


class  a     :
 def __init__( self      ,				__magic_name__      ,				__magic_name__						)					->							str:
      _a			      =							name
      _a			      =							val
 def __str__( self						)					->							Any:
      return f'{self.__class__.__name__}({self.name}, {self.val})'




 def __lt__( self      ,				__magic_name__						)					->							Optional[Any]:
      return self.val < other.val





class  a     :
 def __init__( self      ,				__magic_name__						)					->							int:
      _a			      =							{}
      _a			      =							{}
      _a			      =							self.build_heap(UpperCamelCase__						)
 def __getitem__( self      ,				__magic_name__						)					->							Tuple:
      return self.get_value(UpperCamelCase__						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Any:
      return (idx - 1) // 2
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							str:
      return idx * 2 + 1
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Union[str, Any]:
      return idx * 2 + 2
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							str:
      return self.heap_dict[key]
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Optional[int]:
      _a			      =							len(UpperCamelCase__						) - 1
      _a			      =							self.get_parent_idx(UpperCamelCase__						)

      for idx, i in enumerate(UpperCamelCase__						):
           _a			      =							idx
           _a			      =							i.val

      for i in range(UpperCamelCase__      ,				-1      ,				-1						):
           self.sift_down(UpperCamelCase__      ,				UpperCamelCase__						)
      return array
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							Tuple:





      while True:
           _a			      =							self.get_left_child_idx(UpperCamelCase__						)  # noqa: E741
           _a			      =							self.get_right_child_idx(UpperCamelCase__						)

           _a			      =							idx
           if l < len(UpperCamelCase__						) and array[l] < array[idx]:
                _a			      =							l
           if r < len(UpperCamelCase__						) and array[r] < array[smallest]:
                _a			      =							r

           if smallest != idx:
                _a      ,   _a			      =							array[smallest], array[idx]
                (
                    (
                    _a       
                )      ,   (
                    _a       
                )      ,          
                )			      =							(
                    self.idx_of_element[array[smallest]],
                    self.idx_of_element[array[idx]],
                )
                _a			      =							smallest
           else:
                break
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Dict:
      _a			      =							self.get_parent_idx(UpperCamelCase__						)
      while p >= 0 and self.heap[p] > self.heap[idx]:
           _a      ,   _a			      =							self.heap[idx], self.heap[p]
           _a      ,   _a			      =							(
               self.idx_of_element[self.heap[idx]],
               self.idx_of_element[self.heap[p]],
           )
           _a			      =							p
           _a			      =							self.get_parent_idx(UpperCamelCase__						)
 def 							__UpperCAmelCase		( self						)					->							Dict:
      return self.heap[0]
 def 							__UpperCAmelCase		( self						)					->							List[Any]:
      _a      ,   _a			      =							self.heap[-1], self.heap[0]
      _a      ,   _a			      =							(
          self.idx_of_element[self.heap[-1]],
          self.idx_of_element[self.heap[0]],
      )

      _a			      =							self.heap.pop()
      del self.idx_of_element[x]
      self.sift_down(0      ,				self.heap						)
      return x
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Any:
      self.heap.append(UpperCamelCase__						)
      _a			      =							len(self.heap						) - 1
      _a			      =							node.val
      self.sift_up(len(self.heap						) - 1						)
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      return len(self.heap						) == 0




 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							int:
      assert (
          self.heap[self.idx_of_element[node]].val > new_value
      ), "newValue must be less that current value"
      _a			      =							new_value
      _a			      =							new_value
      self.sift_up(self.idx_of_element[node]						)





a_					:    Optional[int]          =    Node("R", -1)
a_					:    str          =    Node("B", 6)
a_					:    Dict          =    Node("A", 3)
a_					:    str          =    Node("X", 1)
a_					:    List[str]          =    Node("E", 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
a_					:    List[str]          =    MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print("Min Heap - before decrease key")
for i in my_min_heap.heap:
 print(i)

print("Min Heap - After decrease key of node [B -> -17]")
my_min_heap.decrease_key(b, -1_7)

# After
for i in my_min_heap.heap:
 print(i)

if __name__ == "__main__":
 import doctest

 doctest.testmod()





'''simple docstring'''






import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


a_					:    Optional[Any]          =    logging.getLogger(__name__)




@dataclass(frozen=_SCREAMING_SNAKE_CASE       )
class  a     :
 _lowerCAmelCase 					=	42
 _lowerCAmelCase 					=	42
 _lowerCAmelCase 					=	None
 _lowerCAmelCase 					=	None
 _lowerCAmelCase 					=	None





@dataclass(frozen=_SCREAMING_SNAKE_CASE       )
class  a     :
 _lowerCAmelCase 					=	42
 _lowerCAmelCase 					=	None
 _lowerCAmelCase 					=	None
 _lowerCAmelCase 					=	None
 _lowerCAmelCase 					=	None


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




 class  a     (     _SCREAMING_SNAKE_CASE       ):
  _lowerCAmelCase 					=	42
  def __init__( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = None      ,				__magic_name__=False      ,				__magic_name__ = False      ,				)					->							Optional[int]:
       _a			      =							hans_processors[task]()

       _a			      =							os.path.join(
           __magic_name__      ,				'cached_{}_{}_{}_{}'.format(
               'dev' if evaluate else 'train'      ,				tokenizer.__class__.__name__      ,				str(__magic_name__						)      ,				__magic_name__      ,				)      ,				)
       _a			      =							processor.get_labels()
       if tokenizer.__class__ in (
           RobertaTokenizer,
           RobertaTokenizerFast,
           XLMRobertaTokenizer,
           BartTokenizer,
           BartTokenizerFast,
       ):
            # HACK(label indices are swapped in RoBERTa pretrained model)
            _a      ,   _a			      =							label_list[2], label_list[1]
       _a			      =							label_list

       # 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(__magic_name__						):
            if os.path.exists(__magic_name__						) and not overwrite_cache:
                 logger.info(f'Loading features from cached file {cached_features_file}'						)
                 _a			      =							torch.load(__magic_name__						)
            else:
                 logger.info(f'Creating features from dataset file at {data_dir}'						)

                 _a			      =							(
                     processor.get_dev_examples(__magic_name__						) if evaluate else processor.get_train_examples(__magic_name__						)
                 )

                 logger.info('Training examples: %s'      ,				len(__magic_name__						)						)
                 _a			      =							hans_convert_examples_to_features(__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)
                 logger.info('Saving features into cached file %s'      ,				__magic_name__						)
                 torch.save(self.features      ,				__magic_name__						)
  def __len__( self						)					->							List[Any]:
       return len(self.features						)
  def __getitem__( self      ,				__magic_name__						)					->							InputFeatures:
       return self.features[i]




  def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
       return self.label_list


if is_tf_available():
 import tensorflow as tf




 class  a     :
  _lowerCAmelCase 					=	42
  def __init__( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = 1_28      ,				__magic_name__=False      ,				__magic_name__ = False      ,				)					->							str:
       _a			      =							hans_processors[task]()
       _a			      =							processor.get_labels()
       if tokenizer.__class__ in (
           RobertaTokenizer,
           RobertaTokenizerFast,
           XLMRobertaTokenizer,
           BartTokenizer,
           BartTokenizerFast,
       ):
            # HACK(label indices are swapped in RoBERTa pretrained model)
            _a      ,   _a			      =							label_list[2], label_list[1]
       _a			      =							label_list

       _a			      =							processor.get_dev_examples(__magic_name__						) if evaluate else processor.get_train_examples(__magic_name__						)
       _a			      =							hans_convert_examples_to_features(__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)

       def gen():
            for ex_index, ex in tqdm.tqdm(enumerate(self.features						)      ,				desc='convert examples to features'						):
                 if ex_index % 1_00_00 == 0:
                      logger.info('Writing example %d of %d' % (ex_index, len(__magic_name__						))						)

                 yield (
                     {
                         "example_id": 0,
                         "input_ids": ex.input_ids,
                         "attention_mask": ex.attention_mask,
                         "token_type_ids": ex.token_type_ids,
                     },
                     ex.label,
                 )

       _a			      =							tf.data.Dataset.from_generator(
           __magic_name__      ,				(
               {
                   'example_id': tf.intaa,
                   'input_ids': tf.intaa,
                   'attention_mask': tf.intaa,
                   'token_type_ids': tf.intaa,
               },
               tf.intaa,
           )      ,				(
               {
                   'example_id': tf.TensorShape([]						),
                   'input_ids': tf.TensorShape([None, None]						),
                   'attention_mask': tf.TensorShape([None, None]						),
                   'token_type_ids': tf.TensorShape([None, None]						),
               },
               tf.TensorShape([]						),
           )      ,				)
  def 							__UpperCAmelCase		( self						)					->							Tuple:
       return self.dataset
  def __len__( self						)					->							Optional[Any]:
       return len(self.features						)
  def __getitem__( self      ,				__magic_name__						)					->							InputFeatures:
       return self.features[i]




  def 							__UpperCAmelCase		( self						)					->							List[Any]:
       return self.label_list




class  a     (     _SCREAMING_SNAKE_CASE       ):
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Tuple:
      return self._create_examples(self._read_tsv(os.path.join(__magic_name__      ,				'heuristics_train_set.txt'						)						)      ,				'train'						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							List[Any]:
      return self._create_examples(self._read_tsv(os.path.join(__magic_name__      ,				'heuristics_evaluation_set.txt'						)						)      ,				'dev'						)
 def 							__UpperCAmelCase		( self						)					->							Tuple:
      return ["contradiction", "entailment", "neutral"]




 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							Optional[Any]:
      _a			      =							[]
      for i, line in enumerate(__magic_name__						):
           if i == 0:
                continue
           _a			      =							'%s-%s' % (set_type, line[0])
           _a			      =							line[5]
           _a			      =							line[6]
           _a			      =							line[7][2:] if line[7].startswith('ex'						) else line[7]
           _a			      =							line[0]
           examples.append(InputExample(guid=__magic_name__      ,				text_a=__magic_name__      ,				text_b=__magic_name__      ,				label=__magic_name__      ,				pairID=__magic_name__						)						)
      return examples






def 	_A				(lowerCAmelCase__  :List[InputExample]				,						lowerCAmelCase__  :List[str]				,						lowerCAmelCase__  :int				,						lowerCAmelCase__  :PreTrainedTokenizer				,						)   ->  Tuple:




     '''simple docstring'''



     _a			      =							{label: i for i, label in enumerate(lowerCAmelCase__		)}

     _a			      =							[]
     for ex_index, example in tqdm.tqdm(enumerate(lowerCAmelCase__		)				,						desc='convert examples to features'		):
          if ex_index % 1_00_00 == 0:
               logger.info('Writing example %d' % (ex_index)		)

          _a			      =							tokenizer(
              example.text_a				,						example.text_b				,						add_special_tokens=lowerCAmelCase__				,						max_length=lowerCAmelCase__				,						padding='max_length'				,						truncation=lowerCAmelCase__				,						return_overflowing_tokens=lowerCAmelCase__				,						)

          _a			      =							label_map[example.label] if example.label in label_map else 0

          _a			      =							int(example.pairID		)

          features.append(InputFeatures(**lowerCAmelCase__				,						label=lowerCAmelCase__				,						pairID=lowerCAmelCase__		)		)

     for i, example in enumerate(examples[:5]		):
          logger.info('*** Example ***'		)
          logger.info(f'guid: {example}'		)
          logger.info(f'features: {features[i]}'		)

     return features


a_					:    Optional[int]          =    {
    "hans": 3,
}






a_					:    Optional[Any]          =    {
    "hans": HansProcessor,
}

