


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       _UpperCamelCase    (					unittest.TestCase				):

				"""simple docstring"""







				def      _UpperCAmelCase					(						self				,   a__   )	->				Any:
					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(a__   )

				def      _UpperCAmelCase					(						self   )	->				Tuple:
					A								=				"""sshleifer/tiny-gpt2"""
					A								=				PyTorchBenchmarkArguments(
					    models=[MODEL_ID]				,   training=a__				,   inference=a__				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=a__				,   )
					A								=				PyTorchBenchmark(a__   )
					A								=				benchmark.run()
					self.check_results_dict_not_empty(results.time_inference_result   )
					self.check_results_dict_not_empty(results.memory_inference_result   )

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

				def      _UpperCAmelCase					(						self   )	->				Optional[Any]:
					A								=				"""sshleifer/tiny-gpt2"""
					A								=				PyTorchBenchmarkArguments(
					    models=[MODEL_ID]				,   training=a__				,   inference=a__				,   torchscript=a__				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=a__				,   )
					A								=				PyTorchBenchmark(a__   )
					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(torch_device == """cpu"""				,   """Cant do half precision"""   )
				def      _UpperCAmelCase					(						self   )	->				Tuple:
					A								=				"""sshleifer/tiny-gpt2"""
					A								=				PyTorchBenchmarkArguments(
					    models=[MODEL_ID]				,   training=a__				,   inference=a__				,   fpaa=a__				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=a__				,   )
					A								=				PyTorchBenchmark(a__   )
					A								=				benchmark.run()
					self.check_results_dict_not_empty(results.time_inference_result   )
					self.check_results_dict_not_empty(results.memory_inference_result   )

				def      _UpperCAmelCase					(						self   )	->				str:
					A								=				"""sshleifer/tiny-gpt2"""
					A								=				AutoConfig.from_pretrained(a__   )
					# set architectures equal to `None`
					A								=				None
					A								=				PyTorchBenchmarkArguments(
					    models=[MODEL_ID]				,   training=a__				,   inference=a__				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=a__				,   )
					A								=				PyTorchBenchmark(a__				,   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   )

				def      _UpperCAmelCase					(						self   )	->				Dict:
					A								=				"""sshleifer/tiny-gpt2"""
					A								=				PyTorchBenchmarkArguments(
					    models=[MODEL_ID]				,   training=a__				,   inference=a__				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=a__				,   )
					A								=				PyTorchBenchmark(a__   )
					A								=				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      _UpperCAmelCase					(						self   )	->				Dict:
					A								=				"""sshleifer/tiny-gpt2"""
					A								=				PyTorchBenchmarkArguments(
					    models=[MODEL_ID]				,   training=a__				,   inference=a__				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   fpaa=a__				,   multi_process=a__				,   )
					A								=				PyTorchBenchmark(a__   )
					A								=				benchmark.run()
					self.check_results_dict_not_empty(results.time_train_result   )
					self.check_results_dict_not_empty(results.memory_train_result   )

				def      _UpperCAmelCase					(						self   )	->				int:
					A								=				"""sshleifer/tiny-gpt2"""
					A								=				AutoConfig.from_pretrained(a__   )
					A								=				PyTorchBenchmarkArguments(
					    models=[MODEL_ID]				,   training=a__				,   inference=a__				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=a__				,   )
					A								=				PyTorchBenchmark(a__				,   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   )

				def      _UpperCAmelCase					(						self   )	->				List[str]:
					A								=				"""sshleifer/tinier_bart"""
					A								=				AutoConfig.from_pretrained(a__   )
					A								=				PyTorchBenchmarkArguments(
					    models=[MODEL_ID]				,   training=a__				,   inference=a__				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=a__				,   )
					A								=				PyTorchBenchmark(a__				,   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   )

				def      _UpperCAmelCase					(						self   )	->				Dict:
					A								=				"""sshleifer/tiny-gpt2"""
					A								=				AutoConfig.from_pretrained(a__   )
					A								=				PyTorchBenchmarkArguments(
					    models=[MODEL_ID]				,   training=a__				,   inference=a__				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=a__				,   )
					A								=				PyTorchBenchmark(a__				,   configs=[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      _UpperCAmelCase					(						self   )	->				Any:
					A								=				"""sshleifer/tinier_bart"""
					A								=				AutoConfig.from_pretrained(a__   )
					A								=				PyTorchBenchmarkArguments(
					    models=[MODEL_ID]				,   training=a__				,   inference=a__				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=a__				,   )
					A								=				PyTorchBenchmark(a__				,   configs=[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      _UpperCAmelCase					(						self   )	->				Optional[int]:
					A								=				"""sshleifer/tiny-gpt2"""
					with tempfile.TemporaryDirectory() as tmp_dir:
						A								=				PyTorchBenchmarkArguments(
						    models=[MODEL_ID]				,   training=a__				,   inference=a__				,   save_to_csv=a__				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   inference_time_csv_file=os.path.join(a__				,   """inf_time.csv"""   )				,   train_memory_csv_file=os.path.join(a__				,   """train_mem.csv"""   )				,   inference_memory_csv_file=os.path.join(a__				,   """inf_mem.csv"""   )				,   train_time_csv_file=os.path.join(a__				,   """train_time.csv"""   )				,   env_info_csv_file=os.path.join(a__				,   """env.csv"""   )				,   multi_process=a__				,   )
						A								=				PyTorchBenchmark(a__   )
						benchmark.run()
						self.assertTrue(Path(os.path.join(a__				,   """inf_time.csv"""   )   ).exists()   )
						self.assertTrue(Path(os.path.join(a__				,   """train_time.csv"""   )   ).exists()   )
						self.assertTrue(Path(os.path.join(a__				,   """inf_mem.csv"""   )   ).exists()   )
						self.assertTrue(Path(os.path.join(a__				,   """train_mem.csv"""   )   ).exists()   )
						self.assertTrue(Path(os.path.join(a__				,   """env.csv"""   )   ).exists()   )

				def      _UpperCAmelCase					(						self   )	->				Optional[int]:
					A								=				"""sshleifer/tiny-gpt2"""

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

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







def   _lowerCAmelCase				(   UpperCamelCase__:     str						) ->   str:





	"""simple docstring"""







	return " ".join(input_str.split()[::-1]						)


if __name__ == "__main__":
				import doctest

				doctest.testmod()



