

def  _snake_case		(snake_case_					:     Optional[Any]    ,       snake_case_					:     str    ,       snake_case_					:     List[str]  )							->		Optional[Any]:
  __UpperCAmelCase	:  List[Any]		=     index
  __UpperCAmelCase	:  Optional[Any]		=     2 * index + 1
  __UpperCAmelCase	:  Optional[Any]		=     2 * index + 2
  if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
    __UpperCAmelCase	:  Optional[int]		=     left_index

  if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
    __UpperCAmelCase	:  str		=     right_index

  if largest != index:
    __UpperCAmelCase,			__UpperCAmelCase	:  List[Any]		=     unsorted[index], unsorted[largest]
    heapify(snake_case_    ,       snake_case_    ,       snake_case_  )





def  _snake_case		(snake_case_					:     Optional[int]  )							->		Dict:
  __UpperCAmelCase	:  Dict		=     len(snake_case_  )
  for i in range(n // 2 - 1    ,       -1    ,       -1  ):
    heapify(snake_case_    ,       snake_case_    ,       snake_case_  )
  for i in range(n - 1    ,       0    ,       -1  ):
    __UpperCAmelCase,			__UpperCAmelCase	:  Any		=     unsorted[i], unsorted[0]
    heapify(snake_case_    ,       0    ,       snake_case_  )
  return unsorted


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













"""simple docstring"""



import os
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():
 import torch

 from transformers import GPTaLMHeadModel


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



    '''simple docstring'''


    @slow
    @unittest.skip("""Model is not available."""			)
    def 		A__      (      self			):
     UpperCAmelCase__ 						=				"""nvidia/megatron-gpt2-345m/"""
     if "MYDIR" in os.environ:
      UpperCAmelCase__ 						=				os.path.join(os.environ["""MYDIR"""]   ,			__lowercase			)
     UpperCAmelCase__ 						=				GPTaLMHeadModel.from_pretrained(__lowercase			)
     model.to(__lowercase			)
     model.half()

     UpperCAmelCase__ 						=				torch.tensor(
         [[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]]   ,			device=__lowercase   ,			dtype=torch.long   ,			)

     with torch.no_grad():
      UpperCAmelCase__ 						=				model(__lowercase			).logits

     UpperCAmelCase__ 						=				torch.Size((1, 9, 50257)			)
     self.assertEqual(output.shape   ,			__lowercase			)

     UpperCAmelCase__ 						=				torch.tensor(
         [
             4.9_414,
             -0.2_920,
             -1.2_148,
             -4.0_273,
             -0.5_161,
             -5.2_109,
             -1.2_412,
             -1.8_301,
             -1.7_734,
             -4.7_148,
             -0.2_317,
             -1.0_811,
             -2.1_777,
             0.4_141,
             -3.7_969,
             -4.0_586,
             -2.5_332,
             -3.3_809,
             4.3_867,
         ]   ,			device=__lowercase   ,			dtype=torch.half   ,			)

     for i in range(19			):
      UpperCAmelCase__						,	UpperCAmelCase__ 						=				8 * i // 17, 2792 * i  # along the diagonal
      UpperCAmelCase__						,	UpperCAmelCase__ 						=				output[0, r, c], expected_diag[i]
      UpperCAmelCase__ 						=				F'''row={r} col={c} computed={computed} expected={expected}'''
      self.assertAlmostEqual(__lowercase   ,			__lowercase   ,			delta=1e-4   ,			msg=__lowercase			)



