



"""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


A		=						logging.getLogger(__name__)

@dataclass
class       _a  :
			__magic_name__     					=     42
			__magic_name__     					=     42
			__magic_name__     					=     42



@dataclass
class       _a  :
			__magic_name__     					=     42
			__magic_name__     					=     42
			__magic_name__     					=     None
			__magic_name__     					=     None



class       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     """train"""
			__magic_name__     					=     """dev"""
			__magic_name__     					=     """test"""



class       _a  :



			@staticmethod
			def 				__lowercase					(			_lowercase							:				int   ,	_lowercase							:				Union[Split, str]		)							->					List[InputExample]:
									raise NotImplementedError



			@staticmethod
			def 				__lowercase					(			_lowercase							:				str		)							->					List[str]:
									raise NotImplementedError



			@staticmethod
			def 				__lowercase					(			_lowercase							:				List[InputExample]   ,	_lowercase							:				List[str]   ,	_lowercase							:				int   ,	_lowercase							:				PreTrainedTokenizer   ,	_lowercase							:				int=False   ,	_lowercase							:				Dict="[CLS]"   ,	_lowercase							:				Any=1   ,	_lowercase							:				List[str]="[SEP]"   ,	_lowercase							:				Dict=False   ,	_lowercase							:				int=False   ,	_lowercase							:				List[Any]=0   ,	_lowercase							:				List[str]=0   ,	_lowercase							:				str=-100   ,	_lowercase							:				int=0   ,	_lowercase							:				Tuple=True   ,	)							->					List[InputFeatures]:
									snake_case   :			str						    = {label: i for i, label in enumerate(_lowercase		)}

									snake_case   :			str						    = []
									for ex_index, example in enumerate(_lowercase		):
															if ex_index % 10000 == 0:
																					logger.info("Writing example %d of %d"   ,	_lowercase   ,	len(_lowercase		)		)

															snake_case   :			List[Any]						    = []
															snake_case   :			Any						    = []
															for word, label in zip(example.words   ,	example.labels		):
																					snake_case   :			Optional[int]						    = tokenizer.tokenize(_lowercase		)

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

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

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

															snake_case   :			int						    = tokenizer.convert_tokens_to_ids(_lowercase		)

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

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

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

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

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


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

			class       _a  ( SCREAMING_SNAKE_CASE__):
						__magic_name__     					=     42
						__magic_name__     					=     nn.CrossEntropyLoss().ignore_index



						def __init__(			self							:				Dict   ,	_lowercase							:				TokenClassificationTask   ,	_lowercase							:				str   ,	_lowercase							:				PreTrainedTokenizer   ,	_lowercase							:				List[str]   ,	_lowercase							:				str   ,	_lowercase							:				Optional[int] = None   ,	_lowercase							:				Dict=False   ,	_lowercase							:				Split = Split.train   ,	)							->					List[str]:
												# Load data features from cache or dataset file
												snake_case   :			str						    = os.path.join(
												    _lowercase   ,	"cached_{}_{}_{}".format(mode.value   ,	tokenizer.__class__.__name__   ,	str(_lowercase		)		)   ,	)

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



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



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


if is_tf_available():
			import tensorflow as tf

			class       _a  :
						__magic_name__     					=     42
						__magic_name__     					=     -1_00



						def __init__(			self							:				List[str]   ,	_lowercase							:				TokenClassificationTask   ,	_lowercase							:				str   ,	_lowercase							:				PreTrainedTokenizer   ,	_lowercase							:				List[str]   ,	_lowercase							:				str   ,	_lowercase							:				Optional[int] = None   ,	_lowercase							:				int=False   ,	_lowercase							:				Split = Split.train   ,	)							->					Tuple:
												snake_case   :			Union[str, Any]						    = token_classification_task.read_examples_from_file(_lowercase   ,	_lowercase		)
												# TODO clean up all this to leverage built-in features of tokenizers
												snake_case   :			int						    = token_classification_task.convert_examples_to_features(
												    _lowercase   ,	_lowercase   ,	_lowercase   ,	_lowercase   ,	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=_lowercase   ,	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:
																		snake_case   :			List[str]						    = tf.data.Dataset.from_generator(
																		    _lowercase   ,	({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)   ,	(
																		        {"input_ids": tf.TensorShape([None]		), "attention_mask": tf.TensorShape([None]		)},
																		        tf.TensorShape([None]		),
																		    )   ,	)
												else:
																		snake_case   :			List[Any]						    = tf.data.Dataset.from_generator(
																		    _lowercase   ,	({"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 				__lowercase					(			self							:				Optional[int]		)							->					Dict:
												snake_case   :			str						    = self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features		)		)		)

												return self.dataset



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



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








"""simple docstring"""







A		=						8.31_4462  # Unit - J mol-1 K-1

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							float     ,		lowerCamelCase_:							float     ,		lowerCamelCase_:							float ):
						"""simple docstring"""






						if moles < 0 or kelvin < 0 or volume < 0:
												raise ValueError("Invalid inputs. Enter positive value." )
						return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							float     ,		lowerCamelCase_:							float     ,		lowerCamelCase_:							float ):
						"""simple docstring"""






						if moles < 0 or kelvin < 0 or pressure < 0:
												raise ValueError("Invalid inputs. Enter positive value." )
						return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


if __name__ == "__main__":
			from doctest import testmod

			testmod()



