






class     _SCREAMING_SNAKE_CASE       :





	def __init__(		self				:					Optional[int] ,  __lowerCamelCase				:					int	):
		UpperCamelCase     :List[Any]          =	size
		UpperCamelCase     :Optional[Any]          =	[0] * size
		UpperCamelCase     :Any          =	[0] * size





	@staticmethod
	def 						_A (		__lowerCamelCase				:					int	):
		return index | (index + 1)





	@staticmethod
	def 						_A (		__lowerCamelCase				:					int	):
		return (index & (index + 1)) - 1





	def 						_A (		self				:					Dict ,  __lowerCamelCase				:					int ,  __lowerCamelCase				:					int	):
		UpperCamelCase     :Union[str, Any]          =	value
		while index < self.size:
			UpperCamelCase     :int          =	self.get_prev(__lowerCamelCase	) + 1
			if current_left_border == index:
				UpperCamelCase     :List[Any]          =	value
			else:
				UpperCamelCase     :Union[str, Any]          =	max(__lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase	)
			UpperCamelCase     :Tuple          =	self.get_next(__lowerCamelCase	)





	def 						_A (		self				:					List[str] ,  __lowerCamelCase				:					int ,  __lowerCamelCase				:					int	):
		right -= 1  # Because of right is exclusive
		UpperCamelCase     :Union[str, Any]          =	0
		while left <= right:
			UpperCamelCase     :List[Any]          =	self.get_prev(__lowerCamelCase	)
			if left <= current_left:
				UpperCamelCase     :Tuple          =	max(__lowerCamelCase ,  self.tree[right]	)
				UpperCamelCase     :List[Any]          =	current_left
			else:
				UpperCamelCase     :Tuple          =	max(__lowerCamelCase ,  self.arr[right]	)
				right -= 1
		return result


if __name__ == "__main__":
	import doctest

	doctest.testmod()







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 					__lowerCAmelCase       :
			lowerCamelCase_     :     str
			lowerCamelCase_     :     List[str]
			lowerCamelCase_     :     Optional[List[str]]




@dataclass
class 					__lowerCAmelCase       :
			lowerCamelCase_     :     List[int]
			lowerCamelCase_     :     List[int]
			lowerCamelCase_     :     Optional[List[int]]       			=      None
			lowerCamelCase_     :     Optional[List[int]]       			=      None




class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     str       			=      '''train'''
			lowerCamelCase_     :     List[str]       			=      '''dev'''
			lowerCamelCase_     :     List[Any]       			=      '''test'''




class 					__lowerCAmelCase       :






			@staticmethod
			def 			lowerCamelCase  (__magic_name__					,							__magic_name__    )     ->   List[InputExample]:


							'''simple docstring'''



							raise NotImplementedError






			@staticmethod
			def 			lowerCamelCase  (__magic_name__    )     ->   List[str]:


							'''simple docstring'''



							raise NotImplementedError






			@staticmethod
			def 			lowerCamelCase  (__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__=False					,							__magic_name__="[CLS]"					,							__magic_name__=1					,							__magic_name__="[SEP]"					,							__magic_name__=False					,							__magic_name__=False					,							__magic_name__=0					,							__magic_name__=0					,							__magic_name__=-100					,							__magic_name__=0					,							__magic_name__=True					,							)     ->   List[InputFeatures]:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  {label: i for i, label in enumerate(__magic_name__    )}

							snake_case_						:    Dict				=  []
							for ex_index, example in enumerate(__magic_name__    ):
											if ex_index % 1_0000 == 0:
															logger.info('''Writing example %d of %d'''					,							__magic_name__					,							len(__magic_name__    )    )

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

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

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

											if cls_token_at_end:
															tokens += [cls_token]
															label_ids += [pad_token_label_id]
															segment_ids += [cls_token_segment_id]
											else:
															snake_case_						:    Union[str, Any]				=  [cls_token] + tokens
															snake_case_						:    List[Any]				=  [pad_token_label_id] + label_ids
															snake_case_						:    Optional[Any]				=  [cls_token_segment_id] + segment_ids

											snake_case_						:    Optional[Any]				=  tokenizer.convert_tokens_to_ids(__magic_name__    )

											# 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(__magic_name__    )

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

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

											if "token_type_ids" not in tokenizer.model_input_names:
															snake_case_						:    int				=  None

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


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



	class 					__lowerCAmelCase       (			_a     ):
				lowerCamelCase_     :     List[InputFeatures]
				lowerCamelCase_     :     int       			=      nn.CrossEntropyLoss().ignore_index






				def __init__(self					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__ = None					,							__magic_name__=False					,							__magic_name__ = Split.train					,							)     ->   Union[str, Any]:


								'''simple docstring'''



								snake_case_						:    List[str]				=  os.path.join(
								    __magic_name__					,							'''cached_{}_{}_{}'''.format(mode.value					,							tokenizer.__class__.__name__					,							str(__magic_name__    )    )					,							)

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






				def __len__(self    )     ->   Optional[Any]:


								'''simple docstring'''



								return len(self.features    )






				def __getitem__(self					,							__magic_name__    )     ->   InputFeatures:


								'''simple docstring'''



								return self.features[i]


if is_tf_available():
	import tensorflow as tf



	class 					__lowerCAmelCase       :
				lowerCamelCase_     :     List[InputFeatures]
				lowerCamelCase_     :     int       			=      -100






				def __init__(self					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__ = None					,							__magic_name__=False					,							__magic_name__ = Split.train					,							)     ->   Optional[int]:


								'''simple docstring'''



								snake_case_						:    Optional[int]				=  token_classification_task.read_examples_from_file(__magic_name__					,							__magic_name__    )
								# TODO clean up all this to leverage built-in features of tokenizers
								snake_case_						:    int				=  token_classification_task.convert_examples_to_features(
								    __magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							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=__magic_name__					,							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_						:    Optional[Any]				=  tf.data.Dataset.from_generator(
												    __magic_name__					,							({'''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_						:    int				=  tf.data.Dataset.from_generator(
												    __magic_name__					,							({'''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    )     ->   List[Any]:


								'''simple docstring'''



								snake_case_						:    Optional[Any]				=  self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features    )    )    )

								return self.dataset






				def __len__(self    )     ->   str:


								'''simple docstring'''



								return len(self.features    )






				def __getitem__(self					,							__magic_name__    )     ->   InputFeatures:


								'''simple docstring'''



								return self.features[i]
