

from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel

@require_tf
class        snake_case__    :







						"""simple docstring"""




						__lowerCAmelCase					:int      =					PegasusConfig
						__lowerCAmelCase					:Tuple      =					{}
						__lowerCAmelCase					:Optional[Any]      =					"gelu"

						def __init__(       self ,       __lowercase ,       __lowercase=1_3 ,       __lowercase=7 ,       __lowercase=True ,       __lowercase=False ,       __lowercase=9_9 ,       __lowercase=3_2 ,       __lowercase=2 ,       __lowercase=4 ,       __lowercase=3_7 ,       __lowercase=0.1 ,       __lowercase=0.1 ,       __lowercase=4_0 ,       __lowercase=2 ,       __lowercase=1 ,       __lowercase=0 ,       ) ->				Union[str, Any]:
									"""simple docstring"""




									a__       :						Optional[int]							=  parent
									a__       :						int							=  batch_size
									a__       :						Union[str, Any]							=  seq_length
									a__       :						Tuple							=  is_training
									a__       :						Optional[int]							=  use_labels
									a__       :						Union[str, Any]							=  vocab_size
									a__       :						Optional[int]							=  hidden_size
									a__       :						List[str]							=  num_hidden_layers
									a__       :						Optional[Any]							=  num_attention_heads
									a__       :						Optional[Any]							=  intermediate_size

									a__       :						Dict							=  hidden_dropout_prob
									a__       :						Tuple							=  attention_probs_dropout_prob
									a__       :						List[Any]							=  max_position_embeddings
									a__       :						int							=  eos_token_id
									a__       :						Tuple							=  pad_token_id
									a__       :						Dict							=  bos_token_id

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Union[str, Any]:
									"""simple docstring"""




									a__       :						Optional[Any]							=  ids_tensor([self.batch_size, self.seq_length - 1] ,       self.vocab_size  )
									a__       :						Any							=  tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size  ) ,       1  )
									a__       :						Any							=  tf.concat([input_ids, eos_tensor] ,       axis=1  )

									a__       :						Any							=  ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size  )

									a__       :						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 ,       )
									a__       :						Union[str, Any]							=  prepare_pegasus_inputs_dict(__UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase  )
									return config, inputs_dict

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase  ) ->				List[Any]:
									"""simple docstring"""




									a__       :						Union[str, Any]							=  TFPegasusModel(config=__UpperCAmelCase  ).get_decoder()
									a__       :						Optional[Any]							=  inputs_dict["""input_ids"""]

									a__       :						List[str]							=  input_ids[:1, :]
									a__       :						str							=  inputs_dict["""attention_mask"""][:1, :]
									a__       :						str							=  inputs_dict["""head_mask"""]
									a__       :						str							=  1

									# first forward pass
									a__       :						Optional[int]							=  model(__UpperCAmelCase ,       attention_mask=__UpperCAmelCase ,       head_mask=__UpperCAmelCase ,       use_cache=__UpperCAmelCase  )

									a__	,						a__       :						Dict							=  outputs.to_tuple()

									# create hypothetical next token and extent to next_input_ids
									a__       :						Union[str, Any]							=  ids_tensor((self.batch_size, 3) ,       config.vocab_size  )
									a__       :						Tuple							=  tf.cast(ids_tensor((self.batch_size, 3) ,       2  ) ,       tf.inta  )

									# append to next input_ids and
									a__       :						int							=  tf.concat([input_ids, next_tokens] ,       axis=-1  )
									a__       :						List[str]							=  tf.concat([attention_mask, next_attn_mask] ,       axis=-1  )

									a__       :						Optional[Any]							=  model(__UpperCAmelCase ,       attention_mask=__UpperCAmelCase  )[0]
									a__       :						List[Any]							=  model(__UpperCAmelCase ,       attention_mask=__UpperCAmelCase ,       past_key_values=__UpperCAmelCase  )[0]

									self.parent.assertEqual(next_tokens.shape[1] ,       output_from_past.shape[1]  )

									# select random slice
									a__       :						Any							=  int(ids_tensor((1,) ,       output_from_past.shape[-1]  )  )
									a__       :						str							=  output_from_no_past[:, -3:, random_slice_idx]
									a__       :						Optional[int]							=  output_from_past[:, :, random_slice_idx]

									# test that outputs are equal for slice
									tf.debugging.assert_near(__UpperCAmelCase ,       __UpperCAmelCase ,       rtol=1E-3  )




def      lowerCAmelCase_					(  _lowercase    :       Any						,       _lowercase    :       Optional[Any]						,       _lowercase    :       int						,       _lowercase    :       Tuple=None						,       _lowercase    :       Optional[int]=None						,       _lowercase    :       List[Any]=None						,       _lowercase    :       int=None						,       _lowercase    :       Any=None						,       )							->	Union[str, Any]:







			"""simple docstring"""
			if attention_mask is None:
						a__       :						Optional[Any]							=  tf.cast(tf.math.not_equal(_lowercase						,       config.pad_token_id)						,       tf.inta)
			if decoder_attention_mask is None:
						a__       :						Optional[int]							=  tf.concat(
						    [
						        tf.ones(decoder_input_ids[:, :1].shape						,       dtype=tf.inta),
						        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]						,       config.pad_token_id)						,       tf.inta),
						    ]						,       axis=-1						,       )
			if head_mask is None:
						a__       :						Tuple							=  tf.ones((config.encoder_layers, config.encoder_attention_heads))
			if decoder_head_mask is None:
						a__       :						int							=  tf.ones((config.decoder_layers, config.decoder_attention_heads))
			if cross_attn_head_mask is None:
						a__       :						Tuple							=  tf.ones((config.decoder_layers, config.decoder_attention_heads))
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": decoder_attention_mask,
			    "head_mask": head_mask,
			    "decoder_head_mask": decoder_head_mask,
			    "cross_attn_head_mask": cross_attn_head_mask,
			}

@require_tf
class        snake_case__    (A__ ,     A__ ,     unittest.TestCase		):







						"""simple docstring"""




						__lowerCAmelCase					:Dict      =					(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
						__lowerCAmelCase					:Tuple      =					(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
						__lowerCAmelCase					:str      =					(
						    {
						        "conversational": TFPegasusForConditionalGeneration,
						        "feature-extraction": TFPegasusModel,
						        "summarization": TFPegasusForConditionalGeneration,
						        "text2text-generation": TFPegasusForConditionalGeneration,
						        "translation": TFPegasusForConditionalGeneration,
						    }
						    if is_tf_available()
						    else {}
						)
						__lowerCAmelCase					:Optional[Any]      =					True
						__lowerCAmelCase					:Optional[Any]      =					False
						__lowerCAmelCase					:List[Any]      =					False

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Union[str, Any]:
									"""simple docstring"""




									a__       :						Tuple							=  TFPegasusModelTester(self  )
									a__       :						Any							=  ConfigTester(self ,       config_class=__UpperCAmelCase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[Any]:
									"""simple docstring"""




									self.config_tester.run_common_tests()

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									a__       :						Optional[int]							=  self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*__UpperCAmelCase  )




@require_sentencepiece
@require_tokenizers
@require_tf
class        snake_case__    (unittest.TestCase		):







						"""simple docstring"""




						__lowerCAmelCase					:str      =					[
						    " 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!\" ",
						]
						__lowerCAmelCase					:Optional[int]      =					[
						    "California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to"
						    " reduce the risk of wildfires.",
						    "N-Dubz have revealed they\'re \"grateful\" to have been nominated for four Mobo Awards.",
						]  # differs slightly from pytorch, likely due to numerical differences in linear layers
						__lowerCAmelCase					:Any      =					"google/pegasus-xsum"

						@cached_property
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									return AutoTokenizer.from_pretrained(self.model_name  )

						@cached_property
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




									a__       :						Union[str, Any]							=  TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
									return model

						def 							SCREAMING_SNAKE_CASE__(       self ,       **__lowercase  ) ->				Dict:
									"""simple docstring"""




									a__       :						List[Any]							=  self.translate_src_text(**__UpperCAmelCase  )
									assert self.expected_text == generated_words

						def 							SCREAMING_SNAKE_CASE__(       self ,       **__lowercase  ) ->				Optional[int]:
									"""simple docstring"""




									a__       :						Union[str, Any]							=  self.tokenizer(self.src_text ,       **__UpperCAmelCase ,       padding=__UpperCAmelCase ,       return_tensors="""tf"""  )
									a__       :						Tuple							=  self.model.generate(
									    model_inputs.input_ids ,       attention_mask=model_inputs.attention_mask ,       num_beams=2 ,       use_cache=__UpperCAmelCase ,       )
									a__       :						int							=  self.tokenizer.batch_decode(generated_ids.numpy() ,       skip_special_tokens=__UpperCAmelCase  )
									return generated_words

						@slow
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				int:
									"""simple docstring"""




									self._assert_generated_batch_equal_expected()









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


_lowercase		:				int      		=(
    "https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py"
)


_lowercase		:				Dict      		=logging.get_logger(__name__)  # pylint: disable=invalid-name

def      lowerCAmelCase_					(  )							->	Union[str, Any]:







			"""simple docstring"""
			a__       :						List[Any]							=  """https://pypi.org/pypi/diffusers/json"""
			a__       :						Tuple							=  json.loads(request.urlopen(_lowercase).read())["""releases"""].keys()
			return sorted(_lowercase						,       key=lambda _lowercase: version.Version(_lowercase))

def      lowerCAmelCase_					(  )							->	Optional[Any]:







			"""simple docstring"""
			# 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(_lowercase)
			os.makedirs(_lowercase						,       exist_ok=_lowercase)
			a__       :						Tuple							=  Path(_lowercase) / """__init__.py"""
			if not init_path.exists():
						init_path.touch()

def      lowerCAmelCase_					(  _lowercase    :       Union[str, os.PathLike])							->	Optional[Any]:







			"""simple docstring"""
			init_hf_modules()
			a__       :						Optional[int]							=  Path(_lowercase) / 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(_lowercase						,       exist_ok=_lowercase)
			a__       :						Any							=  dynamic_module_path / """__init__.py"""
			if not init_path.exists():
						init_path.touch()

def      lowerCAmelCase_					(  _lowercase    :       Union[str, Any])							->	Optional[int]:







			"""simple docstring"""
			with open(_lowercase						,       """r"""						,       encoding="""utf-8""") as f:
						a__       :						Union[str, Any]							=  f.read()

			# Imports of the form `import .xxx`
			a__       :						Optional[Any]							=  re.findall("""^\s*import\s+\.(\S+)\s*$"""						,       _lowercase						,       flags=re.MULTILINE)
			# Imports of the form `from .xxx import yyy`
			relative_imports += re.findall("""^\s*from\s+\.(\S+)\s+import"""						,       _lowercase						,       flags=re.MULTILINE)
			# Unique-ify
			return list(set(_lowercase))

def      lowerCAmelCase_					(  _lowercase    :       List[Any])							->	Dict:







			"""simple docstring"""
			a__       :						Dict							=  False
			a__       :						str							=  [module_file]
			a__       :						List[Any]							=  []

			# Let's recurse through all relative imports
			while not no_change:
						a__       :						Optional[Any]							=  []
						for f in files_to_check:
									new_imports.extend(get_relative_imports(_lowercase))

						a__       :						Dict							=  Path(_lowercase).parent
						a__       :						Any							=  [str(module_path / m) for m in new_imports]
						a__       :						Any							=  [f for f in new_import_files if f not in all_relative_imports]
						a__       :						List[Any]							=  [F'''{f}.py''' for f in new_import_files]

						a__       :						List[Any]							=  len(_lowercase) == 0
						all_relative_imports.extend(_lowercase)

			return all_relative_imports

def      lowerCAmelCase_					(  _lowercase    :       Optional[int])							->	Any:







			"""simple docstring"""
			with open(_lowercase						,       """r"""						,       encoding="""utf-8""") as f:
						a__       :						Optional[Any]							=  f.read()

			# Imports of the form `import xxx`
			a__       :						Optional[int]							=  re.findall("""^\s*import\s+(\S+)\s*$"""						,       _lowercase						,       flags=re.MULTILINE)
			# Imports of the form `from xxx import yyy`
			imports += re.findall("""^\s*from\s+(\S+)\s+import"""						,       _lowercase						,       flags=re.MULTILINE)
			# Only keep the top-level module
			a__       :						Any							=  [imp.split(""".""")[0] for imp in imports if not imp.startswith(""".""")]

			# Unique-ify and test we got them all
			a__       :						Optional[Any]							=  list(set(_lowercase))
			a__       :						Union[str, Any]							=  []
			for imp in imports:
						try:
									importlib.import_module(_lowercase)
						except ImportError:
									missing_packages.append(_lowercase)

			if len(_lowercase) > 0:
						raise ImportError(
						    """This modeling file requires the following packages that were not found in your environment: """
						    F'''{', '.join(_lowercase)}. Run `pip install {' '.join(_lowercase)}`''')

			return get_relative_imports(_lowercase)

def      lowerCAmelCase_					(  _lowercase    :       int						,       _lowercase    :       Tuple)							->	Union[str, Any]:







			"""simple docstring"""
			a__       :						Dict							=  module_path.replace(os.path.sep						,       """.""")
			a__       :						List[Any]							=  importlib.import_module(_lowercase)

			if class_name is None:
						return find_pipeline_class(_lowercase)
			return getattr(_lowercase						,       _lowercase)

def      lowerCAmelCase_					(  _lowercase    :       Union[str, Any])							->	Optional[int]:







			"""simple docstring"""
			from ..pipelines import DiffusionPipeline

			a__       :						Any							=  dict(inspect.getmembers(_lowercase						,       inspect.isclass))

			a__       :						str							=  None
			for cls_name, cls in cls_members.items():
						if (
						    cls_name != DiffusionPipeline.__name__
						    and issubclass(cls						,       _lowercase)
						    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}.''')
									a__       :						str							=  cls

			return pipeline_class

def      lowerCAmelCase_					(  _lowercase    :       Union[str, os.PathLike]						,       _lowercase    :       str						,       _lowercase    :       Optional[Union[str, os.PathLike]] = None						,       _lowercase    :       bool = False						,       _lowercase    :       bool = False						,       _lowercase    :       Optional[Dict[str, str]] = None						,       _lowercase    :       Optional[Union[bool, str]] = None						,       _lowercase    :       Optional[str] = None						,       _lowercase    :       bool = False						,       )							->	Optional[int]:







			"""simple docstring"""
			a__       :						Union[str, Any]							=  str(_lowercase)

			a__       :						Union[str, Any]							=  os.path.join(_lowercase						,       _lowercase)

			if os.path.isfile(_lowercase):
						a__       :						Dict							=  module_file_or_url
						a__       :						List[str]							=  """local"""
			elif pretrained_model_name_or_path.count("""/""") == 0:
						a__       :						Optional[Any]							=  get_diffusers_versions()
						# cut ".dev0"
						a__       :						Union[str, Any]							=  """v""" + """.""".join(__version__.split(""".""")[:3])

						# retrieve github version that matches
						if revision is None:
									a__       :						List[str]							=  latest_version if latest_version[1:] in available_versions else """main"""
									logger.info(F'''Defaulting to latest_version: {revision}.''')
						elif revision in available_versions:
									a__       :						str							=  F'''v{revision}'''
						elif revision == "main":
									a__       :						List[str]							=  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
						a__       :						Any							=  COMMUNITY_PIPELINES_URL.format(revision=_lowercase						,       pipeline=_lowercase)
						try:
									a__       :						Optional[int]							=  cached_download(
									    _lowercase						,       cache_dir=_lowercase						,       force_download=_lowercase						,       proxies=_lowercase						,       resume_download=_lowercase						,       local_files_only=_lowercase						,       use_auth_token=_lowercase						,       )
									a__       :						Any							=  """git"""
									a__       :						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
									a__       :						Any							=  hf_hub_download(
									    _lowercase						,       _lowercase						,       cache_dir=_lowercase						,       force_download=_lowercase						,       proxies=_lowercase						,       resume_download=_lowercase						,       local_files_only=_lowercase						,       use_auth_token=_lowercase						,       )
									a__       :						Optional[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
			a__       :						List[str]							=  check_imports(_lowercase)

			# Now we move the module inside our cached dynamic modules.
			a__       :						int							=  DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
			create_dynamic_module(_lowercase)
			a__       :						Tuple							=  Path(_lowercase) / 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(_lowercase						,       submodule_path / module_file)
						for module_needed in modules_needed:
									a__       :						Dict							=  F'''{module_needed}.py'''
									shutil.copy(os.path.join(_lowercase						,       _lowercase)						,       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(_lowercase						,       _lowercase):
									a__       :						Optional[Any]							=  use_auth_token
						elif use_auth_token is True:
									a__       :						Dict							=  HfFolder.get_token()
						else:
									a__       :						str							=  None

						a__       :						Any							=  model_info(_lowercase						,       revision=_lowercase						,       token=_lowercase).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.
						a__       :						Optional[int]							=  submodule_path / commit_hash
						a__       :						Tuple							=  full_submodule + os.path.sep + commit_hash
						create_dynamic_module(_lowercase)

						if not (submodule_path / module_file).exists():
									shutil.copy(_lowercase						,       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(
												    _lowercase						,       F'''{module_needed}.py'''						,       cache_dir=_lowercase						,       force_download=_lowercase						,       resume_download=_lowercase						,       proxies=_lowercase						,       use_auth_token=_lowercase						,       revision=_lowercase						,       local_files_only=_lowercase						,       )
			return os.path.join(_lowercase						,       _lowercase)





def      lowerCAmelCase_					(  _lowercase    :       Union[str, os.PathLike]						,       _lowercase    :       str						,       _lowercase    :       Optional[str] = None						,       _lowercase    :       Optional[Union[str, os.PathLike]] = None						,       _lowercase    :       bool = False						,       _lowercase    :       bool = False						,       _lowercase    :       Optional[Dict[str, str]] = None						,       _lowercase    :       Optional[Union[bool, str]] = None						,       _lowercase    :       Optional[str] = None						,       _lowercase    :       bool = False						,       **_lowercase    :       Union[str, Any]						,       )							->	List[str]:







			"""simple docstring"""
			a__       :						int							=  get_cached_module_file(
			    _lowercase						,       _lowercase						,       cache_dir=_lowercase						,       force_download=_lowercase						,       resume_download=_lowercase						,       proxies=_lowercase						,       use_auth_token=_lowercase						,       revision=_lowercase						,       local_files_only=_lowercase						,       )
			return get_class_in_module(_lowercase						,       final_module.replace(""".py"""						,       """"""))




