






"""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


__lowerCamelCase							=  logging.getLogger(__name__)



@dataclass
class       __A		:
					UpperCAmelCase__								=				42
					UpperCAmelCase__								=				42
					UpperCAmelCase__								=				42



@dataclass
class       __A		:
					UpperCAmelCase__								=				42
					UpperCAmelCase__								=				42
					UpperCAmelCase__								=				None
					UpperCAmelCase__								=				None



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				"train"
					UpperCAmelCase__								=				"dev"
					UpperCAmelCase__								=				"test"



class       __A		:

					@staticmethod
					def   lowerCamelCase__   (			__snake_case  :      Union[str, 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  :      int=False     ,		__snake_case  :      Union[str, Any]="[CLS]"     ,		__snake_case  :      str=1     ,		__snake_case  :      Dict="[SEP]"     ,		__snake_case  :      List[Any]=False     ,		__snake_case  :      Dict=False     ,		__snake_case  :      Dict=0     ,		__snake_case  :      Dict=0     ,		__snake_case  :      Optional[int]=-1_0_0     ,		__snake_case  :      Optional[int]=0     ,		__snake_case  :      str=True     ,		)			->							List[InputFeatures]:
											__magic_name__:	str							=       {label: i for i, label in enumerate(__snake_case					)}

											__magic_name__:	Optional[Any]							=       []
											for ex_index, example in enumerate(__snake_case					):
																	if ex_index % 1_0_0_0_0 == 0:
																							logger.info("""Writing example %d of %d"""     ,		__snake_case     ,		len(__snake_case					)					)

																	__magic_name__:	int							=       []
																	__magic_name__:	List[Any]							=       []
																	for word, label in zip(example.words     ,		example.labels					):
																							__magic_name__:	Optional[Any]							=       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.
																	__magic_name__:	int							=       tokenizer.num_special_tokens_to_add()
																	if len(__snake_case					) > max_seq_length - special_tokens_count:
																							__magic_name__:	List[str]							=       tokens[: (max_seq_length - special_tokens_count)]
																							__magic_name__:	Optional[int]							=       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]
																	__magic_name__:	Union[str, Any]							=       [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:
																							__magic_name__:	Union[str, Any]							=       [cls_token] + tokens
																							__magic_name__:	List[str]							=       [pad_token_label_id] + label_ids
																							__magic_name__:	Dict							=       [cls_token_segment_id] + segment_ids

																	__magic_name__:	str							=       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.
																	__magic_name__:	int							=       [1 if mask_padding_with_zero else 0] * len(__snake_case					)

																	# Zero-pad up to the sequence length.
																	__magic_name__:	Union[str, Any]							=       max_seq_length - len(__snake_case					)
																	if pad_on_left:
																							__magic_name__:	Union[str, Any]							=       ([pad_token] * padding_length) + input_ids
																							__magic_name__:	Dict							=       ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
																							__magic_name__:	Dict							=       ([pad_token_segment_id] * padding_length) + segment_ids
																							__magic_name__:	Optional[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(__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:
																							__magic_name__:	Any							=       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       __A		(   SCREAMING_SNAKE_CASE_			):
												UpperCAmelCase__								=				42
												UpperCAmelCase__								=				nn.CrossEntropyLoss().ignore_index

												def __init__(			self  :      int     ,		__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     ,		)			->							Optional[int]:
																		# Load data features from cache or dataset file
																		__magic_name__:	Union[str, Any]							=       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.
																		__magic_name__:	Union[str, Any]							=       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}'					)
																														__magic_name__:	List[Any]							=       torch.load(__snake_case					)
																								else:
																														logger.info(F'Creating features from dataset file at {data_dir}'					)
																														__magic_name__:	int							=       token_classification_task.read_examples_from_file(__snake_case     ,		__snake_case					)
																														# TODO clean up all this to leverage built-in features of tokenizers
																														__magic_name__:	List[str]							=       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  :      str					)			->							str:
																		return len(self.features					)






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


if is_tf_available():
							import tensorflow as tf



							class       __A		:
												UpperCAmelCase__								=				42
												UpperCAmelCase__								=				-1_0_0

												def __init__(			self  :      Tuple     ,		__snake_case  :      TokenClassificationTask     ,		__snake_case  :      str     ,		__snake_case  :      PreTrainedTokenizer     ,		__snake_case  :      List[str]     ,		__snake_case  :      str     ,		__snake_case  :      Optional[int] = None     ,		__snake_case  :      str=False     ,		__snake_case  :      Split = Split.train     ,		)			->							List[Any]:
																		__magic_name__:	Optional[int]							=       token_classification_task.read_examples_from_file(__snake_case     ,		__snake_case					)
																		# TODO clean up all this to leverage built-in features of tokenizers
																		__magic_name__:	Optional[int]							=       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:
																								__magic_name__:	Union[str, Any]							=       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:
																								__magic_name__:	str							=       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  :      Tuple					)			->							Dict:
																		__magic_name__:	Optional[Any]							=       self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features					)					)					)

																		return self.dataset

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






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











def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     []
				for data in source_data:
								for i, el in enumerate(lowerCamelCase_							):
												if len(lowerCamelCase_							) < i + 1:
																data_lists.append([]							)
												data_lists[i].append(float(lowerCamelCase_							)							)
				return data_lists






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     []
				for dlist, weight in zip(lowerCamelCase_						,  lowerCamelCase_							):
								lowercase__  					=     min(lowerCamelCase_							)
								lowercase__  					=     max(lowerCamelCase_							)

								lowercase__  					=     []
								# for weight 0 score is 1 - actual score
								if weight == 0:
												for item in dlist:
																try:
																				score.append(1 - ((item - mind) / (maxd - mind))							)
																except ZeroDivisionError:
																				score.append(1							)

								elif weight == 1:
												for item in dlist:
																try:
																				score.append((item - mind) / (maxd - mind)							)
																except ZeroDivisionError:
																				score.append(0							)

        # weight not 0 or 1
								else:
												lowercase__  					=     F"""Invalid weight of {weight:f} provided"""
												raise ValueError(lowerCamelCase_							)

								score_lists.append(lowerCamelCase_							)

				return score_lists






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     [0 for i in range(len(score_lists[0]							)							)]

				for slist in score_lists:
								for j, ele in enumerate(lowerCamelCase_							):
												lowercase__  					=     final_scores[j] + ele

				return final_scores






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     get_data(lowerCamelCase_							)
				lowercase__  					=     calculate_each_score(lowerCamelCase_						,  lowerCamelCase_							)
				lowercase__  					=     generate_final_scores(lowerCamelCase_							)

				# append scores to source data
				for i, ele in enumerate(lowerCamelCase_							):
								source_data[i].append(lowerCamelCase_							)

				return source_data




