


"""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 					lowerCAmelCase	(					unittest.TestCase    ):




		'''simple docstring'''







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							Tuple:
								for model_result in results.values():
														for batch_size, sequence_length in zip(model_result['bs']						,       model_result['ss']				):
																				SCREAMING_SNAKE_CASE													=					model_result['result'][batch_size][sequence_length]
																				self.assertIsNotNone(lowerCAmelCase__				)







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







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







		def 							__A						(	self				)     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE													=					'sshleifer/tiny-gpt2'
								SCREAMING_SNAKE_CASE													=					PyTorchBenchmarkArguments(
								    models=[MODEL_ID]						,       training=lowerCAmelCase__						,       inference=lowerCAmelCase__						,       torchscript=lowerCAmelCase__						,       sequence_lengths=[8]						,       batch_sizes=[1]						,       multi_process=lowerCAmelCase__						,       )
								SCREAMING_SNAKE_CASE													=					PyTorchBenchmark(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					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 							__A						(	self				)     ->							Dict:
								SCREAMING_SNAKE_CASE													=					'sshleifer/tiny-gpt2'
								SCREAMING_SNAKE_CASE													=					PyTorchBenchmarkArguments(
								    models=[MODEL_ID]						,       training=lowerCAmelCase__						,       inference=lowerCAmelCase__						,       fpaa=lowerCAmelCase__						,       sequence_lengths=[8]						,       batch_sizes=[1]						,       multi_process=lowerCAmelCase__						,       )
								SCREAMING_SNAKE_CASE													=					PyTorchBenchmark(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result				)
								self.check_results_dict_not_empty(results.memory_inference_result				)







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







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







		def 							__A						(	self				)     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE													=					'sshleifer/tiny-gpt2'
								SCREAMING_SNAKE_CASE													=					AutoConfig.from_pretrained(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					PyTorchBenchmarkArguments(
								    models=[MODEL_ID]						,       training=lowerCAmelCase__						,       inference=lowerCAmelCase__						,       sequence_lengths=[8]						,       batch_sizes=[1]						,       multi_process=lowerCAmelCase__						,       )
								SCREAMING_SNAKE_CASE													=					PyTorchBenchmark(lowerCAmelCase__						,       configs=[config]				)
								SCREAMING_SNAKE_CASE													=					benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result				)
								self.check_results_dict_not_empty(results.memory_inference_result				)







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







		def 							__A						(	self				)     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE													=					'sshleifer/tiny-gpt2'
								SCREAMING_SNAKE_CASE													=					AutoConfig.from_pretrained(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					PyTorchBenchmarkArguments(
								    models=[MODEL_ID]						,       training=lowerCAmelCase__						,       inference=lowerCAmelCase__						,       sequence_lengths=[8]						,       batch_sizes=[1]						,       multi_process=lowerCAmelCase__						,       )
								SCREAMING_SNAKE_CASE													=					PyTorchBenchmark(lowerCAmelCase__						,       configs=[config]				)
								SCREAMING_SNAKE_CASE													=					benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result				)
								self.check_results_dict_not_empty(results.memory_train_result				)







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







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







		def 							__A						(	self				)     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE													=					'sshleifer/tiny-gpt2'

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

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






"""simple docstring"""




__UpperCamelCase						=      0  # The first color of the flag.
__UpperCamelCase						=      1  # The second color of the flag.
__UpperCamelCase						=      2  # The third color of the flag.
__UpperCamelCase						=      (red, white, blue)


def     lowercase			(SCREAMING_SNAKE_CASE_		:				list  )   ->						list:
						if not sequence:
												return []
						if len(SCREAMING_SNAKE_CASE_  ) == 1:
												return list(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					0
						SCREAMING_SNAKE_CASE													=					len(SCREAMING_SNAKE_CASE_  ) - 1
						SCREAMING_SNAKE_CASE													=					0
						while mid <= high:
												if sequence[mid] == colors[0]:
																		SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					sequence[mid], sequence[low]
																		low += 1
																		mid += 1
												elif sequence[mid] == colors[1]:
																		mid += 1
												elif sequence[mid] == colors[2]:
																		SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					sequence[high], sequence[mid]
																		high -= 1
												else:
																		SCREAMING_SNAKE_CASE													=					F'The elements inside the sequence must contains only {colors} values'
																		raise ValueError(SCREAMING_SNAKE_CASE_  )
						return sequence


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	__UpperCamelCase						=      input('''Enter numbers separated by commas:\n''').strip()
	__UpperCamelCase						=      [int(item.strip()) for item in user_input.split(''',''')]
	print(f'''{dutch_national_flag_sort(unsorted)}''')


