

UpperCAmelCase_							:					Optional[int]		 =   {
    0: '''0''',
    1: '''1''',
    2: '''2''',
    3: '''3''',
    4: '''4''',
    5: '''5''',
    6: '''6''',
    7: '''7''',
    8: '''8''',
    9: '''9''',
    10: '''a''',
    11: '''b''',
    12: '''c''',
    13: '''d''',
    14: '''e''',
    15: '''f''',
}
def 							__SCREAMING_SNAKE_CASE     (   a__    :   float       )					->				str:
	assert type(a__       ) in (int, float) and decimal == int(a__       )
	__A :			List[str]		    =			int(a__       )
	__A :			List[Any]		    =			""""""
	__A :			int		    =			False
	if decimal < 0:
		__A :			Tuple		    =			True
		decimal *= -1
	while decimal > 0:
		__A    ,				__A :			List[Any]		    =			divmod(a__			,16       )
		__A :			Union[str, Any]		    =			values[remainder] + hexadecimal
	__A :			Optional[Any]		    =			"""0x""" + hexadecimal
	if negative:
		__A :			Tuple		    =			"""-""" + hexadecimal
	return hexadecimal


if __name__ == "__main__":
			import doctest

			doctest.testmod()













'''simple docstring'''



import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[Any],       SCREAMING_SNAKE_CASE__:						Tuple,       SCREAMING_SNAKE_CASE__:						Dict     )	->      List[Any]:

   """simple docstring"""
   # Initialise PyTorch model
   __a					  =     RemBertConfig.from_json_file(SCREAMING_SNAKE_CASE__     )
   print('Building PyTorch model from configuration: {}'.format(str(SCREAMING_SNAKE_CASE__     )     )     )
   __a					  =     RemBertModel(SCREAMING_SNAKE_CASE__     )

   # Load weights from tf checkpoint
   load_tf_weights_in_rembert(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )

   # Save pytorch-model
   print('Save PyTorch model to {}'.format(SCREAMING_SNAKE_CASE__     )     )
   torch.save(model.state_dict(),       SCREAMING_SNAKE_CASE__     )


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(
     """--rembert_config_file""",
     default=None,
     type=str,
     required=True,
     help=(
         """The config json file corresponding to the pre-trained RemBERT 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."""
 )
 __UpperCamelCase   :							List[str]         =   parser.parse_args()
 convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)