



"""simple docstring"""
_UpperCAmelCase  =      {
    """joule""": 1.0,
    """kilojoule""": 1_0_0_0,
    """megajoule""": 1_0_0_0_0_0_0,
    """gigajoule""": 1_0_0_0_0_0_0_0_0_0,
    """wattsecond""": 1.0,
    """watthour""": 3_6_0_0,
    """kilowatthour""": 3_6_0_0_0_0_0,
    """newtonmeter""": 1.0,
    """calorie_nutr""": 4_1_8_6.8,
    """kilocalorie_nutr""": 4_1_8_6_8_0_0.0_0,
    """electronvolt""": 1.602176634e-19,
    """britishthermalunit_it""": 1_0_5_5.0_5_5_8_5,
    """footpound""": 1.3_5_5_8_1_8,
}





def   __magic_name__					(				lowercase				,      lowercase				,      lowercase			):





   if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
      SCREAMING_SNAKE_CASE_:			List[Any]        =(
          f'''Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
          f'''Valid values are: {", ".join(lowercase			)}'''
      )
      raise ValueError(lowercase			)
   return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
   import doctest

   doctest.testmod()







'''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 (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       List[str]      =							BigBirdConfig.from_json_file(SCREAMING_SNAKE_CASE							)
     print(f"Building PyTorch model from configuration: {config}"							)

     if is_trivia_qa:
          __A	:       List[str]      =							BigBirdForQuestionAnswering(SCREAMING_SNAKE_CASE							)
     else:
          __A	:       List[Any]      =							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__":
      _UpperCamelCase 							=  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 							=  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
      )

