





import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						Tuple ,     snake_case_		:						int ,     snake_case_		:						Any						):
     # Initialise PyTorch model
     __magic_name__				=       TaConfig.from_json_file(snake_case_						)
     print(f'Building PyTorch model from configuration: {config}'						)
     __magic_name__				=       TaForConditionalGeneration(snake_case_						)

     # Load weights from tf checkpoint
     load_tf_weights_in_ta(snake_case_ ,     snake_case_ ,     snake_case_						)

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


if __name__ == "__main__":
    a_    :				List[Any]					=			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(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help=(
            'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
        ),
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    a_    :				Tuple					=			parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
a_    :				List[str]					=			TypeVar('T', bound='Enum')



@unique
class  SCREAMING_SNAKE_CASE_  ( SCREAMING_SNAKE_CASE__			):


  """simple docstring"""





  _a								=							80
  _a								=							70
  _a								=							60
  _a								=							50
  _a								=							40
  _a								=							30
  _a								=							20
  _a								=							10



@unique
class  SCREAMING_SNAKE_CASE_  ( SCREAMING_SNAKE_CASE__			):


  """simple docstring"""





  _a								=							24
  _a								=							21
  _a								=							18
  _a								=							15
  _a								=							12
  _a								=							9
  _a								=							6
  _a								=							3
  _a								=							2
  _a								=							1
  _a								=							-1
  _a								=							-2
  _a								=							-3
  _a								=							-6
  _a								=							-9
  _a								=							-12
  _a								=							-15
  _a								=							-18
  _a								=							-21
  _a								=							-24





  @classmethod
  def 			__A      (  cls						)		->   dict:





       '''simple docstring'''







       return {unit.name: unit.value for unit in cls if unit.value > 0}







  @classmethod
  def 			__A      (  cls						)		->   dict:





       '''simple docstring'''







       return {unit.name: unit.value for unit in cls if unit.value < 0}




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						float						):
     __magic_name__				=       SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
     for name_prefix, value_prefix in prefixes.items():
          __magic_name__				=       value / (10**value_prefix)
          if numerical_part > 1:
               return f'{numerical_part!s} {name_prefix}'
     return str(snake_case_						)




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						float						):
     for prefix in BinaryUnit:
          __magic_name__				=       value / (2**prefix.value)
          if numerical_part > 1:
               return f'{numerical_part!s} {prefix.name}'
     return str(snake_case_						)


if __name__ == "__main__":
    import doctest

    doctest.testmod()