code: 
  
  
  
  '''simple docstring'''
  
  
  
  import numpy as np
  
  a__						:			int										=			[
      ['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        UpperCAmelCase__  :
  
  
  
   def __init__(  self			)  ->							None:
    __UpperCamelCase				      =  np.array(lowercase			)
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase			)  ->							np.ndarray:
    __UpperCamelCase      ,		__UpperCamelCase				      =  np.where(letter == self.SQUARE			)
    __UpperCamelCase				      =  np.concatenate([indexa + 1, indexa + 1]			)
    return indexes
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase			)  ->							str:
    return self.SQUARE[indexa - 1, indexa - 1]
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase			)  ->							str:
    __UpperCamelCase				      =  message.lower()
    __UpperCamelCase				      =  message.replace("""j"""		,	"""i"""			)
  
    __UpperCamelCase				      =  """"""
    for letter_index in range(len(lowercase			)			):
     if message[letter_index] != " ":
      __UpperCamelCase				      =  self.letter_to_numbers(message[letter_index]			)
      __UpperCamelCase				      =  encoded_message + str(numbers[0]			) + str(numbers[1]			)
     elif message[letter_index] == " ":
      __UpperCamelCase				      =  encoded_message + """ """
  
    return encoded_message
  
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase			)  ->							str:
    __UpperCamelCase				      =  message.replace(""" """		,	"""  """			)
    __UpperCamelCase				      =  """"""
    for numbers_index in range(int(len(lowercase			) / 2			)			):
     if message[numbers_index * 2] != " ":
      __UpperCamelCase				      =  message[numbers_index * 2]
      __UpperCamelCase				      =  message[numbers_index * 2 + 1]
  
      __UpperCamelCase				      =  self.numbers_to_letter(int(lowercase			)		,	int(lowercase			)			)
      __UpperCamelCase				      =  decoded_message + letter
     elif message[numbers_index * 2] == " ":
      __UpperCamelCase				      =  decoded_message + """ """
  
    return decoded_message
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  import argparse
  
  from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  TaConfig.from_json_file(__A       )
   print(f"Building PyTorch model from configuration: {config}"       )
   __UpperCamelCase				      =  TaForConditionalGeneration(__A       )
  
   # Load weights from tf checkpoint
   load_tf_weights_in_ta(__A							,__A							,__A       )
  
   # Save pytorch-model
   print(f"Save PyTorch model to {pytorch_dump_path}"       )
   model.save_pretrained(__A       )
  
  
  if __name__ == "__main__":
   a__						:			int										=			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 T5 model. \nThis specifies the model architecture.'
       ),
   )
   parser.add_argument(
       '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   a__						:			Optional[Any]										=			parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  
  
  
  
  