



'''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


lowercase__  :	Optional[Any]            =      logging.get_logger(__name__)

lowercase__  :	Dict            =      {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

lowercase__  :	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'
        ),
    },
}

lowercase__  :	int            =      {
    'roberta-base': 5_12,
    'roberta-large': 5_12,
    'roberta-large-mnli': 5_12,
    'distilroberta-base': 5_12,
    'roberta-base-openai-detector': 5_12,
    'roberta-large-openai-detector': 5_12,
}





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





def     snake_case__			(lowerCAmelCase_						):
    UpperCamelCase__	     =      set()
    UpperCamelCase__	     =      word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)						)
        UpperCamelCase__	     =      char
    return pairs





class    lowerCamelCase_				(						lowerCAmelCase_	):






   '''simple docstring'''

   A			:  List[Any]					=						VOCAB_FILES_NAMES
   A			:  Dict					=						PRETRAINED_VOCAB_FILES_MAP
   A			:  Any					=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A			:  Optional[Any]					=						["input_ids", "attention_mask"]


   def __init__(	self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase="replace"  ,    __UpperCamelCase="<s>"  ,    __UpperCamelCase="</s>"  ,    __UpperCamelCase="</s>"  ,    __UpperCamelCase="<s>"  ,    __UpperCamelCase="<unk>"  ,    __UpperCamelCase="<pad>"  ,    __UpperCamelCase="<mask>"  ,    __UpperCamelCase=False  ,    **__UpperCamelCase  ,    ):
       UpperCamelCase__	     =      AddedToken(__UpperCamelCase  ,    lstrip=__UpperCamelCase  ,    rstrip=__UpperCamelCase			) if isinstance(__UpperCamelCase  ,    __UpperCamelCase			) else bos_token
       UpperCamelCase__	     =      AddedToken(__UpperCamelCase  ,    lstrip=__UpperCamelCase  ,    rstrip=__UpperCamelCase			) if isinstance(__UpperCamelCase  ,    __UpperCamelCase			) else eos_token
       UpperCamelCase__	     =      AddedToken(__UpperCamelCase  ,    lstrip=__UpperCamelCase  ,    rstrip=__UpperCamelCase			) if isinstance(__UpperCamelCase  ,    __UpperCamelCase			) else sep_token
       UpperCamelCase__	     =      AddedToken(__UpperCamelCase  ,    lstrip=__UpperCamelCase  ,    rstrip=__UpperCamelCase			) if isinstance(__UpperCamelCase  ,    __UpperCamelCase			) else cls_token
       UpperCamelCase__	     =      AddedToken(__UpperCamelCase  ,    lstrip=__UpperCamelCase  ,    rstrip=__UpperCamelCase			) if isinstance(__UpperCamelCase  ,    __UpperCamelCase			) else unk_token
       UpperCamelCase__	     =      AddedToken(__UpperCamelCase  ,    lstrip=__UpperCamelCase  ,    rstrip=__UpperCamelCase			) if isinstance(__UpperCamelCase  ,    __UpperCamelCase			) else pad_token

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

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

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

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


   @property
   def 						_UpperCamelCase							(	self			):
       return len(self.encoder			)


   def 						_UpperCamelCase							(	self			):
       return dict(self.encoder  ,    **self.added_tokens_encoder			)


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       if token in self.cache:
           return self.cache[token]
       UpperCamelCase__	     =      tuple(__UpperCamelCase			)
       UpperCamelCase__	     =      get_pairs(__UpperCamelCase			)

       if not pairs:
           return token

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

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


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       UpperCamelCase__	     =      []
       for token in re.findall(self.pat  ,    __UpperCamelCase			):
           UpperCamelCase__	     =      "".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(__UpperCamelCase			).split(" "			)			)
       return bpe_tokens


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       return self.encoder.get(__UpperCamelCase  ,    self.encoder.get(self.unk_token			)			)


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       return self.decoder.get(__UpperCamelCase			)


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       UpperCamelCase__	     =      "".join(__UpperCamelCase			)
       UpperCamelCase__	     =      bytearray([self.byte_decoder[c] for c in text]			).decode("utf-8"  ,    errors=self.errors			)
       return text


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase = None			):
       if not os.path.isdir(__UpperCamelCase			):
           logger.error(f'Vocabulary path ({save_directory}) should be a directory'			)
           return
       UpperCamelCase__	     =      os.path.join(
           __UpperCamelCase  ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]			)
       UpperCamelCase__	     =      os.path.join(
           __UpperCamelCase  ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]			)

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

       UpperCamelCase__	     =      0
       with open(__UpperCamelCase  ,    "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 __UpperCamelCase			: 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!"			)
                   UpperCamelCase__	     =      token_index
               writer.write(" ".join(__UpperCamelCase			) + "\n"			)
               index += 1

       return vocab_file, merge_file


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase = None			):
       if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       UpperCamelCase__	     =      [self.cls_token_id]
       UpperCamelCase__	     =      [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase = None  ,    __UpperCamelCase = False			):
       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=__UpperCamelCase  ,    token_ids_a=__UpperCamelCase  ,    already_has_special_tokens=__UpperCamelCase			)

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


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase = None			):
       UpperCamelCase__	     =      [self.sep_token_id]
       UpperCamelCase__	     =      [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 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase=False  ,    **__UpperCamelCase			):
       UpperCamelCase__	     =      kwargs.pop("add_prefix_space"  ,    self.add_prefix_space			)
       if (is_split_into_words or add_prefix_space) and (len(__UpperCamelCase			) > 0 and not text[0].isspace()):
           UpperCamelCase__	     =      " " + text
       return (text, kwargs)









'''simple docstring'''







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


lowercase__  :	str            =      logging.get_logger(__name__)

@dataclass
class    lowerCamelCase_				:






   '''simple docstring'''

   A			:  str
   A			:  str
   A			:  Optional[str]					=						None
   A			:  Optional[str]					=						None


   def 						_UpperCamelCase							(	self			):
       return json.dumps(dataclasses.asdict(self			)  ,    indent=2			) + "\n"

@dataclass(frozen=lowerCAmelCase_	)
class    lowerCamelCase_				:






   '''simple docstring'''

   A			:  List[int]
   A			:  Optional[List[int]]					=						None
   A			:  Optional[List[int]]					=						None
   A			:  Optional[Union[int, float]]					=						None


   def 						_UpperCamelCase							(	self			):
       return json.dumps(dataclasses.asdict(self			)			) + "\n"

class    lowerCamelCase_				:






   '''simple docstring'''

   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       raise NotImplementedError()


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       raise NotImplementedError()


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       raise NotImplementedError()


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       raise NotImplementedError()


   def 						_UpperCamelCase							(	self			):
       raise NotImplementedError()


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       if len(self.get_labels()			) > 1:
           UpperCamelCase__	     =      self.get_labels()[int(example.label			)]
       return example







   @classmethod
   def 						_UpperCamelCase							(	cls  ,    __UpperCamelCase  ,    __UpperCamelCase=None			):
       with open(__UpperCamelCase  ,    "r"  ,    encoding="utf-8-sig"			) as f:
           return list(csv.reader(__UpperCamelCase  ,    delimiter="\t"  ,    quotechar=__UpperCamelCase			)			)

class    lowerCamelCase_				(						lowerCAmelCase_	):






   '''simple docstring'''

   def __init__(	self  ,    __UpperCamelCase=None  ,    __UpperCamelCase=None  ,    __UpperCamelCase="classification"  ,    __UpperCamelCase=False			):
       UpperCamelCase__	     =      [] if labels is None else labels
       UpperCamelCase__	     =      [] if examples is None else examples
       UpperCamelCase__	     =      mode
       UpperCamelCase__	     =      verbose


   def __len__(	self			):
       return len(self.examples			)


   def __getitem__(	self  ,    __UpperCamelCase			):
       if isinstance(__UpperCamelCase  ,    __UpperCamelCase			):
           return SingleSentenceClassificationProcessor(labels=self.labels  ,    examples=self.examples[idx]			)
       return self.examples[idx]


   @classmethod
   def 						_UpperCamelCase							(	cls  ,    __UpperCamelCase  ,    __UpperCamelCase=""  ,    __UpperCamelCase=0  ,    __UpperCamelCase=1  ,    __UpperCamelCase=None  ,    __UpperCamelCase=False  ,    **__UpperCamelCase			):
       UpperCamelCase__	     =      cls(**__UpperCamelCase			)
       processor.add_examples_from_csv(
           __UpperCamelCase  ,    split_name=__UpperCamelCase  ,    column_label=__UpperCamelCase  ,    column_text=__UpperCamelCase  ,    column_id=__UpperCamelCase  ,    skip_first_row=__UpperCamelCase  ,    overwrite_labels=__UpperCamelCase  ,    overwrite_examples=__UpperCamelCase  ,    )
       return processor


   @classmethod
   def 						_UpperCamelCase							(	cls  ,    __UpperCamelCase  ,    __UpperCamelCase=None  ,    **__UpperCamelCase			):
       UpperCamelCase__	     =      cls(**__UpperCamelCase			)
       processor.add_examples(__UpperCamelCase  ,    labels=__UpperCamelCase			)
       return processor


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase=""  ,    __UpperCamelCase=0  ,    __UpperCamelCase=1  ,    __UpperCamelCase=None  ,    __UpperCamelCase=False  ,    __UpperCamelCase=False  ,    __UpperCamelCase=False  ,    ):
       UpperCamelCase__	     =      self._read_tsv(__UpperCamelCase			)
       if skip_first_row:
           UpperCamelCase__	     =      lines[1:]
       UpperCamelCase__	     =      []
       UpperCamelCase__	     =      []
       UpperCamelCase__	     =      []
       for i, line in enumerate(__UpperCamelCase			):
           texts.append(line[column_text]			)
           labels.append(line[column_label]			)
           if column_id is not None:
               ids.append(line[column_id]			)
           else:
               UpperCamelCase__	     =      f'{split_name}-{i}' if split_name else str(__UpperCamelCase			)
               ids.append(__UpperCamelCase			)

       return self.add_examples(
           __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    overwrite_labels=__UpperCamelCase  ,    overwrite_examples=__UpperCamelCase			)


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase=None  ,    __UpperCamelCase=None  ,    __UpperCamelCase=False  ,    __UpperCamelCase=False			):
       if labels is not None and len(__UpperCamelCase			) != len(__UpperCamelCase			):
           raise ValueError(
               f'Text and labels have mismatched lengths {len(__UpperCamelCase			)} and {len(__UpperCamelCase			)}'			)
       if ids is not None and len(__UpperCamelCase			) != len(__UpperCamelCase			):
           raise ValueError(f'Text and ids have mismatched lengths {len(__UpperCamelCase			)} and {len(__UpperCamelCase			)}'			)
       if ids is None:
           UpperCamelCase__	     =      [None] * len(__UpperCamelCase			)
       if labels is None:
           UpperCamelCase__	     =      [None] * len(__UpperCamelCase			)
       UpperCamelCase__	     =      []
       UpperCamelCase__	     =      set()
       for text_or_text_and_label, label, guid in zip(__UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			):
           if isinstance(__UpperCamelCase  ,    (tuple, list)			) and label is None:
               UpperCamelCase__     ,	UpperCamelCase__	     =      text_or_text_and_label
           else:
               UpperCamelCase__	     =      text_or_text_and_label
           added_labels.add(__UpperCamelCase			)
           examples.append(InputExample(guid=__UpperCamelCase  ,    text_a=__UpperCamelCase  ,    text_b=__UpperCamelCase  ,    label=__UpperCamelCase			)			)

       # Update examples
       if overwrite_examples:
           UpperCamelCase__	     =      examples
       else:
           self.examples.extend(__UpperCamelCase			)

       # Update labels
       if overwrite_labels:
           UpperCamelCase__	     =      list(__UpperCamelCase			)
       else:
           UpperCamelCase__	     =      list(set(self.labels			).union(__UpperCamelCase			)			)

       return self.examples







   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase=None  ,    __UpperCamelCase=False  ,    __UpperCamelCase=0  ,    __UpperCamelCase=True  ,    __UpperCamelCase=None  ,    ):
       if max_length is None:
           UpperCamelCase__	     =      tokenizer.max_len

       UpperCamelCase__	     =      {label: i for i, label in enumerate(self.labels			)}

       UpperCamelCase__	     =      []
       for ex_index, example in enumerate(self.examples			):
           if ex_index % 1_00_00 == 0:
               logger.info(f'Tokenizing example {ex_index}'			)

           UpperCamelCase__	     =      tokenizer.encode(
               example.text_a  ,    add_special_tokens=__UpperCamelCase  ,    max_length=min(__UpperCamelCase  ,    tokenizer.max_len			)  ,    )
           all_input_ids.append(__UpperCamelCase			)

       UpperCamelCase__	     =      max(len(__UpperCamelCase			) for input_ids in all_input_ids			)

       UpperCamelCase__	     =      []
       for ex_index, (input_ids, example) in enumerate(zip(__UpperCamelCase  ,    self.examples			)			):
           if ex_index % 1_00_00 == 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.
           UpperCamelCase__	     =      [1 if mask_padding_with_zero else 0] * len(__UpperCamelCase			)

           # Zero-pad up to the sequence length.
           UpperCamelCase__	     =      batch_length - len(__UpperCamelCase			)
           if pad_on_left:
               UpperCamelCase__	     =      ([pad_token] * padding_length) + input_ids
               UpperCamelCase__	     =      ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
           else:
               UpperCamelCase__	     =      input_ids + ([pad_token] * padding_length)
               UpperCamelCase__	     =      attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

           if len(__UpperCamelCase			) != batch_length:
               raise ValueError(f'Error with input length {len(__UpperCamelCase			)} vs {batch_length}'			)
           if len(__UpperCamelCase			) != batch_length:
               raise ValueError(f'Error with input length {len(__UpperCamelCase			)} vs {batch_length}'			)

           if self.mode == "classification":
               UpperCamelCase__	     =      label_map[example.label]
           elif self.mode == "regression":
               UpperCamelCase__	     =      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(__UpperCamelCase			) for x in input_ids]			)}'			)
               logger.info(f'attention_mask: {" ".join([str(__UpperCamelCase			) for x in attention_mask]			)}'			)
               logger.info(f'label: {example.label} (id = {label})'			)

           features.append(InputFeatures(input_ids=__UpperCamelCase  ,    attention_mask=__UpperCamelCase  ,    label=__UpperCamelCase			)			)

       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)

           UpperCamelCase__	     =      tf.data.Dataset.from_generator(
               __UpperCamelCase  ,    ({"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

           UpperCamelCase__	     =      torch.tensor([f.input_ids for f in features]  ,    dtype=torch.long			)
           UpperCamelCase__	     =      torch.tensor([f.attention_mask for f in features]  ,    dtype=torch.long			)
           if self.mode == "classification":
               UpperCamelCase__	     =      torch.tensor([f.label for f in features]  ,    dtype=torch.long			)
           elif self.mode == "regression":
               UpperCamelCase__	     =      torch.tensor([f.label for f in features]  ,    dtype=torch.float			)

           UpperCamelCase__	     =      TensorDataset(__UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			)
           return dataset
       else:
           raise ValueError("return_tensors should be one of 'tf' or 'pt'"			)




