
"""simple docstring"""






from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCamelCase_						   =   logging.get_logger(__name__)





class 							snake_case       (     SCREAMING_SNAKE_CASE_		):
   a_					:		Any     					=     ["""input_ids""", """attention_mask"""]







   def __init__( self       ,							__UpperCAmelCase="[PAD]"       ,							__UpperCAmelCase="[BOS]"       ,							__UpperCAmelCase="[EOS]"       ,							__UpperCAmelCase="[MASK]"       ,							__UpperCAmelCase="[CLS]"       ,							__UpperCAmelCase="[SEP]"       ,							__UpperCAmelCase=20_48       ,							**__UpperCAmelCase       ,							)   ->None:
      a_						   =     AddedToken(__UpperCAmelCase       ,							lstrip=__UpperCAmelCase       ,							rstrip=__UpperCAmelCase) if isinstance(__UpperCAmelCase       ,							__UpperCAmelCase) else pad_token
      a_						   =     AddedToken(__UpperCAmelCase       ,							lstrip=__UpperCAmelCase       ,							rstrip=__UpperCAmelCase) if isinstance(__UpperCAmelCase       ,							__UpperCAmelCase) else bos_token
      a_						   =     AddedToken(__UpperCAmelCase       ,							lstrip=__UpperCAmelCase       ,							rstrip=__UpperCAmelCase) if isinstance(__UpperCAmelCase       ,							__UpperCAmelCase) else eos_token
      a_						   =     AddedToken(__UpperCAmelCase       ,							lstrip=__UpperCAmelCase       ,							rstrip=__UpperCAmelCase) if isinstance(__UpperCAmelCase       ,							__UpperCAmelCase) else mask_token
      a_						   =     AddedToken(__UpperCAmelCase       ,							lstrip=__UpperCAmelCase       ,							rstrip=__UpperCAmelCase) if isinstance(__UpperCAmelCase       ,							__UpperCAmelCase) else cls_token
      a_						   =     AddedToken(__UpperCAmelCase       ,							lstrip=__UpperCAmelCase       ,							rstrip=__UpperCAmelCase) if isinstance(__UpperCAmelCase       ,							__UpperCAmelCase) else sep_token

      super().__init__(
          pad_token=__UpperCAmelCase       ,							bos_token=__UpperCAmelCase       ,							eos_token=__UpperCAmelCase       ,							mask_token=__UpperCAmelCase       ,							cls_token=__UpperCAmelCase       ,							sep_token=__UpperCAmelCase       ,							model_max_length=__UpperCAmelCase       ,							**__UpperCAmelCase       ,							)

      a_						   =     2**8  # utf is 8 bits

      # define special tokens dict
      a_						   =     {
          self.pad_token: 0,
          self.bos_token: 1,
          self.eos_token: 2,
          self.mask_token: 3,
          self.cls_token: 4,
          self.sep_token: 5,
      }
      a_						   =     len(self.special_tokens_encoder)
      a_						   =     {v: k for k, v in self.special_tokens_encoder.items()}







   def    UpperCAmelCase__	( self)   ->Dict[str, int]:
      a_						   =     self.special_tokens_encoder.copy()
      vocab.update(self.added_tokens_encoder)
      for i in range(self._utf_vocab_size):
         a_						   =     chr(__UpperCAmelCase)
         a_						   =     i + len(self.special_tokens_encoder)
      return vocab







   @property
   def    UpperCAmelCase__	( self)   ->Any:
      return self._utf_vocab_size + self._num_special_tokens







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase = None       ,							__UpperCAmelCase = False)   ->List[int]:
      if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=__UpperCAmelCase       ,							token_ids_a=__UpperCAmelCase       ,							already_has_special_tokens=__UpperCAmelCase)

      # normal case: some special tokens
      if token_ids_a is None:
         return [1] + [0] * len(__UpperCAmelCase) + [1]
      return [1] + ([0] * len(__UpperCAmelCase)) + [1] + ([0] * len(__UpperCAmelCase)) + [1]







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase = None)   ->List[int]:
      if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      else:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase)   ->List[str]:
      a_						   =     [chr(__UpperCAmelCase) for i in text.encode("utf-8")]
      return tokens







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase)   ->Tuple:
      if token in self.special_tokens_encoder:
         a_						   =     self.special_tokens_encoder[token]
      elif token in self.added_tokens_encoder:
         a_						   =     self.added_tokens_encoder[token]
      elif len(__UpperCAmelCase) != 1:
         a_						   =     self.unk_token_id
      else:
         a_						   =     ord(__UpperCAmelCase) + self._num_special_tokens
      return token_id







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase)   ->str:
      if index in self.special_tokens_decoder:
         a_						   =     self.special_tokens_decoder[index]
      elif index in self.added_tokens_decoder:
         a_						   =     self.added_tokens_decoder[index]
      else:
         a_						   =     chr(index - self._num_special_tokens)
      return token







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase)   ->List[Any]:
      a_						   =     B""
      for token in tokens:
         if token in self.special_tokens_decoder:
            a_						   =     self.special_tokens_decoder[token].encode("utf-8")
         elif token in self.added_tokens_decoder:
            a_						   =     self.special_tokens_decoder[token].encode("utf-8")
         elif token in self.special_tokens_encoder:
            a_						   =     token.encode("utf-8")
         elif token in self.added_tokens_encoder:
            a_						   =     token.encode("utf-8")
         else:
            a_						   =     bytes([ord(__UpperCAmelCase)])
         bstring += tok_string
      a_						   =     bstring.decode("utf-8"       ,							errors="replace")
      return string







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase = None)   ->Tuple[str]:
      return ()

"""simple docstring"""






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 ,   UpperCAmelCase ,   UpperCAmelCase     )						->Dict:

   """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__":
      UpperCamelCase_						   =   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.'
      )
      UpperCamelCase_						   =   parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)