


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							:	Union[str, Any]					      =				logging.getLogger(__name__)







@dataclass
class 	snake_case_       :
					UpperCAmelCase__ :   str
					UpperCAmelCase__ :   List[str]
					UpperCAmelCase__ :   Optional[List[str]]







@dataclass
class 	snake_case_       :
					UpperCAmelCase__ :   List[int]
					UpperCAmelCase__ :   List[int]
					UpperCAmelCase__ :   Optional[List[int]]   				=      None
					UpperCAmelCase__ :   Optional[List[int]]   				=      None







class 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   Dict   				=      '''train'''
					UpperCAmelCase__ :   List[str]   				=      '''dev'''
					UpperCAmelCase__ :   Dict   				=      '''test'''







class 	snake_case_       :
					@staticmethod
					def 			lowerCamelCase__(				__snake_case       :List[Any]      ,__snake_case       :Union[Split, str]				) ->   List[InputExample]:
												raise NotImplementedError
					@staticmethod
					def 			lowerCamelCase__(				__snake_case       :str				) ->   List[str]:
												raise NotImplementedError



					@staticmethod
					def 			lowerCamelCase__(				__snake_case       :List[InputExample]      ,__snake_case       :List[str]      ,__snake_case       :int      ,__snake_case       :PreTrainedTokenizer      ,__snake_case       :Optional[Any]=False      ,__snake_case       :Any="[CLS]"      ,__snake_case       :str=1      ,__snake_case       :Dict="[SEP]"      ,__snake_case       :List[Any]=False      ,__snake_case       :Any=False      ,__snake_case       :int=0      ,__snake_case       :Tuple=0      ,__snake_case       :Union[str, Any]=-1_00      ,__snake_case       :Any=0      ,__snake_case       :Tuple=True      ,) ->   List[InputFeatures]:
												a__					=					{label: i for i, label in enumerate(__snake_case				)}

												a__					=					[]
												for ex_index, example in enumerate(__snake_case				):
																			if ex_index % 1_00_00 == 0:
																										logger.info('Writing example %d of %d'      ,__snake_case      ,len(__snake_case				)				)

																			a__					=					[]
																			a__					=					[]
																			for word, label in zip(example.words      ,example.labels				):
																										a__					=					tokenizer.tokenize(__snake_case				)

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

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
																			a__					=					tokenizer.num_special_tokens_to_add()
																			if len(__snake_case				) > max_seq_length - special_tokens_count:
																										a__					=					tokens[: (max_seq_length - special_tokens_count)]
																										a__					=					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]
																			a__					=					[sequence_a_segment_id] * len(__snake_case				)

																			if cls_token_at_end:
																										tokens += [cls_token]
																										label_ids += [pad_token_label_id]
																										segment_ids += [cls_token_segment_id]
																			else:
																										a__					=					[cls_token] + tokens
																										a__					=					[pad_token_label_id] + label_ids
																										a__					=					[cls_token_segment_id] + segment_ids

																			a__					=					tokenizer.convert_tokens_to_ids(__snake_case				)

																			# 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(__snake_case				)

																			# Zero-pad up to the sequence length.
																			a__					=					max_seq_length - len(__snake_case				)
																			if pad_on_left:
																										a__					=					([pad_token] * padding_length) + input_ids
																										a__					=					([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
																										a__					=					([pad_token_segment_id] * padding_length) + segment_ids
																										a__					=					([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(__snake_case				) == max_seq_length
																			assert len(__snake_case				) == max_seq_length
																			assert len(__snake_case				) == max_seq_length
																			assert len(__snake_case				) == max_seq_length

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

																			if "token_type_ids" not in tokenizer.model_input_names:
																										a__					=					None

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


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







						class 	snake_case_       (lowerCamelCase_				):
											UpperCAmelCase__ :   List[InputFeatures]
											UpperCAmelCase__ :   int   				=      nn.CrossEntropyLoss().ignore_index
											def __init__(				self       :Optional[Any]      ,__snake_case       :TokenClassificationTask      ,__snake_case       :str      ,__snake_case       :PreTrainedTokenizer      ,__snake_case       :List[str]      ,__snake_case       :str      ,__snake_case       :Optional[int] = None      ,__snake_case       :Tuple=False      ,__snake_case       :Split = Split.train      ,) ->   Optional[Any]:
																		# Load data features from cache or dataset file
																		a__					=					os.path.join(
																		    __snake_case      ,'cached_{}_{}_{}'.format(mode.value      ,tokenizer.__class__.__name__      ,str(__snake_case				)				)      ,)

																		# Make sure only the first process in distributed training processes the dataset,
																		# and the others will use the cache.
																		a__					=					cached_features_file + '.lock'
																		with FileLock(__snake_case				):
																									if os.path.exists(__snake_case				) and not overwrite_cache:
																																logger.info(F'Loading features from cached file {cached_features_file}'				)
																																a__					=					torch.load(__snake_case				)
																									else:
																																logger.info(F'Creating features from dataset file at {data_dir}'				)
																																a__					=					token_classification_task.read_examples_from_file(__snake_case      ,__snake_case				)
																																# TODO clean up all this to leverage built-in features of tokenizers
																																a__					=					token_classification_task.convert_examples_to_features(
																																    __snake_case      ,__snake_case      ,__snake_case      ,__snake_case      ,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=__snake_case      ,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      ,__snake_case				)
											def __len__(				self       :List[str]				) ->   int:
																		return len(self.features				)



											def __getitem__(				self       :Optional[Any]      ,__snake_case       :Optional[int]				) ->   InputFeatures:
																		return self.features[i]


if is_tf_available():
						import tensorflow as tf







						class 	snake_case_       :
											UpperCAmelCase__ :   List[InputFeatures]
											UpperCAmelCase__ :   int   				=      -1_0_0
											def __init__(				self       :List[str]      ,__snake_case       :TokenClassificationTask      ,__snake_case       :str      ,__snake_case       :PreTrainedTokenizer      ,__snake_case       :List[str]      ,__snake_case       :str      ,__snake_case       :Optional[int] = None      ,__snake_case       :Any=False      ,__snake_case       :Split = Split.train      ,) ->   List[Any]:
																		a__					=					token_classification_task.read_examples_from_file(__snake_case      ,__snake_case				)
																		# TODO clean up all this to leverage built-in features of tokenizers
																		a__					=					token_classification_task.convert_examples_to_features(
																		    __snake_case      ,__snake_case      ,__snake_case      ,__snake_case      ,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=__snake_case      ,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:
																									a__					=					tf.data.Dataset.from_generator(
																									    __snake_case      ,({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)      ,(
																									        {'input_ids': tf.TensorShape([None]				), 'attention_mask': tf.TensorShape([None]				)},
																									        tf.TensorShape([None]				),
																									    )      ,)
																		else:
																									a__					=					tf.data.Dataset.from_generator(
																									    __snake_case      ,({'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       :int				) ->   Dict:
																		a__					=					self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features				)				)				)

																		return self.dataset
											def __len__(				self       :Union[str, Any]				) ->   List[Any]:
																		return len(self.features				)



											def __getitem__(				self       :Union[str, Any]      ,__snake_case       :List[Any]				) ->   InputFeatures:
																		return self.features[i]








from __future__ import annotations






def 							__lowercase       (			__lowerCAmelCase							:    float ,   __lowerCAmelCase							:    float ,   __lowerCAmelCase							:    float       ):
							if (voltage, current, resistance).count(0       ) != 1:
														raise ValueError('One and only one argument must be 0'       )
							if resistance < 0:
														raise ValueError('Resistance cannot be negative'       )
							if voltage == 0:
														return {"voltage": float(current * resistance       )}
							elif current == 0:
														return {"current": voltage / resistance}
							elif resistance == 0:
														return {"resistance": voltage / current}
							else:
														raise ValueError('Exactly one argument must be 0'       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()




