





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


snake_case__   :				Any						=	logging.getLogger(__name__)





@dataclass
class A_   :
   lowerCAmelCase__  	=					field(
       metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}  )
   lowerCAmelCase__  	=					field(
       default=_lowerCamelCase					, metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}  )
   lowerCAmelCase__  	=					field(
       default=_lowerCamelCase					, metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}  )
   lowerCAmelCase__  	=					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__  	=					field(
       default=_lowerCamelCase					, metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}					, )





def _a							(      )      ->		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'''   ,							lowerCamelCase					)

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

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

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

      # Initialize our Trainer
      __A			  =							Trainer(
          model=lowerCamelCase   ,							args=lowerCamelCase   ,							train_dataset=lowerCamelCase   ,							eval_dataset=lowerCamelCase   ,							data_collator=lowerCamelCase   ,							)

      # 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 _a							(      lowerCamelCase:  str					)      ->		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__   :				Dict						=	{
    'configuration_videomae': ['VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VideoMAEConfig'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Any						=	[
           'VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST',
           'VideoMAEForPreTraining',
           'VideoMAEModel',
           'VideoMAEPreTrainedModel',
           'VideoMAEForVideoClassification',
       ]

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				str						=	['VideoMAEFeatureExtractor']
       snake_case__   :				Tuple						=	['VideoMAEImageProcessor']

if TYPE_CHECKING:
       from .configuration_videomae import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP, VideoMAEConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_videomae import (
                  VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  VideoMAEForPreTraining,
                  VideoMAEForVideoClassification,
                  VideoMAEModel,
                  VideoMAEPreTrainedModel,
              )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_videomae import VideoMAEFeatureExtractor
              from .image_processing_videomae import VideoMAEImageProcessor

else:
       import sys

       snake_case__   :				Optional[Any]						=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





