
'''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,
)


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


@dataclass(frozen=__lowerCamelCase    )
class  _UpperCAmelCase :

     """simple docstring"""

     a_    	=					42
     a_    	=					42
     a_    	=					None
     a_    	=					None
     a_    	=					None







@dataclass(frozen=__lowerCamelCase    )
class  _UpperCAmelCase :

     """simple docstring"""

     a_    	=					42
     a_    	=					None
     a_    	=					None
     a_    	=					None
     a_    	=					None


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


    class  _UpperCAmelCase ( __lowerCamelCase    ):

         """simple docstring"""

         a_    	=					42


         def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ = None							,		lowerCAmelCase_=False							,		lowerCAmelCase_ = False							,		):

               '''simple docstring'''

               a_      :	Dict      =							hans_processors[task]()

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

               # Make sure only the first process in distributed training processes the dataset,
               # and the others will use the cache.
               a_      :	List[str]      =							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}''' )
                           a_      :	List[Any]      =							torch.load(SCREAMING_SNAKE_CASE_ )
                     else:
                           logger.info(f'''Creating features from dataset file at {data_dir}''' )

                           a_      :	List[str]      =							(
                               processor.get_dev_examples(SCREAMING_SNAKE_CASE_ ) if evaluate else processor.get_train_examples(SCREAMING_SNAKE_CASE_ )
                           )

                           logger.info("""Training examples: %s"""							,		len(SCREAMING_SNAKE_CASE_ ) )
                           a_      :	Union[str, Any]      =							hans_convert_examples_to_features(SCREAMING_SNAKE_CASE_							,		SCREAMING_SNAKE_CASE_							,		SCREAMING_SNAKE_CASE_							,		SCREAMING_SNAKE_CASE_ )
                           logger.info("""Saving features into cached file %s"""							,		SCREAMING_SNAKE_CASE_ )
                           torch.save(self.features							,		SCREAMING_SNAKE_CASE_ )


         def __len__(      self ):

               '''simple docstring'''

               return len(self.features )


         def __getitem__(      self							,		lowerCAmelCase_ ):

               '''simple docstring'''

               return self.features[i]






         def 							_lowerCAmelCase  (      self ):

               '''simple docstring'''

               return self.label_list


if is_tf_available():
    import tensorflow as tf


    class  _UpperCAmelCase :

         """simple docstring"""

         a_    	=					42


         def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ = 1_28							,		lowerCAmelCase_=False							,		lowerCAmelCase_ = False							,		):

               '''simple docstring'''

               a_      :	Dict      =							hans_processors[task]()
               a_      :	Optional[int]      =							processor.get_labels()
               if tokenizer.__class__ in (
                   RobertaTokenizer,
                   RobertaTokenizerFast,
                   XLMRobertaTokenizer,
                   BartTokenizer,
                   BartTokenizerFast,
               ):
                     # HACK(label indices are swapped in RoBERTa pretrained model)
                     a_      , a_      :	str      =							label_list[2], label_list[1]
               a_      :	Union[str, Any]      =							label_list

               a_      :	str      =							processor.get_dev_examples(SCREAMING_SNAKE_CASE_ ) if evaluate else processor.get_train_examples(SCREAMING_SNAKE_CASE_ )
               a_      :	Any      =							hans_convert_examples_to_features(SCREAMING_SNAKE_CASE_							,		SCREAMING_SNAKE_CASE_							,		SCREAMING_SNAKE_CASE_							,		SCREAMING_SNAKE_CASE_ )

               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(SCREAMING_SNAKE_CASE_ )) )

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

               a_      :	Optional[Any]      =							tf.data.Dataset.from_generator(
                   SCREAMING_SNAKE_CASE_							,		(
                       {
                           """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 							_lowerCAmelCase  (      self ):

               '''simple docstring'''

               return self.dataset


         def __len__(      self ):

               '''simple docstring'''

               return len(self.features )


         def __getitem__(      self							,		lowerCAmelCase_ ):

               '''simple docstring'''

               return self.features[i]






         def 							_lowerCAmelCase  (      self ):

               '''simple docstring'''

               return self.label_list


class  _UpperCAmelCase ( __lowerCamelCase    ):

     """simple docstring"""

     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

           '''simple docstring'''

           return self._create_examples(self._read_tsv(os.path.join(SCREAMING_SNAKE_CASE_							,		"""heuristics_train_set.txt""" ) )							,		"""train""" )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

           '''simple docstring'''

           return self._create_examples(self._read_tsv(os.path.join(SCREAMING_SNAKE_CASE_							,		"""heuristics_evaluation_set.txt""" ) )							,		"""dev""" )


     def 							_lowerCAmelCase  (      self ):

           '''simple docstring'''

           return ["contradiction", "entailment", "neutral"]






     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

           '''simple docstring'''

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







def 	_snake_case						(      A_       :    List[InputExample]     ,		A_       :    List[str]     ,		A_       :    int     ,		A_       :    PreTrainedTokenizer     ,		):
      """simple docstring"""







      a_      :	str      =							{label: i for i, label in enumerate(__a   )}

      a_      :	int      =							[]
      for ex_index, example in tqdm.tqdm(enumerate(__a   )     ,		desc="""convert examples to features"""   ):
            if ex_index % 1_0000 == 0:
                  logger.info("""Writing example %d""" % (ex_index)   )

            a_      :	List[str]      =							tokenizer(
                example.text_a     ,		example.text_b     ,		add_special_tokens=__a     ,		max_length=__a     ,		padding="""max_length"""     ,		truncation=__a     ,		return_overflowing_tokens=__a     ,		)

            a_      :	Tuple      =							label_map[example.label] if example.label in label_map else 0

            a_      :	str      =							int(example.pairID   )

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

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

      return features


__snake_case:  int								=			{
    "hans": 3,
}




__snake_case:  Any								=			{
    "hans": HansProcessor,
}




'''simple docstring'''

def 	_snake_case						(      A_       :    list   ):
  """simple docstring"""







  if len(A_   ) <= 1:
    return lst

  a_      :	Any      =							1

  while i < len(A_   ):
    if lst[i - 1] <= lst[i]:
      i += 1
    else:
      a_      , a_      :	int      =							lst[i], lst[i - 1]
      i -= 1
      if i == 0:
        a_      :	List[str]      =							1

  return lst


if __name__ == "__main__":
    __snake_case:  List[Any]								=			input("Enter numbers separated by a comma:\n").strip()
    __snake_case:  Optional[int]								=			[int(item) for item in user_input.split(",")]
    print(gnome_sort(unsorted))


