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from math import factorial, pi
def _snake_case ( lowerCAmelCase : float , lowerCAmelCase : int = 3_0 ):
"""simple docstring"""
if not isinstance(lowerCAmelCase , (int, float) ):
raise ValueError("maclaurin_sin() requires either an int or float for theta" )
if not isinstance(lowerCAmelCase , lowerCAmelCase ) or accuracy <= 0:
raise ValueError("maclaurin_sin() requires a positive int for accuracy" )
SCREAMING_SNAKE_CASE_ : int = float(lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : List[Any] = theta // (2 * pi)
theta -= 2 * div * pi
return sum(
(-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1 ) for r in range(lowerCAmelCase ) )
def _snake_case ( lowerCAmelCase : float , lowerCAmelCase : int = 3_0 ):
"""simple docstring"""
if not isinstance(lowerCAmelCase , (int, float) ):
raise ValueError("maclaurin_cos() requires either an int or float for theta" )
if not isinstance(lowerCAmelCase , lowerCAmelCase ) or accuracy <= 0:
raise ValueError("maclaurin_cos() requires a positive int for accuracy" )
SCREAMING_SNAKE_CASE_ : str = float(lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Optional[int] = theta // (2 * pi)
theta -= 2 * div * pi
return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r ) for r in range(lowerCAmelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(maclaurin_sin(10))
print(maclaurin_sin(-10))
print(maclaurin_sin(10, 15))
print(maclaurin_sin(-10, 15))
print(maclaurin_cos(5))
print(maclaurin_cos(-5))
print(maclaurin_cos(10, 15))
print(maclaurin_cos(-10, 15))
| 18 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
_lowerCAmelCase = abspath(join(dirname(dirname(dirname(__file__))), '''src'''))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)
def __lowerCAmelCase ( snake_case__ ):
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(snake_case__ )
def __lowerCAmelCase ( snake_case__ ):
from transformers.testing_utils import pytest_terminal_summary_main
__UpperCamelCase : int = terminalreporter.config.getoption("--make-reports" )
if make_reports:
pytest_terminal_summary_main(snake_case__ , id=snake_case__ )
| 298 | 0 |
from numpy import exp, pi, sqrt
def A__ ( lowerCamelCase , lowerCamelCase = 0.0 , lowerCamelCase = 1.0 ) -> int:
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 223 |
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ImageGPTImageProcessor
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Optional[int] , snake_case_ : Dict , snake_case_ : Tuple=7 , snake_case_ : Optional[Any]=3 , snake_case_ : Dict=18 , snake_case_ : Dict=30 , snake_case_ : Union[str, Any]=400 , snake_case_ : List[Any]=True , snake_case_ : Any=None , snake_case_ : List[str]=True , ):
UpperCamelCase_: Dict = size if size is not None else {"""height""": 18, """width""": 18}
UpperCamelCase_: Union[str, Any] = parent
UpperCamelCase_: Tuple = batch_size
UpperCamelCase_: List[str] = num_channels
UpperCamelCase_: Optional[int] = image_size
UpperCamelCase_: Dict = min_resolution
UpperCamelCase_: Optional[int] = max_resolution
UpperCamelCase_: str = do_resize
UpperCamelCase_: Tuple = size
UpperCamelCase_: Dict = do_normalize
def lowerCAmelCase__ ( self : str ):
return {
# here we create 2 clusters for the sake of simplicity
"clusters": np.asarray(
[
[0.8866_4436_3403_3203, 0.6618_8293_6954_4983, 0.3891_7464_0178_6804],
[-0.6042_5591_4688_1104, -0.0_2295_0088_6052_8469, 0.5423_7973_6900_3296],
] ),
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
}
@require_torch
@require_vision
class _UpperCamelCase ( _A , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase : Optional[int] = ImageGPTImageProcessor if is_vision_available() else None
def lowerCAmelCase__ ( self : List[str] ):
UpperCamelCase_: Any = ImageGPTImageProcessingTester(self )
@property
def lowerCAmelCase__ ( self : Optional[int] ):
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCAmelCase__ ( self : Optional[int] ):
UpperCamelCase_: List[Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(snake_case_ , """clusters""" ) )
self.assertTrue(hasattr(snake_case_ , """do_resize""" ) )
self.assertTrue(hasattr(snake_case_ , """size""" ) )
self.assertTrue(hasattr(snake_case_ , """do_normalize""" ) )
def lowerCAmelCase__ ( self : int ):
UpperCamelCase_: List[Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""height""": 18, """width""": 18} )
UpperCamelCase_: Tuple = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {"""height""": 42, """width""": 42} )
def lowerCAmelCase__ ( self : Optional[Any] ):
UpperCamelCase_: int = self.image_processing_class(**self.image_processor_dict )
UpperCamelCase_: Optional[int] = json.loads(image_processor.to_json_string() )
for key, value in self.image_processor_dict.items():
if key == "clusters":
self.assertTrue(np.array_equal(snake_case_ , obj[key] ) )
else:
self.assertEqual(obj[key] , snake_case_ )
def lowerCAmelCase__ ( self : List[Any] ):
UpperCamelCase_: Dict = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCamelCase_: int = os.path.join(snake_case_ , """image_processor.json""" )
image_processor_first.to_json_file(snake_case_ )
UpperCamelCase_: Any = self.image_processing_class.from_json_file(snake_case_ ).to_dict()
UpperCamelCase_: str = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(snake_case_ , image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key] , snake_case_ )
def lowerCAmelCase__ ( self : Union[str, Any] ):
UpperCamelCase_: Optional[Any] = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
image_processor_first.save_pretrained(snake_case_ )
UpperCamelCase_: Optional[int] = self.image_processing_class.from_pretrained(snake_case_ ).to_dict()
UpperCamelCase_: Union[str, Any] = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(snake_case_ , image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key] , snake_case_ )
@unittest.skip("""ImageGPT requires clusters at initialization""" )
def lowerCAmelCase__ ( self : List[Any] ):
pass
def A__ ( ) -> Optional[int]:
UpperCamelCase_: Optional[int] = load_dataset("""hf-internal-testing/fixtures_image_utils""" , split="""test""" )
UpperCamelCase_: Tuple = Image.open(dataset[4]["""file"""] )
UpperCamelCase_: Union[str, Any] = Image.open(dataset[5]["""file"""] )
UpperCamelCase_: List[str] = [imagea, imagea]
return images
@require_vision
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def lowerCAmelCase__ ( self : List[str] ):
UpperCamelCase_: List[Any] = ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small""" )
UpperCamelCase_: List[str] = prepare_images()
# test non-batched
UpperCamelCase_: List[str] = image_processing(images[0] , return_tensors="""pt""" )
self.assertIsInstance(encoding.input_ids , torch.LongTensor )
self.assertEqual(encoding.input_ids.shape , (1, 1024) )
UpperCamelCase_: Union[str, Any] = [306, 191, 191]
self.assertEqual(encoding.input_ids[0, :3].tolist() , snake_case_ )
# test batched
UpperCamelCase_: Optional[int] = image_processing(snake_case_ , return_tensors="""pt""" )
self.assertIsInstance(encoding.input_ids , torch.LongTensor )
self.assertEqual(encoding.input_ids.shape , (2, 1024) )
UpperCamelCase_: str = [303, 13, 13]
self.assertEqual(encoding.input_ids[1, -3:].tolist() , snake_case_ )
| 223 | 1 |
"""simple docstring"""
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config
# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)
a = [
'cross_validation.py',
'gradient_accumulation.py',
'local_sgd.py',
'multi_process_metrics.py',
'memory.py',
'automatic_gradient_accumulation.py',
'fsdp_with_peak_mem_tracking.py',
'deepspeed_with_config_support.py',
'megatron_lm_gpt_pretraining.py',
]
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def __lowercase ( self : Union[str, Any] , lowerCAmelCase : str , lowerCAmelCase : bool , lowerCAmelCase : str = None , lowerCAmelCase : list = None ):
lowerCAmelCase = None
lowerCAmelCase = os.path.abspath(os.path.join("""examples""" , """by_feature""" ) )
lowerCAmelCase = os.path.abspath("""examples""" )
for item in os.listdir(lowerCAmelCase ):
if item not in EXCLUDE_EXAMPLES:
lowerCAmelCase = os.path.join(lowerCAmelCase , lowerCAmelCase )
if os.path.isfile(lowerCAmelCase ) and ".py" in item_path:
with self.subTest(
tested_script=lowerCAmelCase , feature_script=lowerCAmelCase , tested_section="""main()""" if parser_only else """training_function()""" , ):
lowerCAmelCase = compare_against_test(
os.path.join(lowerCAmelCase , lowerCAmelCase ) , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
lowerCAmelCase = """\n""".join(lowerCAmelCase )
if special_strings is not None:
for string in special_strings:
lowerCAmelCase = diff.replace(lowerCAmelCase , """""" )
self.assertEqual(lowerCAmelCase , """""" )
def __lowercase ( self : Union[str, Any] ):
self.one_complete_example("""complete_nlp_example.py""" , lowerCAmelCase )
self.one_complete_example("""complete_nlp_example.py""" , lowerCAmelCase )
def __lowercase ( self : Optional[int] ):
lowerCAmelCase = os.path.abspath(os.path.join("""examples""" , """cv_example.py""" ) )
lowerCAmelCase = [
""" """ * 16 + """{\n\n""",
""" """ * 20 + """\"accuracy\": eval_metric[\"accuracy\"],\n\n""",
""" """ * 20 + """\"f1\": eval_metric[\"f1\"],\n\n""",
""" """ * 20 + """\"train_loss\": total_loss.item() / len(train_dataloader),\n\n""",
""" """ * 20 + """\"epoch\": epoch,\n\n""",
""" """ * 16 + """},\n\n""",
""" """ * 16 + """step=epoch,\n""",
""" """ * 12,
""" """ * 8 + """for step, batch in enumerate(active_dataloader):\n""",
]
self.one_complete_example("""complete_cv_example.py""" , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
self.one_complete_example("""complete_cv_example.py""" , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
@mock.patch.dict(os.environ , {'TESTING_MOCKED_DATALOADERS': '1'} )
class SCREAMING_SNAKE_CASE__ ( _a ):
_a = False
@classmethod
def __lowercase ( cls : List[Any] ):
super().setUpClass()
lowerCAmelCase = tempfile.mkdtemp()
lowerCAmelCase = os.path.join(cls._tmpdir , """default_config.yml""" )
write_basic_config(save_location=cls.configPath )
lowerCAmelCase = ["""accelerate""", """launch""", """--config_file""", cls.configPath]
@classmethod
def __lowercase ( cls : Optional[Any] ):
super().tearDownClass()
shutil.rmtree(cls._tmpdir )
def __lowercase ( self : Optional[int] ):
lowerCAmelCase = f'''
examples/by_feature/checkpointing.py
--checkpointing_steps epoch
--output_dir {self.tmpdir}
'''.split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , """epoch_0""" ) ) )
def __lowercase ( self : Tuple ):
lowerCAmelCase = f'''
examples/by_feature/checkpointing.py
--checkpointing_steps 1
--output_dir {self.tmpdir}
'''.split()
lowerCAmelCase = run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , """step_2""" ) ) )
def __lowercase ( self : Optional[int] ):
lowerCAmelCase = f'''
examples/by_feature/checkpointing.py
--resume_from_checkpoint {os.path.join(self.tmpdir , 'epoch_0' )}
'''.split()
lowerCAmelCase = run_command(self._launch_args + testargs , return_stdout=lowerCAmelCase )
self.assertNotIn("""epoch 0:""" , lowerCAmelCase )
self.assertIn("""epoch 1:""" , lowerCAmelCase )
def __lowercase ( self : Union[str, Any] ):
lowerCAmelCase = f'''
examples/by_feature/checkpointing.py
--resume_from_checkpoint {os.path.join(self.tmpdir , 'step_2' )}
'''.split()
lowerCAmelCase = run_command(self._launch_args + testargs , return_stdout=lowerCAmelCase )
if torch.cuda.is_available():
lowerCAmelCase = torch.cuda.device_count()
else:
lowerCAmelCase = 1
if num_processes > 1:
self.assertNotIn("""epoch 0:""" , lowerCAmelCase )
self.assertIn("""epoch 1:""" , lowerCAmelCase )
else:
self.assertIn("""epoch 0:""" , lowerCAmelCase )
self.assertIn("""epoch 1:""" , lowerCAmelCase )
@slow
def __lowercase ( self : str ):
lowerCAmelCase = """
examples/by_feature/cross_validation.py
--num_folds 2
""".split()
with mock.patch.dict(os.environ , {"""TESTING_MOCKED_DATALOADERS""": """0"""} ):
lowerCAmelCase = run_command(self._launch_args + testargs , return_stdout=lowerCAmelCase )
lowerCAmelCase = re.findall("""({.+})""" , lowerCAmelCase )
lowerCAmelCase = [r for r in results if """accuracy""" in r][-1]
lowerCAmelCase = ast.literal_eval(lowerCAmelCase )
self.assertGreaterEqual(results["""accuracy"""] , 0.75 )
def __lowercase ( self : List[Any] ):
lowerCAmelCase = ["""examples/by_feature/multi_process_metrics.py"""]
run_command(self._launch_args + testargs )
@require_trackers
@mock.patch.dict(os.environ , {"""WANDB_MODE""": """offline"""} )
def __lowercase ( self : Any ):
with tempfile.TemporaryDirectory() as tmpdir:
lowerCAmelCase = f'''
examples/by_feature/tracking.py
--with_tracking
--project_dir {tmpdir}
'''.split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase , """tracking""" ) ) )
def __lowercase ( self : List[Any] ):
lowerCAmelCase = ["""examples/by_feature/gradient_accumulation.py"""]
run_command(self._launch_args + testargs )
def __lowercase ( self : Any ):
lowerCAmelCase = ["""examples/by_feature/local_sgd.py"""]
run_command(self._launch_args + testargs )
| 155 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {
'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a = [
'LILT_PRETRAINED_MODEL_ARCHIVE_LIST',
'LiltForQuestionAnswering',
'LiltForSequenceClassification',
'LiltForTokenClassification',
'LiltModel',
'LiltPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_lilt import (
LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
LiltPreTrainedModel,
)
else:
import sys
a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 155 | 1 |
"""simple docstring"""
from typing import Any
def __lowercase ( snake_case_ : list ,snake_case_ : list ,snake_case_ : dict ,snake_case_ : dict ,snake_case_ : dict ,) ->list:
'''simple docstring'''
_validation(
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,)
# Creates data structures and fill initial step
__A : dict = {}
__A : dict = {}
for state in states_space:
__A : str = observations_space[0]
__A : str = (
initial_probabilities[state] * emission_probabilities[state][observation]
)
__A : Dict = None
# Fills the data structure with the probabilities of
# different transitions and pointers to previous states
for o in range(1 ,len(snake_case_ ) ):
__A : Any = observations_space[o]
__A : Any = observations_space[o - 1]
for state in states_space:
# Calculates the argmax for probability function
__A : Tuple = ''''''
__A : Union[str, Any] = -1
for k_state in states_space:
__A : Optional[Any] = (
probabilities[(k_state, prior_observation)]
* transition_probabilities[k_state][state]
* emission_probabilities[state][observation]
)
if probability > max_probability:
__A : List[str] = probability
__A : Union[str, Any] = k_state
# Update probabilities and pointers dicts
__A : Optional[Any] = (
probabilities[(arg_max, prior_observation)]
* transition_probabilities[arg_max][state]
* emission_probabilities[state][observation]
)
__A : Tuple = arg_max
# The final observation
__A : Dict = observations_space[len(snake_case_ ) - 1]
# argmax for given final observation
__A : Union[str, Any] = ''''''
__A : Optional[Any] = -1
for k_state in states_space:
__A : Any = probabilities[(k_state, final_observation)]
if probability > max_probability:
__A : Any = probability
__A : Optional[int] = k_state
__A : Optional[int] = arg_max
# Process pointers backwards
__A : int = last_state
__A : int = []
for o in range(len(snake_case_ ) - 1 ,-1 ,-1 ):
result.append(snake_case_ )
__A : Optional[int] = pointers[previous, observations_space[o]]
result.reverse()
return result
def __lowercase ( snake_case_ : Any ,snake_case_ : Any ,snake_case_ : Any ,snake_case_ : Any ,snake_case_ : Any ,) ->None:
'''simple docstring'''
_validate_not_empty(
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,)
_validate_lists(snake_case_ ,snake_case_ )
_validate_dicts(
snake_case_ ,snake_case_ ,snake_case_ )
def __lowercase ( snake_case_ : Any ,snake_case_ : Any ,snake_case_ : Any ,snake_case_ : Any ,snake_case_ : Any ,) ->None:
'''simple docstring'''
if not all(
[
observations_space,
states_space,
initial_probabilities,
transition_probabilities,
emission_probabilities,
] ):
raise ValueError('''There\'s an empty parameter''' )
def __lowercase ( snake_case_ : Any ,snake_case_ : Any ) ->None:
'''simple docstring'''
_validate_list(snake_case_ ,'''observations_space''' )
_validate_list(snake_case_ ,'''states_space''' )
def __lowercase ( snake_case_ : Any ,snake_case_ : str ) ->None:
'''simple docstring'''
if not isinstance(_object ,snake_case_ ):
__A : Optional[int] = F"""{var_name} must be a list"""
raise ValueError(snake_case_ )
else:
for x in _object:
if not isinstance(snake_case_ ,snake_case_ ):
__A : Optional[Any] = F"""{var_name} must be a list of strings"""
raise ValueError(snake_case_ )
def __lowercase ( snake_case_ : Any ,snake_case_ : Any ,snake_case_ : Any ,) ->None:
'''simple docstring'''
_validate_dict(snake_case_ ,'''initial_probabilities''' ,snake_case_ )
_validate_nested_dict(snake_case_ ,'''transition_probabilities''' )
_validate_nested_dict(snake_case_ ,'''emission_probabilities''' )
def __lowercase ( snake_case_ : Any ,snake_case_ : str ) ->None:
'''simple docstring'''
_validate_dict(_object ,snake_case_ ,snake_case_ )
for x in _object.values():
_validate_dict(snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
def __lowercase ( snake_case_ : Any ,snake_case_ : str ,snake_case_ : type ,snake_case_ : bool = False ) ->None:
'''simple docstring'''
if not isinstance(_object ,snake_case_ ):
__A : int = F"""{var_name} must be a dict"""
raise ValueError(snake_case_ )
if not all(isinstance(snake_case_ ,snake_case_ ) for x in _object ):
__A : List[Any] = F"""{var_name} all keys must be strings"""
raise ValueError(snake_case_ )
if not all(isinstance(snake_case_ ,snake_case_ ) for x in _object.values() ):
__A : int = '''nested dictionary ''' if nested else ''''''
__A : int = F"""{var_name} {nested_text}all values must be {value_type.__name__}"""
raise ValueError(snake_case_ )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 291 |
"""simple docstring"""
a_ = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametre""": """Ym""",
}
# Exponent of the factor(meter)
a_ = {
"""m""": 0,
"""km""": 3,
"""Mm""": 6,
"""Gm""": 9,
"""Tm""": 12,
"""Pm""": 15,
"""Em""": 18,
"""Zm""": 21,
"""Ym""": 24,
}
def __lowercase ( snake_case_ : float ,snake_case_ : str ,snake_case_ : str ) ->float:
'''simple docstring'''
__A : Tuple = from_type.lower().strip('''s''' )
__A : Optional[int] = to_type.lower().strip('''s''' )
__A : List[str] = UNIT_SYMBOL.get(snake_case_ ,snake_case_ )
__A : Any = UNIT_SYMBOL.get(snake_case_ ,snake_case_ )
if from_sanitized not in METRIC_CONVERSION:
__A : int = (
F"""Invalid 'from_type' value: {from_type!r}.\n"""
F"""Conversion abbreviations are: {', '.join(snake_case_ )}"""
)
raise ValueError(snake_case_ )
if to_sanitized not in METRIC_CONVERSION:
__A : str = (
F"""Invalid 'to_type' value: {to_type!r}.\n"""
F"""Conversion abbreviations are: {', '.join(snake_case_ )}"""
)
raise ValueError(snake_case_ )
__A : Optional[Any] = METRIC_CONVERSION[from_sanitized]
__A : Optional[int] = METRIC_CONVERSION[to_sanitized]
__A : Union[str, Any] = 1
if from_exponent > to_exponent:
__A : Dict = from_exponent - to_exponent
else:
__A : Union[str, Any] = -(to_exponent - from_exponent)
return value * pow(10 ,snake_case_ )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 291 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''facebook/data2vec-vision-base-ft''': (
'''https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'''
),
}
class SCREAMING_SNAKE_CASE__ ( lowercase ):
"""simple docstring"""
a : List[Any] ="data2vec-vision"
def __init__( self , snake_case__=768 , snake_case__=12 , snake_case__=12 , snake_case__=3_072 , snake_case__="gelu" , snake_case__=0.0 , snake_case__=0.0 , snake_case__=0.02 , snake_case__=1e-12 , snake_case__=224 , snake_case__=16 , snake_case__=3 , snake_case__=False , snake_case__=False , snake_case__=False , snake_case__=False , snake_case__=0.1 , snake_case__=0.1 , snake_case__=True , snake_case__=[3, 5, 7, 11] , snake_case__=[1, 2, 3, 6] , snake_case__=True , snake_case__=0.4 , snake_case__=256 , snake_case__=1 , snake_case__=False , snake_case__=255 , **snake_case__ , ):
"""simple docstring"""
super().__init__(**snake_case__ )
lowerCAmelCase : Tuple = hidden_size
lowerCAmelCase : List[Any] = num_hidden_layers
lowerCAmelCase : Tuple = num_attention_heads
lowerCAmelCase : Optional[int] = intermediate_size
lowerCAmelCase : Optional[int] = hidden_act
lowerCAmelCase : Dict = hidden_dropout_prob
lowerCAmelCase : Optional[Any] = attention_probs_dropout_prob
lowerCAmelCase : int = initializer_range
lowerCAmelCase : Dict = layer_norm_eps
lowerCAmelCase : Optional[int] = image_size
lowerCAmelCase : Optional[Any] = patch_size
lowerCAmelCase : Optional[Any] = num_channels
lowerCAmelCase : Union[str, Any] = use_mask_token
lowerCAmelCase : str = use_absolute_position_embeddings
lowerCAmelCase : Any = use_relative_position_bias
lowerCAmelCase : List[str] = use_shared_relative_position_bias
lowerCAmelCase : str = layer_scale_init_value
lowerCAmelCase : Union[str, Any] = drop_path_rate
lowerCAmelCase : Any = use_mean_pooling
# decode head attributes (semantic segmentation)
lowerCAmelCase : Optional[int] = out_indices
lowerCAmelCase : Union[str, Any] = pool_scales
# auxiliary head attributes (semantic segmentation)
lowerCAmelCase : str = use_auxiliary_head
lowerCAmelCase : int = auxiliary_loss_weight
lowerCAmelCase : Tuple = auxiliary_channels
lowerCAmelCase : List[str] = auxiliary_num_convs
lowerCAmelCase : Tuple = auxiliary_concat_input
lowerCAmelCase : List[str] = semantic_loss_ignore_index
class SCREAMING_SNAKE_CASE__ ( lowercase ):
"""simple docstring"""
a : Union[str, Any] =version.parse("1.11" )
@property
def lowercase__ ( self ):
"""simple docstring"""
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def lowercase__ ( self ):
"""simple docstring"""
return 1e-4
| 108 |
"""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 _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
if isinstance(__lowerCAmelCase , torch.Tensor ):
return image
elif isinstance(__lowerCAmelCase , PIL.Image.Image ):
SCREAMING_SNAKE_CASE__ : Any = [image]
if isinstance(image[0] , PIL.Image.Image ):
SCREAMING_SNAKE_CASE__ : List[str] = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION["""lanczos"""] ) )[None, :] for i in image]
SCREAMING_SNAKE_CASE__ : List[Any] = np.concatenate(__lowerCAmelCase , axis=0 )
SCREAMING_SNAKE_CASE__ : List[str] = np.array(__lowerCAmelCase ).astype(np.floataa ) / 255.0
SCREAMING_SNAKE_CASE__ : Optional[Any] = image.transpose(0 , 3 , 1 , 2 )
SCREAMING_SNAKE_CASE__ : Tuple = 2.0 * image - 1.0
SCREAMING_SNAKE_CASE__ : Tuple = torch.from_numpy(__lowerCAmelCase )
elif isinstance(image[0] , torch.Tensor ):
SCREAMING_SNAKE_CASE__ : Tuple = torch.cat(__lowerCAmelCase , dim=0 )
return image
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=0.9_995 ) -> Union[str, Any]:
if not isinstance(__lowerCAmelCase , np.ndarray ):
SCREAMING_SNAKE_CASE__ : Dict = True
SCREAMING_SNAKE_CASE__ : int = va.device
SCREAMING_SNAKE_CASE__ : str = va.cpu().numpy()
SCREAMING_SNAKE_CASE__ : str = va.cpu().numpy()
SCREAMING_SNAKE_CASE__ : Any = np.sum(va * va / (np.linalg.norm(__lowerCAmelCase ) * np.linalg.norm(__lowerCAmelCase )) )
if np.abs(__lowerCAmelCase ) > DOT_THRESHOLD:
SCREAMING_SNAKE_CASE__ : Tuple = (1 - t) * va + t * va
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = np.arccos(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.sin(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = theta_a * t
SCREAMING_SNAKE_CASE__ : Tuple = np.sin(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = np.sin(theta_a - theta_t ) / sin_theta_a
SCREAMING_SNAKE_CASE__ : Optional[int] = sin_theta_t / sin_theta_a
SCREAMING_SNAKE_CASE__ : List[Any] = sa * va + sa * va
if inputs_are_torch:
SCREAMING_SNAKE_CASE__ : str = torch.from_numpy(__lowerCAmelCase ).to(__lowerCAmelCase )
return va
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Any:
SCREAMING_SNAKE_CASE__ : Tuple = F.normalize(__lowerCAmelCase , dim=-1 )
SCREAMING_SNAKE_CASE__ : Optional[int] = F.normalize(__lowerCAmelCase , dim=-1 )
return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
for param in model.parameters():
SCREAMING_SNAKE_CASE__ : int = value
class __a (UpperCamelCase_):
'''simple docstring'''
def __init__( self , _a , _a , _a , _a , _a , _a , _a , _a=None , _a=None , _a=None , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__()
self.register_modules(
vae=_a , text_encoder=_a , clip_model=_a , tokenizer=_a , unet=_a , scheduler=_a , feature_extractor=_a , coca_model=_a , coca_tokenizer=_a , coca_transform=_a , )
SCREAMING_SNAKE_CASE__ : Optional[Any] = (
feature_extractor.size
if isinstance(feature_extractor.size , _a )
else feature_extractor.size["""shortest_edge"""]
)
SCREAMING_SNAKE_CASE__ : List[Any] = transforms.Normalize(mean=feature_extractor.image_mean , std=feature_extractor.image_std )
set_requires_grad(self.text_encoder , _a )
set_requires_grad(self.clip_model , _a )
def _a ( self , _a = "auto" ) -> Dict:
"""simple docstring"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(_a )
def _a ( self ) -> List[str]:
"""simple docstring"""
self.enable_attention_slicing(_a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
set_requires_grad(self.vae , _a )
def _a ( self ) -> Dict:
"""simple docstring"""
set_requires_grad(self.vae , _a )
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
set_requires_grad(self.unet , _a )
def _a ( self ) -> int:
"""simple docstring"""
set_requires_grad(self.unet , _a )
def _a ( self , _a , _a , _a ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = min(int(num_inference_steps * strength ) , _a )
SCREAMING_SNAKE_CASE__ : Optional[int] = max(num_inference_steps - init_timestep , 0 )
SCREAMING_SNAKE_CASE__ : Dict = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def _a ( self , _a , _a , _a , _a , _a , _a=None ) -> Optional[Any]:
"""simple docstring"""
if not isinstance(_a , torch.Tensor ):
raise ValueError(f'''`image` has to be of type `torch.Tensor` but is {type(_a )}''' )
SCREAMING_SNAKE_CASE__ : Optional[Any] = image.to(device=_a , dtype=_a )
if isinstance(_a , _a ):
SCREAMING_SNAKE_CASE__ : int = [
self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(_a )
]
SCREAMING_SNAKE_CASE__ : Tuple = torch.cat(_a , dim=0 )
else:
SCREAMING_SNAKE_CASE__ : int = self.vae.encode(_a ).latent_dist.sample(_a )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
SCREAMING_SNAKE_CASE__ : Optional[Any] = 0.18_215 * init_latents
SCREAMING_SNAKE_CASE__ : List[str] = init_latents.repeat_interleave(_a , dim=0 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = randn_tensor(init_latents.shape , generator=_a , device=_a , dtype=_a )
# get latents
SCREAMING_SNAKE_CASE__ : Any = self.scheduler.add_noise(_a , _a , _a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = init_latents
return latents
def _a ( self , _a ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.coca_transform(_a ).unsqueeze(0 )
with torch.no_grad(), torch.cuda.amp.autocast():
SCREAMING_SNAKE_CASE__ : List[Any] = self.coca_model.generate(transformed_image.to(device=self.device , dtype=self.coca_model.dtype ) )
SCREAMING_SNAKE_CASE__ : str = self.coca_tokenizer.decode(generated[0].cpu().numpy() )
return generated.split("""<end_of_text>""" )[0].replace("""<start_of_text>""" , """""" ).rstrip(""" .,""" )
def _a ( self , _a , _a ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.feature_extractor.preprocess(_a )
SCREAMING_SNAKE_CASE__ : str = torch.from_numpy(clip_image_input["""pixel_values"""][0] ).unsqueeze(0 ).to(self.device ).half()
SCREAMING_SNAKE_CASE__ : Any = self.clip_model.get_image_features(_a )
SCREAMING_SNAKE_CASE__ : int = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = image_embeddings_clip.repeat_interleave(_a , dim=0 )
return image_embeddings_clip
@torch.enable_grad()
def _a ( self , _a , _a , _a , _a , _a , _a , _a , ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = latents.detach().requires_grad_()
SCREAMING_SNAKE_CASE__ : str = self.scheduler.scale_model_input(_a , _a )
# predict the noise residual
SCREAMING_SNAKE_CASE__ : Any = self.unet(_a , _a , encoder_hidden_states=_a ).sample
if isinstance(self.scheduler , (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.scheduler.alphas_cumprod[timestep]
SCREAMING_SNAKE_CASE__ : List[Any] = 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
SCREAMING_SNAKE_CASE__ : Optional[Any] = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
SCREAMING_SNAKE_CASE__ : List[str] = torch.sqrt(_a )
SCREAMING_SNAKE_CASE__ : Dict = pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler , _a ):
SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler.sigmas[index]
SCREAMING_SNAKE_CASE__ : Dict = 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
SCREAMING_SNAKE_CASE__ : Optional[Any] = 1 / 0.18_215 * sample
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.vae.decode(_a ).sample
SCREAMING_SNAKE_CASE__ : Any = (image / 2 + 0.5).clamp(0 , 1 )
SCREAMING_SNAKE_CASE__ : Any = transforms.Resize(self.feature_extractor_size )(_a )
SCREAMING_SNAKE_CASE__ : Dict = self.normalize(_a ).to(latents.dtype )
SCREAMING_SNAKE_CASE__ : Tuple = self.clip_model.get_image_features(_a )
SCREAMING_SNAKE_CASE__ : int = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = spherical_dist_loss(_a , _a ).mean() * clip_guidance_scale
SCREAMING_SNAKE_CASE__ : Optional[Any] = -torch.autograd.grad(_a , _a )[0]
if isinstance(self.scheduler , _a ):
SCREAMING_SNAKE_CASE__ : Any = latents.detach() + grads * (sigma**2)
SCREAMING_SNAKE_CASE__ : Optional[int] = noise_pred_original
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = noise_pred_original - torch.sqrt(_a ) * grads
return noise_pred, latents
@torch.no_grad()
def __call__( self , _a , _a , _a = None , _a = None , _a = 512 , _a = 512 , _a = 0.6 , _a = 50 , _a = 7.5 , _a = 1 , _a = 0.0 , _a = 100 , _a = None , _a = "pil" , _a = True , _a = 0.8 , _a = 0.1 , _a = 0.1 , ) -> int:
"""simple docstring"""
if isinstance(_a , _a ) and len(_a ) != batch_size:
raise ValueError(f'''You have passed {batch_size} batch_size, but only {len(_a )} 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(_a , torch.Generator ) and batch_size > 1:
SCREAMING_SNAKE_CASE__ : Optional[Any] = [generator] + [None] * (batch_size - 1)
SCREAMING_SNAKE_CASE__ : List[Any] = [
("""model""", self.coca_model is None),
("""tokenizer""", self.coca_tokenizer is None),
("""transform""", self.coca_transform is None),
]
SCREAMING_SNAKE_CASE__ : Optional[int] = [x[0] for x in coca_is_none if x[1]]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """, """.join(_a )
# generate prompts with coca model if prompt is None
if content_prompt is None:
if len(_a ):
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.''' )
SCREAMING_SNAKE_CASE__ : Any = self.get_image_description(_a )
if style_prompt is None:
if len(_a ):
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.''' )
SCREAMING_SNAKE_CASE__ : Tuple = self.get_image_description(_a )
# get prompt text embeddings for content and style
SCREAMING_SNAKE_CASE__ : Any = self.tokenizer(
_a , padding="""max_length""" , max_length=self.tokenizer.model_max_length , truncation=_a , return_tensors="""pt""" , )
SCREAMING_SNAKE_CASE__ : Any = self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]
SCREAMING_SNAKE_CASE__ : Dict = self.tokenizer(
_a , padding="""max_length""" , max_length=self.tokenizer.model_max_length , truncation=_a , return_tensors="""pt""" , )
SCREAMING_SNAKE_CASE__ : List[str] = self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = slerp(_a , _a , _a )
# duplicate text embeddings for each generation per prompt
SCREAMING_SNAKE_CASE__ : int = text_embeddings.repeat_interleave(_a , dim=0 )
# set timesteps
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """offset""" in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
SCREAMING_SNAKE_CASE__ : Tuple = {}
if accepts_offset:
SCREAMING_SNAKE_CASE__ : List[str] = 1
self.scheduler.set_timesteps(_a , **_a )
# 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 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_timesteps(_a , _a , self.device )
SCREAMING_SNAKE_CASE__ : List[str] = timesteps[:1].repeat(_a )
# Preprocess image
SCREAMING_SNAKE_CASE__ : str = preprocess(_a , _a , _a )
SCREAMING_SNAKE_CASE__ : Dict = self.prepare_latents(
_a , _a , _a , text_embeddings.dtype , self.device , _a )
SCREAMING_SNAKE_CASE__ : List[Any] = preprocess(_a , _a , _a )
SCREAMING_SNAKE_CASE__ : Any = self.prepare_latents(
_a , _a , _a , text_embeddings.dtype , self.device , _a )
SCREAMING_SNAKE_CASE__ : List[Any] = slerp(_a , _a , _a )
if clip_guidance_scale > 0:
SCREAMING_SNAKE_CASE__ : List[str] = self.get_clip_image_embeddings(_a , _a )
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_clip_image_embeddings(_a , _a )
SCREAMING_SNAKE_CASE__ : Dict = slerp(
_a , _a , _a )
# 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.
SCREAMING_SNAKE_CASE__ : str = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = content_text_input.input_ids.shape[-1]
SCREAMING_SNAKE_CASE__ : str = self.tokenizer([""""""] , padding="""max_length""" , max_length=_a , return_tensors="""pt""" )
SCREAMING_SNAKE_CASE__ : Tuple = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt
SCREAMING_SNAKE_CASE__ : Tuple = uncond_embeddings.repeat_interleave(_a , 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
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 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`.
SCREAMING_SNAKE_CASE__ : Dict = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
SCREAMING_SNAKE_CASE__ : Any = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
SCREAMING_SNAKE_CASE__ : List[str] = torch.randn(_a , generator=_a , device="""cpu""" , dtype=_a ).to(
self.device )
else:
SCREAMING_SNAKE_CASE__ : Any = torch.randn(_a , generator=_a , device=self.device , dtype=_a )
else:
if latents.shape != latents_shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
SCREAMING_SNAKE_CASE__ : Optional[Any] = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
SCREAMING_SNAKE_CASE__ : List[str] = 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]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """eta""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
SCREAMING_SNAKE_CASE__ : str = {}
if accepts_eta:
SCREAMING_SNAKE_CASE__ : Optional[Any] = eta
# check if the scheduler accepts generator
SCREAMING_SNAKE_CASE__ : int = """generator""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
if accepts_generator:
SCREAMING_SNAKE_CASE__ : Optional[Any] = generator
with self.progress_bar(total=_a ):
for i, t in enumerate(_a ):
# expand the latents if we are doing classifier free guidance
SCREAMING_SNAKE_CASE__ : List[Any] = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
SCREAMING_SNAKE_CASE__ : List[str] = self.scheduler.scale_model_input(_a , _a )
# predict the noise residual
SCREAMING_SNAKE_CASE__ : List[Any] = self.unet(_a , _a , encoder_hidden_states=_a ).sample
# perform classifier free guidance
if do_classifier_free_guidance:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = noise_pred.chunk(2 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
SCREAMING_SNAKE_CASE__ : List[Any] = (
text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
)
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = self.cond_fn(
_a , _a , _a , _a , _a , _a , _a , )
# compute the previous noisy sample x_t -> x_t-1
SCREAMING_SNAKE_CASE__ : Any = self.scheduler.step(_a , _a , _a , **_a ).prev_sample
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
SCREAMING_SNAKE_CASE__ : List[Any] = 1 / 0.18_215 * latents
SCREAMING_SNAKE_CASE__ : int = self.vae.decode(_a ).sample
SCREAMING_SNAKE_CASE__ : str = (image / 2 + 0.5).clamp(0 , 1 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
SCREAMING_SNAKE_CASE__ : int = self.numpy_to_pil(_a )
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=_a , nsfw_content_detected=_a )
| 132 | 0 |
UpperCAmelCase_ = [0, 2, 4, 6, 8]
UpperCAmelCase_ = [1, 3, 5, 7, 9]
def lowerCAmelCase_ ( __UpperCAmelCase: int , __UpperCAmelCase: int , __UpperCAmelCase: list[int] , __UpperCAmelCase: int ) -> int:
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
for i in range(length // 2 - 1 , -1 , -1 ):
remainder += digits[i] + digits[length - i - 1]
if remainder % 2 == 0:
return 0
remainder //= 10
return 1
if remaining_length == 1:
if remainder % 2 == 0:
return 0
UpperCamelCase__ : Optional[Any] = 0
for digit in range(10 ):
UpperCamelCase__ : List[str] = digit
result += reversible_numbers(
0 , (remainder + 2 * digit) // 10 , __UpperCAmelCase , __UpperCAmelCase )
return result
UpperCamelCase__ : Tuple = 0
for digita in range(10 ):
UpperCamelCase__ : List[Any] = digita
if (remainder + digita) % 2 == 0:
UpperCamelCase__ : Optional[int] = ODD_DIGITS
else:
UpperCamelCase__ : Any = EVEN_DIGITS
for digita in other_parity_digits:
UpperCamelCase__ : List[Any] = digita
result += reversible_numbers(
remaining_length - 2 , (remainder + digita + digita) // 10 , __UpperCAmelCase , __UpperCAmelCase , )
return result
def lowerCAmelCase_ ( __UpperCAmelCase: int = 9 ) -> int:
UpperCamelCase__ : Optional[int] = 0
for length in range(1 , max_power + 1 ):
result += reversible_numbers(__UpperCAmelCase , 0 , [0] * length , __UpperCAmelCase )
return result
if __name__ == "__main__":
print(F'''{solution() = }''')
| 247 |
# 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.
import re
from ..utils import cached_file
# docstyle-ignore
UpperCAmelCase_ = '\nHuman: <<task>>\n\nAssistant: '
UpperCAmelCase_ = 'huggingface-tools/default-prompts'
UpperCAmelCase_ = {'chat': 'chat_prompt_template.txt', 'run': 'run_prompt_template.txt'}
def lowerCAmelCase_ ( __UpperCAmelCase: Optional[Any] , __UpperCAmelCase: List[Any] , __UpperCAmelCase: Optional[Any]="run" ) -> int:
if prompt_or_repo_id is None:
UpperCamelCase__ : List[Any] = DEFAULT_PROMPTS_REPO
# prompt is considered a repo ID when it does not contain any kind of space
if re.search('''\\s''' , __UpperCAmelCase ) is not None:
return prompt_or_repo_id
UpperCamelCase__ : Any = cached_file(
__UpperCAmelCase , PROMPT_FILES[mode] , repo_type='''dataset''' , user_agent={'''agent''': agent_name} )
with open(__UpperCAmelCase , '''r''' , encoding='''utf-8''' ) as f:
return f.read()
| 247 | 1 |
"""simple docstring"""
def a__ ( __SCREAMING_SNAKE_CASE ) -> list[int]:
__lowerCAmelCase: Optional[int] = [0 for i in range(len(__SCREAMING_SNAKE_CASE ) )]
# initialize interval's left pointer and right pointer
__lowerCAmelCase , __lowerCAmelCase: Optional[Any] = 0, 0
for i in range(1 , len(__SCREAMING_SNAKE_CASE ) ):
# case when current index is inside the interval
if i <= right_pointer:
__lowerCAmelCase: Any = min(right_pointer - i + 1 , z_result[i - left_pointer] )
__lowerCAmelCase: List[str] = min_edge
while go_next(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
z_result[i] += 1
# if new index's result gives us more right interval,
# we've to update left_pointer and right_pointer
if i + z_result[i] - 1 > right_pointer:
__lowerCAmelCase , __lowerCAmelCase: Tuple = i, i + z_result[i] - 1
return z_result
def a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> bool:
return i + z_result[i] < len(__SCREAMING_SNAKE_CASE ) and s[z_result[i]] == s[i + z_result[i]]
def a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> int:
__lowerCAmelCase: Tuple = 0
# concatenate 'pattern' and 'input_str' and call z_function
# with concatenated string
__lowerCAmelCase: int = z_function(pattern + input_str )
for val in z_result:
# if value is greater then length of the pattern string
# that means this index is starting position of substring
# which is equal to pattern string
if val >= len(__SCREAMING_SNAKE_CASE ):
answer += 1
return answer
if __name__ == "__main__":
import doctest
doctest.testmod()
| 217 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}
class snake_case ( __snake_case ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = """switch_transformers"""
SCREAMING_SNAKE_CASE_ : Tuple = ["""past_key_values"""]
SCREAMING_SNAKE_CASE_ : Any = {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
def __init__( self : List[str] , UpperCamelCase__ : List[str]=3_2_1_2_8 , UpperCamelCase__ : str=7_6_8 , UpperCamelCase__ : Union[str, Any]=6_4 , UpperCamelCase__ : Optional[int]=2_0_4_8 , UpperCamelCase__ : Dict=6_4 , UpperCamelCase__ : List[str]=1_2 , UpperCamelCase__ : Dict=3 , UpperCamelCase__ : Any=1_2 , UpperCamelCase__ : Dict=3 , UpperCamelCase__ : Dict=1_2 , UpperCamelCase__ : List[str]=8 , UpperCamelCase__ : Optional[Any]=False , UpperCamelCase__ : Optional[int]=0.01 , UpperCamelCase__ : Optional[int]="float32" , UpperCamelCase__ : Any=False , UpperCamelCase__ : Dict=3_2 , UpperCamelCase__ : Union[str, Any]=1_2_8 , UpperCamelCase__ : Union[str, Any]=0.1 , UpperCamelCase__ : Optional[int]=1e-6 , UpperCamelCase__ : Optional[Any]=0.001 , UpperCamelCase__ : Dict=0.001 , UpperCamelCase__ : int=1.0 , UpperCamelCase__ : str="relu" , UpperCamelCase__ : int=True , UpperCamelCase__ : Any=False , UpperCamelCase__ : str=True , UpperCamelCase__ : Tuple=0 , UpperCamelCase__ : str=1 , **UpperCamelCase__ : Tuple , )-> Optional[Any]:
'''simple docstring'''
__lowerCAmelCase: int = vocab_size
__lowerCAmelCase: str = d_model
__lowerCAmelCase: str = d_kv
__lowerCAmelCase: str = d_ff
__lowerCAmelCase: List[str] = num_sparse_encoder_layers
__lowerCAmelCase: List[Any] = num_layers
__lowerCAmelCase: Optional[Any] = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
__lowerCAmelCase: Tuple = num_sparse_decoder_layers
# This tells us, each how many encoder layer we'll have to set a sparse layer.
if self.num_sparse_encoder_layers > 0:
__lowerCAmelCase: int = self.num_layers // self.num_sparse_encoder_layers
else:
__lowerCAmelCase: Union[str, Any] = self.num_layers # HACK: this will create 0 sparse layers
# This tells us, each how many encoder layer we'll have to set a sparse layer.
if self.num_sparse_decoder_layers > 0:
__lowerCAmelCase: Dict = self.num_decoder_layers // self.num_sparse_decoder_layers
else:
__lowerCAmelCase: Any = self.num_decoder_layers # HACK: this will create 0 sparse layers
__lowerCAmelCase: Dict = num_heads
__lowerCAmelCase: Dict = num_experts
__lowerCAmelCase: Any = expert_capacity
__lowerCAmelCase: List[Any] = router_bias
__lowerCAmelCase: int = router_jitter_noise
if router_dtype not in ["float32", "float16", "bfloat16"]:
raise ValueError(f"`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}")
__lowerCAmelCase: Dict = router_dtype
__lowerCAmelCase: Optional[Any] = router_ignore_padding_tokens
__lowerCAmelCase: Union[str, Any] = relative_attention_num_buckets
__lowerCAmelCase: str = relative_attention_max_distance
__lowerCAmelCase: Optional[int] = dropout_rate
__lowerCAmelCase: Optional[Any] = layer_norm_epsilon
__lowerCAmelCase: int = initializer_factor
__lowerCAmelCase: Tuple = feed_forward_proj
__lowerCAmelCase: int = use_cache
__lowerCAmelCase: int = add_router_probs
__lowerCAmelCase: Optional[Any] = router_z_loss_coef
__lowerCAmelCase: Dict = router_aux_loss_coef
__lowerCAmelCase: Union[str, Any] = self.feed_forward_proj.split("-")
__lowerCAmelCase: Tuple = act_info[-1]
__lowerCAmelCase: str = act_info[0] == "gated"
if len(UpperCamelCase__) > 1 and act_info[0] != "gated" or len(UpperCamelCase__) > 2:
raise ValueError(
f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
"Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
"'gated-gelu' or 'relu'")
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
__lowerCAmelCase: List[str] = "gelu_new"
super().__init__(
pad_token_id=UpperCamelCase__ , eos_token_id=UpperCamelCase__ , is_encoder_decoder=UpperCamelCase__ , **UpperCamelCase__ , )
| 217 | 1 |
def a__ ( __UpperCamelCase ):
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
raise TypeError("only integers accepted as input" )
else:
SCREAMING_SNAKE_CASE_ = str(abs(__lowerCamelCase ) )
SCREAMING_SNAKE_CASE_ = [list(__lowerCamelCase ) for char in range(len(__lowerCamelCase ) )]
for index in range(len(__lowerCamelCase ) ):
num_transpositions[index].pop(__lowerCamelCase )
return max(
int("".join(list(__lowerCamelCase ) ) ) for transposition in num_transpositions )
if __name__ == "__main__":
__import__("doctest").testmod()
| 360 | from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a__ ( __UpperCamelCase ):
return "".join(sorted(__UpperCamelCase ) )
def a__ ( __UpperCamelCase ):
return word_by_signature[signature(__UpperCamelCase )]
A : str = Path(__file__).parent.joinpath("words.txt").read_text(encoding="utf-8")
A : int = sorted({word.strip().lower() for word in data.splitlines()})
A : Tuple = collections.defaultdict(list)
for word in word_list:
word_by_signature[signature(word)].append(word)
if __name__ == "__main__":
A : Union[str, Any] = {word: anagram(word) for word in word_list if len(anagram(word)) > 1}
with open("anagrams.txt", "w") as file:
file.write("all_anagrams = \n ")
file.write(pprint.pformat(all_anagrams))
| 305 | 0 |
"""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, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = '''▁'''
SCREAMING_SNAKE_CASE_ = {'''vocab_file''': '''sentencepiece.bpe.model'''}
SCREAMING_SNAKE_CASE_ = {
'''vocab_file''': {
'''facebook/mbart-large-50-one-to-many-mmt''': (
'''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'''
),
}
}
SCREAMING_SNAKE_CASE_ = {
'''facebook/mbart-large-50-one-to-many-mmt''': 1_024,
}
# fmt: off
SCREAMING_SNAKE_CASE_ = ['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''', '''af_ZA''', '''az_AZ''', '''bn_IN''', '''fa_IR''', '''he_IL''', '''hr_HR''', '''id_ID''', '''ka_GE''', '''km_KH''', '''mk_MK''', '''ml_IN''', '''mn_MN''', '''mr_IN''', '''pl_PL''', '''ps_AF''', '''pt_XX''', '''sv_SE''', '''sw_KE''', '''ta_IN''', '''te_IN''', '''th_TH''', '''tl_XX''', '''uk_UA''', '''ur_PK''', '''xh_ZA''', '''gl_ES''', '''sl_SI''']
class lowerCAmelCase_ ( A__ ):
'''simple docstring'''
_snake_case = VOCAB_FILES_NAMES
_snake_case = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_snake_case = PRETRAINED_VOCAB_FILES_MAP
_snake_case = ['''input_ids''', '''attention_mask''']
_snake_case = []
_snake_case = []
def __init__( self , snake_case_ , snake_case_=None , snake_case_=None , snake_case_="</s>" , snake_case_="</s>" , snake_case_="<s>" , snake_case_="<unk>" , snake_case_="<pad>" , snake_case_="<mask>" , snake_case_ = None , **snake_case_ , ) -> None:
# Mask token behave like a normal word, i.e. include the space before it
__lowerCAmelCase = AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else mask_token
__lowerCAmelCase = {} if sp_model_kwargs is None else sp_model_kwargs
__lowerCAmelCase = kwargs.get("""additional_special_tokens""" , [] )
kwargs["additional_special_tokens"] += [
code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
]
super().__init__(
src_lang=snake_case_ , tgt_lang=snake_case_ , eos_token=snake_case_ , unk_token=snake_case_ , sep_token=snake_case_ , cls_token=snake_case_ , pad_token=snake_case_ , mask_token=snake_case_ , sp_model_kwargs=self.sp_model_kwargs , **snake_case_ , )
__lowerCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(snake_case_ ) )
__lowerCAmelCase = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# Mimic fairseq token-to-id alignment for the first 4 token
__lowerCAmelCase = {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
__lowerCAmelCase = 1
__lowerCAmelCase = len(self.sp_model )
__lowerCAmelCase = {
code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(snake_case_ )
}
__lowerCAmelCase = {v: k for k, v in self.lang_code_to_id.items()}
__lowerCAmelCase = len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset
self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
__lowerCAmelCase = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
__lowerCAmelCase = src_lang if src_lang is not None else """en_XX"""
__lowerCAmelCase = self.lang_code_to_id[self._src_lang]
__lowerCAmelCase = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def A__ ( self ) -> int:
return len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset + 1 # Plus 1 for the mask token
@property
def A__ ( self ) -> str:
return self._src_lang
@src_lang.setter
def A__ ( self , snake_case_ ) -> None:
__lowerCAmelCase = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def __getstate__( self ) -> Dict:
__lowerCAmelCase = self.__dict__.copy()
__lowerCAmelCase = None
return state
def __setstate__( self , snake_case_ ) -> None:
__lowerCAmelCase = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
__lowerCAmelCase = {}
__lowerCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def A__ ( self ) -> Dict:
__lowerCAmelCase = {self.convert_ids_to_tokens(snake_case_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def A__ ( self , snake_case_ ) -> List[str]:
return self.sp_model.encode(snake_case_ , out_type=snake_case_ )
def A__ ( self , snake_case_ ) -> int:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
__lowerCAmelCase = self.sp_model.PieceToId(snake_case_ )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def A__ ( self , snake_case_ ) -> str:
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def A__ ( self , snake_case_ ) -> Optional[Any]:
__lowerCAmelCase = []
__lowerCAmelCase = """"""
__lowerCAmelCase = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(snake_case_ ) + token
__lowerCAmelCase = True
__lowerCAmelCase = []
else:
current_sub_tokens.append(snake_case_ )
__lowerCAmelCase = False
out_string += self.sp_model.decode(snake_case_ )
return out_string.strip()
def A__ ( self , snake_case_ , snake_case_ = None ) -> Tuple[str]:
if not os.path.isdir(snake_case_ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
__lowerCAmelCase = os.path.join(
snake_case_ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , snake_case_ )
elif not os.path.isfile(self.vocab_file ):
with open(snake_case_ , """wb""" ) as fi:
__lowerCAmelCase = self.sp_model.serialized_model_proto()
fi.write(snake_case_ )
return (out_vocab_file,)
def A__ ( self , snake_case_ , snake_case_ = None , snake_case_ = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=snake_case_ , token_ids_a=snake_case_ , already_has_special_tokens=snake_case_ )
__lowerCAmelCase = [1] * len(self.prefix_tokens )
__lowerCAmelCase = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(snake_case_ )) + suffix_ones
return prefix_ones + ([0] * len(snake_case_ )) + ([0] * len(snake_case_ )) + suffix_ones
def A__ ( self , snake_case_ , snake_case_ = None ) -> List[int]:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def A__ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , **snake_case_ ) -> Dict:
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
__lowerCAmelCase = src_lang
__lowerCAmelCase = self(snake_case_ , add_special_tokens=snake_case_ , return_tensors=snake_case_ , **snake_case_ )
__lowerCAmelCase = self.convert_tokens_to_ids(snake_case_ )
__lowerCAmelCase = tgt_lang_id
return inputs
def A__ ( self , snake_case_ , snake_case_ = "en_XX" , snake_case_ = None , snake_case_ = "ro_RO" , **snake_case_ , ) -> BatchEncoding:
__lowerCAmelCase = src_lang
__lowerCAmelCase = tgt_lang
return super().prepare_seqaseq_batch(snake_case_ , snake_case_ , **snake_case_ )
def A__ ( self ) -> Tuple:
return self.set_src_lang_special_tokens(self.src_lang )
def A__ ( self ) -> str:
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def A__ ( self , snake_case_ ) -> None:
__lowerCAmelCase = self.lang_code_to_id[src_lang]
__lowerCAmelCase = [self.cur_lang_code_id]
__lowerCAmelCase = [self.eos_token_id]
def A__ ( self , snake_case_ ) -> None:
__lowerCAmelCase = self.lang_code_to_id[tgt_lang]
__lowerCAmelCase = [self.cur_lang_code_id]
__lowerCAmelCase = [self.eos_token_id]
| 301 |
"""simple docstring"""
import sys
import turtle
def lowercase (_lowerCAmelCase , _lowerCAmelCase ):
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def lowercase (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , ):
my_pen.up()
my_pen.goto(vertexa[0] , vertexa[1] )
my_pen.down()
my_pen.goto(vertexa[0] , vertexa[1] )
my_pen.goto(vertexa[0] , vertexa[1] )
my_pen.goto(vertexa[0] , vertexa[1] )
if depth == 0:
return
triangle(_lowerCAmelCase , get_mid(_lowerCAmelCase , _lowerCAmelCase ) , get_mid(_lowerCAmelCase , _lowerCAmelCase ) , depth - 1 )
triangle(_lowerCAmelCase , get_mid(_lowerCAmelCase , _lowerCAmelCase ) , get_mid(_lowerCAmelCase , _lowerCAmelCase ) , depth - 1 )
triangle(_lowerCAmelCase , get_mid(_lowerCAmelCase , _lowerCAmelCase ) , get_mid(_lowerCAmelCase , _lowerCAmelCase ) , depth - 1 )
if __name__ == "__main__":
if len(sys.argv) != 2:
raise ValueError(
'''Correct format for using this script: '''
'''python fractals.py <int:depth_for_fractal>'''
)
SCREAMING_SNAKE_CASE_ = turtle.Turtle()
my_pen.ht()
my_pen.speed(5)
my_pen.pencolor('''red''')
SCREAMING_SNAKE_CASE_ = [(-175, -125), (0, 175), (175, -125)] # vertices of triangle
triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))
| 301 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import Counter
from random import random
class __lowerCamelCase :
"""simple docstring"""
def __init__( self : List[Any]):
_A : List[Any] = {}
def A ( self : int , SCREAMING_SNAKE_CASE : str):
_A : Optional[Any] = {}
def A ( self : Any , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : float):
if nodea not in self.connections:
self.add_node(SCREAMING_SNAKE_CASE)
if nodea not in self.connections:
self.add_node(SCREAMING_SNAKE_CASE)
_A : Any = probability
def A ( self : Any):
return list(self.connections)
def A ( self : Tuple , SCREAMING_SNAKE_CASE : str):
_A : Dict = 0
_A : Optional[int] = random()
for dest in self.connections[node]:
current_probability += self.connections[node][dest]
if current_probability > random_value:
return dest
return ""
def lowerCAmelCase__ ( lowerCamelCase : str ,lowerCamelCase : list[tuple[str, str, float]] ,lowerCamelCase : int ):
_A : Optional[Any] = MarkovChainGraphUndirectedUnweighted()
for nodea, nodea, probability in transitions:
graph.add_transition_probability(lowerCamelCase ,lowerCamelCase ,lowerCamelCase )
_A : Optional[int] = Counter(graph.get_nodes() )
_A : Dict = start
for _ in range(lowerCamelCase ):
_A : List[Any] = graph.transition(lowerCamelCase )
visited[node] += 1
return visited
if __name__ == "__main__":
import doctest
doctest.testmod()
| 227 |
'''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 __lowerCamelCase ( a_ , a_ , a_ , unittest.TestCase ):
"""simple docstring"""
a = StableDiffusionControlNetImgaImgPipeline
a = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
a = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
a = IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"control_image"} )
a = IMAGE_TO_IMAGE_IMAGE_PARAMS
def A ( self : Tuple):
torch.manual_seed(0)
_A : List[str] = 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)
_A : Optional[Any] = 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)
_A : Any = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=SCREAMING_SNAKE_CASE , set_alpha_to_one=SCREAMING_SNAKE_CASE , )
torch.manual_seed(0)
_A : List[str] = 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)
_A : List[str] = 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=1000 , )
_A : Any = CLIPTextModel(SCREAMING_SNAKE_CASE)
_A : List[Any] = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
_A : Tuple = {
'unet': unet,
'controlnet': controlnet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def A ( self : Union[str, Any] , SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : Tuple=0):
if str(SCREAMING_SNAKE_CASE).startswith('mps'):
_A : Optional[int] = torch.manual_seed(SCREAMING_SNAKE_CASE)
else:
_A : Tuple = torch.Generator(device=SCREAMING_SNAKE_CASE).manual_seed(SCREAMING_SNAKE_CASE)
_A : Union[str, Any] = 2
_A : Tuple = randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=SCREAMING_SNAKE_CASE , device=torch.device(SCREAMING_SNAKE_CASE) , )
_A : Tuple = floats_tensor(control_image.shape , rng=random.Random(SCREAMING_SNAKE_CASE)).to(SCREAMING_SNAKE_CASE)
_A : Union[str, Any] = image.cpu().permute(0 , 2 , 3 , 1)[0]
_A : Union[str, Any] = Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE)).convert('RGB').resize((64, 64))
_A : Any = {
'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 A ( self : str):
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 A ( self : Tuple):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)
def A ( self : int):
self._test_inference_batch_single_identical(expected_max_diff=2e-3)
class __lowerCamelCase ( a_ , a_ , unittest.TestCase ):
"""simple docstring"""
a = StableDiffusionControlNetImgaImgPipeline
a = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
a = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
a = frozenset([] ) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
def A ( self : List[str]):
torch.manual_seed(0)
_A : List[str] = 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(SCREAMING_SNAKE_CASE : Union[str, Any]):
if isinstance(SCREAMING_SNAKE_CASE , torch.nn.Convad):
torch.nn.init.normal(m.weight)
m.bias.data.fill_(1.0)
_A : int = 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(SCREAMING_SNAKE_CASE)
torch.manual_seed(0)
_A : str = 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(SCREAMING_SNAKE_CASE)
torch.manual_seed(0)
_A : List[Any] = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=SCREAMING_SNAKE_CASE , set_alpha_to_one=SCREAMING_SNAKE_CASE , )
torch.manual_seed(0)
_A : Tuple = 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)
_A : str = 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=1000 , )
_A : List[Any] = CLIPTextModel(SCREAMING_SNAKE_CASE)
_A : List[Any] = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
_A : List[str] = MultiControlNetModel([controlneta, controlneta])
_A : List[str] = {
'unet': unet,
'controlnet': controlnet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def A ( self : Optional[int] , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : List[str]=0):
if str(SCREAMING_SNAKE_CASE).startswith('mps'):
_A : Optional[Any] = torch.manual_seed(SCREAMING_SNAKE_CASE)
else:
_A : Union[str, Any] = torch.Generator(device=SCREAMING_SNAKE_CASE).manual_seed(SCREAMING_SNAKE_CASE)
_A : Union[str, Any] = 2
_A : List[str] = [
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=SCREAMING_SNAKE_CASE , device=torch.device(SCREAMING_SNAKE_CASE) , ),
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=SCREAMING_SNAKE_CASE , device=torch.device(SCREAMING_SNAKE_CASE) , ),
]
_A : str = floats_tensor(control_image[0].shape , rng=random.Random(SCREAMING_SNAKE_CASE)).to(SCREAMING_SNAKE_CASE)
_A : Optional[int] = image.cpu().permute(0 , 2 , 3 , 1)[0]
_A : Optional[int] = Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE)).convert('RGB').resize((64, 64))
_A : Optional[Any] = {
'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 A ( self : Tuple):
_A : List[str] = self.get_dummy_components()
_A : List[Any] = self.pipeline_class(**SCREAMING_SNAKE_CASE)
pipe.to(SCREAMING_SNAKE_CASE)
_A : int = 10.0
_A : Union[str, Any] = 4
_A : str = self.get_dummy_inputs(SCREAMING_SNAKE_CASE)
_A : List[Any] = steps
_A : List[str] = scale
_A : int = pipe(**SCREAMING_SNAKE_CASE)[0]
_A : Any = self.get_dummy_inputs(SCREAMING_SNAKE_CASE)
_A : Union[str, Any] = steps
_A : Any = scale
_A : Dict = pipe(**SCREAMING_SNAKE_CASE , control_guidance_start=0.1 , control_guidance_end=0.2)[0]
_A : Tuple = self.get_dummy_inputs(SCREAMING_SNAKE_CASE)
_A : str = steps
_A : List[Any] = scale
_A : int = pipe(**SCREAMING_SNAKE_CASE , control_guidance_start=[0.1, 0.3] , control_guidance_end=[0.2, 0.7])[0]
_A : List[str] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE)
_A : Tuple = steps
_A : Tuple = scale
_A : str = pipe(**SCREAMING_SNAKE_CASE , 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 A ( self : Optional[Any]):
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 A ( self : Any):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)
def A ( self : Dict):
self._test_inference_batch_single_identical(expected_max_diff=2e-3)
def A ( self : str):
_A : Optional[int] = self.get_dummy_components()
_A : Dict = self.pipeline_class(**SCREAMING_SNAKE_CASE)
pipe.to(SCREAMING_SNAKE_CASE)
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)
with tempfile.TemporaryDirectory() as tmpdir:
try:
# save_pretrained is not implemented for Multi-ControlNet
pipe.save_pretrained(SCREAMING_SNAKE_CASE)
except NotImplementedError:
pass
@slow
@require_torch_gpu
class __lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
def A ( self : Optional[Any]):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A ( self : Any):
_A : Dict = ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny')
_A : List[Any] = StableDiffusionControlNetImgaImgPipeline.from_pretrained(
'runwayml/stable-diffusion-v1-5' , safety_checker=SCREAMING_SNAKE_CASE , controlnet=SCREAMING_SNAKE_CASE)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)
_A : List[Any] = torch.Generator(device='cpu').manual_seed(0)
_A : List[Any] = 'evil space-punk bird'
_A : int = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png').resize((512, 512))
_A : List[str] = load_image(
'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png').resize((512, 512))
_A : Dict = pipe(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , control_image=SCREAMING_SNAKE_CASE , generator=SCREAMING_SNAKE_CASE , output_type='np' , num_inference_steps=50 , strength=0.6 , )
_A : Optional[int] = output.images[0]
assert image.shape == (512, 512, 3)
_A : int = 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
| 227 | 1 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class __lowerCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
__lowerCamelCase = 1
@register_to_config
def __init__( self , _snake_case=2000 , _snake_case=0.1 , _snake_case=20 , _snake_case=1e-3 ):
"""simple docstring"""
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = None
def snake_case ( self , _snake_case , _snake_case = None ):
"""simple docstring"""
_lowerCAmelCase = torch.linspace(1 , self.config.sampling_eps , _snake_case , device=_snake_case )
def snake_case ( self , _snake_case , _snake_case , _snake_case , _snake_case=None ):
"""simple docstring"""
if self.timesteps is None:
raise ValueError(
"""`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler""" )
# TODO(Patrick) better comments + non-PyTorch
# postprocess model score
_lowerCAmelCase = (
-0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
)
_lowerCAmelCase = torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff ) )
_lowerCAmelCase = std.flatten()
while len(std.shape ) < len(score.shape ):
_lowerCAmelCase = std.unsqueeze(-1 )
_lowerCAmelCase = -score / std
# compute
_lowerCAmelCase = -1.0 / len(self.timesteps )
_lowerCAmelCase = self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
_lowerCAmelCase = beta_t.flatten()
while len(beta_t.shape ) < len(x.shape ):
_lowerCAmelCase = beta_t.unsqueeze(-1 )
_lowerCAmelCase = -0.5 * beta_t * x
_lowerCAmelCase = torch.sqrt(_snake_case )
_lowerCAmelCase = drift - diffusion**2 * score
_lowerCAmelCase = x + drift * dt
# add noise
_lowerCAmelCase = randn_tensor(x.shape , layout=x.layout , generator=_snake_case , device=x.device , dtype=x.dtype )
_lowerCAmelCase = x_mean + diffusion * math.sqrt(-dt ) * noise
return x, x_mean
def __len__( self ):
"""simple docstring"""
return self.config.num_train_timesteps
| 82 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"""snap-research/efficientformer-l1-300""": (
"""https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"""
),
}
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : int = "efficientformer"
def __init__( self : Optional[int] ,lowerCamelCase__ : List[int] = [3, 2, 6, 4] ,lowerCamelCase__ : List[int] = [48, 96, 224, 448] ,lowerCamelCase__ : List[bool] = [True, True, True, True] ,lowerCamelCase__ : int = 448 ,lowerCamelCase__ : int = 32 ,lowerCamelCase__ : int = 4 ,lowerCamelCase__ : int = 7 ,lowerCamelCase__ : int = 5 ,lowerCamelCase__ : int = 8 ,lowerCamelCase__ : int = 4 ,lowerCamelCase__ : float = 0.0 ,lowerCamelCase__ : int = 16 ,lowerCamelCase__ : int = 3 ,lowerCamelCase__ : int = 3 ,lowerCamelCase__ : int = 3 ,lowerCamelCase__ : int = 2 ,lowerCamelCase__ : int = 1 ,lowerCamelCase__ : float = 0.0 ,lowerCamelCase__ : int = 1 ,lowerCamelCase__ : bool = True ,lowerCamelCase__ : bool = True ,lowerCamelCase__ : float = 1e-5 ,lowerCamelCase__ : str = "gelu" ,lowerCamelCase__ : float = 0.02 ,lowerCamelCase__ : float = 1e-1_2 ,lowerCamelCase__ : int = 224 ,lowerCamelCase__ : float = 1e-0_5 ,**lowerCamelCase__ : str ,) -> None:
'''simple docstring'''
super().__init__(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = hidden_sizes
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = patch_size
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = depths
SCREAMING_SNAKE_CASE = mlp_expansion_ratio
SCREAMING_SNAKE_CASE = downsamples
SCREAMING_SNAKE_CASE = dim
SCREAMING_SNAKE_CASE = key_dim
SCREAMING_SNAKE_CASE = attention_ratio
SCREAMING_SNAKE_CASE = resolution
SCREAMING_SNAKE_CASE = pool_size
SCREAMING_SNAKE_CASE = downsample_patch_size
SCREAMING_SNAKE_CASE = downsample_stride
SCREAMING_SNAKE_CASE = downsample_pad
SCREAMING_SNAKE_CASE = drop_path_rate
SCREAMING_SNAKE_CASE = num_metaad_blocks
SCREAMING_SNAKE_CASE = distillation
SCREAMING_SNAKE_CASE = use_layer_scale
SCREAMING_SNAKE_CASE = layer_scale_init_value
SCREAMING_SNAKE_CASE = image_size
SCREAMING_SNAKE_CASE = batch_norm_eps
| 296 | 0 |
'''simple docstring'''
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
lowercase ='platform'
import jax
import jax.numpy as jnp
import numpy as np
from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class __magic_name__ :
UpperCAmelCase =PegasusConfig
UpperCAmelCase ={}
UpperCAmelCase ="gelu"
def __init__( self , snake_case , snake_case=1_3 , snake_case=7 , snake_case=True , snake_case=False , snake_case=9_9 , snake_case=3_2 , snake_case=5 , snake_case=4 , snake_case=3_7 , snake_case=0.1 , snake_case=0.1 , snake_case=2_0 , snake_case=2 , snake_case=1 , snake_case=0 , ) -> Dict:
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] =parent
_UpperCAmelCase : Union[str, Any] =batch_size
_UpperCAmelCase : List[Any] =seq_length
_UpperCAmelCase : Optional[Any] =is_training
_UpperCAmelCase : Dict =use_labels
_UpperCAmelCase : Union[str, Any] =vocab_size
_UpperCAmelCase : int =hidden_size
_UpperCAmelCase : Any =num_hidden_layers
_UpperCAmelCase : int =num_attention_heads
_UpperCAmelCase : str =intermediate_size
_UpperCAmelCase : Union[str, Any] =hidden_dropout_prob
_UpperCAmelCase : Dict =attention_probs_dropout_prob
_UpperCAmelCase : str =max_position_embeddings
_UpperCAmelCase : List[str] =eos_token_id
_UpperCAmelCase : Dict =pad_token_id
_UpperCAmelCase : str =bos_token_id
def lowerCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCAmelCase : Optional[int] =ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size).clip(3 , self.vocab_size)
_UpperCAmelCase : int =np.expand_dims(np.array([self.eos_token_id] * self.batch_size) , 1)
_UpperCAmelCase : int =np.concatenate([input_ids, eos_tensor] , axis=1)
_UpperCAmelCase : Dict =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
_UpperCAmelCase : str =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 , )
_UpperCAmelCase : List[Any] =prepare_pegasus_inputs_dict(snake_case , snake_case , snake_case)
return config, inputs_dict
def lowerCAmelCase ( self , snake_case , snake_case , snake_case) -> int:
'''simple docstring'''
_UpperCAmelCase : List[str] =2_0
_UpperCAmelCase : List[Any] =model_class_name(snake_case)
_UpperCAmelCase : List[str] =model.encode(inputs_dict['input_ids'])
_UpperCAmelCase , _UpperCAmelCase : int =(
inputs_dict['decoder_input_ids'],
inputs_dict['decoder_attention_mask'],
)
_UpperCAmelCase : str =model.init_cache(decoder_input_ids.shape[0] , snake_case , snake_case)
_UpperCAmelCase : Optional[int] =jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype='i4')
_UpperCAmelCase : Any =jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
_UpperCAmelCase : Optional[int] =model.decode(
decoder_input_ids[:, :-1] , snake_case , decoder_attention_mask=snake_case , past_key_values=snake_case , decoder_position_ids=snake_case , )
_UpperCAmelCase : int =jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='i4')
_UpperCAmelCase : Dict =model.decode(
decoder_input_ids[:, -1:] , snake_case , decoder_attention_mask=snake_case , past_key_values=outputs_cache.past_key_values , decoder_position_ids=snake_case , )
_UpperCAmelCase : Dict =model.decode(snake_case , snake_case)
_UpperCAmelCase : Union[str, Any] =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1E-3 , msg=f"Max diff is {diff}")
def lowerCAmelCase ( self , snake_case , snake_case , snake_case) -> Tuple:
'''simple docstring'''
_UpperCAmelCase : Dict =2_0
_UpperCAmelCase : Union[str, Any] =model_class_name(snake_case)
_UpperCAmelCase : Any =model.encode(inputs_dict['input_ids'])
_UpperCAmelCase , _UpperCAmelCase : int =(
inputs_dict['decoder_input_ids'],
inputs_dict['decoder_attention_mask'],
)
_UpperCAmelCase : Any =jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
] , axis=-1 , )
_UpperCAmelCase : Dict =model.init_cache(decoder_input_ids.shape[0] , snake_case , snake_case)
_UpperCAmelCase : Any =jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
_UpperCAmelCase : str =model.decode(
decoder_input_ids[:, :-1] , snake_case , decoder_attention_mask=snake_case , past_key_values=snake_case , decoder_position_ids=snake_case , )
_UpperCAmelCase : str =jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='i4')
_UpperCAmelCase : Any =model.decode(
decoder_input_ids[:, -1:] , snake_case , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=snake_case , decoder_position_ids=snake_case , )
_UpperCAmelCase : Union[str, Any] =model.decode(snake_case , snake_case , decoder_attention_mask=snake_case)
_UpperCAmelCase : str =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1E-3 , msg=f"Max diff is {diff}")
def lowerCamelCase__ ( __lowerCamelCase : List[str] , __lowerCamelCase : List[str] , __lowerCamelCase : Tuple , __lowerCamelCase : Dict=None , __lowerCamelCase : Union[str, Any]=None , ):
'''simple docstring'''
if attention_mask is None:
_UpperCAmelCase : List[str] =np.not_equal(__lowerCamelCase , config.pad_token_id ).astype(np.inta )
if decoder_attention_mask is None:
_UpperCAmelCase : List[Any] =np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape , dtype=np.inta ),
np.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ).astype(np.inta ),
] , axis=-1 , )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_flax
class __magic_name__ ( lowerCAmelCase ,unittest.TestCase ):
UpperCAmelCase =(
(
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
)
if is_flax_available()
else ()
)
UpperCAmelCase =(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
UpperCAmelCase =True
UpperCAmelCase =False
UpperCAmelCase =False
UpperCAmelCase =False
def lowerCAmelCase ( self) -> int:
'''simple docstring'''
_UpperCAmelCase : Dict =FlaxPegasusModelTester(self)
_UpperCAmelCase : int =ConfigTester(self , config_class=snake_case)
def lowerCAmelCase ( self) -> Tuple:
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCAmelCase ( self) -> List[str]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase : Union[str, Any] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(snake_case , snake_case , snake_case)
def lowerCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase : Union[str, Any] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(snake_case , snake_case , snake_case)
def lowerCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase : Union[str, Any] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
_UpperCAmelCase : Optional[Any] =self._prepare_for_class(snake_case , snake_case)
_UpperCAmelCase : Optional[int] =model_class(snake_case)
@jax.jit
def encode_jitted(snake_case , snake_case=None , **snake_case):
return model.encode(input_ids=snake_case , attention_mask=snake_case)
with self.subTest('JIT Enabled'):
_UpperCAmelCase : List[Any] =encode_jitted(**snake_case).to_tuple()
with self.subTest('JIT Disabled'):
with jax.disable_jit():
_UpperCAmelCase : List[Any] =encode_jitted(**snake_case).to_tuple()
self.assertEqual(len(snake_case) , len(snake_case))
for jitted_output, output in zip(snake_case , snake_case):
self.assertEqual(jitted_output.shape , output.shape)
def lowerCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase : int =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
_UpperCAmelCase : Tuple =model_class(snake_case)
_UpperCAmelCase : Optional[int] =model.encode(inputs_dict['input_ids'] , inputs_dict['attention_mask'])
_UpperCAmelCase : int ={
'decoder_input_ids': inputs_dict['decoder_input_ids'],
'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
'encoder_outputs': encoder_outputs,
}
@jax.jit
def decode_jitted(snake_case , snake_case , snake_case):
return model.decode(
decoder_input_ids=snake_case , decoder_attention_mask=snake_case , encoder_outputs=snake_case , )
with self.subTest('JIT Enabled'):
_UpperCAmelCase : Tuple =decode_jitted(**snake_case).to_tuple()
with self.subTest('JIT Disabled'):
with jax.disable_jit():
_UpperCAmelCase : str =decode_jitted(**snake_case).to_tuple()
self.assertEqual(len(snake_case) , len(snake_case))
for jitted_output, output in zip(snake_case , snake_case):
self.assertEqual(jitted_output.shape , output.shape)
@slow
def lowerCAmelCase ( self) -> Dict:
'''simple docstring'''
for model_class_name in self.all_model_classes:
_UpperCAmelCase : Optional[Any] =model_class_name.from_pretrained('google/pegasus-large' , from_pt=snake_case)
_UpperCAmelCase : int =np.ones((1, 1))
_UpperCAmelCase : int =model(snake_case)
self.assertIsNotNone(snake_case)
@slow
def lowerCAmelCase ( self) -> str:
'''simple docstring'''
_UpperCAmelCase : Optional[int] =FlaxPegasusForConditionalGeneration.from_pretrained('google/pegasus-xsum')
_UpperCAmelCase : str =PegasusTokenizer.from_pretrained('google/pegasus-xsum')
_UpperCAmelCase : Dict =[
' 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 : int =[
'California\'s largest electricity provider has turned off power to hundreds of thousands of customers.',
'Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.',
]
_UpperCAmelCase : int =tokenizer(snake_case , return_tensors='np' , truncation=snake_case , max_length=5_1_2 , padding=snake_case)
_UpperCAmelCase : int =model.generate(**snake_case , num_beams=2).sequences
_UpperCAmelCase : Optional[int] =tokenizer.batch_decode(snake_case , skip_special_tokens=snake_case)
assert tgt_text == decoded
| 242 |
'''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.
import re
from ..utils import cached_file
# docstyle-ignore
lowercase ='\nHuman: <<task>>\n\nAssistant: '
lowercase ='huggingface-tools/default-prompts'
lowercase ={'chat': 'chat_prompt_template.txt', 'run': 'run_prompt_template.txt'}
def lowerCamelCase__ ( __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : int="run" ):
'''simple docstring'''
if prompt_or_repo_id is None:
_UpperCAmelCase : List[str] =DEFAULT_PROMPTS_REPO
# prompt is considered a repo ID when it does not contain any kind of space
if re.search('\\s' , __lowerCamelCase ) is not None:
return prompt_or_repo_id
_UpperCAmelCase : Dict =cached_file(
__lowerCamelCase , PROMPT_FILES[mode] , repo_type='dataset' , user_agent={'agent': agent_name} )
with open(__lowerCamelCase , 'r' , encoding='utf-8' ) as f:
return f.read()
| 242 | 1 |
"""simple docstring"""
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = """▁"""
lowercase__ = {"""vocab_file""": """vocab.txt""", """sentencepiece_model_ckpt""": """sentencepiece.bpe.model"""}
lowercase__ = {
"""sentencepiece_model_file""": """sentencepiece.bpe.model""",
"""vocab_file""": """vocab.txt""",
}
lowercase__ = {
"""vocab_file""": {
"""ernie-m-base""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt""",
"""ernie-m-large""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt""",
},
"""sentencepiece_model_file""": {
"""ernie-m-base""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model""",
"""ernie-m-large""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model""",
},
}
lowercase__ = {
"""ernie-m-base""": 514,
"""ernie-m-large""": 514,
}
lowercase__ = {
"""ernie-m-base""": {"""do_lower_case""": False},
"""ernie-m-large""": {"""do_lower_case""": False},
}
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = ["input_ids"]
lowerCamelCase__ = VOCAB_FILES_NAMES
lowerCamelCase__ = PRETRAINED_INIT_CONFIGURATION
lowerCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ = RESOURCE_FILES_NAMES
def __init__( self , lowercase , lowercase=None , lowercase=False , lowercase="utf8" , lowercase="[UNK]" , lowercase="[SEP]" , lowercase="[PAD]" , lowercase="[CLS]" , lowercase="[MASK]" , lowercase = None , **lowercase , ):
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
_lowerCamelCase : Optional[int] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=lowercase , unk_token=lowercase , sep_token=lowercase , pad_token=lowercase , cls_token=lowercase , mask_token=lowercase , vocab_file=lowercase , encoding=lowercase , sp_model_kwargs=self.sp_model_kwargs , **lowercase , )
_lowerCamelCase : str = do_lower_case
_lowerCamelCase : Optional[Any] = sentencepiece_model_ckpt
_lowerCamelCase : int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(lowercase )
# to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
if vocab_file is not None:
_lowerCamelCase : Dict = self.load_vocab(filepath=lowercase )
else:
_lowerCamelCase : Optional[int] = {self.sp_model.id_to_piece(lowercase ): id for id in range(self.sp_model.get_piece_size() )}
_lowerCamelCase : int = {v: k for k, v in self.vocab.items()}
def A_ ( self , lowercase ):
if text is None:
return None
_lowerCamelCase : Tuple = self.tokenize(lowercase )
_lowerCamelCase, _lowerCamelCase : Any = '', []
for i, ch in enumerate(lowercase ):
if ch in self.SP_CHAR_MAPPING:
_lowerCamelCase : List[str] = self.SP_CHAR_MAPPING.get(lowercase )
else:
_lowerCamelCase : Dict = unicodedata.normalize('NFKC' , lowercase )
if self.is_whitespace(lowercase ):
continue
normalized_text += ch
char_mapping.extend([i] * len(lowercase ) )
_lowerCamelCase, _lowerCamelCase, _lowerCamelCase : Union[str, Any] = normalized_text, [], 0
if self.do_lower_case:
_lowerCamelCase : List[str] = text.lower()
for token in split_tokens:
if token[:1] == "▁":
_lowerCamelCase : List[str] = token[1:]
_lowerCamelCase : Union[str, Any] = text[offset:].index(lowercase ) + offset
_lowerCamelCase : Optional[int] = start + len(lowercase )
token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1) )
_lowerCamelCase : Optional[Any] = end
return token_mapping
@property
def A_ ( self ):
return len(self.vocab )
def A_ ( self ):
return dict(self.vocab , **self.added_tokens_encoder )
def __getstate__( self ):
_lowerCamelCase : str = self.__dict__.copy()
_lowerCamelCase : Optional[int] = None
return state
def __setstate__( self , lowercase ):
_lowerCamelCase : Union[str, Any] = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
_lowerCamelCase : List[str] = {}
_lowerCamelCase : str = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.sentencepiece_model_ckpt )
def A_ ( self , lowercase ):
return "".join((self.SP_CHAR_MAPPING.get(lowercase , lowercase ) for c in text) )
def A_ ( self , lowercase , lowercase=False , lowercase=64 , lowercase=0.1 ):
if self.sp_model_kwargs.get('enable_sampling' ) is True:
_lowerCamelCase : Optional[int] = True
if self.sp_model_kwargs.get('alpha' ) is not None:
_lowerCamelCase : Tuple = self.sp_model_kwargs.get('alpha' )
if self.sp_model_kwargs.get('nbest_size' ) is not None:
_lowerCamelCase : Tuple = self.sp_model_kwargs.get('nbest_size' )
if not enable_sampling:
_lowerCamelCase : Optional[Any] = self.sp_model.EncodeAsPieces(lowercase )
else:
_lowerCamelCase : List[str] = self.sp_model.SampleEncodeAsPieces(lowercase , lowercase , lowercase )
_lowerCamelCase : Tuple = []
for pi, piece in enumerate(lowercase ):
if piece == SPIECE_UNDERLINE:
if not pieces[pi + 1].startswith(lowercase ) and pi != 0:
new_pieces.append(lowercase )
continue
else:
continue
_lowerCamelCase : Tuple = 0
for i, chunk in enumerate(lowercase ):
if chunk == SPIECE_UNDERLINE:
continue
if self.is_ch_char(lowercase ) or self.is_punct(lowercase ):
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
new_pieces.append(lowercase )
_lowerCamelCase : int = i + 1
elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
_lowerCamelCase : Any = i
elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
_lowerCamelCase : Tuple = i
if len(lowercase ) > lst_i:
new_pieces.append(piece[lst_i:] )
return new_pieces
def A_ ( self , lowercase ):
_lowerCamelCase : Optional[int] = ''.join(lowercase ).replace(lowercase , ' ' ).strip()
return out_string
def A_ ( self , lowercase ):
_lowerCamelCase : Union[str, Any] = self.convert_ids_to_tokens(lowercase )
_lowerCamelCase : Any = ''.join(lowercase ).replace(lowercase , ' ' ).strip()
return out_string
def A_ ( self , lowercase ):
return self.vocab.get(lowercase , self.vocab.get(self.unk_token ) )
def A_ ( self , lowercase ):
return self.reverse_vocab.get(lowercase , self.unk_token )
def A_ ( self , lowercase , lowercase=None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_lowerCamelCase : List[Any] = [self.cls_token_id]
_lowerCamelCase : Optional[Any] = [self.sep_token_id]
return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep
def A_ ( self , lowercase , lowercase=None ):
if offset_mapping_a is None:
return [(0, 0)] + offset_mapping_a + [(0, 0)]
return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]
def A_ ( self , lowercase , lowercase=None , lowercase=False ):
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'You should not supply a second sequence if the provided sequence of '
'ids is already formatted with special tokens for the model.' )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(lowercase )) + [1, 1] + ([0] * len(lowercase )) + [1]
return [1] + ([0] * len(lowercase )) + [1]
def A_ ( self , lowercase , lowercase = None ):
# called when `add_special_tokens` is True, so align with `build_inputs_with_special_tokens` method
if token_ids_a is None:
# [CLS] X [SEP]
return (len(lowercase ) + 2) * [0]
# [CLS] A [SEP] [SEP] B [SEP]
return [0] * (len(lowercase ) + 1) + [1] * (len(lowercase ) + 3)
def A_ ( self , lowercase ):
if "\u4e00" <= char <= "\u9fff":
return True
return False
def A_ ( self , lowercase ):
if ("a" <= char <= "z") or ("A" <= char <= "Z"):
return True
return False
def A_ ( self , lowercase ):
if char in ",;:.?!~，；：。？！《》【】":
return True
return False
def A_ ( self , lowercase ):
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
if len(lowercase ) == 1:
_lowerCamelCase : Tuple = unicodedata.category(lowercase )
if cat == "Zs":
return True
return False
def A_ ( self , lowercase ):
_lowerCamelCase : Tuple = {}
with io.open(lowercase , 'r' , encoding='utf-8' ) as f:
for index, line in enumerate(lowercase ):
_lowerCamelCase : int = line.rstrip('\n' )
_lowerCamelCase : Optional[Any] = int(lowercase )
return token_to_idx
def A_ ( self , lowercase , lowercase = None ):
_lowerCamelCase : Union[str, Any] = 0
if os.path.isdir(lowercase ):
_lowerCamelCase : List[Any] = os.path.join(
lowercase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
else:
_lowerCamelCase : Optional[int] = (filename_prefix + '-' if filename_prefix else '') + save_directory
with open(lowercase , 'w' , encoding='utf-8' ) as writer:
for token, token_index in sorted(self.vocab.items() , key=lambda lowercase : kv[1] ):
if index != token_index:
logger.warning(
F'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
' Please check that the vocabulary is not corrupted!' )
_lowerCamelCase : Optional[Any] = token_index
writer.write(token + '\n' )
index += 1
_lowerCamelCase : List[str] = os.path.join(lowercase , 'sentencepiece.bpe.model' )
with open(lowercase , 'wb' ) as fi:
_lowerCamelCase : Dict = self.sp_model.serialized_model_proto()
fi.write(lowercase )
return (vocab_file,) | 96 |
"""simple docstring"""
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
require_tensorflow_probability,
require_tf,
slow,
)
from ..bert.test_modeling_bert import BertModelTester
if is_tf_available():
from transformers import (
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelForTableQuestionAnswering,
TFAutoModelForTokenClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFFunnelBaseModel,
TFFunnelModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
TFTapasForQuestionAnswering,
)
from transformers.models.auto.modeling_tf_auto import (
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_MAPPING,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = """new-model"""
if is_tf_available():
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = NewModelConfig
@require_tf
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def A_ ( self ):
_lowerCamelCase : List[str] = 'bert-base-cased'
_lowerCamelCase : Union[str, Any] = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Union[str, Any] = TFAutoModel.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
_lowerCamelCase : List[str] = 'bert-base-cased'
_lowerCamelCase : int = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : int = TFAutoModelForPreTraining.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Dict = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : int = TFAutoModelForCausalLM.from_pretrained(lowercase )
_lowerCamelCase, _lowerCamelCase : str = TFAutoModelForCausalLM.from_pretrained(lowercase , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : List[Any] = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : str = TFAutoModelWithLMHead.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Tuple = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Optional[int] = TFAutoModelForMaskedLM.from_pretrained(lowercase )
_lowerCamelCase, _lowerCamelCase : Tuple = TFAutoModelForMaskedLM.from_pretrained(lowercase , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Optional[int] = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Tuple = TFAutoModelForSeqaSeqLM.from_pretrained(lowercase )
_lowerCamelCase, _lowerCamelCase : Tuple = TFAutoModelForSeqaSeqLM.from_pretrained(lowercase , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
_lowerCamelCase : str = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Union[str, Any] = TFAutoModelForSequenceClassification.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
_lowerCamelCase : Optional[Any] = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : List[str] = TFAutoModelForQuestionAnswering.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
@require_tensorflow_probability
def A_ ( self ):
for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
_lowerCamelCase : Dict = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Dict = TFAutoModelForTableQuestionAnswering.from_pretrained(lowercase )
_lowerCamelCase, _lowerCamelCase : List[Any] = TFAutoModelForTableQuestionAnswering.from_pretrained(
lowercase , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
def A_ ( self ):
_lowerCamelCase : int = TFAutoModelWithLMHead.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
self.assertEqual(model.num_parameters() , 14410 )
self.assertEqual(model.num_parameters(only_trainable=lowercase ) , 14410 )
def A_ ( self ):
_lowerCamelCase : Any = TFAutoModelWithLMHead.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
self.assertEqual(model.num_parameters() , 14410 )
self.assertEqual(model.num_parameters(only_trainable=lowercase ) , 14410 )
def A_ ( self ):
# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
_lowerCamelCase : List[str] = TFAutoModel.from_pretrained('sgugger/funnel-random-tiny' )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Optional[int] = copy.deepcopy(model.config )
_lowerCamelCase : Dict = ['FunnelBaseModel']
_lowerCamelCase : List[Any] = TFAutoModel.from_config(lowercase )
self.assertIsInstance(lowercase , lowercase )
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(lowercase )
_lowerCamelCase : Tuple = TFAutoModel.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
def A_ ( self ):
try:
AutoConfig.register('new-model' , lowercase )
_lowerCamelCase : Tuple = [
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSequenceClassification,
TFAutoModelForTokenClassification,
]
for auto_class in auto_classes:
with self.subTest(auto_class.__name__ ):
# Wrong config class will raise an error
with self.assertRaises(lowercase ):
auto_class.register(lowercase , lowercase )
auto_class.register(lowercase , lowercase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(lowercase ):
auto_class.register(lowercase , lowercase )
# Now that the config is registered, it can be used as any other config with the auto-API
_lowerCamelCase : Optional[Any] = BertModelTester(self ).get_config()
_lowerCamelCase : Dict = NewModelConfig(**tiny_config.to_dict() )
_lowerCamelCase : int = auto_class.from_config(lowercase )
self.assertIsInstance(lowercase , lowercase )
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(lowercase )
_lowerCamelCase : List[Any] = auto_class.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
for mapping in (
TF_MODEL_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
):
if NewModelConfig in mapping._extra_content:
del mapping._extra_content[NewModelConfig]
def A_ ( self ):
with self.assertRaisesRegex(
lowercase , 'bert-base is not a local folder and is not a valid model identifier' ):
_lowerCamelCase : Union[str, Any] = TFAutoModel.from_pretrained('bert-base' )
def A_ ( self ):
with self.assertRaisesRegex(
lowercase , r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)' ):
_lowerCamelCase : str = TFAutoModel.from_pretrained(lowercase , revision='aaaaaa' )
def A_ ( self ):
with self.assertRaisesRegex(
lowercase , 'hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin' , ):
_lowerCamelCase : Tuple = TFAutoModel.from_pretrained('hf-internal-testing/config-no-model' )
def A_ ( self ):
with self.assertRaisesRegex(lowercase , 'Use `from_pt=True` to load this model' ):
_lowerCamelCase : Tuple = TFAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only' )
def A_ ( self ):
# Make sure we have cached the model.
_lowerCamelCase : Optional[int] = TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert' )
with RequestCounter() as counter:
_lowerCamelCase : Optional[int] = TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert' )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 )
# With a sharded checkpoint
_lowerCamelCase : int = TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded' )
with RequestCounter() as counter:
_lowerCamelCase : List[Any] = TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded' )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 ) | 96 | 1 |
"""simple docstring"""
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig
from transformers import WavLMConfig, WavLMModel, logging
logging.set_verbosity_info()
a : int = logging.get_logger(__name__)
a : Dict = {
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
'''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
'''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
'''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
'''self_attn.grep_linear''': '''encoder.layers.*.attention.gru_rel_pos_linear''',
'''self_attn.relative_attention_bias''': '''encoder.layers.*.attention.rel_attn_embed''',
'''self_attn.grep_a''': '''encoder.layers.*.attention.gru_rel_pos_const''',
'''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
'''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
'''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''ctc_proj''',
'''mask_emb''': '''masked_spec_embed''',
}
a : Tuple = [
'''ctc_proj''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
]
def _SCREAMING_SNAKE_CASE ( _lowercase : Dict , _lowercase : Union[str, Any] , _lowercase : Dict , _lowercase : int , _lowercase : List[Any] ) ->Tuple:
'''simple docstring'''
for attribute in key.split("." ):
a : Optional[int] = getattr(_lowercase , _lowercase )
if weight_type is not None:
a : Optional[Any] = getattr(_lowercase , _lowercase ).shape
else:
a : Dict = hf_pointer.shape
assert hf_shape == value.shape, (
F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
F""" {value.shape} for {full_name}"""
)
if weight_type == "weight":
a : str = value
elif weight_type == "weight_g":
a : Union[str, Any] = value
elif weight_type == "weight_v":
a : Any = value
elif weight_type == "bias":
a : List[Any] = value
else:
a : Any = value
logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def _SCREAMING_SNAKE_CASE ( _lowercase : Optional[Any] , _lowercase : Dict ) ->int:
'''simple docstring'''
a : Tuple = []
a : List[str] = fairseq_model.state_dict()
a : Union[str, Any] = hf_model.feature_extractor
for name, value in fairseq_dict.items():
a : Any = False
if "conv_layers" in name:
load_conv_layer(
_lowercase , _lowercase , _lowercase , _lowercase , hf_model.config.feat_extract_norm == "group" , )
a : Tuple = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split("w2v_model." )[-1] == name.split("." )[0]:
a : Optional[int] = True
if "*" in mapped_key:
a : List[str] = name.split(_lowercase )[0].split("." )[-2]
a : Any = mapped_key.replace("*" , _lowercase )
if "weight_g" in name:
a : str = "weight_g"
elif "weight_v" in name:
a : int = "weight_v"
elif "bias" in name and "relative_attention_bias" not in name:
a : Union[str, Any] = "bias"
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
a : int = "weight"
else:
a : Dict = None
set_recursively(_lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
continue
if not is_used:
unused_weights.append(_lowercase )
logger.warning(F"""Unused weights: {unused_weights}""" )
def _SCREAMING_SNAKE_CASE ( _lowercase : Tuple , _lowercase : Optional[int] , _lowercase : str , _lowercase : List[str] , _lowercase : str ) ->Dict:
'''simple docstring'''
a : str = full_name.split("conv_layers." )[-1]
a : int = name.split("." )
a : Tuple = int(items[0] )
a : List[str] = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
)
a : Optional[int] = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
)
a : List[Any] = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
" found."
)
a : Tuple = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
)
a : Optional[int] = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(_lowercase )
@torch.no_grad()
def _SCREAMING_SNAKE_CASE ( _lowercase : List[str] , _lowercase : List[str] , _lowercase : Dict=None ) ->Optional[Any]:
'''simple docstring'''
a : Tuple = torch.load(_lowercase )
a : Dict = WavLMConfigOrig(checkpoint["cfg"] )
a : Union[str, Any] = WavLMOrig(_lowercase )
model.load_state_dict(checkpoint["model"] )
model.eval()
if config_path is not None:
a : Union[str, Any] = WavLMConfig.from_pretrained(_lowercase )
else:
a : Dict = WavLMConfig()
a : List[Any] = WavLMModel(_lowercase )
recursively_load_weights(_lowercase , _lowercase )
hf_wavlm.save_pretrained(_lowercase )
if __name__ == "__main__":
a : List[str] = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
a : Optional[Any] = parser.parse_args()
convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 79 |
"""simple docstring"""
# Copyright 2021 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 argparse import ArgumentParser
from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser
def _SCREAMING_SNAKE_CASE ( ) ->List[str]:
'''simple docstring'''
a : Optional[Any] = ArgumentParser("Accelerate CLI tool" , usage="accelerate <command> [<args>]" , allow_abbrev=_lowercase )
a : Optional[Any] = parser.add_subparsers(help="accelerate command helpers" )
# Register commands
get_config_parser(subparsers=_lowercase )
env_command_parser(subparsers=_lowercase )
launch_command_parser(subparsers=_lowercase )
tpu_command_parser(subparsers=_lowercase )
test_command_parser(subparsers=_lowercase )
# Let's go
a : int = parser.parse_args()
if not hasattr(_lowercase , "func" ):
parser.print_help()
exit(1 )
# Run
args.func(_lowercase )
if __name__ == "__main__":
main()
| 79 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCAmelCase : Any = 400_0000 ):
"""simple docstring"""
__magic_name__ : List[Any] = []
__magic_name__ , __magic_name__ : str = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(_SCREAMING_SNAKE_CASE )
__magic_name__ , __magic_name__ : Dict = b, a + b
return sum(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
print(F'{solution() = }') | 331 |
"""simple docstring"""
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import BertGenerationDecoder, BertGenerationEncoder
class _lowerCamelCase :
def __init__(self , __a , __a=13 , __a=7 , __a=True , __a=True , __a=99 , __a=32 , __a=5 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=50 , __a=0.02 , __a=True , __a=None , ) -> Union[str, Any]:
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = seq_length
UpperCamelCase = is_training
UpperCamelCase = use_input_mask
UpperCamelCase = vocab_size
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_act
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = max_position_embeddings
UpperCamelCase = initializer_range
UpperCamelCase = use_labels
UpperCamelCase = scope
def snake_case_ (self ) -> Union[str, Any]:
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase = None
if self.use_input_mask:
UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
if self.use_labels:
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase = self.get_config()
return config, input_ids, input_mask, token_labels
def snake_case_ (self ) -> List[str]:
return BertGenerationConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , is_decoder=__a , initializer_range=self.initializer_range , )
def snake_case_ (self ) -> List[str]:
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) = self.prepare_config_and_inputs()
UpperCamelCase = True
UpperCamelCase = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
input_mask,
token_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def snake_case_ (self , __a , __a , __a , __a , **__a , ) -> Dict:
UpperCamelCase = BertGenerationEncoder(config=__a )
model.to(__a )
model.eval()
UpperCamelCase = model(__a , attention_mask=__a )
UpperCamelCase = model(__a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case_ (self , __a , __a , __a , __a , __a , __a , **__a , ) -> str:
UpperCamelCase = True
UpperCamelCase = BertGenerationEncoder(config=__a )
model.to(__a )
model.eval()
UpperCamelCase = model(
__a , attention_mask=__a , encoder_hidden_states=__a , encoder_attention_mask=__a , )
UpperCamelCase = model(
__a , attention_mask=__a , encoder_hidden_states=__a , )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case_ (self , __a , __a , __a , __a , __a , __a , **__a , ) -> Optional[int]:
UpperCamelCase = True
UpperCamelCase = True
UpperCamelCase = BertGenerationDecoder(config=__a ).to(__a ).eval()
# first forward pass
UpperCamelCase = model(
__a , attention_mask=__a , encoder_hidden_states=__a , encoder_attention_mask=__a , use_cache=__a , )
UpperCamelCase = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
UpperCamelCase = ids_tensor((self.batch_size, 3) , config.vocab_size )
UpperCamelCase = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
UpperCamelCase = torch.cat([input_ids, next_tokens] , dim=-1 )
UpperCamelCase = torch.cat([input_mask, next_mask] , dim=-1 )
UpperCamelCase = model(
__a , attention_mask=__a , encoder_hidden_states=__a , encoder_attention_mask=__a , output_hidden_states=__a , )["hidden_states"][0]
UpperCamelCase = model(
__a , attention_mask=__a , encoder_hidden_states=__a , encoder_attention_mask=__a , past_key_values=__a , output_hidden_states=__a , )["hidden_states"][0]
# select random slice
UpperCamelCase = ids_tensor((1,) , output_from_past.shape[-1] ).item()
UpperCamelCase = output_from_no_past[:, -3:, random_slice_idx].detach()
UpperCamelCase = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(__a , __a , atol=1e-3 ) )
def snake_case_ (self , __a , __a , __a , __a , *__a , ) -> Optional[Any]:
UpperCamelCase = BertGenerationDecoder(__a )
model.to(__a )
model.eval()
UpperCamelCase = model(__a , attention_mask=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def snake_case_ (self ) -> Dict:
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = self.prepare_config_and_inputs()
UpperCamelCase = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _lowerCamelCase ( _lowercase , _lowercase , _lowercase , unittest.TestCase ):
UpperCAmelCase_ = (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
UpperCAmelCase_ = (BertGenerationDecoder,) if is_torch_available() else ()
UpperCAmelCase_ = (
{"feature-extraction": BertGenerationEncoder, "text-generation": BertGenerationDecoder}
if is_torch_available()
else {}
)
def snake_case_ (self ) -> Any:
UpperCamelCase = BertGenerationEncoderTester(self )
UpperCamelCase = ConfigTester(self , config_class=__a , hidden_size=37 )
def snake_case_ (self ) -> Tuple:
self.config_tester.run_common_tests()
def snake_case_ (self ) -> List[str]:
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__a )
def snake_case_ (self ) -> Any:
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs()
UpperCamelCase = "bert"
self.model_tester.create_and_check_model(__a , __a , __a , __a )
def snake_case_ (self ) -> Optional[Any]:
UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*__a )
def snake_case_ (self ) -> List[str]:
UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*__a )
def snake_case_ (self ) -> Union[str, Any]:
# This regression test was failing with PyTorch < 1.3
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) = self.model_tester.prepare_config_and_inputs_for_decoder()
UpperCamelCase = None
self.model_tester.create_and_check_model_as_decoder(
__a , __a , __a , __a , __a , __a , )
def snake_case_ (self ) -> str:
UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_for_causal_lm(*__a )
@slow
def snake_case_ (self ) -> List[str]:
UpperCamelCase = BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder" )
self.assertIsNotNone(__a )
@require_torch
class _lowerCamelCase ( unittest.TestCase ):
@slow
def snake_case_ (self ) -> int:
UpperCamelCase = BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder" )
UpperCamelCase = torch.tensor([[1_01, 75_92, 10_10, 20_26, 38_99, 20_03, 1_01_40, 1_02]] )
with torch.no_grad():
UpperCamelCase = model(__a )[0]
UpperCamelCase = torch.Size([1, 8, 10_24] )
self.assertEqual(output.shape , __a )
UpperCamelCase = torch.tensor(
[[[0.1775, 0.0083, -0.0321], [1.6002, 0.1287, 0.3912], [2.1473, 0.5791, 0.6066]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __a , atol=1e-4 ) )
@require_torch
class _lowerCamelCase ( unittest.TestCase ):
@slow
def snake_case_ (self ) -> Optional[Any]:
UpperCamelCase = BertGenerationDecoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder" )
UpperCamelCase = torch.tensor([[1_01, 75_92, 10_10, 20_26, 38_99, 20_03, 1_01_40, 1_02]] )
with torch.no_grad():
UpperCamelCase = model(__a )[0]
UpperCamelCase = torch.Size([1, 8, 5_03_58] )
self.assertEqual(output.shape , __a )
UpperCamelCase = torch.tensor(
[[[-0.5788, -2.5994, -3.7054], [0.0438, 4.7997, 1.8795], [1.5862, 6.6409, 4.4638]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __a , atol=1e-4 ) )
| 153 | 0 |
'''simple docstring'''
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)
from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
logging.set_verbosity_info()
def _lowerCAmelCase ( lowerCamelCase_ : str , lowerCamelCase_ : str ):
if "xprophetnet" in prophetnet_checkpoint_path:
__lowercase = XLMProphetNetForConditionalGenerationOld.from_pretrained(lowerCamelCase_ )
__lowercase , __lowercase = XLMProphetNetForConditionalGeneration.from_pretrained(
lowerCamelCase_ , output_loading_info=lowerCamelCase_ )
else:
__lowercase = ProphetNetForConditionalGenerationOld.from_pretrained(lowerCamelCase_ )
__lowercase , __lowercase = ProphetNetForConditionalGeneration.from_pretrained(
lowerCamelCase_ , output_loading_info=lowerCamelCase_ )
__lowercase = ['''key_proj''', '''value_proj''', '''query_proj''']
__lowercase = {
'''self_attn''': '''ngram_self_attn''',
'''cross_attn''': '''encoder_attn''',
'''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
'''feed_forward_layer_norm''': '''final_layer_norm''',
'''feed_forward''': '''''',
'''intermediate''': '''fc1''',
'''output''': '''fc2''',
'''key_proj''': '''k_proj''',
'''query_proj''': '''q_proj''',
'''value_proj''': '''v_proj''',
'''word_embeddings''': '''embed_tokens''',
'''embeddings_layer_norm''': '''emb_layer_norm''',
'''relative_pos_embeddings''': '''relative_linear''',
'''ngram_embeddings''': '''ngram_input_embed''',
'''position_embeddings''': '''embed_positions''',
}
for key in loading_info["missing_keys"]:
__lowercase = key.split('''.''' )
if attributes[0] == "lm_head":
__lowercase = prophet
__lowercase = prophet_old
else:
__lowercase = prophet.prophetnet
__lowercase = prophet_old.model
__lowercase = False
for attribute in attributes:
if attribute in mapping:
__lowercase = mapping[attribute]
if not hasattr(lowerCamelCase_ , lowerCamelCase_ ) and len(lowerCamelCase_ ) > 0:
__lowercase = attribute
elif hasattr(lowerCamelCase_ , lowerCamelCase_ ):
__lowercase = attribute
if attribute == "weight":
assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
__lowercase = old_model.weight
logger.info(f"{attribute} is initialized." )
__lowercase = True
break
elif attribute == "bias":
assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
__lowercase = old_model.bias
logger.info(f"{attribute} is initialized" )
__lowercase = True
break
elif attribute in special_keys and hasattr(lowerCamelCase_ , '''in_proj_weight''' ):
__lowercase = old_model.in_proj_weight.shape[0] // 3
__lowercase = getattr(lowerCamelCase_ , lowerCamelCase_ )
param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
if attribute == "query_proj":
__lowercase = nn.Parameter(old_model.in_proj_weight[:embed_dim, :] )
__lowercase = nn.Parameter(old_model.in_proj_bias[:embed_dim] )
elif attribute == "key_proj":
__lowercase = nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
__lowercase = nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
elif attribute == "value_proj":
__lowercase = nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
__lowercase = nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
__lowercase = True
break
elif attribute == "position_embeddings":
assert (
model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
), "Hidden size has to match"
assert model.position_embeddings.weight.shape[0] == 5_1_2, "We want 512 position_embeddings."
__lowercase = nn.Parameter(old_model.embed_positions.weight[:5_1_2, :] )
__lowercase = True
break
if attribute.isdigit():
__lowercase = model[int(lowerCamelCase_ )]
__lowercase = old_model[int(lowerCamelCase_ )]
else:
__lowercase = getattr(lowerCamelCase_ , lowerCamelCase_ )
if old_attribute == "":
__lowercase = old_model
else:
if not hasattr(lowerCamelCase_ , lowerCamelCase_ ):
raise ValueError(f"{old_model} does not have {old_attribute}" )
__lowercase = getattr(lowerCamelCase_ , lowerCamelCase_ )
if not is_key_init:
raise ValueError(f"{key} was not correctly initialized!" )
print(f"Saving model to {pytorch_dump_folder_path}" )
prophet.save_pretrained(lowerCamelCase_ )
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--prophetnet_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
_SCREAMING_SNAKE_CASE = parser.parse_args()
convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)
| 217 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
if is_torch_available():
import torch
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class __lowercase ( lowerCAmelCase__ ):
'''simple docstring'''
a : int = ["pixel_values"]
def __init__(self ,_lowerCamelCase = True ,_lowerCamelCase = None ,_lowerCamelCase = PILImageResampling.BILINEAR ,_lowerCamelCase = True ,_lowerCamelCase = None ,_lowerCamelCase = True ,_lowerCamelCase = 1 / 255 ,_lowerCamelCase = True ,_lowerCamelCase = None ,_lowerCamelCase = None ,**_lowerCamelCase ,) -> None:
'''simple docstring'''
super().__init__(**_lowerCamelCase )
__lowercase = size if size is not None else {'''shortest_edge''': 256}
__lowercase = get_size_dict(_lowerCamelCase ,default_to_square=_lowerCamelCase )
__lowercase = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
__lowercase = get_size_dict(_lowerCamelCase ,param_name='''crop_size''' )
__lowercase = do_resize
__lowercase = size
__lowercase = resample
__lowercase = do_center_crop
__lowercase = crop_size
__lowercase = do_rescale
__lowercase = rescale_factor
__lowercase = do_normalize
__lowercase = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
__lowercase = image_std if image_std is not None else IMAGENET_STANDARD_STD
def _UpperCAmelCase (self ,_lowerCamelCase ,_lowerCamelCase ,_lowerCamelCase = PILImageResampling.BICUBIC ,_lowerCamelCase = None ,**_lowerCamelCase ,) -> np.ndarray:
'''simple docstring'''
__lowercase = get_size_dict(_lowerCamelCase ,default_to_square=_lowerCamelCase )
if "shortest_edge" not in size:
raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}" )
__lowercase = get_resize_output_image_size(_lowerCamelCase ,size=size['''shortest_edge'''] ,default_to_square=_lowerCamelCase )
return resize(_lowerCamelCase ,size=_lowerCamelCase ,resample=_lowerCamelCase ,data_format=_lowerCamelCase ,**_lowerCamelCase )
def _UpperCAmelCase (self ,_lowerCamelCase ,_lowerCamelCase ,_lowerCamelCase = None ,**_lowerCamelCase ,) -> np.ndarray:
'''simple docstring'''
__lowercase = get_size_dict(_lowerCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(f"The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}" )
return center_crop(_lowerCamelCase ,size=(size['''height'''], size['''width''']) ,data_format=_lowerCamelCase ,**_lowerCamelCase )
def _UpperCAmelCase (self ,_lowerCamelCase ,_lowerCamelCase ,_lowerCamelCase = None ,**_lowerCamelCase ) -> np.ndarray:
'''simple docstring'''
return rescale(_lowerCamelCase ,scale=_lowerCamelCase ,data_format=_lowerCamelCase ,**_lowerCamelCase )
def _UpperCAmelCase (self ,_lowerCamelCase ,_lowerCamelCase ,_lowerCamelCase ,_lowerCamelCase = None ,**_lowerCamelCase ,) -> np.ndarray:
'''simple docstring'''
return normalize(_lowerCamelCase ,mean=_lowerCamelCase ,std=_lowerCamelCase ,data_format=_lowerCamelCase ,**_lowerCamelCase )
def _UpperCAmelCase (self ,_lowerCamelCase ,_lowerCamelCase = None ,_lowerCamelCase = None ,_lowerCamelCase = None ,_lowerCamelCase = None ,_lowerCamelCase = None ,_lowerCamelCase = None ,_lowerCamelCase = None ,_lowerCamelCase = None ,_lowerCamelCase = None ,_lowerCamelCase = None ,_lowerCamelCase = None ,_lowerCamelCase = ChannelDimension.FIRST ,**_lowerCamelCase ,) -> Any:
'''simple docstring'''
__lowercase = do_resize if do_resize is not None else self.do_resize
__lowercase = size if size is not None else self.size
__lowercase = get_size_dict(_lowerCamelCase ,default_to_square=_lowerCamelCase )
__lowercase = resample if resample is not None else self.resample
__lowercase = do_center_crop if do_center_crop is not None else self.do_center_crop
__lowercase = crop_size if crop_size is not None else self.crop_size
__lowercase = get_size_dict(_lowerCamelCase ,param_name='''crop_size''' )
__lowercase = do_rescale if do_rescale is not None else self.do_rescale
__lowercase = rescale_factor if rescale_factor is not None else self.rescale_factor
__lowercase = do_normalize if do_normalize is not None else self.do_normalize
__lowercase = image_mean if image_mean is not None else self.image_mean
__lowercase = image_std if image_std is not None else self.image_std
__lowercase = make_list_of_images(_lowerCamelCase )
if not valid_images(_lowerCamelCase ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# All transformations expect numpy arrays.
__lowercase = [to_numpy_array(_lowerCamelCase ) for image in images]
if do_resize:
__lowercase = [self.resize(image=_lowerCamelCase ,size=_lowerCamelCase ,resample=_lowerCamelCase ) for image in images]
if do_center_crop:
__lowercase = [self.center_crop(image=_lowerCamelCase ,size=_lowerCamelCase ) for image in images]
if do_rescale:
__lowercase = [self.rescale(image=_lowerCamelCase ,scale=_lowerCamelCase ) for image in images]
if do_normalize:
__lowercase = [self.normalize(image=_lowerCamelCase ,mean=_lowerCamelCase ,std=_lowerCamelCase ) for image in images]
__lowercase = [to_channel_dimension_format(_lowerCamelCase ,_lowerCamelCase ) for image in images]
__lowercase = {'''pixel_values''': images}
return BatchFeature(data=_lowerCamelCase ,tensor_type=_lowerCamelCase )
def _UpperCAmelCase (self ,_lowerCamelCase ,_lowerCamelCase = None ) -> str:
'''simple docstring'''
__lowercase = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(_lowerCamelCase ) != len(_lowerCamelCase ):
raise ValueError(
'''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )
if is_torch_tensor(_lowerCamelCase ):
__lowercase = target_sizes.numpy()
__lowercase = []
for idx in range(len(_lowerCamelCase ) ):
__lowercase = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) ,size=target_sizes[idx] ,mode='''bilinear''' ,align_corners=_lowerCamelCase )
__lowercase = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(_lowerCamelCase )
else:
__lowercase = logits.argmax(dim=1 )
__lowercase = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 217 | 1 |
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 lowercase__ ( unittest.TestCase):
def __A ( self : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = tempfile.mkdtemp()
# fmt: off
SCREAMING_SNAKE_CASE : Any = ['''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
SCREAMING_SNAKE_CASE : List[str] = dict(zip(UpperCamelCase__ , range(len(UpperCamelCase__ ) ) ) )
SCREAMING_SNAKE_CASE : Optional[Any] = ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
SCREAMING_SNAKE_CASE : Optional[Any] = {'''unk_token''': '''<unk>'''}
SCREAMING_SNAKE_CASE : Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
SCREAMING_SNAKE_CASE : str = 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(UpperCamelCase__ ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(UpperCamelCase__ ) )
SCREAMING_SNAKE_CASE : str = {
'''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],
}
SCREAMING_SNAKE_CASE : Tuple = os.path.join(self.tmpdirname , UpperCamelCase__ )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(UpperCamelCase__ , UpperCamelCase__ )
def __A ( self : Dict , **UpperCamelCase__ : List[Any] ):
'''simple docstring'''
return CLIPTokenizer.from_pretrained(self.tmpdirname , **UpperCamelCase__ )
def __A ( self : Dict , **UpperCamelCase__ : Union[str, Any] ):
'''simple docstring'''
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **UpperCamelCase__ )
def __A ( self : Any , **UpperCamelCase__ : Union[str, Any] ):
'''simple docstring'''
return CLIPImageProcessor.from_pretrained(self.tmpdirname , **UpperCamelCase__ )
def __A ( self : Optional[Any] ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def __A ( self : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE : int = [Image.fromarray(np.moveaxis(UpperCamelCase__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __A ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = self.get_tokenizer()
SCREAMING_SNAKE_CASE : Optional[int] = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE : Any = self.get_image_processor()
SCREAMING_SNAKE_CASE : List[Any] = CLIPProcessor(tokenizer=UpperCamelCase__ , image_processor=UpperCamelCase__ )
processor_slow.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE : str = CLIPProcessor.from_pretrained(self.tmpdirname , use_fast=UpperCamelCase__ )
SCREAMING_SNAKE_CASE : List[str] = CLIPProcessor(tokenizer=UpperCamelCase__ , image_processor=UpperCamelCase__ )
processor_fast.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE : Dict = 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 , UpperCamelCase__ )
self.assertIsInstance(processor_fast.tokenizer , UpperCamelCase__ )
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 , UpperCamelCase__ )
self.assertIsInstance(processor_fast.image_processor , UpperCamelCase__ )
def __A ( self : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = CLIPProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE : List[Any] = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
SCREAMING_SNAKE_CASE : List[Any] = self.get_image_processor(do_normalize=UpperCamelCase__ , padding_value=1.0 )
SCREAMING_SNAKE_CASE : List[Any] = CLIPProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=UpperCamelCase__ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , UpperCamelCase__ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , UpperCamelCase__ )
def __A ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_image_processor()
SCREAMING_SNAKE_CASE : Tuple = self.get_tokenizer()
SCREAMING_SNAKE_CASE : str = CLIPProcessor(tokenizer=UpperCamelCase__ , image_processor=UpperCamelCase__ )
SCREAMING_SNAKE_CASE : int = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE : Optional[Any] = image_processor(UpperCamelCase__ , return_tensors='''np''' )
SCREAMING_SNAKE_CASE : str = processor(images=UpperCamelCase__ , 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 __A ( self : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self.get_image_processor()
SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_tokenizer()
SCREAMING_SNAKE_CASE : int = CLIPProcessor(tokenizer=UpperCamelCase__ , image_processor=UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Any = '''lower newer'''
SCREAMING_SNAKE_CASE : List[Any] = processor(text=UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Any = tokenizer(UpperCamelCase__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __A ( self : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_image_processor()
SCREAMING_SNAKE_CASE : Tuple = self.get_tokenizer()
SCREAMING_SNAKE_CASE : Union[str, Any] = CLIPProcessor(tokenizer=UpperCamelCase__ , image_processor=UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Optional[Any] = '''lower newer'''
SCREAMING_SNAKE_CASE : Optional[int] = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE : int = processor(text=UpperCamelCase__ , images=UpperCamelCase__ )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with pytest.raises(UpperCamelCase__ ):
processor()
def __A ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self.get_image_processor()
SCREAMING_SNAKE_CASE : Dict = self.get_tokenizer()
SCREAMING_SNAKE_CASE : int = CLIPProcessor(tokenizer=UpperCamelCase__ , image_processor=UpperCamelCase__ )
SCREAMING_SNAKE_CASE : List[Any] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE : List[str] = processor.batch_decode(UpperCamelCase__ )
SCREAMING_SNAKE_CASE : List[str] = tokenizer.batch_decode(UpperCamelCase__ )
self.assertListEqual(UpperCamelCase__ , UpperCamelCase__ )
def __A ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = self.get_image_processor()
SCREAMING_SNAKE_CASE : List[str] = self.get_tokenizer()
SCREAMING_SNAKE_CASE : Optional[Any] = CLIPProcessor(tokenizer=UpperCamelCase__ , image_processor=UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Union[str, Any] = '''lower newer'''
SCREAMING_SNAKE_CASE : int = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE : Optional[Any] = processor(text=UpperCamelCase__ , images=UpperCamelCase__ )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 182 | import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class lowercase__ ( unittest.TestCase):
def __A ( self : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = 0
def __A ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''' )
self.assertIsInstance(UpperCamelCase__ , UpperCamelCase__ )
def __A ( self : Dict ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE : str = Path(UpperCamelCase__ ) / '''preprocessor_config.json'''
SCREAMING_SNAKE_CASE : Dict = Path(UpperCamelCase__ ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(UpperCamelCase__ , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(UpperCamelCase__ , '''w''' ) )
SCREAMING_SNAKE_CASE : Any = AutoImageProcessor.from_pretrained(UpperCamelCase__ )
self.assertIsInstance(UpperCamelCase__ , UpperCamelCase__ )
def __A ( self : int ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE : Tuple = Path(UpperCamelCase__ ) / '''preprocessor_config.json'''
SCREAMING_SNAKE_CASE : Dict = Path(UpperCamelCase__ ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(UpperCamelCase__ , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(UpperCamelCase__ , '''w''' ) )
SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained(UpperCamelCase__ )
self.assertIsInstance(UpperCamelCase__ , UpperCamelCase__ )
def __A ( self : List[str] ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE : Dict = CLIPConfig()
# Create a dummy config file with image_proceesor_type
SCREAMING_SNAKE_CASE : Dict = Path(UpperCamelCase__ ) / '''preprocessor_config.json'''
SCREAMING_SNAKE_CASE : List[str] = Path(UpperCamelCase__ ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(UpperCamelCase__ , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(UpperCamelCase__ , '''w''' ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
SCREAMING_SNAKE_CASE : List[Any] = AutoImageProcessor.from_pretrained(UpperCamelCase__ ).to_dict()
config_dict.pop('''image_processor_type''' )
SCREAMING_SNAKE_CASE : str = CLIPImageProcessor(**UpperCamelCase__ )
# save in new folder
model_config.save_pretrained(UpperCamelCase__ )
config.save_pretrained(UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Union[str, Any] = AutoImageProcessor.from_pretrained(UpperCamelCase__ )
# make sure private variable is not incorrectly saved
SCREAMING_SNAKE_CASE : List[Any] = json.loads(config.to_json_string() )
self.assertTrue('''_processor_class''' not in dict_as_saved )
self.assertIsInstance(UpperCamelCase__ , UpperCamelCase__ )
def __A ( self : Tuple ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE : Optional[int] = Path(UpperCamelCase__ ) / '''preprocessor_config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(UpperCamelCase__ , '''w''' ) , )
SCREAMING_SNAKE_CASE : Dict = AutoImageProcessor.from_pretrained(UpperCamelCase__ )
self.assertIsInstance(UpperCamelCase__ , UpperCamelCase__ )
def __A ( self : int ):
'''simple docstring'''
with self.assertRaisesRegex(
UpperCamelCase__ , '''clip-base is not a local folder and is not a valid model identifier''' ):
SCREAMING_SNAKE_CASE : List[str] = AutoImageProcessor.from_pretrained('''clip-base''' )
def __A ( self : List[str] ):
'''simple docstring'''
with self.assertRaisesRegex(
UpperCamelCase__ , r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(UpperCamelCase__ , revision='''aaaaaa''' )
def __A ( self : Dict ):
'''simple docstring'''
with self.assertRaisesRegex(
UpperCamelCase__ , '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' , ):
SCREAMING_SNAKE_CASE : Dict = AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''' )
def __A ( self : List[Any] ):
'''simple docstring'''
with self.assertRaises(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE : str = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE : List[Any] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=UpperCamelCase__ )
SCREAMING_SNAKE_CASE : List[Any] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=UpperCamelCase__ )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained(UpperCamelCase__ , trust_remote_code=UpperCamelCase__ )
self.assertEqual(reloaded_image_processor.__class__.__name__ , '''NewImageProcessor''' )
def __A ( self : Optional[Any] ):
'''simple docstring'''
try:
AutoConfig.register('''custom''' , UpperCamelCase__ )
AutoImageProcessor.register(UpperCamelCase__ , UpperCamelCase__ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(UpperCamelCase__ ):
AutoImageProcessor.register(UpperCamelCase__ , UpperCamelCase__ )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE : Union[str, Any] = Path(UpperCamelCase__ ) / '''preprocessor_config.json'''
SCREAMING_SNAKE_CASE : Any = Path(UpperCamelCase__ ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(UpperCamelCase__ , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(UpperCamelCase__ , '''w''' ) )
SCREAMING_SNAKE_CASE : Tuple = CustomImageProcessor.from_pretrained(UpperCamelCase__ )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Any = AutoImageProcessor.from_pretrained(UpperCamelCase__ )
self.assertIsInstance(UpperCamelCase__ , UpperCamelCase__ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def __A ( self : Any ):
'''simple docstring'''
class lowercase__ ( UpperCamelCase_):
UpperCamelCase_ = True
try:
AutoConfig.register('''custom''' , UpperCamelCase__ )
AutoImageProcessor.register(UpperCamelCase__ , UpperCamelCase__ )
# If remote code is not set, the default is to use local
SCREAMING_SNAKE_CASE : str = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
SCREAMING_SNAKE_CASE : Union[str, Any] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=UpperCamelCase__ )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=UpperCamelCase__ )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(not hasattr(UpperCamelCase__ , '''is_local''' ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 182 | 1 |
import math
import flax.linen as nn
import jax.numpy as jnp
def UpperCamelCase ( __magic_name__ : jnp.ndarray , __magic_name__ : int , __magic_name__ : float = 1 , __magic_name__ : float = 1 , __magic_name__ : float = 1.0E4 , __magic_name__ : bool = False , __magic_name__ : float = 1.0 , ) -> jnp.ndarray:
"""simple docstring"""
assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
assert embedding_dim % 2 == 0, f'''Embedding dimension {embedding_dim} should be even'''
lowercase__ = float(embedding_dim // 2 )
lowercase__ = math.log(max_timescale / min_timescale ) / (num_timescales - freq_shift)
lowercase__ = min_timescale * jnp.exp(jnp.arange(__magic_name__ , dtype=jnp.floataa ) * -log_timescale_increment )
lowercase__ = jnp.expand_dims(__magic_name__ , 1 ) * jnp.expand_dims(__magic_name__ , 0 )
# scale embeddings
lowercase__ = scale * emb
if flip_sin_to_cos:
lowercase__ = jnp.concatenate([jnp.cos(__magic_name__ ), jnp.sin(__magic_name__ )] , axis=1 )
else:
lowercase__ = jnp.concatenate([jnp.sin(__magic_name__ ), jnp.cos(__magic_name__ )] , axis=1 )
lowercase__ = jnp.reshape(__magic_name__ , [jnp.shape(__magic_name__ )[0], embedding_dim] )
return signal
class A ( nn.Module ):
'''simple docstring'''
A__ = 32
A__ = jnp.floataa
@nn.compact
def __call__(self : int , _UpperCAmelCase : Tuple ) -> int:
"""simple docstring"""
lowercase__ = nn.Dense(self.time_embed_dim , dtype=self.dtype , name="""linear_1""" )(_UpperCAmelCase )
lowercase__ = nn.silu(_UpperCAmelCase )
lowercase__ = nn.Dense(self.time_embed_dim , dtype=self.dtype , name="""linear_2""" )(_UpperCAmelCase )
return temb
class A ( nn.Module ):
'''simple docstring'''
A__ = 32
A__ = False
A__ = 1
@nn.compact
def __call__(self : List[str] , _UpperCAmelCase : Any ) -> Optional[Any]:
"""simple docstring"""
return get_sinusoidal_embeddings(
_UpperCAmelCase , embedding_dim=self.dim , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.freq_shift )
| 363 |
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class A :
'''simple docstring'''
def __init__(self : List[str] , _UpperCAmelCase : str , _UpperCAmelCase : Any=13 , _UpperCAmelCase : int=64 , _UpperCAmelCase : Any=2 , _UpperCAmelCase : str=3 , _UpperCAmelCase : str=True , _UpperCAmelCase : List[Any]=True , _UpperCAmelCase : Optional[int]=32 , _UpperCAmelCase : Tuple=5 , _UpperCAmelCase : Tuple=4 , _UpperCAmelCase : Union[str, Any]=37 , _UpperCAmelCase : Tuple="gelu" , _UpperCAmelCase : Optional[int]=0.1 , _UpperCAmelCase : int=0.1 , _UpperCAmelCase : Any=10 , _UpperCAmelCase : str=0.02 , _UpperCAmelCase : Any=[1, 16, 4, 4] , _UpperCAmelCase : str=None , ) -> int:
"""simple docstring"""
lowercase__ = parent
lowercase__ = batch_size
lowercase__ = image_size
lowercase__ = patch_size
lowercase__ = num_channels
lowercase__ = is_training
lowercase__ = use_labels
lowercase__ = hidden_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = intermediate_size
lowercase__ = hidden_act
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = type_sequence_label_size
lowercase__ = initializer_range
lowercase__ = scope
lowercase__ = backbone_featmap_shape
# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
# the number of patches is based on the feature map of the backbone, which by default uses an output stride
# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
lowercase__ = (self.image_size // 32) ** 2
lowercase__ = num_patches + 1
def lowerCamelCase__ (self : Any ) -> Optional[int]:
"""simple docstring"""
lowercase__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase__ = None
if self.use_labels:
lowercase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ = self.get_config()
return config, pixel_values, labels
def lowerCamelCase__ (self : int ) -> Tuple:
"""simple docstring"""
lowercase__ = {
"""global_padding""": """same""",
"""layer_type""": """bottleneck""",
"""depths""": [3, 4, 9],
"""out_features""": ["""stage1""", """stage2""", """stage3"""],
"""embedding_dynamic_padding""": True,
"""hidden_sizes""": [4, 8, 16, 32],
"""num_groups""": 2,
}
return ViTHybridConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=_UpperCAmelCase , initializer_range=self.initializer_range , backbone_featmap_shape=self.backbone_featmap_shape , backbone_config=_UpperCAmelCase , )
def lowerCamelCase__ (self : Union[str, Any] , _UpperCAmelCase : int , _UpperCAmelCase : Tuple , _UpperCAmelCase : Dict ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = ViTHybridModel(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
lowercase__ = model(_UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : str , _UpperCAmelCase : str , _UpperCAmelCase : int ) -> List[Any]:
"""simple docstring"""
lowercase__ = self.type_sequence_label_size
lowercase__ = ViTHybridForImageClassification(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
lowercase__ = model(_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowerCamelCase__ (self : int ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ = config_and_inputs
lowercase__ = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class A ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
A__ = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
A__ = (
{'''feature-extraction''': ViTHybridModel, '''image-classification''': ViTHybridForImageClassification}
if is_torch_available()
else {}
)
A__ = False
A__ = False
A__ = False
def lowerCamelCase__ (self : Any ) -> Any:
"""simple docstring"""
lowercase__ = ViTHybridModelTester(self )
lowercase__ = ConfigTester(self , config_class=_UpperCAmelCase , has_text_modality=_UpperCAmelCase , hidden_size=37 )
def lowerCamelCase__ (self : Dict ) -> List[str]:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="""ViT does not use inputs_embeds""" )
def lowerCamelCase__ (self : List[str] ) -> Any:
"""simple docstring"""
pass
def lowerCamelCase__ (self : Dict ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ = model_class(_UpperCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowercase__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_UpperCAmelCase , nn.Linear ) )
def lowerCamelCase__ (self : Dict ) -> Optional[Any]:
"""simple docstring"""
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ = model_class(_UpperCAmelCase )
lowercase__ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ = [*signature.parameters.keys()]
lowercase__ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _UpperCAmelCase )
def lowerCamelCase__ (self : Optional[Any] ) -> Dict:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCAmelCase )
def lowerCamelCase__ (self : Tuple ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase )
def lowerCamelCase__ (self : Dict ) -> Any:
"""simple docstring"""
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ = _config_zero_init(_UpperCAmelCase )
for model_class in self.all_model_classes:
lowercase__ = model_class(config=_UpperCAmelCase )
# Skip the check for the backbone
for name, module in model.named_modules():
if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
lowercase__ = [f'''{name}.{key}''' for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=f'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@slow
def lowerCamelCase__ (self : Dict ) -> List[Any]:
"""simple docstring"""
for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = ViTHybridModel.from_pretrained(_UpperCAmelCase )
self.assertIsNotNone(_UpperCAmelCase )
def UpperCamelCase ( ) -> Optional[int]:
"""simple docstring"""
lowercase__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class A ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCamelCase__ (self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
return (
ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def lowerCamelCase__ (self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
_UpperCAmelCase )
lowercase__ = self.default_image_processor
lowercase__ = prepare_img()
lowercase__ = image_processor(images=_UpperCAmelCase , return_tensors="""pt""" ).to(_UpperCAmelCase )
# forward pass
with torch.no_grad():
lowercase__ = model(**_UpperCAmelCase )
# verify the logits
lowercase__ = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , _UpperCAmelCase )
lowercase__ = torch.tensor([-1.9_090, -0.4_993, -0.2_389] ).to(_UpperCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _UpperCAmelCase , atol=1E-4 ) )
@slow
@require_accelerate
def lowerCamelCase__ (self : Any ) -> Optional[int]:
"""simple docstring"""
lowercase__ = ViTHybridImageProcessor.from_pretrained("""google/vit-hybrid-base-bit-384""" )
lowercase__ = ViTHybridForImageClassification.from_pretrained("""google/vit-hybrid-base-bit-384""" , device_map="""auto""" )
lowercase__ = prepare_img()
lowercase__ = image_processor(images=_UpperCAmelCase , return_tensors="""pt""" )
lowercase__ = model(**_UpperCAmelCase )
lowercase__ = outputs.logits
# model predicts one of the 1000 ImageNet classes
lowercase__ = logits.argmax(-1 ).item()
self.assertTrue(model.config.idalabel[predicted_class_idx] , """tabby, tabby cat""" )
| 146 | 0 |
"""simple docstring"""
def _snake_case ( lowercase__ : str , lowercase__ : int ) -> str:
'''simple docstring'''
lowerCAmelCase_ :list[list[str]] = [[] for _ in range(lowercase__ )]
lowerCAmelCase_ :Optional[Any] = key - 1
if key <= 0:
raise ValueError("""Height of grid can't be 0 or negative""" )
if key == 1 or len(lowercase__ ) <= key:
return input_string
for position, character in enumerate(lowercase__ ):
lowerCAmelCase_ :List[str] = position % (lowest * 2) # puts it in bounds
lowerCAmelCase_ :int = min(lowercase__ , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append(lowercase__ )
lowerCAmelCase_ :str = ["""""".join(lowercase__ ) for row in temp_grid]
lowerCAmelCase_ :Any = """""".join(lowercase__ )
return output_string
def _snake_case ( lowercase__ : str , lowercase__ : int ) -> str:
'''simple docstring'''
lowerCAmelCase_ :List[str] = []
lowerCAmelCase_ :List[Any] = key - 1
if key <= 0:
raise ValueError("""Height of grid can't be 0 or negative""" )
if key == 1:
return input_string
lowerCAmelCase_ :list[list[str]] = [[] for _ in range(lowercase__ )] # generates template
for position in range(len(lowercase__ ) ):
lowerCAmelCase_ :Any = position % (lowest * 2) # puts it in bounds
lowerCAmelCase_ :Dict = min(lowercase__ , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append("""*""" )
lowerCAmelCase_ :Tuple = 0
for row in temp_grid: # fills in the characters
lowerCAmelCase_ :Dict = input_string[counter : counter + len(lowercase__ )]
grid.append(list(lowercase__ ) )
counter += len(lowercase__ )
lowerCAmelCase_ :List[Any] = """""" # reads as zigzag
for position in range(len(lowercase__ ) ):
lowerCAmelCase_ :Tuple = position % (lowest * 2) # puts it in bounds
lowerCAmelCase_ :str = min(lowercase__ , lowest * 2 - num ) # creates zigzag pattern
output_string += grid[num][0]
grid[num].pop(0 )
return output_string
def _snake_case ( lowercase__ : str ) -> dict[int, str]:
'''simple docstring'''
lowerCAmelCase_ :int = {}
for key_guess in range(1 , len(lowercase__ ) ): # tries every key
lowerCAmelCase_ :int = decrypt(lowercase__ , lowercase__ )
return results
if __name__ == "__main__":
import doctest
doctest.testmod()
| 84 |
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class _A :
def __init__( self : int , _A : Optional[int] , _A : Any=13 , _A : List[Any]=7 , _A : List[Any]=True , _A : Optional[Any]=True , _A : str=True , _A : Any=True , _A : Dict=True , _A : Optional[Any]=False , _A : Any=False , _A : List[str]=False , _A : Optional[int]=2 , _A : List[Any]=99 , _A : str=0 , _A : Dict=32 , _A : Dict=5 , _A : List[Any]=4 , _A : Optional[Any]=0.1 , _A : Optional[int]=0.1 , _A : Optional[Any]=512 , _A : Optional[Any]=2 , _A : Optional[Any]=0.02 , _A : Optional[int]=2 , _A : Tuple=4 , _A : List[Any]="last" , _A : List[str]=True , _A : Tuple=None , _A : Optional[Any]=0 , ) -> Any:
"""simple docstring"""
lowercase : str = parent
lowercase : Optional[Any] = batch_size
lowercase : Union[str, Any] = seq_length
lowercase : str = is_training
lowercase : str = use_input_lengths
lowercase : List[Any] = use_token_type_ids
lowercase : Union[str, Any] = use_labels
lowercase : Tuple = gelu_activation
lowercase : Dict = sinusoidal_embeddings
lowercase : Any = causal
lowercase : str = asm
lowercase : Optional[Any] = n_langs
lowercase : Dict = vocab_size
lowercase : Dict = n_special
lowercase : List[Any] = hidden_size
lowercase : str = num_hidden_layers
lowercase : int = num_attention_heads
lowercase : str = hidden_dropout_prob
lowercase : Dict = attention_probs_dropout_prob
lowercase : List[Any] = max_position_embeddings
lowercase : Optional[int] = type_sequence_label_size
lowercase : List[str] = initializer_range
lowercase : List[str] = num_labels
lowercase : int = num_choices
lowercase : int = summary_type
lowercase : Tuple = use_proj
lowercase : Union[str, Any] = scope
lowercase : List[str] = bos_token_id
def __a ( self : Any ) -> Dict:
"""simple docstring"""
lowercase : str = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
lowercase : str = None
if self.use_input_lengths:
lowercase : int = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
lowercase : Union[str, Any] = None
if self.use_token_type_ids:
lowercase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
lowercase : Union[str, Any] = None
lowercase : List[str] = None
lowercase : Optional[Any] = None
if self.use_labels:
lowercase : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase : Tuple = ids_tensor([self.batch_size] , 2 ).float()
lowercase : Tuple = ids_tensor([self.batch_size] , self.num_choices )
lowercase : List[Any] = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def __a ( self : Any ) -> List[Any]:
"""simple docstring"""
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def __a ( self : int , _A : str , _A : Optional[Any] , _A : int , _A : List[str] , _A : Any , _A : Dict , _A : Tuple , _A : Union[str, Any] , _A : Tuple , ) -> List[Any]:
"""simple docstring"""
lowercase : List[Any] = XLMModel(config=_A )
model.to(_A )
model.eval()
lowercase : Tuple = model(_A , lengths=_A , langs=_A )
lowercase : Dict = model(_A , langs=_A )
lowercase : int = model(_A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __a ( self : int , _A : Dict , _A : int , _A : int , _A : Union[str, Any] , _A : Tuple , _A : Union[str, Any] , _A : Any , _A : Union[str, Any] , _A : Dict , ) -> Optional[Any]:
"""simple docstring"""
lowercase : Optional[int] = XLMWithLMHeadModel(_A )
model.to(_A )
model.eval()
lowercase : Tuple = model(_A , token_type_ids=_A , labels=_A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __a ( self : Union[str, Any] , _A : List[str] , _A : Union[str, Any] , _A : List[str] , _A : Optional[int] , _A : Optional[Any] , _A : int , _A : Union[str, Any] , _A : Tuple , _A : int , ) -> Union[str, Any]:
"""simple docstring"""
lowercase : Dict = XLMForQuestionAnsweringSimple(_A )
model.to(_A )
model.eval()
lowercase : List[str] = model(_A )
lowercase : Any = model(_A , start_positions=_A , end_positions=_A )
lowercase : Any = outputs
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __a ( self : Union[str, Any] , _A : int , _A : Union[str, Any] , _A : List[Any] , _A : Union[str, Any] , _A : List[str] , _A : Any , _A : Any , _A : str , _A : Union[str, Any] , ) -> Dict:
"""simple docstring"""
lowercase : Optional[int] = XLMForQuestionAnswering(_A )
model.to(_A )
model.eval()
lowercase : Any = model(_A )
lowercase : Tuple = model(
_A , start_positions=_A , end_positions=_A , cls_index=_A , is_impossible=_A , p_mask=_A , )
lowercase : Optional[int] = model(
_A , start_positions=_A , end_positions=_A , cls_index=_A , is_impossible=_A , )
((lowercase) , ) : Optional[int] = result_with_labels.to_tuple()
lowercase : List[str] = model(_A , start_positions=_A , end_positions=_A )
((lowercase) , ) : Any = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def __a ( self : Union[str, Any] , _A : Optional[int] , _A : Dict , _A : int , _A : List[Any] , _A : List[str] , _A : Optional[Any] , _A : Dict , _A : Optional[int] , _A : str , ) -> int:
"""simple docstring"""
lowercase : List[str] = XLMForSequenceClassification(_A )
model.to(_A )
model.eval()
lowercase : List[str] = model(_A )
lowercase : Union[str, Any] = model(_A , labels=_A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __a ( self : Union[str, Any] , _A : str , _A : int , _A : List[str] , _A : Optional[int] , _A : Union[str, Any] , _A : Tuple , _A : Dict , _A : Any , _A : Tuple , ) -> Dict:
"""simple docstring"""
lowercase : Optional[Any] = self.num_labels
lowercase : Tuple = XLMForTokenClassification(_A )
model.to(_A )
model.eval()
lowercase : str = model(_A , attention_mask=_A , labels=_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __a ( self : List[Any] , _A : List[str] , _A : Dict , _A : str , _A : List[str] , _A : List[str] , _A : Union[str, Any] , _A : Tuple , _A : Any , _A : Any , ) -> Union[str, Any]:
"""simple docstring"""
lowercase : int = self.num_choices
lowercase : List[Any] = XLMForMultipleChoice(config=_A )
model.to(_A )
model.eval()
lowercase : str = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase : Dict = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase : List[str] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase : Dict = model(
_A , attention_mask=_A , token_type_ids=_A , labels=_A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __a ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
lowercase : List[Any] = self.prepare_config_and_inputs()
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) ,
) : Union[str, Any] = config_and_inputs
lowercase : Optional[int] = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''lengths''': input_lengths}
return config, inputs_dict
@require_torch
class _A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
_UpperCamelCase : Any = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
_UpperCamelCase : str = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
_UpperCamelCase : Tuple = (
{
'''feature-extraction''': XLMModel,
'''fill-mask''': XLMWithLMHeadModel,
'''question-answering''': XLMForQuestionAnsweringSimple,
'''text-classification''': XLMForSequenceClassification,
'''text-generation''': XLMWithLMHeadModel,
'''token-classification''': XLMForTokenClassification,
'''zero-shot''': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def __a ( self : List[Any] , _A : Tuple , _A : List[str] , _A : Dict , _A : Union[str, Any] , _A : Optional[Any] ) -> List[Any]:
"""simple docstring"""
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('''Fast''' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def __a ( self : Dict , _A : Tuple , _A : List[str] , _A : int=False ) -> Optional[Any]:
"""simple docstring"""
lowercase : List[str] = super()._prepare_for_class(_A , _A , return_labels=_A )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
lowercase : int = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_A )
lowercase : str = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_A )
return inputs_dict
def __a ( self : Any ) -> List[str]:
"""simple docstring"""
lowercase : List[str] = XLMModelTester(self )
lowercase : Any = ConfigTester(self , config_class=_A , emb_dim=37 )
def __a ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
self.config_tester.run_common_tests()
def __a ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
lowercase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*_A )
def __a ( self : Any ) -> Dict:
"""simple docstring"""
lowercase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*_A )
def __a ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
lowercase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*_A )
def __a ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
lowercase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*_A )
def __a ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
lowercase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*_A )
def __a ( self : Dict ) -> int:
"""simple docstring"""
lowercase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*_A )
def __a ( self : Any ) -> List[Any]:
"""simple docstring"""
lowercase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*_A )
def __a ( self : int , _A : Union[str, Any] , _A : int , _A : Union[str, Any] , _A : Optional[Any] , _A : List[Any] , _A : List[Any]=False , _A : Optional[int]=1 ) -> Any:
"""simple docstring"""
self.assertIsInstance(_A , _A )
self.assertListEqual(
[isinstance(_A , _A ) for iter_attentions in attentions] , [True] * len(_A ) )
self.assertEqual(len(_A ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(_A ):
# adds PAD dummy token
lowercase : List[Any] = min_length + idx + 1
lowercase : str = min_length + idx + 1
lowercase : Any = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(_A ) )
def __a ( self : int , _A : Optional[int] , _A : Dict , _A : Any , _A : List[str] , _A : Optional[int] , _A : List[Any]=False , _A : List[Any]=1 ) -> str:
"""simple docstring"""
self.assertIsInstance(_A , _A )
self.assertListEqual(
[isinstance(_A , _A ) for iter_hidden_states in hidden_states] , [True] * len(_A ) , )
self.assertEqual(len(_A ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(_A ):
# adds PAD dummy token
lowercase : Union[str, Any] = min_length + idx + 1
lowercase : Optional[Any] = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(_A ) , )
pass
@slow
def __a ( self : Optional[int] ) -> Any:
"""simple docstring"""
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase : Any = XLMModel.from_pretrained(_A )
self.assertIsNotNone(_A )
@require_torch
class _A ( unittest.TestCase ):
@slow
def __a ( self : Any ) -> Optional[Any]:
"""simple docstring"""
lowercase : Optional[int] = XLMWithLMHeadModel.from_pretrained('''xlm-mlm-en-2048''' )
model.to(_A )
lowercase : str = torch.tensor([[14, 447]] , dtype=torch.long , device=_A ) # the president
lowercase : List[str] = [
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
lowercase : Dict = model.generate(_A , do_sample=_A )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , _A ) | 308 | 0 |
from bisect import bisect
from itertools import accumulate
def lowerCamelCase_ ( lowerCAmelCase: int , lowerCAmelCase: Any , lowerCAmelCase: Optional[int] , lowerCAmelCase: List[str] )-> int:
_snake_case : int = sorted(zip(lowerCAmelCase , lowerCAmelCase ) , key=lambda lowerCAmelCase : x[0] / x[1] , reverse=lowerCAmelCase )
_snake_case , _snake_case : int = [i[0] for i in r], [i[1] for i in r]
_snake_case : str = list(accumulate(lowerCAmelCase ) )
_snake_case : Union[str, Any] = bisect(lowerCAmelCase , lowerCAmelCase )
return (
0
if k == 0
else sum(vl[:k] ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
if k != n
else sum(vl[:k] )
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 260 |
import qiskit
def lowerCamelCase_ ( lowerCAmelCase: int = 2 )-> qiskit.result.counts.Counts:
_snake_case : Dict = qubits
# Using Aer's simulator
_snake_case : List[str] = qiskit.Aer.get_backend('aer_simulator' )
# Creating a Quantum Circuit acting on the q register
_snake_case : Tuple = qiskit.QuantumCircuit(lowerCAmelCase , lowerCAmelCase )
# Adding a H gate on qubit 0 (now q0 in superposition)
circuit.h(0 )
for i in range(1 , lowerCAmelCase ):
# Adding CX (CNOT) gate
circuit.cx(i - 1 , lowerCAmelCase )
# Mapping the quantum measurement to the classical bits
circuit.measure(list(range(lowerCAmelCase ) ) , list(range(lowerCAmelCase ) ) )
# Now measuring any one qubit would affect other qubits to collapse
# their super position and have same state as the measured one.
# Executing the circuit on the simulator
_snake_case : Any = qiskit.execute(lowerCAmelCase , lowerCAmelCase , shots=10_00 )
return job.result().get_counts(lowerCAmelCase )
if __name__ == "__main__":
print(F"""Total count for various states are: {quantum_entanglement(3)}""")
| 260 | 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
#
########################################################################
lowercase : Dict = 16
lowercase : Dict = 32
def SCREAMING_SNAKE_CASE__ ( __A , __A = 16 ) -> str:
_snake_case = AutoTokenizer.from_pretrained('bert-base-cased' )
_snake_case = load_dataset('glue' , 'mrpc' )
def tokenize_function(__A ):
# max_length=None => use the model max length (it's actually the default)
_snake_case = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=__A , max_length=__A )
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():
_snake_case = datasets.map(
__A , batched=__A , 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
_snake_case = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(__A ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_snake_case = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_snake_case = 16
elif accelerator.mixed_precision != "no":
_snake_case = 8
else:
_snake_case = None
return tokenizer.pad(
__A , padding='longest' , max_length=__A , pad_to_multiple_of=__A , return_tensors='pt' , )
# Instantiate dataloaders.
_snake_case = DataLoader(
tokenized_datasets['train'] , shuffle=__A , collate_fn=__A , batch_size=__A )
_snake_case = DataLoader(
tokenized_datasets['validation'] , shuffle=__A , collate_fn=__A , batch_size=__A )
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
lowercase : Any = mocked_dataloaders # noqa: F811
def SCREAMING_SNAKE_CASE__ ( __A , __A ) -> Any:
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS' , __A ) == "1":
_snake_case = 2
# Initialize accelerator
_snake_case = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_snake_case = config['lr']
_snake_case = int(config['num_epochs'] )
_snake_case = int(config['seed'] )
_snake_case = int(config['batch_size'] )
_snake_case = evaluate.load('glue' , 'mrpc' )
# If the batch size is too big we use gradient accumulation
_snake_case = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
_snake_case = batch_size // MAX_GPU_BATCH_SIZE
_snake_case = MAX_GPU_BATCH_SIZE
set_seed(__A )
_snake_case , _snake_case = get_dataloaders(__A , __A )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_snake_case = AutoModelForSequenceClassification.from_pretrained('bert-base-cased' , return_dict=__A )
# 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).
_snake_case = model.to(accelerator.device )
# Instantiate optimizer
_snake_case = AdamW(params=model.parameters() , lr=__A )
# Instantiate scheduler
_snake_case = get_linear_schedule_with_warmup(
optimizer=__A , num_warmup_steps=100 , num_training_steps=(len(__A ) * 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.
_snake_case , _snake_case , _snake_case , _snake_case , _snake_case = accelerator.prepare(
__A , __A , __A , __A , __A )
# Now we train the model
for epoch in range(__A ):
model.train()
for step, batch in enumerate(__A ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
_snake_case = model(**__A )
_snake_case = outputs.loss
_snake_case = loss / gradient_accumulation_steps
accelerator.backward(__A )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
_snake_case = 0
for step, batch in enumerate(__A ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_snake_case = model(**__A )
_snake_case = outputs.logits.argmax(dim=-1 )
_snake_case , _snake_case = 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(__A ) - 1:
# Last batch needs to be truncated on distributed systems as it contains additional samples
_snake_case = predictions[: len(eval_dataloader.dataset ) - samples_seen]
_snake_case = 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=__A , references=__A , )
_snake_case = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'epoch {epoch}:' , __A )
def SCREAMING_SNAKE_CASE__ ( ) -> Optional[int]:
_snake_case = argparse.ArgumentParser(description='Simple example of training script.' )
parser.add_argument(
'--mixed_precision' , type=__A , default=__A , 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.' )
_snake_case = parser.parse_args()
_snake_case = {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
training_function(__A , __A )
if __name__ == "__main__":
main()
| 42 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase : Tuple = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization_xlm": ["XLMTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : int = [
"XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"XLMForMultipleChoice",
"XLMForQuestionAnswering",
"XLMForQuestionAnsweringSimple",
"XLMForSequenceClassification",
"XLMForTokenClassification",
"XLMModel",
"XLMPreTrainedModel",
"XLMWithLMHeadModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : Tuple = [
"TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFXLMForMultipleChoice",
"TFXLMForQuestionAnsweringSimple",
"TFXLMForSequenceClassification",
"TFXLMForTokenClassification",
"TFXLMMainLayer",
"TFXLMModel",
"TFXLMPreTrainedModel",
"TFXLMWithLMHeadModel",
]
if TYPE_CHECKING:
from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
from .tokenization_xlm import XLMTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm import (
XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMPreTrainedModel,
XLMWithLMHeadModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm import (
TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMForMultipleChoice,
TFXLMForQuestionAnsweringSimple,
TFXLMForSequenceClassification,
TFXLMForTokenClassification,
TFXLMMainLayer,
TFXLMModel,
TFXLMPreTrainedModel,
TFXLMWithLMHeadModel,
)
else:
import sys
lowercase : Union[str, Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 42 | 1 |
'''simple docstring'''
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
_lowercase : List[str] = """0.12""" # assumed parallelism: 8
if is_torch_available():
import torch
def lowerCamelCase__ ( A : str , A : str , A : List[Any]=None ):
'''simple docstring'''
if rng is None:
UpperCAmelCase = random.Random()
UpperCAmelCase = 1
for dim in shape:
total_dims *= dim
UpperCAmelCase = []
for _ in range(A ):
values.append(rng.randint(0 , vocab_size - 1 ) )
UpperCAmelCase = np.array(A , dtype=jnp.intaa ).reshape(A )
return output
def lowerCamelCase__ ( A : int , A : Optional[int]=None ):
'''simple docstring'''
UpperCAmelCase = ids_tensor(A , vocab_size=2 , rng=A )
# make sure that at least one token is attended to for each batch
UpperCAmelCase = 1
return attn_mask
@require_flax
class UpperCamelCase__:
__magic_name__ : Optional[int] = None
__magic_name__ : Optional[Any] = ()
def a__( self : str )-> Optional[Any]:
"""simple docstring"""
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
# cut to half length & take max batch_size 3
UpperCAmelCase = 2
UpperCAmelCase = inputs['''input_ids'''].shape[-1] // 2
UpperCAmelCase = inputs['''input_ids'''][:max_batch_size, :sequence_length]
UpperCAmelCase = jnp.ones_like(lowerCAmelCase )
UpperCAmelCase = attention_mask[:max_batch_size, :sequence_length]
# generate max 5 tokens
UpperCAmelCase = input_ids.shape[-1] + 5
if config.eos_token_id is not None and config.pad_token_id is None:
# hack to allow generate for models such as GPT2 as is done in `generate()`
UpperCAmelCase = config.eos_token_id
return config, input_ids, attention_mask, max_length
@is_pt_flax_cross_test
def a__( self : Dict )-> Optional[int]:
"""simple docstring"""
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._get_input_ids_and_config()
UpperCAmelCase = False
UpperCAmelCase = max_length
UpperCAmelCase = 0
for model_class in self.all_generative_model_classes:
UpperCAmelCase = model_class(lowerCAmelCase )
UpperCAmelCase = model_class.__name__[4:] # Skip the "Flax" at the beginning
UpperCAmelCase = getattr(lowerCAmelCase , lowerCAmelCase )
UpperCAmelCase = pt_model_class(lowerCAmelCase ).eval()
UpperCAmelCase = load_flax_weights_in_pytorch_model(lowerCAmelCase , flax_model.params )
UpperCAmelCase = flax_model.generate(lowerCAmelCase ).sequences
UpperCAmelCase = pt_model.generate(torch.tensor(lowerCAmelCase , dtype=torch.long ) )
if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
UpperCAmelCase = flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]
self.assertListEqual(pt_generation_outputs.numpy().tolist() , flax_generation_outputs.tolist() )
def a__( self : Any )-> Optional[Any]:
"""simple docstring"""
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._get_input_ids_and_config()
UpperCAmelCase = False
UpperCAmelCase = max_length
for model_class in self.all_generative_model_classes:
UpperCAmelCase = model_class(lowerCAmelCase )
UpperCAmelCase = model.generate(lowerCAmelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowerCAmelCase )
UpperCAmelCase = jit(model.generate )
UpperCAmelCase = jit_generate(lowerCAmelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def a__( self : Optional[Any] )-> int:
"""simple docstring"""
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._get_input_ids_and_config()
UpperCAmelCase = True
UpperCAmelCase = max_length
for model_class in self.all_generative_model_classes:
UpperCAmelCase = model_class(lowerCAmelCase )
UpperCAmelCase = model.generate(lowerCAmelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowerCAmelCase )
UpperCAmelCase = jit(model.generate )
UpperCAmelCase = jit_generate(lowerCAmelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def a__( self : str )-> List[str]:
"""simple docstring"""
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._get_input_ids_and_config()
UpperCAmelCase = False
UpperCAmelCase = max_length
UpperCAmelCase = 2
for model_class in self.all_generative_model_classes:
UpperCAmelCase = model_class(lowerCAmelCase )
UpperCAmelCase = model.generate(lowerCAmelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowerCAmelCase )
UpperCAmelCase = jit(model.generate )
UpperCAmelCase = jit_generate(lowerCAmelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def a__( self : List[Any] )-> List[str]:
"""simple docstring"""
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._get_input_ids_and_config()
UpperCAmelCase = False
UpperCAmelCase = max_length
UpperCAmelCase = 2
UpperCAmelCase = 2
for model_class in self.all_generative_model_classes:
UpperCAmelCase = model_class(lowerCAmelCase )
UpperCAmelCase = model.generate(lowerCAmelCase ).sequences
self.assertEqual(generation_outputs.shape[0] , input_ids.shape[0] * config.num_return_sequences )
def a__( self : Tuple )-> List[str]:
"""simple docstring"""
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._get_input_ids_and_config()
UpperCAmelCase = True
UpperCAmelCase = max_length
UpperCAmelCase = 0.8
UpperCAmelCase = 10
UpperCAmelCase = 0.3
UpperCAmelCase = 1
UpperCAmelCase = 8
UpperCAmelCase = 9
for model_class in self.all_generative_model_classes:
UpperCAmelCase = model_class(lowerCAmelCase )
UpperCAmelCase = model.generate(lowerCAmelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowerCAmelCase )
UpperCAmelCase = jit(model.generate )
UpperCAmelCase = jit_generate(lowerCAmelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def a__( self : Optional[Any] )-> Optional[int]:
"""simple docstring"""
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._get_input_ids_and_config()
UpperCAmelCase = max_length
UpperCAmelCase = 1
UpperCAmelCase = 8
UpperCAmelCase = 9
for model_class in self.all_generative_model_classes:
UpperCAmelCase = model_class(lowerCAmelCase )
UpperCAmelCase = model.generate(lowerCAmelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowerCAmelCase )
UpperCAmelCase = jit(model.generate )
UpperCAmelCase = jit_generate(lowerCAmelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def a__( self : Tuple )-> Tuple:
"""simple docstring"""
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._get_input_ids_and_config()
UpperCAmelCase = max_length
UpperCAmelCase = 2
UpperCAmelCase = 1
UpperCAmelCase = 8
UpperCAmelCase = 9
for model_class in self.all_generative_model_classes:
UpperCAmelCase = model_class(lowerCAmelCase )
UpperCAmelCase = model.generate(lowerCAmelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowerCAmelCase )
UpperCAmelCase = jit(model.generate )
UpperCAmelCase = jit_generate(lowerCAmelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def a__( self : Union[str, Any] )-> Any:
"""simple docstring"""
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._get_input_ids_and_config()
# pad attention mask on the left
UpperCAmelCase = attention_mask.at[(0, 0)].set(0 )
UpperCAmelCase = False
UpperCAmelCase = max_length
for model_class in self.all_generative_model_classes:
UpperCAmelCase = model_class(lowerCAmelCase )
UpperCAmelCase = model.generate(lowerCAmelCase , attention_mask=lowerCAmelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowerCAmelCase )
UpperCAmelCase = jit(model.generate )
UpperCAmelCase = jit_generate(lowerCAmelCase , attention_mask=lowerCAmelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def a__( self : Optional[Any] )-> int:
"""simple docstring"""
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._get_input_ids_and_config()
# pad attention mask on the left
UpperCAmelCase = attention_mask.at[(0, 0)].set(0 )
UpperCAmelCase = True
UpperCAmelCase = max_length
for model_class in self.all_generative_model_classes:
UpperCAmelCase = model_class(lowerCAmelCase )
UpperCAmelCase = model.generate(lowerCAmelCase , attention_mask=lowerCAmelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowerCAmelCase )
UpperCAmelCase = jit(model.generate )
UpperCAmelCase = jit_generate(lowerCAmelCase , attention_mask=lowerCAmelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def a__( self : Tuple )-> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._get_input_ids_and_config()
# pad attention mask on the left
UpperCAmelCase = attention_mask.at[(0, 0)].set(0 )
UpperCAmelCase = 2
UpperCAmelCase = max_length
for model_class in self.all_generative_model_classes:
UpperCAmelCase = model_class(lowerCAmelCase )
UpperCAmelCase = model.generate(lowerCAmelCase , attention_mask=lowerCAmelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowerCAmelCase )
UpperCAmelCase = jit(model.generate )
UpperCAmelCase = jit_generate(lowerCAmelCase , attention_mask=lowerCAmelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
@require_flax
class UpperCamelCase__( unittest.TestCase ):
def a__( self : Union[str, Any] )-> Optional[int]:
"""simple docstring"""
UpperCAmelCase = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert''' )
UpperCAmelCase = FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''' )
UpperCAmelCase = '''Hello world'''
UpperCAmelCase = tokenizer(lowerCAmelCase , return_tensors='''np''' ).input_ids
# typos are quickly detected (the correct argument is `do_sample`)
with self.assertRaisesRegex(lowerCAmelCase , '''do_samples''' ):
model.generate(lowerCAmelCase , do_samples=lowerCAmelCase )
# arbitrary arguments that will not be used anywhere are also not accepted
with self.assertRaisesRegex(lowerCAmelCase , '''foo''' ):
UpperCAmelCase = {'''foo''': '''bar'''}
model.generate(lowerCAmelCase , **lowerCAmelCase )
| 91 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : int = logging.get_logger(__name__)
_lowercase : Optional[Any] = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class UpperCamelCase__( lowerCAmelCase ):
__magic_name__ : Union[str, Any] = "openai-gpt"
__magic_name__ : Optional[int] = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : Optional[int] , lowerCAmelCase : Optional[Any]=40478 , lowerCAmelCase : str=512 , lowerCAmelCase : List[Any]=768 , lowerCAmelCase : Tuple=12 , lowerCAmelCase : int=12 , lowerCAmelCase : List[str]="gelu" , lowerCAmelCase : List[str]=0.1 , lowerCAmelCase : List[Any]=0.1 , lowerCAmelCase : Dict=0.1 , lowerCAmelCase : Tuple=1E-5 , lowerCAmelCase : Tuple=0.02 , lowerCAmelCase : Optional[int]="cls_index" , lowerCAmelCase : Dict=True , lowerCAmelCase : Optional[int]=None , lowerCAmelCase : Dict=True , lowerCAmelCase : int=0.1 , **lowerCAmelCase : Optional[int] , )-> str:
"""simple docstring"""
UpperCAmelCase = vocab_size
UpperCAmelCase = n_positions
UpperCAmelCase = n_embd
UpperCAmelCase = n_layer
UpperCAmelCase = n_head
UpperCAmelCase = afn
UpperCAmelCase = resid_pdrop
UpperCAmelCase = embd_pdrop
UpperCAmelCase = attn_pdrop
UpperCAmelCase = layer_norm_epsilon
UpperCAmelCase = initializer_range
UpperCAmelCase = summary_type
UpperCAmelCase = summary_use_proj
UpperCAmelCase = summary_activation
UpperCAmelCase = summary_first_dropout
UpperCAmelCase = summary_proj_to_labels
super().__init__(**lowerCAmelCase )
| 91 | 1 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
)
class A__ :
"""simple docstring"""
def __init__( self , lowercase , lowercase=13 , lowercase=7 , lowercase=True , lowercase=True , lowercase=True , lowercase=True , lowercase=99 , lowercase=64 , lowercase=32 , lowercase=5 , lowercase=4 , lowercase=37 , lowercase="gelu" , lowercase=0.1 , lowercase=0.1 , lowercase=512 , lowercase=16 , lowercase=2 , lowercase=0.02 , lowercase=3 , lowercase=4 , lowercase=None , ) -> Tuple:
'''simple docstring'''
a__ : int = parent
a__ : Any = batch_size
a__ : Union[str, Any] = seq_length
a__ : Dict = is_training
a__ : Optional[Any] = use_input_mask
a__ : str = use_token_type_ids
a__ : Union[str, Any] = use_labels
a__ : List[Any] = vocab_size
a__ : str = hidden_size
a__ : str = embedding_size
a__ : Tuple = num_hidden_layers
a__ : List[str] = num_attention_heads
a__ : Optional[Any] = intermediate_size
a__ : List[str] = hidden_act
a__ : Optional[Any] = hidden_dropout_prob
a__ : Optional[int] = attention_probs_dropout_prob
a__ : Dict = max_position_embeddings
a__ : Optional[Any] = type_vocab_size
a__ : List[str] = type_sequence_label_size
a__ : Tuple = initializer_range
a__ : int = num_labels
a__ : str = num_choices
a__ : Optional[int] = scope
def __lowercase ( self) -> Optional[int]:
'''simple docstring'''
a__ : Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
a__ : Dict = None
if self.use_input_mask:
a__ : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length])
a__ : Dict = None
if self.use_token_type_ids:
a__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
a__ : Union[str, Any] = None
a__ : Union[str, Any] = None
a__ : str = None
if self.use_labels:
a__ : Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size)
a__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
a__ : Tuple = ids_tensor([self.batch_size] , self.num_choices)
a__ : List[str] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowercase ( self) -> int:
'''simple docstring'''
return MegatronBertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , embedding_size=self.embedding_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowercase , initializer_range=self.initializer_range , )
def __lowercase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase) -> Optional[int]:
'''simple docstring'''
a__ : Union[str, Any] = MegatronBertModel(config=lowercase)
model.to(lowercase)
model.eval()
a__ : List[str] = model(lowercase , attention_mask=lowercase , token_type_ids=lowercase)
a__ : Any = model(lowercase , token_type_ids=lowercase)
a__ : List[str] = model(lowercase)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size))
def __lowercase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase) -> Optional[int]:
'''simple docstring'''
a__ : int = MegatronBertForMaskedLM(config=lowercase)
model.to(lowercase)
model.eval()
a__ : List[Any] = model(lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def __lowercase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase) -> List[Any]:
'''simple docstring'''
a__ : Optional[Any] = MegatronBertForCausalLM(config=lowercase)
model.to(lowercase)
model.eval()
a__ : Dict = model(lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def __lowercase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase) -> Any:
'''simple docstring'''
a__ : Optional[int] = MegatronBertForNextSentencePrediction(config=lowercase)
model.to(lowercase)
model.eval()
a__ : Any = model(
lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2))
def __lowercase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase) -> Dict:
'''simple docstring'''
a__ : Tuple = MegatronBertForPreTraining(config=lowercase)
model.to(lowercase)
model.eval()
a__ : Optional[Any] = model(
lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase , next_sentence_label=lowercase , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2))
def __lowercase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase) -> Optional[int]:
'''simple docstring'''
a__ : List[str] = MegatronBertForQuestionAnswering(config=lowercase)
model.to(lowercase)
model.eval()
a__ : str = model(
lowercase , attention_mask=lowercase , token_type_ids=lowercase , start_positions=lowercase , end_positions=lowercase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length))
def __lowercase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase) -> List[str]:
'''simple docstring'''
a__ : Optional[Any] = self.num_labels
a__ : Optional[int] = MegatronBertForSequenceClassification(lowercase)
model.to(lowercase)
model.eval()
a__ : int = model(lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def __lowercase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase) -> Optional[int]:
'''simple docstring'''
a__ : Any = self.num_labels
a__ : Dict = MegatronBertForTokenClassification(config=lowercase)
model.to(lowercase)
model.eval()
a__ : Dict = model(lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels))
def __lowercase ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase) -> Dict:
'''simple docstring'''
a__ : Any = self.num_choices
a__ : Any = MegatronBertForMultipleChoice(config=lowercase)
model.to(lowercase)
model.eval()
a__ : Any = input_ids.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
a__ : Any = token_type_ids.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
a__ : List[Any] = input_mask.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
a__ : Dict = model(
lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices))
def __lowercase ( self) -> Tuple:
'''simple docstring'''
a__ : Dict = self.prepare_config_and_inputs()
(
(
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) ,
) : Optional[int] = config_and_inputs
a__ : Optional[int] = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class A__ ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
__A : str = (
(
MegatronBertModel,
MegatronBertForMaskedLM,
MegatronBertForCausalLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
)
if is_torch_available()
else ()
)
__A : List[Any] = (
{
'''feature-extraction''': MegatronBertModel,
'''fill-mask''': MegatronBertForMaskedLM,
'''question-answering''': MegatronBertForQuestionAnswering,
'''text-classification''': MegatronBertForSequenceClassification,
'''text-generation''': MegatronBertForCausalLM,
'''token-classification''': MegatronBertForTokenClassification,
'''zero-shot''': MegatronBertForSequenceClassification,
}
if is_torch_available()
else {}
)
__A : List[Any] = True
# test_resize_embeddings = False
__A : Tuple = False
def __lowercase ( self , lowercase , lowercase , lowercase=False) -> Optional[int]:
'''simple docstring'''
a__ : List[Any] = super()._prepare_for_class(lowercase , lowercase , return_labels=lowercase)
if return_labels:
if model_class in get_values(lowercase):
a__ : List[Any] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=lowercase)
a__ : Union[str, Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowercase)
return inputs_dict
def __lowercase ( self) -> Optional[Any]:
'''simple docstring'''
a__ : int = MegatronBertModelTester(self)
a__ : List[Any] = ConfigTester(self , config_class=lowercase , hidden_size=37)
def __lowercase ( self) -> Union[str, Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def __lowercase ( self) -> int:
'''simple docstring'''
a__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_model(*lowercase)
def __lowercase ( self) -> int:
'''simple docstring'''
a__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_masked_lm(*lowercase)
def __lowercase ( self) -> Optional[int]:
'''simple docstring'''
a__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*lowercase)
def __lowercase ( self) -> List[str]:
'''simple docstring'''
a__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*lowercase)
def __lowercase ( self) -> Dict:
'''simple docstring'''
a__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_pretraining(*lowercase)
def __lowercase ( self) -> List[str]:
'''simple docstring'''
a__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_question_answering(*lowercase)
def __lowercase ( self) -> Optional[Any]:
'''simple docstring'''
a__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*lowercase)
def __lowercase ( self) -> Any:
'''simple docstring'''
a__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_token_classification(*lowercase)
def A_ ( A__ ) -> Any:
return torch.tensor(
A__ , dtype=torch.long , device=A__ , )
lowercase : Any = 1e-4
@require_torch
@require_sentencepiece
@require_tokenizers
class A__ ( unittest.TestCase ):
"""simple docstring"""
@slow
@unittest.skip('Model is not available.')
def __lowercase ( self) -> List[Any]:
'''simple docstring'''
a__ : List[str] = 'nvidia/megatron-bert-uncased-345m'
if "MYDIR" in os.environ:
a__ : Optional[int] = os.path.join(os.environ['MYDIR'] , lowercase)
a__ : str = MegatronBertModel.from_pretrained(lowercase)
model.to(lowercase)
model.half()
a__ : List[str] = _long_tensor([[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]])
with torch.no_grad():
a__ : int = model(lowercase)[0]
a__ : Optional[Any] = torch.Size((1, 9, 1024))
self.assertEqual(output.shape , lowercase)
a__ : Tuple = [-0.60_40, -0.25_17, -0.10_25, 0.34_20, -0.67_58, -0.00_17, -0.10_89, -0.19_90, 0.57_28]
for ii in range(3):
for jj in range(3):
a__ : Optional[Any] = output[0, ii, jj]
a__ : Tuple = expected[3 * ii + jj]
a__ : Union[str, Any] = 'ii={} jj={} a={} b={}'.format(lowercase , lowercase , lowercase , lowercase)
self.assertTrue(math.isclose(lowercase , lowercase , rel_tol=lowercase , abs_tol=lowercase) , msg=lowercase)
| 99 |
"""simple docstring"""
import qiskit
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
A_ : Tuple = qiskit.Aer.get_backend('aer_simulator' )
A_ : str = qiskit.QuantumCircuit(4 , 2 )
# encode inputs in qubits 0 and 1
if bita == 1:
qc_ha.x(0 )
if bita == 1:
qc_ha.x(1 )
qc_ha.barrier()
# use cnots to write XOR of the inputs on qubit2
qc_ha.cx(0 , 2 )
qc_ha.cx(1 , 2 )
# use ccx / toffoli gate to write AND of the inputs on qubit3
qc_ha.ccx(0 , 1 , 3 )
qc_ha.barrier()
# extract outputs
qc_ha.measure(2 , 0 ) # extract XOR value
qc_ha.measure(3 , 1 ) # extract AND value
# Execute the circuit on the qasm simulator
A_ : Optional[Any] = qiskit.execute(_UpperCAmelCase , _UpperCAmelCase , shots=1000 )
# Return the histogram data of the results of the experiment
return job.result().get_counts(_UpperCAmelCase )
if __name__ == "__main__":
lowerCamelCase_ : List[str] = half_adder(1, 1)
print(F"Half Adder Output Qubit Counts: {counts}") | 286 | 0 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
def snake_case (UpperCAmelCase__ ) -> List[str]:
UpperCamelCase_: int = [False] * len(UpperCAmelCase__ )
UpperCamelCase_: Any = [-1] * len(UpperCAmelCase__ )
def dfs(UpperCAmelCase__ , UpperCAmelCase__ ):
UpperCamelCase_: Tuple = True
UpperCamelCase_: Optional[int] = c
for u in graph[v]:
if not visited[u]:
dfs(UpperCAmelCase__ , 1 - c )
for i in range(len(UpperCAmelCase__ ) ):
if not visited[i]:
dfs(UpperCAmelCase__ , 0 )
for i in range(len(UpperCAmelCase__ ) ):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
A_ : Dict = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph)) | 292 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class _lowerCAmelCase( unittest.TestCase ):
"""simple docstring"""
def _a ( self ):
UpperCamelCase_: Any = torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0] )
UpperCamelCase_: List[str] = get_activation('gelu' )
self.assertTrue(torch.allclose(gelu_python(_lowerCamelCase ) , torch_builtin(_lowerCamelCase ) ) )
self.assertFalse(torch.allclose(gelu_python(_lowerCamelCase ) , gelu_new(_lowerCamelCase ) ) )
def _a ( self ):
UpperCamelCase_: Optional[Any] = torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0] )
UpperCamelCase_: Union[str, Any] = get_activation('gelu' )
UpperCamelCase_: int = get_activation('gelu_10' )
UpperCamelCase_: Union[str, Any] = torch_builtin(_lowerCamelCase )
UpperCamelCase_: List[str] = geluaa(_lowerCamelCase )
UpperCamelCase_: Dict = torch.where(y_gelu_aa < 1_0.0 , 1 , 0 )
self.assertTrue(torch.max(_lowerCamelCase ).item() == 1_0.0 )
self.assertTrue(torch.allclose(y_gelu * clipped_mask , y_gelu_aa * clipped_mask ) )
def _a ( self ):
get_activation('gelu' )
get_activation('gelu_10' )
get_activation('gelu_fast' )
get_activation('gelu_new' )
get_activation('gelu_python' )
get_activation('gelu_pytorch_tanh' )
get_activation('linear' )
get_activation('mish' )
get_activation('quick_gelu' )
get_activation('relu' )
get_activation('sigmoid' )
get_activation('silu' )
get_activation('swish' )
get_activation('tanh' )
with self.assertRaises(_lowerCamelCase ):
get_activation('bogus' )
with self.assertRaises(_lowerCamelCase ):
get_activation(_lowerCamelCase )
def _a ( self ):
UpperCamelCase_: str = get_activation('gelu' )
UpperCamelCase_: str = 1
UpperCamelCase_: int = get_activation('gelu' )
self.assertEqual(acta.a , 1 )
with self.assertRaises(_lowerCamelCase ):
UpperCamelCase_: Tuple = acta.a | 292 | 1 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")
class UpperCAmelCase :
'''simple docstring'''
def __init__( self : List[Any] , __lowercase : Union[str, Any] , __lowercase : Union[str, Any] , __lowercase : List[str] = True , __lowercase : List[str] = False ):
"""simple docstring"""
snake_case_ = scheduler
snake_case_ = optimizers if isinstance(_SCREAMING_SNAKE_CASE , (list, tuple) ) else [optimizers]
snake_case_ = split_batches
snake_case_ = step_with_optimizer
snake_case_ = GradientState()
def snake_case__ ( self : int , *__lowercase : List[Any] , **__lowercase : Optional[int] ):
"""simple docstring"""
if not self.step_with_optimizer:
# No link between scheduler and optimizer -> just step
self.scheduler.step(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
return
# Otherwise, first make sure the optimizer was stepped.
if not self.gradient_state.sync_gradients:
if self.gradient_state.adjust_scheduler:
self.scheduler._step_count += 1
return
for opt in self.optimizers:
if opt.step_was_skipped:
return
if self.split_batches:
# Split batches -> the training dataloader batch size is not changed so one step per training step
self.scheduler.step(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
else:
# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
# num_processes steps per training step
snake_case_ = AcceleratorState().num_processes
for _ in range(_SCREAMING_SNAKE_CASE ):
# Special case when using OneCycle and `drop_last` was not used
if hasattr(self.scheduler , "total_steps" ):
if self.scheduler._step_count <= self.scheduler.total_steps:
self.scheduler.step(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
else:
self.scheduler.step(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def snake_case__ ( self : Dict ):
"""simple docstring"""
return self.scheduler.get_last_lr()
def snake_case__ ( self : Any ):
"""simple docstring"""
return self.scheduler.state_dict()
def snake_case__ ( self : List[str] , __lowercase : int ):
"""simple docstring"""
self.scheduler.load_state_dict(_SCREAMING_SNAKE_CASE )
def snake_case__ ( self : Optional[Any] ):
"""simple docstring"""
return self.scheduler.get_lr()
def snake_case__ ( self : int , *__lowercase : int , **__lowercase : Tuple ):
"""simple docstring"""
return self.scheduler.print_lr(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
| 187 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, 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 DonutImageProcessor
class __a ( unittest.TestCase ):
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=7 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=18 , _SCREAMING_SNAKE_CASE=30 , _SCREAMING_SNAKE_CASE=400 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5] , _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5] , ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = image_size
_UpperCAmelCase = min_resolution
_UpperCAmelCase = max_resolution
_UpperCAmelCase = do_resize
_UpperCAmelCase = size if size is not None else {'height': 18, 'width': 20}
_UpperCAmelCase = do_thumbnail
_UpperCAmelCase = do_align_axis
_UpperCAmelCase = do_pad
_UpperCAmelCase = do_normalize
_UpperCAmelCase = image_mean
_UpperCAmelCase = image_std
def UpperCAmelCase__ ( self ) -> Any:
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_thumbnail": self.do_thumbnail,
"do_align_long_axis": self.do_align_axis,
"do_pad": self.do_pad,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class __a ( UpperCAmelCase , unittest.TestCase ):
_a : List[str] = DonutImageProcessor if is_vision_available() else None
def UpperCAmelCase__ ( self ) -> str:
"""simple docstring"""
_UpperCAmelCase = DonutImageProcessingTester(self )
@property
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCAmelCase__ ( self ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , 'do_resize' ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , 'size' ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , 'do_thumbnail' ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , 'do_align_long_axis' ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , 'do_pad' ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , 'do_normalize' ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , 'image_mean' ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , 'image_std' ) )
def UpperCAmelCase__ ( self ) -> Any:
"""simple docstring"""
_UpperCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'height': 18, 'width': 20} )
_UpperCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'height': 42, 'width': 42} )
# Previous config had dimensions in (width, height) order
_UpperCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict , size=(42, 84) )
self.assertEqual(image_processor.size , {'height': 84, 'width': 42} )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
pass
@is_flaky()
def UpperCAmelCase__ ( self ) -> Any:
"""simple docstring"""
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_UpperCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=_SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(_SCREAMING_SNAKE_CASE , Image.Image )
# Test not batched input
_UpperCAmelCase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
# Test batched
_UpperCAmelCase = image_processing(_SCREAMING_SNAKE_CASE , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
@is_flaky()
def UpperCAmelCase__ ( self ) -> str:
"""simple docstring"""
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_UpperCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=_SCREAMING_SNAKE_CASE , numpify=_SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(_SCREAMING_SNAKE_CASE , np.ndarray )
# Test not batched input
_UpperCAmelCase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
# Test batched
_UpperCAmelCase = image_processing(_SCREAMING_SNAKE_CASE , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
@is_flaky()
def UpperCAmelCase__ ( self ) -> int:
"""simple docstring"""
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_UpperCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=_SCREAMING_SNAKE_CASE , torchify=_SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(_SCREAMING_SNAKE_CASE , torch.Tensor )
# Test not batched input
_UpperCAmelCase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
# Test batched
_UpperCAmelCase = image_processing(_SCREAMING_SNAKE_CASE , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
| 329 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE__ = {"""configuration_beit""": ["""BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BeitConfig""", """BeitOnnxConfig"""]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = ["""BeitFeatureExtractor"""]
SCREAMING_SNAKE_CASE__ = ["""BeitImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
"""BEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""BeitForImageClassification""",
"""BeitForMaskedImageModeling""",
"""BeitForSemanticSegmentation""",
"""BeitModel""",
"""BeitPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
"""FlaxBeitForImageClassification""",
"""FlaxBeitForMaskedImageModeling""",
"""FlaxBeitModel""",
"""FlaxBeitPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_beit import BeitFeatureExtractor
from .image_processing_beit import BeitImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_beit import (
BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
BeitPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_beit import (
FlaxBeitForImageClassification,
FlaxBeitForMaskedImageModeling,
FlaxBeitModel,
FlaxBeitPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 350 |
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: int ):
'''simple docstring'''
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(__lowerCamelCase , __lowerCamelCase ):
raise TypeError("Input value must be a 'int' type" )
return bin(__lowerCamelCase ).count("1" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 297 | 0 |
class lowerCAmelCase_ :
def __init__( self, SCREAMING_SNAKE_CASE_ ) -> None:
UpperCamelCase : int = size
UpperCamelCase : Union[str, Any] = [0] * size
UpperCamelCase : str = [0] * size
@staticmethod
def snake_case_ ( SCREAMING_SNAKE_CASE_ ) -> int:
return index | (index + 1)
@staticmethod
def snake_case_ ( SCREAMING_SNAKE_CASE_ ) -> int:
return (index & (index + 1)) - 1
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> None:
UpperCamelCase : Dict = value
while index < self.size:
UpperCamelCase : Optional[int] = self.get_prev(SCREAMING_SNAKE_CASE_ ) + 1
if current_left_border == index:
UpperCamelCase : Any = value
else:
UpperCamelCase : Union[str, Any] = max(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = self.get_next(SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> int:
right -= 1 # Because of right is exclusive
UpperCamelCase : Any = 0
while left <= right:
UpperCamelCase : Optional[int] = self.get_prev(SCREAMING_SNAKE_CASE_ )
if left <= current_left:
UpperCamelCase : Union[str, Any] = max(SCREAMING_SNAKE_CASE_, self.tree[right] )
UpperCamelCase : List[str] = current_left
else:
UpperCamelCase : Tuple = max(SCREAMING_SNAKE_CASE_, self.arr[right] )
right -= 1
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| 119 |
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
import PIL.Image
from .features import FeatureType
__UpperCAmelCase = None
__UpperCAmelCase = '''<''' if sys.byteorder == '''little''' else '''>'''
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
__UpperCAmelCase = [
np.dtype('''|b1'''),
np.dtype('''|u1'''),
np.dtype('''<u2'''),
np.dtype('''>u2'''),
np.dtype('''<i2'''),
np.dtype('''>i2'''),
np.dtype('''<u4'''),
np.dtype('''>u4'''),
np.dtype('''<i4'''),
np.dtype('''>i4'''),
np.dtype('''<f4'''),
np.dtype('''>f4'''),
np.dtype('''<f8'''),
np.dtype('''>f8'''),
]
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase__ : bool = True
UpperCAmelCase__ : Optional[str] = None
# Automatically constructed
UpperCAmelCase__ : ClassVar[str] = "PIL.Image.Image"
UpperCAmelCase__ : ClassVar[Any] = pa.struct({"bytes": pa.binary(), "path": pa.string()} )
UpperCAmelCase__ : str = field(default="Image" , init=a__ , repr=a__ )
def __call__( self ) -> Any:
return self.pa_type
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> dict:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('To support encoding images, please install \'Pillow\'.' )
if isinstance(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = np.array(SCREAMING_SNAKE_CASE_ )
if isinstance(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ):
return {"path": value, "bytes": None}
elif isinstance(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ):
return {"path": None, "bytes": value}
elif isinstance(SCREAMING_SNAKE_CASE_, np.ndarray ):
# convert the image array to PNG/TIFF bytes
return encode_np_array(SCREAMING_SNAKE_CASE_ )
elif isinstance(SCREAMING_SNAKE_CASE_, PIL.Image.Image ):
# convert the PIL image to bytes (default format is PNG/TIFF)
return encode_pil_image(SCREAMING_SNAKE_CASE_ )
elif value.get('path' ) is not None and os.path.isfile(value['path'] ):
# we set "bytes": None to not duplicate the data if they're already available locally
return {"bytes": None, "path": value.get('path' )}
elif value.get('bytes' ) is not None or value.get('path' ) is not None:
# store the image bytes, and path is used to infer the image format using the file extension
return {"bytes": value.get('bytes' ), "path": value.get('path' )}
else:
raise ValueError(
F"""An image sample should have one of 'path' or 'bytes' but they are missing or None in {value}.""" )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_=None ) -> "PIL.Image.Image":
if not self.decode:
raise RuntimeError('Decoding is disabled for this feature. Please use Image(decode=True) instead.' )
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('To support decoding images, please install \'Pillow\'.' )
if token_per_repo_id is None:
UpperCamelCase : Any = {}
UpperCamelCase , UpperCamelCase : Union[str, Any] = value['path'], value['bytes']
if bytes_ is None:
if path is None:
raise ValueError(F"""An image should have one of 'path' or 'bytes' but both are None in {value}.""" )
else:
if is_local_path(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : int = PIL.Image.open(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : int = path.split('::' )[-1]
try:
UpperCamelCase : Optional[Any] = string_to_dict(SCREAMING_SNAKE_CASE_, config.HUB_DATASETS_URL )['repo_id']
UpperCamelCase : str = token_per_repo_id.get(SCREAMING_SNAKE_CASE_ )
except ValueError:
UpperCamelCase : Tuple = None
with xopen(SCREAMING_SNAKE_CASE_, 'rb', use_auth_token=SCREAMING_SNAKE_CASE_ ) as f:
UpperCamelCase : Optional[int] = BytesIO(f.read() )
UpperCamelCase : int = PIL.Image.open(bytes_ )
else:
UpperCamelCase : Optional[int] = PIL.Image.open(BytesIO(bytes_ ) )
image.load() # to avoid "Too many open files" errors
return image
def snake_case_ ( self ) -> Union["FeatureType", Dict[str, "FeatureType"]]:
from .features import Value
return (
self
if self.decode
else {
"bytes": Value('binary' ),
"path": Value('string' ),
}
)
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> pa.StructArray:
if pa.types.is_string(storage.type ):
UpperCamelCase : List[str] = pa.array([None] * len(SCREAMING_SNAKE_CASE_ ), type=pa.binary() )
UpperCamelCase : Dict = pa.StructArray.from_arrays([bytes_array, storage], ['bytes', 'path'], mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
UpperCamelCase : Optional[int] = pa.array([None] * len(SCREAMING_SNAKE_CASE_ ), type=pa.string() )
UpperCamelCase : Union[str, Any] = pa.StructArray.from_arrays([storage, path_array], ['bytes', 'path'], mask=storage.is_null() )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index('bytes' ) >= 0:
UpperCamelCase : List[str] = storage.field('bytes' )
else:
UpperCamelCase : List[str] = pa.array([None] * len(SCREAMING_SNAKE_CASE_ ), type=pa.binary() )
if storage.type.get_field_index('path' ) >= 0:
UpperCamelCase : List[str] = storage.field('path' )
else:
UpperCamelCase : Optional[Any] = pa.array([None] * len(SCREAMING_SNAKE_CASE_ ), type=pa.string() )
UpperCamelCase : Optional[int] = pa.StructArray.from_arrays([bytes_array, path_array], ['bytes', 'path'], mask=storage.is_null() )
elif pa.types.is_list(storage.type ):
UpperCamelCase : Optional[Any] = pa.array(
[encode_np_array(np.array(SCREAMING_SNAKE_CASE_ ) )['bytes'] if arr is not None else None for arr in storage.to_pylist()], type=pa.binary(), )
UpperCamelCase : List[str] = pa.array([None] * len(SCREAMING_SNAKE_CASE_ ), type=pa.string() )
UpperCamelCase : int = pa.StructArray.from_arrays(
[bytes_array, path_array], ['bytes', 'path'], mask=bytes_array.is_null() )
return array_cast(SCREAMING_SNAKE_CASE_, self.pa_type )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> pa.StructArray:
@no_op_if_value_is_null
def path_to_bytes(SCREAMING_SNAKE_CASE_ ):
with xopen(SCREAMING_SNAKE_CASE_, 'rb' ) as f:
UpperCamelCase : Optional[int] = f.read()
return bytes_
UpperCamelCase : Union[str, Any] = pa.array(
[
(path_to_bytes(x['path'] ) if x['bytes'] is None else x['bytes']) if x is not None else None
for x in storage.to_pylist()
], type=pa.binary(), )
UpperCamelCase : Any = pa.array(
[os.path.basename(SCREAMING_SNAKE_CASE_ ) if path is not None else None for path in storage.field('path' ).to_pylist()], type=pa.string(), )
UpperCamelCase : int = pa.StructArray.from_arrays([bytes_array, path_array], ['bytes', 'path'], mask=bytes_array.is_null() )
return array_cast(SCREAMING_SNAKE_CASE_, self.pa_type )
def UpperCamelCase ( ) -> List[str]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('To support encoding images, please install \'Pillow\'.' )
global _IMAGE_COMPRESSION_FORMATS
if _IMAGE_COMPRESSION_FORMATS is None:
PIL.Image.init()
UpperCamelCase : Dict = list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
return _IMAGE_COMPRESSION_FORMATS
def UpperCamelCase ( snake_case__ : "PIL.Image.Image" ) -> bytes:
UpperCamelCase : Any = BytesIO()
if image.format in list_image_compression_formats():
UpperCamelCase : Tuple = image.format
else:
UpperCamelCase : List[str] = 'PNG' if image.mode in ['1', 'L', 'LA', 'RGB', 'RGBA'] else 'TIFF'
image.save(snake_case__ , format=snake_case__ )
return buffer.getvalue()
def UpperCamelCase ( snake_case__ : "PIL.Image.Image" ) -> dict:
if hasattr(snake_case__ , 'filename' ) and image.filename != "":
return {"path": image.filename, "bytes": None}
else:
return {"path": None, "bytes": image_to_bytes(snake_case__ )}
def UpperCamelCase ( snake_case__ : np.ndarray ) -> dict:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('To support encoding images, please install \'Pillow\'.' )
UpperCamelCase : Union[str, Any] = array.dtype
UpperCamelCase : List[Any] = dtype.byteorder if dtype.byteorder != '=' else _NATIVE_BYTEORDER
UpperCamelCase : Optional[Any] = dtype.kind
UpperCamelCase : Any = dtype.itemsize
UpperCamelCase : int = None
# Multi-channel array case (only np.dtype("|u1") is allowed)
if array.shape[2:]:
UpperCamelCase : Optional[Any] = np.dtype('|u1' )
if dtype_kind not in ["u", "i"]:
raise TypeError(
F"""Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.""" )
if dtype is not dest_dtype:
warnings.warn(F"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'""" )
# Exact match
elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
UpperCamelCase : List[Any] = dtype
else: # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
while dtype_itemsize >= 1:
UpperCamelCase : Dict = dtype_byteorder + dtype_kind + str(snake_case__ )
UpperCamelCase : str = np.dtype(snake_case__ )
if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
warnings.warn(F"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'""" )
break
else:
dtype_itemsize //= 2
if dest_dtype is None:
raise TypeError(
F"""Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}""" )
UpperCamelCase : Union[str, Any] = PIL.Image.fromarray(array.astype(snake_case__ ) )
return {"path": None, "bytes": image_to_bytes(snake_case__ )}
def UpperCamelCase ( snake_case__ : Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]] ) -> List[dict]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('To support encoding images, please install \'Pillow\'.' )
if objs:
UpperCamelCase , UpperCamelCase : Union[str, Any] = first_non_null_value(snake_case__ )
if isinstance(snake_case__ , snake_case__ ):
return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
if isinstance(snake_case__ , np.ndarray ):
UpperCamelCase : List[Any] = no_op_if_value_is_null(snake_case__ )
return [obj_to_image_dict_func(snake_case__ ) for obj in objs]
elif isinstance(snake_case__ , PIL.Image.Image ):
UpperCamelCase : Optional[int] = no_op_if_value_is_null(snake_case__ )
return [obj_to_image_dict_func(snake_case__ ) for obj in objs]
else:
return objs
else:
return objs
| 119 | 1 |
"""simple docstring"""
from math import factorial
UpperCAmelCase: dict[str, int] = {str(digit): factorial(digit) for digit in range(10)}
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise TypeError("""Parameter number must be int""" )
if number < 0:
raise ValueError("""Parameter number must be greater than or equal to 0""" )
# Converts number in string to iterate on its digits and adds its factorial.
return sum(DIGIT_FACTORIAL[digit] for digit in str(__UpperCAmelCase ) )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase = 60 , __UpperCAmelCase = 1000000 ):
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ) or not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise TypeError("""Parameters chain_length and number_limit must be int""" )
if chain_length <= 0 or number_limit <= 0:
raise ValueError(
"""Parameters chain_length and number_limit must be greater than 0""" )
# the counter for the chains with the exact desired length
_lowercase : str = 0
# the cached sizes of the previous chains
_lowercase : dict[int, int] = {}
for start_chain_element in range(1 , __UpperCAmelCase ):
# The temporary set will contain the elements of the chain
_lowercase : Optional[int] = set()
_lowercase : List[Any] = 0
# Stop computing the chain when you find a cached size, a repeating item or the
# length is greater then the desired one.
_lowercase : Optional[int] = start_chain_element
while (
chain_element not in chain_sets_lengths
and chain_element not in chain_set
and chain_set_length <= chain_length
):
chain_set.add(__UpperCAmelCase )
chain_set_length += 1
_lowercase : Optional[Any] = digit_factorial_sum(__UpperCAmelCase )
if chain_element in chain_sets_lengths:
chain_set_length += chain_sets_lengths[chain_element]
_lowercase : List[str] = chain_set_length
# If chain contains the exact amount of elements increase the counter
if chain_set_length == chain_length:
chains_counter += 1
return chains_counter
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F'{solution()}')
| 336 |
"""simple docstring"""
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
is_remote_filesystem,
rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int
UpperCAmelCase: str = datasets.utils.logging.get_logger(__name__)
if TYPE_CHECKING:
import pyspark
@dataclass
class UpperCamelCase ( datasets.BuilderConfig ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[datasets.Features] = None
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , ):
import pyspark
def generate_fn():
_lowercase : List[Any] = df.select("""*""" , pyspark.sql.functions.spark_partition_id().alias("""part_id""" ) )
for partition_id in partition_order:
_lowercase : Optional[int] = df_with_partition_id.select("""*""" ).where(F"""part_id = {partition_id}""" ).drop("""part_id""" )
_lowercase : int = partition_df.collect()
_lowercase : Dict = 0
for row in rows:
yield F"""{partition_id}_{row_id}""", row.asDict()
row_id += 1
return generate_fn
class UpperCamelCase ( _BaseExamplesIterable ):
"""simple docstring"""
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_=None ,):
_lowercase : Union[str, Any] = df
_lowercase : List[str] = partition_order or range(self.df.rdd.getNumPartitions() )
_lowercase : Tuple = _generate_iterable_examples(self.df ,self.partition_order )
def __iter__( self ):
yield from self.generate_examples_fn()
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
_lowercase : List[str] = list(range(self.df.rdd.getNumPartitions() ) )
generator.shuffle(UpperCAmelCase_ )
return SparkExamplesIterable(self.df ,partition_order=UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Union[str, Any] = self.split_shard_indices_by_worker(UpperCAmelCase_ ,UpperCAmelCase_ )
return SparkExamplesIterable(self.df ,partition_order=UpperCAmelCase_ )
@property
def lowerCamelCase__ ( self ):
return len(self.partition_order )
class UpperCamelCase ( datasets.DatasetBuilder ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = SparkConfig
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
import pyspark
_lowercase : List[Any] = pyspark.sql.SparkSession.builder.getOrCreate()
_lowercase : List[Any] = df
_lowercase : int = working_dir
super().__init__(
cache_dir=UpperCAmelCase_ ,config_name=str(self.df.semanticHash() ) ,**UpperCAmelCase_ ,)
def lowerCamelCase__ ( self ):
# Returns the path of the created file.
def create_cache_and_write_probe(UpperCAmelCase_ ):
# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
# already exist.
os.makedirs(self._cache_dir ,exist_ok=UpperCAmelCase_ )
_lowercase : Union[str, Any] = os.path.join(self._cache_dir ,"""fs_test""" + uuid.uuida().hex )
# Opening the file in append mode will create a new file unless it already exists, in which case it will not
# change the file contents.
open(UpperCAmelCase_ ,"""a""" )
return [probe_file]
if self._spark.conf.get("""spark.master""" ,"""""" ).startswith("""local""" ):
return
# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
# accessible to the driver.
# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
if self._cache_dir:
_lowercase : List[str] = (
self._spark.sparkContext.parallelize(range(1 ) ,1 ).mapPartitions(UpperCAmelCase_ ).collect()
)
if os.path.isfile(probe[0] ):
return
raise ValueError(
"""When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir""" )
def lowerCamelCase__ ( self ):
return datasets.DatasetInfo(features=self.config.features )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
import pyspark
def get_arrow_batch_size(UpperCAmelCase_ ):
for batch in it:
yield pa.RecordBatch.from_pydict({"""batch_bytes""": [batch.nbytes]} )
_lowercase : List[str] = self.df.count()
_lowercase : List[str] = df_num_rows if df_num_rows <= 1_00 else 1_00
# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
_lowercase : Union[str, Any] = (
self.df.limit(UpperCAmelCase_ )
.repartition(1 )
.mapInArrow(UpperCAmelCase_ ,"""batch_bytes: long""" )
.agg(pyspark.sql.functions.sum("""batch_bytes""" ).alias("""sample_bytes""" ) )
.collect()[0]
.sample_bytes
/ sample_num_rows
)
_lowercase : List[Any] = approx_bytes_per_row * df_num_rows
if approx_total_size > max_shard_size:
# Make sure there is at least one row per partition.
_lowercase : int = min(UpperCAmelCase_ ,int(approx_total_size / max_shard_size ) )
_lowercase : List[Any] = self.df.repartition(UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,):
import pyspark
_lowercase : Union[str, Any] = ParquetWriter if file_format == """parquet""" else ArrowWriter
_lowercase : List[Any] = os.path.join(self._working_dir ,os.path.basename(UpperCAmelCase_ ) ) if self._working_dir else fpath
_lowercase : Any = file_format == """parquet"""
# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
# pickling the SparkContext.
_lowercase : Union[str, Any] = self.config.features
_lowercase : Optional[int] = self._writer_batch_size
_lowercase : Optional[Any] = self._fs.storage_options
def write_arrow(UpperCAmelCase_ ):
# Within the same SparkContext, no two task attempts will share the same attempt ID.
_lowercase : Any = pyspark.TaskContext().taskAttemptId()
_lowercase : List[str] = next(UpperCAmelCase_ ,UpperCAmelCase_ )
if first_batch is None:
# Some partitions might not receive any data.
return pa.RecordBatch.from_arrays(
[[task_id], [0], [0]] ,names=["""task_id""", """num_examples""", """num_bytes"""] ,)
_lowercase : List[Any] = 0
_lowercase : int = writer_class(
features=UpperCAmelCase_ ,path=working_fpath.replace("""SSSSS""" ,f"""{shard_id:05d}""" ).replace("""TTTTT""" ,f"""{task_id:05d}""" ) ,writer_batch_size=UpperCAmelCase_ ,storage_options=UpperCAmelCase_ ,embed_local_files=UpperCAmelCase_ ,)
_lowercase : Optional[int] = pa.Table.from_batches([first_batch] )
writer.write_table(UpperCAmelCase_ )
for batch in it:
if max_shard_size is not None and writer._num_bytes >= max_shard_size:
_lowercase , _lowercase : Optional[Any] = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] ,names=["""task_id""", """num_examples""", """num_bytes"""] ,)
shard_id += 1
_lowercase : Union[str, Any] = writer_class(
features=writer._features ,path=working_fpath.replace("""SSSSS""" ,f"""{shard_id:05d}""" ).replace("""TTTTT""" ,f"""{task_id:05d}""" ) ,writer_batch_size=UpperCAmelCase_ ,storage_options=UpperCAmelCase_ ,embed_local_files=UpperCAmelCase_ ,)
_lowercase : Dict = pa.Table.from_batches([batch] )
writer.write_table(UpperCAmelCase_ )
if writer._num_bytes > 0:
_lowercase , _lowercase : Dict = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] ,names=["""task_id""", """num_examples""", """num_bytes"""] ,)
if working_fpath != fpath:
for file in os.listdir(os.path.dirname(UpperCAmelCase_ ) ):
_lowercase : Dict = os.path.join(os.path.dirname(UpperCAmelCase_ ) ,os.path.basename(UpperCAmelCase_ ) )
shutil.move(UpperCAmelCase_ ,UpperCAmelCase_ )
_lowercase : List[str] = (
self.df.mapInArrow(UpperCAmelCase_ ,"""task_id: long, num_examples: long, num_bytes: long""" )
.groupBy("""task_id""" )
.agg(
pyspark.sql.functions.sum("""num_examples""" ).alias("""total_num_examples""" ) ,pyspark.sql.functions.sum("""num_bytes""" ).alias("""total_num_bytes""" ) ,pyspark.sql.functions.count("""num_bytes""" ).alias("""num_shards""" ) ,pyspark.sql.functions.collect_list("""num_examples""" ).alias("""shard_lengths""" ) ,)
.collect()
)
for row in stats:
yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ = "arrow" ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
self._validate_cache_dir()
_lowercase : Tuple = convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
self._repartition_df_if_needed(UpperCAmelCase_ )
_lowercase : Optional[int] = not is_remote_filesystem(self._fs )
_lowercase : Dict = os.path.join if is_local else posixpath.join
_lowercase : int = """-TTTTT-SSSSS-of-NNNNN"""
_lowercase : Optional[Any] = f"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
_lowercase : Dict = path_join(self._output_dir ,UpperCAmelCase_ )
_lowercase : List[Any] = 0
_lowercase : Optional[Any] = 0
_lowercase : int = 0
_lowercase : Any = []
_lowercase : Any = []
for task_id, content in self._prepare_split_single(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
(
(
_lowercase
) , (
_lowercase
) , (
_lowercase
) , (
_lowercase
) ,
) : Tuple = content
if num_bytes > 0:
total_num_examples += num_examples
total_num_bytes += num_bytes
total_shards += num_shards
task_id_and_num_shards.append((task_id, num_shards) )
all_shard_lengths.extend(UpperCAmelCase_ )
_lowercase : Optional[int] = total_num_examples
_lowercase : List[Any] = total_num_bytes
# should rename everything at the end
logger.debug(f"""Renaming {total_shards} shards.""" )
if total_shards > 1:
_lowercase : List[Any] = all_shard_lengths
# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
# pickling error due to pickling the SparkContext.
_lowercase : Union[str, Any] = self._fs
# use the -SSSSS-of-NNNNN pattern
def _rename_shard(
UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,):
rename(
UpperCAmelCase_ ,fpath.replace("""SSSSS""" ,f"""{shard_id:05d}""" ).replace("""TTTTT""" ,f"""{task_id:05d}""" ) ,fpath.replace("""TTTTT-SSSSS""" ,f"""{global_shard_id:05d}""" ).replace("""NNNNN""" ,f"""{total_shards:05d}""" ) ,)
_lowercase : Optional[Any] = []
_lowercase : List[str] = 0
for i in range(len(UpperCAmelCase_ ) ):
_lowercase , _lowercase : List[str] = task_id_and_num_shards[i]
for shard_id in range(UpperCAmelCase_ ):
args.append([task_id, shard_id, global_shard_id] )
global_shard_id += 1
self._spark.sparkContext.parallelize(UpperCAmelCase_ ,len(UpperCAmelCase_ ) ).map(lambda UpperCAmelCase_ : _rename_shard(*UpperCAmelCase_ ) ).collect()
else:
# don't use any pattern
_lowercase : Tuple = 0
_lowercase : Optional[Any] = task_id_and_num_shards[0][0]
self._rename(
fpath.replace("""SSSSS""" ,f"""{shard_id:05d}""" ).replace("""TTTTT""" ,f"""{task_id:05d}""" ) ,fpath.replace(UpperCAmelCase_ ,"""""" ) ,)
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,):
return SparkExamplesIterable(self.df )
| 336 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
_a = logging.get_logger(__name__)
if is_vision_available():
import PIL
class A_ ( snake_case__ ):
_lowercase : Optional[int] = ['pixel_values']
def __init__( self : int , UpperCAmelCase : bool = True , UpperCAmelCase : Dict[str, int] = None , UpperCAmelCase : PILImageResampling = PILImageResampling.BICUBIC , UpperCAmelCase : bool = True , UpperCAmelCase : Dict[str, int] = None , UpperCAmelCase : bool = True , UpperCAmelCase : Union[int, float] = 1 / 2_5_5 , UpperCAmelCase : bool = True , UpperCAmelCase : Optional[Union[float, List[float]]] = None , UpperCAmelCase : Optional[Union[float, List[float]]] = None , UpperCAmelCase : bool = True , **UpperCAmelCase : Any , ) -> None:
super().__init__(**UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = size if size is not None else {'shortest_edge': 2_2_4}
__lowerCAmelCase: Tuple = get_size_dict(UpperCAmelCase , default_to_square=UpperCAmelCase )
__lowerCAmelCase: Dict = crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
__lowerCAmelCase: Union[str, Any] = get_size_dict(UpperCAmelCase , default_to_square=UpperCAmelCase , param_name='crop_size' )
__lowerCAmelCase: Tuple = do_resize
__lowerCAmelCase: int = size
__lowerCAmelCase: Union[str, Any] = resample
__lowerCAmelCase: Optional[int] = do_center_crop
__lowerCAmelCase: Dict = crop_size
__lowerCAmelCase: Dict = do_rescale
__lowerCAmelCase: List[str] = rescale_factor
__lowerCAmelCase: Tuple = do_normalize
__lowerCAmelCase: Union[str, Any] = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
__lowerCAmelCase: int = image_std if image_std is not None else OPENAI_CLIP_STD
__lowerCAmelCase: str = do_convert_rgb
def UpperCAmelCase ( self : Optional[int] , UpperCAmelCase : np.ndarray , UpperCAmelCase : Dict[str, int] , UpperCAmelCase : PILImageResampling = PILImageResampling.BICUBIC , UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase : Optional[int] , ) -> np.ndarray:
__lowerCAmelCase: List[Any] = get_size_dict(UpperCAmelCase , default_to_square=UpperCAmelCase )
if "shortest_edge" not in size:
raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
__lowerCAmelCase: Optional[Any] = get_resize_output_image_size(UpperCAmelCase , size=size['shortest_edge'] , default_to_square=UpperCAmelCase )
return resize(UpperCAmelCase , size=UpperCAmelCase , resample=UpperCAmelCase , data_format=UpperCAmelCase , **UpperCAmelCase )
def UpperCAmelCase ( self : Dict , UpperCAmelCase : np.ndarray , UpperCAmelCase : Dict[str, int] , UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase : Any , ) -> np.ndarray:
__lowerCAmelCase: Tuple = get_size_dict(UpperCAmelCase )
if "height" not in size or "width" not in size:
raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''' )
return center_crop(UpperCAmelCase , size=(size['height'], size['width']) , data_format=UpperCAmelCase , **UpperCAmelCase )
def UpperCAmelCase ( self : Dict , UpperCAmelCase : np.ndarray , UpperCAmelCase : Union[int, float] , UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase : Optional[Any] , ) -> str:
return rescale(UpperCAmelCase , scale=UpperCAmelCase , data_format=UpperCAmelCase , **UpperCAmelCase )
def UpperCAmelCase ( self : Tuple , UpperCAmelCase : np.ndarray , UpperCAmelCase : Union[float, List[float]] , UpperCAmelCase : Union[float, List[float]] , UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase : Union[str, Any] , ) -> np.ndarray:
return normalize(UpperCAmelCase , mean=UpperCAmelCase , std=UpperCAmelCase , data_format=UpperCAmelCase , **UpperCAmelCase )
def UpperCAmelCase ( self : Optional[Any] , UpperCAmelCase : ImageInput , UpperCAmelCase : bool = None , UpperCAmelCase : Dict[str, int] = None , UpperCAmelCase : PILImageResampling = None , UpperCAmelCase : bool = None , UpperCAmelCase : int = None , UpperCAmelCase : bool = None , UpperCAmelCase : float = None , UpperCAmelCase : bool = None , UpperCAmelCase : Optional[Union[float, List[float]]] = None , UpperCAmelCase : Optional[Union[float, List[float]]] = None , UpperCAmelCase : bool = None , UpperCAmelCase : Optional[Union[str, TensorType]] = None , UpperCAmelCase : Optional[ChannelDimension] = ChannelDimension.FIRST , **UpperCAmelCase : int , ) -> PIL.Image.Image:
__lowerCAmelCase: Union[str, Any] = do_resize if do_resize is not None else self.do_resize
__lowerCAmelCase: List[str] = size if size is not None else self.size
__lowerCAmelCase: Dict = get_size_dict(UpperCAmelCase , param_name='size' , default_to_square=UpperCAmelCase )
__lowerCAmelCase: Union[str, Any] = resample if resample is not None else self.resample
__lowerCAmelCase: Union[str, Any] = do_center_crop if do_center_crop is not None else self.do_center_crop
__lowerCAmelCase: Dict = crop_size if crop_size is not None else self.crop_size
__lowerCAmelCase: str = get_size_dict(UpperCAmelCase , param_name='crop_size' , default_to_square=UpperCAmelCase )
__lowerCAmelCase: Any = do_rescale if do_rescale is not None else self.do_rescale
__lowerCAmelCase: Dict = rescale_factor if rescale_factor is not None else self.rescale_factor
__lowerCAmelCase: List[str] = do_normalize if do_normalize is not None else self.do_normalize
__lowerCAmelCase: Tuple = image_mean if image_mean is not None else self.image_mean
__lowerCAmelCase: List[str] = image_std if image_std is not None else self.image_std
__lowerCAmelCase: List[str] = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
__lowerCAmelCase: List[Any] = make_list_of_images(UpperCAmelCase )
if not valid_images(UpperCAmelCase ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None:
raise ValueError('Size must be specified if do_resize is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
__lowerCAmelCase: List[str] = [convert_to_rgb(UpperCAmelCase ) for image in images]
# All transformations expect numpy arrays.
__lowerCAmelCase: Tuple = [to_numpy_array(UpperCAmelCase ) for image in images]
if do_resize:
__lowerCAmelCase: Optional[int] = [self.resize(image=UpperCAmelCase , size=UpperCAmelCase , resample=UpperCAmelCase ) for image in images]
if do_center_crop:
__lowerCAmelCase: int = [self.center_crop(image=UpperCAmelCase , size=UpperCAmelCase ) for image in images]
if do_rescale:
__lowerCAmelCase: Dict = [self.rescale(image=UpperCAmelCase , scale=UpperCAmelCase ) for image in images]
if do_normalize:
__lowerCAmelCase: Any = [self.normalize(image=UpperCAmelCase , mean=UpperCAmelCase , std=UpperCAmelCase ) for image in images]
__lowerCAmelCase: Tuple = [to_channel_dimension_format(UpperCAmelCase , UpperCAmelCase ) for image in images]
__lowerCAmelCase: List[str] = {'pixel_values': images}
return BatchFeature(data=UpperCAmelCase , tensor_type=UpperCAmelCase )
| 322 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A_ ( snake_case__ ):
_lowercase : int = (DPMSolverSinglestepScheduler,)
_lowercase : Optional[Any] = (('num_inference_steps', 2_5),)
def UpperCAmelCase ( self : Dict , **UpperCAmelCase : List[Any] ) -> Optional[Any]:
__lowerCAmelCase: Union[str, Any] = {
'num_train_timesteps': 1_0_0_0,
'beta_start': 0.0001,
'beta_end': 0.02,
'beta_schedule': 'linear',
'solver_order': 2,
'prediction_type': 'epsilon',
'thresholding': False,
'sample_max_value': 1.0,
'algorithm_type': 'dpmsolver++',
'solver_type': 'midpoint',
'lambda_min_clipped': -float('inf' ),
'variance_type': None,
}
config.update(**UpperCAmelCase )
return config
def UpperCAmelCase ( self : str , UpperCAmelCase : List[Any]=0 , **UpperCAmelCase : str ) -> Any:
__lowerCAmelCase: Optional[int] = dict(self.forward_default_kwargs )
__lowerCAmelCase: int = kwargs.pop('num_inference_steps' , UpperCAmelCase )
__lowerCAmelCase: int = self.dummy_sample
__lowerCAmelCase: Union[str, Any] = 0.1 * sample
__lowerCAmelCase: str = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
__lowerCAmelCase: Union[str, Any] = self.get_scheduler_config(**UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = scheduler_class(**UpperCAmelCase )
scheduler.set_timesteps(UpperCAmelCase )
# copy over dummy past residuals
__lowerCAmelCase: Optional[int] = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(UpperCAmelCase )
__lowerCAmelCase: Dict = scheduler_class.from_pretrained(UpperCAmelCase )
new_scheduler.set_timesteps(UpperCAmelCase )
# copy over dummy past residuals
__lowerCAmelCase: Optional[int] = dummy_past_residuals[: new_scheduler.config.solver_order]
__lowerCAmelCase , __lowerCAmelCase: Optional[int] = sample, sample
for t in range(UpperCAmelCase , time_step + scheduler.config.solver_order + 1 ):
__lowerCAmelCase: str = scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ).prev_sample
__lowerCAmelCase: str = new_scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def UpperCAmelCase ( self : str ) -> str:
pass
def UpperCAmelCase ( self : Optional[Any] , UpperCAmelCase : Any=0 , **UpperCAmelCase : Optional[int] ) -> Tuple:
__lowerCAmelCase: Tuple = dict(self.forward_default_kwargs )
__lowerCAmelCase: Tuple = kwargs.pop('num_inference_steps' , UpperCAmelCase )
__lowerCAmelCase: Tuple = self.dummy_sample
__lowerCAmelCase: Union[str, Any] = 0.1 * sample
__lowerCAmelCase: Tuple = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
__lowerCAmelCase: Dict = self.get_scheduler_config()
__lowerCAmelCase: Any = scheduler_class(**UpperCAmelCase )
scheduler.set_timesteps(UpperCAmelCase )
# copy over dummy past residuals (must be after setting timesteps)
__lowerCAmelCase: List[Any] = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(UpperCAmelCase )
__lowerCAmelCase: List[str] = scheduler_class.from_pretrained(UpperCAmelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(UpperCAmelCase )
# copy over dummy past residual (must be after setting timesteps)
__lowerCAmelCase: Optional[Any] = dummy_past_residuals[: new_scheduler.config.solver_order]
__lowerCAmelCase: Any = scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ).prev_sample
__lowerCAmelCase: Dict = new_scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def UpperCAmelCase ( self : int , UpperCAmelCase : Dict=None , **UpperCAmelCase : List[str] ) -> Union[str, Any]:
if scheduler is None:
__lowerCAmelCase: str = self.scheduler_classes[0]
__lowerCAmelCase: int = self.get_scheduler_config(**UpperCAmelCase )
__lowerCAmelCase: Any = scheduler_class(**UpperCAmelCase )
__lowerCAmelCase: List[Any] = self.scheduler_classes[0]
__lowerCAmelCase: List[str] = self.get_scheduler_config(**UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = scheduler_class(**UpperCAmelCase )
__lowerCAmelCase: List[Any] = 1_0
__lowerCAmelCase: Dict = self.dummy_model()
__lowerCAmelCase: Dict = self.dummy_sample_deter
scheduler.set_timesteps(UpperCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
__lowerCAmelCase: Dict = model(UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: List[Any] = scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ).prev_sample
return sample
def UpperCAmelCase ( self : List[str] ) -> Union[str, Any]:
__lowerCAmelCase: List[str] = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
__lowerCAmelCase: Any = 5_0
__lowerCAmelCase: int = self.dummy_model()
__lowerCAmelCase: List[str] = self.dummy_sample_deter
scheduler.set_timesteps(UpperCAmelCase )
# make sure that the first t is uneven
for i, t in enumerate(scheduler.timesteps[3:] ):
__lowerCAmelCase: List[Any] = model(UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: List[Any] = scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ).prev_sample
__lowerCAmelCase: Optional[int] = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.2574 ) < 1E-3
def UpperCAmelCase ( self : Optional[int] ) -> Dict:
for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=UpperCAmelCase )
def UpperCAmelCase ( self : Optional[Any] ) -> Any:
# make sure that iterating over schedulers with same config names gives same results
# for defaults
__lowerCAmelCase: List[str] = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
__lowerCAmelCase: Dict = self.full_loop(scheduler=UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.2791 ) < 1E-3
__lowerCAmelCase: Tuple = DEISMultistepScheduler.from_config(scheduler.config )
__lowerCAmelCase: List[str] = DPMSolverMultistepScheduler.from_config(scheduler.config )
__lowerCAmelCase: Any = UniPCMultistepScheduler.from_config(scheduler.config )
__lowerCAmelCase: Optional[int] = DPMSolverSinglestepScheduler.from_config(scheduler.config )
__lowerCAmelCase: Union[str, Any] = self.full_loop(scheduler=UpperCAmelCase )
__lowerCAmelCase: List[Any] = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.2791 ) < 1E-3
def UpperCAmelCase ( self : List[str] ) -> List[str]:
self.check_over_configs(thresholding=UpperCAmelCase )
for order in [1, 2, 3]:
for solver_type in ["midpoint", "heun"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=UpperCAmelCase , prediction_type=UpperCAmelCase , sample_max_value=UpperCAmelCase , algorithm_type='dpmsolver++' , solver_order=UpperCAmelCase , solver_type=UpperCAmelCase , )
def UpperCAmelCase ( self : Any ) -> Union[str, Any]:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=UpperCAmelCase )
def UpperCAmelCase ( self : Tuple ) -> str:
for algorithm_type in ["dpmsolver", "dpmsolver++"]:
for solver_type in ["midpoint", "heun"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=UpperCAmelCase , solver_type=UpperCAmelCase , prediction_type=UpperCAmelCase , algorithm_type=UpperCAmelCase , )
__lowerCAmelCase: Dict = self.full_loop(
solver_order=UpperCAmelCase , solver_type=UpperCAmelCase , prediction_type=UpperCAmelCase , algorithm_type=UpperCAmelCase , )
assert not torch.isnan(UpperCAmelCase ).any(), "Samples have nan numbers"
def UpperCAmelCase ( self : Optional[Any] ) -> str:
self.check_over_configs(lower_order_final=UpperCAmelCase )
self.check_over_configs(lower_order_final=UpperCAmelCase )
def UpperCAmelCase ( self : str ) -> Any:
self.check_over_configs(lambda_min_clipped=-float('inf' ) )
self.check_over_configs(lambda_min_clipped=-5.1 )
def UpperCAmelCase ( self : List[Any] ) -> str:
self.check_over_configs(variance_type=UpperCAmelCase )
self.check_over_configs(variance_type='learned_range' )
def UpperCAmelCase ( self : Union[str, Any] ) -> List[Any]:
for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
self.check_over_forward(num_inference_steps=UpperCAmelCase , time_step=0 )
def UpperCAmelCase ( self : Any ) -> int:
__lowerCAmelCase: Any = self.full_loop()
__lowerCAmelCase: Tuple = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.2791 ) < 1E-3
def UpperCAmelCase ( self : Any ) -> Union[str, Any]:
__lowerCAmelCase: List[str] = self.full_loop(use_karras_sigmas=UpperCAmelCase )
__lowerCAmelCase: str = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.2248 ) < 1E-3
def UpperCAmelCase ( self : Dict ) -> Optional[Any]:
__lowerCAmelCase: Tuple = self.full_loop(prediction_type='v_prediction' )
__lowerCAmelCase: List[str] = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.1453 ) < 1E-3
def UpperCAmelCase ( self : str ) -> List[str]:
__lowerCAmelCase: int = self.full_loop(prediction_type='v_prediction' , use_karras_sigmas=UpperCAmelCase )
__lowerCAmelCase: Tuple = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.0649 ) < 1E-3
def UpperCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
__lowerCAmelCase: Any = self.scheduler_classes[0]
__lowerCAmelCase: Optional[Any] = self.get_scheduler_config(thresholding=UpperCAmelCase , dynamic_thresholding_ratio=0 )
__lowerCAmelCase: List[str] = scheduler_class(**UpperCAmelCase )
__lowerCAmelCase: Optional[int] = 1_0
__lowerCAmelCase: Union[str, Any] = self.dummy_model()
__lowerCAmelCase: int = self.dummy_sample_deter.half()
scheduler.set_timesteps(UpperCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
__lowerCAmelCase: Any = model(UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: Any = scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ).prev_sample
assert sample.dtype == torch.floataa
| 322 | 1 |
"""simple docstring"""
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
__SCREAMING_SNAKE_CASE =logging.getLogger(__name__)
class UpperCamelCase :
def __init__( self ) -> Tuple:
'''simple docstring'''
lowercase_ : Dict = False
def _UpperCAmelCase ( self ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> Any:
'''simple docstring'''
if not self.initialized:
lowercase_ : Optional[Any] = RagRetriever(
__UpperCamelCase ,question_encoder_tokenizer=__UpperCamelCase ,generator_tokenizer=__UpperCamelCase ,index=__UpperCamelCase ,init_retrieval=__UpperCamelCase ,)
lowercase_ : Dict = True
def _UpperCAmelCase ( self ) -> int:
'''simple docstring'''
self.retriever.index.init_index()
def _UpperCAmelCase ( self ,__UpperCamelCase ,__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ , lowercase_ : Any = self.retriever._main_retrieve(__UpperCamelCase ,__UpperCamelCase )
return doc_ids, retrieved_doc_embeds
class UpperCamelCase ( lowercase_ ):
def __init__( self ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase=None ) -> Optional[int]:
'''simple docstring'''
if index is not None and index.is_initialized() and len(__UpperCamelCase ) > 0:
raise ValueError(
'When using Ray for distributed fine-tuning, '
'you\'ll need to provide the paths instead, '
'as the dataset and the index are loaded '
'separately. More info in examples/rag/use_own_knowledge_dataset.py ' )
super().__init__(
__UpperCamelCase ,question_encoder_tokenizer=__UpperCamelCase ,generator_tokenizer=__UpperCamelCase ,index=__UpperCamelCase ,init_retrieval=__UpperCamelCase ,)
lowercase_ : List[Any] = retrieval_workers
if len(self.retrieval_workers ) > 0:
ray.get(
[
worker.create_rag_retriever.remote(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
for worker in self.retrieval_workers
] )
def _UpperCAmelCase ( self ) -> Tuple:
'''simple docstring'''
logger.info('initializing retrieval' )
if len(self.retrieval_workers ) > 0:
ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers] )
else:
# Non-distributed training. Load index into this same process.
self.index.init_index()
def _UpperCAmelCase ( self ,__UpperCamelCase ,__UpperCamelCase ) -> Tuple:
'''simple docstring'''
if len(self.retrieval_workers ) > 0:
# Select a random retrieval actor.
lowercase_ : Any = self.retrieval_workers[random.randint(0 ,len(self.retrieval_workers ) - 1 )]
lowercase_ , lowercase_ : Optional[int] = ray.get(random_worker.retrieve.remote(__UpperCamelCase ,__UpperCamelCase ) )
else:
lowercase_ , lowercase_ : int = self._main_retrieve(__UpperCamelCase ,__UpperCamelCase )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__UpperCamelCase )
@classmethod
def _UpperCAmelCase ( cls ,__UpperCamelCase ,__UpperCamelCase=None ,**__UpperCamelCase ) -> Optional[Any]:
'''simple docstring'''
return super(__UpperCamelCase ,cls ).get_tokenizers(__UpperCamelCase ,__UpperCamelCase ,**__UpperCamelCase )
@classmethod
def _UpperCAmelCase ( cls ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase=None ,**__UpperCamelCase ) -> List[Any]:
'''simple docstring'''
lowercase_ : Tuple = kwargs.pop('config' ,__UpperCamelCase ) or RagConfig.from_pretrained(__UpperCamelCase ,**__UpperCamelCase )
lowercase_ : List[Any] = RagTokenizer.from_pretrained(__UpperCamelCase ,config=__UpperCamelCase )
lowercase_ : str = rag_tokenizer.question_encoder
lowercase_ : Dict = rag_tokenizer.generator
if indexed_dataset is not None:
lowercase_ : Union[str, Any] = 'custom'
lowercase_ : List[Any] = CustomHFIndex(config.retrieval_vector_size ,__UpperCamelCase )
else:
lowercase_ : Union[str, Any] = cls._build_index(__UpperCamelCase )
return cls(
__UpperCamelCase ,question_encoder_tokenizer=__UpperCamelCase ,generator_tokenizer=__UpperCamelCase ,retrieval_workers=__UpperCamelCase ,index=__UpperCamelCase ,)
| 321 | """simple docstring"""
def lowercase__( __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : list[int] , __SCREAMING_SNAKE_CASE : int ):
def count_of_possible_combinations(__SCREAMING_SNAKE_CASE : int ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(target - item ) for item in array )
return count_of_possible_combinations(__SCREAMING_SNAKE_CASE )
def lowercase__( __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : list[int] , __SCREAMING_SNAKE_CASE : int ):
def count_of_possible_combinations_with_dp_array(
__SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : list[int] ) -> int:
if target < 0:
return 0
if target == 0:
return 1
if dp_array[target] != -1:
return dp_array[target]
lowercase_ : str = sum(
count_of_possible_combinations_with_dp_array(target - item , __SCREAMING_SNAKE_CASE )
for item in array )
lowercase_ : Tuple = answer
return answer
lowercase_ : Optional[Any] = [-1] * (target + 1)
return count_of_possible_combinations_with_dp_array(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def lowercase__( __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : list[int] , __SCREAMING_SNAKE_CASE : int ):
lowercase_ : Dict = [0] * (target + 1)
lowercase_ : Dict = 1
for i in range(1 , target + 1 ):
for j in range(__SCREAMING_SNAKE_CASE ):
if i - array[j] >= 0:
dp_array[i] += dp_array[i - array[j]]
return dp_array[target]
if __name__ == "__main__":
import doctest
doctest.testmod()
__SCREAMING_SNAKE_CASE =3
__SCREAMING_SNAKE_CASE =5
__SCREAMING_SNAKE_CASE =[1, 2, 5]
print(combination_sum_iv(n, array, target))
| 321 | 1 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.testing_utils import (
is_torch_available,
require_accelerate,
require_bitsandbytes,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
def A_ ( A__ ) -> Any:
if model.config.model_type == "gpt2":
return model.transformer.h[0].mlp.c_fc
return model.transformer.h[0].mlp.dense_ah_to_h
if is_torch_available():
import torch
import torch.nn as nn
class A__ ( nn.Module ):
"""simple docstring"""
def __init__( self , lowercase , lowercase) -> Optional[int]:
'''simple docstring'''
super().__init__()
a__ : int = module
a__ : Dict = nn.Sequential(
nn.Linear(module.in_features , lowercase , bias=lowercase) , nn.Linear(lowercase , module.out_features , bias=lowercase) , )
a__ : Dict = (2.0 / (5 * min(module.in_features , module.out_features))) ** 0.5
nn.init.normal_(self.adapter[0].weight , std=lowercase)
nn.init.zeros_(self.adapter[1].weight)
self.adapter.to(module.weight.device)
def __lowercase ( self , lowercase , *lowercase , **lowercase) -> Any:
'''simple docstring'''
return self.module(lowercase , *lowercase , **lowercase) + self.adapter(lowercase)
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class A__ ( unittest.TestCase ):
"""simple docstring"""
__A : Tuple = '''bigscience/bloom-1b7'''
# Constant values
__A : str = 2.109_6595_5269_2574
__A : List[Any] = '''Hello my name is'''
__A : List[str] = set()
EXPECTED_OUTPUTS.add('''Hello my name is John and I am a professional photographer. I''' )
EXPECTED_OUTPUTS.add('''Hello my name is John.\nI am a friend of your father.\n''' )
EXPECTED_OUTPUTS.add('''Hello my name is John Doe, I am a student at the University''' )
__A : List[Any] = 1_0
def __lowercase ( self) -> str:
'''simple docstring'''
a__ : Union[str, Any] = AutoTokenizer.from_pretrained(self.model_name)
class A__ ( __UpperCAmelCase ):
"""simple docstring"""
def __lowercase ( self) -> int:
'''simple docstring'''
super().setUp()
# Models and tokenizer
a__ : str = AutoModelForCausalLM.from_pretrained(
self.model_name , torch_dtype=torch.floataa , device_map='auto')
a__ : Union[str, Any] = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=lowercase , device_map='auto')
def __lowercase ( self) -> Any:
'''simple docstring'''
del self.model_fpaa
del self.model_abit
gc.collect()
torch.cuda.empty_cache()
def __lowercase ( self) -> Optional[int]:
'''simple docstring'''
a__ : Dict = self.model_abit.config
self.assertTrue(hasattr(lowercase , 'quantization_config'))
a__ : str = config.to_dict()
a__ : Dict = config.to_diff_dict()
a__ : Dict = config.to_json_string()
def __lowercase ( self) -> Optional[Any]:
'''simple docstring'''
from bitsandbytes.nn import Paramsabit
a__ : Optional[Any] = self.model_fpaa.get_memory_footprint()
a__ : Any = self.model_abit.get_memory_footprint()
self.assertAlmostEqual(mem_fpaa / mem_abit , self.EXPECTED_RELATIVE_DIFFERENCE)
a__ : Optional[int] = get_some_linear_layer(self.model_abit)
self.assertTrue(linear.weight.__class__ == Paramsabit)
def __lowercase ( self) -> str:
'''simple docstring'''
from transformers import TaPreTrainedModel
self.model_fpaa.get_memory_footprint()
self.model_abit.get_memory_footprint()
for name, module in self.model_abit.named_modules():
if isinstance(lowercase , torch.nn.Linear):
if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
# 4-bit parameters are packed in uint8 variables
self.assertTrue(module.weight.dtype == torch.uinta)
def __lowercase ( self) -> Optional[int]:
'''simple docstring'''
a__ : List[str] = self.tokenizer(self.input_text , return_tensors='pt')
a__ : List[str] = self.model_abit.generate(input_ids=encoded_input['input_ids'].to(0) , max_new_tokens=10)
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=lowercase) , self.EXPECTED_OUTPUTS)
def __lowercase ( self) -> int:
'''simple docstring'''
a__ : Dict = BitsAndBytesConfig()
a__ : Union[str, Any] = True
a__ : Optional[Any] = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=lowercase , device_map='auto')
a__ : Any = self.tokenizer(self.input_text , return_tensors='pt')
a__ : List[str] = model_abit_from_config.generate(
input_ids=encoded_input['input_ids'].to(0) , max_new_tokens=10)
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=lowercase) , self.EXPECTED_OUTPUTS)
def __lowercase ( self) -> Any:
'''simple docstring'''
with self.assertRaises(lowercase), tempfile.TemporaryDirectory() as tmpdirname:
self.model_abit.save_pretrained(lowercase)
def __lowercase ( self) -> str:
'''simple docstring'''
a__ : Any = BitsAndBytesConfig()
with self.assertRaises(lowercase):
a__ : Any = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=lowercase , load_in_abit=lowercase , device_map='auto' , bnb_abit_quant_type='nf4' , )
def __lowercase ( self) -> Optional[Any]:
'''simple docstring'''
with self.assertRaises(lowercase):
# Tries with `str`
self.model_abit.to('cpu')
with self.assertRaises(lowercase):
# Tries with a `dtype``
self.model_abit.to(torch.floataa)
with self.assertRaises(lowercase):
# Tries with a `device`
self.model_abit.to(torch.device('cuda:0'))
with self.assertRaises(lowercase):
# Tries with a `device`
self.model_abit.float()
with self.assertRaises(lowercase):
# Tries with a `device`
self.model_abit.half()
# Test if we did not break anything
a__ : Optional[int] = self.tokenizer(self.input_text , return_tensors='pt')
a__ : Optional[Any] = self.model_fpaa.to(torch.floataa)
a__ : int = self.model_fpaa.generate(input_ids=encoded_input['input_ids'].to(0) , max_new_tokens=10)
# Check this does not throw an error
a__ : Tuple = self.model_fpaa.to('cpu')
# Check this does not throw an error
a__ : List[str] = self.model_fpaa.half()
# Check this does not throw an error
a__ : Any = self.model_fpaa.float()
def __lowercase ( self) -> Optional[Any]:
'''simple docstring'''
a__ : List[Any] = AutoModelForSeqaSeqLM.from_pretrained('t5-small' , load_in_abit=lowercase , device_map='auto')
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa)
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class A__ ( unittest.TestCase ):
"""simple docstring"""
@classmethod
def __lowercase ( cls) -> List[str]:
'''simple docstring'''
a__ : Union[str, Any] = 't5-small'
a__ : str = 'google/flan-t5-small' # flan-t5 uses dense-act instead of dense-relu-dense
a__ : Dict = AutoTokenizer.from_pretrained(cls.model_name)
a__ : List[str] = 'Translate in German: Hello, my dog is cute'
def __lowercase ( self) -> List[Any]:
'''simple docstring'''
gc.collect()
torch.cuda.empty_cache()
def __lowercase ( self) -> List[Any]:
'''simple docstring'''
from transformers import TaForConditionalGeneration
a__ : Tuple = TaForConditionalGeneration._keep_in_fpaa_modules
a__ : Any = None
# test with `t5-small`
a__ : Tuple = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=lowercase , device_map='auto')
a__ : Any = self.tokenizer(self.input_text , return_tensors='pt').to(0)
a__ : Optional[Any] = model.generate(**lowercase)
# test with `flan-t5-small`
a__ : Dict = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=lowercase , device_map='auto')
a__ : Optional[Any] = self.tokenizer(self.input_text , return_tensors='pt').to(0)
a__ : Union[str, Any] = model.generate(**lowercase)
a__ : Dict = modules
def __lowercase ( self) -> List[str]:
'''simple docstring'''
import bitsandbytes as bnb
from transformers import TaForConditionalGeneration
# test with `t5-small`
a__ : Tuple = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=lowercase , device_map='auto')
# there was a bug with decoders - this test checks that it is fixed
self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q , bnb.nn.Linearabit))
a__ : Optional[int] = self.tokenizer(self.input_text , return_tensors='pt').to(0)
a__ : Union[str, Any] = model.generate(**lowercase)
# test with `flan-t5-small`
a__ : int = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=lowercase , device_map='auto')
a__ : str = self.tokenizer(self.input_text , return_tensors='pt').to(0)
a__ : Any = model.generate(**lowercase)
class A__ ( __UpperCAmelCase ):
"""simple docstring"""
def __lowercase ( self) -> Tuple:
'''simple docstring'''
super().setUp()
# model_name
a__ : List[Any] = 'bigscience/bloom-560m'
a__ : str = 't5-small'
# Different types of model
a__ : Optional[int] = AutoModel.from_pretrained(self.model_name , load_in_abit=lowercase , device_map='auto')
# Sequence classification model
a__ : Optional[int] = AutoModelForSequenceClassification.from_pretrained(
self.model_name , load_in_abit=lowercase , device_map='auto')
# CausalLM model
a__ : List[Any] = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=lowercase , device_map='auto')
# Seq2seq model
a__ : List[Any] = AutoModelForSeqaSeqLM.from_pretrained(
self.seq_to_seq_name , load_in_abit=lowercase , device_map='auto')
def __lowercase ( self) -> List[Any]:
'''simple docstring'''
del self.base_model
del self.sequence_model
del self.model_abit
del self.seq_to_seq_model
gc.collect()
torch.cuda.empty_cache()
def __lowercase ( self) -> List[str]:
'''simple docstring'''
from bitsandbytes.nn import Paramsabit
self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit)
# Other heads should be nn.Parameter
self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter)
self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter)
self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter)
class A__ ( __UpperCAmelCase ):
"""simple docstring"""
def __lowercase ( self) -> Union[str, Any]:
'''simple docstring'''
super().setUp()
def __lowercase ( self) -> Optional[int]:
'''simple docstring'''
del self.pipe
gc.collect()
torch.cuda.empty_cache()
def __lowercase ( self) -> str:
'''simple docstring'''
a__ : Optional[Any] = pipeline(
'text-generation' , model=self.model_name , model_kwargs={'device_map': 'auto', 'load_in_4bit': True, 'torch_dtype': torch.floataa} , max_new_tokens=self.MAX_NEW_TOKENS , )
# Real second forward pass
a__ : int = self.pipe(self.input_text)
self.assertIn(pipeline_output[0]['generated_text'] , self.EXPECTED_OUTPUTS)
@require_torch_multi_gpu
class A__ ( __UpperCAmelCase ):
"""simple docstring"""
def __lowercase ( self) -> Union[str, Any]:
'''simple docstring'''
super().setUp()
def __lowercase ( self) -> int:
'''simple docstring'''
a__ : Optional[Any] = AutoModelForCausalLM.from_pretrained(
self.model_name , load_in_abit=lowercase , device_map='balanced')
# Check correct device map
self.assertEqual(set(model_parallel.hf_device_map.values()) , {0, 1})
# Check that inference pass works on the model
a__ : Tuple = self.tokenizer(self.input_text , return_tensors='pt')
# Second real batch
a__ : Optional[Any] = model_parallel.generate(input_ids=encoded_input['input_ids'].to(0) , max_new_tokens=10)
self.assertIn(self.tokenizer.decode(output_parallel[0] , skip_special_tokens=lowercase) , self.EXPECTED_OUTPUTS)
class A__ ( __UpperCAmelCase ):
"""simple docstring"""
def __lowercase ( self) -> Optional[Any]:
'''simple docstring'''
a__ : Optional[int] = 'facebook/opt-350m'
super().setUp()
def __lowercase ( self) -> List[Any]:
'''simple docstring'''
if version.parse(importlib.metadata.version('bitsandbytes')) < version.parse('0.37.0'):
return
# Step 1: freeze all parameters
a__ : Optional[int] = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=lowercase)
self.assertEqual(set(model.hf_device_map.values()) , {torch.cuda.current_device()})
for param in model.parameters():
a__ : Any = False # freeze the model - train adapters later
if param.ndim == 1:
# cast the small parameters (e.g. layernorm) to fp32 for stability
a__ : List[Any] = param.data.to(torch.floataa)
# Step 2: add adapters
for _, module in model.named_modules():
if "OPTAttention" in repr(type(lowercase)):
a__ : Optional[int] = LoRALayer(module.q_proj , rank=16)
a__ : Optional[Any] = LoRALayer(module.k_proj , rank=16)
a__ : Optional[Any] = LoRALayer(module.v_proj , rank=16)
# Step 3: dummy batch
a__ : Optional[int] = self.tokenizer('Test batch ' , return_tensors='pt').to(0)
# Step 4: Check if the gradient is not None
with torch.cuda.amp.autocast():
a__ : int = model.forward(**lowercase)
out.logits.norm().backward()
for module in model.modules():
if isinstance(lowercase , lowercase):
self.assertTrue(module.adapter[1].weight.grad is not None)
self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0)
elif isinstance(lowercase , nn.Embedding):
self.assertTrue(module.weight.grad is None)
class A__ ( __UpperCAmelCase ):
"""simple docstring"""
__A : Tuple = '''gpt2-xl'''
__A : Dict = 3.3191_8548_5415_2187
| 99 |
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def A_ ( A__ ) -> float:
return np.dot(A__ , A__ )
class A__ :
"""simple docstring"""
def __init__( self , *,
lowercase = np.inf , lowercase = "linear" , lowercase = 0.0 , ) -> None:
'''simple docstring'''
a__ : Tuple = regularization
a__ : Optional[Any] = gamma
if kernel == "linear":
a__ : Optional[Any] = self.__linear
elif kernel == "rbf":
if self.gamma == 0:
raise ValueError('rbf kernel requires gamma')
if not isinstance(self.gamma , (float, int)):
raise ValueError('gamma must be float or int')
if not self.gamma > 0:
raise ValueError('gamma must be > 0')
a__ : str = self.__rbf
# in the future, there could be a default value like in sklearn
# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
# previously it was 1/(n_features)
else:
a__ : Optional[int] = F'Unknown kernel: {kernel}'
raise ValueError(lowercase)
def __lowercase ( self , lowercase , lowercase) -> float:
'''simple docstring'''
return np.dot(lowercase , lowercase)
def __lowercase ( self , lowercase , lowercase) -> float:
'''simple docstring'''
return np.exp(-(self.gamma * norm_squared(vectora - vectora)))
def __lowercase ( self , lowercase , lowercase) -> None:
'''simple docstring'''
a__ : List[str] = observations
a__ : Dict = classes
# using Wolfe's Dual to calculate w.
# Primal problem: minimize 1/2*norm_squared(w)
# constraint: yn(w . xn + b) >= 1
#
# With l a vector
# Dual problem: maximize sum_n(ln) -
# 1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
# constraint: self.C >= ln >= 0
# and sum_n(ln*yn) = 0
# Then we get w using w = sum_n(ln*yn*xn)
# At the end we can get b ~= mean(yn - w . xn)
#
# Since we use kernels, we only need l_star to calculate b
# and to classify observations
((a__) , ) : Optional[int] = np.shape(lowercase)
def to_minimize(lowercase) -> float:
a__ : Tuple = 0
((a__) , ) : Optional[int] = np.shape(lowercase)
for i in range(lowercase):
for j in range(lowercase):
s += (
candidate[i]
* candidate[j]
* classes[i]
* classes[j]
* self.kernel(observations[i] , observations[j])
)
return 1 / 2 * s - sum(lowercase)
a__ : Optional[Any] = LinearConstraint(lowercase , 0 , 0)
a__ : str = Bounds(0 , self.regularization)
a__ : List[str] = minimize(
lowercase , np.ones(lowercase) , bounds=lowercase , constraints=[ly_contraint]).x
a__ : Dict = l_star
# calculating mean offset of separation plane to points
a__ : int = 0
for i in range(lowercase):
for j in range(lowercase):
s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
observations[i] , observations[j])
a__ : List[str] = s / n
def __lowercase ( self , lowercase) -> int:
'''simple docstring'''
a__ : int = sum(
self.optimum[n]
* self.classes[n]
* self.kernel(self.observations[n] , lowercase)
for n in range(len(self.classes)))
return 1 if s + self.offset >= 0 else -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 99 | 1 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING
lowerCAmelCase__ :Dict = logging.get_logger(__name__)
@add_end_docstrings(lowerCAmelCase__ )
class __a ( lowerCAmelCase__ ):
def __init__( self , **_SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(**_SCREAMING_SNAKE_CASE )
requires_backends(self , 'vision' )
requires_backends(self , 'torch' )
if self.framework != "pt":
raise ValueError(f'''The {self.__class__} is only available in PyTorch.''' )
self.check_model_type(_SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self , **_SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
_UpperCAmelCase = {}
_UpperCAmelCase = {}
_UpperCAmelCase = {}
# preprocess args
if "points_per_batch" in kwargs:
_UpperCAmelCase = kwargs['points_per_batch']
if "points_per_crop" in kwargs:
_UpperCAmelCase = kwargs['points_per_crop']
if "crops_n_layers" in kwargs:
_UpperCAmelCase = kwargs['crops_n_layers']
if "crop_overlap_ratio" in kwargs:
_UpperCAmelCase = kwargs['crop_overlap_ratio']
if "crop_n_points_downscale_factor" in kwargs:
_UpperCAmelCase = kwargs['crop_n_points_downscale_factor']
# postprocess args
if "pred_iou_thresh" in kwargs:
_UpperCAmelCase = kwargs['pred_iou_thresh']
if "stability_score_offset" in kwargs:
_UpperCAmelCase = kwargs['stability_score_offset']
if "mask_threshold" in kwargs:
_UpperCAmelCase = kwargs['mask_threshold']
if "stability_score_thresh" in kwargs:
_UpperCAmelCase = kwargs['stability_score_thresh']
if "crops_nms_thresh" in kwargs:
_UpperCAmelCase = kwargs['crops_nms_thresh']
if "output_rle_mask" in kwargs:
_UpperCAmelCase = kwargs['output_rle_mask']
if "output_bboxes_mask" in kwargs:
_UpperCAmelCase = kwargs['output_bboxes_mask']
return preprocess_kwargs, forward_params, postprocess_kwargs
def __call__( self , _SCREAMING_SNAKE_CASE , *_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
return super().__call__(_SCREAMING_SNAKE_CASE , *_SCREAMING_SNAKE_CASE , num_workers=_SCREAMING_SNAKE_CASE , batch_size=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=64 , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = 512 / 1500 , _SCREAMING_SNAKE_CASE = 32 , _SCREAMING_SNAKE_CASE = 1 , ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = load_image(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = self.image_processor.size['longest_edge']
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = self.image_processor.generate_crop_boxes(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = self.image_processor(images=_SCREAMING_SNAKE_CASE , return_tensors='pt' )
with self.device_placement():
if self.framework == "pt":
_UpperCAmelCase = self.get_inference_context()
with inference_context():
_UpperCAmelCase = self._ensure_tensor_on_device(_SCREAMING_SNAKE_CASE , device=self.device )
_UpperCAmelCase = self.model.get_image_embeddings(model_inputs.pop('pixel_values' ) )
_UpperCAmelCase = image_embeddings
_UpperCAmelCase = grid_points.shape[1]
_UpperCAmelCase = points_per_batch if points_per_batch is not None else n_points
if points_per_batch <= 0:
raise ValueError(
'Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. '
'To return all points at once, set points_per_batch to None' )
for i in range(0 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = grid_points[:, i : i + points_per_batch, :, :]
_UpperCAmelCase = input_labels[:, i : i + points_per_batch]
_UpperCAmelCase = i == n_points - points_per_batch
yield {
"input_points": batched_points,
"input_labels": labels,
"input_boxes": crop_boxes,
"is_last": is_last,
**model_inputs,
}
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=0.88 , _SCREAMING_SNAKE_CASE=0.95 , _SCREAMING_SNAKE_CASE=0 , _SCREAMING_SNAKE_CASE=1 , ) -> Dict:
"""simple docstring"""
_UpperCAmelCase = model_inputs.pop('input_boxes' )
_UpperCAmelCase = model_inputs.pop('is_last' )
_UpperCAmelCase = model_inputs.pop('original_sizes' ).tolist()
_UpperCAmelCase = model_inputs.pop('reshaped_input_sizes' ).tolist()
_UpperCAmelCase = self.model(**_SCREAMING_SNAKE_CASE )
# post processing happens here in order to avoid CPU GPU copies of ALL the masks
_UpperCAmelCase = model_outputs['pred_masks']
_UpperCAmelCase = self.image_processor.post_process_masks(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , binarize=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = model_outputs['iou_scores']
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = self.image_processor.filter_masks(
masks[0] , iou_scores[0] , original_sizes[0] , input_boxes[0] , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , )
return {
"masks": masks,
"is_last": is_last,
"boxes": boxes,
"iou_scores": iou_scores,
}
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=0.7 , ) -> int:
"""simple docstring"""
_UpperCAmelCase = []
_UpperCAmelCase = []
_UpperCAmelCase = []
for model_output in model_outputs:
all_scores.append(model_output.pop('iou_scores' ) )
all_masks.extend(model_output.pop('masks' ) )
all_boxes.append(model_output.pop('boxes' ) )
_UpperCAmelCase = torch.cat(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = torch.cat(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = self.image_processor.post_process_for_mask_generation(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = defaultdict(_SCREAMING_SNAKE_CASE )
for output in model_outputs:
for k, v in output.items():
extra[k].append(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = {}
if output_rle_mask:
_UpperCAmelCase = rle_mask
if output_bboxes_mask:
_UpperCAmelCase = bounding_boxes
return {"masks": output_masks, "scores": iou_scores, **optional, **extra}
| 355 |
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def lowerCAmelCase__ ( a__: Sequence[float] , a__: int , a__: int ) -> tuple[int | None, int | None, float]:
'''simple docstring'''
if not arr:
return None, None, 0
if low == high:
return low, high, arr[low]
_UpperCAmelCase = (low + high) // 2
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = max_subarray(a__ , a__ , a__ )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = max_subarray(a__ , mid + 1 , a__ )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = max_cross_sum(a__ , a__ , a__ , a__ )
if left_sum >= right_sum and left_sum >= cross_sum:
return left_low, left_high, left_sum
elif right_sum >= left_sum and right_sum >= cross_sum:
return right_low, right_high, right_sum
return cross_left, cross_right, cross_sum
def lowerCAmelCase__ ( a__: Sequence[float] , a__: int , a__: int , a__: int ) -> tuple[int, int, float]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = float('-inf' ), -1
_UpperCAmelCase , _UpperCAmelCase = float('-inf' ), -1
_UpperCAmelCase = 0
for i in range(a__ , low - 1 , -1 ):
summ += arr[i]
if summ > left_sum:
_UpperCAmelCase = summ
_UpperCAmelCase = i
_UpperCAmelCase = 0
for i in range(mid + 1 , high + 1 ):
summ += arr[i]
if summ > right_sum:
_UpperCAmelCase = summ
_UpperCAmelCase = i
return max_left, max_right, (left_sum + right_sum)
def lowerCAmelCase__ ( a__: int ) -> float:
'''simple docstring'''
_UpperCAmelCase = [randint(1 , a__ ) for _ in range(a__ )]
_UpperCAmelCase = time.time()
max_subarray(a__ , 0 , input_size - 1 )
_UpperCAmelCase = time.time()
return end - start
def lowerCAmelCase__ ( ) -> None:
'''simple docstring'''
_UpperCAmelCase = [1_0, 1_0_0, 1_0_0_0, 1_0_0_0_0, 5_0_0_0_0, 1_0_0_0_0_0, 2_0_0_0_0_0, 3_0_0_0_0_0, 4_0_0_0_0_0, 5_0_0_0_0_0]
_UpperCAmelCase = [time_max_subarray(a__ ) for input_size in input_sizes]
print('No of Inputs\t\tTime Taken' )
for input_size, runtime in zip(a__ , a__ ):
print(a__ , '\t\t' , a__ )
plt.plot(a__ , a__ )
plt.xlabel('Number of Inputs' )
plt.ylabel('Time taken in seconds' )
plt.show()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 185 | 0 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
if is_tf_available():
import tensorflow as tf
if is_tensorflow_text_available():
from transformers.models.bert import TFBertTokenizer
lowercase : Dict = ["""bert-base-uncased""", """bert-base-cased"""]
lowercase : Optional[Any] = """hf-internal-testing/tiny-bert-tf-only"""
if is_tf_available():
class A__ ( tf.keras.Model ):
"""simple docstring"""
def __init__( self , lowercase) -> Dict:
'''simple docstring'''
super().__init__()
a__ : Tuple = tokenizer
a__ : int = AutoConfig.from_pretrained(lowercase)
a__ : Dict = TFAutoModel.from_config(lowercase)
def __lowercase ( self , lowercase) -> Tuple:
'''simple docstring'''
a__ : List[Any] = self.tokenizer(lowercase)
a__ : Dict = self.bert(**lowercase)
return out["pooler_output"]
@require_tf
@require_tensorflow_text
class A__ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self) -> List[Any]:
'''simple docstring'''
super().setUp()
a__ : int = [
BertTokenizer.from_pretrained(lowercase) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
] # repeat for when fast_bert_tokenizer=false
a__ : Union[str, Any] = [TFBertTokenizer.from_pretrained(lowercase) for checkpoint in TOKENIZER_CHECKPOINTS] + [
TFBertTokenizer.from_pretrained(lowercase , use_fast_bert_tokenizer=lowercase)
for checkpoint in TOKENIZER_CHECKPOINTS
]
assert len(self.tokenizers) == len(self.tf_tokenizers)
a__ : Tuple = [
'This is a straightforward English test sentence.',
'This one has some weird characters\rto\nsee\r\nif those\u00E9break things.',
'Now we\'re going to add some Chinese: 一 二 三 一二三',
'And some much more rare Chinese: 齉 堃 齉堃',
'Je vais aussi écrire en français pour tester les accents',
'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
]
a__ : Optional[Any] = list(zip(self.test_sentences , self.test_sentences[::-1]))
def __lowercase ( self) -> Dict:
'''simple docstring'''
for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers):
for test_inputs in (self.test_sentences, self.paired_sentences):
a__ : Tuple = tokenizer(lowercase , return_tensors='tf' , padding='longest')
a__ : Any = tf_tokenizer(lowercase)
for key in python_outputs.keys():
self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape))
self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key] , tf.intaa) == tf_outputs[key]))
@slow
def __lowercase ( self) -> Optional[Any]:
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
a__ : Dict = tf_tokenizer(self.paired_sentences)
a__ : Tuple = tf_tokenizer(
text=[sentence[0] for sentence in self.paired_sentences] , text_pair=[sentence[1] for sentence in self.paired_sentences] , )
for key in merged_outputs.keys():
self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key] , tf.intaa) == separated_outputs[key]))
@slow
def __lowercase ( self) -> int:
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
a__ : Tuple = tf.function(lowercase)
for test_inputs in (self.test_sentences, self.paired_sentences):
a__ : Optional[int] = tf.constant(lowercase)
a__ : Optional[Any] = compiled_tokenizer(lowercase)
a__ : Union[str, Any] = tf_tokenizer(lowercase)
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))
@slow
def __lowercase ( self) -> str:
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
a__ : str = ModelToSave(tokenizer=lowercase)
a__ : str = tf.convert_to_tensor(self.test_sentences)
a__ : List[Any] = model(lowercase) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
a__ : Optional[int] = Path(lowercase) / 'saved.model'
model.save(lowercase)
a__ : Any = tf.keras.models.load_model(lowercase)
a__ : List[Any] = loaded_model(lowercase)
# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output)) , 1e-5)
| 99 |
def A_ ( A__ , A__ ) -> str:
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
a__ : List[str] = str(bin(A__ ) )[2:] # remove the leading "0b"
a__ : Optional[int] = str(bin(A__ ) )[2:] # remove the leading "0b"
a__ : List[str] = max(len(A__ ) , len(A__ ) )
return "0b" + "".join(
str(int(char_a == '1' and char_b == '1' ) )
for char_a, char_b in zip(a_binary.zfill(A__ ) , b_binary.zfill(A__ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 99 | 1 |
'''simple docstring'''
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
renew_vae_resnet_paths,
)
def a ( __a , __a ) -> int:
'''simple docstring'''
UpperCamelCase__ :Optional[Any] = checkpoint
UpperCamelCase__ :Optional[int] = {}
UpperCamelCase__ :Any = vae_state_dict['''encoder.conv_in.weight''']
UpperCamelCase__ :Union[str, Any] = vae_state_dict['''encoder.conv_in.bias''']
UpperCamelCase__ :List[Any] = vae_state_dict['''encoder.conv_out.weight''']
UpperCamelCase__ :List[str] = vae_state_dict['''encoder.conv_out.bias''']
UpperCamelCase__ :Optional[Any] = vae_state_dict['''encoder.norm_out.weight''']
UpperCamelCase__ :Any = vae_state_dict['''encoder.norm_out.bias''']
UpperCamelCase__ :Any = vae_state_dict['''decoder.conv_in.weight''']
UpperCamelCase__ :List[Any] = vae_state_dict['''decoder.conv_in.bias''']
UpperCamelCase__ :Union[str, Any] = vae_state_dict['''decoder.conv_out.weight''']
UpperCamelCase__ :Union[str, Any] = vae_state_dict['''decoder.conv_out.bias''']
UpperCamelCase__ :Any = vae_state_dict['''decoder.norm_out.weight''']
UpperCamelCase__ :List[str] = vae_state_dict['''decoder.norm_out.bias''']
UpperCamelCase__ :Tuple = vae_state_dict['''quant_conv.weight''']
UpperCamelCase__ :Optional[int] = vae_state_dict['''quant_conv.bias''']
UpperCamelCase__ :List[str] = vae_state_dict['''post_quant_conv.weight''']
UpperCamelCase__ :int = vae_state_dict['''post_quant_conv.bias''']
# Retrieves the keys for the encoder down blocks only
UpperCamelCase__ :List[Any] = len({'''.'''.join(layer.split('''.''' )[:3] ) for layer in vae_state_dict if '''encoder.down''' in layer} )
UpperCamelCase__ :int = {
layer_id: [key for key in vae_state_dict if f'''down.{layer_id}''' in key] for layer_id in range(__a )
}
# Retrieves the keys for the decoder up blocks only
UpperCamelCase__ :Tuple = len({'''.'''.join(layer.split('''.''' )[:3] ) for layer in vae_state_dict if '''decoder.up''' in layer} )
UpperCamelCase__ :Optional[Any] = {
layer_id: [key for key in vae_state_dict if f'''up.{layer_id}''' in key] for layer_id in range(__a )
}
for i in range(__a ):
UpperCamelCase__ :Union[str, Any] = [key for key in down_blocks[i] if f'''down.{i}''' in key and f'''down.{i}.downsample''' not in key]
if f'''encoder.down.{i}.downsample.conv.weight''' in vae_state_dict:
UpperCamelCase__ :Dict = vae_state_dict.pop(
f'''encoder.down.{i}.downsample.conv.weight''' )
UpperCamelCase__ :str = vae_state_dict.pop(
f'''encoder.down.{i}.downsample.conv.bias''' )
UpperCamelCase__ :Optional[Any] = renew_vae_resnet_paths(__a )
UpperCamelCase__ :Union[str, Any] = {'''old''': f'''down.{i}.block''', '''new''': f'''down_blocks.{i}.resnets'''}
assign_to_checkpoint(__a , __a , __a , additional_replacements=[meta_path] , config=__a )
UpperCamelCase__ :Optional[Any] = [key for key in vae_state_dict if '''encoder.mid.block''' in key]
UpperCamelCase__ :Dict = 2
for i in range(1 , num_mid_res_blocks + 1 ):
UpperCamelCase__ :Dict = [key for key in mid_resnets if f'''encoder.mid.block_{i}''' in key]
UpperCamelCase__ :Optional[Any] = renew_vae_resnet_paths(__a )
UpperCamelCase__ :Dict = {'''old''': f'''mid.block_{i}''', '''new''': f'''mid_block.resnets.{i - 1}'''}
assign_to_checkpoint(__a , __a , __a , additional_replacements=[meta_path] , config=__a )
UpperCamelCase__ :str = [key for key in vae_state_dict if '''encoder.mid.attn''' in key]
UpperCamelCase__ :Union[str, Any] = renew_vae_attention_paths(__a )
UpperCamelCase__ :int = {'''old''': '''mid.attn_1''', '''new''': '''mid_block.attentions.0'''}
assign_to_checkpoint(__a , __a , __a , additional_replacements=[meta_path] , config=__a )
conv_attn_to_linear(__a )
for i in range(__a ):
UpperCamelCase__ :int = num_up_blocks - 1 - i
UpperCamelCase__ :int = [
key for key in up_blocks[block_id] if f'''up.{block_id}''' in key and f'''up.{block_id}.upsample''' not in key
]
if f'''decoder.up.{block_id}.upsample.conv.weight''' in vae_state_dict:
UpperCamelCase__ :Tuple = vae_state_dict[
f'''decoder.up.{block_id}.upsample.conv.weight'''
]
UpperCamelCase__ :Dict = vae_state_dict[
f'''decoder.up.{block_id}.upsample.conv.bias'''
]
UpperCamelCase__ :Union[str, Any] = renew_vae_resnet_paths(__a )
UpperCamelCase__ :Union[str, Any] = {'''old''': f'''up.{block_id}.block''', '''new''': f'''up_blocks.{i}.resnets'''}
assign_to_checkpoint(__a , __a , __a , additional_replacements=[meta_path] , config=__a )
UpperCamelCase__ :List[str] = [key for key in vae_state_dict if '''decoder.mid.block''' in key]
UpperCamelCase__ :Tuple = 2
for i in range(1 , num_mid_res_blocks + 1 ):
UpperCamelCase__ :int = [key for key in mid_resnets if f'''decoder.mid.block_{i}''' in key]
UpperCamelCase__ :int = renew_vae_resnet_paths(__a )
UpperCamelCase__ :str = {'''old''': f'''mid.block_{i}''', '''new''': f'''mid_block.resnets.{i - 1}'''}
assign_to_checkpoint(__a , __a , __a , additional_replacements=[meta_path] , config=__a )
UpperCamelCase__ :Any = [key for key in vae_state_dict if '''decoder.mid.attn''' in key]
UpperCamelCase__ :Any = renew_vae_attention_paths(__a )
UpperCamelCase__ :List[Any] = {'''old''': '''mid.attn_1''', '''new''': '''mid_block.attentions.0'''}
assign_to_checkpoint(__a , __a , __a , additional_replacements=[meta_path] , config=__a )
conv_attn_to_linear(__a )
return new_checkpoint
def a ( __a , __a , ) -> List[str]:
'''simple docstring'''
UpperCamelCase__ :Optional[int] = requests.get(
''' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml''' )
UpperCamelCase__ :Union[str, Any] = io.BytesIO(r.content )
UpperCamelCase__ :Any = OmegaConf.load(__a )
UpperCamelCase__ :List[Any] = 512
UpperCamelCase__ :int = '''cuda''' if torch.cuda.is_available() else '''cpu'''
if checkpoint_path.endswith('''safetensors''' ):
from safetensors import safe_open
UpperCamelCase__ :Union[str, Any] = {}
with safe_open(__a , framework='''pt''' , device='''cpu''' ) as f:
for key in f.keys():
UpperCamelCase__ :List[Any] = f.get_tensor(__a )
else:
UpperCamelCase__ :List[str] = torch.load(__a , map_location=__a )['''state_dict''']
# Convert the VAE model.
UpperCamelCase__ :str = create_vae_diffusers_config(__a , image_size=__a )
UpperCamelCase__ :Union[str, Any] = custom_convert_ldm_vae_checkpoint(__a , __a )
UpperCamelCase__ :Dict = AutoencoderKL(**__a )
vae.load_state_dict(__a )
vae.save_pretrained(__a )
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
parser.add_argument('''--vae_pt_path''', default=None, type=str, required=True, help='''Path to the VAE.pt to convert.''')
parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the VAE.pt to convert.''')
__snake_case = parser.parse_args()
vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)
| 350 |
'''simple docstring'''
from math import ceil
def a ( __a , __a ) -> Any:
'''simple docstring'''
UpperCamelCase__ :str = list(range(0 , __a ) )
UpperCamelCase__ :Optional[int] = [item for sublist in list(device_map.values() ) for item in sublist]
# Duplicate check
UpperCamelCase__ :Optional[int] = []
for i in device_map_blocks:
if device_map_blocks.count(__a ) > 1 and i not in duplicate_blocks:
duplicate_blocks.append(__a )
# Missing blocks
UpperCamelCase__ :List[str] = [i for i in blocks if i not in device_map_blocks]
UpperCamelCase__ :Optional[Any] = [i for i in device_map_blocks if i not in blocks]
if len(__a ) != 0:
raise ValueError(
'''Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device.'''
''' These attention blocks were specified more than once: ''' + str(__a ) )
if len(__a ) != 0:
raise ValueError(
'''There are attention blocks for this model that are not specified in the device_map. Add these attention '''
'''blocks to a device on the device_map: ''' + str(__a ) )
if len(__a ) != 0:
raise ValueError(
'''The device_map contains more attention blocks than this model has. Remove these from the device_map:'''
+ str(__a ) )
def a ( __a , __a ) -> Tuple:
'''simple docstring'''
UpperCamelCase__ :Optional[Any] = list(range(__a ) )
UpperCamelCase__ :Any = int(ceil(n_layers / len(__a ) ) )
UpperCamelCase__ :List[Any] = [layers[i : i + n_blocks] for i in range(0 , __a , __a )]
return dict(zip(__a , __a ) ) | 219 | 0 |
"""simple docstring"""
def __lowerCAmelCase ( lowercase : int ) -> bool:
"""simple docstring"""
snake_case : List[str] = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 203 |
"""simple docstring"""
from typing import Dict
from .base import GenericTensor, Pipeline
class _lowerCAmelCase ( snake_case_ ):
def lowerCamelCase ( self , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=None , **UpperCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
if tokenize_kwargs is None:
snake_case : Optional[Any] = {}
if truncation is not None:
if "truncation" in tokenize_kwargs:
raise ValueError(
"truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)" )
snake_case : List[str] = truncation
snake_case : Union[str, Any] = tokenize_kwargs
snake_case : List[Any] = {}
if return_tensors is not None:
snake_case : Tuple = return_tensors
return preprocess_params, {}, postprocess_params
def lowerCamelCase ( self , UpperCamelCase__ , **UpperCamelCase__ ) -> Dict[str, GenericTensor]:
'''simple docstring'''
snake_case : List[Any] = self.framework
snake_case : str = self.tokenizer(UpperCamelCase__ , return_tensors=UpperCamelCase__ , **UpperCamelCase__ )
return model_inputs
def lowerCamelCase ( self , UpperCamelCase__ ) -> Tuple:
'''simple docstring'''
snake_case : int = self.model(**UpperCamelCase__ )
return model_outputs
def lowerCamelCase ( self , UpperCamelCase__ , UpperCamelCase__=False ) -> Union[str, Any]:
'''simple docstring'''
if return_tensors:
return model_outputs[0]
if self.framework == "pt":
return model_outputs[0].tolist()
elif self.framework == "tf":
return model_outputs[0].numpy().tolist()
def __call__( self , *UpperCamelCase__ , **UpperCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
return super().__call__(*UpperCamelCase__ , **UpperCamelCase__ )
| 203 | 1 |
"""simple docstring"""
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
)
from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST
class snake_case :
def __init__( self : Tuple , UpperCamelCase__ : List[Any] , UpperCamelCase__ : int=1_3 , UpperCamelCase__ : Tuple=7 , UpperCamelCase__ : Dict=True , UpperCamelCase__ : Dict=True , UpperCamelCase__ : Union[str, Any]=True , UpperCamelCase__ : Tuple=True , UpperCamelCase__ : int=9_9 , UpperCamelCase__ : List[Any]=2_4 , UpperCamelCase__ : List[str]=2 , UpperCamelCase__ : Union[str, Any]=6 , UpperCamelCase__ : Dict=3_7 , UpperCamelCase__ : int="gelu" , UpperCamelCase__ : Dict=0.1 , UpperCamelCase__ : Optional[Any]=0.1 , UpperCamelCase__ : Dict=5_1_2 , UpperCamelCase__ : Dict=1_6 , UpperCamelCase__ : Dict=2 , UpperCamelCase__ : Any=0.02 , UpperCamelCase__ : Dict=3 , UpperCamelCase__ : Any=None , UpperCamelCase__ : Union[str, Any]=1_0_0_0 , )-> int:
'''simple docstring'''
__lowerCAmelCase: Any = parent
__lowerCAmelCase: Any = batch_size
__lowerCAmelCase: List[Any] = seq_length
__lowerCAmelCase: Optional[Any] = is_training
__lowerCAmelCase: List[Any] = use_input_mask
__lowerCAmelCase: List[Any] = use_token_type_ids
__lowerCAmelCase: Union[str, Any] = use_labels
__lowerCAmelCase: Optional[int] = vocab_size
__lowerCAmelCase: Union[str, Any] = hidden_size
__lowerCAmelCase: Optional[Any] = num_hidden_layers
__lowerCAmelCase: Optional[int] = num_attention_heads
__lowerCAmelCase: List[Any] = intermediate_size
__lowerCAmelCase: Dict = hidden_act
__lowerCAmelCase: List[Any] = hidden_dropout_prob
__lowerCAmelCase: Union[str, Any] = attention_probs_dropout_prob
__lowerCAmelCase: str = max_position_embeddings
__lowerCAmelCase: List[str] = type_vocab_size
__lowerCAmelCase: Optional[Any] = type_sequence_label_size
__lowerCAmelCase: str = initializer_range
__lowerCAmelCase: int = num_labels
__lowerCAmelCase: Union[str, Any] = scope
__lowerCAmelCase: Any = range_bbox
def lowercase_ ( self : Dict)-> List[str]:
'''simple docstring'''
__lowerCAmelCase: int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
__lowerCAmelCase: Any = ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox)
# Ensure that bbox is legal
for i in range(bbox.shape[0]):
for j in range(bbox.shape[1]):
if bbox[i, j, 3] < bbox[i, j, 1]:
__lowerCAmelCase: List[str] = bbox[i, j, 3]
__lowerCAmelCase: Union[str, Any] = bbox[i, j, 1]
__lowerCAmelCase: int = t
if bbox[i, j, 2] < bbox[i, j, 0]:
__lowerCAmelCase: Dict = bbox[i, j, 2]
__lowerCAmelCase: List[str] = bbox[i, j, 0]
__lowerCAmelCase: Optional[int] = t
__lowerCAmelCase: Optional[int] = None
if self.use_input_mask:
__lowerCAmelCase: List[str] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2)
__lowerCAmelCase: List[Any] = None
if self.use_token_type_ids:
__lowerCAmelCase: int = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
__lowerCAmelCase: str = None
__lowerCAmelCase: Union[str, Any] = None
if self.use_labels:
__lowerCAmelCase: Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size)
__lowerCAmelCase: Any = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
__lowerCAmelCase: Dict = self.get_config()
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
def lowercase_ ( self : Dict)-> List[Any]:
'''simple docstring'''
return LiltConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
def lowercase_ ( self : Dict , UpperCamelCase__ : int , UpperCamelCase__ : Dict , UpperCamelCase__ : str , UpperCamelCase__ : Any , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : str , UpperCamelCase__ : Optional[int] , )-> Optional[int]:
'''simple docstring'''
__lowerCAmelCase: Union[str, Any] = LiltModel(config=UpperCamelCase__)
model.to(UpperCamelCase__)
model.eval()
__lowerCAmelCase: Dict = model(UpperCamelCase__ , bbox=UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__)
__lowerCAmelCase: List[Any] = model(UpperCamelCase__ , bbox=UpperCamelCase__ , token_type_ids=UpperCamelCase__)
__lowerCAmelCase: Tuple = model(UpperCamelCase__ , bbox=UpperCamelCase__)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size))
def lowercase_ ( self : Optional[Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Dict , UpperCamelCase__ : Tuple , UpperCamelCase__ : Tuple , UpperCamelCase__ : Dict , UpperCamelCase__ : Optional[int] , )-> Dict:
'''simple docstring'''
__lowerCAmelCase: List[Any] = self.num_labels
__lowerCAmelCase: Tuple = LiltForTokenClassification(config=UpperCamelCase__)
model.to(UpperCamelCase__)
model.eval()
__lowerCAmelCase: str = model(
UpperCamelCase__ , bbox=UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ , labels=UpperCamelCase__)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels))
def lowercase_ ( self : List[Any] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : int , UpperCamelCase__ : Tuple , UpperCamelCase__ : Any , UpperCamelCase__ : Any , )-> Union[str, Any]:
'''simple docstring'''
__lowerCAmelCase: Union[str, Any] = LiltForQuestionAnswering(config=UpperCamelCase__)
model.to(UpperCamelCase__)
model.eval()
__lowerCAmelCase: List[str] = model(
UpperCamelCase__ , bbox=UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ , start_positions=UpperCamelCase__ , end_positions=UpperCamelCase__ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length))
def lowercase_ ( self : Optional[Any])-> Optional[int]:
'''simple docstring'''
__lowerCAmelCase: str = self.prepare_config_and_inputs()
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
): Optional[int] = config_and_inputs
__lowerCAmelCase: List[Any] = {
"input_ids": input_ids,
"bbox": bbox,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class snake_case ( __snake_case, __snake_case, __snake_case, unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : Any = (
(
LiltModel,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltForQuestionAnswering,
)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE_ : Union[str, Any] = (
{
"""feature-extraction""": LiltModel,
"""question-answering""": LiltForQuestionAnswering,
"""text-classification""": LiltForSequenceClassification,
"""token-classification""": LiltForTokenClassification,
"""zero-shot""": LiltForSequenceClassification,
}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE_ : Any = False
SCREAMING_SNAKE_CASE_ : Tuple = False
def lowercase_ ( self : List[str] , UpperCamelCase__ : Any , UpperCamelCase__ : Tuple , UpperCamelCase__ : Tuple , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : int)-> Dict:
'''simple docstring'''
return True
def lowercase_ ( self : List[Any])-> Dict:
'''simple docstring'''
__lowerCAmelCase: Dict = LiltModelTester(self)
__lowerCAmelCase: Optional[int] = ConfigTester(self , config_class=UpperCamelCase__ , hidden_size=3_7)
def lowercase_ ( self : Tuple)-> Any:
'''simple docstring'''
self.config_tester.run_common_tests()
def lowercase_ ( self : List[Any])-> Union[str, Any]:
'''simple docstring'''
__lowerCAmelCase: List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase__)
def lowercase_ ( self : str)-> int:
'''simple docstring'''
__lowerCAmelCase: Dict = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__lowerCAmelCase: List[Any] = type
self.model_tester.create_and_check_model(*UpperCamelCase__)
def lowercase_ ( self : str)-> Union[str, Any]:
'''simple docstring'''
__lowerCAmelCase: List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__)
def lowercase_ ( self : Tuple)-> int:
'''simple docstring'''
__lowerCAmelCase: List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__)
@slow
def lowercase_ ( self : Dict)-> List[str]:
'''simple docstring'''
for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCAmelCase: Any = LiltModel.from_pretrained(UpperCamelCase__)
self.assertIsNotNone(UpperCamelCase__)
@require_torch
@slow
class snake_case ( unittest.TestCase ):
def lowercase_ ( self : Dict)-> Any:
'''simple docstring'''
__lowerCAmelCase: Union[str, Any] = LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base").to(UpperCamelCase__)
__lowerCAmelCase: Optional[int] = torch.tensor([[1, 2]] , device=UpperCamelCase__)
__lowerCAmelCase: int = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]] , device=UpperCamelCase__)
# forward pass
with torch.no_grad():
__lowerCAmelCase: str = model(input_ids=UpperCamelCase__ , bbox=UpperCamelCase__)
__lowerCAmelCase: Optional[int] = torch.Size([1, 2, 7_6_8])
__lowerCAmelCase: Optional[int] = torch.tensor(
[[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]] , device=UpperCamelCase__ , )
self.assertTrue(outputs.last_hidden_state.shape , UpperCamelCase__)
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3] , UpperCamelCase__ , atol=1e-3))
| 108 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
__A = {
"configuration_speecht5": [
"SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP",
"SpeechT5Config",
"SpeechT5HifiGanConfig",
],
"feature_extraction_speecht5": ["SpeechT5FeatureExtractor"],
"processing_speecht5": ["SpeechT5Processor"],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = ["SpeechT5Tokenizer"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
"SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST",
"SpeechT5ForSpeechToText",
"SpeechT5ForSpeechToSpeech",
"SpeechT5ForTextToSpeech",
"SpeechT5Model",
"SpeechT5PreTrainedModel",
"SpeechT5HifiGan",
]
if TYPE_CHECKING:
from .configuration_speechta import (
SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
SpeechTaConfig,
SpeechTaHifiGanConfig,
)
from .feature_extraction_speechta import SpeechTaFeatureExtractor
from .processing_speechta import SpeechTaProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speechta import SpeechTaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speechta import (
SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaHifiGan,
SpeechTaModel,
SpeechTaPreTrainedModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 108 | 1 |
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizerOutput
from transformers.testing_utils import require_phonemizer
from ...test_tokenization_common import TokenizerTesterMixin
@require_phonemizer
class __A ( lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase_ = WavaVecaPhonemeCTCTokenizer
lowerCAmelCase_ = False
def __lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
lowerCamelCase__ = (
'''<s> <pad> </s> <unk> n s t ə l a i k d m ɛ ɾ e ɪ p o ɐ z ð f j v b ɹ ʁ ʊ iː r w ʌ u ɡ æ aɪ ʃ h ɔ ɑː '''
'''ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː '''
'''ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 '''
'''oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ '''
'''pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ '''
'''yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ '''
'''əʊ S ɡʲ onɡ2 u" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ '''
'''ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ '''
'''ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ '''
'''uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ '''
'''ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ '''
'''ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ '''
'''ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4'''
).split(''' ''' )
lowerCamelCase__ = dict(zip(__lowerCAmelCase , range(len(__lowerCAmelCase ) ) ) )
lowerCamelCase__ = {'''pad_token''': '''<pad>''', '''unk_token''': '''<unk>''', '''bos_token''': '''<s>''', '''eos_token''': '''</s>'''}
lowerCamelCase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(__lowerCAmelCase ) + '''\n''' )
def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase=False , __lowerCAmelCase=2_0 , __lowerCAmelCase=5 ):
'''simple docstring'''
lowerCamelCase__ = [(i, tokenizer.decode([i] , clean_up_tokenization_spaces=__lowerCAmelCase )) for i in range(len(__lowerCAmelCase ) )]
lowerCamelCase__ = list(filter(lambda __lowerCAmelCase : [t[0]] == tokenizer.encode(t[1] , do_phonemize=__lowerCAmelCase ) , __lowerCAmelCase ) )
if max_length is not None and len(__lowerCAmelCase ) > max_length:
lowerCamelCase__ = toks[:max_length]
if min_length is not None and len(__lowerCAmelCase ) < min_length and len(__lowerCAmelCase ) > 0:
while len(__lowerCAmelCase ) < min_length:
lowerCamelCase__ = toks + toks
# toks_str = [t[1] for t in toks]
lowerCamelCase__ = [t[0] for t in toks]
# Ensure consistency
lowerCamelCase__ = tokenizer.decode(__lowerCAmelCase , clean_up_tokenization_spaces=__lowerCAmelCase )
if " " not in output_txt and len(__lowerCAmelCase ) > 1:
lowerCamelCase__ = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=__lowerCAmelCase )
+ ''' '''
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=__lowerCAmelCase )
)
if with_prefix_space:
lowerCamelCase__ = ''' ''' + output_txt
lowerCamelCase__ = tokenizer.encode(__lowerCAmelCase , add_special_tokens=__lowerCAmelCase )
return output_txt, output_ids
def __lowerCamelCase ( self , **__lowerCAmelCase ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname , **__lowerCAmelCase )
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''' )
# check adding a single token
tokenizer.add_tokens('''xxx''' )
lowerCamelCase__ = tokenizer('''m xxx ɪ''' , do_phonemize=__lowerCAmelCase ).input_ids
self.assertEqual(__lowerCAmelCase , [1_3, 3_9_2, 1_7] ) # xxx should be last token
tokenizer.add_tokens(['''aaa''', '''bbb''', '''ccc'''] )
lowerCamelCase__ = tokenizer('''m aaa ɪ ccc''' , do_phonemize=__lowerCAmelCase ).input_ids
self.assertEqual(__lowerCAmelCase , [1_3, 3_9_3, 1_7, 3_9_5] ) # aaa and ccc should be after xxx and 2 after aaa
lowerCamelCase__ = tokenizer('''maɪ c''' , do_phonemize=__lowerCAmelCase ).input_ids
self.assertEqual(__lowerCAmelCase , [3, 2_0_0] ) # mai should be <unk> (=3)
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''' )
lowerCamelCase__ = '''Hello how are you'''
lowerCamelCase__ = tokenizer.phonemize(__lowerCAmelCase , phonemizer_lang='''en-us''' )
self.assertEqual(__lowerCAmelCase , '''h ə l oʊ h aʊ ɑːɹ j uː''' )
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''' )
lowerCamelCase__ = '''Hello how are you'''
lowerCamelCase__ = tokenizer.phonemize(__lowerCAmelCase , phonemizer_lang='''en-us''' )
self.assertEqual(tokenizer(__lowerCAmelCase ).input_ids , tokenizer(__lowerCAmelCase , do_phonemize=__lowerCAmelCase ).input_ids )
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''' )
lowerCamelCase__ = '''Hello how are you'''
lowerCamelCase__ = tokenizer.phonemize(__lowerCAmelCase , phonemizer_lang='''en-us''' )
lowerCamelCase__ = tokenizer.decode(tokenizer(__lowerCAmelCase ).input_ids )
self.assertEqual(__lowerCAmelCase , __lowerCAmelCase )
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''' )
lowerCamelCase__ = [
[1_1, 5, 1_5, tokenizer.pad_token_id, 1_5, 8, 9_8],
[2_4, 2_2, 5, 2_4, 2_2, 5, 7_7],
]
lowerCamelCase__ = tokenizer.decode(sample_ids[0] )
lowerCamelCase__ = tokenizer.batch_decode(__lowerCAmelCase )
self.assertEqual(__lowerCAmelCase , batch_tokens[0] )
self.assertEqual(__lowerCAmelCase , ['''k s ɾ ɾ l ɭʲ''', '''j ð s j ð s oːɹ'''] )
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = self.tokenizer_class.from_pretrained(
'''facebook/wav2vec2-lv-60-espeak-cv-ft''' , word_delimiter_token='''|''' )
tokenizer.add_tokens('''|''' )
lowerCamelCase__ = '''Hello how are you'''
lowerCamelCase__ = tokenizer.phonemize(__lowerCAmelCase , phonemizer_lang='''en-us''' )
self.assertEqual(__lowerCAmelCase , '''h ə l oʊ | h aʊ | ɑːɹ | j uː |''' )
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = self.tokenizer_class.from_pretrained(
'''facebook/wav2vec2-lv-60-espeak-cv-ft''' , word_delimiter_token='''|''' )
tokenizer.add_tokens('''|''' )
lowerCamelCase__ = '''Hello how are you'''
lowerCamelCase__ = tokenizer.phonemize(__lowerCAmelCase , phonemizer_lang='''en-us''' )
self.assertEqual(tokenizer(__lowerCAmelCase ).input_ids , tokenizer(__lowerCAmelCase , do_phonemize=__lowerCAmelCase ).input_ids )
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = self.tokenizer_class.from_pretrained(
'''facebook/wav2vec2-lv-60-espeak-cv-ft''' , word_delimiter_token='''|''' )
tokenizer.add_tokens('''|''' )
# fmt: off
lowerCamelCase__ = [
[1_1, 5, 1_5, tokenizer.pad_token_id, tokenizer.word_delimiter_token_id, 1_5, 8, tokenizer.word_delimiter_token_id, 9_8],
[tokenizer.word_delimiter_token_id, 2_4, 2_2, tokenizer.word_delimiter_token_id, 5, 2_4, 2_2, 5, 7_7],
]
# fmt: on
# decode with word_del_token filter
lowerCamelCase__ = tokenizer.decode(sample_ids[0] )
lowerCamelCase__ = tokenizer.batch_decode(__lowerCAmelCase )
self.assertEqual(__lowerCAmelCase , batch_tokens[0] )
self.assertEqual(__lowerCAmelCase , ['''k s ɾ ɾ l ɭʲ''', '''j ð s j ð s oːɹ'''] )
# decode with no word_del_token filter
lowerCamelCase__ = tokenizer.decode(sample_ids[0] , filter_word_delimiter_token=__lowerCAmelCase )
lowerCamelCase__ = tokenizer.batch_decode(__lowerCAmelCase , filter_word_delimiter_token=__lowerCAmelCase )
self.assertEqual(__lowerCAmelCase , batch_tokens[0] )
self.assertEqual(__lowerCAmelCase , ['''k s ɾ | ɾ l | ɭʲ''', '''| j ð | s j ð s oːɹ'''] )
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = self.tokenizer_class.from_pretrained(
'''facebook/wav2vec2-lv-60-espeak-cv-ft''' , word_delimiter_token='''|''' )
tokenizer.add_tokens('''|''' )
lowerCamelCase__ = '''Hello how are you'''
lowerCamelCase__ = tokenizer.phonemize(__lowerCAmelCase , phonemizer_lang='''en-us''' )
lowerCamelCase__ = tokenizer.decode(tokenizer(__lowerCAmelCase ).input_ids , filter_word_delimiter_token=__lowerCAmelCase )
self.assertEqual(__lowerCAmelCase , __lowerCAmelCase )
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = self.tokenizer_class.from_pretrained(
'''facebook/wav2vec2-lv-60-espeak-cv-ft''' , word_delimiter_token='''|''' )
tokenizer.add_tokens('''|''' )
lowerCamelCase__ = '''Hello how are you'''
lowerCamelCase__ = tokenizer.phonemize(__lowerCAmelCase , phonemizer_lang='''en-us''' )
lowerCamelCase__ = tokenizer.decode(tokenizer(__lowerCAmelCase ).input_ids , filter_word_delimiter_token=__lowerCAmelCase )
self.assertEqual(''' '''.join([p.strip() for p in phonemes.split(''' |''' )] ).strip() , __lowerCAmelCase )
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = self.tokenizer_class.from_pretrained(
'''facebook/wav2vec2-lv-60-espeak-cv-ft''' , word_delimiter_token=__lowerCAmelCase )
lowerCamelCase__ = '''Hello how are you'''
lowerCamelCase__ = tokenizer(__lowerCAmelCase , phonemizer_lang='''en-us''' ).input_ids
lowerCamelCase__ = tokenizer(__lowerCAmelCase , phonemizer_lang='''fr-fr''' ).input_ids
self.assertNotEqual(__lowerCAmelCase , __lowerCAmelCase )
lowerCamelCase__ = tokenizer.decode(__lowerCAmelCase )
lowerCamelCase__ = tokenizer.decode(__lowerCAmelCase )
self.assertEqual(__lowerCAmelCase , '''h ə l oʊ h aʊ ɑːɹ j uː''' )
self.assertEqual(__lowerCAmelCase , '''ɛ l o h aʊ a ʁ j u''' )
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''' )
lowerCamelCase__ = '''Hello how Are you'''
lowerCamelCase__ = '''hello how are you'''
lowerCamelCase__ = tokenizer(__lowerCAmelCase ).input_ids
lowerCamelCase__ = tokenizer(__lowerCAmelCase ).input_ids
self.assertEqual(__lowerCAmelCase , __lowerCAmelCase )
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''' )
tokenizer.add_tokens(['''!''', '''?'''] )
tokenizer.add_special_tokens({'''cls_token''': '''$$$'''} )
# fmt: off
lowerCamelCase__ = [
[1_1, 5, 1_5, tokenizer.pad_token_id, 1_5, 8, 9_8, 3_9_2, 3_9_2, 3_9_3, 3_9_2, 3_9_2, 3_9_3, 3_9_4, 3_9_4],
[2_4, 2_2, 5, 2_4, 2_2, 5, 7_7, tokenizer.pad_token_id, 3_9_4, 3_9_4],
]
# fmt: on
lowerCamelCase__ = tokenizer.batch_decode(__lowerCAmelCase )
self.assertEqual(__lowerCAmelCase , ['''k s ɾ ɾ l ɭʲ!?!? $$$''', '''j ð s j ð s oːɹ $$$'''] )
@staticmethod
def __lowerCamelCase ( __lowerCAmelCase , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = [d[key] for d in offsets]
return retrieved_list
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = self.get_tokenizer(word_delimiter_token='''|''' )
tokenizer.add_tokens('''|''' )
# fmt: off
# ksssɾɾ|ɾɾ<pad>ɾɾ|<pad>ɾlll|ɭʲ -> k s ɾ ɾ | ɾ l | ɭʲ"
lowerCamelCase__ = [1_1, 5, 5, 5, 1_5, 1_5, tokenizer.pad_token_id, 1_5, 1_5, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 1_5, 8, 8, 8, tokenizer.word_delimiter_token_id, 9_8]
# fmt: on
lowerCamelCase__ = tokenizer.decode(__lowerCAmelCase , output_char_offsets=__lowerCAmelCase , filter_word_delimiter_token=__lowerCAmelCase )
# check Wav2Vec2CTCTokenizerOutput keys for char
self.assertEqual(len(outputs.keys() ) , 2 )
self.assertTrue('''text''' in outputs )
self.assertTrue('''char_offsets''' in outputs )
self.assertTrue(isinstance(__lowerCAmelCase , __lowerCAmelCase ) )
# check that order of chars is correct and identical for both outputs
self.assertEqual(''' '''.join(self.get_from_offsets(outputs['''char_offsets'''] , '''char''' ) ) , outputs.text )
self.assertListEqual(
self.get_from_offsets(outputs['''char_offsets'''] , '''char''' ) , ['''k''', '''s''', '''ɾ''', '''ɾ''', '''|''', '''ɾ''', '''l''', '''|''', '''ɭʲ'''] )
# check that offsets are actually correct for char
# 0-1 is 11, 1-4 is 5, 4-6 is first 15, 6-7 is <pad> (thus not shown), 7-9 is second 15, 9-10 is word_delimiter_token,
# 10-11 is <pad> (thus not shown), 11-12 is third 15, 12-15 is 8, 15-16 is word_delimiter_token, 16-17 is 98
self.assertListEqual(
self.get_from_offsets(outputs['''char_offsets'''] , '''start_offset''' ) , [0, 1, 4, 7, 9, 1_1, 1_2, 1_5, 1_6] )
self.assertListEqual(
self.get_from_offsets(outputs['''char_offsets'''] , '''end_offset''' ) , [1, 4, 6, 9, 1_0, 1_2, 1_5, 1_6, 1_7] )
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = self.get_tokenizer(word_delimiter_token='''|''' )
def check_list_tuples_equal(__lowerCAmelCase , __lowerCAmelCase ):
self.assertTrue(isinstance(__lowerCAmelCase , __lowerCAmelCase ) )
self.assertTrue(isinstance(outputs_list[0] , __lowerCAmelCase ) )
# transform list to ModelOutput
lowerCamelCase__ = WavaVecaPhonemeCTCTokenizerOutput(
{k: [d[k] for d in outputs_list] for k in outputs_list[0]} )
self.assertListEqual(outputs_batch['''text'''] , outputs_batch_a['''text'''] )
def recursive_check(__lowerCAmelCase , __lowerCAmelCase ):
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
[recursive_check(__lowerCAmelCase , __lowerCAmelCase ) for la, la in zip(__lowerCAmelCase , __lowerCAmelCase )]
self.assertEqual(__lowerCAmelCase , __lowerCAmelCase )
if "char_offsets" in outputs_batch:
recursive_check(outputs_batch['''char_offsets'''] , outputs_batch_a['''char_offsets'''] )
# fmt: off
lowerCamelCase__ = [
[1_1, 5, 1_5, tokenizer.pad_token_id, 1_5, 4, 8, 9_8, 3_2, 3_2, 3_2, 3_2, 4, 3_3, tokenizer.word_delimiter_token_id, 3_2, 3_2, 3_3, 3_4, 3_4],
[2_4, 2_2, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 2_4, 2_2, 2_2, 2_2, 4, 5, 7_7, tokenizer.pad_token_id, 2_2, 2_2, 4, 3_4, 3_4, 3_4, 3_4],
]
# fmt: on
# We assume that `decode` works as expected. All we will check now is
# the output type is correct and the output is identical to `decode`
# char
lowerCamelCase__ = tokenizer.batch_decode(__lowerCAmelCase , output_char_offsets=__lowerCAmelCase )
lowerCamelCase__ = [tokenizer.decode(__lowerCAmelCase , output_char_offsets=__lowerCAmelCase ) for ids in sample_ids]
check_list_tuples_equal(__lowerCAmelCase , __lowerCAmelCase )
@unittest.skip('''Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes''' )
def __lowerCamelCase ( self ):
'''simple docstring'''
pass
@unittest.skip('''Wav2Vec2PhonemeTokenizer always puts spaces between phonemes''' )
def __lowerCamelCase ( self ):
'''simple docstring'''
pass
@unittest.skip('''encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency''' )
def __lowerCamelCase ( self ):
'''simple docstring'''
pass
@unittest.skip('''Wav2Vec2PhonemeModel has no max model length => no testing''' )
def __lowerCamelCase ( self ):
'''simple docstring'''
pass
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = self.get_tokenizers(do_lower_case=__lowerCAmelCase )
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
lowerCamelCase__ = tokenizer.vocab_size
lowerCamelCase__ = len(__lowerCAmelCase )
self.assertNotEqual(__lowerCAmelCase , 0 )
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
lowerCamelCase__ = ['''aaaaa bbbbbb''', '''cccccccccdddddddd''']
lowerCamelCase__ = tokenizer.add_tokens(__lowerCAmelCase )
lowerCamelCase__ = tokenizer.vocab_size
lowerCamelCase__ = len(__lowerCAmelCase )
self.assertNotEqual(__lowerCAmelCase , 0 )
self.assertEqual(__lowerCAmelCase , __lowerCAmelCase )
self.assertEqual(__lowerCAmelCase , len(__lowerCAmelCase ) )
self.assertEqual(__lowerCAmelCase , all_size + len(__lowerCAmelCase ) )
lowerCamelCase__ = tokenizer.encode('''aaaaa bbbbbb low cccccccccdddddddd l''' , add_special_tokens=__lowerCAmelCase )
self.assertGreaterEqual(len(__lowerCAmelCase ) , 4 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
lowerCamelCase__ = {'''eos_token''': '''>>>>|||<||<<|<<''', '''pad_token''': '''<<<<<|||>|>>>>|>'''}
lowerCamelCase__ = tokenizer.add_special_tokens(__lowerCAmelCase )
lowerCamelCase__ = tokenizer.vocab_size
lowerCamelCase__ = len(__lowerCAmelCase )
self.assertNotEqual(__lowerCAmelCase , 0 )
self.assertEqual(__lowerCAmelCase , __lowerCAmelCase )
self.assertEqual(__lowerCAmelCase , len(__lowerCAmelCase ) )
self.assertEqual(__lowerCAmelCase , all_size_a + len(__lowerCAmelCase ) )
lowerCamelCase__ = tokenizer.encode(
'''>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l''' , add_special_tokens=__lowerCAmelCase )
self.assertGreaterEqual(len(__lowerCAmelCase ) , 6 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[0] , tokens[1] )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokens[-4] )
self.assertEqual(tokens[0] , tokenizer.eos_token_id )
self.assertEqual(tokens[-3] , tokenizer.pad_token_id )
@unittest.skip('''The tokenizer shouldn\'t be used to encode input IDs (except for labels), only to decode.''' )
def __lowerCamelCase ( self ):
'''simple docstring'''
pass
@unittest.skip('''The tokenizer shouldn\'t be used to encode input IDs (except for labels), only to decode.''' )
def __lowerCamelCase ( self ):
'''simple docstring'''
pass
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = self.get_tokenizers(fast=__lowerCAmelCase , do_lower_case=__lowerCAmelCase )
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
lowerCamelCase__ = ['''ð''', '''ɪ''', '''s''', '''ɪ''', '''z''', '''ɐ''', '''t''', '''ɛ''', '''k''', '''s''', '''t''']
lowerCamelCase__ = tokenizer.convert_tokens_to_string(__lowerCAmelCase )
self.assertIsInstance(output['''text'''] , __lowerCAmelCase )
| 209 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_a = logging.get_logger(__name__)
_a = "▁"
_a = {
"vocab_file": "vocab.json",
"spm_file": "sentencepiece.bpe.model",
"tokenizer_config_file": "tokenizer_config.json",
}
_a = {
"vocab_file": {
"facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json",
"facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json",
},
"spm_file": {
"facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model",
"facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model",
},
"tokenizer_config_file": {
"facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json",
"facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json",
},
}
_a = {
"facebook/m2m100_418M": 1_024,
}
# fmt: off
_a = {
"m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
"wmt21": ["en", "ha", "is", "ja", "cs", "ru", "zh", "de"]
}
class __A ( lowerCAmelCase ):
'''simple docstring'''
lowerCAmelCase_ = VOCAB_FILES_NAMES
lowerCAmelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase_ = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase_ = ["""input_ids""", """attention_mask"""]
lowerCAmelCase_ = []
lowerCAmelCase_ = []
def __init__( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase="<s>" , __lowerCAmelCase="</s>" , __lowerCAmelCase="</s>" , __lowerCAmelCase="<pad>" , __lowerCAmelCase="<unk>" , __lowerCAmelCase="m2m100" , __lowerCAmelCase = None , __lowerCAmelCase=8 , **__lowerCAmelCase , ):
'''simple docstring'''
lowerCamelCase__ = {} if sp_model_kwargs is None else sp_model_kwargs
lowerCamelCase__ = language_codes
lowerCamelCase__ = FAIRSEQ_LANGUAGE_CODES[language_codes]
lowerCamelCase__ = {lang_code: F'__{lang_code}__' for lang_code in fairseq_language_code}
lowerCamelCase__ = kwargs.get('''additional_special_tokens''' , [] )
kwargs["additional_special_tokens"] += [
self.get_lang_token(__lowerCAmelCase )
for lang_code in fairseq_language_code
if self.get_lang_token(__lowerCAmelCase ) not in kwargs["additional_special_tokens"]
]
super().__init__(
src_lang=__lowerCAmelCase , tgt_lang=__lowerCAmelCase , bos_token=__lowerCAmelCase , eos_token=__lowerCAmelCase , sep_token=__lowerCAmelCase , unk_token=__lowerCAmelCase , pad_token=__lowerCAmelCase , language_codes=__lowerCAmelCase , sp_model_kwargs=self.sp_model_kwargs , num_madeup_words=__lowerCAmelCase , **__lowerCAmelCase , )
lowerCamelCase__ = vocab_file
lowerCamelCase__ = load_json(__lowerCAmelCase )
lowerCamelCase__ = {v: k for k, v in self.encoder.items()}
lowerCamelCase__ = spm_file
lowerCamelCase__ = load_spm(__lowerCAmelCase , self.sp_model_kwargs )
lowerCamelCase__ = len(self.encoder )
lowerCamelCase__ = {
self.get_lang_token(__lowerCAmelCase ): self.encoder_size + i for i, lang_code in enumerate(__lowerCAmelCase )
}
lowerCamelCase__ = {lang_code: self.encoder_size + i for i, lang_code in enumerate(__lowerCAmelCase )}
lowerCamelCase__ = {v: k for k, v in self.lang_token_to_id.items()}
lowerCamelCase__ = src_lang if src_lang is not None else '''en'''
lowerCamelCase__ = tgt_lang
lowerCamelCase__ = self.get_lang_id(self._src_lang )
self.set_src_lang_special_tokens(self._src_lang )
lowerCamelCase__ = num_madeup_words
@property
def __lowerCamelCase ( self ):
'''simple docstring'''
return len(self.encoder ) + len(self.lang_token_to_id )
@property
def __lowerCamelCase ( self ):
'''simple docstring'''
return self._src_lang
@src_lang.setter
def __lowerCamelCase ( self , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def __lowerCamelCase ( self , __lowerCAmelCase ):
'''simple docstring'''
return self.sp_model.encode(__lowerCAmelCase , out_type=__lowerCAmelCase )
def __lowerCamelCase ( self , __lowerCAmelCase ):
'''simple docstring'''
if token in self.lang_token_to_id:
return self.lang_token_to_id[token]
return self.encoder.get(__lowerCAmelCase , self.encoder[self.unk_token] )
def __lowerCamelCase ( self , __lowerCAmelCase ):
'''simple docstring'''
if index in self.id_to_lang_token:
return self.id_to_lang_token[index]
return self.decoder.get(__lowerCAmelCase , self.unk_token )
def __lowerCamelCase ( self , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = []
lowerCamelCase__ = ''''''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(__lowerCAmelCase ) + token
lowerCamelCase__ = []
else:
current_sub_tokens.append(__lowerCAmelCase )
out_string += self.sp_model.decode(__lowerCAmelCase )
return out_string.strip()
def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase = None , __lowerCAmelCase = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__lowerCAmelCase , token_ids_a=__lowerCAmelCase , already_has_special_tokens=__lowerCAmelCase )
lowerCamelCase__ = [1] * len(self.prefix_tokens )
lowerCamelCase__ = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(__lowerCAmelCase )) + suffix_ones
return prefix_ones + ([0] * len(__lowerCAmelCase )) + ([0] * len(__lowerCAmelCase )) + suffix_ones
def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase = None ):
'''simple docstring'''
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = {self.convert_ids_to_tokens(__lowerCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
'''simple docstring'''
lowerCamelCase__ = self.__dict__.copy()
lowerCamelCase__ = None
return state
def __setstate__( self , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
lowerCamelCase__ = {}
lowerCamelCase__ = load_spm(self.spm_file , self.sp_model_kwargs )
def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase = None ):
'''simple docstring'''
lowerCamelCase__ = Path(__lowerCAmelCase )
if not save_dir.is_dir():
raise OSError(F'{save_directory} should be a directory' )
lowerCamelCase__ = save_dir / (
(filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
)
lowerCamelCase__ = save_dir / (
(filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
)
save_json(self.encoder , __lowerCAmelCase )
if os.path.abspath(self.spm_file ) != os.path.abspath(__lowerCAmelCase ) and os.path.isfile(self.spm_file ):
copyfile(self.spm_file , __lowerCAmelCase )
elif not os.path.isfile(self.spm_file ):
with open(__lowerCAmelCase , '''wb''' ) as fi:
lowerCamelCase__ = self.sp_model.serialized_model_proto()
fi.write(__lowerCAmelCase )
return (str(__lowerCAmelCase ), str(__lowerCAmelCase ))
def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase = "en" , __lowerCAmelCase = None , __lowerCAmelCase = "ro" , **__lowerCAmelCase , ):
'''simple docstring'''
lowerCamelCase__ = src_lang
lowerCamelCase__ = tgt_lang
self.set_src_lang_special_tokens(self.src_lang )
return super().prepare_seqaseq_batch(__lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase )
def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ):
'''simple docstring'''
if src_lang is None or tgt_lang is None:
raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''' )
lowerCamelCase__ = src_lang
lowerCamelCase__ = self(__lowerCAmelCase , add_special_tokens=__lowerCAmelCase , **__lowerCAmelCase )
lowerCamelCase__ = self.get_lang_id(__lowerCAmelCase )
lowerCamelCase__ = tgt_lang_id
return inputs
def __lowerCamelCase ( self ):
'''simple docstring'''
self.set_src_lang_special_tokens(self.src_lang )
def __lowerCamelCase ( self ):
'''simple docstring'''
self.set_tgt_lang_special_tokens(self.tgt_lang )
def __lowerCamelCase ( self , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = self.get_lang_token(__lowerCAmelCase )
lowerCamelCase__ = self.lang_token_to_id[lang_token]
lowerCamelCase__ = [self.cur_lang_id]
lowerCamelCase__ = [self.eos_token_id]
def __lowerCamelCase ( self , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = self.get_lang_token(__lowerCAmelCase )
lowerCamelCase__ = self.lang_token_to_id[lang_token]
lowerCamelCase__ = [self.cur_lang_id]
lowerCamelCase__ = [self.eos_token_id]
def __lowerCamelCase ( self , __lowerCAmelCase ):
'''simple docstring'''
return self.lang_code_to_token[lang]
def __lowerCamelCase ( self , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = self.get_lang_token(__lowerCAmelCase )
return self.lang_token_to_id[lang_token]
def lowerCAmelCase__(__snake_case ,__snake_case ) -> sentencepiece.SentencePieceProcessor:
'''simple docstring'''
lowerCamelCase__ = sentencepiece.SentencePieceProcessor(**__snake_case )
spm.Load(str(__snake_case ) )
return spm
def lowerCAmelCase__(__snake_case ) -> Union[Dict, List]:
'''simple docstring'''
with open(__snake_case ,'''r''' ) as f:
return json.load(__snake_case )
def lowerCAmelCase__(__snake_case ,__snake_case ) -> None:
'''simple docstring'''
with open(__snake_case ,'''w''' ) as f:
json.dump(__snake_case ,__snake_case ,indent=2 )
| 209 | 1 |
"""simple docstring"""
import enum
import shutil
import sys
lowercase__ : List[str] = shutil.get_terminal_size()
lowercase__ : Dict = {"""UP""": """A""", """DOWN""": """B""", """RIGHT""": """C""", """LEFT""": """D"""}
class UpperCamelCase__ ( enum.Enum ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = 1
def UpperCamelCase_ ( lowerCAmelCase__ : Dict , lowerCAmelCase__ : Tuple="" ) -> Dict:
"""simple docstring"""
sys.stdout.write(str(__snake_case ) + end )
sys.stdout.flush()
def UpperCamelCase_ ( lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Dict="" ) -> Optional[Any]:
"""simple docstring"""
forceWrite(f"\u001b[{color}m{content}\u001b[0m" , __snake_case )
def UpperCamelCase_ ( ) -> int:
"""simple docstring"""
forceWrite('\r' )
def UpperCamelCase_ ( lowerCAmelCase__ : int , lowerCAmelCase__ : str ) -> Tuple:
"""simple docstring"""
forceWrite(f"\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}" )
def UpperCamelCase_ ( ) -> Any:
"""simple docstring"""
forceWrite(' ' * TERMINAL_WIDTH )
reset_cursor()
def UpperCamelCase_ ( ) -> int:
"""simple docstring"""
reset_cursor()
forceWrite('-' * TERMINAL_WIDTH )
| 356 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
FlaubertForMultipleChoice,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertModel,
FlaubertWithLMHeadModel,
)
from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
def __init__( self : Dict , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Union[str, Any]=1_3 , SCREAMING_SNAKE_CASE_ : Dict=7 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=True , SCREAMING_SNAKE_CASE_ : List[str]=True , SCREAMING_SNAKE_CASE_ : Dict=True , SCREAMING_SNAKE_CASE_ : Dict=True , SCREAMING_SNAKE_CASE_ : List[str]=True , SCREAMING_SNAKE_CASE_ : str=False , SCREAMING_SNAKE_CASE_ : str=False , SCREAMING_SNAKE_CASE_ : Any=False , SCREAMING_SNAKE_CASE_ : Optional[Any]=2 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=9_9 , SCREAMING_SNAKE_CASE_ : Dict=0 , SCREAMING_SNAKE_CASE_ : int=3_2 , SCREAMING_SNAKE_CASE_ : Dict=5 , SCREAMING_SNAKE_CASE_ : Dict=4 , SCREAMING_SNAKE_CASE_ : List[Any]=0.1 , SCREAMING_SNAKE_CASE_ : List[Any]=0.1 , SCREAMING_SNAKE_CASE_ : List[str]=5_1_2 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=1_2 , SCREAMING_SNAKE_CASE_ : Tuple=2 , SCREAMING_SNAKE_CASE_ : Optional[int]=0.02 , SCREAMING_SNAKE_CASE_ : Optional[int]=3 , SCREAMING_SNAKE_CASE_ : int=4 , SCREAMING_SNAKE_CASE_ : List[Any]="last" , SCREAMING_SNAKE_CASE_ : Union[str, Any]=None , SCREAMING_SNAKE_CASE_ : str=None , ):
lowerCAmelCase_ : Tuple = parent
lowerCAmelCase_ : Tuple = batch_size
lowerCAmelCase_ : str = seq_length
lowerCAmelCase_ : List[Any] = is_training
lowerCAmelCase_ : Optional[int] = use_input_lengths
lowerCAmelCase_ : Union[str, Any] = use_token_type_ids
lowerCAmelCase_ : str = use_labels
lowerCAmelCase_ : str = gelu_activation
lowerCAmelCase_ : str = sinusoidal_embeddings
lowerCAmelCase_ : List[Any] = causal
lowerCAmelCase_ : Union[str, Any] = asm
lowerCAmelCase_ : Union[str, Any] = n_langs
lowerCAmelCase_ : Optional[Any] = vocab_size
lowerCAmelCase_ : Any = n_special
lowerCAmelCase_ : Union[str, Any] = hidden_size
lowerCAmelCase_ : str = num_hidden_layers
lowerCAmelCase_ : Tuple = num_attention_heads
lowerCAmelCase_ : Optional[Any] = hidden_dropout_prob
lowerCAmelCase_ : List[Any] = attention_probs_dropout_prob
lowerCAmelCase_ : Dict = max_position_embeddings
lowerCAmelCase_ : Union[str, Any] = type_vocab_size
lowerCAmelCase_ : List[Any] = type_sequence_label_size
lowerCAmelCase_ : str = initializer_range
lowerCAmelCase_ : Dict = num_labels
lowerCAmelCase_ : Union[str, Any] = num_choices
lowerCAmelCase_ : Union[str, Any] = summary_type
lowerCAmelCase_ : Optional[Any] = use_proj
lowerCAmelCase_ : List[Any] = scope
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
lowerCAmelCase_ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCAmelCase_ : str = random_attention_mask([self.batch_size, self.seq_length] )
lowerCAmelCase_ : Dict = None
if self.use_input_lengths:
lowerCAmelCase_ : Any = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
lowerCAmelCase_ : Any = None
if self.use_token_type_ids:
lowerCAmelCase_ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
lowerCAmelCase_ : Dict = None
lowerCAmelCase_ : Optional[int] = None
lowerCAmelCase_ : List[str] = None
if self.use_labels:
lowerCAmelCase_ : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase_ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCAmelCase_ : Any = ids_tensor([self.batch_size] , 2 ).float()
lowerCAmelCase_ : List[Any] = ids_tensor([self.batch_size] , self.num_choices )
lowerCAmelCase_ : Union[str, Any] = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
return FlaubertConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , )
def SCREAMING_SNAKE_CASE__ ( self : Dict , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str , ):
lowerCAmelCase_ : Union[str, Any] = FlaubertModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCAmelCase_ : Any = model(SCREAMING_SNAKE_CASE_ , lengths=SCREAMING_SNAKE_CASE_ , langs=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Any = model(SCREAMING_SNAKE_CASE_ , langs=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Optional[Any] = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self : Any , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : List[Any] , ):
lowerCAmelCase_ : Any = FlaubertWithLMHeadModel(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCAmelCase_ : Dict = model(SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self : int , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : int , ):
lowerCAmelCase_ : Tuple = FlaubertForQuestionAnsweringSimple(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCAmelCase_ : Union[str, Any] = model(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Optional[int] = model(SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Union[str, Any] , ):
lowerCAmelCase_ : Optional[int] = FlaubertForQuestionAnswering(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCAmelCase_ : int = model(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Any = model(
SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ , cls_index=SCREAMING_SNAKE_CASE_ , is_impossible=SCREAMING_SNAKE_CASE_ , p_mask=SCREAMING_SNAKE_CASE_ , )
lowerCAmelCase_ : Optional[int] = model(
SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ , cls_index=SCREAMING_SNAKE_CASE_ , is_impossible=SCREAMING_SNAKE_CASE_ , )
((lowerCAmelCase_) ,) : int = result_with_labels.to_tuple()
lowerCAmelCase_ : int = model(SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ )
((lowerCAmelCase_) ,) : Dict = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Dict , ):
lowerCAmelCase_ : Optional[int] = FlaubertForSequenceClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCAmelCase_ : Any = model(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : List[Any] = model(SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def SCREAMING_SNAKE_CASE__ ( self : Any , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Dict , ):
lowerCAmelCase_ : List[Any] = self.num_labels
lowerCAmelCase_ : Optional[Any] = FlaubertForTokenClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCAmelCase_ : int = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self : Dict , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : List[str] , ):
lowerCAmelCase_ : Dict = self.num_choices
lowerCAmelCase_ : Optional[Any] = FlaubertForMultipleChoice(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCAmelCase_ : Tuple = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCAmelCase_ : Union[str, Any] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCAmelCase_ : List[Any] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCAmelCase_ : Optional[Any] = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
lowerCAmelCase_ : List[Any] = self.prepare_config_and_inputs()
(
(
lowerCAmelCase_
) ,(
lowerCAmelCase_
) ,(
lowerCAmelCase_
) ,(
lowerCAmelCase_
) ,(
lowerCAmelCase_
) ,(
lowerCAmelCase_
) ,(
lowerCAmelCase_
) ,(
lowerCAmelCase_
) ,(
lowerCAmelCase_
) ,
) : Dict = config_and_inputs
lowerCAmelCase_ : List[str] = {
'input_ids': input_ids,
'token_type_ids': token_type_ids,
'lengths': input_lengths,
'attention_mask': input_mask,
}
return config, inputs_dict
@require_torch
class UpperCamelCase__ ( lowercase_, lowercase_, unittest.TestCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = (
(
FlaubertModel,
FlaubertWithLMHeadModel,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertForMultipleChoice,
)
if is_torch_available()
else ()
)
_SCREAMING_SNAKE_CASE = (
{
"""feature-extraction""": FlaubertModel,
"""fill-mask""": FlaubertWithLMHeadModel,
"""question-answering""": FlaubertForQuestionAnsweringSimple,
"""text-classification""": FlaubertForSequenceClassification,
"""token-classification""": FlaubertForTokenClassification,
"""zero-shot""": FlaubertForSequenceClassification,
}
if is_torch_available()
else {}
)
def SCREAMING_SNAKE_CASE__ ( self : int , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('Fast' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Optional[Any]=False ):
lowerCAmelCase_ : Any = super()._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
if return_labels:
if model_class.__name__ == "FlaubertForQuestionAnswering":
lowerCAmelCase_ : Tuple = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : int = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ )
return inputs_dict
def SCREAMING_SNAKE_CASE__ ( self : Any ):
lowerCAmelCase_ : Tuple = FlaubertModelTester(self )
lowerCAmelCase_ : str = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , emb_dim=3_7 )
def SCREAMING_SNAKE_CASE__ ( self : Any ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
lowerCAmelCase_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_model(*SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
lowerCAmelCase_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_lm_head(*SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
lowerCAmelCase_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_simple_qa(*SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
lowerCAmelCase_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_qa(*SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
lowerCAmelCase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_sequence_classif(*SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
lowerCAmelCase_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_token_classif(*SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
lowerCAmelCase_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_multiple_choice(*SCREAMING_SNAKE_CASE_ )
@slow
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ : Optional[Any] = FlaubertModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@slow
@require_torch_gpu
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
lowerCAmelCase_ ,lowerCAmelCase_ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == FlaubertForMultipleChoice:
return
lowerCAmelCase_ : int = True
lowerCAmelCase_ : Union[str, Any] = model_class(config=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Any = self._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : int = torch.jit.trace(
SCREAMING_SNAKE_CASE_ , (inputs_dict['input_ids'].to('cpu' ), inputs_dict['attention_mask'].to('cpu' )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(SCREAMING_SNAKE_CASE_ , os.path.join(SCREAMING_SNAKE_CASE_ , 'traced_model.pt' ) )
lowerCAmelCase_ : Optional[Any] = torch.jit.load(os.path.join(SCREAMING_SNAKE_CASE_ , 'traced_model.pt' ) , map_location=SCREAMING_SNAKE_CASE_ )
loaded(inputs_dict['input_ids'].to(SCREAMING_SNAKE_CASE_ ) , inputs_dict['attention_mask'].to(SCREAMING_SNAKE_CASE_ ) )
@require_torch
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
@slow
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
lowerCAmelCase_ : int = FlaubertModel.from_pretrained('flaubert/flaubert_base_cased' )
lowerCAmelCase_ : List[Any] = torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
with torch.no_grad():
lowerCAmelCase_ : Optional[Any] = model(SCREAMING_SNAKE_CASE_ )[0]
lowerCAmelCase_ : Optional[Any] = torch.Size((1, 1_1, 7_6_8) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Optional[int] = torch.tensor(
[[[-2.62_51, -1.42_98, -0.02_27], [-2.85_10, -1.63_87, 0.22_58], [-2.81_14, -1.18_32, -0.30_66]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-4 ) )
| 289 | 0 |
"""simple docstring"""
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class lowercase__ :
_UpperCAmelCase :Tuple = None
def UpperCAmelCase__ ( self : Tuple ):
lowerCamelCase_ : int =self.feature_extraction_class(**self.feat_extract_dict )
lowerCamelCase_ : int =json.loads(feat_extract.to_json_string() )
for key, value in self.feat_extract_dict.items():
self.assertEqual(obj[key] , snake_case__ )
def UpperCAmelCase__ ( self : Tuple ):
lowerCamelCase_ : List[str] =self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCamelCase_ : Optional[int] =os.path.join(snake_case__ , "feat_extract.json" )
feat_extract_first.to_json_file(snake_case__ )
lowerCamelCase_ : Optional[Any] =self.feature_extraction_class.from_json_file(snake_case__ )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def UpperCAmelCase__ ( self : str ):
lowerCamelCase_ : Union[str, Any] =self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCamelCase_ : List[str] =feat_extract_first.save_pretrained(snake_case__ )[0]
check_json_file_has_correct_format(snake_case__ )
lowerCamelCase_ : str =self.feature_extraction_class.from_pretrained(snake_case__ )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def UpperCAmelCase__ ( self : Optional[Any] ):
lowerCamelCase_ : Tuple =self.feature_extraction_class()
self.assertIsNotNone(snake_case__ )
| 144 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : Dict = logging.get_logger(__name__)
A__ : Union[str, Any] = {
'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
# See all CANINE models at https://huggingface.co/models?filter=canine
}
class lowercase__ ( snake_case__ ):
_UpperCAmelCase :List[str] = "canine"
def __init__( self : Optional[Any] , snake_case__ : Union[str, Any]=768 , snake_case__ : Tuple=12 , snake_case__ : Optional[Any]=12 , snake_case__ : Union[str, Any]=3072 , snake_case__ : Optional[Any]="gelu" , snake_case__ : Tuple=0.1 , snake_case__ : Tuple=0.1 , snake_case__ : int=1_6384 , snake_case__ : str=16 , snake_case__ : Tuple=0.02 , snake_case__ : Dict=1E-12 , snake_case__ : Any=0 , snake_case__ : Optional[int]=0xe_000 , snake_case__ : List[str]=0xe_001 , snake_case__ : List[str]=4 , snake_case__ : List[str]=4 , snake_case__ : List[Any]=8 , snake_case__ : List[str]=1_6384 , snake_case__ : Union[str, Any]=128 , **snake_case__ : Tuple , ):
super().__init__(pad_token_id=snake_case__ , bos_token_id=snake_case__ , eos_token_id=snake_case__ , **snake_case__ )
lowerCamelCase_ : Tuple =max_position_embeddings
lowerCamelCase_ : Optional[int] =hidden_size
lowerCamelCase_ : Tuple =num_hidden_layers
lowerCamelCase_ : Dict =num_attention_heads
lowerCamelCase_ : str =intermediate_size
lowerCamelCase_ : Dict =hidden_act
lowerCamelCase_ : List[Any] =hidden_dropout_prob
lowerCamelCase_ : Union[str, Any] =attention_probs_dropout_prob
lowerCamelCase_ : Dict =initializer_range
lowerCamelCase_ : Tuple =type_vocab_size
lowerCamelCase_ : Optional[Any] =layer_norm_eps
# Character config:
lowerCamelCase_ : List[str] =downsampling_rate
lowerCamelCase_ : List[Any] =upsampling_kernel_size
lowerCamelCase_ : Any =num_hash_functions
lowerCamelCase_ : Optional[int] =num_hash_buckets
lowerCamelCase_ : Union[str, Any] =local_transformer_stride
| 144 | 1 |
def lowerCamelCase__ ( A : int , A : int ):
'''simple docstring'''
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(snake_case_ , int(b / 2 ) ) * actual_power(snake_case_ , int(b / 2 ) )
else:
return a * actual_power(snake_case_ , int(b / 2 ) ) * actual_power(snake_case_ , int(b / 2 ) )
def lowerCamelCase__ ( A : int , A : int ):
'''simple docstring'''
if b < 0:
return 1 / actual_power(snake_case_ , snake_case_ )
return actual_power(snake_case_ , snake_case_ )
if __name__ == "__main__":
print(power(-2, -3))
| 355 |
'''simple docstring'''
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
"""pipelines_utils""",
"""0.22.0""",
"""Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
standard_warn=False,
stacklevel=3,
)
| 91 | 0 |
'''simple docstring'''
def lowercase__( __UpperCamelCase: List[str] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = 1
for i in range(1 ,num + 1 ):
fact *= i
return fact
def lowercase__( __UpperCamelCase: int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = 0
while number > 0:
SCREAMING_SNAKE_CASE : Optional[Any] = number % 10
sum_of_digits += last_digit
SCREAMING_SNAKE_CASE : str = number // 10 # Removing the last_digit from the given number
return sum_of_digits
def lowercase__( __UpperCamelCase: str = 1_00 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = factorial(__UpperCamelCase )
SCREAMING_SNAKE_CASE : Dict = split_and_add(__UpperCamelCase )
return result
if __name__ == "__main__":
print(solution(int(input("Enter the Number: ").strip())))
| 251 | import numpy as np
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 1e-12 , SCREAMING_SNAKE_CASE = 100 , ):
'''simple docstring'''
assert np.shape(SCREAMING_SNAKE_CASE )[0] == np.shape(SCREAMING_SNAKE_CASE )[1]
# Ensure proper dimensionality.
assert np.shape(SCREAMING_SNAKE_CASE )[0] == np.shape(SCREAMING_SNAKE_CASE )[0]
# Ensure inputs are either both complex or both real
assert np.iscomplexobj(SCREAMING_SNAKE_CASE ) == np.iscomplexobj(SCREAMING_SNAKE_CASE )
__UpperCamelCase :List[Any] = np.iscomplexobj(SCREAMING_SNAKE_CASE )
if is_complex:
# Ensure complex input_matrix is Hermitian
assert np.array_equal(SCREAMING_SNAKE_CASE , input_matrix.conj().T )
# Set convergence to False. Will define convergence when we exceed max_iterations
# or when we have small changes from one iteration to next.
__UpperCamelCase :str = False
__UpperCamelCase :int = 0
__UpperCamelCase :Optional[Any] = 0
__UpperCamelCase :Union[str, Any] = 1e12
while not convergence:
# Multiple matrix by the vector.
__UpperCamelCase :List[str] = np.dot(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Normalize the resulting output vector.
__UpperCamelCase :Tuple = w / np.linalg.norm(SCREAMING_SNAKE_CASE )
# Find rayleigh quotient
# (faster than usual b/c we know vector is normalized already)
__UpperCamelCase :int = vector.conj().T if is_complex else vector.T
__UpperCamelCase :Optional[int] = np.dot(SCREAMING_SNAKE_CASE , np.dot(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) )
# Check convergence.
__UpperCamelCase :Optional[Any] = np.abs(lambda_ - lambda_previous ) / lambda_
iterations += 1
if error <= error_tol or iterations >= max_iterations:
__UpperCamelCase :Dict = True
__UpperCamelCase :List[Any] = lambda_
if is_complex:
__UpperCamelCase :Tuple = np.real(lambda_ )
return lambda_, vector
def lowerCamelCase ( ):
'''simple docstring'''
__UpperCamelCase :int = np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]] )
__UpperCamelCase :Optional[Any] = np.array([41, 4, 20] )
__UpperCamelCase :Any = real_input_matrix.astype(np.complexaaa )
__UpperCamelCase :Dict = np.triu(1j * complex_input_matrix , 1 )
complex_input_matrix += imag_matrix
complex_input_matrix += -1 * imag_matrix.T
__UpperCamelCase :Optional[int] = np.array([41, 4, 20] ).astype(np.complexaaa )
for problem_type in ["real", "complex"]:
if problem_type == "real":
__UpperCamelCase :Any = real_input_matrix
__UpperCamelCase :int = real_vector
elif problem_type == "complex":
__UpperCamelCase :Tuple = complex_input_matrix
__UpperCamelCase :Optional[Any] = complex_vector
# Our implementation.
__UpperCamelCase , __UpperCamelCase :Dict = power_iteration(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Numpy implementation.
# Get eigenvalues and eigenvectors using built-in numpy
# eigh (eigh used for symmetric or hermetian matrices).
__UpperCamelCase , __UpperCamelCase :List[Any] = np.linalg.eigh(SCREAMING_SNAKE_CASE )
# Last eigenvalue is the maximum one.
__UpperCamelCase :List[Any] = eigen_values[-1]
# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
__UpperCamelCase :str = eigen_vectors[:, -1]
# Check our implementation and numpy gives close answers.
assert np.abs(eigen_value - eigen_value_max ) <= 1e-6
# Take absolute values element wise of each eigenvector.
# as they are only unique to a minus sign.
assert np.linalg.norm(np.abs(SCREAMING_SNAKE_CASE ) - np.abs(SCREAMING_SNAKE_CASE ) ) <= 1e-6
if __name__ == "__main__":
import doctest
doctest.testmod()
test_power_iteration()
| 43 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class a__ :
def __init__( self ):
"""simple docstring"""
_lowercase : list[Any] = []
_lowercase : int = 0
_lowercase : int = 0
def _lowerCamelCase ( self ):
"""simple docstring"""
return self.head == self.tail
def _lowerCamelCase ( self , _UpperCamelCase ):
"""simple docstring"""
self.data.append(_UpperCamelCase )
_lowercase : Optional[int] = self.tail + 1
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : Union[str, Any] = self.data[self.head]
_lowercase : Tuple = self.head + 1
return ret
def _lowerCamelCase ( self ):
"""simple docstring"""
return self.tail - self.head
def _lowerCamelCase ( self ):
"""simple docstring"""
print(self.data )
print("**************" )
print(self.data[self.head : self.tail] )
class a__ :
def __init__( self , _UpperCamelCase ):
"""simple docstring"""
_lowercase : Dict = data
_lowercase : MyNode | None = None
_lowercase : MyNode | None = None
_lowercase : int = 1
def _lowerCamelCase ( self ):
"""simple docstring"""
return self.data
def _lowerCamelCase ( self ):
"""simple docstring"""
return self.left
def _lowerCamelCase ( self ):
"""simple docstring"""
return self.right
def _lowerCamelCase ( self ):
"""simple docstring"""
return self.height
def _lowerCamelCase ( self , _UpperCamelCase ):
"""simple docstring"""
_lowercase : Optional[Any] = data
def _lowerCamelCase ( self , _UpperCamelCase ):
"""simple docstring"""
_lowercase : Union[str, Any] = node
def _lowerCamelCase ( self , _UpperCamelCase ):
"""simple docstring"""
_lowercase : Dict = node
def _lowerCamelCase ( self , _UpperCamelCase ):
"""simple docstring"""
_lowercase : List[str] = height
def _A ( snake_case ) -> int:
if node is None:
return 0
return node.get_height()
def _A ( snake_case , snake_case ) -> int:
if a > b:
return a
return b
def _A ( snake_case ) -> MyNode:
print("left rotation node:" , node.get_data() )
_lowercase : str = node.get_left()
assert ret is not None
node.set_left(ret.get_right() )
ret.set_right(snake_case )
_lowercase : Dict = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(snake_case )
_lowercase : Dict = my_max(get_height(ret.get_right() ) , get_height(ret.get_left() ) ) + 1
ret.set_height(snake_case )
return ret
def _A ( snake_case ) -> MyNode:
print("right rotation node:" , node.get_data() )
_lowercase : Any = node.get_right()
assert ret is not None
node.set_right(ret.get_left() )
ret.set_left(snake_case )
_lowercase : str = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(snake_case )
_lowercase : Tuple = my_max(get_height(ret.get_right() ) , get_height(ret.get_left() ) ) + 1
ret.set_height(snake_case )
return ret
def _A ( snake_case ) -> MyNode:
_lowercase : Optional[Any] = node.get_left()
assert left_child is not None
node.set_left(left_rotation(snake_case ) )
return right_rotation(snake_case )
def _A ( snake_case ) -> MyNode:
_lowercase : List[str] = node.get_right()
assert right_child is not None
node.set_right(right_rotation(snake_case ) )
return left_rotation(snake_case )
def _A ( snake_case , snake_case ) -> MyNode | None:
if node is None:
return MyNode(snake_case )
if data < node.get_data():
node.set_left(insert_node(node.get_left() , snake_case ) )
if (
get_height(node.get_left() ) - get_height(node.get_right() ) == 2
): # an unbalance detected
_lowercase : Dict = node.get_left()
assert left_child is not None
if (
data < left_child.get_data()
): # new node is the left child of the left child
_lowercase : Optional[int] = right_rotation(snake_case )
else:
_lowercase : Union[str, Any] = lr_rotation(snake_case )
else:
node.set_right(insert_node(node.get_right() , snake_case ) )
if get_height(node.get_right() ) - get_height(node.get_left() ) == 2:
_lowercase : int = node.get_right()
assert right_child is not None
if data < right_child.get_data():
_lowercase : Union[str, Any] = rl_rotation(snake_case )
else:
_lowercase : Any = left_rotation(snake_case )
_lowercase : Any = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(snake_case )
return node
def _A ( snake_case ) -> Any:
while True:
_lowercase : Tuple = root.get_right()
if right_child is None:
break
_lowercase : Tuple = right_child
return root.get_data()
def _A ( snake_case ) -> Any:
while True:
_lowercase : List[str] = root.get_left()
if left_child is None:
break
_lowercase : int = left_child
return root.get_data()
def _A ( snake_case , snake_case ) -> MyNode | None:
_lowercase : Dict = root.get_left()
_lowercase : Any = root.get_right()
if root.get_data() == data:
if left_child is not None and right_child is not None:
_lowercase : Tuple = get_left_most(snake_case )
root.set_data(snake_case )
root.set_right(del_node(snake_case , snake_case ) )
elif left_child is not None:
_lowercase : Optional[Any] = left_child
elif right_child is not None:
_lowercase : Union[str, Any] = right_child
else:
return None
elif root.get_data() > data:
if left_child is None:
print("No such data" )
return root
else:
root.set_left(del_node(snake_case , snake_case ) )
else: # root.get_data() < data
if right_child is None:
return root
else:
root.set_right(del_node(snake_case , snake_case ) )
if get_height(snake_case ) - get_height(snake_case ) == 2:
assert right_child is not None
if get_height(right_child.get_right() ) > get_height(right_child.get_left() ):
_lowercase : Dict = left_rotation(snake_case )
else:
_lowercase : Optional[Any] = rl_rotation(snake_case )
elif get_height(snake_case ) - get_height(snake_case ) == -2:
assert left_child is not None
if get_height(left_child.get_left() ) > get_height(left_child.get_right() ):
_lowercase : Optional[Any] = right_rotation(snake_case )
else:
_lowercase : Optional[Any] = lr_rotation(snake_case )
_lowercase : List[str] = my_max(get_height(root.get_right() ) , get_height(root.get_left() ) ) + 1
root.set_height(snake_case )
return root
class a__ :
def __init__( self ):
"""simple docstring"""
_lowercase : MyNode | None = None
def _lowerCamelCase ( self ):
"""simple docstring"""
return get_height(self.root )
def _lowerCamelCase ( self , _UpperCamelCase ):
"""simple docstring"""
print("insert:" + str(_UpperCamelCase ) )
_lowercase : str = insert_node(self.root , _UpperCamelCase )
def _lowerCamelCase ( self , _UpperCamelCase ):
"""simple docstring"""
print("delete:" + str(_UpperCamelCase ) )
if self.root is None:
print("Tree is empty!" )
return
_lowercase : Tuple = del_node(self.root , _UpperCamelCase )
def __str__( self , ): # a level traversale, gives a more intuitive look on the tree
"""simple docstring"""
_lowercase : str = ""
_lowercase : Optional[Any] = MyQueue()
q.push(self.root )
_lowercase : Tuple = self.get_height()
if layer == 0:
return output
_lowercase : Optional[Any] = 0
while not q.is_empty():
_lowercase : List[Any] = q.pop()
_lowercase : str = " " * int(math.pow(2 , layer - 1 ) )
output += space
if node is None:
output += "*"
q.push(_UpperCamelCase )
q.push(_UpperCamelCase )
else:
output += str(node.get_data() )
q.push(node.get_left() )
q.push(node.get_right() )
output += space
_lowercase : Optional[int] = cnt + 1
for i in range(100 ):
if cnt == math.pow(2 , _UpperCamelCase ) - 1:
_lowercase : List[Any] = layer - 1
if layer == 0:
output += "\n*************************************"
return output
output += "\n"
break
output += "\n*************************************"
return output
def _A ( ) -> None:
import doctest
doctest.testmod()
if __name__ == "__main__":
_test()
_snake_case = AVLtree()
_snake_case = list(range(10))
random.shuffle(lst)
for i in lst:
t.insert(i)
print(str(t))
random.shuffle(lst)
for i in lst:
t.del_node(i)
print(str(t))
| 199 |
'''simple docstring'''
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
_snake_case = logging.getLogger()
@unittest.skip('Temporarily disable the doc tests.' )
@require_torch
@require_tf
@slow
class a__ ( unittest.TestCase ):
def _lowerCamelCase ( self , _UpperCamelCase , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = True , ):
"""simple docstring"""
_lowercase : str = [file for file in os.listdir(_UpperCamelCase ) if os.path.isfile(os.path.join(_UpperCamelCase , _UpperCamelCase ) )]
if identifier is not None:
_lowercase : str = [file for file in files if identifier in file]
if n_identifier is not None:
if isinstance(_UpperCamelCase , _UpperCamelCase ):
for n_ in n_identifier:
_lowercase : Dict = [file for file in files if n_ not in file]
else:
_lowercase : Optional[Any] = [file for file in files if n_identifier not in file]
_lowercase : Dict = ignore_files or []
ignore_files.append("__init__.py" )
_lowercase : List[str] = [file for file in files if file not in ignore_files]
for file in files:
# Open all files
print("Testing" , _UpperCamelCase )
if only_modules:
_lowercase : Optional[Any] = file.split("." )[0]
try:
_lowercase : Union[str, Any] = getattr(_UpperCamelCase , _UpperCamelCase )
_lowercase : Optional[int] = doctest.DocTestSuite(_UpperCamelCase )
_lowercase : Tuple = unittest.TextTestRunner().run(_UpperCamelCase )
self.assertIs(len(result.failures ) , 0 )
except AttributeError:
logger.info(f'''{module_identifier} is not a module.''' )
else:
_lowercase : Optional[Any] = doctest.testfile(str(".." / directory / file ) , optionflags=doctest.ELLIPSIS )
self.assertIs(result.failed , 0 )
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : Tuple = Path("src/transformers" )
_lowercase : str = "modeling"
_lowercase : Tuple = [
"modeling_ctrl.py",
"modeling_tf_ctrl.py",
]
self.analyze_directory(_UpperCamelCase , identifier=_UpperCamelCase , ignore_files=_UpperCamelCase )
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : Optional[int] = Path("src/transformers" )
_lowercase : Any = "tokenization"
self.analyze_directory(_UpperCamelCase , identifier=_UpperCamelCase )
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : Tuple = Path("src/transformers" )
_lowercase : Optional[Any] = "configuration"
self.analyze_directory(_UpperCamelCase , identifier=_UpperCamelCase )
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : Union[str, Any] = Path("src/transformers" )
_lowercase : List[Any] = ["configuration", "modeling", "tokenization"]
self.analyze_directory(_UpperCamelCase , n_identifier=_UpperCamelCase )
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : Dict = Path("docs/source" )
_lowercase : int = ["favicon.ico"]
self.analyze_directory(_UpperCamelCase , ignore_files=_UpperCamelCase , only_modules=_UpperCamelCase )
| 199 | 1 |
from math import factorial, pi
def SCREAMING_SNAKE_CASE_ ( __A : float , __A : int = 30 ) -> float:
"""simple docstring"""
if not isinstance(__A , (int, float) ):
raise ValueError('maclaurin_sin() requires either an int or float for theta' )
if not isinstance(__A , __A ) or accuracy <= 0:
raise ValueError('maclaurin_sin() requires a positive int for accuracy' )
a_ : Tuple = float(__A )
a_ : int = theta // (2 * pi)
theta -= 2 * div * pi
return sum(
(-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1 ) for r in range(__A ) )
def SCREAMING_SNAKE_CASE_ ( __A : float , __A : int = 30 ) -> float:
"""simple docstring"""
if not isinstance(__A , (int, float) ):
raise ValueError('maclaurin_cos() requires either an int or float for theta' )
if not isinstance(__A , __A ) or accuracy <= 0:
raise ValueError('maclaurin_cos() requires a positive int for accuracy' )
a_ : Dict = float(__A )
a_ : Union[str, Any] = theta // (2 * pi)
theta -= 2 * div * pi
return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r ) for r in range(__A ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(maclaurin_sin(10))
print(maclaurin_sin(-10))
print(maclaurin_sin(10, 15))
print(maclaurin_sin(-10, 15))
print(maclaurin_cos(5))
print(maclaurin_cos(-5))
print(maclaurin_cos(10, 15))
print(maclaurin_cos(-10, 15))
| 32 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines_common import ANY
# These 2 model types require different inputs than those of the usual text models.
UpperCAmelCase_ : Dict = {'LayoutLMv2Config', 'LayoutLMv3Config'}
@is_pipeline_test
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
snake_case__ : List[str] = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
snake_case__ : Optional[Any] = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if model_mapping is not None:
snake_case__ : str = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
snake_case__ : List[Any] = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
@require_torch
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> Tuple:
a_ : List[Any] = pipeline(
task='text-classification' , model='hf-internal-testing/tiny-random-distilbert' , framework='pt' )
a_ : int = text_classifier('This is great !' )
self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__ ) , [{'label': 'LABEL_0', 'score': 0.504}] )
a_ : Tuple = text_classifier('This is great !' , top_k=2 )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE__ ) , [{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}] )
a_ : List[str] = text_classifier(['This is great !', 'This is bad'] , top_k=2 )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE__ ) , [
[{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}],
[{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}],
] , )
a_ : Tuple = text_classifier('This is great !' , top_k=1 )
self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__ ) , [{'label': 'LABEL_0', 'score': 0.504}] )
# Legacy behavior
a_ : Union[str, Any] = text_classifier('This is great !' , return_all_scores=SCREAMING_SNAKE_CASE__ )
self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__ ) , [{'label': 'LABEL_0', 'score': 0.504}] )
a_ : List[str] = text_classifier('This is great !' , return_all_scores=SCREAMING_SNAKE_CASE__ )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE__ ) , [[{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}]] )
a_ : int = text_classifier(['This is great !', 'Something else'] , return_all_scores=SCREAMING_SNAKE_CASE__ )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE__ ) , [
[{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}],
[{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}],
] , )
a_ : str = text_classifier(['This is great !', 'Something else'] , return_all_scores=SCREAMING_SNAKE_CASE__ )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE__ ) , [
{'label': 'LABEL_0', 'score': 0.504},
{'label': 'LABEL_0', 'score': 0.504},
] , )
@require_torch
def SCREAMING_SNAKE_CASE ( self : int ) -> Dict:
import torch
a_ : List[Any] = pipeline(
task='text-classification' , model='hf-internal-testing/tiny-random-distilbert' , framework='pt' , device=torch.device('cpu' ) , )
a_ : Any = text_classifier('This is great !' )
self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__ ) , [{'label': 'LABEL_0', 'score': 0.504}] )
@require_tf
def SCREAMING_SNAKE_CASE ( self : str ) -> Optional[Any]:
a_ : List[str] = pipeline(
task='text-classification' , model='hf-internal-testing/tiny-random-distilbert' , framework='tf' )
a_ : Optional[int] = text_classifier('This is great !' )
self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__ ) , [{'label': 'LABEL_0', 'score': 0.504}] )
@slow
@require_torch
def SCREAMING_SNAKE_CASE ( self : Dict ) -> Optional[int]:
a_ : List[str] = pipeline('text-classification' )
a_ : Dict = text_classifier('This is great !' )
self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__ ) , [{'label': 'POSITIVE', 'score': 1.0}] )
a_ : Union[str, Any] = text_classifier('This is bad !' )
self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__ ) , [{'label': 'NEGATIVE', 'score': 1.0}] )
a_ : Tuple = text_classifier('Birds are a type of animal' )
self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__ ) , [{'label': 'POSITIVE', 'score': 0.988}] )
@slow
@require_tf
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> List[Any]:
a_ : Dict = pipeline('text-classification' , framework='tf' )
a_ : Optional[Any] = text_classifier('This is great !' )
self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__ ) , [{'label': 'POSITIVE', 'score': 1.0}] )
a_ : int = text_classifier('This is bad !' )
self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__ ) , [{'label': 'NEGATIVE', 'score': 1.0}] )
a_ : Optional[int] = text_classifier('Birds are a type of animal' )
self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__ ) , [{'label': 'POSITIVE', 'score': 0.988}] )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Optional[Any] ) -> Any:
a_ : Optional[Any] = TextClassificationPipeline(model=SCREAMING_SNAKE_CASE__ , tokenizer=SCREAMING_SNAKE_CASE__ )
return text_classifier, ["HuggingFace is in", "This is another test"]
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Optional[int] ) -> Union[str, Any]:
a_ : List[str] = text_classifier.model
# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
a_ : Union[str, Any] = 'HuggingFace is in'
a_ : int = text_classifier(SCREAMING_SNAKE_CASE__ )
self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__ ) , [{'label': ANY(SCREAMING_SNAKE_CASE__ ), 'score': ANY(SCREAMING_SNAKE_CASE__ )}] )
self.assertTrue(outputs[0]['label'] in model.config.idalabel.values() )
a_ : Union[str, Any] = ['HuggingFace is in ', 'Paris is in France']
a_ : int = text_classifier(SCREAMING_SNAKE_CASE__ )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE__ ) , [{'label': ANY(SCREAMING_SNAKE_CASE__ ), 'score': ANY(SCREAMING_SNAKE_CASE__ )}, {'label': ANY(SCREAMING_SNAKE_CASE__ ), 'score': ANY(SCREAMING_SNAKE_CASE__ )}] , )
self.assertTrue(outputs[0]['label'] in model.config.idalabel.values() )
self.assertTrue(outputs[1]['label'] in model.config.idalabel.values() )
# Forcing to get all results with `top_k=None`
# This is NOT the legacy format
a_ : List[Any] = text_classifier(SCREAMING_SNAKE_CASE__ , top_k=SCREAMING_SNAKE_CASE__ )
a_ : Dict = len(model.config.idalabel.values() )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE__ ) , [[{'label': ANY(SCREAMING_SNAKE_CASE__ ), 'score': ANY(SCREAMING_SNAKE_CASE__ )}] * N, [{'label': ANY(SCREAMING_SNAKE_CASE__ ), 'score': ANY(SCREAMING_SNAKE_CASE__ )}] * N] , )
a_ : int = {'text': 'HuggingFace is in ', 'text_pair': 'Paris is in France'}
a_ : Optional[int] = text_classifier(SCREAMING_SNAKE_CASE__ )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE__ ) , {'label': ANY(SCREAMING_SNAKE_CASE__ ), 'score': ANY(SCREAMING_SNAKE_CASE__ )} , )
self.assertTrue(outputs['label'] in model.config.idalabel.values() )
# This might be used a text pair, but tokenizer + pipe interaction
# makes it hard to understand that it's not using the pair properly
# https://github.com/huggingface/transformers/issues/17305
# We disabled this usage instead as it was outputting wrong outputs.
a_ : Any = [['HuggingFace is in ', 'Paris is in France']]
with self.assertRaises(SCREAMING_SNAKE_CASE__ ):
text_classifier(SCREAMING_SNAKE_CASE__ )
# This used to be valid for doing text pairs
# We're keeping it working because of backward compatibility
a_ : Tuple = text_classifier([[['HuggingFace is in ', 'Paris is in France']]] )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE__ ) , [{'label': ANY(SCREAMING_SNAKE_CASE__ ), 'score': ANY(SCREAMING_SNAKE_CASE__ )}] , )
self.assertTrue(outputs[0]['label'] in model.config.idalabel.values() )
| 32 | 1 |
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
get_torch_dist_unique_port,
require_apex,
require_bitsandbytes,
require_fairscale,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
require_torch_non_multi_gpu,
slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed
lowerCAmelCase__ :Any = os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f'''{bindir}/../../examples/pytorch/translation'''):
from run_translation import main # noqa
set_seed(4_2)
lowerCAmelCase__ :List[str] = '''sshleifer/student_marian_en_ro_6_1'''
lowerCAmelCase__ :Dict = '''sshleifer/tiny-mbart'''
@require_torch
class __a ( A__ ):
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = self.run_trainer(
eval_steps=1 , max_len=12 , model_name=__snake_case , num_train_epochs=1 , distributed=__snake_case , extra_args_str=__snake_case , predict_with_generate=__snake_case , do_train=__snake_case , do_eval=__snake_case , do_predict=__snake_case , )
_UpperCAmelCase = TrainerState.load_from_json(os.path.join(__snake_case , 'trainer_state.json' ) ).log_history
if not do_eval:
return
_UpperCAmelCase = [log for log in logs if 'eval_loss' in log.keys()]
_UpperCAmelCase = eval_metrics[0]
if predict_with_generate:
assert "eval_bleu" in first_step_stats
_UpperCAmelCase = eval_metrics[-1]
assert isinstance(last_step_stats['eval_bleu'] , __snake_case )
assert not math.isnan(float(last_step_stats['eval_loss'] ) ), "eval_loss must not be `nan`"
@require_torch_non_multi_gpu
def UpperCAmelCase__ ( self ) -> int:
"""simple docstring"""
self.run_seqaseq_quick()
@require_torch_multi_gpu
def UpperCAmelCase__ ( self ) -> Dict:
"""simple docstring"""
self.run_seqaseq_quick(distributed=__snake_case )
@require_torch_multi_gpu
def UpperCAmelCase__ ( self ) -> List[Any]:
"""simple docstring"""
self.run_seqaseq_quick(distributed=__snake_case )
@unittest.skip('Requires an update of the env running those tests' )
@require_torch_multi_gpu
@require_fairscale
def UpperCAmelCase__ ( self ) -> Dict:
"""simple docstring"""
self.run_seqaseq_quick(distributed=__snake_case , extra_args_str='--sharded_ddp simple' )
@unittest.skip('Requires an update of the env running those tests' )
@require_torch_multi_gpu
@require_fairscale
def UpperCAmelCase__ ( self ) -> Dict:
"""simple docstring"""
self.run_seqaseq_quick(distributed=__snake_case , extra_args_str='--sharded_ddp simple --fp16' )
@unittest.skip('Requires an update of the env running those tests' )
@require_torch_multi_gpu
@require_fairscale
def UpperCAmelCase__ ( self ) -> str:
"""simple docstring"""
self.run_seqaseq_quick(distributed=__snake_case , extra_args_str='--sharded_ddp zero_dp_2' , predict_with_generate=__snake_case )
@unittest.skip('Requires an update of the env running those tests' )
@require_torch_multi_gpu
@require_fairscale
def UpperCAmelCase__ ( self ) -> List[Any]:
"""simple docstring"""
self.run_seqaseq_quick(
distributed=__snake_case , extra_args_str='--sharded_ddp zero_dp_2 --fp16' , predict_with_generate=__snake_case )
@require_apex
@require_torch_gpu
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
"""simple docstring"""
self.run_seqaseq_quick(distributed=__snake_case , extra_args_str='--fp16 --fp16_backend=apex' )
# test 2nd time - was getting eval_loss': nan'
# to reproduce the problem set distributed=False
self.run_seqaseq_quick(distributed=__snake_case , extra_args_str='--fp16 --fp16_backend=apex' )
@parameterized.expand(['base', 'low', 'high', 'mixed'] )
@require_torch_multi_gpu
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = {
# test with the default log_level - should be info and thus log info once
'base': {'extra_args_str': '', 'n_matches': 1},
# test with low log_level and log_level_replica - should be noisy on all processes
# now the info string should appear twice on 2 processes
'low': {'extra_args_str': '--log_level debug --log_level_replica debug', 'n_matches': 2},
# test with high log_level and low log_level_replica
# now the info string should appear once only on the replica
'high': {'extra_args_str': '--log_level error --log_level_replica debug', 'n_matches': 1},
# test with high log_level and log_level_replica - should be quiet on all processes
'mixed': {'extra_args_str': '--log_level error --log_level_replica error', 'n_matches': 0},
}
_UpperCAmelCase = experiments[experiment_id]
_UpperCAmelCase = {'distributed': True, 'predict_with_generate': False, 'do_eval': False, 'do_predict': False}
_UpperCAmelCase = 'Running training'
with CaptureStderr() as cl:
self.run_seqaseq_quick(**__snake_case , extra_args_str=data['extra_args_str'] )
_UpperCAmelCase = len(re.findall(__snake_case , cl.err ) )
self.assertEqual(__snake_case , data['n_matches'] )
@slow
def UpperCAmelCase__ ( self ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = self.run_trainer(
eval_steps=2 , max_len=128 , model_name=__snake_case , learning_rate=3e-4 , num_train_epochs=10 , distributed=__snake_case , )
# Check metrics
_UpperCAmelCase = TrainerState.load_from_json(os.path.join(__snake_case , 'trainer_state.json' ) ).log_history
_UpperCAmelCase = [log for log in logs if 'eval_loss' in log.keys()]
_UpperCAmelCase = eval_metrics[0]
_UpperCAmelCase = eval_metrics[-1]
assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
assert isinstance(last_step_stats['eval_bleu'] , __snake_case )
# test if do_predict saves generations and metrics
_UpperCAmelCase = os.listdir(__snake_case )
_UpperCAmelCase = {os.path.basename(__snake_case ) for p in contents}
assert "generated_predictions.txt" in contents
assert "predict_results.json" in contents
@slow
@require_bitsandbytes
def UpperCAmelCase__ ( self ) -> Any:
"""simple docstring"""
from transformers.training_args import OptimizerNames
def train_and_return_metrics(_SCREAMING_SNAKE_CASE ) -> Tuple[int, float]:
_UpperCAmelCase = '--skip_memory_metrics 0'
_UpperCAmelCase = self.run_trainer(
max_len=128 , model_name=__snake_case , learning_rate=3e-4 , num_train_epochs=1 , optim=__snake_case , distributed=__snake_case , extra_args_str=__snake_case , do_eval=__snake_case , do_predict=__snake_case , n_gpus_to_use=1 , )
# Check metrics
_UpperCAmelCase = TrainerState.load_from_json(Path(__snake_case , 'trainer_state.json' ) ).log_history
_UpperCAmelCase = int(logs[0]['train_mem_gpu_peaked_delta'] / 2**20 )
_UpperCAmelCase = int(logs[0]['train_mem_gpu_alloc_delta'] / 2**20 )
_UpperCAmelCase = logs[0]['train_loss']
return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = train_and_return_metrics(OptimizerNames.ADAMW_BNB.value )
_UpperCAmelCase = gpu_alloc_mem_orig - gpu_alloc_mem_bnb
_UpperCAmelCase = gpu_peak_mem_orig + gpu_alloc_mem_orig
_UpperCAmelCase = gpu_peak_mem_bnb + gpu_alloc_mem_bnb
_UpperCAmelCase = gpu_total_mem_orig - gpu_total_mem_bnb
# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
# in 2 bytes and the diff in optim memory usage is derived as so:
#
# - normal 25*8=~200MB (8 bytes per param)
# - bnb 25*2= ~50MB (2 bytes per param)
#
# Thus we should expect ~150MB total memory saved.
#
# Peak memory should be the same - the total should be different by about that same margin
#
# After leaving a small margin to accommodate for differences between gpus let's check
# that we have at least 120MB in savings
_UpperCAmelCase = 120
# uncomment the following if this test starts failing - requires py38 for a new print feature
# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
# print(f" {gpu_alloc_mem_bnb=}MB {gpu_peak_mem_bnb=}MB {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
# print(f"{gpu_alloc_mem_diff=}MB")
# print(f"{gpu_peak_mem_diff=}MB")
# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")
self.assertGreater(
__snake_case , __snake_case , 'should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got'
f''' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'''
f''' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB''' , )
self.assertGreater(
__snake_case , __snake_case , 'should use ~150MB less total gpu memory with BNB, compared to without it for this model but got'
f''' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'''
f''' gpu_total_mem_bnb={gpu_total_mem_bnb}MB''' , )
self.assertEqual(
__snake_case , __snake_case , f'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}''' )
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 3e-3 , _SCREAMING_SNAKE_CASE = "adafactor" , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = None , ) -> Dict:
"""simple docstring"""
_UpperCAmelCase = self.test_file_dir / '../fixtures/tests_samples/wmt_en_ro'
_UpperCAmelCase = self.get_auto_remove_tmp_dir()
_UpperCAmelCase = f'''
--model_name_or_path {model_name}
--train_file {data_dir}/train.json
--validation_file {data_dir}/val.json
--test_file {data_dir}/test.json
--output_dir {output_dir}
--overwrite_output_dir
--max_train_samples 8
--max_source_length {max_len}
--max_target_length {max_len}
--do_train
--num_train_epochs {str(__snake_case )}
--per_device_train_batch_size 4
--learning_rate {learning_rate}
--warmup_steps 8
--logging_steps 0
--logging_strategy no
--save_steps {str(__snake_case )}
--group_by_length
--label_smoothing_factor 0.1
--target_lang ro_RO
--source_lang en_XX
'''.split()
_UpperCAmelCase = f'''
--do_eval
--per_device_eval_batch_size 4
--max_eval_samples 8
--val_max_target_length {max_len}
--evaluation_strategy steps
--eval_steps {str(__snake_case )}
'''.split()
_UpperCAmelCase = '\n --do_predict\n '.split()
_UpperCAmelCase = []
if do_train:
args += args_train
if do_eval:
args += args_eval
if do_predict:
args += args_predict
if predict_with_generate:
args += "--predict_with_generate".split()
if do_train:
if optim == "adafactor":
args += "--adafactor".split()
else:
args += f'''--optim {optim}'''.split()
if extra_args_str is not None:
args += extra_args_str.split()
if distributed:
if n_gpus_to_use is None:
_UpperCAmelCase = get_gpu_count()
_UpperCAmelCase = get_torch_dist_unique_port()
_UpperCAmelCase = f'''
-m torch.distributed.run
--nproc_per_node={n_gpus_to_use}
--master_port={master_port}
{self.examples_dir_str}/pytorch/translation/run_translation.py
'''.split()
_UpperCAmelCase = [sys.executable] + distributed_args + args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(__snake_case , env=self.get_env() )
else:
_UpperCAmelCase = ['run_translation.py'] + args
with patch.object(__snake_case , 'argv' , __snake_case ):
main()
return output_dir
| 353 |
# Copyright 2022 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.
import argparse
import os
import subprocess
from packaging.version import Version, parse
from accelerate.commands.config.config_args import default_config_file, load_config_from_file
lowerCAmelCase__ :Dict = '''Run commands across TPU VMs for initial setup before running `accelerate launch`.'''
def lowerCAmelCase__ ( a__: Optional[Any]=None ) -> List[Any]:
'''simple docstring'''
if subparsers is not None:
_UpperCAmelCase = subparsers.add_parser('tpu-config' , description=_description )
else:
_UpperCAmelCase = argparse.ArgumentParser('Accelerate tpu-config command' , description=_description )
# Core arguments
_UpperCAmelCase = parser.add_argument_group(
'Config Arguments' , 'Arguments that can be configured through `accelerate config`.' )
config_args.add_argument(
'--config_file' , type=a__ , default=a__ , help='Path to the config file to use for accelerate.' , )
config_args.add_argument(
'--tpu_name' , default=a__ , help='The name of the TPU to use. If not specified, will use the TPU specified in the config file.' , )
config_args.add_argument(
'--tpu_zone' , default=a__ , help='The zone of the TPU to use. If not specified, will use the zone specified in the config file.' , )
_UpperCAmelCase = parser.add_argument_group('TPU Arguments' , 'Arguments for options ran inside the TPU.' )
pod_args.add_argument(
'--use_alpha' , action='store_true' , help='Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.' , )
pod_args.add_argument(
'--command_file' , default=a__ , help='The path to the file containing the commands to run on the pod on startup.' , )
pod_args.add_argument(
'--command' , action='append' , nargs='+' , help='A command to run on the pod. Can be passed multiple times.' , )
pod_args.add_argument(
'--install_accelerate' , action='store_true' , help='Whether to install accelerate on the pod. Defaults to False.' , )
pod_args.add_argument(
'--accelerate_version' , default='latest' , help='The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.' , )
pod_args.add_argument(
'--debug' , action='store_true' , help='If set, will print the command that would be run instead of running it.' )
if subparsers is not None:
parser.set_defaults(func=a__ )
return parser
def lowerCAmelCase__ ( a__: str ) -> Any:
'''simple docstring'''
_UpperCAmelCase = None
# Get the default from the config file if it exists.
if args.config_file is not None or os.path.isfile(a__ ):
_UpperCAmelCase = load_config_from_file(args.config_file )
if not args.command_file and defaults.command_file is not None and not args.command:
_UpperCAmelCase = defaults.command_file
if not args.command and defaults.commands is not None:
_UpperCAmelCase = defaults.commands
if not args.tpu_name:
_UpperCAmelCase = defaults.tpu_name
if not args.tpu_zone:
_UpperCAmelCase = defaults.tpu_zone
if args.accelerate_version == "dev":
_UpperCAmelCase = 'git+https://github.com/huggingface/accelerate.git'
elif args.accelerate_version == "latest":
_UpperCAmelCase = 'accelerate -U'
elif isinstance(parse(args.accelerate_version ) , a__ ):
_UpperCAmelCase = F'''accelerate=={args.accelerate_version}'''
if not args.command_file and not args.command:
raise ValueError('You must specify either a command file or a command to run on the pod.' )
if args.command_file:
with open(args.command_file , 'r' ) as f:
_UpperCAmelCase = [f.read().splitlines()]
# To turn list of lists into list of strings
if isinstance(args.command[0] , a__ ):
_UpperCAmelCase = [line for cmd in args.command for line in cmd]
# Default to the shared folder and install accelerate
_UpperCAmelCase = ['cd /usr/share']
if args.install_accelerate:
new_cmd += [F'''pip install {args.accelerate_version}''']
new_cmd += args.command
_UpperCAmelCase = '; '.join(a__ )
# Then send it to gcloud
# Eventually try to use google-api-core to do this instead of subprocess
_UpperCAmelCase = ['gcloud']
if args.use_alpha:
cmd += ["alpha"]
cmd += [
"compute",
"tpus",
"tpu-vm",
"ssh",
args.tpu_name,
"--zone",
args.tpu_zone,
"--command",
args.command,
"--worker",
"all",
]
if args.debug:
print(F'''Running {" ".join(a__ )}''' )
return
subprocess.run(a__ )
print('Successfully setup pod.' )
def lowerCAmelCase__ ( ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = tpu_command_parser()
_UpperCAmelCase = parser.parse_args()
tpu_command_launcher(a__ )
| 185 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_nllb import NllbTokenizer
else:
__UpperCamelCase = None
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}
__UpperCamelCase = {
'''vocab_file''': {
'''facebook/nllb-200-distilled-600M''': (
'''https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model'''
),
},
'''tokenizer_file''': {
'''facebook/nllb-200-distilled-600M''': (
'''https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json'''
),
},
}
__UpperCamelCase = {
'''facebook/nllb-large-en-ro''': 1024,
'''facebook/nllb-200-distilled-600M''': 1024,
}
# fmt: off
__UpperCamelCase = ['''ace_Arab''', '''ace_Latn''', '''acm_Arab''', '''acq_Arab''', '''aeb_Arab''', '''afr_Latn''', '''ajp_Arab''', '''aka_Latn''', '''amh_Ethi''', '''apc_Arab''', '''arb_Arab''', '''ars_Arab''', '''ary_Arab''', '''arz_Arab''', '''asm_Beng''', '''ast_Latn''', '''awa_Deva''', '''ayr_Latn''', '''azb_Arab''', '''azj_Latn''', '''bak_Cyrl''', '''bam_Latn''', '''ban_Latn''', '''bel_Cyrl''', '''bem_Latn''', '''ben_Beng''', '''bho_Deva''', '''bjn_Arab''', '''bjn_Latn''', '''bod_Tibt''', '''bos_Latn''', '''bug_Latn''', '''bul_Cyrl''', '''cat_Latn''', '''ceb_Latn''', '''ces_Latn''', '''cjk_Latn''', '''ckb_Arab''', '''crh_Latn''', '''cym_Latn''', '''dan_Latn''', '''deu_Latn''', '''dik_Latn''', '''dyu_Latn''', '''dzo_Tibt''', '''ell_Grek''', '''eng_Latn''', '''epo_Latn''', '''est_Latn''', '''eus_Latn''', '''ewe_Latn''', '''fao_Latn''', '''pes_Arab''', '''fij_Latn''', '''fin_Latn''', '''fon_Latn''', '''fra_Latn''', '''fur_Latn''', '''fuv_Latn''', '''gla_Latn''', '''gle_Latn''', '''glg_Latn''', '''grn_Latn''', '''guj_Gujr''', '''hat_Latn''', '''hau_Latn''', '''heb_Hebr''', '''hin_Deva''', '''hne_Deva''', '''hrv_Latn''', '''hun_Latn''', '''hye_Armn''', '''ibo_Latn''', '''ilo_Latn''', '''ind_Latn''', '''isl_Latn''', '''ita_Latn''', '''jav_Latn''', '''jpn_Jpan''', '''kab_Latn''', '''kac_Latn''', '''kam_Latn''', '''kan_Knda''', '''kas_Arab''', '''kas_Deva''', '''kat_Geor''', '''knc_Arab''', '''knc_Latn''', '''kaz_Cyrl''', '''kbp_Latn''', '''kea_Latn''', '''khm_Khmr''', '''kik_Latn''', '''kin_Latn''', '''kir_Cyrl''', '''kmb_Latn''', '''kon_Latn''', '''kor_Hang''', '''kmr_Latn''', '''lao_Laoo''', '''lvs_Latn''', '''lij_Latn''', '''lim_Latn''', '''lin_Latn''', '''lit_Latn''', '''lmo_Latn''', '''ltg_Latn''', '''ltz_Latn''', '''lua_Latn''', '''lug_Latn''', '''luo_Latn''', '''lus_Latn''', '''mag_Deva''', '''mai_Deva''', '''mal_Mlym''', '''mar_Deva''', '''min_Latn''', '''mkd_Cyrl''', '''plt_Latn''', '''mlt_Latn''', '''mni_Beng''', '''khk_Cyrl''', '''mos_Latn''', '''mri_Latn''', '''zsm_Latn''', '''mya_Mymr''', '''nld_Latn''', '''nno_Latn''', '''nob_Latn''', '''npi_Deva''', '''nso_Latn''', '''nus_Latn''', '''nya_Latn''', '''oci_Latn''', '''gaz_Latn''', '''ory_Orya''', '''pag_Latn''', '''pan_Guru''', '''pap_Latn''', '''pol_Latn''', '''por_Latn''', '''prs_Arab''', '''pbt_Arab''', '''quy_Latn''', '''ron_Latn''', '''run_Latn''', '''rus_Cyrl''', '''sag_Latn''', '''san_Deva''', '''sat_Beng''', '''scn_Latn''', '''shn_Mymr''', '''sin_Sinh''', '''slk_Latn''', '''slv_Latn''', '''smo_Latn''', '''sna_Latn''', '''snd_Arab''', '''som_Latn''', '''sot_Latn''', '''spa_Latn''', '''als_Latn''', '''srd_Latn''', '''srp_Cyrl''', '''ssw_Latn''', '''sun_Latn''', '''swe_Latn''', '''swh_Latn''', '''szl_Latn''', '''tam_Taml''', '''tat_Cyrl''', '''tel_Telu''', '''tgk_Cyrl''', '''tgl_Latn''', '''tha_Thai''', '''tir_Ethi''', '''taq_Latn''', '''taq_Tfng''', '''tpi_Latn''', '''tsn_Latn''', '''tso_Latn''', '''tuk_Latn''', '''tum_Latn''', '''tur_Latn''', '''twi_Latn''', '''tzm_Tfng''', '''uig_Arab''', '''ukr_Cyrl''', '''umb_Latn''', '''urd_Arab''', '''uzn_Latn''', '''vec_Latn''', '''vie_Latn''', '''war_Latn''', '''wol_Latn''', '''xho_Latn''', '''ydd_Hebr''', '''yor_Latn''', '''yue_Hant''', '''zho_Hans''', '''zho_Hant''', '''zul_Latn''']
class UpperCamelCase ( lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE_ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE_ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE_ = ["input_ids", "attention_mask"]
SCREAMING_SNAKE_CASE_ = NllbTokenizer
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = []
def __init__( self, lowerCAmelCase__=None, lowerCAmelCase__=None, lowerCAmelCase__="<s>", lowerCAmelCase__="</s>", lowerCAmelCase__="</s>", lowerCAmelCase__="<s>", lowerCAmelCase__="<unk>", lowerCAmelCase__="<pad>", lowerCAmelCase__="<mask>", lowerCAmelCase__=None, lowerCAmelCase__=None, lowerCAmelCase__=None, lowerCAmelCase__=False, **lowerCAmelCase__, ) -> List[str]:
# Mask token behave like a normal word, i.e. include the space before it
snake_case_ = AddedToken(lowerCAmelCase__, lstrip=lowerCAmelCase__, rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__, lowerCAmelCase__) else mask_token
snake_case_ = legacy_behaviour
super().__init__(
vocab_file=lowerCAmelCase__, tokenizer_file=lowerCAmelCase__, bos_token=lowerCAmelCase__, eos_token=lowerCAmelCase__, sep_token=lowerCAmelCase__, cls_token=lowerCAmelCase__, unk_token=lowerCAmelCase__, pad_token=lowerCAmelCase__, mask_token=lowerCAmelCase__, src_lang=lowerCAmelCase__, tgt_lang=lowerCAmelCase__, additional_special_tokens=lowerCAmelCase__, legacy_behaviour=lowerCAmelCase__, **lowerCAmelCase__, )
snake_case_ = vocab_file
snake_case_ = False if not self.vocab_file else True
snake_case_ = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens])
self.add_special_tokens({'additional_special_tokens': _additional_special_tokens})
snake_case_ = {
lang_code: self.convert_tokens_to_ids(lowerCAmelCase__) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
snake_case_ = src_lang if src_lang is not None else 'eng_Latn'
snake_case_ = self.convert_tokens_to_ids(self._src_lang)
snake_case_ = tgt_lang
self.set_src_lang_special_tokens(self._src_lang)
@property
def a_ ( self) -> str:
return self._src_lang
@src_lang.setter
def a_ ( self, lowerCAmelCase__) -> None:
snake_case_ = new_src_lang
self.set_src_lang_special_tokens(self._src_lang)
def a_ ( self, lowerCAmelCase__, lowerCAmelCase__ = None) -> List[int]:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def a_ ( self, lowerCAmelCase__, lowerCAmelCase__ = None) -> List[int]:
snake_case_ = [self.sep_token_id]
snake_case_ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]
def a_ ( self, lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__, **lowerCAmelCase__) -> str:
if src_lang is None or tgt_lang is None:
raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model')
snake_case_ = src_lang
snake_case_ = self(lowerCAmelCase__, add_special_tokens=lowerCAmelCase__, return_tensors=lowerCAmelCase__, **lowerCAmelCase__)
snake_case_ = self.convert_tokens_to_ids(lowerCAmelCase__)
snake_case_ = tgt_lang_id
return inputs
def a_ ( self, lowerCAmelCase__, lowerCAmelCase__ = "eng_Latn", lowerCAmelCase__ = None, lowerCAmelCase__ = "fra_Latn", **lowerCAmelCase__, ) -> BatchEncoding:
snake_case_ = src_lang
snake_case_ = tgt_lang
return super().prepare_seqaseq_batch(lowerCAmelCase__, lowerCAmelCase__, **lowerCAmelCase__)
def a_ ( self) -> List[Any]:
return self.set_src_lang_special_tokens(self.src_lang)
def a_ ( self) -> Tuple:
return self.set_tgt_lang_special_tokens(self.tgt_lang)
def a_ ( self, lowerCAmelCase__) -> None:
snake_case_ = self.convert_tokens_to_ids(lowerCAmelCase__)
if self.legacy_behaviour:
snake_case_ = []
snake_case_ = [self.eos_token_id, self.cur_lang_code]
else:
snake_case_ = [self.cur_lang_code]
snake_case_ = [self.eos_token_id]
snake_case_ = self.convert_ids_to_tokens(self.prefix_tokens)
snake_case_ = self.convert_ids_to_tokens(self.suffix_tokens)
snake_case_ = processors.TemplateProcessing(
single=prefix_tokens_str + ['$A'] + suffix_tokens_str, pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str, special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens)), )
def a_ ( self, lowerCAmelCase__) -> None:
snake_case_ = self.convert_tokens_to_ids(lowerCAmelCase__)
if self.legacy_behaviour:
snake_case_ = []
snake_case_ = [self.eos_token_id, self.cur_lang_code]
else:
snake_case_ = [self.cur_lang_code]
snake_case_ = [self.eos_token_id]
snake_case_ = self.convert_ids_to_tokens(self.prefix_tokens)
snake_case_ = self.convert_ids_to_tokens(self.suffix_tokens)
snake_case_ = processors.TemplateProcessing(
single=prefix_tokens_str + ['$A'] + suffix_tokens_str, pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str, special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens)), )
def a_ ( self, lowerCAmelCase__, lowerCAmelCase__ = None) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
'tokenizer.')
if not os.path.isdir(lowerCAmelCase__):
logger.error(f'Vocabulary path ({save_directory}) should be a directory.')
return
snake_case_ = os.path.join(
lowerCAmelCase__, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
if os.path.abspath(self.vocab_file) != os.path.abspath(lowerCAmelCase__):
copyfile(self.vocab_file, lowerCAmelCase__)
return (out_vocab_file,)
| 69 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
lowercase : List[str] = logging.get_logger(__name__)
lowercase : str = {
'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
# See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class A ( __snake_case ):
__magic_name__ = '''gpt_neo'''
__magic_name__ = ['''past_key_values''']
__magic_name__ = {'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
def __init__( self , SCREAMING_SNAKE_CASE=50257 , SCREAMING_SNAKE_CASE=2048 , SCREAMING_SNAKE_CASE=2048 , SCREAMING_SNAKE_CASE=24 , SCREAMING_SNAKE_CASE=[[["global", "local"], 12]] , SCREAMING_SNAKE_CASE=16 , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=256 , SCREAMING_SNAKE_CASE="gelu_new" , SCREAMING_SNAKE_CASE=0.0 , SCREAMING_SNAKE_CASE=0.0 , SCREAMING_SNAKE_CASE=0.0 , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=1e-5 , SCREAMING_SNAKE_CASE=0.02 , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=50256 , SCREAMING_SNAKE_CASE=50256 , **SCREAMING_SNAKE_CASE , ) -> int:
"""simple docstring"""
A : Union[str, Any] = vocab_size
A : Optional[Any] = max_position_embeddings
A : Dict = hidden_size
A : Optional[Any] = num_layers
A : Tuple = num_heads
A : int = intermediate_size
A : Optional[Any] = window_size
A : List[Any] = activation_function
A : Union[str, Any] = resid_dropout
A : Any = embed_dropout
A : List[Any] = attention_dropout
A : str = classifier_dropout
A : List[Any] = layer_norm_epsilon
A : str = initializer_range
A : List[str] = use_cache
A : Optional[int] = bos_token_id
A : List[Any] = eos_token_id
A : int = attention_types
A : int = self.expand_attention_types_params(SCREAMING_SNAKE_CASE )
if len(self.attention_layers ) != self.num_layers:
raise ValueError(
'''Configuration for convolutional module is incorrect. '''
'''It is required that `len(config.attention_layers)` == `config.num_layers` '''
F'but is `len(config.attention_layers) = {len(self.attention_layers )}`, '
F'`config.num_layers = {self.num_layers}`. '
'''`config.attention_layers` is prepared using `config.attention_types`. '''
'''Please verify the value of `config.attention_types` argument.''' )
super().__init__(bos_token_id=SCREAMING_SNAKE_CASE , eos_token_id=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
@staticmethod
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
A : List[str] = []
for item in attention_types:
for _ in range(item[1] ):
attentions.extend(item[0] )
return attentions
def lowerCAmelCase_ ( snake_case__ , snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
import torch
A : Tuple = input.size()
A : Union[str, Any] = len(snake_case__ )
A : List[str] = shape[dimension]
A : Union[str, Any] = torch.arange(0 , snake_case__ , snake_case__ )
A : List[str] = torch.div(sizedim - size , snake_case__ , rounding_mode='''floor''' ) + 1
A : Optional[int] = torch.arange(snake_case__ ) + low_indices[:min_length][:, None]
A : str = [slice(snake_case__ )] * rank
A : List[Any] = indices
A : Union[str, Any] = input[s]
A : List[str] = list(range(0 , rank + 1 ) )
perm.append(perm.pop(dimension + 1 ) )
return sliced.permute(snake_case__ )
def lowerCAmelCase_ ( snake_case__ , snake_case__ ):
'''simple docstring'''
import torch
A : List[str] = torch.arange(1 , snake_case__ )
A : Optional[int] = torch.remainder(snake_case__ , snake_case__ )
A : Optional[int] = remainders == 0
A : Optional[Any] = candidates[divisor_indices]
A : Optional[int] = torch.max(snake_case__ )
return largest_divisor, torch.div(snake_case__ , snake_case__ , rounding_mode='''floor''' )
class A ( __snake_case ):
@property
def __lowerCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
A : Tuple = OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} )
if self.use_past:
self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE , direction='''inputs''' )
A : Optional[Any] = {0: '''batch''', 1: '''past_sequence + sequence'''}
else:
A : Dict = {0: '''batch''', 1: '''sequence'''}
return common_inputs
@property
def __lowerCAmelCase ( self ) -> int:
"""simple docstring"""
return self._config.num_heads
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = -1 , SCREAMING_SNAKE_CASE = -1 , SCREAMING_SNAKE_CASE = False , SCREAMING_SNAKE_CASE = None , ) -> Mapping[str, Any]:
"""simple docstring"""
A : List[str] = super(SCREAMING_SNAKE_CASE , self ).generate_dummy_inputs(
SCREAMING_SNAKE_CASE , batch_size=SCREAMING_SNAKE_CASE , seq_length=SCREAMING_SNAKE_CASE , is_pair=SCREAMING_SNAKE_CASE , framework=SCREAMING_SNAKE_CASE )
# We need to order the input in the way they appears in the forward()
A : Any = 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
A, A : Dict = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
A : str = seqlen + 2
A : List[Any] = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
A : Any = [
(torch.zeros(SCREAMING_SNAKE_CASE ), torch.zeros(SCREAMING_SNAKE_CASE )) for _ in range(self.num_layers )
]
A : str = common_inputs['''attention_mask''']
if self.use_past:
A : Optional[int] = ordered_inputs['''attention_mask'''].dtype
A : List[str] = torch.cat(
[ordered_inputs['''attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , dtype=SCREAMING_SNAKE_CASE )] , dim=1 )
return ordered_inputs
@property
def __lowerCAmelCase ( self ) -> int:
"""simple docstring"""
return 13
| 3 | 0 |
'''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 snake_case_ (UpperCamelCase : int ):
'''simple docstring'''
if isinstance(UpperCamelCase , collections.abc.Iterable ):
return x
return (x, x)
@require_flax
class A :
def __lowerCAmelCase ( self : str , lowerCAmelCase_ : Any , lowerCAmelCase_ : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
pass
def __lowerCAmelCase ( self : Tuple ) -> Any:
"""simple docstring"""
pass
def __lowerCAmelCase ( self : Tuple ) -> str:
"""simple docstring"""
pass
def __lowerCAmelCase ( self : Optional[int] , lowerCAmelCase_ : np.ndarray , lowerCAmelCase_ : np.ndarray , lowerCAmelCase_ : float ) -> List[Any]:
"""simple docstring"""
_a = np.abs((a - b) ).max()
self.assertLessEqual(lowerCAmelCase_ , lowerCAmelCase_ , F'Difference between torch and flax is {diff} (>= {tol}).' )
def __lowerCAmelCase ( self : List[str] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[Any]=None , **lowerCAmelCase_ : int ) -> List[Any]:
"""simple docstring"""
_a = VisionTextDualEncoderConfig.from_vision_text_configs(lowerCAmelCase_ , lowerCAmelCase_ )
_a = FlaxVisionTextDualEncoderModel(lowerCAmelCase_ )
_a = 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 __lowerCAmelCase ( self : Optional[int] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : List[str]=None , **lowerCAmelCase_ : List[str] ) -> Union[str, Any]:
"""simple docstring"""
_a , _a = self.get_vision_text_model(lowerCAmelCase_ , lowerCAmelCase_ )
_a = {'''vision_model''': vision_model, '''text_model''': text_model}
_a = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCAmelCase_ )
_a = 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 __lowerCAmelCase ( self : Optional[int] , lowerCAmelCase_ : int , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : int , lowerCAmelCase_ : Any , lowerCAmelCase_ : Tuple=None , **lowerCAmelCase_ : Optional[Any] ) -> Any:
"""simple docstring"""
_a , _a = self.get_vision_text_model(lowerCAmelCase_ , lowerCAmelCase_ )
_a = {'''vision_model''': vision_model, '''text_model''': text_model}
_a = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCAmelCase_ )
_a = model(input_ids=lowerCAmelCase_ , pixel_values=lowerCAmelCase_ , attention_mask=lowerCAmelCase_ )
_a = output[0]
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowerCAmelCase_ )
_a = FlaxVisionTextDualEncoderModel.from_pretrained(lowerCAmelCase_ )
_a = model(input_ids=lowerCAmelCase_ , pixel_values=lowerCAmelCase_ , attention_mask=lowerCAmelCase_ )
_a = after_output[0]
_a = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowerCAmelCase_ , 1e-3 )
def __lowerCAmelCase ( self : str , lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Any=None , **lowerCAmelCase_ : Dict ) -> int:
"""simple docstring"""
_a , _a = self.get_vision_text_model(lowerCAmelCase_ , lowerCAmelCase_ )
_a = {'''vision_model''': vision_model, '''text_model''': text_model}
_a = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCAmelCase_ )
_a = model(
input_ids=lowerCAmelCase_ , pixel_values=lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , output_attentions=lowerCAmelCase_ )
_a = 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)
_a = to_atuple(vision_model.config.image_size )
_a = to_atuple(vision_model.config.patch_size )
_a = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
_a = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
_a = 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 __lowerCAmelCase ( self : Union[str, Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Optional[Any] ) -> int:
"""simple docstring"""
pt_model.to(lowerCAmelCase_ )
pt_model.eval()
# prepare inputs
_a = inputs_dict
_a = {k: torch.tensor(v.tolist() ) for k, v in flax_inputs.items()}
with torch.no_grad():
_a = pt_model(**lowerCAmelCase_ ).to_tuple()
_a = 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_ )
_a = FlaxVisionTextDualEncoderModel.from_pretrained(lowerCAmelCase_ , from_pt=lowerCAmelCase_ )
_a = 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_ )
_a = VisionTextDualEncoderModel.from_pretrained(lowerCAmelCase_ , from_flax=lowerCAmelCase_ )
pt_model_loaded.to(lowerCAmelCase_ )
pt_model_loaded.eval()
with torch.no_grad():
_a = 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 __lowerCAmelCase ( self : Any , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Optional[Any] ) -> int:
"""simple docstring"""
_a = VisionTextDualEncoderConfig.from_vision_text_configs(lowerCAmelCase_ , lowerCAmelCase_ )
_a = VisionTextDualEncoderModel(lowerCAmelCase_ )
_a = FlaxVisionTextDualEncoderModel(lowerCAmelCase_ )
_a = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , lowerCAmelCase_ )
_a = fx_state
self.check_pt_flax_equivalence(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
def __lowerCAmelCase ( self : int , lowerCAmelCase_ : int , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Optional[int] ) -> str:
"""simple docstring"""
_a = VisionTextDualEncoderConfig.from_vision_text_configs(lowerCAmelCase_ , lowerCAmelCase_ )
_a = VisionTextDualEncoderModel(lowerCAmelCase_ )
_a = FlaxVisionTextDualEncoderModel(lowerCAmelCase_ )
_a = load_flax_weights_in_pytorch_model(lowerCAmelCase_ , fx_model.params )
self.check_pt_flax_equivalence(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
def __lowerCAmelCase ( self : Optional[Any] ) -> str:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**lowerCAmelCase_ )
def __lowerCAmelCase ( self : Union[str, Any] ) -> int:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**lowerCAmelCase_ )
def __lowerCAmelCase ( self : List[str] ) -> List[str]:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
self.check_save_load(**lowerCAmelCase_ )
def __lowerCAmelCase ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**lowerCAmelCase_ )
@is_pt_flax_cross_test
def __lowerCAmelCase ( self : str ) -> List[str]:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
_a = config_inputs_dict.pop('''vision_config''' )
_a = config_inputs_dict.pop('''text_config''' )
_a = config_inputs_dict
self.check_equivalence_pt_to_flax(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
self.check_equivalence_flax_to_pt(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
@slow
def __lowerCAmelCase ( self : Dict ) -> List[str]:
"""simple docstring"""
_a , _a = self.get_pretrained_model_and_inputs()
_a = model_a(**lowerCAmelCase_ )
_a = outputs[0]
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(lowerCAmelCase_ )
_a = FlaxVisionTextDualEncoderModel.from_pretrained(lowerCAmelCase_ )
_a = model_a(**lowerCAmelCase_ )
_a = after_outputs[0]
_a = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowerCAmelCase_ , 1e-5 )
@require_flax
class A ( _a ,unittest.TestCase ):
def __lowerCAmelCase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
_a = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
'''hf-internal-testing/tiny-random-vit''' , '''hf-internal-testing/tiny-bert''' , vision_from_pt=lowerCAmelCase_ , text_from_pt=lowerCAmelCase_ , )
_a = 13
_a = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
_a = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
_a = random_attention_mask([batch_size, 4] )
_a = {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}
return model, inputs
def __lowerCAmelCase ( self : List[Any] , lowerCAmelCase_ : int , lowerCAmelCase_ : List[Any] ) -> List[str]:
"""simple docstring"""
_a = FlaxViTModel(lowerCAmelCase_ )
_a = FlaxBertModel(lowerCAmelCase_ )
return vision_model, text_model
def __lowerCAmelCase ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
_a = FlaxViTModelTester(self )
_a = FlaxBertModelTester(self )
_a = vit_model_tester.prepare_config_and_inputs()
_a = bert_model_tester.prepare_config_and_inputs()
_a , _a = vision_config_and_inputs
_a , _a , _a , _a = 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 A ( _a ,unittest.TestCase ):
def __lowerCAmelCase ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
_a = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
'''hf-internal-testing/tiny-random-clip''' , '''hf-internal-testing/tiny-bert''' , vision_from_pt=lowerCAmelCase_ , text_from_pt=lowerCAmelCase_ , )
_a = 13
_a = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
_a = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
_a = random_attention_mask([batch_size, 4] )
_a = {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}
return model, inputs
def __lowerCAmelCase ( self : Dict , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : List[Any] ) -> Tuple:
"""simple docstring"""
_a = FlaxCLIPVisionModel(lowerCAmelCase_ )
_a = FlaxBertModel(lowerCAmelCase_ )
return vision_model, text_model
def __lowerCAmelCase ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
_a = FlaxCLIPVisionModelTester(self )
_a = FlaxBertModelTester(self )
_a = clip_model_tester.prepare_config_and_inputs()
_a = bert_model_tester.prepare_config_and_inputs()
_a , _a = vision_config_and_inputs
_a , _a , _a , _a = 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 A ( unittest.TestCase ):
@slow
def __lowerCAmelCase ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
_a = FlaxVisionTextDualEncoderModel.from_pretrained('''clip-italian/clip-italian''' , logit_scale_init_value=1.0 )
_a = VisionTextDualEncoderProcessor.from_pretrained('''clip-italian/clip-italian''' )
_a = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
_a = processor(
text=['''una foto di un gatto''', '''una foto di un cane'''] , images=lowerCAmelCase_ , padding=lowerCAmelCase_ , return_tensors='''np''' )
_a = 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]) , )
_a = np.array([[1.2_2_8_4_7_2_7, 0.3_1_0_4_1_2_2]] )
self.assertTrue(np.allclose(outputs.logits_per_image , lowerCAmelCase_ , atol=1e-3 ) )
| 179 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEModel,
)
from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class A :
def __init__( self : Any , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : List[str]=13 , lowerCAmelCase_ : Optional[Any]=10 , lowerCAmelCase_ : Optional[int]=3 , lowerCAmelCase_ : Union[str, Any]=2 , lowerCAmelCase_ : Tuple=2 , lowerCAmelCase_ : Dict=2 , lowerCAmelCase_ : str=True , lowerCAmelCase_ : Optional[Any]=True , lowerCAmelCase_ : Dict=32 , lowerCAmelCase_ : Tuple=5 , lowerCAmelCase_ : Optional[int]=4 , lowerCAmelCase_ : int=37 , lowerCAmelCase_ : int="gelu" , lowerCAmelCase_ : List[str]=0.1 , lowerCAmelCase_ : str=0.1 , lowerCAmelCase_ : Dict=10 , lowerCAmelCase_ : int=0.0_2 , lowerCAmelCase_ : Union[str, Any]=0.9 , lowerCAmelCase_ : str=None , ) -> int:
"""simple docstring"""
_a = parent
_a = batch_size
_a = image_size
_a = num_channels
_a = patch_size
_a = tubelet_size
_a = num_frames
_a = is_training
_a = use_labels
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = intermediate_size
_a = hidden_act
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = type_sequence_label_size
_a = initializer_range
_a = mask_ratio
_a = scope
# in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
_a = (image_size // patch_size) ** 2
_a = (num_frames // tubelet_size) * self.num_patches_per_frame
# use this variable to define bool_masked_pos
_a = int(mask_ratio * self.seq_length )
def __lowerCAmelCase ( self : Any ) -> Optional[int]:
"""simple docstring"""
_a = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
_a = None
if self.use_labels:
_a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_a = self.get_config()
return config, pixel_values, labels
def __lowerCAmelCase ( self : List[str] ) -> Any:
"""simple docstring"""
return VideoMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , tubelet_size=self.tubelet_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=lowerCAmelCase_ , initializer_range=self.initializer_range , )
def __lowerCAmelCase ( self : List[str] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
_a = VideoMAEModel(config=lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
_a = model(lowerCAmelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCAmelCase ( self : Any , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
_a = VideoMAEForPreTraining(lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
# important: each video needs to have the same number of masked patches
# hence we define a single mask, which we then repeat for each example in the batch
_a = torch.ones((self.num_masks,) )
_a = torch.cat([mask, torch.zeros(self.seq_length - mask.size(0 ) )] )
_a = mask.expand(self.batch_size , -1 ).bool()
_a = model(lowerCAmelCase_ , lowerCAmelCase_ )
# model only returns predictions for masked patches
_a = mask.sum().item()
_a = 3 * self.tubelet_size * self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_masked_patches, decoder_num_labels) )
def __lowerCAmelCase ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
_a , _a , _a = config_and_inputs
_a = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class A ( _a ,_a ,unittest.TestCase ):
lowercase_ = (
(VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
)
lowercase_ = (
{'feature-extraction': VideoMAEModel, 'video-classification': VideoMAEForVideoClassification}
if is_torch_available()
else {}
)
lowercase_ = False
lowercase_ = False
lowercase_ = False
lowercase_ = False
def __lowerCAmelCase ( self : Dict ) -> int:
"""simple docstring"""
_a = VideoMAEModelTester(self )
_a = ConfigTester(self , config_class=lowerCAmelCase_ , has_text_modality=lowerCAmelCase_ , hidden_size=37 )
def __lowerCAmelCase ( self : Optional[int] , lowerCAmelCase_ : Any , lowerCAmelCase_ : int , lowerCAmelCase_ : List[Any]=False ) -> Tuple:
"""simple docstring"""
_a = copy.deepcopy(lowerCAmelCase_ )
if model_class == VideoMAEForPreTraining:
# important: each video needs to have the same number of masked patches
# hence we define a single mask, which we then repeat for each example in the batch
_a = torch.ones((self.model_tester.num_masks,) )
_a = torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0 ) )] )
_a = mask.expand(self.model_tester.batch_size , -1 ).bool()
_a = bool_masked_pos.to(lowerCAmelCase_ )
if return_labels:
if model_class in [
*get_values(lowerCAmelCase_ ),
]:
_a = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase_ )
return inputs_dict
def __lowerCAmelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='''VideoMAE does not use inputs_embeds''' )
def __lowerCAmelCase ( self : Any ) -> Any:
"""simple docstring"""
pass
def __lowerCAmelCase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(lowerCAmelCase_ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
_a = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCAmelCase_ , nn.Linear ) )
def __lowerCAmelCase ( self : Any ) -> Any:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(lowerCAmelCase_ )
_a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a = [*signature.parameters.keys()]
_a = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , lowerCAmelCase_ )
def __lowerCAmelCase ( self : int ) -> Any:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase_ )
def __lowerCAmelCase ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*lowerCAmelCase_ )
@slow
def __lowerCAmelCase ( self : Dict ) -> Optional[int]:
"""simple docstring"""
for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a = VideoMAEModel.from_pretrained(lowerCAmelCase_ )
self.assertIsNotNone(lowerCAmelCase_ )
def __lowerCAmelCase ( self : int ) -> Any:
"""simple docstring"""
if not self.has_attentions:
pass
else:
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = True
for model_class in self.all_model_classes:
_a = self.model_tester.seq_length - self.model_tester.num_masks
_a = (
num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
)
_a = True
_a = False
_a = True
_a = model_class(lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_ ) )
_a = outputs.attentions
self.assertEqual(len(lowerCAmelCase_ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
_a = True
_a = model_class(lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_ ) )
_a = outputs.attentions
self.assertEqual(len(lowerCAmelCase_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
_a = len(lowerCAmelCase_ )
# Check attention is always last and order is fine
_a = True
_a = True
_a = model_class(lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_ ) )
self.assertEqual(out_len + 1 , len(lowerCAmelCase_ ) )
_a = outputs.attentions
self.assertEqual(len(lowerCAmelCase_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
def __lowerCAmelCase ( self : Tuple ) -> Tuple:
"""simple docstring"""
def check_hidden_states_output(lowerCAmelCase_ : str , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Tuple ):
_a = model_class(lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_ ) )
_a = outputs.hidden_states
_a = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(lowerCAmelCase_ ) , lowerCAmelCase_ )
_a = self.model_tester.seq_length - self.model_tester.num_masks
_a = num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = True
check_hidden_states_output(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_a = True
check_hidden_states_output(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __lowerCAmelCase ( self : Optional[int] ) -> Dict:
"""simple docstring"""
pass
def snake_case_ ():
'''simple docstring'''
_a = hf_hub_download(
repo_id='''hf-internal-testing/spaghetti-video''' , filename='''eating_spaghetti.npy''' , repo_type='''dataset''' )
_a = np.load(UpperCamelCase )
return list(UpperCamelCase )
@require_torch
@require_vision
class A ( unittest.TestCase ):
@cached_property
def __lowerCAmelCase ( self : str ) -> List[Any]:
"""simple docstring"""
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def __lowerCAmelCase ( self : Dict ) -> Dict:
"""simple docstring"""
_a = VideoMAEForVideoClassification.from_pretrained('''MCG-NJU/videomae-base-finetuned-kinetics''' ).to(
lowerCAmelCase_ )
_a = self.default_image_processor
_a = prepare_video()
_a = image_processor(lowerCAmelCase_ , return_tensors='''pt''' ).to(lowerCAmelCase_ )
# forward pass
with torch.no_grad():
_a = model(**lowerCAmelCase_ )
# verify the logits
_a = torch.Size((1, 4_00) )
self.assertEqual(outputs.logits.shape , lowerCAmelCase_ )
_a = torch.tensor([0.3_6_6_9, -0.0_6_8_8, -0.2_4_2_1] ).to(lowerCAmelCase_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCAmelCase_ , atol=1e-4 ) )
@slow
def __lowerCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
_a = VideoMAEForPreTraining.from_pretrained('''MCG-NJU/videomae-base-short''' ).to(lowerCAmelCase_ )
_a = self.default_image_processor
_a = prepare_video()
_a = image_processor(lowerCAmelCase_ , return_tensors='''pt''' ).to(lowerCAmelCase_ )
# add boolean mask, indicating which patches to mask
_a = hf_hub_download(repo_id='''hf-internal-testing/bool-masked-pos''' , filename='''bool_masked_pos.pt''' )
_a = torch.load(lowerCAmelCase_ )
# forward pass
with torch.no_grad():
_a = model(**lowerCAmelCase_ )
# verify the logits
_a = torch.Size([1, 14_08, 15_36] )
_a = torch.tensor(
[[0.7_9_9_4, 0.9_6_1_2, 0.8_5_0_8], [0.7_4_0_1, 0.8_9_5_8, 0.8_3_0_2], [0.5_8_6_2, 0.7_4_6_8, 0.7_3_2_5]] , device=lowerCAmelCase_ )
self.assertEqual(outputs.logits.shape , lowerCAmelCase_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , lowerCAmelCase_ , atol=1e-4 ) )
# verify the loss (`config.norm_pix_loss` = `True`)
_a = torch.tensor([0.5_1_4_2] , device=lowerCAmelCase_ )
self.assertTrue(torch.allclose(outputs.loss , lowerCAmelCase_ , atol=1e-4 ) )
# verify the loss (`config.norm_pix_loss` = `False`)
_a = VideoMAEForPreTraining.from_pretrained('''MCG-NJU/videomae-base-short''' , norm_pix_loss=lowerCAmelCase_ ).to(
lowerCAmelCase_ )
with torch.no_grad():
_a = model(**lowerCAmelCase_ )
_a = torch.tensor(torch.tensor([0.6_4_6_9] ) , device=lowerCAmelCase_ )
self.assertTrue(torch.allclose(outputs.loss , lowerCAmelCase_ , atol=1e-4 ) )
| 179 | 1 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blenderbot,
blenderbot_small,
blip,
blip_a,
bloom,
bridgetower,
byta,
camembert,
canine,
chinese_clip,
clap,
clip,
clipseg,
codegen,
conditional_detr,
convbert,
convnext,
convnextva,
cpm,
cpmant,
ctrl,
cvt,
dataavec,
deberta,
deberta_va,
decision_transformer,
deformable_detr,
deit,
deprecated,
deta,
detr,
dialogpt,
dinat,
distilbert,
dit,
donut,
dpr,
dpt,
efficientformer,
efficientnet,
electra,
encodec,
encoder_decoder,
ernie,
ernie_m,
esm,
falcon,
flaubert,
flava,
fnet,
focalnet,
fsmt,
funnel,
git,
glpn,
gpta,
gpt_bigcode,
gpt_neo,
gpt_neox,
gpt_neox_japanese,
gpt_swa,
gptj,
gptsan_japanese,
graphormer,
groupvit,
herbert,
hubert,
ibert,
imagegpt,
informer,
instructblip,
jukebox,
layoutlm,
layoutlmva,
layoutlmva,
layoutxlm,
led,
levit,
lilt,
llama,
longformer,
longta,
luke,
lxmert,
mam_aaa,
marian,
markuplm,
maskaformer,
maskformer,
mbart,
mbartaa,
mega,
megatron_bert,
megatron_gpta,
mgp_str,
mluke,
mobilebert,
mobilenet_va,
mobilenet_va,
mobilevit,
mobilevitva,
mpnet,
mra,
mta,
musicgen,
mvp,
nat,
nezha,
nllb,
nllb_moe,
nystromformer,
oneformer,
open_llama,
openai,
opt,
owlvit,
pegasus,
pegasus_x,
perceiver,
phobert,
pixastruct,
plbart,
poolformer,
prophetnet,
qdqbert,
rag,
realm,
reformer,
regnet,
rembert,
resnet,
roberta,
roberta_prelayernorm,
roc_bert,
roformer,
rwkv,
sam,
segformer,
sew,
sew_d,
speech_encoder_decoder,
speech_to_text,
speech_to_text_a,
speechta,
splinter,
squeezebert,
swiftformer,
swin,
swinasr,
swinva,
switch_transformers,
ta,
table_transformer,
tapas,
time_series_transformer,
timesformer,
timm_backbone,
transfo_xl,
trocr,
tvlt,
umta,
unispeech,
unispeech_sat,
upernet,
videomae,
vilt,
vision_encoder_decoder,
vision_text_dual_encoder,
visual_bert,
vit,
vit_hybrid,
vit_mae,
vit_msn,
vivit,
wavaveca,
wavaveca_conformer,
wavaveca_phoneme,
wavaveca_with_lm,
wavlm,
whisper,
x_clip,
xglm,
xlm,
xlm_prophetnet,
xlm_roberta,
xlm_roberta_xl,
xlnet,
xmod,
yolos,
yoso,
)
| 116 |
from manim import *
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
def _lowerCAmelCase ( self ):
A : Union[str, Any] = Rectangle(height=0.5, width=0.5 )
A : Optional[int] = Rectangle(height=0.25, width=0.25 )
A : Optional[Any] = Rectangle(height=0.46, width=0.46 ).set_stroke(width=0 )
A : List[str] = [mem.copy() for i in range(6 )]
A : Any = [mem.copy() for i in range(6 )]
A : int = VGroup(*lowerCamelCase__ ).arrange(lowerCamelCase__, buff=0 )
A : Tuple = VGroup(*lowerCamelCase__ ).arrange(lowerCamelCase__, buff=0 )
A : str = VGroup(lowerCamelCase__, lowerCamelCase__ ).arrange(lowerCamelCase__, buff=0 )
A : List[Any] = Text("""CPU""", font_size=24 )
A : Optional[int] = Group(lowerCamelCase__, lowerCamelCase__ ).arrange(lowerCamelCase__, buff=0.5, aligned_edge=lowerCamelCase__ )
cpu.move_to([-2.5, -0.5, 0] )
self.add(lowerCamelCase__ )
A : List[Any] = [mem.copy() for i in range(4 )]
A : Optional[Any] = VGroup(*lowerCamelCase__ ).arrange(lowerCamelCase__, buff=0 )
A : Dict = Text("""GPU""", font_size=24 )
A : Any = Group(lowerCamelCase__, lowerCamelCase__ ).arrange(lowerCamelCase__, buff=0.5, aligned_edge=lowerCamelCase__ )
gpu.move_to([-1, -1, 0] )
self.add(lowerCamelCase__ )
A : Optional[int] = [mem.copy() for i in range(6 )]
A : List[str] = VGroup(*lowerCamelCase__ ).arrange(lowerCamelCase__, buff=0 )
A : Optional[int] = Text("""Model""", font_size=24 )
A : List[Any] = Group(lowerCamelCase__, lowerCamelCase__ ).arrange(lowerCamelCase__, buff=0.5, aligned_edge=lowerCamelCase__ )
model.move_to([3, -1.0, 0] )
self.add(lowerCamelCase__ )
A : Tuple = []
A : Tuple = []
A : Any = []
for i, rect in enumerate(lowerCamelCase__ ):
rect.set_stroke(lowerCamelCase__ )
A : Any = Rectangle(height=0.46 / 4, width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(lowerCamelCase__, opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ), buff=0.02, direction=lowerCamelCase__ )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(model_cpu_arr[0], direction=lowerCamelCase__, buff=0.0 )
else:
cpu_target.next_to(model_cpu_arr[i - 1], direction=lowerCamelCase__, buff=0.0 )
self.add(lowerCamelCase__ )
model_cpu_arr.append(lowerCamelCase__ )
self.add(*lowerCamelCase__, *lowerCamelCase__, *lowerCamelCase__ )
A : int = [mem.copy() for i in range(6 )]
A : List[Any] = VGroup(*lowerCamelCase__ ).arrange(lowerCamelCase__, buff=0 )
A : str = Text("""Loaded Checkpoint""", font_size=24 )
A : List[str] = Group(lowerCamelCase__, lowerCamelCase__ ).arrange(lowerCamelCase__, buff=0.5, aligned_edge=lowerCamelCase__ )
checkpoint.move_to([3, 0.5, 0] )
self.add(lowerCamelCase__ )
A : Optional[int] = []
A : List[Any] = []
for i, rect in enumerate(lowerCamelCase__ ):
A : int = fill.copy().set_fill(lowerCamelCase__, opacity=0.7 )
target.move_to(lowerCamelCase__ )
ckpt_arr.append(lowerCamelCase__ )
A : List[Any] = target.copy()
if i < 5:
cpu_target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.move_to(cpu_right_col_base[i - 5] )
ckpt_cpu_arr.append(lowerCamelCase__ )
self.add(*lowerCamelCase__, *lowerCamelCase__ )
A : str = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
A : List[Any] = MarkupText(
f'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''', font_size=18, )
key_text.move_to([-5, 2.4, 0] )
self.add(lowerCamelCase__, lowerCamelCase__ )
A : Union[str, Any] = MarkupText(
f'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''', font_size=18, )
blue_text.next_to(lowerCamelCase__, DOWN * 2.4, aligned_edge=key_text.get_left() )
self.add(lowerCamelCase__ )
A : List[str] = MarkupText(
f'''Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.''', font_size=24, )
step_a.move_to([2, 2, 0] )
A : List[str] = [meta_mem.copy() for i in range(6 )]
A : List[Any] = [meta_mem.copy() for i in range(6 )]
A : List[Any] = VGroup(*lowerCamelCase__ ).arrange(lowerCamelCase__, buff=0 )
A : List[str] = VGroup(*lowerCamelCase__ ).arrange(lowerCamelCase__, buff=0 )
A : Dict = VGroup(lowerCamelCase__, lowerCamelCase__ ).arrange(lowerCamelCase__, buff=0 )
A : Optional[Any] = Text("""Disk""", font_size=24 )
A : List[str] = Group(lowerCamelCase__, lowerCamelCase__ ).arrange(lowerCamelCase__, buff=0.5, aligned_edge=lowerCamelCase__ )
disk.move_to([-4.0, -1.25, 0] )
self.play(Write(lowerCamelCase__, run_time=3 ), Write(lowerCamelCase__, run_time=1 ), Create(lowerCamelCase__, run_time=1 ) )
A : str = []
for i, rect in enumerate(lowerCamelCase__ ):
A : Optional[Any] = rect.copy()
target.generate_target()
target.target.move_to(disk_left_col_base[i] ).scale(0.5 )
animations.append(MoveToTarget(lowerCamelCase__, run_time=1.5 ) )
self.play(*lowerCamelCase__ )
self.play(FadeOut(lowerCamelCase__ ) )
A : List[str] = MarkupText(f'''Then, the checkpoint is removed from memory\nthrough garbage collection.''', font_size=24 )
step_a.move_to([2, 2, 0] )
self.play(Write(lowerCamelCase__, run_time=3 ) )
self.play(
FadeOut(lowerCamelCase__, lowerCamelCase__, *lowerCamelCase__, *lowerCamelCase__ ), )
self.wait()
| 116 | 1 |
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
__magic_name__ = argparse.ArgumentParser(
description=(
"Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"
" Distillation"
)
)
parser.add_argument("--model_type", default="bert", choices=["bert"])
parser.add_argument("--model_name", default="bert-base-uncased", type=str)
parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_bert-base-uncased_0247911.pth", type=str)
parser.add_argument("--vocab_transform", action="store_true")
__magic_name__ = parser.parse_args()
if args.model_type == "bert":
__magic_name__ = BertForMaskedLM.from_pretrained(args.model_name)
__magic_name__ = "bert"
else:
raise ValueError("args.model_type should be \"bert\".")
__magic_name__ = model.state_dict()
__magic_name__ = {}
for w in ["word_embeddings", "position_embeddings"]:
__magic_name__ = state_dict[f'''{prefix}.embeddings.{w}.weight''']
for w in ["weight", "bias"]:
__magic_name__ = state_dict[f'''{prefix}.embeddings.LayerNorm.{w}''']
__magic_name__ = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
for w in ["weight", "bias"]:
__magic_name__ = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
]
__magic_name__ = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
]
__magic_name__ = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
]
__magic_name__ = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
]
__magic_name__ = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
]
__magic_name__ = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
]
__magic_name__ = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
]
__magic_name__ = state_dict[
f'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
]
std_idx += 1
__magic_name__ = state_dict["cls.predictions.decoder.weight"]
__magic_name__ = state_dict["cls.predictions.bias"]
if args.vocab_transform:
for w in ["weight", "bias"]:
__magic_name__ = state_dict[f'''cls.predictions.transform.dense.{w}''']
__magic_name__ = state_dict[f'''cls.predictions.transform.LayerNorm.{w}''']
print(f'''N layers selected for distillation: {std_idx}''')
print(f'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')
print(f'''Save transferred checkpoint to {args.dump_checkpoint}.''')
torch.save(compressed_sd, args.dump_checkpoint)
| 152 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def _lowerCAmelCase ( A__: str , A__: List[str] , A__: str ):
'''simple docstring'''
UpperCAmelCase = AlbertConfig.from_json_file(A__ )
print(F"""Building PyTorch model from configuration: {config}""" )
UpperCAmelCase = AlbertForPreTraining(A__ )
# Load weights from tf checkpoint
load_tf_weights_in_albert(A__ , A__ , A__ )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , A__ )
if __name__ == "__main__":
__magic_name__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--albert_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained ALBERT model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
__magic_name__ = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)
| 152 | 1 |
def __lowerCAmelCase ( a__ ) -> str:
return "".join([hex(a__ )[2:].zfill(2 ).upper() for byte in list(a__ )] )
def __lowerCAmelCase ( a__ ) -> bytes:
# Check data validity, following RFC3548
# https://www.ietf.org/rfc/rfc3548.txt
if (len(a__ ) % 2) != 0:
raise ValueError(
'''Base16 encoded data is invalid:
Data does not have an even number of hex digits.''' )
# Check the character set - the standard base16 alphabet
# is uppercase according to RFC3548 section 6
if not set(a__ ) <= set('''0123456789ABCDEF''' ):
raise ValueError(
'''Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters.''' )
# For every two hexadecimal digits (= a byte), turn it into an integer.
# Then, string the result together into bytes, and return it.
return bytes(int(data[i] + data[i + 1] , 16 ) for i in range(0 , len(a__ ) , 2 ) )
if __name__ == "__main__":
import doctest
doctest.testmod() | 6 |
'''simple docstring'''
import os
__snake_case ={"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1_000}
def a_ ( lowerCamelCase : str ):
lowerCAmelCase = 0
lowerCAmelCase = 0
while index < len(lowerCamelCase ) - 1:
lowerCAmelCase = SYMBOLS[numerals[index]]
lowerCAmelCase = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def a_ ( lowerCamelCase : int ):
lowerCAmelCase = ''
lowerCAmelCase = num // 1000
numerals += m_count * "M"
num %= 1000
lowerCAmelCase = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
lowerCAmelCase = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def a_ ( lowerCamelCase : str = "/p089_roman.txt" ):
lowerCAmelCase = 0
with open(os.path.dirname(lowerCamelCase ) + roman_numerals_filename ) as filea:
lowerCAmelCase = filea.readlines()
for line in lines:
lowerCAmelCase = line.strip()
lowerCAmelCase = parse_roman_numerals(lowerCamelCase )
lowerCAmelCase = generate_roman_numerals(lowerCamelCase )
savings += len(lowerCamelCase ) - len(lowerCamelCase )
return savings
if __name__ == "__main__":
print(F'''{solution() = }''')
| 4 | 0 |
"""simple docstring"""
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS
logging.basicConfig(level=logging.DEBUG)
__A : int = logging.getLogger()
def A_ ( snake_case_ : Path ,snake_case_ : list ):
'''simple docstring'''
UpperCamelCase : int = """\n""".join(snake_case_ )
Path(snake_case_ ).open("""w""" ).writelines(snake_case_ )
__A : Tuple = '''patrickvonplaten/t5-tiny-random'''
__A : List[str] = '''sshleifer/bart-tiny-random'''
__A : Union[str, Any] = '''sshleifer/tiny-mbart'''
__A : Any = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL) # remove noisy download output from tracebacks
class lowerCamelCase ( _UpperCAmelCase ):
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Dict = Path(self.get_auto_remove_tmp_dir() ) / """utest_input.source"""
UpperCamelCase : List[Any] = input_file_name.parent / """utest_output.txt"""
assert not output_file_name.exists()
UpperCamelCase : Optional[int] = [""" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."""]
_dump_articles(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = str(Path(self.get_auto_remove_tmp_dir() ) / """scores.json""" )
UpperCamelCase : Optional[int] = """translation_en_to_de""" if model == T5_TINY else """summarization"""
UpperCamelCase : str = f'\n run_eval_search.py\n {model}\n {input_file_name}\n {output_file_name}\n --score_path {score_path}\n --task {task}\n --num_beams 2\n --length_penalty 2.0\n '.split()
with patch.object(SCREAMING_SNAKE_CASE_ , """argv""" , SCREAMING_SNAKE_CASE_ ):
run_generate()
assert Path(SCREAMING_SNAKE_CASE_ ).exists()
# os.remove(Path(output_file_name))
def a_ ( self ):
self.run_eval_tester(SCREAMING_SNAKE_CASE_ )
@parameterized.expand([BART_TINY, MBART_TINY] )
@slow
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
self.run_eval_tester(SCREAMING_SNAKE_CASE_ )
@parameterized.expand([T5_TINY, MBART_TINY] )
@slow
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : int = Path(self.get_auto_remove_tmp_dir() ) / """utest_input.source"""
UpperCamelCase : str = input_file_name.parent / """utest_output.txt"""
assert not output_file_name.exists()
UpperCamelCase : Dict = {
"""en""": ["""Machine learning is great, isn't it?""", """I like to eat bananas""", """Tomorrow is another great day!"""],
"""de""": [
"""Maschinelles Lernen ist großartig, oder?""",
"""Ich esse gerne Bananen""",
"""Morgen ist wieder ein toller Tag!""",
],
}
UpperCamelCase : List[Any] = Path(self.get_auto_remove_tmp_dir() )
UpperCamelCase : Tuple = str(tmp_dir / """scores.json""" )
UpperCamelCase : Optional[int] = str(tmp_dir / """val.target""" )
_dump_articles(SCREAMING_SNAKE_CASE_ , text["""en"""] )
_dump_articles(SCREAMING_SNAKE_CASE_ , text["""de"""] )
UpperCamelCase : Dict = """translation_en_to_de""" if model == T5_TINY else """summarization"""
UpperCamelCase : str = f'\n run_eval_search.py\n {model}\n {str(SCREAMING_SNAKE_CASE_ )}\n {str(SCREAMING_SNAKE_CASE_ )}\n --score_path {score_path}\n --reference_path {reference_path}\n --task {task}\n '.split()
testargs.extend(["""--search""", """num_beams=1:2 length_penalty=0.9:1.0"""] )
with patch.object(SCREAMING_SNAKE_CASE_ , """argv""" , SCREAMING_SNAKE_CASE_ ):
with CaptureStdout() as cs:
run_search()
UpperCamelCase : Tuple = [""" num_beams | length_penalty""", model, """Best score args"""]
UpperCamelCase : int = ["""Info"""]
if "translation" in task:
expected_strings.append("""bleu""" )
else:
expected_strings.extend(SCREAMING_SNAKE_CASE_ )
for w in expected_strings:
assert w in cs.out
for w in un_expected_strings:
assert w not in cs.out
assert Path(SCREAMING_SNAKE_CASE_ ).exists()
os.remove(Path(SCREAMING_SNAKE_CASE_ ) )
| 363 |
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__A : Any = logging.get_logger(__name__)
__A : Dict = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__A : Optional[Any] = {
'''vocab_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
},
'''merges_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
},
}
__A : Any = {'''allegro/herbert-base-cased''': 514}
__A : Optional[Any] = {}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Dict = VOCAB_FILES_NAMES
lowercase : Any = PRETRAINED_VOCAB_FILES_MAP
lowercase : List[str] = PRETRAINED_INIT_CONFIGURATION
lowercase : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : Union[str, Any] = HerbertTokenizer
def __init__( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="<unk>" , SCREAMING_SNAKE_CASE_="<pad>" , SCREAMING_SNAKE_CASE_="<mask>" , SCREAMING_SNAKE_CASE_="</s>" , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , tokenizer_file=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Dict = [self.cls_token_id]
UpperCamelCase : str = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE_ , token_ids_a=SCREAMING_SNAKE_CASE_ , already_has_special_tokens=SCREAMING_SNAKE_CASE_ )
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Tuple = [self.sep_token_id]
UpperCamelCase : List[str] = [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 a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Optional[int] = self._tokenizer.model.save(SCREAMING_SNAKE_CASE_ , name=SCREAMING_SNAKE_CASE_ )
return tuple(SCREAMING_SNAKE_CASE_ )
| 27 | 0 |
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
NystromformerModel,
)
from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
class UpperCAmelCase :
'''simple docstring'''
def __init__( self : List[Any] ,A : Optional[int] ,A : str=13 ,A : Optional[int]=7 ,A : Any=True ,A : Tuple=True ,A : Any=True ,A : str=True ,A : Tuple=99 ,A : str=32 ,A : Dict=5 ,A : List[str]=4 ,A : Tuple=37 ,A : int="gelu" ,A : str=0.1 ,A : Tuple=0.1 ,A : int=5_12 ,A : List[Any]=16 ,A : Optional[Any]=2 ,A : Dict=0.02 ,A : str=3 ,A : Dict=4 ,A : Dict=None ,):
__A = parent
__A = batch_size
__A = seq_length
__A = is_training
__A = use_input_mask
__A = use_token_type_ids
__A = use_labels
__A = vocab_size
__A = hidden_size
__A = num_hidden_layers
__A = num_attention_heads
__A = intermediate_size
__A = hidden_act
__A = hidden_dropout_prob
__A = attention_probs_dropout_prob
__A = max_position_embeddings
__A = type_vocab_size
__A = type_sequence_label_size
__A = initializer_range
__A = num_labels
__A = num_choices
__A = scope
def UpperCamelCase_ ( self : Dict ):
__A = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
__A = None
if self.use_input_mask:
__A = random_attention_mask([self.batch_size, self.seq_length] )
__A = None
if self.use_token_type_ids:
__A = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
__A = None
__A = None
__A = None
if self.use_labels:
__A = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
__A = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
__A = ids_tensor([self.batch_size] ,self.num_choices )
__A = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase_ ( self : int ):
return NystromformerConfig(
vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,type_vocab_size=self.type_vocab_size ,is_decoder=A ,initializer_range=self.initializer_range ,)
def UpperCamelCase_ ( self : Dict ,A : Tuple ,A : Optional[Any] ,A : int ,A : Optional[int] ,A : Union[str, Any] ,A : int ,A : Optional[int] ):
__A = NystromformerModel(config=A )
model.to(A )
model.eval()
__A = model(A ,attention_mask=A ,token_type_ids=A )
__A = model(A ,token_type_ids=A )
__A = model(A )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase_ ( self : Optional[int] ,A : Tuple ,A : Union[str, Any] ,A : Optional[int] ,A : Union[str, Any] ,A : int ,A : List[str] ,A : Optional[Any] ):
__A = NystromformerForMaskedLM(config=A )
model.to(A )
model.eval()
__A = model(A ,attention_mask=A ,token_type_ids=A ,labels=A )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase_ ( self : int ,A : List[Any] ,A : str ,A : Any ,A : Union[str, Any] ,A : Any ,A : List[Any] ,A : Dict ):
__A = NystromformerForQuestionAnswering(config=A )
model.to(A )
model.eval()
__A = model(
A ,attention_mask=A ,token_type_ids=A ,start_positions=A ,end_positions=A ,)
self.parent.assertEqual(result.start_logits.shape ,(self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape ,(self.batch_size, self.seq_length) )
def UpperCamelCase_ ( self : Optional[int] ,A : Tuple ,A : List[Any] ,A : str ,A : List[Any] ,A : Union[str, Any] ,A : Dict ,A : str ):
__A = self.num_labels
__A = NystromformerForSequenceClassification(A )
model.to(A )
model.eval()
__A = model(A ,attention_mask=A ,token_type_ids=A ,labels=A )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def UpperCamelCase_ ( self : str ,A : int ,A : Dict ,A : Tuple ,A : Optional[Any] ,A : List[Any] ,A : Tuple ,A : Optional[Any] ):
__A = self.num_labels
__A = NystromformerForTokenClassification(config=A )
model.to(A )
model.eval()
__A = model(A ,attention_mask=A ,token_type_ids=A ,labels=A )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase_ ( self : Optional[Any] ,A : List[Any] ,A : List[str] ,A : Union[str, Any] ,A : Optional[Any] ,A : Dict ,A : Tuple ,A : Any ):
__A = self.num_choices
__A = NystromformerForMultipleChoice(config=A )
model.to(A )
model.eval()
__A = input_ids.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
__A = token_type_ids.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
__A = input_mask.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
__A = model(
A ,attention_mask=A ,token_type_ids=A ,labels=A ,)
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_choices) )
def UpperCamelCase_ ( self : Dict ):
__A = self.prepare_config_and_inputs()
(
__A
) = config_and_inputs
__A = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class UpperCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , unittest.TestCase ):
'''simple docstring'''
snake_case_ = (
(
NystromformerModel,
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
)
if is_torch_available()
else ()
)
snake_case_ = (
{
"feature-extraction": NystromformerModel,
"fill-mask": NystromformerForMaskedLM,
"question-answering": NystromformerForQuestionAnswering,
"text-classification": NystromformerForSequenceClassification,
"token-classification": NystromformerForTokenClassification,
"zero-shot": NystromformerForSequenceClassification,
}
if is_torch_available()
else {}
)
snake_case_ = False
snake_case_ = False
def UpperCamelCase_ ( self : Any ):
__A = NystromformerModelTester(self )
__A = ConfigTester(self ,config_class=A ,hidden_size=37 )
def UpperCamelCase_ ( self : Tuple ):
self.config_tester.run_common_tests()
def UpperCamelCase_ ( self : List[str] ):
__A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def UpperCamelCase_ ( self : Tuple ):
__A = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__A = type
self.model_tester.create_and_check_model(*A )
def UpperCamelCase_ ( self : List[str] ):
__A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*A )
def UpperCamelCase_ ( self : Optional[int] ):
__A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*A )
def UpperCamelCase_ ( self : Any ):
__A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*A )
def UpperCamelCase_ ( self : str ):
__A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*A )
def UpperCamelCase_ ( self : Dict ):
__A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*A )
@slow
def UpperCamelCase_ ( self : List[Any] ):
for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__A = NystromformerModel.from_pretrained(A )
self.assertIsNotNone(A )
@require_torch
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def UpperCamelCase_ ( self : str ):
__A = NystromformerModel.from_pretrained("uw-madison/nystromformer-512" )
__A = torch.tensor([[0, 1, 2, 3, 4, 5]] )
with torch.no_grad():
__A = model(A )[0]
__A = torch.Size((1, 6, 7_68) )
self.assertEqual(output.shape ,A )
__A = torch.tensor(
[[[-0.45_32, -0.09_36, 0.51_37], [-0.26_76, 0.06_28, 0.61_86], [-0.36_29, -0.17_26, 0.47_16]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] ,A ,atol=1E-4 ) )
@slow
def UpperCamelCase_ ( self : Union[str, Any] ):
__A = """the [MASK] of Belgium is Brussels"""
__A = AutoTokenizer.from_pretrained("uw-madison/nystromformer-512" )
__A = NystromformerForMaskedLM.from_pretrained("uw-madison/nystromformer-512" )
__A = tokenizer(A ,return_tensors="pt" )
with torch.no_grad():
__A = model(encoding.input_ids ).logits
__A = token_logits[:, 2, :].argmax(-1 )[0]
self.assertEqual(tokenizer.decode(A ) ,"capital" )
| 15 |
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase : Tuple = logging.get_logger(__name__)
lowerCamelCase : int = {
'nvidia/segformer-b0-finetuned-ade-512-512': (
'https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json'
),
# See all SegFormer models at https://huggingface.co/models?filter=segformer
}
class __lowercase (UpperCamelCase__ ):
"""simple docstring"""
_snake_case = """segformer"""
def __init__( self , A=3 , A=4 , A=[2, 2, 2, 2] , A=[8, 4, 2, 1] , A=[3_2, 6_4, 1_6_0, 2_5_6] , A=[7, 3, 3, 3] , A=[4, 2, 2, 2] , A=[1, 2, 5, 8] , A=[4, 4, 4, 4] , A="gelu" , A=0.0 , A=0.0 , A=0.1 , A=0.02 , A=0.1 , A=1e-6 , A=2_5_6 , A=2_5_5 , **A , ) -> Dict:
super().__init__(**A )
if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
warnings.warn(
"""Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"""
""" removed, as the behaviour will default to that of reshape_last_stage = True.""" , A , )
snake_case : List[str] = num_channels
snake_case : Optional[int] = num_encoder_blocks
snake_case : Optional[int] = depths
snake_case : str = sr_ratios
snake_case : str = hidden_sizes
snake_case : Any = patch_sizes
snake_case : Tuple = strides
snake_case : List[str] = mlp_ratios
snake_case : Optional[Any] = num_attention_heads
snake_case : int = hidden_act
snake_case : Tuple = hidden_dropout_prob
snake_case : Any = attention_probs_dropout_prob
snake_case : List[Any] = classifier_dropout_prob
snake_case : Optional[Any] = initializer_range
snake_case : Optional[Any] = drop_path_rate
snake_case : int = layer_norm_eps
snake_case : Optional[Any] = decoder_hidden_size
snake_case : Tuple = kwargs.get("""reshape_last_stage""" , A )
snake_case : List[str] = semantic_loss_ignore_index
class __lowercase (UpperCamelCase__ ):
"""simple docstring"""
_snake_case = version.parse("""1.11""" )
@property
def UpperCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def UpperCAmelCase ( self ) -> float:
return 1e-4
@property
def UpperCAmelCase ( self ) -> int:
return 1_2
| 124 | 0 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def lowerCAmelCase_ ( __a ) -> int:
"""simple docstring"""
lowerCamelCase__: List[Any] =args.pruning_method
lowerCamelCase__: int =args.threshold
lowerCamelCase__: Any =args.model_name_or_path.rstrip("/" )
lowerCamelCase__: str =args.target_model_path
print(F"""Load fine-pruned model from {model_name_or_path}""" )
lowerCamelCase__: int =torch.load(os.path.join(__a , "pytorch_model.bin" ) )
lowerCamelCase__: Optional[int] ={}
for name, tensor in model.items():
if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
lowerCamelCase__: Any =tensor
print(F"""Copied layer {name}""" )
elif "classifier" in name or "qa_output" in name:
lowerCamelCase__: Dict =tensor
print(F"""Copied layer {name}""" )
elif "bias" in name:
lowerCamelCase__: List[Any] =tensor
print(F"""Copied layer {name}""" )
else:
if pruning_method == "magnitude":
lowerCamelCase__: Any =MagnitudeBinarizer.apply(inputs=__a , threshold=__a )
lowerCamelCase__: Optional[int] =tensor * mask
print(F"""Pruned layer {name}""" )
elif pruning_method == "topK":
if "mask_scores" in name:
continue
lowerCamelCase__: Union[str, Any] =name[:-6]
lowerCamelCase__: Any =model[F"""{prefix_}mask_scores"""]
lowerCamelCase__: List[Any] =TopKBinarizer.apply(__a , __a )
lowerCamelCase__: Optional[int] =tensor * mask
print(F"""Pruned layer {name}""" )
elif pruning_method == "sigmoied_threshold":
if "mask_scores" in name:
continue
lowerCamelCase__: Tuple =name[:-6]
lowerCamelCase__: List[Any] =model[F"""{prefix_}mask_scores"""]
lowerCamelCase__: Any =ThresholdBinarizer.apply(__a , __a , __a )
lowerCamelCase__: Tuple =tensor * mask
print(F"""Pruned layer {name}""" )
elif pruning_method == "l0":
if "mask_scores" in name:
continue
lowerCamelCase__: str =name[:-6]
lowerCamelCase__: List[str] =model[F"""{prefix_}mask_scores"""]
lowerCamelCase__ , lowerCamelCase__: List[str] =-0.1, 1.1
lowerCamelCase__: Optional[Any] =torch.sigmoid(__a )
lowerCamelCase__: Any =s * (r - l) + l
lowerCamelCase__: List[Any] =s_bar.clamp(min=0.0 , max=1.0 )
lowerCamelCase__: Optional[int] =tensor * mask
print(F"""Pruned layer {name}""" )
else:
raise ValueError("Unknown pruning method" )
if target_model_path is None:
lowerCamelCase__: Optional[int] =os.path.join(
os.path.dirname(__a ) , F"""bertarized_{os.path.basename(__a )}""" )
if not os.path.isdir(__a ):
shutil.copytree(__a , __a )
print(F"""\nCreated folder {target_model_path}""" )
torch.save(__a , os.path.join(__a , "pytorch_model.bin" ) )
print("\nPruned model saved! See you later!" )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
parser.add_argument(
"--pruning_method",
choices=["l0", "magnitude", "topK", "sigmoied_threshold"],
type=str,
required=True,
help=(
"Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,"
" sigmoied_threshold = Soft movement pruning)"
),
)
parser.add_argument(
"--threshold",
type=float,
required=False,
help=(
"For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model."
"For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."
"Not needed for `l0`"
),
)
parser.add_argument(
"--model_name_or_path",
type=str,
required=True,
help="Folder containing the model that was previously fine-pruned",
)
parser.add_argument(
"--target_model_path",
default=None,
type=str,
required=False,
help="Folder containing the model that was previously fine-pruned",
)
__A = parser.parse_args()
main(args)
| 273 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_environment
@require_huggingface_suite
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ (self : Optional[int]) ->Tuple:
'''simple docstring'''
lowerCamelCase__: List[str] =inspect.getfile(accelerate.test_utils)
lowerCamelCase__: str =os.path.sep.join(
mod_file.split(os.path.sep)[:-1] + ["scripts", "external_deps", "test_metrics.py"])
from accelerate.test_utils.scripts.external_deps import test_metrics # noqa: F401
lowerCamelCase__: str =test_metrics
@require_cpu
def SCREAMING_SNAKE_CASE_ (self : int) ->Any:
'''simple docstring'''
debug_launcher(self.test_metrics.main , num_processes=1)
@require_cpu
def SCREAMING_SNAKE_CASE_ (self : int) ->int:
'''simple docstring'''
debug_launcher(self.test_metrics.main)
@require_single_gpu
def SCREAMING_SNAKE_CASE_ (self : Any) ->int:
'''simple docstring'''
self.test_metrics.main()
@require_multi_gpu
def SCREAMING_SNAKE_CASE_ (self : Tuple) ->Dict:
'''simple docstring'''
print(F"""Found {torch.cuda.device_count()} devices.""")
lowerCamelCase__: Optional[Any] =["torchrun", F"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
with patch_environment(omp_num_threads=1):
execute_subprocess_async(UpperCAmelCase_ , env=os.environ.copy())
| 273 | 1 |
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
lowerCamelCase = logging.get_logger(__name__)
class A ( UpperCamelCase_ ):
def __init__( self : int , *lowercase_ : Tuple , **lowercase_ : Union[str, Any] ) -> None:
"""simple docstring"""
warnings.warn(
'The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use DeformableDetrImageProcessor instead.' , lowercase_ , )
super().__init__(*lowercase_ , **lowercase_ )
| 199 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class A ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
UpperCamelCase__ : List[Any] =[('size', ctypes.c_int), ('visible', ctypes.c_byte)]
def a_ ( ):
'''simple docstring'''
if os.name == "nt":
_lowerCamelCase : Optional[Any] =CursorInfo()
_lowerCamelCase : Dict =ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(SCREAMING_SNAKE_CASE__ , ctypes.byref(SCREAMING_SNAKE_CASE__ ) )
_lowerCamelCase : Any =False
ctypes.windll.kernelaa.SetConsoleCursorInfo(SCREAMING_SNAKE_CASE__ , ctypes.byref(SCREAMING_SNAKE_CASE__ ) )
elif os.name == "posix":
sys.stdout.write('\033[?25l' )
sys.stdout.flush()
def a_ ( ):
'''simple docstring'''
if os.name == "nt":
_lowerCamelCase : Any =CursorInfo()
_lowerCamelCase : Optional[Any] =ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(SCREAMING_SNAKE_CASE__ , ctypes.byref(SCREAMING_SNAKE_CASE__ ) )
_lowerCamelCase : Union[str, Any] =True
ctypes.windll.kernelaa.SetConsoleCursorInfo(SCREAMING_SNAKE_CASE__ , ctypes.byref(SCREAMING_SNAKE_CASE__ ) )
elif os.name == "posix":
sys.stdout.write('\033[?25h' )
sys.stdout.flush()
@contextmanager
def a_ ( ):
'''simple docstring'''
try:
hide_cursor()
yield
finally:
show_cursor()
| 199 | 1 |
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : List[Any] = ['''flax''']
def __init__( self : str , *UpperCAmelCase_ : Tuple , **UpperCAmelCase_ : Dict ):
requires_backends(self , ["flax"] )
@classmethod
def _A ( cls : Union[str, Any] , *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : Dict ):
requires_backends(cls , ["flax"] )
@classmethod
def _A ( cls : Tuple , *UpperCAmelCase_ : Any , **UpperCAmelCase_ : int ):
requires_backends(cls , ["flax"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : List[str] = ['''flax''']
def __init__( self : Optional[int] , *UpperCAmelCase_ : Optional[int] , **UpperCAmelCase_ : Dict ):
requires_backends(self , ["flax"] )
@classmethod
def _A ( cls : Any , *UpperCAmelCase_ : Optional[int] , **UpperCAmelCase_ : Dict ):
requires_backends(cls , ["flax"] )
@classmethod
def _A ( cls : Tuple , *UpperCAmelCase_ : int , **UpperCAmelCase_ : str ):
requires_backends(cls , ["flax"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : List[str] = ['''flax''']
def __init__( self : Union[str, Any] , *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : Dict ):
requires_backends(self , ["flax"] )
@classmethod
def _A ( cls : List[Any] , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : Union[str, Any] ):
requires_backends(cls , ["flax"] )
@classmethod
def _A ( cls : int , *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : List[Any] ):
requires_backends(cls , ["flax"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : List[str] = ['''flax''']
def __init__( self : List[Any] , *UpperCAmelCase_ : Tuple , **UpperCAmelCase_ : List[Any] ):
requires_backends(self , ["flax"] )
@classmethod
def _A ( cls : int , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : str ):
requires_backends(cls , ["flax"] )
@classmethod
def _A ( cls : Tuple , *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : Dict ):
requires_backends(cls , ["flax"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Any = ['''flax''']
def __init__( self : List[Any] , *UpperCAmelCase_ : Optional[int] , **UpperCAmelCase_ : Tuple ):
requires_backends(self , ["flax"] )
@classmethod
def _A ( cls : List[str] , *UpperCAmelCase_ : str , **UpperCAmelCase_ : List[Any] ):
requires_backends(cls , ["flax"] )
@classmethod
def _A ( cls : List[str] , *UpperCAmelCase_ : Optional[int] , **UpperCAmelCase_ : List[Any] ):
requires_backends(cls , ["flax"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : int = ['''flax''']
def __init__( self : Tuple , *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : Tuple ):
requires_backends(self , ["flax"] )
@classmethod
def _A ( cls : Any , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : List[Any] ):
requires_backends(cls , ["flax"] )
@classmethod
def _A ( cls : Tuple , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : Optional[Any] ):
requires_backends(cls , ["flax"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : List[str] = ['''flax''']
def __init__( self : int , *UpperCAmelCase_ : str , **UpperCAmelCase_ : Optional[int] ):
requires_backends(self , ["flax"] )
@classmethod
def _A ( cls : Union[str, Any] , *UpperCAmelCase_ : int , **UpperCAmelCase_ : Union[str, Any] ):
requires_backends(cls , ["flax"] )
@classmethod
def _A ( cls : Optional[int] , *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : Dict ):
requires_backends(cls , ["flax"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Optional[int] = ['''flax''']
def __init__( self : Optional[Any] , *UpperCAmelCase_ : Tuple , **UpperCAmelCase_ : Dict ):
requires_backends(self , ["flax"] )
@classmethod
def _A ( cls : Optional[Any] , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : List[str] ):
requires_backends(cls , ["flax"] )
@classmethod
def _A ( cls : List[str] , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : Tuple ):
requires_backends(cls , ["flax"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Dict = ['''flax''']
def __init__( self : str , *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : List[Any] ):
requires_backends(self , ["flax"] )
@classmethod
def _A ( cls : Tuple , *UpperCAmelCase_ : Optional[int] , **UpperCAmelCase_ : Optional[Any] ):
requires_backends(cls , ["flax"] )
@classmethod
def _A ( cls : Dict , *UpperCAmelCase_ : str , **UpperCAmelCase_ : Union[str, Any] ):
requires_backends(cls , ["flax"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Union[str, Any] = ['''flax''']
def __init__( self : int , *UpperCAmelCase_ : Optional[Any] , **UpperCAmelCase_ : int ):
requires_backends(self , ["flax"] )
@classmethod
def _A ( cls : List[str] , *UpperCAmelCase_ : Tuple , **UpperCAmelCase_ : Union[str, Any] ):
requires_backends(cls , ["flax"] )
@classmethod
def _A ( cls : Dict , *UpperCAmelCase_ : str , **UpperCAmelCase_ : str ):
requires_backends(cls , ["flax"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Union[str, Any] = ['''flax''']
def __init__( self : Tuple , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : Tuple ):
requires_backends(self , ["flax"] )
@classmethod
def _A ( cls : Any , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : List[Any] ):
requires_backends(cls , ["flax"] )
@classmethod
def _A ( cls : int , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : Any ):
requires_backends(cls , ["flax"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Dict = ['''flax''']
def __init__( self : List[Any] , *UpperCAmelCase_ : Tuple , **UpperCAmelCase_ : int ):
requires_backends(self , ["flax"] )
@classmethod
def _A ( cls : List[Any] , *UpperCAmelCase_ : List[str] , **UpperCAmelCase_ : str ):
requires_backends(cls , ["flax"] )
@classmethod
def _A ( cls : Optional[Any] , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : List[str] ):
requires_backends(cls , ["flax"] )
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : int = ['''flax''']
def __init__( self : Optional[int] , *UpperCAmelCase_ : Any , **UpperCAmelCase_ : List[Any] ):
requires_backends(self , ["flax"] )
@classmethod
def _A ( cls : Union[str, Any] , *UpperCAmelCase_ : Optional[int] , **UpperCAmelCase_ : Union[str, Any] ):
requires_backends(cls , ["flax"] )
@classmethod
def _A ( cls : Any , *UpperCAmelCase_ : Any , **UpperCAmelCase_ : Optional[int] ):
requires_backends(cls , ["flax"] )
| 367 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Dict = '''timm_backbone'''
def __init__( self : List[Any] , UpperCAmelCase_ : Optional[int]=None , UpperCAmelCase_ : List[str]=3 , UpperCAmelCase_ : List[Any]=True , UpperCAmelCase_ : List[str]=True , UpperCAmelCase_ : Union[str, Any]=None , **UpperCAmelCase_ : Optional[Any] , ):
super().__init__(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Dict = backbone
SCREAMING_SNAKE_CASE : List[str] = num_channels
SCREAMING_SNAKE_CASE : Optional[Any] = features_only
SCREAMING_SNAKE_CASE : Dict = use_pretrained_backbone
SCREAMING_SNAKE_CASE : Optional[int] = True
SCREAMING_SNAKE_CASE : List[Any] = out_indices if out_indices is not None else (-1,)
| 319 | 0 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def _lowercase ( __snake_case = "laptop" ) -> DataFrame:
__lowerCAmelCase : str = F"""https://www.amazon.in/laptop/s?k={product}"""
__lowerCAmelCase : Union[str, Any] = {
"User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36\n (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36",
"Accept-Language": "en-US, en;q=0.5",
}
__lowerCAmelCase : List[str] = BeautifulSoup(requests.get(__snake_case ,headers=__snake_case ).text )
# Initialize a Pandas dataframe with the column titles
__lowerCAmelCase : Dict = DataFrame(
columns=[
"Product Title",
"Product Link",
"Current Price of the product",
"Product Rating",
"MRP of the product",
"Discount",
] )
# Loop through each entry and store them in the dataframe
for item, _ in zip_longest(
soup.find_all(
"div" ,attrs={"class": "s-result-item", "data-component-type": "s-search-result"} ,) ,soup.find_all("div" ,attrs={"class": "a-row a-size-base a-color-base"} ) ,):
try:
__lowerCAmelCase : Any = item.ha.text
__lowerCAmelCase : Union[str, Any] = "https://www.amazon.in/" + item.ha.a["href"]
__lowerCAmelCase : Any = item.find("span" ,attrs={"class": "a-offscreen"} ).text
try:
__lowerCAmelCase : Union[str, Any] = item.find("span" ,attrs={"class": "a-icon-alt"} ).text
except AttributeError:
__lowerCAmelCase : Optional[Any] = "Not available"
try:
__lowerCAmelCase : Union[str, Any] = (
"₹"
+ item.find(
"span" ,attrs={"class": "a-price a-text-price"} ).text.split("₹" )[1]
)
except AttributeError:
__lowerCAmelCase : Dict = ""
try:
__lowerCAmelCase : str = float(
(
(
float(product_mrp.strip("₹" ).replace("," ,"" ) )
- float(product_price.strip("₹" ).replace("," ,"" ) )
)
/ float(product_mrp.strip("₹" ).replace("," ,"" ) )
)
* 100 )
except ValueError:
__lowerCAmelCase : List[str] = float("nan" )
except AttributeError:
pass
__lowerCAmelCase : int = [
product_title,
product_link,
product_price,
product_rating,
product_mrp,
discount,
]
__lowerCAmelCase : Union[str, Any] = " "
__lowerCAmelCase : Union[str, Any] = " "
data_frame.index += 1
return data_frame
if __name__ == "__main__":
__snake_case : Any = 'headphones'
get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""") | 269 |
"""simple docstring"""
from __future__ import annotations
def _lowercase ( __snake_case ,__snake_case ,__snake_case ,__snake_case ) -> list:
__lowerCAmelCase : Dict = []
__lowerCAmelCase , __lowerCAmelCase : Any = input_list[low:mid], input_list[mid : high + 1]
while left and right:
result.append((left if left[0] <= right[0] else right).pop(0 ) )
__lowerCAmelCase : int = result + left + right
return input_list
def _lowercase ( __snake_case ) -> list:
if len(__snake_case ) <= 1:
return input_list
__lowerCAmelCase : int = list(__snake_case )
# iteration for two-way merging
__lowerCAmelCase : Optional[int] = 2
while p <= len(__snake_case ):
# getting low, high and middle value for merge-sort of single list
for i in range(0 ,len(__snake_case ) ,__snake_case ):
__lowerCAmelCase : Union[str, Any] = i
__lowerCAmelCase : Tuple = i + p - 1
__lowerCAmelCase : Optional[Any] = (low + high + 1) // 2
__lowerCAmelCase : Any = merge(__snake_case ,__snake_case ,__snake_case ,__snake_case )
# final merge of last two parts
if p * 2 >= len(__snake_case ):
__lowerCAmelCase : Optional[Any] = i
__lowerCAmelCase : Union[str, Any] = merge(__snake_case ,0 ,__snake_case ,len(__snake_case ) - 1 )
break
p *= 2
return input_list
if __name__ == "__main__":
__snake_case : Union[str, Any] = input('Enter numbers separated by a comma:\n').strip()
if user_input == "":
__snake_case : Optional[int] = []
else:
__snake_case : int = [int(item.strip()) for item in user_input.split(',')]
print(iter_merge_sort(unsorted)) | 269 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class _SCREAMING_SNAKE_CASE ( __a ,unittest.TestCase ):
__SCREAMING_SNAKE_CASE :Any = ShapEImgaImgPipeline
__SCREAMING_SNAKE_CASE :Dict = ["""image"""]
__SCREAMING_SNAKE_CASE :Tuple = ["""image"""]
__SCREAMING_SNAKE_CASE :List[str] = [
"""num_images_per_prompt""",
"""num_inference_steps""",
"""generator""",
"""latents""",
"""guidance_scale""",
"""frame_size""",
"""output_type""",
"""return_dict""",
]
__SCREAMING_SNAKE_CASE :Any = False
@property
def snake_case__ ( self : Optional[int] ):
return 32
@property
def snake_case__ ( self : int ):
return 32
@property
def snake_case__ ( self : int ):
return self.time_input_dim * 4
@property
def snake_case__ ( self : Tuple ):
return 8
@property
def snake_case__ ( self : List[str] ):
torch.manual_seed(0 )
__magic_name__ = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=64 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=1 , )
__magic_name__ = CLIPVisionModel(a__ )
return model
@property
def snake_case__ ( self : Union[str, Any] ):
__magic_name__ = CLIPImageProcessor(
crop_size=224 , do_center_crop=a__ , do_normalize=a__ , do_resize=a__ , image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073] , image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711] , resample=3 , size=224 , )
return image_processor
@property
def snake_case__ ( self : List[Any] ):
torch.manual_seed(0 )
__magic_name__ = {
'''num_attention_heads''': 2,
'''attention_head_dim''': 16,
'''embedding_dim''': self.time_input_dim,
'''num_embeddings''': 32,
'''embedding_proj_dim''': self.text_embedder_hidden_size,
'''time_embed_dim''': self.time_embed_dim,
'''num_layers''': 1,
'''clip_embed_dim''': self.time_input_dim * 2,
'''additional_embeddings''': 0,
'''time_embed_act_fn''': '''gelu''',
'''norm_in_type''': '''layer''',
'''embedding_proj_norm_type''': '''layer''',
'''encoder_hid_proj_type''': None,
'''added_emb_type''': None,
}
__magic_name__ = PriorTransformer(**a__ )
return model
@property
def snake_case__ ( self : List[str] ):
torch.manual_seed(0 )
__magic_name__ = {
'''param_shapes''': (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'''d_latent''': self.time_input_dim,
'''d_hidden''': self.renderer_dim,
'''n_output''': 12,
'''background''': (
0.1,
0.1,
0.1,
),
}
__magic_name__ = ShapERenderer(**a__ )
return model
def snake_case__ ( self : Optional[Any] ):
__magic_name__ = self.dummy_prior
__magic_name__ = self.dummy_image_encoder
__magic_name__ = self.dummy_image_processor
__magic_name__ = self.dummy_renderer
__magic_name__ = HeunDiscreteScheduler(
beta_schedule='''exp''' , num_train_timesteps=1024 , prediction_type='''sample''' , use_karras_sigmas=a__ , clip_sample=a__ , clip_sample_range=1.0 , )
__magic_name__ = {
'''prior''': prior,
'''image_encoder''': image_encoder,
'''image_processor''': image_processor,
'''renderer''': renderer,
'''scheduler''': scheduler,
}
return components
def snake_case__ ( self : Optional[Any] , a__ : List[Any] , a__ : Union[str, Any]=0 ):
__magic_name__ = floats_tensor((1, 3, 64, 64) , rng=random.Random(a__ ) ).to(a__ )
if str(a__ ).startswith('''mps''' ):
__magic_name__ = torch.manual_seed(a__ )
else:
__magic_name__ = torch.Generator(device=a__ ).manual_seed(a__ )
__magic_name__ = {
'''image''': input_image,
'''generator''': generator,
'''num_inference_steps''': 1,
'''frame_size''': 32,
'''output_type''': '''np''',
}
return inputs
def snake_case__ ( self : Dict ):
__magic_name__ = '''cpu'''
__magic_name__ = self.get_dummy_components()
__magic_name__ = self.pipeline_class(**a__ )
__magic_name__ = pipe.to(a__ )
pipe.set_progress_bar_config(disable=a__ )
__magic_name__ = pipe(**self.get_dummy_inputs(a__ ) )
__magic_name__ = output.images[0]
__magic_name__ = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
__magic_name__ = np.array(
[
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def snake_case__ ( self : Optional[int] ):
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def snake_case__ ( self : Any ):
__magic_name__ = torch_device == '''cpu'''
__magic_name__ = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=a__ , relax_max_difference=a__ , )
def snake_case__ ( self : List[str] ):
__magic_name__ = self.get_dummy_components()
__magic_name__ = self.pipeline_class(**a__ )
__magic_name__ = pipe.to(a__ )
pipe.set_progress_bar_config(disable=a__ )
__magic_name__ = 1
__magic_name__ = 2
__magic_name__ = self.get_dummy_inputs(a__ )
for key in inputs.keys():
if key in self.batch_params:
__magic_name__ = batch_size * [inputs[key]]
__magic_name__ = pipe(**a__ , num_images_per_prompt=a__ )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def snake_case__ ( self : Tuple ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case__ ( self : Dict ):
__magic_name__ = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png''' )
__magic_name__ = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/shap_e/test_shap_e_img2img_out.npy''' )
__magic_name__ = ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img''' )
__magic_name__ = pipe.to(a__ )
pipe.set_progress_bar_config(disable=a__ )
__magic_name__ = torch.Generator(device=a__ ).manual_seed(0 )
__magic_name__ = pipe(
a__ , generator=a__ , guidance_scale=3.0 , num_inference_steps=64 , frame_size=64 , output_type='''np''' , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(a__ , a__ )
| 98 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class _SCREAMING_SNAKE_CASE :
def __init__( self : Union[str, Any] ):
__magic_name__ = []
__magic_name__ = 0
__magic_name__ = 0
def snake_case__ ( self : int ):
return self.head == self.tail
def snake_case__ ( self : int , a__ : Any ):
self.data.append(a__ )
__magic_name__ = self.tail + 1
def snake_case__ ( self : Tuple ):
__magic_name__ = self.data[self.head]
__magic_name__ = self.head + 1
return ret
def snake_case__ ( self : Optional[Any] ):
return self.tail - self.head
def snake_case__ ( self : List[Any] ):
print(self.data )
print('''**************''' )
print(self.data[self.head : self.tail] )
class _SCREAMING_SNAKE_CASE :
def __init__( self : List[str] , a__ : Any ):
__magic_name__ = data
__magic_name__ = None
__magic_name__ = None
__magic_name__ = 1
def snake_case__ ( self : Optional[int] ):
return self.data
def snake_case__ ( self : List[Any] ):
return self.left
def snake_case__ ( self : Tuple ):
return self.right
def snake_case__ ( self : Any ):
return self.height
def snake_case__ ( self : Optional[Any] , a__ : Any ):
__magic_name__ = data
def snake_case__ ( self : int , a__ : MyNode | None ):
__magic_name__ = node
def snake_case__ ( self : Tuple , a__ : MyNode | None ):
__magic_name__ = node
def snake_case__ ( self : List[str] , a__ : int ):
__magic_name__ = height
def UpperCamelCase ( a ) -> int:
'''simple docstring'''
if node is None:
return 0
return node.get_height()
def UpperCamelCase ( a , a ) -> int:
'''simple docstring'''
if a > b:
return a
return b
def UpperCamelCase ( a ) -> MyNode:
'''simple docstring'''
print('''left rotation node:''' , node.get_data() )
__magic_name__ = node.get_left()
assert ret is not None
node.set_left(ret.get_right() )
ret.set_right(a )
__magic_name__ = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(a )
__magic_name__ = my_max(get_height(ret.get_right() ) , get_height(ret.get_left() ) ) + 1
ret.set_height(a )
return ret
def UpperCamelCase ( a ) -> MyNode:
'''simple docstring'''
print('''right rotation node:''' , node.get_data() )
__magic_name__ = node.get_right()
assert ret is not None
node.set_right(ret.get_left() )
ret.set_left(a )
__magic_name__ = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(a )
__magic_name__ = my_max(get_height(ret.get_right() ) , get_height(ret.get_left() ) ) + 1
ret.set_height(a )
return ret
def UpperCamelCase ( a ) -> MyNode:
'''simple docstring'''
__magic_name__ = node.get_left()
assert left_child is not None
node.set_left(left_rotation(a ) )
return right_rotation(a )
def UpperCamelCase ( a ) -> MyNode:
'''simple docstring'''
__magic_name__ = node.get_right()
assert right_child is not None
node.set_right(right_rotation(a ) )
return left_rotation(a )
def UpperCamelCase ( a , a ) -> MyNode | None:
'''simple docstring'''
if node is None:
return MyNode(a )
if data < node.get_data():
node.set_left(insert_node(node.get_left() , a ) )
if (
get_height(node.get_left() ) - get_height(node.get_right() ) == 2
): # an unbalance detected
__magic_name__ = node.get_left()
assert left_child is not None
if (
data < left_child.get_data()
): # new node is the left child of the left child
__magic_name__ = right_rotation(a )
else:
__magic_name__ = lr_rotation(a )
else:
node.set_right(insert_node(node.get_right() , a ) )
if get_height(node.get_right() ) - get_height(node.get_left() ) == 2:
__magic_name__ = node.get_right()
assert right_child is not None
if data < right_child.get_data():
__magic_name__ = rl_rotation(a )
else:
__magic_name__ = left_rotation(a )
__magic_name__ = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(a )
return node
def UpperCamelCase ( a ) -> Any:
'''simple docstring'''
while True:
__magic_name__ = root.get_right()
if right_child is None:
break
__magic_name__ = right_child
return root.get_data()
def UpperCamelCase ( a ) -> Any:
'''simple docstring'''
while True:
__magic_name__ = root.get_left()
if left_child is None:
break
__magic_name__ = left_child
return root.get_data()
def UpperCamelCase ( a , a ) -> MyNode | None:
'''simple docstring'''
__magic_name__ = root.get_left()
__magic_name__ = root.get_right()
if root.get_data() == data:
if left_child is not None and right_child is not None:
__magic_name__ = get_left_most(a )
root.set_data(a )
root.set_right(del_node(a , a ) )
elif left_child is not None:
__magic_name__ = left_child
elif right_child is not None:
__magic_name__ = right_child
else:
return None
elif root.get_data() > data:
if left_child is None:
print('''No such data''' )
return root
else:
root.set_left(del_node(a , a ) )
else: # root.get_data() < data
if right_child is None:
return root
else:
root.set_right(del_node(a , a ) )
if get_height(a ) - get_height(a ) == 2:
assert right_child is not None
if get_height(right_child.get_right() ) > get_height(right_child.get_left() ):
__magic_name__ = left_rotation(a )
else:
__magic_name__ = rl_rotation(a )
elif get_height(a ) - get_height(a ) == -2:
assert left_child is not None
if get_height(left_child.get_left() ) > get_height(left_child.get_right() ):
__magic_name__ = right_rotation(a )
else:
__magic_name__ = lr_rotation(a )
__magic_name__ = my_max(get_height(root.get_right() ) , get_height(root.get_left() ) ) + 1
root.set_height(a )
return root
class _SCREAMING_SNAKE_CASE :
def __init__( self : List[Any] ):
__magic_name__ = None
def snake_case__ ( self : List[Any] ):
return get_height(self.root )
def snake_case__ ( self : Optional[int] , a__ : Any ):
print('''insert:''' + str(a__ ) )
__magic_name__ = insert_node(self.root , a__ )
def snake_case__ ( self : Dict , a__ : Any ):
print('''delete:''' + str(a__ ) )
if self.root is None:
print('''Tree is empty!''' )
return
__magic_name__ = del_node(self.root , a__ )
def __str__( self : Optional[Any] , ): # a level traversale, gives a more intuitive look on the tree
__magic_name__ = ''''''
__magic_name__ = MyQueue()
q.push(self.root )
__magic_name__ = self.get_height()
if layer == 0:
return output
__magic_name__ = 0
while not q.is_empty():
__magic_name__ = q.pop()
__magic_name__ = ''' ''' * int(math.pow(2 , layer - 1 ) )
output += space
if node is None:
output += "*"
q.push(a__ )
q.push(a__ )
else:
output += str(node.get_data() )
q.push(node.get_left() )
q.push(node.get_right() )
output += space
__magic_name__ = cnt + 1
for i in range(100 ):
if cnt == math.pow(2 , a__ ) - 1:
__magic_name__ = layer - 1
if layer == 0:
output += "\n*************************************"
return output
output += "\n"
break
output += "\n*************************************"
return output
def UpperCamelCase ( ) -> None:
'''simple docstring'''
import doctest
doctest.testmod()
if __name__ == "__main__":
_test()
_lowerCAmelCase = AVLtree()
_lowerCAmelCase = list(range(10))
random.shuffle(lst)
for i in lst:
t.insert(i)
print(str(t))
random.shuffle(lst)
for i in lst:
t.del_node(i)
print(str(t))
| 98 | 1 |
import random
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase ):
lowercase , lowercase , lowercase :List[Any] = [], [], []
for element in data:
if element < pivot:
less.append(__lowerCamelCase )
elif element > pivot:
greater.append(__lowerCamelCase )
else:
equal.append(__lowerCamelCase )
return less, equal, greater
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase ):
# index = len(items) // 2 when trying to find the median
# (value of index when items is sorted)
# invalid input
if index >= len(__lowerCamelCase ) or index < 0:
return None
lowercase :int = items[random.randint(0, len(__lowerCamelCase ) - 1 )]
lowercase :str = 0
lowercase , lowercase , lowercase :List[str] = _partition(__lowerCamelCase, __lowerCamelCase )
lowercase :Dict = len(__lowerCamelCase )
lowercase :Union[str, Any] = len(__lowerCamelCase )
# index is the pivot
if m <= index < m + count:
return pivot
# must be in smaller
elif m > index:
return quick_select(__lowerCamelCase, __lowerCamelCase )
# must be in larger
else:
return quick_select(__lowerCamelCase, index - (m + count) )
| 236 |
"""simple docstring"""
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
UpperCAmelCase__ = 'http://www.mocksite.com/file1.txt'
UpperCAmelCase__ = '"text": ["foo", "foo"]'
UpperCAmelCase__ = '6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'
class lowerCAmelCase__ :
__a = 200
__a = {"""Content-Length""": """100"""}
__a = {}
def lowercase ( self : List[str] , **_lowerCamelCase : List[str] ):
return [bytes(_lowerCamelCase , '''utf-8''' )]
def _UpperCAmelCase ( *__lowerCamelCase : List[str] , **__lowerCamelCase : Dict ) -> Dict:
return MockResponse()
@pytest.mark.parametrize('''urls_type''' , [str, list, dict] )
def _UpperCAmelCase ( __lowerCamelCase : Union[str, Any] , __lowerCamelCase : List[str] , __lowerCamelCase : str ) -> int:
import requests
monkeypatch.setattr(__lowerCamelCase , '''request''' , __lowerCamelCase )
_snake_case = URL
if issubclass(__lowerCamelCase , __lowerCamelCase ):
_snake_case = url
elif issubclass(__lowerCamelCase , __lowerCamelCase ):
_snake_case = [url]
elif issubclass(__lowerCamelCase , __lowerCamelCase ):
_snake_case = {'''train''': url}
_snake_case = '''dummy'''
_snake_case = '''downloads'''
_snake_case = tmp_path
_snake_case = DownloadConfig(
cache_dir=os.path.join(__lowerCamelCase , __lowerCamelCase ) , use_etag=__lowerCamelCase , )
_snake_case = DownloadManager(dataset_name=__lowerCamelCase , download_config=__lowerCamelCase )
_snake_case = dl_manager.download(__lowerCamelCase )
_snake_case = urls
for downloaded_paths in [downloaded_paths]:
if isinstance(__lowerCamelCase , __lowerCamelCase ):
_snake_case = [downloaded_paths]
_snake_case = [urls]
elif isinstance(__lowerCamelCase , __lowerCamelCase ):
assert "train" in downloaded_paths.keys()
_snake_case = downloaded_paths.values()
_snake_case = urls.values()
assert downloaded_paths
for downloaded_path, input_url in zip(__lowerCamelCase , __lowerCamelCase ):
assert downloaded_path == dl_manager.downloaded_paths[input_url]
_snake_case = Path(__lowerCamelCase )
_snake_case = downloaded_path.parts
assert parts[-1] == HASH
assert parts[-2] == cache_subdir
assert downloaded_path.exists()
_snake_case = downloaded_path.read_text()
assert content == CONTENT
_snake_case = downloaded_path.with_suffix('''.json''' )
assert metadata_downloaded_path.exists()
_snake_case = json.loads(metadata_downloaded_path.read_text() )
assert metadata_content == {"url": URL, "etag": None}
@pytest.mark.parametrize('''paths_type''' , [str, list, dict] )
def _UpperCAmelCase ( __lowerCamelCase : int , __lowerCamelCase : str , __lowerCamelCase : Optional[int] ) -> int:
_snake_case = str(__lowerCamelCase )
if issubclass(__lowerCamelCase , __lowerCamelCase ):
_snake_case = filename
elif issubclass(__lowerCamelCase , __lowerCamelCase ):
_snake_case = [filename]
elif issubclass(__lowerCamelCase , __lowerCamelCase ):
_snake_case = {'''train''': filename}
_snake_case = '''dummy'''
_snake_case = xz_file.parent
_snake_case = '''extracted'''
_snake_case = DownloadConfig(
cache_dir=__lowerCamelCase , use_etag=__lowerCamelCase , )
_snake_case = DownloadManager(dataset_name=__lowerCamelCase , download_config=__lowerCamelCase )
_snake_case = dl_manager.extract(__lowerCamelCase )
_snake_case = paths
for extracted_paths in [extracted_paths]:
if isinstance(__lowerCamelCase , __lowerCamelCase ):
_snake_case = [extracted_paths]
_snake_case = [paths]
elif isinstance(__lowerCamelCase , __lowerCamelCase ):
assert "train" in extracted_paths.keys()
_snake_case = extracted_paths.values()
_snake_case = paths.values()
assert extracted_paths
for extracted_path, input_path in zip(__lowerCamelCase , __lowerCamelCase ):
assert extracted_path == dl_manager.extracted_paths[input_path]
_snake_case = Path(__lowerCamelCase )
_snake_case = extracted_path.parts
assert parts[-1] == hash_url_to_filename(__lowerCamelCase , etag=__lowerCamelCase )
assert parts[-2] == extracted_subdir
assert extracted_path.exists()
_snake_case = extracted_path.read_text()
_snake_case = text_file.read_text()
assert extracted_file_content == expected_file_content
def _UpperCAmelCase ( __lowerCamelCase : Tuple , __lowerCamelCase : List[Any] ) -> Dict:
assert path.endswith('''.jsonl''' )
for num_items, line in enumerate(__lowerCamelCase , start=1 ):
_snake_case = json.loads(line.decode('''utf-8''' ) )
assert item.keys() == {"col_1", "col_2", "col_3"}
assert num_items == 4
@pytest.mark.parametrize('''archive_jsonl''' , ['''tar_jsonl_path''', '''zip_jsonl_path'''] )
def _UpperCAmelCase ( __lowerCamelCase : Dict , __lowerCamelCase : str ) -> Dict:
_snake_case = request.getfixturevalue(__lowerCamelCase )
_snake_case = DownloadManager()
for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(__lowerCamelCase ) , start=1 ):
_test_jsonl(__lowerCamelCase , __lowerCamelCase )
assert num_jsonl == 2
@pytest.mark.parametrize('''archive_nested_jsonl''' , ['''tar_nested_jsonl_path''', '''zip_nested_jsonl_path'''] )
def _UpperCAmelCase ( __lowerCamelCase : str , __lowerCamelCase : List[Any] ) -> Tuple:
_snake_case = request.getfixturevalue(__lowerCamelCase )
_snake_case = DownloadManager()
for num_tar, (path, file) in enumerate(dl_manager.iter_archive(__lowerCamelCase ) , start=1 ):
for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(__lowerCamelCase ) , start=1 ):
_test_jsonl(__lowerCamelCase , __lowerCamelCase )
assert num_tar == 1
assert num_jsonl == 2
def _UpperCAmelCase ( __lowerCamelCase : Tuple ) -> List[Any]:
_snake_case = DownloadManager()
for num_file, file in enumerate(dl_manager.iter_files(__lowerCamelCase ) , start=1 ):
assert os.path.basename(__lowerCamelCase ) == ("test.txt" if num_file == 1 else "train.txt")
assert num_file == 2
| 288 | 0 |
import unittest
from knapsack import knapsack as k
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
'''simple docstring'''
def _lowerCAmelCase ( self ):
A : Optional[Any] = 0
A : str = [0]
A : Tuple = [0]
A : str = len(lowerCamelCase__ )
self.assertEqual(k.knapsack(lowerCamelCase__, lowerCamelCase__, lowerCamelCase__, lowerCamelCase__ ), 0 )
A : int = [60]
A : Union[str, Any] = [10]
A : Tuple = len(lowerCamelCase__ )
self.assertEqual(k.knapsack(lowerCamelCase__, lowerCamelCase__, lowerCamelCase__, lowerCamelCase__ ), 0 )
def _lowerCAmelCase ( self ):
A : List[str] = 3
A : int = [1, 2, 3]
A : Optional[int] = [3, 2, 1]
A : str = len(lowerCamelCase__ )
self.assertEqual(k.knapsack(lowerCamelCase__, lowerCamelCase__, lowerCamelCase__, lowerCamelCase__ ), 5 )
def _lowerCAmelCase ( self ):
A : Any = 50
A : List[str] = [60, 100, 120]
A : Any = [10, 20, 30]
A : Tuple = len(lowerCamelCase__ )
self.assertEqual(k.knapsack(lowerCamelCase__, lowerCamelCase__, lowerCamelCase__, lowerCamelCase__ ), 220 )
if __name__ == "__main__":
unittest.main()
| 115 |
from __future__ import annotations
def __UpperCamelCase ( _lowerCAmelCase ) -> list[int]:
"""simple docstring"""
A : Tuple = 2
A : List[Any] = []
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()
| 115 | 1 |
'''simple docstring'''
def snake_case_ ( __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
return [sentence[i : i + ngram_size] for i in range(len(__SCREAMING_SNAKE_CASE ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 93 |
"""simple docstring"""
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
UpperCAmelCase_ : Optional[Any] = datasets.logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = """\
@InProceedings{moosavi2019minimum,
author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},
title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},
year = {2019},
booktitle = {Proceedings of the 57th Annual Meeting of
the Association for Computational Linguistics (Volume 1: Long Papers)},
publisher = {Association for Computational Linguistics},
address = {Florence, Italy},
}
@inproceedings{10.3115/1072399.1072405,
author = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},
title = {A Model-Theoretic Coreference Scoring Scheme},
year = {1995},
isbn = {1558604022},
publisher = {Association for Computational Linguistics},
address = {USA},
url = {https://doi.org/10.3115/1072399.1072405},
doi = {10.3115/1072399.1072405},
booktitle = {Proceedings of the 6th Conference on Message Understanding},
pages = {45–52},
numpages = {8},
location = {Columbia, Maryland},
series = {MUC6 ’95}
}
@INPROCEEDINGS{Bagga98algorithmsfor,
author = {Amit Bagga and Breck Baldwin},
title = {Algorithms for Scoring Coreference Chains},
booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},
year = {1998},
pages = {563--566}
}
@INPROCEEDINGS{Luo05oncoreference,
author = {Xiaoqiang Luo},
title = {On coreference resolution performance metrics},
booktitle = {In Proc. of HLT/EMNLP},
year = {2005},
pages = {25--32},
publisher = {URL}
}
@inproceedings{moosavi-strube-2016-coreference,
title = \"Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric\",
author = \"Moosavi, Nafise Sadat and
Strube, Michael\",
booktitle = \"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",
month = aug,
year = \"2016\",
address = \"Berlin, Germany\",
publisher = \"Association for Computational Linguistics\",
url = \"https://www.aclweb.org/anthology/P16-1060\",
doi = \"10.18653/v1/P16-1060\",
pages = \"632--642\",
}
"""
UpperCAmelCase_ : Tuple = """\
CoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which
implements of the common evaluation metrics including MUC [Vilain et al, 1995],
B-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],
LEA [Moosavi and Strube, 2016] and the averaged CoNLL score
(the average of the F1 values of MUC, B-cubed and CEAFe)
[Denis and Baldridge, 2009a; Pradhan et al., 2011].
This wrapper of CoVal currently only work with CoNLL line format:
The CoNLL format has one word per line with all the annotation for this word in column separated by spaces:
Column Type Description
1 Document ID This is a variation on the document filename
2 Part number Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
3 Word number
4 Word itself This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.
5 Part-of-Speech
6 Parse bit This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the \"([pos] [word])\" string (or leaf) and concatenating the items in the rows of that column.
7 Predicate lemma The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a \"-\"
8 Predicate Frameset ID This is the PropBank frameset ID of the predicate in Column 7.
9 Word sense This is the word sense of the word in Column 3.
10 Speaker/Author This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.
11 Named Entities These columns identifies the spans representing various named entities.
12:N Predicate Arguments There is one column each of predicate argument structure information for the predicate mentioned in Column 7.
N Coreference Coreference chain information encoded in a parenthesis structure.
More informations on the format can be found here (section \"*_conll File Format\"): http://www.conll.cemantix.org/2012/data.html
Details on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md
CoVal code was written by @ns-moosavi.
Some parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py
The test suite is taken from https://github.com/conll/reference-coreference-scorers/
Mention evaluation and the test suite are added by @andreasvc.
Parsing CoNLL files is developed by Leo Born.
"""
UpperCAmelCase_ : Union[str, Any] = """
Calculates coreference evaluation metrics.
Args:
predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.
Each prediction is a word with its annotations as a string made of columns joined with spaces.
Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
See the details on the format in the description of the metric.
references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.
Each reference is a word with its annotations as a string made of columns joined with spaces.
Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
See the details on the format in the description of the metric.
keep_singletons: After extracting all mentions of key or system files,
mentions whose corresponding coreference chain is of size one,
are considered as singletons. The default evaluation mode will include
singletons in evaluations if they are included in the key or the system files.
By setting 'keep_singletons=False', all singletons in the key and system files
will be excluded from the evaluation.
NP_only: Most of the recent coreference resolvers only resolve NP mentions and
leave out the resolution of VPs. By setting the 'NP_only' option, the scorer will only evaluate the resolution of NPs.
min_span: By setting 'min_span', the scorer reports the results based on automatically detected minimum spans.
Minimum spans are determined using the MINA algorithm.
Returns:
'mentions': mentions
'muc': MUC metric [Vilain et al, 1995]
'bcub': B-cubed [Bagga and Baldwin, 1998]
'ceafe': CEAFe [Luo et al., 2005]
'lea': LEA [Moosavi and Strube, 2016]
'conll_score': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)
Examples:
>>> coval = datasets.load_metric('coval')
>>> words = ['bc/cctv/00/cctv_0005 0 0 Thank VBP (TOP(S(VP* thank 01 1 Xu_li * (V*) * -',
... 'bc/cctv/00/cctv_0005 0 1 you PRP (NP*) - - - Xu_li * (ARG1*) (ARG0*) (116)',
... 'bc/cctv/00/cctv_0005 0 2 everyone NN (NP*) - - - Xu_li * (ARGM-DIS*) * (116)',
... 'bc/cctv/00/cctv_0005 0 3 for IN (PP* - - - Xu_li * (ARG2* * -',
... 'bc/cctv/00/cctv_0005 0 4 watching VBG (S(VP*)))) watch 01 1 Xu_li * *) (V*) -',
... 'bc/cctv/00/cctv_0005 0 5 . . *)) - - - Xu_li * * * -']
>>> references = [words]
>>> predictions = [words]
>>> results = coval.compute(predictions=predictions, references=references)
>>> print(results) # doctest:+ELLIPSIS
{'mentions/recall': 1.0,[...] 'conll_score': 100.0}
"""
def _A (__a , __a , __a=False , __a=False , __a=True , __a=False , __a="dummy_doc" ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = {doc: key_lines}
SCREAMING_SNAKE_CASE_ : List[str] = {doc: sys_lines}
SCREAMING_SNAKE_CASE_ : Dict = {}
SCREAMING_SNAKE_CASE_ : Dict = 0
SCREAMING_SNAKE_CASE_ : List[str] = 0
SCREAMING_SNAKE_CASE_ : Tuple = 0
SCREAMING_SNAKE_CASE_ : int = 0
SCREAMING_SNAKE_CASE_ : List[str] = 0
SCREAMING_SNAKE_CASE_ : Any = 0
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Tuple = reader.get_doc_mentions(__a , key_doc_lines[doc] , __a )
key_singletons_num += singletons_num
if NP_only or min_span:
SCREAMING_SNAKE_CASE_ : Optional[Any] = reader.set_annotated_parse_trees(__a , key_doc_lines[doc] , __a , __a )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : int = reader.get_doc_mentions(__a , sys_doc_lines[doc] , __a )
sys_singletons_num += singletons_num
if NP_only or min_span:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = reader.set_annotated_parse_trees(__a , key_doc_lines[doc] , __a , __a )
if remove_nested:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : List[str] = reader.remove_nested_coref_mentions(__a , __a )
key_nested_coref_num += nested_mentions
key_removed_nested_clusters += removed_clusters
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : List[str] = reader.remove_nested_coref_mentions(__a , __a )
sys_nested_coref_num += nested_mentions
sys_removed_nested_clusters += removed_clusters
SCREAMING_SNAKE_CASE_ : Optional[Any] = reader.get_mention_assignments(__a , __a )
SCREAMING_SNAKE_CASE_ : Optional[Any] = reader.get_mention_assignments(__a , __a )
SCREAMING_SNAKE_CASE_ : Optional[Any] = (key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)
if remove_nested:
logger.info(
'''Number of removed nested coreferring mentions in the key '''
f'annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}' )
logger.info(
'''Number of resulting singleton clusters in the key '''
f'annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}' )
if not keep_singletons:
logger.info(
f'{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system '
'''files, respectively''' )
return doc_coref_infos
def _A (__a , __a , __a , __a , __a , __a , __a ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = get_coref_infos(__a , __a , __a , __a , __a , __a )
SCREAMING_SNAKE_CASE_ : str = {}
SCREAMING_SNAKE_CASE_ : Union[str, Any] = 0
SCREAMING_SNAKE_CASE_ : str = 0
for name, metric in metrics:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Tuple = evaluator.evaluate_documents(__a , __a , beta=1 )
if name in ["muc", "bcub", "ceafe"]:
conll += fa
conll_subparts_num += 1
output_scores.update({f'{name}/recall': recall, f'{name}/precision': precision, f'{name}/f1': fa} )
logger.info(
name.ljust(10 ) , f'Recall: {recall * 1_00:.2f}' , f' Precision: {precision * 1_00:.2f}' , f' F1: {fa * 1_00:.2f}' , )
if conll_subparts_num == 3:
SCREAMING_SNAKE_CASE_ : Tuple = (conll / 3) * 1_00
logger.info(f'CoNLL score: {conll:.2f}' )
output_scores.update({'''conll_score''': conll} )
return output_scores
def _A (__a ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = False
for line in key_lines:
if not line.startswith('''#''' ):
if len(line.split() ) > 6:
SCREAMING_SNAKE_CASE_ : Any = line.split()[5]
if not parse_col == "-":
SCREAMING_SNAKE_CASE_ : Any = True
break
else:
break
return has_gold_parse
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCAmelCase__ ( datasets.Metric ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self : Optional[Any]):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''string''')),
'''references''': datasets.Sequence(datasets.Value('''string''')),
}) , codebase_urls=['''https://github.com/ns-moosavi/coval'''] , reference_urls=[
'''https://github.com/ns-moosavi/coval''',
'''https://www.aclweb.org/anthology/P16-1060''',
'''http://www.conll.cemantix.org/2012/data.html''',
] , )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , lowercase_ : List[str] , lowercase_ : Optional[int] , lowercase_ : Dict=True , lowercase_ : Optional[Any]=False , lowercase_ : Optional[Any]=False , lowercase_ : Dict=False):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : int = [
('''mentions''', evaluator.mentions),
('''muc''', evaluator.muc),
('''bcub''', evaluator.b_cubed),
('''ceafe''', evaluator.ceafe),
('''lea''', evaluator.lea),
]
if min_span:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = util.check_gold_parse_annotation(lowercase_)
if not has_gold_parse:
raise NotImplementedError('''References should have gold parse annotation to use \'min_span\'.''')
# util.parse_key_file(key_file)
# key_file = key_file + ".parsed"
SCREAMING_SNAKE_CASE_ : Optional[Any] = evaluate(
key_lines=lowercase_ , sys_lines=lowercase_ , metrics=lowercase_ , NP_only=lowercase_ , remove_nested=lowercase_ , keep_singletons=lowercase_ , min_span=lowercase_ , )
return score
| 91 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DPTImageProcessor
class lowercase__ :
def __init__( self : List[str] ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : List[Any]=2 ,lowerCamelCase__ : Optional[int]=32 ,lowerCamelCase__ : Optional[Any]=16 ,lowerCamelCase__ : Optional[int]=3 ,lowerCamelCase__ : List[Any]=True ,lowerCamelCase__ : str=True ,lowerCamelCase__ : str=32 ,lowerCamelCase__ : Optional[Any]=4 ,lowerCamelCase__ : Union[str, Any]=[0, 1, 2, 3] ,lowerCamelCase__ : Dict=4 ,lowerCamelCase__ : Union[str, Any]=37 ,lowerCamelCase__ : Optional[Any]="gelu" ,lowerCamelCase__ : int=0.1 ,lowerCamelCase__ : Dict=0.1 ,lowerCamelCase__ : Union[str, Any]=0.0_2 ,lowerCamelCase__ : Dict=3 ,lowerCamelCase__ : Optional[int]=[1, 384, 24, 24] ,lowerCamelCase__ : Tuple=True ,lowerCamelCase__ : Optional[Any]=None ,):
'''simple docstring'''
_UpperCamelCase : Tuple = parent
_UpperCamelCase : Optional[int] = batch_size
_UpperCamelCase : str = image_size
_UpperCamelCase : str = patch_size
_UpperCamelCase : int = num_channels
_UpperCamelCase : Any = is_training
_UpperCamelCase : str = use_labels
_UpperCamelCase : List[Any] = hidden_size
_UpperCamelCase : List[str] = num_hidden_layers
_UpperCamelCase : int = backbone_out_indices
_UpperCamelCase : Optional[int] = num_attention_heads
_UpperCamelCase : int = intermediate_size
_UpperCamelCase : Optional[Any] = hidden_act
_UpperCamelCase : Optional[Any] = hidden_dropout_prob
_UpperCamelCase : Tuple = attention_probs_dropout_prob
_UpperCamelCase : Any = initializer_range
_UpperCamelCase : List[str] = num_labels
_UpperCamelCase : Tuple = backbone_featmap_shape
_UpperCamelCase : Dict = scope
_UpperCamelCase : Tuple = is_hybrid
# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
_UpperCamelCase : Optional[int] = (image_size // patch_size) ** 2
_UpperCamelCase : Dict = num_patches + 1
def UpperCamelCase_ ( self : str ):
'''simple docstring'''
_UpperCamelCase : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_UpperCamelCase : int = None
if self.use_labels:
_UpperCamelCase : int = ids_tensor([self.batch_size, self.image_size, self.image_size] ,self.num_labels )
_UpperCamelCase : int = self.get_config()
return config, pixel_values, labels
def UpperCamelCase_ ( self : List[Any] ):
'''simple docstring'''
_UpperCamelCase : List[str] = {
'global_padding': 'same',
'layer_type': 'bottleneck',
'depths': [3, 4, 9],
'out_features': ['stage1', 'stage2', 'stage3'],
'embedding_dynamic_padding': True,
'hidden_sizes': [96, 192, 384, 768],
'num_groups': 2,
}
return DPTConfig(
image_size=self.image_size ,patch_size=self.patch_size ,num_channels=self.num_channels ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,backbone_out_indices=self.backbone_out_indices ,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 ,is_hybrid=self.is_hybrid ,backbone_config=lowerCamelCase__ ,backbone_featmap_shape=self.backbone_featmap_shape ,)
def UpperCamelCase_ ( self : str ,lowerCamelCase__ : str ,lowerCamelCase__ : Optional[int] ,lowerCamelCase__ : Optional[Any] ):
'''simple docstring'''
_UpperCamelCase : Optional[int] = DPTModel(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
_UpperCamelCase : List[Any] = model(lowerCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase_ ( self : Any ,lowerCamelCase__ : int ,lowerCamelCase__ : str ,lowerCamelCase__ : int ):
'''simple docstring'''
_UpperCamelCase : Tuple = self.num_labels
_UpperCamelCase : int = DPTForDepthEstimation(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
_UpperCamelCase : int = model(lowerCamelCase__ )
self.parent.assertEqual(result.predicted_depth.shape ,(self.batch_size, self.image_size, self.image_size) )
def UpperCamelCase_ ( self : Optional[Any] ,lowerCamelCase__ : List[str] ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : List[str] ):
'''simple docstring'''
_UpperCamelCase : Tuple = self.num_labels
_UpperCamelCase : Tuple = DPTForSemanticSegmentation(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
_UpperCamelCase : str = model(lowerCamelCase__ ,labels=lowerCamelCase__ )
self.parent.assertEqual(
result.logits.shape ,(self.batch_size, self.num_labels, self.image_size, self.image_size) )
def UpperCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
_UpperCamelCase : Optional[int] = self.prepare_config_and_inputs()
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase : List[Any] = config_and_inputs
_UpperCamelCase : Optional[int] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class lowercase__ ( lowercase , lowercase , unittest.TestCase ):
lowercase__ = (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
lowercase__ = (
{
"""depth-estimation""": DPTForDepthEstimation,
"""feature-extraction""": DPTModel,
"""image-segmentation""": DPTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
lowercase__ = False
lowercase__ = False
lowercase__ = False
def UpperCamelCase_ ( self : str ):
'''simple docstring'''
_UpperCamelCase : Tuple = DPTModelTester(self )
_UpperCamelCase : Dict = ConfigTester(self ,config_class=lowerCamelCase__ ,has_text_modality=lowerCamelCase__ ,hidden_size=37 )
def UpperCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='DPT does not use inputs_embeds' )
def UpperCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
pass
def UpperCamelCase_ ( self : str ):
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCamelCase : int = model_class(lowerCamelCase__ )
self.assertIsInstance(model.get_input_embeddings() ,(nn.Module) )
_UpperCamelCase : List[Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCamelCase__ ,nn.Linear ) )
def UpperCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCamelCase : Optional[int] = model_class(lowerCamelCase__ )
_UpperCamelCase : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCamelCase : Union[str, Any] = [*signature.parameters.keys()]
_UpperCamelCase : Tuple = ['pixel_values']
self.assertListEqual(arg_names[:1] ,lowerCamelCase__ )
def UpperCamelCase_ ( self : List[Any] ):
'''simple docstring'''
_UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase__ )
def UpperCamelCase_ ( self : List[str] ):
'''simple docstring'''
_UpperCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_depth_estimation(*lowerCamelCase__ )
def UpperCamelCase_ ( self : Dict ):
'''simple docstring'''
_UpperCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase__ )
def UpperCamelCase_ ( self : List[str] ):
'''simple docstring'''
for model_class in self.all_model_classes:
if model_class.__name__ == "DPTForDepthEstimation":
continue
_UpperCamelCase , _UpperCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCamelCase : Union[str, Any] = True
if model_class in get_values(lowerCamelCase__ ):
continue
_UpperCamelCase : int = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.train()
_UpperCamelCase : List[str] = self._prepare_for_class(lowerCamelCase__ ,lowerCamelCase__ ,return_labels=lowerCamelCase__ )
_UpperCamelCase : Any = model(**lowerCamelCase__ ).loss
loss.backward()
def UpperCamelCase_ ( self : str ):
'''simple docstring'''
for model_class in self.all_model_classes:
if model_class.__name__ == "DPTForDepthEstimation":
continue
_UpperCamelCase , _UpperCamelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCamelCase : List[Any] = False
_UpperCamelCase : Optional[Any] = True
if model_class in get_values(lowerCamelCase__ ) or not model_class.supports_gradient_checkpointing:
continue
_UpperCamelCase : Optional[Any] = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.gradient_checkpointing_enable()
model.train()
_UpperCamelCase : Union[str, Any] = self._prepare_for_class(lowerCamelCase__ ,lowerCamelCase__ ,return_labels=lowerCamelCase__ )
_UpperCamelCase : Tuple = model(**lowerCamelCase__ ).loss
loss.backward()
def UpperCamelCase_ ( self : List[str] ):
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCamelCase : Optional[int] = _config_zero_init(lowerCamelCase__ )
for model_class in self.all_model_classes:
_UpperCamelCase : Any = model_class(config=lowerCamelCase__ )
# Skip the check for the backbone
_UpperCamelCase : List[str] = []
for name, module in model.named_modules():
if module.__class__.__name__ == "DPTViTHybridEmbeddings":
_UpperCamelCase : Optional[int] = [F'{name}.{key}' for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() ,[0.0, 1.0] ,msg=F'Parameter {name} of model {model_class} seems not properly initialized' ,)
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def UpperCamelCase_ ( self : Dict ):
'''simple docstring'''
pass
@slow
def UpperCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
_UpperCamelCase : Dict = DPTModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def UpperCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
_UpperCamelCase , _UpperCamelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCamelCase : int = 'add'
with self.assertRaises(lowerCamelCase__ ):
_UpperCamelCase : Optional[int] = DPTForDepthEstimation(lowerCamelCase__ )
def A__ ( ):
_UpperCamelCase : Dict = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
@slow
class lowercase__ ( unittest.TestCase ):
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
_UpperCamelCase : Dict = DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas' )
_UpperCamelCase : Optional[Any] = DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas' ).to(lowerCamelCase__ )
_UpperCamelCase : Union[str, Any] = prepare_img()
_UpperCamelCase : Dict = image_processor(images=lowerCamelCase__ ,return_tensors='pt' ).to(lowerCamelCase__ )
# forward pass
with torch.no_grad():
_UpperCamelCase : Tuple = model(**lowerCamelCase__ )
_UpperCamelCase : List[str] = outputs.predicted_depth
# verify the predicted depth
_UpperCamelCase : Optional[int] = torch.Size((1, 384, 384) )
self.assertEqual(predicted_depth.shape ,lowerCamelCase__ )
_UpperCamelCase : List[str] = torch.tensor(
[[[5.6_4_3_7, 5.6_1_4_6, 5.6_5_1_1], [5.4_3_7_1, 5.5_6_4_9, 5.5_9_5_8], [5.5_2_1_5, 5.5_1_8_4, 5.5_2_9_3]]] ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100 ,lowerCamelCase__ ,atol=1E-4 ) )
| 236 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_nllb import NllbTokenizer
else:
snake_case_ : List[Any] = None
snake_case_ : Dict = logging.get_logger(__name__)
snake_case_ : Dict = {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}
snake_case_ : List[str] = {
'vocab_file': {
'facebook/nllb-200-distilled-600M': (
'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model'
),
},
'tokenizer_file': {
'facebook/nllb-200-distilled-600M': (
'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json'
),
},
}
snake_case_ : str = {
'facebook/nllb-large-en-ro': 1024,
'facebook/nllb-200-distilled-600M': 1024,
}
# fmt: off
snake_case_ : Optional[Any] = ['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn']
class lowercase__ ( lowercase ):
lowercase__ = VOCAB_FILES_NAMES
lowercase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ = PRETRAINED_VOCAB_FILES_MAP
lowercase__ = ["""input_ids""", """attention_mask"""]
lowercase__ = NllbTokenizer
lowercase__ = []
lowercase__ = []
def __init__( self : List[Any] ,lowerCamelCase__ : Optional[Any]=None ,lowerCamelCase__ : Dict=None ,lowerCamelCase__ : List[Any]="<s>" ,lowerCamelCase__ : Dict="</s>" ,lowerCamelCase__ : List[Any]="</s>" ,lowerCamelCase__ : Union[str, Any]="<s>" ,lowerCamelCase__ : List[Any]="<unk>" ,lowerCamelCase__ : Any="<pad>" ,lowerCamelCase__ : Optional[Any]="<mask>" ,lowerCamelCase__ : Optional[Any]=None ,lowerCamelCase__ : str=None ,lowerCamelCase__ : Tuple=None ,lowerCamelCase__ : Union[str, Any]=False ,**lowerCamelCase__ : Optional[Any] ,):
'''simple docstring'''
# Mask token behave like a normal word, i.e. include the space before it
_UpperCamelCase : Optional[int] = AddedToken(lowerCamelCase__ ,lstrip=lowerCamelCase__ ,rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ ,lowerCamelCase__ ) else mask_token
_UpperCamelCase : Union[str, Any] = legacy_behaviour
super().__init__(
vocab_file=lowerCamelCase__ ,tokenizer_file=lowerCamelCase__ ,bos_token=lowerCamelCase__ ,eos_token=lowerCamelCase__ ,sep_token=lowerCamelCase__ ,cls_token=lowerCamelCase__ ,unk_token=lowerCamelCase__ ,pad_token=lowerCamelCase__ ,mask_token=lowerCamelCase__ ,src_lang=lowerCamelCase__ ,tgt_lang=lowerCamelCase__ ,additional_special_tokens=lowerCamelCase__ ,legacy_behaviour=lowerCamelCase__ ,**lowerCamelCase__ ,)
_UpperCamelCase : int = vocab_file
_UpperCamelCase : int = False if not self.vocab_file else True
_UpperCamelCase : Dict = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({'additional_special_tokens': _additional_special_tokens} )
_UpperCamelCase : List[str] = {
lang_code: self.convert_tokens_to_ids(lowerCamelCase__ ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
_UpperCamelCase : List[str] = src_lang if src_lang is not None else 'eng_Latn'
_UpperCamelCase : int = self.convert_tokens_to_ids(self._src_lang )
_UpperCamelCase : Dict = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
return self._src_lang
@src_lang.setter
def UpperCamelCase_ ( self : Union[str, Any] ,lowerCamelCase__ : str ):
'''simple docstring'''
_UpperCamelCase : str = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def UpperCamelCase_ ( self : List[str] ,lowerCamelCase__ : List[int] ,lowerCamelCase__ : Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def UpperCamelCase_ ( self : Dict ,lowerCamelCase__ : List[int] ,lowerCamelCase__ : Optional[List[int]] = None ):
'''simple docstring'''
_UpperCamelCase : Dict = [self.sep_token_id]
_UpperCamelCase : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def UpperCamelCase_ ( self : Union[str, Any] ,lowerCamelCase__ : str ,lowerCamelCase__ : str ,lowerCamelCase__ : Optional[str] ,lowerCamelCase__ : Optional[str] ,**lowerCamelCase__ : Dict ):
'''simple docstring'''
if src_lang is None or tgt_lang is None:
raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model' )
_UpperCamelCase : Tuple = src_lang
_UpperCamelCase : Optional[Any] = self(lowerCamelCase__ ,add_special_tokens=lowerCamelCase__ ,return_tensors=lowerCamelCase__ ,**lowerCamelCase__ )
_UpperCamelCase : Tuple = self.convert_tokens_to_ids(lowerCamelCase__ )
_UpperCamelCase : str = tgt_lang_id
return inputs
def UpperCamelCase_ ( self : int ,lowerCamelCase__ : List[str] ,lowerCamelCase__ : str = "eng_Latn" ,lowerCamelCase__ : Optional[List[str]] = None ,lowerCamelCase__ : str = "fra_Latn" ,**lowerCamelCase__ : Union[str, Any] ,):
'''simple docstring'''
_UpperCamelCase : Tuple = src_lang
_UpperCamelCase : List[str] = tgt_lang
return super().prepare_seqaseq_batch(lowerCamelCase__ ,lowerCamelCase__ ,**lowerCamelCase__ )
def UpperCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
return self.set_src_lang_special_tokens(self.src_lang )
def UpperCamelCase_ ( self : Any ):
'''simple docstring'''
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def UpperCamelCase_ ( self : str ,lowerCamelCase__ : List[Any] ):
'''simple docstring'''
_UpperCamelCase : int = self.convert_tokens_to_ids(lowerCamelCase__ )
if self.legacy_behaviour:
_UpperCamelCase : Optional[Any] = []
_UpperCamelCase : int = [self.eos_token_id, self.cur_lang_code]
else:
_UpperCamelCase : List[Any] = [self.cur_lang_code]
_UpperCamelCase : List[Any] = [self.eos_token_id]
_UpperCamelCase : Union[str, Any] = self.convert_ids_to_tokens(self.prefix_tokens )
_UpperCamelCase : int = self.convert_ids_to_tokens(self.suffix_tokens )
_UpperCamelCase : Any = processors.TemplateProcessing(
single=prefix_tokens_str + ['$A'] + suffix_tokens_str ,pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str ,special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,self.prefix_tokens + self.suffix_tokens ) ) ,)
def UpperCamelCase_ ( self : int ,lowerCamelCase__ : str ):
'''simple docstring'''
_UpperCamelCase : Any = self.convert_tokens_to_ids(lowerCamelCase__ )
if self.legacy_behaviour:
_UpperCamelCase : Tuple = []
_UpperCamelCase : str = [self.eos_token_id, self.cur_lang_code]
else:
_UpperCamelCase : Tuple = [self.cur_lang_code]
_UpperCamelCase : Optional[Any] = [self.eos_token_id]
_UpperCamelCase : int = self.convert_ids_to_tokens(self.prefix_tokens )
_UpperCamelCase : List[Any] = self.convert_ids_to_tokens(self.suffix_tokens )
_UpperCamelCase : Optional[int] = processors.TemplateProcessing(
single=prefix_tokens_str + ['$A'] + suffix_tokens_str ,pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str ,special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,self.prefix_tokens + self.suffix_tokens ) ) ,)
def UpperCamelCase_ ( self : Any ,lowerCamelCase__ : str ,lowerCamelCase__ : Optional[str] = None ):
'''simple docstring'''
if not self.can_save_slow_tokenizer:
raise ValueError(
'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
'tokenizer.' )
if not os.path.isdir(lowerCamelCase__ ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory.' )
return
_UpperCamelCase : List[Any] = os.path.join(
lowerCamelCase__ ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCamelCase__ ):
copyfile(self.vocab_file ,lowerCamelCase__ )
return (out_vocab_file,)
| 236 | 1 |
'''simple docstring'''
from statistics import mean, stdev
def A__ ( UpperCAmelCase_ , UpperCAmelCase_ = 3 ):
_UpperCamelCase : List[Any] = min(UpperCAmelCase_ )
_UpperCamelCase : Dict = max(UpperCAmelCase_ )
# normalize data
return [round((x - x_min) / (x_max - x_min) , UpperCAmelCase_ ) for x in data]
def A__ ( UpperCAmelCase_ , UpperCAmelCase_ = 3 ):
_UpperCamelCase : str = mean(UpperCAmelCase_ )
_UpperCamelCase : Tuple = stdev(UpperCAmelCase_ )
# standardize data
return [round((x - mu) / (sigma) , UpperCAmelCase_ ) for x in data]
| 83 | """simple docstring"""
def __UpperCAmelCase ( UpperCAmelCase_ : int , UpperCAmelCase_ : int ) -> int:
'''simple docstring'''
while a != 0:
__snake_case , __snake_case : Optional[Any] = b % a, a
return b
def __UpperCAmelCase ( UpperCAmelCase_ : int , UpperCAmelCase_ : int ) -> int:
'''simple docstring'''
if gcd(UpperCAmelCase_ , UpperCAmelCase_ ) != 1:
__snake_case : Optional[Any] = F"mod inverse of {a!r} and {m!r} does not exist"
raise ValueError(UpperCAmelCase_ )
__snake_case , __snake_case , __snake_case : Optional[int] = 1, 0, a
__snake_case , __snake_case , __snake_case : int = 0, 1, m
while va != 0:
__snake_case : Union[str, Any] = ua // va
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case : Union[str, Any] = (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
return ua % m
| 172 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase__ = {
'''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
'''tokenization_canine''': ['''CanineTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = [
'''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
lowerCamelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 352 | from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
lowerCamelCase__ = {'''configuration_gpt_neox''': ['''GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXConfig''']}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = ['''GPTNeoXTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = [
'''GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTNeoXForCausalLM''',
'''GPTNeoXForQuestionAnswering''',
'''GPTNeoXForSequenceClassification''',
'''GPTNeoXForTokenClassification''',
'''GPTNeoXLayer''',
'''GPTNeoXModel''',
'''GPTNeoXPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox import (
GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXLayer,
GPTNeoXModel,
GPTNeoXPreTrainedModel,
)
else:
import sys
lowerCamelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 63 | 0 |
'''simple docstring'''
import os
import sys
import unittest
lowercase : Dict = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
get_model_to_tester_mapping,
get_test_to_tester_mapping,
)
lowercase : Any = os.path.join('tests', 'models', 'bert', 'test_modeling_bert.py')
lowercase : Optional[int] = os.path.join('tests', 'models', 'blip', 'test_modeling_blip.py')
class A ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> List[str]:
"""simple docstring"""
A : Tuple = get_test_to_tester_mapping(SCREAMING_SNAKE_CASE )
A : Any = get_test_to_tester_mapping(SCREAMING_SNAKE_CASE )
A : List[Any] = {'''BertModelTest''': '''BertModelTester'''}
A : int = {
'''BlipModelTest''': '''BlipModelTester''',
'''BlipTextImageModelTest''': '''BlipTextImageModelsModelTester''',
'''BlipTextModelTest''': '''BlipTextModelTester''',
'''BlipTextRetrievalModelTest''': '''BlipTextRetrievalModelTester''',
'''BlipVQAModelTest''': '''BlipVQAModelTester''',
'''BlipVisionModelTest''': '''BlipVisionModelTester''',
}
self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE )
self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self ) -> Any:
"""simple docstring"""
A : Tuple = get_model_to_test_mapping(SCREAMING_SNAKE_CASE )
A : Tuple = get_model_to_test_mapping(SCREAMING_SNAKE_CASE )
A : List[str] = {
'''BertForMaskedLM''': ['''BertModelTest'''],
'''BertForMultipleChoice''': ['''BertModelTest'''],
'''BertForNextSentencePrediction''': ['''BertModelTest'''],
'''BertForPreTraining''': ['''BertModelTest'''],
'''BertForQuestionAnswering''': ['''BertModelTest'''],
'''BertForSequenceClassification''': ['''BertModelTest'''],
'''BertForTokenClassification''': ['''BertModelTest'''],
'''BertLMHeadModel''': ['''BertModelTest'''],
'''BertModel''': ['''BertModelTest'''],
}
A : Union[str, Any] = {
'''BlipForConditionalGeneration''': ['''BlipTextImageModelTest'''],
'''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTest'''],
'''BlipForQuestionAnswering''': ['''BlipVQAModelTest'''],
'''BlipModel''': ['''BlipModelTest'''],
'''BlipTextModel''': ['''BlipTextModelTest'''],
'''BlipVisionModel''': ['''BlipVisionModelTest'''],
}
self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE )
self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self ) -> Optional[int]:
"""simple docstring"""
A : int = get_model_to_tester_mapping(SCREAMING_SNAKE_CASE )
A : Union[str, Any] = get_model_to_tester_mapping(SCREAMING_SNAKE_CASE )
A : Dict = {
'''BertForMaskedLM''': ['''BertModelTester'''],
'''BertForMultipleChoice''': ['''BertModelTester'''],
'''BertForNextSentencePrediction''': ['''BertModelTester'''],
'''BertForPreTraining''': ['''BertModelTester'''],
'''BertForQuestionAnswering''': ['''BertModelTester'''],
'''BertForSequenceClassification''': ['''BertModelTester'''],
'''BertForTokenClassification''': ['''BertModelTester'''],
'''BertLMHeadModel''': ['''BertModelTester'''],
'''BertModel''': ['''BertModelTester'''],
}
A : str = {
'''BlipForConditionalGeneration''': ['''BlipTextImageModelsModelTester'''],
'''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTester'''],
'''BlipForQuestionAnswering''': ['''BlipVQAModelTester'''],
'''BlipModel''': ['''BlipModelTester'''],
'''BlipTextModel''': ['''BlipTextModelTester'''],
'''BlipVisionModel''': ['''BlipVisionModelTester'''],
}
self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE )
self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE )
| 3 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
lowercase_ = numpy.array([0, 0])
lowercase_ = numpy.array([0.5, 0.866_0254])
lowercase_ = numpy.array([1, 0])
lowercase_ = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]
def a__ ( snake_case , snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = initial_vectors
for _ in range(snake_case ):
__SCREAMING_SNAKE_CASE : Dict = iteration_step(snake_case )
return vectors
def a__ ( snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = []
for i, start_vector in enumerate(vectors[:-1] ):
__SCREAMING_SNAKE_CASE : str = vectors[i + 1]
new_vectors.append(snake_case )
__SCREAMING_SNAKE_CASE : List[str] = end_vector - start_vector
new_vectors.append(start_vector + difference_vector / 3 )
new_vectors.append(
start_vector + difference_vector / 3 + rotate(difference_vector / 3 , 60 ) )
new_vectors.append(start_vector + difference_vector * 2 / 3 )
new_vectors.append(vectors[-1] )
return new_vectors
def a__ ( snake_case , snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Tuple = numpy.radians(snake_case )
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : str = numpy.cos(snake_case ), numpy.sin(snake_case )
__SCREAMING_SNAKE_CASE : Any = numpy.array(((c, -s), (s, c)) )
return numpy.dot(snake_case , snake_case )
def a__ ( snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : int = plt.gca()
axes.set_aspect('''equal''' )
# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
# y-coordinates as inputs, which are constructed from the vector-list using
# zip()
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : Tuple = zip(*snake_case )
plt.plot(snake_case , snake_case )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
lowercase_ = iterate(INITIAL_VECTORS, 5)
plot(processed_vectors)
| 303 | 0 |
'''simple docstring'''
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def lowerCamelCase ( lowerCAmelCase : Dict[str, torch.Tensor] ):
"""simple docstring"""
__magic_name__ : Optional[Any] = []
__magic_name__ : str = []
__magic_name__ : Optional[Any] = []
for rt in rc.restypes:
__magic_name__ : Tuple = rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names] )
__magic_name__ : Optional[int] = {name: i for i, name in enumerate(lowerCAmelCase )}
restype_atomaa_to_atomaa_list.append(
[(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types] )
restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names] )
# Add dummy mapping for restype 'UNK'
restype_atomaa_to_atomaa_list.append([0] * 14 )
restype_atomaa_to_atomaa_list.append([0] * 37 )
restype_atomaa_mask_list.append([0.0] * 14 )
__magic_name__ : Dict = torch.tensor(
lowerCAmelCase , dtype=torch.intaa , device=protein['aatype'].device , )
__magic_name__ : Union[str, Any] = torch.tensor(
lowerCAmelCase , dtype=torch.intaa , device=protein['aatype'].device , )
__magic_name__ : Dict = torch.tensor(
lowerCAmelCase , dtype=torch.floataa , device=protein['aatype'].device , )
__magic_name__ : Optional[int] = protein['aatype'].to(torch.long )
# create the mapping for (residx, atom14) --> atom37, i.e. an array
# with shape (num_res, 14) containing the atom37 indices for this protein
__magic_name__ : Any = restype_atomaa_to_atomaa[protein_aatype]
__magic_name__ : int = restype_atomaa_mask[protein_aatype]
__magic_name__ : Optional[Any] = residx_atomaa_mask
__magic_name__ : Any = residx_atomaa_to_atomaa.long()
# create the gather indices for mapping back
__magic_name__ : Any = restype_atomaa_to_atomaa[protein_aatype]
__magic_name__ : Optional[int] = residx_atomaa_to_atomaa.long()
# create the corresponding mask
__magic_name__ : str = torch.zeros([21, 37] , dtype=torch.floataa , device=protein['aatype'].device )
for restype, restype_letter in enumerate(rc.restypes ):
__magic_name__ : Any = rc.restype_atoa[restype_letter]
__magic_name__ : Optional[Any] = rc.residue_atoms[restype_name]
for atom_name in atom_names:
__magic_name__ : List[str] = rc.atom_order[atom_name]
__magic_name__ : List[str] = 1
__magic_name__ : List[str] = restype_atomaa_mask[protein_aatype]
__magic_name__ : List[str] = residx_atomaa_mask
return protein
def lowerCamelCase ( lowerCAmelCase : Dict[str, torch.Tensor] ):
"""simple docstring"""
__magic_name__ : Tuple = tree_map(lambda lowerCAmelCase : torch.tensor(lowerCAmelCase , device=batch['aatype'].device ) , lowerCAmelCase , np.ndarray )
__magic_name__ : Optional[int] = tensor_tree_map(lambda lowerCAmelCase : np.array(lowerCAmelCase ) , make_atomaa_masks(lowerCAmelCase ) )
return out
| 352 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
from .text_encoder import MultilingualCLIP
lowerCAmelCase :str = logging.get_logger(__name__) # pylint: disable=invalid-name
lowerCAmelCase :int = '''
Examples:
```py
>>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
>>> import torch
>>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")
>>> pipe_prior.to("cuda")
>>> prompt = "red cat, 4k photo"
>>> out = pipe_prior(prompt)
>>> image_emb = out.image_embeds
>>> negative_image_emb = out.negative_image_embeds
>>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")
>>> pipe.to("cuda")
>>> image = pipe(
... prompt,
... image_embeds=image_emb,
... negative_image_embeds=negative_image_emb,
... height=768,
... width=768,
... num_inference_steps=100,
... ).images
>>> image[0].save("cat.png")
```
'''
def lowerCamelCase ( lowerCAmelCase : Optional[int] , lowerCAmelCase : Union[str, Any] , lowerCAmelCase : List[Any]=8 ):
"""simple docstring"""
__magic_name__ : List[str] = h // scale_factor**2
if h % scale_factor**2 != 0:
new_h += 1
__magic_name__ : str = w // scale_factor**2
if w % scale_factor**2 != 0:
new_w += 1
return new_h * scale_factor, new_w * scale_factor
class _lowerCamelCase ( lowercase__ ):
'''simple docstring'''
def __init__( self : Union[str, Any] , _A : MultilingualCLIP , _A : XLMRobertaTokenizer , _A : UNetaDConditionModel , _A : Union[DDIMScheduler, DDPMScheduler] , _A : VQModel , ) -> int:
super().__init__()
self.register_modules(
text_encoder=_A , tokenizer=_A , unet=_A , scheduler=_A , movq=_A , )
__magic_name__ : List[Any] = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def __lowerCAmelCase ( self : List[Any] , _A : Tuple , _A : Optional[Any] , _A : Optional[int] , _A : Dict , _A : str , _A : List[str] ) -> str:
if latents is None:
__magic_name__ : Any = randn_tensor(_A , generator=_A , device=_A , dtype=_A )
else:
if latents.shape != shape:
raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}' )
__magic_name__ : int = latents.to(_A )
__magic_name__ : Union[str, Any] = latents * scheduler.init_noise_sigma
return latents
def __lowerCAmelCase ( self : List[Any] , _A : List[str] , _A : List[str] , _A : List[str] , _A : List[Any] , _A : str=None , ) -> Dict:
__magic_name__ : Optional[Any] = len(_A ) if isinstance(_A , _A ) else 1
# get prompt text embeddings
__magic_name__ : str = self.tokenizer(
_A , padding='max_length' , truncation=_A , max_length=77 , return_attention_mask=_A , add_special_tokens=_A , return_tensors='pt' , )
__magic_name__ : Optional[Any] = text_inputs.input_ids
__magic_name__ : Optional[Any] = self.tokenizer(_A , padding='longest' , return_tensors='pt' ).input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(_A , _A ):
__magic_name__ : str = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] )
logger.warning(
'The following part of your input was truncated because CLIP can only handle sequences up to'
F' {self.tokenizer.model_max_length} tokens: {removed_text}' )
__magic_name__ : Union[str, Any] = text_input_ids.to(_A )
__magic_name__ : Dict = text_inputs.attention_mask.to(_A )
__magic_name__ , __magic_name__ : str = self.text_encoder(
input_ids=_A , attention_mask=_A )
__magic_name__ : Tuple = prompt_embeds.repeat_interleave(_A , dim=0 )
__magic_name__ : int = text_encoder_hidden_states.repeat_interleave(_A , dim=0 )
__magic_name__ : Union[str, Any] = text_mask.repeat_interleave(_A , dim=0 )
if do_classifier_free_guidance:
__magic_name__ : List[str]
if negative_prompt is None:
__magic_name__ : Optional[Any] = [''] * batch_size
elif type(_A ) is not type(_A ):
raise TypeError(
F'`negative_prompt` should be the same type to `prompt`, but got {type(_A )} !='
F' {type(_A )}.' )
elif isinstance(_A , _A ):
__magic_name__ : int = [negative_prompt]
elif batch_size != len(_A ):
raise ValueError(
F'`negative_prompt`: {negative_prompt} has batch size {len(_A )}, but `prompt`:'
F' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
' the batch size of `prompt`.' )
else:
__magic_name__ : Dict = negative_prompt
__magic_name__ : List[str] = self.tokenizer(
_A , padding='max_length' , max_length=77 , truncation=_A , return_attention_mask=_A , add_special_tokens=_A , return_tensors='pt' , )
__magic_name__ : Optional[int] = uncond_input.input_ids.to(_A )
__magic_name__ : Optional[Any] = uncond_input.attention_mask.to(_A )
__magic_name__ , __magic_name__ : int = self.text_encoder(
input_ids=_A , attention_mask=_A )
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
__magic_name__ : List[str] = negative_prompt_embeds.shape[1]
__magic_name__ : str = negative_prompt_embeds.repeat(1 , _A )
__magic_name__ : Dict = negative_prompt_embeds.view(batch_size * num_images_per_prompt , _A )
__magic_name__ : Any = uncond_text_encoder_hidden_states.shape[1]
__magic_name__ : Optional[int] = uncond_text_encoder_hidden_states.repeat(1 , _A , 1 )
__magic_name__ : Tuple = uncond_text_encoder_hidden_states.view(
batch_size * num_images_per_prompt , _A , -1 )
__magic_name__ : List[Any] = uncond_text_mask.repeat_interleave(_A , dim=0 )
# done duplicates
# 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
__magic_name__ : Tuple = torch.cat([negative_prompt_embeds, prompt_embeds] )
__magic_name__ : str = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states] )
__magic_name__ : str = torch.cat([uncond_text_mask, text_mask] )
return prompt_embeds, text_encoder_hidden_states, text_mask
def __lowerCAmelCase ( self : Dict , _A : List[Any]=0 ) -> Tuple:
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('Please install accelerate via `pip install accelerate`' )
__magic_name__ : List[Any] = torch.device(F'cuda:{gpu_id}' )
__magic_name__ : Dict = [
self.unet,
self.text_encoder,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(_A , _A )
def __lowerCAmelCase ( self : List[Any] , _A : List[str]=0 ) -> str:
if is_accelerate_available() and is_accelerate_version('>=' , '0.17.0.dev0' ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.' )
__magic_name__ : int = torch.device(F'cuda:{gpu_id}' )
if self.device.type != "cpu":
self.to('cpu' , silence_dtype_warnings=_A )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
__magic_name__ : Optional[int] = None
for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
__magic_name__ , __magic_name__ : Union[str, Any] = cpu_offload_with_hook(_A , _A , prev_module_hook=_A )
if self.safety_checker is not None:
__magic_name__ , __magic_name__ : List[str] = cpu_offload_with_hook(self.safety_checker , _A , prev_module_hook=_A )
# We'll offload the last model manually.
__magic_name__ : Any = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def __lowerCAmelCase ( self : int ) -> List[str]:
if not hasattr(self.unet , '_hf_hook' ):
return self.device
for module in self.unet.modules():
if (
hasattr(_A , '_hf_hook' )
and hasattr(module._hf_hook , 'execution_device' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(_A )
def __call__( self : int , _A : Union[str, List[str]] , _A : Union[torch.FloatTensor, List[torch.FloatTensor]] , _A : Union[torch.FloatTensor, List[torch.FloatTensor]] , _A : Optional[Union[str, List[str]]] = None , _A : int = 512 , _A : int = 512 , _A : int = 100 , _A : float = 4.0 , _A : int = 1 , _A : Optional[Union[torch.Generator, List[torch.Generator]]] = None , _A : Optional[torch.FloatTensor] = None , _A : Optional[str] = "pil" , _A : bool = True , ) -> Optional[int]:
if isinstance(_A , _A ):
__magic_name__ : Optional[int] = 1
elif isinstance(_A , _A ):
__magic_name__ : Union[str, Any] = len(_A )
else:
raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(_A )}' )
__magic_name__ : Tuple = self._execution_device
__magic_name__ : Any = batch_size * num_images_per_prompt
__magic_name__ : int = guidance_scale > 1.0
__magic_name__ , __magic_name__ , __magic_name__ : Optional[int] = self._encode_prompt(
_A , _A , _A , _A , _A )
if isinstance(_A , _A ):
__magic_name__ : Union[str, Any] = torch.cat(_A , dim=0 )
if isinstance(_A , _A ):
__magic_name__ : Dict = torch.cat(_A , dim=0 )
if do_classifier_free_guidance:
__magic_name__ : Dict = image_embeds.repeat_interleave(_A , dim=0 )
__magic_name__ : Optional[int] = negative_image_embeds.repeat_interleave(_A , dim=0 )
__magic_name__ : Optional[int] = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(
dtype=prompt_embeds.dtype , device=_A )
self.scheduler.set_timesteps(_A , device=_A )
__magic_name__ : Tuple = self.scheduler.timesteps
__magic_name__ : Optional[int] = self.unet.config.in_channels
__magic_name__ , __magic_name__ : Dict = get_new_h_w(_A , _A , self.movq_scale_factor )
# create initial latent
__magic_name__ : Union[str, Any] = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , text_encoder_hidden_states.dtype , _A , _A , _A , self.scheduler , )
for i, t in enumerate(self.progress_bar(_A ) ):
# expand the latents if we are doing classifier free guidance
__magic_name__ : Dict = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
__magic_name__ : Tuple = {'text_embeds': prompt_embeds, 'image_embeds': image_embeds}
__magic_name__ : Union[str, Any] = self.unet(
sample=_A , timestep=_A , encoder_hidden_states=_A , added_cond_kwargs=_A , return_dict=_A , )[0]
if do_classifier_free_guidance:
__magic_name__ , __magic_name__ : Optional[Any] = noise_pred.split(latents.shape[1] , dim=1 )
__magic_name__ , __magic_name__ : Dict = noise_pred.chunk(2 )
__magic_name__ , __magic_name__ : List[str] = variance_pred.chunk(2 )
__magic_name__ : List[Any] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
__magic_name__ : Any = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , 'variance_type' )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
__magic_name__ , __magic_name__ : Optional[Any] = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
__magic_name__ : List[Any] = self.scheduler.step(
_A , _A , _A , generator=_A , ).prev_sample
# post-processing
__magic_name__ : int = self.movq.decode(_A , force_not_quantize=_A )['sample']
if output_type not in ["pt", "np", "pil"]:
raise ValueError(F'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}' )
if output_type in ["np", "pil"]:
__magic_name__ : Dict = image * 0.5 + 0.5
__magic_name__ : str = image.clamp(0 , 1 )
__magic_name__ : List[Any] = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
__magic_name__ : str = self.numpy_to_pil(_A )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=_A ) | 275 | 0 |
'''simple docstring'''
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
a__ : List[str] ='''\
@misc{chen2021evaluating,
title={Evaluating Large Language Models Trained on Code},
author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \
and Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \
and Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \
and Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \
and Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \
and Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \
and Mohammad Bavarian and Clemens Winter and Philippe Tillet \
and Felipe Petroski Such and Dave Cummings and Matthias Plappert \
and Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \
and William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \
and Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \
and William Saunders and Christopher Hesse and Andrew N. Carr \
and Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \
and Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \
and Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \
and Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},
year={2021},
eprint={2107.03374},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
'''
a__ : Optional[Any] ='''\
This metric implements the evaluation harness for the HumanEval problem solving dataset
described in the paper "Evaluating Large Language Models Trained on Code"
(https://arxiv.org/abs/2107.03374).
'''
a__ : Optional[int] ='''
Calculates how good are predictions given some references, using certain scores
Args:
predictions: list of candidates to evaluate. Each candidates should be a list
of strings with several code candidates to solve the problem.
references: a list with a test for each prediction. Each test should evaluate the
correctness of a code candidate.
k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])
num_workers: number of workers used to evaluate the canidate programs (Default: 4).
timeout:
Returns:
pass_at_k: dict with pass rates for each k
results: dict with granular results of each unittest
Examples:
>>> code_eval = datasets.load_metric("code_eval")
>>> test_cases = ["assert add(2,3)==5"]
>>> candidates = [["def add(a,b): return a*b", "def add(a, b): return a+b"]]
>>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])
>>> print(pass_at_k)
{\'pass@1\': 0.5, \'pass@2\': 1.0}
'''
a__ : Dict ='''
################################################################################
!!!WARNING!!!
################################################################################
The "code_eval" metric executes untrusted model-generated code in Python.
Although it is highly unlikely that model-generated code will do something
overtly malicious in response to this test suite, model-generated code may act
destructively due to a lack of model capability or alignment.
Users are strongly encouraged to sandbox this evaluation suite so that it
does not perform destructive actions on their host or network. For more
information on how OpenAI sandboxes its code, see the paper "Evaluating Large
Language Models Trained on Code" (https://arxiv.org/abs/2107.03374).
Once you have read this disclaimer and taken appropriate precautions,
set the environment variable HF_ALLOW_CODE_EVAL="1". Within Python you can to this
with:
>>> import os
>>> os.environ["HF_ALLOW_CODE_EVAL"] = "1"
################################################################################\
'''
a__ : Optional[Any] ='''The MIT License
Copyright (c) OpenAI (https://openai.com)
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class snake_case ( datasets.Metric ):
"""simple docstring"""
def _lowerCamelCase ( self : Union[str, Any] ):
return datasets.MetricInfo(
# This is the description that will appear on the metrics page.
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Sequence(datasets.Value('string' ) ),
'references': datasets.Value('string' ),
} ) , homepage='https://github.com/openai/human-eval' , codebase_urls=['https://github.com/openai/human-eval'] , reference_urls=['https://github.com/openai/human-eval'] , license=_LICENSE , )
def _lowerCamelCase ( self : Union[str, Any] , __A : Dict , __A : Tuple , __A : List[str]=[1, 1_0, 1_0_0] , __A : Dict=4 , __A : List[str]=3.0 ):
if os.getenv('HF_ALLOW_CODE_EVAL' , 0 ) != "1":
raise ValueError(_WARNING )
if os.name == "nt":
raise NotImplementedError('This metric is currently not supported on Windows.' )
with ThreadPoolExecutor(max_workers=__A ) as executor:
__UpperCamelCase = []
__UpperCamelCase = Counter()
__UpperCamelCase = 0
__UpperCamelCase = defaultdict(__A )
for task_id, (candidates, test_case) in enumerate(zip(__A , __A ) ):
for candidate in candidates:
__UpperCamelCase = candidate + '\n' + test_case
__UpperCamelCase = (test_program, timeout, task_id, completion_id[task_id])
__UpperCamelCase = executor.submit(__A , *__A )
futures.append(__A )
completion_id[task_id] += 1
n_samples += 1
for future in as_completed(__A ):
__UpperCamelCase = future.result()
results[result["task_id"]].append((result['completion_id'], result) )
__UpperCamelCase , __UpperCamelCase = [], []
for result in results.values():
result.sort()
__UpperCamelCase = [r[1]['passed'] for r in result]
total.append(len(__A ) )
correct.append(sum(__A ) )
__UpperCamelCase = np.array(__A )
__UpperCamelCase = np.array(__A )
__UpperCamelCase = k
__UpperCamelCase = {f'''pass@{k}''': estimate_pass_at_k(__A , __A , __A ).mean() for k in ks if (total >= k).all()}
return pass_at_k, results
def lowercase__ ( __lowercase : Optional[Any] , __lowercase : int , __lowercase : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
def estimator(__lowercase : int , __lowercase : int , __lowercase : int ) -> float:
if n - c < k:
return 1.0
return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1 , n + 1 ) )
if isinstance(__lowercase , __lowercase ):
__UpperCamelCase = itertools.repeat(__lowercase , len(__lowercase ) )
else:
assert len(__lowercase ) == len(__lowercase )
__UpperCamelCase = iter(__lowercase )
return np.array([estimator(int(__lowercase ) , int(__lowercase ) , __lowercase ) for n, c in zip(__lowercase , __lowercase )] )
| 53 |
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class _UpperCAmelCase ( A__ ):
"""simple docstring"""
def __init__( self : Dict, *lowerCamelCase : Union[str, Any], lowerCamelCase : Union[str, Any]=None, lowerCamelCase : Any=None, **lowerCamelCase : str ):
'''simple docstring'''
super().__init__(*lowerCamelCase, **lowerCamelCase )
lowercase__ = eval_examples
lowercase__ = post_process_function
def lowercase__ ( self : int, lowerCamelCase : str=None, lowerCamelCase : Optional[Any]=None, lowerCamelCase : Union[str, Any]=None, lowerCamelCase : str = "eval" ):
'''simple docstring'''
lowercase__ = self.eval_dataset if eval_dataset is None else eval_dataset
lowercase__ = self.get_eval_dataloader(lowerCamelCase )
lowercase__ = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
lowercase__ = self.compute_metrics
lowercase__ = None
lowercase__ = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
lowercase__ = time.time()
try:
lowercase__ = eval_loop(
lowerCamelCase, description='''Evaluation''', prediction_loss_only=True if compute_metrics is None else None, ignore_keys=lowerCamelCase, metric_key_prefix=lowerCamelCase, )
finally:
lowercase__ = compute_metrics
lowercase__ = self.args.eval_batch_size * self.args.world_size
if F"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
start_time += output.metrics[F"""{metric_key_prefix}_jit_compilation_time"""]
output.metrics.update(
speed_metrics(
lowerCamelCase, lowerCamelCase, num_samples=output.num_samples, num_steps=math.ceil(output.num_samples / total_batch_size ), ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
lowercase__ = self.post_process_function(lowerCamelCase, lowerCamelCase, output.predictions )
lowercase__ = self.compute_metrics(lowerCamelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F"""{metric_key_prefix}_""" ):
lowercase__ = metrics.pop(lowerCamelCase )
metrics.update(output.metrics )
else:
lowercase__ = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(lowerCamelCase )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
lowercase__ = self.callback_handler.on_evaluate(self.args, self.state, self.control, lowerCamelCase )
return metrics
def lowercase__ ( self : List[Any], lowerCamelCase : Any, lowerCamelCase : Dict, lowerCamelCase : int=None, lowerCamelCase : str = "test" ):
'''simple docstring'''
lowercase__ = self.get_test_dataloader(lowerCamelCase )
# Temporarily disable metric computation, we will do it in the loop here.
lowercase__ = self.compute_metrics
lowercase__ = None
lowercase__ = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
lowercase__ = time.time()
try:
lowercase__ = eval_loop(
lowerCamelCase, description='''Prediction''', prediction_loss_only=True if compute_metrics is None else None, ignore_keys=lowerCamelCase, metric_key_prefix=lowerCamelCase, )
finally:
lowercase__ = compute_metrics
lowercase__ = self.args.eval_batch_size * self.args.world_size
if F"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
start_time += output.metrics[F"""{metric_key_prefix}_jit_compilation_time"""]
output.metrics.update(
speed_metrics(
lowerCamelCase, lowerCamelCase, num_samples=output.num_samples, num_steps=math.ceil(output.num_samples / total_batch_size ), ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
lowercase__ = self.post_process_function(lowerCamelCase, lowerCamelCase, output.predictions, '''predict''' )
lowercase__ = self.compute_metrics(lowerCamelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F"""{metric_key_prefix}_""" ):
lowercase__ = metrics.pop(lowerCamelCase )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions, label_ids=predictions.label_ids, metrics=lowerCamelCase )
| 207 | 0 |
def lowerCamelCase__ (__lowerCamelCase ):
_SCREAMING_SNAKE_CASE : Optional[Any] = [0] * len(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Any = []
_SCREAMING_SNAKE_CASE : List[Any] = [1] * len(__lowerCamelCase )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__lowerCamelCase ) ):
if indegree[i] == 0:
queue.append(__lowerCamelCase )
while queue:
_SCREAMING_SNAKE_CASE : Any = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
_SCREAMING_SNAKE_CASE : Union[str, Any] = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(__lowerCamelCase )
print(max(__lowerCamelCase ) )
# Adjacency list of Graph
UpperCamelCase__ ={0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph) | 366 |
from maths.prime_check import is_prime
def lowerCamelCase__ (__lowerCamelCase ):
if not isinstance(__lowerCamelCase, __lowerCamelCase ):
_SCREAMING_SNAKE_CASE : List[str] = f"""Input value of [number={number}] must be an integer"""
raise TypeError(__lowerCamelCase )
if is_prime(__lowerCamelCase ) and is_prime(number + 2 ):
return number + 2
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod() | 325 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __lowerCAmelCase ( lowerCAmelCase , unittest.TestCase):
_a = DanceDiffusionPipeline
_a = UNCONDITIONAL_AUDIO_GENERATION_PARAMS
_a = PipelineTesterMixin.required_optional_params - {
'''callback''',
'''latents''',
'''callback_steps''',
'''output_type''',
'''num_images_per_prompt''',
}
_a = UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
_a = False
_a = False
def SCREAMING_SNAKE_CASE ( self: Optional[int] ):
torch.manual_seed(0 )
lowercase :str = UNetaDModel(
block_out_channels=(32, 32, 64) , extra_in_channels=16 , sample_size=5_12 , sample_rate=1_60_00 , in_channels=2 , out_channels=2 , flip_sin_to_cos=_lowerCAmelCase , use_timestep_embedding=_lowerCAmelCase , time_embedding_type="fourier" , mid_block_type="UNetMidBlock1D" , down_block_types=("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") , up_block_types=("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") , )
lowercase :List[Any] = IPNDMScheduler()
lowercase :Optional[int] = {
"unet": unet,
"scheduler": scheduler,
}
return components
def SCREAMING_SNAKE_CASE ( self: List[Any] , _lowerCAmelCase: Union[str, Any] , _lowerCAmelCase: str=0 ):
if str(_lowerCAmelCase ).startswith("mps" ):
lowercase :Optional[Any] = torch.manual_seed(_lowerCAmelCase )
else:
lowercase :Union[str, Any] = torch.Generator(device=_lowerCAmelCase ).manual_seed(_lowerCAmelCase )
lowercase :Any = {
"batch_size": 1,
"generator": generator,
"num_inference_steps": 4,
}
return inputs
def SCREAMING_SNAKE_CASE ( self: str ):
lowercase :List[str] = "cpu" # ensure determinism for the device-dependent torch.Generator
lowercase :List[Any] = self.get_dummy_components()
lowercase :int = DanceDiffusionPipeline(**_lowerCAmelCase )
lowercase :Tuple = pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowercase :Dict = self.get_dummy_inputs(_lowerCAmelCase )
lowercase :Dict = pipe(**_lowerCAmelCase )
lowercase :Optional[int] = output.audios
lowercase :Tuple = audio[0, -3:, -3:]
assert audio.shape == (1, 2, components["unet"].sample_size)
lowercase :Optional[int] = np.array([-0.72_65, 1.00_00, -0.83_88, 0.11_75, 0.94_98, -1.00_00] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def SCREAMING_SNAKE_CASE ( self: Any ):
return super().test_save_load_local()
@skip_mps
def SCREAMING_SNAKE_CASE ( self: Tuple ):
return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )
@skip_mps
def SCREAMING_SNAKE_CASE ( self: List[str] ):
return super().test_save_load_optional_components()
@skip_mps
def SCREAMING_SNAKE_CASE ( self: Any ):
return super().test_attention_slicing_forward_pass()
def SCREAMING_SNAKE_CASE ( self: str ):
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase):
def SCREAMING_SNAKE_CASE ( self: Optional[Any] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE ( self: List[Any] ):
lowercase :List[Any] = torch_device
lowercase :List[Any] = DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k" )
lowercase :Tuple = pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowercase :List[Any] = torch.manual_seed(0 )
lowercase :str = pipe(generator=_lowerCAmelCase , num_inference_steps=1_00 , audio_length_in_s=4.0_96 )
lowercase :Union[str, Any] = output.audios
lowercase :Optional[Any] = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
lowercase :List[str] = np.array([-0.01_92, -0.02_31, -0.03_18, -0.00_59, 0.00_02, -0.00_20] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1e-2
def SCREAMING_SNAKE_CASE ( self: int ):
lowercase :Optional[Any] = torch_device
lowercase :Any = DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k" , torch_dtype=torch.floataa )
lowercase :Dict = pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowercase :int = torch.manual_seed(0 )
lowercase :Dict = pipe(generator=_lowerCAmelCase , num_inference_steps=1_00 , audio_length_in_s=4.0_96 )
lowercase :Dict = output.audios
lowercase :Optional[int] = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
lowercase :Union[str, Any] = np.array([-0.03_67, -0.04_88, -0.07_71, -0.05_25, -0.04_44, -0.03_41] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1e-2
| 236 |
import os
from math import logaa
def UpperCAmelCase__ ( lowerCamelCase = "base_exp.txt" ):
lowercase :float = 0
lowercase :str = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(lowerCamelCase ), lowerCamelCase ) ) ):
lowercase , lowercase :str = list(map(lowerCamelCase, line.split("," ) ) )
if x * logaa(lowerCamelCase ) > largest:
lowercase :Optional[Any] = x * logaa(lowerCamelCase )
lowercase :Any = i + 1
return result
if __name__ == "__main__":
print(solution())
| 236 | 1 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import UperNetForSemanticSegmentation
from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class A_ :
'''simple docstring'''
def __init__( self: int , a: str , a: List[Any]=13 , a: Optional[int]=32 , a: Any=3 , a: int=4 , a: Optional[int]=[10, 20, 30, 40] , a: List[Any]=[2, 2, 3, 2] , a: List[Any]=True , a: Optional[Any]=True , a: int=37 , a: Union[str, Any]="gelu" , a: Tuple=10 , a: int=0.0_2 , a: Any=["stage2", "stage3", "stage4"] , a: Optional[Any]=3 , a: Tuple=None , ):
__lowerCamelCase : str = parent
__lowerCamelCase : Optional[int] = batch_size
__lowerCamelCase : List[str] = image_size
__lowerCamelCase : Dict = num_channels
__lowerCamelCase : Optional[Any] = num_stages
__lowerCamelCase : Optional[Any] = hidden_sizes
__lowerCamelCase : List[Any] = depths
__lowerCamelCase : List[Any] = is_training
__lowerCamelCase : int = use_labels
__lowerCamelCase : Optional[Any] = intermediate_size
__lowerCamelCase : str = hidden_act
__lowerCamelCase : List[Any] = type_sequence_label_size
__lowerCamelCase : Dict = initializer_range
__lowerCamelCase : Optional[Any] = out_features
__lowerCamelCase : Any = num_labels
__lowerCamelCase : Union[str, Any] = scope
__lowerCamelCase : Optional[int] = num_stages
def _snake_case ( self: str ):
__lowerCamelCase : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowerCamelCase : List[str] = None
if self.use_labels:
__lowerCamelCase : List[str] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCamelCase : List[Any] = self.get_config()
return config, pixel_values, labels
def _snake_case ( self: str ):
return ConvNextConfig(
num_channels=self.num_channels , num_stages=self.num_stages , hidden_sizes=self.hidden_sizes , depths=self.depths , is_training=self.is_training , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , out_features=self.out_features , )
def _snake_case ( self: List[Any] ):
return UperNetConfig(
backbone_config=self.get_backbone_config() , hidden_size=512 , pool_scales=[1, 2, 3, 6] , use_auxiliary_head=lowercase__ , auxiliary_loss_weight=0.4 , auxiliary_in_channels=40 , auxiliary_channels=256 , auxiliary_num_convs=1 , auxiliary_concat_input=lowercase__ , loss_ignore_index=255 , num_labels=self.num_labels , )
def _snake_case ( self: Any , a: List[str] , a: Any , a: Any ):
__lowerCamelCase : Tuple = UperNetForSemanticSegmentation(config=lowercase__ )
model.to(lowercase__ )
model.eval()
__lowerCamelCase : str = model(lowercase__ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size) )
def _snake_case ( self: Union[str, Any] ):
__lowerCamelCase : str = self.prepare_config_and_inputs()
(
(
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) ,
) : Optional[Any] = config_and_inputs
__lowerCamelCase : Union[str, Any] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class A_ ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
'''simple docstring'''
__snake_case = (UperNetForSemanticSegmentation,) if is_torch_available() else ()
__snake_case = {'image-segmentation': UperNetForSemanticSegmentation} if is_torch_available() else {}
__snake_case = False
__snake_case = False
__snake_case = False
__snake_case = False
__snake_case = False
__snake_case = False
def _snake_case ( self: Union[str, Any] ):
__lowerCamelCase : List[str] = UperNetModelTester(self )
__lowerCamelCase : List[Any] = ConfigTester(self , config_class=lowercase__ , has_text_modality=lowercase__ , hidden_size=37 )
def _snake_case ( self: Union[str, Any] ):
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 _snake_case ( self: Optional[int] ):
return
def _snake_case ( self: Optional[Any] ):
__lowerCamelCase , __lowerCamelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase : int = model_class(lowercase__ )
__lowerCamelCase : str = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCamelCase : List[Any] = [*signature.parameters.keys()]
__lowerCamelCase : str = ['pixel_values']
self.assertListEqual(arg_names[:1] , lowercase__ )
def _snake_case ( self: Optional[Any] ):
__lowerCamelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*lowercase__ )
@unittest.skip(reason='UperNet does not use inputs_embeds' )
def _snake_case ( self: int ):
pass
@unittest.skip(reason='UperNet does not support input and output embeddings' )
def _snake_case ( self: str ):
pass
@unittest.skip(reason='UperNet does not have a base model' )
def _snake_case ( self: Optional[Any] ):
pass
@unittest.skip(reason='UperNet does not have a base model' )
def _snake_case ( self: Union[str, Any] ):
pass
@require_torch_multi_gpu
@unittest.skip(reason='UperNet has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`' )
def _snake_case ( self: Any ):
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def _snake_case ( self: List[Any] ):
pass
def _snake_case ( self: Any ):
def check_hidden_states_output(a: List[str] , a: int , a: List[str] ):
__lowerCamelCase : Tuple = model_class(lowercase__ )
model.to(lowercase__ )
model.eval()
with torch.no_grad():
__lowerCamelCase : Dict = model(**self._prepare_for_class(lowercase__ , lowercase__ ) )
__lowerCamelCase : Optional[int] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__lowerCamelCase : Any = self.model_tester.num_stages
self.assertEqual(len(lowercase__ ) , expected_num_stages + 1 )
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
__lowerCamelCase , __lowerCamelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase : Optional[Any] = True
check_hidden_states_output(lowercase__ , lowercase__ , lowercase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowerCamelCase : Union[str, Any] = True
check_hidden_states_output(lowercase__ , lowercase__ , lowercase__ )
def _snake_case ( self: Dict ):
__lowerCamelCase , __lowerCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCamelCase : Optional[int] = _config_zero_init(lowercase__ )
__lowerCamelCase : Tuple = _config_zero_init(configs_no_init.backbone_config )
for model_class in self.all_model_classes:
__lowerCamelCase : Tuple = model_class(config=lowercase__ )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F'Parameter {name} of model {model_class} seems not properly initialized' , )
@unittest.skip(reason='UperNet does not have tied weights' )
def _snake_case ( self: Optional[Any] ):
pass
@slow
def _snake_case ( self: Optional[Any] ):
for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCamelCase : int = UperNetForSemanticSegmentation.from_pretrained(lowercase__ )
self.assertIsNotNone(lowercase__ )
def UpperCamelCase__ ( ):
__lowerCamelCase : List[str] = hf_hub_download(
repo_id='hf-internal-testing/fixtures_ade20k' , repo_type='dataset' , filename='ADE_val_00000001.jpg' )
__lowerCamelCase : str = Image.open(A__ ).convert('RGB' )
return image
@require_torch
@require_vision
@slow
class A_ ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self: Optional[Any] ):
__lowerCamelCase : List[Any] = AutoImageProcessor.from_pretrained('openmmlab/upernet-swin-tiny' )
__lowerCamelCase : Any = UperNetForSemanticSegmentation.from_pretrained('openmmlab/upernet-swin-tiny' ).to(lowercase__ )
__lowerCamelCase : Union[str, Any] = prepare_img()
__lowerCamelCase : Any = processor(images=lowercase__ , return_tensors='pt' ).to(lowercase__ )
with torch.no_grad():
__lowerCamelCase : Tuple = model(**lowercase__ )
__lowerCamelCase : Union[str, Any] = torch.Size((1, model.config.num_labels, 512, 512) )
self.assertEqual(outputs.logits.shape , lowercase__ )
__lowerCamelCase : Tuple = torch.tensor(
[[-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.4_7_9_7, -7.4_7_9_7, -7.3_0_6_8]] ).to(lowercase__ )
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , lowercase__ , atol=1e-4 ) )
def _snake_case ( self: Tuple ):
__lowerCamelCase : Optional[int] = AutoImageProcessor.from_pretrained('openmmlab/upernet-convnext-tiny' )
__lowerCamelCase : Optional[Any] = UperNetForSemanticSegmentation.from_pretrained('openmmlab/upernet-convnext-tiny' ).to(lowercase__ )
__lowerCamelCase : Tuple = prepare_img()
__lowerCamelCase : Tuple = processor(images=lowercase__ , return_tensors='pt' ).to(lowercase__ )
with torch.no_grad():
__lowerCamelCase : int = model(**lowercase__ )
__lowerCamelCase : Tuple = torch.Size((1, model.config.num_labels, 512, 512) )
self.assertEqual(outputs.logits.shape , lowercase__ )
__lowerCamelCase : List[Any] = torch.tensor(
[[-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.7_7_4_6, -8.7_7_4_6, -8.6_1_3_0]] ).to(lowercase__ )
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , lowercase__ , atol=1e-4 ) )
| 358 |
from __future__ import annotations
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase : Optional[Any] = 0.00
__lowerCamelCase : Tuple = 0
for resistor in resistors:
if resistor <= 0:
__lowerCamelCase : Union[str, Any] = f'Resistor at index {index} has a negative or zero value!'
raise ValueError(SCREAMING_SNAKE_CASE__ )
first_sum += 1 / float(SCREAMING_SNAKE_CASE__ )
index += 1
return 1 / first_sum
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase : Union[str, Any] = 0.00
__lowerCamelCase : str = 0
for resistor in resistors:
sum_r += resistor
if resistor < 0:
__lowerCamelCase : Any = f'Resistor at index {index} has a negative value!'
raise ValueError(SCREAMING_SNAKE_CASE__ )
index += 1
return sum_r
if __name__ == "__main__":
import doctest
doctest.testmod()
| 194 | 0 |
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
SCREAMING_SNAKE_CASE__ = {
"vocab_file": {
"allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json"
},
"merges_file": {
"allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt"
},
}
SCREAMING_SNAKE_CASE__ = {"allegro/herbert-base-cased": 514}
SCREAMING_SNAKE_CASE__ = {}
class lowercase ( _UpperCAmelCase ):
_SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE = PRETRAINED_INIT_CONFIGURATION
_SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE = HerbertTokenizer
def __init__( self , lowercase=None , lowercase=None , lowercase=None , lowercase="<s>" , lowercase="<unk>" , lowercase="<pad>" , lowercase="<mask>" , lowercase="</s>" , **lowercase , ) -> List[Any]:
super().__init__(
lowercase , lowercase , tokenizer_file=lowercase , cls_token=lowercase , unk_token=lowercase , pad_token=lowercase , mask_token=lowercase , sep_token=lowercase , **lowercase , )
def _snake_case ( self , lowercase , lowercase = None ) -> List[int]:
lowerCAmelCase = [self.cls_token_id]
lowerCAmelCase = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def _snake_case ( self , lowercase , lowercase = None , lowercase = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowercase , token_ids_a=lowercase , already_has_special_tokens=lowercase )
if token_ids_a is None:
return [1] + ([0] * len(lowercase )) + [1]
return [1] + ([0] * len(lowercase )) + [1] + ([0] * len(lowercase )) + [1]
def _snake_case ( self , lowercase , lowercase = None ) -> List[int]:
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 _snake_case ( self , lowercase , lowercase = None ) -> Tuple[str]:
lowerCAmelCase = self._tokenizer.model.save(lowercase , name=lowercase )
return tuple(lowercase )
| 46 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> set:
lowerCamelCase__ : Optional[Any] = set()
# edges = list of graph's edges
lowerCamelCase__ : List[str] = get_edges(_UpperCAmelCase )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extremity to chosen_vertices and then
# remove all arcs adjacent to the from_node and to_node
while edges:
lowerCamelCase__ , lowerCamelCase__ : str = edges.pop()
chosen_vertices.add(_UpperCAmelCase )
chosen_vertices.add(_UpperCAmelCase )
for edge in edges.copy():
if from_node in edge or to_node in edge:
edges.discard(_UpperCAmelCase )
return chosen_vertices
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> set:
lowerCamelCase__ : Union[str, Any] = set()
for from_node, to_nodes in graph.items():
for to_node in to_nodes:
edges.add((from_node, to_node) )
return edges
if __name__ == "__main__":
import doctest
doctest.testmod()
# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")
| 50 | 0 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
a : Optional[Any] = """pt"""
elif is_tf_available():
a : Union[str, Any] = """tf"""
else:
a : Any = """jax"""
class __UpperCAmelCase( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = PerceiverTokenizer
__lowerCamelCase = False
def UpperCAmelCase_ ( self ):
'''simple docstring'''
super().setUp()
lowercase__ : str= PerceiverTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def UpperCAmelCase_ ( self ):
'''simple docstring'''
return PerceiverTokenizer.from_pretrained("deepmind/language-perceiver" )
def UpperCAmelCase_ ( self , **snake_case__ ):
'''simple docstring'''
return self.tokenizer_class.from_pretrained(self.tmpdirname , **snake_case__ )
def UpperCAmelCase_ ( self , snake_case__ , snake_case__=False , snake_case__=20 , snake_case__=5 ):
'''simple docstring'''
lowercase__ : Union[str, Any]= []
for i in range(len(snake_case__ ) ):
try:
lowercase__ : Any= tokenizer.decode([i] , clean_up_tokenization_spaces=snake_case__ )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
lowercase__ : int= list(filter(lambda snake_case__ : re.match(r"^[ a-zA-Z]+$" , t[1] ) , snake_case__ ) )
lowercase__ : Union[str, Any]= list(filter(lambda snake_case__ : [t[0]] == tokenizer.encode(t[1] , add_special_tokens=snake_case__ ) , snake_case__ ) )
if max_length is not None and len(snake_case__ ) > max_length:
lowercase__ : int= toks[:max_length]
if min_length is not None and len(snake_case__ ) < min_length and len(snake_case__ ) > 0:
while len(snake_case__ ) < min_length:
lowercase__ : List[str]= toks + toks
# toks_str = [t[1] for t in toks]
lowercase__ : str= [t[0] for t in toks]
# Ensure consistency
lowercase__ : Optional[Any]= tokenizer.decode(snake_case__ , clean_up_tokenization_spaces=snake_case__ )
if " " not in output_txt and len(snake_case__ ) > 1:
lowercase__ : Dict= (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=snake_case__ )
+ " "
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=snake_case__ )
)
if with_prefix_space:
lowercase__ : List[Any]= " " + output_txt
lowercase__ : Any= tokenizer.encode(snake_case__ , add_special_tokens=snake_case__ )
return output_txt, output_ids
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Optional[Any]= self.perceiver_tokenizer
lowercase__ : Union[str, Any]= "Unicode €."
lowercase__ : Tuple= tokenizer(snake_case__ )
lowercase__ : Dict= [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
self.assertEqual(encoded["input_ids"] , snake_case__ )
# decoding
lowercase__ : List[str]= tokenizer.decode(snake_case__ )
self.assertEqual(snake_case__ , "[CLS]Unicode €.[SEP]" )
lowercase__ : List[str]= tokenizer("e è é ê ë" )
lowercase__ : int= [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
self.assertEqual(encoded["input_ids"] , snake_case__ )
# decoding
lowercase__ : Any= tokenizer.decode(snake_case__ )
self.assertEqual(snake_case__ , "[CLS]e è é ê ë[SEP]" )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode("e è é ê ë" ) ) , "[CLS]e è é ê ë[SEP]" )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : str= self.perceiver_tokenizer
lowercase__ : Tuple= ["A long paragraph for summarization.", "Another paragraph for summarization."]
# fmt: off
lowercase__ : List[Any]= [4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
# fmt: on
lowercase__ : Dict= tokenizer(snake_case__ , padding=snake_case__ , return_tensors=snake_case__ )
self.assertIsInstance(snake_case__ , snake_case__ )
if FRAMEWORK != "jax":
lowercase__ : List[Any]= list(batch.input_ids.numpy()[0] )
else:
lowercase__ : str= list(batch.input_ids.tolist()[0] )
self.assertListEqual(snake_case__ , snake_case__ )
self.assertEqual((2, 38) , batch.input_ids.shape )
self.assertEqual((2, 38) , batch.attention_mask.shape )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Optional[Any]= self.perceiver_tokenizer
lowercase__ : int= ["A long paragraph for summarization.", "Another paragraph for summarization."]
lowercase__ : Union[str, Any]= tokenizer(snake_case__ , padding=snake_case__ , return_tensors=snake_case__ )
# check if input_ids are returned and no decoder_input_ids
self.assertIn("input_ids" , snake_case__ )
self.assertIn("attention_mask" , snake_case__ )
self.assertNotIn("decoder_input_ids" , snake_case__ )
self.assertNotIn("decoder_attention_mask" , snake_case__ )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Tuple= self.perceiver_tokenizer
lowercase__ : int= [
"Summary of the text.",
"Another summary.",
]
lowercase__ : List[Any]= tokenizer(
text_target=snake_case__ , max_length=32 , padding="max_length" , truncation=snake_case__ , return_tensors=snake_case__ )
self.assertEqual(32 , targets["input_ids"].shape[1] )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Union[str, Any]= self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
self.assertNotEqual(tokenizer.model_max_length , 42 )
# Now let's start the test
lowercase__ : List[str]= self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
# Isolate this from the other tests because we save additional tokens/etc
lowercase__ : str= tempfile.mkdtemp()
lowercase__ : str= " He is very happy, UNwant\u00E9d,running"
lowercase__ : int= tokenizer.encode(snake_case__ , add_special_tokens=snake_case__ )
tokenizer.save_pretrained(snake_case__ )
lowercase__ : List[str]= tokenizer.__class__.from_pretrained(snake_case__ )
lowercase__ : Tuple= after_tokenizer.encode(snake_case__ , add_special_tokens=snake_case__ )
self.assertListEqual(snake_case__ , snake_case__ )
shutil.rmtree(snake_case__ )
lowercase__ : List[str]= self.get_tokenizers(model_max_length=42 )
for tokenizer in tokenizers:
with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
# Isolate this from the other tests because we save additional tokens/etc
lowercase__ : Tuple= tempfile.mkdtemp()
lowercase__ : List[Any]= " He is very happy, UNwant\u00E9d,running"
tokenizer.add_tokens(["bim", "bambam"] )
lowercase__ : Tuple= tokenizer.additional_special_tokens
additional_special_tokens.append("new_additional_special_token" )
tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens} )
lowercase__ : List[str]= tokenizer.encode(snake_case__ , add_special_tokens=snake_case__ )
tokenizer.save_pretrained(snake_case__ )
lowercase__ : Tuple= tokenizer.__class__.from_pretrained(snake_case__ )
lowercase__ : Tuple= after_tokenizer.encode(snake_case__ , add_special_tokens=snake_case__ )
self.assertListEqual(snake_case__ , snake_case__ )
self.assertIn("new_additional_special_token" , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 42 )
lowercase__ : Tuple= tokenizer.__class__.from_pretrained(snake_case__ , model_max_length=43 )
self.assertEqual(tokenizer.model_max_length , 43 )
shutil.rmtree(snake_case__ )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Union[str, Any]= []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(snake_case__ )
with open(os.path.join(snake_case__ , "special_tokens_map.json" ) , encoding="utf-8" ) as json_file:
lowercase__ : Optional[Any]= json.load(snake_case__ )
with open(os.path.join(snake_case__ , "tokenizer_config.json" ) , encoding="utf-8" ) as json_file:
lowercase__ : Optional[Any]= json.load(snake_case__ )
lowercase__ : List[str]= [F'''<extra_id_{i}>''' for i in range(125 )]
lowercase__ : Optional[int]= added_tokens_extra_ids + [
"an_additional_special_token"
]
lowercase__ : Union[str, Any]= added_tokens_extra_ids + [
"an_additional_special_token"
]
with open(os.path.join(snake_case__ , "special_tokens_map.json" ) , "w" , encoding="utf-8" ) as outfile:
json.dump(snake_case__ , snake_case__ )
with open(os.path.join(snake_case__ , "tokenizer_config.json" ) , "w" , encoding="utf-8" ) as outfile:
json.dump(snake_case__ , snake_case__ )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
lowercase__ : Optional[int]= tokenizer_class.from_pretrained(
snake_case__ , )
self.assertIn(
"an_additional_special_token" , tokenizer_without_change_in_init.additional_special_tokens )
self.assertEqual(
["an_additional_special_token"] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(["an_additional_special_token"] ) ) , )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
lowercase__ : int= added_tokens_extra_ids + [AddedToken("a_new_additional_special_token" , lstrip=snake_case__ )]
lowercase__ : int= tokenizer_class.from_pretrained(
snake_case__ , additional_special_tokens=snake_case__ , )
self.assertIn("a_new_additional_special_token" , tokenizer.additional_special_tokens )
self.assertEqual(
["a_new_additional_special_token"] , tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"] ) ) , )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Union[str, Any]= self.perceiver_tokenizer
self.assertEqual(tokenizer.decode([178] ) , "�" )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
pass
def UpperCAmelCase_ ( self ):
'''simple docstring'''
pass
def UpperCAmelCase_ ( self ):
'''simple docstring'''
pass
def UpperCAmelCase_ ( self ):
'''simple docstring'''
pass
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Optional[int]= self.get_tokenizers(fast=snake_case__ , do_lower_case=snake_case__ )
for tokenizer in tokenizers:
with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
lowercase__ : Optional[int]= ["[CLS]", "t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "s", "t", "[SEP]"]
lowercase__ : Optional[int]= tokenizer.convert_tokens_to_string(snake_case__ )
self.assertIsInstance(snake_case__ , snake_case__ )
| 370 |
"""simple docstring"""
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu
class __UpperCAmelCase( nn.Module ):
"""simple docstring"""
def __init__( self ):
'''simple docstring'''
super().__init__()
lowercase__ : Any= nn.Linear(3 , 4 )
lowercase__ : Tuple= nn.BatchNormad(4 )
lowercase__ : Dict= nn.Linear(4 , 5 )
def UpperCAmelCase_ ( self , snake_case__ ):
'''simple docstring'''
return self.lineara(self.batchnorm(self.lineara(snake_case__ ) ) )
class __UpperCAmelCase( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def UpperCAmelCase_ ( self , snake_case__ , *snake_case__ , **snake_case__ ):
'''simple docstring'''
return (args[0] + 1,) + args[1:], kwargs
class __UpperCAmelCase( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def UpperCAmelCase_ ( self , snake_case__ , snake_case__ ):
'''simple docstring'''
return output + 1
class __UpperCAmelCase( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : str= ModelForTest()
lowercase__ : str= ModelHook()
add_hook_to_module(snake_case__ , snake_case__ )
self.assertEqual(test_model._hf_hook , snake_case__ )
self.assertTrue(hasattr(snake_case__ , "_old_forward" ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , "forward" )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ["x"] )
remove_hook_from_module(snake_case__ )
self.assertFalse(hasattr(snake_case__ , "_hf_hook" ) )
self.assertFalse(hasattr(snake_case__ , "_old_forward" ) )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : int= ModelForTest()
lowercase__ : int= ModelHook()
add_hook_to_module(snake_case__ , snake_case__ )
add_hook_to_module(snake_case__ , snake_case__ , append=snake_case__ )
self.assertEqual(isinstance(test_model._hf_hook , snake_case__ ) , snake_case__ )
self.assertEqual(len(test_model._hf_hook.hooks ) , 2 )
self.assertTrue(hasattr(snake_case__ , "_old_forward" ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , "forward" )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ["x"] )
remove_hook_from_module(snake_case__ )
self.assertFalse(hasattr(snake_case__ , "_hf_hook" ) )
self.assertFalse(hasattr(snake_case__ , "_old_forward" ) )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Dict= ModelForTest()
lowercase__ : int= torch.randn(2 , 3 )
lowercase__ : Optional[Any]= test_model(x + 1 )
lowercase__ : Tuple= test_model(x + 2 )
lowercase__ : str= PreForwardHook()
add_hook_to_module(snake_case__ , snake_case__ )
lowercase__ : Tuple= test_model(snake_case__ )
self.assertTrue(torch.allclose(snake_case__ , snake_case__ , atol=1e-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
lowercase__ : Tuple= PreForwardHook()
add_hook_to_module(snake_case__ , snake_case__ )
lowercase__ : Optional[Any]= test_model(snake_case__ )
self.assertTrue(torch.allclose(snake_case__ , snake_case__ , atol=1e-5 ) )
# You need to use the sequential hook to chain two or more hooks
lowercase__ : List[str]= SequentialHook(PreForwardHook() , PreForwardHook() )
add_hook_to_module(snake_case__ , snake_case__ )
lowercase__ : Dict= test_model(snake_case__ )
assert torch.allclose(snake_case__ , snake_case__ , atol=1e-5 )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Tuple= ModelForTest()
lowercase__ : Optional[int]= torch.randn(2 , 3 )
lowercase__ : Optional[int]= test_model(snake_case__ )
lowercase__ : str= PostForwardHook()
add_hook_to_module(snake_case__ , snake_case__ )
lowercase__ : Optional[int]= test_model(snake_case__ )
self.assertTrue(torch.allclose(snake_case__ , output + 1 , atol=1e-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
lowercase__ : Tuple= PostForwardHook()
add_hook_to_module(snake_case__ , snake_case__ )
lowercase__ : Dict= test_model(snake_case__ )
self.assertTrue(torch.allclose(snake_case__ , output + 1 , atol=1e-5 ) )
# You need to use the sequential hook to chain two or more hooks
lowercase__ : Optional[Any]= SequentialHook(PostForwardHook() , PostForwardHook() )
add_hook_to_module(snake_case__ , snake_case__ )
lowercase__ : List[str]= test_model(snake_case__ )
assert torch.allclose(snake_case__ , output + 2 , atol=1e-5 )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : int= ModelForTest()
lowercase__ : Optional[Any]= torch.randn(2 , 3 )
lowercase__ : int= test_model(snake_case__ )
lowercase__ : Union[str, Any]= PostForwardHook()
add_hook_to_module(snake_case__ , snake_case__ )
lowercase__ : Dict= test_model(snake_case__ )
self.assertTrue(torch.allclose(snake_case__ , output + 1 ) )
self.assertTrue(outputa.requires_grad )
lowercase__ : Any= True
lowercase__ : Optional[int]= test_model(snake_case__ )
self.assertFalse(outputa.requires_grad )
@require_multi_gpu
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Dict= ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("cpu" ) )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
# This will move each submodule on different devices
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=1 ) )
self.assertEqual(model.lineara.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device(0 ) )
self.assertEqual(model.lineara.weight.device , torch.device(1 ) )
# We can still make a forward pass. The input does not need to be on any particular device
lowercase__ : int= torch.randn(2 , 3 )
lowercase__ : List[str]= model(snake_case__ )
self.assertEqual(output.device , torch.device(1 ) )
# We can add a general hook to put back output on same device as input.
add_hook_to_module(snake_case__ , AlignDevicesHook(io_same_device=snake_case__ ) )
lowercase__ : Tuple= torch.randn(2 , 3 ).to(0 )
lowercase__ : Optional[Any]= model(snake_case__ )
self.assertEqual(output.device , torch.device(0 ) )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Optional[Any]= ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("cpu" ) )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
# This will move each submodule on different devices
lowercase__ : Optional[int]= {"execution_device": 0 if torch.cuda.is_available() else "cpu", "offload": True}
add_hook_to_module(model.lineara , AlignDevicesHook(**snake_case__ ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**snake_case__ ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**snake_case__ ) )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("meta" ) )
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
# Buffers are not included in the offload by default, so are on the execution device
lowercase__ : Optional[int]= torch.device(hook_kwargs["execution_device"] )
self.assertEqual(model.batchnorm.running_mean.device , snake_case__ )
lowercase__ : List[Any]= torch.randn(2 , 3 )
lowercase__ : str= model(snake_case__ )
self.assertEqual(output.device , snake_case__ )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("cpu" ) )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
# Now test with buffers included in the offload
lowercase__ : Optional[int]= {
"execution_device": 0 if torch.cuda.is_available() else "cpu",
"offload": True,
"offload_buffers": True,
}
add_hook_to_module(model.lineara , AlignDevicesHook(**snake_case__ ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**snake_case__ ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**snake_case__ ) )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("meta" ) )
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device("meta" ) )
lowercase__ : str= torch.randn(2 , 3 )
lowercase__ : str= model(snake_case__ )
self.assertEqual(output.device , snake_case__ )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("cpu" ) )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Dict= ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("cpu" ) )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
# This will move each submodule on different devices
lowercase__ : str= 0 if torch.cuda.is_available() else "cpu"
attach_align_device_hook(snake_case__ , execution_device=snake_case__ , offload=snake_case__ )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("meta" ) )
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
# Buffers are not included in the offload by default, so are on the execution device
lowercase__ : Dict= torch.device(snake_case__ )
self.assertEqual(model.batchnorm.running_mean.device , snake_case__ )
lowercase__ : Optional[Any]= torch.randn(2 , 3 )
lowercase__ : List[Any]= model(snake_case__ )
self.assertEqual(output.device , snake_case__ )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(snake_case__ )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("cpu" ) )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
# Now test with buffers included in the offload
attach_align_device_hook(snake_case__ , execution_device=snake_case__ , offload=snake_case__ , offload_buffers=snake_case__ )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("meta" ) )
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device("meta" ) )
lowercase__ : List[str]= torch.randn(2 , 3 )
lowercase__ : List[Any]= model(snake_case__ )
self.assertEqual(output.device , snake_case__ )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(snake_case__ )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("cpu" ) )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Any= ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("cpu" ) )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
# This will move each submodule on different devices
lowercase__ : Optional[Any]= 0 if torch.cuda.is_available() else "cpu"
attach_align_device_hook(
snake_case__ , execution_device=snake_case__ , offload=snake_case__ , weights_map=model.state_dict() )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("meta" ) )
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
# Buffers are not included in the offload by default, so are on the execution device
lowercase__ : Tuple= torch.device(snake_case__ )
self.assertEqual(model.batchnorm.running_mean.device , snake_case__ )
lowercase__ : str= torch.randn(2 , 3 )
lowercase__ : List[Any]= model(snake_case__ )
self.assertEqual(output.device , snake_case__ )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(snake_case__ )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("cpu" ) )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
# Now test with buffers included in the offload
attach_align_device_hook(
snake_case__ , execution_device=snake_case__ , offload=snake_case__ , weights_map=model.state_dict() , offload_buffers=snake_case__ , )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("meta" ) )
self.assertEqual(model.lineara.weight.device , torch.device("meta" ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device("meta" ) )
lowercase__ : Dict= torch.randn(2 , 3 )
lowercase__ : List[str]= model(snake_case__ )
self.assertEqual(output.device , snake_case__ )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(snake_case__ )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
self.assertEqual(model.batchnorm.weight.device , torch.device("cpu" ) )
self.assertEqual(model.lineara.weight.device , torch.device("cpu" ) )
| 150 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
def _SCREAMING_SNAKE_CASE (A ) -> list[int]:
"""simple docstring"""
if num <= 0:
lowercase__ = f"{num}: Invalid input, please enter a positive integer."
raise ValueError(A )
lowercase__ = [True] * (num + 1)
lowercase__ = []
lowercase__ = 2
lowercase__ = int(math.sqrt(A ) )
while start <= end:
# If start is a prime
if sieve[start] is True:
prime.append(A )
# Set multiples of start be False
for i in range(start * start , num + 1 , A ):
if sieve[i] is True:
lowercase__ = False
start += 1
for j in range(end + 1 , num + 1 ):
if sieve[j] is True:
prime.append(A )
return prime
if __name__ == "__main__":
print(prime_sieve(int(input('Enter a positive integer: ').strip())))
| 2 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING
lowercase__ : Dict = logging.get_logger(__name__)
@add_end_docstrings(UpperCAmelCase__ )
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self : Optional[Any] , **__lowercase : Union[str, Any] ):
"""simple docstring"""
super().__init__(**__lowercase )
requires_backends(self , "vision" )
requires_backends(self , "torch" )
if self.framework != "pt":
raise ValueError(f"The {self.__class__} is only available in PyTorch." )
self.check_model_type(__lowercase )
def snake_case__ ( self : Optional[int] , **__lowercase : Optional[Any] ):
"""simple docstring"""
snake_case_ = {}
snake_case_ = {}
snake_case_ = {}
# preprocess args
if "points_per_batch" in kwargs:
snake_case_ = kwargs["points_per_batch"]
if "points_per_crop" in kwargs:
snake_case_ = kwargs["points_per_crop"]
if "crops_n_layers" in kwargs:
snake_case_ = kwargs["crops_n_layers"]
if "crop_overlap_ratio" in kwargs:
snake_case_ = kwargs["crop_overlap_ratio"]
if "crop_n_points_downscale_factor" in kwargs:
snake_case_ = kwargs["crop_n_points_downscale_factor"]
# postprocess args
if "pred_iou_thresh" in kwargs:
snake_case_ = kwargs["pred_iou_thresh"]
if "stability_score_offset" in kwargs:
snake_case_ = kwargs["stability_score_offset"]
if "mask_threshold" in kwargs:
snake_case_ = kwargs["mask_threshold"]
if "stability_score_thresh" in kwargs:
snake_case_ = kwargs["stability_score_thresh"]
if "crops_nms_thresh" in kwargs:
snake_case_ = kwargs["crops_nms_thresh"]
if "output_rle_mask" in kwargs:
snake_case_ = kwargs["output_rle_mask"]
if "output_bboxes_mask" in kwargs:
snake_case_ = kwargs["output_bboxes_mask"]
return preprocess_kwargs, forward_params, postprocess_kwargs
def __call__( self : Optional[int] , __lowercase : List[str] , *__lowercase : Optional[Any] , __lowercase : Dict=None , __lowercase : List[str]=None , **__lowercase : Optional[Any] ):
"""simple docstring"""
return super().__call__(__lowercase , *__lowercase , num_workers=__lowercase , batch_size=__lowercase , **__lowercase )
def snake_case__ ( self : str , __lowercase : int , __lowercase : List[str]=64 , __lowercase : int = 0 , __lowercase : float = 5_12 / 15_00 , __lowercase : Optional[int] = 32 , __lowercase : Optional[int] = 1 , ):
"""simple docstring"""
snake_case_ = load_image(__lowercase )
snake_case_ = self.image_processor.size["longest_edge"]
snake_case_ , snake_case_ , snake_case_ , snake_case_ = self.image_processor.generate_crop_boxes(
__lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase )
snake_case_ = self.image_processor(images=__lowercase , return_tensors="pt" )
with self.device_placement():
if self.framework == "pt":
snake_case_ = self.get_inference_context()
with inference_context():
snake_case_ = self._ensure_tensor_on_device(__lowercase , device=self.device )
snake_case_ = self.model.get_image_embeddings(model_inputs.pop("pixel_values" ) )
snake_case_ = image_embeddings
snake_case_ = grid_points.shape[1]
snake_case_ = points_per_batch if points_per_batch is not None else n_points
if points_per_batch <= 0:
raise ValueError(
"Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. "
"To return all points at once, set points_per_batch to None" )
for i in range(0 , __lowercase , __lowercase ):
snake_case_ = grid_points[:, i : i + points_per_batch, :, :]
snake_case_ = input_labels[:, i : i + points_per_batch]
snake_case_ = i == n_points - points_per_batch
yield {
"input_points": batched_points,
"input_labels": labels,
"input_boxes": crop_boxes,
"is_last": is_last,
**model_inputs,
}
def snake_case__ ( self : Tuple , __lowercase : Union[str, Any] , __lowercase : Union[str, Any]=0.88 , __lowercase : Union[str, Any]=0.95 , __lowercase : int=0 , __lowercase : int=1 , ):
"""simple docstring"""
snake_case_ = model_inputs.pop("input_boxes" )
snake_case_ = model_inputs.pop("is_last" )
snake_case_ = model_inputs.pop("original_sizes" ).tolist()
snake_case_ = model_inputs.pop("reshaped_input_sizes" ).tolist()
snake_case_ = self.model(**__lowercase )
# post processing happens here in order to avoid CPU GPU copies of ALL the masks
snake_case_ = model_outputs["pred_masks"]
snake_case_ = self.image_processor.post_process_masks(
__lowercase , __lowercase , __lowercase , __lowercase , binarize=__lowercase )
snake_case_ = model_outputs["iou_scores"]
snake_case_ , snake_case_ , snake_case_ = self.image_processor.filter_masks(
masks[0] , iou_scores[0] , original_sizes[0] , input_boxes[0] , __lowercase , __lowercase , __lowercase , __lowercase , )
return {
"masks": masks,
"is_last": is_last,
"boxes": boxes,
"iou_scores": iou_scores,
}
def snake_case__ ( self : str , __lowercase : Any , __lowercase : Optional[int]=False , __lowercase : int=False , __lowercase : List[str]=0.7 , ):
"""simple docstring"""
snake_case_ = []
snake_case_ = []
snake_case_ = []
for model_output in model_outputs:
all_scores.append(model_output.pop("iou_scores" ) )
all_masks.extend(model_output.pop("masks" ) )
all_boxes.append(model_output.pop("boxes" ) )
snake_case_ = torch.cat(__lowercase )
snake_case_ = torch.cat(__lowercase )
snake_case_ , snake_case_ , snake_case_ , snake_case_ = self.image_processor.post_process_for_mask_generation(
__lowercase , __lowercase , __lowercase , __lowercase )
snake_case_ = defaultdict(__lowercase )
for output in model_outputs:
for k, v in output.items():
extra[k].append(__lowercase )
snake_case_ = {}
if output_rle_mask:
snake_case_ = rle_mask
if output_bboxes_mask:
snake_case_ = bounding_boxes
return {"masks": output_masks, "scores": iou_scores, **optional, **extra}
| 187 | 0 |
'''simple docstring'''
import math
def __magic_name__( lowerCamelCase):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5, int(math.sqrt(lowerCamelCase) + 1), 6):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def __magic_name__( lowerCamelCase = 0.1):
__lowerCAmelCase = 3
__lowerCAmelCase = 3
while primes / (2 * j - 1) >= ratio:
for i in range(j * j + j + 1, (j + 2) * (j + 2), j + 1):
primes += is_prime(lowerCamelCase)
j += 2
return j
if __name__ == "__main__":
import doctest
doctest.testmod()
| 367 |
'''simple docstring'''
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import BridgeTowerImageProcessor
class a__ ( unittest.TestCase ):
"""simple docstring"""
def __init__(self , __lowercase , __lowercase = True , __lowercase = None , __lowercase = 32 , __lowercase = True , __lowercase = 1 / 2_55 , __lowercase = True , __lowercase = True , __lowercase = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3] , __lowercase = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1] , __lowercase = True , __lowercase=7 , __lowercase=30 , __lowercase=4_00 , __lowercase=3 , ):
__lowerCAmelCase = parent
__lowerCAmelCase = do_resize
__lowerCAmelCase = size if size is not None else {'''shortest_edge''': 2_88}
__lowerCAmelCase = size_divisor
__lowerCAmelCase = do_rescale
__lowerCAmelCase = rescale_factor
__lowerCAmelCase = do_normalize
__lowerCAmelCase = do_center_crop
__lowerCAmelCase = image_mean
__lowerCAmelCase = image_std
__lowerCAmelCase = do_pad
__lowerCAmelCase = batch_size
__lowerCAmelCase = num_channels
__lowerCAmelCase = min_resolution
__lowerCAmelCase = max_resolution
def _snake_case (self ):
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
"size_divisor": self.size_divisor,
}
def _snake_case (self , __lowercase , __lowercase=False ):
if not batched:
__lowerCAmelCase = self.size['''shortest_edge''']
__lowerCAmelCase = image_inputs[0]
if isinstance(__lowercase , Image.Image ):
__lowerCAmelCase , __lowerCAmelCase = image.size
else:
__lowerCAmelCase , __lowerCAmelCase = image.shape[1], image.shape[2]
__lowerCAmelCase = size / min(__lowercase , __lowercase )
if h < w:
__lowerCAmelCase , __lowerCAmelCase = size, scale * w
else:
__lowerCAmelCase , __lowerCAmelCase = scale * h, size
__lowerCAmelCase = int((13_33 / 8_00) * size )
if max(__lowercase , __lowercase ) > max_size:
__lowerCAmelCase = max_size / max(__lowercase , __lowercase )
__lowerCAmelCase = newh * scale
__lowerCAmelCase = neww * scale
__lowerCAmelCase , __lowerCAmelCase = int(newh + 0.5 ), int(neww + 0.5 )
__lowerCAmelCase , __lowerCAmelCase = (
newh // self.size_divisor * self.size_divisor,
neww // self.size_divisor * self.size_divisor,
)
else:
__lowerCAmelCase = []
for image in image_inputs:
__lowerCAmelCase , __lowerCAmelCase = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
__lowerCAmelCase = max(__lowercase , key=lambda __lowercase : item[0] )[0]
__lowerCAmelCase = max(__lowercase , key=lambda __lowercase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class a__ ( __A , unittest.TestCase ):
"""simple docstring"""
__UpperCamelCase : Any = BridgeTowerImageProcessor if is_vision_available() else None
def _snake_case (self ):
__lowerCAmelCase = BridgeTowerImageProcessingTester(self )
@property
def _snake_case (self ):
return self.image_processor_tester.prepare_image_processor_dict()
def _snake_case (self ):
__lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__lowercase , '''image_mean''' ) )
self.assertTrue(hasattr(__lowercase , '''image_std''' ) )
self.assertTrue(hasattr(__lowercase , '''do_normalize''' ) )
self.assertTrue(hasattr(__lowercase , '''do_resize''' ) )
self.assertTrue(hasattr(__lowercase , '''size''' ) )
self.assertTrue(hasattr(__lowercase , '''size_divisor''' ) )
def _snake_case (self ):
pass
def _snake_case (self ):
# Initialize image processor
__lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowercase )
for image in image_inputs:
self.assertIsInstance(__lowercase , Image.Image )
# Test not batched input
__lowerCAmelCase = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__lowerCAmelCase , __lowerCAmelCase = self.image_processor_tester.get_expected_values(__lowercase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__lowerCAmelCase = image_processing(__lowercase , return_tensors='''pt''' ).pixel_values
__lowerCAmelCase , __lowerCAmelCase = self.image_processor_tester.get_expected_values(__lowercase , batched=__lowercase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _snake_case (self ):
# Initialize image processor
__lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowercase , numpify=__lowercase )
for image in image_inputs:
self.assertIsInstance(__lowercase , np.ndarray )
# Test not batched input
__lowerCAmelCase = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__lowerCAmelCase , __lowerCAmelCase = self.image_processor_tester.get_expected_values(__lowercase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__lowerCAmelCase = image_processing(__lowercase , return_tensors='''pt''' ).pixel_values
__lowerCAmelCase , __lowerCAmelCase = self.image_processor_tester.get_expected_values(__lowercase , batched=__lowercase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _snake_case (self ):
# Initialize image processor
__lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowercase , torchify=__lowercase )
for image in image_inputs:
self.assertIsInstance(__lowercase , torch.Tensor )
# Test not batched input
__lowerCAmelCase = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__lowerCAmelCase , __lowerCAmelCase = self.image_processor_tester.get_expected_values(__lowercase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__lowerCAmelCase = image_processing(__lowercase , return_tensors='''pt''' ).pixel_values
__lowerCAmelCase , __lowerCAmelCase = self.image_processor_tester.get_expected_values(__lowercase , batched=__lowercase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
| 9 | 0 |
"""simple docstring"""
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def lowerCamelCase ( *_UpperCamelCase : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
__UpperCAmelCase : Optional[int] = list(_UpperCamelCase )
for i in range(len(_UpperCamelCase ) ):
__UpperCAmelCase : Optional[int] = None
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
return objects
def lowerCamelCase ( _UpperCamelCase : Exception ) -> bool:
'''simple docstring'''
__UpperCAmelCase : List[str] = [
"""CUDA out of memory.""", # CUDA OOM
"""cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""", # CUDNN SNAFU
"""DefaultCPUAllocator: can't allocate memory""", # CPU OOM
]
if isinstance(_UpperCamelCase , _UpperCamelCase ) and len(exception.args ) == 1:
return any(err in exception.args[0] for err in _statements )
return False
def lowerCamelCase ( _UpperCamelCase : callable = None , _UpperCamelCase : int = 1_2_8 ) -> int:
'''simple docstring'''
if function is None:
return functools.partial(_UpperCamelCase , starting_batch_size=_UpperCamelCase )
__UpperCAmelCase : List[str] = starting_batch_size
def decorator(*_UpperCamelCase : str , **_UpperCamelCase : Any ):
nonlocal batch_size
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
__UpperCAmelCase : int = list(inspect.signature(_UpperCamelCase ).parameters.keys() )
# Guard against user error
if len(_UpperCamelCase ) < (len(_UpperCamelCase ) + 1):
__UpperCAmelCase : str = """, """.join([f'''{arg}={value}''' for arg, value in zip(params[1:] , args[1:] )] )
raise TypeError(
f'''Batch size was passed into `{function.__name__}` as the first argument when called.'''
f'''Remove this as the decorator already does so: `{function.__name__}({arg_str})`''' )
while True:
if batch_size == 0:
raise RuntimeError("""No executable batch size found, reached zero.""" )
try:
return function(_UpperCamelCase , *_UpperCamelCase , **_UpperCamelCase )
except Exception as e:
if should_reduce_batch_size(_UpperCamelCase ):
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
batch_size //= 2
else:
raise
return decorator
| 115 |
"""simple docstring"""
import numpy as np
UpperCAmelCase : Optional[Any] = [
['a', 'b', 'c', 'd', 'e'],
['f', 'g', 'h', 'i', 'k'],
['l', 'm', 'n', 'o', 'p'],
['q', 'r', 's', 't', 'u'],
['v', 'w', 'x', 'y', 'z'],
]
class lowerCamelCase__ :
"""simple docstring"""
def __init__( self : Union[str, Any] ):
'''simple docstring'''
__UpperCAmelCase : List[Any] = np.array(UpperCamelCase )
def lowerCamelCase__ ( self : int , UpperCamelCase : str ):
'''simple docstring'''
__UpperCAmelCase ,__UpperCAmelCase : Optional[Any] = np.where(letter == self.SQUARE )
__UpperCAmelCase : Optional[Any] = np.concatenate([indexa + 1, indexa + 1] )
return indexes
def lowerCamelCase__ ( self : Any , UpperCamelCase : int , UpperCamelCase : int ):
'''simple docstring'''
__UpperCAmelCase : Tuple = self.SQUARE[indexa - 1, indexa - 1]
return letter
def lowerCamelCase__ ( self : Optional[int] , UpperCamelCase : str ):
'''simple docstring'''
__UpperCAmelCase : str = message.lower()
__UpperCAmelCase : List[Any] = message.replace(""" """ , """""" )
__UpperCAmelCase : List[Any] = message.replace("""j""" , """i""" )
__UpperCAmelCase : Optional[int] = np.empty((2, len(UpperCamelCase )) )
for letter_index in range(len(UpperCamelCase ) ):
__UpperCAmelCase : List[Any] = self.letter_to_numbers(message[letter_index] )
__UpperCAmelCase : str = numbers[0]
__UpperCAmelCase : int = numbers[1]
__UpperCAmelCase : Union[str, Any] = first_step.reshape(2 * len(UpperCamelCase ) )
__UpperCAmelCase : Optional[Any] = """"""
for numbers_index in range(len(UpperCamelCase ) ):
__UpperCAmelCase : Any = int(second_step[numbers_index * 2] )
__UpperCAmelCase : Any = int(second_step[(numbers_index * 2) + 1] )
__UpperCAmelCase : str = self.numbers_to_letter(UpperCamelCase , UpperCamelCase )
__UpperCAmelCase : Optional[Any] = encoded_message + letter
return encoded_message
def lowerCamelCase__ ( self : Optional[int] , UpperCamelCase : str ):
'''simple docstring'''
__UpperCAmelCase : Any = message.lower()
message.replace(""" """ , """""" )
__UpperCAmelCase : int = np.empty(2 * len(UpperCamelCase ) )
for letter_index in range(len(UpperCamelCase ) ):
__UpperCAmelCase : Any = self.letter_to_numbers(message[letter_index] )
__UpperCAmelCase : Any = numbers[0]
__UpperCAmelCase : Dict = numbers[1]
__UpperCAmelCase : str = first_step.reshape((2, len(UpperCamelCase )) )
__UpperCAmelCase : Union[str, Any] = """"""
for numbers_index in range(len(UpperCamelCase ) ):
__UpperCAmelCase : Optional[int] = int(second_step[0, numbers_index] )
__UpperCAmelCase : Tuple = int(second_step[1, numbers_index] )
__UpperCAmelCase : Tuple = self.numbers_to_letter(UpperCamelCase , UpperCamelCase )
__UpperCAmelCase : Any = decoded_message + letter
return decoded_message
| 115 | 1 |
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def lowerCAmelCase__ ( _a : str , _a : float | Decimal , _a : float = 10**-10 ):
snake_case_ : Optional[int] = a
while True:
snake_case_ : Tuple = Decimal(_a ) - (
Decimal(eval(_a ) ) / Decimal(eval(str(diff(_a ) ) ) ) # noqa: S307
)
# This number dictates the accuracy of the answer
if abs(eval(_a ) ) < precision: # noqa: S307
return float(_a )
# Let's Execute
if __name__ == "__main__":
# Find root of trigonometric function
# Find value of pi
print(F"""The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}""")
# Find root of polynomial
print(F"""The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}""")
# Find Square Root of 5
print(F"""The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}""")
# Exponential Roots
print(F"""The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}""")
| 370 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def lowerCAmelCase__ ( ):
snake_case_ : str = ArgumentParser(
description=(
"PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"
) )
# Optional arguments for the launch helper
parser.add_argument("--num_cores" , type=_a , default=1 , help="Number of TPU cores to use (1 or 8)." )
# positional
parser.add_argument(
"training_script" , type=_a , help=(
"The full path to the single TPU training "
"program/script to be launched in parallel, "
"followed by all the arguments for the "
"training script"
) , )
# rest from the training program
parser.add_argument("training_script_args" , nargs=_a )
return parser.parse_args()
def lowerCAmelCase__ ( ):
snake_case_ : str = parse_args()
# Import training_script as a module.
snake_case_ : Any = Path(args.training_script )
sys.path.append(str(script_fpath.parent.resolve() ) )
snake_case_ : Tuple = script_fpath.stem
snake_case_ : str = importlib.import_module(_a )
# Patch sys.argv
snake_case_ : Optional[int] = [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores )]
xmp.spawn(mod._mp_fn , args=() , nprocs=args.num_cores )
if __name__ == "__main__":
main()
| 36 | 0 |
"""simple docstring"""
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
_UpperCamelCase: Union[str, Any] = {
'sample_size': 3_2,
'in_channels': 3,
'out_channels': 3,
'layers_per_block': 2,
'num_class_embeds': 1_0_0_0,
'block_out_channels': [3_2, 6_4],
'attention_head_dim': 8,
'down_block_types': [
'ResnetDownsampleBlock2D',
'AttnDownBlock2D',
],
'up_block_types': [
'AttnUpBlock2D',
'ResnetUpsampleBlock2D',
],
'resnet_time_scale_shift': 'scale_shift',
'upsample_type': 'resnet',
'downsample_type': 'resnet',
}
_UpperCamelCase: Union[str, Any] = {
'sample_size': 6_4,
'in_channels': 3,
'out_channels': 3,
'layers_per_block': 3,
'num_class_embeds': 1_0_0_0,
'block_out_channels': [1_9_2, 1_9_2 * 2, 1_9_2 * 3, 1_9_2 * 4],
'attention_head_dim': 6_4,
'down_block_types': [
'ResnetDownsampleBlock2D',
'AttnDownBlock2D',
'AttnDownBlock2D',
'AttnDownBlock2D',
],
'up_block_types': [
'AttnUpBlock2D',
'AttnUpBlock2D',
'AttnUpBlock2D',
'ResnetUpsampleBlock2D',
],
'resnet_time_scale_shift': 'scale_shift',
'upsample_type': 'resnet',
'downsample_type': 'resnet',
}
_UpperCamelCase: int = {
'sample_size': 2_5_6,
'in_channels': 3,
'out_channels': 3,
'layers_per_block': 2,
'num_class_embeds': None,
'block_out_channels': [2_5_6, 2_5_6, 2_5_6 * 2, 2_5_6 * 2, 2_5_6 * 4, 2_5_6 * 4],
'attention_head_dim': 6_4,
'down_block_types': [
'ResnetDownsampleBlock2D',
'ResnetDownsampleBlock2D',
'ResnetDownsampleBlock2D',
'AttnDownBlock2D',
'AttnDownBlock2D',
'AttnDownBlock2D',
],
'up_block_types': [
'AttnUpBlock2D',
'AttnUpBlock2D',
'AttnUpBlock2D',
'ResnetUpsampleBlock2D',
'ResnetUpsampleBlock2D',
'ResnetUpsampleBlock2D',
],
'resnet_time_scale_shift': 'default',
'upsample_type': 'resnet',
'downsample_type': 'resnet',
}
_UpperCamelCase: Optional[Any] = {
'num_train_timesteps': 4_0,
'sigma_min': 0.0_0_2,
'sigma_max': 8_0.0,
}
_UpperCamelCase: Any = {
'num_train_timesteps': 2_0_1,
'sigma_min': 0.0_0_2,
'sigma_max': 8_0.0,
}
_UpperCamelCase: Dict = {
'num_train_timesteps': 1_5_1,
'sigma_min': 0.0_0_2,
'sigma_max': 8_0.0,
}
def lowercase__ ( _UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError('boolean value expected' )
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=False ) -> Any:
'''simple docstring'''
lowercase : Dict = checkpoint[f'''{old_prefix}.in_layers.0.weight''']
lowercase : Tuple = checkpoint[f'''{old_prefix}.in_layers.0.bias''']
lowercase : Optional[Any] = checkpoint[f'''{old_prefix}.in_layers.2.weight''']
lowercase : Union[str, Any] = checkpoint[f'''{old_prefix}.in_layers.2.bias''']
lowercase : List[Any] = checkpoint[f'''{old_prefix}.emb_layers.1.weight''']
lowercase : str = checkpoint[f'''{old_prefix}.emb_layers.1.bias''']
lowercase : Optional[Any] = checkpoint[f'''{old_prefix}.out_layers.0.weight''']
lowercase : Dict = checkpoint[f'''{old_prefix}.out_layers.0.bias''']
lowercase : Dict = checkpoint[f'''{old_prefix}.out_layers.3.weight''']
lowercase : List[Any] = checkpoint[f'''{old_prefix}.out_layers.3.bias''']
if has_skip:
lowercase : Any = checkpoint[f'''{old_prefix}.skip_connection.weight''']
lowercase : Tuple = checkpoint[f'''{old_prefix}.skip_connection.bias''']
return new_checkpoint
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=None ) -> Dict:
'''simple docstring'''
lowercase , lowercase , lowercase : Dict = checkpoint[f'''{old_prefix}.qkv.weight'''].chunk(3 , dim=0 )
lowercase , lowercase , lowercase : int = checkpoint[f'''{old_prefix}.qkv.bias'''].chunk(3 , dim=0 )
lowercase : int = checkpoint[f'''{old_prefix}.norm.weight''']
lowercase : int = checkpoint[f'''{old_prefix}.norm.bias''']
lowercase : Dict = weight_q.squeeze(-1 ).squeeze(-1 )
lowercase : Optional[int] = bias_q.squeeze(-1 ).squeeze(-1 )
lowercase : Optional[Any] = weight_k.squeeze(-1 ).squeeze(-1 )
lowercase : int = bias_k.squeeze(-1 ).squeeze(-1 )
lowercase : Dict = weight_v.squeeze(-1 ).squeeze(-1 )
lowercase : Tuple = bias_v.squeeze(-1 ).squeeze(-1 )
lowercase : Tuple = (
checkpoint[f'''{old_prefix}.proj_out.weight'''].squeeze(-1 ).squeeze(-1 )
)
lowercase : str = checkpoint[f'''{old_prefix}.proj_out.bias'''].squeeze(-1 ).squeeze(-1 )
return new_checkpoint
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase ) -> str:
'''simple docstring'''
lowercase : Optional[Any] = torch.load(_UpperCAmelCase , map_location='cpu' )
lowercase : str = {}
lowercase : List[str] = checkpoint['time_embed.0.weight']
lowercase : Dict = checkpoint['time_embed.0.bias']
lowercase : Optional[Any] = checkpoint['time_embed.2.weight']
lowercase : int = checkpoint['time_embed.2.bias']
if unet_config["num_class_embeds"] is not None:
lowercase : Dict = checkpoint['label_emb.weight']
lowercase : List[Any] = checkpoint['input_blocks.0.0.weight']
lowercase : Union[str, Any] = checkpoint['input_blocks.0.0.bias']
lowercase : Optional[int] = unet_config['down_block_types']
lowercase : Dict = unet_config['layers_per_block']
lowercase : Tuple = unet_config['attention_head_dim']
lowercase : Union[str, Any] = unet_config['block_out_channels']
lowercase : str = 1
lowercase : str = channels_list[0]
for i, layer_type in enumerate(_UpperCAmelCase ):
lowercase : Dict = channels_list[i]
lowercase : Optional[int] = current_channels != prev_channels
if layer_type == "ResnetDownsampleBlock2D":
for j in range(_UpperCAmelCase ):
lowercase : Union[str, Any] = f'''down_blocks.{i}.resnets.{j}'''
lowercase : Optional[int] = f'''input_blocks.{current_layer}.0'''
lowercase : Dict = True if j == 0 and downsample_block_has_skip else False
lowercase : Tuple = convert_resnet(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , has_skip=_UpperCAmelCase )
current_layer += 1
elif layer_type == "AttnDownBlock2D":
for j in range(_UpperCAmelCase ):
lowercase : List[Any] = f'''down_blocks.{i}.resnets.{j}'''
lowercase : str = f'''input_blocks.{current_layer}.0'''
lowercase : Optional[Any] = True if j == 0 and downsample_block_has_skip else False
lowercase : List[Any] = convert_resnet(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , has_skip=_UpperCAmelCase )
lowercase : Optional[Any] = f'''down_blocks.{i}.attentions.{j}'''
lowercase : Tuple = f'''input_blocks.{current_layer}.1'''
lowercase : List[str] = convert_attention(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
current_layer += 1
if i != len(_UpperCAmelCase ) - 1:
lowercase : Any = f'''down_blocks.{i}.downsamplers.0'''
lowercase : Any = f'''input_blocks.{current_layer}.0'''
lowercase : Optional[int] = convert_resnet(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
current_layer += 1
lowercase : Tuple = current_channels
# hardcoded the mid-block for now
lowercase : List[str] = 'mid_block.resnets.0'
lowercase : str = 'middle_block.0'
lowercase : str = convert_resnet(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
lowercase : List[str] = 'mid_block.attentions.0'
lowercase : List[str] = 'middle_block.1'
lowercase : Dict = convert_attention(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
lowercase : Any = 'mid_block.resnets.1'
lowercase : List[str] = 'middle_block.2'
lowercase : str = convert_resnet(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
lowercase : Any = 0
lowercase : Optional[Any] = unet_config['up_block_types']
for i, layer_type in enumerate(_UpperCAmelCase ):
if layer_type == "ResnetUpsampleBlock2D":
for j in range(layers_per_block + 1 ):
lowercase : Dict = f'''up_blocks.{i}.resnets.{j}'''
lowercase : Union[str, Any] = f'''output_blocks.{current_layer}.0'''
lowercase : str = convert_resnet(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , has_skip=_UpperCAmelCase )
current_layer += 1
if i != len(_UpperCAmelCase ) - 1:
lowercase : List[str] = f'''up_blocks.{i}.upsamplers.0'''
lowercase : List[Any] = f'''output_blocks.{current_layer-1}.1'''
lowercase : int = convert_resnet(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
elif layer_type == "AttnUpBlock2D":
for j in range(layers_per_block + 1 ):
lowercase : Optional[Any] = f'''up_blocks.{i}.resnets.{j}'''
lowercase : int = f'''output_blocks.{current_layer}.0'''
lowercase : int = convert_resnet(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , has_skip=_UpperCAmelCase )
lowercase : Dict = f'''up_blocks.{i}.attentions.{j}'''
lowercase : List[str] = f'''output_blocks.{current_layer}.1'''
lowercase : Dict = convert_attention(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
current_layer += 1
if i != len(_UpperCAmelCase ) - 1:
lowercase : int = f'''up_blocks.{i}.upsamplers.0'''
lowercase : List[str] = f'''output_blocks.{current_layer-1}.2'''
lowercase : Optional[Any] = convert_resnet(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
lowercase : str = checkpoint['out.0.weight']
lowercase : Any = checkpoint['out.0.bias']
lowercase : Optional[int] = checkpoint['out.2.weight']
lowercase : int = checkpoint['out.2.bias']
return new_checkpoint
if __name__ == "__main__":
_UpperCamelCase: int = argparse.ArgumentParser()
parser.add_argument('--unet_path', default=None, type=str, required=True, help='Path to the unet.pt to convert.')
parser.add_argument(
'--dump_path', default=None, type=str, required=True, help='Path to output the converted UNet model.'
)
parser.add_argument('--class_cond', default=True, type=str, help='Whether the model is class-conditional.')
_UpperCamelCase: Optional[int] = parser.parse_args()
_UpperCamelCase: List[str] = strabool(args.class_cond)
_UpperCamelCase: int = os.path.basename(args.unet_path)
print(f'''Checkpoint: {ckpt_name}''')
# Get U-Net config
if "imagenet64" in ckpt_name:
_UpperCamelCase: str = IMAGENET_64_UNET_CONFIG
elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
_UpperCamelCase: Any = LSUN_256_UNET_CONFIG
elif "test" in ckpt_name:
_UpperCamelCase: List[str] = TEST_UNET_CONFIG
else:
raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')
if not args.class_cond:
_UpperCamelCase: Optional[Any] = None
_UpperCamelCase: List[Any] = con_pt_to_diffuser(args.unet_path, unet_config)
_UpperCamelCase: str = UNetaDModel(**unet_config)
image_unet.load_state_dict(converted_unet_ckpt)
# Get scheduler config
if "cd" in ckpt_name or "test" in ckpt_name:
_UpperCamelCase: int = CD_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
_UpperCamelCase: Optional[int] = CT_IMAGENET_64_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
_UpperCamelCase: Any = CT_LSUN_256_SCHEDULER_CONFIG
else:
raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')
_UpperCamelCase: Tuple = CMStochasticIterativeScheduler(**scheduler_config)
_UpperCamelCase: Optional[int] = ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
consistency_model.save_pretrained(args.dump_path)
| 255 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
@dataclass
class a__ ( SCREAMING_SNAKE_CASE__ ):
_lowerCamelCase = 42
class a__ ( SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ ):
@register_to_config
def __init__( self : Optional[int], lowerCAmelCase : int = 32, lowerCAmelCase : int = 64, lowerCAmelCase : int = 20, lowerCAmelCase : int = 768, lowerCAmelCase : Optional[Any]=77, lowerCAmelCase : Tuple=4, lowerCAmelCase : float = 0.0, lowerCAmelCase : str = "silu", lowerCAmelCase : Optional[str] = None, lowerCAmelCase : Optional[str] = None, lowerCAmelCase : Optional[str] = "linear", lowerCAmelCase : Optional[str] = "prd", lowerCAmelCase : Optional[int] = None, lowerCAmelCase : Optional[int] = None, lowerCAmelCase : Optional[int] = None, ) -> List[Any]:
super().__init__()
lowercase : List[Any] = num_attention_heads
lowercase : int = attention_head_dim
lowercase : List[Any] = num_attention_heads * attention_head_dim
lowercase : Tuple = additional_embeddings
lowercase : Dict = time_embed_dim or inner_dim
lowercase : Optional[Any] = embedding_proj_dim or embedding_dim
lowercase : int = clip_embed_dim or embedding_dim
lowercase : List[str] = Timesteps(lowerCAmelCase, lowerCAmelCase, 0 )
lowercase : List[str] = TimestepEmbedding(lowerCAmelCase, lowerCAmelCase, out_dim=lowerCAmelCase, act_fn=lowerCAmelCase )
lowercase : List[str] = nn.Linear(lowerCAmelCase, lowerCAmelCase )
if embedding_proj_norm_type is None:
lowercase : str = None
elif embedding_proj_norm_type == "layer":
lowercase : Tuple = nn.LayerNorm(lowerCAmelCase )
else:
raise ValueError(f'''unsupported embedding_proj_norm_type: {embedding_proj_norm_type}''' )
lowercase : List[str] = nn.Linear(lowerCAmelCase, lowerCAmelCase )
if encoder_hid_proj_type is None:
lowercase : Optional[int] = None
elif encoder_hid_proj_type == "linear":
lowercase : Dict = nn.Linear(lowerCAmelCase, lowerCAmelCase )
else:
raise ValueError(f'''unsupported encoder_hid_proj_type: {encoder_hid_proj_type}''' )
lowercase : Dict = nn.Parameter(torch.zeros(1, num_embeddings + additional_embeddings, lowerCAmelCase ) )
if added_emb_type == "prd":
lowercase : Union[str, Any] = nn.Parameter(torch.zeros(1, 1, lowerCAmelCase ) )
elif added_emb_type is None:
lowercase : str = None
else:
raise ValueError(
f'''`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.''' )
lowercase : Dict = nn.ModuleList(
[
BasicTransformerBlock(
lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, dropout=lowerCAmelCase, activation_fn='gelu', attention_bias=lowerCAmelCase, )
for d in range(lowerCAmelCase )
] )
if norm_in_type == "layer":
lowercase : str = nn.LayerNorm(lowerCAmelCase )
elif norm_in_type is None:
lowercase : Optional[int] = None
else:
raise ValueError(f'''Unsupported norm_in_type: {norm_in_type}.''' )
lowercase : int = nn.LayerNorm(lowerCAmelCase )
lowercase : str = nn.Linear(lowerCAmelCase, lowerCAmelCase )
lowercase : Optional[Any] = torch.full(
[num_embeddings + additional_embeddings, num_embeddings + additional_embeddings], -1_0000.0 )
causal_attention_mask.triu_(1 )
lowercase : List[str] = causal_attention_mask[None, ...]
self.register_buffer('causal_attention_mask', lowerCAmelCase, persistent=lowerCAmelCase )
lowercase : Any = nn.Parameter(torch.zeros(1, lowerCAmelCase ) )
lowercase : Any = nn.Parameter(torch.zeros(1, lowerCAmelCase ) )
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def lowercase ( self : Tuple ) -> Dict[str, AttentionProcessor]:
lowercase : Any = {}
def fn_recursive_add_processors(lowerCAmelCase : str, lowerCAmelCase : torch.nn.Module, lowerCAmelCase : Dict[str, AttentionProcessor] ):
if hasattr(lowerCAmelCase, 'set_processor' ):
lowercase : List[str] = module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(f'''{name}.{sub_name}''', lowerCAmelCase, lowerCAmelCase )
return processors
for name, module in self.named_children():
fn_recursive_add_processors(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
return processors
def lowercase ( self : Union[str, Any], lowerCAmelCase : Union[AttentionProcessor, Dict[str, AttentionProcessor]] ) -> Tuple:
lowercase : str = len(self.attn_processors.keys() )
if isinstance(lowerCAmelCase, lowerCAmelCase ) and len(lowerCAmelCase ) != count:
raise ValueError(
f'''A dict of processors was passed, but the number of processors {len(lowerCAmelCase )} does not match the'''
f''' number of attention layers: {count}. Please make sure to pass {count} processor classes.''' )
def fn_recursive_attn_processor(lowerCAmelCase : str, lowerCAmelCase : torch.nn.Module, lowerCAmelCase : Union[str, Any] ):
if hasattr(lowerCAmelCase, 'set_processor' ):
if not isinstance(lowerCAmelCase, lowerCAmelCase ):
module.set_processor(lowerCAmelCase )
else:
module.set_processor(processor.pop(f'''{name}.processor''' ) )
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f'''{name}.{sub_name}''', lowerCAmelCase, lowerCAmelCase )
for name, module in self.named_children():
fn_recursive_attn_processor(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
def lowercase ( self : Optional[Any] ) -> Optional[Any]:
self.set_attn_processor(AttnProcessor() )
def lowercase ( self : Any, lowerCAmelCase : int, lowerCAmelCase : Union[torch.Tensor, float, int], lowerCAmelCase : torch.FloatTensor, lowerCAmelCase : Optional[torch.FloatTensor] = None, lowerCAmelCase : Optional[torch.BoolTensor] = None, lowerCAmelCase : bool = True, ) -> List[Any]:
lowercase : Optional[Any] = hidden_states.shape[0]
lowercase : Union[str, Any] = timestep
if not torch.is_tensor(lowerCAmelCase ):
lowercase : List[str] = torch.tensor([timesteps], dtype=torch.long, device=hidden_states.device )
elif torch.is_tensor(lowerCAmelCase ) and len(timesteps.shape ) == 0:
lowercase : List[str] = timesteps[None].to(hidden_states.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
lowercase : Optional[int] = timesteps * torch.ones(lowerCAmelCase, dtype=timesteps.dtype, device=timesteps.device )
lowercase : Dict = self.time_proj(lowerCAmelCase )
# timesteps does not contain any weights and will always return f32 tensors
# but time_embedding might be fp16, so we need to cast here.
lowercase : Optional[int] = timesteps_projected.to(dtype=self.dtype )
lowercase : Any = self.time_embedding(lowerCAmelCase )
if self.embedding_proj_norm is not None:
lowercase : Any = self.embedding_proj_norm(lowerCAmelCase )
lowercase : List[str] = self.embedding_proj(lowerCAmelCase )
if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
lowercase : str = self.encoder_hidden_states_proj(lowerCAmelCase )
elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
raise ValueError('`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set' )
lowercase : Optional[Any] = self.proj_in(lowerCAmelCase )
lowercase : Optional[int] = self.positional_embedding.to(hidden_states.dtype )
lowercase : Dict = []
lowercase : Optional[int] = 0
if encoder_hidden_states is not None:
additional_embeds.append(lowerCAmelCase )
additional_embeddings_len += encoder_hidden_states.shape[1]
if len(proj_embeddings.shape ) == 2:
lowercase : str = proj_embeddings[:, None, :]
if len(hidden_states.shape ) == 2:
lowercase : Union[str, Any] = hidden_states[:, None, :]
lowercase : int = additional_embeds + [
proj_embeddings,
time_embeddings[:, None, :],
hidden_states,
]
if self.prd_embedding is not None:
lowercase : List[str] = self.prd_embedding.to(hidden_states.dtype ).expand(lowerCAmelCase, -1, -1 )
additional_embeds.append(lowerCAmelCase )
lowercase : Union[str, Any] = torch.cat(
lowerCAmelCase, dim=1, )
# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
lowercase : Optional[int] = additional_embeddings_len + proj_embeddings.shape[1] + 1
if positional_embeddings.shape[1] < hidden_states.shape[1]:
lowercase : List[Any] = F.pad(
lowerCAmelCase, (
0,
0,
additional_embeddings_len,
self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
), value=0.0, )
lowercase : str = hidden_states + positional_embeddings
if attention_mask is not None:
lowercase : Tuple = (1 - attention_mask.to(hidden_states.dtype )) * -1_0000.0
lowercase : List[Any] = F.pad(lowerCAmelCase, (0, self.additional_embeddings), value=0.0 )
lowercase : int = (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype )
lowercase : Union[str, Any] = attention_mask.repeat_interleave(self.config.num_attention_heads, dim=0 )
if self.norm_in is not None:
lowercase : List[Any] = self.norm_in(lowerCAmelCase )
for block in self.transformer_blocks:
lowercase : Tuple = block(lowerCAmelCase, attention_mask=lowerCAmelCase )
lowercase : Optional[Any] = self.norm_out(lowerCAmelCase )
if self.prd_embedding is not None:
lowercase : Optional[Any] = hidden_states[:, -1]
else:
lowercase : Any = hidden_states[:, additional_embeddings_len:]
lowercase : Optional[int] = self.proj_to_clip_embeddings(lowerCAmelCase )
if not return_dict:
return (predicted_image_embedding,)
return PriorTransformerOutput(predicted_image_embedding=lowerCAmelCase )
def lowercase ( self : Any, lowerCAmelCase : Dict ) -> Dict:
lowercase : int = (prior_latents * self.clip_std) + self.clip_mean
return prior_latents
| 255 | 1 |
'''simple docstring'''
def _A ( _lowerCAmelCase ):
"""simple docstring"""
if any(not isinstance(_lowerCAmelCase , _lowerCAmelCase ) or x < 0 for x in sequence ):
raise TypeError('Sequence must be list of non-negative integers' )
for _ in range(len(_lowerCAmelCase ) ):
for i, (rod_upper, rod_lower) in enumerate(zip(_lowerCAmelCase , sequence[1:] ) ):
if rod_upper > rod_lower:
sequence[i] -= rod_upper - rod_lower
sequence[i + 1] += rod_upper - rod_lower
return sequence
if __name__ == "__main__":
assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]
| 48 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _UpperCamelCase ( A , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = XLMTokenizer
lowerCAmelCase__ = False
def __lowerCamelCase ( self : Union[str, Any]):
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
__lowercase =[
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'w</w>',
'r</w>',
't</w>',
'lo',
'low',
'er</w>',
'low</w>',
'lowest</w>',
'newer</w>',
'wider</w>',
'<unk>',
]
__lowercase =dict(zip(_lowerCAmelCase , range(len(_lowerCAmelCase))))
__lowercase =['l o 123', 'lo w 1456', 'e r</w> 1789', '']
__lowercase =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'])
__lowercase =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'])
with open(self.vocab_file , 'w') as fp:
fp.write(json.dumps(_lowerCAmelCase))
with open(self.merges_file , 'w') as fp:
fp.write('\n'.join(_lowerCAmelCase))
def __lowerCamelCase ( self : List[str] , _lowerCAmelCase : Any):
'''simple docstring'''
__lowercase ='lower newer'
__lowercase ='lower newer'
return input_text, output_text
def __lowerCamelCase ( self : str):
'''simple docstring'''
__lowercase =XLMTokenizer(self.vocab_file , self.merges_file)
__lowercase ='lower'
__lowercase =['low', 'er</w>']
__lowercase =tokenizer.tokenize(_lowerCAmelCase)
self.assertListEqual(_lowerCAmelCase , _lowerCAmelCase)
__lowercase =tokens + ['<unk>']
__lowercase =[1_4, 1_5, 2_0]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase) , _lowerCAmelCase)
@slow
def __lowerCamelCase ( self : str):
'''simple docstring'''
__lowercase =XLMTokenizer.from_pretrained('xlm-mlm-en-2048')
__lowercase =tokenizer.encode('sequence builders' , add_special_tokens=_lowerCAmelCase)
__lowercase =tokenizer.encode('multi-sequence build' , add_special_tokens=_lowerCAmelCase)
__lowercase =tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase)
__lowercase =tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase , _lowerCAmelCase)
assert encoded_sentence == [0] + text + [1]
assert encoded_pair == [0] + text + [1] + text_a + [1]
| 48 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.json',
# See all Cvt models at https://huggingface.co/models?filter=cvt
}
class A_ (lowercase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = """cvt"""
def __init__( self , lowercase_=3 , lowercase_=[7, 3, 3] , lowercase_=[4, 2, 2] , lowercase_=[2, 1, 1] , lowercase_=[64, 192, 384] , lowercase_=[1, 3, 6] , lowercase_=[1, 2, 10] , lowercase_=[4.0, 4.0, 4.0] , lowercase_=[0.0, 0.0, 0.0] , lowercase_=[0.0, 0.0, 0.0] , lowercase_=[0.0, 0.0, 0.1] , lowercase_=[True, True, True] , lowercase_=[False, False, True] , lowercase_=["dw_bn", "dw_bn", "dw_bn"] , lowercase_=[3, 3, 3] , lowercase_=[1, 1, 1] , lowercase_=[2, 2, 2] , lowercase_=[1, 1, 1] , lowercase_=[1, 1, 1] , lowercase_=0.02 , lowercase_=1E-1_2 , **lowercase_ , ):
"""simple docstring"""
super().__init__(**lowercase_ )
UpperCAmelCase_ : Tuple = num_channels
UpperCAmelCase_ : int = patch_sizes
UpperCAmelCase_ : Dict = patch_stride
UpperCAmelCase_ : Union[str, Any] = patch_padding
UpperCAmelCase_ : Optional[int] = embed_dim
UpperCAmelCase_ : Tuple = num_heads
UpperCAmelCase_ : Tuple = depth
UpperCAmelCase_ : Optional[Any] = mlp_ratio
UpperCAmelCase_ : List[Any] = attention_drop_rate
UpperCAmelCase_ : str = drop_rate
UpperCAmelCase_ : Any = drop_path_rate
UpperCAmelCase_ : Union[str, Any] = qkv_bias
UpperCAmelCase_ : Union[str, Any] = cls_token
UpperCAmelCase_ : Any = qkv_projection_method
UpperCAmelCase_ : Optional[Any] = kernel_qkv
UpperCAmelCase_ : int = padding_kv
UpperCAmelCase_ : List[Any] = stride_kv
UpperCAmelCase_ : Tuple = padding_q
UpperCAmelCase_ : int = stride_q
UpperCAmelCase_ : Any = initializer_range
UpperCAmelCase_ : Union[str, Any] = layer_norm_eps
| 61 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
_a = 'platform'
import jax
import jax.numpy as jnp
from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
FlaxBlenderbotSmallForConditionalGeneration,
FlaxBlenderbotSmallModel,
shift_tokens_right,
)
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase=None, __lowerCamelCase=None, __lowerCamelCase=None, __lowerCamelCase=None, __lowerCamelCase=None, __lowerCamelCase=None, ):
if attention_mask is None:
UpperCAmelCase_ : Union[str, Any] = np.where(input_ids != config.pad_token_id, 1, 0 )
if decoder_attention_mask is None:
UpperCAmelCase_ : Optional[int] = np.where(decoder_input_ids != config.pad_token_id, 1, 0 )
if head_mask is None:
UpperCAmelCase_ : int = np.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
UpperCAmelCase_ : Union[str, Any] = np.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
UpperCAmelCase_ : List[Any] = np.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": attention_mask,
}
class A_ :
'''simple docstring'''
def __init__( self , lowercase_ , lowercase_=13 , lowercase_=7 , lowercase_=True , lowercase_=False , lowercase_=99 , lowercase_=16 , lowercase_=2 , lowercase_=4 , lowercase_=4 , lowercase_="gelu" , lowercase_=0.1 , lowercase_=0.1 , lowercase_=32 , lowercase_=2 , lowercase_=1 , lowercase_=0 , lowercase_=0.02 , ):
"""simple docstring"""
UpperCAmelCase_ : List[str] = parent
UpperCAmelCase_ : Tuple = batch_size
UpperCAmelCase_ : str = seq_length
UpperCAmelCase_ : Dict = is_training
UpperCAmelCase_ : List[Any] = use_labels
UpperCAmelCase_ : Optional[int] = vocab_size
UpperCAmelCase_ : int = hidden_size
UpperCAmelCase_ : Optional[Any] = num_hidden_layers
UpperCAmelCase_ : Dict = num_attention_heads
UpperCAmelCase_ : List[str] = intermediate_size
UpperCAmelCase_ : Optional[int] = hidden_act
UpperCAmelCase_ : str = hidden_dropout_prob
UpperCAmelCase_ : int = attention_probs_dropout_prob
UpperCAmelCase_ : Optional[Any] = max_position_embeddings
UpperCAmelCase_ : str = eos_token_id
UpperCAmelCase_ : str = pad_token_id
UpperCAmelCase_ : str = bos_token_id
UpperCAmelCase_ : List[Any] = initializer_range
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : Optional[Any] = np.clip(ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ) , 3 , self.vocab_size )
UpperCAmelCase_ : Any = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) , dtype=np.intaa )) , -1 )
UpperCAmelCase_ : str = shift_tokens_right(lowercase_ , 1 , 2 )
UpperCAmelCase_ : str = BlenderbotSmallConfig(
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_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , initializer_range=self.initializer_range , use_cache=lowercase_ , )
UpperCAmelCase_ : Optional[int] = prepare_blenderbot_inputs_dict(lowercase_ , lowercase_ , lowercase_ )
return config, inputs_dict
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ : Optional[int] = self.prepare_config_and_inputs()
return config, inputs_dict
def UpperCamelCase__ ( self , lowercase_ , lowercase_ , lowercase_ ):
"""simple docstring"""
UpperCAmelCase_ : List[str] = 20
UpperCAmelCase_ : int = model_class_name(lowercase_ )
UpperCAmelCase_ : Optional[int] = model.encode(inputs_dict["input_ids"] )
UpperCAmelCase_ , UpperCAmelCase_ : Any = (
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
UpperCAmelCase_ : Any = model.init_cache(decoder_input_ids.shape[0] , lowercase_ , lowercase_ )
UpperCAmelCase_ : Tuple = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype="i4" )
UpperCAmelCase_ : Union[str, Any] = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
UpperCAmelCase_ : int = model.decode(
decoder_input_ids[:, :-1] , lowercase_ , decoder_attention_mask=lowercase_ , past_key_values=lowercase_ , decoder_position_ids=lowercase_ , )
UpperCAmelCase_ : int = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="i4" )
UpperCAmelCase_ : Dict = model.decode(
decoder_input_ids[:, -1:] , lowercase_ , decoder_attention_mask=lowercase_ , past_key_values=outputs_cache.past_key_values , decoder_position_ids=lowercase_ , )
UpperCAmelCase_ : Optional[Any] = model.decode(lowercase_ , lowercase_ )
UpperCAmelCase_ : Tuple = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=F"""Max diff is {diff}""" )
def UpperCamelCase__ ( self , lowercase_ , lowercase_ , lowercase_ ):
"""simple docstring"""
UpperCAmelCase_ : List[str] = 20
UpperCAmelCase_ : Any = model_class_name(lowercase_ )
UpperCAmelCase_ : Tuple = model.encode(inputs_dict["input_ids"] )
UpperCAmelCase_ , UpperCAmelCase_ : List[Any] = (
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
UpperCAmelCase_ : Optional[Any] = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] , axis=-1 , )
UpperCAmelCase_ : int = model.init_cache(decoder_input_ids.shape[0] , lowercase_ , lowercase_ )
UpperCAmelCase_ : List[str] = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
UpperCAmelCase_ : List[str] = model.decode(
decoder_input_ids[:, :-1] , lowercase_ , decoder_attention_mask=lowercase_ , past_key_values=lowercase_ , decoder_position_ids=lowercase_ , )
UpperCAmelCase_ : Any = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="i4" )
UpperCAmelCase_ : Dict = model.decode(
decoder_input_ids[:, -1:] , lowercase_ , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=lowercase_ , decoder_position_ids=lowercase_ , )
UpperCAmelCase_ : Dict = model.decode(lowercase_ , lowercase_ , decoder_attention_mask=lowercase_ )
UpperCAmelCase_ : Optional[Any] = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=F"""Max diff is {diff}""" )
@require_flax
class A_ (unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = 99
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : Optional[Any] = np.array(
[
[71, 82, 18, 33, 46, 91, 2],
[68, 34, 26, 58, 30, 82, 2],
[5, 97, 17, 39, 94, 40, 2],
[76, 83, 94, 25, 70, 78, 2],
[87, 59, 41, 35, 48, 66, 2],
[55, 13, 16, 58, 5, 2, 1], # note padding
[64, 27, 31, 51, 12, 75, 2],
[52, 64, 86, 17, 83, 39, 2],
[48, 61, 9, 24, 71, 82, 2],
[26, 1, 60, 48, 22, 13, 2],
[21, 5, 62, 28, 14, 76, 2],
[45, 98, 37, 86, 59, 48, 2],
[70, 70, 50, 9, 28, 0, 2],
] , dtype=np.intaa , )
UpperCAmelCase_ : Any = input_ids.shape[0]
UpperCAmelCase_ : Dict = BlenderbotSmallConfig(
vocab_size=self.vocab_size , d_model=24 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=32 , decoder_ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Tuple = self._get_config_and_data()
UpperCAmelCase_ : List[str] = FlaxBlenderbotSmallForConditionalGeneration(lowercase_ )
UpperCAmelCase_ : Optional[int] = lm_model(input_ids=lowercase_ )
UpperCAmelCase_ : Optional[int] = (batch_size, input_ids.shape[1], config.vocab_size)
self.assertEqual(outputs["logits"].shape , lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : List[str] = BlenderbotSmallConfig(
vocab_size=self.vocab_size , d_model=14 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=8 , decoder_ffn_dim=8 , max_position_embeddings=48 , )
UpperCAmelCase_ : Optional[int] = FlaxBlenderbotSmallForConditionalGeneration(lowercase_ )
UpperCAmelCase_ : str = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] , dtype=np.intaa )
UpperCAmelCase_ : str = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] , dtype=np.intaa )
UpperCAmelCase_ : Tuple = lm_model(input_ids=lowercase_ , decoder_input_ids=lowercase_ )
UpperCAmelCase_ : Tuple = (*summary.shape, config.vocab_size)
self.assertEqual(outputs["logits"].shape , lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : List[Any] = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] , dtype=np.intaa )
UpperCAmelCase_ : Dict = shift_tokens_right(lowercase_ , 1 , 2 )
UpperCAmelCase_ : Tuple = np.equal(lowercase_ , 1 ).astype(np.floataa ).sum()
UpperCAmelCase_ : Optional[Any] = np.equal(lowercase_ , 1 ).astype(np.floataa ).sum()
self.assertEqual(shifted.shape , input_ids.shape )
self.assertEqual(lowercase_ , n_pad_before - 1 )
self.assertTrue(np.equal(shifted[:, 0] , 2 ).all() )
@require_flax
class A_ (lowercase__ ,unittest.TestCase ,lowercase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = True
SCREAMING_SNAKE_CASE__ : Union[str, Any] = (
(
FlaxBlenderbotSmallModel,
FlaxBlenderbotSmallForConditionalGeneration,
)
if is_flax_available()
else ()
)
SCREAMING_SNAKE_CASE__ : List[Any] = (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : Dict = FlaxBlenderbotSmallModelTester(self )
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ : Tuple = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(lowercase_ , lowercase_ , lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(lowercase_ , lowercase_ , lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
UpperCAmelCase_ : List[Any] = self._prepare_for_class(lowercase_ , lowercase_ )
UpperCAmelCase_ : Dict = model_class(lowercase_ )
@jax.jit
def encode_jitted(lowercase_ , lowercase_=None , **lowercase_ ):
return model.encode(input_ids=lowercase_ , attention_mask=lowercase_ )
with self.subTest("JIT Enabled" ):
UpperCAmelCase_ : List[Any] = encode_jitted(**lowercase_ ).to_tuple()
with self.subTest("JIT Disabled" ):
with jax.disable_jit():
UpperCAmelCase_ : Optional[Any] = encode_jitted(**lowercase_ ).to_tuple()
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) )
for jitted_output, output in zip(lowercase_ , lowercase_ ):
self.assertEqual(jitted_output.shape , output.shape )
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
UpperCAmelCase_ : Optional[int] = model_class(lowercase_ )
UpperCAmelCase_ : Tuple = model.encode(inputs_dict["input_ids"] , inputs_dict["attention_mask"] )
UpperCAmelCase_ : int = {
"decoder_input_ids": inputs_dict["decoder_input_ids"],
"decoder_attention_mask": inputs_dict["decoder_attention_mask"],
"encoder_outputs": encoder_outputs,
}
@jax.jit
def decode_jitted(lowercase_ , lowercase_ , lowercase_ ):
return model.decode(
decoder_input_ids=lowercase_ , decoder_attention_mask=lowercase_ , encoder_outputs=lowercase_ , )
with self.subTest("JIT Enabled" ):
UpperCAmelCase_ : str = decode_jitted(**lowercase_ ).to_tuple()
with self.subTest("JIT Disabled" ):
with jax.disable_jit():
UpperCAmelCase_ : List[Any] = decode_jitted(**lowercase_ ).to_tuple()
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) )
for jitted_output, output in zip(lowercase_ , lowercase_ ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
for model_class_name in self.all_model_classes:
UpperCAmelCase_ : Optional[Any] = model_class_name.from_pretrained("facebook/blenderbot_small-90M" )
# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
UpperCAmelCase_ : List[str] = np.ones((1, 1) ) * model.config.eos_token_id
UpperCAmelCase_ : Optional[int] = model(lowercase_ )
self.assertIsNotNone(lowercase_ )
| 61 | 1 |
from __future__ import annotations
class a__ :
def __init__( self , _A ):
"""simple docstring"""
__lowerCAmelCase = TypeError(
"Matrices must be formed from a list of zero or more lists containing at "
"least one and the same number of values, each of which must be of type "
"int or float." )
if len(_A ) != 0:
__lowerCAmelCase = len(rows[0] )
if cols == 0:
raise error
for row in rows:
if len(_A ) != cols:
raise error
for value in row:
if not isinstance(_A , (int, float) ):
raise error
__lowerCAmelCase = rows
else:
__lowerCAmelCase = []
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return [[row[i] for row in self.rows] for i in range(len(self.rows[0] ) )]
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return len(self.rows )
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return len(self.rows[0] )
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return (self.num_rows, self.num_columns)
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return self.order[0] == self.order[1]
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = [
[0 if column_num != row_num else 1 for column_num in range(self.num_rows )]
for row_num in range(self.num_rows )
]
return Matrix(_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
if not self.is_square:
return 0
if self.order == (0, 0):
return 1
if self.order == (1, 1):
return int(self.rows[0][0] )
if self.order == (2, 2):
return int(
(self.rows[0][0] * self.rows[1][1])
- (self.rows[0][1] * self.rows[1][0]) )
else:
return sum(
self.rows[0][column] * self.cofactors().rows[0][column]
for column in range(self.num_columns ) )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return bool(self.determinant() )
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = [
[
self.rows[other_row][other_column]
for other_column in range(self.num_columns )
if other_column != column
]
for other_row in range(self.num_rows )
if other_row != row
]
return Matrix(_A ).determinant()
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
if (row + column) % 2 == 0:
return self.get_minor(_A , _A )
return -1 * self.get_minor(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return Matrix(
[
[self.get_minor(_A , _A ) for column in range(self.num_columns )]
for row in range(self.num_rows )
] )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return Matrix(
[
[
self.minors().rows[row][column]
if (row + column) % 2 == 0
else self.minors().rows[row][column] * -1
for column in range(self.minors().num_columns )
]
for row in range(self.minors().num_rows )
] )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = [
[self.cofactors().rows[column][row] for column in range(self.num_columns )]
for row in range(self.num_rows )
]
return Matrix(_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.determinant()
if not determinant:
raise TypeError("Only matrices with a non-zero determinant have an inverse" )
return self.adjugate() * (1 / determinant)
def __repr__( self ):
"""simple docstring"""
return str(self.rows )
def __str__( self ):
"""simple docstring"""
if self.num_rows == 0:
return "[]"
if self.num_rows == 1:
return "[[" + ". ".join(str(self.rows[0] ) ) + "]]"
return (
"["
+ "\n ".join(
[
"[" + ". ".join([str(_A ) for value in row] ) + ".]"
for row in self.rows
] )
+ "]"
)
def __SCREAMING_SNAKE_CASE( self , _A , _A = None ):
"""simple docstring"""
__lowerCAmelCase = TypeError("Row must be a list containing all ints and/or floats" )
if not isinstance(_A , _A ):
raise type_error
for value in row:
if not isinstance(_A , (int, float) ):
raise type_error
if len(_A ) != self.num_columns:
raise ValueError(
"Row must be equal in length to the other rows in the matrix" )
if position is None:
self.rows.append(_A )
else:
__lowerCAmelCase = self.rows[0:position] + [row] + self.rows[position:]
def __SCREAMING_SNAKE_CASE( self , _A , _A = None ):
"""simple docstring"""
__lowerCAmelCase = TypeError(
"Column must be a list containing all ints and/or floats" )
if not isinstance(_A , _A ):
raise type_error
for value in column:
if not isinstance(_A , (int, float) ):
raise type_error
if len(_A ) != self.num_rows:
raise ValueError(
"Column must be equal in length to the other columns in the matrix" )
if position is None:
__lowerCAmelCase = [self.rows[i] + [column[i]] for i in range(self.num_rows )]
else:
__lowerCAmelCase = [
self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
for i in range(self.num_rows )
]
def __eq__( self , _A ):
"""simple docstring"""
if not isinstance(_A , _A ):
return NotImplemented
return self.rows == other.rows
def __ne__( self , _A ):
"""simple docstring"""
return not self == other
def __neg__( self ):
"""simple docstring"""
return self * -1
def __add__( self , _A ):
"""simple docstring"""
if self.order != other.order:
raise ValueError("Addition requires matrices of the same order" )
return Matrix(
[
[self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns )]
for i in range(self.num_rows )
] )
def __sub__( self , _A ):
"""simple docstring"""
if self.order != other.order:
raise ValueError("Subtraction requires matrices of the same order" )
return Matrix(
[
[self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns )]
for i in range(self.num_rows )
] )
def __mul__( self , _A ):
"""simple docstring"""
if isinstance(_A , (int, float) ):
return Matrix(
[[int(element * other ) for element in row] for row in self.rows] )
elif isinstance(_A , _A ):
if self.num_columns != other.num_rows:
raise ValueError(
"The number of columns in the first matrix must "
"be equal to the number of rows in the second" )
return Matrix(
[
[Matrix.dot_product(_A , _A ) for column in other.columns()]
for row in self.rows
] )
else:
raise TypeError(
"A Matrix can only be multiplied by an int, float, or another matrix" )
def __pow__( self , _A ):
"""simple docstring"""
if not isinstance(_A , _A ):
raise TypeError("A Matrix can only be raised to the power of an int" )
if not self.is_square:
raise ValueError("Only square matrices can be raised to a power" )
if other == 0:
return self.identity()
if other < 0:
if self.is_invertable():
return self.inverse() ** (-other)
raise ValueError(
"Only invertable matrices can be raised to a negative power" )
__lowerCAmelCase = self
for _ in range(other - 1 ):
result *= self
return result
@classmethod
def __SCREAMING_SNAKE_CASE( cls , _A , _A ):
"""simple docstring"""
return sum(row[i] * column[i] for i in range(len(_A ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 363 |
from sklearn.metrics import mean_squared_error
import datasets
UpperCamelCase__ = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
"""
UpperCamelCase__ = """\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""
UpperCamelCase__ = """
Args:
predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
references: array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
sample_weight: array-like of shape (n_samples,), default=None
Sample weights.
multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
Defines aggregating of multiple output values. Array-like value defines weights used to average errors.
\"raw_values\" : Returns a full set of errors in case of multioutput input.
\"uniform_average\" : Errors of all outputs are averaged with uniform weight.
squared : bool, default=True
If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.
Returns:
mse : mean squared error.
Examples:
>>> mse_metric = datasets.load_metric(\"mse\")
>>> predictions = [2.5, 0.0, 2, 8]
>>> references = [3, -0.5, 2, 7]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'mse': 0.375}
>>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
>>> print(rmse_result)
{'mse': 0.6123724356957945}
If you're using multi-dimensional lists, then set the config as follows :
>>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")
>>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
>>> references = [[0, 2], [-1, 2], [8, -5]]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'mse': 0.7083333333333334}
>>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
>>> print(results) # doctest: +NORMALIZE_WHITESPACE
{'mse': array([0.41666667, 1. ])}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a__ ( datasets.Metric ):
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , reference_urls=[
"https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"
] , )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
if self.config_name == "multilist":
return {
"predictions": datasets.Sequence(datasets.Value("float" ) ),
"references": datasets.Sequence(datasets.Value("float" ) ),
}
else:
return {
"predictions": datasets.Value("float" ),
"references": datasets.Value("float" ),
}
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A=None , _A="uniform_average" , _A=True ):
"""simple docstring"""
__lowerCAmelCase = mean_squared_error(
_A , _A , sample_weight=_A , multioutput=_A , squared=_A )
return {"mse": mse}
| 102 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase__ : int = {
'configuration_ctrl': ['CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CTRLConfig'],
'tokenization_ctrl': ['CTRLTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ : List[str] = [
'CTRL_PRETRAINED_MODEL_ARCHIVE_LIST',
'CTRLForSequenceClassification',
'CTRLLMHeadModel',
'CTRLModel',
'CTRLPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ : int = [
'TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFCTRLForSequenceClassification',
'TFCTRLLMHeadModel',
'TFCTRLModel',
'TFCTRLPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
from .tokenization_ctrl import CTRLTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ctrl import (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
CTRLPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_ctrl import (
TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFCTRLForSequenceClassification,
TFCTRLLMHeadModel,
TFCTRLModel,
TFCTRLPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 98 |
'''simple docstring'''
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def __lowerCAmelCase (__lowerCAmelCase ):
_UpperCAmelCase : List[str] = [
"encoder.version",
"decoder.version",
"model.encoder.version",
"model.decoder.version",
"decoder.output_projection.weight",
"_float_tensor",
"encoder.embed_positions._float_tensor",
"decoder.embed_positions._float_tensor",
]
for k in ignore_keys:
state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
def __lowerCAmelCase (__lowerCAmelCase ):
_UpperCAmelCase , _UpperCAmelCase : Optional[Any] = emb.weight.shape
_UpperCAmelCase : str = nn.Linear(__lowerCAmelCase , __lowerCAmelCase , bias=__lowerCAmelCase )
_UpperCAmelCase : Optional[int] = emb.weight.data
return lin_layer
def __lowerCAmelCase (__lowerCAmelCase , __lowerCAmelCase=None ):
_UpperCAmelCase : int = {}
for old_key in state_dict.keys():
_UpperCAmelCase : Tuple = old_key
if "moe_layer.experts." in key:
if expert_idx is not None:
_UpperCAmelCase : Optional[int] = key.replace("moe_layer.experts.0" , F"""ffn.experts.expert_{expert_idx}""" )
else:
_UpperCAmelCase : Any = key.replace("moe_layer.experts." , "ffn.experts.expert_" )
if "gate" in key:
_UpperCAmelCase : List[Any] = key.replace(".moe_layer.gate.wg" , ".ffn.router.classifier" )
if "fc2" and "experts" not in key:
_UpperCAmelCase : Tuple = key.replace(".fc2." , ".ffn.fc2." )
if "fc1" and "experts" not in key:
_UpperCAmelCase : List[Any] = key.replace(".fc1." , ".ffn.fc1." )
if ".encoder_attn." in key:
_UpperCAmelCase : List[Any] = key.replace(".encoder_attn." , ".cross_attention." )
if "encoder_attn_layer_norm" in key:
_UpperCAmelCase : Any = key.replace("encoder_attn_layer_norm" , "cross_attention_layer_norm" )
if "final_layer_norm" in key:
_UpperCAmelCase : int = key.replace("final_layer_norm" , "ff_layer_norm" )
_UpperCAmelCase : Tuple = state_dict[old_key]
return new_dict
def __lowerCAmelCase (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = WEIGHTS_NAME ):
_UpperCAmelCase : Optional[int] = []
_UpperCAmelCase : Optional[Any] = 0
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
for expert in range(__lowerCAmelCase ):
_UpperCAmelCase : Tuple = switch_checkpoint_path + F"""-rank-{expert}.pt"""
if os.path.isfile(__lowerCAmelCase ):
_UpperCAmelCase : Tuple = torch.load(__lowerCAmelCase )["model"]
remove_ignore_keys_(__lowerCAmelCase )
_UpperCAmelCase : Dict = rename_fairseq_keys(__lowerCAmelCase , __lowerCAmelCase )
_UpperCAmelCase : List[str] = os.path.join(
__lowerCAmelCase , weights_name.replace(".bin" , F"""-{len(__lowerCAmelCase )+1:05d}-of-???.bin""" ) )
torch.save(__lowerCAmelCase , __lowerCAmelCase )
sharded_state_dicts.append(expert_state.keys() )
total_size += sum([value.numel() for key, value in expert_state.items()] ) * dtype_byte_size(
expert_state[list(__lowerCAmelCase )[0]].dtype )
# Add the last block
_UpperCAmelCase : Tuple = os.path.join(__lowerCAmelCase , weights_name.replace(".bin" , F"""-{len(__lowerCAmelCase )+1:05d}-of-???.bin""" ) )
_UpperCAmelCase : Union[str, Any] = torch.load(switch_checkpoint_path + "-shared.pt" )["model"]
remove_ignore_keys_(__lowerCAmelCase )
_UpperCAmelCase : Union[str, Any] = rename_fairseq_keys(__lowerCAmelCase , __lowerCAmelCase )
_UpperCAmelCase : Any = shared_weights["decoder.embed_tokens.weight"]
sharded_state_dicts.append(shared_weights.keys() )
# If we only have the shared weights (dummy model/experts saved on the same file)
if len(__lowerCAmelCase ) == 1:
_UpperCAmelCase : List[str] = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
torch.save(__lowerCAmelCase , __lowerCAmelCase )
return {weights_name: sharded_state_dicts[0]}, None
else:
torch.save(__lowerCAmelCase , __lowerCAmelCase )
# Otherwise, let's build the index
_UpperCAmelCase : Union[str, Any] = {}
for idx, shard in enumerate(__lowerCAmelCase ):
_UpperCAmelCase : Tuple = weights_name.replace(".bin" , F"""-{idx+1:05d}-of-{len(__lowerCAmelCase ):05d}.bin""" )
_UpperCAmelCase : List[Any] = os.path.join(__lowerCAmelCase , weights_name.replace(".bin" , F"""-{idx+1:05d}-of-???.bin""" ) )
os.rename(__lowerCAmelCase , os.path.join(__lowerCAmelCase , __lowerCAmelCase ) )
for key in shard:
_UpperCAmelCase : List[Any] = shard_file
# Add the metadata
_UpperCAmelCase : Any = {"total_size": total_size}
_UpperCAmelCase : List[str] = {"metadata": metadata, "weight_map": weight_map}
with open(os.path.join(__lowerCAmelCase , __lowerCAmelCase ) , "w" , encoding="utf-8" ) as f:
_UpperCAmelCase : Tuple = json.dumps(__lowerCAmelCase , indent=2 , sort_keys=__lowerCAmelCase ) + "\n"
f.write(__lowerCAmelCase )
return metadata, index
if __name__ == "__main__":
lowerCamelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--nllb_moe_checkpoint_path',
default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000',
type=str,
required=False,
help='Path to a directory containing a folder per layer. Follows the original Google format.',
)
parser.add_argument('--dtype', default='float32', type=str, required=False, help='dtype of the saved model')
parser.add_argument(
'--pytorch_dump_folder_path',
default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b',
type=str,
required=False,
help='Path to the output pytorch model.',
)
lowerCamelCase__ = parser.parse_args()
lowerCamelCase__ ,lowerCamelCase__ = shard_on_the_fly(
args.nllb_moe_checkpoint_path,
args.pytorch_dump_folder_path,
128,
args.dtype,
)
lowerCamelCase__ = NllbMoeConfig.from_pretrained(
'facebook/nllb-200-3.3B', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
)
config.save_pretrained(args.pytorch_dump_folder_path)
lowerCamelCase__ = NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
print('Done')
model.save_pretrained(args.pytorch_dump_folder_path)
| 234 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'''kssteven/ibert-roberta-base''': '''https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json''',
'''kssteven/ibert-roberta-large''': '''https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json''',
'''kssteven/ibert-roberta-large-mnli''': (
'''https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'''
),
}
class _snake_case ( _lowercase ):
lowerCamelCase__: int = "ibert"
def __init__( self: Union[str, Any] , __lowerCamelCase: List[str]=3_05_22 , __lowerCamelCase: str=7_68 , __lowerCamelCase: str=12 , __lowerCamelCase: List[str]=12 , __lowerCamelCase: Optional[int]=30_72 , __lowerCamelCase: List[str]="gelu" , __lowerCamelCase: Dict=0.1 , __lowerCamelCase: Union[str, Any]=0.1 , __lowerCamelCase: Optional[Any]=5_12 , __lowerCamelCase: List[Any]=2 , __lowerCamelCase: Tuple=0.02 , __lowerCamelCase: List[str]=1e-12 , __lowerCamelCase: Optional[int]=1 , __lowerCamelCase: int=0 , __lowerCamelCase: Optional[int]=2 , __lowerCamelCase: Tuple="absolute" , __lowerCamelCase: Union[str, Any]=False , __lowerCamelCase: List[str]="none" , **__lowerCamelCase: Dict , ) -> int:
super().__init__(pad_token_id=__lowerCamelCase , bos_token_id=__lowerCamelCase , eos_token_id=__lowerCamelCase , **__lowerCamelCase )
__UpperCAmelCase : List[Any] = vocab_size
__UpperCAmelCase : Dict = hidden_size
__UpperCAmelCase : Any = num_hidden_layers
__UpperCAmelCase : int = num_attention_heads
__UpperCAmelCase : Union[str, Any] = hidden_act
__UpperCAmelCase : Union[str, Any] = intermediate_size
__UpperCAmelCase : List[str] = hidden_dropout_prob
__UpperCAmelCase : List[Any] = attention_probs_dropout_prob
__UpperCAmelCase : int = max_position_embeddings
__UpperCAmelCase : List[Any] = type_vocab_size
__UpperCAmelCase : Optional[int] = initializer_range
__UpperCAmelCase : List[Any] = layer_norm_eps
__UpperCAmelCase : Dict = position_embedding_type
__UpperCAmelCase : Dict = quant_mode
__UpperCAmelCase : Tuple = force_dequant
class _snake_case ( _lowercase ):
@property
def _lowerCamelCase ( self: Optional[int] ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
__UpperCAmelCase : int = {0: "batch", 1: "choice", 2: "sequence"}
else:
__UpperCAmelCase : Tuple = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] )
| 342 | import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_snake_case = logging.get_logger(__name__)
_snake_case = {
'''facebook/detr-resnet-50''': '''https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json''',
# See all DETR models at https://huggingface.co/models?filter=detr
}
class _snake_case ( _lowercase ):
lowerCamelCase__: str = "detr"
lowerCamelCase__: Dict = ["past_key_values"]
lowerCamelCase__: str = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
}
def __init__( self: List[str] , __lowerCamelCase: List[Any]=True , __lowerCamelCase: Any=None , __lowerCamelCase: Dict=3 , __lowerCamelCase: str=1_00 , __lowerCamelCase: Union[str, Any]=6 , __lowerCamelCase: Union[str, Any]=20_48 , __lowerCamelCase: Dict=8 , __lowerCamelCase: Optional[int]=6 , __lowerCamelCase: List[Any]=20_48 , __lowerCamelCase: int=8 , __lowerCamelCase: Tuple=0.0 , __lowerCamelCase: Dict=0.0 , __lowerCamelCase: Any=True , __lowerCamelCase: Tuple="relu" , __lowerCamelCase: Tuple=2_56 , __lowerCamelCase: Dict=0.1 , __lowerCamelCase: Union[str, Any]=0.0 , __lowerCamelCase: Optional[int]=0.0 , __lowerCamelCase: Union[str, Any]=0.02 , __lowerCamelCase: str=1.0 , __lowerCamelCase: List[str]=False , __lowerCamelCase: Dict="sine" , __lowerCamelCase: Optional[int]="resnet50" , __lowerCamelCase: Optional[int]=True , __lowerCamelCase: int=False , __lowerCamelCase: Union[str, Any]=1 , __lowerCamelCase: Tuple=5 , __lowerCamelCase: int=2 , __lowerCamelCase: Dict=1 , __lowerCamelCase: Dict=1 , __lowerCamelCase: Union[str, Any]=5 , __lowerCamelCase: Dict=2 , __lowerCamelCase: int=0.1 , **__lowerCamelCase: str , ) -> int:
if backbone_config is not None and use_timm_backbone:
raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`." )
if not use_timm_backbone:
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
__UpperCAmelCase : Optional[int] = CONFIG_MAPPING["resnet"](out_features=["stage4"] )
elif isinstance(__lowerCamelCase , __lowerCamelCase ):
__UpperCAmelCase : List[Any] = backbone_config.get("model_type" )
__UpperCAmelCase : List[str] = CONFIG_MAPPING[backbone_model_type]
__UpperCAmelCase : List[str] = config_class.from_dict(__lowerCamelCase )
# set timm attributes to None
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : List[Any] = None, None, None
__UpperCAmelCase : Any = use_timm_backbone
__UpperCAmelCase : Optional[Any] = backbone_config
__UpperCAmelCase : Optional[Any] = num_channels
__UpperCAmelCase : List[Any] = num_queries
__UpperCAmelCase : Optional[int] = d_model
__UpperCAmelCase : Optional[Any] = encoder_ffn_dim
__UpperCAmelCase : Dict = encoder_layers
__UpperCAmelCase : List[Any] = encoder_attention_heads
__UpperCAmelCase : int = decoder_ffn_dim
__UpperCAmelCase : Tuple = decoder_layers
__UpperCAmelCase : int = decoder_attention_heads
__UpperCAmelCase : List[Any] = dropout
__UpperCAmelCase : Dict = attention_dropout
__UpperCAmelCase : Optional[Any] = activation_dropout
__UpperCAmelCase : int = activation_function
__UpperCAmelCase : Any = init_std
__UpperCAmelCase : str = init_xavier_std
__UpperCAmelCase : int = encoder_layerdrop
__UpperCAmelCase : Tuple = decoder_layerdrop
__UpperCAmelCase : List[Any] = encoder_layers
__UpperCAmelCase : Optional[Any] = auxiliary_loss
__UpperCAmelCase : int = position_embedding_type
__UpperCAmelCase : Optional[int] = backbone
__UpperCAmelCase : str = use_pretrained_backbone
__UpperCAmelCase : Dict = dilation
# Hungarian matcher
__UpperCAmelCase : Optional[int] = class_cost
__UpperCAmelCase : Optional[Any] = bbox_cost
__UpperCAmelCase : Optional[int] = giou_cost
# Loss coefficients
__UpperCAmelCase : Any = mask_loss_coefficient
__UpperCAmelCase : Any = dice_loss_coefficient
__UpperCAmelCase : Any = bbox_loss_coefficient
__UpperCAmelCase : Optional[int] = giou_loss_coefficient
__UpperCAmelCase : Optional[Any] = eos_coefficient
super().__init__(is_encoder_decoder=__lowerCamelCase , **__lowerCamelCase )
@property
def _lowerCamelCase ( self: Dict ) -> int:
return self.encoder_attention_heads
@property
def _lowerCamelCase ( self: str ) -> int:
return self.d_model
@classmethod
def _lowerCamelCase ( cls: Optional[int] , __lowerCamelCase: PretrainedConfig , **__lowerCamelCase: List[Any] ) -> List[Any]:
return cls(backbone_config=__lowerCamelCase , **__lowerCamelCase )
def _lowerCamelCase ( self: str ) -> Dict[str, any]:
__UpperCAmelCase : Optional[int] = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
__UpperCAmelCase : int = self.backbone_config.to_dict()
__UpperCAmelCase : List[str] = self.__class__.model_type
return output
class _snake_case ( _lowercase ):
lowerCamelCase__: Optional[int] = version.parse("1.11" )
@property
def _lowerCamelCase ( self: Optional[Any] ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
("pixel_mask", {0: "batch"}),
] )
@property
def _lowerCamelCase ( self: Optional[Any] ) -> float:
return 1e-5
@property
def _lowerCamelCase ( self: List[str] ) -> int:
return 12
| 342 | 1 |
'''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 UpperCamelCase_ ( _UpperCAmelCase : List[str] ) -> str:
"""simple docstring"""
_UpperCAmelCase : Tuple = []
for line in lines:
_UpperCAmelCase : Optional[Any] = re.sub(R"#.*" , "" , _UpperCAmelCase ) # remove comments
if line:
filtered_lines.append(_UpperCAmelCase )
_UpperCAmelCase : Optional[int] = "\n".join(_UpperCAmelCase )
# Make a hash from all this code
_UpperCAmelCase : Optional[int] = full_str.encode("utf-8" )
return shaaaa(_UpperCAmelCase ).hexdigest()
# get importable module names and hash for caching
__SCREAMING_SNAKE_CASE : Optional[Any] = {
"""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
__SCREAMING_SNAKE_CASE : Tuple = {
""".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})
__SCREAMING_SNAKE_CASE : str = {"""imagefolder""", """audiofolder"""}
# Used to filter data files based on extensions given a module name
__SCREAMING_SNAKE_CASE : 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""")
| 31 |
_a = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def _a ( SCREAMING_SNAKE_CASE : dict , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Any ) -> list[str]:
"""simple docstring"""
__lowerCAmelCase: int = set()
# keep track of all the paths to be checked
__lowerCAmelCase: str = [[start]]
# return path if start is goal
if start == goal:
return [start]
# keeps looping until all possible paths have been checked
while queue:
# pop the first path from the queue
__lowerCAmelCase: str = queue.pop(0 )
# get the last node from the path
__lowerCAmelCase: Union[str, Any] = path[-1]
if node not in explored:
__lowerCAmelCase: Dict = graph[node]
# go through all neighbour nodes, construct a new path and
# push it into the queue
for neighbour in neighbours:
__lowerCAmelCase: Dict = list(SCREAMING_SNAKE_CASE )
new_path.append(SCREAMING_SNAKE_CASE )
queue.append(SCREAMING_SNAKE_CASE )
# return path if neighbour is goal
if neighbour == goal:
return new_path
# mark node as explored
explored.add(SCREAMING_SNAKE_CASE )
# in case there's no path between the 2 nodes
return []
def _a ( SCREAMING_SNAKE_CASE : dict , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : Any ) -> int:
"""simple docstring"""
if not graph or start not in graph or target not in graph:
return -1
if start == target:
return 0
__lowerCAmelCase: Optional[int] = [start]
__lowerCAmelCase: Dict = set(SCREAMING_SNAKE_CASE )
# Keep tab on distances from `start` node.
__lowerCAmelCase: Optional[int] = {start: 0, target: -1}
while queue:
__lowerCAmelCase: Any = queue.pop(0 )
if node == target:
__lowerCAmelCase: Optional[int] = (
dist[node] if dist[target] == -1 else min(dist[target] , dist[node] )
)
for adjacent in graph[node]:
if adjacent not in visited:
visited.add(SCREAMING_SNAKE_CASE )
queue.append(SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Union[str, Any] = dist[node] + 1
return dist[target]
if __name__ == "__main__":
print(bfs_shortest_path(demo_graph, '''G''', '''D''')) # returns ['G', 'C', 'A', 'B', 'D']
print(bfs_shortest_path_distance(demo_graph, '''G''', '''D''')) # returns 4
| 322 | 0 |
"""simple docstring"""
import numpy as np
__SCREAMING_SNAKE_CASE : Optional[int] = [
['a', 'b', 'c', 'd', 'e'],
['f', 'g', 'h', 'i', 'k'],
['l', 'm', 'n', 'o', 'p'],
['q', 'r', 's', 't', 'u'],
['v', 'w', 'x', 'y', 'z'],
]
class __A :
'''simple docstring'''
def __init__( self : Dict ) ->None:
"""simple docstring"""
snake_case_ = np.array(UpperCAmelCase_ )
def lowerCAmelCase ( self : Any , UpperCAmelCase_ : str ) ->np.ndarray:
"""simple docstring"""
snake_case_ , snake_case_ = np.where(letter == self.SQUARE )
snake_case_ = np.concatenate([indexa + 1, indexa + 1] )
return indexes
def lowerCAmelCase ( self : Optional[Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : int ) ->str:
"""simple docstring"""
snake_case_ = self.SQUARE[indexa - 1, indexa - 1]
return letter
def lowerCAmelCase ( self : Optional[Any] , UpperCAmelCase_ : str ) ->str:
"""simple docstring"""
snake_case_ = message.lower()
snake_case_ = message.replace(""" """ , """""" )
snake_case_ = message.replace("""j""" , """i""" )
snake_case_ = np.empty((2, len(UpperCAmelCase_ )) )
for letter_index in range(len(UpperCAmelCase_ ) ):
snake_case_ = self.letter_to_numbers(message[letter_index] )
snake_case_ = numbers[0]
snake_case_ = numbers[1]
snake_case_ = first_step.reshape(2 * len(UpperCAmelCase_ ) )
snake_case_ = """"""
for numbers_index in range(len(UpperCAmelCase_ ) ):
snake_case_ = int(second_step[numbers_index * 2] )
snake_case_ = int(second_step[(numbers_index * 2) + 1] )
snake_case_ = self.numbers_to_letter(UpperCAmelCase_ , UpperCAmelCase_ )
snake_case_ = encoded_message + letter
return encoded_message
def lowerCAmelCase ( self : str , UpperCAmelCase_ : str ) ->str:
"""simple docstring"""
snake_case_ = message.lower()
message.replace(""" """ , """""" )
snake_case_ = np.empty(2 * len(UpperCAmelCase_ ) )
for letter_index in range(len(UpperCAmelCase_ ) ):
snake_case_ = self.letter_to_numbers(message[letter_index] )
snake_case_ = numbers[0]
snake_case_ = numbers[1]
snake_case_ = first_step.reshape((2, len(UpperCAmelCase_ )) )
snake_case_ = """"""
for numbers_index in range(len(UpperCAmelCase_ ) ):
snake_case_ = int(second_step[0, numbers_index] )
snake_case_ = int(second_step[1, numbers_index] )
snake_case_ = self.numbers_to_letter(UpperCAmelCase_ , UpperCAmelCase_ )
snake_case_ = decoded_message + letter
return decoded_message
| 371 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Tuple = {
'google/mobilenet_v1_1.0_224': 'https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json',
'google/mobilenet_v1_0.75_192': 'https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class __A (snake_case__):
'''simple docstring'''
__lowercase: List[Any] = """mobilenet_v1"""
def __init__( self : Union[str, Any] , UpperCAmelCase_ : str=3 , UpperCAmelCase_ : List[Any]=224 , UpperCAmelCase_ : List[Any]=1.0 , UpperCAmelCase_ : Any=8 , UpperCAmelCase_ : int="relu6" , UpperCAmelCase_ : str=True , UpperCAmelCase_ : List[str]=0.999 , UpperCAmelCase_ : Optional[Any]=0.02 , UpperCAmelCase_ : List[Any]=0.001 , **UpperCAmelCase_ : Any , ) ->Union[str, Any]:
"""simple docstring"""
super().__init__(**UpperCAmelCase_ )
if depth_multiplier <= 0:
raise ValueError("""depth_multiplier must be greater than zero.""" )
snake_case_ = num_channels
snake_case_ = image_size
snake_case_ = depth_multiplier
snake_case_ = min_depth
snake_case_ = hidden_act
snake_case_ = tf_padding
snake_case_ = classifier_dropout_prob
snake_case_ = initializer_range
snake_case_ = layer_norm_eps
class __A (snake_case__):
'''simple docstring'''
__lowercase: int = version.parse("""1.11""")
@property
def lowerCAmelCase ( self : Union[str, Any] ) ->Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict([("""pixel_values""", {0: """batch"""})] )
@property
def lowerCAmelCase ( self : int ) ->Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "image-classification":
return OrderedDict([("""logits""", {0: """batch"""})] )
else:
return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})] )
@property
def lowerCAmelCase ( self : int ) ->float:
"""simple docstring"""
return 1E-4
| 233 | 0 |
'''simple docstring'''
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwiftFormerForImageClassification, SwiftFormerModel
from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class _lowerCAmelCase :
'''simple docstring'''
def __init__(self , UpperCAmelCase , UpperCAmelCase=13 , UpperCAmelCase=3 , UpperCAmelCase=True , UpperCAmelCase=True , UpperCAmelCase=0.1 , UpperCAmelCase=0.1 , UpperCAmelCase=224 , UpperCAmelCase=1000 , UpperCAmelCase=[3, 3, 6, 4] , UpperCAmelCase=[48, 56, 112, 220] , ) -> Union[str, Any]:
_snake_case = parent
_snake_case = batch_size
_snake_case = num_channels
_snake_case = is_training
_snake_case = use_labels
_snake_case = hidden_dropout_prob
_snake_case = attention_probs_dropout_prob
_snake_case = num_labels
_snake_case = image_size
_snake_case = layer_depths
_snake_case = embed_dims
def lowercase (self ) -> Any:
_snake_case = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_snake_case = None
if self.use_labels:
_snake_case = ids_tensor([self.batch_size] , self.num_labels )
_snake_case = self.get_config()
return config, pixel_values, labels
def lowercase (self ) -> int:
return SwiftFormerConfig(
depths=self.layer_depths , embed_dims=self.embed_dims , mlp_ratio=4 , downsamples=[True, True, True, True] , hidden_act="""gelu""" , num_labels=self.num_labels , down_patch_size=3 , down_stride=2 , down_pad=1 , drop_rate=0.0 , drop_path_rate=0.0 , use_layer_scale=UpperCAmelCase , layer_scale_init_value=1e-5 , )
def lowercase (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> Tuple:
_snake_case = SwiftFormerModel(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
_snake_case = model(UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dims[-1], 7, 7) )
def lowercase (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> Tuple:
_snake_case = self.num_labels
_snake_case = SwiftFormerForImageClassification(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
_snake_case = model(UpperCAmelCase , labels=UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
_snake_case = SwiftFormerForImageClassification(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
_snake_case = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_snake_case = model(UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowercase (self ) -> Dict:
((_snake_case), (_snake_case), (_snake_case)) = self.prepare_config_and_inputs()
_snake_case = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class _lowerCAmelCase ( __snake_case , __snake_case , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase_ = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
lowerCAmelCase_ = (
{"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
if is_torch_available()
else {}
)
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
def lowercase (self ) -> Union[str, Any]:
_snake_case = SwiftFormerModelTester(self )
_snake_case = ConfigTester(
self , config_class=UpperCAmelCase , has_text_modality=UpperCAmelCase , hidden_size=37 , num_attention_heads=12 , num_hidden_layers=12 , )
def lowercase (self ) -> Dict:
self.config_tester.run_common_tests()
@unittest.skip(reason="""SwiftFormer does not use inputs_embeds""" )
def lowercase (self ) -> List[str]:
pass
def lowercase (self ) -> Any:
_snake_case, _snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_snake_case = model_class(UpperCAmelCase )
_snake_case = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(UpperCAmelCase , nn.Linear ) )
def lowercase (self ) -> int:
_snake_case, _snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_snake_case = model_class(UpperCAmelCase )
_snake_case = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_snake_case = [*signature.parameters.keys()]
_snake_case = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , UpperCAmelCase )
def lowercase (self ) -> Union[str, Any]:
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase )
def lowercase (self ) -> Dict:
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase )
@slow
def lowercase (self ) -> List[str]:
for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_snake_case = SwiftFormerModel.from_pretrained(UpperCAmelCase )
self.assertIsNotNone(UpperCAmelCase )
@unittest.skip(reason="""SwiftFormer does not output attentions""" )
def lowercase (self ) -> Optional[Any]:
pass
def lowercase (self ) -> Optional[int]:
def check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
_snake_case = model_class(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
with torch.no_grad():
_snake_case = model(**self._prepare_for_class(UpperCAmelCase , UpperCAmelCase ) )
_snake_case = outputs.hidden_states
_snake_case = 8
self.assertEqual(len(UpperCAmelCase ) , UpperCAmelCase ) # TODO
# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
# with the width and height being successively divided by 2, after every 2 blocks
for i in range(len(UpperCAmelCase ) ):
self.assertEqual(
hidden_states[i].shape , torch.Size(
[
self.model_tester.batch_size,
self.model_tester.embed_dims[i // 2],
(self.model_tester.image_size // 4) // 2 ** (i // 2),
(self.model_tester.image_size // 4) // 2 ** (i // 2),
] ) , )
_snake_case, _snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_snake_case = True
check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_snake_case = True
check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def lowercase (self ) -> Dict:
def _config_zero_init(UpperCAmelCase ):
_snake_case = copy.deepcopy(UpperCAmelCase )
for key in configs_no_init.__dict__.keys():
if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
setattr(UpperCAmelCase , UpperCAmelCase , 1e-1_0 )
if isinstance(getattr(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) , UpperCAmelCase ):
_snake_case = _config_zero_init(getattr(UpperCAmelCase , UpperCAmelCase ) )
setattr(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
return configs_no_init
_snake_case, _snake_case = self.model_tester.prepare_config_and_inputs_for_common()
_snake_case = _config_zero_init(UpperCAmelCase )
for model_class in self.all_model_classes:
_snake_case = model_class(config=UpperCAmelCase )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9) / 1e9).round().item() , [0.0, 1.0] , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def lowercase (self ) -> Any:
pass
def __SCREAMING_SNAKE_CASE ( ):
_snake_case = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class _lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowercase (self ) -> Dict:
return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs""" ) if is_vision_available() else None
@slow
def lowercase (self ) -> Any:
_snake_case = SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs""" ).to(UpperCAmelCase )
_snake_case = self.default_image_processor
_snake_case = prepare_img()
_snake_case = image_processor(images=UpperCAmelCase , return_tensors="""pt""" ).to(UpperCAmelCase )
# forward pass
with torch.no_grad():
_snake_case = model(**UpperCAmelCase )
# verify the logits
_snake_case = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , UpperCAmelCase )
_snake_case = torch.tensor([[-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0]] ).to(UpperCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , UpperCAmelCase , atol=1e-4 ) ) | 341 |
'''simple docstring'''
__lowerCAmelCase = [
(1_000, 'M'),
(900, 'CM'),
(500, 'D'),
(400, 'CD'),
(100, 'C'),
(90, 'XC'),
(50, 'L'),
(40, 'XL'),
(10, 'X'),
(9, 'IX'),
(5, 'V'),
(4, 'IV'),
(1, 'I'),
]
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE ):
_snake_case = {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000}
_snake_case = 0
_snake_case = 0
while place < len(_SCREAMING_SNAKE_CASE ):
if (place + 1 < len(_SCREAMING_SNAKE_CASE )) and (vals[roman[place]] < vals[roman[place + 1]]):
total += vals[roman[place + 1]] - vals[roman[place]]
place += 2
else:
total += vals[roman[place]]
place += 1
return total
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE ):
_snake_case = []
for arabic, roman in ROMAN:
((_snake_case), (_snake_case)) = divmod(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
result.append(roman * factor )
if number == 0:
break
return "".join(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
import doctest
doctest.testmod() | 341 | 1 |
"""simple docstring"""
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
from .features import FeatureType
_lowercase ,_lowercase ,_lowercase : str = False, False, False
@dataclass
class _UpperCAmelCase :
a__ : Optional[int] = None
a__ : bool = True
a__ : bool = True
a__ : Optional[str] = None
# Automatically constructed
a__ : ClassVar[str] = "dict"
a__ : ClassVar[Any] = pa.struct({"bytes": pa.binary(), "path": pa.string()} )
a__ : str = field(default="Audio" , init=_lowerCAmelCase , repr=_lowerCAmelCase )
def __call__( self : Optional[int] ):
return self.pa_type
def a ( self : Any , _lowercase : Union[str, bytes, dict] ):
try:
import soundfile as sf # soundfile is a dependency of librosa, needed to decode audio files.
except ImportError as err:
raise ImportError('''To support encoding audio data, please install \'soundfile\'.''' ) from err
if isinstance(_lowercase , _lowercase ):
return {"bytes": None, "path": value}
elif isinstance(_lowercase , _lowercase ):
return {"bytes": value, "path": None}
elif "array" in value:
# convert the audio array to wav bytes
__UpperCAmelCase = BytesIO()
sf.write(_lowercase , value['''array'''] , value['''sampling_rate'''] , format='''wav''' )
return {"bytes": buffer.getvalue(), "path": None}
elif value.get('''path''' ) is not None and os.path.isfile(value['''path'''] ):
# we set "bytes": None to not duplicate the data if they're already available locally
if value["path"].endswith('''pcm''' ):
# "PCM" only has raw audio bytes
if value.get('''sampling_rate''' ) is None:
# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
raise KeyError('''To use PCM files, please specify a \'sampling_rate\' in Audio object''' )
if value.get('''bytes''' ):
# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
__UpperCAmelCase = np.frombuffer(value['''bytes'''] , dtype=np.intaa ).astype(np.floataa ) / 3_27_67
else:
__UpperCAmelCase = np.memmap(value['''path'''] , dtype='''h''' , mode='''r''' ).astype(np.floataa ) / 3_27_67
__UpperCAmelCase = BytesIO(bytes() )
sf.write(_lowercase , _lowercase , value['''sampling_rate'''] , format='''wav''' )
return {"bytes": buffer.getvalue(), "path": None}
else:
return {"bytes": None, "path": value.get('''path''' )}
elif value.get('''bytes''' ) is not None or value.get('''path''' ) is not None:
# store the audio bytes, and path is used to infer the audio format using the file extension
return {"bytes": value.get('''bytes''' ), "path": value.get('''path''' )}
else:
raise ValueError(
F'''An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.''' )
def a ( self : Dict , _lowercase : dict , _lowercase : Optional[Dict[str, Union[str, bool, None]]] = None ):
if not self.decode:
raise RuntimeError('''Decoding is disabled for this feature. Please use Audio(decode=True) instead.''' )
__UpperCAmelCase , __UpperCAmelCase = (value['''path'''], BytesIO(value['''bytes'''] )) if value['''bytes'''] is not None else (value['''path'''], None)
if path is None and file is None:
raise ValueError(F'''An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.''' )
try:
import librosa
import soundfile as sf
except ImportError as err:
raise ImportError('''To support decoding audio files, please install \'librosa\' and \'soundfile\'.''' ) from err
__UpperCAmelCase = xsplitext(_lowercase )[1][1:].lower() if path is not None else None
if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
raise RuntimeError(
'''Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, '''
'''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. ''' )
elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
raise RuntimeError(
'''Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, '''
'''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. ''' )
if file is None:
__UpperCAmelCase = token_per_repo_id or {}
__UpperCAmelCase = path.split('''::''' )[-1]
try:
__UpperCAmelCase = string_to_dict(_lowercase , config.HUB_DATASETS_URL )['''repo_id''']
__UpperCAmelCase = token_per_repo_id[repo_id]
except (ValueError, KeyError):
__UpperCAmelCase = None
with xopen(_lowercase , '''rb''' , use_auth_token=_lowercase ) as f:
__UpperCAmelCase , __UpperCAmelCase = sf.read(_lowercase )
else:
__UpperCAmelCase , __UpperCAmelCase = sf.read(_lowercase )
__UpperCAmelCase = array.T
if self.mono:
__UpperCAmelCase = librosa.to_mono(_lowercase )
if self.sampling_rate and self.sampling_rate != sampling_rate:
__UpperCAmelCase = librosa.resample(_lowercase , orig_sr=_lowercase , target_sr=self.sampling_rate )
__UpperCAmelCase = self.sampling_rate
return {"path": path, "array": array, "sampling_rate": sampling_rate}
def a ( self : int ):
from .features import Value
if self.decode:
raise ValueError('''Cannot flatten a decoded Audio feature.''' )
return {
"bytes": Value('''binary''' ),
"path": Value('''string''' ),
}
def a ( self : Tuple , _lowercase : Union[pa.StringArray, pa.StructArray] ):
if pa.types.is_string(storage.type ):
__UpperCAmelCase = pa.array([None] * len(_lowercase ) , type=pa.binary() )
__UpperCAmelCase = pa.StructArray.from_arrays([bytes_array, storage] , ['''bytes''', '''path'''] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
__UpperCAmelCase = pa.array([None] * len(_lowercase ) , type=pa.string() )
__UpperCAmelCase = pa.StructArray.from_arrays([storage, path_array] , ['''bytes''', '''path'''] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ) and storage.type.get_all_field_indices('''array''' ):
__UpperCAmelCase = pa.array([Audio().encode_example(_lowercase ) if x is not None else None for x in storage.to_pylist()] )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index('''bytes''' ) >= 0:
__UpperCAmelCase = storage.field('''bytes''' )
else:
__UpperCAmelCase = pa.array([None] * len(_lowercase ) , type=pa.binary() )
if storage.type.get_field_index('''path''' ) >= 0:
__UpperCAmelCase = storage.field('''path''' )
else:
__UpperCAmelCase = pa.array([None] * len(_lowercase ) , type=pa.string() )
__UpperCAmelCase = pa.StructArray.from_arrays([bytes_array, path_array] , ['''bytes''', '''path'''] , mask=storage.is_null() )
return array_cast(_lowercase , self.pa_type )
def a ( self : Tuple , _lowercase : pa.StructArray ):
@no_op_if_value_is_null
def path_to_bytes(_lowercase : int ):
with xopen(_lowercase , '''rb''' ) as f:
__UpperCAmelCase = f.read()
return bytes_
__UpperCAmelCase = pa.array(
[
(path_to_bytes(x['''path'''] ) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
__UpperCAmelCase = pa.array(
[os.path.basename(_lowercase ) if path is not None else None for path in storage.field('''path''' ).to_pylist()] , type=pa.string() , )
__UpperCAmelCase = pa.StructArray.from_arrays([bytes_array, path_array] , ['''bytes''', '''path'''] , mask=bytes_array.is_null() )
return array_cast(_lowercase , self.pa_type )
| 86 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
_lowercase : List[Any] = logging.get_logger(__name__)
@add_end_docstrings(_lowerCAmelCase )
class _UpperCAmelCase ( _lowerCAmelCase ):
def __init__( self : str , *_lowercase : Tuple , **_lowercase : List[Any] ):
super().__init__(*_lowercase , **_lowercase )
self.check_model_type(_lowercase )
def a ( self : int , _lowercase : Dict=None , _lowercase : List[Any]=None , _lowercase : int=None , **_lowercase : Dict ):
__UpperCAmelCase , __UpperCAmelCase = {}, {}
if padding is not None:
__UpperCAmelCase = padding
if truncation is not None:
__UpperCAmelCase = truncation
if top_k is not None:
__UpperCAmelCase = top_k
return preprocess_params, {}, postprocess_params
def __call__( self : List[str] , _lowercase : Union["Image.Image", str] , _lowercase : str = None , **_lowercase : Optional[Any] ):
if isinstance(_lowercase , (Image.Image, str) ) and isinstance(_lowercase , _lowercase ):
__UpperCAmelCase = {'''image''': image, '''question''': question}
else:
__UpperCAmelCase = image
__UpperCAmelCase = super().__call__(_lowercase , **_lowercase )
return results
def a ( self : Union[str, Any] , _lowercase : List[str] , _lowercase : Any=False , _lowercase : Union[str, Any]=False ):
__UpperCAmelCase = load_image(inputs['''image'''] )
__UpperCAmelCase = self.tokenizer(
inputs['''question'''] , return_tensors=self.framework , padding=_lowercase , truncation=_lowercase )
__UpperCAmelCase = self.image_processor(images=_lowercase , return_tensors=self.framework )
model_inputs.update(_lowercase )
return model_inputs
def a ( self : Optional[Any] , _lowercase : str ):
__UpperCAmelCase = self.model(**_lowercase )
return model_outputs
def a ( self : str , _lowercase : Optional[int] , _lowercase : Any=5 ):
if top_k > self.model.config.num_labels:
__UpperCAmelCase = self.model.config.num_labels
if self.framework == "pt":
__UpperCAmelCase = model_outputs.logits.sigmoid()[0]
__UpperCAmelCase , __UpperCAmelCase = probs.topk(_lowercase )
else:
raise ValueError(F'''Unsupported framework: {self.framework}''' )
__UpperCAmelCase = scores.tolist()
__UpperCAmelCase = ids.tolist()
return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(_lowercase , _lowercase )]
| 86 | 1 |
"""simple docstring"""
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
lowerCamelCase_ : List[Any] = logging.getLogger(__name__)
def _A ( ):
"""simple docstring"""
a =argparse.ArgumentParser(
description='''Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.''' )
parser.add_argument(
'''--dataset_name''' , type=lowercase , default='''wikitext''' , help='''Name of the training. Explore datasets at: hf.co/datasets.''' , )
parser.add_argument(
'''--dataset_config''' , type=lowercase , default='''wikitext-103-raw-v1''' , help='''Configuration name of the dataset.''' )
parser.add_argument(
'''--tokenizer_name_or_path''' , type=lowercase , default='''sayakpaul/unigram-tokenizer-wikitext''' , help='''Tokenizer identifier. Can be a local filepath or a Hub identifier.''' , )
parser.add_argument(
'''--shard_size''' , type=lowercase , default=10_00 , help='''Number of entries to go in a single shard.''' , )
parser.add_argument('''--split''' , type=lowercase , default='''train''' , choices=['''train''', '''test''', '''validation'''] )
parser.add_argument(
'''--limit''' , default=lowercase , type=lowercase , help='''Limit the number of shards (used for debugging).''' , )
parser.add_argument(
'''--max_length''' , type=lowercase , default=5_12 , help='''Maximum sequence length. For training on TPUs, it helps to have a maximum'''
''' sequence length that is a multiple of 8.''' , )
parser.add_argument(
'''--output_dir''' , default='''tf-tpu''' , type=lowercase , help='''Output directory where the TFRecord shards will be saved. If the'''
''' path is appended with `gs://` (\'gs://tf-tpu\', for example) then the TFRecord'''
''' shards will be directly saved to a Google Cloud Storage bucket.''' , )
a =parser.parse_args()
return args
def _A ( lowercase ):
"""simple docstring"""
def fn(lowercase ):
return tokenizer(examples['''text'''] )
return fn
def _A ( lowercase ):
"""simple docstring"""
a =[]
for i in range(len(tokenized_data['''input_ids'''] ) ):
a ={
'''input_ids''': tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data['''input_ids'''][i] ) ),
'''attention_mask''': tf.train.Feature(
intaa_list=tf.train.IntaaList(value=tokenized_data['''attention_mask'''][i] ) ),
}
a =tf.train.Features(feature=lowercase )
a =tf.train.Example(features=lowercase )
a =example.SerializeToString()
records.append(lowercase )
return records
def _A ( lowercase ):
"""simple docstring"""
a =datasets.load_dataset(args.dataset_name , args.dataset_config , split=args.split )
if args.limit is not None:
a =min(len(lowercase ) , args.limit )
a =dataset.select(range(lowercase ) )
print(f'''Limiting the dataset to {args.limit} entries.''' )
a =AutoTokenizer.from_pretrained(args.tokenizer_name_or_path )
# Handle output directory creation.
# For serializing into a Google Cloud Storage Bucket, one needs to first
# create a bucket.
if "gs" not in args.output_dir:
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
a =os.path.join(args.output_dir , args.split )
if not os.path.exists(lowercase ):
os.makedirs(lowercase )
else:
a =os.path.join(args.output_dir , args.split )
# Tokenize the whole dataset at once.
a =tokenize_function(lowercase )
a =dataset.map(lowercase , batched=lowercase , num_proc=4 , remove_columns=['''text'''] )
# We need to concatenate all our texts together, and then split the result
# into chunks of a fixed size, which we will call block_size. To do this, we
# will use the map method again, with the option batched=True. When we use batched=True,
# the function we pass to map() will be passed multiple inputs at once, allowing us
# to group them into more or fewer examples than we had in the input.
# This allows us to create our new fixed-length samples. The advantage of this
# method is that we don't lose a whole lot of content from the dataset compared to the
# case where we simply tokenize with a pre-defined max_length.
def group_texts(lowercase ):
# Concatenate all texts.
a ={k: sum(examples[k] , [] ) for k in examples.keys()}
a =len(concatenated_examples[list(examples.keys() )[0]] )
# We drop the small remainder, though you could add padding instead if the model supports it
# In this, as in all things, we advise you to follow your heart 🫀
a =(total_length // args.max_length) * args.max_length
# Split by chunks of max_len.
a ={
k: [t[i : i + args.max_length] for i in range(0 , lowercase , args.max_length )]
for k, t in concatenated_examples.items()
}
return result
a =dataset_tokenized.map(lowercase , batched=lowercase , batch_size=10_00 , num_proc=4 )
a =0
a =0
for shard in range(0 , len(lowercase ) , args.shard_size ):
a =grouped_dataset[shard : shard + args.shard_size]
a =len(dataset_snapshot['''input_ids'''] )
a =os.path.join(lowercase , f'''dataset-{shard_count}-{records_containing}.tfrecord''' )
a =get_serialized_examples(lowercase )
with tf.io.TFRecordWriter(lowercase ) as out_file:
for i in range(len(lowercase ) ):
a =serialized_examples[i]
out_file.write(lowercase )
print('''Wrote file {} containing {} records'''.format(lowercase , lowercase ) )
shard_count += 1
total_records += records_containing
with open(f'''split-{args.split}-records-count.txt''' , '''w''' ) as f:
print(f'''Total {args.split} records: {total_records}''' , file=lowercase )
if __name__ == "__main__":
lowerCamelCase_ : Dict = parse_args()
main(args) | 81 |
from __future__ import annotations
def lowerCAmelCase_ ( __UpperCAmelCase: list[int] , __UpperCAmelCase: int ) -> list[int]:
UpperCamelCase__ : Optional[int] = 0
UpperCamelCase__ : Tuple = len(__UpperCAmelCase ) - 1
while i < j:
if nums[i] + nums[j] == target:
return [i, j]
elif nums[i] + nums[j] < target:
UpperCamelCase__ : Tuple = i + 1
else:
UpperCamelCase__ : str = j - 1
return []
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F'''{two_pointer([2, 7, 11, 15], 9) = }''')
| 201 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
# See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class A__ ( _snake_case ):
lowercase = "vit_msn"
def __init__( self , UpperCamelCase__=768 , UpperCamelCase__=12 , UpperCamelCase__=12 , UpperCamelCase__=3072 , UpperCamelCase__="gelu" , UpperCamelCase__=0.0 , UpperCamelCase__=0.0 , UpperCamelCase__=0.02 , UpperCamelCase__=1e-0_6 , UpperCamelCase__=224 , UpperCamelCase__=16 , UpperCamelCase__=3 , UpperCamelCase__=True , **UpperCamelCase__ , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(**UpperCamelCase__ )
A_ = hidden_size
A_ = num_hidden_layers
A_ = num_attention_heads
A_ = intermediate_size
A_ = hidden_act
A_ = hidden_dropout_prob
A_ = attention_probs_dropout_prob
A_ = initializer_range
A_ = layer_norm_eps
A_ = image_size
A_ = patch_size
A_ = num_channels
A_ = qkv_bias
| 353 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase = {
'''configuration_blenderbot''': [
'''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BlenderbotConfig''',
'''BlenderbotOnnxConfig''',
],
'''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase = ['''BlenderbotTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase = [
'''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BlenderbotForCausalLM''',
'''BlenderbotForConditionalGeneration''',
'''BlenderbotModel''',
'''BlenderbotPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase = [
'''TFBlenderbotForConditionalGeneration''',
'''TFBlenderbotModel''',
'''TFBlenderbotPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase = [
'''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
__lowerCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 101 | 0 |
'''simple docstring'''
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
def a ( self : Tuple ) -> Tuple:
__lowerCAmelCase = torch.nn.Linear(10 , 10 )
__lowerCAmelCase = torch.optim.SGD(model.parameters() , 0.1 )
__lowerCAmelCase = Accelerator()
__lowerCAmelCase = accelerator.prepare(__SCREAMING_SNAKE_CASE )
try:
pickle.loads(pickle.dumps(__SCREAMING_SNAKE_CASE ) )
except Exception as e:
self.fail(f"""Accelerated optimizer pickling failed with {e}""" )
AcceleratorState._reset_state()
| 229 |
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
# Initialise PyTorch model
__a = BigBirdConfig.from_json_file(_UpperCAmelCase )
print(f'Building PyTorch model from configuration: {config}' )
if is_trivia_qa:
__a = BigBirdForQuestionAnswering(_UpperCAmelCase )
else:
__a = BigBirdForPreTraining(_UpperCAmelCase )
# Load weights from tf checkpoint
load_tf_weights_in_big_bird(_UpperCAmelCase , _UpperCAmelCase , is_trivia_qa=_UpperCAmelCase )
# Save pytorch-model
print(f'Save PyTorch model to {pytorch_dump_path}' )
model.save_pretrained(_UpperCAmelCase )
if __name__ == "__main__":
__snake_case :Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--big_bird_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained BERT model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--is_trivia_qa''', action='''store_true''', help='''Whether to convert a model with a trivia_qa head.'''
)
__snake_case :Any = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
)
| 49 | 0 |
import os
import re
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
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {"""vocab_file""": """spiece.model"""}
SCREAMING_SNAKE_CASE_ = {
"""vocab_file""": {
"""google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
"""google/bigbird-roberta-large""": (
"""https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
),
"""google/bigbird-base-trivia-itc""": (
"""https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
),
}
}
SCREAMING_SNAKE_CASE_ = {
"""google/bigbird-roberta-base""": 4_0_9_6,
"""google/bigbird-roberta-large""": 4_0_9_6,
"""google/bigbird-base-trivia-itc""": 4_0_9_6,
}
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Any = VOCAB_FILES_NAMES
__snake_case : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
__snake_case : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__snake_case : str = ["input_ids", "attention_mask"]
__snake_case : List[int] = []
def __init__( self : Union[str, Any] ,lowerCamelCase__ : List[str] ,lowerCamelCase__ : str="<unk>" ,lowerCamelCase__ : List[Any]="<s>" ,lowerCamelCase__ : Dict="</s>" ,lowerCamelCase__ : List[str]="<pad>" ,lowerCamelCase__ : Tuple="[SEP]" ,lowerCamelCase__ : Dict="[MASK]" ,lowerCamelCase__ : List[Any]="[CLS]" ,lowerCamelCase__ : Optional[Dict[str, Any]] = None ,**lowerCamelCase__ : Union[str, Any] ,) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AddedToken(lowerCamelCase__ ,lstrip=lowerCamelCase__ ,rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ ,lowerCamelCase__ ) else bos_token
SCREAMING_SNAKE_CASE = AddedToken(lowerCamelCase__ ,lstrip=lowerCamelCase__ ,rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ ,lowerCamelCase__ ) else eos_token
SCREAMING_SNAKE_CASE = AddedToken(lowerCamelCase__ ,lstrip=lowerCamelCase__ ,rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ ,lowerCamelCase__ ) else unk_token
SCREAMING_SNAKE_CASE = AddedToken(lowerCamelCase__ ,lstrip=lowerCamelCase__ ,rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ ,lowerCamelCase__ ) else pad_token
SCREAMING_SNAKE_CASE = AddedToken(lowerCamelCase__ ,lstrip=lowerCamelCase__ ,rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ ,lowerCamelCase__ ) else cls_token
SCREAMING_SNAKE_CASE = AddedToken(lowerCamelCase__ ,lstrip=lowerCamelCase__ ,rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ ,lowerCamelCase__ ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
SCREAMING_SNAKE_CASE = AddedToken(lowerCamelCase__ ,lstrip=lowerCamelCase__ ,rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ ,lowerCamelCase__ ) else mask_token
SCREAMING_SNAKE_CASE = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=lowerCamelCase__ ,eos_token=lowerCamelCase__ ,unk_token=lowerCamelCase__ ,pad_token=lowerCamelCase__ ,sep_token=lowerCamelCase__ ,mask_token=lowerCamelCase__ ,cls_token=lowerCamelCase__ ,sp_model_kwargs=self.sp_model_kwargs ,**lowerCamelCase__ ,)
SCREAMING_SNAKE_CASE = vocab_file
SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(lowerCamelCase__ )
@property
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> str:
'''simple docstring'''
return self.sp_model.get_piece_size()
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = {self.convert_ids_to_tokens(lowerCamelCase__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : str ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.__dict__.copy()
SCREAMING_SNAKE_CASE = None
return state
def __setstate__( self : Any ,lowerCamelCase__ : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = d
# for backward compatibility
if not hasattr(self ,"""sp_model_kwargs""" ):
SCREAMING_SNAKE_CASE = {}
SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ,lowerCamelCase__ : str ) -> List[str]:
'''simple docstring'''
return self.sp_model.encode(lowerCamelCase__ ,out_type=lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ,lowerCamelCase__ : Optional[Any] ) -> Dict:
'''simple docstring'''
return self.sp_model.piece_to_id(lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ,lowerCamelCase__ : int ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.sp_model.IdToPiece(lowerCamelCase__ )
return token
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ,lowerCamelCase__ : List[Any] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = """"""
SCREAMING_SNAKE_CASE = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(lowerCamelCase__ ) + token
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = []
else:
current_sub_tokens.append(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = False
out_string += self.sp_model.decode(lowerCamelCase__ )
return out_string.strip()
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ,lowerCamelCase__ : List[int] ,lowerCamelCase__ : bool = False ,lowerCamelCase__ : bool = None ,lowerCamelCase__ : bool = True ,**lowerCamelCase__ : Any ,) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = kwargs.pop("""use_source_tokenizer""" ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.convert_ids_to_tokens(lowerCamelCase__ ,skip_special_tokens=lowerCamelCase__ )
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(lowerCamelCase__ ) )
SCREAMING_SNAKE_CASE = []
sub_texts.append(lowerCamelCase__ )
else:
current_sub_text.append(lowerCamelCase__ )
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(lowerCamelCase__ ) )
# Mimic the behavior of the Rust tokenizer:
# No space before [MASK] and [SEP]
if spaces_between_special_tokens:
SCREAMING_SNAKE_CASE = re.sub(R""" (\[(MASK|SEP)\])""" ,R"""\1""" ,""" """.join(lowerCamelCase__ ) )
else:
SCREAMING_SNAKE_CASE = """""".join(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
SCREAMING_SNAKE_CASE = self.clean_up_tokenization(lowerCamelCase__ )
return clean_text
else:
return text
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ,lowerCamelCase__ : str ,lowerCamelCase__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(lowerCamelCase__ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
SCREAMING_SNAKE_CASE = os.path.join(
lowerCamelCase__ ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""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:
SCREAMING_SNAKE_CASE = self.sp_model.serialized_model_proto()
fi.write(lowerCamelCase__ )
return (out_vocab_file,)
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ,lowerCamelCase__ : List[int] ,lowerCamelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
SCREAMING_SNAKE_CASE = [self.cls_token_id]
SCREAMING_SNAKE_CASE = [self.sep_token_id]
return cls + token_ids_a + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE__ ( self : int ,lowerCamelCase__ : List[int] ,lowerCamelCase__ : Optional[List[int]] = None ,lowerCamelCase__ : bool = False ) -> List[int]:
'''simple docstring'''
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] + ([0] * len(lowerCamelCase__ )) + [1]
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ,lowerCamelCase__ : List[int] ,lowerCamelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = [self.sep_token_id]
SCREAMING_SNAKE_CASE = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
| 193 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ = {"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = ["""ReformerTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = ["""ReformerTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
"""REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ReformerAttention""",
"""ReformerForMaskedLM""",
"""ReformerForQuestionAnswering""",
"""ReformerForSequenceClassification""",
"""ReformerLayer""",
"""ReformerModel""",
"""ReformerModelWithLMHead""",
"""ReformerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_reformer import ReformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_reformer_fast import ReformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_reformer import (
REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ReformerAttention,
ReformerForMaskedLM,
ReformerForQuestionAnswering,
ReformerForSequenceClassification,
ReformerLayer,
ReformerModel,
ReformerModelWithLMHead,
ReformerPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 193 | 1 |
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
_snake_case = datasets.utils.logging.get_logger(__name__)
_snake_case = ["names", "prefix"]
_snake_case = ["warn_bad_lines", "error_bad_lines", "mangle_dupe_cols"]
_snake_case = ["encoding_errors", "on_bad_lines"]
_snake_case = ["date_format"]
@dataclass
class UpperCAmelCase_ ( datasets.BuilderConfig):
lowerCamelCase__ = ","
lowerCamelCase__ = None
lowerCamelCase__ = "infer"
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = True
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = False
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = True
lowerCamelCase__ = True
lowerCamelCase__ = False
lowerCamelCase__ = True
lowerCamelCase__ = None
lowerCamelCase__ = "."
lowerCamelCase__ = None
lowerCamelCase__ = '"'
lowerCamelCase__ = 0
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = True
lowerCamelCase__ = True
lowerCamelCase__ = 0
lowerCamelCase__ = True
lowerCamelCase__ = False
lowerCamelCase__ = None
lowerCamelCase__ = 10000
lowerCamelCase__ = None
lowerCamelCase__ = "strict"
lowerCamelCase__ = "error"
lowerCamelCase__ = None
def snake_case__ ( self):
'''simple docstring'''
if self.delimiter is not None:
_lowerCAmelCase : List[str] = self.delimiter
if self.column_names is not None:
_lowerCAmelCase : int = self.column_names
@property
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = {
'''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):
lowerCamelCase__ = CsvConfig
def snake_case__ ( self):
'''simple docstring'''
return datasets.DatasetInfo(features=self.config.features)
def snake_case__ ( self, __a):
'''simple docstring'''
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 : Tuple = dl_manager.download_and_extract(self.config.data_files)
if isinstance(lowerCamelCase__, (str, list, tuple)):
_lowerCAmelCase : Optional[Any] = data_files
if isinstance(lowerCamelCase__, lowerCamelCase__):
_lowerCAmelCase : Union[str, Any] = [files]
_lowerCAmelCase : List[Any] = [dl_manager.iter_files(lowerCamelCase__) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"files": files})]
_lowerCAmelCase : Optional[int] = []
for split_name, files in data_files.items():
if isinstance(lowerCamelCase__, lowerCamelCase__):
_lowerCAmelCase : Optional[Any] = [files]
_lowerCAmelCase : Dict = [dl_manager.iter_files(lowerCamelCase__) for file in files]
splits.append(datasets.SplitGenerator(name=lowerCamelCase__, gen_kwargs={"files": files}))
return splits
def snake_case__ ( self, __a):
'''simple docstring'''
if self.config.features is not None:
_lowerCAmelCase : List[Any] = self.config.features.arrow_schema
if all(not require_storage_cast(lowerCamelCase__) for feature in self.config.features.values()):
# cheaper cast
_lowerCAmelCase : Optional[int] = 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 : List[str] = table_cast(lowerCamelCase__, lowerCamelCase__)
return pa_table
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : int = self.config.features.arrow_schema if self.config.features else None
# dtype allows reading an int column as str
_lowerCAmelCase : Any = (
{
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 : Dict = pd.read_csv(lowerCamelCase__, iterator=lowerCamelCase__, dtype=lowerCamelCase__, **self.config.pd_read_csv_kwargs)
try:
for batch_idx, df in enumerate(lowerCamelCase__):
_lowerCAmelCase : Optional[int] = 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
| 36 |
import os
import sys
__UpperCamelCase : Optional[Any] = os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
AutoTokenizer,
add_start_docstrings,
)
__UpperCamelCase : Tuple = [
"torch",
"numpy",
"tokenizers",
"filelock",
"requests",
"tqdm",
"regex",
"sentencepiece",
"sacremoses",
"importlib_metadata",
"huggingface_hub",
]
@add_start_docstrings(AutoConfig.__doc__ )
def _a ( *SCREAMING_SNAKE_CASE : Union[str, Any] , **SCREAMING_SNAKE_CASE : Any ):
"""simple docstring"""
return AutoConfig.from_pretrained(*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
@add_start_docstrings(AutoTokenizer.__doc__ )
def _a ( *SCREAMING_SNAKE_CASE : Union[str, Any] , **SCREAMING_SNAKE_CASE : List[Any] ):
"""simple docstring"""
return AutoTokenizer.from_pretrained(*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
@add_start_docstrings(AutoModel.__doc__ )
def _a ( *SCREAMING_SNAKE_CASE : Dict , **SCREAMING_SNAKE_CASE : Optional[int] ):
"""simple docstring"""
return AutoModel.from_pretrained(*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
@add_start_docstrings(AutoModelForCausalLM.__doc__ )
def _a ( *SCREAMING_SNAKE_CASE : Any , **SCREAMING_SNAKE_CASE : Dict ):
"""simple docstring"""
return AutoModelForCausalLM.from_pretrained(*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
@add_start_docstrings(AutoModelForMaskedLM.__doc__ )
def _a ( *SCREAMING_SNAKE_CASE : List[Any] , **SCREAMING_SNAKE_CASE : Any ):
"""simple docstring"""
return AutoModelForMaskedLM.from_pretrained(*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
@add_start_docstrings(AutoModelForSequenceClassification.__doc__ )
def _a ( *SCREAMING_SNAKE_CASE : int , **SCREAMING_SNAKE_CASE : Dict ):
"""simple docstring"""
return AutoModelForSequenceClassification.from_pretrained(*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
@add_start_docstrings(AutoModelForQuestionAnswering.__doc__ )
def _a ( *SCREAMING_SNAKE_CASE : Optional[int] , **SCREAMING_SNAKE_CASE : Dict ):
"""simple docstring"""
return AutoModelForQuestionAnswering.from_pretrained(*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
| 146 | 0 |
"""simple docstring"""
def UpperCAmelCase ( UpperCamelCase__ ):
"""simple docstring"""
return " ".join(
''.join(word[::-1] ) if len(UpperCamelCase__ ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words("Hey wollef sroirraw"))
| 367 | """simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
BertTokenizerFast,
BlipImageProcessor,
GPTaTokenizer,
InstructBlipProcessor,
PreTrainedTokenizerFast,
)
@require_vision
class UpperCamelCase__( unittest.TestCase ):
def snake_case__ ( self ) -> int:
A__ = tempfile.mkdtemp()
A__ = BlipImageProcessor()
A__ = GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model' )
A__ = BertTokenizerFast.from_pretrained('hf-internal-testing/tiny-random-bert' )
A__ = InstructBlipProcessor(__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase )
processor.save_pretrained(self.tmpdirname )
def snake_case__ ( self ,**__UpperCAmelCase ) -> str:
return AutoProcessor.from_pretrained(self.tmpdirname ,**__UpperCAmelCase ).tokenizer
def snake_case__ ( self ,**__UpperCAmelCase ) -> Tuple:
return AutoProcessor.from_pretrained(self.tmpdirname ,**__UpperCAmelCase ).image_processor
def snake_case__ ( self ,**__UpperCAmelCase ) -> Tuple:
return AutoProcessor.from_pretrained(self.tmpdirname ,**__UpperCAmelCase ).qformer_tokenizer
def snake_case__ ( self ) -> Optional[Any]:
shutil.rmtree(self.tmpdirname )
def snake_case__ ( self ) -> str:
A__ = [np.random.randint(2_55 ,size=(3, 30, 4_00) ,dtype=np.uinta )]
A__ = [Image.fromarray(np.moveaxis(__UpperCAmelCase ,0 ,-1 ) ) for x in image_inputs]
return image_inputs
def snake_case__ ( self ) -> Any:
A__ = InstructBlipProcessor(
tokenizer=self.get_tokenizer() ,image_processor=self.get_image_processor() ,qformer_tokenizer=self.get_qformer_tokenizer() ,)
processor.save_pretrained(self.tmpdirname )
A__ = self.get_tokenizer(bos_token='(BOS)' ,eos_token='(EOS)' )
A__ = self.get_image_processor(do_normalize=__UpperCAmelCase ,padding_value=1.0 )
A__ = InstructBlipProcessor.from_pretrained(
self.tmpdirname ,bos_token='(BOS)' ,eos_token='(EOS)' ,do_normalize=__UpperCAmelCase ,padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() ,tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer ,__UpperCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() ,image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor ,__UpperCAmelCase )
self.assertIsInstance(processor.qformer_tokenizer ,__UpperCAmelCase )
def snake_case__ ( self ) -> str:
A__ = self.get_image_processor()
A__ = self.get_tokenizer()
A__ = self.get_qformer_tokenizer()
A__ = InstructBlipProcessor(
tokenizer=__UpperCAmelCase ,image_processor=__UpperCAmelCase ,qformer_tokenizer=__UpperCAmelCase )
A__ = self.prepare_image_inputs()
A__ = image_processor(__UpperCAmelCase ,return_tensors='np' )
A__ = processor(images=__UpperCAmelCase ,return_tensors='np' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() ,input_processor[key].sum() ,delta=1e-2 )
def snake_case__ ( self ) -> Tuple:
A__ = self.get_image_processor()
A__ = self.get_tokenizer()
A__ = self.get_qformer_tokenizer()
A__ = InstructBlipProcessor(
tokenizer=__UpperCAmelCase ,image_processor=__UpperCAmelCase ,qformer_tokenizer=__UpperCAmelCase )
A__ = 'lower newer'
A__ = processor(text=__UpperCAmelCase )
A__ = tokenizer(__UpperCAmelCase ,return_token_type_ids=__UpperCAmelCase )
A__ = qformer_tokenizer(__UpperCAmelCase ,return_token_type_ids=__UpperCAmelCase )
for key in encoded_tokens.keys():
self.assertListEqual(encoded_tokens[key] ,encoded_processor[key] )
for key in encoded_tokens_qformer.keys():
self.assertListEqual(encoded_tokens_qformer[key] ,encoded_processor['qformer_' + key] )
def snake_case__ ( self ) -> str:
A__ = self.get_image_processor()
A__ = self.get_tokenizer()
A__ = self.get_qformer_tokenizer()
A__ = InstructBlipProcessor(
tokenizer=__UpperCAmelCase ,image_processor=__UpperCAmelCase ,qformer_tokenizer=__UpperCAmelCase )
A__ = 'lower newer'
A__ = self.prepare_image_inputs()
A__ = processor(text=__UpperCAmelCase ,images=__UpperCAmelCase )
self.assertListEqual(
list(inputs.keys() ) ,['input_ids', 'attention_mask', 'qformer_input_ids', 'qformer_attention_mask', 'pixel_values'] ,)
# test if it raises when no input is passed
with pytest.raises(__UpperCAmelCase ):
processor()
def snake_case__ ( self ) -> Tuple:
A__ = self.get_image_processor()
A__ = self.get_tokenizer()
A__ = self.get_qformer_tokenizer()
A__ = InstructBlipProcessor(
tokenizer=__UpperCAmelCase ,image_processor=__UpperCAmelCase ,qformer_tokenizer=__UpperCAmelCase )
A__ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
A__ = processor.batch_decode(__UpperCAmelCase )
A__ = tokenizer.batch_decode(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase ,__UpperCAmelCase )
def snake_case__ ( self ) -> Any:
A__ = self.get_image_processor()
A__ = self.get_tokenizer()
A__ = self.get_qformer_tokenizer()
A__ = InstructBlipProcessor(
tokenizer=__UpperCAmelCase ,image_processor=__UpperCAmelCase ,qformer_tokenizer=__UpperCAmelCase )
A__ = 'lower newer'
A__ = self.prepare_image_inputs()
A__ = processor(text=__UpperCAmelCase ,images=__UpperCAmelCase )
self.assertListEqual(
list(inputs.keys() ) ,['input_ids', 'attention_mask', 'qformer_input_ids', 'qformer_attention_mask', 'pixel_values'] ,)
| 154 | 0 |
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __A ( lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase_ = None
lowerCAmelCase_ = BloomTokenizerFast
lowerCAmelCase_ = BloomTokenizerFast
lowerCAmelCase_ = True
lowerCAmelCase_ = False
lowerCAmelCase_ = """tokenizer_file"""
lowerCAmelCase_ = {"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}
def __lowerCamelCase ( self ):
'''simple docstring'''
super().setUp()
lowerCamelCase__ = BloomTokenizerFast.from_pretrained('''bigscience/tokenizer''' )
tokenizer.save_pretrained(self.tmpdirname )
def __lowerCamelCase ( self , **__lowerCAmelCase ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return BloomTokenizerFast.from_pretrained(self.tmpdirname , **__lowerCAmelCase )
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = self.get_rust_tokenizer()
lowerCamelCase__ = ['''The quick brown fox</s>''', '''jumps over the lazy dog</s>''']
lowerCamelCase__ = [[2_1_7_5, 2_3_7_1_4, 7_3_1_7_3, 1_4_4_2_5_2, 2], [7_7, 1_3_2_6_1_9, 3_4_7_8, 3_6_8, 1_0_9_5_8_6, 3_5_4_3_3, 2]]
lowerCamelCase__ = tokenizer.batch_encode_plus(__lowerCAmelCase )['''input_ids''']
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase )
lowerCamelCase__ = tokenizer.batch_decode(__lowerCAmelCase )
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase )
def __lowerCamelCase ( self , __lowerCAmelCase=6 ):
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})' ):
lowerCamelCase__ = self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase , **__lowerCAmelCase )
# tokenizer_r.pad_token = None # Hotfixing padding = None
# Simple input
lowerCamelCase__ = '''This is a simple input'''
lowerCamelCase__ = ['''This is a simple input 1''', '''This is a simple input 2''']
lowerCamelCase__ = ('''This is a simple input''', '''This is a pair''')
lowerCamelCase__ = [
('''This is a simple input 1''', '''This is a simple input 2'''),
('''This is a simple pair 1''', '''This is a simple pair 2'''),
]
# Simple input tests
try:
tokenizer_r.encode(__lowerCAmelCase , max_length=__lowerCAmelCase )
tokenizer_r.encode_plus(__lowerCAmelCase , max_length=__lowerCAmelCase )
tokenizer_r.batch_encode_plus(__lowerCAmelCase , max_length=__lowerCAmelCase )
tokenizer_r.encode(__lowerCAmelCase , max_length=__lowerCAmelCase )
tokenizer_r.batch_encode_plus(__lowerCAmelCase , max_length=__lowerCAmelCase )
except ValueError:
self.fail('''Bloom Tokenizer should be able to deal with padding''' )
lowerCamelCase__ = None # Hotfixing padding = None
self.assertRaises(__lowerCAmelCase , tokenizer_r.encode , __lowerCAmelCase , max_length=__lowerCAmelCase , padding='''max_length''' )
# Simple input
self.assertRaises(__lowerCAmelCase , tokenizer_r.encode_plus , __lowerCAmelCase , max_length=__lowerCAmelCase , padding='''max_length''' )
# Simple input
self.assertRaises(
__lowerCAmelCase , tokenizer_r.batch_encode_plus , __lowerCAmelCase , max_length=__lowerCAmelCase , padding='''max_length''' , )
# Pair input
self.assertRaises(__lowerCAmelCase , tokenizer_r.encode , __lowerCAmelCase , max_length=__lowerCAmelCase , padding='''max_length''' )
# Pair input
self.assertRaises(__lowerCAmelCase , tokenizer_r.encode_plus , __lowerCAmelCase , max_length=__lowerCAmelCase , padding='''max_length''' )
# Pair input
self.assertRaises(
__lowerCAmelCase , tokenizer_r.batch_encode_plus , __lowerCAmelCase , max_length=__lowerCAmelCase , padding='''max_length''' , )
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = self.get_rust_tokenizer()
lowerCamelCase__ = load_dataset('''xnli''' , '''all_languages''' , split='''test''' , streaming=__lowerCAmelCase )
lowerCamelCase__ = next(iter(__lowerCAmelCase ) )['''premise'''] # pick up one data
lowerCamelCase__ = list(sample_data.values() )
lowerCamelCase__ = list(map(tokenizer.encode , __lowerCAmelCase ) )
lowerCamelCase__ = [tokenizer.decode(__lowerCAmelCase , clean_up_tokenization_spaces=__lowerCAmelCase ) for x in output_tokens]
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase )
def __lowerCamelCase ( self ):
'''simple docstring'''
self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map ) , 1 )
self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values() )[0] ) , 1 )
| 209 |
_a = 65_521
def lowerCAmelCase__(__snake_case ) -> int:
'''simple docstring'''
lowerCamelCase__ = 1
lowerCamelCase__ = 0
for plain_chr in plain_text:
lowerCamelCase__ = (a + ord(__snake_case )) % MOD_ADLER
lowerCamelCase__ = (b + a) % MOD_ADLER
return (b << 16) | a
| 209 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class _lowerCAmelCase ( lowercase ,unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase : Any = BlenderbotSmallTokenizer
__UpperCAmelCase : Dict = False
def _lowercase ( self : Any ):
super().setUp()
__lowercase = ["__start__", "adapt", "act", "ap@@", "te", "__end__", "__unk__"]
__lowercase = dict(zip(UpperCAmelCase__, range(len(UpperCAmelCase__ ) ) ) )
__lowercase = ["#version: 0.2", "a p", "t e</w>", "ap t</w>", "a d", "ad apt</w>", "a c", "ac t</w>", ""]
__lowercase = {"unk_token": "__unk__", "bos_token": "__start__", "eos_token": "__end__"}
__lowercase = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"] )
__lowercase = 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(UpperCAmelCase__ ) + "\n" )
with open(self.merges_file, "w", encoding="utf-8" ) as fp:
fp.write("\n".join(UpperCAmelCase__ ) )
def _lowercase ( self : List[str], **UpperCAmelCase__ : int ):
kwargs.update(self.special_tokens_map )
return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname, **UpperCAmelCase__ )
def _lowercase ( self : int, UpperCAmelCase__ : Dict ):
__lowercase = "adapt act apte"
__lowercase = "adapt act apte"
return input_text, output_text
def _lowercase ( self : Optional[Any] ):
__lowercase = BlenderbotSmallTokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map )
__lowercase = "adapt act apte"
__lowercase = ["adapt", "act", "ap@@", "te"]
__lowercase = tokenizer.tokenize(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__, UpperCAmelCase__ )
__lowercase = [tokenizer.bos_token] + tokens + [tokenizer.eos_token]
__lowercase = [0, 1, 2, 3, 4, 5]
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__ ), UpperCAmelCase__ )
def _lowercase ( self : int ):
__lowercase = BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M" )
assert tok("sam" ).input_ids == [1_3_8_4]
__lowercase = "I am a small frog."
__lowercase = tok([src_text], padding=UpperCAmelCase__, truncation=UpperCAmelCase__ )["input_ids"]
__lowercase = tok.batch_decode(UpperCAmelCase__, skip_special_tokens=UpperCAmelCase__, clean_up_tokenization_spaces=UpperCAmelCase__ )[0]
assert src_text != decoded # I wish it did!
assert decoded == "i am a small frog ."
def _lowercase ( self : List[Any] ):
__lowercase = BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M" )
__lowercase = "I am a small frog ."
__lowercase = "."
__lowercase = tok(UpperCAmelCase__ )["input_ids"]
__lowercase = tok(UpperCAmelCase__ )["input_ids"]
assert encoded[-1] == encoded_dot[0]
| 144 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
)
@flax.struct.dataclass
class _lowerCAmelCase ( lowercase ):
"""simple docstring"""
__UpperCAmelCase : jnp.ndarray
__UpperCAmelCase : jnp.ndarray
class _lowerCAmelCase ( nn.Module ):
"""simple docstring"""
__UpperCAmelCase : int
__UpperCAmelCase : Tuple[int] = (1_6, 3_2, 9_6, 2_5_6)
__UpperCAmelCase : jnp.dtype = jnp.floataa
def _lowercase ( self : Union[str, Any] ):
__lowercase = nn.Conv(
self.block_out_channels[0], kernel_size=(3, 3), padding=((1, 1), (1, 1)), dtype=self.dtype, )
__lowercase = []
for i in range(len(self.block_out_channels ) - 1 ):
__lowercase = self.block_out_channels[i]
__lowercase = self.block_out_channels[i + 1]
__lowercase = nn.Conv(
UpperCAmelCase__, kernel_size=(3, 3), padding=((1, 1), (1, 1)), dtype=self.dtype, )
blocks.append(UpperCAmelCase__ )
__lowercase = nn.Conv(
UpperCAmelCase__, kernel_size=(3, 3), strides=(2, 2), padding=((1, 1), (1, 1)), dtype=self.dtype, )
blocks.append(UpperCAmelCase__ )
__lowercase = blocks
__lowercase = nn.Conv(
self.conditioning_embedding_channels, kernel_size=(3, 3), padding=((1, 1), (1, 1)), kernel_init=nn.initializers.zeros_init(), bias_init=nn.initializers.zeros_init(), dtype=self.dtype, )
def __call__( self : Any, UpperCAmelCase__ : Union[str, Any] ):
__lowercase = self.conv_in(UpperCAmelCase__ )
__lowercase = nn.silu(UpperCAmelCase__ )
for block in self.blocks:
__lowercase = block(UpperCAmelCase__ )
__lowercase = nn.silu(UpperCAmelCase__ )
__lowercase = self.conv_out(UpperCAmelCase__ )
return embedding
@flax_register_to_config
class _lowerCAmelCase ( nn.Module ,lowercase ,lowercase ):
"""simple docstring"""
__UpperCAmelCase : int = 3_2
__UpperCAmelCase : int = 4
__UpperCAmelCase : Tuple[str] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
__UpperCAmelCase : Union[bool, Tuple[bool]] = False
__UpperCAmelCase : Tuple[int] = (3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
__UpperCAmelCase : int = 2
__UpperCAmelCase : Union[int, Tuple[int]] = 8
__UpperCAmelCase : Optional[Union[int, Tuple[int]]] = None
__UpperCAmelCase : int = 1_2_8_0
__UpperCAmelCase : float = 0.0
__UpperCAmelCase : bool = False
__UpperCAmelCase : jnp.dtype = jnp.floataa
__UpperCAmelCase : bool = True
__UpperCAmelCase : int = 0
__UpperCAmelCase : str = "rgb"
__UpperCAmelCase : Tuple[int] = (1_6, 3_2, 9_6, 2_5_6)
def _lowercase ( self : List[Any], UpperCAmelCase__ : jax.random.KeyArray ):
# init input tensors
__lowercase = (1, self.in_channels, self.sample_size, self.sample_size)
__lowercase = jnp.zeros(UpperCAmelCase__, dtype=jnp.floataa )
__lowercase = jnp.ones((1,), dtype=jnp.intaa )
__lowercase = jnp.zeros((1, 1, self.cross_attention_dim), dtype=jnp.floataa )
__lowercase = (1, 3, self.sample_size * 8, self.sample_size * 8)
__lowercase = jnp.zeros(UpperCAmelCase__, dtype=jnp.floataa )
__lowercase ,__lowercase = jax.random.split(UpperCAmelCase__ )
__lowercase = {"params": params_rng, "dropout": dropout_rng}
return self.init(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ )["params"]
def _lowercase ( self : Union[str, Any] ):
__lowercase = self.block_out_channels
__lowercase = block_out_channels[0] * 4
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
__lowercase = self.num_attention_heads or self.attention_head_dim
# input
__lowercase = nn.Conv(
block_out_channels[0], kernel_size=(3, 3), strides=(1, 1), padding=((1, 1), (1, 1)), dtype=self.dtype, )
# time
__lowercase = FlaxTimesteps(
block_out_channels[0], flip_sin_to_cos=self.flip_sin_to_cos, freq_shift=self.config.freq_shift )
__lowercase = FlaxTimestepEmbedding(UpperCAmelCase__, dtype=self.dtype )
__lowercase = FlaxControlNetConditioningEmbedding(
conditioning_embedding_channels=block_out_channels[0], block_out_channels=self.conditioning_embedding_out_channels, )
__lowercase = self.only_cross_attention
if isinstance(UpperCAmelCase__, UpperCAmelCase__ ):
__lowercase = (only_cross_attention,) * len(self.down_block_types )
if isinstance(UpperCAmelCase__, UpperCAmelCase__ ):
__lowercase = (num_attention_heads,) * len(self.down_block_types )
# down
__lowercase = []
__lowercase = []
__lowercase = block_out_channels[0]
__lowercase = nn.Conv(
UpperCAmelCase__, kernel_size=(1, 1), padding="VALID", kernel_init=nn.initializers.zeros_init(), bias_init=nn.initializers.zeros_init(), dtype=self.dtype, )
controlnet_down_blocks.append(UpperCAmelCase__ )
for i, down_block_type in enumerate(self.down_block_types ):
__lowercase = output_channel
__lowercase = block_out_channels[i]
__lowercase = i == len(UpperCAmelCase__ ) - 1
if down_block_type == "CrossAttnDownBlock2D":
__lowercase = FlaxCrossAttnDownBlockaD(
in_channels=UpperCAmelCase__, out_channels=UpperCAmelCase__, dropout=self.dropout, num_layers=self.layers_per_block, num_attention_heads=num_attention_heads[i], add_downsample=not is_final_block, use_linear_projection=self.use_linear_projection, only_cross_attention=only_cross_attention[i], dtype=self.dtype, )
else:
__lowercase = FlaxDownBlockaD(
in_channels=UpperCAmelCase__, out_channels=UpperCAmelCase__, dropout=self.dropout, num_layers=self.layers_per_block, add_downsample=not is_final_block, dtype=self.dtype, )
down_blocks.append(UpperCAmelCase__ )
for _ in range(self.layers_per_block ):
__lowercase = nn.Conv(
UpperCAmelCase__, kernel_size=(1, 1), padding="VALID", kernel_init=nn.initializers.zeros_init(), bias_init=nn.initializers.zeros_init(), dtype=self.dtype, )
controlnet_down_blocks.append(UpperCAmelCase__ )
if not is_final_block:
__lowercase = nn.Conv(
UpperCAmelCase__, kernel_size=(1, 1), padding="VALID", kernel_init=nn.initializers.zeros_init(), bias_init=nn.initializers.zeros_init(), dtype=self.dtype, )
controlnet_down_blocks.append(UpperCAmelCase__ )
__lowercase = down_blocks
__lowercase = controlnet_down_blocks
# mid
__lowercase = block_out_channels[-1]
__lowercase = FlaxUNetMidBlockaDCrossAttn(
in_channels=UpperCAmelCase__, dropout=self.dropout, num_attention_heads=num_attention_heads[-1], use_linear_projection=self.use_linear_projection, dtype=self.dtype, )
__lowercase = nn.Conv(
UpperCAmelCase__, kernel_size=(1, 1), padding="VALID", kernel_init=nn.initializers.zeros_init(), bias_init=nn.initializers.zeros_init(), dtype=self.dtype, )
def __call__( self : str, UpperCAmelCase__ : Union[str, Any], UpperCAmelCase__ : List[Any], UpperCAmelCase__ : Tuple, UpperCAmelCase__ : List[str], UpperCAmelCase__ : float = 1.0, UpperCAmelCase__ : bool = True, UpperCAmelCase__ : bool = False, ):
__lowercase = self.controlnet_conditioning_channel_order
if channel_order == "bgr":
__lowercase = jnp.flip(UpperCAmelCase__, axis=1 )
# 1. time
if not isinstance(UpperCAmelCase__, jnp.ndarray ):
__lowercase = jnp.array([timesteps], dtype=jnp.intaa )
elif isinstance(UpperCAmelCase__, jnp.ndarray ) and len(timesteps.shape ) == 0:
__lowercase = timesteps.astype(dtype=jnp.floataa )
__lowercase = jnp.expand_dims(UpperCAmelCase__, 0 )
__lowercase = self.time_proj(UpperCAmelCase__ )
__lowercase = self.time_embedding(UpperCAmelCase__ )
# 2. pre-process
__lowercase = jnp.transpose(UpperCAmelCase__, (0, 2, 3, 1) )
__lowercase = self.conv_in(UpperCAmelCase__ )
__lowercase = jnp.transpose(UpperCAmelCase__, (0, 2, 3, 1) )
__lowercase = self.controlnet_cond_embedding(UpperCAmelCase__ )
sample += controlnet_cond
# 3. down
__lowercase = (sample,)
for down_block in self.down_blocks:
if isinstance(UpperCAmelCase__, UpperCAmelCase__ ):
__lowercase ,__lowercase = down_block(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, deterministic=not train )
else:
__lowercase ,__lowercase = down_block(UpperCAmelCase__, UpperCAmelCase__, deterministic=not train )
down_block_res_samples += res_samples
# 4. mid
__lowercase = self.mid_block(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, deterministic=not train )
# 5. contronet blocks
__lowercase = ()
for down_block_res_sample, controlnet_block in zip(UpperCAmelCase__, self.controlnet_down_blocks ):
__lowercase = controlnet_block(UpperCAmelCase__ )
controlnet_down_block_res_samples += (down_block_res_sample,)
__lowercase = controlnet_down_block_res_samples
__lowercase = self.controlnet_mid_block(UpperCAmelCase__ )
# 6. scaling
__lowercase = [sample * conditioning_scale for sample in down_block_res_samples]
mid_block_res_sample *= conditioning_scale
if not return_dict:
return (down_block_res_samples, mid_block_res_sample)
return FlaxControlNetOutput(
down_block_res_samples=UpperCAmelCase__, mid_block_res_sample=UpperCAmelCase__ )
| 144 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ :Any = {
"configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ :List[Any] = [
"RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"ResNetForImageClassification",
"ResNetModel",
"ResNetPreTrainedModel",
"ResNetBackbone",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ :Any = [
"TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFResNetForImageClassification",
"TFResNetModel",
"TFResNetPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ :List[Any] = [
"FlaxResNetForImageClassification",
"FlaxResNetModel",
"FlaxResNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_resnet import (
RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
ResNetBackbone,
ResNetForImageClassification,
ResNetModel,
ResNetPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_resnet import (
TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFResNetForImageClassification,
TFResNetModel,
TFResNetPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel
else:
import sys
a_ :Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 277 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification
logging.set_verbosity_info()
a_ :List[Any] = logging.get_logger(__name__)
a_ :List[Any] = {
"post_extract_proj": "feature_projection.projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.k_proj": "encoder.layers.*.attention.k_proj",
"self_attn.v_proj": "encoder.layers.*.attention.v_proj",
"self_attn.q_proj": "encoder.layers.*.attention.q_proj",
"self_attn.out_proj": "encoder.layers.*.attention.out_proj",
"self_attn_layer_norm": "encoder.layers.*.layer_norm",
"fc1": "encoder.layers.*.feed_forward.intermediate_dense",
"fc2": "encoder.layers.*.feed_forward.output_dense",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "encoder.layer_norm",
"adapter_layer": "encoder.layers.*.adapter_layer",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "lm_head",
"mask_emb": "masked_spec_embed",
"pooling_layer.linear": "projector",
"pooling_layer.projection": "classifier",
}
a_ :List[Any] = [
"lm_head",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
"projector",
"classifier",
]
def lowercase_ (A : Dict ):
snake_case__ : Optional[Any] = {}
with open(A , 'r' ) as file:
for line_number, line in enumerate(A ):
snake_case__ : Dict = line.strip()
if line:
snake_case__ : int = line.split()
snake_case__ : List[str] = line_number
snake_case__ : Dict = words[0]
snake_case__ : Optional[Any] = value
return result
def lowercase_ (A : int , A : int , A : Optional[int] , A : Optional[Any] , A : Tuple ):
for attribute in key.split('.' ):
snake_case__ : Optional[int] = getattr(A , A )
snake_case__ : Union[str, Any] = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(A ):
snake_case__ : List[str] = PARAM_MAPPING[full_name.split('.' )[-1]]
snake_case__ : Dict = 'param'
if weight_type is not None and weight_type != "param":
snake_case__ : Union[str, Any] = getattr(A , A ).shape
elif weight_type is not None and weight_type == "param":
snake_case__ : Optional[int] = hf_pointer
for attribute in hf_param_name.split('.' ):
snake_case__ : Optional[Any] = getattr(A , A )
snake_case__ : Dict = shape_pointer.shape
# let's reduce dimension
snake_case__ : List[Any] = value[0]
else:
snake_case__ : Union[str, Any] = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
F''' {value.shape} for {full_name}''' )
if weight_type == "weight":
snake_case__ : Any = value
elif weight_type == "weight_g":
snake_case__ : List[Any] = value
elif weight_type == "weight_v":
snake_case__ : Any = value
elif weight_type == "bias":
snake_case__ : List[Any] = value
elif weight_type == "param":
for attribute in hf_param_name.split('.' ):
snake_case__ : int = getattr(A , A )
snake_case__ : Optional[int] = value
else:
snake_case__ : Optional[Any] = value
logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' )
def lowercase_ (A : Tuple , A : List[Any] , A : int , A : str , A : Tuple ):
snake_case__ : Optional[int] = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(A ):
snake_case__ : List[str] = PARAM_MAPPING[full_name.split('.' )[-1]]
snake_case__ : str = 'param'
if weight_type is not None and weight_type != "param":
snake_case__ : int = '.'.join([key, weight_type] )
elif weight_type is not None and weight_type == "param":
snake_case__ : Any = '.'.join([key, hf_param_name] )
else:
snake_case__ : Dict = key
snake_case__ : List[str] = value if 'lm_head' in full_key else value[0]
a_ :List[str] = {
"W_a": "linear_1.weight",
"W_b": "linear_2.weight",
"b_a": "linear_1.bias",
"b_b": "linear_2.bias",
"ln_W": "norm.weight",
"ln_b": "norm.bias",
}
def lowercase_ (A : str , A : Optional[Any] , A : Optional[Any]=None , A : List[str]=None ):
snake_case__ : Optional[int] = False
for key, mapped_key in MAPPING.items():
snake_case__ : Tuple = 'wav2vec2.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
snake_case__ : Optional[int] = True
if "*" in mapped_key:
snake_case__ : List[Any] = name.split(A )[0].split('.' )[-2]
snake_case__ : Union[str, Any] = mapped_key.replace('*' , A )
if "weight_g" in name:
snake_case__ : Tuple = 'weight_g'
elif "weight_v" in name:
snake_case__ : List[str] = 'weight_v'
elif "bias" in name:
snake_case__ : Dict = 'bias'
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
snake_case__ : Optional[int] = 'weight'
else:
snake_case__ : str = None
if hf_dict is not None:
rename_dict(A , A , A , A , A )
else:
set_recursively(A , A , A , A , A )
return is_used
return is_used
def lowercase_ (A : Optional[Any] , A : Dict , A : Optional[int] ):
snake_case__ : Dict = []
snake_case__ : Tuple = fairseq_model.state_dict()
snake_case__ : str = hf_model.wavaveca.feature_extractor
for name, value in fairseq_dict.items():
snake_case__ : str = False
if "conv_layers" in name:
load_conv_layer(
A , A , A , A , hf_model.config.feat_extract_norm == 'group' , )
snake_case__ : Any = True
else:
snake_case__ : Dict = load_wavaveca_layer(A , A , A )
if not is_used:
unused_weights.append(A )
logger.warning(F'''Unused weights: {unused_weights}''' )
def lowercase_ (A : Dict , A : Optional[Any] , A : Tuple , A : str , A : List[str] ):
snake_case__ : List[Any] = full_name.split('conv_layers.' )[-1]
snake_case__ : List[str] = name.split('.' )
snake_case__ : List[Any] = int(items[0] )
snake_case__ : str = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''' )
snake_case__ : Any = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''' )
snake_case__ : str = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.''' )
snake_case__ : str = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.''' )
snake_case__ : int = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(A )
@torch.no_grad()
def lowercase_ (A : Union[str, Any] , A : str , A : Tuple=None , A : List[str]=None , A : Any=True , A : Optional[int]=False ):
if config_path is not None:
snake_case__ : List[Any] = WavaVecaConfig.from_pretrained(A )
else:
snake_case__ : List[Any] = WavaVecaConfig()
if is_seq_class:
snake_case__ : Dict = read_txt_into_dict(A )
snake_case__ : Any = idalabel
snake_case__ : Union[str, Any] = WavaVecaForSequenceClassification(A )
snake_case__ : Any = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6_0_0_0 , padding_value=0 , do_normalize=A , return_attention_mask=A , )
feature_extractor.save_pretrained(A )
elif is_finetuned:
if dict_path:
snake_case__ : str = Dictionary.load(A )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
snake_case__ : List[str] = target_dict.pad_index
snake_case__ : Optional[int] = target_dict.bos_index
snake_case__ : Optional[int] = target_dict.eos_index
snake_case__ : List[Any] = len(target_dict.symbols )
snake_case__ : str = os.path.join(A , 'vocab.json' )
if not os.path.isdir(A ):
logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(A ) )
return
os.makedirs(A , exist_ok=A )
snake_case__ : Optional[Any] = target_dict.indices
# fairseq has the <pad> and <s> switched
snake_case__ : Optional[Any] = 0
snake_case__ : Union[str, Any] = 1
with open(A , 'w' , encoding='utf-8' ) as vocab_handle:
json.dump(A , A )
snake_case__ : List[Any] = WavaVecaCTCTokenizer(
A , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='|' , do_lower_case=A , )
snake_case__ : str = True if config.feat_extract_norm == 'layer' else False
snake_case__ : Optional[Any] = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6_0_0_0 , padding_value=0 , do_normalize=A , return_attention_mask=A , )
snake_case__ : Union[str, Any] = WavaVecaProcessor(feature_extractor=A , tokenizer=A )
processor.save_pretrained(A )
snake_case__ : str = WavaVecaForCTC(A )
else:
snake_case__ : int = WavaVecaForPreTraining(A )
if is_finetuned or is_seq_class:
snake_case__ , snake_case__ , snake_case__ : str = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
else:
snake_case__ : Tuple = argparse.Namespace(task='audio_pretraining' )
snake_case__ : str = fairseq.tasks.setup_task(A )
snake_case__ , snake_case__ , snake_case__ : Any = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=A )
snake_case__ : List[Any] = model[0].eval()
recursively_load_weights(A , A , not is_finetuned )
hf_wavavec.save_pretrained(A )
if __name__ == "__main__":
a_ :List[Any] = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
)
parser.add_argument(
"--is_seq_class",
action="store_true",
help="Whether the model to convert is a fine-tuned sequence classification model or not",
)
a_ :str = parser.parse_args()
a_ :Tuple = not args.not_finetuned and not args.is_seq_class
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.dict_path,
is_finetuned,
args.is_seq_class,
)
| 277 | 1 |
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
_lowerCamelCase : Any = '''base_with_context'''
def a_ ( __lowercase : Union[str, Any] , __lowercase : Tuple ) -> Any:
_snake_case = nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding'] ) )
_snake_case = nn.Parameter(
torch.FloatTensor(weights['Embed_0']['embedding'] ) , requires_grad=__lowercase )
for lyr_num, lyr in enumerate(model.encoders ):
_snake_case = weights[f'''layers_{lyr_num}''']
_snake_case = nn.Parameter(
torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale'] ) )
_snake_case = ly_weight['attention']
_snake_case = nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T ) )
_snake_case = nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T ) )
_snake_case = nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T ) )
_snake_case = nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T ) )
_snake_case = nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale'] ) )
_snake_case = nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T ) )
_snake_case = nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T ) )
_snake_case = nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T ) )
_snake_case = nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale'] ) )
return model
def a_ ( __lowercase : Union[str, Any] , __lowercase : int ) -> List[Any]:
_snake_case = nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T ) )
_snake_case = nn.Parameter(
torch.FloatTensor(weights['Embed_0']['embedding'] ) , requires_grad=__lowercase )
for lyr_num, lyr in enumerate(model.encoders ):
_snake_case = weights[f'''layers_{lyr_num}''']
_snake_case = ly_weight['attention']
_snake_case = nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T ) )
_snake_case = nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T ) )
_snake_case = nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T ) )
_snake_case = nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T ) )
_snake_case = nn.Parameter(
torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale'] ) )
_snake_case = nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T ) )
_snake_case = nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T ) )
_snake_case = nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T ) )
_snake_case = nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale'] ) )
_snake_case = nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale'] ) )
return model
def a_ ( __lowercase : Dict , __lowercase : Tuple ) -> Dict:
_snake_case = nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T ) )
_snake_case = nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T ) )
_snake_case = nn.Parameter(
torch.FloatTensor(weights['Embed_0']['embedding'] ) , requires_grad=__lowercase )
_snake_case = nn.Parameter(
torch.FloatTensor(weights['continuous_inputs_projection']['kernel'].T ) )
for lyr_num, lyr in enumerate(model.decoders ):
_snake_case = weights[f'''layers_{lyr_num}''']
_snake_case = nn.Parameter(
torch.FloatTensor(ly_weight['pre_self_attention_layer_norm']['scale'] ) )
_snake_case = nn.Parameter(
torch.FloatTensor(ly_weight['FiLMLayer_0']['DenseGeneral_0']['kernel'].T ) )
_snake_case = ly_weight['self_attention']
_snake_case = nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T ) )
_snake_case = nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T ) )
_snake_case = nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T ) )
_snake_case = nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T ) )
_snake_case = ly_weight['MultiHeadDotProductAttention_0']
_snake_case = nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T ) )
_snake_case = nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T ) )
_snake_case = nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T ) )
_snake_case = nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T ) )
_snake_case = nn.Parameter(
torch.FloatTensor(ly_weight['pre_cross_attention_layer_norm']['scale'] ) )
_snake_case = nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale'] ) )
_snake_case = nn.Parameter(
torch.FloatTensor(ly_weight['FiLMLayer_1']['DenseGeneral_0']['kernel'].T ) )
_snake_case = nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T ) )
_snake_case = nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T ) )
_snake_case = nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T ) )
_snake_case = nn.Parameter(torch.FloatTensor(weights['decoder_norm']['scale'] ) )
_snake_case = nn.Parameter(torch.FloatTensor(weights['spec_out_dense']['kernel'].T ) )
return model
def a_ ( __lowercase : Union[str, Any] ) -> Tuple:
_snake_case = checkpoints.load_tax_checkpoint(args.checkpoint_path )
_snake_case = jnp.tree_util.tree_map(onp.array , __lowercase )
_snake_case = [
'from __gin__ import dynamic_registration',
'from music_spectrogram_diffusion.models.diffusion import diffusion_utils',
'diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0',
'diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()',
]
_snake_case = os.path.join(args.checkpoint_path , '..' , 'config.gin' )
_snake_case = inference.parse_training_gin_file(__lowercase , __lowercase )
_snake_case = inference.InferenceModel(args.checkpoint_path , __lowercase )
_snake_case = DDPMScheduler(beta_schedule='squaredcos_cap_v2' , variance_type='fixed_large' )
_snake_case = SpectrogramNotesEncoder(
max_length=synth_model.sequence_length['inputs'] , vocab_size=synth_model.model.module.config.vocab_size , d_model=synth_model.model.module.config.emb_dim , dropout_rate=synth_model.model.module.config.dropout_rate , num_layers=synth_model.model.module.config.num_encoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , feed_forward_proj='gated-gelu' , )
_snake_case = SpectrogramContEncoder(
input_dims=synth_model.audio_codec.n_dims , targets_context_length=synth_model.sequence_length['targets_context'] , d_model=synth_model.model.module.config.emb_dim , dropout_rate=synth_model.model.module.config.dropout_rate , num_layers=synth_model.model.module.config.num_encoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , feed_forward_proj='gated-gelu' , )
_snake_case = TaFilmDecoder(
input_dims=synth_model.audio_codec.n_dims , targets_length=synth_model.sequence_length['targets_context'] , max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time , d_model=synth_model.model.module.config.emb_dim , num_layers=synth_model.model.module.config.num_decoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , dropout_rate=synth_model.model.module.config.dropout_rate , )
_snake_case = load_notes_encoder(ta_checkpoint['target']['token_encoder'] , __lowercase )
_snake_case = load_continuous_encoder(ta_checkpoint['target']['continuous_encoder'] , __lowercase )
_snake_case = load_decoder(ta_checkpoint['target']['decoder'] , __lowercase )
_snake_case = OnnxRuntimeModel.from_pretrained('kashif/soundstream_mel_decoder' )
_snake_case = SpectrogramDiffusionPipeline(
notes_encoder=__lowercase , continuous_encoder=__lowercase , decoder=__lowercase , scheduler=__lowercase , melgan=__lowercase , )
if args.save:
pipe.save_pretrained(args.output_path )
if __name__ == "__main__":
_lowerCamelCase : Dict = argparse.ArgumentParser()
parser.add_argument('''--output_path''', default=None, type=str, required=True, help='''Path to the converted model.''')
parser.add_argument(
'''--save''', default=True, type=bool, required=False, help='''Whether to save the converted model or not.'''
)
parser.add_argument(
'''--checkpoint_path''',
default=F'{MODEL}/checkpoint_500000',
type=str,
required=False,
help='''Path to the original jax model checkpoint.''',
)
_lowerCamelCase : Dict = parser.parse_args()
main(args) | 130 |
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig
_lowerCamelCase : List[Any] = logging.get_logger(__name__)
# General docstring
_lowerCamelCase : Dict = '''PoolFormerConfig'''
# Base docstring
_lowerCamelCase : int = '''sail/poolformer_s12'''
_lowerCamelCase : Optional[Any] = [1, 512, 7, 7]
# Image classification docstring
_lowerCamelCase : Optional[int] = '''sail/poolformer_s12'''
_lowerCamelCase : List[Any] = '''tabby, tabby cat'''
_lowerCamelCase : List[str] = [
'''sail/poolformer_s12''',
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]
def a_ ( __lowercase : List[Any] , __lowercase : float = 0.0 , __lowercase : bool = False ) -> Optional[int]:
if drop_prob == 0.0 or not training:
return input
_snake_case = 1 - drop_prob
_snake_case = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
_snake_case = keep_prob + torch.rand(__lowercase , dtype=input.dtype , device=input.device )
random_tensor.floor_() # binarize
_snake_case = input.div(__lowercase ) * random_tensor
return output
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[Any] , lowercase : Optional[float] = None ):
'''simple docstring'''
super().__init__()
_snake_case = drop_prob
def A ( self : Any , lowercase : torch.Tensor ):
'''simple docstring'''
return drop_path(lowercase , self.drop_prob , self.training )
def A ( self : Tuple ):
'''simple docstring'''
return "p={}".format(self.drop_prob )
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
'''simple docstring'''
def __init__( self : Tuple , lowercase : Dict , lowercase : Dict , lowercase : str , lowercase : int , lowercase : Optional[Any] , lowercase : str=None ):
'''simple docstring'''
super().__init__()
_snake_case = patch_size if isinstance(lowercase , collections.abc.Iterable ) else (patch_size, patch_size)
_snake_case = stride if isinstance(lowercase , collections.abc.Iterable ) else (stride, stride)
_snake_case = padding if isinstance(lowercase , collections.abc.Iterable ) else (padding, padding)
_snake_case = nn.Convad(lowercase , lowercase , kernel_size=lowercase , stride=lowercase , padding=lowercase )
_snake_case = norm_layer(lowercase ) if norm_layer else nn.Identity()
def A ( self : int , lowercase : Union[str, Any] ):
'''simple docstring'''
_snake_case = self.projection(lowercase )
_snake_case = self.norm(lowercase )
return embeddings
class SCREAMING_SNAKE_CASE__ ( nn.GroupNorm ):
'''simple docstring'''
def __init__( self : Dict , lowercase : List[Any] , **lowercase : str ):
'''simple docstring'''
super().__init__(1 , lowercase , **lowercase )
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
'''simple docstring'''
def __init__( self : int , lowercase : List[Any] ):
'''simple docstring'''
super().__init__()
_snake_case = nn.AvgPoolad(lowercase , stride=1 , padding=pool_size // 2 , count_include_pad=lowercase )
def A ( self : int , lowercase : List[str] ):
'''simple docstring'''
return self.pool(lowercase ) - hidden_states
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict , lowercase : Tuple , lowercase : str , lowercase : Optional[Any] , lowercase : Union[str, Any] ):
'''simple docstring'''
super().__init__()
_snake_case = nn.Convad(lowercase , lowercase , 1 )
_snake_case = nn.Convad(lowercase , lowercase , 1 )
_snake_case = PoolFormerDropPath(lowercase )
if isinstance(config.hidden_act , lowercase ):
_snake_case = ACTaFN[config.hidden_act]
else:
_snake_case = config.hidden_act
def A ( self : Optional[int] , lowercase : str ):
'''simple docstring'''
_snake_case = self.conva(lowercase )
_snake_case = self.act_fn(lowercase )
_snake_case = self.drop(lowercase )
_snake_case = self.conva(lowercase )
_snake_case = self.drop(lowercase )
return hidden_states
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
'''simple docstring'''
def __init__( self : str , lowercase : Tuple , lowercase : int , lowercase : str , lowercase : Union[str, Any] , lowercase : str , lowercase : Dict ):
'''simple docstring'''
super().__init__()
_snake_case = PoolFormerPooling(lowercase )
_snake_case = PoolFormerOutput(lowercase , lowercase , lowercase , lowercase )
_snake_case = PoolFormerGroupNorm(lowercase )
_snake_case = PoolFormerGroupNorm(lowercase )
# Useful for training neural nets
_snake_case = PoolFormerDropPath(lowercase ) if drop_path > 0.0 else nn.Identity()
_snake_case = config.use_layer_scale
if config.use_layer_scale:
_snake_case = nn.Parameter(
config.layer_scale_init_value * torch.ones((lowercase) ) , requires_grad=lowercase )
_snake_case = nn.Parameter(
config.layer_scale_init_value * torch.ones((lowercase) ) , requires_grad=lowercase )
def A ( self : Optional[int] , lowercase : Union[str, Any] ):
'''simple docstring'''
if self.use_layer_scale:
_snake_case = self.pooling(self.before_norm(lowercase ) )
_snake_case = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * pooling_output
# First residual connection
_snake_case = hidden_states + self.drop_path(lowercase )
_snake_case = ()
_snake_case = self.output(self.after_norm(lowercase ) )
_snake_case = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * layer_output
# Second residual connection
_snake_case = hidden_states + self.drop_path(lowercase )
_snake_case = (output,) + outputs
return outputs
else:
_snake_case = self.drop_path(self.pooling(self.before_norm(lowercase ) ) )
# First residual connection
_snake_case = pooling_output + hidden_states
_snake_case = ()
# Second residual connection inside the PoolFormerOutput block
_snake_case = self.drop_path(self.output(self.after_norm(lowercase ) ) )
_snake_case = hidden_states + layer_output
_snake_case = (output,) + outputs
return outputs
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[Any] , lowercase : Optional[int] ):
'''simple docstring'''
super().__init__()
_snake_case = config
# stochastic depth decay rule
_snake_case = [x.item() for x in torch.linspace(0 , config.drop_path_rate , sum(config.depths ) )]
# patch embeddings
_snake_case = []
for i in range(config.num_encoder_blocks ):
embeddings.append(
PoolFormerEmbeddings(
patch_size=config.patch_sizes[i] , stride=config.strides[i] , padding=config.padding[i] , num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1] , hidden_size=config.hidden_sizes[i] , ) )
_snake_case = nn.ModuleList(lowercase )
# Transformer blocks
_snake_case = []
_snake_case = 0
for i in range(config.num_encoder_blocks ):
# each block consists of layers
_snake_case = []
if i != 0:
cur += config.depths[i - 1]
for j in range(config.depths[i] ):
layers.append(
PoolFormerLayer(
lowercase , num_channels=config.hidden_sizes[i] , pool_size=config.pool_size , hidden_size=config.hidden_sizes[i] , intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio ) , drop_path=dpr[cur + j] , ) )
blocks.append(nn.ModuleList(lowercase ) )
_snake_case = nn.ModuleList(lowercase )
def A ( self : Any , lowercase : List[str] , lowercase : str=False , lowercase : Tuple=True ):
'''simple docstring'''
_snake_case = () if output_hidden_states else None
_snake_case = pixel_values
for idx, layers in enumerate(zip(self.patch_embeddings , self.block ) ):
_snake_case , _snake_case = layers
# Get patch embeddings from hidden_states
_snake_case = embedding_layer(lowercase )
# Send the embeddings through the blocks
for _, blk in enumerate(lowercase ):
_snake_case = blk(lowercase )
_snake_case = layer_outputs[0]
if output_hidden_states:
_snake_case = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=lowercase , hidden_states=lowercase )
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase ):
'''simple docstring'''
_UpperCAmelCase : List[Any] = PoolFormerConfig
_UpperCAmelCase : Optional[int] = "poolformer"
_UpperCAmelCase : str = "pixel_values"
_UpperCAmelCase : int = True
def A ( self : Tuple , lowercase : str ):
'''simple docstring'''
if isinstance(lowercase , (nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(lowercase , nn.LayerNorm ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
def A ( self : Optional[Any] , lowercase : str , lowercase : Dict=False ):
'''simple docstring'''
if isinstance(lowercase , lowercase ):
_snake_case = value
_lowerCamelCase : Optional[Any] = r'''
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''
_lowerCamelCase : Tuple = r'''
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`PoolFormerImageProcessor.__call__`] for details.
'''
@add_start_docstrings(
"The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top." ,UpperCAmelCase ,)
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase ):
'''simple docstring'''
def __init__( self : str , lowercase : List[Any] ):
'''simple docstring'''
super().__init__(lowercase )
_snake_case = config
_snake_case = PoolFormerEncoder(lowercase )
# Initialize weights and apply final processing
self.post_init()
def A ( self : List[str] ):
'''simple docstring'''
return self.embeddings.patch_embeddings
@add_start_docstrings_to_model_forward(lowercase )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=lowercase , config_class=_CONFIG_FOR_DOC , modality='vision' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def A ( self : Tuple , lowercase : Optional[torch.FloatTensor] = None , lowercase : Optional[bool] = None , lowercase : Optional[bool] = None , ):
'''simple docstring'''
_snake_case = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_snake_case = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError('You have to specify pixel_values' )
_snake_case = self.encoder(
lowercase , output_hidden_states=lowercase , return_dict=lowercase , )
_snake_case = encoder_outputs[0]
if not return_dict:
return (sequence_output, None) + encoder_outputs[1:]
return BaseModelOutputWithNoAttention(
last_hidden_state=lowercase , hidden_states=encoder_outputs.hidden_states , )
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
'''simple docstring'''
def __init__( self : List[str] , lowercase : Union[str, Any] ):
'''simple docstring'''
super().__init__()
_snake_case = nn.Linear(config.hidden_size , config.hidden_size )
def A ( self : Optional[Any] , lowercase : Optional[int] ):
'''simple docstring'''
_snake_case = self.dense(lowercase )
return output
@add_start_docstrings(
"\n PoolFormer Model transformer with an image classification head on top\n " ,UpperCAmelCase ,)
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase ):
'''simple docstring'''
def __init__( self : str , lowercase : Any ):
'''simple docstring'''
super().__init__(lowercase )
_snake_case = config.num_labels
_snake_case = PoolFormerModel(lowercase )
# Final norm
_snake_case = PoolFormerGroupNorm(config.hidden_sizes[-1] )
# Classifier head
_snake_case = (
nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity()
)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(lowercase )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=lowercase , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def A ( self : Union[str, Any] , lowercase : Optional[torch.FloatTensor] = None , lowercase : Optional[torch.LongTensor] = None , lowercase : Optional[bool] = None , lowercase : Optional[bool] = None , ):
'''simple docstring'''
_snake_case = return_dict if return_dict is not None else self.config.use_return_dict
_snake_case = self.poolformer(
lowercase , output_hidden_states=lowercase , return_dict=lowercase , )
_snake_case = outputs[0]
_snake_case = self.classifier(self.norm(lowercase ).mean([-2, -1] ) )
_snake_case = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
_snake_case = 'regression'
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
_snake_case = 'single_label_classification'
else:
_snake_case = 'multi_label_classification'
if self.config.problem_type == "regression":
_snake_case = MSELoss()
if self.num_labels == 1:
_snake_case = loss_fct(logits.squeeze() , labels.squeeze() )
else:
_snake_case = loss_fct(lowercase , lowercase )
elif self.config.problem_type == "single_label_classification":
_snake_case = CrossEntropyLoss()
_snake_case = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
_snake_case = BCEWithLogitsLoss()
_snake_case = loss_fct(lowercase , lowercase )
if not return_dict:
_snake_case = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=lowercase , logits=lowercase , hidden_states=outputs.hidden_states ) | 130 | 1 |
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging
logging.set_verbosity_info()
# should not include what is already done by the `from_pt` argument
UpperCAmelCase : int = {
'''/attention/''': '''/0/SelfAttention/''',
'''/self_attention/''': '''/0/SelfAttention/''',
'''/encoder_decoder_attention/''': '''/1/EncDecAttention/''',
'''value''': '''v''',
'''query''': '''q''',
'''key''': '''k''',
'''out''': '''o''',
'''pre_self_attention_layer_norm''': '''0/layer_norm''',
'''pre_cross_attention_layer_norm''': '''1/layer_norm''',
'''pre_attention_layer_norm''': '''0/layer_norm''', # previously 1, but seems wrong
'''token_embedder''': '''shared''',
'''encoder_norm''': '''final_layer_norm''',
'''decoder_norm''': '''final_layer_norm''',
'''relpos_bias/rel_embedding''': '''block/0/layer/0/SelfAttention/relative_attention_bias/weight''',
'''router/router_weights/w/''': '''router/classifier/''',
'''roer/roer_weights/w/''': '''router/classifier/''',
'''logits_dense''': '''lm_head''',
}
def _SCREAMING_SNAKE_CASE ( a ) -> Optional[Any]:
# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
# the original model
__A : int = list(s_dict.keys() )
for key in keys:
__A : Union[str, Any] = r'.*/layers_(\d+)'
__A : Dict = key
if re.match(a , a ):
__A : List[str] = re.sub(r'layers_(\d+)' , r'block/\1/layer' , a )
__A : List[Any] = r'(encoder|decoder)\/'
if re.match(a , a ):
__A : Tuple = re.match(a , a ).groups()
if groups[0] == "encoder":
__A : Union[str, Any] = re.sub(r'/mlp/' , r'/1/mlp/' , a )
__A : Any = re.sub(r'/pre_mlp_layer_norm/' , r'/1/layer_norm/' , a )
elif groups[0] == "decoder":
__A : Optional[int] = re.sub(r'/mlp/' , r'/2/mlp/' , a )
__A : Optional[int] = re.sub(r'/pre_mlp_layer_norm/' , r'/2/layer_norm/' , a )
# 2. Convert other classic mappings
for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
if old_key in new_key:
__A : str = new_key.replace(a , a )
print(F"""{key} -> {new_key}""" )
__A : Dict = s_dict.pop(a )
if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
__A : Any = s_dict[
'encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
].T
if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
__A : List[Any] = s_dict[
'decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
].T
# 3. Take extra care of the EXPERTS layer
for key in list(s_dict.keys() ):
if "expert" in key:
__A : Optional[int] = s_dict[key].shape[0]
__A : Tuple = s_dict[key]
for idx in range(a ):
__A : str = expert_weihts[idx]
print(F"""{key} -> {key.replace("expert/" , "nested fstring" )}""" )
s_dict.pop(a )
return s_dict
UpperCAmelCase : Tuple = {
'''NUM_ENCODER_LAYERS''': '''num_layers''',
'''NUM_DECODER_LAYERS''': '''num_decoder_layers''',
'''NUM_HEADS''': '''num_heads''',
'''HEAD_DIM''': '''d_kv''',
'''EMBED_DIM''': '''d_model''',
'''MLP_DIM''': '''d_ff''',
'''NUM_SELECTED_EXPERTS''': '''num_selected_experts''',
'''NUM_ENCODER_SPARSE_LAYERS''': '''num_sparse_encoder_layers''',
'''NUM_DECODER_SPARSE_LAYERS''': '''num_sparse_decoder_layers''',
'''dense.MlpBlock.activations''': '''feed_forward_proj''',
}
def _SCREAMING_SNAKE_CASE ( a , a ) -> Optional[Any]:
# Convert a google style config to the hugging face fromat
import regex as re
with open(a , 'r' ) as f:
__A : List[str] = f.read()
__A : int = re.findall(r'(.*) = ([0-9.]*)' , a )
__A : Optional[Any] = {}
for param, value in regex_match:
if param in GIN_TO_CONFIG_MAPPING and value != "":
__A : str = float(a ) if '.' in value else int(a )
__A : Any = re.findall(r'(.*activations) = \(\'(.*)\',\)' , a )[0]
__A : List[Any] = str(activation[1] )
__A : List[str] = num_experts
__A : Union[str, Any] = SwitchTransformersConfig(**a )
return config
def _SCREAMING_SNAKE_CASE ( a , a , a=None , a="./" , a=8 ) -> List[Any]:
# Initialise PyTorch model
print(F"""Loading flax weights from : {flax_checkpoint_path}""" )
__A : List[Any] = checkpoints.load_tax_checkpoint(a )
if gin_file is not None:
__A : str = convert_gin_to_config(a , a )
else:
__A : int = SwitchTransformersConfig.from_pretrained(a )
__A : Union[str, Any] = SwitchTransformersForConditionalGeneration(a )
__A : Optional[Any] = flax_params['target']
__A : Optional[int] = flatten_dict(a , sep='/' )
__A : Union[str, Any] = rename_keys(a )
__A : Any = unflatten_dict(a , sep='/' )
# Load the flax params in the PT model
load_flax_weights_in_pytorch_model(a , a )
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
pt_model.save_pretrained(a )
if __name__ == "__main__":
UpperCAmelCase : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--switch_t5x_checkpoint_path''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the'''
''' model architecture. If not provided, a `gin_file` has to be provided.'''
),
)
parser.add_argument(
'''--gin_file''',
default=None,
type=str,
required=False,
help='''Path to the gin config file. If not provided, a `config_file` has to be passed ''',
)
parser.add_argument(
'''--config_name''', default=None, type=str, required=False, help='''Config name of SwitchTransformers model.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output pytorch model.'''
)
parser.add_argument('''--num_experts''', default=8, type=int, required=False, help='''Number of experts''')
UpperCAmelCase : Optional[int] = parser.parse_args()
convert_flax_checkpoint_to_pytorch(
args.switch_tax_checkpoint_path,
args.config_name,
args.gin_file,
args.pytorch_dump_folder_path,
args.num_experts,
)
| 280 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class _A:
"""simple docstring"""
def __init__( self , _A = None ):
if components is None:
__A : int = []
__A : Tuple = list(_A )
def __len__( self ):
return len(self.__components )
def __str__( self ):
return "(" + ",".join(map(_A , self.__components ) ) + ")"
def __add__( self , _A ):
__A : Optional[int] = len(self )
if size == len(_A ):
__A : Any = [self.__components[i] + other.component(_A ) for i in range(_A )]
return Vector(_A )
else:
raise Exception('must have the same size' )
def __sub__( self , _A ):
__A : Tuple = len(self )
if size == len(_A ):
__A : Union[str, Any] = [self.__components[i] - other.component(_A ) for i in range(_A )]
return Vector(_A )
else: # error case
raise Exception('must have the same size' )
@overload
def __mul__( self , _A ):
...
@overload
def __mul__( self , _A ):
...
def __mul__( self , _A ):
if isinstance(_A , (float, int) ):
__A : str = [c * other for c in self.__components]
return Vector(_A )
elif isinstance(_A , _A ) and len(self ) == len(_A ):
__A : Union[str, Any] = len(self )
__A : Dict = [self.__components[i] * other.component(_A ) for i in range(_A )]
return sum(_A )
else: # error case
raise Exception('invalid operand!' )
def UpperCAmelCase_ ( self ):
return Vector(self.__components )
def UpperCAmelCase_ ( self , _A ):
if isinstance(_A , _A ) and -len(self.__components ) <= i < len(self.__components ):
return self.__components[i]
else:
raise Exception('index out of range' )
def UpperCAmelCase_ ( self , _A , _A ):
assert -len(self.__components ) <= pos < len(self.__components )
__A : Optional[int] = value
def UpperCAmelCase_ ( self ):
if len(self.__components ) == 0:
raise Exception('Vector is empty' )
__A : Optional[Any] = [c**2 for c in self.__components]
return math.sqrt(sum(_A ) )
def UpperCAmelCase_ ( self , _A , _A = False ):
__A : Optional[Any] = self * other
__A : Optional[Any] = self.euclidean_length() * other.euclidean_length()
if deg:
return math.degrees(math.acos(num / den ) )
else:
return math.acos(num / den )
def _SCREAMING_SNAKE_CASE ( a ) -> Vector:
assert isinstance(a , a )
return Vector([0] * dimension )
def _SCREAMING_SNAKE_CASE ( a , a ) -> Vector:
assert isinstance(a , a ) and (isinstance(a , a ))
__A : Optional[Any] = [0] * dimension
__A : Tuple = 1
return Vector(a )
def _SCREAMING_SNAKE_CASE ( a , a , a ) -> Vector:
assert (
isinstance(a , a )
and isinstance(a , a )
and (isinstance(a , (int, float) ))
)
return x * scalar + y
def _SCREAMING_SNAKE_CASE ( a , a , a ) -> Vector:
random.seed(a )
__A : str = [random.randint(a , a ) for _ in range(a )]
return Vector(a )
class _A:
"""simple docstring"""
def __init__( self , _A , _A , _A ):
__A : Optional[Any] = matrix
__A : Dict = w
__A : Optional[int] = h
def __str__( self ):
__A : Tuple = ''
for i in range(self.__height ):
ans += "|"
for j in range(self.__width ):
if j < self.__width - 1:
ans += str(self.__matrix[i][j] ) + ","
else:
ans += str(self.__matrix[i][j] ) + "|\n"
return ans
def __add__( self , _A ):
if self.__width == other.width() and self.__height == other.height():
__A : Optional[Any] = []
for i in range(self.__height ):
__A : Optional[Any] = [
self.__matrix[i][j] + other.component(_A , _A )
for j in range(self.__width )
]
matrix.append(_A )
return Matrix(_A , self.__width , self.__height )
else:
raise Exception('matrix must have the same dimension!' )
def __sub__( self , _A ):
if self.__width == other.width() and self.__height == other.height():
__A : Tuple = []
for i in range(self.__height ):
__A : str = [
self.__matrix[i][j] - other.component(_A , _A )
for j in range(self.__width )
]
matrix.append(_A )
return Matrix(_A , self.__width , self.__height )
else:
raise Exception('matrices must have the same dimension!' )
@overload
def __mul__( self , _A ):
...
@overload
def __mul__( self , _A ):
...
def __mul__( self , _A ):
if isinstance(_A , _A ): # matrix-vector
if len(_A ) == self.__width:
__A : List[Any] = zero_vector(self.__height )
for i in range(self.__height ):
__A : List[str] = [
self.__matrix[i][j] * other.component(_A )
for j in range(self.__width )
]
ans.change_component(_A , sum(_A ) )
return ans
else:
raise Exception(
'vector must have the same size as the '
'number of columns of the matrix!' )
elif isinstance(_A , (int, float) ): # matrix-scalar
__A : List[str] = [
[self.__matrix[i][j] * other for j in range(self.__width )]
for i in range(self.__height )
]
return Matrix(_A , self.__width , self.__height )
return None
def UpperCAmelCase_ ( self ):
return self.__height
def UpperCAmelCase_ ( self ):
return self.__width
def UpperCAmelCase_ ( self , _A , _A ):
if 0 <= x < self.__height and 0 <= y < self.__width:
return self.__matrix[x][y]
else:
raise Exception('change_component: indices out of bounds' )
def UpperCAmelCase_ ( self , _A , _A , _A ):
if 0 <= x < self.__height and 0 <= y < self.__width:
__A : int = value
else:
raise Exception('change_component: indices out of bounds' )
def UpperCAmelCase_ ( self , _A , _A ):
if self.__height != self.__width:
raise Exception('Matrix is not square' )
__A : List[str] = self.__matrix[:x] + self.__matrix[x + 1 :]
for i in range(len(_A ) ):
__A : Optional[int] = minor[i][:y] + minor[i][y + 1 :]
return Matrix(_A , self.__width - 1 , self.__height - 1 ).determinant()
def UpperCAmelCase_ ( self , _A , _A ):
if self.__height != self.__width:
raise Exception('Matrix is not square' )
if 0 <= x < self.__height and 0 <= y < self.__width:
return (-1) ** (x + y) * self.minor(_A , _A )
else:
raise Exception('Indices out of bounds' )
def UpperCAmelCase_ ( self ):
if self.__height != self.__width:
raise Exception('Matrix is not square' )
if self.__height < 1:
raise Exception('Matrix has no element' )
elif self.__height == 1:
return self.__matrix[0][0]
elif self.__height == 2:
return (
self.__matrix[0][0] * self.__matrix[1][1]
- self.__matrix[0][1] * self.__matrix[1][0]
)
else:
__A : List[str] = [
self.__matrix[0][y] * self.cofactor(0 , _A ) for y in range(self.__width )
]
return sum(_A )
def _SCREAMING_SNAKE_CASE ( a ) -> Matrix:
__A : list[list[float]] = [[0] * n for _ in range(a )]
return Matrix(a , a , a )
def _SCREAMING_SNAKE_CASE ( a , a , a , a ) -> Matrix:
random.seed(a )
__A : list[list[float]] = [
[random.randint(a , a ) for _ in range(a )] for _ in range(a )
]
return Matrix(a , a , a )
| 280 | 1 |
"""simple docstring"""
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class _lowerCAmelCase ( snake_case_ ):
__UpperCAmelCase : torch.FloatTensor
__UpperCAmelCase : Optional[torch.FloatTensor] = None
def __lowerCAmelCase ( lowercase : Any , lowercase : Dict=0.999 , lowercase : Dict="cosine" , ) -> Optional[Any]:
"""simple docstring"""
if alpha_transform_type == "cosine":
def alpha_bar_fn(lowercase : Union[str, Any] ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(lowercase : Tuple ):
return math.exp(t * -12.0 )
else:
raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}' )
snake_case : List[str] = []
for i in range(lowercase ):
snake_case : int = i / num_diffusion_timesteps
snake_case : str = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(lowercase ) / alpha_bar_fn(lowercase ) , lowercase ) )
return torch.tensor(lowercase , dtype=torch.floataa )
class _lowerCAmelCase ( snake_case_ , snake_case_ ):
__UpperCAmelCase : Optional[int] = 1
@register_to_config
def __init__( self , UpperCamelCase__ = 1000 , UpperCamelCase__ = 0.0001 , UpperCamelCase__ = 0.02 , UpperCamelCase__ = "linear" , UpperCamelCase__ = None , UpperCamelCase__ = True , UpperCamelCase__ = True , UpperCamelCase__ = 0 , UpperCamelCase__ = "epsilon" , UpperCamelCase__ = 1.0 , **UpperCamelCase__ , ) -> str:
'''simple docstring'''
if kwargs.get("set_alpha_to_one" , UpperCamelCase__ ) is not None:
snake_case : str = (
"The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."
)
deprecate("set_alpha_to_one" , "1.0.0" , UpperCamelCase__ , standard_warn=UpperCamelCase__ )
snake_case : Optional[int] = kwargs["set_alpha_to_one"]
if trained_betas is not None:
snake_case : List[Any] = torch.tensor(UpperCamelCase__ , dtype=torch.floataa )
elif beta_schedule == "linear":
snake_case : Union[str, Any] = torch.linspace(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
snake_case : str = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , UpperCamelCase__ , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
snake_case : Any = betas_for_alpha_bar(UpperCamelCase__ )
else:
raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}' )
snake_case : Optional[Any] = 1.0 - self.betas
snake_case : Union[str, Any] = torch.cumprod(self.alphas , dim=0 )
# At every step in inverted ddim, we are looking into the next alphas_cumprod
# For the final step, there is no next alphas_cumprod, and the index is out of bounds
# `set_alpha_to_zero` decides whether we set this parameter simply to zero
# in this case, self.step() just output the predicted noise
# or whether we use the final alpha of the "non-previous" one.
snake_case : List[str] = torch.tensor(0.0 ) if set_alpha_to_zero else self.alphas_cumprod[-1]
# standard deviation of the initial noise distribution
snake_case : Optional[Any] = 1.0
# setable values
snake_case : Optional[Any] = None
snake_case : Optional[int] = torch.from_numpy(np.arange(0 , UpperCamelCase__ ).copy().astype(np.intaa ) )
def lowerCamelCase ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> torch.FloatTensor:
'''simple docstring'''
return sample
def lowerCamelCase ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> str:
'''simple docstring'''
if num_inference_steps > self.config.num_train_timesteps:
raise ValueError(
F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
F' maximal {self.config.num_train_timesteps} timesteps.' )
snake_case : int = num_inference_steps
snake_case : Union[str, Any] = self.config.num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
snake_case : List[str] = (np.arange(0 , UpperCamelCase__ ) * step_ratio).round().copy().astype(np.intaa )
snake_case : Tuple = torch.from_numpy(UpperCamelCase__ ).to(UpperCamelCase__ )
self.timesteps += self.config.steps_offset
def lowerCamelCase ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 0.0 , UpperCamelCase__ = False , UpperCamelCase__ = None , UpperCamelCase__ = True , ) -> Union[DDIMSchedulerOutput, Tuple]:
'''simple docstring'''
snake_case : Dict = timestep + self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
# change original implementation to exactly match noise levels for analogous forward process
snake_case : Any = self.alphas_cumprod[timestep]
snake_case : Dict = (
self.alphas_cumprod[prev_timestep]
if prev_timestep < self.config.num_train_timesteps
else self.final_alpha_cumprod
)
snake_case : str = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
if self.config.prediction_type == "epsilon":
snake_case : List[str] = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
snake_case : Union[str, Any] = model_output
elif self.config.prediction_type == "sample":
snake_case : str = model_output
snake_case : Optional[int] = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
elif self.config.prediction_type == "v_prediction":
snake_case : Any = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
snake_case : Optional[int] = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
else:
raise ValueError(
F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
" `v_prediction`" )
# 4. Clip or threshold "predicted x_0"
if self.config.clip_sample:
snake_case : int = pred_original_sample.clamp(
-self.config.clip_sample_range , self.config.clip_sample_range )
# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
snake_case : List[Any] = (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon
# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
snake_case : Dict = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
if not return_dict:
return (prev_sample, pred_original_sample)
return DDIMSchedulerOutput(prev_sample=UpperCamelCase__ , pred_original_sample=UpperCamelCase__ )
def __len__( self ) -> Any:
'''simple docstring'''
return self.config.num_train_timesteps
| 112 |
"""simple docstring"""
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
__snake_case = data_utils.TransfoXLTokenizer
__snake_case = data_utils.TransfoXLCorpus
__snake_case = data_utils
__snake_case = data_utils
def __lowerCAmelCase ( lowercase : Optional[int] , lowercase : int , lowercase : List[Any] , lowercase : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
if transfo_xl_dataset_file:
# Convert a pre-processed corpus (see original TensorFlow repo)
with open(lowercase , "rb" ) as fp:
snake_case : int = pickle.load(lowercase , encoding="latin1" )
# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
snake_case : int = pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["pretrained_vocab_file"]
print(F'Save vocabulary to {pytorch_vocab_dump_path}' )
snake_case : str = corpus.vocab.__dict__
torch.save(lowercase , lowercase )
snake_case : str = corpus.__dict__
corpus_dict_no_vocab.pop("vocab" , lowercase )
snake_case : Dict = pytorch_dump_folder_path + "/" + CORPUS_NAME
print(F'Save dataset to {pytorch_dataset_dump_path}' )
torch.save(lowercase , lowercase )
if tf_checkpoint_path:
# Convert a pre-trained TensorFlow model
snake_case : Union[str, Any] = os.path.abspath(lowercase )
snake_case : str = os.path.abspath(lowercase )
print(F'Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.' )
# Initialise PyTorch model
if transfo_xl_config_file == "":
snake_case : int = TransfoXLConfig()
else:
snake_case : Optional[int] = TransfoXLConfig.from_json_file(lowercase )
print(F'Building PyTorch model from configuration: {config}' )
snake_case : str = TransfoXLLMHeadModel(lowercase )
snake_case : str = load_tf_weights_in_transfo_xl(lowercase , lowercase , lowercase )
# Save pytorch-model
snake_case : Union[str, Any] = os.path.join(lowercase , lowercase )
snake_case : Optional[Any] = os.path.join(lowercase , lowercase )
print(F'Save PyTorch model to {os.path.abspath(lowercase )}' )
torch.save(model.state_dict() , lowercase )
print(F'Save configuration file to {os.path.abspath(lowercase )}' )
with open(lowercase , "w" , encoding="utf-8" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
type=str,
required=True,
help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
)
parser.add_argument(
"""--tf_checkpoint_path""",
default="""""",
type=str,
help="""An optional path to a TensorFlow checkpoint path to be converted.""",
)
parser.add_argument(
"""--transfo_xl_config_file""",
default="""""",
type=str,
help=(
"""An optional config json file corresponding to the pre-trained BERT model. \n"""
"""This specifies the model architecture."""
),
)
parser.add_argument(
"""--transfo_xl_dataset_file""",
default="""""",
type=str,
help="""An optional dataset file to be converted in a vocabulary.""",
)
__snake_case = parser.parse_args()
convert_transfo_xl_checkpoint_to_pytorch(
args.tf_checkpoint_path,
args.transfo_xl_config_file,
args.pytorch_dump_folder_path,
args.transfo_xl_dataset_file,
)
| 112 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class lowerCAmelCase_ (a__ , unittest.TestCase ):
"""simple docstring"""
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class lowerCAmelCase_ (unittest.TestCase ):
"""simple docstring"""
@property
def __magic_name__ (self ) -> Optional[int]:
"""simple docstring"""
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def __magic_name__ (self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = ort.SessionOptions()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = False
return options
def __magic_name__ (self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo.png""" )
SCREAMING_SNAKE_CASE__ : Dict = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo_mask.png""" )
SCREAMING_SNAKE_CASE__ : List[str] = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"""runwayml/stable-diffusion-inpainting""" , revision="""onnx""" , safety_checker=SCREAMING_SNAKE_CASE__ , feature_extractor=SCREAMING_SNAKE_CASE__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = """A red cat sitting on a park bench"""
SCREAMING_SNAKE_CASE__ : int = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE__ : Dict = pipe(
prompt=SCREAMING_SNAKE_CASE__ , image=SCREAMING_SNAKE_CASE__ , mask_image=SCREAMING_SNAKE_CASE__ , guidance_scale=7.5 , num_inference_steps=10 , generator=SCREAMING_SNAKE_CASE__ , output_type="""np""" , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = output.images
SCREAMING_SNAKE_CASE__ : Dict = images[0, 2_55:2_58, 2_55:2_58, -1]
assert images.shape == (1, 5_12, 5_12, 3)
SCREAMING_SNAKE_CASE__ : Any = np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def __magic_name__ (self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo.png""" )
SCREAMING_SNAKE_CASE__ : Tuple = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo_mask.png""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = LMSDiscreteScheduler.from_pretrained(
"""runwayml/stable-diffusion-inpainting""" , subfolder="""scheduler""" , revision="""onnx""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"""runwayml/stable-diffusion-inpainting""" , revision="""onnx""" , scheduler=SCREAMING_SNAKE_CASE__ , safety_checker=SCREAMING_SNAKE_CASE__ , feature_extractor=SCREAMING_SNAKE_CASE__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Optional[int] = """A red cat sitting on a park bench"""
SCREAMING_SNAKE_CASE__ : Dict = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE__ : str = pipe(
prompt=SCREAMING_SNAKE_CASE__ , image=SCREAMING_SNAKE_CASE__ , mask_image=SCREAMING_SNAKE_CASE__ , guidance_scale=7.5 , num_inference_steps=20 , generator=SCREAMING_SNAKE_CASE__ , output_type="""np""" , )
SCREAMING_SNAKE_CASE__ : Any = output.images
SCREAMING_SNAKE_CASE__ : int = images[0, 2_55:2_58, 2_55:2_58, -1]
assert images.shape == (1, 5_12, 5_12, 3)
SCREAMING_SNAKE_CASE__ : List[str] = np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
| 25 |
from __future__ import annotations
from typing import Generic, TypeVar
lowerCamelCase_ = TypeVar('''T''')
class __A( Generic[T] ):
"""simple docstring"""
def __init__(self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = data
UpperCamelCase__ = self
UpperCamelCase__ = 0
class __A( Generic[T] ):
"""simple docstring"""
def __init__(self ):
# map from node name to the node object
UpperCamelCase__ = {}
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ ):
# create a new set with x as its member
UpperCamelCase__ = DisjointSetTreeNode(SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ ):
# find the set x belongs to (with path-compression)
UpperCamelCase__ = self.map[data]
if elem_ref != elem_ref.parent:
UpperCamelCase__ = self.find_set(elem_ref.parent.data )
return elem_ref.parent
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
# helper function for union operation
if nodea.rank > nodea.rank:
UpperCamelCase__ = nodea
else:
UpperCamelCase__ = nodea
if nodea.rank == nodea.rank:
nodea.rank += 1
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
# merge 2 disjoint sets
self.link(self.find_set(SCREAMING_SNAKE_CASE_ ) , self.find_set(SCREAMING_SNAKE_CASE_ ) )
class __A( Generic[T] ):
"""simple docstring"""
def __init__(self ):
# connections: map from the node to the neighbouring nodes (with weights)
UpperCamelCase__ = {}
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ ):
# add a node ONLY if its not present in the graph
if node not in self.connections:
UpperCamelCase__ = {}
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
# add an edge with the given weight
self.add_node(SCREAMING_SNAKE_CASE_ )
self.add_node(SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = weight
UpperCamelCase__ = weight
def UpperCAmelCase_ (self ):
UpperCamelCase__ = []
UpperCamelCase__ = set()
for start in self.connections:
for end in self.connections[start]:
if (start, end) not in seen:
seen.add((end, start) )
edges.append((start, end, self.connections[start][end]) )
edges.sort(key=lambda SCREAMING_SNAKE_CASE_ : x[2] )
# creating the disjoint set
UpperCamelCase__ = DisjointSetTree[T]()
for node in self.connections:
disjoint_set.make_set(SCREAMING_SNAKE_CASE_ )
# MST generation
UpperCamelCase__ = 0
UpperCamelCase__ = 0
UpperCamelCase__ = GraphUndirectedWeighted[T]()
while num_edges < len(self.connections ) - 1:
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = edges[index]
index += 1
UpperCamelCase__ = disjoint_set.find_set(SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = disjoint_set.find_set(SCREAMING_SNAKE_CASE_ )
if parent_u != parent_v:
num_edges += 1
graph.add_edge(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
disjoint_set.union(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return graph
| 244 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
enable_full_determinism()
class __snake_case ( unittest.TestCase ):
a__ = StableDiffusionLDMaDPipeline
a__ = TEXT_TO_IMAGE_PARAMS
a__ = TEXT_TO_IMAGE_BATCH_PARAMS
a__ = TEXT_TO_IMAGE_IMAGE_PARAMS
def lowerCamelCase_ ( self) -> Optional[int]:
'''simple docstring'''
torch.manual_seed(0)
a__: Tuple = 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 , )
a__: Any = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=lowercase , set_alpha_to_one=lowercase , )
torch.manual_seed(0)
a__: Optional[Any] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=6 , out_channels=6 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , )
torch.manual_seed(0)
a__: Any = 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 , )
a__: List[str] = CLIPTextModel(lowercase)
a__: Optional[int] = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
a__: Any = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def lowerCamelCase_ ( self , lowercase , lowercase=0) -> List[str]:
'''simple docstring'''
if str(lowercase).startswith('mps'):
a__: Tuple = torch.manual_seed(lowercase)
else:
a__: Optional[Any] = torch.Generator(device=lowercase).manual_seed(lowercase)
a__: Optional[Any] = {
'prompt': 'A painting of a squirrel eating a burger',
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
a__: str = 'cpu' # ensure determinism for the device-dependent torch.Generator
a__: Tuple = self.get_dummy_components()
a__: str = StableDiffusionLDMaDPipeline(**lowercase)
a__: str = ldmad_pipe.to(lowercase)
ldmad_pipe.set_progress_bar_config(disable=lowercase)
a__: Any = self.get_dummy_inputs(lowercase)
a__: Any = ldmad_pipe(**lowercase)
a__: str = output.rgb, output.depth
a__: Dict = rgb[0, -3:, -3:, -1]
a__: List[str] = depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
a__: Tuple = np.array(
[0.37338176, 0.70247, 0.74203193, 0.51643604, 0.58256793, 0.60932136, 0.4181095, 0.48355877, 0.46535262])
a__: int = np.array([103.46727, 85.812004, 87.849236])
assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb).max() < 1e-2
assert np.abs(image_slice_depth.flatten() - expected_slice_depth).max() < 1e-2
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
a__: str = self.get_dummy_components()
a__: Any = StableDiffusionLDMaDPipeline(**lowercase)
a__: Dict = ldmad_pipe.to(lowercase)
ldmad_pipe.set_progress_bar_config(disable=lowercase)
a__: str = self.get_dummy_inputs(lowercase)
a__: int = 3 * [inputs['prompt']]
# forward
a__: Any = ldmad_pipe(**lowercase)
a__: Any = output.rgb, output.depth
a__: Dict = rgb_slice_a[0, -3:, -3:, -1]
a__: List[Any] = depth_slice_a[0, -3:, -1]
a__: List[Any] = self.get_dummy_inputs(lowercase)
a__: int = 3 * [inputs.pop('prompt')]
a__: str = ldmad_pipe.tokenizer(
lowercase , padding='max_length' , max_length=ldmad_pipe.tokenizer.model_max_length , truncation=lowercase , return_tensors='pt' , )
a__: List[str] = text_inputs['input_ids'].to(lowercase)
a__: Dict = ldmad_pipe.text_encoder(lowercase)[0]
a__: List[Any] = prompt_embeds
# forward
a__: Optional[int] = ldmad_pipe(**lowercase)
a__: List[str] = output.rgb, output.depth
a__: str = rgb_slice_a[0, -3:, -3:, -1]
a__: Optional[Any] = depth_slice_a[0, -3:, -1]
assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()).max() < 1e-4
assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()).max() < 1e-4
def lowerCamelCase_ ( self) -> List[str]:
'''simple docstring'''
a__: List[str] = 'cpu' # ensure determinism for the device-dependent torch.Generator
a__: str = self.get_dummy_components()
a__: int = PNDMScheduler(skip_prk_steps=lowercase)
a__: List[Any] = StableDiffusionLDMaDPipeline(**lowercase)
a__: Tuple = ldmad_pipe.to(lowercase)
ldmad_pipe.set_progress_bar_config(disable=lowercase)
a__: Union[str, Any] = self.get_dummy_inputs(lowercase)
a__: Any = 'french fries'
a__: Optional[Any] = ldmad_pipe(**lowercase , negative_prompt=lowercase)
a__: Any = output.rgb, output.depth
a__: Dict = rgb[0, -3:, -3:, -1]
a__: Union[str, Any] = depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
a__: Tuple = np.array(
[0.37044, 0.71811503, 0.7223251, 0.48603675, 0.5638391, 0.6364948, 0.42833704, 0.4901315, 0.47926217])
a__: Dict = np.array([107.84738, 84.62802, 89.962135])
assert np.abs(rgb_slice.flatten() - expected_slice_rgb).max() < 1e-2
assert np.abs(depth_slice.flatten() - expected_slice_depth).max() < 1e-2
@slow
@require_torch_gpu
class __snake_case ( unittest.TestCase ):
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self , lowercase , lowercase="cpu" , lowercase=torch.floataa , lowercase=0) -> Tuple:
'''simple docstring'''
a__: int = torch.Generator(device=lowercase).manual_seed(lowercase)
a__: Optional[Any] = np.random.RandomState(lowercase).standard_normal((1, 4, 64, 64))
a__: Any = torch.from_numpy(lowercase).to(device=lowercase , dtype=lowercase)
a__: str = {
'prompt': 'a photograph of an astronaut riding a horse',
'latents': latents,
'generator': generator,
'num_inference_steps': 3,
'guidance_scale': 7.5,
'output_type': 'numpy',
}
return inputs
def lowerCamelCase_ ( self) -> List[str]:
'''simple docstring'''
a__: List[str] = StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d')
a__: Optional[Any] = ldmad_pipe.to(lowercase)
ldmad_pipe.set_progress_bar_config(disable=lowercase)
a__: Optional[Any] = self.get_inputs(lowercase)
a__: Union[str, Any] = ldmad_pipe(**lowercase)
a__: Optional[Any] = output.rgb, output.depth
a__: str = rgb[0, -3:, -3:, -1].flatten()
a__: str = rgb[0, -3:, -1].flatten()
assert rgb.shape == (1, 5_12, 5_12, 3)
assert depth.shape == (1, 5_12, 5_12)
a__: Optional[Any] = np.array(
[0.53805465, 0.56707305, 0.5486515, 0.57012236, 0.5814511, 0.56253487, 0.54843014, 0.55092263, 0.6459706])
a__: str = np.array(
[0.9263781, 0.6678672, 0.5486515, 0.92202145, 0.67831135, 0.56253487, 0.9241694, 0.7551478, 0.6459706])
assert np.abs(rgb_slice - expected_slice_rgb).max() < 3e-3
assert np.abs(depth_slice - expected_slice_depth).max() < 3e-3
@nightly
@require_torch_gpu
class __snake_case ( unittest.TestCase ):
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self , lowercase , lowercase="cpu" , lowercase=torch.floataa , lowercase=0) -> Dict:
'''simple docstring'''
a__: Optional[Any] = torch.Generator(device=lowercase).manual_seed(lowercase)
a__: str = np.random.RandomState(lowercase).standard_normal((1, 4, 64, 64))
a__: Dict = torch.from_numpy(lowercase).to(device=lowercase , dtype=lowercase)
a__: str = {
'prompt': 'a photograph of an astronaut riding a horse',
'latents': latents,
'generator': generator,
'num_inference_steps': 50,
'guidance_scale': 7.5,
'output_type': 'numpy',
}
return inputs
def lowerCamelCase_ ( self) -> Optional[int]:
'''simple docstring'''
a__: List[str] = StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d').to(lowercase)
ldmad_pipe.set_progress_bar_config(disable=lowercase)
a__: List[str] = self.get_inputs(lowercase)
a__: Dict = ldmad_pipe(**lowercase)
a__: Dict = output.rgb, output.depth
a__: Union[str, Any] = 0.495586
a__: Union[str, Any] = 0.33795515
a__: str = 112.48518
a__: List[Any] = 98.489746
assert np.abs(expected_rgb_mean - rgb.mean()) < 1e-3
assert np.abs(expected_rgb_std - rgb.std()) < 1e-3
assert np.abs(expected_depth_mean - depth.mean()) < 1e-3
assert np.abs(expected_depth_std - depth.std()) < 1e-3
def lowerCamelCase_ ( self) -> str:
'''simple docstring'''
a__: Union[str, Any] = StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d-4c').to(lowercase)
ldmad_pipe.set_progress_bar_config(disable=lowercase)
a__: List[str] = self.get_inputs(lowercase)
a__: Union[str, Any] = ldmad_pipe(**lowercase)
a__: List[Any] = output.rgb, output.depth
a__: Optional[Any] = 0.4194127
a__: Optional[Any] = 0.35375586
a__: int = 0.5638502
a__: Dict = 0.34686103
assert rgb.shape == (1, 5_12, 5_12, 3)
assert depth.shape == (1, 5_12, 5_12, 1)
assert np.abs(expected_rgb_mean - rgb.mean()) < 1e-3
assert np.abs(expected_rgb_std - rgb.std()) < 1e-3
assert np.abs(expected_depth_mean - depth.mean()) < 1e-3
assert np.abs(expected_depth_std - depth.std()) < 1e-3
| 358 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE ) ->bool:
return credit_card_number.startswith(('34', '35', '37', '4', '5', '6') )
def __a ( _SCREAMING_SNAKE_CASE ) ->bool:
a__: Any = credit_card_number
a__: Tuple = 0
a__: List[str] = len(_SCREAMING_SNAKE_CASE ) - 2
for i in range(_SCREAMING_SNAKE_CASE , -1 , -2 ):
# double the value of every second digit
a__: Tuple = int(cc_number[i] )
digit *= 2
# If doubling of a number results in a two digit number
# i.e greater than 9(e.g., 6 × 2 = 12),
# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
# to get a single digit number.
if digit > 9:
digit %= 10
digit += 1
a__: Optional[Any] = cc_number[:i] + str(_SCREAMING_SNAKE_CASE ) + cc_number[i + 1 :]
total += digit
# Sum up the remaining digits
for i in range(len(_SCREAMING_SNAKE_CASE ) - 1 , -1 , -2 ):
total += int(cc_number[i] )
return total % 10 == 0
def __a ( _SCREAMING_SNAKE_CASE ) ->bool:
a__: Optional[int] = F'{credit_card_number} is an invalid credit card number because'
if not credit_card_number.isdigit():
print(F'{error_message} it has nonnumerical characters.' )
return False
if not 13 <= len(_SCREAMING_SNAKE_CASE ) <= 16:
print(F'{error_message} of its length.' )
return False
if not validate_initial_digits(_SCREAMING_SNAKE_CASE ):
print(F'{error_message} of its first two digits.' )
return False
if not luhn_validation(_SCREAMING_SNAKE_CASE ):
print(F'{error_message} it fails the Luhn check.' )
return False
print(F'{credit_card_number} is a valid credit card number.' )
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
validate_credit_card_number('4111111111111111')
validate_credit_card_number('32323')
| 203 | 0 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class SCREAMING_SNAKE_CASE_ :
def __init__( self : List[Any] , lowerCamelCase_ : Any ):
"""simple docstring"""
UpperCamelCase = data
UpperCamelCase = None
class SCREAMING_SNAKE_CASE_ :
def __init__( self : Any ):
"""simple docstring"""
UpperCamelCase = None
UpperCamelCase = None
def __iter__( self : Any ):
"""simple docstring"""
UpperCamelCase = self.head
while self.head:
yield node.data
UpperCamelCase = node.next
if node == self.head:
break
def __len__( self : Any ):
"""simple docstring"""
return sum(1 for _ in self )
def __repr__( self : Union[str, Any] ):
"""simple docstring"""
return "->".join(str(lowerCamelCase_ ) for item in iter(self ) )
def lowerCamelCase_ ( self : str , lowerCamelCase_ : Any ):
"""simple docstring"""
self.insert_nth(len(self ) , lowerCamelCase_ )
def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : Any ):
"""simple docstring"""
self.insert_nth(0 , lowerCamelCase_ )
def lowerCamelCase_ ( self : int , lowerCamelCase_ : int , lowerCamelCase_ : Any ):
"""simple docstring"""
if index < 0 or index > len(self ):
raise IndexError("""list index out of range.""" )
UpperCamelCase = Node(lowerCamelCase_ )
if self.head is None:
UpperCamelCase = new_node # first node points itself
UpperCamelCase = UpperCamelCase = new_node
elif index == 0: # insert at head
UpperCamelCase = self.head
UpperCamelCase = UpperCamelCase = new_node
else:
UpperCamelCase = self.head
for _ in range(index - 1 ):
UpperCamelCase = temp.next
UpperCamelCase = temp.next
UpperCamelCase = new_node
if index == len(self ) - 1: # insert at tail
UpperCamelCase = new_node
def lowerCamelCase_ ( self : Dict ):
"""simple docstring"""
return self.delete_nth(0 )
def lowerCamelCase_ ( self : Union[str, Any] ):
"""simple docstring"""
return self.delete_nth(len(self ) - 1 )
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : int = 0 ):
"""simple docstring"""
if not 0 <= index < len(self ):
raise IndexError("""list index out of range.""" )
UpperCamelCase = self.head
if self.head == self.tail: # just one node
UpperCamelCase = UpperCamelCase = None
elif index == 0: # delete head node
UpperCamelCase = self.tail.next.next
UpperCamelCase = self.head.next
else:
UpperCamelCase = self.head
for _ in range(index - 1 ):
UpperCamelCase = temp.next
UpperCamelCase = temp.next
UpperCamelCase = temp.next.next
if index == len(self ) - 1: # delete at tail
UpperCamelCase = temp
return delete_node.data
def lowerCamelCase_ ( self : Dict ):
"""simple docstring"""
return len(self ) == 0
def lowercase( ) -> None:
'''simple docstring'''
UpperCamelCase = CircularLinkedList()
assert len(UpperCamelCase__ ) == 0
assert circular_linked_list.is_empty() is True
assert str(UpperCamelCase__ ) == ""
try:
circular_linked_list.delete_front()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_tail()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_nth(-1 )
raise AssertionError
except IndexError:
assert True
try:
circular_linked_list.delete_nth(0 )
raise AssertionError
except IndexError:
assert True
assert circular_linked_list.is_empty() is True
for i in range(5 ):
assert len(UpperCamelCase__ ) == i
circular_linked_list.insert_nth(UpperCamelCase__ , i + 1 )
assert str(UpperCamelCase__ ) == "->".join(str(UpperCamelCase__ ) for i in range(1 , 6 ) )
circular_linked_list.insert_tail(6 )
assert str(UpperCamelCase__ ) == "->".join(str(UpperCamelCase__ ) for i in range(1 , 7 ) )
circular_linked_list.insert_head(0 )
assert str(UpperCamelCase__ ) == "->".join(str(UpperCamelCase__ ) for i in range(0 , 7 ) )
assert circular_linked_list.delete_front() == 0
assert circular_linked_list.delete_tail() == 6
assert str(UpperCamelCase__ ) == "->".join(str(UpperCamelCase__ ) for i in range(1 , 6 ) )
assert circular_linked_list.delete_nth(2 ) == 3
circular_linked_list.insert_nth(2 , 3 )
assert str(UpperCamelCase__ ) == "->".join(str(UpperCamelCase__ ) for i in range(1 , 6 ) )
assert circular_linked_list.is_empty() is False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 343 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase ={"configuration_vit_msn": ["VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMSNConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase =[
"VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST",
"ViTMSNModel",
"ViTMSNForImageClassification",
"ViTMSNPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_msn import (
VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMSNForImageClassification,
ViTMSNModel,
ViTMSNPreTrainedModel,
)
else:
import sys
__UpperCAmelCase =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 67 | 0 |
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version
from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging
__lowerCamelCase : Union[str, Any] = (
"""https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py"""
)
__lowerCamelCase : Optional[Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
def SCREAMING_SNAKE_CASE ( ):
snake_case__ : List[Any] = "https://pypi.org/pypi/diffusers/json"
snake_case__ : Dict = json.loads(request.urlopen(snake_case_ ).read() )["releases"].keys()
return sorted(snake_case_ , key=lambda snake_case_ : version.Version(snake_case_ ) )
def SCREAMING_SNAKE_CASE ( ):
# This function has already been executed if HF_MODULES_CACHE already is in the Python path.
if HF_MODULES_CACHE in sys.path:
return
sys.path.append(snake_case_ )
os.makedirs(snake_case_ , exist_ok=snake_case_ )
snake_case__ : Optional[int] = Path(snake_case_ ) / "__init__.py"
if not init_path.exists():
init_path.touch()
def SCREAMING_SNAKE_CASE ( snake_case_ : Union[str, os.PathLike] ):
init_hf_modules()
snake_case__ : Union[str, Any] = Path(snake_case_ ) / name
# If the parent module does not exist yet, recursively create it.
if not dynamic_module_path.parent.exists():
create_dynamic_module(dynamic_module_path.parent )
os.makedirs(snake_case_ , exist_ok=snake_case_ )
snake_case__ : str = dynamic_module_path / "__init__.py"
if not init_path.exists():
init_path.touch()
def SCREAMING_SNAKE_CASE ( snake_case_ : Tuple ):
with open(snake_case_ , "r" , encoding="utf-8" ) as f:
snake_case__ : Optional[Any] = f.read()
# Imports of the form `import .xxx`
snake_case__ : Union[str, Any] = re.findall("^\s*import\s+\.(\S+)\s*$" , snake_case_ , flags=re.MULTILINE )
# Imports of the form `from .xxx import yyy`
relative_imports += re.findall("^\s*from\s+\.(\S+)\s+import" , snake_case_ , flags=re.MULTILINE )
# Unique-ify
return list(set(snake_case_ ) )
def SCREAMING_SNAKE_CASE ( snake_case_ : Dict ):
snake_case__ : Optional[int] = False
snake_case__ : Tuple = [module_file]
snake_case__ : Tuple = []
# Let's recurse through all relative imports
while not no_change:
snake_case__ : Tuple = []
for f in files_to_check:
new_imports.extend(get_relative_imports(snake_case_ ) )
snake_case__ : int = Path(snake_case_ ).parent
snake_case__ : int = [str(module_path / m ) for m in new_imports]
snake_case__ : Union[str, Any] = [f for f in new_import_files if f not in all_relative_imports]
snake_case__ : int = [F'''{f}.py''' for f in new_import_files]
snake_case__ : Optional[Any] = len(snake_case_ ) == 0
all_relative_imports.extend(snake_case_ )
return all_relative_imports
def SCREAMING_SNAKE_CASE ( snake_case_ : Any ):
with open(snake_case_ , "r" , encoding="utf-8" ) as f:
snake_case__ : Optional[Any] = f.read()
# Imports of the form `import xxx`
snake_case__ : Any = re.findall("^\s*import\s+(\S+)\s*$" , snake_case_ , flags=re.MULTILINE )
# Imports of the form `from xxx import yyy`
imports += re.findall("^\s*from\s+(\S+)\s+import" , snake_case_ , flags=re.MULTILINE )
# Only keep the top-level module
snake_case__ : Tuple = [imp.split("." )[0] for imp in imports if not imp.startswith("." )]
# Unique-ify and test we got them all
snake_case__ : Dict = list(set(snake_case_ ) )
snake_case__ : List[Any] = []
for imp in imports:
try:
importlib.import_module(snake_case_ )
except ImportError:
missing_packages.append(snake_case_ )
if len(snake_case_ ) > 0:
raise ImportError(
"This modeling file requires the following packages that were not found in your environment: "
F'''{', '.join(snake_case_ )}. Run `pip install {' '.join(snake_case_ )}`''' )
return get_relative_imports(snake_case_ )
def SCREAMING_SNAKE_CASE ( snake_case_ : Dict , snake_case_ : List[Any] ):
snake_case__ : Dict = module_path.replace(os.path.sep , "." )
snake_case__ : Union[str, Any] = importlib.import_module(snake_case_ )
if class_name is None:
return find_pipeline_class(snake_case_ )
return getattr(snake_case_ , snake_case_ )
def SCREAMING_SNAKE_CASE ( snake_case_ : int ):
from ..pipelines import DiffusionPipeline
snake_case__ : str = dict(inspect.getmembers(snake_case_ , inspect.isclass ) )
snake_case__ : Tuple = None
for cls_name, cls in cls_members.items():
if (
cls_name != DiffusionPipeline.__name__
and issubclass(cls , snake_case_ )
and cls.__module__.split("." )[0] != "diffusers"
):
if pipeline_class is not None:
raise ValueError(
F'''Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:'''
F''' {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in'''
F''' {loaded_module}.''' )
snake_case__ : Any = cls
return pipeline_class
def SCREAMING_SNAKE_CASE ( snake_case_ : Union[str, os.PathLike] , snake_case_ : str , snake_case_ : Optional[Union[str, os.PathLike]] = None , snake_case_ : bool = False , snake_case_ : bool = False , snake_case_ : Optional[Dict[str, str]] = None , snake_case_ : Optional[Union[bool, str]] = None , snake_case_ : Optional[str] = None , snake_case_ : bool = False , ):
snake_case__ : Optional[Any] = str(snake_case_ )
snake_case__ : Union[str, Any] = os.path.join(snake_case_ , snake_case_ )
if os.path.isfile(snake_case_ ):
snake_case__ : Dict = module_file_or_url
snake_case__ : Tuple = "local"
elif pretrained_model_name_or_path.count("/" ) == 0:
snake_case__ : Optional[Any] = get_diffusers_versions()
# cut ".dev0"
snake_case__ : Tuple = "v" + ".".join(__version__.split("." )[:3] )
# retrieve github version that matches
if revision is None:
snake_case__ : Optional[int] = latest_version if latest_version[1:] in available_versions else "main"
logger.info(F'''Defaulting to latest_version: {revision}.''' )
elif revision in available_versions:
snake_case__ : Any = F'''v{revision}'''
elif revision == "main":
snake_case__ : Tuple = revision
else:
raise ValueError(
F'''`custom_revision`: {revision} does not exist. Please make sure to choose one of'''
F''' {', '.join(available_versions + ['main'] )}.''' )
# community pipeline on GitHub
snake_case__ : Dict = COMMUNITY_PIPELINES_URL.format(revision=snake_case_ , pipeline=snake_case_ )
try:
snake_case__ : Tuple = cached_download(
snake_case_ , cache_dir=snake_case_ , force_download=snake_case_ , proxies=snake_case_ , resume_download=snake_case_ , local_files_only=snake_case_ , use_auth_token=snake_case_ , )
snake_case__ : int = "git"
snake_case__ : Optional[int] = pretrained_model_name_or_path + ".py"
except EnvironmentError:
logger.error(F'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.''' )
raise
else:
try:
# Load from URL or cache if already cached
snake_case__ : Dict = hf_hub_download(
snake_case_ , snake_case_ , cache_dir=snake_case_ , force_download=snake_case_ , proxies=snake_case_ , resume_download=snake_case_ , local_files_only=snake_case_ , use_auth_token=snake_case_ , )
snake_case__ : List[Any] = os.path.join("local" , "--".join(pretrained_model_name_or_path.split("/" ) ) )
except EnvironmentError:
logger.error(F'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.''' )
raise
# Check we have all the requirements in our environment
snake_case__ : List[str] = check_imports(snake_case_ )
# Now we move the module inside our cached dynamic modules.
snake_case__ : Any = DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
create_dynamic_module(snake_case_ )
snake_case__ : List[Any] = Path(snake_case_ ) / full_submodule
if submodule == "local" or submodule == "git":
# We always copy local files (we could hash the file to see if there was a change, and give them the name of
# that hash, to only copy when there is a modification but it seems overkill for now).
# The only reason we do the copy is to avoid putting too many folders in sys.path.
shutil.copy(snake_case_ , submodule_path / module_file )
for module_needed in modules_needed:
snake_case__ : Dict = F'''{module_needed}.py'''
shutil.copy(os.path.join(snake_case_ , snake_case_ ) , submodule_path / module_needed )
else:
# Get the commit hash
# TODO: we will get this info in the etag soon, so retrieve it from there and not here.
if isinstance(snake_case_ , snake_case_ ):
snake_case__ : List[Any] = use_auth_token
elif use_auth_token is True:
snake_case__ : List[Any] = HfFolder.get_token()
else:
snake_case__ : List[Any] = None
snake_case__ : Union[str, Any] = model_info(snake_case_ , revision=snake_case_ , token=snake_case_ ).sha
# The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
# benefit of versioning.
snake_case__ : Union[str, Any] = submodule_path / commit_hash
snake_case__ : Any = full_submodule + os.path.sep + commit_hash
create_dynamic_module(snake_case_ )
if not (submodule_path / module_file).exists():
shutil.copy(snake_case_ , submodule_path / module_file )
# Make sure we also have every file with relative
for module_needed in modules_needed:
if not (submodule_path / module_needed).exists():
get_cached_module_file(
snake_case_ , F'''{module_needed}.py''' , cache_dir=snake_case_ , force_download=snake_case_ , resume_download=snake_case_ , proxies=snake_case_ , use_auth_token=snake_case_ , revision=snake_case_ , local_files_only=snake_case_ , )
return os.path.join(snake_case_ , snake_case_ )
def SCREAMING_SNAKE_CASE ( snake_case_ : Union[str, os.PathLike] , snake_case_ : str , snake_case_ : Optional[str] = None , snake_case_ : Optional[Union[str, os.PathLike]] = None , snake_case_ : bool = False , snake_case_ : bool = False , snake_case_ : Optional[Dict[str, str]] = None , snake_case_ : Optional[Union[bool, str]] = None , snake_case_ : Optional[str] = None , snake_case_ : bool = False , **snake_case_ : Any , ):
snake_case__ : Tuple = get_cached_module_file(
snake_case_ , snake_case_ , cache_dir=snake_case_ , force_download=snake_case_ , resume_download=snake_case_ , proxies=snake_case_ , use_auth_token=snake_case_ , revision=snake_case_ , local_files_only=snake_case_ , )
return get_class_in_module(snake_case_ , final_module.replace(".py" , "" ) )
| 368 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def SCREAMING_SNAKE_CASE ( snake_case_ : int ):
for param in module.parameters():
snake_case__ : Tuple = False
def SCREAMING_SNAKE_CASE ( ):
snake_case__ : Any = "cuda" if torch.cuda.is_available() else "cpu"
if torch.backends.mps.is_available() and torch.backends.mps.is_built():
snake_case__ : List[Any] = "mps"
if device == "mps":
print(
"WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"
" errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"
" with generations." )
return device
def SCREAMING_SNAKE_CASE ( snake_case_ : str ):
snake_case__ : List[str] = plt.imshow(snake_case_ )
fig.axes.get_xaxis().set_visible(snake_case_ )
fig.axes.get_yaxis().set_visible(snake_case_ )
plt.show()
def SCREAMING_SNAKE_CASE ( ):
snake_case__ : str = datetime.now()
snake_case__ : Optional[Any] = current_time.strftime("%H:%M:%S" )
return timestamp
| 286 | 0 |
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