

'''simple docstring'''

import math

def 					__UpperCamelCase				(						lowercase__    :	list,       lowercase__    :	int = 0,       lowercase__    :	int = 0			):






     '''simple docstring'''





     __lowercase      		=end or len(lowercase__			)
     for i in range(lowercase__,       lowercase__			):
          __lowercase      		=i
          __lowercase      		=array[i]
          while temp_index != start and temp_index_value < array[temp_index - 1]:
               __lowercase      		=array[temp_index - 1]
               temp_index -= 1
          __lowercase      		=temp_index_value
     return array

def 					__UpperCamelCase				(						lowercase__    :	list,       lowercase__    :	int,       lowercase__    :	int			):  # Max Heap






     '''simple docstring'''





     __lowercase      		=index
     __lowercase      		=2 * index + 1  # Left Node
     __lowercase      		=2 * index + 2  # Right Node

     if left_index < heap_size and array[largest] < array[left_index]:
          __lowercase      		=left_index

     if right_index < heap_size and array[largest] < array[right_index]:
          __lowercase      		=right_index

     if largest != index:
          __lowercase		,  __lowercase      		=array[largest], array[index]
          heapify(lowercase__,       lowercase__,       lowercase__			)

def 					__UpperCamelCase				(						lowercase__    :	list			):






     '''simple docstring'''





     __lowercase      		=len(lowercase__			)

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

     for i in range(n - 1,       0,       -1			):
          __lowercase		,  __lowercase      		=array[0], array[i]
          heapify(lowercase__,       0,       lowercase__			)

     return array

def 					__UpperCamelCase				(						lowercase__    :	list,       lowercase__    :	int,       lowercase__    :	int,       lowercase__    :	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 					__UpperCamelCase				(						lowercase__    :	list,       lowercase__    :	int,       lowercase__    :	int,       lowercase__    :	int			):






     '''simple docstring'''





     __lowercase      		=low
     __lowercase      		=high
     while True:
          while array[i] < pivot:
               i += 1
          j -= 1
          while pivot < array[j]:
               j -= 1
          if i >= j:
               return i
          __lowercase		,  __lowercase      		=array[j], array[i]
          i += 1

def 					__UpperCamelCase				(						lowercase__    :	list			):






     '''simple docstring'''





     if len(lowercase__			) == 0:
          return array
     __lowercase      		=2 * math.ceil(math.loga(len(lowercase__			)			)			)
     __lowercase      		=16
     return intro_sort(lowercase__,       0,       len(lowercase__			),       lowercase__,       lowercase__			)

def 					__UpperCamelCase				(						lowercase__    :	list,       lowercase__    :	int,       lowercase__    :	int,       lowercase__    :	int,       lowercase__    :	int			):






     '''simple docstring'''





     while end - start > size_threshold:
          if max_depth == 0:
               return heap_sort(lowercase__			)
          max_depth -= 1
          __lowercase      		=median_of_a(lowercase__,       lowercase__,       start + ((end - start) // 2) + 1,       end - 1			)
          __lowercase      		=partition(lowercase__,       lowercase__,       lowercase__,       lowercase__			)
          intro_sort(lowercase__,       lowercase__,       lowercase__,       lowercase__,       lowercase__			)
          __lowercase      		=p
     return insertion_sort(lowercase__,       lowercase__,       lowercase__			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()

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





'''simple docstring'''

import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / '''utils'''))

from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


UpperCAmelCase					 =	get_tests_dir('''fixtures''')


class 	lowerCAmelCase  (   unittest.TestCase ):







       def       snake_case (				self      :			str   ):







            """simple docstring"""







            __lowercase      		=mock.Mock()
            __lowercase      		=500
            __lowercase      		={}
            __lowercase      		=HTTPError
            __lowercase      		={}

            # Download this model to make sure it's in the cache.
            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'   )
            # Under the mock environment we get a 500 error when trying to reach the model.
            with mock.patch('requests.Session.request'    ,   return_value=__lowercase   ) as mock_head:
                 __lowercase      		=WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'   )
                 # This check we did call the fake head request
                 mock_head.assert_called()







       def       snake_case (				self      :			Dict   ):







            """simple docstring"""







            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(
                'https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json'   )






@is_staging_test
class 	lowerCAmelCase  (   unittest.TestCase ):







       @classmethod
       def       snake_case (				cls      :			str   ):







            """simple docstring"""







            __lowercase      		=TOKEN
            HfFolder.save_token(__lowercase   )







       @classmethod
       def       snake_case (				cls      :			str   ):







            """simple docstring"""







            try:
                 delete_repo(token=cls._token    ,   repo_id='test-feature-extractor'   )
            except HTTPError:
                 pass

            try:
                 delete_repo(token=cls._token    ,   repo_id='valid_org/test-feature-extractor-org'   )
            except HTTPError:
                 pass

            try:
                 delete_repo(token=cls._token    ,   repo_id='test-dynamic-feature-extractor'   )
            except HTTPError:
                 pass







       def       snake_case (				self      :			int   ):







            """simple docstring"""







            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(__lowercase   )
            feature_extractor.push_to_hub('test-feature-extractor'    ,   use_auth_token=self._token   )

            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(f'''{USER}/test-feature-extractor'''   )
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(__lowercase    ,   getattr(__lowercase    ,   __lowercase   )   )

            # Reset repo
            delete_repo(token=self._token    ,   repo_id='test-feature-extractor'   )

            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                 feature_extractor.save_pretrained(
                     __lowercase    ,   repo_id='test-feature-extractor'    ,   push_to_hub=__lowercase    ,   use_auth_token=self._token   )

            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(f'''{USER}/test-feature-extractor'''   )
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(__lowercase    ,   getattr(__lowercase    ,   __lowercase   )   )







       def       snake_case (				self      :			List[Any]   ):







            """simple docstring"""







            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(__lowercase   )
            feature_extractor.push_to_hub('valid_org/test-feature-extractor'    ,   use_auth_token=self._token   )

            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor'   )
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(__lowercase    ,   getattr(__lowercase    ,   __lowercase   )   )

            # Reset repo
            delete_repo(token=self._token    ,   repo_id='valid_org/test-feature-extractor'   )

            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                 feature_extractor.save_pretrained(
                     __lowercase    ,   repo_id='valid_org/test-feature-extractor-org'    ,   push_to_hub=__lowercase    ,   use_auth_token=self._token   )

            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor-org'   )
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(__lowercase    ,   getattr(__lowercase    ,   __lowercase   )   )







       def       snake_case (				self      :			Optional[Any]   ):







            """simple docstring"""







            CustomFeatureExtractor.register_for_auto_class()
            __lowercase      		=CustomFeatureExtractor.from_pretrained(__lowercase   )

            feature_extractor.push_to_hub('test-dynamic-feature-extractor'    ,   use_auth_token=self._token   )

            # This has added the proper auto_map field to the config
            self.assertDictEqual(
                feature_extractor.auto_map    ,   {'AutoFeatureExtractor': 'custom_feature_extraction.CustomFeatureExtractor'}    ,   )

            __lowercase      		=AutoFeatureExtractor.from_pretrained(
                f'''{USER}/test-dynamic-feature-extractor'''    ,   trust_remote_code=__lowercase   )
            # Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
            self.assertEqual(new_feature_extractor.__class__.__name__    ,   'CustomFeatureExtractor'   )


