




import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       ConvBertConfig.from_json_file(snake_case_ )
			SCREAMING_SNAKE_CASE     =       ConvBertModel(snake_case_ )

			SCREAMING_SNAKE_CASE     =       load_tf_weights_in_convbert(snake_case_       ,						snake_case_       ,						snake_case_ )
			model.save_pretrained(snake_case_ )

			SCREAMING_SNAKE_CASE     =       TFConvBertModel.from_pretrained(snake_case_       ,						from_pt=snake_case_ )
			tf_model.save_pretrained(snake_case_ )


if __name__ == "__main__":
		__A    :				int    						=	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(
		    """--convbert_config_file""",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        """The config json file corresponding to the pre-trained ConvBERT 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."""
		)
		__A    :				str    						=	parser.parse_args()
		convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_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()