code: 
  
  
  
  
  """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   UpperCamelCase__     (		lowercase__  :     List[Any]       ,     lowercase__  :     Optional[int]       ,     lowercase__  :     int   ):
  		# Initialise PyTorch model
  		snake_case		:						Dict								=				RoFormerConfig.from_json_file(lowercase__   )
  		print(F'''Building PyTorch model from configuration: {config}'''   )
  		snake_case		:						List[Any]								=				RoFormerForMaskedLM(lowercase__   )
  
  		# Load weights from tf checkpoint
  		load_tf_weights_in_roformer(lowercase__       ,     lowercase__       ,     lowercase__   )
  
  		# Save pytorch-model
  		print(F'''Save PyTorch model to {pytorch_dump_path}'''   )
  		torch.save(model.state_dict()       ,     lowercase__       ,     _use_new_zipfile_serialization=lowercase__   )
  
  
  if __name__ == "__main__":
  							__A 		=			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 		=			parser.parse_args()
  							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding
  class 			lowerCamelCase__    (  lowerCamelCase_			):
  			a__ :      Tuple								=     ["""image_processor""", """tokenizer"""]
  			a__ :      List[Any]								=     """EfficientNetImageProcessor"""
  			a__ :      str								=     ("""BertTokenizer""", """BertTokenizerFast""")
  
  
  			def __init__(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					super().__init__(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
  
  
  			def __call__(     self				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE="max_length"				, SCREAMING_SNAKE_CASE=64				, SCREAMING_SNAKE_CASE=None				, **SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					if text is None and images is None:
  							raise ValueError("You have to specify either text or images. Both cannot be none."							)
  
  					if text is not None:
  							snake_case		:						Dict								=				self.tokenizer(
  							    SCREAMING_SNAKE_CASE				, padding=SCREAMING_SNAKE_CASE				, max_length=SCREAMING_SNAKE_CASE				, return_tensors=SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  
  					if images is not None:
  							snake_case		:						Tuple								=				self.image_processor(SCREAMING_SNAKE_CASE				, return_tensors=SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  
  					if text is not None and images is not None:
  							snake_case		:						Dict								=				image_features.pixel_values
  							return encoding
  					elif text is not None:
  							return encoding
  					else:
  							return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE							)				, tensor_type=SCREAMING_SNAKE_CASE							)
  
  
  			def 							lowerCamelCase_				(     self				, *SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  
  
  			def 							lowerCamelCase_				(     self				, *SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return self.tokenizer.decode(*SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  
  
  
  			@property
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[Any]								=				self.tokenizer.model_input_names
  					snake_case		:						Any								=				self.image_processor.model_input_names
  					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)
  
  