code: 
  
  
  
  '''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
  
  
  a__						:			List[str]										=			logging.get_logger(__name__)
  @dataclass
  class        UpperCAmelCase__  :
   __SCREAMING_SNAKE_CASE					      =				42
   __SCREAMING_SNAKE_CASE					      =				42
   __SCREAMING_SNAKE_CASE					      =				None
   __SCREAMING_SNAKE_CASE					      =				None
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Optional[Any]:
    return json.dumps(dataclasses.asdict(self			)		,	indent=2			) + "\n"
  
  
  @dataclass(frozen=UpperCAmelCase_)
  class        UpperCAmelCase__  :
   __SCREAMING_SNAKE_CASE					      =				42
   __SCREAMING_SNAKE_CASE					      =				None
   __SCREAMING_SNAKE_CASE					      =				None
   __SCREAMING_SNAKE_CASE					      =				None
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Tuple:
    return json.dumps(dataclasses.asdict(self			)			) + "\n"
  
  
  class        UpperCAmelCase__  :
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase			)  ->							str:
    raise NotImplementedError()
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase			)  ->							str:
    raise NotImplementedError()
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase			)  ->							Dict:
    raise NotImplementedError()
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase			)  ->							Optional[int]:
    raise NotImplementedError()
  
  
  
   def    __lowerCamelCase				(  self			)  ->							int:
    raise NotImplementedError()
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase			)  ->							int:
    if len(self.get_labels()			) > 1:
     __UpperCamelCase				      =  self.get_labels()[int(example.label			)]
    return example
  
  
  
  
   @classmethod
   def    __lowerCamelCase				(  cls		,	lowercase		,	lowercase=None			)  ->							int:
    with open(lowercase		,	"""r"""		,	encoding="""utf-8-sig"""			) as f:
     return list(csv.reader(lowercase		,	delimiter="""\t"""		,	quotechar=lowercase			)			)
  
  
  class        UpperCAmelCase__  (    UpperCAmelCase_):
  
  
  
   def __init__(  self		,	lowercase=None		,	lowercase=None		,	lowercase="classification"		,	lowercase=False			)  ->							Optional[Any]:
    __UpperCamelCase				      =  [] if labels is None else labels
    __UpperCamelCase				      =  [] if examples is None else examples
    __UpperCamelCase				      =  mode
    __UpperCamelCase				      =  verbose
  
  
  
   def __len__(  self			)  ->							List[str]:
    return len(self.examples			)
  
  
  
   def __getitem__(  self		,	lowercase			)  ->							str:
    if isinstance(lowercase		,	lowercase			):
     return SingleSentenceClassificationProcessor(labels=self.labels		,	examples=self.examples[idx]			)
    return self.examples[idx]
  
  
  
   @classmethod
   def    __lowerCamelCase				(  cls		,	lowercase		,	lowercase=""		,	lowercase=0		,	lowercase=1		,	lowercase=None		,	lowercase=False		,	**lowercase			)  ->							Any:
    __UpperCamelCase				      =  cls(**lowercase			)
    processor.add_examples_from_csv(
        lowercase		,	split_name=lowercase		,	column_label=lowercase		,	column_text=lowercase		,	column_id=lowercase		,	skip_first_row=lowercase		,	overwrite_labels=lowercase		,	overwrite_examples=lowercase		,	)
    return processor
  
  
  
   @classmethod
   def    __lowerCamelCase				(  cls		,	lowercase		,	lowercase=None		,	**lowercase			)  ->							Union[str, Any]:
    __UpperCamelCase				      =  cls(**lowercase			)
    processor.add_examples(lowercase		,	labels=lowercase			)
    return processor
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase=""		,	lowercase=0		,	lowercase=1		,	lowercase=None		,	lowercase=False		,	lowercase=False		,	lowercase=False		,	)  ->							int:
    __UpperCamelCase				      =  self._read_tsv(lowercase			)
    if skip_first_row:
     __UpperCamelCase				      =  lines[1:]
    __UpperCamelCase				      =  []
    __UpperCamelCase				      =  []
    __UpperCamelCase				      =  []
    for i, line in enumerate(lowercase			):
     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(lowercase			)
      ids.append(lowercase			)
  
    return self.add_examples(
        lowercase		,	lowercase		,	lowercase		,	overwrite_labels=lowercase		,	overwrite_examples=lowercase			)
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase=None		,	lowercase=None		,	lowercase=False		,	lowercase=False			)  ->							Any:
    if labels is not None and len(lowercase			) != len(lowercase			):
     raise ValueError(
         f"Text and labels have mismatched lengths {len(lowercase			)} and {len(lowercase			)}"			)
    if ids is not None and len(lowercase			) != len(lowercase			):
     raise ValueError(f"Text and ids have mismatched lengths {len(lowercase			)} and {len(lowercase			)}"			)
    if ids is None:
     __UpperCamelCase				      =  [None] * len(lowercase			)
    if labels is None:
     __UpperCamelCase				      =  [None] * len(lowercase			)
    __UpperCamelCase				      =  []
    __UpperCamelCase				      =  set()
    for text_or_text_and_label, label, guid in zip(lowercase		,	lowercase		,	lowercase			):
     if isinstance(lowercase		,	(tuple, list)			) and label is None:
      __UpperCamelCase      ,		__UpperCamelCase				      =  text_or_text_and_label
     else:
      __UpperCamelCase				      =  text_or_text_and_label
     added_labels.add(lowercase			)
     examples.append(InputExample(guid=lowercase		,	text_a=lowercase		,	text_b=lowercase		,	label=lowercase			)			)
  
    # Update examples
    if overwrite_examples:
     __UpperCamelCase				      =  examples
    else:
     self.examples.extend(lowercase			)
  
    # Update labels
    if overwrite_labels:
     __UpperCamelCase				      =  list(lowercase			)
    else:
     __UpperCamelCase				      =  list(set(self.labels			).union(lowercase			)			)
  
    return self.examples
  
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase=None		,	lowercase=False		,	lowercase=0		,	lowercase=True		,	lowercase=None		,	)  ->							Any:
    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_0_0_0_0 == 0:
      logger.info(f"Tokenizing example {ex_index}"			)
  
     __UpperCamelCase				      =  tokenizer.encode(
         example.text_a		,	add_special_tokens=lowercase		,	max_length=min(lowercase		,	tokenizer.max_len			)		,	)
     all_input_ids.append(lowercase			)
  
    __UpperCamelCase				      =  max(len(lowercase			) for input_ids in all_input_ids			)
  
    __UpperCamelCase				      =  []
    for ex_index, (input_ids, example) in enumerate(zip(lowercase		,	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.
     __UpperCamelCase				      =  [1 if mask_padding_with_zero else 0] * len(lowercase			)
  
     # Zero-pad up to the sequence length.
     __UpperCamelCase				      =  batch_length - len(lowercase			)
     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(lowercase			) != batch_length:
      raise ValueError(f"Error with input length {len(lowercase			)} vs {batch_length}"			)
     if len(lowercase			) != batch_length:
      raise ValueError(f"Error with input length {len(lowercase			)} 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(lowercase			) for x in input_ids]			)}"			)
      logger.info(f"attention_mask: {' '.join([str(lowercase			) for x in attention_mask]			)}"			)
      logger.info(f"label: {example.label} (id = {label})"			)
  
     features.append(InputFeatures(input_ids=lowercase		,	attention_mask=lowercase		,	label=lowercase			)			)
  
    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(
         lowercase		,	({"""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(lowercase		,	lowercase		,	lowercase			)
     return dataset
    else:
     raise ValueError("""return_tensors should be one of 'tf' or 'pt'"""			)
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  from .stack import Stack
  
  
  
  
  
  
  
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  Stack()
   __UpperCamelCase				      =  {"""(""": """)""", """[""": """]""", """{""": """}"""}
   for bracket in parentheses:
    if bracket in bracket_pairs:
     stack.push(__A       )
    elif bracket in (")", "]", "}"):
     if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
      return False
   return stack.is_empty()
  
  
  if __name__ == "__main__":
   from doctest import testmod
  
   testmod()
  
   a__						:			List[Any]										=			['((()))', '((())', '(()))']
   print('Balanced parentheses demonstration:\n')
   for example in examples:
    a__						:			int										=			'' if balanced_parentheses(example) else 'not '
    print(f'''{example} is {not_str}balanced''')
  
  
  
  
  
  