



"""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


lowerCamelCase				:     Tuple								=  logging.get_logger(__name__)

lowerCamelCase				:     List[str]								=  {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

lowerCamelCase				:     Optional[int]								=  {
    """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"""
        ),
    },
}

lowerCamelCase				:     List[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 							A__ (     ):


  '''simple docstring'''


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


def 							A__ (     UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			set()
  _SCREAMING_SNAKE_CASE        =			word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)    )
    _SCREAMING_SNAKE_CASE        =			char
  return pairs







class    __snake_case(					__A       ):
    _A       = VOCAB_FILES_NAMES
    _A       = PRETRAINED_VOCAB_FILES_MAP
    _A       = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _A       = ['''input_ids''', '''attention_mask''']




    def __init__(   self    ,							A_    ,							A_    ,							A_="replace"    ,							A_="<s>"    ,							A_="</s>"    ,							A_="</s>"    ,							A_="<s>"    ,							A_="<unk>"    ,							A_="<pad>"    ,							A_="<mask>"    ,							A_=False    ,							**A_    ,							):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			AddedToken(A_    ,							lstrip=A_    ,							rstrip=A_			) if isinstance(A_    ,							A_			) else bos_token
      _SCREAMING_SNAKE_CASE        =			AddedToken(A_    ,							lstrip=A_    ,							rstrip=A_			) if isinstance(A_    ,							A_			) else eos_token
      _SCREAMING_SNAKE_CASE        =			AddedToken(A_    ,							lstrip=A_    ,							rstrip=A_			) if isinstance(A_    ,							A_			) else sep_token
      _SCREAMING_SNAKE_CASE        =			AddedToken(A_    ,							lstrip=A_    ,							rstrip=A_			) if isinstance(A_    ,							A_			) else cls_token
      _SCREAMING_SNAKE_CASE        =			AddedToken(A_    ,							lstrip=A_    ,							rstrip=A_			) if isinstance(A_    ,							A_			) else unk_token
      _SCREAMING_SNAKE_CASE        =			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
      _SCREAMING_SNAKE_CASE        =			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:
        _SCREAMING_SNAKE_CASE        =			json.load(A_			)
      _SCREAMING_SNAKE_CASE        =			{v: k for k, v in self.encoder.items()}
      _SCREAMING_SNAKE_CASE        =			errors  # how to handle errors in decoding
      _SCREAMING_SNAKE_CASE        =			bytes_to_unicode()
      _SCREAMING_SNAKE_CASE        =			{v: k for k, v in self.byte_encoder.items()}
      with open(A_    ,							encoding='''utf-8'''			) as merges_handle:
        _SCREAMING_SNAKE_CASE        =			merges_handle.read().split('''\n'''			)[1:-1]
      _SCREAMING_SNAKE_CASE        =			[tuple(merge.split()			) for merge in bpe_merges]
      _SCREAMING_SNAKE_CASE        =			dict(zip(A_    ,							range(len(A_			)			)			)			)
      _SCREAMING_SNAKE_CASE        =			{}
      _SCREAMING_SNAKE_CASE        =			add_prefix_space

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




    @property
    def   A   (   self			):





      '''simple docstring'''



      return len(self.encoder			)




    def   A   (   self			):





      '''simple docstring'''



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




    def   A   (   self    ,							A_			):





      '''simple docstring'''



      if token in self.cache:
        return self.cache[token]
      _SCREAMING_SNAKE_CASE        =			tuple(A_			)
      _SCREAMING_SNAKE_CASE        =			get_pairs(A_			)

      if not pairs:
        return token

      while True:
        _SCREAMING_SNAKE_CASE        =			min(A_    ,							key=lambda A_			: self.bpe_ranks.get(A_    ,							float('''inf'''			)			)			)
        if bigram not in self.bpe_ranks:
          break
        _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			bigram
        _SCREAMING_SNAKE_CASE        =			[]
        _SCREAMING_SNAKE_CASE        =			0
        while i < len(A_			):
          try:
            _SCREAMING_SNAKE_CASE        =			word.index(A_    ,							A_			)
          except ValueError:
            new_word.extend(word[i:]			)
            break
          else:
            new_word.extend(word[i:j]			)
            _SCREAMING_SNAKE_CASE        =			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
        _SCREAMING_SNAKE_CASE        =			tuple(A_			)
        _SCREAMING_SNAKE_CASE        =			new_word
        if len(A_			) == 1:
          break
        else:
          _SCREAMING_SNAKE_CASE        =			get_pairs(A_			)
      _SCREAMING_SNAKE_CASE        =			''' '''.join(A_			)
      _SCREAMING_SNAKE_CASE        =			word
      return word




    def   A   (   self    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			[]
      for token in re.findall(self.pat    ,							A_			):
        _SCREAMING_SNAKE_CASE        =			''''''.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   A   (   self    ,							A_			):





      '''simple docstring'''



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




    def   A   (   self    ,							A_			):





      '''simple docstring'''



      return self.decoder.get(A_			)




    def   A   (   self    ,							A_			):





      '''simple docstring'''



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




    def   A   (   self    ,							A_    ,							A_ = None			):





      '''simple docstring'''



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

      _SCREAMING_SNAKE_CASE        =			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!'''			)
            _SCREAMING_SNAKE_CASE        =			token_index
          writer.write(''' '''.join(A_			) + '''\n'''			)
          index += 1

      return vocab_file, merge_file




    def   A   (   self    ,							A_    ,							A_ = None			):





      '''simple docstring'''



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




    def   A   (   self    ,							A_    ,							A_ = None    ,							A_ = False			):





      '''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   A   (   self    ,							A_    ,							A_ = None			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			[self.sep_token_id]
      _SCREAMING_SNAKE_CASE        =			[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   A   (   self    ,							A_    ,							A_=False    ,							**A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			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()):
        _SCREAMING_SNAKE_CASE        =			''' ''' + text
      return (text, kwargs)





"""simple docstring"""






from transformers import ProcessorMixin




class    __snake_case(					__A       ):
    _A       = '''AutoFeatureExtractor'''
    _A       = '''AutoTokenizer'''
