
'''simple docstring'''





import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


a__		: str  							=							logging.get_logger(__name__)

a__		: Dict  							=							{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

a__		: List[Any]  							=							{
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
}

a__		: str  							=							{
    "roberta-base": 5_1_2,
    "roberta-large": 5_1_2,
    "roberta-large-mnli": 5_1_2,
    "distilroberta-base": 5_1_2,
    "roberta-base-openai-detector": 5_1_2,
    "roberta-large-openai-detector": 5_1_2,
}





@lru_cache()
def 							snake_case				(		)->      List[str]:







      """simple docstring"""
      __A													=      (
          list(range(ord('!'						)   ,	ord('~'						) + 1						)						) + list(range(ord('¡'						)   ,	ord('¬'						) + 1						)						) + list(range(ord('®'						)   ,	ord('ÿ'						) + 1						)						)
      )
      __A													=      bs[:]
      __A													=      0
      for b in range(2**8						):
            if b not in bs:
                  bs.append(UpperCAmelCase						)
                  cs.append(2**8 + n						)
                  n += 1
      __A													=      [chr(UpperCAmelCase						) for n in cs]
      return dict(zip(UpperCAmelCase   ,	UpperCAmelCase						)						)





def 							snake_case				(		UpperCAmelCase						)->      str:







      """simple docstring"""
      __A													=      set()
      __A													=      word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)						)
            __A													=      char
      return pairs


class 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE):
 UpperCAmelCase__     :     Dict    					=       VOCAB_FILES_NAMES
 UpperCAmelCase__     :     List[Any]    					=       PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase__     :     Any    					=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase__     :     str    					=       ['input_ids', 'attention_mask']






 def __init__(      self	:Tuple						,					_A	:Union[str, Any]						,					_A	:Optional[Any]						,					_A	:List[Any]="replace"						,					_A	:Tuple="<s>"						,					_A	:Optional[Any]="</s>"						,					_A	:int="</s>"						,					_A	:List[str]="<s>"						,					_A	:Optional[Any]="<unk>"						,					_A	:Union[str, Any]="<pad>"						,					_A	:Any="<mask>"						,					_A	:Any=False						,					**_A	:Tuple						,					)					->      Union[str, Any]:







       '''simple docstring'''



       __A													=      AddedToken(_A						,					lstrip=_A						,					rstrip=_A      ) if isinstance(_A						,					_A      ) else bos_token
       __A													=      AddedToken(_A						,					lstrip=_A						,					rstrip=_A      ) if isinstance(_A						,					_A      ) else eos_token
       __A													=      AddedToken(_A						,					lstrip=_A						,					rstrip=_A      ) if isinstance(_A						,					_A      ) else sep_token
       __A													=      AddedToken(_A						,					lstrip=_A						,					rstrip=_A      ) if isinstance(_A						,					_A      ) else cls_token
       __A													=      AddedToken(_A						,					lstrip=_A						,					rstrip=_A      ) if isinstance(_A						,					_A      ) else unk_token
       __A													=      AddedToken(_A						,					lstrip=_A						,					rstrip=_A      ) if isinstance(_A						,					_A      ) else pad_token

       # Mask token behave like a normal word, i.e. include the space before it
       __A													=      AddedToken(_A						,					lstrip=_A						,					rstrip=_A      ) if isinstance(_A						,					_A      ) else mask_token

       super().__init__(
           errors=_A						,					bos_token=_A						,					eos_token=_A						,					unk_token=_A						,					sep_token=_A						,					cls_token=_A						,					pad_token=_A						,					mask_token=_A						,					add_prefix_space=_A						,					**_A						,					)

       with open(_A						,					encoding='utf-8'      ) as vocab_handle:
             __A													=      json.load(_A      )
       __A													=      {v: k for k, v in self.encoder.items()}
       __A													=      errors  # how to handle errors in decoding
       __A													=      bytes_to_unicode()
       __A													=      {v: k for k, v in self.byte_encoder.items()}
       with open(_A						,					encoding='utf-8'      ) as merges_handle:
             __A													=      merges_handle.read().split('\n'      )[1:-1]
       __A													=      [tuple(merge.split()      ) for merge in bpe_merges]
       __A													=      dict(zip(_A						,					range(len(_A      )      )      )      )
       __A													=      {}
       __A													=      add_prefix_space

       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       __A													=      re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'      )






 @property
 def 						lowercase_    (      self	:Dict      )					->      Dict:







       '''simple docstring'''



       return len(self.encoder      )






 def 						lowercase_    (      self	:Union[str, Any]      )					->      Tuple:







       '''simple docstring'''



       return dict(self.encoder						,					**self.added_tokens_encoder      )






 def 						lowercase_    (      self	:List[Any]						,					_A	:Optional[Any]      )					->      Optional[Any]:







       '''simple docstring'''



       if token in self.cache:
             return self.cache[token]
       __A													=      tuple(_A      )
       __A													=      get_pairs(_A      )

       if not pairs:
             return token

       while True:
             __A													=      min(_A						,					key=lambda _A      : self.bpe_ranks.get(_A						,					float('inf'      )      )      )
             if bigram not in self.bpe_ranks:
                   break
             __A						,   __A													=      bigram
             __A													=      []
             __A													=      0
             while i < len(_A      ):
                   try:
                         __A													=      word.index(_A						,					_A      )
                   except ValueError:
                         new_word.extend(word[i:]      )
                         break
                   else:
                         new_word.extend(word[i:j]      )
                         __A													=      j

                   if word[i] == first and i < len(_A      ) - 1 and word[i + 1] == second:
                         new_word.append(first + second      )
                         i += 2
                   else:
                         new_word.append(word[i]      )
                         i += 1
             __A													=      tuple(_A      )
             __A													=      new_word
             if len(_A      ) == 1:
                   break
             else:
                   __A													=      get_pairs(_A      )
       __A													=      ' '.join(_A      )
       __A													=      word
       return word






 def 						lowercase_    (      self	:Tuple						,					_A	:Union[str, Any]      )					->      Optional[Any]:







       '''simple docstring'''



       __A													=      []
       for token in re.findall(self.pat						,					_A      ):
             __A													=      ''.join(
                 self.byte_encoder[b] for b in token.encode('utf-8'      )      )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
             bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_A      ).split(' '      )      )
       return bpe_tokens






 def 						lowercase_    (      self	:Optional[Any]						,					_A	:List[Any]      )					->      List[Any]:







       '''simple docstring'''



       return self.encoder.get(_A						,					self.encoder.get(self.unk_token      )      )






 def 						lowercase_    (      self	:List[str]						,					_A	:int      )					->      Union[str, Any]:







       '''simple docstring'''



       return self.decoder.get(_A      )






 def 						lowercase_    (      self	:List[Any]						,					_A	:int      )					->      str:







       '''simple docstring'''



       __A													=      ''.join(_A      )
       __A													=      bytearray([self.byte_decoder[c] for c in text]      ).decode('utf-8'						,					errors=self.errors      )
       return text






 def 						lowercase_    (      self	:List[str]						,					_A	:str						,					_A	:Optional[str] = None      )					->      Tuple[str]:







       '''simple docstring'''



       if not os.path.isdir(_A      ):
             logger.error(F'Vocabulary path ({save_directory}) should be a directory'      )
             return
       __A													=      os.path.join(
           _A						,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )
       __A													=      os.path.join(
           _A						,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']      )

       with open(_A						,					'w'						,					encoding='utf-8'      ) as f:
             f.write(json.dumps(self.encoder						,					indent=2						,					sort_keys=_A						,					ensure_ascii=_A      ) + '\n'      )

       __A													=      0
       with open(_A						,					'w'						,					encoding='utf-8'      ) as writer:
             writer.write('#version: 0.2\n'      )
             for bpe_tokens, token_index in sorted(self.bpe_ranks.items()						,					key=lambda _A      : kv[1]      ):
                   if index != token_index:
                         logger.warning(
                             F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                             ' Please check that the tokenizer is not corrupted!'      )
                         __A													=      token_index
                   writer.write(' '.join(_A      ) + '\n'      )
                   index += 1

       return vocab_file, merge_file






 def 						lowercase_    (      self	:str						,					_A	:List[int]						,					_A	:Optional[List[int]] = None      )					->      List[int]:







       '''simple docstring'''



       if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       __A													=      [self.cls_token_id]
       __A													=      [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep






 def 						lowercase_    (      self	:Tuple						,					_A	:List[int]						,					_A	:Optional[List[int]] = None						,					_A	:bool = False      )					->      List[int]:







       '''simple docstring'''



       if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=_A						,					token_ids_a=_A						,					already_has_special_tokens=_A      )

       if token_ids_a is None:
             return [1] + ([0] * len(_A      )) + [1]
       return [1] + ([0] * len(_A      )) + [1, 1] + ([0] * len(_A      )) + [1]






 def 						lowercase_    (      self	:List[Any]						,					_A	:List[int]						,					_A	:Optional[List[int]] = None      )					->      List[int]:







       '''simple docstring'''



       __A													=      [self.sep_token_id]
       __A													=      [self.cls_token_id]

       if token_ids_a is None:
             return len(cls + token_ids_a + sep      ) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]






 def 						lowercase_    (      self	:int						,					_A	:Optional[int]						,					_A	:Union[str, Any]=False						,					**_A	:str      )					->      Dict:







       '''simple docstring'''



       __A													=      kwargs.pop('add_prefix_space'						,					self.add_prefix_space      )
       if (is_split_into_words or add_prefix_space) and (len(_A      ) > 0 and not text[0].isspace()):
             __A													=      ' ' + text
       return (text, kwargs)








'''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 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Union[str, Any]:







      """simple docstring"""
      # Initialise PyTorch model
      __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__		: Tuple  							=							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__		: Union[str, Any]  							=							parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






