





class   a_ :

			def __init__(   self   :  Tuple     , UpperCAmelCase__   :  int						):






							"""simple docstring"""
							snake_case   :					Union[str, Any]    						=				size
							snake_case   :					List[Any]    						=				[0] * size
							snake_case   :					Any    						=				[0] * size

			@staticmethod
			def      lowerCAmelCase(   UpperCAmelCase__   :  int						):






							"""simple docstring"""
							return index | (index + 1)

			@staticmethod
			def      lowerCAmelCase(   UpperCAmelCase__   :  int						):






							"""simple docstring"""
							return (index & (index + 1)) - 1

			def      lowerCAmelCase(   self   :  Union[str, Any]     , UpperCAmelCase__   :  int     , UpperCAmelCase__   :  int						):






							"""simple docstring"""
							snake_case   :					Dict    						=				value
							while index < self.size:
											snake_case   :					Union[str, Any]    						=				self.get_prev(__UpperCAmelCase						) + 1
											if current_left_border == index:
															snake_case   :					Union[str, Any]    						=				value
											else:
															snake_case   :					List[str]    						=				max(__UpperCAmelCase     , __UpperCAmelCase     , __UpperCAmelCase						)
											snake_case   :					Optional[Any]    						=				self.get_next(__UpperCAmelCase						)



			def      lowerCAmelCase(   self   :  int     , UpperCAmelCase__   :  int     , UpperCAmelCase__   :  int						):






							"""simple docstring"""
							right -= 1  # Because of right is exclusive
							snake_case   :					Optional[int]    						=				0
							while left <= right:
											snake_case   :					Any    						=				self.get_prev(__UpperCAmelCase						)
											if left <= current_left:
															snake_case   :					Dict    						=				max(__UpperCAmelCase     , self.tree[right]						)
															snake_case   :					Optional[Any]    						=				current_left
											else:
															snake_case   :					Dict    						=				max(__UpperCAmelCase     , 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


UpperCAmelCase__							  =						logging.getLogger(__name__)






@dataclass
class 						lowercase_   :






      '''simple docstring'''




      __snake_case	    =    42
      __snake_case	    =    42
      __snake_case	    =    42






@dataclass
class 						lowercase_   :






      '''simple docstring'''




      __snake_case	    =    42
      __snake_case	    =    42
      __snake_case	    =    None
      __snake_case	    =    None






class 						lowercase_   (      lowercase  ):






      '''simple docstring'''




      __snake_case	    =    '''train'''
      __snake_case	    =    '''dev'''
      __snake_case	    =    '''test'''






class 						lowercase_   :






      '''simple docstring'''




      @staticmethod
      def  __lowerCAmelCase   ( __UpperCAmelCase :		int					,   __UpperCAmelCase :		Union[Split, str]    )       ->List[InputExample]:




       """simple docstring"""


       raise NotImplementedError







      @staticmethod
      def  __lowerCAmelCase   ( __UpperCAmelCase :		str    )       ->List[str]:




       """simple docstring"""


       raise NotImplementedError







      @staticmethod
      def  __lowerCAmelCase   ( __UpperCAmelCase :		List[InputExample]					,   __UpperCAmelCase :		List[str]					,   __UpperCAmelCase :		int					,   __UpperCAmelCase :		PreTrainedTokenizer					,   __UpperCAmelCase :		Tuple=False					,   __UpperCAmelCase :		str="[CLS]"					,   __UpperCAmelCase :		str=1					,   __UpperCAmelCase :		List[str]="[SEP]"					,   __UpperCAmelCase :		Union[str, Any]=False					,   __UpperCAmelCase :		Tuple=False					,   __UpperCAmelCase :		List[str]=0					,   __UpperCAmelCase :		int=0					,   __UpperCAmelCase :		Tuple=-100					,   __UpperCAmelCase :		Optional[int]=0					,   __UpperCAmelCase :		Any=True					,   )       ->List[InputFeatures]:




       """simple docstring"""


       a						    = {label: i for i, label in enumerate(__UpperCAmelCase    )}

       a						    = []
       for ex_index, example in enumerate(__UpperCAmelCase    ):
        if ex_index % 10_000 == 0:
         logger.info('''Writing example %d of %d'''					,   __UpperCAmelCase					,   len(__UpperCAmelCase    )    )

        a						    = []
        a						    = []
        for word, label in zip(example.words					,   example.labels    ):
         a						    = tokenizer.tokenize(__UpperCAmelCase    )

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

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
        a						    = tokenizer.num_special_tokens_to_add()
        if len(__UpperCAmelCase    ) > max_seq_length - special_tokens_count:
         a						    = tokens[: (max_seq_length - special_tokens_count)]
         a						    = 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						    = [sequence_a_segment_id] * len(__UpperCAmelCase    )

        if cls_token_at_end:
         tokens += [cls_token]
         label_ids += [pad_token_label_id]
         segment_ids += [cls_token_segment_id]
        else:
         a						    = [cls_token] + tokens
         a						    = [pad_token_label_id] + label_ids
         a						    = [cls_token_segment_id] + segment_ids

        a						    = tokenizer.convert_tokens_to_ids(__UpperCAmelCase    )

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

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

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

        if "token_type_ids" not in tokenizer.model_input_names:
         a						    = None

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


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






     class 						lowercase_   (      lowercase  ):






           '''simple docstring'''




           __snake_case	    =    42
           __snake_case	    =    nn.CrossEntropyLoss().ignore_index







           def __init__( self :		str					,   __UpperCAmelCase :		TokenClassificationTask					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		PreTrainedTokenizer					,   __UpperCAmelCase :		List[str]					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		Optional[int] = None					,   __UpperCAmelCase :		Any=False					,   __UpperCAmelCase :		Split = Split.train					,   )       ->List[str]:




            """simple docstring"""


            a						    = os.path.join(
                __UpperCAmelCase					,   '''cached_{}_{}_{}'''.format(mode.value					,   tokenizer.__class__.__name__					,   str(__UpperCAmelCase    )    )					,   )

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







           def __len__( self :		List[str]    )       ->Union[str, Any]:




            """simple docstring"""


            return len(self.features    )







           def __getitem__( self :		List[Any]					,   __UpperCAmelCase :		Dict    )       ->InputFeatures:




            """simple docstring"""


            return self.features[i]


if is_tf_available():
     import tensorflow as tf






     class 						lowercase_   :






           '''simple docstring'''




           __snake_case	    =    42
           __snake_case	    =    -1_00







           def __init__( self :		Any					,   __UpperCAmelCase :		TokenClassificationTask					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		PreTrainedTokenizer					,   __UpperCAmelCase :		List[str]					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		Optional[int] = None					,   __UpperCAmelCase :		List[Any]=False					,   __UpperCAmelCase :		Split = Split.train					,   )       ->Optional[int]:




            """simple docstring"""


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




            """simple docstring"""


            a						    = self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features    )    )    )

            return self.dataset







           def __len__( self :		List[str]    )       ->Dict:




            """simple docstring"""


            return len(self.features    )







           def __getitem__( self :		Tuple					,   __UpperCAmelCase :		List[Any]    )       ->InputFeatures:




            """simple docstring"""


            return self.features[i]


