



"""simple docstring"""




import math

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		list		,    SCREAMING_SNAKE_CASE__      :		int = 0		,    SCREAMING_SNAKE_CASE__      :		int = 0							)    ->  list:





     '''simple docstring'''

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

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		list		,    SCREAMING_SNAKE_CASE__      :		int		,    SCREAMING_SNAKE_CASE__      :		int							)    ->  None:  # Max Heap





     '''simple docstring'''

     _UpperCAmelCase     :      List[Any]														=       index
     _UpperCAmelCase     :      List[str]														=       2 * index + 1  # Left Node
     _UpperCAmelCase     :      str														=       2 * index + 2  # Right Node

     if left_index < heap_size and array[largest] < array[left_index]:
          _UpperCAmelCase     :      Any														=       left_index

     if right_index < heap_size and array[largest] < array[right_index]:
          _UpperCAmelCase     :      Optional[int]														=       right_index

     if largest != index:
          _UpperCAmelCase							,			_UpperCAmelCase     :      str														=       array[largest], array[index]
          heapify(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		list							)    ->  list:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       len(SCREAMING_SNAKE_CASE__							)

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

     for i in range(n - 1		,    0		,    -1							):
          _UpperCAmelCase							,			_UpperCAmelCase     :      Union[str, Any]														=       array[0], array[i]
          heapify(SCREAMING_SNAKE_CASE__		,    0		,    SCREAMING_SNAKE_CASE__							)

     return array

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		list		,    SCREAMING_SNAKE_CASE__      :		int		,    SCREAMING_SNAKE_CASE__      :		int		,    SCREAMING_SNAKE_CASE__      :		int							)    ->  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 		__snake_case		( SCREAMING_SNAKE_CASE__      :		list		,    SCREAMING_SNAKE_CASE__      :		int		,    SCREAMING_SNAKE_CASE__      :		int		,    SCREAMING_SNAKE_CASE__      :		int							)    ->  int:





     '''simple docstring'''

     _UpperCAmelCase     :      List[Any]														=       low
     _UpperCAmelCase     :      Tuple														=       high
     while True:
          while array[i] < pivot:
               i += 1
          j -= 1
          while pivot < array[j]:
               j -= 1
          if i >= j:
               return i
          _UpperCAmelCase							,			_UpperCAmelCase     :      Optional[Any]														=       array[j], array[i]
          i += 1

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		list							)    ->  list:





     '''simple docstring'''

     if len(SCREAMING_SNAKE_CASE__							) == 0:
          return array
     _UpperCAmelCase     :      List[Any]														=       2 * math.ceil(math.loga(len(SCREAMING_SNAKE_CASE__							)							)							)
     _UpperCAmelCase     :      Optional[Any]														=       16
     return intro_sort(SCREAMING_SNAKE_CASE__		,    0		,    len(SCREAMING_SNAKE_CASE__							)		,    SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		list		,    SCREAMING_SNAKE_CASE__      :		int		,    SCREAMING_SNAKE_CASE__      :		int		,    SCREAMING_SNAKE_CASE__      :		int		,    SCREAMING_SNAKE_CASE__      :		int							)    ->  list:





     '''simple docstring'''

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


if __name__ == "__main__":
 import doctest

 doctest.testmod()

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





"""simple docstring"""




import unittest

from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
 from PIL import Image
else:






 class       UpperCAmelCase_    :



       @staticmethod
       def 		snake_case_   (	*A	:      Union[str, Any]	,      **A	:      Dict		):
            pass






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



      @require_torch
      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      Union[str, Any]														=       pipeline(
               model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification"	,      )
           _UpperCAmelCase     :      Union[str, Any]														=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
           _UpperCAmelCase     :      Tuple														=       image_classifier(A	,      candidate_labels=["a", "b", "c"]		)

           # The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
           # python and torch versions.
           self.assertIn(
               nested_simplify(A		)	,      [
                   [{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}],
                   [{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "c"}, {"score": 0.333, "label": "b"}],
               ]	,      )

           _UpperCAmelCase     :      str														=       image_classifier([image] * 5	,      candidate_labels=["A", "B", "C"]	,      batch_size=2		)
           self.assertEqual(
               nested_simplify(A		)	,      [
                   [
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                   ],
                   [
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                   ],
                   [
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                   ],
                   [
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                   ],
                   [
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                   ],
               ]	,      )



      @require_tf
      def 		snake_case_   (	self	:      Any		):
           _UpperCAmelCase     :      List[str]														=       pipeline(
               model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification"	,      framework="tf"		)
           _UpperCAmelCase     :      int														=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
           _UpperCAmelCase     :      Union[str, Any]														=       image_classifier(A	,      candidate_labels=["a", "b", "c"]		)

           self.assertEqual(
               nested_simplify(A		)	,      [{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}]	,      )

           _UpperCAmelCase     :      Optional[Any]														=       image_classifier([image] * 5	,      candidate_labels=["A", "B", "C"]	,      batch_size=2		)
           self.assertEqual(
               nested_simplify(A		)	,      [
                   [
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                   ],
                   [
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                   ],
                   [
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                   ],
                   [
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                   ],
                   [
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                   ],
               ]	,      )



      @slow
      @require_torch
      def 		snake_case_   (	self	:      Any		):
           _UpperCAmelCase     :      Optional[Any]														=       pipeline(
               task="zero-shot-image-classification"	,      model="openai/clip-vit-base-patch32"	,      )
           # This is an image of 2 cats with remotes and no planes
           _UpperCAmelCase     :      Dict														=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
           _UpperCAmelCase     :      List[Any]														=       image_classifier(A	,      candidate_labels=["cat", "plane", "remote"]		)

           self.assertEqual(
               nested_simplify(A		)	,      [
                   {"score": 0.511, "label": "remote"},
                   {"score": 0.485, "label": "cat"},
                   {"score": 0.004, "label": "plane"},
               ]	,      )

           _UpperCAmelCase     :      Tuple														=       image_classifier([image] * 5	,      candidate_labels=["cat", "plane", "remote"]	,      batch_size=2		)
           self.assertEqual(
               nested_simplify(A		)	,      [
                   [
                       {"score": 0.511, "label": "remote"},
                       {"score": 0.485, "label": "cat"},
                       {"score": 0.004, "label": "plane"},
                   ],
               ]
               * 5	,      )



      @slow
      @require_tf
      def 		snake_case_   (	self	:      Any		):
           _UpperCAmelCase     :      List[Any]														=       pipeline(
               task="zero-shot-image-classification"	,      model="openai/clip-vit-base-patch32"	,      framework="tf"		)
           # This is an image of 2 cats with remotes and no planes
           _UpperCAmelCase     :      Union[str, Any]														=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
           _UpperCAmelCase     :      Union[str, Any]														=       image_classifier(A	,      candidate_labels=["cat", "plane", "remote"]		)
           self.assertEqual(
               nested_simplify(A		)	,      [
                   {"score": 0.511, "label": "remote"},
                   {"score": 0.485, "label": "cat"},
                   {"score": 0.004, "label": "plane"},
               ]	,      )

           _UpperCAmelCase     :      Union[str, Any]														=       image_classifier([image] * 5	,      candidate_labels=["cat", "plane", "remote"]	,      batch_size=2		)
           self.assertEqual(
               nested_simplify(A		)	,      [
                   [
                       {"score": 0.511, "label": "remote"},
                       {"score": 0.485, "label": "cat"},
                       {"score": 0.004, "label": "plane"},
                   ],
               ]
               * 5	,      )
