
import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow




@is_pipeline_test
@require_torch
class        UpperCAmelCase_			( unittest.TestCase     ):


  '''simple docstring'''







  @require_torch
  def 							_lowercase							( self			: Any    )			->	Optional[Any]:







    """simple docstring"""






    __magic_name__				     =       pipeline(
        task="""zero-shot-audio-classification"""			,							model="""hf-internal-testing/tiny-clap-htsat-unfused"""    )
    __magic_name__				     =       load_dataset("""ashraq/esc50"""    )
    __magic_name__				     =       dataset["""train"""]["""audio"""][-1]["""array"""]
    __magic_name__				     =       audio_classifier(UpperCAmelCase__			,							candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]    )
    self.assertEqual(
        nested_simplify(UpperCAmelCase__    )			,							[{"""score""": 0.501, """label""": """Sound of a dog"""}, {"""score""": 0.499, """label""": """Sound of vaccum cleaner"""}]			,							)
  @unittest.skip("""No models are available in TF"""    )
  def 							_lowercase							( self			: Any    )			->	Optional[Any]:







    """simple docstring"""






    pass
  @slow
  @require_torch
  def 							_lowercase							( self			: Tuple    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       pipeline(
        task="""zero-shot-audio-classification"""			,							model="""laion/clap-htsat-unfused"""			,							)
    # This is an audio of a dog
    __magic_name__				     =       load_dataset("""ashraq/esc50"""    )
    __magic_name__				     =       dataset["""train"""]["""audio"""][-1]["""array"""]
    __magic_name__				     =       audio_classifier(UpperCAmelCase__			,							candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]    )

    self.assertEqual(
        nested_simplify(UpperCAmelCase__    )			,							[
            {"""score""": 0.999, """label""": """Sound of a dog"""},
            {"""score""": 0.001, """label""": """Sound of vaccum cleaner"""},
        ]			,							)

    __magic_name__				     =       audio_classifier([audio] * 5			,							candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]    )
    self.assertEqual(
        nested_simplify(UpperCAmelCase__    )			,							[
            [
                {"""score""": 0.999, """label""": """Sound of a dog"""},
                {"""score""": 0.001, """label""": """Sound of vaccum cleaner"""},
            ],
        ]
        * 5			,							)
    __magic_name__				     =       audio_classifier(
        [audio] * 5			,							candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]			,							batch_size=5    )
    self.assertEqual(
        nested_simplify(UpperCAmelCase__    )			,							[
            [
                {"""score""": 0.999, """label""": """Sound of a dog"""},
                {"""score""": 0.001, """label""": """Sound of vaccum cleaner"""},
            ],
        ]
        * 5			,							)





  @unittest.skip("""No models are available in TF"""    )
  def 							_lowercase							( self			: Any    )			->	Union[str, Any]:







    """simple docstring"""






    pass





'''simple docstring'''




from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge

@total_ordering
class 		UpperCAmelCase_      (  __lowercase		):





     def __lt__( self       :      Optional[int]    ,      UpperCAmelCase__       :      List[str]    )				->  List[Any]:
        return self[-1] < other[-1]





     def __eq__( self       :      str    ,      UpperCAmelCase__       :      List[str]    )				->  Tuple:
        return self[-1] == other[-1]





def 	a_					( lowerCamelCase	:     list				):
   lowerCAmelCase     =			[]
   # sort into stacks
   for element in collection:
      lowerCAmelCase     =			Stack([element]				)
      lowerCAmelCase     =			bisect_left(lowerCamelCase	, lowerCamelCase				)
      if i != len(lowerCamelCase				):
         stacks[i].append(lowerCamelCase				)
      else:
         stacks.append(lowerCamelCase				)

    # use a heap-based merge to merge stack efficiently
   lowerCAmelCase     =			merge(*(reversed(lowerCamelCase				) for stack in stacks)				)
   return collection


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





