code: 
  import hashlib
  from typing import Union
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       bytes:
  
  
  		'''simple docstring'''
  
  
  		if isinstance(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ ):
  				# Data already in bytes, returns as it as
  				return hash_data
  		elif isinstance(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ ):
  				# We keep the data as it as for it ot be later encoded to UTF-8
  				# However replace `\\` with `/` for Windows compatibility
  				snake_case     :					Any     =	hash_data.replace('''\\'''   ,					'''/''' )
  		elif isinstance(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ ):
  				snake_case     :					Any     =	str(SCREAMING_SNAKE_CASE__ )
  		else:
  				# If data is not of the required type, raise error
  				raise InvalidKeyError(SCREAMING_SNAKE_CASE__ )
  
  		return hash_data.encode('''utf-8''' )
  
  
  
  
  
  
  class   snake_case__					(			__SCREAMING_SNAKE_CASE				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		def __init__(			self							: List[Any]     ,  UpperCamelCase__							: Union[str, Any]			)	-> List[str]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Dict     =	'''\nFAILURE TO GENERATE DATASET: Invalid key type detected'''
  				snake_case     :					List[Any]     =	f'\nFound Key {hash_data} of type {type(UpperCamelCase__			)}'
  				snake_case     :					Dict     =	'''\nKeys should be either str, int or bytes type'''
  				super().__init__(f'{self.prefix}{self.err_msg}{self.suffix}'			)
  
  
  
  
  
  
  
  class   snake_case__					(			__SCREAMING_SNAKE_CASE				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		def __init__(			self							: Any     ,  UpperCamelCase__							: Tuple     ,  UpperCamelCase__							: Optional[Any]     ,  UpperCamelCase__							: int=""			)	-> List[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Union[str, Any]     =	key
  				snake_case     :					Any     =	duplicate_key_indices
  				snake_case     :					Union[str, Any]     =	fix_msg
  				snake_case     :					Optional[int]     =	'''Found multiple examples generated with the same key'''
  				if len(UpperCamelCase__			) <= 20:
  						snake_case     :					str     =	f'\nThe examples at index {", ".join(UpperCamelCase__			)} have the key {key}'
  				else:
  						snake_case     :					List[Any]     =	f'\nThe examples at index {", ".join(duplicate_key_indices[:20]			)}... ({len(UpperCamelCase__			) - 20} more) have the key {key}'
  				snake_case     :					Tuple     =	'''\n''' + fix_msg if fix_msg else ''''''
  				super().__init__(f'{self.prefix}{self.err_msg}{self.suffix}'			)
  
  
  
  
  
  
  
  class   snake_case__					:
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		def __init__(			self							: List[str]     ,  UpperCamelCase__							: str			)	-> Union[str, Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Dict     =	hashlib.mda(_as_bytes(UpperCamelCase__			)			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Optional[int]     ,  UpperCamelCase__							: Union[str, int, bytes]			)	-> int:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					int     =	self._split_mda.copy()
  				snake_case     :					List[str]     =	_as_bytes(UpperCamelCase__			)
  				mda.update(UpperCamelCase__			)
  				# Convert to integer with hexadecimal conversion
  				return int(mda.hexdigest()     ,  16			)
  
  
  
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)
  
  
  