


import inspect
import unittest

from transformers import YolosConfig
from transformers.testing_utils import 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, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import YolosForObjectDetection, YolosModel
						from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor







class        snake_case_   :
					def __init__(      self   :Dict			,__snake_case   :Union[str, Any]			,__snake_case   :Union[str, Any]=13			,__snake_case   :List[str]=[30, 30]			,__snake_case   :int=2			,__snake_case   :Tuple=3			,__snake_case   :Tuple=True			,__snake_case   :Any=True			,__snake_case   :str=32			,__snake_case   :Dict=5			,__snake_case   :str=4			,__snake_case   :Tuple=37			,__snake_case   :Dict="gelu"			,__snake_case   :List[Any]=0.1			,__snake_case   :int=0.1			,__snake_case   :Dict=10			,__snake_case   :List[str]=0.02			,__snake_case   :Any=3			,__snake_case   :Tuple=None			,__snake_case   :Optional[Any]=8			,__snake_case   :Dict=10			,)					->    int:
												a__             =     parent
												a__             =     batch_size
												a__             =     image_size
												a__             =     patch_size
												a__             =     num_channels
												a__             =     is_training
												a__             =     use_labels
												a__             =     hidden_size
												a__             =     num_hidden_layers
												a__             =     num_attention_heads
												a__             =     intermediate_size
												a__             =     hidden_act
												a__             =     hidden_dropout_prob
												a__             =     attention_probs_dropout_prob
												a__             =     type_sequence_label_size
												a__             =     initializer_range
												a__             =     num_labels
												a__             =     scope
												a__             =     n_targets
												a__             =     num_detection_tokens
												# we set the expected sequence length (which is used in several tests)
												# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
												a__             =     (image_size[1] // patch_size) * (image_size[0] // patch_size)
												a__             =     num_patches + 1 + self.num_detection_tokens
					def  lowerCamelCase__(      self   :Union[str, Any]      )					->    List[Any]:
												a__             =     floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]      )

												a__             =     None
												if self.use_labels:
																			# labels is a list of Dict (each Dict being the labels for a given example in the batch)
																			a__             =     []
																			for i in range(self.batch_size      ):
																										a__             =     {}
																										a__             =     torch.randint(
																										    high=self.num_labels			,size=(self.n_targets,)			,device=__snake_case      )
																										a__             =     torch.rand(self.n_targets			,4			,device=__snake_case      )
																										labels.append(__snake_case      )

												a__             =     self.get_config()

												return config, pixel_values, labels
					def  lowerCamelCase__(      self   :Optional[Any]      )					->    Any:
												return YolosConfig(
												    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=__snake_case			,initializer_range=self.initializer_range			,num_detection_tokens=self.num_detection_tokens			,num_labels=self.num_labels			,)
					def  lowerCamelCase__(      self   :Dict			,__snake_case   :Dict			,__snake_case   :Optional[int]			,__snake_case   :Dict      )					->    Union[str, Any]:
												a__             =     YolosModel(config=__snake_case      )
												model.to(__snake_case      )
												model.eval()
												a__             =     model(__snake_case      )
												self.parent.assertEqual(
												    result.last_hidden_state.shape			,(self.batch_size, self.expected_seq_len, self.hidden_size)      )
					def  lowerCamelCase__(      self   :Dict			,__snake_case   :str			,__snake_case   :int			,__snake_case   :Union[str, Any]      )					->    str:
												a__             =     YolosForObjectDetection(__snake_case      )
												model.to(__snake_case      )
												model.eval()

												a__             =     model(pixel_values=__snake_case      )
												a__             =     model(__snake_case      )

												self.parent.assertEqual(result.logits.shape			,(self.batch_size, self.num_detection_tokens, self.num_labels + 1)      )
												self.parent.assertEqual(result.pred_boxes.shape			,(self.batch_size, self.num_detection_tokens, 4)      )

												a__             =     model(pixel_values=__snake_case			,labels=__snake_case      )

												self.parent.assertEqual(result.loss.shape			,()      )
												self.parent.assertEqual(result.logits.shape			,(self.batch_size, self.num_detection_tokens, self.num_labels + 1)      )
												self.parent.assertEqual(result.pred_boxes.shape			,(self.batch_size, self.num_detection_tokens, 4)      )



					def  lowerCamelCase__(      self   :Dict      )					->    str:
												a__             =     self.prepare_config_and_inputs()
												a__      ,					a__      ,					a__             =     config_and_inputs
												a__             =     {'pixel_values': pixel_values}
												return config, inputs_dict







@require_torch
class        snake_case_   (lowerCamelCase_	,						lowerCamelCase_	,						unittest.TestCase      ):
					UpperCAmelCase__					:    Optional[Any]          =			(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
					UpperCAmelCase__					:    int          =			(
					    {'''feature-extraction''': YolosModel, '''object-detection''': YolosForObjectDetection} if is_torch_available() else {}
					)

					UpperCAmelCase__					:    Optional[Any]          =			False
					UpperCAmelCase__					:    Any          =			False
					UpperCAmelCase__					:    str          =			False
					UpperCAmelCase__					:    List[Any]          =			False
					def  lowerCamelCase__(      self   :List[Any]			,__snake_case   :Any			,__snake_case   :Dict			,__snake_case   :Optional[int]=False      )					->    List[str]:
												a__             =     super()._prepare_for_class(__snake_case			,__snake_case			,return_labels=__snake_case      )

												if return_labels:
																			if model_class.__name__ == "YolosForObjectDetection":
																										a__             =     []
																										for i in range(self.model_tester.batch_size      ):
																																	a__             =     {}
																																	a__             =     torch.ones(
																																	    size=(self.model_tester.n_targets,)			,device=__snake_case			,dtype=torch.long      )
																																	a__             =     torch.ones(
																																	    self.model_tester.n_targets			,4			,device=__snake_case			,dtype=torch.float      )
																																	labels.append(__snake_case      )
																										a__             =     labels

												return inputs_dict
					def  lowerCamelCase__(      self   :Dict      )					->    Union[str, Any]:
												a__             =     YolosModelTester(self      )
												a__             =     ConfigTester(self			,config_class=__snake_case			,has_text_modality=__snake_case			,hidden_size=37      )
					def  lowerCamelCase__(      self   :Optional[int]      )					->    str:
												self.config_tester.run_common_tests()
					def  lowerCamelCase__(      self   :Dict      )					->    str:
												# YOLOS does not use inputs_embeds
												pass
					def  lowerCamelCase__(      self   :List[Any]      )					->    Optional[int]:
												a__      ,					a__             =     self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			a__             =     model_class(__snake_case      )
																			self.assertIsInstance(model.get_input_embeddings()			,(nn.Module)      )
																			a__             =     model.get_output_embeddings()
																			self.assertTrue(x is None or isinstance(__snake_case			,nn.Linear      )      )
					def  lowerCamelCase__(      self   :List[str]      )					->    List[Any]:
												a__      ,					a__             =     self.model_tester.prepare_config_and_inputs_for_common()

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

																			a__             =     ['pixel_values']
																			self.assertListEqual(arg_names[:1]			,__snake_case      )
					def  lowerCamelCase__(      self   :List[Any]      )					->    Tuple:
												a__             =     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__snake_case      )
					def  lowerCamelCase__(      self   :Union[str, Any]      )					->    Optional[int]:
												a__      ,					a__             =     self.model_tester.prepare_config_and_inputs_for_common()
												a__             =     True

												# in YOLOS, the seq_len is different
												a__             =     self.model_tester.expected_seq_len
												for model_class in self.all_model_classes:
																			a__             =     True
																			a__             =     False
																			a__             =     True
																			a__             =     model_class(__snake_case      )
																			model.to(__snake_case      )
																			model.eval()
																			with torch.no_grad():
																										a__             =     model(**self._prepare_for_class(__snake_case			,__snake_case      )      )
																			a__             =     outputs.attentions
																			self.assertEqual(len(__snake_case      )			,self.model_tester.num_hidden_layers      )

																			# check that output_attentions also work using config
																			del inputs_dict["output_attentions"]
																			a__             =     True
																			a__             =     model_class(__snake_case      )
																			model.to(__snake_case      )
																			model.eval()
																			with torch.no_grad():
																										a__             =     model(**self._prepare_for_class(__snake_case			,__snake_case      )      )
																			a__             =     outputs.attentions
																			self.assertEqual(len(__snake_case      )			,self.model_tester.num_hidden_layers      )

																			self.assertListEqual(
																			    list(attentions[0].shape[-3:]      )			,[self.model_tester.num_attention_heads, seq_len, seq_len]			,)
																			a__             =     len(__snake_case      )

																			# Check attention is always last and order is fine
																			a__             =     True
																			a__             =     True
																			a__             =     model_class(__snake_case      )
																			model.to(__snake_case      )
																			model.eval()
																			with torch.no_grad():
																										a__             =     model(**self._prepare_for_class(__snake_case			,__snake_case      )      )

																			a__             =     1
																			self.assertEqual(out_len + added_hidden_states			,len(__snake_case      )      )

																			a__             =     outputs.attentions

																			self.assertEqual(len(__snake_case      )			,self.model_tester.num_hidden_layers      )
																			self.assertListEqual(
																			    list(self_attentions[0].shape[-3:]      )			,[self.model_tester.num_attention_heads, seq_len, seq_len]			,)
					def  lowerCamelCase__(      self   :List[Any]      )					->    Tuple:
												def check_hidden_states_output(__snake_case   :Union[str, Any]			,__snake_case   :List[Any]			,__snake_case   :List[Any]      ):
																			a__             =     model_class(__snake_case      )
																			model.to(__snake_case      )
																			model.eval()

																			with torch.no_grad():
																										a__             =     model(**self._prepare_for_class(__snake_case			,__snake_case      )      )

																			a__             =     outputs.hidden_states

																			a__             =     getattr(
																			    self.model_tester			,'expected_num_hidden_layers'			,self.model_tester.num_hidden_layers + 1      )
																			self.assertEqual(len(__snake_case      )			,__snake_case      )

																			# YOLOS has a different seq_length
																			a__             =     self.model_tester.expected_seq_len

																			self.assertListEqual(
																			    list(hidden_states[0].shape[-2:]      )			,[seq_length, self.model_tester.hidden_size]			,)

												a__      ,					a__             =     self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			a__             =     True
																			check_hidden_states_output(__snake_case			,__snake_case			,__snake_case      )

																			# check that output_hidden_states also work using config
																			del inputs_dict["output_hidden_states"]
																			a__             =     True

																			check_hidden_states_output(__snake_case			,__snake_case			,__snake_case      )
					def  lowerCamelCase__(      self   :List[Any]      )					->    Dict:
												a__             =     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_object_detection(*__snake_case      )



					@slow
					def  lowerCamelCase__(      self   :List[str]      )					->    Tuple:
												for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			a__             =     YolosModel.from_pretrained(__snake_case      )
																			self.assertIsNotNone(__snake_case      )






def 							__lowercase   ( ):
							a__             =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
							return image







@require_torch
@require_vision
class        snake_case_   (unittest.TestCase      ):
					@cached_property
					def  lowerCamelCase__(      self   :List[str]      )					->    Tuple:
												return AutoImageProcessor.from_pretrained('hustvl/yolos-small'      ) if is_vision_available() else None



					@slow
					def  lowerCamelCase__(      self   :Tuple      )					->    Optional[Any]:
												a__             =     YolosForObjectDetection.from_pretrained('hustvl/yolos-small'      ).to(__snake_case      )

												a__             =     self.default_image_processor
												a__             =     prepare_img()
												a__             =     image_processor(images=__snake_case			,return_tensors='pt'      ).to(__snake_case      )

												# forward pass
												with torch.no_grad():
																			a__             =     model(inputs.pixel_values      )

												# verify outputs
												a__             =     torch.Size((1, 1_00, 92)      )
												self.assertEqual(outputs.logits.shape			,__snake_case      )

												a__             =     torch.tensor(
												    [[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]]			,device=__snake_case			,)
												a__             =     torch.tensor(
												    [[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]]			,device=__snake_case      )
												self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]			,__snake_case			,atol=1E-4      )      )
												self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]			,__snake_case			,atol=1E-4      )      )

												# verify postprocessing
												a__             =     image_processor.post_process_object_detection(
												    __snake_case			,threshold=0.3			,target_sizes=[image.size[::-1]]      )[0]
												a__             =     torch.tensor([0.99_94, 0.97_90, 0.99_64, 0.99_72, 0.98_61]      ).to(__snake_case      )
												a__             =     [75, 75, 17, 63, 17]
												a__             =     torch.tensor([3_35.06_09, 79.38_48, 3_75.42_16, 1_87.24_95]      ).to(__snake_case      )

												self.assertEqual(len(results['scores']      )			,5      )
												self.assertTrue(torch.allclose(results['scores']			,__snake_case			,atol=1E-4      )      )
												self.assertSequenceEqual(results['labels'].tolist()			,__snake_case      )
												self.assertTrue(torch.allclose(results['boxes'][0, :]			,__snake_case      )      )








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        snake_case_   (unittest.TestCase      ):
					def  lowerCamelCase__(      self   :Any			,__snake_case   :List[Any]      )					->    Union[str, 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(__snake_case      )
					def  lowerCamelCase__(      self   :List[str]      )					->    Union[str, Any]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     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]      )					->    Union[str, Any]:
												a__             =     'sgugger/tiny-distilbert-classification'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,only_pretrain_model=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     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]      )					->    Optional[Any]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,torchscript=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												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  lowerCamelCase__(      self   :int      )					->    str:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,fpaa=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     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[int]      )					->    Union[str, Any]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												# set architectures equal to `None`
												a__             =     None
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,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  lowerCamelCase__(      self   :Dict      )					->    int:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												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  lowerCamelCase__(      self   :int      )					->    List[str]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,fpaa=__snake_case			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     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      )					->    Union[str, Any]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,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  lowerCamelCase__(      self   :List[Any]      )					->    Any:
												a__             =     'sshleifer/tinier_bart'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,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  lowerCamelCase__(      self   :Tuple      )					->    Dict:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,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  lowerCamelCase__(      self   :Union[str, Any]      )					->    Optional[int]:
												a__             =     'sshleifer/tinier_bart'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,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  lowerCamelCase__(      self   :Optional[int]      )					->    List[Any]:
												a__             =     'sshleifer/tiny-gpt2'
												with tempfile.TemporaryDirectory() as tmp_dir:
																			a__             =     PyTorchBenchmarkArguments(
																			    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,save_to_csv=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,inference_time_csv_file=os.path.join(__snake_case			,'inf_time.csv'      )			,train_memory_csv_file=os.path.join(__snake_case			,'train_mem.csv'      )			,inference_memory_csv_file=os.path.join(__snake_case			,'inf_mem.csv'      )			,train_time_csv_file=os.path.join(__snake_case			,'train_time.csv'      )			,env_info_csv_file=os.path.join(__snake_case			,'env.csv'      )			,multi_process=__snake_case			,)
																			a__             =     PyTorchBenchmark(__snake_case      )
																			benchmark.run()
																			self.assertTrue(Path(os.path.join(__snake_case			,'inf_time.csv'      )      ).exists()      )
																			self.assertTrue(Path(os.path.join(__snake_case			,'train_time.csv'      )      ).exists()      )
																			self.assertTrue(Path(os.path.join(__snake_case			,'inf_mem.csv'      )      ).exists()      )
																			self.assertTrue(Path(os.path.join(__snake_case			,'train_mem.csv'      )      ).exists()      )
																			self.assertTrue(Path(os.path.join(__snake_case			,'env.csv'      )      ).exists()      )



					def  lowerCamelCase__(      self   :Optional[int]      )					->    List[str]:
												a__             =     'sshleifer/tiny-gpt2'

												def _check_summary_is_not_empty(__snake_case   :List[str]      ):
																			self.assertTrue(hasattr(__snake_case			,'sequential'      )      )
																			self.assertTrue(hasattr(__snake_case			,'cumulative'      )      )
																			self.assertTrue(hasattr(__snake_case			,'current'      )      )
																			self.assertTrue(hasattr(__snake_case			,'total'      )      )

												with tempfile.TemporaryDirectory() as tmp_dir:
																			a__             =     PyTorchBenchmarkArguments(
																			    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,log_filename=os.path.join(__snake_case			,'log.txt'      )			,log_print=__snake_case			,trace_memory_line_by_line=__snake_case			,multi_process=__snake_case			,)
																			a__             =     PyTorchBenchmark(__snake_case      )
																			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(__snake_case			,'log.txt'      )      ).exists()      )




