
'''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  lowerCAmelCase__   (  unittest.TestCase     ):






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





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






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Tuple													=       'sshleifer/tiny-gpt2'
											lowercase_	:     str													=       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				,							)
											lowercase_	:     List[Any]													=       TensorFlowBenchmark(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Any													=       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		):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       'sgugger/tiny-distilbert-classification'
											lowercase_	:     List[str]													=       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				,							)
											lowercase_	:     str													=       TensorFlowBenchmark(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     int													=       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		):


											"""simple docstring"""





											lowercase_	:     Union[str, Any]													=       'sshleifer/tiny-gpt2'
											lowercase_	:     Dict													=       TensorFlowBenchmarkArguments(
											    models=[MODEL_ID]				,							training=__SCREAMING_SNAKE_CASE				,							inference=__SCREAMING_SNAKE_CASE				,							sequence_lengths=[8]				,							batch_sizes=[1]				,							multi_process=__SCREAMING_SNAKE_CASE				,							)
											lowercase_	:     List[str]													=       TensorFlowBenchmark(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Tuple													=       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		):


											"""simple docstring"""





											lowercase_	:     str													=       'sshleifer/tiny-gpt2'
											lowercase_	:     str													=       AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Any													=       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				,							)
											lowercase_	:     Optional[Any]													=       TensorFlowBenchmark(__SCREAMING_SNAKE_CASE				,							[config]		)
											lowercase_	:     Union[str, Any]													=       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		):


											"""simple docstring"""





											lowercase_	:     Dict													=       'sshleifer/tiny-gpt2'
											lowercase_	:     Union[str, Any]													=       AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[int]													=       TensorFlowBenchmarkArguments(
											    models=[MODEL_ID]				,							training=__SCREAMING_SNAKE_CASE				,							inference=__SCREAMING_SNAKE_CASE				,							sequence_lengths=[8]				,							batch_sizes=[1]				,							multi_process=__SCREAMING_SNAKE_CASE				,							)
											lowercase_	:     str													=       TensorFlowBenchmark(__SCREAMING_SNAKE_CASE				,							[config]		)
											lowercase_	:     List[str]													=       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		):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       'sshleifer/tiny-gpt2'
											lowercase_	:     Any													=       TensorFlowBenchmarkArguments(
											    models=[MODEL_ID]				,							training=__SCREAMING_SNAKE_CASE				,							inference=__SCREAMING_SNAKE_CASE				,							sequence_lengths=[8]				,							batch_sizes=[1]				,							multi_process=__SCREAMING_SNAKE_CASE				,							)
											lowercase_	:     Tuple													=       TensorFlowBenchmark(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     str													=       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		):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       'sshleifer/tiny-gpt2'
											lowercase_	:     List[Any]													=       AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     List[Any]													=       TensorFlowBenchmarkArguments(
											    models=[MODEL_ID]				,							training=__SCREAMING_SNAKE_CASE				,							inference=__SCREAMING_SNAKE_CASE				,							sequence_lengths=[8]				,							batch_sizes=[1]				,							multi_process=__SCREAMING_SNAKE_CASE				,							)
											lowercase_	:     Optional[Any]													=       TensorFlowBenchmark(__SCREAMING_SNAKE_CASE				,							[config]		)
											lowercase_	:     List[Any]													=       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		):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       'patrickvonplaten/t5-tiny-random'
											lowercase_	:     Optional[int]													=       AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Any													=       TensorFlowBenchmarkArguments(
											    models=[MODEL_ID]				,							training=__SCREAMING_SNAKE_CASE				,							inference=__SCREAMING_SNAKE_CASE				,							sequence_lengths=[8]				,							batch_sizes=[1]				,							multi_process=__SCREAMING_SNAKE_CASE				,							)
											lowercase_	:     List[Any]													=       TensorFlowBenchmark(__SCREAMING_SNAKE_CASE				,							configs=[config]		)
											lowercase_	:     Union[str, Any]													=       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		):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       'sshleifer/tiny-gpt2'
											lowercase_	:     str													=       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				,							)
											lowercase_	:     int													=       TensorFlowBenchmark(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Tuple													=       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		):


											"""simple docstring"""





											lowercase_	:     str													=       'sshleifer/tiny-gpt2'
											with tempfile.TemporaryDirectory() as tmp_dir:
																	lowercase_	:     int													=       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				,							)
																	lowercase_	:     List[str]													=       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		):


											"""simple docstring"""





											lowercase_	:     Union[str, Any]													=       'sshleifer/tiny-gpt2'

											def _check_summary_is_not_empty(__SCREAMING_SNAKE_CASE		):
																	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:
																	lowercase_	:     List[str]													=       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				,							)
																	lowercase_	:     List[str]													=       TensorFlowBenchmark(__SCREAMING_SNAKE_CASE		)
																	lowercase_	:     Any													=       benchmark.run()
																	_check_summary_is_not_empty(result.inference_summary		)
																	self.assertTrue(Path(os.path.join(__SCREAMING_SNAKE_CASE				,							'''log.txt'''		)		).exists()		)










def A_	(		A__				)  ->		int:
       stooge(A__		,      0		,      len(A__				) - 1				)
       return arr



def A_	(		A__		,      A__		,      A__				)  ->		List[Any]:
       if i >= h:
              return

       # If first element is smaller than the last then swap them
       if arr[i] > arr[h]:
              a__ ,							a__						:	List[str] =				arr[h], arr[i]

       # If there are more than 2 elements in the array
       if h - i + 1 > 2:
              a__						:	Dict =				(int)((h - i + 1) / 3				)

              # Recursively sort first 2/3 elements
              stooge(A__		,      A__		,      (h - t)				)

              # Recursively sort last 2/3 elements
              stooge(A__		,      i + t		,      (A__)				)

              # Recursively sort first 2/3 elements
              stooge(A__		,      A__		,      (h - t)				)


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


