



"""simple docstring"""


import math







def  lowercase						(      _SCREAMING_SNAKE_CASE   :						list					,     _SCREAMING_SNAKE_CASE   :						int = 0					,     _SCREAMING_SNAKE_CASE   :						int = 0				):






				'''simple docstring'''



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







def  lowercase						(      _SCREAMING_SNAKE_CASE   :						list					,     _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						int				):  # Max Heap






				'''simple docstring'''



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

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

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

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







def  lowercase						(      _SCREAMING_SNAKE_CASE   :						list				):






				'''simple docstring'''



				_UpperCAmelCase					=    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				):
								_UpperCAmelCase					=    array[0], array[i]
								heapify(snake_case_					,     0					,     snake_case_				)

				return array







def  lowercase						(      _SCREAMING_SNAKE_CASE   :						list					,     _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						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						(      _SCREAMING_SNAKE_CASE   :						list					,     _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



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







def  lowercase						(      _SCREAMING_SNAKE_CASE   :						list				):






				'''simple docstring'''



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







def  lowercase						(      _SCREAMING_SNAKE_CASE   :						list					,     _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



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


if __name__ == "__main__":
					import doctest

					doctest.testmod()

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








"""simple docstring"""


import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
					from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class 			_a (					unittest.TestCase):
			"""simple docstring"""



			def  lowercase__						(		self     :    Tuple				,   __UpperCamelCase     :    str   )->Tuple:
							for model_result in results.values():
											for batch_size, sequence_length in zip(model_result['''bs''']				,   model_result['''ss''']   ):
															_UpperCAmelCase					=    model_result['''result'''][batch_size][sequence_length]
															self.assertIsNotNone(__UpperCamelCase   )



			def  lowercase__						(		self     :    Union[str, Any]   )->Optional[Any]:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )



			def  lowercase__						(		self     :    Tuple   )->Optional[Any]:
							_UpperCAmelCase					=    '''sgugger/tiny-distilbert-classification'''
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   only_pretrain_model=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )



			def  lowercase__						(		self     :    Tuple   )->str:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   torchscript=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )



			@unittest.skipIf(torch_device == '''cpu'''				,   '''Cant do half precision'''   )
			def  lowercase__						(		self     :    Any   )->Optional[Any]:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   fpaa=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )



			def  lowercase__						(		self     :    Tuple   )->Any:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    AutoConfig.from_pretrained(__UpperCamelCase   )
							# set architectures equal to `None`
							_UpperCAmelCase					=    None
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase				,   configs=[config]   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )



			def  lowercase__						(		self     :    List[Any]   )->str:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_train_result   )
							self.check_results_dict_not_empty(results.memory_train_result   )



			@unittest.skipIf(torch_device == '''cpu'''				,   '''Can\'t do half precision'''   )
			def  lowercase__						(		self     :    List[str]   )->int:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   fpaa=__UpperCamelCase				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_train_result   )
							self.check_results_dict_not_empty(results.memory_train_result   )



			def  lowercase__						(		self     :    str   )->Dict:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    AutoConfig.from_pretrained(__UpperCamelCase   )
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase				,   configs=[config]   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )



			def  lowercase__						(		self     :    Optional[int]   )->Optional[Any]:
							_UpperCAmelCase					=    '''sshleifer/tinier_bart'''
							_UpperCAmelCase					=    AutoConfig.from_pretrained(__UpperCamelCase   )
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase				,   configs=[config]   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )



			def  lowercase__						(		self     :    List[str]   )->str:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    AutoConfig.from_pretrained(__UpperCamelCase   )
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase				,   configs=[config]   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_train_result   )
							self.check_results_dict_not_empty(results.memory_train_result   )



			def  lowercase__						(		self     :    Optional[int]   )->int:
							_UpperCAmelCase					=    '''sshleifer/tinier_bart'''
							_UpperCAmelCase					=    AutoConfig.from_pretrained(__UpperCamelCase   )
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase				,   configs=[config]   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_train_result   )
							self.check_results_dict_not_empty(results.memory_train_result   )



			def  lowercase__						(		self     :    Optional[Any]   )->int:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							with tempfile.TemporaryDirectory() as tmp_dir:
											_UpperCAmelCase					=    PyTorchBenchmarkArguments(
											    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   save_to_csv=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   inference_time_csv_file=os.path.join(__UpperCamelCase				,   '''inf_time.csv'''   )				,   train_memory_csv_file=os.path.join(__UpperCamelCase				,   '''train_mem.csv'''   )				,   inference_memory_csv_file=os.path.join(__UpperCamelCase				,   '''inf_mem.csv'''   )				,   train_time_csv_file=os.path.join(__UpperCamelCase				,   '''train_time.csv'''   )				,   env_info_csv_file=os.path.join(__UpperCamelCase				,   '''env.csv'''   )				,   multi_process=__UpperCamelCase				,   )
											_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
											benchmark.run()
											self.assertTrue(Path(os.path.join(__UpperCamelCase				,   '''inf_time.csv'''   )   ).exists()   )
											self.assertTrue(Path(os.path.join(__UpperCamelCase				,   '''train_time.csv'''   )   ).exists()   )
											self.assertTrue(Path(os.path.join(__UpperCamelCase				,   '''inf_mem.csv'''   )   ).exists()   )
											self.assertTrue(Path(os.path.join(__UpperCamelCase				,   '''train_mem.csv'''   )   ).exists()   )
											self.assertTrue(Path(os.path.join(__UpperCamelCase				,   '''env.csv'''   )   ).exists()   )



			def  lowercase__						(		self     :    Tuple   )->List[Any]:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''

							def _check_summary_is_not_empty(__UpperCamelCase     :    str   ):
											self.assertTrue(hasattr(__UpperCamelCase				,   '''sequential'''   )   )
											self.assertTrue(hasattr(__UpperCamelCase				,   '''cumulative'''   )   )
											self.assertTrue(hasattr(__UpperCamelCase				,   '''current'''   )   )
											self.assertTrue(hasattr(__UpperCamelCase				,   '''total'''   )   )

							with tempfile.TemporaryDirectory() as tmp_dir:
											_UpperCAmelCase					=    PyTorchBenchmarkArguments(
											    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   log_filename=os.path.join(__UpperCamelCase				,   '''log.txt'''   )				,   log_print=__UpperCamelCase				,   trace_memory_line_by_line=__UpperCamelCase				,   multi_process=__UpperCamelCase				,   )
											_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
											_UpperCAmelCase					=    benchmark.run()
											_check_summary_is_not_empty(result.inference_summary   )
											_check_summary_is_not_empty(result.train_summary   )
											self.assertTrue(Path(os.path.join(__UpperCamelCase				,   '''log.txt'''   )   ).exists()   )



