




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_    :       Union[str, Any]       =							logging.getLogger(__name__)







@dataclass
class 		__UpperCAmelCase:





     """simple docstring"""



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






def 							__lowerCAmelCase			():
    # 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			(UpperCAmelCase_		):
    # 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() = }""")

