

"""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


snake_case_   :   List[Any]        =       logging.getLogger(__name__)







@dataclass
class       snake_case__  :
			SCREAMING_SNAKE_CASE__     				=						42
			SCREAMING_SNAKE_CASE__     				=						42
			SCREAMING_SNAKE_CASE__     				=						42







@dataclass
class       snake_case__  :
			SCREAMING_SNAKE_CASE__     				=						42
			SCREAMING_SNAKE_CASE__     				=						42
			SCREAMING_SNAKE_CASE__     				=						None
			SCREAMING_SNAKE_CASE__     				=						None







class       snake_case__  ( _lowercase     ):
			SCREAMING_SNAKE_CASE__     				=						'''train'''
			SCREAMING_SNAKE_CASE__     				=						'''dev'''
			SCREAMING_SNAKE_CASE__     				=						'''test'''







class       snake_case__  :







			@staticmethod
			def 			__lowerCAmelCase				(			lowercase			: List[str]			,  lowercase			: Union[Split, str]  ):





								'''simple docstring'''



								raise NotImplementedError







			@staticmethod
			def 			__lowerCAmelCase				(			lowercase			: str  ):





								'''simple docstring'''



								raise NotImplementedError







			@staticmethod
			def 			__lowerCAmelCase				(			lowercase			: List[InputExample]			,  lowercase			: List[str]			,  lowercase			: int			,  lowercase			: PreTrainedTokenizer			,  lowercase			: int=False			,  lowercase			: Optional[int]="[CLS]"			,  lowercase			: Union[str, Any]=1			,  lowercase			: Union[str, Any]="[SEP]"			,  lowercase			: int=False			,  lowercase			: List[Any]=False			,  lowercase			: Any=0			,  lowercase			: List[str]=0			,  lowercase			: int=-1_00			,  lowercase			: List[str]=0			,  lowercase			: Tuple=True			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				{label: i for i, label in enumerate(A_  )}

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

													UpperCAmelCase			:   int      =				[]
													UpperCAmelCase			:   Tuple      =				[]
													for word, label in zip(example.words			,  example.labels  ):
																		UpperCAmelCase			:   Dict      =				tokenizer.tokenize(A_  )

																		# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
																		if len(A_  ) > 0:
																							tokens.extend(A_  )
																							# 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(A_  ) - 1)  )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
													UpperCAmelCase			:   int      =				tokenizer.num_special_tokens_to_add()
													if len(A_  ) > max_seq_length - special_tokens_count:
																		UpperCAmelCase			:   Optional[int]      =				tokens[: (max_seq_length - special_tokens_count)]
																		UpperCAmelCase			:   List[Any]      =				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]
													UpperCAmelCase			:   int      =				[sequence_a_segment_id] * len(A_  )

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

													UpperCAmelCase			:   List[Any]      =				tokenizer.convert_tokens_to_ids(A_  )

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

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

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

													if "token_type_ids" not in tokenizer.model_input_names:
																		UpperCAmelCase			:   Tuple      =				None

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


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







	class       snake_case__  ( _lowercase     ):
				SCREAMING_SNAKE_CASE__     				=						42
				SCREAMING_SNAKE_CASE__     				=						nn.CrossEntropyLoss().ignore_index







				def __init__(			self			: Tuple			,  lowercase			: TokenClassificationTask			,  lowercase			: str			,  lowercase			: PreTrainedTokenizer			,  lowercase			: List[str]			,  lowercase			: str			,  lowercase			: Optional[int] = None			,  lowercase			: Optional[int]=False			,  lowercase			: Split = Split.train			,  ):





									'''simple docstring'''



									UpperCAmelCase			:   Tuple      =				os.path.join(
									    A_			,  "cached_{}_{}_{}".format(mode.value			,  tokenizer.__class__.__name__			,  str(A_  )  )			,  )

									# Make sure only the first process in distributed training processes the dataset,
									# and the others will use the cache.
									UpperCAmelCase			:   Tuple      =				cached_features_file + ".lock"
									with FileLock(A_  ):
														if os.path.exists(A_  ) and not overwrite_cache:
																			logger.info(f"""Loading features from cached file {cached_features_file}"""  )
																			UpperCAmelCase			:   int      =				torch.load(A_  )
														else:
																			logger.info(f"""Creating features from dataset file at {data_dir}"""  )
																			UpperCAmelCase			:   Optional[Any]      =				token_classification_task.read_examples_from_file(A_			,  A_  )
																			# TODO clean up all this to leverage built-in features of tokenizers
																			UpperCAmelCase			:   Optional[Any]      =				token_classification_task.convert_examples_to_features(
																			    A_			,  A_			,  A_			,  A_			,  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=A_			,  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			,  A_  )







				def __len__(			self			: Optional[int]  ):





									'''simple docstring'''



									return len(self.features  )







				def __getitem__(			self			: List[Any]			,  lowercase			: Optional[int]  ):





									'''simple docstring'''



									return self.features[i]


if is_tf_available():
	import tensorflow as tf







	class       snake_case__  :
				SCREAMING_SNAKE_CASE__     				=						42
				SCREAMING_SNAKE_CASE__     				=						-100







				def __init__(			self			: Any			,  lowercase			: TokenClassificationTask			,  lowercase			: str			,  lowercase			: PreTrainedTokenizer			,  lowercase			: List[str]			,  lowercase			: str			,  lowercase			: Optional[int] = None			,  lowercase			: str=False			,  lowercase			: Split = Split.train			,  ):





									'''simple docstring'''



									UpperCAmelCase			:   List[Any]      =				token_classification_task.read_examples_from_file(A_			,  A_  )
									# TODO clean up all this to leverage built-in features of tokenizers
									UpperCAmelCase			:   List[str]      =				token_classification_task.convert_examples_to_features(
									    A_			,  A_			,  A_			,  A_			,  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=A_			,  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:
														UpperCAmelCase			:   str      =				tf.data.Dataset.from_generator(
														    A_			,  ({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)			,  (
														        {"input_ids": tf.TensorShape([None]  ), "attention_mask": tf.TensorShape([None]  )},
														        tf.TensorShape([None]  ),
														    )			,  )
									else:
														UpperCAmelCase			:   List[Any]      =				tf.data.Dataset.from_generator(
														    A_			,  ({"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 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





									'''simple docstring'''



									UpperCAmelCase			:   int      =				self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features  )  )  )

									return self.dataset







				def __len__(			self			: Optional[Any]  ):





									'''simple docstring'''



									return len(self.features  )







				def __getitem__(			self			: Union[str, Any]			,  lowercase			: Optional[Any]  ):





									'''simple docstring'''



									return self.features[i]









"""simple docstring"""






def   lowercase_	(    _lowercase       :		int							,      _lowercase       :		int	):




					'''simple docstring'''






					return int(input_a == input_a == 0	)



def   lowercase_	(    ):




					'''simple docstring'''






					print("Truth Table of NOR Gate:"	)
					print("|   Input 1   |  Input 2  | Output |"	)
					print(F"""|      0      |    0      |  {nor_gate(0							,      0	)}     |"""	)
					print(F"""|      0      |    1      |  {nor_gate(0							,      1	)}     |"""	)
					print(F"""|      1      |    0      |  {nor_gate(1							,      0	)}     |"""	)
					print(F"""|      1      |    1      |  {nor_gate(1							,      1	)}     |"""	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()


