
"""simple docstring"""




import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


UpperCAmelCase__   :					Optional[Any]												=				logging.getLogger(__name__)



@dataclass
class 			lowerCAmelCase_		:


				"""simple docstring"""



				__UpperCamelCase   :		str
				__UpperCamelCase   :		List[str]
				__UpperCamelCase   :		Optional[List[str]]






@dataclass
class 			lowerCAmelCase_		:


				"""simple docstring"""



				__UpperCamelCase   :		List[int]
				__UpperCamelCase   :		List[int]
				__UpperCamelCase   :		Optional[List[int]]       =     None
				__UpperCamelCase   :		Optional[List[int]]       =     None






class 			lowerCAmelCase_		(__UpperCAmelCase						):


				"""simple docstring"""



				__UpperCamelCase   :		str       =     "train"
				__UpperCamelCase   :		Optional[int]       =     "dev"
				__UpperCamelCase   :		int       =     "test"






class 			lowerCAmelCase_		:


				"""simple docstring"""



				@staticmethod
				def 			__magic_name__					(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Any:



											"""simple docstring"""




											raise NotImplementedError



				@staticmethod
				def 			__magic_name__					(SCREAMING_SNAKE_CASE__					)      ->	Union[str, Any]:



											"""simple docstring"""




											raise NotImplementedError



				@staticmethod
				def 			__magic_name__					(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__="[CLS]"     , SCREAMING_SNAKE_CASE__=1     , SCREAMING_SNAKE_CASE__="[SEP]"     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__=0     , SCREAMING_SNAKE_CASE__=0     , SCREAMING_SNAKE_CASE__=-1_00     , SCREAMING_SNAKE_CASE__=0     , SCREAMING_SNAKE_CASE__=True     , )      ->	Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: int     =     {label: i for i, label in enumerate(_lowerCamelCase					)}

											SCREAMING_SNAKE_CASE__		: List[Any]     =     []
											for ex_index, example in enumerate(_lowerCamelCase					):
																		if ex_index % 1_00_00 == 0:
																									logger.info("""Writing example %d of %d"""     , _lowerCamelCase     , len(_lowerCamelCase					)					)

																		SCREAMING_SNAKE_CASE__		: str     =     []
																		SCREAMING_SNAKE_CASE__		: int     =     []
																		for word, label in zip(example.words     , example.labels					):
																									SCREAMING_SNAKE_CASE__		: List[str]     =     tokenizer.tokenize(_lowerCamelCase					)

																									# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
																									if len(_lowerCamelCase					) > 0:
																																tokens.extend(_lowerCamelCase					)
																																# Use the real label id for the first token of the word, and padding ids for the remaining tokens
																																label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(_lowerCamelCase					) - 1)					)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
																		SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     tokenizer.num_special_tokens_to_add()
																		if len(_lowerCamelCase					) > max_seq_length - special_tokens_count:
																									SCREAMING_SNAKE_CASE__		: List[Any]     =     tokens[: (max_seq_length - special_tokens_count)]
																									SCREAMING_SNAKE_CASE__		: Dict     =     label_ids[: (max_seq_length - special_tokens_count)]

																		# The convention in BERT is:
																		# (a) For sequence pairs:
																		#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
																		#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
																		# (b) For single sequences:
																		#  tokens:   [CLS] the dog is hairy . [SEP]
																		#  type_ids:   0   0   0   0  0     0   0
																		#
																		# Where "type_ids" are used to indicate whether this is the first
																		# sequence or the second sequence. The embedding vectors for `type=0` and
																		# `type=1` were learned during pre-training and are added to the wordpiece
																		# embedding vector (and position vector). This is not *strictly* necessary
																		# since the [SEP] token unambiguously separates the sequences, but it makes
																		# it easier for the model to learn the concept of sequences.
																		#
																		# For classification tasks, the first vector (corresponding to [CLS]) is
																		# used as the "sentence vector". Note that this only makes sense because
																		# the entire model is fine-tuned.
																		tokens += [sep_token]
																		label_ids += [pad_token_label_id]
																		if sep_token_extra:
																									# roberta uses an extra separator b/w pairs of sentences
																									tokens += [sep_token]
																									label_ids += [pad_token_label_id]
																		SCREAMING_SNAKE_CASE__		: List[Any]     =     [sequence_a_segment_id] * len(_lowerCamelCase					)

																		if cls_token_at_end:
																									tokens += [cls_token]
																									label_ids += [pad_token_label_id]
																									segment_ids += [cls_token_segment_id]
																		else:
																									SCREAMING_SNAKE_CASE__		: List[Any]     =     [cls_token] + tokens
																									SCREAMING_SNAKE_CASE__		: str     =     [pad_token_label_id] + label_ids
																									SCREAMING_SNAKE_CASE__		: Optional[int]     =     [cls_token_segment_id] + segment_ids

																		SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     tokenizer.convert_tokens_to_ids(_lowerCamelCase					)

																		# The mask has 1 for real tokens and 0 for padding tokens. Only real
																		# tokens are attended to.
																		SCREAMING_SNAKE_CASE__		: int     =     [1 if mask_padding_with_zero else 0] * len(_lowerCamelCase					)

																		# Zero-pad up to the sequence length.
																		SCREAMING_SNAKE_CASE__		: Optional[Any]     =     max_seq_length - len(_lowerCamelCase					)
																		if pad_on_left:
																									SCREAMING_SNAKE_CASE__		: List[str]     =     ([pad_token] * padding_length) + input_ids
																									SCREAMING_SNAKE_CASE__		: Tuple     =     ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
																									SCREAMING_SNAKE_CASE__		: Tuple     =     ([pad_token_segment_id] * padding_length) + segment_ids
																									SCREAMING_SNAKE_CASE__		: int     =     ([pad_token_label_id] * padding_length) + label_ids
																		else:
																									input_ids += [pad_token] * padding_length
																									input_mask += [0 if mask_padding_with_zero else 1] * padding_length
																									segment_ids += [pad_token_segment_id] * padding_length
																									label_ids += [pad_token_label_id] * padding_length

																		assert len(_lowerCamelCase					) == max_seq_length
																		assert len(_lowerCamelCase					) == max_seq_length
																		assert len(_lowerCamelCase					) == max_seq_length
																		assert len(_lowerCamelCase					) == max_seq_length

																		if ex_index < 5:
																									logger.info("""*** Example ***"""					)
																									logger.info("""guid: %s"""     , example.guid					)
																									logger.info("""tokens: %s"""     , """ """.join([str(_lowerCamelCase					) for x in tokens]					)					)
																									logger.info("""input_ids: %s"""     , """ """.join([str(_lowerCamelCase					) for x in input_ids]					)					)
																									logger.info("""input_mask: %s"""     , """ """.join([str(_lowerCamelCase					) for x in input_mask]					)					)
																									logger.info("""segment_ids: %s"""     , """ """.join([str(_lowerCamelCase					) for x in segment_ids]					)					)
																									logger.info("""label_ids: %s"""     , """ """.join([str(_lowerCamelCase					) for x in label_ids]					)					)

																		if "token_type_ids" not in tokenizer.model_input_names:
																									SCREAMING_SNAKE_CASE__		: str     =     None

																		features.append(
																		    InputFeatures(
																		        input_ids=_lowerCamelCase     , attention_mask=_lowerCamelCase     , token_type_ids=_lowerCamelCase     , label_ids=_lowerCamelCase					)					)
											return features


if is_torch_available():
							import torch
							from torch import nn
							from torch.utils.data import Dataset



							class 			lowerCAmelCase_		(__UpperCAmelCase						):


											"""simple docstring"""



											__UpperCamelCase   :		List[InputFeatures]
											__UpperCamelCase   :		int       =     nn.CrossEntropyLoss().ignore_index



											def __init__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__ = Split.train     , )      ->	Tuple:



																		"""simple docstring"""




																		SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     os.path.join(
																		    _lowerCamelCase     , """cached_{}_{}_{}""".format(mode.value     , tokenizer.__class__.__name__     , str(_lowerCamelCase					)					)     , )

																		# Make sure only the first process in distributed training processes the dataset,
																		# and the others will use the cache.
																		SCREAMING_SNAKE_CASE__		: List[Any]     =     cached_features_file + """.lock"""
																		with FileLock(_lowerCamelCase					):
																									if os.path.exists(_lowerCamelCase					) and not overwrite_cache:
																																logger.info(F'''Loading features from cached file {cached_features_file}'''					)
																																SCREAMING_SNAKE_CASE__		: int     =     torch.load(_lowerCamelCase					)
																									else:
																																logger.info(F'''Creating features from dataset file at {data_dir}'''					)
																																SCREAMING_SNAKE_CASE__		: Optional[Any]     =     token_classification_task.read_examples_from_file(_lowerCamelCase     , _lowerCamelCase					)
																																# TODO clean up all this to leverage built-in features of tokenizers
																																SCREAMING_SNAKE_CASE__		: Any     =     token_classification_task.convert_examples_to_features(
																																    _lowerCamelCase     , _lowerCamelCase     , _lowerCamelCase     , _lowerCamelCase     , cls_token_at_end=bool(model_type in ["""xlnet"""]					)     , cls_token=tokenizer.cls_token     , cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0     , sep_token=tokenizer.sep_token     , sep_token_extra=_lowerCamelCase     , pad_on_left=bool(tokenizer.padding_side == """left"""					)     , pad_token=tokenizer.pad_token_id     , pad_token_segment_id=tokenizer.pad_token_type_id     , pad_token_label_id=self.pad_token_label_id     , )
																																logger.info(F'''Saving features into cached file {cached_features_file}'''					)
																																torch.save(self.features     , _lowerCamelCase					)



											def __len__(self					)      ->	List[str]:



																		"""simple docstring"""




																		return len(self.features					)



											def __getitem__(self     , SCREAMING_SNAKE_CASE__					)      ->	int:



																		"""simple docstring"""




																		return self.features[i]


if is_tf_available():
							import tensorflow as tf



							class 			lowerCAmelCase_		:


											"""simple docstring"""



											__UpperCamelCase   :		List[InputFeatures]
											__UpperCamelCase   :		int       =     -100



											def __init__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__ = Split.train     , )      ->	int:



																		"""simple docstring"""




																		SCREAMING_SNAKE_CASE__		: Optional[int]     =     token_classification_task.read_examples_from_file(_lowerCamelCase     , _lowerCamelCase					)
																		# TODO clean up all this to leverage built-in features of tokenizers
																		SCREAMING_SNAKE_CASE__		: int     =     token_classification_task.convert_examples_to_features(
																		    _lowerCamelCase     , _lowerCamelCase     , _lowerCamelCase     , _lowerCamelCase     , cls_token_at_end=bool(model_type in ["""xlnet"""]					)     , cls_token=tokenizer.cls_token     , cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0     , sep_token=tokenizer.sep_token     , sep_token_extra=_lowerCamelCase     , pad_on_left=bool(tokenizer.padding_side == """left"""					)     , pad_token=tokenizer.pad_token_id     , pad_token_segment_id=tokenizer.pad_token_type_id     , pad_token_label_id=self.pad_token_label_id     , )

																		def gen():
																									for ex in self.features:
																																if ex.token_type_ids is None:
																																							yield (
																																							    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
																																							    ex.label_ids,
																																							)
																																else:
																																							yield (
																																							    {
																																							        "input_ids": ex.input_ids,
																																							        "attention_mask": ex.attention_mask,
																																							        "token_type_ids": ex.token_type_ids,
																																							    },
																																							    ex.label_ids,
																																							)

																		if "token_type_ids" not in tokenizer.model_input_names:
																									SCREAMING_SNAKE_CASE__		: Tuple     =     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([None]					),
																									    )     , )
																		else:
																									SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     tf.data.Dataset.from_generator(
																									    _lowerCamelCase     , ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa)     , (
																									        {
																									            """input_ids""": tf.TensorShape([None]					),
																									            """attention_mask""": tf.TensorShape([None]					),
																									            """token_type_ids""": tf.TensorShape([None]					),
																									        },
																									        tf.TensorShape([None]					),
																									    )     , )



											def 			__magic_name__					(self					)      ->	Tuple:



																		"""simple docstring"""




																		SCREAMING_SNAKE_CASE__		: List[str]     =     self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features					)					)					)

																		return self.dataset



											def __len__(self					)      ->	Any:



																		"""simple docstring"""




																		return len(self.features					)



											def __getitem__(self     , SCREAMING_SNAKE_CASE__					)      ->	str:



																		"""simple docstring"""




																		return self.features[i]






"""simple docstring"""




def lowercase_						(     _snake_case     ,_snake_case		):
							return int((input_a, input_a).count(1		) != 0		)

def lowercase_						(     ):
							assert or_gate(0     ,0		) == 0
							assert or_gate(0     ,1		) == 1
							assert or_gate(1     ,0		) == 1
							assert or_gate(1     ,1		) == 1


if __name__ == "__main__":
					print(or_gate(0, 1))
					print(or_gate(1, 0))
					print(or_gate(0, 0))
					print(or_gate(1, 1))




