





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




def 			lowerCAmelCase_			(				)  ->		Any:
       # 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_ 							=	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_ 							=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
       else:
              UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_ 							=	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.

       UpperCamelCase_ 							=	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_ 							=	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_       ,		)
       UpperCamelCase_ 							=	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
       UpperCamelCase_ 							=	hasattr(model.config       ,		"lang2id"	)
       UpperCamelCase_ 							=	(
           SquadDataset(
               UpperCamelCase_       ,		tokenizer=UpperCamelCase_       ,		is_language_sensitive=UpperCamelCase_       ,		cache_dir=model_args.cache_dir	)
           if training_args.do_train
           else None
       )
       UpperCamelCase_ 							=	(
           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
       UpperCamelCase_ 							=	DataCollatorWithPadding(UpperCamelCase_       ,		pad_to_multiple_of=8	) if training_args.fpaa else None

       # Initialize our Trainer
       UpperCamelCase_ 							=	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]:
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
     main()





'''simple docstring'''

import math

def 				__magic_name__(    lowerCamelCase):
						__lowerCAmelCase											=					math.log(2,					1_0) * number
						__lowerCAmelCase											=					round((log_number - int(lowerCamelCase)),					1_5)

						return difference

def 				__magic_name__(    lowerCamelCase = 6_7_8_9_1_0):
						__lowerCAmelCase											=					9_0
						__lowerCAmelCase											=					0

						__lowerCAmelCase											=					math.log(1.23,					1_0)
						__lowerCAmelCase											=					math.log(1.24,					1_0)
						__lowerCAmelCase											=					0

						while position < number:
												__lowerCAmelCase											=					log_difference(lowerCamelCase)

												if difference >= upper_limit:
																		power_iterator += 9_3

												elif difference < lower_limit:
																		power_iterator += 1_9_6

												else:
																		__lowerCAmelCase											=					power_iterator
																		power_iterator += 1_9_6
																		position += 1

						return previous_power


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	print(f"""{solution() = }""")

