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  :					Tuple												=	logging.get_logger(__name__)
  
  
  
  
  
  @dataclass
  class     __UpperCamelCase				:
  		SCREAMING_SNAKE_CASE              =					42
  		SCREAMING_SNAKE_CASE              =					42
  		SCREAMING_SNAKE_CASE              =					None
  		SCREAMING_SNAKE_CASE              =					None
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	str):
  							return json.dumps(dataclasses.asdict(self)  ,  indent=2) + "\n"
  
  
  
  
  
  @dataclass(frozen=_A		)
  class     __UpperCamelCase				:
  		SCREAMING_SNAKE_CASE              =					42
  		SCREAMING_SNAKE_CASE              =					None
  		SCREAMING_SNAKE_CASE              =					None
  		SCREAMING_SNAKE_CASE              =					None
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]):
  							return json.dumps(dataclasses.asdict(self)) + "\n"
  
  
  
  
  
  class     __UpperCamelCase				:
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Tuple  ,  __SCREAMING_SNAKE_CASE				:	Dict):
  							raise NotImplementedError()
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[Any]  ,  __SCREAMING_SNAKE_CASE				:	Tuple):
  							raise NotImplementedError()
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]):
  							raise NotImplementedError()
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	str  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]):
  							raise NotImplementedError()
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Any):
  							raise NotImplementedError()
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	int  ,  __SCREAMING_SNAKE_CASE				:	Tuple):
  
  
  
  
  
  
  
  							if len(self.get_labels()) > 1:
  												A    =      self.get_labels()[int(example.label)]
  							return example
  
  
  		@classmethod
  		def       SCREAMING_SNAKE_CASE__     (cls				:	Any  ,  __SCREAMING_SNAKE_CASE				:	str  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=None):
  
  
  
  
  
  
  
  							with open(__SCREAMING_SNAKE_CASE  ,  "r"  ,  encoding="utf-8-sig") as f:
  												return list(csv.reader(__SCREAMING_SNAKE_CASE  ,  delimiter="\t"  ,  quotechar=__SCREAMING_SNAKE_CASE))
  
  
  
  
  
  class     __UpperCamelCase				(     _A		):
  
  
  		def __init__(self				:	Any  ,  __SCREAMING_SNAKE_CASE				:	Any=None  ,  __SCREAMING_SNAKE_CASE				:	List[str]=None  ,  __SCREAMING_SNAKE_CASE				:	Dict="classification"  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]=False):
  							A    =      [] if labels is None else labels
  							A    =      [] if examples is None else examples
  							A    =      mode
  							A    =      verbose
  
  
  		def __len__(self				:	Union[str, Any]):
  							return len(self.examples)
  
  
  		def __getitem__(self				:	Optional[Any]  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]):
  
  
  
  
  
  
  
  							if isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
  												return SingleSentenceClassificationProcessor(labels=self.labels  ,  examples=self.examples[idx])
  							return self.examples[idx]
  
  
  		@classmethod
  		def       SCREAMING_SNAKE_CASE__     (cls				:	str  ,  __SCREAMING_SNAKE_CASE				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]=""  ,  __SCREAMING_SNAKE_CASE				:	str=0  ,  __SCREAMING_SNAKE_CASE				:	Dict=1  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]=None  ,  __SCREAMING_SNAKE_CASE				:	Tuple=False  ,  **__SCREAMING_SNAKE_CASE				:	Dict):
  							A    =      cls(**__SCREAMING_SNAKE_CASE)
  							processor.add_examples_from_csv(
  							    __SCREAMING_SNAKE_CASE  ,  split_name=__SCREAMING_SNAKE_CASE  ,  column_label=__SCREAMING_SNAKE_CASE  ,  column_text=__SCREAMING_SNAKE_CASE  ,  column_id=__SCREAMING_SNAKE_CASE  ,  skip_first_row=__SCREAMING_SNAKE_CASE  ,  overwrite_labels=__SCREAMING_SNAKE_CASE  ,  overwrite_examples=__SCREAMING_SNAKE_CASE  ,  )
  							return processor
  
  
  		@classmethod
  		def       SCREAMING_SNAKE_CASE__     (cls				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	List[str]=None  ,  **__SCREAMING_SNAKE_CASE				:	Tuple):
  							A    =      cls(**__SCREAMING_SNAKE_CASE)
  							processor.add_examples(__SCREAMING_SNAKE_CASE  ,  labels=__SCREAMING_SNAKE_CASE)
  							return processor
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	Any=""  ,  __SCREAMING_SNAKE_CASE				:	List[str]=0  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=1  ,  __SCREAMING_SNAKE_CASE				:	List[str]=None  ,  __SCREAMING_SNAKE_CASE				:	List[str]=False  ,  __SCREAMING_SNAKE_CASE				:	int=False  ,  __SCREAMING_SNAKE_CASE				:	Any=False  ,  ):
  							A    =      self._read_tsv(__SCREAMING_SNAKE_CASE)
  							if skip_first_row:
  												A    =      lines[1:]
  							A    =      []
  							A    =      []
  							A    =      []
  							for i, line in enumerate(__SCREAMING_SNAKE_CASE):
  												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(__SCREAMING_SNAKE_CASE)
  																	ids.append(__SCREAMING_SNAKE_CASE)
  
  							return self.add_examples(
  							    __SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  overwrite_labels=__SCREAMING_SNAKE_CASE  ,  overwrite_examples=__SCREAMING_SNAKE_CASE)
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	Any  ,  __SCREAMING_SNAKE_CASE				:	Tuple=None  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=None  ,  __SCREAMING_SNAKE_CASE				:	int=False  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=False):
  
  
  
  
  
  
  
  							if labels is not None and len(__SCREAMING_SNAKE_CASE) != len(__SCREAMING_SNAKE_CASE):
  												raise ValueError(
  												    F"""Text and labels have mismatched lengths {len(__SCREAMING_SNAKE_CASE)} and {len(__SCREAMING_SNAKE_CASE)}""")
  							if ids is not None and len(__SCREAMING_SNAKE_CASE) != len(__SCREAMING_SNAKE_CASE):
  												raise ValueError(F"""Text and ids have mismatched lengths {len(__SCREAMING_SNAKE_CASE)} and {len(__SCREAMING_SNAKE_CASE)}""")
  							if ids is None:
  												A    =      [None] * len(__SCREAMING_SNAKE_CASE)
  							if labels is None:
  												A    =      [None] * len(__SCREAMING_SNAKE_CASE)
  							A    =      []
  							A    =      set()
  							for text_or_text_and_label, label, guid in zip(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
  												if isinstance(__SCREAMING_SNAKE_CASE  ,  (tuple, list)) and label is None:
  																	A    ,					A    =      text_or_text_and_label
  												else:
  																	A    =      text_or_text_and_label
  												added_labels.add(__SCREAMING_SNAKE_CASE)
  												examples.append(InputExample(guid=__SCREAMING_SNAKE_CASE  ,  text_a=__SCREAMING_SNAKE_CASE  ,  text_b=__SCREAMING_SNAKE_CASE  ,  label=__SCREAMING_SNAKE_CASE))
  
  							# Update examples
  							if overwrite_examples:
  												A    =      examples
  							else:
  												self.examples.extend(__SCREAMING_SNAKE_CASE)
  
  							# Update labels
  							if overwrite_labels:
  												A    =      list(__SCREAMING_SNAKE_CASE)
  							else:
  												A    =      list(set(self.labels).union(__SCREAMING_SNAKE_CASE))
  
  							return self.examples
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	List[Any]  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]  ,  __SCREAMING_SNAKE_CASE				:	str=None  ,  __SCREAMING_SNAKE_CASE				:	str=False  ,  __SCREAMING_SNAKE_CASE				:	List[str]=0  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]=True  ,  __SCREAMING_SNAKE_CASE				:	List[str]=None  ,  ):
  
  
  
  
  
  
  
  							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=__SCREAMING_SNAKE_CASE  ,  max_length=min(__SCREAMING_SNAKE_CASE  ,  tokenizer.max_len)  ,  )
  												all_input_ids.append(__SCREAMING_SNAKE_CASE)
  
  							A    =      max(len(__SCREAMING_SNAKE_CASE) for input_ids in all_input_ids)
  
  							A    =      []
  							for ex_index, (input_ids, example) in enumerate(zip(__SCREAMING_SNAKE_CASE  ,  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(__SCREAMING_SNAKE_CASE)
  
  												# Zero-pad up to the sequence length.
  												A    =      batch_length - len(__SCREAMING_SNAKE_CASE)
  												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(__SCREAMING_SNAKE_CASE) != batch_length:
  																	raise ValueError(F"""Error with input length {len(__SCREAMING_SNAKE_CASE)} vs {batch_length}""")
  												if len(__SCREAMING_SNAKE_CASE) != batch_length:
  																	raise ValueError(F"""Error with input length {len(__SCREAMING_SNAKE_CASE)} 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(__SCREAMING_SNAKE_CASE) for x in input_ids])}""")
  																	logger.info(F"""attention_mask: {" ".join([str(__SCREAMING_SNAKE_CASE) for x in attention_mask])}""")
  																	logger.info(F"""label: {example.label} (id = {label})""")
  
  												features.append(InputFeatures(input_ids=__SCREAMING_SNAKE_CASE  ,  attention_mask=__SCREAMING_SNAKE_CASE  ,  label=__SCREAMING_SNAKE_CASE))
  
  							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(
  												    __SCREAMING_SNAKE_CASE  ,  ({"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(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
  												return dataset
  							else:
  												raise ValueError("return_tensors should be one of \'tf\' or \'pt\'")
  
  
  
  
  
  
style_context: 
  
  """simple docstring"""
  
  
  
  
  def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
  					"""simple docstring"""
  					if not isinstance(lowercase__    ,							lowercase__   ):
  										raise ValueError("double_factorial() only accepts integral values"   )
  					if n < 0:
  										raise ValueError("double_factorial() not defined for negative values"   )
  					return 1 if n <= 1 else n * double_factorial(n - 2   )
  
  
  if __name__ == "__main__":
  	import doctest
  
  	doctest.testmod()
  
  
  
  
  
  