCase ):
return self.tokenizer.batch_decode(*_lowerCAmelCase ,**_lowerCAmelCase )
def UpperCamelCase_ ( self ,*_lowerCAmelCase ,**_lowerCAmelCase ):
return self.tokenizer.decode(*_lowerCAmelCase ,**_lowerCAmelCase )
@property
def UpperCamelCase_ ( self ):
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" ,_lowerCAmelCase ,)
return self.image_processor_class
@property
def UpperCamelCase_ ( self ):
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" ,_lowerCAmelCase ,)
return self.image_processor
| 50 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
UpperCamelCase : List[str] = logging.get_logger(__name__)
UpperCamelCase : Dict = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
UpperCamelCase : int = {
'vocab_file': {
'squeezebert/squeezebert-uncased': (
'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'
),
'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt',
'squeezebert/squeezebert-mnli-headless': (
'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'squeezebert/squeezebert-uncased': (
'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'
),
'squeezebert/squeezebert-mnli': (
'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'
),
'squeezebert/squeezebert-mnli-headless': (
'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'
),
},
}
UpperCamelCase : Tuple = {
'squeezebert/squeezebert-uncased': 5_12,
'squeezebert/squeezebert-mnli': 5_12,
'squeezebert/squeezebert-mnli-headless': 5_12,
}
UpperCamelCase : Dict = {
'squeezebert/squeezebert-uncased': {'do_lower_case': True},
'squeezebert/squeezebert-mnli': {'do_lower_case': True},
'squeezebert/squeezebert-mnli-headless': {'do_lower_case': True},
}
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = VOCAB_FILES_NAMES
_UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
_UpperCamelCase = PRETRAINED_INIT_CONFIGURATION
_UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCamelCase = SqueezeBertTokenizer
def __init__( self ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=True ,_lowerCAmelCase="[UNK]" ,_lowerCAmelCase="[SEP]" ,_lowerCAmelCase="[PAD]" ,_lowerCAmelCase="[CLS]" ,_lowerCAmelCase="[MASK]" ,_lowerCAmelCase=True ,_lowerCAmelCase=None ,**_lowerCAmelCase ,):
super().__init__(
_lowerCAmelCase ,tokenizer_file=_lowerCAmelCase ,do_lower_case=_lowerCAmelCase ,unk_token=_lowerCAmelCase ,sep_token=_lowerCAmelCase ,pad_token=_lowerCAmelCase ,cls_token=_lowerCAmelCase ,mask_token=_lowerCAmelCase ,tokenize_chinese_chars=_lowerCAmelCase ,strip_accents=_lowerCAmelCase ,**_lowerCAmelCase ,)
lowerCamelCase__ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("""lowercase""" ,_lowerCAmelCase ) != do_lower_case
or normalizer_state.get("""strip_accents""" ,_lowerCAmelCase ) != strip_accents
or normalizer_state.get("""handle_chinese_chars""" ,_lowerCAmelCase ) != tokenize_chinese_chars
):
lowerCamelCase__ = getattr(_lowerCAmelCase ,normalizer_state.pop("""type""" ) )
lowerCamelCase__ = do_lower_case
lowerCamelCase__ = strip_accents
lowerCamelCase__ = tokenize_chinese_chars
lowerCamelCase__ = normalizer_class(**_lowerCAmelCase )
lowerCamelCase__ = do_lower_case
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase=None ):
lowerCamelCase__ = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase = None ):
lowerCamelCase__ = [self.sep_token_id]
lowerCamelCase__ = [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 UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase = None ):
lowerCamelCase__ = self._tokenizer.model.save(_lowerCAmelCase ,name=_lowerCAmelCase )
return tuple(_lowerCAmelCase )
| 50 | 1 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ):
return number | (1 << position)
def A__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ):
return number & ~(1 << position)
def A__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ):
return number ^ (1 << position)
def A__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ):
return ((number >> position) & 1) == 1
def A__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ):
return int((number & (1 << position)) != 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 50 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def A__ ( __lowerCAmelCase : Any ):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0x4_e_0_0 and cp <= 0x9_f_f_f)
or (cp >= 0x3_4_0_0 and cp <= 0x4_d_b_f) #
or (cp >= 0x2_0_0_0_0 and cp <= 0x2_a_6_d_f) #
or (cp >= 0x2_a_7_0_0 and cp <= 0x2_b_7_3_f) #
or (cp >= 0x2_b_7_4_0 and cp <= 0x2_b_8_1_f) #
or (cp >= 0x2_b_8_2_0 and cp <= 0x2_c_e_a_f) #
or (cp >= 0xf_9_0_0 and cp <= 0xf_a_f_f)
or (cp >= 0x2_f_8_0_0 and cp <= 0x2_f_a_1_f) #
): #
return True
return False
def A__ ( __lowerCAmelCase : str ):
# word like '180' or '身高' or '神'
for char in word:
lowerCamelCase__ = ord(__lowerCAmelCase )
if not _is_chinese_char(__lowerCAmelCase ):
return 0
return 1
def A__ ( __lowerCAmelCase : List[str] ):
lowerCamelCase__ = set()
for token in tokens:
lowerCamelCase__ = len(__lowerCAmelCase ) > 1 and is_chinese(__lowerCAmelCase )
if chinese_word:
word_set.add(__lowerCAmelCase )
lowerCamelCase__ = list(__lowerCAmelCase )
return word_list
def A__ ( __lowerCAmelCase : List[str] , __lowerCAmelCase : set() ):
if not chinese_word_set:
return bert_tokens
lowerCamelCase__ = max([len(__lowerCAmelCase ) for w in chinese_word_set] )
lowerCamelCase__ = bert_tokens
lowerCamelCase__ , lowerCamelCase__ = 0, len(__lowerCAmelCase )
while start < end:
lowerCamelCase__ = True
if is_chinese(bert_word[start] ):
lowerCamelCase__ = min(end - start , __lowerCAmelCase )
for i in range(__lowerCAmelCase , 1 , -1 ):
lowerCamelCase__ = """""".join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1 , start + i ):
lowerCamelCase__ = """##""" + bert_word[j]
lowerCamelCase__ = start + i
lowerCamelCase__ = False
break
if single_word:
start += 1
return bert_word
def A__ ( __lowerCAmelCase : List[str] , __lowerCAmelCase : LTP , __lowerCAmelCase : BertTokenizer ):
lowerCamelCase__ = []
for i in range(0 , len(__lowerCAmelCase ) , 100 ):
lowerCamelCase__ = ltp_tokenizer.pipeline(lines[i : i + 100] , tasks=["""cws"""] ).cws
lowerCamelCase__ = [get_chinese_word(__lowerCAmelCase ) for r in res]
ltp_res.extend(__lowerCAmelCase )
assert len(__lowerCAmelCase ) == len(__lowerCAmelCase )
lowerCamelCase__ = []
for i in range(0 , len(__lowerCAmelCase ) , 100 ):
lowerCamelCase__ = bert_tokenizer(lines[i : i + 100] , add_special_tokens=__lowerCAmelCase , truncation=__lowerCAmelCase , max_length=512 )
bert_res.extend(res["""input_ids"""] )
assert len(__lowerCAmelCase ) == len(__lowerCAmelCase )
lowerCamelCase__ = []
for input_ids, chinese_word in zip(__lowerCAmelCase , __lowerCAmelCase ):
lowerCamelCase__ = []
for id in input_ids:
lowerCamelCase__ = bert_tokenizer._convert_id_to_token(__lowerCAmelCase )
input_tokens.append(__lowerCAmelCase )
lowerCamelCase__ = add_sub_symbol(__lowerCAmelCase , __lowerCAmelCase )
lowerCamelCase__ = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(__lowerCAmelCase ):
if token[:2] == "##":
lowerCamelCase__ = token[2:]
# save chinese tokens' pos
if len(__lowerCAmelCase ) == 1 and _is_chinese_char(ord(__lowerCAmelCase ) ):
ref_id.append(__lowerCAmelCase )
ref_ids.append(__lowerCAmelCase )
assert len(__lowerCAmelCase ) == len(__lowerCAmelCase )
return ref_ids
def A__ ( __lowerCAmelCase : Optional[int] ):
# For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
# If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
with open(args.file_name , """r""" , encoding="""utf-8""" ) as f:
lowerCamelCase__ = f.readlines()
lowerCamelCase__ = [line.strip() for line in data if len(__lowerCAmelCase ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
lowerCamelCase__ = LTP(args.ltp ) # faster in GPU device
lowerCamelCase__ = BertTokenizer.from_pretrained(args.bert )
lowerCamelCase__ = prepare_ref(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
with open(args.save_path , """w""" , encoding="""utf-8""" ) as f:
lowerCamelCase__ = [json.dumps(__lowerCAmelCase ) + """\n""" for ref in ref_ids]
f.writelines(__lowerCAmelCase )
if __name__ == "__main__":
UpperCamelCase : Optional[int] = argparse.ArgumentParser(description='prepare_chinese_ref')
parser.add_argument(
'--file_name',
required=False,
type=str,
default='./resources/chinese-demo.txt',
help='file need process, same as training data in lm',
)
parser.add_argument(
'--ltp',
required=False,
type=str,
default='./resources/ltp',
help='resources for LTP tokenizer, usually a path',
)
parser.add_argument(
'--bert',
required=False,
type=str,
default='./resources/robert',
help='resources for Bert tokenizer',
)
parser.add_argument(
'--save_path',
required=False,
type=str,
default='./resources/ref.txt',
help='path to save res',
)
UpperCamelCase : Any = parser.parse_args()
main(args)
| 50 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase : List[Any] = logging.get_logger(__name__)
UpperCamelCase : str = '▁'
UpperCamelCase : Union[str, Any] = {'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}
UpperCamelCase : Optional[Any] = {
'vocab_file': {
'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model',
},
'monolingual_vocab_file': {
'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt',
},
}
UpperCamelCase : str = {'vinai/bartpho-syllable': 10_24}
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = VOCAB_FILES_NAMES
_UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
_UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCamelCase = ['input_ids', 'attention_mask']
def __init__( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase="<s>" ,_lowerCAmelCase="</s>" ,_lowerCAmelCase="</s>" ,_lowerCAmelCase="<s>" ,_lowerCAmelCase="<unk>" ,_lowerCAmelCase="<pad>" ,_lowerCAmelCase="<mask>" ,_lowerCAmelCase = None ,**_lowerCAmelCase ,):
# Mask token behave like a normal word, i.e. include the space before it
lowerCamelCase__ = AddedToken(_lowerCAmelCase ,lstrip=_lowerCAmelCase ,rstrip=_lowerCAmelCase ) if isinstance(_lowerCAmelCase ,_lowerCAmelCase ) else mask_token
lowerCamelCase__ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_lowerCAmelCase ,eos_token=_lowerCAmelCase ,unk_token=_lowerCAmelCase ,sep_token=_lowerCAmelCase ,cls_token=_lowerCAmelCase ,pad_token=_lowerCAmelCase ,mask_token=_lowerCAmelCase ,sp_model_kwargs=self.sp_model_kwargs ,**_lowerCAmelCase ,)
lowerCamelCase__ = vocab_file
lowerCamelCase__ = monolingual_vocab_file
lowerCamelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(_lowerCAmelCase ) )
# Load the reduced vocab
# Keep order of special tokens for backward compatibility
lowerCamelCase__ = {}
lowerCamelCase__ = 0
for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
if str(_lowerCAmelCase ) not in self.fairseq_tokens_to_ids:
lowerCamelCase__ = cnt
cnt += 1
with open(_lowerCAmelCase ,"""r""" ,encoding="""utf-8""" ) as f:
for line in f.readlines():
lowerCamelCase__ = line.strip().split()[0]
lowerCamelCase__ = len(self.fairseq_tokens_to_ids )
if str(_lowerCAmelCase ) not in self.fairseq_tokens_to_ids:
lowerCamelCase__ = len(self.fairseq_tokens_to_ids )
lowerCamelCase__ = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self ):
lowerCamelCase__ = self.__dict__.copy()
lowerCamelCase__ = None
lowerCamelCase__ = self.sp_model.serialized_model_proto()
return state
def __setstate__( self ,_lowerCAmelCase ):
lowerCamelCase__ = d
# for backward compatibility
if not hasattr(self ,"""sp_model_kwargs""" ):
lowerCamelCase__ = {}
lowerCamelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowerCamelCase__ = [self.cls_token_id]
lowerCamelCase__ = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase = None ,_lowerCAmelCase = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCAmelCase ,token_ids_a=_lowerCAmelCase ,already_has_special_tokens=_lowerCAmelCase )
if token_ids_a is None:
return [1] + ([0] * len(_lowerCAmelCase )) + [1]
return [1] + ([0] * len(_lowerCAmelCase )) + [1, 1] + ([0] * len(_lowerCAmelCase )) + [1]
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase = None ):
lowerCamelCase__ = [self.sep_token_id]
lowerCamelCase__ = [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]
@property
def UpperCamelCase_ ( self ):
return len(self.fairseq_ids_to_tokens )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = {self.convert_ids_to_tokens(_lowerCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
return self.sp_model.encode(_lowerCAmelCase ,out_type=_lowerCAmelCase )
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
else:
return self.unk_token_id
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
return self.fairseq_ids_to_tokens[index]
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
lowerCamelCase__ = """""".join(_lowerCAmelCase ).replace(_lowerCAmelCase ,""" """ ).strip()
return out_string
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase = None ):
if not os.path.isdir(_lowerCAmelCase ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowerCamelCase__ = os.path.join(
_lowerCAmelCase ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
lowerCamelCase__ = os.path.join(
_lowerCAmelCase ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""monolingual_vocab_file"""] ,)
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file ,_lowerCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_lowerCAmelCase ,"""wb""" ) as fi:
lowerCamelCase__ = self.sp_model.serialized_model_proto()
fi.write(_lowerCAmelCase )
if os.path.abspath(self.monolingual_vocab_file ) != os.path.abspath(
_lowerCAmelCase ) and os.path.isfile(self.monolingual_vocab_file ):
copyfile(self.monolingual_vocab_file ,_lowerCAmelCase )
elif not os.path.isfile(self.monolingual_vocab_file ):
with open(_lowerCAmelCase ,"""w""" ,encoding="""utf-8""" ) as fp:
for token in self.fairseq_tokens_to_ids:
if token not in self.all_special_tokens:
fp.write(F'''{str(_lowerCAmelCase )} \n''' )
return out_vocab_file, out_monolingual_vocab_file
| 50 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase : Tuple = logging.get_logger(__name__)
def A__ ( __lowerCAmelCase : int ):
lowerCamelCase__ = DPTConfig(embedding_type="""hybrid""" )
if "large" in checkpoint_url:
lowerCamelCase__ = 1024
lowerCamelCase__ = 4096
lowerCamelCase__ = 24
lowerCamelCase__ = 16
lowerCamelCase__ = [5, 11, 17, 23]
lowerCamelCase__ = [256, 512, 1024, 1024]
lowerCamelCase__ = (1, 384, 384)
if "nyu" or "midas" in checkpoint_url:
lowerCamelCase__ = 768
lowerCamelCase__ = [1, 1, 1, 0.5]
lowerCamelCase__ = [256, 512, 768, 768]
lowerCamelCase__ = 150
lowerCamelCase__ = 16
lowerCamelCase__ = (1, 384, 384)
lowerCamelCase__ = False
lowerCamelCase__ = """project"""
if "ade" in checkpoint_url:
lowerCamelCase__ = True
lowerCamelCase__ = 768
lowerCamelCase__ = [1, 1, 1, 0.5]
lowerCamelCase__ = 150
lowerCamelCase__ = 16
lowerCamelCase__ = """huggingface/label-files"""
lowerCamelCase__ = """ade20k-id2label.json"""
lowerCamelCase__ = json.load(open(cached_download(hf_hub_url(__lowerCAmelCase , __lowerCAmelCase , repo_type="""dataset""" ) ) , """r""" ) )
lowerCamelCase__ = {int(__lowerCAmelCase ): v for k, v in idalabel.items()}
lowerCamelCase__ = idalabel
lowerCamelCase__ = {v: k for k, v in idalabel.items()}
lowerCamelCase__ = [1, 150, 480, 480]
return config, expected_shape
def A__ ( __lowerCAmelCase : Optional[int] ):
lowerCamelCase__ = ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
for k in ignore_keys:
state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
def A__ ( __lowerCAmelCase : List[Any] ):
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
lowerCamelCase__ = name.replace("""pretrained.model""" , """dpt.encoder""" )
if "pretrained.model" in name:
lowerCamelCase__ = name.replace("""pretrained.model""" , """dpt.embeddings""" )
if "patch_embed" in name:
lowerCamelCase__ = name.replace("""patch_embed""" , """""" )
if "pos_embed" in name:
lowerCamelCase__ = name.replace("""pos_embed""" , """position_embeddings""" )
if "attn.proj" in name:
lowerCamelCase__ = name.replace("""attn.proj""" , """attention.output.dense""" )
if "proj" in name and "project" not in name:
lowerCamelCase__ = name.replace("""proj""" , """projection""" )
if "blocks" in name:
lowerCamelCase__ = name.replace("""blocks""" , """layer""" )
if "mlp.fc1" in name:
lowerCamelCase__ = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
lowerCamelCase__ = name.replace("""mlp.fc2""" , """output.dense""" )
if "norm1" in name and "backbone" not in name:
lowerCamelCase__ = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name and "backbone" not in name:
lowerCamelCase__ = name.replace("""norm2""" , """layernorm_after""" )
if "scratch.output_conv" in name:
lowerCamelCase__ = name.replace("""scratch.output_conv""" , """head""" )
if "scratch" in name:
lowerCamelCase__ = name.replace("""scratch""" , """neck""" )
if "layer1_rn" in name:
lowerCamelCase__ = name.replace("""layer1_rn""" , """convs.0""" )
if "layer2_rn" in name:
lowerCamelCase__ = name.replace("""layer2_rn""" , """convs.1""" )
if "layer3_rn" in name:
lowerCamelCase__ = name.replace("""layer3_rn""" , """convs.2""" )
if "layer4_rn" in name:
lowerCamelCase__ = name.replace("""layer4_rn""" , """convs.3""" )
if "refinenet" in name:
lowerCamelCase__ = int(name[len("""neck.refinenet""" ) : len("""neck.refinenet""" ) + 1] )
# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
lowerCamelCase__ = name.replace(F'''refinenet{layer_idx}''' , F'''fusion_stage.layers.{abs(layer_idx-4 )}''' )
if "out_conv" in name:
lowerCamelCase__ = name.replace("""out_conv""" , """projection""" )
if "resConfUnit1" in name:
lowerCamelCase__ = name.replace("""resConfUnit1""" , """residual_layer1""" )
if "resConfUnit2" in name:
lowerCamelCase__ = name.replace("""resConfUnit2""" , """residual_layer2""" )
if "conv1" in name:
lowerCamelCase__ = name.replace("""conv1""" , """convolution1""" )
if "conv2" in name:
lowerCamelCase__ = name.replace("""conv2""" , """convolution2""" )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess1.0.project.0""" , """neck.reassemble_stage.readout_projects.0.0""" )
if "pretrained.act_postprocess2.0.project.0" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess2.0.project.0""" , """neck.reassemble_stage.readout_projects.1.0""" )
if "pretrained.act_postprocess3.0.project.0" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess3.0.project.0""" , """neck.reassemble_stage.readout_projects.2.0""" )
if "pretrained.act_postprocess4.0.project.0" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess4.0.project.0""" , """neck.reassemble_stage.readout_projects.3.0""" )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess1.3""" , """neck.reassemble_stage.layers.0.projection""" )
if "pretrained.act_postprocess1.4" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess1.4""" , """neck.reassemble_stage.layers.0.resize""" )
if "pretrained.act_postprocess2.3" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess2.3""" , """neck.reassemble_stage.layers.1.projection""" )
if "pretrained.act_postprocess2.4" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess2.4""" , """neck.reassemble_stage.layers.1.resize""" )
if "pretrained.act_postprocess3.3" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess3.3""" , """neck.reassemble_stage.layers.2.projection""" )
if "pretrained.act_postprocess4.3" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess4.3""" , """neck.reassemble_stage.layers.3.projection""" )
if "pretrained.act_postprocess4.4" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess4.4""" , """neck.reassemble_stage.layers.3.resize""" )
if "pretrained" in name:
lowerCamelCase__ = name.replace("""pretrained""" , """dpt""" )
if "bn" in name:
lowerCamelCase__ = name.replace("""bn""" , """batch_norm""" )
if "head" in name:
lowerCamelCase__ = name.replace("""head""" , """head.head""" )
if "encoder.norm" in name:
lowerCamelCase__ = name.replace("""encoder.norm""" , """layernorm""" )
if "auxlayer" in name:
lowerCamelCase__ = name.replace("""auxlayer""" , """auxiliary_head.head""" )
if "backbone" in name:
lowerCamelCase__ = name.replace("""backbone""" , """backbone.bit.encoder""" )
if ".." in name:
lowerCamelCase__ = name.replace("""..""" , """.""" )
if "stem.conv" in name:
lowerCamelCase__ = name.replace("""stem.conv""" , """bit.embedder.convolution""" )
if "blocks" in name:
lowerCamelCase__ = name.replace("""blocks""" , """layers""" )
if "convolution" in name and "backbone" in name:
lowerCamelCase__ = name.replace("""convolution""" , """conv""" )
if "layer" in name and "backbone" in name:
lowerCamelCase__ = name.replace("""layer""" , """layers""" )
if "backbone.bit.encoder.bit" in name:
lowerCamelCase__ = name.replace("""backbone.bit.encoder.bit""" , """backbone.bit""" )
if "embedder.conv" in name:
lowerCamelCase__ = name.replace("""embedder.conv""" , """embedder.convolution""" )
if "backbone.bit.encoder.stem.norm" in name:
lowerCamelCase__ = name.replace("""backbone.bit.encoder.stem.norm""" , """backbone.bit.embedder.norm""" )
return name
def A__ ( __lowerCAmelCase : str , __lowerCAmelCase : int ):
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowerCamelCase__ = state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight''' )
lowerCamelCase__ = state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
lowerCamelCase__ = in_proj_weight[: config.hidden_size, :]
lowerCamelCase__ = in_proj_bias[: config.hidden_size]
lowerCamelCase__ = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowerCamelCase__ = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowerCamelCase__ = in_proj_weight[
-config.hidden_size :, :
]
lowerCamelCase__ = in_proj_bias[-config.hidden_size :]
def A__ ( ):
lowerCamelCase__ = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowerCamelCase__ = Image.open(requests.get(__lowerCAmelCase , stream=__lowerCAmelCase ).raw )
return im
@torch.no_grad()
def A__ ( __lowerCAmelCase : Dict , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Any ):
lowerCamelCase__ , lowerCamelCase__ = get_dpt_config(__lowerCAmelCase )
# load original state_dict from URL
# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
lowerCamelCase__ = torch.load(__lowerCAmelCase , map_location="""cpu""" )
# remove certain keys
remove_ignore_keys_(__lowerCAmelCase )
# rename keys
for key in state_dict.copy().keys():
lowerCamelCase__ = state_dict.pop(__lowerCAmelCase )
lowerCamelCase__ = val
# read in qkv matrices
read_in_q_k_v(__lowerCAmelCase , __lowerCAmelCase )
# load HuggingFace model
lowerCamelCase__ = DPTForSemanticSegmentation(__lowerCAmelCase ) if """ade""" in checkpoint_url else DPTForDepthEstimation(__lowerCAmelCase )
model.load_state_dict(__lowerCAmelCase )
model.eval()
# Check outputs on an image
lowerCamelCase__ = 480 if """ade""" in checkpoint_url else 384
lowerCamelCase__ = DPTImageProcessor(size=__lowerCAmelCase )
lowerCamelCase__ = prepare_img()
lowerCamelCase__ = image_processor(__lowerCAmelCase , return_tensors="""pt""" )
# forward pass
lowerCamelCase__ = model(**__lowerCAmelCase ).logits if """ade""" in checkpoint_url else model(**__lowerCAmelCase ).predicted_depth
if show_prediction:
lowerCamelCase__ = (
torch.nn.functional.interpolate(
outputs.unsqueeze(1 ) , size=(image.size[1], image.size[0]) , mode="""bicubic""" , align_corners=__lowerCAmelCase , )
.squeeze()
.cpu()
.numpy()
)
Image.fromarray((prediction / prediction.max()) * 255 ).show()
if pytorch_dump_folder_path is not None:
Path(__lowerCAmelCase ).mkdir(exist_ok=__lowerCAmelCase )
print(F'''Saving model 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:
model.push_to_hub("""ybelkada/dpt-hybrid-midas""" )
image_processor.push_to_hub("""ybelkada/dpt-hybrid-midas""" )
if __name__ == "__main__":
UpperCamelCase : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint_url',
default='https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt',
type=str,
help='URL of the original DPT checkpoint you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default=None,
type=str,
required=False,
help='Path to the output PyTorch model directory.',
)
parser.add_argument(
'--push_to_hub',
action='store_true',
)
parser.add_argument(
'--model_name',
default='dpt-large',
type=str,
help='Name of the model, in case you\'re pushing to the hub.',
)
parser.add_argument(
'--show_prediction',
action='store_true',
)
UpperCamelCase : List[str] = parser.parse_args()
convert_dpt_checkpoint(
args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
)
| 50 | 1 |
'''simple docstring'''
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('>=', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType
UpperCamelCase : Union[str, Any] = get_logger(__name__)
def A__ ( __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : List[str]=0 ):
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
with FSDP.state_dict_type(
__lowerCAmelCase , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
lowerCamelCase__ = model.state_dict()
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
lowerCamelCase__ = F'''{MODEL_NAME}.bin''' if model_index == 0 else F'''{MODEL_NAME}_{model_index}.bin'''
lowerCamelCase__ = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
if accelerator.process_index == 0:
logger.info(F'''Saving model to {output_model_file}''' )
torch.save(__lowerCAmelCase , __lowerCAmelCase )
logger.info(F'''Model saved to {output_model_file}''' )
elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
lowerCamelCase__ = (
F'''{MODEL_NAME}_rank{accelerator.process_index}.bin'''
if model_index == 0
else F'''{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'''
)
lowerCamelCase__ = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
logger.info(F'''Saving model to {output_model_file}''' )
torch.save(__lowerCAmelCase , __lowerCAmelCase )
logger.info(F'''Model saved to {output_model_file}''' )
elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
lowerCamelCase__ = os.path.join(__lowerCAmelCase , F'''{MODEL_NAME}_{model_index}''' )
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
logger.info(F'''Saving model to {ckpt_dir}''' )
lowerCamelCase__ = {"""model""": state_dict}
dist_cp.save_state_dict(
state_dict=__lowerCAmelCase , storage_writer=dist_cp.FileSystemWriter(__lowerCAmelCase ) , planner=DefaultSavePlanner() , )
logger.info(F'''Model saved to {ckpt_dir}''' )
def A__ ( __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : str , __lowerCAmelCase : str , __lowerCAmelCase : Dict=0 ):
accelerator.wait_for_everyone()
with FSDP.state_dict_type(
__lowerCAmelCase , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
if type(__lowerCAmelCase ) != FSDP and accelerator.process_index != 0:
if not fsdp_plugin.sync_module_states:
raise ValueError(
"""Set the `sync_module_states` flag to `True` so that model states are synced across processes when """
"""initializing FSDP object""" )
return
lowerCamelCase__ = F'''{MODEL_NAME}.bin''' if model_index == 0 else F'''{MODEL_NAME}_{model_index}.bin'''
lowerCamelCase__ = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
logger.info(F'''Loading model from {input_model_file}''' )
lowerCamelCase__ = torch.load(__lowerCAmelCase )
logger.info(F'''Model loaded from {input_model_file}''' )
elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
lowerCamelCase__ = (
F'''{MODEL_NAME}_rank{accelerator.process_index}.bin'''
if model_index == 0
else F'''{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'''
)
lowerCamelCase__ = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
logger.info(F'''Loading model from {input_model_file}''' )
lowerCamelCase__ = torch.load(__lowerCAmelCase )
logger.info(F'''Model loaded from {input_model_file}''' )
elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
lowerCamelCase__ = (
os.path.join(__lowerCAmelCase , F'''{MODEL_NAME}_{model_index}''' )
if F'''{MODEL_NAME}''' not in input_dir
else input_dir
)
logger.info(F'''Loading model from {ckpt_dir}''' )
lowerCamelCase__ = {"""model""": model.state_dict()}
dist_cp.load_state_dict(
state_dict=__lowerCAmelCase , storage_reader=dist_cp.FileSystemReader(__lowerCAmelCase ) , planner=DefaultLoadPlanner() , )
lowerCamelCase__ = state_dict["""model"""]
logger.info(F'''Model loaded from {ckpt_dir}''' )
model.load_state_dict(__lowerCAmelCase )
def A__ ( __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Any , __lowerCAmelCase : int , __lowerCAmelCase : str , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : int=0 ):
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
with FSDP.state_dict_type(
__lowerCAmelCase , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
lowerCamelCase__ = FSDP.optim_state_dict(__lowerCAmelCase , __lowerCAmelCase )
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
if accelerator.process_index == 0:
lowerCamelCase__ = (
F'''{OPTIMIZER_NAME}.bin''' if optimizer_index == 0 else F'''{OPTIMIZER_NAME}_{optimizer_index}.bin'''
)
lowerCamelCase__ = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
logger.info(F'''Saving Optimizer state to {output_optimizer_file}''' )
torch.save(__lowerCAmelCase , __lowerCAmelCase )
logger.info(F'''Optimizer state saved in {output_optimizer_file}''' )
else:
lowerCamelCase__ = os.path.join(__lowerCAmelCase , F'''{OPTIMIZER_NAME}_{optimizer_index}''' )
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
logger.info(F'''Saving Optimizer state to {ckpt_dir}''' )
dist_cp.save_state_dict(
state_dict={"""optimizer""": optim_state} , storage_writer=dist_cp.FileSystemWriter(__lowerCAmelCase ) , planner=DefaultSavePlanner() , )
logger.info(F'''Optimizer state saved in {ckpt_dir}''' )
def A__ ( __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : str , __lowerCAmelCase : str , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[Any]=0 ):
accelerator.wait_for_everyone()
with FSDP.state_dict_type(
__lowerCAmelCase , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
lowerCamelCase__ = None
# below check should work but currently it isn't working (mostly opytorch issue),
# in the meantime disabling it at the cost of excess memory usage
# if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
lowerCamelCase__ = (
F'''{OPTIMIZER_NAME}.bin''' if optimizer_index == 0 else F'''{OPTIMIZER_NAME}_{optimizer_index}.bin'''
)
lowerCamelCase__ = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
logger.info(F'''Loading Optimizer state from {input_optimizer_file}''' )
lowerCamelCase__ = torch.load(__lowerCAmelCase )
logger.info(F'''Optimizer state loaded from {input_optimizer_file}''' )
else:
lowerCamelCase__ = (
os.path.join(__lowerCAmelCase , F'''{OPTIMIZER_NAME}_{optimizer_index}''' )
if F'''{OPTIMIZER_NAME}''' not in input_dir
else input_dir
)
logger.info(F'''Loading Optimizer from {ckpt_dir}''' )
lowerCamelCase__ = load_sharded_optimizer_state_dict(
model_state_dict=model.state_dict() , optimizer_key="""optimizer""" , storage_reader=dist_cp.FileSystemReader(__lowerCAmelCase ) , )
lowerCamelCase__ = optim_state["""optimizer"""]
logger.info(F'''Optimizer loaded from {ckpt_dir}''' )
lowerCamelCase__ = FSDP.optim_state_dict_to_load(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
optimizer.load_state_dict(__lowerCAmelCase )
| 50 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase : Tuple = {
'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
'tokenization_mvp': ['MvpTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : str = ['MvpTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Optional[int] = [
'MVP_PRETRAINED_MODEL_ARCHIVE_LIST',
'MvpForCausalLM',
'MvpForConditionalGeneration',
'MvpForQuestionAnswering',
'MvpForSequenceClassification',
'MvpModel',
'MvpPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
UpperCamelCase : Optional[int] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 50 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. 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 ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = 'philschmid/bart-large-cnn-samsum'
_UpperCamelCase = (
'This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '
'and returns a summary of the text.'
)
_UpperCamelCase = 'summarizer'
_UpperCamelCase = AutoTokenizer
_UpperCamelCase = AutoModelForSeqaSeqLM
_UpperCamelCase = ['text']
_UpperCamelCase = ['text']
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
return self.pre_processor(_lowerCAmelCase ,return_tensors="""pt""" ,truncation=_lowerCAmelCase )
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
return self.model.generate(**_lowerCAmelCase )[0]
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
return self.pre_processor.decode(_lowerCAmelCase ,skip_special_tokens=_lowerCAmelCase ,clean_up_tokenization_spaces=_lowerCAmelCase )
| 50 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
UpperCamelCase : Tuple = logging.get_logger(__name__)
UpperCamelCase : Dict = {
'Salesforce/codegen-350M-nl': 'https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json',
'Salesforce/codegen-350M-multi': 'https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json',
'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json',
'Salesforce/codegen-2B-nl': 'https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json',
'Salesforce/codegen-2B-multi': 'https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json',
'Salesforce/codegen-2B-mono': 'https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json',
'Salesforce/codegen-6B-nl': 'https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json',
'Salesforce/codegen-6B-multi': 'https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json',
'Salesforce/codegen-6B-mono': 'https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json',
'Salesforce/codegen-16B-nl': 'https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json',
'Salesforce/codegen-16B-multi': 'https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json',
'Salesforce/codegen-16B-mono': 'https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json',
}
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = 'codegen'
_UpperCamelCase = {
'max_position_embeddings': 'n_positions',
'hidden_size': 'n_embd',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self ,_lowerCAmelCase=5_04_00 ,_lowerCAmelCase=20_48 ,_lowerCAmelCase=20_48 ,_lowerCAmelCase=40_96 ,_lowerCAmelCase=28 ,_lowerCAmelCase=16 ,_lowerCAmelCase=64 ,_lowerCAmelCase=None ,_lowerCAmelCase="gelu_new" ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=1E-5 ,_lowerCAmelCase=0.02 ,_lowerCAmelCase=True ,_lowerCAmelCase=5_02_56 ,_lowerCAmelCase=5_02_56 ,_lowerCAmelCase=False ,**_lowerCAmelCase ,):
lowerCamelCase__ = vocab_size
lowerCamelCase__ = n_ctx
lowerCamelCase__ = n_positions
lowerCamelCase__ = n_embd
lowerCamelCase__ = n_layer
lowerCamelCase__ = n_head
lowerCamelCase__ = n_inner
lowerCamelCase__ = rotary_dim
lowerCamelCase__ = activation_function
lowerCamelCase__ = resid_pdrop
lowerCamelCase__ = embd_pdrop
lowerCamelCase__ = attn_pdrop
lowerCamelCase__ = layer_norm_epsilon
lowerCamelCase__ = initializer_range
lowerCamelCase__ = use_cache
lowerCamelCase__ = bos_token_id
lowerCamelCase__ = eos_token_id
super().__init__(
bos_token_id=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase ,tie_word_embeddings=_lowerCAmelCase ,**_lowerCAmelCase )
class UpperCamelCase__ (a ):
'''simple docstring'''
def __init__( self ,_lowerCAmelCase ,_lowerCAmelCase = "default" ,_lowerCAmelCase = None ,_lowerCAmelCase = False ,):
super().__init__(_lowerCAmelCase ,task=_lowerCAmelCase ,patching_specs=_lowerCAmelCase ,use_past=_lowerCAmelCase )
if not getattr(self._config ,"""pad_token_id""" ,_lowerCAmelCase ):
# TODO: how to do that better?
lowerCamelCase__ = 0
@property
def UpperCamelCase_ ( self ):
lowerCamelCase__ = OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}} )
if self.use_past:
self.fill_with_past_key_values_(_lowerCAmelCase ,direction="""inputs""" )
lowerCamelCase__ = {0: """batch""", 1: """past_sequence + sequence"""}
else:
lowerCamelCase__ = {0: """batch""", 1: """sequence"""}
return common_inputs
@property
def UpperCamelCase_ ( self ):
return self._config.n_layer
@property
def UpperCamelCase_ ( self ):
return self._config.n_head
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase = -1 ,_lowerCAmelCase = -1 ,_lowerCAmelCase = False ,_lowerCAmelCase = None ,):
lowerCamelCase__ = super(_lowerCAmelCase ,self ).generate_dummy_inputs(
_lowerCAmelCase ,batch_size=_lowerCAmelCase ,seq_length=_lowerCAmelCase ,is_pair=_lowerCAmelCase ,framework=_lowerCAmelCase )
# We need to order the input in the way they appears in the forward()
lowerCamelCase__ = OrderedDict({"""input_ids""": common_inputs["""input_ids"""]} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
lowerCamelCase__ , lowerCamelCase__ = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
lowerCamelCase__ = seqlen + 2
lowerCamelCase__ = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
lowerCamelCase__ = [
(torch.zeros(_lowerCAmelCase ), torch.zeros(_lowerCAmelCase )) for _ in range(self.num_layers )
]
lowerCamelCase__ = common_inputs["""attention_mask"""]
if self.use_past:
lowerCamelCase__ = ordered_inputs["""attention_mask"""].dtype
lowerCamelCase__ = torch.cat(
[ordered_inputs["""attention_mask"""], torch.ones(_lowerCAmelCase ,_lowerCAmelCase ,dtype=_lowerCAmelCase )] ,dim=1 )
return ordered_inputs
@property
def UpperCamelCase_ ( self ):
return 13
| 50 | 1 |
'''simple docstring'''
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()
UpperCamelCase : str = logging.get_logger(__name__)
def A__ ( __lowerCAmelCase : int , __lowerCAmelCase : Dict=False , __lowerCAmelCase : Tuple=False , __lowerCAmelCase : List[str]=False ):
lowerCamelCase__ = []
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 A__ ( __lowerCAmelCase : Any , __lowerCAmelCase : Tuple ):
for i in range(config.num_hidden_layers ):
lowerCamelCase__ = """vilt."""
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowerCamelCase__ = state_dict.pop(F'''transformer.blocks.{i}.attn.qkv.weight''' )
lowerCamelCase__ = state_dict.pop(F'''transformer.blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
lowerCamelCase__ = in_proj_weight[
: config.hidden_size, :
]
lowerCamelCase__ = in_proj_bias[: config.hidden_size]
lowerCamelCase__ = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowerCamelCase__ = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowerCamelCase__ = in_proj_weight[
-config.hidden_size :, :
]
lowerCamelCase__ = in_proj_bias[-config.hidden_size :]
def A__ ( __lowerCAmelCase : Optional[Any] ):
lowerCamelCase__ = ["""head.weight""", """head.bias"""]
for k in ignore_keys:
state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
def A__ ( __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Union[str, Any] ):
lowerCamelCase__ = dct.pop(__lowerCAmelCase )
lowerCamelCase__ = val
@torch.no_grad()
def A__ ( __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Optional[Any] ):
lowerCamelCase__ = ViltConfig(image_size=384 , patch_size=32 , tie_word_embeddings=__lowerCAmelCase )
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
if "vqa" in checkpoint_url:
lowerCamelCase__ = True
lowerCamelCase__ = 3129
lowerCamelCase__ = """huggingface/label-files"""
lowerCamelCase__ = """vqa2-id2label.json"""
lowerCamelCase__ = json.load(open(hf_hub_download(__lowerCAmelCase , __lowerCAmelCase , repo_type="""dataset""" ) , """r""" ) )
lowerCamelCase__ = {int(__lowerCAmelCase ): v for k, v in idalabel.items()}
lowerCamelCase__ = idalabel
lowerCamelCase__ = {v: k for k, v in idalabel.items()}
lowerCamelCase__ = ViltForQuestionAnswering(__lowerCAmelCase )
elif "nlvr" in checkpoint_url:
lowerCamelCase__ = True
lowerCamelCase__ = 2
lowerCamelCase__ = {0: """False""", 1: """True"""}
lowerCamelCase__ = {v: k for k, v in config.idalabel.items()}
lowerCamelCase__ = 3
lowerCamelCase__ = ViltForImagesAndTextClassification(__lowerCAmelCase )
elif "irtr" in checkpoint_url:
lowerCamelCase__ = True
lowerCamelCase__ = ViltForImageAndTextRetrieval(__lowerCAmelCase )
elif "mlm_itm" in checkpoint_url:
lowerCamelCase__ = True
lowerCamelCase__ = ViltForMaskedLM(__lowerCAmelCase )
else:
raise ValueError("""Unknown model type""" )
# load state_dict of original model, remove and rename some keys
lowerCamelCase__ = torch.hub.load_state_dict_from_url(__lowerCAmelCase , map_location="""cpu""" )["""state_dict"""]
lowerCamelCase__ = 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:
lowerCamelCase__ = ["""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:
lowerCamelCase__ , lowerCamelCase__ = model.load_state_dict(__lowerCAmelCase , strict=__lowerCAmelCase )
assert missing_keys == ["mlm_score.decoder.bias"]
else:
model.load_state_dict(__lowerCAmelCase )
# Define processor
lowerCamelCase__ = ViltImageProcessor(size=384 )
lowerCamelCase__ = BertTokenizer.from_pretrained("""bert-base-uncased""" )
lowerCamelCase__ = ViltProcessor(__lowerCAmelCase , __lowerCAmelCase )
# Forward pass on example inputs (image + text)
if nlvr_model:
lowerCamelCase__ = Image.open(requests.get("""https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg""" , stream=__lowerCAmelCase ).raw )
lowerCamelCase__ = Image.open(requests.get("""https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg""" , stream=__lowerCAmelCase ).raw )
lowerCamelCase__ = (
"""The left image contains twice the number of dogs as the right image, and at least two dogs in total are"""
""" standing."""
)
lowerCamelCase__ = processor(__lowerCAmelCase , __lowerCAmelCase , return_tensors="""pt""" )
lowerCamelCase__ = processor(__lowerCAmelCase , __lowerCAmelCase , return_tensors="""pt""" )
lowerCamelCase__ = model(
input_ids=encoding_a.input_ids , pixel_values=encoding_a.pixel_values , pixel_values_a=encoding_a.pixel_values , )
else:
lowerCamelCase__ = Image.open(requests.get("""http://images.cocodataset.org/val2017/000000039769.jpg""" , stream=__lowerCAmelCase ).raw )
if mlm_model:
lowerCamelCase__ = """a bunch of [MASK] laying on a [MASK]."""
else:
lowerCamelCase__ = """How many cats are there?"""
lowerCamelCase__ = processor(__lowerCAmelCase , __lowerCAmelCase , return_tensors="""pt""" )
lowerCamelCase__ = model(**__lowerCAmelCase )
# Verify outputs
if mlm_model:
lowerCamelCase__ = torch.Size([1, 11, 3_0522] )
lowerCamelCase__ = 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"
lowerCamelCase__ = outputs.logits[0, 4, :].argmax(-1 ).item()
assert tokenizer.decode([predicted_id] ) == "cats"
elif vqa_model:
lowerCamelCase__ = torch.Size([1, 3129] )
lowerCamelCase__ = 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"
lowerCamelCase__ = outputs.logits.argmax(-1 ).item()
assert model.config.idalabel[predicted_idx] == "2"
elif nlvr_model:
lowerCamelCase__ = torch.Size([1, 2] )
lowerCamelCase__ = 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__":
UpperCamelCase : str = 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.'
)
UpperCamelCase : Union[str, Any] = parser.parse_args()
convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 50 |
'''simple docstring'''
# 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
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase : int = {
'configuration_xmod': [
'XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XmodConfig',
'XmodOnnxConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Tuple = [
'XMOD_PRETRAINED_MODEL_ARCHIVE_LIST',
'XmodForCausalLM',
'XmodForMaskedLM',
'XmodForMultipleChoice',
'XmodForQuestionAnswering',
'XmodForSequenceClassification',
'XmodForTokenClassification',
'XmodModel',
'XmodPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xmod import (
XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
XmodForCausalLM,
XmodForMaskedLM,
XmodForMultipleChoice,
XmodForQuestionAnswering,
XmodForSequenceClassification,
XmodForTokenClassification,
XmodModel,
XmodPreTrainedModel,
)
else:
import sys
UpperCamelCase : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 50 | 1 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ):
return int((input_a, input_a).count(1 ) != 0 )
def A__ ( ):
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))
| 50 |
'''simple docstring'''
from typing import Union
import fire
import torch
from tqdm import tqdm
def A__ ( __lowerCAmelCase : str , __lowerCAmelCase : str = "cpu" , __lowerCAmelCase : Union[str, None] = None ):
lowerCamelCase__ = torch.load(__lowerCAmelCase , map_location=__lowerCAmelCase )
for k, v in tqdm(state_dict.items() ):
if not isinstance(__lowerCAmelCase , torch.Tensor ):
raise TypeError("""FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin""" )
lowerCamelCase__ = v.half()
if save_path is None: # overwrite src_path
lowerCamelCase__ = src_path
torch.save(__lowerCAmelCase , __lowerCAmelCase )
if __name__ == "__main__":
fire.Fire(convert)
| 50 | 1 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal
UpperCamelCase : int = datasets.utils.logging.get_logger(__name__)
UpperCamelCase : List[str] = ['names', 'prefix']
UpperCamelCase : List[str] = ['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
UpperCamelCase : Optional[int] = ['encoding_errors', 'on_bad_lines']
UpperCamelCase : Optional[int] = ['date_format']
@dataclass
class UpperCamelCase__ (datasets.BuilderConfig ):
'''simple docstring'''
_UpperCamelCase = ","
_UpperCamelCase = None
_UpperCamelCase = "infer"
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = True
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = False
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = True
_UpperCamelCase = True
_UpperCamelCase = False
_UpperCamelCase = True
_UpperCamelCase = None
_UpperCamelCase = "."
_UpperCamelCase = None
_UpperCamelCase = '"'
_UpperCamelCase = 0
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = True
_UpperCamelCase = True
_UpperCamelCase = 0
_UpperCamelCase = True
_UpperCamelCase = False
_UpperCamelCase = None
_UpperCamelCase = 10000
_UpperCamelCase = None
_UpperCamelCase = "strict"
_UpperCamelCase = "error"
_UpperCamelCase = None
def UpperCamelCase_ ( self ):
if self.delimiter is not None:
lowerCamelCase__ = self.delimiter
if self.column_names is not None:
lowerCamelCase__ = self.column_names
@property
def UpperCamelCase_ ( self ):
lowerCamelCase__ = {
"""sep""": self.sep,
"""header""": self.header,
"""names""": self.names,
"""index_col""": self.index_col,
"""usecols""": self.usecols,
"""prefix""": self.prefix,
"""mangle_dupe_cols""": self.mangle_dupe_cols,
"""engine""": self.engine,
"""converters""": self.converters,
"""true_values""": self.true_values,
"""false_values""": self.false_values,
"""skipinitialspace""": self.skipinitialspace,
"""skiprows""": self.skiprows,
"""nrows""": self.nrows,
"""na_values""": self.na_values,
"""keep_default_na""": self.keep_default_na,
"""na_filter""": self.na_filter,
"""verbose""": self.verbose,
"""skip_blank_lines""": self.skip_blank_lines,
"""thousands""": self.thousands,
"""decimal""": self.decimal,
"""lineterminator""": self.lineterminator,
"""quotechar""": self.quotechar,
"""quoting""": self.quoting,
"""escapechar""": self.escapechar,
"""comment""": self.comment,
"""encoding""": self.encoding,
"""dialect""": self.dialect,
"""error_bad_lines""": self.error_bad_lines,
"""warn_bad_lines""": self.warn_bad_lines,
"""skipfooter""": self.skipfooter,
"""doublequote""": self.doublequote,
"""memory_map""": self.memory_map,
"""float_precision""": self.float_precision,
"""chunksize""": self.chunksize,
"""encoding_errors""": self.encoding_errors,
"""on_bad_lines""": self.on_bad_lines,
"""date_format""": self.date_format,
}
# some kwargs must not be passed if they don't have a default value
# some others are deprecated and we can also not pass them if they are the default value
for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig() ,_lowerCAmelCase ):
del pd_read_csv_kwargs[pd_read_csv_parameter]
# Remove 2.0 new arguments
if not (datasets.config.PANDAS_VERSION.major >= 2):
for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
del pd_read_csv_kwargs[pd_read_csv_parameter]
# Remove 1.3 new arguments
if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
del pd_read_csv_kwargs[pd_read_csv_parameter]
return pd_read_csv_kwargs
class UpperCamelCase__ (datasets.ArrowBasedBuilder ):
'''simple docstring'''
_UpperCamelCase = CsvConfig
def UpperCamelCase_ ( self ):
return datasets.DatasetInfo(features=self.config.features )
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
if not self.config.data_files:
raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}''' )
lowerCamelCase__ = dl_manager.download_and_extract(self.config.data_files )
if isinstance(_lowerCAmelCase ,(str, list, tuple) ):
lowerCamelCase__ = data_files
if isinstance(_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = [files]
lowerCamelCase__ = [dl_manager.iter_files(_lowerCAmelCase ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,gen_kwargs={"""files""": files} )]
lowerCamelCase__ = []
for split_name, files in data_files.items():
if isinstance(_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = [files]
lowerCamelCase__ = [dl_manager.iter_files(_lowerCAmelCase ) for file in files]
splits.append(datasets.SplitGenerator(name=_lowerCAmelCase ,gen_kwargs={"""files""": files} ) )
return splits
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
if self.config.features is not None:
lowerCamelCase__ = self.config.features.arrow_schema
if all(not require_storage_cast(_lowerCAmelCase ) for feature in self.config.features.values() ):
# cheaper cast
lowerCamelCase__ = pa.Table.from_arrays([pa_table[field.name] for field in schema] ,schema=_lowerCAmelCase )
else:
# more expensive cast; allows str <-> int/float or str to Audio for example
lowerCamelCase__ = table_cast(_lowerCAmelCase ,_lowerCAmelCase )
return pa_table
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
lowerCamelCase__ = self.config.features.arrow_schema if self.config.features else None
# dtype allows reading an int column as str
lowerCamelCase__ = (
{
name: dtype.to_pandas_dtype() if not require_storage_cast(_lowerCAmelCase ) else object
for name, dtype, feature in zip(schema.names ,schema.types ,self.config.features.values() )
}
if schema is not None
else None
)
for file_idx, file in enumerate(itertools.chain.from_iterable(_lowerCAmelCase ) ):
lowerCamelCase__ = pd.read_csv(_lowerCAmelCase ,iterator=_lowerCAmelCase ,dtype=_lowerCAmelCase ,**self.config.pd_read_csv_kwargs )
try:
for batch_idx, df in enumerate(_lowerCAmelCase ):
lowerCamelCase__ = pa.Table.from_pandas(_lowerCAmelCase )
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(_lowerCAmelCase )
except ValueError as e:
logger.error(F'''Failed to read file \'{file}\' with error {type(_lowerCAmelCase )}: {e}''' )
raise
| 50 |
'''simple docstring'''
import os
from pathlib import Path
def A__ ( ):
from torch.utils.cpp_extension import load
lowerCamelCase__ = Path(__lowerCAmelCase ).resolve().parent.parent.parent / """kernels""" / """deformable_detr"""
lowerCamelCase__ = [
root / filename
for filename in [
"""vision.cpp""",
os.path.join("""cpu""" , """ms_deform_attn_cpu.cpp""" ),
os.path.join("""cuda""" , """ms_deform_attn_cuda.cu""" ),
]
]
load(
"""MultiScaleDeformableAttention""" , __lowerCAmelCase , with_cuda=__lowerCAmelCase , extra_include_paths=[str(__lowerCAmelCase )] , extra_cflags=["""-DWITH_CUDA=1"""] , extra_cuda_cflags=[
"""-DCUDA_HAS_FP16=1""",
"""-D__CUDA_NO_HALF_OPERATORS__""",
"""-D__CUDA_NO_HALF_CONVERSIONS__""",
"""-D__CUDA_NO_HALF2_OPERATORS__""",
] , )
import MultiScaleDeformableAttention as MSDA
return MSDA
| 50 | 1 |
'''simple docstring'''
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_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 torch import nn
from transformers import ASTForAudioClassification, ASTModel
from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_torchaudio_available():
import torchaudio
from transformers import ASTFeatureExtractor
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self ,_lowerCAmelCase ,_lowerCAmelCase=13 ,_lowerCAmelCase=2 ,_lowerCAmelCase=24 ,_lowerCAmelCase=16 ,_lowerCAmelCase=True ,_lowerCAmelCase=True ,_lowerCAmelCase=32 ,_lowerCAmelCase=5 ,_lowerCAmelCase=4 ,_lowerCAmelCase=37 ,_lowerCAmelCase="gelu" ,_lowerCAmelCase=0.1 ,_lowerCAmelCase=0.1 ,_lowerCAmelCase=10 ,_lowerCAmelCase=0.02 ,_lowerCAmelCase=None ,_lowerCAmelCase=2 ,_lowerCAmelCase=2 ,):
lowerCamelCase__ = parent
lowerCamelCase__ = batch_size
lowerCamelCase__ = patch_size
lowerCamelCase__ = max_length
lowerCamelCase__ = num_mel_bins
lowerCamelCase__ = is_training
lowerCamelCase__ = use_labels
lowerCamelCase__ = hidden_size
lowerCamelCase__ = num_hidden_layers
lowerCamelCase__ = num_attention_heads
lowerCamelCase__ = intermediate_size
lowerCamelCase__ = hidden_act
lowerCamelCase__ = hidden_dropout_prob
lowerCamelCase__ = attention_probs_dropout_prob
lowerCamelCase__ = type_sequence_label_size
lowerCamelCase__ = initializer_range
lowerCamelCase__ = scope
lowerCamelCase__ = frequency_stride
lowerCamelCase__ = time_stride
# in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
lowerCamelCase__ = (self.num_mel_bins - self.patch_size) // self.frequency_stride + 1
lowerCamelCase__ = (self.max_length - self.patch_size) // self.time_stride + 1
lowerCamelCase__ = frequency_out_dimension * time_out_dimension
lowerCamelCase__ = num_patches + 2
def UpperCamelCase_ ( self ):
lowerCamelCase__ = floats_tensor([self.batch_size, self.max_length, self.num_mel_bins] )
lowerCamelCase__ = None
if self.use_labels:
lowerCamelCase__ = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
lowerCamelCase__ = self.get_config()
return config, input_values, labels
def UpperCamelCase_ ( self ):
return ASTConfig(
patch_size=self.patch_size ,max_length=self.max_length ,num_mel_bins=self.num_mel_bins ,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=_lowerCAmelCase ,initializer_range=self.initializer_range ,frequency_stride=self.frequency_stride ,time_stride=self.time_stride ,)
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = ASTModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
lowerCamelCase__ = model(_lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.prepare_config_and_inputs()
(
(
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) ,
) = config_and_inputs
lowerCamelCase__ = {"""input_values""": input_values}
return config, inputs_dict
@require_torch
class UpperCamelCase__ (a ,a ,unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase = (
(
ASTModel,
ASTForAudioClassification,
)
if is_torch_available()
else ()
)
_UpperCamelCase = (
{'audio-classification': ASTForAudioClassification, 'feature-extraction': ASTModel}
if is_torch_available()
else {}
)
_UpperCamelCase = False
_UpperCamelCase = False
_UpperCamelCase = False
_UpperCamelCase = False
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
if pipeline_test_casse_name == "AudioClassificationPipelineTests":
return True
return False
def UpperCamelCase_ ( self ):
lowerCamelCase__ = ASTModelTester(self )
lowerCamelCase__ = ConfigTester(self ,config_class=_lowerCAmelCase ,has_text_modality=_lowerCAmelCase ,hidden_size=37 )
def UpperCamelCase_ ( self ):
self.config_tester.run_common_tests()
@unittest.skip(reason="""AST does not use inputs_embeds""" )
def UpperCamelCase_ ( self ):
pass
def UpperCamelCase_ ( self ):
lowerCamelCase__ , lowerCamelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase__ = model_class(_lowerCAmelCase )
self.assertIsInstance(model.get_input_embeddings() ,(nn.Module) )
lowerCamelCase__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_lowerCAmelCase ,nn.Linear ) )
def UpperCamelCase_ ( self ):
lowerCamelCase__ , lowerCamelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase__ = model_class(_lowerCAmelCase )
lowerCamelCase__ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCamelCase__ = [*signature.parameters.keys()]
lowerCamelCase__ = ["""input_values"""]
self.assertListEqual(arg_names[:1] ,_lowerCAmelCase )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
@slow
def UpperCamelCase_ ( self ):
for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase__ = ASTModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
def A__ ( ):
lowerCamelCase__ = hf_hub_download(
repo_id="""nielsr/audio-spectogram-transformer-checkpoint""" , filename="""sample_audio.flac""" , repo_type="""dataset""" )
lowerCamelCase__ , lowerCamelCase__ = torchaudio.load(__lowerCAmelCase )
return audio, sampling_rate
@require_torch
@require_torchaudio
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
@cached_property
def UpperCamelCase_ ( self ):
return (
ASTFeatureExtractor.from_pretrained("""MIT/ast-finetuned-audioset-10-10-0.4593""" )
if is_torchaudio_available()
else None
)
@slow
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.default_feature_extractor
lowerCamelCase__ = ASTForAudioClassification.from_pretrained("""MIT/ast-finetuned-audioset-10-10-0.4593""" ).to(_lowerCAmelCase )
lowerCamelCase__ = self.default_feature_extractor
lowerCamelCase__ , lowerCamelCase__ = prepare_audio()
lowerCamelCase__ = audio.squeeze().numpy()
lowerCamelCase__ = feature_extractor(_lowerCAmelCase ,sampling_rate=_lowerCAmelCase ,return_tensors="""pt""" ).to(_lowerCAmelCase )
# forward pass
with torch.no_grad():
lowerCamelCase__ = model(**_lowerCAmelCase )
# verify the logits
lowerCamelCase__ = torch.Size((1, 5_27) )
self.assertEqual(outputs.logits.shape ,_lowerCAmelCase )
lowerCamelCase__ = torch.tensor([-0.8760, -7.0042, -8.6602] ).to(_lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] ,_lowerCAmelCase ,atol=1E-4 ) )
| 50 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : list[int] , __lowerCAmelCase : list[int] ):
lowerCamelCase__ = len(__lowerCAmelCase )
print("""The following activities are selected:""" )
# The first activity is always selected
lowerCamelCase__ = 0
print(__lowerCAmelCase , end=""",""" )
# Consider rest of the activities
for j in range(__lowerCAmelCase ):
# If this activity has start time greater than
# or equal to the finish time of previously
# selected activity, then select it
if start[j] >= finish[i]:
print(__lowerCAmelCase , end=""",""" )
lowerCamelCase__ = j
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCamelCase : Union[str, Any] = [1, 3, 0, 5, 8, 5]
UpperCamelCase : int = [2, 4, 6, 7, 9, 9]
print_max_activities(start, finish)
| 50 | 1 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : int ):
lowerCamelCase__ = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def A__ ( __lowerCAmelCase : int = 5000 ):
lowerCamelCase__ = [(i * (3 * i - 1)) // 2 for i in range(1 , __lowerCAmelCase )]
for i, pentagonal_i in enumerate(__lowerCAmelCase ):
for j in range(__lowerCAmelCase , len(__lowerCAmelCase ) ):
lowerCamelCase__ = pentagonal_nums[j]
lowerCamelCase__ = pentagonal_i + pentagonal_j
lowerCamelCase__ = pentagonal_j - pentagonal_i
if is_pentagonal(__lowerCAmelCase ) and is_pentagonal(__lowerCAmelCase ):
return b
return -1
if __name__ == "__main__":
print(F'{solution() = }')
| 50 |
'''simple docstring'''
import warnings
from ..trainer import Trainer
from ..utils import logging
UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
class UpperCamelCase__ (a ):
'''simple docstring'''
def __init__( self ,_lowerCAmelCase=None ,**_lowerCAmelCase ):
warnings.warn(
"""`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` """
"""instead.""" ,_lowerCAmelCase ,)
super().__init__(args=_lowerCAmelCase ,**_lowerCAmelCase )
| 50 | 1 |
'''simple docstring'''
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler
def A__ ( __lowerCAmelCase : Any , __lowerCAmelCase : Dict , __lowerCAmelCase : int=[] ):
lowerCamelCase__ = size[0] - overlap_pixels * 2
lowerCamelCase__ = size[1] - overlap_pixels * 2
for letter in ["l", "r"]:
if letter in remove_borders:
size_x += overlap_pixels
for letter in ["t", "b"]:
if letter in remove_borders:
size_y += overlap_pixels
lowerCamelCase__ = np.ones((size_y, size_x) , dtype=np.uinta ) * 255
lowerCamelCase__ = np.pad(__lowerCAmelCase , mode="""linear_ramp""" , pad_width=__lowerCAmelCase , end_values=0 )
if "l" in remove_borders:
lowerCamelCase__ = mask[:, overlap_pixels : mask.shape[1]]
if "r" in remove_borders:
lowerCamelCase__ = mask[:, 0 : mask.shape[1] - overlap_pixels]
if "t" in remove_borders:
lowerCamelCase__ = mask[overlap_pixels : mask.shape[0], :]
if "b" in remove_borders:
lowerCamelCase__ = mask[0 : mask.shape[0] - overlap_pixels, :]
return mask
def A__ ( __lowerCAmelCase : Tuple , __lowerCAmelCase : Any , __lowerCAmelCase : str ):
return max(__lowerCAmelCase , min(__lowerCAmelCase , __lowerCAmelCase ) )
def A__ ( __lowerCAmelCase : [int] , __lowerCAmelCase : [int] , __lowerCAmelCase : [int] ):
return (
clamp(rect[0] , min[0] , max[0] ),
clamp(rect[1] , min[1] , max[1] ),
clamp(rect[2] , min[0] , max[0] ),
clamp(rect[3] , min[1] , max[1] ),
)
def A__ ( __lowerCAmelCase : [int] , __lowerCAmelCase : int , __lowerCAmelCase : [int] ):
lowerCamelCase__ = list(__lowerCAmelCase )
rect[0] -= overlap
rect[1] -= overlap
rect[2] += overlap
rect[3] += overlap
lowerCamelCase__ = clamp_rect(__lowerCAmelCase , [0, 0] , [image_size[0], image_size[1]] )
return rect
def A__ ( __lowerCAmelCase : List[str] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Tuple ):
lowerCamelCase__ = Image.new("""RGB""" , (tile.size[0] + original_slice, tile.size[1]) )
result.paste(
original_image.resize((tile.size[0], tile.size[1]) , Image.BICUBIC ).crop(
(slice_x, 0, slice_x + original_slice, tile.size[1]) ) , (0, 0) , )
result.paste(__lowerCAmelCase , (original_slice, 0) )
return result
def A__ ( __lowerCAmelCase : Dict , __lowerCAmelCase : Any ):
lowerCamelCase__ = (original_image_slice * 4, 0, tile.size[0], tile.size[1])
lowerCamelCase__ = tile.crop(__lowerCAmelCase )
return tile
def A__ ( __lowerCAmelCase : Any , __lowerCAmelCase : Tuple ):
lowerCamelCase__ = n % d
return n - divisor
class UpperCamelCase__ (a ):
'''simple docstring'''
def __init__( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase = 3_50 ,):
super().__init__(
vae=_lowerCAmelCase ,text_encoder=_lowerCAmelCase ,tokenizer=_lowerCAmelCase ,unet=_lowerCAmelCase ,low_res_scheduler=_lowerCAmelCase ,scheduler=_lowerCAmelCase ,max_noise_level=_lowerCAmelCase ,)
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,**_lowerCAmelCase ):
torch.manual_seed(0 )
lowerCamelCase__ = (
min(image.size[0] - (tile_size + original_image_slice) ,x * tile_size ),
min(image.size[1] - (tile_size + original_image_slice) ,y * tile_size ),
min(image.size[0] ,(x + 1) * tile_size ),
min(image.size[1] ,(y + 1) * tile_size ),
)
lowerCamelCase__ = add_overlap_rect(_lowerCAmelCase ,_lowerCAmelCase ,image.size )
lowerCamelCase__ = image.crop(_lowerCAmelCase )
lowerCamelCase__ = ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
lowerCamelCase__ = translated_slice_x - (original_image_slice / 2)
lowerCamelCase__ = max(0 ,_lowerCAmelCase )
lowerCamelCase__ = squeeze_tile(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = to_input.size
lowerCamelCase__ = to_input.resize((tile_size, tile_size) ,Image.BICUBIC )
lowerCamelCase__ = super(_lowerCAmelCase ,self ).__call__(image=_lowerCAmelCase ,**_lowerCAmelCase ).images[0]
lowerCamelCase__ = upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4) ,Image.BICUBIC )
lowerCamelCase__ = unsqueeze_tile(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4) ,Image.BICUBIC )
lowerCamelCase__ = []
if x == 0:
remove_borders.append("""l""" )
elif crop_rect[2] == image.size[0]:
remove_borders.append("""r""" )
if y == 0:
remove_borders.append("""t""" )
elif crop_rect[3] == image.size[1]:
remove_borders.append("""b""" )
lowerCamelCase__ = Image.fromarray(
make_transparency_mask(
(upscaled_tile.size[0], upscaled_tile.size[1]) ,tile_border * 4 ,remove_borders=_lowerCAmelCase ) ,mode="""L""" ,)
final_image.paste(
_lowerCAmelCase ,(crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4) ,_lowerCAmelCase )
@torch.no_grad()
def __call__( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase = 75 ,_lowerCAmelCase = 9.0 ,_lowerCAmelCase = 50 ,_lowerCAmelCase = None ,_lowerCAmelCase = 1 ,_lowerCAmelCase = 0.0 ,_lowerCAmelCase = None ,_lowerCAmelCase = None ,_lowerCAmelCase = None ,_lowerCAmelCase = 1 ,_lowerCAmelCase = 1_28 ,_lowerCAmelCase = 32 ,_lowerCAmelCase = 32 ,):
lowerCamelCase__ = Image.new("""RGB""" ,(image.size[0] * 4, image.size[1] * 4) )
lowerCamelCase__ = math.ceil(image.size[0] / tile_size )
lowerCamelCase__ = math.ceil(image.size[1] / tile_size )
lowerCamelCase__ = tcx * tcy
lowerCamelCase__ = 0
for y in range(_lowerCAmelCase ):
for x in range(_lowerCAmelCase ):
self._process_tile(
_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,prompt=_lowerCAmelCase ,num_inference_steps=_lowerCAmelCase ,guidance_scale=_lowerCAmelCase ,noise_level=_lowerCAmelCase ,negative_prompt=_lowerCAmelCase ,num_images_per_prompt=_lowerCAmelCase ,eta=_lowerCAmelCase ,generator=_lowerCAmelCase ,latents=_lowerCAmelCase ,)
current_count += 1
if callback is not None:
callback({"""progress""": current_count / total_tile_count, """image""": final_image} )
return final_image
def A__ ( ):
# Run a demo
lowerCamelCase__ = """stabilityai/stable-diffusion-x4-upscaler"""
lowerCamelCase__ = StableDiffusionTiledUpscalePipeline.from_pretrained(__lowerCAmelCase , revision="""fp16""" , torch_dtype=torch.floataa )
lowerCamelCase__ = pipe.to("""cuda""" )
lowerCamelCase__ = Image.open("""../../docs/source/imgs/diffusers_library.jpg""" )
def callback(__lowerCAmelCase : str ):
print(F'''progress: {obj["progress"]:.4f}''' )
obj["image"].save("""diffusers_library_progress.jpg""" )
lowerCamelCase__ = pipe(image=__lowerCAmelCase , prompt="""Black font, white background, vector""" , noise_level=40 , callback=__lowerCAmelCase )
final_image.save("""diffusers_library.jpg""" )
if __name__ == "__main__":
main()
| 50 |
'''simple docstring'''
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def A__ ( __lowerCAmelCase : List[str] ):
lowerCamelCase__ = []
for line in lines:
lowerCamelCase__ = re.sub(R"""#.*""" , """""" , __lowerCAmelCase ) # remove comments
if line:
filtered_lines.append(__lowerCAmelCase )
lowerCamelCase__ = """\n""".join(__lowerCAmelCase )
# Make a hash from all this code
lowerCamelCase__ = full_str.encode("""utf-8""" )
return shaaaa(__lowerCAmelCase ).hexdigest()
# get importable module names and hash for caching
UpperCamelCase : Dict = {
'csv': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
'json': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
'pandas': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
'parquet': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
'arrow': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
'text': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
'imagefolder': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
'audiofolder': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
UpperCamelCase : str = {
'.csv': ('csv', {}),
'.tsv': ('csv', {'sep': '\t'}),
'.json': ('json', {}),
'.jsonl': ('json', {}),
'.parquet': ('parquet', {}),
'.arrow': ('arrow', {}),
'.txt': ('text', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
UpperCamelCase : List[Any] = {'imagefolder', 'audiofolder'}
# Used to filter data files based on extensions given a module name
UpperCamelCase : Dict[str, List[str]] = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append('.zip')
_MODULE_TO_EXTENSIONS["audiofolder"].append('.zip')
| 50 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel
@require_tf
class UpperCamelCase__ :
'''simple docstring'''
_UpperCamelCase = PegasusConfig
_UpperCamelCase = {}
_UpperCamelCase = 'gelu'
def __init__( self ,_lowerCAmelCase ,_lowerCAmelCase=13 ,_lowerCAmelCase=7 ,_lowerCAmelCase=True ,_lowerCAmelCase=False ,_lowerCAmelCase=99 ,_lowerCAmelCase=32 ,_lowerCAmelCase=2 ,_lowerCAmelCase=4 ,_lowerCAmelCase=37 ,_lowerCAmelCase=0.1 ,_lowerCAmelCase=0.1 ,_lowerCAmelCase=40 ,_lowerCAmelCase=2 ,_lowerCAmelCase=1 ,_lowerCAmelCase=0 ,):
lowerCamelCase__ = parent
lowerCamelCase__ = batch_size
lowerCamelCase__ = seq_length
lowerCamelCase__ = is_training
lowerCamelCase__ = use_labels
lowerCamelCase__ = vocab_size
lowerCamelCase__ = hidden_size
lowerCamelCase__ = num_hidden_layers
lowerCamelCase__ = num_attention_heads
lowerCamelCase__ = intermediate_size
lowerCamelCase__ = hidden_dropout_prob
lowerCamelCase__ = attention_probs_dropout_prob
lowerCamelCase__ = max_position_embeddings
lowerCamelCase__ = eos_token_id
lowerCamelCase__ = pad_token_id
lowerCamelCase__ = bos_token_id
def UpperCamelCase_ ( self ):
lowerCamelCase__ = ids_tensor([self.batch_size, self.seq_length - 1] ,self.vocab_size )
lowerCamelCase__ = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) ,1 )
lowerCamelCase__ = tf.concat([input_ids, eos_tensor] ,axis=1 )
lowerCamelCase__ = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
lowerCamelCase__ = self.config_cls(
vocab_size=self.vocab_size ,d_model=self.hidden_size ,encoder_layers=self.num_hidden_layers ,decoder_layers=self.num_hidden_layers ,encoder_attention_heads=self.num_attention_heads ,decoder_attention_heads=self.num_attention_heads ,encoder_ffn_dim=self.intermediate_size ,decoder_ffn_dim=self.intermediate_size ,dropout=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,eos_token_ids=[2] ,bos_token_id=self.bos_token_id ,pad_token_id=self.pad_token_id ,decoder_start_token_id=self.pad_token_id ,**self.config_updates ,)
lowerCamelCase__ = prepare_pegasus_inputs_dict(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
return config, inputs_dict
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = TFPegasusModel(config=_lowerCAmelCase ).get_decoder()
lowerCamelCase__ = inputs_dict["""input_ids"""]
lowerCamelCase__ = input_ids[:1, :]
lowerCamelCase__ = inputs_dict["""attention_mask"""][:1, :]
lowerCamelCase__ = inputs_dict["""head_mask"""]
lowerCamelCase__ = 1
# first forward pass
lowerCamelCase__ = model(_lowerCAmelCase ,attention_mask=_lowerCAmelCase ,head_mask=_lowerCAmelCase ,use_cache=_lowerCAmelCase )
lowerCamelCase__ , lowerCamelCase__ = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
lowerCamelCase__ = ids_tensor((self.batch_size, 3) ,config.vocab_size )
lowerCamelCase__ = tf.cast(ids_tensor((self.batch_size, 3) ,2 ) ,tf.inta )
# append to next input_ids and
lowerCamelCase__ = tf.concat([input_ids, next_tokens] ,axis=-1 )
lowerCamelCase__ = tf.concat([attention_mask, next_attn_mask] ,axis=-1 )
lowerCamelCase__ = model(_lowerCAmelCase ,attention_mask=_lowerCAmelCase )[0]
lowerCamelCase__ = model(_lowerCAmelCase ,attention_mask=_lowerCAmelCase ,past_key_values=_lowerCAmelCase )[0]
self.parent.assertEqual(next_tokens.shape[1] ,output_from_past.shape[1] )
# select random slice
lowerCamelCase__ = int(ids_tensor((1,) ,output_from_past.shape[-1] ) )
lowerCamelCase__ = output_from_no_past[:, -3:, random_slice_idx]
lowerCamelCase__ = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(_lowerCAmelCase ,_lowerCAmelCase ,rtol=1E-3 )
def A__ ( __lowerCAmelCase : List[Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[Any]=None , __lowerCAmelCase : Dict=None , __lowerCAmelCase : int=None , __lowerCAmelCase : Optional[Any]=None , __lowerCAmelCase : str=None , ):
if attention_mask is None:
lowerCamelCase__ = tf.cast(tf.math.not_equal(__lowerCAmelCase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
lowerCamelCase__ = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
lowerCamelCase__ = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
lowerCamelCase__ = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
lowerCamelCase__ = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class UpperCamelCase__ (a ,a ,unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase = (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
_UpperCamelCase = (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
_UpperCamelCase = (
{
'conversational': TFPegasusForConditionalGeneration,
'feature-extraction': TFPegasusModel,
'summarization': TFPegasusForConditionalGeneration,
'text2text-generation': TFPegasusForConditionalGeneration,
'translation': TFPegasusForConditionalGeneration,
}
if is_tf_available()
else {}
)
_UpperCamelCase = True
_UpperCamelCase = False
_UpperCamelCase = False
def UpperCamelCase_ ( self ):
lowerCamelCase__ = TFPegasusModelTester(self )
lowerCamelCase__ = ConfigTester(self ,config_class=_lowerCAmelCase )
def UpperCamelCase_ ( self ):
self.config_tester.run_common_tests()
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*_lowerCAmelCase )
@require_sentencepiece
@require_tokenizers
@require_tf
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase = [
' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.',
' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ',
]
_UpperCamelCase = [
'California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to'
' reduce the risk of wildfires.',
'N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.',
] # differs slightly from pytorch, likely due to numerical differences in linear layers
_UpperCamelCase = 'google/pegasus-xsum'
@cached_property
def UpperCamelCase_ ( self ):
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def UpperCamelCase_ ( self ):
lowerCamelCase__ = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def UpperCamelCase_ ( self ,**_lowerCAmelCase ):
lowerCamelCase__ = self.translate_src_text(**_lowerCAmelCase )
assert self.expected_text == generated_words
def UpperCamelCase_ ( self ,**_lowerCAmelCase ):
lowerCamelCase__ = self.tokenizer(self.src_text ,**_lowerCAmelCase ,padding=_lowerCAmelCase ,return_tensors="""tf""" )
lowerCamelCase__ = self.model.generate(
model_inputs.input_ids ,attention_mask=model_inputs.attention_mask ,num_beams=2 ,use_cache=_lowerCAmelCase ,)
lowerCamelCase__ = self.tokenizer.batch_decode(generated_ids.numpy() ,skip_special_tokens=_lowerCAmelCase )
return generated_words
@slow
def UpperCamelCase_ ( self ):
self._assert_generated_batch_equal_expected()
| 50 |
'''simple docstring'''
import operator
def A__ ( __lowerCAmelCase : list , __lowerCAmelCase : bool = False , __lowerCAmelCase : list | None = None ):
lowerCamelCase__ = operator.lt if reverse else operator.gt
lowerCamelCase__ = solution or []
if not arr:
return solution
lowerCamelCase__ = [arr.pop(0 )]
for i, item in enumerate(__lowerCAmelCase ):
if _operator(__lowerCAmelCase , sublist[-1] ):
sublist.append(__lowerCAmelCase )
arr.pop(__lowerCAmelCase )
# merging sublist into solution list
if not solution:
solution.extend(__lowerCAmelCase )
else:
while sublist:
lowerCamelCase__ = sublist.pop(0 )
for i, xx in enumerate(__lowerCAmelCase ):
if not _operator(__lowerCAmelCase , __lowerCAmelCase ):
solution.insert(__lowerCAmelCase , __lowerCAmelCase )
break
else:
solution.append(__lowerCAmelCase )
strand_sort(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
return solution
if __name__ == "__main__":
assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]
| 50 | 1 |
'''simple docstring'''
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
UpperCamelCase : List[str] = [
# (stable-diffusion, HF Diffusers)
('time_embed.0.weight', 'time_embedding.linear_1.weight'),
('time_embed.0.bias', 'time_embedding.linear_1.bias'),
('time_embed.2.weight', 'time_embedding.linear_2.weight'),
('time_embed.2.bias', 'time_embedding.linear_2.bias'),
('input_blocks.0.0.weight', 'conv_in.weight'),
('input_blocks.0.0.bias', 'conv_in.bias'),
('out.0.weight', 'conv_norm_out.weight'),
('out.0.bias', 'conv_norm_out.bias'),
('out.2.weight', 'conv_out.weight'),
('out.2.bias', 'conv_out.bias'),
]
UpperCamelCase : List[str] = [
# (stable-diffusion, HF Diffusers)
('in_layers.0', 'norm1'),
('in_layers.2', 'conv1'),
('out_layers.0', 'norm2'),
('out_layers.3', 'conv2'),
('emb_layers.1', 'time_emb_proj'),
('skip_connection', 'conv_shortcut'),
]
UpperCamelCase : List[str] = []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
# loop over downblocks/upblocks
for j in range(2):
# loop over resnets/attentions for downblocks
UpperCamelCase : str = F'down_blocks.{i}.resnets.{j}.'
UpperCamelCase : List[Any] = F'input_blocks.{3*i + j + 1}.0.'
unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
if i < 3:
# no attention layers in down_blocks.3
UpperCamelCase : Optional[int] = F'down_blocks.{i}.attentions.{j}.'
UpperCamelCase : List[str] = F'input_blocks.{3*i + j + 1}.1.'
unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
for j in range(3):
# loop over resnets/attentions for upblocks
UpperCamelCase : Union[str, Any] = F'up_blocks.{i}.resnets.{j}.'
UpperCamelCase : int = F'output_blocks.{3*i + j}.0.'
unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
if i > 0:
# no attention layers in up_blocks.0
UpperCamelCase : str = F'up_blocks.{i}.attentions.{j}.'
UpperCamelCase : Optional[Any] = F'output_blocks.{3*i + j}.1.'
unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
if i < 3:
# no downsample in down_blocks.3
UpperCamelCase : Optional[Any] = F'down_blocks.{i}.downsamplers.0.conv.'
UpperCamelCase : Dict = F'input_blocks.{3*(i+1)}.0.op.'
unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
# no upsample in up_blocks.3
UpperCamelCase : List[Any] = F'up_blocks.{i}.upsamplers.0.'
UpperCamelCase : Optional[int] = F'output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'
unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
UpperCamelCase : List[str] = 'mid_block.attentions.0.'
UpperCamelCase : Any = 'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
for j in range(2):
UpperCamelCase : List[str] = F'mid_block.resnets.{j}.'
UpperCamelCase : str = F'middle_block.{2*j}.'
unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
def A__ ( __lowerCAmelCase : List[str] ):
# buyer beware: this is a *brittle* function,
# and correct output requires that all of these pieces interact in
# the exact order in which I have arranged them.
lowerCamelCase__ = {k: k for k in unet_state_dict.keys()}
for sd_name, hf_name in unet_conversion_map:
lowerCamelCase__ = sd_name
for k, v in mapping.items():
if "resnets" in k:
for sd_part, hf_part in unet_conversion_map_resnet:
lowerCamelCase__ = v.replace(__lowerCAmelCase , __lowerCAmelCase )
lowerCamelCase__ = v
for k, v in mapping.items():
for sd_part, hf_part in unet_conversion_map_layer:
lowerCamelCase__ = v.replace(__lowerCAmelCase , __lowerCAmelCase )
lowerCamelCase__ = v
lowerCamelCase__ = {v: unet_state_dict[k] for k, v in mapping.items()}
return new_state_dict
# ================#
# VAE Conversion #
# ================#
UpperCamelCase : Optional[Any] = [
# (stable-diffusion, HF Diffusers)
('nin_shortcut', 'conv_shortcut'),
('norm_out', 'conv_norm_out'),
('mid.attn_1.', 'mid_block.attentions.0.'),
]
for i in range(4):
# down_blocks have two resnets
for j in range(2):
UpperCamelCase : List[Any] = F'encoder.down_blocks.{i}.resnets.{j}.'
UpperCamelCase : Tuple = F'encoder.down.{i}.block.{j}.'
vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
if i < 3:
UpperCamelCase : Dict = F'down_blocks.{i}.downsamplers.0.'
UpperCamelCase : int = F'down.{i}.downsample.'
vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
UpperCamelCase : int = F'up_blocks.{i}.upsamplers.0.'
UpperCamelCase : Tuple = F'up.{3-i}.upsample.'
vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
# up_blocks have three resnets
# also, up blocks in hf are numbered in reverse from sd
for j in range(3):
UpperCamelCase : str = F'decoder.up_blocks.{i}.resnets.{j}.'
UpperCamelCase : int = F'decoder.up.{3-i}.block.{j}.'
vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
UpperCamelCase : Optional[Any] = F'mid_block.resnets.{i}.'
UpperCamelCase : List[str] = F'mid.block_{i+1}.'
vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
UpperCamelCase : Any = [
# (stable-diffusion, HF Diffusers)
('norm.', 'group_norm.'),
('q.', 'query.'),
('k.', 'key.'),
('v.', 'value.'),
('proj_out.', 'proj_attn.'),
]
def A__ ( __lowerCAmelCase : Dict ):
# convert HF linear weights to SD conv2d weights
return w.reshape(*w.shape , 1 , 1 )
def A__ ( __lowerCAmelCase : Optional[int] ):
lowerCamelCase__ = {k: k for k in vae_state_dict.keys()}
for k, v in mapping.items():
for sd_part, hf_part in vae_conversion_map:
lowerCamelCase__ = v.replace(__lowerCAmelCase , __lowerCAmelCase )
lowerCamelCase__ = v
for k, v in mapping.items():
if "attentions" in k:
for sd_part, hf_part in vae_conversion_map_attn:
lowerCamelCase__ = v.replace(__lowerCAmelCase , __lowerCAmelCase )
lowerCamelCase__ = v
lowerCamelCase__ = {v: vae_state_dict[k] for k, v in mapping.items()}
lowerCamelCase__ = ["""q""", """k""", """v""", """proj_out"""]
for k, v in new_state_dict.items():
for weight_name in weights_to_convert:
if F'''mid.attn_1.{weight_name}.weight''' in k:
print(F'''Reshaping {k} for SD format''' )
lowerCamelCase__ = reshape_weight_for_sd(__lowerCAmelCase )
return new_state_dict
# =========================#
# Text Encoder Conversion #
# =========================#
UpperCamelCase : List[Any] = [
# (stable-diffusion, HF Diffusers)
('resblocks.', 'text_model.encoder.layers.'),
('ln_1', 'layer_norm1'),
('ln_2', 'layer_norm2'),
('.c_fc.', '.fc1.'),
('.c_proj.', '.fc2.'),
('.attn', '.self_attn'),
('ln_final.', 'transformer.text_model.final_layer_norm.'),
('token_embedding.weight', 'transformer.text_model.embeddings.token_embedding.weight'),
('positional_embedding', 'transformer.text_model.embeddings.position_embedding.weight'),
]
UpperCamelCase : List[Any] = {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
UpperCamelCase : Tuple = re.compile('|'.join(protected.keys()))
# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
UpperCamelCase : List[Any] = {'q': 0, 'k': 1, 'v': 2}
def A__ ( __lowerCAmelCase : str ):
lowerCamelCase__ = {}
lowerCamelCase__ = {}
lowerCamelCase__ = {}
for k, v in text_enc_dict.items():
if (
k.endswith(""".self_attn.q_proj.weight""" )
or k.endswith(""".self_attn.k_proj.weight""" )
or k.endswith(""".self_attn.v_proj.weight""" )
):
lowerCamelCase__ = k[: -len(""".q_proj.weight""" )]
lowerCamelCase__ = k[-len("""q_proj.weight""" )]
if k_pre not in capture_qkv_weight:
lowerCamelCase__ = [None, None, None]
lowerCamelCase__ = v
continue
if (
k.endswith(""".self_attn.q_proj.bias""" )
or k.endswith(""".self_attn.k_proj.bias""" )
or k.endswith(""".self_attn.v_proj.bias""" )
):
lowerCamelCase__ = k[: -len(""".q_proj.bias""" )]
lowerCamelCase__ = k[-len("""q_proj.bias""" )]
if k_pre not in capture_qkv_bias:
lowerCamelCase__ = [None, None, None]
lowerCamelCase__ = v
continue
lowerCamelCase__ = textenc_pattern.sub(lambda __lowerCAmelCase : protected[re.escape(m.group(0 ) )] , __lowerCAmelCase )
lowerCamelCase__ = v
for k_pre, tensors in capture_qkv_weight.items():
if None in tensors:
raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing""" )
lowerCamelCase__ = textenc_pattern.sub(lambda __lowerCAmelCase : protected[re.escape(m.group(0 ) )] , __lowerCAmelCase )
lowerCamelCase__ = torch.cat(__lowerCAmelCase )
for k_pre, tensors in capture_qkv_bias.items():
if None in tensors:
raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing""" )
lowerCamelCase__ = textenc_pattern.sub(lambda __lowerCAmelCase : protected[re.escape(m.group(0 ) )] , __lowerCAmelCase )
lowerCamelCase__ = torch.cat(__lowerCAmelCase )
return new_state_dict
def A__ ( __lowerCAmelCase : Optional[Any] ):
return text_enc_dict
if __name__ == "__main__":
UpperCamelCase : Any = argparse.ArgumentParser()
parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
parser.add_argument(
'--use_safetensors', action='store_true', help='Save weights use safetensors, default is ckpt.'
)
UpperCamelCase : Union[str, Any] = parser.parse_args()
assert args.model_path is not None, "Must provide a model path!"
assert args.checkpoint_path is not None, "Must provide a checkpoint path!"
# Path for safetensors
UpperCamelCase : int = osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
UpperCamelCase : Optional[Any] = osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
UpperCamelCase : Dict = osp.join(args.model_path, 'text_encoder', 'model.safetensors')
# Load models from safetensors if it exists, if it doesn't pytorch
if osp.exists(unet_path):
UpperCamelCase : Optional[Any] = load_file(unet_path, device='cpu')
else:
UpperCamelCase : Optional[int] = osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
UpperCamelCase : List[str] = torch.load(unet_path, map_location='cpu')
if osp.exists(vae_path):
UpperCamelCase : Any = load_file(vae_path, device='cpu')
else:
UpperCamelCase : List[Any] = osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
UpperCamelCase : List[str] = torch.load(vae_path, map_location='cpu')
if osp.exists(text_enc_path):
UpperCamelCase : Tuple = load_file(text_enc_path, device='cpu')
else:
UpperCamelCase : Optional[Any] = osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
UpperCamelCase : Dict = torch.load(text_enc_path, map_location='cpu')
# Convert the UNet model
UpperCamelCase : Optional[int] = convert_unet_state_dict(unet_state_dict)
UpperCamelCase : Union[str, Any] = {'model.diffusion_model.' + k: v for k, v in unet_state_dict.items()}
# Convert the VAE model
UpperCamelCase : Optional[Any] = convert_vae_state_dict(vae_state_dict)
UpperCamelCase : Any = {'first_stage_model.' + k: v for k, v in vae_state_dict.items()}
# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
UpperCamelCase : Dict = 'text_model.encoder.layers.22.layer_norm2.bias' in text_enc_dict
if is_vaa_model:
# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
UpperCamelCase : int = {'transformer.' + k: v for k, v in text_enc_dict.items()}
UpperCamelCase : Optional[Any] = convert_text_enc_state_dict_vaa(text_enc_dict)
UpperCamelCase : List[Any] = {'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
else:
UpperCamelCase : Dict = convert_text_enc_state_dict(text_enc_dict)
UpperCamelCase : Dict = {'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}
# Put together new checkpoint
UpperCamelCase : List[str] = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
if args.half:
UpperCamelCase : Optional[Any] = {k: v.half() for k, v in state_dict.items()}
if args.use_safetensors:
save_file(state_dict, args.checkpoint_path)
else:
UpperCamelCase : Union[str, Any] = {'state_dict': state_dict}
torch.save(state_dict, args.checkpoint_path)
| 50 |
'''simple docstring'''
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def A__ ( __lowerCAmelCase : dict ):
return (data["data"], data["target"])
def A__ ( __lowerCAmelCase : np.ndarray , __lowerCAmelCase : np.ndarray , __lowerCAmelCase : np.ndarray ):
lowerCamelCase__ = XGBRegressor(verbosity=0 , random_state=42 )
xgb.fit(__lowerCAmelCase , __lowerCAmelCase )
# Predict target for test data
lowerCamelCase__ = xgb.predict(__lowerCAmelCase )
lowerCamelCase__ = predictions.reshape(len(__lowerCAmelCase ) , 1 )
return predictions
def A__ ( ):
lowerCamelCase__ = fetch_california_housing()
lowerCamelCase__ , lowerCamelCase__ = data_handling(__lowerCAmelCase )
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = train_test_split(
__lowerCAmelCase , __lowerCAmelCase , test_size=0.25 , random_state=1 )
lowerCamelCase__ = xgboost(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# Error printing
print(F'''Mean Absolute Error : {mean_absolute_error(__lowerCAmelCase , __lowerCAmelCase )}''' )
print(F'''Mean Square Error : {mean_squared_error(__lowerCAmelCase , __lowerCAmelCase )}''' )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 50 | 1 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : list[int] , __lowerCAmelCase : list[int] ):
lowerCamelCase__ = len(__lowerCAmelCase )
print("""The following activities are selected:""" )
# The first activity is always selected
lowerCamelCase__ = 0
print(__lowerCAmelCase , end=""",""" )
# Consider rest of the activities
for j in range(__lowerCAmelCase ):
# If this activity has start time greater than
# or equal to the finish time of previously
# selected activity, then select it
if start[j] >= finish[i]:
print(__lowerCAmelCase , end=""",""" )
lowerCamelCase__ = j
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCamelCase : Union[str, Any] = [1, 3, 0, 5, 8, 5]
UpperCamelCase : int = [2, 4, 6, 7, 9, 9]
print_max_activities(start, finish)
| 50 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBOSTokenLogitsProcessor,
FlaxForcedEOSTokenLogitsProcessor,
FlaxLogitsProcessorList,
FlaxMinLengthLogitsProcessor,
FlaxTemperatureLogitsWarper,
FlaxTopKLogitsWarper,
FlaxTopPLogitsWarper,
)
@require_flax
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = jnp.ones((batch_size, length) ) / length
return scores
def UpperCamelCase_ ( self ):
lowerCamelCase__ = None
lowerCamelCase__ = 20
lowerCamelCase__ = self._get_uniform_logits(batch_size=2 ,length=_lowerCAmelCase )
# tweak scores to not be uniform anymore
lowerCamelCase__ = scores.at[1, 5].set((1 / length) + 0.1 ) # peak, 1st batch
lowerCamelCase__ = scores.at[1, 10].set((1 / length) - 0.4 ) # valley, 1st batch
# compute softmax
lowerCamelCase__ = jax.nn.softmax(_lowerCAmelCase ,axis=-1 )
lowerCamelCase__ = FlaxTemperatureLogitsWarper(temperature=0.5 )
lowerCamelCase__ = FlaxTemperatureLogitsWarper(temperature=1.3 )
lowerCamelCase__ = jax.nn.softmax(temp_dist_warper_sharper(_lowerCAmelCase ,scores.copy() ,cur_len=_lowerCAmelCase ) ,axis=-1 )
lowerCamelCase__ = jax.nn.softmax(temp_dist_warper_smoother(_lowerCAmelCase ,scores.copy() ,cur_len=_lowerCAmelCase ) ,axis=-1 )
# uniform distribution stays uniform
self.assertTrue(jnp.allclose(probs[0, :] ,warped_prob_sharp[0, :] ,atol=1E-3 ) )
self.assertTrue(jnp.allclose(probs[0, :] ,warped_prob_smooth[0, :] ,atol=1E-3 ) )
# sharp peaks get higher, valleys get lower
self.assertLess(probs[1, :].max() ,warped_prob_sharp[1, :].max() )
self.assertGreater(probs[1, :].min() ,warped_prob_sharp[1, :].min() )
# smooth peaks get lower, valleys get higher
self.assertGreater(probs[1, :].max() ,warped_prob_smooth[1, :].max() )
self.assertLess(probs[1, :].min() ,warped_prob_smooth[1, :].min() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = None
lowerCamelCase__ = 10
lowerCamelCase__ = 2
# create ramp distribution
lowerCamelCase__ = np.broadcast_to(np.arange(_lowerCAmelCase )[None, :] ,(batch_size, vocab_size) ).copy()
lowerCamelCase__ = ramp_logits[1:, : vocab_size // 2] + vocab_size
lowerCamelCase__ = FlaxTopKLogitsWarper(3 )
lowerCamelCase__ = top_k_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# check that correct tokens are filtered
self.assertListEqual(jnp.isinf(scores[0] ).tolist() ,7 * [True] + 3 * [False] )
self.assertListEqual(jnp.isinf(scores[1] ).tolist() ,2 * [True] + 3 * [False] + 5 * [True] )
# check special case
lowerCamelCase__ = 5
lowerCamelCase__ = FlaxTopKLogitsWarper(top_k=1 ,filter_value=0.0 ,min_tokens_to_keep=3 )
lowerCamelCase__ = np.broadcast_to(np.arange(_lowerCAmelCase )[None, :] ,(batch_size, length) ).copy()
lowerCamelCase__ = top_k_warp_safety_check(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
self.assertListEqual((scores == 0.0).sum(axis=-1 ).tolist() ,[2, 2] )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = None
lowerCamelCase__ = 10
lowerCamelCase__ = 2
# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
lowerCamelCase__ = np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]] ) )
lowerCamelCase__ = FlaxTopPLogitsWarper(0.8 )
lowerCamelCase__ = np.exp(top_p_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase ) )
# dist should be filtered to keep min num values so that sum is >= top_p
# exp (-inf) => 0
lowerCamelCase__ = np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]] )
self.assertTrue(np.allclose(_lowerCAmelCase ,_lowerCAmelCase ,atol=1E-3 ) )
# check edge cases with negative and extreme logits
lowerCamelCase__ = np.broadcast_to(np.arange(_lowerCAmelCase )[None, :] ,(batch_size, vocab_size) ).copy() - (
vocab_size // 2
)
# make ramp_logits more extreme
lowerCamelCase__ = ramp_logits[1] * 100.0
# make sure at least 2 tokens are kept
lowerCamelCase__ = FlaxTopPLogitsWarper(0.9 ,min_tokens_to_keep=2 ,filter_value=0.0 )
lowerCamelCase__ = top_p_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
self.assertListEqual((filtered_dist != 0.0).sum(axis=-1 ).tolist() ,[3, 2] )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 20
lowerCamelCase__ = 4
lowerCamelCase__ = 0
lowerCamelCase__ = FlaxMinLengthLogitsProcessor(min_length=10 ,eos_token_id=_lowerCAmelCase )
# check that min length is applied at length 5
lowerCamelCase__ = ids_tensor((batch_size, 20) ,vocab_size=20 )
lowerCamelCase__ = 5
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = min_dist_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist() ,4 * [-float("""inf""" )] )
# check that min length is not applied anymore at length 15
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = 15
lowerCamelCase__ = min_dist_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertFalse(jnp.isinf(_lowerCAmelCase ).any() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 20
lowerCamelCase__ = 4
lowerCamelCase__ = 0
lowerCamelCase__ = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase )
# check that all scores are -inf except the bos_token_id score
lowerCamelCase__ = ids_tensor((batch_size, 1) ,vocab_size=20 )
lowerCamelCase__ = 1
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = logits_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, bos_token_id].tolist() ,4 * [0] ) # score for bos_token_id shold be zero
# check that bos_token_id is not forced if current length is greater than 1
lowerCamelCase__ = 3
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = logits_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertFalse(jnp.isinf(_lowerCAmelCase ).any() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 20
lowerCamelCase__ = 4
lowerCamelCase__ = 0
lowerCamelCase__ = 5
lowerCamelCase__ = FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase )
# check that all scores are -inf except the eos_token_id when max_length is reached
lowerCamelCase__ = ids_tensor((batch_size, 4) ,vocab_size=20 )
lowerCamelCase__ = 4
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = logits_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, eos_token_id].tolist() ,4 * [0] ) # score for eos_token_id should be zero
# check that eos_token_id is not forced if max_length is not reached
lowerCamelCase__ = 3
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = logits_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertFalse(jnp.isinf(_lowerCAmelCase ).any() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 4
lowerCamelCase__ = 10
lowerCamelCase__ = 15
lowerCamelCase__ = 2
lowerCamelCase__ = 1
lowerCamelCase__ = 15
# dummy input_ids and scores
lowerCamelCase__ = ids_tensor((batch_size, sequence_length) ,_lowerCAmelCase )
lowerCamelCase__ = input_ids.copy()
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = scores.copy()
# instantiate all dist processors
lowerCamelCase__ = FlaxTemperatureLogitsWarper(temperature=0.5 )
lowerCamelCase__ = FlaxTopKLogitsWarper(3 )
lowerCamelCase__ = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
lowerCamelCase__ = FlaxMinLengthLogitsProcessor(min_length=10 ,eos_token_id=_lowerCAmelCase )
lowerCamelCase__ = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase )
lowerCamelCase__ = FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase )
lowerCamelCase__ = 10
# no processor list
lowerCamelCase__ = temp_dist_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = top_k_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = top_p_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = min_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = bos_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = eos_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# with processor list
lowerCamelCase__ = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
lowerCamelCase__ = processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# scores should be equal
self.assertTrue(jnp.allclose(_lowerCAmelCase ,_lowerCAmelCase ,atol=1E-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() ,input_ids_comp.tolist() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 4
lowerCamelCase__ = 10
lowerCamelCase__ = 15
lowerCamelCase__ = 2
lowerCamelCase__ = 1
lowerCamelCase__ = 15
# dummy input_ids and scores
lowerCamelCase__ = ids_tensor((batch_size, sequence_length) ,_lowerCAmelCase )
lowerCamelCase__ = input_ids.copy()
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = scores.copy()
# instantiate all dist processors
lowerCamelCase__ = FlaxTemperatureLogitsWarper(temperature=0.5 )
lowerCamelCase__ = FlaxTopKLogitsWarper(3 )
lowerCamelCase__ = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
lowerCamelCase__ = FlaxMinLengthLogitsProcessor(min_length=10 ,eos_token_id=_lowerCAmelCase )
lowerCamelCase__ = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase )
lowerCamelCase__ = FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase )
lowerCamelCase__ = 10
# no processor list
def run_no_processor_list(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = temp_dist_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = top_k_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = top_p_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = min_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = bos_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = eos_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
return scores
# with processor list
def run_processor_list(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
lowerCamelCase__ = processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
return scores
lowerCamelCase__ = jax.jit(_lowerCAmelCase )
lowerCamelCase__ = jax.jit(_lowerCAmelCase )
lowerCamelCase__ = jitted_run_no_processor_list(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = jitted_run_processor_list(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
# scores should be equal
self.assertTrue(jnp.allclose(_lowerCAmelCase ,_lowerCAmelCase ,atol=1E-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() ,input_ids_comp.tolist() )
| 50 | 1 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def A__ ( __lowerCAmelCase : str = "AAPL" ):
lowerCamelCase__ = F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
lowerCamelCase__ = BeautifulSoup(requests.get(__lowerCAmelCase ).text , """html.parser""" )
lowerCamelCase__ = """My(6px) Pos(r) smartphone_Mt(6px)"""
return soup.find("""div""" , class_=class_ ).find("""span""" ).text
if __name__ == "__main__":
for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
print(F'Current {symbol:<4} stock price is {stock_price(symbol):>8}')
| 50 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase : Any = {
'configuration_groupvit': [
'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'GroupViTConfig',
'GroupViTOnnxConfig',
'GroupViTTextConfig',
'GroupViTVisionConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : List[str] = [
'GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'GroupViTModel',
'GroupViTPreTrainedModel',
'GroupViTTextModel',
'GroupViTVisionModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : List[str] = [
'TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFGroupViTModel',
'TFGroupViTPreTrainedModel',
'TFGroupViTTextModel',
'TFGroupViTVisionModel',
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
UpperCamelCase : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 50 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase : Tuple = {
'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
'tokenization_mvp': ['MvpTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : str = ['MvpTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Optional[int] = [
'MVP_PRETRAINED_MODEL_ARCHIVE_LIST',
'MvpForCausalLM',
'MvpForConditionalGeneration',
'MvpForQuestionAnswering',
'MvpForSequenceClassification',
'MvpModel',
'MvpPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
UpperCamelCase : Optional[int] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 50 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ):
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 50 | 1 |
'''simple docstring'''
from __future__ import annotations
from PIL import Image
# Define glider example
UpperCamelCase : Optional[Any] = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
]
# Define blinker example
UpperCamelCase : Tuple = [[0, 1, 0], [0, 1, 0], [0, 1, 0]]
def A__ ( __lowerCAmelCase : list[list[int]] ):
lowerCamelCase__ = []
for i in range(len(__lowerCAmelCase ) ):
lowerCamelCase__ = []
for j in range(len(cells[i] ) ):
# Get the number of live neighbours
lowerCamelCase__ = 0
if i > 0 and j > 0:
neighbour_count += cells[i - 1][j - 1]
if i > 0:
neighbour_count += cells[i - 1][j]
if i > 0 and j < len(cells[i] ) - 1:
neighbour_count += cells[i - 1][j + 1]
if j > 0:
neighbour_count += cells[i][j - 1]
if j < len(cells[i] ) - 1:
neighbour_count += cells[i][j + 1]
if i < len(__lowerCAmelCase ) - 1 and j > 0:
neighbour_count += cells[i + 1][j - 1]
if i < len(__lowerCAmelCase ) - 1:
neighbour_count += cells[i + 1][j]
if i < len(__lowerCAmelCase ) - 1 and j < len(cells[i] ) - 1:
neighbour_count += cells[i + 1][j + 1]
# Rules of the game of life (excerpt from Wikipedia):
# 1. Any live cell with two or three live neighbours survives.
# 2. Any dead cell with three live neighbours becomes a live cell.
# 3. All other live cells die in the next generation.
# Similarly, all other dead cells stay dead.
lowerCamelCase__ = cells[i][j] == 1
if (
(alive and 2 <= neighbour_count <= 3)
or not alive
and neighbour_count == 3
):
next_generation_row.append(1 )
else:
next_generation_row.append(0 )
next_generation.append(__lowerCAmelCase )
return next_generation
def A__ ( __lowerCAmelCase : list[list[int]] , __lowerCAmelCase : int ):
lowerCamelCase__ = []
for _ in range(__lowerCAmelCase ):
# Create output image
lowerCamelCase__ = Image.new("""RGB""" , (len(cells[0] ), len(__lowerCAmelCase )) )
lowerCamelCase__ = img.load()
# Save cells to image
for x in range(len(__lowerCAmelCase ) ):
for y in range(len(cells[0] ) ):
lowerCamelCase__ = 255 - cells[y][x] * 255
lowerCamelCase__ = (colour, colour, colour)
# Save image
images.append(__lowerCAmelCase )
lowerCamelCase__ = new_generation(__lowerCAmelCase )
return images
if __name__ == "__main__":
UpperCamelCase : Dict = generate_images(GLIDER, 16)
images[0].save('out.gif', save_all=True, append_images=images[1:])
| 50 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase : Union[str, Any] = {
'configuration_canine': ['CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CanineConfig'],
'tokenization_canine': ['CanineTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Any = [
'CANINE_PRETRAINED_MODEL_ARCHIVE_LIST',
'CanineForMultipleChoice',
'CanineForQuestionAnswering',
'CanineForSequenceClassification',
'CanineForTokenClassification',
'CanineLayer',
'CanineModel',
'CaninePreTrainedModel',
'load_tf_weights_in_canine',
]
if TYPE_CHECKING:
from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
from .tokenization_canine import CanineTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_canine import (
CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
CanineForMultipleChoice,
CanineForQuestionAnswering,
CanineForSequenceClassification,
CanineForTokenClassification,
CanineLayer,
CanineModel,
CaninePreTrainedModel,
load_tf_weights_in_canine,
)
else:
import sys
UpperCamelCase : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 50 | 1 |
'''simple docstring'''
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def A__ ( __lowerCAmelCase : bool = True , *__lowerCAmelCase : int , **__lowerCAmelCase : Union[str, Any] ):
if not is_tqdm_available():
raise ImportError("""Accelerate's `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`.""" )
lowerCamelCase__ = False
if main_process_only:
lowerCamelCase__ = PartialState().local_process_index == 0
return _tqdm(*__lowerCAmelCase , **__lowerCAmelCase , disable=__lowerCAmelCase )
| 50 |
'''simple docstring'''
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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.
# this script dumps information about the environment
import os
import sys
import transformers
UpperCamelCase : int = '3'
print('Python version:', sys.version)
print('transformers version:', transformers.__version__)
try:
import torch
print('Torch version:', torch.__version__)
print('Cuda available:', torch.cuda.is_available())
print('Cuda version:', torch.version.cuda)
print('CuDNN version:', torch.backends.cudnn.version())
print('Number of GPUs available:', torch.cuda.device_count())
print('NCCL version:', torch.cuda.nccl.version())
except ImportError:
print('Torch version:', None)
try:
import deepspeed
print('DeepSpeed version:', deepspeed.__version__)
except ImportError:
print('DeepSpeed version:', None)
try:
import tensorflow as tf
print('TensorFlow version:', tf.__version__)
print('TF GPUs available:', bool(tf.config.list_physical_devices('GPU')))
print('Number of TF GPUs available:', len(tf.config.list_physical_devices('GPU')))
except ImportError:
print('TensorFlow version:', None)
| 50 | 1 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ):
lowerCamelCase__ = [
"""safety_checker/pytorch_model.bin""",
"""safety_checker/model.safetensors""",
"""vae/diffusion_pytorch_model.bin""",
"""vae/diffusion_pytorch_model.safetensors""",
"""text_encoder/pytorch_model.bin""",
"""text_encoder/model.safetensors""",
"""unet/diffusion_pytorch_model.bin""",
"""unet/diffusion_pytorch_model.safetensors""",
]
self.assertTrue(is_safetensors_compatible(_lowerCAmelCase ) )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = [
"""unet/diffusion_pytorch_model.bin""",
"""unet/diffusion_pytorch_model.safetensors""",
]
self.assertTrue(is_safetensors_compatible(_lowerCAmelCase ) )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = [
"""safety_checker/pytorch_model.bin""",
"""safety_checker/model.safetensors""",
"""vae/diffusion_pytorch_model.bin""",
"""vae/diffusion_pytorch_model.safetensors""",
"""text_encoder/pytorch_model.bin""",
"""text_encoder/model.safetensors""",
"""unet/diffusion_pytorch_model.bin""",
# Removed: 'unet/diffusion_pytorch_model.safetensors',
]
self.assertFalse(is_safetensors_compatible(_lowerCAmelCase ) )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = [
"""text_encoder/pytorch_model.bin""",
"""text_encoder/model.safetensors""",
]
self.assertTrue(is_safetensors_compatible(_lowerCAmelCase ) )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = [
"""safety_checker/pytorch_model.bin""",
"""safety_checker/model.safetensors""",
"""vae/diffusion_pytorch_model.bin""",
"""vae/diffusion_pytorch_model.safetensors""",
"""text_encoder/pytorch_model.bin""",
# Removed: 'text_encoder/model.safetensors',
"""unet/diffusion_pytorch_model.bin""",
"""unet/diffusion_pytorch_model.safetensors""",
]
self.assertFalse(is_safetensors_compatible(_lowerCAmelCase ) )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = [
"""safety_checker/pytorch_model.fp16.bin""",
"""safety_checker/model.fp16.safetensors""",
"""vae/diffusion_pytorch_model.fp16.bin""",
"""vae/diffusion_pytorch_model.fp16.safetensors""",
"""text_encoder/pytorch_model.fp16.bin""",
"""text_encoder/model.fp16.safetensors""",
"""unet/diffusion_pytorch_model.fp16.bin""",
"""unet/diffusion_pytorch_model.fp16.safetensors""",
]
lowerCamelCase__ = """fp16"""
self.assertTrue(is_safetensors_compatible(_lowerCAmelCase ,variant=_lowerCAmelCase ) )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = [
"""unet/diffusion_pytorch_model.fp16.bin""",
"""unet/diffusion_pytorch_model.fp16.safetensors""",
]
lowerCamelCase__ = """fp16"""
self.assertTrue(is_safetensors_compatible(_lowerCAmelCase ,variant=_lowerCAmelCase ) )
def UpperCamelCase_ ( self ):
# pass variant but use the non-variant filenames
lowerCamelCase__ = [
"""unet/diffusion_pytorch_model.bin""",
"""unet/diffusion_pytorch_model.safetensors""",
]
lowerCamelCase__ = """fp16"""
self.assertTrue(is_safetensors_compatible(_lowerCAmelCase ,variant=_lowerCAmelCase ) )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = [
"""safety_checker/pytorch_model.fp16.bin""",
"""safety_checker/model.fp16.safetensors""",
"""vae/diffusion_pytorch_model.fp16.bin""",
"""vae/diffusion_pytorch_model.fp16.safetensors""",
"""text_encoder/pytorch_model.fp16.bin""",
"""text_encoder/model.fp16.safetensors""",
"""unet/diffusion_pytorch_model.fp16.bin""",
# Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
]
lowerCamelCase__ = """fp16"""
self.assertFalse(is_safetensors_compatible(_lowerCAmelCase ,variant=_lowerCAmelCase ) )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = [
"""text_encoder/pytorch_model.fp16.bin""",
"""text_encoder/model.fp16.safetensors""",
]
lowerCamelCase__ = """fp16"""
self.assertTrue(is_safetensors_compatible(_lowerCAmelCase ,variant=_lowerCAmelCase ) )
def UpperCamelCase_ ( self ):
# pass variant but use the non-variant filenames
lowerCamelCase__ = [
"""text_encoder/pytorch_model.bin""",
"""text_encoder/model.safetensors""",
]
lowerCamelCase__ = """fp16"""
self.assertTrue(is_safetensors_compatible(_lowerCAmelCase ,variant=_lowerCAmelCase ) )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = [
"""safety_checker/pytorch_model.fp16.bin""",
"""safety_checker/model.fp16.safetensors""",
"""vae/diffusion_pytorch_model.fp16.bin""",
"""vae/diffusion_pytorch_model.fp16.safetensors""",
"""text_encoder/pytorch_model.fp16.bin""",
# 'text_encoder/model.fp16.safetensors',
"""unet/diffusion_pytorch_model.fp16.bin""",
"""unet/diffusion_pytorch_model.fp16.safetensors""",
]
lowerCamelCase__ = """fp16"""
self.assertFalse(is_safetensors_compatible(_lowerCAmelCase ,variant=_lowerCAmelCase ) )
| 50 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase : Tuple = logging.get_logger(__name__)
UpperCamelCase : Union[str, Any] = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = 'gpt_bigcode'
_UpperCamelCase = ['past_key_values']
_UpperCamelCase = {
'hidden_size': 'n_embd',
'max_position_embeddings': 'n_positions',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self ,_lowerCAmelCase=5_02_57 ,_lowerCAmelCase=10_24 ,_lowerCAmelCase=7_68 ,_lowerCAmelCase=12 ,_lowerCAmelCase=12 ,_lowerCAmelCase=None ,_lowerCAmelCase="gelu_pytorch_tanh" ,_lowerCAmelCase=0.1 ,_lowerCAmelCase=0.1 ,_lowerCAmelCase=0.1 ,_lowerCAmelCase=1E-5 ,_lowerCAmelCase=0.02 ,_lowerCAmelCase=True ,_lowerCAmelCase=True ,_lowerCAmelCase=5_02_56 ,_lowerCAmelCase=5_02_56 ,_lowerCAmelCase=True ,_lowerCAmelCase=True ,_lowerCAmelCase=True ,**_lowerCAmelCase ,):
lowerCamelCase__ = vocab_size
lowerCamelCase__ = n_positions
lowerCamelCase__ = n_embd
lowerCamelCase__ = n_layer
lowerCamelCase__ = n_head
lowerCamelCase__ = n_inner
lowerCamelCase__ = activation_function
lowerCamelCase__ = resid_pdrop
lowerCamelCase__ = embd_pdrop
lowerCamelCase__ = attn_pdrop
lowerCamelCase__ = layer_norm_epsilon
lowerCamelCase__ = initializer_range
lowerCamelCase__ = scale_attn_weights
lowerCamelCase__ = use_cache
lowerCamelCase__ = attention_softmax_in_fpaa
lowerCamelCase__ = scale_attention_softmax_in_fpaa
lowerCamelCase__ = multi_query
lowerCamelCase__ = bos_token_id
lowerCamelCase__ = eos_token_id
super().__init__(bos_token_id=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase ,**_lowerCAmelCase )
| 50 | 1 |
'''simple docstring'''
import argparse
import os
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
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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
#
########################################################################
UpperCamelCase : Any = 16
UpperCamelCase : Any = 32
def A__ ( __lowerCAmelCase : Accelerator , __lowerCAmelCase : int = 16 ):
lowerCamelCase__ = AutoTokenizer.from_pretrained("""bert-base-cased""" )
lowerCamelCase__ = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(__lowerCAmelCase : Union[str, Any] ):
# max_length=None => use the model max length (it's actually the default)
lowerCamelCase__ = 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():
lowerCamelCase__ = 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
lowerCamelCase__ = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(__lowerCAmelCase : Dict ):
# On TPU it's best to pad everything to the same length or training will be very slow.
lowerCamelCase__ = 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":
lowerCamelCase__ = 16
elif accelerator.mixed_precision != "no":
lowerCamelCase__ = 8
else:
lowerCamelCase__ = None
return tokenizer.pad(
__lowerCAmelCase , padding="""longest""" , max_length=__lowerCAmelCase , pad_to_multiple_of=__lowerCAmelCase , return_tensors="""pt""" , )
# Instantiate dataloaders.
lowerCamelCase__ = DataLoader(
tokenized_datasets["""train"""] , shuffle=__lowerCAmelCase , collate_fn=__lowerCAmelCase , batch_size=__lowerCAmelCase )
lowerCamelCase__ = 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
UpperCamelCase : int = mocked_dataloaders # noqa: F811
def A__ ( __lowerCAmelCase : Dict , __lowerCAmelCase : List[str] ):
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , __lowerCAmelCase ) == "1":
lowerCamelCase__ = 2
# Initialize accelerator
lowerCamelCase__ = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowerCamelCase__ = config["""lr"""]
lowerCamelCase__ = int(config["""num_epochs"""] )
lowerCamelCase__ = int(config["""seed"""] )
lowerCamelCase__ = int(config["""batch_size"""] )
lowerCamelCase__ = evaluate.load("""glue""" , """mrpc""" )
# If the batch size is too big we use gradient accumulation
lowerCamelCase__ = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
lowerCamelCase__ = batch_size // MAX_GPU_BATCH_SIZE
lowerCamelCase__ = MAX_GPU_BATCH_SIZE
set_seed(__lowerCAmelCase )
lowerCamelCase__ , lowerCamelCase__ = get_dataloaders(__lowerCAmelCase , __lowerCAmelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowerCamelCase__ = 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).
lowerCamelCase__ = model.to(accelerator.device )
# Instantiate optimizer
lowerCamelCase__ = AdamW(params=model.parameters() , lr=__lowerCAmelCase )
# Instantiate scheduler
lowerCamelCase__ = get_linear_schedule_with_warmup(
optimizer=__lowerCAmelCase , num_warmup_steps=100 , num_training_steps=(len(__lowerCAmelCase ) * num_epochs) // gradient_accumulation_steps , )
# 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.
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = 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 )
lowerCamelCase__ = model(**__lowerCAmelCase )
lowerCamelCase__ = outputs.loss
lowerCamelCase__ = loss / gradient_accumulation_steps
accelerator.backward(__lowerCAmelCase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
lowerCamelCase__ = 0
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():
lowerCamelCase__ = model(**__lowerCAmelCase )
lowerCamelCase__ = outputs.logits.argmax(dim=-1 )
lowerCamelCase__ , lowerCamelCase__ = accelerator.gather((predictions, batch["""labels"""]) )
# New Code #
# First we check if it's a distributed system
if accelerator.use_distributed:
# Then see if we're on the last batch of our eval dataloader
if step == len(__lowerCAmelCase ) - 1:
# Last batch needs to be truncated on distributed systems as it contains additional samples
lowerCamelCase__ = predictions[: len(eval_dataloader.dataset ) - samples_seen]
lowerCamelCase__ = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
# Otherwise we add the number of samples seen
samples_seen += references.shape[0]
# All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
# accelerator.gather_for_metrics((predictions, batch["labels"]))
metric.add_batch(
predictions=__lowerCAmelCase , references=__lowerCAmelCase , )
lowerCamelCase__ = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , __lowerCAmelCase )
def A__ ( ):
lowerCamelCase__ = 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.""" )
lowerCamelCase__ = parser.parse_args()
lowerCamelCase__ = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(__lowerCAmelCase , __lowerCAmelCase )
if __name__ == "__main__":
main()
| 50 |
'''simple docstring'''
from PIL import Image
def A__ ( __lowerCAmelCase : Image , __lowerCAmelCase : float ):
def brightness(__lowerCAmelCase : int ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError("""level must be between -255.0 (black) and 255.0 (white)""" )
return img.point(__lowerCAmelCase )
if __name__ == "__main__":
# Load image
with Image.open('image_data/lena.jpg') as img:
# Change brightness to 100
UpperCamelCase : Union[str, Any] = change_brightness(img, 1_00)
brigt_img.save('image_data/lena_brightness.png', format='png')
| 50 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, SamImageProcessor, SamProcessor
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
@require_vision
@require_torchvision
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ):
lowerCamelCase__ = tempfile.mkdtemp()
lowerCamelCase__ = SamImageProcessor()
lowerCamelCase__ = SamProcessor(_lowerCAmelCase )
processor.save_pretrained(self.tmpdirname )
def UpperCamelCase_ ( self ,**_lowerCAmelCase ):
return AutoProcessor.from_pretrained(self.tmpdirname ,**_lowerCAmelCase ).image_processor
def UpperCamelCase_ ( self ):
shutil.rmtree(self.tmpdirname )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = [np.random.randint(2_55 ,size=(3, 30, 4_00) ,dtype=np.uinta )]
lowerCamelCase__ = [Image.fromarray(np.moveaxis(_lowerCAmelCase ,0 ,-1 ) ) for x in image_inputs]
return image_inputs
def UpperCamelCase_ ( self ):
lowerCamelCase__ = SamProcessor(image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
lowerCamelCase__ = self.get_image_processor(do_normalize=_lowerCAmelCase ,padding_value=1.0 )
lowerCamelCase__ = SamProcessor.from_pretrained(self.tmpdirname ,do_normalize=_lowerCAmelCase ,padding_value=1.0 )
self.assertEqual(processor.image_processor.to_json_string() ,image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor ,_lowerCAmelCase )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.get_image_processor()
lowerCamelCase__ = SamProcessor(image_processor=_lowerCAmelCase )
lowerCamelCase__ = self.prepare_image_inputs()
lowerCamelCase__ = image_processor(_lowerCAmelCase ,return_tensors="""np""" )
lowerCamelCase__ = processor(images=_lowerCAmelCase ,return_tensors="""np""" )
input_feat_extract.pop("""original_sizes""" ) # pop original_sizes as it is popped in the processor
input_feat_extract.pop("""reshaped_input_sizes""" ) # pop original_sizes as it is popped in the processor
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() ,input_processor[key].sum() ,delta=1E-2 )
@require_torch
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.get_image_processor()
lowerCamelCase__ = SamProcessor(image_processor=_lowerCAmelCase )
lowerCamelCase__ = [torch.ones((1, 3, 5, 5) )]
lowerCamelCase__ = [[17_64, 26_46]]
lowerCamelCase__ = [[6_83, 10_24]]
lowerCamelCase__ = processor.post_process_masks(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
self.assertEqual(masks[0].shape ,(1, 3, 17_64, 26_46) )
lowerCamelCase__ = processor.post_process_masks(
_lowerCAmelCase ,torch.tensor(_lowerCAmelCase ) ,torch.tensor(_lowerCAmelCase ) )
self.assertEqual(masks[0].shape ,(1, 3, 17_64, 26_46) )
# should also work with np
lowerCamelCase__ = [np.ones((1, 3, 5, 5) )]
lowerCamelCase__ = processor.post_process_masks(_lowerCAmelCase ,np.array(_lowerCAmelCase ) ,np.array(_lowerCAmelCase ) )
self.assertEqual(masks[0].shape ,(1, 3, 17_64, 26_46) )
lowerCamelCase__ = [[1, 0], [0, 1]]
with self.assertRaises(_lowerCAmelCase ):
lowerCamelCase__ = processor.post_process_masks(_lowerCAmelCase ,np.array(_lowerCAmelCase ) ,np.array(_lowerCAmelCase ) )
@require_vision
@require_tf
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ):
lowerCamelCase__ = tempfile.mkdtemp()
lowerCamelCase__ = SamImageProcessor()
lowerCamelCase__ = SamProcessor(_lowerCAmelCase )
processor.save_pretrained(self.tmpdirname )
def UpperCamelCase_ ( self ,**_lowerCAmelCase ):
return AutoProcessor.from_pretrained(self.tmpdirname ,**_lowerCAmelCase ).image_processor
def UpperCamelCase_ ( self ):
shutil.rmtree(self.tmpdirname )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = [np.random.randint(2_55 ,size=(3, 30, 4_00) ,dtype=np.uinta )]
lowerCamelCase__ = [Image.fromarray(np.moveaxis(_lowerCAmelCase ,0 ,-1 ) ) for x in image_inputs]
return image_inputs
def UpperCamelCase_ ( self ):
lowerCamelCase__ = SamProcessor(image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
lowerCamelCase__ = self.get_image_processor(do_normalize=_lowerCAmelCase ,padding_value=1.0 )
lowerCamelCase__ = SamProcessor.from_pretrained(self.tmpdirname ,do_normalize=_lowerCAmelCase ,padding_value=1.0 )
self.assertEqual(processor.image_processor.to_json_string() ,image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor ,_lowerCAmelCase )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.get_image_processor()
lowerCamelCase__ = SamProcessor(image_processor=_lowerCAmelCase )
lowerCamelCase__ = self.prepare_image_inputs()
lowerCamelCase__ = image_processor(_lowerCAmelCase ,return_tensors="""np""" )
lowerCamelCase__ = processor(images=_lowerCAmelCase ,return_tensors="""np""" )
input_feat_extract.pop("""original_sizes""" ) # pop original_sizes as it is popped in the processor
input_feat_extract.pop("""reshaped_input_sizes""" ) # pop reshaped_input_sizes as it is popped in the processor
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() ,input_processor[key].sum() ,delta=1E-2 )
@require_tf
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.get_image_processor()
lowerCamelCase__ = SamProcessor(image_processor=_lowerCAmelCase )
lowerCamelCase__ = [tf.ones((1, 3, 5, 5) )]
lowerCamelCase__ = [[17_64, 26_46]]
lowerCamelCase__ = [[6_83, 10_24]]
lowerCamelCase__ = processor.post_process_masks(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,return_tensors="""tf""" )
self.assertEqual(masks[0].shape ,(1, 3, 17_64, 26_46) )
lowerCamelCase__ = processor.post_process_masks(
_lowerCAmelCase ,tf.convert_to_tensor(_lowerCAmelCase ) ,tf.convert_to_tensor(_lowerCAmelCase ) ,return_tensors="""tf""" ,)
self.assertEqual(masks[0].shape ,(1, 3, 17_64, 26_46) )
# should also work with np
lowerCamelCase__ = [np.ones((1, 3, 5, 5) )]
lowerCamelCase__ = processor.post_process_masks(
_lowerCAmelCase ,np.array(_lowerCAmelCase ) ,np.array(_lowerCAmelCase ) ,return_tensors="""tf""" )
self.assertEqual(masks[0].shape ,(1, 3, 17_64, 26_46) )
lowerCamelCase__ = [[1, 0], [0, 1]]
with self.assertRaises(tf.errors.InvalidArgumentError ):
lowerCamelCase__ = processor.post_process_masks(
_lowerCAmelCase ,np.array(_lowerCAmelCase ) ,np.array(_lowerCAmelCase ) ,return_tensors="""tf""" )
@require_vision
@require_torchvision
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ):
lowerCamelCase__ = tempfile.mkdtemp()
lowerCamelCase__ = SamImageProcessor()
lowerCamelCase__ = SamProcessor(_lowerCAmelCase )
processor.save_pretrained(self.tmpdirname )
def UpperCamelCase_ ( self ,**_lowerCAmelCase ):
return AutoProcessor.from_pretrained(self.tmpdirname ,**_lowerCAmelCase ).image_processor
def UpperCamelCase_ ( self ):
shutil.rmtree(self.tmpdirname )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = [np.random.randint(2_55 ,size=(3, 30, 4_00) ,dtype=np.uinta )]
lowerCamelCase__ = [Image.fromarray(np.moveaxis(_lowerCAmelCase ,0 ,-1 ) ) for x in image_inputs]
return image_inputs
@is_pt_tf_cross_test
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.get_image_processor()
lowerCamelCase__ = SamProcessor(image_processor=_lowerCAmelCase )
lowerCamelCase__ = np.random.randint(0 ,2 ,size=(1, 3, 5, 5) ).astype(np.floataa )
lowerCamelCase__ = [tf.convert_to_tensor(_lowerCAmelCase )]
lowerCamelCase__ = [torch.tensor(_lowerCAmelCase )]
lowerCamelCase__ = [[17_64, 26_46]]
lowerCamelCase__ = [[6_83, 10_24]]
lowerCamelCase__ = processor.post_process_masks(
_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,return_tensors="""tf""" )
lowerCamelCase__ = processor.post_process_masks(
_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,return_tensors="""pt""" )
self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy() ) )
@is_pt_tf_cross_test
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.get_image_processor()
lowerCamelCase__ = SamProcessor(image_processor=_lowerCAmelCase )
lowerCamelCase__ = self.prepare_image_inputs()
lowerCamelCase__ = image_processor(_lowerCAmelCase ,return_tensors="""pt""" )["""pixel_values"""].numpy()
lowerCamelCase__ = processor(images=_lowerCAmelCase ,return_tensors="""pt""" )["""pixel_values"""].numpy()
lowerCamelCase__ = image_processor(_lowerCAmelCase ,return_tensors="""tf""" )["""pixel_values"""].numpy()
lowerCamelCase__ = processor(images=_lowerCAmelCase ,return_tensors="""tf""" )["""pixel_values"""].numpy()
self.assertTrue(np.allclose(_lowerCAmelCase ,_lowerCAmelCase ) )
self.assertTrue(np.allclose(_lowerCAmelCase ,_lowerCAmelCase ) )
self.assertTrue(np.allclose(_lowerCAmelCase ,_lowerCAmelCase ) )
| 50 |
'''simple docstring'''
def A__ ( ):
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
UpperCamelCase : Dict = generate_large_matrix()
UpperCamelCase : Any = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7, 7, 6], [-1, -2, -3]],
grid,
)
def A__ ( __lowerCAmelCase : list[list[int]] ):
assert all(row == sorted(__lowerCAmelCase , reverse=__lowerCAmelCase ) for row in grid )
assert all(list(__lowerCAmelCase ) == sorted(__lowerCAmelCase , reverse=__lowerCAmelCase ) for col in zip(*__lowerCAmelCase ) )
def A__ ( __lowerCAmelCase : list[int] ):
lowerCamelCase__ = 0
lowerCamelCase__ = len(__lowerCAmelCase ) - 1
# Edge cases such as no values or all numbers are negative.
if not array or array[0] < 0:
return 0
while right + 1 > left:
lowerCamelCase__ = (left + right) // 2
lowerCamelCase__ = array[mid]
# Num must be negative and the index must be greater than or equal to 0.
if num < 0 and array[mid - 1] >= 0:
return mid
if num >= 0:
lowerCamelCase__ = mid + 1
else:
lowerCamelCase__ = mid - 1
# No negative numbers so return the last index of the array + 1 which is the length.
return len(__lowerCAmelCase )
def A__ ( __lowerCAmelCase : list[list[int]] ):
lowerCamelCase__ = 0
lowerCamelCase__ = len(grid[0] )
for i in range(len(__lowerCAmelCase ) ):
lowerCamelCase__ = find_negative_index(grid[i][:bound] )
total += bound
return (len(__lowerCAmelCase ) * len(grid[0] )) - total
def A__ ( __lowerCAmelCase : list[list[int]] ):
return len([number for row in grid for number in row if number < 0] )
def A__ ( __lowerCAmelCase : list[list[int]] ):
lowerCamelCase__ = 0
for row in grid:
for i, number in enumerate(__lowerCAmelCase ):
if number < 0:
total += len(__lowerCAmelCase ) - i
break
return total
def A__ ( ):
from timeit import timeit
print("""Running benchmarks""" )
lowerCamelCase__ = (
"""from __main__ import count_negatives_binary_search, """
"""count_negatives_brute_force, count_negatives_brute_force_with_break, grid"""
)
for func in (
"count_negatives_binary_search", # took 0.7727 seconds
"count_negatives_brute_force_with_break", # took 4.6505 seconds
"count_negatives_brute_force", # took 12.8160 seconds
):
lowerCamelCase__ = timeit(F'''{func}(grid=grid)''' , setup=__lowerCAmelCase , number=500 )
print(F'''{func}() took {time:0.4f} seconds''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 50 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase : Tuple = logging.get_logger(__name__)
UpperCamelCase : Union[str, Any] = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = 'gpt_bigcode'
_UpperCamelCase = ['past_key_values']
_UpperCamelCase = {
'hidden_size': 'n_embd',
'max_position_embeddings': 'n_positions',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self ,_lowerCAmelCase=5_02_57 ,_lowerCAmelCase=10_24 ,_lowerCAmelCase=7_68 ,_lowerCAmelCase=12 ,_lowerCAmelCase=12 ,_lowerCAmelCase=None ,_lowerCAmelCase="gelu_pytorch_tanh" ,_lowerCAmelCase=0.1 ,_lowerCAmelCase=0.1 ,_lowerCAmelCase=0.1 ,_lowerCAmelCase=1E-5 ,_lowerCAmelCase=0.02 ,_lowerCAmelCase=True ,_lowerCAmelCase=True ,_lowerCAmelCase=5_02_56 ,_lowerCAmelCase=5_02_56 ,_lowerCAmelCase=True ,_lowerCAmelCase=True ,_lowerCAmelCase=True ,**_lowerCAmelCase ,):
lowerCamelCase__ = vocab_size
lowerCamelCase__ = n_positions
lowerCamelCase__ = n_embd
lowerCamelCase__ = n_layer
lowerCamelCase__ = n_head
lowerCamelCase__ = n_inner
lowerCamelCase__ = activation_function
lowerCamelCase__ = resid_pdrop
lowerCamelCase__ = embd_pdrop
lowerCamelCase__ = attn_pdrop
lowerCamelCase__ = layer_norm_epsilon
lowerCamelCase__ = initializer_range
lowerCamelCase__ = scale_attn_weights
lowerCamelCase__ = use_cache
lowerCamelCase__ = attention_softmax_in_fpaa
lowerCamelCase__ = scale_attention_softmax_in_fpaa
lowerCamelCase__ = multi_query
lowerCamelCase__ = bos_token_id
lowerCamelCase__ = eos_token_id
super().__init__(bos_token_id=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase ,**_lowerCAmelCase )
| 50 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
UpperCamelCase : List[Any] = 'examples/'
UpperCamelCase : int = {
'examples': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
'init': (re.compile(r'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'),
'setup': (re.compile(r'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), r'\1version="VERSION",'),
'doc': (re.compile(r'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), 'release = "VERSION"\n'),
}
UpperCamelCase : Any = {
'init': 'src/transformers/__init__.py',
'setup': 'setup.py',
}
UpperCamelCase : Any = 'README.md'
def A__ ( __lowerCAmelCase : Any , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[int] ):
with open(__lowerCAmelCase , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
lowerCamelCase__ = f.read()
lowerCamelCase__ , lowerCamelCase__ = REPLACE_PATTERNS[pattern]
lowerCamelCase__ = replace.replace("""VERSION""" , __lowerCAmelCase )
lowerCamelCase__ = re_pattern.sub(__lowerCAmelCase , __lowerCAmelCase )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.write(__lowerCAmelCase )
def A__ ( __lowerCAmelCase : str ):
for folder, directories, fnames in os.walk(__lowerCAmelCase ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove("""research_projects""" )
if "legacy" in directories:
directories.remove("""legacy""" )
for fname in fnames:
if fname.endswith(""".py""" ):
update_version_in_file(os.path.join(__lowerCAmelCase , __lowerCAmelCase ) , __lowerCAmelCase , pattern="""examples""" )
def A__ ( __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[Any]=False ):
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
if not patch:
update_version_in_examples(__lowerCAmelCase )
def A__ ( ):
lowerCamelCase__ = """🤗 Transformers currently provides the following architectures"""
lowerCamelCase__ = """1. Want to contribute a new model?"""
with open(__lowerCAmelCase , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
lowerCamelCase__ = f.readlines()
# Find the start of the list.
lowerCamelCase__ = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
lowerCamelCase__ = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith("""1.""" ):
lowerCamelCase__ = lines[index].replace(
"""https://huggingface.co/docs/transformers/main/model_doc""" , """https://huggingface.co/docs/transformers/model_doc""" , )
index += 1
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.writelines(__lowerCAmelCase )
def A__ ( ):
with open(REPLACE_FILES["""init"""] , """r""" ) as f:
lowerCamelCase__ = f.read()
lowerCamelCase__ = REPLACE_PATTERNS["""init"""][0].search(__lowerCAmelCase ).groups()[0]
return packaging.version.parse(__lowerCAmelCase )
def A__ ( __lowerCAmelCase : Union[str, Any]=False ):
lowerCamelCase__ = get_version()
if patch and default_version.is_devrelease:
raise ValueError("""Can't create a patch version from the dev branch, checkout a released version!""" )
if default_version.is_devrelease:
lowerCamelCase__ = default_version.base_version
elif patch:
lowerCamelCase__ = F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
else:
lowerCamelCase__ = F'''{default_version.major}.{default_version.minor + 1}.0'''
# Now let's ask nicely if that's the right one.
lowerCamelCase__ = input(F'''Which version are you releasing? [{default_version}]''' )
if len(__lowerCAmelCase ) == 0:
lowerCamelCase__ = default_version
print(F'''Updating version to {version}.''' )
global_version_update(__lowerCAmelCase , patch=__lowerCAmelCase )
if not patch:
print("""Cleaning main README, don't forget to run `make fix-copies`.""" )
clean_main_ref_in_model_list()
def A__ ( ):
lowerCamelCase__ = get_version()
lowerCamelCase__ = F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
lowerCamelCase__ = current_version.base_version
# Check with the user we got that right.
lowerCamelCase__ = input(F'''Which version are we developing now? [{dev_version}]''' )
if len(__lowerCAmelCase ) == 0:
lowerCamelCase__ = dev_version
print(F'''Updating version to {version}.''' )
global_version_update(__lowerCAmelCase )
print("""Cleaning main README, don't forget to run `make fix-copies`.""" )
clean_main_ref_in_model_list()
if __name__ == "__main__":
UpperCamelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')
parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')
UpperCamelCase : Any = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print('Nothing to do after a patch :-)')
else:
post_release_work()
| 50 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from datasets import load_dataset
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 BeitImageProcessor
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
def __init__( self ,_lowerCAmelCase ,_lowerCAmelCase=7 ,_lowerCAmelCase=3 ,_lowerCAmelCase=18 ,_lowerCAmelCase=30 ,_lowerCAmelCase=4_00 ,_lowerCAmelCase=True ,_lowerCAmelCase=None ,_lowerCAmelCase=True ,_lowerCAmelCase=None ,_lowerCAmelCase=True ,_lowerCAmelCase=[0.5, 0.5, 0.5] ,_lowerCAmelCase=[0.5, 0.5, 0.5] ,_lowerCAmelCase=False ,):
lowerCamelCase__ = size if size is not None else {"""height""": 20, """width""": 20}
lowerCamelCase__ = crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
lowerCamelCase__ = parent
lowerCamelCase__ = batch_size
lowerCamelCase__ = num_channels
lowerCamelCase__ = image_size
lowerCamelCase__ = min_resolution
lowerCamelCase__ = max_resolution
lowerCamelCase__ = do_resize
lowerCamelCase__ = size
lowerCamelCase__ = do_center_crop
lowerCamelCase__ = crop_size
lowerCamelCase__ = do_normalize
lowerCamelCase__ = image_mean
lowerCamelCase__ = image_std
lowerCamelCase__ = do_reduce_labels
def UpperCamelCase_ ( self ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_reduce_labels": self.do_reduce_labels,
}
def A__ ( ):
lowerCamelCase__ = load_dataset("""hf-internal-testing/fixtures_ade20k""" , split="""test""" )
lowerCamelCase__ = Image.open(dataset[0]["""file"""] )
lowerCamelCase__ = Image.open(dataset[1]["""file"""] )
return image, map
def A__ ( ):
lowerCamelCase__ = load_dataset("""hf-internal-testing/fixtures_ade20k""" , split="""test""" )
lowerCamelCase__ = Image.open(ds[0]["""file"""] )
lowerCamelCase__ = Image.open(ds[1]["""file"""] )
lowerCamelCase__ = Image.open(ds[2]["""file"""] )
lowerCamelCase__ = Image.open(ds[3]["""file"""] )
return [imagea, imagea], [mapa, mapa]
@require_torch
@require_vision
class UpperCamelCase__ (a ,unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase = BeitImageProcessor if is_vision_available() else None
def UpperCamelCase_ ( self ):
lowerCamelCase__ = BeitImageProcessingTester(self )
@property
def UpperCamelCase_ ( self ):
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowerCAmelCase ,"""do_resize""" ) )
self.assertTrue(hasattr(_lowerCAmelCase ,"""size""" ) )
self.assertTrue(hasattr(_lowerCAmelCase ,"""do_center_crop""" ) )
self.assertTrue(hasattr(_lowerCAmelCase ,"""center_crop""" ) )
self.assertTrue(hasattr(_lowerCAmelCase ,"""do_normalize""" ) )
self.assertTrue(hasattr(_lowerCAmelCase ,"""image_mean""" ) )
self.assertTrue(hasattr(_lowerCAmelCase ,"""image_std""" ) )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size ,{"""height""": 20, """width""": 20} )
self.assertEqual(image_processor.crop_size ,{"""height""": 18, """width""": 18} )
self.assertEqual(image_processor.do_reduce_labels ,_lowerCAmelCase )
lowerCamelCase__ = self.image_processing_class.from_dict(
self.image_processor_dict ,size=42 ,crop_size=84 ,reduce_labels=_lowerCAmelCase )
self.assertEqual(image_processor.size ,{"""height""": 42, """width""": 42} )
self.assertEqual(image_processor.crop_size ,{"""height""": 84, """width""": 84} )
self.assertEqual(image_processor.do_reduce_labels ,_lowerCAmelCase )
def UpperCamelCase_ ( self ):
pass
def UpperCamelCase_ ( self ):
# Initialize image_processing
lowerCamelCase__ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowerCamelCase__ = prepare_image_inputs(self.image_processor_tester ,equal_resolution=_lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCAmelCase ,Image.Image )
# Test not batched input
lowerCamelCase__ = 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.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
# Test batched
lowerCamelCase__ = image_processing(_lowerCAmelCase ,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.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
def UpperCamelCase_ ( self ):
# Initialize image_processing
lowerCamelCase__ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowerCamelCase__ = prepare_image_inputs(self.image_processor_tester ,equal_resolution=_lowerCAmelCase ,numpify=_lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCAmelCase ,np.ndarray )
# Test not batched input
lowerCamelCase__ = 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.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
# Test batched
lowerCamelCase__ = image_processing(_lowerCAmelCase ,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.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
def UpperCamelCase_ ( self ):
# Initialize image_processing
lowerCamelCase__ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowerCamelCase__ = prepare_image_inputs(self.image_processor_tester ,equal_resolution=_lowerCAmelCase ,torchify=_lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCAmelCase ,torch.Tensor )
# Test not batched input
lowerCamelCase__ = 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.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
# Test batched
lowerCamelCase__ = image_processing(_lowerCAmelCase ,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.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
def UpperCamelCase_ ( self ):
# Initialize image_processing
lowerCamelCase__ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowerCamelCase__ = prepare_image_inputs(self.image_processor_tester ,equal_resolution=_lowerCAmelCase ,torchify=_lowerCAmelCase )
lowerCamelCase__ = []
for image in image_inputs:
self.assertIsInstance(_lowerCAmelCase ,torch.Tensor )
maps.append(torch.zeros(image.shape[-2:] ).long() )
# Test not batched input
lowerCamelCase__ = image_processing(image_inputs[0] ,maps[0] ,return_tensors="""pt""" )
self.assertEqual(
encoding["""pixel_values"""].shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
self.assertEqual(
encoding["""labels"""].shape ,(
1,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
self.assertEqual(encoding["""labels"""].dtype ,torch.long )
self.assertTrue(encoding["""labels"""].min().item() >= 0 )
self.assertTrue(encoding["""labels"""].max().item() <= 2_55 )
# Test batched
lowerCamelCase__ = image_processing(_lowerCAmelCase ,_lowerCAmelCase ,return_tensors="""pt""" )
self.assertEqual(
encoding["""pixel_values"""].shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
self.assertEqual(
encoding["""labels"""].shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
self.assertEqual(encoding["""labels"""].dtype ,torch.long )
self.assertTrue(encoding["""labels"""].min().item() >= 0 )
self.assertTrue(encoding["""labels"""].max().item() <= 2_55 )
# Test not batched input (PIL images)
lowerCamelCase__ , lowerCamelCase__ = prepare_semantic_single_inputs()
lowerCamelCase__ = image_processing(_lowerCAmelCase ,_lowerCAmelCase ,return_tensors="""pt""" )
self.assertEqual(
encoding["""pixel_values"""].shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
self.assertEqual(
encoding["""labels"""].shape ,(
1,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
self.assertEqual(encoding["""labels"""].dtype ,torch.long )
self.assertTrue(encoding["""labels"""].min().item() >= 0 )
self.assertTrue(encoding["""labels"""].max().item() <= 2_55 )
# Test batched input (PIL images)
lowerCamelCase__ , lowerCamelCase__ = prepare_semantic_batch_inputs()
lowerCamelCase__ = image_processing(_lowerCAmelCase ,_lowerCAmelCase ,return_tensors="""pt""" )
self.assertEqual(
encoding["""pixel_values"""].shape ,(
2,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
self.assertEqual(
encoding["""labels"""].shape ,(
2,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
self.assertEqual(encoding["""labels"""].dtype ,torch.long )
self.assertTrue(encoding["""labels"""].min().item() >= 0 )
self.assertTrue(encoding["""labels"""].max().item() <= 2_55 )
def UpperCamelCase_ ( self ):
# Initialize image_processing
lowerCamelCase__ = self.image_processing_class(**self.image_processor_dict )
# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
lowerCamelCase__ , lowerCamelCase__ = prepare_semantic_single_inputs()
lowerCamelCase__ = image_processing(_lowerCAmelCase ,_lowerCAmelCase ,return_tensors="""pt""" )
self.assertTrue(encoding["""labels"""].min().item() >= 0 )
self.assertTrue(encoding["""labels"""].max().item() <= 1_50 )
lowerCamelCase__ = True
lowerCamelCase__ = image_processing(_lowerCAmelCase ,_lowerCAmelCase ,return_tensors="""pt""" )
self.assertTrue(encoding["""labels"""].min().item() >= 0 )
self.assertTrue(encoding["""labels"""].max().item() <= 2_55 )
| 50 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
UpperCamelCase : List[str] = logging.get_logger(__name__)
UpperCamelCase : Dict = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
UpperCamelCase : int = {
'vocab_file': {
'squeezebert/squeezebert-uncased': (
'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'
),
'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt',
'squeezebert/squeezebert-mnli-headless': (
'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'squeezebert/squeezebert-uncased': (
'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'
),
'squeezebert/squeezebert-mnli': (
'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'
),
'squeezebert/squeezebert-mnli-headless': (
'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'
),
},
}
UpperCamelCase : Tuple = {
'squeezebert/squeezebert-uncased': 5_12,
'squeezebert/squeezebert-mnli': 5_12,
'squeezebert/squeezebert-mnli-headless': 5_12,
}
UpperCamelCase : Dict = {
'squeezebert/squeezebert-uncased': {'do_lower_case': True},
'squeezebert/squeezebert-mnli': {'do_lower_case': True},
'squeezebert/squeezebert-mnli-headless': {'do_lower_case': True},
}
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = VOCAB_FILES_NAMES
_UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
_UpperCamelCase = PRETRAINED_INIT_CONFIGURATION
_UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCamelCase = SqueezeBertTokenizer
def __init__( self ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=True ,_lowerCAmelCase="[UNK]" ,_lowerCAmelCase="[SEP]" ,_lowerCAmelCase="[PAD]" ,_lowerCAmelCase="[CLS]" ,_lowerCAmelCase="[MASK]" ,_lowerCAmelCase=True ,_lowerCAmelCase=None ,**_lowerCAmelCase ,):
super().__init__(
_lowerCAmelCase ,tokenizer_file=_lowerCAmelCase ,do_lower_case=_lowerCAmelCase ,unk_token=_lowerCAmelCase ,sep_token=_lowerCAmelCase ,pad_token=_lowerCAmelCase ,cls_token=_lowerCAmelCase ,mask_token=_lowerCAmelCase ,tokenize_chinese_chars=_lowerCAmelCase ,strip_accents=_lowerCAmelCase ,**_lowerCAmelCase ,)
lowerCamelCase__ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("""lowercase""" ,_lowerCAmelCase ) != do_lower_case
or normalizer_state.get("""strip_accents""" ,_lowerCAmelCase ) != strip_accents
or normalizer_state.get("""handle_chinese_chars""" ,_lowerCAmelCase ) != tokenize_chinese_chars
):
lowerCamelCase__ = getattr(_lowerCAmelCase ,normalizer_state.pop("""type""" ) )
lowerCamelCase__ = do_lower_case
lowerCamelCase__ = strip_accents
lowerCamelCase__ = tokenize_chinese_chars
lowerCamelCase__ = normalizer_class(**_lowerCAmelCase )
lowerCamelCase__ = do_lower_case
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase=None ):
lowerCamelCase__ = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase = None ):
lowerCamelCase__ = [self.sep_token_id]
lowerCamelCase__ = [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 UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase = None ):
lowerCamelCase__ = self._tokenizer.model.save(_lowerCAmelCase ,name=_lowerCAmelCase )
return tuple(_lowerCAmelCase )
| 50 | 1 |
'''simple docstring'''
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available
from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester
if is_flax_available():
from transformers import (
FlaxBertModel,
FlaxCLIPVisionModel,
FlaxVisionTextDualEncoderModel,
FlaxViTModel,
VisionTextDualEncoderConfig,
VisionTextDualEncoderProcessor,
)
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
if is_torch_available():
import torch
from transformers import VisionTextDualEncoderModel
if is_vision_available():
from PIL import Image
def A__ ( __lowerCAmelCase : Any ):
if isinstance(__lowerCAmelCase , collections.abc.Iterable ):
return x
return (x, x)
@require_flax
class UpperCamelCase__ :
'''simple docstring'''
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ):
pass
def UpperCamelCase_ ( self ):
pass
def UpperCamelCase_ ( self ):
pass
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = np.abs((a - b) ).max()
self.assertLessEqual(_lowerCAmelCase ,_lowerCAmelCase ,F'''Difference between torch and flax is {diff} (>= {tol}).''' )
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase=None ,**_lowerCAmelCase ):
lowerCamelCase__ = VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = FlaxVisionTextDualEncoderModel(_lowerCAmelCase )
lowerCamelCase__ = model(input_ids=_lowerCAmelCase ,pixel_values=_lowerCAmelCase ,attention_mask=_lowerCAmelCase )
self.assertEqual(output["""text_embeds"""].shape ,(input_ids.shape[0], config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape ,(pixel_values.shape[0], config.projection_dim) )
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase=None ,**_lowerCAmelCase ):
lowerCamelCase__ , lowerCamelCase__ = self.get_vision_text_model(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = {"""vision_model""": vision_model, """text_model""": text_model}
lowerCamelCase__ = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCAmelCase )
lowerCamelCase__ = model(input_ids=_lowerCAmelCase ,pixel_values=_lowerCAmelCase ,attention_mask=_lowerCAmelCase )
self.assertEqual(output["""text_embeds"""].shape ,(input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape ,(pixel_values.shape[0], model.config.projection_dim) )
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase=None ,**_lowerCAmelCase ):
lowerCamelCase__ , lowerCamelCase__ = self.get_vision_text_model(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = {"""vision_model""": vision_model, """text_model""": text_model}
lowerCamelCase__ = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCAmelCase )
lowerCamelCase__ = model(input_ids=_lowerCAmelCase ,pixel_values=_lowerCAmelCase ,attention_mask=_lowerCAmelCase )
lowerCamelCase__ = output[0]
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_lowerCAmelCase )
lowerCamelCase__ = FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase )
lowerCamelCase__ = model(input_ids=_lowerCAmelCase ,pixel_values=_lowerCAmelCase ,attention_mask=_lowerCAmelCase )
lowerCamelCase__ = after_output[0]
lowerCamelCase__ = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_lowerCAmelCase ,1E-3 )
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase=None ,**_lowerCAmelCase ):
lowerCamelCase__ , lowerCamelCase__ = self.get_vision_text_model(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = {"""vision_model""": vision_model, """text_model""": text_model}
lowerCamelCase__ = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCAmelCase )
lowerCamelCase__ = model(
input_ids=_lowerCAmelCase ,pixel_values=_lowerCAmelCase ,attention_mask=_lowerCAmelCase ,output_attentions=_lowerCAmelCase )
lowerCamelCase__ = output.vision_model_output.attentions
self.assertEqual(len(_lowerCAmelCase ) ,vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
lowerCamelCase__ = to_atuple(vision_model.config.image_size )
lowerCamelCase__ = to_atuple(vision_model.config.patch_size )
lowerCamelCase__ = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
lowerCamelCase__ = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] ,(vision_config.num_attention_heads, seq_len, seq_len) )
lowerCamelCase__ = output.text_model_output.attentions
self.assertEqual(len(_lowerCAmelCase ) ,text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] ,(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) ,)
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
pt_model.to(_lowerCAmelCase )
pt_model.eval()
# prepare inputs
lowerCamelCase__ = inputs_dict
lowerCamelCase__ = {k: torch.tensor(v.tolist() ) for k, v in flax_inputs.items()}
with torch.no_grad():
lowerCamelCase__ = pt_model(**_lowerCAmelCase ).to_tuple()
lowerCamelCase__ = fx_model(**_lowerCAmelCase ).to_tuple()
self.assertEqual(len(_lowerCAmelCase ) ,len(_lowerCAmelCase ) ,"""Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(fx_outputs[:4] ,pt_outputs[:4] ):
self.assert_almost_equals(_lowerCAmelCase ,pt_output.numpy() ,4E-2 )
# PT -> Flax
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(_lowerCAmelCase )
lowerCamelCase__ = FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase ,from_pt=_lowerCAmelCase )
lowerCamelCase__ = fx_model_loaded(**_lowerCAmelCase ).to_tuple()
self.assertEqual(len(_lowerCAmelCase ) ,len(_lowerCAmelCase ) ,"""Output lengths differ between Flax and PyTorch""" )
for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4] ,pt_outputs[:4] ):
self.assert_almost_equals(_lowerCAmelCase ,pt_output.numpy() ,4E-2 )
# Flax -> PT
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(_lowerCAmelCase )
lowerCamelCase__ = VisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase ,from_flax=_lowerCAmelCase )
pt_model_loaded.to(_lowerCAmelCase )
pt_model_loaded.eval()
with torch.no_grad():
lowerCamelCase__ = pt_model_loaded(**_lowerCAmelCase ).to_tuple()
self.assertEqual(len(_lowerCAmelCase ) ,len(_lowerCAmelCase ) ,"""Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output_loaded in zip(fx_outputs[:4] ,pt_outputs_loaded[:4] ):
self.assert_almost_equals(_lowerCAmelCase ,pt_output_loaded.numpy() ,4E-2 )
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = VisionTextDualEncoderModel(_lowerCAmelCase )
lowerCamelCase__ = FlaxVisionTextDualEncoderModel(_lowerCAmelCase )
lowerCamelCase__ = convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,_lowerCAmelCase )
lowerCamelCase__ = fx_state
self.check_pt_flax_equivalence(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = VisionTextDualEncoderModel(_lowerCAmelCase )
lowerCamelCase__ = FlaxVisionTextDualEncoderModel(_lowerCAmelCase )
lowerCamelCase__ = load_flax_weights_in_pytorch_model(_lowerCAmelCase ,fx_model.params )
self.check_pt_flax_equivalence(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**_lowerCAmelCase )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**_lowerCAmelCase )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.prepare_config_and_inputs()
self.check_save_load(**_lowerCAmelCase )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**_lowerCAmelCase )
@is_pt_flax_cross_test
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.prepare_config_and_inputs()
lowerCamelCase__ = config_inputs_dict.pop("""vision_config""" )
lowerCamelCase__ = config_inputs_dict.pop("""text_config""" )
lowerCamelCase__ = config_inputs_dict
self.check_equivalence_pt_to_flax(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
self.check_equivalence_flax_to_pt(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
@slow
def UpperCamelCase_ ( self ):
lowerCamelCase__ , lowerCamelCase__ = self.get_pretrained_model_and_inputs()
lowerCamelCase__ = model_a(**_lowerCAmelCase )
lowerCamelCase__ = outputs[0]
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(_lowerCAmelCase )
lowerCamelCase__ = FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase )
lowerCamelCase__ = model_a(**_lowerCAmelCase )
lowerCamelCase__ = after_outputs[0]
lowerCamelCase__ = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_lowerCAmelCase ,1E-5 )
@require_flax
class UpperCamelCase__ (a ,unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ):
lowerCamelCase__ = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-vit""" ,"""hf-internal-testing/tiny-bert""" ,vision_from_pt=_lowerCAmelCase ,text_from_pt=_lowerCAmelCase ,)
lowerCamelCase__ = 13
lowerCamelCase__ = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
lowerCamelCase__ = ids_tensor([batch_size, 4] ,model.config.text_config.vocab_size )
lowerCamelCase__ = random_attention_mask([batch_size, 4] )
lowerCamelCase__ = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = FlaxViTModel(_lowerCAmelCase )
lowerCamelCase__ = FlaxBertModel(_lowerCAmelCase )
return vision_model, text_model
def UpperCamelCase_ ( self ):
lowerCamelCase__ = FlaxViTModelTester(self )
lowerCamelCase__ = FlaxBertModelTester(self )
lowerCamelCase__ = vit_model_tester.prepare_config_and_inputs()
lowerCamelCase__ = bert_model_tester.prepare_config_and_inputs()
lowerCamelCase__ , lowerCamelCase__ = vision_config_and_inputs
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = text_config_and_inputs
# make sure that cross attention layers are added
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": attention_mask,
"input_ids": input_ids,
"token_type_ids": token_type_ids,
}
@require_torch
class UpperCamelCase__ (a ,unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ):
lowerCamelCase__ = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-clip""" ,"""hf-internal-testing/tiny-bert""" ,vision_from_pt=_lowerCAmelCase ,text_from_pt=_lowerCAmelCase ,)
lowerCamelCase__ = 13
lowerCamelCase__ = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
lowerCamelCase__ = ids_tensor([batch_size, 4] ,model.config.text_config.vocab_size )
lowerCamelCase__ = random_attention_mask([batch_size, 4] )
lowerCamelCase__ = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = FlaxCLIPVisionModel(_lowerCAmelCase )
lowerCamelCase__ = FlaxBertModel(_lowerCAmelCase )
return vision_model, text_model
def UpperCamelCase_ ( self ):
lowerCamelCase__ = FlaxCLIPVisionModelTester(self )
lowerCamelCase__ = FlaxBertModelTester(self )
lowerCamelCase__ = clip_model_tester.prepare_config_and_inputs()
lowerCamelCase__ = bert_model_tester.prepare_config_and_inputs()
lowerCamelCase__ , lowerCamelCase__ = vision_config_and_inputs
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = text_config_and_inputs
# make sure that cross attention layers are added
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": attention_mask,
"input_ids": input_ids,
"token_type_ids": token_type_ids,
}
@require_flax
@require_vision
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
@slow
def UpperCamelCase_ ( self ):
lowerCamelCase__ = FlaxVisionTextDualEncoderModel.from_pretrained("""clip-italian/clip-italian""" ,logit_scale_init_value=1.0 )
lowerCamelCase__ = VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian""" )
lowerCamelCase__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
lowerCamelCase__ = processor(
text=["""una foto di un gatto""", """una foto di un cane"""] ,images=_lowerCAmelCase ,padding=_lowerCAmelCase ,return_tensors="""np""" )
lowerCamelCase__ = model(**_lowerCAmelCase )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape ,(inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape ,(inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) ,)
lowerCamelCase__ = np.array([[1.228_4727, 0.310_4122]] )
self.assertTrue(np.allclose(outputs.logits_per_image ,_lowerCAmelCase ,atol=1E-3 ) )
| 50 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def A__ ( __lowerCAmelCase : Any ):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0x4_e_0_0 and cp <= 0x9_f_f_f)
or (cp >= 0x3_4_0_0 and cp <= 0x4_d_b_f) #
or (cp >= 0x2_0_0_0_0 and cp <= 0x2_a_6_d_f) #
or (cp >= 0x2_a_7_0_0 and cp <= 0x2_b_7_3_f) #
or (cp >= 0x2_b_7_4_0 and cp <= 0x2_b_8_1_f) #
or (cp >= 0x2_b_8_2_0 and cp <= 0x2_c_e_a_f) #
or (cp >= 0xf_9_0_0 and cp <= 0xf_a_f_f)
or (cp >= 0x2_f_8_0_0 and cp <= 0x2_f_a_1_f) #
): #
return True
return False
def A__ ( __lowerCAmelCase : str ):
# word like '180' or '身高' or '神'
for char in word:
lowerCamelCase__ = ord(__lowerCAmelCase )
if not _is_chinese_char(__lowerCAmelCase ):
return 0
return 1
def A__ ( __lowerCAmelCase : List[str] ):
lowerCamelCase__ = set()
for token in tokens:
lowerCamelCase__ = len(__lowerCAmelCase ) > 1 and is_chinese(__lowerCAmelCase )
if chinese_word:
word_set.add(__lowerCAmelCase )
lowerCamelCase__ = list(__lowerCAmelCase )
return word_list
def A__ ( __lowerCAmelCase : List[str] , __lowerCAmelCase : set() ):
if not chinese_word_set:
return bert_tokens
lowerCamelCase__ = max([len(__lowerCAmelCase ) for w in chinese_word_set] )
lowerCamelCase__ = bert_tokens
lowerCamelCase__ , lowerCamelCase__ = 0, len(__lowerCAmelCase )
while start < end:
lowerCamelCase__ = True
if is_chinese(bert_word[start] ):
lowerCamelCase__ = min(end - start , __lowerCAmelCase )
for i in range(__lowerCAmelCase , 1 , -1 ):
lowerCamelCase__ = """""".join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1 , start + i ):
lowerCamelCase__ = """##""" + bert_word[j]
lowerCamelCase__ = start + i
lowerCamelCase__ = False
break
if single_word:
start += 1
return bert_word
def A__ ( __lowerCAmelCase : List[str] , __lowerCAmelCase : LTP , __lowerCAmelCase : BertTokenizer ):
lowerCamelCase__ = []
for i in range(0 , len(__lowerCAmelCase ) , 100 ):
lowerCamelCase__ = ltp_tokenizer.pipeline(lines[i : i + 100] , tasks=["""cws"""] ).cws
lowerCamelCase__ = [get_chinese_word(__lowerCAmelCase ) for r in res]
ltp_res.extend(__lowerCAmelCase )
assert len(__lowerCAmelCase ) == len(__lowerCAmelCase )
lowerCamelCase__ = []
for i in range(0 , len(__lowerCAmelCase ) , 100 ):
lowerCamelCase__ = bert_tokenizer(lines[i : i + 100] , add_special_tokens=__lowerCAmelCase , truncation=__lowerCAmelCase , max_length=512 )
bert_res.extend(res["""input_ids"""] )
assert len(__lowerCAmelCase ) == len(__lowerCAmelCase )
lowerCamelCase__ = []
for input_ids, chinese_word in zip(__lowerCAmelCase , __lowerCAmelCase ):
lowerCamelCase__ = []
for id in input_ids:
lowerCamelCase__ = bert_tokenizer._convert_id_to_token(__lowerCAmelCase )
input_tokens.append(__lowerCAmelCase )
lowerCamelCase__ = add_sub_symbol(__lowerCAmelCase , __lowerCAmelCase )
lowerCamelCase__ = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(__lowerCAmelCase ):
if token[:2] == "##":
lowerCamelCase__ = token[2:]
# save chinese tokens' pos
if len(__lowerCAmelCase ) == 1 and _is_chinese_char(ord(__lowerCAmelCase ) ):
ref_id.append(__lowerCAmelCase )
ref_ids.append(__lowerCAmelCase )
assert len(__lowerCAmelCase ) == len(__lowerCAmelCase )
return ref_ids
def A__ ( __lowerCAmelCase : Optional[int] ):
# For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
# If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
with open(args.file_name , """r""" , encoding="""utf-8""" ) as f:
lowerCamelCase__ = f.readlines()
lowerCamelCase__ = [line.strip() for line in data if len(__lowerCAmelCase ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
lowerCamelCase__ = LTP(args.ltp ) # faster in GPU device
lowerCamelCase__ = BertTokenizer.from_pretrained(args.bert )
lowerCamelCase__ = prepare_ref(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
with open(args.save_path , """w""" , encoding="""utf-8""" ) as f:
lowerCamelCase__ = [json.dumps(__lowerCAmelCase ) + """\n""" for ref in ref_ids]
f.writelines(__lowerCAmelCase )
if __name__ == "__main__":
UpperCamelCase : Optional[int] = argparse.ArgumentParser(description='prepare_chinese_ref')
parser.add_argument(
'--file_name',
required=False,
type=str,
default='./resources/chinese-demo.txt',
help='file need process, same as training data in lm',
)
parser.add_argument(
'--ltp',
required=False,
type=str,
default='./resources/ltp',
help='resources for LTP tokenizer, usually a path',
)
parser.add_argument(
'--bert',
required=False,
type=str,
default='./resources/robert',
help='resources for Bert tokenizer',
)
parser.add_argument(
'--save_path',
required=False,
type=str,
default='./resources/ref.txt',
help='path to save res',
)
UpperCamelCase : Any = parser.parse_args()
main(args)
| 50 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Generator
def A__ ( ):
lowerCamelCase__ = {}
lowerCamelCase__ = 2
while True:
lowerCamelCase__ = factor_map.pop(__lowerCAmelCase , __lowerCAmelCase )
if factor:
lowerCamelCase__ = factor + prime
while x in factor_map:
x += factor
lowerCamelCase__ = factor
else:
lowerCamelCase__ = prime
yield prime
prime += 1
def A__ ( __lowerCAmelCase : float = 1e10 ):
lowerCamelCase__ = sieve()
lowerCamelCase__ = 1
while True:
lowerCamelCase__ = next(__lowerCAmelCase )
if (2 * prime * n) > limit:
return n
# Ignore the next prime as the reminder will be 2.
next(__lowerCAmelCase )
n += 2
if __name__ == "__main__":
print(solution())
| 50 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase : Tuple = logging.get_logger(__name__)
def A__ ( __lowerCAmelCase : int ):
lowerCamelCase__ = DPTConfig(embedding_type="""hybrid""" )
if "large" in checkpoint_url:
lowerCamelCase__ = 1024
lowerCamelCase__ = 4096
lowerCamelCase__ = 24
lowerCamelCase__ = 16
lowerCamelCase__ = [5, 11, 17, 23]
lowerCamelCase__ = [256, 512, 1024, 1024]
lowerCamelCase__ = (1, 384, 384)
if "nyu" or "midas" in checkpoint_url:
lowerCamelCase__ = 768
lowerCamelCase__ = [1, 1, 1, 0.5]
lowerCamelCase__ = [256, 512, 768, 768]
lowerCamelCase__ = 150
lowerCamelCase__ = 16
lowerCamelCase__ = (1, 384, 384)
lowerCamelCase__ = False
lowerCamelCase__ = """project"""
if "ade" in checkpoint_url:
lowerCamelCase__ = True
lowerCamelCase__ = 768
lowerCamelCase__ = [1, 1, 1, 0.5]
lowerCamelCase__ = 150
lowerCamelCase__ = 16
lowerCamelCase__ = """huggingface/label-files"""
lowerCamelCase__ = """ade20k-id2label.json"""
lowerCamelCase__ = json.load(open(cached_download(hf_hub_url(__lowerCAmelCase , __lowerCAmelCase , repo_type="""dataset""" ) ) , """r""" ) )
lowerCamelCase__ = {int(__lowerCAmelCase ): v for k, v in idalabel.items()}
lowerCamelCase__ = idalabel
lowerCamelCase__ = {v: k for k, v in idalabel.items()}
lowerCamelCase__ = [1, 150, 480, 480]
return config, expected_shape
def A__ ( __lowerCAmelCase : Optional[int] ):
lowerCamelCase__ = ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
for k in ignore_keys:
state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
def A__ ( __lowerCAmelCase : List[Any] ):
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
lowerCamelCase__ = name.replace("""pretrained.model""" , """dpt.encoder""" )
if "pretrained.model" in name:
lowerCamelCase__ = name.replace("""pretrained.model""" , """dpt.embeddings""" )
if "patch_embed" in name:
lowerCamelCase__ = name.replace("""patch_embed""" , """""" )
if "pos_embed" in name:
lowerCamelCase__ = name.replace("""pos_embed""" , """position_embeddings""" )
if "attn.proj" in name:
lowerCamelCase__ = name.replace("""attn.proj""" , """attention.output.dense""" )
if "proj" in name and "project" not in name:
lowerCamelCase__ = name.replace("""proj""" , """projection""" )
if "blocks" in name:
lowerCamelCase__ = name.replace("""blocks""" , """layer""" )
if "mlp.fc1" in name:
lowerCamelCase__ = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
lowerCamelCase__ = name.replace("""mlp.fc2""" , """output.dense""" )
if "norm1" in name and "backbone" not in name:
lowerCamelCase__ = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name and "backbone" not in name:
lowerCamelCase__ = name.replace("""norm2""" , """layernorm_after""" )
if "scratch.output_conv" in name:
lowerCamelCase__ = name.replace("""scratch.output_conv""" , """head""" )
if "scratch" in name:
lowerCamelCase__ = name.replace("""scratch""" , """neck""" )
if "layer1_rn" in name:
lowerCamelCase__ = name.replace("""layer1_rn""" , """convs.0""" )
if "layer2_rn" in name:
lowerCamelCase__ = name.replace("""layer2_rn""" , """convs.1""" )
if "layer3_rn" in name:
lowerCamelCase__ = name.replace("""layer3_rn""" , """convs.2""" )
if "layer4_rn" in name:
lowerCamelCase__ = name.replace("""layer4_rn""" , """convs.3""" )
if "refinenet" in name:
lowerCamelCase__ = int(name[len("""neck.refinenet""" ) : len("""neck.refinenet""" ) + 1] )
# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
lowerCamelCase__ = name.replace(F'''refinenet{layer_idx}''' , F'''fusion_stage.layers.{abs(layer_idx-4 )}''' )
if "out_conv" in name:
lowerCamelCase__ = name.replace("""out_conv""" , """projection""" )
if "resConfUnit1" in name:
lowerCamelCase__ = name.replace("""resConfUnit1""" , """residual_layer1""" )
if "resConfUnit2" in name:
lowerCamelCase__ = name.replace("""resConfUnit2""" , """residual_layer2""" )
if "conv1" in name:
lowerCamelCase__ = name.replace("""conv1""" , """convolution1""" )
if "conv2" in name:
lowerCamelCase__ = name.replace("""conv2""" , """convolution2""" )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess1.0.project.0""" , """neck.reassemble_stage.readout_projects.0.0""" )
if "pretrained.act_postprocess2.0.project.0" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess2.0.project.0""" , """neck.reassemble_stage.readout_projects.1.0""" )
if "pretrained.act_postprocess3.0.project.0" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess3.0.project.0""" , """neck.reassemble_stage.readout_projects.2.0""" )
if "pretrained.act_postprocess4.0.project.0" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess4.0.project.0""" , """neck.reassemble_stage.readout_projects.3.0""" )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess1.3""" , """neck.reassemble_stage.layers.0.projection""" )
if "pretrained.act_postprocess1.4" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess1.4""" , """neck.reassemble_stage.layers.0.resize""" )
if "pretrained.act_postprocess2.3" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess2.3""" , """neck.reassemble_stage.layers.1.projection""" )
if "pretrained.act_postprocess2.4" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess2.4""" , """neck.reassemble_stage.layers.1.resize""" )
if "pretrained.act_postprocess3.3" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess3.3""" , """neck.reassemble_stage.layers.2.projection""" )
if "pretrained.act_postprocess4.3" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess4.3""" , """neck.reassemble_stage.layers.3.projection""" )
if "pretrained.act_postprocess4.4" in name:
lowerCamelCase__ = name.replace("""pretrained.act_postprocess4.4""" , """neck.reassemble_stage.layers.3.resize""" )
if "pretrained" in name:
lowerCamelCase__ = name.replace("""pretrained""" , """dpt""" )
if "bn" in name:
lowerCamelCase__ = name.replace("""bn""" , """batch_norm""" )
if "head" in name:
lowerCamelCase__ = name.replace("""head""" , """head.head""" )
if "encoder.norm" in name:
lowerCamelCase__ = name.replace("""encoder.norm""" , """layernorm""" )
if "auxlayer" in name:
lowerCamelCase__ = name.replace("""auxlayer""" , """auxiliary_head.head""" )
if "backbone" in name:
lowerCamelCase__ = name.replace("""backbone""" , """backbone.bit.encoder""" )
if ".." in name:
lowerCamelCase__ = name.replace("""..""" , """.""" )
if "stem.conv" in name:
lowerCamelCase__ = name.replace("""stem.conv""" , """bit.embedder.convolution""" )
if "blocks" in name:
lowerCamelCase__ = name.replace("""blocks""" , """layers""" )
if "convolution" in name and "backbone" in name:
lowerCamelCase__ = name.replace("""convolution""" , """conv""" )
if "layer" in name and "backbone" in name:
lowerCamelCase__ = name.replace("""layer""" , """layers""" )
if "backbone.bit.encoder.bit" in name:
lowerCamelCase__ = name.replace("""backbone.bit.encoder.bit""" , """backbone.bit""" )
if "embedder.conv" in name:
lowerCamelCase__ = name.replace("""embedder.conv""" , """embedder.convolution""" )
if "backbone.bit.encoder.stem.norm" in name:
lowerCamelCase__ = name.replace("""backbone.bit.encoder.stem.norm""" , """backbone.bit.embedder.norm""" )
return name
def A__ ( __lowerCAmelCase : str , __lowerCAmelCase : int ):
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowerCamelCase__ = state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight''' )
lowerCamelCase__ = state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
lowerCamelCase__ = in_proj_weight[: config.hidden_size, :]
lowerCamelCase__ = in_proj_bias[: config.hidden_size]
lowerCamelCase__ = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowerCamelCase__ = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowerCamelCase__ = in_proj_weight[
-config.hidden_size :, :
]
lowerCamelCase__ = in_proj_bias[-config.hidden_size :]
def A__ ( ):
lowerCamelCase__ = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowerCamelCase__ = Image.open(requests.get(__lowerCAmelCase , stream=__lowerCAmelCase ).raw )
return im
@torch.no_grad()
def A__ ( __lowerCAmelCase : Dict , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Any ):
lowerCamelCase__ , lowerCamelCase__ = get_dpt_config(__lowerCAmelCase )
# load original state_dict from URL
# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
lowerCamelCase__ = torch.load(__lowerCAmelCase , map_location="""cpu""" )
# remove certain keys
remove_ignore_keys_(__lowerCAmelCase )
# rename keys
for key in state_dict.copy().keys():
lowerCamelCase__ = state_dict.pop(__lowerCAmelCase )
lowerCamelCase__ = val
# read in qkv matrices
read_in_q_k_v(__lowerCAmelCase , __lowerCAmelCase )
# load HuggingFace model
lowerCamelCase__ = DPTForSemanticSegmentation(__lowerCAmelCase ) if """ade""" in checkpoint_url else DPTForDepthEstimation(__lowerCAmelCase )
model.load_state_dict(__lowerCAmelCase )
model.eval()
# Check outputs on an image
lowerCamelCase__ = 480 if """ade""" in checkpoint_url else 384
lowerCamelCase__ = DPTImageProcessor(size=__lowerCAmelCase )
lowerCamelCase__ = prepare_img()
lowerCamelCase__ = image_processor(__lowerCAmelCase , return_tensors="""pt""" )
# forward pass
lowerCamelCase__ = model(**__lowerCAmelCase ).logits if """ade""" in checkpoint_url else model(**__lowerCAmelCase ).predicted_depth
if show_prediction:
lowerCamelCase__ = (
torch.nn.functional.interpolate(
outputs.unsqueeze(1 ) , size=(image.size[1], image.size[0]) , mode="""bicubic""" , align_corners=__lowerCAmelCase , )
.squeeze()
.cpu()
.numpy()
)
Image.fromarray((prediction / prediction.max()) * 255 ).show()
if pytorch_dump_folder_path is not None:
Path(__lowerCAmelCase ).mkdir(exist_ok=__lowerCAmelCase )
print(F'''Saving model 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:
model.push_to_hub("""ybelkada/dpt-hybrid-midas""" )
image_processor.push_to_hub("""ybelkada/dpt-hybrid-midas""" )
if __name__ == "__main__":
UpperCamelCase : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint_url',
default='https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt',
type=str,
help='URL of the original DPT checkpoint you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default=None,
type=str,
required=False,
help='Path to the output PyTorch model directory.',
)
parser.add_argument(
'--push_to_hub',
action='store_true',
)
parser.add_argument(
'--model_name',
default='dpt-large',
type=str,
help='Name of the model, in case you\'re pushing to the hub.',
)
parser.add_argument(
'--show_prediction',
action='store_true',
)
UpperCamelCase : List[str] = parser.parse_args()
convert_dpt_checkpoint(
args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
)
| 50 | 1 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : int ):
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 50 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase : Tuple = {
'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
'tokenization_mvp': ['MvpTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : str = ['MvpTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Optional[int] = [
'MVP_PRETRAINED_MODEL_ARCHIVE_LIST',
'MvpForCausalLM',
'MvpForConditionalGeneration',
'MvpForQuestionAnswering',
'MvpForSequenceClassification',
'MvpModel',
'MvpPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
UpperCamelCase : Optional[int] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 50 | 1 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask
import transformers
from transformers import (
AutoConfig,
AutoModelForTokenClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
UpperCamelCase : int = logging.getLogger(__name__)
@dataclass
class UpperCamelCase__ :
'''simple docstring'''
_UpperCamelCase = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
_UpperCamelCase = field(
default=a ,metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
_UpperCamelCase = field(
default='NER' ,metadata={'help': 'Task type to fine tune in training (e.g. NER, POS, etc)'} )
_UpperCamelCase = field(
default=a ,metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
_UpperCamelCase = field(default=a ,metadata={'help': 'Set this flag to use fast tokenization.'} )
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
_UpperCamelCase = field(
default=a ,metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} ,)
@dataclass
class UpperCamelCase__ :
'''simple docstring'''
_UpperCamelCase = field(
metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'} )
_UpperCamelCase = field(
default=a ,metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'} ,)
_UpperCamelCase = field(
default=128 ,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=a ,metadata={'help': 'Overwrite the cached training and evaluation sets'} )
def A__ ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
lowerCamelCase__ = 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.
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
""" --overwrite_output_dir to overcome.""" )
lowerCamelCase__ = import_module("""tasks""" )
try:
lowerCamelCase__ = getattr(__lowerCAmelCase , model_args.task_type )
lowerCamelCase__ = token_classification_task_clazz()
except AttributeError:
raise ValueError(
F'''Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
F'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}''' )
# 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.local_rank != -1 ) , training_args.fpaa , )
# 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()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("""Training/evaluation parameters %s""" , __lowerCAmelCase )
# Set seed
set_seed(training_args.seed )
# Prepare CONLL-2003 task
lowerCamelCase__ = token_classification_task.get_labels(data_args.labels )
lowerCamelCase__ = dict(enumerate(__lowerCAmelCase ) )
lowerCamelCase__ = len(__lowerCAmelCase )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowerCamelCase__ = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__lowerCAmelCase , idalabel=__lowerCAmelCase , labelaid={label: i for i, label in enumerate(__lowerCAmelCase )} , cache_dir=model_args.cache_dir , )
lowerCamelCase__ = 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 , use_fast=model_args.use_fast , )
lowerCamelCase__ = AutoModelForTokenClassification.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 , )
# Get datasets
lowerCamelCase__ = (
TokenClassificationDataset(
token_classification_task=__lowerCAmelCase , data_dir=data_args.data_dir , tokenizer=__lowerCAmelCase , labels=__lowerCAmelCase , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
lowerCamelCase__ = (
TokenClassificationDataset(
token_classification_task=__lowerCAmelCase , data_dir=data_args.data_dir , tokenizer=__lowerCAmelCase , labels=__lowerCAmelCase , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def align_predictions(__lowerCAmelCase : np.ndarray , __lowerCAmelCase : np.ndarray ) -> Tuple[List[int], List[int]]:
lowerCamelCase__ = np.argmax(__lowerCAmelCase , axis=2 )
lowerCamelCase__ , lowerCamelCase__ = preds.shape
lowerCamelCase__ = [[] for _ in range(__lowerCAmelCase )]
lowerCamelCase__ = [[] for _ in range(__lowerCAmelCase )]
for i in range(__lowerCAmelCase ):
for j in range(__lowerCAmelCase ):
if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
out_label_list[i].append(label_map[label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
return preds_list, out_label_list
def compute_metrics(__lowerCAmelCase : EvalPrediction ) -> Dict:
lowerCamelCase__ , lowerCamelCase__ = align_predictions(p.predictions , p.label_ids )
return {
"accuracy_score": accuracy_score(__lowerCAmelCase , __lowerCAmelCase ),
"precision": precision_score(__lowerCAmelCase , __lowerCAmelCase ),
"recall": recall_score(__lowerCAmelCase , __lowerCAmelCase ),
"f1": fa_score(__lowerCAmelCase , __lowerCAmelCase ),
}
# Data collator
lowerCamelCase__ = DataCollatorWithPadding(__lowerCAmelCase , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
lowerCamelCase__ = Trainer(
model=__lowerCAmelCase , args=__lowerCAmelCase , train_dataset=__lowerCAmelCase , eval_dataset=__lowerCAmelCase , compute_metrics=__lowerCAmelCase , data_collator=__lowerCAmelCase , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_process_zero():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
lowerCamelCase__ = {}
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
lowerCamelCase__ = trainer.evaluate()
lowerCamelCase__ = os.path.join(training_args.output_dir , """eval_results.txt""" )
if trainer.is_world_process_zero():
with open(__lowerCAmelCase , """w""" ) as writer:
logger.info("""***** Eval results *****""" )
for key, value in result.items():
logger.info(""" %s = %s""" , __lowerCAmelCase , __lowerCAmelCase )
writer.write("""%s = %s\n""" % (key, value) )
results.update(__lowerCAmelCase )
# Predict
if training_args.do_predict:
lowerCamelCase__ = TokenClassificationDataset(
token_classification_task=__lowerCAmelCase , data_dir=data_args.data_dir , tokenizer=__lowerCAmelCase , labels=__lowerCAmelCase , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.test , )
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = trainer.predict(__lowerCAmelCase )
lowerCamelCase__ , lowerCamelCase__ = align_predictions(__lowerCAmelCase , __lowerCAmelCase )
lowerCamelCase__ = os.path.join(training_args.output_dir , """test_results.txt""" )
if trainer.is_world_process_zero():
with open(__lowerCAmelCase , """w""" ) as writer:
for key, value in metrics.items():
logger.info(""" %s = %s""" , __lowerCAmelCase , __lowerCAmelCase )
writer.write("""%s = %s\n""" % (key, value) )
# Save predictions
lowerCamelCase__ = os.path.join(training_args.output_dir , """test_predictions.txt""" )
if trainer.is_world_process_zero():
with open(__lowerCAmelCase , """w""" ) as writer:
with open(os.path.join(data_args.data_dir , """test.txt""" ) , """r""" ) as f:
token_classification_task.write_predictions_to_file(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
return results
def A__ ( __lowerCAmelCase : Union[str, Any] ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 50 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
UpperCamelCase : Tuple = logging.get_logger(__name__)
UpperCamelCase : Dict = {
'Salesforce/codegen-350M-nl': 'https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json',
'Salesforce/codegen-350M-multi': 'https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json',
'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json',
'Salesforce/codegen-2B-nl': 'https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json',
'Salesforce/codegen-2B-multi': 'https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json',
'Salesforce/codegen-2B-mono': 'https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json',
'Salesforce/codegen-6B-nl': 'https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json',
'Salesforce/codegen-6B-multi': 'https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json',
'Salesforce/codegen-6B-mono': 'https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json',
'Salesforce/codegen-16B-nl': 'https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json',
'Salesforce/codegen-16B-multi': 'https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json',
'Salesforce/codegen-16B-mono': 'https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json',
}
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = 'codegen'
_UpperCamelCase = {
'max_position_embeddings': 'n_positions',
'hidden_size': 'n_embd',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self ,_lowerCAmelCase=5_04_00 ,_lowerCAmelCase=20_48 ,_lowerCAmelCase=20_48 ,_lowerCAmelCase=40_96 ,_lowerCAmelCase=28 ,_lowerCAmelCase=16 ,_lowerCAmelCase=64 ,_lowerCAmelCase=None ,_lowerCAmelCase="gelu_new" ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=1E-5 ,_lowerCAmelCase=0.02 ,_lowerCAmelCase=True ,_lowerCAmelCase=5_02_56 ,_lowerCAmelCase=5_02_56 ,_lowerCAmelCase=False ,**_lowerCAmelCase ,):
lowerCamelCase__ = vocab_size
lowerCamelCase__ = n_ctx
lowerCamelCase__ = n_positions
lowerCamelCase__ = n_embd
lowerCamelCase__ = n_layer
lowerCamelCase__ = n_head
lowerCamelCase__ = n_inner
lowerCamelCase__ = rotary_dim
lowerCamelCase__ = activation_function
lowerCamelCase__ = resid_pdrop
lowerCamelCase__ = embd_pdrop
lowerCamelCase__ = attn_pdrop
lowerCamelCase__ = layer_norm_epsilon
lowerCamelCase__ = initializer_range
lowerCamelCase__ = use_cache
lowerCamelCase__ = bos_token_id
lowerCamelCase__ = eos_token_id
super().__init__(
bos_token_id=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase ,tie_word_embeddings=_lowerCAmelCase ,**_lowerCAmelCase )
class UpperCamelCase__ (a ):
'''simple docstring'''
def __init__( self ,_lowerCAmelCase ,_lowerCAmelCase = "default" ,_lowerCAmelCase = None ,_lowerCAmelCase = False ,):
super().__init__(_lowerCAmelCase ,task=_lowerCAmelCase ,patching_specs=_lowerCAmelCase ,use_past=_lowerCAmelCase )
if not getattr(self._config ,"""pad_token_id""" ,_lowerCAmelCase ):
# TODO: how to do that better?
lowerCamelCase__ = 0
@property
def UpperCamelCase_ ( self ):
lowerCamelCase__ = OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}} )
if self.use_past:
self.fill_with_past_key_values_(_lowerCAmelCase ,direction="""inputs""" )
lowerCamelCase__ = {0: """batch""", 1: """past_sequence + sequence"""}
else:
lowerCamelCase__ = {0: """batch""", 1: """sequence"""}
return common_inputs
@property
def UpperCamelCase_ ( self ):
return self._config.n_layer
@property
def UpperCamelCase_ ( self ):
return self._config.n_head
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase = -1 ,_lowerCAmelCase = -1 ,_lowerCAmelCase = False ,_lowerCAmelCase = None ,):
lowerCamelCase__ = super(_lowerCAmelCase ,self ).generate_dummy_inputs(
_lowerCAmelCase ,batch_size=_lowerCAmelCase ,seq_length=_lowerCAmelCase ,is_pair=_lowerCAmelCase ,framework=_lowerCAmelCase )
# We need to order the input in the way they appears in the forward()
lowerCamelCase__ = OrderedDict({"""input_ids""": common_inputs["""input_ids"""]} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
lowerCamelCase__ , lowerCamelCase__ = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
lowerCamelCase__ = seqlen + 2
lowerCamelCase__ = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
lowerCamelCase__ = [
(torch.zeros(_lowerCAmelCase ), torch.zeros(_lowerCAmelCase )) for _ in range(self.num_layers )
]
lowerCamelCase__ = common_inputs["""attention_mask"""]
if self.use_past:
lowerCamelCase__ = ordered_inputs["""attention_mask"""].dtype
lowerCamelCase__ = torch.cat(
[ordered_inputs["""attention_mask"""], torch.ones(_lowerCAmelCase ,_lowerCAmelCase ,dtype=_lowerCAmelCase )] ,dim=1 )
return ordered_inputs
@property
def UpperCamelCase_ ( self ):
return 13
| 50 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBOSTokenLogitsProcessor,
FlaxForcedEOSTokenLogitsProcessor,
FlaxLogitsProcessorList,
FlaxMinLengthLogitsProcessor,
FlaxTemperatureLogitsWarper,
FlaxTopKLogitsWarper,
FlaxTopPLogitsWarper,
)
@require_flax
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = jnp.ones((batch_size, length) ) / length
return scores
def UpperCamelCase_ ( self ):
lowerCamelCase__ = None
lowerCamelCase__ = 20
lowerCamelCase__ = self._get_uniform_logits(batch_size=2 ,length=_lowerCAmelCase )
# tweak scores to not be uniform anymore
lowerCamelCase__ = scores.at[1, 5].set((1 / length) + 0.1 ) # peak, 1st batch
lowerCamelCase__ = scores.at[1, 10].set((1 / length) - 0.4 ) # valley, 1st batch
# compute softmax
lowerCamelCase__ = jax.nn.softmax(_lowerCAmelCase ,axis=-1 )
lowerCamelCase__ = FlaxTemperatureLogitsWarper(temperature=0.5 )
lowerCamelCase__ = FlaxTemperatureLogitsWarper(temperature=1.3 )
lowerCamelCase__ = jax.nn.softmax(temp_dist_warper_sharper(_lowerCAmelCase ,scores.copy() ,cur_len=_lowerCAmelCase ) ,axis=-1 )
lowerCamelCase__ = jax.nn.softmax(temp_dist_warper_smoother(_lowerCAmelCase ,scores.copy() ,cur_len=_lowerCAmelCase ) ,axis=-1 )
# uniform distribution stays uniform
self.assertTrue(jnp.allclose(probs[0, :] ,warped_prob_sharp[0, :] ,atol=1E-3 ) )
self.assertTrue(jnp.allclose(probs[0, :] ,warped_prob_smooth[0, :] ,atol=1E-3 ) )
# sharp peaks get higher, valleys get lower
self.assertLess(probs[1, :].max() ,warped_prob_sharp[1, :].max() )
self.assertGreater(probs[1, :].min() ,warped_prob_sharp[1, :].min() )
# smooth peaks get lower, valleys get higher
self.assertGreater(probs[1, :].max() ,warped_prob_smooth[1, :].max() )
self.assertLess(probs[1, :].min() ,warped_prob_smooth[1, :].min() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = None
lowerCamelCase__ = 10
lowerCamelCase__ = 2
# create ramp distribution
lowerCamelCase__ = np.broadcast_to(np.arange(_lowerCAmelCase )[None, :] ,(batch_size, vocab_size) ).copy()
lowerCamelCase__ = ramp_logits[1:, : vocab_size // 2] + vocab_size
lowerCamelCase__ = FlaxTopKLogitsWarper(3 )
lowerCamelCase__ = top_k_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# check that correct tokens are filtered
self.assertListEqual(jnp.isinf(scores[0] ).tolist() ,7 * [True] + 3 * [False] )
self.assertListEqual(jnp.isinf(scores[1] ).tolist() ,2 * [True] + 3 * [False] + 5 * [True] )
# check special case
lowerCamelCase__ = 5
lowerCamelCase__ = FlaxTopKLogitsWarper(top_k=1 ,filter_value=0.0 ,min_tokens_to_keep=3 )
lowerCamelCase__ = np.broadcast_to(np.arange(_lowerCAmelCase )[None, :] ,(batch_size, length) ).copy()
lowerCamelCase__ = top_k_warp_safety_check(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
self.assertListEqual((scores == 0.0).sum(axis=-1 ).tolist() ,[2, 2] )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = None
lowerCamelCase__ = 10
lowerCamelCase__ = 2
# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
lowerCamelCase__ = np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]] ) )
lowerCamelCase__ = FlaxTopPLogitsWarper(0.8 )
lowerCamelCase__ = np.exp(top_p_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase ) )
# dist should be filtered to keep min num values so that sum is >= top_p
# exp (-inf) => 0
lowerCamelCase__ = np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]] )
self.assertTrue(np.allclose(_lowerCAmelCase ,_lowerCAmelCase ,atol=1E-3 ) )
# check edge cases with negative and extreme logits
lowerCamelCase__ = np.broadcast_to(np.arange(_lowerCAmelCase )[None, :] ,(batch_size, vocab_size) ).copy() - (
vocab_size // 2
)
# make ramp_logits more extreme
lowerCamelCase__ = ramp_logits[1] * 100.0
# make sure at least 2 tokens are kept
lowerCamelCase__ = FlaxTopPLogitsWarper(0.9 ,min_tokens_to_keep=2 ,filter_value=0.0 )
lowerCamelCase__ = top_p_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
self.assertListEqual((filtered_dist != 0.0).sum(axis=-1 ).tolist() ,[3, 2] )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 20
lowerCamelCase__ = 4
lowerCamelCase__ = 0
lowerCamelCase__ = FlaxMinLengthLogitsProcessor(min_length=10 ,eos_token_id=_lowerCAmelCase )
# check that min length is applied at length 5
lowerCamelCase__ = ids_tensor((batch_size, 20) ,vocab_size=20 )
lowerCamelCase__ = 5
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = min_dist_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist() ,4 * [-float("""inf""" )] )
# check that min length is not applied anymore at length 15
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = 15
lowerCamelCase__ = min_dist_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertFalse(jnp.isinf(_lowerCAmelCase ).any() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 20
lowerCamelCase__ = 4
lowerCamelCase__ = 0
lowerCamelCase__ = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase )
# check that all scores are -inf except the bos_token_id score
lowerCamelCase__ = ids_tensor((batch_size, 1) ,vocab_size=20 )
lowerCamelCase__ = 1
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = logits_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, bos_token_id].tolist() ,4 * [0] ) # score for bos_token_id shold be zero
# check that bos_token_id is not forced if current length is greater than 1
lowerCamelCase__ = 3
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = logits_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertFalse(jnp.isinf(_lowerCAmelCase ).any() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 20
lowerCamelCase__ = 4
lowerCamelCase__ = 0
lowerCamelCase__ = 5
lowerCamelCase__ = FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase )
# check that all scores are -inf except the eos_token_id when max_length is reached
lowerCamelCase__ = ids_tensor((batch_size, 4) ,vocab_size=20 )
lowerCamelCase__ = 4
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = logits_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, eos_token_id].tolist() ,4 * [0] ) # score for eos_token_id should be zero
# check that eos_token_id is not forced if max_length is not reached
lowerCamelCase__ = 3
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = logits_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertFalse(jnp.isinf(_lowerCAmelCase ).any() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 4
lowerCamelCase__ = 10
lowerCamelCase__ = 15
lowerCamelCase__ = 2
lowerCamelCase__ = 1
lowerCamelCase__ = 15
# dummy input_ids and scores
lowerCamelCase__ = ids_tensor((batch_size, sequence_length) ,_lowerCAmelCase )
lowerCamelCase__ = input_ids.copy()
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = scores.copy()
# instantiate all dist processors
lowerCamelCase__ = FlaxTemperatureLogitsWarper(temperature=0.5 )
lowerCamelCase__ = FlaxTopKLogitsWarper(3 )
lowerCamelCase__ = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
lowerCamelCase__ = FlaxMinLengthLogitsProcessor(min_length=10 ,eos_token_id=_lowerCAmelCase )
lowerCamelCase__ = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase )
lowerCamelCase__ = FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase )
lowerCamelCase__ = 10
# no processor list
lowerCamelCase__ = temp_dist_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = top_k_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = top_p_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = min_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = bos_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = eos_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# with processor list
lowerCamelCase__ = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
lowerCamelCase__ = processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# scores should be equal
self.assertTrue(jnp.allclose(_lowerCAmelCase ,_lowerCAmelCase ,atol=1E-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() ,input_ids_comp.tolist() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 4
lowerCamelCase__ = 10
lowerCamelCase__ = 15
lowerCamelCase__ = 2
lowerCamelCase__ = 1
lowerCamelCase__ = 15
# dummy input_ids and scores
lowerCamelCase__ = ids_tensor((batch_size, sequence_length) ,_lowerCAmelCase )
lowerCamelCase__ = input_ids.copy()
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = scores.copy()
# instantiate all dist processors
lowerCamelCase__ = FlaxTemperatureLogitsWarper(temperature=0.5 )
lowerCamelCase__ = FlaxTopKLogitsWarper(3 )
lowerCamelCase__ = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
lowerCamelCase__ = FlaxMinLengthLogitsProcessor(min_length=10 ,eos_token_id=_lowerCAmelCase )
lowerCamelCase__ = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase )
lowerCamelCase__ = FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase )
lowerCamelCase__ = 10
# no processor list
def run_no_processor_list(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = temp_dist_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = top_k_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = top_p_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = min_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = bos_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = eos_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
return scores
# with processor list
def run_processor_list(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
lowerCamelCase__ = processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
return scores
lowerCamelCase__ = jax.jit(_lowerCAmelCase )
lowerCamelCase__ = jax.jit(_lowerCAmelCase )
lowerCamelCase__ = jitted_run_no_processor_list(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = jitted_run_processor_list(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
# scores should be equal
self.assertTrue(jnp.allclose(_lowerCAmelCase ,_lowerCAmelCase ,atol=1E-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() ,input_ids_comp.tolist() )
| 50 |
'''simple docstring'''
# 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
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase : int = {
'configuration_xmod': [
'XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XmodConfig',
'XmodOnnxConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Tuple = [
'XMOD_PRETRAINED_MODEL_ARCHIVE_LIST',
'XmodForCausalLM',
'XmodForMaskedLM',
'XmodForMultipleChoice',
'XmodForQuestionAnswering',
'XmodForSequenceClassification',
'XmodForTokenClassification',
'XmodModel',
'XmodPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xmod import (
XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
XmodForCausalLM,
XmodForMaskedLM,
XmodForMultipleChoice,
XmodForQuestionAnswering,
XmodForSequenceClassification,
XmodForTokenClassification,
XmodModel,
XmodPreTrainedModel,
)
else:
import sys
UpperCamelCase : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 50 | 1 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
UpperCamelCase : str = logging.get_logger('transformers.models.speecht5')
def A__ ( __lowerCAmelCase : int , __lowerCAmelCase : Tuple , __lowerCAmelCase : Any ):
hf_model.apply_weight_norm()
lowerCamelCase__ = checkpoint["""input_conv.weight_g"""]
lowerCamelCase__ = checkpoint["""input_conv.weight_v"""]
lowerCamelCase__ = checkpoint["""input_conv.bias"""]
for i in range(len(config.upsample_rates ) ):
lowerCamelCase__ = checkpoint[F'''upsamples.{i}.1.weight_g''']
lowerCamelCase__ = checkpoint[F'''upsamples.{i}.1.weight_v''']
lowerCamelCase__ = checkpoint[F'''upsamples.{i}.1.bias''']
for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ):
for j in range(len(config.resblock_dilation_sizes ) ):
lowerCamelCase__ = checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_g''']
lowerCamelCase__ = checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_v''']
lowerCamelCase__ = checkpoint[F'''blocks.{i}.convs1.{j}.1.bias''']
lowerCamelCase__ = checkpoint[F'''blocks.{i}.convs2.{j}.1.weight_g''']
lowerCamelCase__ = checkpoint[F'''blocks.{i}.convs2.{j}.1.weight_v''']
lowerCamelCase__ = checkpoint[F'''blocks.{i}.convs2.{j}.1.bias''']
lowerCamelCase__ = checkpoint["""output_conv.1.weight_g"""]
lowerCamelCase__ = checkpoint["""output_conv.1.weight_v"""]
lowerCamelCase__ = checkpoint["""output_conv.1.bias"""]
hf_model.remove_weight_norm()
@torch.no_grad()
def A__ ( __lowerCAmelCase : Any , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : List[Any]=None , __lowerCAmelCase : Dict=None , ):
if config_path is not None:
lowerCamelCase__ = SpeechTaHifiGanConfig.from_pretrained(__lowerCAmelCase )
else:
lowerCamelCase__ = SpeechTaHifiGanConfig()
lowerCamelCase__ = SpeechTaHifiGan(__lowerCAmelCase )
lowerCamelCase__ = torch.load(__lowerCAmelCase )
load_weights(orig_checkpoint["""model"""]["""generator"""] , __lowerCAmelCase , __lowerCAmelCase )
lowerCamelCase__ = np.load(__lowerCAmelCase )
lowerCamelCase__ = stats[0].reshape(-1 )
lowerCamelCase__ = stats[1].reshape(-1 )
lowerCamelCase__ = torch.from_numpy(__lowerCAmelCase ).float()
lowerCamelCase__ = torch.from_numpy(__lowerCAmelCase ).float()
model.save_pretrained(__lowerCAmelCase )
if repo_id:
print("""Pushing to the hub...""" )
model.push_to_hub(__lowerCAmelCase )
if __name__ == "__main__":
UpperCamelCase : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
parser.add_argument('--stats_path', required=True, default=None, type=str, help='Path to stats.npy file')
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.'
)
UpperCamelCase : Optional[Any] = parser.parse_args()
convert_hifigan_checkpoint(
args.checkpoint_path,
args.stats_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 50 |
'''simple docstring'''
from typing import Union
import fire
import torch
from tqdm import tqdm
def A__ ( __lowerCAmelCase : str , __lowerCAmelCase : str = "cpu" , __lowerCAmelCase : Union[str, None] = None ):
lowerCamelCase__ = torch.load(__lowerCAmelCase , map_location=__lowerCAmelCase )
for k, v in tqdm(state_dict.items() ):
if not isinstance(__lowerCAmelCase , torch.Tensor ):
raise TypeError("""FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin""" )
lowerCamelCase__ = v.half()
if save_path is None: # overwrite src_path
lowerCamelCase__ = src_path
torch.save(__lowerCAmelCase , __lowerCAmelCase )
if __name__ == "__main__":
fire.Fire(convert)
| 50 | 1 |
'''simple docstring'''
import torch
from diffusers import DiffusionPipeline
class UpperCamelCase__ (a ):
'''simple docstring'''
def __init__( self ,_lowerCAmelCase ,_lowerCAmelCase ):
super().__init__()
self.register_modules(unet=_lowerCAmelCase ,scheduler=_lowerCAmelCase )
def __call__( self ):
lowerCamelCase__ = torch.randn(
(1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,)
lowerCamelCase__ = 1
lowerCamelCase__ = self.unet(_lowerCAmelCase ,_lowerCAmelCase ).sample
lowerCamelCase__ = self.scheduler.step(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ).prev_sample
lowerCamelCase__ = scheduler_output - scheduler_output + torch.ones_like(_lowerCAmelCase )
return result
| 50 |
'''simple docstring'''
import os
from pathlib import Path
def A__ ( ):
from torch.utils.cpp_extension import load
lowerCamelCase__ = Path(__lowerCAmelCase ).resolve().parent.parent.parent / """kernels""" / """deformable_detr"""
lowerCamelCase__ = [
root / filename
for filename in [
"""vision.cpp""",
os.path.join("""cpu""" , """ms_deform_attn_cpu.cpp""" ),
os.path.join("""cuda""" , """ms_deform_attn_cuda.cu""" ),
]
]
load(
"""MultiScaleDeformableAttention""" , __lowerCAmelCase , with_cuda=__lowerCAmelCase , extra_include_paths=[str(__lowerCAmelCase )] , extra_cflags=["""-DWITH_CUDA=1"""] , extra_cuda_cflags=[
"""-DCUDA_HAS_FP16=1""",
"""-D__CUDA_NO_HALF_OPERATORS__""",
"""-D__CUDA_NO_HALF_CONVERSIONS__""",
"""-D__CUDA_NO_HALF2_OPERATORS__""",
] , )
import MultiScaleDeformableAttention as MSDA
return MSDA
| 50 | 1 |
'''simple docstring'''
import functools
def A__ ( __lowerCAmelCase : list[int] , __lowerCAmelCase : list[int] ):
# Validation
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or not all(isinstance(__lowerCAmelCase , __lowerCAmelCase ) for day in days ):
raise ValueError("""The parameter days should be a list of integers""" )
if len(__lowerCAmelCase ) != 3 or not all(isinstance(__lowerCAmelCase , __lowerCAmelCase ) for cost in costs ):
raise ValueError("""The parameter costs should be a list of three integers""" )
if len(__lowerCAmelCase ) == 0:
return 0
if min(__lowerCAmelCase ) <= 0:
raise ValueError("""All days elements should be greater than 0""" )
if max(__lowerCAmelCase ) >= 366:
raise ValueError("""All days elements should be less than 366""" )
lowerCamelCase__ = set(__lowerCAmelCase )
@functools.cache
def dynamic_programming(__lowerCAmelCase : int ) -> int:
if index > 365:
return 0
if index not in days_set:
return dynamic_programming(index + 1 )
return min(
costs[0] + dynamic_programming(index + 1 ) , costs[1] + dynamic_programming(index + 7 ) , costs[2] + dynamic_programming(index + 30 ) , )
return dynamic_programming(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 50 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : list[int] , __lowerCAmelCase : list[int] ):
lowerCamelCase__ = len(__lowerCAmelCase )
print("""The following activities are selected:""" )
# The first activity is always selected
lowerCamelCase__ = 0
print(__lowerCAmelCase , end=""",""" )
# Consider rest of the activities
for j in range(__lowerCAmelCase ):
# If this activity has start time greater than
# or equal to the finish time of previously
# selected activity, then select it
if start[j] >= finish[i]:
print(__lowerCAmelCase , end=""",""" )
lowerCamelCase__ = j
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCamelCase : Union[str, Any] = [1, 3, 0, 5, 8, 5]
UpperCamelCase : int = [2, 4, 6, 7, 9, 9]
print_max_activities(start, finish)
| 50 | 1 |
'''simple docstring'''
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase : str = logging.get_logger(__name__)
def A__ ( __lowerCAmelCase : Tuple ):
lowerCamelCase__ = MobileNetVaConfig(layer_norm_eps=0.001 )
if "_quant" in model_name:
raise ValueError("""Quantized models are not supported.""" )
lowerCamelCase__ = re.match(R"""^mobilenet_v1_([^_]*)_([^_]*)$""" , __lowerCAmelCase )
if matches:
lowerCamelCase__ = float(matches[1] )
lowerCamelCase__ = int(matches[2] )
# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
# the usual 1000. The first class (index 0) is "background".
lowerCamelCase__ = 1001
lowerCamelCase__ = """imagenet-1k-id2label.json"""
lowerCamelCase__ = """huggingface/label-files"""
lowerCamelCase__ = json.load(open(hf_hub_download(__lowerCAmelCase , __lowerCAmelCase , repo_type="""dataset""" ) , """r""" ) )
lowerCamelCase__ = {int(__lowerCAmelCase ) + 1: v for k, v in idalabel.items()}
lowerCamelCase__ = """background"""
lowerCamelCase__ = idalabel
lowerCamelCase__ = {v: k for k, v in idalabel.items()}
return config
def A__ ( ):
lowerCamelCase__ = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowerCamelCase__ = Image.open(requests.get(__lowerCAmelCase , stream=__lowerCAmelCase ).raw )
return im
@torch.no_grad()
def A__ ( __lowerCAmelCase : Any , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Any=False ):
lowerCamelCase__ = get_mobilenet_va_config(__lowerCAmelCase )
# Load 🤗 model
lowerCamelCase__ = MobileNetVaForImageClassification(__lowerCAmelCase ).eval()
# Load weights from TensorFlow checkpoint
load_tf_weights_in_mobilenet_va(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# Check outputs on an image, prepared by MobileNetV1ImageProcessor
lowerCamelCase__ = MobileNetVaImageProcessor(
crop_size={"""width""": config.image_size, """height""": config.image_size} , size={"""shortest_edge""": config.image_size + 32} , )
lowerCamelCase__ = image_processor(images=prepare_img() , return_tensors="""pt""" )
lowerCamelCase__ = model(**__lowerCAmelCase )
lowerCamelCase__ = outputs.logits
assert logits.shape == (1, 1001)
if model_name == "mobilenet_v1_1.0_224":
lowerCamelCase__ = torch.tensor([-4.1739, -1.1233, 3.1205] )
elif model_name == "mobilenet_v1_0.75_192":
lowerCamelCase__ = torch.tensor([-3.9440, -2.3141, -0.3333] )
else:
lowerCamelCase__ = None
if expected_logits is not None:
assert torch.allclose(logits[0, :3] , __lowerCAmelCase , atol=1e-4 )
Path(__lowerCAmelCase ).mkdir(exist_ok=__lowerCAmelCase )
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("""Pushing to the hub...""" )
lowerCamelCase__ = """google/""" + model_name
image_processor.push_to_hub(__lowerCAmelCase )
model.push_to_hub(__lowerCAmelCase )
if __name__ == "__main__":
UpperCamelCase : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='mobilenet_v1_1.0_224',
type=str,
help='Name of the MobileNetV1 model you\'d like to convert. Should in the form \'mobilenet_v1_<depth>_<size>\'.',
)
parser.add_argument(
'--checkpoint_path', required=True, type=str, help='Path to the original TensorFlow checkpoint (.ckpt file).'
)
parser.add_argument(
'--pytorch_dump_folder_path', required=True, 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.'
)
UpperCamelCase : Tuple = parser.parse_args()
convert_movilevit_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 50 |
'''simple docstring'''
import warnings
from ..trainer import Trainer
from ..utils import logging
UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
class UpperCamelCase__ (a ):
'''simple docstring'''
def __init__( self ,_lowerCAmelCase=None ,**_lowerCAmelCase ):
warnings.warn(
"""`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` """
"""instead.""" ,_lowerCAmelCase ,)
super().__init__(args=_lowerCAmelCase ,**_lowerCAmelCase )
| 50 | 1 |
'''simple docstring'''
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class UpperCamelCase__ :
'''simple docstring'''
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
raise NotImplementedError()
def UpperCamelCase_ ( self ):
raise NotImplementedError()
class UpperCamelCase__ (a ):
'''simple docstring'''
def __init__( self ,_lowerCAmelCase ,_lowerCAmelCase = False ,**_lowerCAmelCase ):
lowerCamelCase__ = tokenizer
lowerCamelCase__ = skip_prompt
lowerCamelCase__ = decode_kwargs
# variables used in the streaming process
lowerCamelCase__ = []
lowerCamelCase__ = 0
lowerCamelCase__ = True
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
if len(value.shape ) > 1 and value.shape[0] > 1:
raise ValueError("""TextStreamer only supports batch size 1""" )
elif len(value.shape ) > 1:
lowerCamelCase__ = value[0]
if self.skip_prompt and self.next_tokens_are_prompt:
lowerCamelCase__ = False
return
# Add the new token to the cache and decodes the entire thing.
self.token_cache.extend(value.tolist() )
lowerCamelCase__ = self.tokenizer.decode(self.token_cache ,**self.decode_kwargs )
# After the symbol for a new line, we flush the cache.
if text.endswith("""\n""" ):
lowerCamelCase__ = text[self.print_len :]
lowerCamelCase__ = []
lowerCamelCase__ = 0
# If the last token is a CJK character, we print the characters.
elif len(_lowerCAmelCase ) > 0 and self._is_chinese_char(ord(text[-1] ) ):
lowerCamelCase__ = text[self.print_len :]
self.print_len += len(_lowerCAmelCase )
# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
# which may change with the subsequent token -- there are probably smarter ways to do this!)
else:
lowerCamelCase__ = text[self.print_len : text.rfind(""" """ ) + 1]
self.print_len += len(_lowerCAmelCase )
self.on_finalized_text(_lowerCAmelCase )
def UpperCamelCase_ ( self ):
# Flush the cache, if it exists
if len(self.token_cache ) > 0:
lowerCamelCase__ = self.tokenizer.decode(self.token_cache ,**self.decode_kwargs )
lowerCamelCase__ = text[self.print_len :]
lowerCamelCase__ = []
lowerCamelCase__ = 0
else:
lowerCamelCase__ = """"""
lowerCamelCase__ = True
self.on_finalized_text(_lowerCAmelCase ,stream_end=_lowerCAmelCase )
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase = False ):
print(_lowerCAmelCase ,flush=_lowerCAmelCase ,end="""""" if not stream_end else None )
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0x4e_00 and cp <= 0x9f_ff)
or (cp >= 0x34_00 and cp <= 0x4d_bf) #
or (cp >= 0x2_00_00 and cp <= 0x2_a6_df) #
or (cp >= 0x2_a7_00 and cp <= 0x2_b7_3f) #
or (cp >= 0x2_b7_40 and cp <= 0x2_b8_1f) #
or (cp >= 0x2_b8_20 and cp <= 0x2_ce_af) #
or (cp >= 0xf9_00 and cp <= 0xfa_ff)
or (cp >= 0x2_f8_00 and cp <= 0x2_fa_1f) #
): #
return True
return False
class UpperCamelCase__ (a ):
'''simple docstring'''
def __init__( self ,_lowerCAmelCase ,_lowerCAmelCase = False ,_lowerCAmelCase = None ,**_lowerCAmelCase ):
super().__init__(_lowerCAmelCase ,_lowerCAmelCase ,**_lowerCAmelCase )
lowerCamelCase__ = Queue()
lowerCamelCase__ = None
lowerCamelCase__ = timeout
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase = False ):
self.text_queue.put(_lowerCAmelCase ,timeout=self.timeout )
if stream_end:
self.text_queue.put(self.stop_signal ,timeout=self.timeout )
def __iter__( self ):
return self
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.text_queue.get(timeout=self.timeout )
if value == self.stop_signal:
raise StopIteration()
else:
return value
| 50 |
'''simple docstring'''
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def A__ ( __lowerCAmelCase : List[str] ):
lowerCamelCase__ = []
for line in lines:
lowerCamelCase__ = re.sub(R"""#.*""" , """""" , __lowerCAmelCase ) # remove comments
if line:
filtered_lines.append(__lowerCAmelCase )
lowerCamelCase__ = """\n""".join(__lowerCAmelCase )
# Make a hash from all this code
lowerCamelCase__ = full_str.encode("""utf-8""" )
return shaaaa(__lowerCAmelCase ).hexdigest()
# get importable module names and hash for caching
UpperCamelCase : Dict = {
'csv': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
'json': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
'pandas': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
'parquet': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
'arrow': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
'text': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
'imagefolder': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
'audiofolder': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
UpperCamelCase : str = {
'.csv': ('csv', {}),
'.tsv': ('csv', {'sep': '\t'}),
'.json': ('json', {}),
'.jsonl': ('json', {}),
'.parquet': ('parquet', {}),
'.arrow': ('arrow', {}),
'.txt': ('text', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
UpperCamelCase : List[Any] = {'imagefolder', 'audiofolder'}
# Used to filter data files based on extensions given a module name
UpperCamelCase : Dict[str, List[str]] = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append('.zip')
_MODULE_TO_EXTENSIONS["audiofolder"].append('.zip')
| 50 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
UpperCamelCase : List[str] = {
'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
'susnato/ernie-m-large_pytorch': 'https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json',
}
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = 'ernie_m'
_UpperCamelCase = {"dropout": "classifier_dropout", "num_classes": "num_labels"}
def __init__( self ,_lowerCAmelCase = 25_00_02 ,_lowerCAmelCase = 7_68 ,_lowerCAmelCase = 12 ,_lowerCAmelCase = 12 ,_lowerCAmelCase = 30_72 ,_lowerCAmelCase = "gelu" ,_lowerCAmelCase = 0.1 ,_lowerCAmelCase = 0.1 ,_lowerCAmelCase = 5_14 ,_lowerCAmelCase = 0.02 ,_lowerCAmelCase = 1 ,_lowerCAmelCase = 1E-05 ,_lowerCAmelCase=None ,_lowerCAmelCase=False ,_lowerCAmelCase=0.0 ,**_lowerCAmelCase ,):
super().__init__(pad_token_id=_lowerCAmelCase ,**_lowerCAmelCase )
lowerCamelCase__ = vocab_size
lowerCamelCase__ = hidden_size
lowerCamelCase__ = num_hidden_layers
lowerCamelCase__ = num_attention_heads
lowerCamelCase__ = intermediate_size
lowerCamelCase__ = hidden_act
lowerCamelCase__ = hidden_dropout_prob
lowerCamelCase__ = attention_probs_dropout_prob
lowerCamelCase__ = max_position_embeddings
lowerCamelCase__ = initializer_range
lowerCamelCase__ = layer_norm_eps
lowerCamelCase__ = classifier_dropout
lowerCamelCase__ = is_decoder
lowerCamelCase__ = act_dropout
| 50 |
'''simple docstring'''
import operator
def A__ ( __lowerCAmelCase : list , __lowerCAmelCase : bool = False , __lowerCAmelCase : list | None = None ):
lowerCamelCase__ = operator.lt if reverse else operator.gt
lowerCamelCase__ = solution or []
if not arr:
return solution
lowerCamelCase__ = [arr.pop(0 )]
for i, item in enumerate(__lowerCAmelCase ):
if _operator(__lowerCAmelCase , sublist[-1] ):
sublist.append(__lowerCAmelCase )
arr.pop(__lowerCAmelCase )
# merging sublist into solution list
if not solution:
solution.extend(__lowerCAmelCase )
else:
while sublist:
lowerCamelCase__ = sublist.pop(0 )
for i, xx in enumerate(__lowerCAmelCase ):
if not _operator(__lowerCAmelCase , __lowerCAmelCase ):
solution.insert(__lowerCAmelCase , __lowerCAmelCase )
break
else:
solution.append(__lowerCAmelCase )
strand_sort(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
return solution
if __name__ == "__main__":
assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]
| 50 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase : List[str] = {
'configuration_x_clip': [
'XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XCLIPConfig',
'XCLIPTextConfig',
'XCLIPVisionConfig',
],
'processing_x_clip': ['XCLIPProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Union[str, Any] = [
'XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'XCLIPModel',
'XCLIPPreTrainedModel',
'XCLIPTextModel',
'XCLIPVisionModel',
]
if TYPE_CHECKING:
from .configuration_x_clip import (
XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
XCLIPConfig,
XCLIPTextConfig,
XCLIPVisionConfig,
)
from .processing_x_clip import XCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_x_clip import (
XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
XCLIPModel,
XCLIPPreTrainedModel,
XCLIPTextModel,
XCLIPVisionModel,
)
else:
import sys
UpperCamelCase : Dict = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 50 |
'''simple docstring'''
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def A__ ( __lowerCAmelCase : dict ):
return (data["data"], data["target"])
def A__ ( __lowerCAmelCase : np.ndarray , __lowerCAmelCase : np.ndarray , __lowerCAmelCase : np.ndarray ):
lowerCamelCase__ = XGBRegressor(verbosity=0 , random_state=42 )
xgb.fit(__lowerCAmelCase , __lowerCAmelCase )
# Predict target for test data
lowerCamelCase__ = xgb.predict(__lowerCAmelCase )
lowerCamelCase__ = predictions.reshape(len(__lowerCAmelCase ) , 1 )
return predictions
def A__ ( ):
lowerCamelCase__ = fetch_california_housing()
lowerCamelCase__ , lowerCamelCase__ = data_handling(__lowerCAmelCase )
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = train_test_split(
__lowerCAmelCase , __lowerCAmelCase , test_size=0.25 , random_state=1 )
lowerCamelCase__ = xgboost(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# Error printing
print(F'''Mean Absolute Error : {mean_absolute_error(__lowerCAmelCase , __lowerCAmelCase )}''' )
print(F'''Mean Square Error : {mean_squared_error(__lowerCAmelCase , __lowerCAmelCase )}''' )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 50 | 1 |
'''simple docstring'''
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBertTokenizer,
GPTaConfig,
GPTaLMHeadModel,
GPTaTokenizer,
RobertaConfig,
RobertaForMaskedLM,
RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed
UpperCamelCase : int = {
'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
'bert': (BertConfig, BertForMaskedLM, BertTokenizer),
'gpt2': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}
def A__ ( __lowerCAmelCase : str ):
assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
if args.mlm:
assert os.path.isfile(args.token_counts )
assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
else:
assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])
assert args.teacher_type == args.student_type or (
args.student_type == "distilbert" and args.teacher_type == "bert"
)
assert os.path.isfile(args.student_config )
if args.student_pretrained_weights is not None:
assert os.path.isfile(args.student_pretrained_weights )
if args.freeze_token_type_embds:
assert args.student_type in ["roberta"]
assert args.alpha_ce >= 0.0
assert args.alpha_mlm >= 0.0
assert args.alpha_clm >= 0.0
assert args.alpha_mse >= 0.0
assert args.alpha_cos >= 0.0
assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0
def A__ ( __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] ):
if args.student_type == "roberta":
lowerCamelCase__ = False
elif args.student_type == "gpt2":
lowerCamelCase__ = False
def A__ ( __lowerCAmelCase : Dict , __lowerCAmelCase : Tuple ):
if args.student_type == "roberta":
lowerCamelCase__ = False
def A__ ( ):
lowerCamelCase__ = argparse.ArgumentParser(description="""Training""" )
parser.add_argument("""--force""" , action="""store_true""" , help="""Overwrite dump_path if it already exists.""" )
parser.add_argument(
"""--dump_path""" , type=__lowerCAmelCase , required=__lowerCAmelCase , help="""The output directory (log, checkpoints, parameters, etc.)""" )
parser.add_argument(
"""--data_file""" , type=__lowerCAmelCase , required=__lowerCAmelCase , help="""The binarized file (tokenized + tokens_to_ids) and grouped by sequence.""" , )
parser.add_argument(
"""--student_type""" , type=__lowerCAmelCase , choices=["""distilbert""", """roberta""", """gpt2"""] , required=__lowerCAmelCase , help="""The student type (DistilBERT, RoBERTa).""" , )
parser.add_argument("""--student_config""" , type=__lowerCAmelCase , required=__lowerCAmelCase , help="""Path to the student configuration.""" )
parser.add_argument(
"""--student_pretrained_weights""" , default=__lowerCAmelCase , type=__lowerCAmelCase , help="""Load student initialization checkpoint.""" )
parser.add_argument(
"""--teacher_type""" , choices=["""bert""", """roberta""", """gpt2"""] , required=__lowerCAmelCase , help="""Teacher type (BERT, RoBERTa).""" )
parser.add_argument("""--teacher_name""" , type=__lowerCAmelCase , required=__lowerCAmelCase , help="""The teacher model.""" )
parser.add_argument("""--temperature""" , default=2.0 , type=__lowerCAmelCase , help="""Temperature for the softmax temperature.""" )
parser.add_argument(
"""--alpha_ce""" , default=0.5 , type=__lowerCAmelCase , help="""Linear weight for the distillation loss. Must be >=0.""" )
parser.add_argument(
"""--alpha_mlm""" , default=0.0 , type=__lowerCAmelCase , help="""Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.""" , )
parser.add_argument("""--alpha_clm""" , default=0.5 , type=__lowerCAmelCase , help="""Linear weight for the CLM loss. Must be >=0.""" )
parser.add_argument("""--alpha_mse""" , default=0.0 , type=__lowerCAmelCase , help="""Linear weight of the MSE loss. Must be >=0.""" )
parser.add_argument(
"""--alpha_cos""" , default=0.0 , type=__lowerCAmelCase , help="""Linear weight of the cosine embedding loss. Must be >=0.""" )
parser.add_argument(
"""--mlm""" , action="""store_true""" , help="""The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.""" )
parser.add_argument(
"""--mlm_mask_prop""" , default=0.15 , type=__lowerCAmelCase , help="""Proportion of tokens for which we need to make a prediction.""" , )
parser.add_argument("""--word_mask""" , default=0.8 , type=__lowerCAmelCase , help="""Proportion of tokens to mask out.""" )
parser.add_argument("""--word_keep""" , default=0.1 , type=__lowerCAmelCase , help="""Proportion of tokens to keep.""" )
parser.add_argument("""--word_rand""" , default=0.1 , type=__lowerCAmelCase , help="""Proportion of tokens to randomly replace.""" )
parser.add_argument(
"""--mlm_smoothing""" , default=0.7 , type=__lowerCAmelCase , help="""Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).""" , )
parser.add_argument("""--token_counts""" , type=__lowerCAmelCase , help="""The token counts in the data_file for MLM.""" )
parser.add_argument(
"""--restrict_ce_to_mask""" , action="""store_true""" , help="""If true, compute the distillation loss only the [MLM] prediction distribution.""" , )
parser.add_argument(
"""--freeze_pos_embs""" , action="""store_true""" , help="""Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only.""" , )
parser.add_argument(
"""--freeze_token_type_embds""" , action="""store_true""" , help="""Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only.""" , )
parser.add_argument("""--n_epoch""" , type=__lowerCAmelCase , default=3 , help="""Number of pass on the whole dataset.""" )
parser.add_argument("""--batch_size""" , type=__lowerCAmelCase , default=5 , help="""Batch size (for each process).""" )
parser.add_argument(
"""--group_by_size""" , action="""store_false""" , help="""If true, group sequences that have similar length into the same batch. Default is true.""" , )
parser.add_argument(
"""--gradient_accumulation_steps""" , type=__lowerCAmelCase , default=50 , help="""Gradient accumulation for larger training batches.""" , )
parser.add_argument("""--warmup_prop""" , default=0.05 , type=__lowerCAmelCase , help="""Linear warmup proportion.""" )
parser.add_argument("""--weight_decay""" , default=0.0 , type=__lowerCAmelCase , help="""Weight decay if we apply some.""" )
parser.add_argument("""--learning_rate""" , default=5e-4 , type=__lowerCAmelCase , help="""The initial learning rate for Adam.""" )
parser.add_argument("""--adam_epsilon""" , default=1e-6 , type=__lowerCAmelCase , help="""Epsilon for Adam optimizer.""" )
parser.add_argument("""--max_grad_norm""" , default=5.0 , type=__lowerCAmelCase , help="""Max gradient norm.""" )
parser.add_argument("""--initializer_range""" , default=0.02 , type=__lowerCAmelCase , help="""Random initialization range.""" )
parser.add_argument(
"""--fp16""" , action="""store_true""" , help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit""" , )
parser.add_argument(
"""--fp16_opt_level""" , type=__lowerCAmelCase , default="""O1""" , help=(
"""For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
"""See details at https://nvidia.github.io/apex/amp.html"""
) , )
parser.add_argument("""--n_gpu""" , type=__lowerCAmelCase , default=1 , help="""Number of GPUs in the node.""" )
parser.add_argument("""--local_rank""" , type=__lowerCAmelCase , default=-1 , help="""Distributed training - Local rank""" )
parser.add_argument("""--seed""" , type=__lowerCAmelCase , default=56 , help="""Random seed""" )
parser.add_argument("""--log_interval""" , type=__lowerCAmelCase , default=500 , help="""Tensorboard logging interval.""" )
parser.add_argument("""--checkpoint_interval""" , type=__lowerCAmelCase , default=4000 , help="""Checkpoint interval.""" )
lowerCamelCase__ = parser.parse_args()
sanity_checks(__lowerCAmelCase )
# ARGS #
init_gpu_params(__lowerCAmelCase )
set_seed(__lowerCAmelCase )
if args.is_master:
if os.path.exists(args.dump_path ):
if not args.force:
raise ValueError(
F'''Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'''
""" itUse `--force` if you want to overwrite it""" )
else:
shutil.rmtree(args.dump_path )
if not os.path.exists(args.dump_path ):
os.makedirs(args.dump_path )
logger.info(F'''Experiment will be dumped and logged in {args.dump_path}''' )
# SAVE PARAMS #
logger.info(F'''Param: {args}''' )
with open(os.path.join(args.dump_path , """parameters.json""" ) , """w""" ) as f:
json.dump(vars(__lowerCAmelCase ) , __lowerCAmelCase , indent=4 )
git_log(args.dump_path )
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = MODEL_CLASSES[args.student_type]
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = MODEL_CLASSES[args.teacher_type]
# TOKENIZER #
lowerCamelCase__ = teacher_tokenizer_class.from_pretrained(args.teacher_name )
lowerCamelCase__ = {}
for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
lowerCamelCase__ = tokenizer.all_special_tokens.index(__lowerCAmelCase )
lowerCamelCase__ = tokenizer.all_special_ids[idx]
logger.info(F'''Special tokens {special_tok_ids}''' )
lowerCamelCase__ = special_tok_ids
lowerCamelCase__ = tokenizer.max_model_input_sizes[args.teacher_name]
# DATA LOADER #
logger.info(F'''Loading data from {args.data_file}''' )
with open(args.data_file , """rb""" ) as fp:
lowerCamelCase__ = pickle.load(__lowerCAmelCase )
if args.mlm:
logger.info(F'''Loading token counts from {args.token_counts} (already pre-computed)''' )
with open(args.token_counts , """rb""" ) as fp:
lowerCamelCase__ = pickle.load(__lowerCAmelCase )
lowerCamelCase__ = np.maximum(__lowerCAmelCase , 1 ) ** -args.mlm_smoothing
for idx in special_tok_ids.values():
lowerCamelCase__ = 0.0 # do not predict special tokens
lowerCamelCase__ = torch.from_numpy(__lowerCAmelCase )
else:
lowerCamelCase__ = None
lowerCamelCase__ = LmSeqsDataset(params=__lowerCAmelCase , data=__lowerCAmelCase )
logger.info("""Data loader created.""" )
# STUDENT #
logger.info(F'''Loading student config from {args.student_config}''' )
lowerCamelCase__ = student_config_class.from_pretrained(args.student_config )
lowerCamelCase__ = True
if args.student_pretrained_weights is not None:
logger.info(F'''Loading pretrained weights from {args.student_pretrained_weights}''' )
lowerCamelCase__ = student_model_class.from_pretrained(args.student_pretrained_weights , config=__lowerCAmelCase )
else:
lowerCamelCase__ = student_model_class(__lowerCAmelCase )
if args.n_gpu > 0:
student.to(F'''cuda:{args.local_rank}''' )
logger.info("""Student loaded.""" )
# TEACHER #
lowerCamelCase__ = teacher_model_class.from_pretrained(args.teacher_name , output_hidden_states=__lowerCAmelCase )
if args.n_gpu > 0:
teacher.to(F'''cuda:{args.local_rank}''' )
logger.info(F'''Teacher loaded from {args.teacher_name}.''' )
# FREEZING #
if args.freeze_pos_embs:
freeze_pos_embeddings(__lowerCAmelCase , __lowerCAmelCase )
if args.freeze_token_type_embds:
freeze_token_type_embeddings(__lowerCAmelCase , __lowerCAmelCase )
# SANITY CHECKS #
assert student.config.vocab_size == teacher.config.vocab_size
assert student.config.hidden_size == teacher.config.hidden_size
assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
if args.mlm:
assert token_probs.size(0 ) == stu_architecture_config.vocab_size
# DISTILLER #
torch.cuda.empty_cache()
lowerCamelCase__ = Distiller(
params=__lowerCAmelCase , dataset=__lowerCAmelCase , token_probs=__lowerCAmelCase , student=__lowerCAmelCase , teacher=__lowerCAmelCase )
distiller.train()
logger.info("""Let's go get some drinks.""" )
if __name__ == "__main__":
main()
| 50 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBOSTokenLogitsProcessor,
FlaxForcedEOSTokenLogitsProcessor,
FlaxLogitsProcessorList,
FlaxMinLengthLogitsProcessor,
FlaxTemperatureLogitsWarper,
FlaxTopKLogitsWarper,
FlaxTopPLogitsWarper,
)
@require_flax
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = jnp.ones((batch_size, length) ) / length
return scores
def UpperCamelCase_ ( self ):
lowerCamelCase__ = None
lowerCamelCase__ = 20
lowerCamelCase__ = self._get_uniform_logits(batch_size=2 ,length=_lowerCAmelCase )
# tweak scores to not be uniform anymore
lowerCamelCase__ = scores.at[1, 5].set((1 / length) + 0.1 ) # peak, 1st batch
lowerCamelCase__ = scores.at[1, 10].set((1 / length) - 0.4 ) # valley, 1st batch
# compute softmax
lowerCamelCase__ = jax.nn.softmax(_lowerCAmelCase ,axis=-1 )
lowerCamelCase__ = FlaxTemperatureLogitsWarper(temperature=0.5 )
lowerCamelCase__ = FlaxTemperatureLogitsWarper(temperature=1.3 )
lowerCamelCase__ = jax.nn.softmax(temp_dist_warper_sharper(_lowerCAmelCase ,scores.copy() ,cur_len=_lowerCAmelCase ) ,axis=-1 )
lowerCamelCase__ = jax.nn.softmax(temp_dist_warper_smoother(_lowerCAmelCase ,scores.copy() ,cur_len=_lowerCAmelCase ) ,axis=-1 )
# uniform distribution stays uniform
self.assertTrue(jnp.allclose(probs[0, :] ,warped_prob_sharp[0, :] ,atol=1E-3 ) )
self.assertTrue(jnp.allclose(probs[0, :] ,warped_prob_smooth[0, :] ,atol=1E-3 ) )
# sharp peaks get higher, valleys get lower
self.assertLess(probs[1, :].max() ,warped_prob_sharp[1, :].max() )
self.assertGreater(probs[1, :].min() ,warped_prob_sharp[1, :].min() )
# smooth peaks get lower, valleys get higher
self.assertGreater(probs[1, :].max() ,warped_prob_smooth[1, :].max() )
self.assertLess(probs[1, :].min() ,warped_prob_smooth[1, :].min() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = None
lowerCamelCase__ = 10
lowerCamelCase__ = 2
# create ramp distribution
lowerCamelCase__ = np.broadcast_to(np.arange(_lowerCAmelCase )[None, :] ,(batch_size, vocab_size) ).copy()
lowerCamelCase__ = ramp_logits[1:, : vocab_size // 2] + vocab_size
lowerCamelCase__ = FlaxTopKLogitsWarper(3 )
lowerCamelCase__ = top_k_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# check that correct tokens are filtered
self.assertListEqual(jnp.isinf(scores[0] ).tolist() ,7 * [True] + 3 * [False] )
self.assertListEqual(jnp.isinf(scores[1] ).tolist() ,2 * [True] + 3 * [False] + 5 * [True] )
# check special case
lowerCamelCase__ = 5
lowerCamelCase__ = FlaxTopKLogitsWarper(top_k=1 ,filter_value=0.0 ,min_tokens_to_keep=3 )
lowerCamelCase__ = np.broadcast_to(np.arange(_lowerCAmelCase )[None, :] ,(batch_size, length) ).copy()
lowerCamelCase__ = top_k_warp_safety_check(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
self.assertListEqual((scores == 0.0).sum(axis=-1 ).tolist() ,[2, 2] )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = None
lowerCamelCase__ = 10
lowerCamelCase__ = 2
# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
lowerCamelCase__ = np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]] ) )
lowerCamelCase__ = FlaxTopPLogitsWarper(0.8 )
lowerCamelCase__ = np.exp(top_p_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase ) )
# dist should be filtered to keep min num values so that sum is >= top_p
# exp (-inf) => 0
lowerCamelCase__ = np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]] )
self.assertTrue(np.allclose(_lowerCAmelCase ,_lowerCAmelCase ,atol=1E-3 ) )
# check edge cases with negative and extreme logits
lowerCamelCase__ = np.broadcast_to(np.arange(_lowerCAmelCase )[None, :] ,(batch_size, vocab_size) ).copy() - (
vocab_size // 2
)
# make ramp_logits more extreme
lowerCamelCase__ = ramp_logits[1] * 100.0
# make sure at least 2 tokens are kept
lowerCamelCase__ = FlaxTopPLogitsWarper(0.9 ,min_tokens_to_keep=2 ,filter_value=0.0 )
lowerCamelCase__ = top_p_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
self.assertListEqual((filtered_dist != 0.0).sum(axis=-1 ).tolist() ,[3, 2] )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 20
lowerCamelCase__ = 4
lowerCamelCase__ = 0
lowerCamelCase__ = FlaxMinLengthLogitsProcessor(min_length=10 ,eos_token_id=_lowerCAmelCase )
# check that min length is applied at length 5
lowerCamelCase__ = ids_tensor((batch_size, 20) ,vocab_size=20 )
lowerCamelCase__ = 5
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = min_dist_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist() ,4 * [-float("""inf""" )] )
# check that min length is not applied anymore at length 15
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = 15
lowerCamelCase__ = min_dist_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertFalse(jnp.isinf(_lowerCAmelCase ).any() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 20
lowerCamelCase__ = 4
lowerCamelCase__ = 0
lowerCamelCase__ = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase )
# check that all scores are -inf except the bos_token_id score
lowerCamelCase__ = ids_tensor((batch_size, 1) ,vocab_size=20 )
lowerCamelCase__ = 1
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = logits_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, bos_token_id].tolist() ,4 * [0] ) # score for bos_token_id shold be zero
# check that bos_token_id is not forced if current length is greater than 1
lowerCamelCase__ = 3
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = logits_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertFalse(jnp.isinf(_lowerCAmelCase ).any() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 20
lowerCamelCase__ = 4
lowerCamelCase__ = 0
lowerCamelCase__ = 5
lowerCamelCase__ = FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase )
# check that all scores are -inf except the eos_token_id when max_length is reached
lowerCamelCase__ = ids_tensor((batch_size, 4) ,vocab_size=20 )
lowerCamelCase__ = 4
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = logits_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, eos_token_id].tolist() ,4 * [0] ) # score for eos_token_id should be zero
# check that eos_token_id is not forced if max_length is not reached
lowerCamelCase__ = 3
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = logits_processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
self.assertFalse(jnp.isinf(_lowerCAmelCase ).any() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 4
lowerCamelCase__ = 10
lowerCamelCase__ = 15
lowerCamelCase__ = 2
lowerCamelCase__ = 1
lowerCamelCase__ = 15
# dummy input_ids and scores
lowerCamelCase__ = ids_tensor((batch_size, sequence_length) ,_lowerCAmelCase )
lowerCamelCase__ = input_ids.copy()
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = scores.copy()
# instantiate all dist processors
lowerCamelCase__ = FlaxTemperatureLogitsWarper(temperature=0.5 )
lowerCamelCase__ = FlaxTopKLogitsWarper(3 )
lowerCamelCase__ = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
lowerCamelCase__ = FlaxMinLengthLogitsProcessor(min_length=10 ,eos_token_id=_lowerCAmelCase )
lowerCamelCase__ = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase )
lowerCamelCase__ = FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase )
lowerCamelCase__ = 10
# no processor list
lowerCamelCase__ = temp_dist_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = top_k_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = top_p_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = min_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = bos_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = eos_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# with processor list
lowerCamelCase__ = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
lowerCamelCase__ = processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
# scores should be equal
self.assertTrue(jnp.allclose(_lowerCAmelCase ,_lowerCAmelCase ,atol=1E-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() ,input_ids_comp.tolist() )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = 4
lowerCamelCase__ = 10
lowerCamelCase__ = 15
lowerCamelCase__ = 2
lowerCamelCase__ = 1
lowerCamelCase__ = 15
# dummy input_ids and scores
lowerCamelCase__ = ids_tensor((batch_size, sequence_length) ,_lowerCAmelCase )
lowerCamelCase__ = input_ids.copy()
lowerCamelCase__ = self._get_uniform_logits(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = scores.copy()
# instantiate all dist processors
lowerCamelCase__ = FlaxTemperatureLogitsWarper(temperature=0.5 )
lowerCamelCase__ = FlaxTopKLogitsWarper(3 )
lowerCamelCase__ = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
lowerCamelCase__ = FlaxMinLengthLogitsProcessor(min_length=10 ,eos_token_id=_lowerCAmelCase )
lowerCamelCase__ = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase )
lowerCamelCase__ = FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase )
lowerCamelCase__ = 10
# no processor list
def run_no_processor_list(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = temp_dist_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = top_k_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = top_p_warp(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = min_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = bos_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
lowerCamelCase__ = eos_dist_proc(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
return scores
# with processor list
def run_processor_list(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
lowerCamelCase__ = processor(_lowerCAmelCase ,_lowerCAmelCase ,cur_len=_lowerCAmelCase )
return scores
lowerCamelCase__ = jax.jit(_lowerCAmelCase )
lowerCamelCase__ = jax.jit(_lowerCAmelCase )
lowerCamelCase__ = jitted_run_no_processor_list(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = jitted_run_processor_list(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
# scores should be equal
self.assertTrue(jnp.allclose(_lowerCAmelCase ,_lowerCAmelCase ,atol=1E-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() ,input_ids_comp.tolist() )
| 50 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=a )
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = field(default='question-answering-extractive' ,metadata={'include_in_asdict_even_if_is_default': True} )
_UpperCamelCase = Features({'question': Value('string' ), 'context': Value('string' )} )
_UpperCamelCase = Features(
{
'answers': Sequence(
{
'text': Value('string' ),
'answer_start': Value('int32' ),
} )
} )
_UpperCamelCase = "question"
_UpperCamelCase = "context"
_UpperCamelCase = "answers"
@property
def UpperCamelCase_ ( self ):
return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}
| 50 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase : Any = {
'configuration_groupvit': [
'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'GroupViTConfig',
'GroupViTOnnxConfig',
'GroupViTTextConfig',
'GroupViTVisionConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : List[str] = [
'GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'GroupViTModel',
'GroupViTPreTrainedModel',
'GroupViTTextModel',
'GroupViTVisionModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : List[str] = [
'TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFGroupViTModel',
'TFGroupViTPreTrainedModel',
'TFGroupViTTextModel',
'TFGroupViTVisionModel',
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
UpperCamelCase : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 50 | 1 |
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