



import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Tuple			,       UpperCAmelCase__			:Optional[int]			,       UpperCAmelCase__			:str       ):






 '''simple docstring'''

 a							=		RoFormerConfig.from_json_file(UpperCAmelCase__       )
 print(F"""Building PyTorch model from configuration: {config}"""       )
 a							=		RoFormerForMaskedLM(UpperCAmelCase__       )

 # Load weights from tf checkpoint
 load_tf_weights_in_roformer(UpperCAmelCase__			,       UpperCAmelCase__			,       UpperCAmelCase__       )

 # Save pytorch-model
 print(F"""Save PyTorch model to {pytorch_dump_path}"""       )
 torch.save(model.state_dict()			,       UpperCAmelCase__			,       _use_new_zipfile_serialization=UpperCAmelCase__       )


if __name__ == "__main__":
     A_		:  Any										=  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(
         '''--bert_config_file''',
         default=None,
         type=str,
         required=True,
         help=(
             '''The config json file corresponding to the pre-trained BERT 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_		:  Optional[Any]										=  parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)


'''simple docstring'''

import numpy as np

_lowerCamelCase :				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    lowerCamelCase__  :



   def __init__(    self     )				->     None:
        """simple docstring"""





        _UpperCamelCase  :Any       =np.array(lowerCAmelCase__     )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__     )				->     np.ndarray:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase  :str       =np.where(letter == self.SQUARE     )
        _UpperCamelCase  :Union[str, Any]       =np.concatenate([indexa + 1, indexa + 1]     )
        return indexes



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     str:
        """simple docstring"""





        return self.SQUARE[indexa - 1, indexa - 1]



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__     )				->     str:
        """simple docstring"""





        _UpperCamelCase  :str       =message.lower()
        _UpperCamelCase  :List[Any]       =message.replace("""j"""					,      """i"""     )

        _UpperCamelCase  :List[str]       =""""""
        for letter_index in range(len(lowerCAmelCase__     )     ):
             if message[letter_index] != " ":
                  _UpperCamelCase  :Dict       =self.letter_to_numbers(message[letter_index]     )
                  _UpperCamelCase  :List[Any]       =encoded_message + str(numbers[0]     ) + str(numbers[1]     )
             elif message[letter_index] == " ":
                  _UpperCamelCase  :List[Any]       =encoded_message + """ """

        return encoded_message




   def  _UpperCamelCase		(    self					,      lowerCAmelCase__     )				->     str:
        """simple docstring"""





        _UpperCamelCase  :List[str]       =message.replace(""" """					,      """  """     )
        _UpperCamelCase  :Dict       =""""""
        for numbers_index in range(int(len(lowerCAmelCase__     ) / 2     )     ):
             if message[numbers_index * 2] != " ":
                  _UpperCamelCase  :Tuple       =message[numbers_index * 2]
                  _UpperCamelCase  :List[str]       =message[numbers_index * 2 + 1]

                  _UpperCamelCase  :Dict       =self.numbers_to_letter(int(lowerCAmelCase__     )					,      int(lowerCAmelCase__     )     )
                  _UpperCamelCase  :Union[str, Any]       =decoded_message + letter
             elif message[numbers_index * 2] == " ":
                  _UpperCamelCase  :Dict       =decoded_message + """ """

        return decoded_message