code: 
  
  
  
  
  
  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
  
  
  UpperCAmelCase			:			Tuple	  = logging.get_logger(__name__)
  
  UpperCAmelCase			:			Dict	  = {
      """vocab_file""": """vocab.json""",
      """merges_file""": """merges.txt""",
  }
  
  UpperCAmelCase			:			Tuple	  = {
      """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"""
          ),
      },
  }
  
  UpperCAmelCase			:			Tuple	  = {
      """roberta-base""": 512,
      """roberta-large""": 512,
      """roberta-large-mnli""": 512,
      """distilroberta-base""": 512,
      """roberta-base-openai-detector""": 512,
      """roberta-large-openai-detector""": 512,
  }
  @lru_cache()
  def   _A (	):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__			:      Union[str, Any]            =(
        list(range(ord("!"		) , ord("~"		) + 1		)		) + list(range(ord("¡"		) , ord("¬"		) + 1		)		) + list(range(ord("®"		) , ord("ÿ"		) + 1		)		)
    )
    a__			:      Optional[int]            =bs[:]
    a__			:      str            =0
    for b in range(2**8		):
      if b not in bs:
        bs.append(SCREAMING_SNAKE_CASE		)
        cs.append(2**8 + n		)
        n += 1
    a__			:      Union[str, Any]            =[chr(SCREAMING_SNAKE_CASE		) for n in cs]
    return dict(zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		)		)
  def   _A (	SCREAMING_SNAKE_CASE  :     Optional[int]		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__			:      Any            =set()
    a__			:      Optional[int]            =word[0]
    for char in word[1:]:
      pairs.add((prev_char, char)		)
      a__			:      str            =char
    return pairs
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					UpperCamelCase__):
        _lowercase       :      Any        =    VOCAB_FILES_NAMES
        _lowercase       :      Tuple        =    PRETRAINED_VOCAB_FILES_MAP
        _lowercase       :      Union[str, Any]        =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
        _lowercase       :      int        =    ["""input_ids""", """attention_mask"""]
  
  
  
  
  
  
  
        def __init__(					self	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__="replace"	,   lowerCAmelCase__="<s>"	,   lowerCAmelCase__="</s>"	,   lowerCAmelCase__="</s>"	,   lowerCAmelCase__="<s>"	,   lowerCAmelCase__="<unk>"	,   lowerCAmelCase__="<pad>"	,   lowerCAmelCase__="<mask>"	,   lowerCAmelCase__=False	,   **lowerCAmelCase__	,   )     ->		Optional[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Tuple            =AddedToken(lowerCAmelCase__	,   lstrip=lowerCAmelCase__	,   rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__	,   lowerCAmelCase__							) else bos_token
          a__			:      List[Any]            =AddedToken(lowerCAmelCase__	,   lstrip=lowerCAmelCase__	,   rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__	,   lowerCAmelCase__							) else eos_token
          a__			:      Any            =AddedToken(lowerCAmelCase__	,   lstrip=lowerCAmelCase__	,   rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__	,   lowerCAmelCase__							) else sep_token
          a__			:      int            =AddedToken(lowerCAmelCase__	,   lstrip=lowerCAmelCase__	,   rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__	,   lowerCAmelCase__							) else cls_token
          a__			:      Any            =AddedToken(lowerCAmelCase__	,   lstrip=lowerCAmelCase__	,   rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__	,   lowerCAmelCase__							) else unk_token
          a__			:      Tuple            =AddedToken(lowerCAmelCase__	,   lstrip=lowerCAmelCase__	,   rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__	,   lowerCAmelCase__							) else pad_token
  
          # Mask token behave like a normal word, i.e. include the space before it
          a__			:      Optional[int]            =AddedToken(lowerCAmelCase__	,   lstrip=lowerCAmelCase__	,   rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__	,   lowerCAmelCase__							) else mask_token
  
          super().__init__(
              errors=lowerCAmelCase__	,   bos_token=lowerCAmelCase__	,   eos_token=lowerCAmelCase__	,   unk_token=lowerCAmelCase__	,   sep_token=lowerCAmelCase__	,   cls_token=lowerCAmelCase__	,   pad_token=lowerCAmelCase__	,   mask_token=lowerCAmelCase__	,   add_prefix_space=lowerCAmelCase__	,   **lowerCAmelCase__	,   )
  
          with open(lowerCAmelCase__	,   encoding="utf-8"							) as vocab_handle:
            a__			:      Optional[Any]            =json.load(lowerCAmelCase__							)
          a__			:      int            ={v: k for k, v in self.encoder.items()}
          a__			:      Dict            =errors  # how to handle errors in decoding
          a__			:      int            =bytes_to_unicode()
          a__			:      List[Any]            ={v: k for k, v in self.byte_encoder.items()}
          with open(lowerCAmelCase__	,   encoding="utf-8"							) as merges_handle:
            a__			:      List[Any]            =merges_handle.read().split("\n"							)[1:-1]
          a__			:      int            =[tuple(merge.split()							) for merge in bpe_merges]
          a__			:      Tuple            =dict(zip(lowerCAmelCase__	,   range(len(lowerCAmelCase__							)							)							)							)
          a__			:      Tuple            ={}
          a__			:      Dict            =add_prefix_space
  
          # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
          a__			:      Union[str, Any]            =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							)     ->		Optional[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          return len(self.encoder							)
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Tuple:
  
  
  
          '''simple docstring'''
  
  
  
  
          return dict(self.encoder	,   **self.added_tokens_encoder							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		List[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          if token in self.cache:
            return self.cache[token]
          a__			:      Tuple            =tuple(lowerCAmelCase__							)
          a__			:      List[Any]            =get_pairs(lowerCAmelCase__							)
  
          if not pairs:
            return token
  
          while True:
            a__			:      Dict            =min(lowerCAmelCase__	,   key=lambda lowerCAmelCase__							: self.bpe_ranks.get(lowerCAmelCase__	,   float("inf"							)							)							)
            if bigram not in self.bpe_ranks:
              break
            a__      ,							a__			:      Union[str, Any]            =bigram
            a__			:      Union[str, Any]            =[]
            a__			:      Optional[int]            =0
            while i < len(lowerCAmelCase__							):
              try:
                a__			:      List[str]            =word.index(lowerCAmelCase__	,   lowerCAmelCase__							)
              except ValueError:
                new_word.extend(word[i:]							)
                break
              else:
                new_word.extend(word[i:j]							)
                a__			:      Any            =j
  
              if word[i] == first and i < len(lowerCAmelCase__							) - 1 and word[i + 1] == second:
                new_word.append(first + second							)
                i += 2
              else:
                new_word.append(word[i]							)
                i += 1
            a__			:      List[str]            =tuple(lowerCAmelCase__							)
            a__			:      int            =new_word
            if len(lowerCAmelCase__							) == 1:
              break
            else:
              a__			:      int            =get_pairs(lowerCAmelCase__							)
          a__			:      Optional[Any]            =" ".join(lowerCAmelCase__							)
          a__			:      int            =word
          return word
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Optional[int]            =[]
          for token in re.findall(self.pat	,   lowerCAmelCase__							):
            a__			:      Optional[Any]            ="".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(lowerCAmelCase__							).split(" "							)							)
          return bpe_tokens
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Union[str, Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          return self.encoder.get(lowerCAmelCase__	,   self.encoder.get(self.unk_token							)							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          return self.decoder.get(lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      str            ="".join(lowerCAmelCase__							)
          a__			:      str            =bytearray([self.byte_decoder[c] for c in text]							).decode("utf-8"	,   errors=self.errors							)
          return text
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__ = None							)     ->		Tuple[str]:
  
  
  
          '''simple docstring'''
  
  
  
  
          if not os.path.isdir(lowerCAmelCase__							):
            logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''							)
            return
          a__			:      Optional[Any]            =os.path.join(
              lowerCAmelCase__	,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)
          a__			:      Any            =os.path.join(
              lowerCAmelCase__	,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]							)
  
          with open(lowerCAmelCase__	,   "w"	,   encoding="utf-8"							) as f:
            f.write(json.dumps(self.encoder	,   indent=2	,   sort_keys=lowerCAmelCase__	,   ensure_ascii=lowerCAmelCase__							) + "\n"							)
  
          a__			:      Optional[int]            =0
          with open(lowerCAmelCase__	,   "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 lowerCAmelCase__							: 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__			:      Union[str, Any]            =token_index
              writer.write(" ".join(lowerCAmelCase__							) + "\n"							)
              index += 1
  
          return vocab_file, merge_file
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__ = None							)     ->		List[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          if token_ids_a is None:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          a__			:      Union[str, Any]            =[self.cls_token_id]
          a__			:      List[str]            =[self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = False							)     ->		List[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=lowerCAmelCase__	,   token_ids_a=lowerCAmelCase__	,   already_has_special_tokens=lowerCAmelCase__							)
  
          if token_ids_a is None:
            return [1] + ([0] * len(lowerCAmelCase__							)) + [1]
          return [1] + ([0] * len(lowerCAmelCase__							)) + [1, 1] + ([0] * len(lowerCAmelCase__							)) + [1]
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__ = None							)     ->		List[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[Any]            =[self.sep_token_id]
          a__			:      List[Any]            =[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	,   lowerCAmelCase__	,   lowerCAmelCase__=False	,   **lowerCAmelCase__							)     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[str]            =kwargs.pop("add_prefix_space"	,   self.add_prefix_space							)
          if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase__							) > 0 and not text[0].isspace()):
            a__			:      List[Any]            =" " + text
          return (text, kwargs)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  import csv
  import dataclasses
  import json
  from dataclasses import dataclass
  from typing import List, Optional, Union
  
  from ...utils import is_tf_available, is_torch_available, logging
  
  
  UpperCAmelCase			:			int	  = logging.get_logger(__name__)
  
  
  
  
  
  
  
  @dataclass
  class        __lowerCAmelCase :
        _lowercase       :      str
        _lowercase       :      str
        _lowercase       :      Optional[str]        =    None
        _lowercase       :      Optional[str]        =    None
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		List[str]:
  
  
  
          '''simple docstring'''
  
  
  
  
          return json.dumps(dataclasses.asdict(self							)	,   indent=2							) + "\n"
  
  
  
  
  
  
  
  @dataclass(frozen=UpperCamelCase__)
  class        __lowerCAmelCase :
        _lowercase       :      List[int]
        _lowercase       :      Optional[List[int]]        =    None
        _lowercase       :      Optional[List[int]]        =    None
        _lowercase       :      Optional[Union[int, float]]        =    None
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Optional[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          return json.dumps(dataclasses.asdict(self							)							) + "\n"
  
  
  
  
  
  
  
  class        __lowerCAmelCase :
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Union[str, Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          raise NotImplementedError()
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		str:
  
  
  
          '''simple docstring'''
  
  
  
  
          raise NotImplementedError()
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		List[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          raise NotImplementedError()
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Optional[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          raise NotImplementedError()
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Any:
  
  
  
          '''simple docstring'''
  
  
  
  
          raise NotImplementedError()
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Dict:
  
  
  
          '''simple docstring'''
  
  
  
  
          if len(self.get_labels()							) > 1:
            a__			:      Dict            =self.get_labels()[int(example.label							)]
          return example
  
  
  
  
  
  
  
        @classmethod
        def      _lowercase						(					cls	,   lowerCAmelCase__	,   lowerCAmelCase__=None							)     ->		Union[str, Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          with open(lowerCAmelCase__	,   "r"	,   encoding="utf-8-sig"							) as f:
            return list(csv.reader(lowerCAmelCase__	,   delimiter="\t"	,   quotechar=lowerCAmelCase__							)							)
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					UpperCamelCase__):
  
  
  
  
  
  
  
        def __init__(					self	,   lowerCAmelCase__=None	,   lowerCAmelCase__=None	,   lowerCAmelCase__="classification"	,   lowerCAmelCase__=False							)     ->		Any:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Any            =[] if labels is None else labels
          a__			:      List[Any]            =[] if examples is None else examples
          a__			:      List[str]            =mode
          a__			:      Tuple            =verbose
  
  
  
  
  
  
  
        def __len__(					self							)     ->		Optional[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          return len(self.examples							)
  
  
  
  
  
  
  
        def __getitem__(					self	,   lowerCAmelCase__							)     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          if isinstance(lowerCAmelCase__	,   lowerCAmelCase__							):
            return SingleSentenceClassificationProcessor(labels=self.labels	,   examples=self.examples[idx]							)
          return self.examples[idx]
  
  
  
  
  
  
  
        @classmethod
        def      _lowercase						(					cls	,   lowerCAmelCase__	,   lowerCAmelCase__=""	,   lowerCAmelCase__=0	,   lowerCAmelCase__=1	,   lowerCAmelCase__=None	,   lowerCAmelCase__=False	,   **lowerCAmelCase__							)     ->		List[str]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Tuple            =cls(**lowerCAmelCase__							)
          processor.add_examples_from_csv(
              lowerCAmelCase__	,   split_name=lowerCAmelCase__	,   column_label=lowerCAmelCase__	,   column_text=lowerCAmelCase__	,   column_id=lowerCAmelCase__	,   skip_first_row=lowerCAmelCase__	,   overwrite_labels=lowerCAmelCase__	,   overwrite_examples=lowerCAmelCase__	,   )
          return processor
  
  
  
  
  
  
  
        @classmethod
        def      _lowercase						(					cls	,   lowerCAmelCase__	,   lowerCAmelCase__=None	,   **lowerCAmelCase__							)     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[str]            =cls(**lowerCAmelCase__							)
          processor.add_examples(lowerCAmelCase__	,   labels=lowerCAmelCase__							)
          return processor
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__=""	,   lowerCAmelCase__=0	,   lowerCAmelCase__=1	,   lowerCAmelCase__=None	,   lowerCAmelCase__=False	,   lowerCAmelCase__=False	,   lowerCAmelCase__=False	,   )     ->		Dict:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Dict            =self._read_tsv(lowerCAmelCase__							)
          if skip_first_row:
            a__			:      Tuple            =lines[1:]
          a__			:      List[Any]            =[]
          a__			:      int            =[]
          a__			:      str            =[]
          for i, line in enumerate(lowerCAmelCase__							):
            texts.append(line[column_text]							)
            labels.append(line[column_label]							)
            if column_id is not None:
              ids.append(line[column_id]							)
            else:
              a__			:      List[str]            =F'''{split_name}-{i}''' if split_name else str(lowerCAmelCase__							)
              ids.append(lowerCAmelCase__							)
  
          return self.add_examples(
              lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   overwrite_labels=lowerCAmelCase__	,   overwrite_examples=lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__=None	,   lowerCAmelCase__=None	,   lowerCAmelCase__=False	,   lowerCAmelCase__=False							)     ->		List[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          if labels is not None and len(lowerCAmelCase__							) != len(lowerCAmelCase__							):
            raise ValueError(
                F'''Text and labels have mismatched lengths {len(lowerCAmelCase__							)} and {len(lowerCAmelCase__							)}'''							)
          if ids is not None and len(lowerCAmelCase__							) != len(lowerCAmelCase__							):
            raise ValueError(F'''Text and ids have mismatched lengths {len(lowerCAmelCase__							)} and {len(lowerCAmelCase__							)}'''							)
          if ids is None:
            a__			:      Dict            =[None] * len(lowerCAmelCase__							)
          if labels is None:
            a__			:      str            =[None] * len(lowerCAmelCase__							)
          a__			:      Union[str, Any]            =[]
          a__			:      Optional[Any]            =set()
          for text_or_text_and_label, label, guid in zip(lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__							):
            if isinstance(lowerCAmelCase__	,   (tuple, list)							) and label is None:
              a__      ,							a__			:      int            =text_or_text_and_label
            else:
              a__			:      List[str]            =text_or_text_and_label
            added_labels.add(lowerCAmelCase__							)
            examples.append(InputExample(guid=lowerCAmelCase__	,   text_a=lowerCAmelCase__	,   text_b=lowerCAmelCase__	,   label=lowerCAmelCase__							)							)
  
          # Update examples
          if overwrite_examples:
            a__			:      Dict            =examples
          else:
            self.examples.extend(lowerCAmelCase__							)
  
          # Update labels
          if overwrite_labels:
            a__			:      Any            =list(lowerCAmelCase__							)
          else:
            a__			:      List[Any]            =list(set(self.labels							).union(lowerCAmelCase__							)							)
  
          return self.examples
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__=None	,   lowerCAmelCase__=False	,   lowerCAmelCase__=0	,   lowerCAmelCase__=True	,   lowerCAmelCase__=None	,   )     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          if max_length is None:
            a__			:      int            =tokenizer.max_len
  
          a__			:      int            ={label: i for i, label in enumerate(self.labels							)}
  
          a__			:      Union[str, Any]            =[]
          for ex_index, example in enumerate(self.examples							):
            if ex_index % 1_0_0_0_0 == 0:
              logger.info(F'''Tokenizing example {ex_index}'''							)
  
            a__			:      Dict            =tokenizer.encode(
                example.text_a	,   add_special_tokens=lowerCAmelCase__	,   max_length=min(lowerCAmelCase__	,   tokenizer.max_len							)	,   )
            all_input_ids.append(lowerCAmelCase__							)
  
          a__			:      List[Any]            =max(len(lowerCAmelCase__							) for input_ids in all_input_ids							)
  
          a__			:      Dict            =[]
          for ex_index, (input_ids, example) in enumerate(zip(lowerCAmelCase__	,   self.examples							)							):
            if ex_index % 1_0_0_0_0 == 0:
              logger.info(F'''Writing example {ex_index}/{len(self.examples							)}'''							)
            # The mask has 1 for real tokens and 0 for padding tokens. Only real
            # tokens are attended to.
            a__			:      Optional[Any]            =[1 if mask_padding_with_zero else 0] * len(lowerCAmelCase__							)
  
            # Zero-pad up to the sequence length.
            a__			:      List[str]            =batch_length - len(lowerCAmelCase__							)
            if pad_on_left:
              a__			:      Tuple            =([pad_token] * padding_length) + input_ids
              a__			:      Dict            =([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
            else:
              a__			:      List[str]            =input_ids + ([pad_token] * padding_length)
              a__			:      Any            =attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
  
            if len(lowerCAmelCase__							) != batch_length:
              raise ValueError(F'''Error with input length {len(lowerCAmelCase__							)} vs {batch_length}'''							)
            if len(lowerCAmelCase__							) != batch_length:
              raise ValueError(F'''Error with input length {len(lowerCAmelCase__							)} vs {batch_length}'''							)
  
            if self.mode == "classification":
              a__			:      Dict            =label_map[example.label]
            elif self.mode == "regression":
              a__			:      Any            =float(example.label							)
            else:
              raise ValueError(self.mode							)
  
            if ex_index < 5 and self.verbose:
              logger.info("*** Example ***"							)
              logger.info(F'''guid: {example.guid}'''							)
              logger.info(F'''input_ids: {" ".join([str(lowerCAmelCase__							) for x in input_ids]							)}'''							)
              logger.info(F'''attention_mask: {" ".join([str(lowerCAmelCase__							) for x in attention_mask]							)}'''							)
              logger.info(F'''label: {example.label} (id = {label})'''							)
  
            features.append(InputFeatures(input_ids=lowerCAmelCase__	,   attention_mask=lowerCAmelCase__	,   label=lowerCAmelCase__							)							)
  
          if return_tensors is None:
            return features
          elif return_tensors == "tf":
            if not is_tf_available():
              raise RuntimeError("return_tensors set to 'tf' but TensorFlow 2.0 can't be imported"							)
            import tensorflow as tf
  
            def gen():
              for ex in features:
                yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)
  
            a__			:      Any            =tf.data.Dataset.from_generator(
                lowerCAmelCase__	,   ({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)	,   ({"input_ids": tf.TensorShape([None]							), "attention_mask": tf.TensorShape([None]							)}, tf.TensorShape([]							))	,   )
            return dataset
          elif return_tensors == "pt":
            if not is_torch_available():
              raise RuntimeError("return_tensors set to 'pt' but PyTorch can't be imported"							)
            import torch
            from torch.utils.data import TensorDataset
  
            a__			:      str            =torch.tensor([f.input_ids for f in features]	,   dtype=torch.long							)
            a__			:      str            =torch.tensor([f.attention_mask for f in features]	,   dtype=torch.long							)
            if self.mode == "classification":
              a__			:      Dict            =torch.tensor([f.label for f in features]	,   dtype=torch.long							)
            elif self.mode == "regression":
              a__			:      Tuple            =torch.tensor([f.label for f in features]	,   dtype=torch.float							)
  
            a__			:      List[str]            =TensorDataset(lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__							)
            return dataset
          else:
            raise ValueError("return_tensors should be one of 'tf' or 'pt'"							)
  
  
  
  
  
  
  