





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


_A				:							Tuple							= logging.getLogger(__name__)



@dataclass
class 				__snake_case  :


    '''simple docstring'''







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





def        __snake_case       (						)							->	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.

    SCREAMING_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.
        SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
    else:
        SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_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'''   ,       _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.

    SCREAMING_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   ,       )
    SCREAMING_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=_UpperCAmelCase   ,       )
    SCREAMING_SNAKE_CASE__       							= 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
    SCREAMING_SNAKE_CASE__       							= hasattr(model.config   ,       '''lang2id'''  )
    SCREAMING_SNAKE_CASE__       							= (
        SquadDataset(
            _UpperCAmelCase   ,       tokenizer=_UpperCAmelCase   ,       is_language_sensitive=_UpperCAmelCase   ,       cache_dir=model_args.cache_dir  )
        if training_args.do_train
        else None
    )
    SCREAMING_SNAKE_CASE__       							= (
        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
    SCREAMING_SNAKE_CASE__       							= DataCollatorWithPadding(_UpperCAmelCase   ,       pad_to_multiple_of=8  ) if training_args.fpaa else None

    # Initialize our Trainer
    SCREAMING_SNAKE_CASE__       							= 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        __snake_case       (						lowerCAmelCase_  )							->	Optional[Any]:
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
  main()








import numpy as np

_A				:							List[Any]							= [
    ["""a""", """b""", """c""", """d""", """e"""],
    ["""f""", """g""", """h""", """i""", """k"""],
    ["""l""", """m""", """n""", """o""", """p"""],
    ["""q""", """r""", """s""", """t""", """u"""],
    ["""v""", """w""", """x""", """y""", """z"""],
]



class 				__snake_case  :


    '''simple docstring'''







    def __init__(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= np.array(A_    )



    def    lowercase_				(					self      ,							A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= np.where(letter == self.SQUARE    )
        SCREAMING_SNAKE_CASE__       							= np.concatenate([indexa + 1, indexa + 1]    )
        return indexes



    def    lowercase_				(					self      ,							A_      ,							A_    ):





        '''simple docstring'''

        return self.SQUARE[indexa - 1, indexa - 1]



    def    lowercase_				(					self      ,							A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= message.lower()
        SCREAMING_SNAKE_CASE__       							= message.replace('''j'''      ,							'''i'''    )

        SCREAMING_SNAKE_CASE__       							= ''''''
        for letter_index in range(len(A_    )    ):
            if message[letter_index] != " ":
                SCREAMING_SNAKE_CASE__       							= self.letter_to_numbers(message[letter_index]    )
                SCREAMING_SNAKE_CASE__       							= encoded_message + str(numbers[0]    ) + str(numbers[1]    )
            elif message[letter_index] == " ":
                SCREAMING_SNAKE_CASE__       							= encoded_message + ''' '''

        return encoded_message




    def    lowercase_				(					self      ,							A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= message.replace(''' '''      ,							'''  '''    )
        SCREAMING_SNAKE_CASE__       							= ''''''
        for numbers_index in range(int(len(A_    ) / 2    )    ):
            if message[numbers_index * 2] != " ":
                SCREAMING_SNAKE_CASE__       							= message[numbers_index * 2]
                SCREAMING_SNAKE_CASE__       							= message[numbers_index * 2 + 1]

                SCREAMING_SNAKE_CASE__       							= self.numbers_to_letter(int(A_    )      ,							int(A_    )    )
                SCREAMING_SNAKE_CASE__       							= decoded_message + letter
            elif message[numbers_index * 2] == " ":
                SCREAMING_SNAKE_CASE__       							= decoded_message + ''' '''

        return decoded_message
