






"""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   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




	_A							:					str
	_A							:					List[str]
	_A							:					Optional[List[str]]






@dataclass
class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




	_A							:					List[int]
	_A							:					List[int]
	_A							:					Optional[List[int]] 			=					None
	_A							:					Optional[List[int]] 			=					None






class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	_A							:					Optional[Any] 			=					"""train"""
	_A							:					List[Any] 			=					"""dev"""
	_A							:					Dict 			=					"""test"""






class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




	@staticmethod
	def  lowerCamelCase(lowerCAmelCase_  ,  lowerCAmelCase_				):
		raise NotImplementedError





	@staticmethod
	def  lowerCamelCase(lowerCAmelCase_				):
		raise NotImplementedError





	@staticmethod
	def  lowerCamelCase(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_=False  ,  lowerCAmelCase_="[CLS]"  ,  lowerCAmelCase_=1  ,  lowerCAmelCase_="[SEP]"  ,  lowerCAmelCase_=False  ,  lowerCAmelCase_=False  ,  lowerCAmelCase_=0  ,  lowerCAmelCase_=0  ,  lowerCAmelCase_=-100  ,  lowerCAmelCase_=0  ,  lowerCAmelCase_=True  ,  ):
		A_		:    int						     = {label: i for i, label in enumerate(lowerCAmelCase_				)}

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

			A_		:    List[str]						     = []
			A_		:    str						     = []
			for word, label in zip(example.words  ,  example.labels				):
				A_		:    Any						     = tokenizer.tokenize(lowerCAmelCase_				)

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

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

			if cls_token_at_end:
				tokens += [cls_token]
				label_ids += [pad_token_label_id]
				segment_ids += [cls_token_segment_id]
			else:
				A_		:    str						     = [cls_token] + tokens
				A_		:    Dict						     = [pad_token_label_id] + label_ids
				A_		:    str						     = [cls_token_segment_id] + segment_ids

			A_		:    Optional[int]						     = tokenizer.convert_tokens_to_ids(lowerCAmelCase_				)

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

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

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

			if "token_type_ids" not in tokenizer.model_input_names:
				A_		:    List[str]						     = None

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


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



							class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





								"""simple docstring"""




								_A							:					List[InputFeatures]
								_A							:					int 			=					nn.CrossEntropyLoss().ignore_index





								def __init__(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_=False  ,  lowerCAmelCase_ = Split.train  ,  ):
									# Load data features from cache or dataset file
									A_		:    Any						     = os.path.join(
									    lowerCAmelCase_  ,  """cached_{}_{}_{}""".format(mode.value  ,  tokenizer.__class__.__name__  ,  str(lowerCAmelCase_				)				)  ,  )

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





								def __len__(self				):
									return len(self.features				)





								def __getitem__(self  ,  lowerCAmelCase_				):
									return self.features[i]


if is_tf_available():
							import tensorflow as tf



							class   SCREAMING_SNAKE_CASE	:





								"""simple docstring"""




								_A							:					List[InputFeatures]
								_A							:					int 			=					-100





								def __init__(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_=False  ,  lowerCAmelCase_ = Split.train  ,  ):
									A_		:    str						     = token_classification_task.read_examples_from_file(lowerCAmelCase_  ,  lowerCAmelCase_				)
									# TODO clean up all this to leverage built-in features of tokenizers
									A_		:    Optional[int]						     = token_classification_task.convert_examples_to_features(
									    lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  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=lowerCAmelCase_  ,  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_		:    List[Any]						     = tf.data.Dataset.from_generator(
										    lowerCAmelCase_  ,  ({"""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_		:    Tuple						     = tf.data.Dataset.from_generator(
										    lowerCAmelCase_  ,  ({"""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				):
									A_		:    Optional[int]						     = self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features				)				)				)

									return self.dataset





								def __len__(self				):
									return len(self.features				)





								def __getitem__(self  ,  lowerCAmelCase_				):
									return self.features[i]









"""simple docstring"""
class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




	def __init__(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=None				):
		A_		:    Dict						     = data
		A_		:    Dict						     = previous
		A_		:    int						     = next_node





	def __str__(self				):
		return f"""{self.data}"""





	def  lowerCamelCase(self				):
		return self.data





	def  lowerCamelCase(self				):
		return self.next





	def  lowerCamelCase(self				):
		return self.previous






class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




	def __init__(self  ,  lowerCAmelCase_				):
		A_		:    Optional[Any]						     = head





	def __iter__(self				):
		return self





	def  lowerCamelCase(self				):
		if not self.current:
			raise StopIteration
		else:
			A_		:    str						     = self.current.get_data()
			A_		:    List[str]						     = self.current.get_next()
			return value






class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




	def __init__(self				):
		A_		:    str						     = None  # First node in list
		A_		:    Tuple						     = None  # Last node in list





	def __str__(self				):
		A_		:    int						     = self.head
		A_		:    str						     = []
		while current is not None:
			nodes.append(current.get_data()				)
			A_		:    Any						     = current.get_next()
		return " ".join(str(lowerCAmelCase_				) for node in nodes				)





	def __contains__(self  ,  lowerCAmelCase_				):
		A_		:    Union[str, Any]						     = self.head
		while current:
			if current.get_data() == value:
				return True
			A_		:    Union[str, Any]						     = current.get_next()
		return False





	def __iter__(self				):
		return LinkedListIterator(self.head				)





	def  lowerCamelCase(self				):
		if self.head:
			return self.head.get_data()
		return None





	def  lowerCamelCase(self				):
		if self.tail:
			return self.tail.get_data()
		return None





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		if self.head is None:
			A_		:    str						     = node
			A_		:    Optional[int]						     = node
		else:
			self.insert_before_node(self.head  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		if self.head is None:
			self.set_head(lowerCAmelCase_				)
		else:
			self.insert_after_node(self.tail  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		A_		:    List[Any]						     = Node(lowerCAmelCase_				)
		if self.head is None:
			self.set_head(lowerCAmelCase_				)
		else:
			self.set_tail(lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    str						     = node
		A_		:    Union[str, Any]						     = node.previous

		if node.get_previous() is None:
			A_		:    Dict						     = node_to_insert
		else:
			A_		:    Optional[Any]						     = node_to_insert

		A_		:    Any						     = node_to_insert





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    Dict						     = node
		A_		:    Optional[Any]						     = node.next

		if node.get_next() is None:
			A_		:    Optional[int]						     = node_to_insert
		else:
			A_		:    List[Any]						     = node_to_insert

		A_		:    Optional[Any]						     = node_to_insert





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    str						     = 1
		A_		:    Tuple						     = Node(lowerCAmelCase_				)
		A_		:    Dict						     = self.head
		while node:
			if current_position == position:
				self.insert_before_node(lowerCAmelCase_  ,  lowerCAmelCase_				)
				return
			current_position += 1
			A_		:    List[str]						     = node.next
		self.insert_after_node(self.tail  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		A_		:    Dict						     = self.head
		while node:
			if node.get_data() == item:
				return node
			A_		:    Optional[int]						     = node.get_next()
		raise Exception("""Node not found"""				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		if (node := self.get_node(lowerCAmelCase_				)) is not None:
			if node == self.head:
				A_		:    Tuple						     = self.head.get_next()

			if node == self.tail:
				A_		:    Dict						     = self.tail.get_previous()

			self.remove_node_pointers(lowerCAmelCase_				)





	@staticmethod
	def  lowerCamelCase(lowerCAmelCase_				):
		if node.get_next():
			A_		:    Any						     = node.previous

		if node.get_previous():
			A_		:    int						     = node.next

		A_		:    str						     = None
		A_		:    int						     = None





	def  lowerCamelCase(self				):
		return self.head is None






def       __UpperCamelCase						(		):
	pass


if __name__ == "__main__":
							import doctest

							doctest.testmod()

