






'''simple docstring'''



import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()


def 							lowercase_				(    __A :       Union[str, Any]					,      __A :       List[str]					,      __A :       str					,      __A :       Optional[Any]			)       ->      Union[str, Any]:



 """simple docstring"""




 lowercase					:   Optional[Any]				  =BigBirdConfig.from_json_file(SCREAMING_SNAKE_CASE_			)
 print(F'Building PyTorch model from configuration: {config}'			)

 if is_trivia_qa:
  lowercase					:   Optional[int]				  =BigBirdForQuestionAnswering(SCREAMING_SNAKE_CASE_			)
 else:
  lowercase					:   Tuple				  =BigBirdForPreTraining(SCREAMING_SNAKE_CASE_			)

 # Load weights from tf checkpoint
 load_tf_weights_in_big_bird(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      is_trivia_qa=SCREAMING_SNAKE_CASE_			)

 # Save pytorch-model
 print(F'Save PyTorch model to {pytorch_dump_path}'			)
 model.save_pretrained(SCREAMING_SNAKE_CASE_			)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE						=	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
      )
      parser.add_argument(
          '--big_bird_config_file',
          default=None,
          type=str,
          required=True,
          help=(
              'The config json file corresponding to the pre-trained BERT model. \n'
              'This specifies the model architecture.'
          ),
      )
      parser.add_argument(
          '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
      )
      parser.add_argument(
          '--is_trivia_qa', action='store_true', help='Whether to convert a model with a trivia_qa head.'
      )
      SCREAMING_SNAKE_CASE						=	parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
      )











'''simple docstring'''



from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE						=	{
    'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
    'tokenization_m2m_100': ['M2M100Tokenizer'],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	[
          'M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST',
          'M2M100ForConditionalGeneration',
          'M2M100Model',
          'M2M100PreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
      from .tokenization_mam_aaa import MaMaaaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mam_aaa import (
                M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
                MaMaaaForConditionalGeneration,
                MaMaaaModel,
                MaMaaaPreTrainedModel,
            )


else:
      import sys

      SCREAMING_SNAKE_CASE						=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



