code: 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 							_lowerCamelCase(				__snake_case						,						__snake_case						,						__snake_case	)  ->   str:
  		# Initialise PyTorch model
  		__snake_case		       =     json.load(open(__snake_case						,						"r"	)	)
  		__snake_case		       =     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		       =     GPTNeoForCausalLM(__snake_case	)
  
  		# Load weights from tf checkpoint
  		load_tf_weights_in_gpt_neo(__snake_case						,						__snake_case						,						__snake_case	)
  
  		# Save pytorch-model
  		print(f"""Save PyTorch model to {pytorch_dump_path}"""	)
  		model.save_pretrained(__snake_case	)
  
  
  if __name__ == "__main__":
  		lowerCamelCase__           =				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.'
  		)
  		lowerCamelCase__           =				parser.parse_args()
  		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  
  
  
  
  
style_context: import os
  from string import ascii_letters
  
  lowerCamelCase__           =				ascii_letters + ' \t\n'
  
  
  
  def 							_lowerCamelCase(				)  ->   dict[str, None]:
  		__snake_case		       =     os.path.split(os.path.realpath(__snake_case	)	)
  		__snake_case		       =     {}
  		with open(path[0] + "/dictionary.txt"	) as dictionary_file:
  				for word in dictionary_file.read().split("\n"	):
  						__snake_case		       =     None
  		return english_words
  
  
  lowerCamelCase__           =				load_dictionary()
  
  
  
  def 							_lowerCamelCase(				__snake_case	)  ->   float:
  		__snake_case		       =     message.upper()
  		__snake_case		       =     remove_non_letters(__snake_case	)
  		__snake_case		       =     message.split()
  		__snake_case		       =     len([word for word in possible_words if word in ENGLISH_WORDS]	)
  		return float(__snake_case	) / len(__snake_case	)
  
  
  
  def 							_lowerCamelCase(				__snake_case	)  ->   str:
  		return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE	)
  
  
  
  def 							_lowerCamelCase(				__snake_case						,						__snake_case = 20						,						__snake_case = 85	)  ->   bool:
  		__snake_case		       =     get_english_count(__snake_case	) * 100 >= word_percentage
  		__snake_case		       =     len(remove_non_letters(__snake_case	)	)
  		__snake_case		       =     (float(__snake_case	) / len(__snake_case	)) * 100
  		__snake_case		       =     message_letters_percentage >= letter_percentage
  		return words_match and letters_match
  
  
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  
  
  
  
  
  