


"""simple docstring"""



import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
  from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image







class _lowerCAmelCase       :







   def __init__(			self		,			UpperCamelCase__		,			UpperCamelCase__=13		,			UpperCamelCase__=64		,			UpperCamelCase__=2		,			UpperCamelCase__=3		,			UpperCamelCase__=True		,			UpperCamelCase__=True		,			UpperCamelCase__=32		,			UpperCamelCase__=5		,			UpperCamelCase__=4		,			UpperCamelCase__=37		,			UpperCamelCase__="gelu"		,			UpperCamelCase__=0.1		,			UpperCamelCase__=0.1		,			UpperCamelCase__=10		,			UpperCamelCase__=0.02		,			UpperCamelCase__=[1, 16, 4, 4]		,			UpperCamelCase__=None		,			)			->	List[Any]:

       '''simple docstring'''




       snake_case					:   Tuple         =					parent
       snake_case					:   Dict         =					batch_size
       snake_case					:   Union[str, Any]         =					image_size
       snake_case					:   List[Any]         =					patch_size
       snake_case					:   Dict         =					num_channels
       snake_case					:   Optional[Any]         =					is_training
       snake_case					:   Tuple         =					use_labels
       snake_case					:   str         =					hidden_size
       snake_case					:   Union[str, Any]         =					num_hidden_layers
       snake_case					:   Optional[int]         =					num_attention_heads
       snake_case					:   Any         =					intermediate_size
       snake_case					:   Dict         =					hidden_act
       snake_case					:   Union[str, Any]         =					hidden_dropout_prob
       snake_case					:   str         =					attention_probs_dropout_prob
       snake_case					:   Dict         =					type_sequence_label_size
       snake_case					:   List[str]         =					initializer_range
       snake_case					:   List[Any]         =					scope
       snake_case					:   Dict         =					backbone_featmap_shape

       # in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
       # the number of patches is based on the feature map of the backbone, which by default uses an output stride
       # of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
       snake_case					:   Tuple         =					(self.image_size // 32) ** 2
       snake_case					:   Optional[int]         =					num_patches + 1







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   Tuple         =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

       snake_case					:   Dict         =					None
       if self.use_labels:
           snake_case					:   List[Any]         =					ids_tensor([self.batch_size]		,			self.type_sequence_label_size		)

       snake_case					:   Any         =					self.get_config()

       return config, pixel_values, labels







   def 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   Tuple         =					{
           "global_padding": "same",
           "layer_type": "bottleneck",
           "depths": [3, 4, 9],
           "out_features": ["stage1", "stage2", "stage3"],
           "embedding_dynamic_padding": True,
           "hidden_sizes": [4, 8, 16, 32],
           "num_groups": 2,
       }

       return ViTHybridConfig(
           image_size=self.image_size		,			patch_size=self.patch_size		,			num_channels=self.num_channels		,			hidden_size=self.hidden_size		,			num_hidden_layers=self.num_hidden_layers		,			num_attention_heads=self.num_attention_heads		,			intermediate_size=self.intermediate_size		,			hidden_act=self.hidden_act		,			hidden_dropout_prob=self.hidden_dropout_prob		,			attention_probs_dropout_prob=self.attention_probs_dropout_prob		,			is_decoder=UpperCamelCase__		,			initializer_range=self.initializer_range		,			backbone_featmap_shape=self.backbone_featmap_shape		,			backbone_config=UpperCamelCase__		,			)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					ViTHybridModel(config=UpperCamelCase__		)
       model.to(UpperCamelCase__		)
       model.eval()
       snake_case					:   int         =					model(UpperCamelCase__		)
       self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, self.seq_length, self.hidden_size)		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		)			->	List[Any]:

       '''simple docstring'''




       snake_case					:   Union[str, Any]         =					self.type_sequence_label_size
       snake_case					:   Optional[Any]         =					ViTHybridForImageClassification(UpperCamelCase__		)
       model.to(UpperCamelCase__		)
       model.eval()
       snake_case					:   Dict         =					model(UpperCamelCase__		,			labels=UpperCamelCase__		)
       self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.type_sequence_label_size)		)







   def 				lowerCamelCase	(			self		)			->	List[str]:

       '''simple docstring'''




       snake_case					:   Any         =					self.prepare_config_and_inputs()
       snake_case					:   Optional[Any]         =					config_and_inputs
       snake_case					:   Optional[Any]         =					{"pixel_values": pixel_values}
       return config, inputs_dict







@require_torch
class _lowerCAmelCase       (   snake_case_					,							snake_case_					,							unittest.TestCase			):
   __UpperCAmelCase				: Optional[int]													=						(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
   __UpperCAmelCase				: Any													=						(
       {'''feature-extraction''': ViTHybridModel, '''image-classification''': ViTHybridForImageClassification}
       if is_torch_available()
       else {}
   )
   __UpperCAmelCase				: Any													=						False
   __UpperCAmelCase				: List[Any]													=						False
   __UpperCAmelCase				: str													=						False







   def 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   Union[str, Any]         =					ViTHybridModelTester(self		)
       snake_case					:   Dict         =					ConfigTester(self		,			config_class=UpperCamelCase__		,			has_text_modality=UpperCamelCase__		,			hidden_size=37		)







   def 				lowerCamelCase	(			self		)			->	Union[str, Any]:

       '''simple docstring'''




       self.config_tester.run_common_tests()







   @unittest.skip(reason="ViT does not use inputs_embeds"		)
   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       pass







   def 				lowerCamelCase	(			self		)			->	int:

       '''simple docstring'''




       snake_case					:   Union[str, Any]         =					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           snake_case					:   str         =					model_class(UpperCamelCase__		)
           self.assertIsInstance(model.get_input_embeddings()		,			(nn.Module)		)
           snake_case					:   Optional[Any]         =					model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(UpperCamelCase__		,			nn.Linear		)		)







   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




       snake_case					:   int         =					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           snake_case					:   Dict         =					model_class(UpperCamelCase__		)
           snake_case					:   Dict         =					inspect.signature(model.forward		)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           snake_case					:   List[Any]         =					[*signature.parameters.keys()]

           snake_case					:   Union[str, Any]         =					["pixel_values"]
           self.assertListEqual(arg_names[:1]		,			UpperCamelCase__		)







   def 				lowerCamelCase	(			self		)			->	List[str]:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCamelCase__		)







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__		)







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       snake_case					:   List[str]         =					self.model_tester.prepare_config_and_inputs_for_common()

       snake_case					:   Any         =					_config_zero_init(UpperCamelCase__		)
       for model_class in self.all_model_classes:
           snake_case					:   Tuple         =					model_class(config=UpperCamelCase__		)
           # Skip the check for the backbone
           for name, module in model.named_modules():
               if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                   snake_case					:   List[str]         =					[F'{name}.{key}' for key in module.state_dict().keys()]
                   break

           for name, param in model.named_parameters():
               if param.requires_grad:
                   if name in backbone_params:
                       continue
                   self.assertIn(
                       ((param.data.mean() * 1e9).round() / 1e9).item()		,			[0.0, 1.0]		,			msg=F'Parameter {name} of model {model_class} seems not properly initialized'		,			)







   @slow
   def 				lowerCamelCase	(			self		)			->	List[str]:

       '''simple docstring'''




       for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           snake_case					:   str         =					ViTHybridModel.from_pretrained(UpperCamelCase__		)
           self.assertIsNotNone(UpperCamelCase__		)







def 						__lowerCAmelCase		(		)			->		List[Any]:


    """simple docstring"""





    snake_case					:   List[str]         =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
    return image







@require_torch
@require_vision
class _lowerCAmelCase       (   unittest.TestCase			):







   @cached_property
   def 				lowerCamelCase	(			self		)			->	List[Any]:

       '''simple docstring'''




       return (
           ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
           if is_vision_available()
           else None
       )







   @slow
   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(
           UpperCamelCase__		)

       snake_case					:   Optional[Any]         =					self.default_image_processor
       snake_case					:   int         =					prepare_img()
       snake_case					:   Tuple         =					image_processor(images=UpperCamelCase__		,			return_tensors="pt"		).to(UpperCamelCase__		)

       # forward pass
       with torch.no_grad():
           snake_case					:   Any         =					model(**UpperCamelCase__		)

       # verify the logits
       snake_case					:   Any         =					torch.Size((1, 1000)		)
       self.assertEqual(outputs.logits.shape		,			UpperCamelCase__		)

       snake_case					:   str         =					torch.tensor([-1.9090, -0.4993, -0.2389]		).to(UpperCamelCase__		)

       self.assertTrue(torch.allclose(outputs.logits[0, :3]		,			UpperCamelCase__		,			atol=1e-4		)		)







   @slow
   @require_accelerate
   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




       snake_case					:   List[Any]         =					ViTHybridImageProcessor.from_pretrained("google/vit-hybrid-base-bit-384"		)
       snake_case					:   Any         =					ViTHybridForImageClassification.from_pretrained("google/vit-hybrid-base-bit-384"		,			device_map="auto"		)

       snake_case					:   Tuple         =					prepare_img()

       snake_case					:   Union[str, Any]         =					image_processor(images=UpperCamelCase__		,			return_tensors="pt"		)
       snake_case					:   Dict         =					model(**UpperCamelCase__		)
       snake_case					:   int         =					outputs.logits
       # model predicts one of the 1000 ImageNet classes
       snake_case					:   Optional[Any]         =					logits.argmax(-1		).item()

       self.assertTrue(model.config.idalabel[predicted_class_idx]		,			"tabby, tabby cat"		)



"""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		,			UpperCamelCase__		)			->	Dict:

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




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

       '''simple docstring'''




       snake_case					:   str         =					"sgugger/tiny-distilbert-classification"
       snake_case					:   int         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			only_pretrain_model=UpperCamelCase__		,			)
       snake_case					:   Union[str, Any]         =					PyTorchBenchmark(UpperCamelCase__		)
       snake_case					:   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		)			->	List[str]:

       '''simple docstring'''




       snake_case					:   List[Any]         =					"sshleifer/tiny-gpt2"
       snake_case					:   Tuple         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			torchscript=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   int         =					PyTorchBenchmark(UpperCamelCase__		)
       snake_case					:   int         =					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[Any]:

       '''simple docstring'''




       snake_case					:   List[Any]         =					"sshleifer/tiny-gpt2"
       snake_case					:   int         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			fpaa=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   List[Any]         =					PyTorchBenchmark(UpperCamelCase__		)
       snake_case					:   str         =					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[str]:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					"sshleifer/tiny-gpt2"
       snake_case					:   int         =					AutoConfig.from_pretrained(UpperCamelCase__		)
       # set architectures equal to `None`
       snake_case					:   str         =					None
       snake_case					:   List[Any]         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   List[Any]         =					PyTorchBenchmark(UpperCamelCase__		,			configs=[config]		)
       snake_case					:   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 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       snake_case					:   Tuple         =					"sshleifer/tiny-gpt2"
       snake_case					:   str         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   List[str]         =					PyTorchBenchmark(UpperCamelCase__		)
       snake_case					:   Dict         =					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		)			->	int:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					"sshleifer/tiny-gpt2"
       snake_case					:   Optional[Any]         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			fpaa=UpperCamelCase__		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   Optional[int]         =					PyTorchBenchmark(UpperCamelCase__		)
       snake_case					:   str         =					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]:

       '''simple docstring'''




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

       '''simple docstring'''




       snake_case					:   Optional[int]         =					"sshleifer/tinier_bart"
       snake_case					:   List[str]         =					AutoConfig.from_pretrained(UpperCamelCase__		)
       snake_case					:   Optional[int]         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   Union[str, Any]         =					PyTorchBenchmark(UpperCamelCase__		,			configs=[config]		)
       snake_case					:   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 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					"sshleifer/tiny-gpt2"
       snake_case					:   str         =					AutoConfig.from_pretrained(UpperCamelCase__		)
       snake_case					:   List[str]         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   Optional[int]         =					PyTorchBenchmark(UpperCamelCase__		,			configs=[config]		)
       snake_case					:   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		)			->	Optional[int]:

       '''simple docstring'''




       snake_case					:   str         =					"sshleifer/tinier_bart"
       snake_case					:   Union[str, Any]         =					AutoConfig.from_pretrained(UpperCamelCase__		)
       snake_case					:   Any         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   Union[str, Any]         =					PyTorchBenchmark(UpperCamelCase__		,			configs=[config]		)
       snake_case					:   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		)			->	Any:

       '''simple docstring'''




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

       '''simple docstring'''




       snake_case					:   Union[str, Any]         =					"sshleifer/tiny-gpt2"

       def _check_summary_is_not_empty(UpperCamelCase__		):
           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:
           snake_case					:   Tuple         =					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__		,			)
           snake_case					:   Dict         =					PyTorchBenchmark(UpperCamelCase__		)
           snake_case					:   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(UpperCamelCase__		,			"log.txt"		)		).exists()		)





