code: 
  
  """simple docstring"""
  import numpy as np
  
  _A						=     [
      ["""a""", """b""", """c""", """d""", """e"""],
      ["""f""", """g""", """h""", """i""", """k"""],
      ["""l""", """m""", """n""", """o""", """p"""],
      ["""q""", """r""", """s""", """t""", """u"""],
      ["""v""", """w""", """x""", """y""", """z"""],
  ]
  
  class 			lowerCamelCase  :
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Dict						=						np.array(_lowerCamelCase  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Any						=						np.where(letter == self.SQUARE  )
          UpperCAmelCase__ :       str						=						np.concatenate([indexa + 1, indexa + 1]  )
          return indexes
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return self.SQUARE[indexa - 1, indexa - 1]
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       int						=						message.lower()
          UpperCAmelCase__ :       Tuple						=						message.replace("""j"""       ,					"""i"""  )
  
          UpperCAmelCase__ :       Optional[Any]						=						""""""
          for letter_index in range(len(_lowerCamelCase  )  ):
                if message[letter_index] != " ":
                      UpperCAmelCase__ :       Union[str, Any]						=						self.letter_to_numbers(message[letter_index]  )
                      UpperCAmelCase__ :       int						=						encoded_message + str(numbers[0]  ) + str(numbers[1]  )
                elif message[letter_index] == " ":
                      UpperCAmelCase__ :       Optional[int]						=						encoded_message + """ """
  
          return encoded_message
  
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[Any]						=						message.replace(""" """       ,					"""  """  )
          UpperCAmelCase__ :       Optional[Any]						=						""""""
          for numbers_index in range(int(len(_lowerCamelCase  ) / 2  )  ):
                if message[numbers_index * 2] != " ":
                      UpperCAmelCase__ :       Optional[int]						=						message[numbers_index * 2]
                      UpperCAmelCase__ :       List[str]						=						message[numbers_index * 2 + 1]
  
                      UpperCAmelCase__ :       Dict						=						self.numbers_to_letter(int(_lowerCamelCase  )       ,					int(_lowerCamelCase  )  )
                      UpperCAmelCase__ :       Tuple						=						decoded_message + letter
                elif message[numbers_index * 2] == " ":
                      UpperCAmelCase__ :       List[str]						=						decoded_message + """ """
  
          return decoded_message
  
  
  
  
  
  
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 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   Dict:
        # Initialise PyTorch model
        UpperCAmelCase__ :       Union[str, Any]						=						json.load(open(lowerCAmelCase ,  """r"""						)						)
        UpperCAmelCase__ :       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}"""						)
        UpperCAmelCase__ :       Dict						=						GPTNeoForCausalLM(lowerCAmelCase						)
  
        # Load weights from tf checkpoint
        load_tf_weights_in_gpt_neo(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)
  
        # Save pytorch-model
        print(F"""Save PyTorch model to {pytorch_dump_path}"""						)
        model.save_pretrained(lowerCAmelCase						)
  
  
  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(
          """--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."""
      )
      _A						=     parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  