




"""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       snake_case    (     self					:	str    ,   lowercase__					:	str						):
						for model_result in results.values():
								for batch_size, sequence_length in zip(model_result["bs"]    ,   model_result["ss"]						):
										__lowercase				:		Optional[int]												=				model_result["result"][batch_size][sequence_length]
										self.assertIsNotNone(lowercase__						)



				def       snake_case    (     self					:	Dict						):
						__lowercase				:		Optional[Any]												=				"sshleifer/tiny-gpt2"
						__lowercase				:		int												=				PyTorchBenchmarkArguments(
						    models=[MODEL_ID]    ,   training=lowercase__    ,   inference=lowercase__    ,   sequence_lengths=[8]    ,   batch_sizes=[1]    ,   multi_process=lowercase__    ,   )
						__lowercase				:		str												=				PyTorchBenchmark(lowercase__						)
						__lowercase				:		Optional[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					:	Any						):
						__lowercase				:		List[Any]												=				"sgugger/tiny-distilbert-classification"
						__lowercase				:		List[Any]												=				PyTorchBenchmarkArguments(
						    models=[MODEL_ID]    ,   training=lowercase__    ,   inference=lowercase__    ,   sequence_lengths=[8]    ,   batch_sizes=[1]    ,   multi_process=lowercase__    ,   only_pretrain_model=lowercase__    ,   )
						__lowercase				:		Optional[Any]												=				PyTorchBenchmark(lowercase__						)
						__lowercase				:		List[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					:	str						):
						__lowercase				:		Optional[int]												=				"sshleifer/tiny-gpt2"
						__lowercase				:		str												=				PyTorchBenchmarkArguments(
						    models=[MODEL_ID]    ,   training=lowercase__    ,   inference=lowercase__    ,   torchscript=lowercase__    ,   sequence_lengths=[8]    ,   batch_sizes=[1]    ,   multi_process=lowercase__    ,   )
						__lowercase				:		Optional[int]												=				PyTorchBenchmark(lowercase__						)
						__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						)



				@unittest.skipIf(torch_device == "cpu"    ,   "Cant do half precision"						)
				def       snake_case    (     self					:	str						):
						__lowercase				:		Tuple												=				"sshleifer/tiny-gpt2"
						__lowercase				:		List[str]												=				PyTorchBenchmarkArguments(
						    models=[MODEL_ID]    ,   training=lowercase__    ,   inference=lowercase__    ,   fpaa=lowercase__    ,   sequence_lengths=[8]    ,   batch_sizes=[1]    ,   multi_process=lowercase__    ,   )
						__lowercase				:		Dict												=				PyTorchBenchmark(lowercase__						)
						__lowercase				:		Dict												=				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						):
						__lowercase				:		Union[str, Any]												=				"sshleifer/tiny-gpt2"
						__lowercase				:		List[Any]												=				AutoConfig.from_pretrained(lowercase__						)
						# set architectures equal to `None`
						__lowercase				:		str												=				None
						__lowercase				:		Union[str, Any]												=				PyTorchBenchmarkArguments(
						    models=[MODEL_ID]    ,   training=lowercase__    ,   inference=lowercase__    ,   sequence_lengths=[8]    ,   batch_sizes=[1]    ,   multi_process=lowercase__    ,   )
						__lowercase				:		str												=				PyTorchBenchmark(lowercase__    ,   configs=[config]						)
						__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					:	List[str]						):
						__lowercase				:		Tuple												=				"sshleifer/tiny-gpt2"
						__lowercase				:		Union[str, Any]												=				PyTorchBenchmarkArguments(
						    models=[MODEL_ID]    ,   training=lowercase__    ,   inference=lowercase__    ,   sequence_lengths=[8]    ,   batch_sizes=[1]    ,   multi_process=lowercase__    ,   )
						__lowercase				:		List[Any]												=				PyTorchBenchmark(lowercase__						)
						__lowercase				:		Union[str, Any]												=				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       snake_case    (     self					:	str						):
						__lowercase				:		Dict												=				"sshleifer/tiny-gpt2"
						__lowercase				:		Dict												=				PyTorchBenchmarkArguments(
						    models=[MODEL_ID]    ,   training=lowercase__    ,   inference=lowercase__    ,   sequence_lengths=[8]    ,   batch_sizes=[1]    ,   fpaa=lowercase__    ,   multi_process=lowercase__    ,   )
						__lowercase				:		int												=				PyTorchBenchmark(lowercase__						)
						__lowercase				:		Tuple												=				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					:	Optional[Any]						):
						__lowercase				:		Optional[Any]												=				"sshleifer/tiny-gpt2"
						__lowercase				:		Any												=				AutoConfig.from_pretrained(lowercase__						)
						__lowercase				:		int												=				PyTorchBenchmarkArguments(
						    models=[MODEL_ID]    ,   training=lowercase__    ,   inference=lowercase__    ,   sequence_lengths=[8]    ,   batch_sizes=[1]    ,   multi_process=lowercase__    ,   )
						__lowercase				:		Tuple												=				PyTorchBenchmark(lowercase__    ,   configs=[config]						)
						__lowercase				:		Dict												=				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[Any]						):
						__lowercase				:		Dict												=				"sshleifer/tinier_bart"
						__lowercase				:		List[Any]												=				AutoConfig.from_pretrained(lowercase__						)
						__lowercase				:		Any												=				PyTorchBenchmarkArguments(
						    models=[MODEL_ID]    ,   training=lowercase__    ,   inference=lowercase__    ,   sequence_lengths=[8]    ,   batch_sizes=[1]    ,   multi_process=lowercase__    ,   )
						__lowercase				:		List[Any]												=				PyTorchBenchmark(lowercase__    ,   configs=[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					:	Any						):
						__lowercase				:		List[str]												=				"sshleifer/tiny-gpt2"
						__lowercase				:		Union[str, Any]												=				AutoConfig.from_pretrained(lowercase__						)
						__lowercase				:		Tuple												=				PyTorchBenchmarkArguments(
						    models=[MODEL_ID]    ,   training=lowercase__    ,   inference=lowercase__    ,   sequence_lengths=[8]    ,   batch_sizes=[1]    ,   multi_process=lowercase__    ,   )
						__lowercase				:		Union[str, Any]												=				PyTorchBenchmark(lowercase__    ,   configs=[config]						)
						__lowercase				:		Tuple												=				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						):
						__lowercase				:		Any												=				"sshleifer/tinier_bart"
						__lowercase				:		Optional[int]												=				AutoConfig.from_pretrained(lowercase__						)
						__lowercase				:		Tuple												=				PyTorchBenchmarkArguments(
						    models=[MODEL_ID]    ,   training=lowercase__    ,   inference=lowercase__    ,   sequence_lengths=[8]    ,   batch_sizes=[1]    ,   multi_process=lowercase__    ,   )
						__lowercase				:		Optional[int]												=				PyTorchBenchmark(lowercase__    ,   configs=[config]						)
						__lowercase				:		int												=				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						):
						__lowercase				:		Dict												=				"sshleifer/tiny-gpt2"
						with tempfile.TemporaryDirectory() as tmp_dir:
								__lowercase				:		Tuple												=				PyTorchBenchmarkArguments(
								    models=[MODEL_ID]    ,   training=lowercase__    ,   inference=lowercase__    ,   save_to_csv=lowercase__    ,   sequence_lengths=[8]    ,   batch_sizes=[1]    ,   inference_time_csv_file=os.path.join(lowercase__    ,   "inf_time.csv"						)    ,   train_memory_csv_file=os.path.join(lowercase__    ,   "train_mem.csv"						)    ,   inference_memory_csv_file=os.path.join(lowercase__    ,   "inf_mem.csv"						)    ,   train_time_csv_file=os.path.join(lowercase__    ,   "train_time.csv"						)    ,   env_info_csv_file=os.path.join(lowercase__    ,   "env.csv"						)    ,   multi_process=lowercase__    ,   )
								__lowercase				:		Union[str, Any]												=				PyTorchBenchmark(lowercase__						)
								benchmark.run()
								self.assertTrue(Path(os.path.join(lowercase__    ,   "inf_time.csv"						)						).exists()						)
								self.assertTrue(Path(os.path.join(lowercase__    ,   "train_time.csv"						)						).exists()						)
								self.assertTrue(Path(os.path.join(lowercase__    ,   "inf_mem.csv"						)						).exists()						)
								self.assertTrue(Path(os.path.join(lowercase__    ,   "train_mem.csv"						)						).exists()						)
								self.assertTrue(Path(os.path.join(lowercase__    ,   "env.csv"						)						).exists()						)



				def       snake_case    (     self					:	Optional[Any]						):
						__lowercase				:		List[str]												=				"sshleifer/tiny-gpt2"

						def _check_summary_is_not_empty(lowercase__					:	Any						):
								self.assertTrue(hasattr(lowercase__    ,   "sequential"						)						)
								self.assertTrue(hasattr(lowercase__    ,   "cumulative"						)						)
								self.assertTrue(hasattr(lowercase__    ,   "current"						)						)
								self.assertTrue(hasattr(lowercase__    ,   "total"						)						)

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






"""simple docstring"""







def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->int:






		"""simple docstring"""


		return abs(_lowerCamelCase				) if a == 0 else greatest_common_divisor(b % a,  _lowerCamelCase				)

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->int:






		"""simple docstring"""


		while y:  # --> when y=0 then loop will terminate and return x as final GCD.
				__lowercase						,__lowercase				:		Any												=				y, x % y
		return abs(_lowerCamelCase				)

def 						snake_case__       (					)			->Optional[int]:






		"""simple docstring"""


		try:
				__lowercase				:		Optional[int]												=				input("Enter two integers separated by comma (,): "				).split(","				)
				__lowercase				:		Optional[Any]												=				int(nums[0]				)
				__lowercase				:		str												=				int(nums[1]				)
				print(
				    F'greatest_common_divisor({num_a}, {num_a}) = '
				    F'{greatest_common_divisor(_lowerCamelCase,  _lowerCamelCase				)}'				)
				print(F'By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(_lowerCamelCase,  _lowerCamelCase				)}'				)
		except (IndexError, UnboundLocalError, ValueError):
				print("Wrong input"				)


if __name__ == "__main__":
						main()
