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
  
  
  _lowercase		:     Any     					=logging.get_logger(__name__)
  
  
  
  
  
  
  @dataclass
  class 	UpperCamelCase_     :
  					_a			:	str
  					_a			:	str
  					_a			:	Optional[str]							=				None
  					_a			:	Optional[str]							=				None
  
  
  
  					def      __a    (		self    :    str	):
  							return json.dumps(dataclasses.asdict(self	)					,			indent=2	) + "\n"
  
  
  
  
  
  
  @dataclass(frozen=snake_case__       )
  class 	UpperCamelCase_     :
  					_a			:	List[int]
  					_a			:	Optional[List[int]]							=				None
  					_a			:	Optional[List[int]]							=				None
  					_a			:	Optional[Union[int, float]]							=				None
  
  
  
  					def      __a    (		self    :    List[Any]	):
  							return json.dumps(dataclasses.asdict(self	)	) + "\n"
  
  
  
  
  
  
  class 	UpperCamelCase_     :
  
  
  
  					def      __a    (		self    :    List[str]					,			lowerCamelCase    :    Tuple	):
  							raise NotImplementedError()
  
  
  
  					def      __a    (		self    :    Union[str, Any]					,			lowerCamelCase    :    int	):
  							raise NotImplementedError()
  
  
  
  					def      __a    (		self    :    str					,			lowerCamelCase    :    Optional[Any]	):
  							raise NotImplementedError()
  
  
  
  					def      __a    (		self    :    Union[str, Any]					,			lowerCamelCase    :    Optional[int]	):
  							raise NotImplementedError()
  
  
  
  					def      __a    (		self    :    Union[str, Any]	):
  							raise NotImplementedError()
  
  
  
  					def      __a    (		self    :    List[str]					,			lowerCamelCase    :    Any	):
  							if len(self.get_labels()	) > 1:
  									lowerCamelCase_  :    Optional[Any]   	=			self.get_labels()[int(example.label	)]
  							return example
  
  
  
  
  
  					@classmethod
  					def      __a    (		cls    :    List[str]					,			lowerCamelCase    :    Any					,			lowerCamelCase    :    List[str]=None	):
  							with open(lowerCamelCase					,			'r'					,			encoding='utf-8-sig'	) as f:
  									return list(csv.reader(lowerCamelCase					,			delimiter='\t'					,			quotechar=lowerCamelCase	)	)
  
  
  
  
  
  
  class 	UpperCamelCase_     (     snake_case__       ):
  
  
  
  					def __init__(		self    :    Any					,			lowerCamelCase    :    Dict=None					,			lowerCamelCase    :    List[Any]=None					,			lowerCamelCase    :    Optional[int]="classification"					,			lowerCamelCase    :    str=False	):
  							lowerCamelCase_  :    Any   	=			[] if labels is None else labels
  							lowerCamelCase_  :    str   	=			[] if examples is None else examples
  							lowerCamelCase_  :    Optional[Any]   	=			mode
  							lowerCamelCase_  :    Tuple   	=			verbose
  
  
  
  					def __len__(		self    :    Any	):
  							return len(self.examples	)
  
  
  
  					def __getitem__(		self    :    Any					,			lowerCamelCase    :    Optional[Any]	):
  							if isinstance(lowerCamelCase					,			lowerCamelCase	):
  									return SingleSentenceClassificationProcessor(labels=self.labels					,			examples=self.examples[idx]	)
  							return self.examples[idx]
  
  
  
  					@classmethod
  					def      __a    (		cls    :    Union[str, Any]					,			lowerCamelCase    :    List[Any]					,			lowerCamelCase    :    str=""					,			lowerCamelCase    :    Optional[int]=0					,			lowerCamelCase    :    List[str]=1					,			lowerCamelCase    :    Dict=None					,			lowerCamelCase    :    Dict=False					,			**lowerCamelCase    :    int	):
  							lowerCamelCase_  :    Any   	=			cls(**lowerCamelCase	)
  							processor.add_examples_from_csv(
  							    lowerCamelCase					,			split_name=lowerCamelCase					,			column_label=lowerCamelCase					,			column_text=lowerCamelCase					,			column_id=lowerCamelCase					,			skip_first_row=lowerCamelCase					,			overwrite_labels=lowerCamelCase					,			overwrite_examples=lowerCamelCase					,			)
  							return processor
  
  
  
  					@classmethod
  					def      __a    (		cls    :    Tuple					,			lowerCamelCase    :    Optional[Any]					,			lowerCamelCase    :    Any=None					,			**lowerCamelCase    :    Optional[int]	):
  							lowerCamelCase_  :    Tuple   	=			cls(**lowerCamelCase	)
  							processor.add_examples(lowerCamelCase					,			labels=lowerCamelCase	)
  							return processor
  
  
  
  					def      __a    (		self    :    List[str]					,			lowerCamelCase    :    str					,			lowerCamelCase    :    int=""					,			lowerCamelCase    :    Optional[int]=0					,			lowerCamelCase    :    Dict=1					,			lowerCamelCase    :    Dict=None					,			lowerCamelCase    :    Optional[Any]=False					,			lowerCamelCase    :    Tuple=False					,			lowerCamelCase    :    Optional[int]=False					,			):
  							lowerCamelCase_  :    Tuple   	=			self._read_tsv(lowerCamelCase	)
  							if skip_first_row:
  									lowerCamelCase_  :    List[Any]   	=			lines[1:]
  							lowerCamelCase_  :    Any   	=			[]
  							lowerCamelCase_  :    str   	=			[]
  							lowerCamelCase_  :    str   	=			[]
  							for i, line in enumerate(lowerCamelCase	):
  									texts.append(line[column_text]	)
  									labels.append(line[column_label]	)
  									if column_id is not None:
  											ids.append(line[column_id]	)
  									else:
  											lowerCamelCase_  :    List[str]   	=			F"{split_name}-{i}" if split_name else str(lowerCamelCase	)
  											ids.append(lowerCamelCase	)
  
  							return self.add_examples(
  							    lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			overwrite_labels=lowerCamelCase					,			overwrite_examples=lowerCamelCase	)
  
  
  
  					def      __a    (		self    :    Dict					,			lowerCamelCase    :    Optional[int]					,			lowerCamelCase    :    Union[str, Any]=None					,			lowerCamelCase    :    Optional[int]=None					,			lowerCamelCase    :    str=False					,			lowerCamelCase    :    str=False	):
  							if labels is not None and len(lowerCamelCase	) != len(lowerCamelCase	):
  									raise ValueError(
  									    F"Text and labels have mismatched lengths {len(lowerCamelCase	)} and {len(lowerCamelCase	)}"	)
  							if ids is not None and len(lowerCamelCase	) != len(lowerCamelCase	):
  									raise ValueError(F"Text and ids have mismatched lengths {len(lowerCamelCase	)} and {len(lowerCamelCase	)}"	)
  							if ids is None:
  									lowerCamelCase_  :    Any   	=			[None] * len(lowerCamelCase	)
  							if labels is None:
  									lowerCamelCase_  :    Any   	=			[None] * len(lowerCamelCase	)
  							lowerCamelCase_  :    str   	=			[]
  							lowerCamelCase_  :    int   	=			set()
  							for text_or_text_and_label, label, guid in zip(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase	):
  									if isinstance(lowerCamelCase					,			(tuple, list)	) and label is None:
  											lowerCamelCase_  :    Dict   	=			text_or_text_and_label
  									else:
  											lowerCamelCase_  :    str   	=			text_or_text_and_label
  									added_labels.add(lowerCamelCase	)
  									examples.append(InputExample(guid=lowerCamelCase					,			text_a=lowerCamelCase					,			text_b=lowerCamelCase					,			label=lowerCamelCase	)	)
  
  							# Update examples
  							if overwrite_examples:
  									lowerCamelCase_  :    Tuple   	=			examples
  							else:
  									self.examples.extend(lowerCamelCase	)
  
  							# Update labels
  							if overwrite_labels:
  									lowerCamelCase_  :    int   	=			list(lowerCamelCase	)
  							else:
  									lowerCamelCase_  :    List[str]   	=			list(set(self.labels	).union(lowerCamelCase	)	)
  
  							return self.examples
  
  
  
  
  
  					def      __a    (		self    :    Optional[int]					,			lowerCamelCase    :    Optional[Any]					,			lowerCamelCase    :    Tuple=None					,			lowerCamelCase    :    Tuple=False					,			lowerCamelCase    :    Optional[int]=0					,			lowerCamelCase    :    Optional[Any]=True					,			lowerCamelCase    :    List[str]=None					,			):
  							if max_length is None:
  									lowerCamelCase_  :    List[Any]   	=			tokenizer.max_len
  
  							lowerCamelCase_  :    List[Any]   	=			{label: i for i, label in enumerate(self.labels	)}
  
  							lowerCamelCase_  :    Union[str, Any]   	=			[]
  							for ex_index, example in enumerate(self.examples	):
  									if ex_index % 1_00_00 == 0:
  											logger.info(F"Tokenizing example {ex_index}"	)
  
  									lowerCamelCase_  :    Tuple   	=			tokenizer.encode(
  									    example.text_a					,			add_special_tokens=lowerCamelCase					,			max_length=min(lowerCamelCase					,			tokenizer.max_len	)					,			)
  									all_input_ids.append(lowerCamelCase	)
  
  							lowerCamelCase_  :    Optional[int]   	=			max(len(lowerCamelCase	) for input_ids in all_input_ids	)
  
  							lowerCamelCase_  :    Dict   	=			[]
  							for ex_index, (input_ids, example) in enumerate(zip(lowerCamelCase					,			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.
  									lowerCamelCase_  :    Tuple   	=			[1 if mask_padding_with_zero else 0] * len(lowerCamelCase	)
  
  									# Zero-pad up to the sequence length.
  									lowerCamelCase_  :    Any   	=			batch_length - len(lowerCamelCase	)
  									if pad_on_left:
  											lowerCamelCase_  :    Any   	=			([pad_token] * padding_length) + input_ids
  											lowerCamelCase_  :    int   	=			([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
  									else:
  											lowerCamelCase_  :    Any   	=			input_ids + ([pad_token] * padding_length)
  											lowerCamelCase_  :    Optional[Any]   	=			attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
  
  									if len(lowerCamelCase	) != batch_length:
  											raise ValueError(F"Error with input length {len(lowerCamelCase	)} vs {batch_length}"	)
  									if len(lowerCamelCase	) != batch_length:
  											raise ValueError(F"Error with input length {len(lowerCamelCase	)} vs {batch_length}"	)
  
  									if self.mode == "classification":
  											lowerCamelCase_  :    Optional[int]   	=			label_map[example.label]
  									elif self.mode == "regression":
  											lowerCamelCase_  :    List[str]   	=			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(lowerCamelCase	) for x in input_ids]	)}"	)
  											logger.info(F"attention_mask: {' '.join([str(lowerCamelCase	) for x in attention_mask]	)}"	)
  											logger.info(F"label: {example.label} (id = {label})"	)
  
  									features.append(InputFeatures(input_ids=lowerCamelCase					,			attention_mask=lowerCamelCase					,			label=lowerCamelCase	)	)
  
  							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)
  
  									lowerCamelCase_  :    Dict   	=			tf.data.Dataset.from_generator(
  									    lowerCamelCase					,			({'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
  
  									lowerCamelCase_  :    Optional[int]   	=			torch.tensor([f.input_ids for f in features]					,			dtype=torch.long	)
  									lowerCamelCase_  :    Dict   	=			torch.tensor([f.attention_mask for f in features]					,			dtype=torch.long	)
  									if self.mode == "classification":
  											lowerCamelCase_  :    Tuple   	=			torch.tensor([f.label for f in features]					,			dtype=torch.long	)
  									elif self.mode == "regression":
  											lowerCamelCase_  :    Union[str, Any]   	=			torch.tensor([f.label for f in features]					,			dtype=torch.float	)
  
  									lowerCamelCase_  :    Dict   	=			TensorDataset(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase	)
  									return dataset
  							else:
  									raise ValueError('return_tensors should be one of \'tf\' or \'pt\''	)
  
style_context: 
  
  
  
  
  
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__	):
  		return (input_a, input_a).count(0	) % 2
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		assert xor_gate(0       ,0	) == 0
  		assert xor_gate(0       ,1	) == 1
  		assert xor_gate(1       ,0	) == 1
  		assert xor_gate(1       ,1	) == 0
  
  
  if __name__ == "__main__":
  		print(xor_gate(0, 0))
  		print(xor_gate(0, 1))
  