




'''simple docstring'''
import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
							import tensorflow as tf

							from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class   __UpperCAmelCase				(     unittest.TestCase				):



					'''simple docstring'''





					def     __snake_case						(	self     :						Any      ,       _lowercase     :						Optional[int])    ->							List[str]:





								for model_result in results.values():
											for batch_size, sequence_length in zip(model_result['bs']      ,       model_result['ss']):
														A_									= model_result['result'][batch_size][sequence_length]
														self.assertIsNotNone(_SCREAMING_SNAKE_CASE)



					def     __snake_case						(	self     :						int)    ->							Optional[int]:
								A_									= 'sshleifer/tiny-gpt2'
								A_									= TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]      ,       training=_SCREAMING_SNAKE_CASE      ,       inference=_SCREAMING_SNAKE_CASE      ,       sequence_lengths=[8]      ,       batch_sizes=[1]      ,       eager_mode=_SCREAMING_SNAKE_CASE      ,       multi_process=_SCREAMING_SNAKE_CASE      ,       )
								A_									= TensorFlowBenchmark(_SCREAMING_SNAKE_CASE)
								A_									= benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result)
								self.check_results_dict_not_empty(results.memory_inference_result)



					def     __snake_case						(	self     :						List[str])    ->							int:
								A_									= 'sgugger/tiny-distilbert-classification'
								A_									= TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]      ,       training=_SCREAMING_SNAKE_CASE      ,       inference=_SCREAMING_SNAKE_CASE      ,       sequence_lengths=[8]      ,       batch_sizes=[1]      ,       multi_process=_SCREAMING_SNAKE_CASE      ,       only_pretrain_model=_SCREAMING_SNAKE_CASE      ,       )
								A_									= TensorFlowBenchmark(_SCREAMING_SNAKE_CASE)
								A_									= benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result)
								self.check_results_dict_not_empty(results.memory_inference_result)



					def     __snake_case						(	self     :						Any)    ->							List[Any]:
								A_									= 'sshleifer/tiny-gpt2'
								A_									= TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]      ,       training=_SCREAMING_SNAKE_CASE      ,       inference=_SCREAMING_SNAKE_CASE      ,       sequence_lengths=[8]      ,       batch_sizes=[1]      ,       multi_process=_SCREAMING_SNAKE_CASE      ,       )
								A_									= TensorFlowBenchmark(_SCREAMING_SNAKE_CASE)
								A_									= benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result)
								self.check_results_dict_not_empty(results.memory_inference_result)



					def     __snake_case						(	self     :						Union[str, Any])    ->							List[Any]:
								A_									= 'sshleifer/tiny-gpt2'
								A_									= AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE)
								A_									= TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]      ,       training=_SCREAMING_SNAKE_CASE      ,       inference=_SCREAMING_SNAKE_CASE      ,       sequence_lengths=[8]      ,       batch_sizes=[1]      ,       eager_mode=_SCREAMING_SNAKE_CASE      ,       multi_process=_SCREAMING_SNAKE_CASE      ,       )
								A_									= TensorFlowBenchmark(_SCREAMING_SNAKE_CASE      ,       [config])
								A_									= benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result)
								self.check_results_dict_not_empty(results.memory_inference_result)



					def     __snake_case						(	self     :						Tuple)    ->							List[Any]:
								A_									= 'sshleifer/tiny-gpt2'
								A_									= AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE)
								A_									= TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]      ,       training=_SCREAMING_SNAKE_CASE      ,       inference=_SCREAMING_SNAKE_CASE      ,       sequence_lengths=[8]      ,       batch_sizes=[1]      ,       multi_process=_SCREAMING_SNAKE_CASE      ,       )
								A_									= TensorFlowBenchmark(_SCREAMING_SNAKE_CASE      ,       [config])
								A_									= benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result)
								self.check_results_dict_not_empty(results.memory_inference_result)



					def     __snake_case						(	self     :						int)    ->							List[Any]:
								A_									= 'sshleifer/tiny-gpt2'
								A_									= TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]      ,       training=_SCREAMING_SNAKE_CASE      ,       inference=_SCREAMING_SNAKE_CASE      ,       sequence_lengths=[8]      ,       batch_sizes=[1]      ,       multi_process=_SCREAMING_SNAKE_CASE      ,       )
								A_									= TensorFlowBenchmark(_SCREAMING_SNAKE_CASE)
								A_									= benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result)
								self.check_results_dict_not_empty(results.memory_train_result)



					def     __snake_case						(	self     :						str)    ->							List[Any]:
								A_									= 'sshleifer/tiny-gpt2'
								A_									= AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE)
								A_									= TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]      ,       training=_SCREAMING_SNAKE_CASE      ,       inference=_SCREAMING_SNAKE_CASE      ,       sequence_lengths=[8]      ,       batch_sizes=[1]      ,       multi_process=_SCREAMING_SNAKE_CASE      ,       )
								A_									= TensorFlowBenchmark(_SCREAMING_SNAKE_CASE      ,       [config])
								A_									= benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result)
								self.check_results_dict_not_empty(results.memory_train_result)



					def     __snake_case						(	self     :						int)    ->							Any:
								A_									= 'patrickvonplaten/t5-tiny-random'
								A_									= AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE)
								A_									= TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]      ,       training=_SCREAMING_SNAKE_CASE      ,       inference=_SCREAMING_SNAKE_CASE      ,       sequence_lengths=[8]      ,       batch_sizes=[1]      ,       multi_process=_SCREAMING_SNAKE_CASE      ,       )
								A_									= TensorFlowBenchmark(_SCREAMING_SNAKE_CASE      ,       configs=[config])
								A_									= benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result)
								self.check_results_dict_not_empty(results.memory_inference_result)



					@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('GPU')) == 0      ,       'Cannot do xla on CPU.')
					def     __snake_case						(	self     :						Optional[Any])    ->							int:
								A_									= 'sshleifer/tiny-gpt2'
								A_									= TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]      ,       training=_SCREAMING_SNAKE_CASE      ,       inference=_SCREAMING_SNAKE_CASE      ,       sequence_lengths=[8]      ,       batch_sizes=[1]      ,       use_xla=_SCREAMING_SNAKE_CASE      ,       multi_process=_SCREAMING_SNAKE_CASE      ,       )
								A_									= TensorFlowBenchmark(_SCREAMING_SNAKE_CASE)
								A_									= benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result)
								self.check_results_dict_not_empty(results.memory_inference_result)



					def     __snake_case						(	self     :						Optional[Any])    ->							str:
								A_									= 'sshleifer/tiny-gpt2'
								with tempfile.TemporaryDirectory() as tmp_dir:
											A_									= TensorFlowBenchmarkArguments(
											    models=[MODEL_ID]      ,       inference=_SCREAMING_SNAKE_CASE      ,       save_to_csv=_SCREAMING_SNAKE_CASE      ,       sequence_lengths=[8]      ,       batch_sizes=[1]      ,       inference_time_csv_file=os.path.join(_SCREAMING_SNAKE_CASE      ,       'inf_time.csv')      ,       inference_memory_csv_file=os.path.join(_SCREAMING_SNAKE_CASE      ,       'inf_mem.csv')      ,       env_info_csv_file=os.path.join(_SCREAMING_SNAKE_CASE      ,       'env.csv')      ,       multi_process=_SCREAMING_SNAKE_CASE      ,       )
											A_									= TensorFlowBenchmark(_SCREAMING_SNAKE_CASE)
											benchmark.run()
											self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE      ,       'inf_time.csv')).exists())
											self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE      ,       'inf_mem.csv')).exists())
											self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE      ,       'env.csv')).exists())



					def     __snake_case						(	self     :						List[str])    ->							Tuple:
								A_									= 'sshleifer/tiny-gpt2'

								def _check_summary_is_not_empty(_lowercase     :						Any):
											self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,       'sequential'))
											self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,       'cumulative'))
											self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,       'current'))
											self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,       'total'))

								with tempfile.TemporaryDirectory() as tmp_dir:
											A_									= TensorFlowBenchmarkArguments(
											    models=[MODEL_ID]      ,       inference=_SCREAMING_SNAKE_CASE      ,       sequence_lengths=[8]      ,       batch_sizes=[1]      ,       log_filename=os.path.join(_SCREAMING_SNAKE_CASE      ,       'log.txt')      ,       log_print=_SCREAMING_SNAKE_CASE      ,       trace_memory_line_by_line=_SCREAMING_SNAKE_CASE      ,       eager_mode=_SCREAMING_SNAKE_CASE      ,       multi_process=_SCREAMING_SNAKE_CASE      ,       )
											A_									= TensorFlowBenchmark(_SCREAMING_SNAKE_CASE)
											A_									= benchmark.run()
											_check_summary_is_not_empty(result.inference_summary)
											self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE      ,       'log.txt')).exists())








def 			lowerCAmelCase__					(     a__:						list			)    ->						list:


							'''simple docstring'''





							if len(a__			) < 2:
														return collection

							def circle_sort_util(a__:						list     ,    a__:						int     ,    a__:						int			) -> bool:
														_UpperCAmelCase											=     False

														if low == high:
																					return swapped

														_UpperCAmelCase											=     low
														_UpperCAmelCase											=     high

														while left < right:
																					if collection[left] > collection[right]:
																												_UpperCAmelCase						,			_UpperCAmelCase											=     (
																												    collection[right],
																												    collection[left],
																												)
																												_UpperCAmelCase											=     True

																					left += 1
																					right -= 1

														if left == right and collection[left] > collection[right + 1]:
																					_UpperCAmelCase						,			_UpperCAmelCase											=     (
																					    collection[right + 1],
																					    collection[left],
																					)

																					_UpperCAmelCase											=     True

														_UpperCAmelCase											=     low + int((high - low) / 2			)
														_UpperCAmelCase											=     circle_sort_util(a__     ,    a__     ,    a__			)
														_UpperCAmelCase											=     circle_sort_util(a__     ,    mid + 1     ,    a__			)

														return swapped or left_swap or right_swap

							_UpperCAmelCase											=     True

							while is_not_sorted is True:
														_UpperCAmelCase											=     circle_sort_util(a__     ,    0     ,    len(a__			) - 1			)

							return collection


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