




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		):
						def 	UpperCAmelCase_						(			self   :Dict ,      lowerCamelCase   :Dict      )     ->			Any:
							for model_result in results.values():
								for batch_size, sequence_length in zip(model_result["bs"] ,      model_result["ss"]      ):
									UpperCAmelCase__							 =						model_result['''result'''][batch_size][sequence_length]
									self.assertIsNotNone(_UpperCAmelCase      )
						def 	UpperCAmelCase_						(			self   :Any      )     ->			Any:
							UpperCAmelCase__							 =						'''sshleifer/tiny-gpt2'''
							UpperCAmelCase__							 =						PyTorchBenchmarkArguments(
							    models=[MODEL_ID] ,      training=_UpperCAmelCase ,      inference=_UpperCAmelCase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      multi_process=_UpperCAmelCase ,      )
							UpperCAmelCase__							 =						PyTorchBenchmark(_UpperCAmelCase      )
							UpperCAmelCase__							 =						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   :Tuple      )     ->			Dict:
							UpperCAmelCase__							 =						'''sgugger/tiny-distilbert-classification'''
							UpperCAmelCase__							 =						PyTorchBenchmarkArguments(
							    models=[MODEL_ID] ,      training=_UpperCAmelCase ,      inference=_UpperCAmelCase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      multi_process=_UpperCAmelCase ,      only_pretrain_model=_UpperCAmelCase ,      )
							UpperCAmelCase__							 =						PyTorchBenchmark(_UpperCAmelCase      )
							UpperCAmelCase__							 =						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[Any]      )     ->			Optional[Any]:
							UpperCAmelCase__							 =						'''sshleifer/tiny-gpt2'''
							UpperCAmelCase__							 =						PyTorchBenchmarkArguments(
							    models=[MODEL_ID] ,      training=_UpperCAmelCase ,      inference=_UpperCAmelCase ,      torchscript=_UpperCAmelCase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      multi_process=_UpperCAmelCase ,      )
							UpperCAmelCase__							 =						PyTorchBenchmark(_UpperCAmelCase      )
							UpperCAmelCase__							 =						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   :int      )     ->			Optional[Any]:
							UpperCAmelCase__							 =						'''sshleifer/tiny-gpt2'''
							UpperCAmelCase__							 =						PyTorchBenchmarkArguments(
							    models=[MODEL_ID] ,      training=_UpperCAmelCase ,      inference=_UpperCAmelCase ,      fpaa=_UpperCAmelCase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      multi_process=_UpperCAmelCase ,      )
							UpperCAmelCase__							 =						PyTorchBenchmark(_UpperCAmelCase      )
							UpperCAmelCase__							 =						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   :int      )     ->			List[str]:
							UpperCAmelCase__							 =						'''sshleifer/tiny-gpt2'''
							UpperCAmelCase__							 =						AutoConfig.from_pretrained(_UpperCAmelCase      )
							# set architectures equal to `None`
							UpperCAmelCase__							 =						None
							UpperCAmelCase__							 =						PyTorchBenchmarkArguments(
							    models=[MODEL_ID] ,      training=_UpperCAmelCase ,      inference=_UpperCAmelCase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      multi_process=_UpperCAmelCase ,      )
							UpperCAmelCase__							 =						PyTorchBenchmark(_UpperCAmelCase ,      configs=[config]      )
							UpperCAmelCase__							 =						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   :int      )     ->			List[str]:
							UpperCAmelCase__							 =						'''sshleifer/tiny-gpt2'''
							UpperCAmelCase__							 =						PyTorchBenchmarkArguments(
							    models=[MODEL_ID] ,      training=_UpperCAmelCase ,      inference=_UpperCAmelCase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      multi_process=_UpperCAmelCase ,      )
							UpperCAmelCase__							 =						PyTorchBenchmark(_UpperCAmelCase      )
							UpperCAmelCase__							 =						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      )     ->			Union[str, Any]:
							UpperCAmelCase__							 =						'''sshleifer/tiny-gpt2'''
							UpperCAmelCase__							 =						PyTorchBenchmarkArguments(
							    models=[MODEL_ID] ,      training=_UpperCAmelCase ,      inference=_UpperCAmelCase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      fpaa=_UpperCAmelCase ,      multi_process=_UpperCAmelCase ,      )
							UpperCAmelCase__							 =						PyTorchBenchmark(_UpperCAmelCase      )
							UpperCAmelCase__							 =						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[Any]      )     ->			List[Any]:
							UpperCAmelCase__							 =						'''sshleifer/tiny-gpt2'''
							UpperCAmelCase__							 =						AutoConfig.from_pretrained(_UpperCAmelCase      )
							UpperCAmelCase__							 =						PyTorchBenchmarkArguments(
							    models=[MODEL_ID] ,      training=_UpperCAmelCase ,      inference=_UpperCAmelCase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      multi_process=_UpperCAmelCase ,      )
							UpperCAmelCase__							 =						PyTorchBenchmark(_UpperCAmelCase ,      configs=[config]      )
							UpperCAmelCase__							 =						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      )     ->			List[str]:
							UpperCAmelCase__							 =						'''sshleifer/tinier_bart'''
							UpperCAmelCase__							 =						AutoConfig.from_pretrained(_UpperCAmelCase      )
							UpperCAmelCase__							 =						PyTorchBenchmarkArguments(
							    models=[MODEL_ID] ,      training=_UpperCAmelCase ,      inference=_UpperCAmelCase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      multi_process=_UpperCAmelCase ,      )
							UpperCAmelCase__							 =						PyTorchBenchmark(_UpperCAmelCase ,      configs=[config]      )
							UpperCAmelCase__							 =						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      )     ->			Optional[int]:
							UpperCAmelCase__							 =						'''sshleifer/tiny-gpt2'''
							UpperCAmelCase__							 =						AutoConfig.from_pretrained(_UpperCAmelCase      )
							UpperCAmelCase__							 =						PyTorchBenchmarkArguments(
							    models=[MODEL_ID] ,      training=_UpperCAmelCase ,      inference=_UpperCAmelCase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      multi_process=_UpperCAmelCase ,      )
							UpperCAmelCase__							 =						PyTorchBenchmark(_UpperCAmelCase ,      configs=[config]      )
							UpperCAmelCase__							 =						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[Any]      )     ->			Any:
							UpperCAmelCase__							 =						'''sshleifer/tinier_bart'''
							UpperCAmelCase__							 =						AutoConfig.from_pretrained(_UpperCAmelCase      )
							UpperCAmelCase__							 =						PyTorchBenchmarkArguments(
							    models=[MODEL_ID] ,      training=_UpperCAmelCase ,      inference=_UpperCAmelCase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      multi_process=_UpperCAmelCase ,      )
							UpperCAmelCase__							 =						PyTorchBenchmark(_UpperCAmelCase ,      configs=[config]      )
							UpperCAmelCase__							 =						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   :List[Any]      )     ->			List[str]:
							UpperCAmelCase__							 =						'''sshleifer/tiny-gpt2'''
							with tempfile.TemporaryDirectory() as tmp_dir:
								UpperCAmelCase__							 =						PyTorchBenchmarkArguments(
								    models=[MODEL_ID] ,      training=_UpperCAmelCase ,      inference=_UpperCAmelCase ,      save_to_csv=_UpperCAmelCase ,      sequence_lengths=[8] ,      batch_sizes=[1] ,      inference_time_csv_file=os.path.join(_UpperCAmelCase ,      "inf_time.csv"      ) ,      train_memory_csv_file=os.path.join(_UpperCAmelCase ,      "train_mem.csv"      ) ,      inference_memory_csv_file=os.path.join(_UpperCAmelCase ,      "inf_mem.csv"      ) ,      train_time_csv_file=os.path.join(_UpperCAmelCase ,      "train_time.csv"      ) ,      env_info_csv_file=os.path.join(_UpperCAmelCase ,      "env.csv"      ) ,      multi_process=_UpperCAmelCase ,      )
								UpperCAmelCase__							 =						PyTorchBenchmark(_UpperCAmelCase      )
								benchmark.run()
								self.assertTrue(Path(os.path.join(_UpperCAmelCase ,      "inf_time.csv"      )      ).exists()      )
								self.assertTrue(Path(os.path.join(_UpperCAmelCase ,      "train_time.csv"      )      ).exists()      )
								self.assertTrue(Path(os.path.join(_UpperCAmelCase ,      "inf_mem.csv"      )      ).exists()      )
								self.assertTrue(Path(os.path.join(_UpperCAmelCase ,      "train_mem.csv"      )      ).exists()      )
								self.assertTrue(Path(os.path.join(_UpperCAmelCase ,      "env.csv"      )      ).exists()      )







						def 	UpperCAmelCase_						(			self   :Union[str, Any]      )     ->			Dict:
							UpperCAmelCase__							 =						'''sshleifer/tiny-gpt2'''

							def _check_summary_is_not_empty(lowerCamelCase   :int      ):
								self.assertTrue(hasattr(_UpperCAmelCase ,      "sequential"      )      )
								self.assertTrue(hasattr(_UpperCAmelCase ,      "cumulative"      )      )
								self.assertTrue(hasattr(_UpperCAmelCase ,      "current"      )      )
								self.assertTrue(hasattr(_UpperCAmelCase ,      "total"      )      )

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











import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


_lowerCAmelCase						:   int		=   logging.get_logger(__name__)
class 			_UpperCamelCase      (				lowerCAmelCase		):
      def __init__(			self   :Optional[Any] ,      *lowerCamelCase   :List[str] ,      **lowerCamelCase   :Tuple      )     ->			None:
       warnings.warn(
           "The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
           " use YolosImageProcessor instead." ,      lowerCamelCase ,      )
       super().__init__(*lowerCamelCase ,      **lowerCamelCase      )





