'''simple docstring'''

import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel







def      UpperCamelCase_						(					snake_case_       :       str=32						,		snake_case_       :       Dict=10						,		snake_case_       :       Tuple=1_00						,		snake_case_       :       List[str]=10_26						,		snake_case_       :       Optional[int]=True						,		snake_case_       :       Any="data/tokenized_stories_train_wikitext103.jbl"						,		snake_case_       :       Union[str, Any]="igf_context_pairs.jbl"						,		)					->     Tuple:






 '''simple docstring'''

 set_seed(3      )
 # generate train_data and objective_set
 __lowerCAmelCase							,       __lowerCAmelCase											=						generate_datasets(
     snake_case_						,		snake_case_						,		number=snake_case_						,		min_len=10_26						,		trim=snake_case_      )
 # keeps model same across runs
 set_seed(4      )
 # model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
 # can we train on GPU?
 __lowerCAmelCase											=						torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""      )

 # load pretrained model
 __lowerCAmelCase											=						load_gpta("""gpt2"""      ).to(snake_case_      )
 print("""computing perplexity on objective set"""      )
 __lowerCAmelCase											=						compute_perplexity(snake_case_						,		snake_case_						,		snake_case_      ).item()
 print("""perplexity on objective set:"""						,		snake_case_      )

 # collect igf pairs and save to file demo.jbl
 collect_objective_set(snake_case_						,		snake_case_						,		snake_case_						,		snake_case_						,		snake_case_						,		snake_case_						,		snake_case_						,		snake_case_      )

 # clean up, delete model and data we don't need anymore
 del model, train_data, objective_set
 torch.cuda.empty_cache()







def      UpperCamelCase_						(					snake_case_       :       int						,		snake_case_       :       Tuple=15						,		snake_case_       :       Optional[Any]=1_28						,		snake_case_       :       List[Any]=1_00						,		snake_case_       :       Union[str, Any]="igf_model.pt"						,		)					->     List[str]:






 '''simple docstring'''

 set_seed(42      )

 # Load pre-trained model
 __lowerCAmelCase											=						GPTaLMHeadModel.from_pretrained("""gpt2"""      )

 # Initialize secondary learner to use embedding weights of model
 __lowerCAmelCase											=						SecondaryLearner(snake_case_      )

 # Train secondary learner
 __lowerCAmelCase											=						train_secondary_learner(
     snake_case_						,		snake_case_						,		max_epochs=snake_case_						,		batch_size=snake_case_						,		eval_freq=1_00						,		igf_model_path=snake_case_						,		)

 del model, secondary_learner_train_data
 torch.cuda.empty_cache()

 return secondary_learner







def      UpperCamelCase_						(					snake_case_       :       List[Any]						,		snake_case_       :       Union[str, Any]						,		snake_case_       :       Any						,		snake_case_       :       Dict=32						,		snake_case_       :       str=10_00						,		snake_case_       :       Union[str, Any]=16						,		snake_case_       :       int=1.0						,		snake_case_       :       List[str]=recopy_gpta						,		snake_case_       :       Tuple=None						,		snake_case_       :       List[Any]=10						,		snake_case_       :       Union[str, Any]="gpt2_finetuned.pt"						,		)					->     Tuple:






 '''simple docstring'''

 __lowerCAmelCase											=						torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""      )
 __lowerCAmelCase											=						RandomSampler(snake_case_      )
 __lowerCAmelCase											=						DataLoader(snake_case_						,		sampler=snake_case_      )

 __lowerCAmelCase											=						max_steps // (len(snake_case_      )) + 1
 __lowerCAmelCase											=						0
 __lowerCAmelCase											=						torch.zeros((1, context_len)						,		dtype=torch.long						,		device=snake_case_      )
 __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=						recopy_model(snake_case_						,		snake_case_						,		snake_case_      )

 model.train()
 if secondary_learner is not None:
  secondary_learner.to(snake_case_      )
  secondary_learner.eval()
 __lowerCAmelCase											=						[]
 __lowerCAmelCase											=						0

 __lowerCAmelCase											=						[]
 __lowerCAmelCase											=						[]

 # Compute the performance of the transformer model at the beginning
 __lowerCAmelCase											=						compute_perplexity(snake_case_						,		snake_case_						,		snake_case_      )
 test_perps.append(snake_case_      )
 print("""Test perplexity, step"""						,		snake_case_						,		""":"""						,		snake_case_      )
 for epoch in range(int(snake_case_      )      ):
  for step, example in enumerate(snake_case_      ):
   torch.cuda.empty_cache()
   __lowerCAmelCase											=						random.randint(0						,		example.size(2      ) - context_len - 1      )
   __lowerCAmelCase											=						example[0, 0, start : start + context_len]
   lm_optimizer.zero_grad()
   __lowerCAmelCase											=						model(snake_case_						,		labels=snake_case_      )
   __lowerCAmelCase											=						True

   if secondary_learner is not None:
    __lowerCAmelCase											=						secondary_learner.forward(
        torch.tensor(snake_case_						,		dtype=torch.long						,		device=snake_case_      ).unsqueeze(0      )      )[0].item()
    observed_qs.append(float(snake_case_      )      )

    # Here we implement the simple non-constant threshold for the predicted IG(X) value
    # We will decay the selectivity of our secondary learner filter from
    # 1 standard deviation above average to 1 below average after 10 batches.

    if global_step == 10:
     __lowerCAmelCase											=						-1
    if predicted_q < threshold:
     __lowerCAmelCase											=						False

            # If we passed the filter, add the context to the batch!
   if do_backprop:
    contexts.append(np.array(context.cpu()      )      )
    __lowerCAmelCase											=						outputs[0]
    lm_loss.backward()
    examples += 1

   del outputs

   # Once the batch is filled with enough contexts, backprop on the batch.
   if examples == batch_size:
    torch.cuda.empty_cache()
    __lowerCAmelCase											=						0
    # Do LM backprop
    torch.nn.utils.clip_grad_norm_(model.parameters()						,		3.0      )
    lm_optimizer.step()
    lm_scheduler.step()  # Update learning rate schedule
    global_step += 1
    # Compute the performance of the transformer model at this batch
    if global_step % eval_interval == 0:
     __lowerCAmelCase											=						compute_perplexity(snake_case_						,		snake_case_						,		snake_case_      )
     test_perps.append(snake_case_      )

     print("""Test perplexity, step"""						,		snake_case_						,		""":"""						,		snake_case_      )
            # Break out of the loop after 60 batches
   if max_steps > 0 and global_step > 60:
    break
  if max_steps > 0 and global_step > 60:
   break

    # save finetuned transformer model
 torch.save(model.state_dict()						,		snake_case_      )
 torch.cuda.empty_cache()
 # Do some cleaning up so we can reinitialize for the next run of this function
 del lm_optimizer
 del lm_scheduler
 return model







def      UpperCamelCase_						(					)					->     List[Any]:






 '''simple docstring'''

 __lowerCAmelCase											=						argparse.ArgumentParser(description="""Fine-tune a transformer model with IGF on a language modeling task"""      )

 # Required parameters
 parser.add_argument(
     """--data_dir"""						,		default=snake_case_						,		type=snake_case_						,		required=snake_case_						,		help="""The input data dir. Should contain data files for WikiText."""						,		)
 parser.add_argument(
     """--model_name_or_path"""						,		default=snake_case_						,		type=snake_case_						,		required=snake_case_						,		help="""Path to pretrained model or model identifier from huggingface.co/models"""						,		)
 parser.add_argument(
     """--data_file"""						,		type=snake_case_						,		default=snake_case_						,		help=(
         """A jbl file containing tokenized data which can be split as objective dataset, """
         """train_dataset and test_dataset."""
     )						,		)

 parser.add_argument(
     """--igf_data_file"""						,		type=snake_case_						,		default=snake_case_						,		help="""A jbl file containing the context and information gain pairs to train secondary learner."""						,		)

 parser.add_argument(
     """--output_dir"""						,		default=snake_case_						,		type=snake_case_						,		required=snake_case_						,		help="""The output directory where the final fine-tuned model is stored."""						,		)

 parser.add_argument(
     """--tokenizer_name"""						,		default=snake_case_						,		type=snake_case_						,		help="""Pretrained tokenizer name or path if not the same as model_name"""						,		)
 parser.add_argument("""--seed"""						,		type=snake_case_						,		default=snake_case_						,		help="""A seed for reproducible training."""      )

 parser.add_argument(
     """--context_len"""						,		default=32						,		type=snake_case_						,		help=(
         """The maximum total input sequence length after tokenization. Sequences longer """
         """than this will be truncated, sequences shorter will be padded."""
     )						,		)

 parser.add_argument(
     """--size_objective_set"""						,		default=1_00						,		type=snake_case_						,		help="""number of articles that are long enough to be used as our objective set"""						,		)
 parser.add_argument(
     """--eval_freq"""						,		default=1_00						,		type=snake_case_						,		help="""secondary model evaluation is triggered at eval_freq"""      )

 parser.add_argument("""--max_steps"""						,		default=10_00						,		type=snake_case_						,		help="""To calculate training epochs"""      )

 parser.add_argument(
     """--secondary_learner_batch_size"""						,		default=1_28						,		type=snake_case_						,		help="""batch size of training data for secondary learner"""						,		)

 parser.add_argument(
     """--batch_size"""						,		default=16						,		type=snake_case_						,		help="""batch size of training data of language model(gpt2) """      )

 parser.add_argument(
     """--eval_interval"""						,		default=10						,		type=snake_case_						,		help=(
         """decay the selectivity of our secondary learner filter from"""
         """1 standard deviation above average to 1 below average after 10 batches"""
     )						,		)

 parser.add_argument(
     """--number"""						,		default=1_00						,		type=snake_case_						,		help="""The number of examples split to be used as objective_set/test_data"""      )

 parser.add_argument(
     """--min_len"""						,		default=10_26						,		type=snake_case_						,		help="""The minimum length of the article to be used as objective set"""      )

 parser.add_argument(
     """--secondary_learner_max_epochs"""						,		default=15						,		type=snake_case_						,		help="""number of epochs to train secondary learner"""      )

 parser.add_argument("""--trim"""						,		default=snake_case_						,		type=snake_case_						,		help="""truncate the example if it exceeds context length"""      )

 parser.add_argument(
     """--threshold"""						,		default=1.0						,		type=snake_case_						,		help=(
         """The threshold value used by secondary learner to filter the train_data and allow only"""
         """ informative data as input to the model"""
     )						,		)

 parser.add_argument("""--finetuned_model_name"""						,		default="""gpt2_finetuned.pt"""						,		type=snake_case_						,		help="""finetuned_model_name"""      )

 parser.add_argument(
     """--recopy_model"""						,		default=snake_case_						,		type=snake_case_						,		help="""Reset the model to the original pretrained GPT-2 weights after each iteration"""						,		)

 # function calls
 # Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
 generate_n_pairs(
     context_len=32						,		max_steps=10						,		size_objective_set=1_00						,		min_len=10_26						,		trim=snake_case_						,		data_file="""data/tokenized_stories_train_wikitext103.jbl"""						,		igf_data_file="""igf_context_pairs.jbl"""						,		)

 # Load train data for secondary learner
 __lowerCAmelCase											=						joblib.load("""data/IGF_values.jbl"""      )

 # Train secondary learner
 __lowerCAmelCase											=						training_secondary_learner(
     snake_case_						,		secondary_learner_max_epochs=15						,		secondary_learner_batch_size=1_28						,		eval_freq=1_00						,		igf_model_path="""igf_model.pt"""						,		)

 # load pretrained gpt2 model
 __lowerCAmelCase											=						GPTaLMHeadModel.from_pretrained("""gpt2"""      )
 set_seed(42      )

 # Generate train and test data to train and evaluate gpt2 model
 __lowerCAmelCase							,       __lowerCAmelCase											=						generate_datasets(
     context_len=32						,		file="""data/tokenized_stories_train_wikitext103.jbl"""						,		number=1_00						,		min_len=10_26						,		trim=snake_case_      )

 # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
 finetune(
     snake_case_						,		snake_case_						,		snake_case_						,		context_len=32						,		max_steps=10_00						,		batch_size=16						,		threshold=1.0						,		recopy_model=snake_case_						,		secondary_learner=snake_case_						,		eval_interval=10						,		finetuned_model_name="""gpt2_finetuned.pt"""						,		)


if __name__ == "__main__":
 main()







'''simple docstring'''

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


_A			:      List[Any]					      =				(
    '''https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py'''
)


_A			:      List[Any]					      =				logging.get_logger(__name__)  # pylint: disable=invalid-name







def      UpperCamelCase_						(					)					->     Dict:






 '''simple docstring'''

 __lowerCAmelCase											=						"""https://pypi.org/pypi/diffusers/json"""
 __lowerCAmelCase											=						json.loads(request.urlopen(snake_case_      ).read()      )["""releases"""].keys()
 return sorted(snake_case_						,		key=lambda snake_case_      : version.Version(snake_case_      )      )







def      UpperCamelCase_						(					)					->     int:






 '''simple docstring'''

 if HF_MODULES_CACHE in sys.path:
  return

 sys.path.append(snake_case_      )
 os.makedirs(snake_case_						,		exist_ok=snake_case_      )
 __lowerCAmelCase											=						Path(snake_case_      ) / """__init__.py"""
 if not init_path.exists():
  init_path.touch()







def      UpperCamelCase_						(					snake_case_       :       Union[str, os.PathLike]      )					->     Dict:






 '''simple docstring'''

 init_hf_modules()
 __lowerCAmelCase											=						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_      )
 __lowerCAmelCase											=						dynamic_module_path / """__init__.py"""
 if not init_path.exists():
  init_path.touch()







def      UpperCamelCase_						(					snake_case_       :       int      )					->     str:






 '''simple docstring'''

 with open(snake_case_						,		"""r"""						,		encoding="""utf-8"""      ) as f:
  __lowerCAmelCase											=						f.read()

 # Imports of the form `import .xxx`
 __lowerCAmelCase											=						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      UpperCamelCase_						(					snake_case_       :       int      )					->     Dict:






 '''simple docstring'''

 __lowerCAmelCase											=						False
 __lowerCAmelCase											=						[module_file]
 __lowerCAmelCase											=						[]

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

  __lowerCAmelCase											=						Path(snake_case_      ).parent
  __lowerCAmelCase											=						[str(module_path / m      ) for m in new_imports]
  __lowerCAmelCase											=						[f for f in new_import_files if f not in all_relative_imports]
  __lowerCAmelCase											=						[f"""{f}.py""" for f in new_import_files]

  __lowerCAmelCase											=						len(snake_case_      ) == 0
  all_relative_imports.extend(snake_case_      )

 return all_relative_imports







def      UpperCamelCase_						(					snake_case_       :       List[Any]      )					->     Optional[Any]:






 '''simple docstring'''

 with open(snake_case_						,		"""r"""						,		encoding="""utf-8"""      ) as f:
  __lowerCAmelCase											=						f.read()

 # Imports of the form `import xxx`
 __lowerCAmelCase											=						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
 __lowerCAmelCase											=						[imp.split("""."""      )[0] for imp in imports if not imp.startswith("""."""      )]

 # Unique-ify and test we got them all
 __lowerCAmelCase											=						list(set(snake_case_      )      )
 __lowerCAmelCase											=						[]
 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      UpperCamelCase_						(					snake_case_       :       Dict						,		snake_case_       :       Optional[int]      )					->     List[str]:






 '''simple docstring'''

 __lowerCAmelCase											=						module_path.replace(os.path.sep						,		"""."""      )
 __lowerCAmelCase											=						importlib.import_module(snake_case_      )

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







def      UpperCamelCase_						(					snake_case_       :       str      )					->     Optional[Any]:






 '''simple docstring'''

 from ..pipelines import DiffusionPipeline

 __lowerCAmelCase											=						dict(inspect.getmembers(snake_case_						,		inspect.isclass      )      )

 __lowerCAmelCase											=						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}."""      )
   __lowerCAmelCase											=						cls

 return pipeline_class







def      UpperCamelCase_						(					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						,		)					->     Any:






 '''simple docstring'''

 __lowerCAmelCase											=						str(snake_case_      )

 __lowerCAmelCase											=						os.path.join(snake_case_						,		snake_case_      )

 if os.path.isfile(snake_case_      ):
  __lowerCAmelCase											=						module_file_or_url
  __lowerCAmelCase											=						"""local"""
 elif pretrained_model_name_or_path.count("""/"""      ) == 0:
  __lowerCAmelCase											=						get_diffusers_versions()
  # cut ".dev0"
  __lowerCAmelCase											=						"""v""" + """.""".join(__version__.split("""."""      )[:3]      )

  # retrieve github version that matches
  if revision is None:
   __lowerCAmelCase											=						latest_version if latest_version[1:] in available_versions else """main"""
   logger.info(f"""Defaulting to latest_version: {revision}."""      )
  elif revision in available_versions:
   __lowerCAmelCase											=						f"""v{revision}"""
  elif revision == "main":
   __lowerCAmelCase											=						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
  __lowerCAmelCase											=						COMMUNITY_PIPELINES_URL.format(revision=snake_case_						,		pipeline=snake_case_      )
  try:
   __lowerCAmelCase											=						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_						,		)
   __lowerCAmelCase											=						"""git"""
   __lowerCAmelCase											=						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
   __lowerCAmelCase											=						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_						,		)
   __lowerCAmelCase											=						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
 __lowerCAmelCase											=						check_imports(snake_case_      )

 # Now we move the module inside our cached dynamic modules.
 __lowerCAmelCase											=						DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
 create_dynamic_module(snake_case_      )
 __lowerCAmelCase											=						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:
   __lowerCAmelCase											=						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_      ):
   __lowerCAmelCase											=						use_auth_token
  elif use_auth_token is True:
   __lowerCAmelCase											=						HfFolder.get_token()
  else:
   __lowerCAmelCase											=						None

  __lowerCAmelCase											=						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.
  __lowerCAmelCase											=						submodule_path / commit_hash
  __lowerCAmelCase											=						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      UpperCamelCase_						(					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_       :       Dict						,		)					->     Tuple:






 '''simple docstring'''

 __lowerCAmelCase											=						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"""						,		""""""      )      )






