






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


A        =			logging.getLogger(__name__)


@dataclass
class 							__a							:





  '''simple docstring'''



  UpperCAmelCase__      :				int				    =						42
  UpperCAmelCase__      :				List[Any]				    =						42
  UpperCAmelCase__      :				Dict				    =						42






@dataclass
class 							__a							:





  '''simple docstring'''



  UpperCAmelCase__      :				List[Any]				    =						42
  UpperCAmelCase__      :				str				    =						42
  UpperCAmelCase__      :				Any				    =						None
  UpperCAmelCase__      :				Optional[Any]				    =						None






class 							__a							(  __A						):





  '''simple docstring'''



  UpperCAmelCase__      :				List[str]				    =						"""train"""
  UpperCAmelCase__      :				Any				    =						"""dev"""
  UpperCAmelCase__      :				Optional[Any]				    =						"""test"""






class 							__a							:





  '''simple docstring'''



  @staticmethod
  def   __snake_case			(						UpperCamelCase__ ,    UpperCamelCase__       ):
      raise NotImplementedError


  @staticmethod
  def   __snake_case			(						UpperCamelCase__       ):
      raise NotImplementedError






  @staticmethod
  def   __snake_case			(						UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__=False ,    UpperCamelCase__="[CLS]" ,    UpperCamelCase__=1 ,    UpperCamelCase__="[SEP]" ,    UpperCamelCase__=False ,    UpperCamelCase__=False ,    UpperCamelCase__=0 ,    UpperCamelCase__=0 ,    UpperCamelCase__=-100 ,    UpperCamelCase__=0 ,    UpperCamelCase__=True ,    ):
      SCREAMING_SNAKE_CASE_   :	Tuple													=   {label: i for i, label in enumerate(_SCREAMING_SNAKE_CASE       )}

      SCREAMING_SNAKE_CASE_   :	Any													=   []
      for ex_index, example in enumerate(_SCREAMING_SNAKE_CASE       ):
          if ex_index % 10000 == 0:
              logger.info('Writing example %d of %d' ,    _SCREAMING_SNAKE_CASE ,    len(_SCREAMING_SNAKE_CASE       )       )

          SCREAMING_SNAKE_CASE_   :	List[Any]													=   []
          SCREAMING_SNAKE_CASE_   :	int													=   []
          for word, label in zip(example.words ,    example.labels       ):
              SCREAMING_SNAKE_CASE_   :	Any													=   tokenizer.tokenize(_SCREAMING_SNAKE_CASE       )

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

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

          if cls_token_at_end:
              tokens += [cls_token]
              label_ids += [pad_token_label_id]
              segment_ids += [cls_token_segment_id]
          else:
              SCREAMING_SNAKE_CASE_   :	List[str]													=   [cls_token] + tokens
              SCREAMING_SNAKE_CASE_   :	Any													=   [pad_token_label_id] + label_ids
              SCREAMING_SNAKE_CASE_   :	Optional[int]													=   [cls_token_segment_id] + segment_ids

          SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE       )

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

          # Zero-pad up to the sequence length.
          SCREAMING_SNAKE_CASE_   :	int													=   max_seq_length - len(_SCREAMING_SNAKE_CASE       )
          if pad_on_left:
              SCREAMING_SNAKE_CASE_   :	str													=   ([pad_token] * padding_length) + input_ids
              SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
              SCREAMING_SNAKE_CASE_   :	List[Any]													=   ([pad_token_segment_id] * padding_length) + segment_ids
              SCREAMING_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(_SCREAMING_SNAKE_CASE       ) == max_seq_length
          assert len(_SCREAMING_SNAKE_CASE       ) == max_seq_length
          assert len(_SCREAMING_SNAKE_CASE       ) == max_seq_length
          assert len(_SCREAMING_SNAKE_CASE       ) == max_seq_length

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

          if "token_type_ids" not in tokenizer.model_input_names:
              SCREAMING_SNAKE_CASE_   :	Optional[int]													=   None

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


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


    class 							__a							(  __A						):





      '''simple docstring'''



      UpperCAmelCase__      :				Optional[Any]				    =						42
      UpperCAmelCase__      :				int				    =						nn.CrossEntropyLoss().ignore_index


      def __init__(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    UpperCamelCase__=False ,    UpperCamelCase__ = Split.train ,    ):
          SCREAMING_SNAKE_CASE_   :	Dict													=   os.path.join(
              _SCREAMING_SNAKE_CASE ,    'cached_{}_{}_{}'.format(mode.value ,    tokenizer.__class__.__name__ ,    str(_SCREAMING_SNAKE_CASE       )       ) ,    )

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


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






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


if is_tf_available():
    import tensorflow as tf


    class 							__a							:





      '''simple docstring'''



      UpperCAmelCase__      :				List[Any]				    =						42
      UpperCAmelCase__      :				List[str]				    =						-100


      def __init__(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    UpperCamelCase__=False ,    UpperCamelCase__ = Split.train ,    ):
          SCREAMING_SNAKE_CASE_   :	Optional[int]													=   token_classification_task.read_examples_from_file(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE       )
          # TODO clean up all this to leverage built-in features of tokenizers
          SCREAMING_SNAKE_CASE_   :	Tuple													=   token_classification_task.convert_examples_to_features(
              _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_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=_SCREAMING_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:
              SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   tf.data.Dataset.from_generator(
                  _SCREAMING_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:
              SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   tf.data.Dataset.from_generator(
                  _SCREAMING_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   __snake_case			(						self       ):
          SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features       )       )       )

          return self.dataset


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






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







from timeit import timeit



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				if number < 0:
								raise ValueError('the value of input must not be negative'      )
				SCREAMING_SNAKE_CASE_   :	Tuple													=   0
				while number:
								number &= number - 1
								result += 1
				return result



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				if number < 0:
								raise ValueError('the value of input must not be negative'      )
				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   0
				while number:
								if number % 2 == 1:
												result += 1
								number >>= 1
				return result



def 					_lowerCamelCase(			):





				'''simple docstring'''







				def do_benchmark(lowerCAmelCase__				:		int      ) -> None:
								SCREAMING_SNAKE_CASE_   :	int													=   'import __main__ as z'
								print(F'''Benchmark when {number = }:'''      )
								print(F'''{get_set_bits_count_using_modulo_operator(lowerCAmelCase__      ) = }'''      )
								SCREAMING_SNAKE_CASE_   :	int													=   timeit('z.get_set_bits_count_using_modulo_operator(25)'    ,    setup=lowerCAmelCase__      )
								print(F'''timeit() runs in {timing} seconds'''      )
								print(F'''{get_set_bits_count_using_brian_kernighans_algorithm(lowerCAmelCase__      ) = }'''      )
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   timeit(
								    'z.get_set_bits_count_using_brian_kernighans_algorithm(25)'    ,    setup=lowerCAmelCase__    ,    )
								print(F'''timeit() runs in {timing} seconds'''      )

				for number in (25, 37, 58, 0):
								do_benchmark(lowerCAmelCase__      )
								print()


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				benchmark()