


import argparse

import torch

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


logging.set_verbosity_info()
def 				UpperCamelCase_	(					__snake_case      :    int						,      __snake_case      :    int						,      __snake_case      :    List[Any]  )->   Any:




						'''simple docstring'''
						# Initialise PyTorch model
						__magic_name__					=      RoFormerConfig.from_json_file(__snake_case  )
						print(F'''Building PyTorch model from configuration: {config}'''  )
						__magic_name__					=      RoFormerForMaskedLM(__snake_case  )

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

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


if __name__ == "__main__":
					UpperCamelCase			:					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(
					    '''--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.'''
					)
					UpperCamelCase			:					Optional[int]         =							parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





from ..utils import DummyObject, requires_backends

class      __snake_case				(   metaclass=_UpperCAmelCase):
					SCREAMING_SNAKE_CASE_		:       str								=			["torch", "transformers", "k_diffusion"]
					def __init__(						self       :						Tuple	,      *_lowerCAmelCase       :						Union[str, Any]	,      **_lowerCAmelCase       :						Union[str, Any]  )							->					Dict:



											'''simple docstring'''





											requires_backends(self	,      ['torch', 'transformers', 'k_diffusion']  )
					@classmethod
					def 	a			(						cls       :						Optional[int]	,      *_lowerCAmelCase       :						Any	,      **_lowerCAmelCase       :						Optional[Any]  )							->					Optional[Any]:



											'''simple docstring'''





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







					@classmethod
					def 	a			(						cls       :						Union[str, Any]	,      *_lowerCAmelCase       :						Tuple	,      **_lowerCAmelCase       :						str  )							->					Tuple:



											'''simple docstring'''





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

