


'''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		):




			def 							lowerCAmelCase__			(						self     :   List[str]		,     lowerCamelCase__     :   Optional[int]       )      ->Union[str, Any]:



					'''simple docstring'''







					for model_result in results.values():
							for batch_size, sequence_length in zip(model_result["bs"]		,     model_result["ss"]       ):
									_UpperCAmelCase						:				str	    =					model_result["result"][batch_size][sequence_length]
									self.assertIsNotNone(lowerCamelCase__       )




			def 							lowerCAmelCase__			(						self     :   str       )      ->Dict:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[int]	    =					"sshleifer/tiny-gpt2"
					_UpperCAmelCase						:				List[str]	    =					PyTorchBenchmarkArguments(
					    models=[MODEL_ID]		,     training=lowerCamelCase__		,     inference=lowerCamelCase__		,     sequence_lengths=[8]		,     batch_sizes=[1]		,     multi_process=lowerCamelCase__		,     )
					_UpperCAmelCase						:				Any	    =					PyTorchBenchmark(lowerCamelCase__       )
					_UpperCAmelCase						:				Any	    =					benchmark.run()
					self.check_results_dict_not_empty(results.time_inference_result       )
					self.check_results_dict_not_empty(results.memory_inference_result       )




			def 							lowerCAmelCase__			(						self     :   str       )      ->Dict:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[int]	    =					"sgugger/tiny-distilbert-classification"
					_UpperCAmelCase						:				Union[str, Any]	    =					PyTorchBenchmarkArguments(
					    models=[MODEL_ID]		,     training=lowerCamelCase__		,     inference=lowerCamelCase__		,     sequence_lengths=[8]		,     batch_sizes=[1]		,     multi_process=lowerCamelCase__		,     only_pretrain_model=lowerCamelCase__		,     )
					_UpperCAmelCase						:				Tuple	    =					PyTorchBenchmark(lowerCamelCase__       )
					_UpperCAmelCase						:				Any	    =					benchmark.run()
					self.check_results_dict_not_empty(results.time_inference_result       )
					self.check_results_dict_not_empty(results.memory_inference_result       )




			def 							lowerCAmelCase__			(						self     :   Any       )      ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase						:				Dict	    =					"sshleifer/tiny-gpt2"
					_UpperCAmelCase						:				Tuple	    =					PyTorchBenchmarkArguments(
					    models=[MODEL_ID]		,     training=lowerCamelCase__		,     inference=lowerCamelCase__		,     torchscript=lowerCamelCase__		,     sequence_lengths=[8]		,     batch_sizes=[1]		,     multi_process=lowerCamelCase__		,     )
					_UpperCAmelCase						:				Any	    =					PyTorchBenchmark(lowerCamelCase__       )
					_UpperCAmelCase						:				Optional[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(torch_device == "cpu"		,     "Cant do half precision"       )
			def 							lowerCAmelCase__			(						self     :   Optional[int]       )      ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				Tuple	    =					"sshleifer/tiny-gpt2"
					_UpperCAmelCase						:				Optional[Any]	    =					PyTorchBenchmarkArguments(
					    models=[MODEL_ID]		,     training=lowerCamelCase__		,     inference=lowerCamelCase__		,     fpaa=lowerCamelCase__		,     sequence_lengths=[8]		,     batch_sizes=[1]		,     multi_process=lowerCamelCase__		,     )
					_UpperCAmelCase						:				List[str]	    =					PyTorchBenchmark(lowerCamelCase__       )
					_UpperCAmelCase						:				Any	    =					benchmark.run()
					self.check_results_dict_not_empty(results.time_inference_result       )
					self.check_results_dict_not_empty(results.memory_inference_result       )




			def 							lowerCAmelCase__			(						self     :   List[Any]       )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				int	    =					"sshleifer/tiny-gpt2"
					_UpperCAmelCase						:				Tuple	    =					AutoConfig.from_pretrained(lowerCamelCase__       )
					# set architectures equal to `None`
					_UpperCAmelCase						:				int	    =					None
					_UpperCAmelCase						:				List[str]	    =					PyTorchBenchmarkArguments(
					    models=[MODEL_ID]		,     training=lowerCamelCase__		,     inference=lowerCamelCase__		,     sequence_lengths=[8]		,     batch_sizes=[1]		,     multi_process=lowerCamelCase__		,     )
					_UpperCAmelCase						:				int	    =					PyTorchBenchmark(lowerCamelCase__		,     configs=[config]       )
					_UpperCAmelCase						:				Optional[int]	    =					benchmark.run()
					self.check_results_dict_not_empty(results.time_inference_result       )
					self.check_results_dict_not_empty(results.memory_inference_result       )




			def 							lowerCAmelCase__			(						self     :   Optional[Any]       )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				Dict	    =					"sshleifer/tiny-gpt2"
					_UpperCAmelCase						:				Union[str, Any]	    =					PyTorchBenchmarkArguments(
					    models=[MODEL_ID]		,     training=lowerCamelCase__		,     inference=lowerCamelCase__		,     sequence_lengths=[8]		,     batch_sizes=[1]		,     multi_process=lowerCamelCase__		,     )
					_UpperCAmelCase						:				List[str]	    =					PyTorchBenchmark(lowerCamelCase__       )
					_UpperCAmelCase						:				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 							lowerCAmelCase__			(						self     :   List[Any]       )      ->str:



					'''simple docstring'''







					_UpperCAmelCase						:				Union[str, Any]	    =					"sshleifer/tiny-gpt2"
					_UpperCAmelCase						:				Union[str, Any]	    =					PyTorchBenchmarkArguments(
					    models=[MODEL_ID]		,     training=lowerCamelCase__		,     inference=lowerCamelCase__		,     sequence_lengths=[8]		,     batch_sizes=[1]		,     fpaa=lowerCamelCase__		,     multi_process=lowerCamelCase__		,     )
					_UpperCAmelCase						:				List[str]	    =					PyTorchBenchmark(lowerCamelCase__       )
					_UpperCAmelCase						:				Optional[int]	    =					benchmark.run()
					self.check_results_dict_not_empty(results.time_train_result       )
					self.check_results_dict_not_empty(results.memory_train_result       )




			def 							lowerCAmelCase__			(						self     :   str       )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					"sshleifer/tiny-gpt2"
					_UpperCAmelCase						:				Union[str, Any]	    =					AutoConfig.from_pretrained(lowerCamelCase__       )
					_UpperCAmelCase						:				Tuple	    =					PyTorchBenchmarkArguments(
					    models=[MODEL_ID]		,     training=lowerCamelCase__		,     inference=lowerCamelCase__		,     sequence_lengths=[8]		,     batch_sizes=[1]		,     multi_process=lowerCamelCase__		,     )
					_UpperCAmelCase						:				Union[str, Any]	    =					PyTorchBenchmark(lowerCamelCase__		,     configs=[config]       )
					_UpperCAmelCase						:				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 							lowerCAmelCase__			(						self     :   Tuple       )      ->str:



					'''simple docstring'''







					_UpperCAmelCase						:				Any	    =					"sshleifer/tinier_bart"
					_UpperCAmelCase						:				Union[str, Any]	    =					AutoConfig.from_pretrained(lowerCamelCase__       )
					_UpperCAmelCase						:				int	    =					PyTorchBenchmarkArguments(
					    models=[MODEL_ID]		,     training=lowerCamelCase__		,     inference=lowerCamelCase__		,     sequence_lengths=[8]		,     batch_sizes=[1]		,     multi_process=lowerCamelCase__		,     )
					_UpperCAmelCase						:				Dict	    =					PyTorchBenchmark(lowerCamelCase__		,     configs=[config]       )
					_UpperCAmelCase						:				Any	    =					benchmark.run()
					self.check_results_dict_not_empty(results.time_inference_result       )
					self.check_results_dict_not_empty(results.memory_inference_result       )




			def 							lowerCAmelCase__			(						self     :   Optional[Any]       )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				Tuple	    =					"sshleifer/tiny-gpt2"
					_UpperCAmelCase						:				List[Any]	    =					AutoConfig.from_pretrained(lowerCamelCase__       )
					_UpperCAmelCase						:				Dict	    =					PyTorchBenchmarkArguments(
					    models=[MODEL_ID]		,     training=lowerCamelCase__		,     inference=lowerCamelCase__		,     sequence_lengths=[8]		,     batch_sizes=[1]		,     multi_process=lowerCamelCase__		,     )
					_UpperCAmelCase						:				Any	    =					PyTorchBenchmark(lowerCamelCase__		,     configs=[config]       )
					_UpperCAmelCase						:				Tuple	    =					benchmark.run()
					self.check_results_dict_not_empty(results.time_train_result       )
					self.check_results_dict_not_empty(results.memory_train_result       )




			def 							lowerCAmelCase__			(						self     :   str       )      ->Any:



					'''simple docstring'''







					_UpperCAmelCase						:				int	    =					"sshleifer/tinier_bart"
					_UpperCAmelCase						:				str	    =					AutoConfig.from_pretrained(lowerCamelCase__       )
					_UpperCAmelCase						:				Any	    =					PyTorchBenchmarkArguments(
					    models=[MODEL_ID]		,     training=lowerCamelCase__		,     inference=lowerCamelCase__		,     sequence_lengths=[8]		,     batch_sizes=[1]		,     multi_process=lowerCamelCase__		,     )
					_UpperCAmelCase						:				Tuple	    =					PyTorchBenchmark(lowerCamelCase__		,     configs=[config]       )
					_UpperCAmelCase						:				Any	    =					benchmark.run()
					self.check_results_dict_not_empty(results.time_train_result       )
					self.check_results_dict_not_empty(results.memory_train_result       )




			def 							lowerCAmelCase__			(						self     :   Optional[int]       )      ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[int]	    =					"sshleifer/tiny-gpt2"
					with tempfile.TemporaryDirectory() as tmp_dir:
							_UpperCAmelCase						:				Optional[int]	    =					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__		,     )
							_UpperCAmelCase						:				Dict	    =					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 							lowerCAmelCase__			(						self     :   Any       )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[Any]	    =					"sshleifer/tiny-gpt2"

					def _check_summary_is_not_empty(lowerCamelCase__     :   Dict       ):
							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:
							_UpperCAmelCase						:				Any	    =					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__		,     )
							_UpperCAmelCase						:				List[Any]	    =					PyTorchBenchmark(lowerCamelCase__       )
							_UpperCAmelCase						:				Dict	    =					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'''


import math






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		return math.sqrt(__lowerCAmelCase      ) * math.sqrt(__lowerCAmelCase      ) == num






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				int	    =					0
		_UpperCAmelCase						:				Tuple	    =					n
		while left <= right:
				_UpperCAmelCase						:				int	    =					(left + right) // 2
				if mid**2 == n:
						return True
				elif mid**2 > n:
						_UpperCAmelCase						:				str	    =					mid - 1
				else:
						_UpperCAmelCase						:				List[str]	    =					mid + 1
		return False


if __name__ == "__main__":
						import doctest

						doctest.testmod()



