
'''simple docstring'''




import math







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__ = 0		,				snake_case__ = 0						):




    '''simple docstring'''



    A       :   List[Any]					=						end or len(snake_case__						)
    for i in range(snake_case__		,				snake_case__						):
        A       :   Union[str, Any]					=						i
        A       :   Optional[int]					=						array[i]
        while temp_index != start and temp_index_value < array[temp_index - 1]:
            A       :   Optional[Any]					=						array[temp_index - 1]
            temp_index -= 1
        A       :   str					=						temp_index_value
    return array







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__						):  # Max Heap




    '''simple docstring'''



    A       :   Optional[int]					=						index
    A       :   List[str]					=						2 * index + 1  # Left Node
    A       :   Optional[int]					=						2 * index + 2  # Right Node

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

    if right_index < heap_size and array[largest] < array[right_index]:
        A       :   Dict					=						right_index

    if largest != index:
        A,  A       :   List[str]					=						array[largest], array[index]
        heapify(snake_case__		,				snake_case__		,				snake_case__						)







def 		lowerCAmelCase_				(   snake_case__						):




    '''simple docstring'''



    A       :   int					=						len(snake_case__						)

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

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

    return array







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__						):




    '''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 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   Optional[Any]					=						low
    A       :   str					=						high
    while True:
        while array[i] < pivot:
            i += 1
        j -= 1
        while pivot < array[j]:
            j -= 1
        if i >= j:
            return i
        A,  A       :   int					=						array[j], array[i]
        i += 1







def 		lowerCAmelCase_				(   snake_case__						):




    '''simple docstring'''



    if len(snake_case__						) == 0:
        return array
    A       :   Any					=						2 * math.ceil(math.loga(len(snake_case__						)						)						)
    A       :   Tuple					=						16
    return intro_sort(snake_case__		,				0		,				len(snake_case__						)		,				snake_case__		,				snake_case__						)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



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


if __name__ == "__main__":
      import doctest

      doctest.testmod()

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






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


lowercase			:				Dict    		=			{
    'configuration_speech_to_text': ['SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Speech2TextConfig'],
    'processing_speech_to_text': ['Speech2TextProcessor'],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				List[Any]    		=			['Speech2TextTokenizer']

try:
      if not is_speech_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				List[str]    		=			['Speech2TextFeatureExtractor']

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				Dict    		=			[
          'TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFSpeech2TextForConditionalGeneration',
          'TFSpeech2TextModel',
          'TFSpeech2TextPreTrainedModel',
      ]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				Any    		=			[
          'SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'Speech2TextForConditionalGeneration',
          'Speech2TextModel',
          'Speech2TextPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
      from .processing_speech_to_text import SpeechaTextProcessor

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_speech_to_text import SpeechaTextTokenizer

      try:
            if not is_speech_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_speech_to_text import (
                TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFSpeechaTextForConditionalGeneration,
                TFSpeechaTextModel,
                TFSpeechaTextPreTrainedModel,
            )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_speech_to_text import (
                SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                SpeechaTextForConditionalGeneration,
                SpeechaTextModel,
                SpeechaTextPreTrainedModel,
            )

else:
      import sys

      lowercase			:				List[Any]    		=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




