






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,
)


__lowerCamelCase	:		List[Any]										=   logging.getLogger(__name__)







@dataclass(frozen=_A						)
class  _lowercase   :
      _a							:						List[str]										=							42
      _a							:						Tuple										=							42
      _a							:						Optional[int]										=							None
      _a							:						Dict										=							None
      _a							:						int										=							None







@dataclass(frozen=_A						)
class  _lowercase   :
      _a							:						Tuple										=							42
      _a							:						Dict										=							None
      _a							:						List[str]										=							None
      _a							:						Tuple										=							None
      _a							:						List[str]										=							None


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







       class  _lowercase   (   _A						):
             _a							:						List[Any]										=							42
             def __init__(     self				,			a				,			a				,			a				,			a = None				,			a=False				,			a = False				,			):
              snake_case__  :    Optional[Any]							       =hans_processors[task]()

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

              # 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(_lowerCAmelCase   ):
               if os.path.exists(_lowerCAmelCase   ) and not overwrite_cache:
                logger.info(F"Loading features from cached file {cached_features_file}"   )
                snake_case__  :    Tuple							       =torch.load(_lowerCAmelCase   )
               else:
                logger.info(F"Creating features from dataset file at {data_dir}"   )

                snake_case__  :    Optional[int]							       =(
                    processor.get_dev_examples(_lowerCAmelCase   ) if evaluate else processor.get_train_examples(_lowerCAmelCase   )
                )

                logger.info("""Training examples: %s"""				,			len(_lowerCAmelCase   )   )
                snake_case__  :    Dict							       =hans_convert_examples_to_features(_lowerCAmelCase				,			_lowerCAmelCase				,			_lowerCAmelCase				,			_lowerCAmelCase   )
                logger.info("""Saving features into cached file %s"""				,			_lowerCAmelCase   )
                torch.save(self.features				,			_lowerCAmelCase   )
             def __len__(     self   ):
              return len(self.features   )
             def __getitem__(     self				,			a   ):
              return self.features[i]



             def 						lowercase__					(     self   ):
              return self.label_list


if is_tf_available():
       import tensorflow as tf







       class  _lowercase   :
             _a							:						List[str]										=							42
             def __init__(     self				,			a				,			a				,			a				,			a = 1_2_8				,			a=False				,			a = False				,			):
              snake_case__  :    Dict							       =hans_processors[task]()
              snake_case__  :    Tuple							       =processor.get_labels()
              if tokenizer.__class__ in (
                  RobertaTokenizer,
                  RobertaTokenizerFast,
                  XLMRobertaTokenizer,
                  BartTokenizer,
                  BartTokenizerFast,
              ):
               # HACK(label indices are swapped in RoBERTa pretrained model)
               snake_case__       ,   snake_case__  :    Tuple							       =label_list[2], label_list[1]
              snake_case__  :    Any							       =label_list

              snake_case__  :    str							       =processor.get_dev_examples(_lowerCAmelCase   ) if evaluate else processor.get_train_examples(_lowerCAmelCase   )
              snake_case__  :    List[str]							       =hans_convert_examples_to_features(_lowerCAmelCase				,			_lowerCAmelCase				,			_lowerCAmelCase				,			_lowerCAmelCase   )

              def gen():
               for ex_index, ex in tqdm.tqdm(enumerate(self.features   )				,			desc="""convert examples to features"""   ):
                if ex_index % 1_0_0_0_0 == 0:
                 logger.info("""Writing example %d of %d""" % (ex_index, len(_lowerCAmelCase   ))   )

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

              snake_case__  :    Optional[Any]							       =tf.data.Dataset.from_generator(
                  _lowerCAmelCase				,			(
                      {
                          """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 						lowercase__					(     self   ):
              return self.dataset
             def __len__(     self   ):
              return len(self.features   )
             def __getitem__(     self				,			a   ):
              return self.features[i]



             def 						lowercase__					(     self   ):
              return self.label_list







class  _lowercase   (   _A						):
      def 						lowercase__					(     self				,			a   ):
       return self._create_examples(self._read_tsv(os.path.join(_lowerCAmelCase				,			"""heuristics_train_set.txt"""   )   )				,			"""train"""   )
      def 						lowercase__					(     self				,			a   ):
       return self._create_examples(self._read_tsv(os.path.join(_lowerCAmelCase				,			"""heuristics_evaluation_set.txt"""   )   )				,			"""dev"""   )
      def 						lowercase__					(     self   ):
       return ["contradiction", "entailment", "neutral"]



      def 						lowercase__					(     self				,			a				,			a   ):
       snake_case__  :    int							       =[]
       for i, line in enumerate(_lowerCAmelCase   ):
        if i == 0:
         continue
        snake_case__  :    Union[str, Any]							       ="""%s-%s""" % (set_type, line[0])
        snake_case__  :    Union[str, Any]							       =line[5]
        snake_case__  :    int							       =line[6]
        snake_case__  :    List[str]							       =line[7][2:] if line[7].startswith("""ex"""   ) else line[7]
        snake_case__  :    Optional[int]							       =line[0]
        examples.append(InputExample(guid=_lowerCAmelCase				,			text_a=_lowerCAmelCase				,			text_b=_lowerCAmelCase				,			label=_lowerCAmelCase				,			pairID=_lowerCAmelCase   )   )
       return examples







def 			A__							(      _a  :					List[InputExample]		,  _a  :					List[str]		,  _a  :					int		,  _a  :					PreTrainedTokenizer		,  ):





 '''simple docstring'''







 snake_case__  :    Union[str, Any]							       ={label: i for i, label in enumerate(__lowerCAmelCase	)}

 snake_case__  :    Tuple							       =[]
 for ex_index, example in tqdm.tqdm(enumerate(__lowerCAmelCase	)		,  desc="""convert examples to features"""	):
  if ex_index % 10000 == 0:
   logger.info("""Writing example %d""" % (ex_index)	)

  snake_case__  :    Tuple							       =tokenizer(
      example.text_a		,  example.text_b		,  add_special_tokens=__lowerCAmelCase		,  max_length=__lowerCAmelCase		,  padding="""max_length"""		,  truncation=__lowerCAmelCase		,  return_overflowing_tokens=__lowerCAmelCase		,  )

  snake_case__  :    List[Any]							       =label_map[example.label] if example.label in label_map else 0

  snake_case__  :    Optional[int]							       =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


__lowerCamelCase	:		List[Any]										=   {
    'hans': 3,
}

__lowerCamelCase	:		Tuple										=   {
    'hans': HansProcessor,
}













import math







def 			A__							(      _a  :					list		,  _a  :					int = 0		,  _a  :					int = 0	):





 '''simple docstring'''







 snake_case__  :    Tuple							       =end or len(_a	)
 for i in range(_a		,  _a	):
  snake_case__  :    int							       =i
  snake_case__  :    int							       =array[i]
  while temp_index != start and temp_index_value < array[temp_index - 1]:
   snake_case__  :    Dict							       =array[temp_index - 1]
   temp_index -= 1
  snake_case__  :    Dict							       =temp_index_value
 return array







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int	):  # Max Heap





 '''simple docstring'''







 snake_case__  :    Tuple							       =index
 snake_case__  :    Dict							       =2 * index + 1  # Left Node
 snake_case__  :    Union[str, Any]							       =2 * index + 2  # Right Node

 if left_index < heap_size and array[largest] < array[left_index]:
  snake_case__  :    Dict							       =left_index

 if right_index < heap_size and array[largest] < array[right_index]:
  snake_case__  :    List[Any]							       =right_index

 if largest != index:
  snake_case__       ,   snake_case__  :    List[Any]							       =array[largest], array[index]
  heapify(_a		,  _a		,  _a	)







def 			A__							(      _a  :					list	):





 '''simple docstring'''







 snake_case__  :    List[str]							       =len(_a	)

 for i in range(n // 2		,  -1		,  -1	):
  heapify(_a		,  _a		,  _a	)

 for i in range(n - 1		,  0		,  -1	):
  snake_case__       ,   snake_case__  :    Tuple							       =array[0], array[i]
  heapify(_a		,  0		,  _a	)

 return array







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 if (array[first_index] > array[middle_index]) != (
     array[first_index] > array[last_index]
 ):
  return array[first_index]
 elif (array[middle_index] > array[first_index]) != (
     array[middle_index] > array[last_index]
 ):
  return array[middle_index]
 else:
  return array[last_index]







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 snake_case__  :    Dict							       =low
 snake_case__  :    Union[str, Any]							       =high
 while True:
  while array[i] < pivot:
   i += 1
  j -= 1
  while pivot < array[j]:
   j -= 1
  if i >= j:
   return i
  snake_case__       ,   snake_case__  :    List[Any]							       =array[j], array[i]
  i += 1







def 			A__							(      _a  :					list	):





 '''simple docstring'''







 if len(_a	) == 0:
  return array
 snake_case__  :    Optional[int]							       =2 * math.ceil(math.loga(len(_a	)	)	)
 snake_case__  :    Tuple							       =16
 return intro_sort(_a		,  0		,  len(_a	)		,  _a		,  _a	)







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int		,  _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 while end - start > size_threshold:
  if max_depth == 0:
   return heap_sort(_a	)
  max_depth -= 1
  snake_case__  :    Dict							       =median_of_a(_a		,  _a		,  start + ((end - start) // 2) + 1		,  end - 1	)
  snake_case__  :    List[str]							       =partition(_a		,  _a		,  _a		,  _a	)
  intro_sort(_a		,  _a		,  _a		,  _a		,  _a	)
  snake_case__  :    Union[str, Any]							       =p
 return insertion_sort(_a		,  _a		,  _a	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       __lowerCamelCase	:		Optional[int]										=   input("""Enter numbers separated by a comma : """).strip()
       __lowerCamelCase	:		Dict										=   [float(item) for item in user_input.split(""",""")]
       print(sort(unsorted))





