





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       : List[Any]						    = logging.getLogger(__name__)







@dataclass
class     lowerCamelCase:
     '''simple docstring'''


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





def 							__lowerCAmelCase(					)       ->   Dict:




    """simple docstring"""




    _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'       ,    _SCREAMING_SNAKE_CASE							)

    # 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=_SCREAMING_SNAKE_CASE       ,    )
    _A    =       AutoModelForQuestionAnswering.from_pretrained(
        model_args.model_name_or_path       ,    from_tf=bool('.ckpt' in model_args.model_name_or_path							)       ,    config=_SCREAMING_SNAKE_CASE       ,    cache_dir=model_args.cache_dir       ,    )

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

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

    # Initialize our Trainer
    _A    =       Trainer(
        model=_SCREAMING_SNAKE_CASE       ,    args=_SCREAMING_SNAKE_CASE       ,    train_dataset=_SCREAMING_SNAKE_CASE       ,    eval_dataset=_SCREAMING_SNAKE_CASE       ,    data_collator=_SCREAMING_SNAKE_CASE       ,    )

    # 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(					_SCREAMING_SNAKE_CASE							)       ->   Any:




    """simple docstring"""




    main()


if __name__ == "__main__":
 main()












from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest







@require_tokenizers
class     lowerCamelCase(	__snake_case  ):
     '''simple docstring'''


     __magic_name__        = SqueezeBertTokenizer
     __magic_name__        = SqueezeBertTokenizerFast
     __magic_name__        = True




     def 					lowerCAmelCase__		(						self							,    **snake_case_       ):
         return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname							,    **snake_case_       )




     @slow
     def 					lowerCAmelCase__		(						self       ):
         _A    =       SqueezeBertTokenizer.from_pretrained('squeezebert/squeezebert-mnli-headless'       )

         _A    =       tokenizer.encode('sequence builders'							,    add_special_tokens=snake_case_       )
         _A    =       tokenizer.encode('multi-sequence build'							,    add_special_tokens=snake_case_       )

         _A    =       tokenizer.build_inputs_with_special_tokens(snake_case_       )
         _A    =       tokenizer.build_inputs_with_special_tokens(snake_case_							,    snake_case_       )

         assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
         assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
             tokenizer.sep_token_id
         ]





