


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		:Tuple											=       logging.getLogger(__name__)


@dataclass
class       __lowerCAmelCase       :






  """simple docstring"""





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






def      _lowerCAmelCase  (							)->List[str]:
   '''simple docstring'''






   snake_case_         =   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.
      snake_case_      ,      snake_case_      ,      snake_case_         =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
   else:
      snake_case_      ,      snake_case_      ,      snake_case_         =   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"						,  lowerCAmelCase_							)

   # 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.

   snake_case_         =   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						,  )
   snake_case_         =   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=lowerCAmelCase_						,  )
   snake_case_         =   AutoModelForQuestionAnswering.from_pretrained(
       model_args.model_name_or_path						,  from_tf=bool(".ckpt" in model_args.model_name_or_path							)						,  config=lowerCAmelCase_						,  cache_dir=model_args.cache_dir						,  )

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

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

   # Initialize our Trainer
   snake_case_         =   Trainer(
       model=lowerCAmelCase_						,  args=lowerCAmelCase_						,  train_dataset=lowerCAmelCase_						,  eval_dataset=lowerCAmelCase_						,  data_collator=lowerCAmelCase_						,  )

   # 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  (							lowerCAmelCase_							:Optional[Any]							)->str:
   '''simple docstring'''






   main()


if __name__ == "__main__":
   main()








def      _lowerCAmelCase  (							lowerCAmelCase_							:Dict						,  lowerCAmelCase_							:Optional[int]						,  lowerCAmelCase_							:Tuple							)->Optional[Any]:
   '''simple docstring'''






   assert b != 0, "This cannot accept modulo that is == 0"
   if n == 0:
      return 1

   if n % 2 == 1:
      return (bin_exp_mod(lowerCAmelCase_						,  n - 1						,  lowerCAmelCase_							) * a) % b

   snake_case_         =   bin_exp_mod(lowerCAmelCase_						,  n / 2						,  lowerCAmelCase_							)
   return (r * r) % b


if __name__ == "__main__":
   try:
      SCREAMING_SNAKE_CASE		:str											=       int(input('''Enter Base : ''').strip())
      SCREAMING_SNAKE_CASE		:List[Any]											=       int(input('''Enter Power : ''').strip())
      SCREAMING_SNAKE_CASE		:Dict											=       int(input('''Enter Modulo : ''').strip())
   except ValueError:
      print('''Invalid literal for integer''')

   print(bin_exp_mod(BASE, POWER, MODULO))




