
import math



def 		lowercase_    (     A__				,	A__ = 0				,	A__ = 0	)							->							list:
    """simple docstring"""

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



def 		lowercase_    (     A__				,	A__				,	A__	)							->							None:  # Max Heap
    """simple docstring"""

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

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

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

    if largest != index:
        snake_case  ,							snake_case				  =      array[largest], array[index]
        heapify(A__				,	A__				,	A__	)



def 		lowercase_    (     A__	)							->							list:
    """simple docstring"""

    snake_case				  =      len(A__	)

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

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

    return array



def 		lowercase_    (     A__				,	A__				,	A__				,	A__	)							->							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 		lowercase_    (     A__				,	A__				,	A__				,	A__	)							->							int:
    """simple docstring"""

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



def 		lowercase_    (     A__	)							->							list:
    """simple docstring"""

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



def 		lowercase_    (     A__				,	A__				,	A__				,	A__				,	A__	)							->							list:
    """simple docstring"""

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


if __name__ == "__main__":
   import doctest

   doctest.testmod()

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



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer







@require_torch
@require_sentencepiece
@require_tokenizers
class   lowerCamelCase	(						unittest.TestCase						):





     @slow
     def 	UpperCAmelCase(self   :					List[str]     )		->		Optional[int]:
         snake_case				  =      AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"       ,					return_dict=_A     ).to(_A     )
         snake_case				  =      AutoTokenizer.from_pretrained("google/mt5-small"     )

         snake_case				  =      tokenizer("Hello there"       ,					return_tensors="pt"     ).input_ids
         snake_case				  =      tokenizer("Hi I am"       ,					return_tensors="pt"     ).input_ids

         snake_case				  =      model(input_ids.to(_A     )       ,					labels=labels.to(_A     )     ).loss
         snake_case				  =      -(labels.shape[-1] * loss.item())

         snake_case				  =      -84.91_27
         self.assertTrue(abs(mtf_score - EXPECTED_SCORE     ) < 1E-4     )

