
'''simple docstring'''





from ..utils import DummyObject, requires_backends


class 			UpperCamelCase__	(     metaclass=SCREAMING_SNAKE_CASE):
 UpperCAmelCase__     :     Tuple    					=       ['torch', 'transformers', 'k_diffusion']






 def __init__(      self	:str						,					*_A	:Optional[int]						,					**_A	:List[str]      )					->      Union[str, Any]:







       '''simple docstring'''



       requires_backends(self						,					['torch', 'transformers', 'k_diffusion']      )






 @classmethod
 def 						lowercase_    (      cls	:str						,					*_A	:Optional[int]						,					**_A	:Optional[int]      )					->      List[Any]:







       '''simple docstring'''



       requires_backends(cls						,					['torch', 'transformers', 'k_diffusion']      )






 @classmethod
 def 						lowercase_    (      cls	:Any						,					*_A	:Tuple						,					**_A	:List[Any]      )					->      Union[str, Any]:







       '''simple docstring'''



       requires_backends(cls						,					['torch', 'transformers', 'k_diffusion']      )








'''simple docstring'''





import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Union[str, Any]:







      """simple docstring"""
      # Initialise PyTorch model
      __A													=      RoFormerConfig.from_json_file(UpperCAmelCase						)
      print(f'Building PyTorch model from configuration: {config}'						)
      __A													=      RoFormerForMaskedLM(UpperCAmelCase						)

      # Load weights from tf checkpoint
      load_tf_weights_in_roformer(UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)

      # Save pytorch-model
      print(f'Save PyTorch model to {pytorch_dump_path}'						)
      torch.save(model.state_dict()   ,	UpperCAmelCase   ,	_use_new_zipfile_serialization=UpperCAmelCase						)


if __name__ == "__main__":
 a__		: Tuple  							=							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(
     "--bert_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."
 )
 a__		: Union[str, Any]  							=							parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






