






"""simple docstring"""




import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


UpperCamelCase											=     logging.getLogger(__name__)




@dataclass
class  lowercase_     :
      A__						:		Any							      =					field(
          metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}  )
      A__						:		int							      =					field(
          default=lowerCAmelCase_,     metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}  )
      A__						:		Dict							      =					field(
          default=lowerCAmelCase_,     metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}  )
      A__						:		List[Any]							      =					field(default=lowerCAmelCase_,     metadata={'''help''': '''Set this flag to use fast tokenization.'''}  )
      # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
      # or just modify its tokenizer_config.json.
      A__						:		List[str]							      =					field(
          default=lowerCAmelCase_,     metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''},     )







def 							lowerCAmelCase			(   )							->				Optional[Any]:







  '''simple docstring'''







  _a					      =						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )

  if len(sys.argv       ) == 2 and sys.argv[1].endswith(".json"       ):
    # If we pass only one argument to the script and it's the path to a json file,
    # let's parse it to get our arguments.
    _a					      =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
  else:
    _a					      =						parser.parse_args_into_dataclasses()

  if (
      os.path.exists(training_args.output_dir       )
      and os.listdir(training_args.output_dir       )
      and training_args.do_train
      and not training_args.overwrite_output_dir
  ):
    raise ValueError(
        f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
        " --overwrite_output_dir to overcome."       )

  # Setup logging
  logging.basicConfig(
      format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"  ,			datefmt="%m/%d/%Y %H:%M:%S"  ,			level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN  ,			)
  logger.warning(
      "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"  ,			training_args.local_rank  ,			training_args.device  ,			training_args.n_gpu  ,			bool(training_args.local_rank != -1       )  ,			training_args.fpaa  ,			)
  # Set the verbosity to info of the Transformers logger (on main process only):
  if is_main_process(training_args.local_rank       ):
    transformers.utils.logging.set_verbosity_info()
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()
  logger.info("Training/evaluation parameters %s"  ,			UpperCamelCase_       )

  # Prepare Question-Answering task
  # Load pretrained model and tokenizer
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.

  _a					      =						AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,			cache_dir=model_args.cache_dir  ,			)
  _a					      =						AutoTokenizer.from_pretrained(
      model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path  ,			cache_dir=model_args.cache_dir  ,			use_fast=UpperCamelCase_  ,			)
  _a					      =						AutoModelForQuestionAnswering.from_pretrained(
      model_args.model_name_or_path  ,			from_tf=bool(".ckpt" in model_args.model_name_or_path       )  ,			config=UpperCamelCase_  ,			cache_dir=model_args.cache_dir  ,			)

  # Get datasets
  _a					      =						hasattr(model.config  ,			"lang2id"       )
  _a					      =						(
      SquadDataset(
          UpperCamelCase_  ,			tokenizer=UpperCamelCase_  ,			is_language_sensitive=UpperCamelCase_  ,			cache_dir=model_args.cache_dir       )
      if training_args.do_train
      else None
  )
  _a					      =						(
      SquadDataset(
          UpperCamelCase_  ,			tokenizer=UpperCamelCase_  ,			mode="dev"  ,			is_language_sensitive=UpperCamelCase_  ,			cache_dir=model_args.cache_dir  ,			)
      if training_args.do_eval
      else None
  )

  # Data collator
  _a					      =						DataCollatorWithPadding(UpperCamelCase_  ,			pad_to_multiple_of=8       ) if training_args.fpaa else None

  # Initialize our Trainer
  _a					      =						Trainer(
      model=UpperCamelCase_  ,			args=UpperCamelCase_  ,			train_dataset=UpperCamelCase_  ,			eval_dataset=UpperCamelCase_  ,			data_collator=UpperCamelCase_  ,			)

  # Training
  if training_args.do_train:
    trainer.train(
        model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path       ) else None       )
    trainer.save_model()
    # For convenience, we also re-save the tokenizer to the same directory,
    # so that you can share your model easily on huggingface.co/models =)
    if trainer.is_world_master():
      tokenizer.save_pretrained(training_args.output_dir       )






def 							lowerCAmelCase			(   UpperCamelCase_:				Union[str, Any]       )							->				Tuple:







  '''simple docstring'''







  main()


if __name__ == "__main__":
     main()




"""simple docstring"""




def 				a__			(					lowerCAmelCase						:							int		):






     '''simple docstring'''
     if number != int(lowerCAmelCase		):
          raise ValueError("factorial() only accepts integral values"		)
     if number < 0:
          raise ValueError("factorial() not defined for negative values"		)
     UpperCAmelCase__				:			int							=    1
     for i in range(1							,     number + 1		):
          value *= i
     return value



def 				a__			(					lowerCAmelCase						:							int		):






     '''simple docstring'''
     if not isinstance(lowerCAmelCase							,     lowerCAmelCase		):
          raise ValueError("factorial() only accepts integral values"		)
     if n < 0:
          raise ValueError("factorial() not defined for negative values"		)
     return 1 if n == 0 or n == 1 else n * factorial(n - 1		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    A__   :						Any 							=     int(input("""Enter a positive integer: """).strip() or 0)
    print(f"""factorial{n} is {factorial(n)}""")



