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    __lowerCAmelCase  :



 '''simple docstring'''
 _lowerCamelCase						:	str				    = field(
     metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}						)
 _lowerCamelCase						:	Optional[str]				    = field(
     default=a_,	metadata={'help': 'Pretrained config name or path if not the same as model_name'}						)
 _lowerCamelCase						:	Optional[str]				    = field(
     default=a_,	metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}						)
 _lowerCamelCase						:	bool				    = 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.
 _lowerCamelCase						:	Optional[str]				    = field(
     default=a_,	metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'},	)







def SCREAMING_SNAKE_CASE				(     ):
       # 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.

       _UpperCamelCase     :       Dict  						=  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.
              _UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase     :       Union[str, Any]  						=  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
       else:
              _UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase     :       List[str]  						=  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'		,						lowercase_				)

       # 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.

       _UpperCamelCase     :       Optional[Any]  						=  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		,						)
       _UpperCamelCase     :       Dict  						=  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=lowercase_		,						)
       _UpperCamelCase     :       List[Any]  						=  AutoModelForQuestionAnswering.from_pretrained(
           model_args.model_name_or_path		,						from_tf=bool('.ckpt' in model_args.model_name_or_path				)		,						config=lowercase_		,						cache_dir=model_args.cache_dir		,						)

       # Get datasets
       _UpperCamelCase     :       List[Any]  						=  hasattr(model.config		,						'lang2id'				)
       _UpperCamelCase     :       int  						=  (
           SquadDataset(
               lowercase_		,						tokenizer=lowercase_		,						is_language_sensitive=lowercase_		,						cache_dir=model_args.cache_dir				)
           if training_args.do_train
           else None
       )
       _UpperCamelCase     :       Dict  						=  (
           SquadDataset(
               lowercase_		,						tokenizer=lowercase_		,						mode='dev'		,						is_language_sensitive=lowercase_		,						cache_dir=model_args.cache_dir		,						)
           if training_args.do_eval
           else None
       )

       # Data collator
       _UpperCamelCase     :       Tuple  						=  DataCollatorWithPadding(lowercase_		,						pad_to_multiple_of=8				) if training_args.fpaa else None

       # Initialize our Trainer
       _UpperCamelCase     :       List[Any]  						=  Trainer(
           model=lowercase_		,						args=lowercase_		,						train_dataset=lowercase_		,						eval_dataset=lowercase_		,						data_collator=lowercase_		,						)

       # 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 SCREAMING_SNAKE_CASE				(     lowercase_:	Dict				):
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
 main()




from __future__ import annotations


def SCREAMING_SNAKE_CASE				(     lowercase_:	int		,						lowercase_:	int		,						lowercase_:	int				):
       assert (
           c % greatest_common_divisor(lowercase_		,						lowercase_				) == 0
       )  # greatest_common_divisor(a,b) function implemented below
       ((_UpperCamelCase)						,      (_UpperCamelCase)						,      (_UpperCamelCase))     :       int  						=  extended_gcd(lowercase_		,						lowercase_				)  # extended_gcd(a,b) function implemented below
       _UpperCamelCase     :       List[Any]  						=  c / d
       return (r * x, r * y)


def SCREAMING_SNAKE_CASE				(     lowercase_:	int		,						lowercase_:	int		,						lowercase_:	int		,						lowercase_:	int = 2				):
       ((_UpperCamelCase)						,      (_UpperCamelCase))     :       Tuple  						=  diophantine(lowercase_		,						lowercase_		,						lowercase_				)  # Initial value
       _UpperCamelCase     :       List[Any]  						=  greatest_common_divisor(lowercase_		,						lowercase_				)
       _UpperCamelCase     :       Optional[int]  						=  a // d
       _UpperCamelCase     :       Optional[Any]  						=  b // d

       for i in range(lowercase_				):
              _UpperCamelCase     :       Union[str, Any]  						=  xa + i * q
              _UpperCamelCase     :       List[Any]  						=  ya - i * p
              print(lowercase_		,						lowercase_				)


def SCREAMING_SNAKE_CASE				(     lowercase_:	int		,						lowercase_:	int				):
       if a < b:
              _UpperCamelCase						,      _UpperCamelCase     :       Optional[Any]  						=  b, a

       while a % b != 0:
              _UpperCamelCase						,      _UpperCamelCase     :       Union[str, Any]  						=  b, a % b

       return b


def SCREAMING_SNAKE_CASE				(     lowercase_:	int		,						lowercase_:	int				):
       assert a >= 0 and b >= 0

       if b == 0:
              _UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase     :       Optional[Any]  						=  a, 1, 0
       else:
              ((_UpperCamelCase)						,      (_UpperCamelCase)						,      (_UpperCamelCase))     :       Any  						=  extended_gcd(lowercase_		,						a % b				)
              _UpperCamelCase     :       Any  						=  q
              _UpperCamelCase     :       str  						=  p - q * (a // b)

       assert a % d == 0 and b % d == 0
       assert d == a * x + b * y

       return (d, x, y)


if __name__ == "__main__":
 from doctest import testmod

 testmod(name='''diophantine''', verbose=True)
 testmod(name='''diophantine_all_soln''', verbose=True)
 testmod(name='''extended_gcd''', verbose=True)
 testmod(name='''greatest_common_divisor''', verbose=True)



