



'''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'"""			)










'''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''')





