


import argparse

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


logging.set_verbosity_info()







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[int]						,					__A				:    Union[str, Any]						,					__A				:    Optional[int]						,					__A				:    List[str]       )			->	Dict:

       """simple docstring"""

       a_      :	str							=						BigBirdConfig.from_json_file(__A       )
       print(F"""Building PyTorch model from configuration: {config}"""       )

       if is_trivia_qa:
              a_      :	Any							=						BigBirdForQuestionAnswering(__A       )
       else:
              a_      :	Tuple							=						BigBirdForPreTraining(__A       )

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

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


if __name__ == "__main__":
   UpperCAmelCase_   :				List[str]						      = 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.'
   )
   UpperCAmelCase_   :				str						      = 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
   )






UpperCAmelCase_   :				Optional[int]						      = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

UpperCAmelCase_   :				List[str]						      = [{'type': 'code', 'content': INSTALL_CONTENT}]
UpperCAmelCase_   :				int						      = {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}


