code: 
  '''simple docstring'''
  import copy
  
  from transformers.configuration_utils import PretrainedConfig
  from transformers.utils import logging
  
  
  lowercase__						=			logging.get_logger(__name__)
  
  
  
  
  
  
  class   snake_case__					(			__SCREAMING_SNAKE_CASE				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		lowerCamelCase           =	"""hybrid-clip"""
  		lowerCamelCase           =	True
  
  
  
  		def __init__(			self							: Any     ,  UpperCamelCase__							: Optional[Any]=512     ,  **UpperCamelCase__							: Dict			)	-> Dict:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				super().__init__(**UpperCamelCase__			)
  
  				if "text_config" not in kwargs:
  						raise ValueError('''`text_config` can not be `None`.'''			)
  
  				if "vision_config" not in kwargs:
  						raise ValueError('''`vision_config` can not be `None`.'''			)
  
  				snake_case     :					Any     =	kwargs.pop('''text_config'''			)
  				snake_case     :					str     =	kwargs.pop('''vision_config'''			)
  
  				snake_case     :					int     =	text_config.pop('''model_type'''			)
  				snake_case     :					str     =	vision_config.pop('''model_type'''			)
  
  				from transformers import AutoConfig
  
  				snake_case     :					str     =	AutoConfig.for_model(UpperCamelCase__     ,  **UpperCamelCase__			)
  
  				if vision_model_type == "clip":
  						snake_case     :					Optional[int]     =	AutoConfig.for_model(UpperCamelCase__     ,  **UpperCamelCase__			).vision_config
  				elif vision_model_type == "clip_vision_model":
  						from transformers import CLIPVisionConfig
  
  						snake_case     :					Dict     =	CLIPVisionConfig(**UpperCamelCase__			)
  				else:
  						snake_case     :					Tuple     =	AutoConfig.for_model(UpperCamelCase__     ,  **UpperCamelCase__			)
  
  				snake_case     :					Optional[Any]     =	projection_dim
  				snake_case     :					Optional[int]     =	1.0
  
  
  
  		@classmethod
  		def 			lowerCAmelCase							(			cls							: List[str]     ,  UpperCamelCase__							: PretrainedConfig     ,  UpperCamelCase__							: PretrainedConfig     ,  **UpperCamelCase__							: List[Any]			)	-> Union[str, Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				return cls(text_config=text_config.to_dict()     ,  vision_config=vision_config.to_dict()     ,  **UpperCamelCase__			)
  
  
  
  		def 			lowerCAmelCase							(			self							: int			)	-> Optional[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Dict     =	copy.deepcopy(self.__dict__			)
  				snake_case     :					List[str]     =	self.text_config.to_dict()
  				snake_case     :					List[str]     =	self.vision_config.to_dict()
  				snake_case     :					Any     =	self.__class__.model_type
  				return output
  
  
  
style_context: 
  '''simple docstring'''
  import argparse
  import json
  
  from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Dict:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Tuple     =	json.load(open(SCREAMING_SNAKE_CASE__   ,					'''r''' ) )
  		snake_case     :					Any     =	GPTNeoConfig(
  		    hidden_size=config_json['''n_embd''']   ,					num_layers=config_json['''n_layer''']   ,					num_heads=config_json['''n_head''']   ,					attention_types=config_json['''attention_types''']   ,					max_position_embeddings=config_json['''n_positions''']   ,					resid_dropout=config_json['''res_dropout''']   ,					embed_dropout=config_json['''embed_dropout''']   ,					attention_dropout=config_json['''attn_dropout''']   ,					)
  		print(F'Building PyTorch model from configuration: {config}' )
  		snake_case     :					Union[str, Any]     =	GPTNeoForCausalLM(SCREAMING_SNAKE_CASE__ )
  
  		# Load weights from tf checkpoint
  		load_tf_weights_in_gpt_neo(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )
  
  		# Save pytorch-model
  		print(F'Save PyTorch model to {pytorch_dump_path}' )
  		model.save_pretrained(SCREAMING_SNAKE_CASE__ )
  
  
  if __name__ == "__main__":
  						lowercase__						=			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(
  						    "--config_file",
  						    default=None,
  						    type=str,
  						    required=True,
  						    help=(
  						        "The config json file corresponding to the pre-trained mesh-tf 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."
  						)
  						lowercase__						=			parser.parse_args()
  						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  
  