



"""simple docstring"""
import math





def   __magic_name__					(				lowercase				,      lowercase			):





   if 0 not in (x, y):
      # We use the relation x^y = y*log10(x), where 10 is the base.
      return y * math.logaa(lowercase			)
   else:
      if x == 0:  # 0 raised to any number is 0
         return 0
      elif y == 0:
         return 1  # any number raised to 0 is 1
   raise AssertionError("""This should never happen"""			)


if __name__ == "__main__":  # Main function
   # Read two numbers from input and typecast them to int using map function.
   # Here x is the base and y is the power.
   _UpperCAmelCase  =      """Enter the base and the power separated by a comma: """
   _UpperCAmelCase,			_UpperCAmelCase  =      map(int, input(prompt).split(""","""))
   _UpperCAmelCase,			_UpperCAmelCase  =      map(int, input(prompt).split(""","""))

   # We find the log of each number, using the function res(), which takes two
   # arguments.
   _UpperCAmelCase  =      res(xa, ya)
   _UpperCAmelCase  =      res(xa, ya)

   # We check for the largest number
   if resa > resa:
      print("""Largest number is""", xa, """^""", ya)
   elif resa > resa:
      print("""Largest number is""", xa, """^""", ya)
   else:
      print("""Both are equal""")







'''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
      )

