





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


lowercase_		:				List[str]				=						logging.getLogger(__name__)


@dataclass
class 			lowercase				:



						"""simple docstring"""

						_UpperCamelCase  :	Union[str, Any]								=	42
						_UpperCamelCase  :	Optional[int]								=	42
						_UpperCamelCase  :	Optional[int]								=	42



@dataclass
class 			lowercase				:



						"""simple docstring"""

						_UpperCamelCase  :	Tuple								=	42
						_UpperCamelCase  :	Dict								=	42
						_UpperCamelCase  :	Optional[int]								=	None
						_UpperCamelCase  :	List[Any]								=	None



class 			lowercase				(							_lowerCamelCase     ):



						"""simple docstring"""

						_UpperCamelCase  :	Dict								=	"train"
						_UpperCamelCase  :	Union[str, Any]								=	"dev"
						_UpperCamelCase  :	str								=	"test"



class 			lowercase				:



						"""simple docstring"""

						@staticmethod
						def 		__UpperCAmelCase       (    lowerCamelCase_			:		Optional[int] ,				lowerCamelCase_			:		Union[str, Any]					):





										'''simple docstring'''







										raise NotImplementedError







						@staticmethod
						def 		__UpperCAmelCase       (    lowerCamelCase_			:		Optional[int]					):





										'''simple docstring'''







										raise NotImplementedError







						@staticmethod
						def 		__UpperCAmelCase       (    lowerCamelCase_			:		Union[str, Any] ,				lowerCamelCase_			:		Any ,				lowerCamelCase_			:		str ,				lowerCamelCase_			:		Any ,				lowerCamelCase_			:		Any=False ,				lowerCamelCase_			:		List[Any]="[CLS]" ,				lowerCamelCase_			:		Optional[int]=1 ,				lowerCamelCase_			:		Optional[int]="[SEP]" ,				lowerCamelCase_			:		Dict=False ,				lowerCamelCase_			:		Union[str, Any]=False ,				lowerCamelCase_			:		Optional[int]=0 ,				lowerCamelCase_			:		List[Any]=0 ,				lowerCamelCase_			:		Dict=-1_00 ,				lowerCamelCase_			:		List[Any]=0 ,				lowerCamelCase_			:		Union[str, Any]=True ,				):





										'''simple docstring'''







										_snake_case :			Any  						=   {label: i for i, label in enumerate(A__					)}

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

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

														_snake_case :			Optional[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.
														_snake_case :			Optional[Any]  						=   [1 if mask_padding_with_zero else 0] * len(A__					)

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



								"""simple docstring"""

								_UpperCamelCase  :	Tuple								=	42
								_UpperCamelCase  :	Union[str, Any]								=	nn.CrossEntropyLoss().ignore_index







								def __init__(    self			:		int ,				lowerCamelCase_			:		Tuple ,				lowerCamelCase_			:		Any ,				lowerCamelCase_			:		Union[str, Any] ,				lowerCamelCase_			:		Dict ,				lowerCamelCase_			:		Any ,				lowerCamelCase_			:		int = None ,				lowerCamelCase_			:		List[Any]=False ,				lowerCamelCase_			:		List[str] = Split.train ,				):





												'''simple docstring'''







												_snake_case :			Optional[Any]  						=   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.
												_snake_case :			Any  						=   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}'''					)
																				_snake_case :			int  						=   torch.load(A__					)
																else:
																				logger.info(f'''Creating features from dataset file at {data_dir}'''					)
																				_snake_case :			Optional[int]  						=   token_classification_task.read_examples_from_file(A__ ,				A__					)
																				# TODO clean up all this to leverage built-in features of tokenizers
																				_snake_case :			Dict  						=   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			:		Union[str, Any]					):





												'''simple docstring'''







												return len(self.features					)







								def __getitem__(    self			:		str ,				lowerCamelCase_			:		int					):





												'''simple docstring'''







												return self.features[i]


if is_tf_available():
		import tensorflow as tf


		class 			lowercase				:



								"""simple docstring"""

								_UpperCamelCase  :	str								=	42
								_UpperCamelCase  :	str								=	-100







								def __init__(    self			:		List[str] ,				lowerCamelCase_			:		Optional[Any] ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		Optional[int] ,				lowerCamelCase_			:		Optional[int] ,				lowerCamelCase_			:		Any ,				lowerCamelCase_			:		Tuple = None ,				lowerCamelCase_			:		Optional[int]=False ,				lowerCamelCase_			:		Union[str, Any] = Split.train ,				):





												'''simple docstring'''







												_snake_case :			Dict  						=   token_classification_task.read_examples_from_file(A__ ,				A__					)
												# TODO clean up all this to leverage built-in features of tokenizers
												_snake_case :			Tuple  						=   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:
																_snake_case :			Any  						=   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:
																_snake_case :			Dict  						=   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 		__UpperCAmelCase       (    self			:		Tuple					):





												'''simple docstring'''







												_snake_case :			Union[str, Any]  						=   self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features					)					)					)

												return self.dataset







								def __len__(    self			:		Dict					):





												'''simple docstring'''







												return len(self.features					)







								def __getitem__(    self			:		List[Any] ,				lowerCamelCase_			:		Any					):





												'''simple docstring'''







												return self.features[i]













import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


lowercase_		:				Tuple				=						logging.getLogger(__name__)


class 			lowercase				(							a_     ):



						"""simple docstring"""

						def __init__(    self			:		Dict ,				lowerCamelCase_			:		Dict=-1					):





										'''simple docstring'''







										_snake_case :			str  						=   label_idx







						def 		__UpperCAmelCase       (    self			:		Any ,				lowerCamelCase_			:		Optional[int] ,				lowerCamelCase_			:		Union[Split, str]					):





										'''simple docstring'''







										if isinstance(lowerCamelCase_ ,				lowerCamelCase_					):
														_snake_case :			Union[str, Any]  						=   mode.value
										_snake_case :			Optional[int]  						=   os.path.join(lowerCamelCase_ ,				f'''{mode}.txt'''					)
										_snake_case :			Dict  						=   1
										_snake_case :			List[str]  						=   []
										with open(lowerCamelCase_ ,				encoding='utf-8'					) as f:
														_snake_case :			List[Any]  						=   []
														_snake_case :			int  						=   []
														for line in f:
																		if line.startswith('-DOCSTART-'					) or line == "" or line == "\n":
																						if words:
																										examples.append(InputExample(guid=f'''{mode}-{guid_index}''' ,				words=lowerCamelCase_ ,				labels=lowerCamelCase_					)					)
																										guid_index += 1
																										_snake_case :			Optional[Any]  						=   []
																										_snake_case :			Union[str, Any]  						=   []
																		else:
																						_snake_case :			Tuple  						=   line.split(' '					)
																						words.append(splits[0]					)
																						if len(lowerCamelCase_					) > 1:
																										labels.append(splits[self.label_idx].replace('\n' ,				''					)					)
																						else:
																										# Examples could have no label for mode = "test"
																										labels.append('O'					)
														if words:
																		examples.append(InputExample(guid=f'''{mode}-{guid_index}''' ,				words=lowerCamelCase_ ,				labels=lowerCamelCase_					)					)
										return examples







						def 		__UpperCAmelCase       (    self			:		Union[str, Any] ,				lowerCamelCase_			:		TextIO ,				lowerCamelCase_			:		TextIO ,				lowerCamelCase_			:		List					):





										'''simple docstring'''







										_snake_case :			str  						=   0
										for line in test_input_reader:
														if line.startswith('-DOCSTART-'					) or line == "" or line == "\n":
																		writer.write(lowerCamelCase_					)
																		if not preds_list[example_id]:
																						example_id += 1
														elif preds_list[example_id]:
																		_snake_case :			List[str]  						=   line.split()[0] + ' ' + preds_list[example_id].pop(0					) + '\n'
																		writer.write(lowerCamelCase_					)
														else:
																		logger.warning('Maximum sequence length exceeded: No prediction for \'%s\'.' ,				line.split()[0]					)







						def 		__UpperCAmelCase       (    self			:		List[str] ,				lowerCamelCase_			:		str					):





										'''simple docstring'''







										if path:
														with open(lowerCamelCase_ ,				'r'					) as f:
																		_snake_case :			Optional[int]  						=   f.read().splitlines()
														if "O" not in labels:
																		_snake_case :			Optional[int]  						=   ['O'] + labels
														return labels
										else:
														return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]



class 			lowercase				(							a_     ):



						"""simple docstring"""

						def __init__(    self			:		Optional[int]					):





										'''simple docstring'''







										super().__init__(label_idx=-2					)







						def 		__UpperCAmelCase       (    self			:		Optional[int] ,				lowerCamelCase_			:		str					):





										'''simple docstring'''







										if path:
														with open(lowerCamelCase_ ,				'r'					) as f:
																		_snake_case :			str  						=   f.read().splitlines()
														if "O" not in labels:
																		_snake_case :			Union[str, Any]  						=   ['O'] + labels
														return labels
										else:
														return [
														    "O",
														    "B-ADVP",
														    "B-INTJ",
														    "B-LST",
														    "B-PRT",
														    "B-NP",
														    "B-SBAR",
														    "B-VP",
														    "B-ADJP",
														    "B-CONJP",
														    "B-PP",
														    "I-ADVP",
														    "I-INTJ",
														    "I-LST",
														    "I-PRT",
														    "I-NP",
														    "I-SBAR",
														    "I-VP",
														    "I-ADJP",
														    "I-CONJP",
														    "I-PP",
														]



class 			lowercase				(							a_     ):



						"""simple docstring"""

						def 		__UpperCAmelCase       (    self			:		Optional[Any] ,				lowerCamelCase_			:		Union[str, Any] ,				lowerCamelCase_			:		Union[Split, str]					):





										'''simple docstring'''







										if isinstance(lowerCamelCase_ ,				lowerCamelCase_					):
														_snake_case :			str  						=   mode.value
										_snake_case :			List[str]  						=   os.path.join(lowerCamelCase_ ,				f'''{mode}.txt'''					)
										_snake_case :			Tuple  						=   1
										_snake_case :			List[str]  						=   []

										with open(lowerCamelCase_ ,				encoding='utf-8'					) as f:
														for sentence in parse_incr(lowerCamelCase_					):
																		_snake_case :			List[str]  						=   []
																		_snake_case :			str  						=   []
																		for token in sentence:
																						words.append(token['form']					)
																						labels.append(token['upos']					)
																		assert len(lowerCamelCase_					) == len(lowerCamelCase_					)
																		if words:
																						examples.append(InputExample(guid=f'''{mode}-{guid_index}''' ,				words=lowerCamelCase_ ,				labels=lowerCamelCase_					)					)
																						guid_index += 1
										return examples







						def 		__UpperCAmelCase       (    self			:		Optional[int] ,				lowerCamelCase_			:		TextIO ,				lowerCamelCase_			:		TextIO ,				lowerCamelCase_			:		List					):





										'''simple docstring'''







										_snake_case :			Dict  						=   0
										for sentence in parse_incr(lowerCamelCase_					):
														_snake_case :			Optional[int]  						=   preds_list[example_id]
														_snake_case :			List[Any]  						=   ''
														for token in sentence:
																		out += f'''{token['form']} ({token['upos']}|{s_p.pop(0					)}) '''
														out += "\n"
														writer.write(lowerCamelCase_					)
														example_id += 1







						def 		__UpperCAmelCase       (    self			:		Optional[Any] ,				lowerCamelCase_			:		str					):





										'''simple docstring'''







										if path:
														with open(lowerCamelCase_ ,				'r'					) as f:
																		return f.read().splitlines()
										else:
														return [
														    "ADJ",
														    "ADP",
														    "ADV",
														    "AUX",
														    "CCONJ",
														    "DET",
														    "INTJ",
														    "NOUN",
														    "NUM",
														    "PART",
														    "PRON",
														    "PROPN",
														    "PUNCT",
														    "SCONJ",
														    "SYM",
														    "VERB",
														    "X",
														]






