




'''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


_SCREAMING_SNAKE_CASE =	logging.getLogger(__name__)
@dataclass
class      __UpperCAmelCase							:



     '''simple docstring'''





     _UpperCamelCase  	=		field(
         metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}							)
     _UpperCamelCase  	=		field(
         default=lowerCamelCase__ ,metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}							)
     _UpperCamelCase  	=		field(
         default=lowerCamelCase__ ,metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}							)
     _UpperCamelCase  	=		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.
     _UpperCamelCase  	=		field(
         default=lowerCamelCase__ ,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} ,)





def  lowerCamelCase(       )		->		Dict:
   # See all possible arguments in src/transformers/training_args.py
   # or by passing the --help flag to this script.
   # We now keep distinct sets of args, for a cleaner separation of concerns.

   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_		,   A_		,   A_       		=   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
   else:
      A_		,   A_		,   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(       SCREAMING_SNAKE_CASE_    )		->		Optional[Any]:
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
       main()










'''simple docstring'''
def  lowerCamelCase(       SCREAMING_SNAKE_CASE_    )		->		list:
			A_       		=   [True for i in range(n + 1    )]
			A_       		=   [i - 1 for i in range(n + 1    )]
			A_       		=   []
			for i in range(2					,n + 1    ):
						if is_prime[i]:
									primes.append(SCREAMING_SNAKE_CASE_    )
						for j in range(0					,len(SCREAMING_SNAKE_CASE_    )    ):
									if i * primes[j] >= n:
												break
									A_       		=   False

									if i % primes[j] == 0:
												A_       		=   totients[i] * primes[j]
												break

									A_       		=   totients[i] * (primes[j] - 1)

			return totients


if __name__ == "__main__":
							import doctest

							doctest.testmod()




