

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__     :					int								=			logging.getLogger(__name__)



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






def     _UpperCAmelCase				(     ):





    """simple docstring"""


    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'       ,      _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.

    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=_SCREAMING_SNAKE_CASE       ,      )
    SCREAMING_SNAKE_CASE_													=  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
    SCREAMING_SNAKE_CASE_													=  hasattr(model.config       ,      'lang2id'				)
    SCREAMING_SNAKE_CASE_													=  (
        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
    )
    SCREAMING_SNAKE_CASE_													=  (
        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
    SCREAMING_SNAKE_CASE_													=  DataCollatorWithPadding(_SCREAMING_SNAKE_CASE       ,      pad_to_multiple_of=8				) if training_args.fpaa else None

    # Initialize our Trainer
    SCREAMING_SNAKE_CASE_													=  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     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						List[str]				):





    """simple docstring"""


    main()


if __name__ == "__main__":
       main()









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_snake_case				= {"""configuration_bit""": ["""BIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BitConfig""", """BitOnnxConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= [
      """BIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """BitForImageClassification""",
      """BitModel""",
      """BitPreTrainedModel""",
      """BitBackbone""",
  ]


try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= ["""BitImageProcessor"""]


if TYPE_CHECKING:
  from .configuration_bit import BIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BitConfig, BitOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bit import (
        BIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BitBackbone,
        BitForImageClassification,
        BitModel,
        BitPreTrainedModel,
    )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_bit import BitImageProcessor

else:
  import sys

  _snake_case				= _LazyModule(__name__, globals()["""__file__"""], _import_structure)






