code: 
  
  
  
  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
  
  
  __lowerCamelCase  				=					logging.get_logger(__name__)
  
  
  
  @dataclass
  class 		_snake_case      :
  
  
  
  
  
   """simple docstring"""
  
  
  
   lowerCamelCase_          =			42
   lowerCamelCase_          =			42
   lowerCamelCase_          =			None
   lowerCamelCase_          =			None
   def lowercase_		(						self					)	->							Any:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return json.dumps(dataclasses.asdict(self					)	,    indent=2					) + "\n"
  
  
  
  
  
  
  
  @dataclass(frozen=lowerCamelCase				)
  class 		_snake_case      :
  
  
  
  
  
   """simple docstring"""
  
  
  
   lowerCamelCase_          =			42
   lowerCamelCase_          =			None
   lowerCamelCase_          =			None
   lowerCamelCase_          =			None
   def lowercase_		(						self					)	->							Any:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return json.dumps(dataclasses.asdict(self					)					) + "\n"
  
  
  
  
  
  
  
  class 		_snake_case      :
  
  
  
  
  
   """simple docstring"""
  
  
  
   def lowercase_		(						self	,    a					)	->							Optional[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     raise NotImplementedError()
   def lowercase_		(						self	,    a					)	->							int:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     raise NotImplementedError()
   def lowercase_		(						self	,    a					)	->							str:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     raise NotImplementedError()
   def lowercase_		(						self	,    a					)	->							str:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     raise NotImplementedError()
   def lowercase_		(						self					)	->							Optional[int]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     raise NotImplementedError()
   def lowercase_		(						self	,    a					)	->							Any:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if len(self.get_labels()					) > 1:
       _A           =    self.get_labels()[int(example.label					)]
     return example
  
  
  
   @classmethod
   def lowercase_		(						cls	,    a	,    a=None					)	->							Union[str, Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     with open(a	,    '''r'''	,    encoding='''utf-8-sig'''					) as f:
       return list(csv.reader(a	,    delimiter='''\t'''	,    quotechar=a					)					)
  
  
  
  
  
  
  
  class 		_snake_case      (			lowerCamelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   def __init__(						self	,    a=None	,    a=None	,    a="classification"	,    a=False					)	->							str:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    [] if labels is None else labels
     _A           =    [] if examples is None else examples
     _A           =    mode
     _A           =    verbose
   def __len__(						self					)	->							Tuple:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return len(self.examples					)
   def __getitem__(						self	,    a					)	->							Tuple:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if isinstance(a	,    a					):
       return SingleSentenceClassificationProcessor(labels=self.labels	,    examples=self.examples[idx]					)
     return self.examples[idx]
   @classmethod
   def lowercase_		(						cls	,    a	,    a=""	,    a=0	,    a=1	,    a=None	,    a=False	,    **a					)	->							List[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    cls(**a					)
     processor.add_examples_from_csv(
         a	,    split_name=a	,    column_label=a	,    column_text=a	,    column_id=a	,    skip_first_row=a	,    overwrite_labels=a	,    overwrite_examples=a	,    )
     return processor
   @classmethod
   def lowercase_		(						cls	,    a	,    a=None	,    **a					)	->							str:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    cls(**a					)
     processor.add_examples(a	,    labels=a					)
     return processor
   def lowercase_		(						self	,    a	,    a=""	,    a=0	,    a=1	,    a=None	,    a=False	,    a=False	,    a=False	,    )	->							Any:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    self._read_tsv(a					)
     if skip_first_row:
       _A           =    lines[1:]
     _A           =    []
     _A           =    []
     _A           =    []
     for i, line in enumerate(a					):
       texts.append(line[column_text]					)
       labels.append(line[column_label]					)
       if column_id is not None:
         ids.append(line[column_id]					)
       else:
         _A           =    f'''{split_name}-{i}''' if split_name else str(a					)
         ids.append(a					)
  
     return self.add_examples(
         a	,    a	,    a	,    overwrite_labels=a	,    overwrite_examples=a					)
   def lowercase_		(						self	,    a	,    a=None	,    a=None	,    a=False	,    a=False					)	->							Optional[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if labels is not None and len(a					) != len(a					):
       raise ValueError(
           f'''Text and labels have mismatched lengths {len(a					)} and {len(a					)}'''					)
     if ids is not None and len(a					) != len(a					):
       raise ValueError(f'''Text and ids have mismatched lengths {len(a					)} and {len(a					)}'''					)
     if ids is None:
       _A           =    [None] * len(a					)
     if labels is None:
       _A           =    [None] * len(a					)
     _A           =    []
     _A           =    set()
     for text_or_text_and_label, label, guid in zip(a	,    a	,    a					):
       if isinstance(a	,    (tuple, list)					) and label is None:
         _A    ,   _A           =    text_or_text_and_label
       else:
         _A           =    text_or_text_and_label
       added_labels.add(a					)
       examples.append(InputExample(guid=a	,    text_a=a	,    text_b=a	,    label=a					)					)
  
     # Update examples
     if overwrite_examples:
       _A           =    examples
     else:
       self.examples.extend(a					)
  
     # Update labels
     if overwrite_labels:
       _A           =    list(a					)
     else:
       _A           =    list(set(self.labels					).union(a					)					)
  
     return self.examples
  
  
  
   def lowercase_		(						self	,    a	,    a=None	,    a=False	,    a=0	,    a=True	,    a=None	,    )	->							Dict:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if max_length is None:
       _A           =    tokenizer.max_len
  
     _A           =    {label: i for i, label in enumerate(self.labels					)}
  
     _A           =    []
     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           =    tokenizer.encode(
           example.text_a	,    add_special_tokens=a	,    max_length=min(a	,    tokenizer.max_len					)	,    )
       all_input_ids.append(a					)
  
     _A           =    max(len(a					) for input_ids in all_input_ids					)
  
     _A           =    []
     for ex_index, (input_ids, example) in enumerate(zip(a	,    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           =    [1 if mask_padding_with_zero else 0] * len(a					)
  
       # Zero-pad up to the sequence length.
       _A           =    batch_length - len(a					)
       if pad_on_left:
         _A           =    ([pad_token] * padding_length) + input_ids
         _A           =    ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
       else:
         _A           =    input_ids + ([pad_token] * padding_length)
         _A           =    attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
  
       if len(a					) != batch_length:
         raise ValueError(f'''Error with input length {len(a					)} vs {batch_length}'''					)
       if len(a					) != batch_length:
         raise ValueError(f'''Error with input length {len(a					)} vs {batch_length}'''					)
  
       if self.mode == "classification":
         _A           =    label_map[example.label]
       elif self.mode == "regression":
         _A           =    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(a					) for x in input_ids]					)}'''					)
         logger.info(f'''attention_mask: {" ".join([str(a					) for x in attention_mask]					)}'''					)
         logger.info(f'''label: {example.label} (id = {label})'''					)
  
       features.append(InputFeatures(input_ids=a	,    attention_mask=a	,    label=a					)					)
  
     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           =    tf.data.Dataset.from_generator(
           a	,    ({'''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           =    torch.tensor([f.input_ids for f in features]	,    dtype=torch.long					)
       _A           =    torch.tensor([f.attention_mask for f in features]	,    dtype=torch.long					)
       if self.mode == "classification":
         _A           =    torch.tensor([f.label for f in features]	,    dtype=torch.long					)
       elif self.mode == "regression":
         _A           =    torch.tensor([f.label for f in features]	,    dtype=torch.float					)
  
       _A           =    TensorDataset(a	,    a	,    a					)
       return dataset
     else:
       raise ValueError('''return_tensors should be one of \'tf\' or \'pt\''''					)
style_context: 
  
  
  
  from ..utils import requires_backends
  
  
  __lowerCamelCase  				=					None
  
  
  
  class 		_snake_case      :
  
  
  
  
  
   """simple docstring"""
  
  
  
   def __init__(						self	,    *a	,    **a					)	->							Tuple:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     requires_backends(self	,    ['''detectron2''']					)
  
  
  
   @classmethod
   def lowercase_		(						cls	,    *a	,    **a					)	->							Optional[int]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     requires_backends(cls	,    ['''detectron2''']					)