
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import ChineseCLIPImageProcessor





class     __UpperCamelCase (							unittest.TestCase       ):






     """simple docstring"""







     def __init__(				self							:							str     ,						_A							:							List[str]     ,						_A							:							str=7     ,						_A							:							Dict=3     ,						_A							:							Tuple=18     ,						_A							:							Optional[Any]=30     ,						_A							:							str=400     ,						_A							:							str=True     ,						_A							:							Tuple=None     ,						_A							:							Optional[int]=True     ,						_A							:							Optional[Any]=None     ,						_A							:							Optional[int]=True     ,						_A							:							Dict=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]     ,						_A							:							List[str]=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]     ,						_A							:							Any=True     ,						):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	int					=		size if size is not None else {'''height''': 224, '''width''': 224}
       __SCREAMING_SNAKE_CASE      :	List[str]					=		crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		parent
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		batch_size
       __SCREAMING_SNAKE_CASE      :	List[str]					=		num_channels
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		image_size
       __SCREAMING_SNAKE_CASE      :	int					=		min_resolution
       __SCREAMING_SNAKE_CASE      :	Tuple					=		max_resolution
       __SCREAMING_SNAKE_CASE      :	Any					=		do_resize
       __SCREAMING_SNAKE_CASE      :	Tuple					=		size
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		do_center_crop
       __SCREAMING_SNAKE_CASE      :	List[str]					=		crop_size
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		do_normalize
       __SCREAMING_SNAKE_CASE      :	str					=		image_mean
       __SCREAMING_SNAKE_CASE      :	int					=		image_std
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		do_convert_rgb



     def  UpperCAmelCase__							(				self							:							Union[str, Any] ):







       """simple docstring"""




       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_center_crop": self.do_center_crop,
           "crop_size": self.crop_size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_convert_rgb": self.do_convert_rgb,
       }




     def  UpperCAmelCase__							(				self							:							List[str]     ,						_A							:							Optional[int]=False     ,						_A							:							int=False     ,						_A							:							int=False ):







       """simple docstring"""




       assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

       if equal_resolution:
         __SCREAMING_SNAKE_CASE      :	Tuple					=		[]
         for i in range(self.batch_size ):
           image_inputs.append(
               np.random.randint(
                   255     ,						size=(self.num_channels, self.max_resolution, self.max_resolution)     ,						dtype=np.uinta ) )
       else:
         __SCREAMING_SNAKE_CASE      :	Any					=		[]
         for i in range(self.batch_size ):
           __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Optional[Any]					=		np.random.choice(np.arange(self.min_resolution     ,						self.max_resolution )     ,						2 )
           image_inputs.append(np.random.randint(255     ,						size=(self.num_channels, width, height)     ,						dtype=np.uinta ) )

       if not numpify and not torchify:
         # PIL expects the channel dimension as last dimension
         __SCREAMING_SNAKE_CASE      :	int					=		[Image.fromarray(np.moveaxis(_A     ,						0     ,						-1 ) ) for x in image_inputs]

       if torchify:
         __SCREAMING_SNAKE_CASE      :	List[str]					=		[torch.from_numpy(_A ) for x in image_inputs]

       return image_inputs






@require_torch
@require_vision
class     __UpperCamelCase (							lowerCAmelCase__ ,						unittest.TestCase       ):






     """simple docstring"""







     lowerCAmelCase_							   =			ChineseCLIPImageProcessor if is_vision_available() else None



     def  UpperCAmelCase__							(				self							:							Dict ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Any					=		ChineseCLIPImageProcessingTester(self     ,						do_center_crop=_A )



     @property
     def  UpperCAmelCase__							(				self							:							Tuple ):







       """simple docstring"""




       return self.image_processor_tester.prepare_image_processor_dict()



     def  UpperCAmelCase__							(				self							:							Dict ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	int					=		self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(_A     ,						'''do_resize''' ) )
       self.assertTrue(hasattr(_A     ,						'''size''' ) )
       self.assertTrue(hasattr(_A     ,						'''do_center_crop''' ) )
       self.assertTrue(hasattr(_A     ,						'''center_crop''' ) )
       self.assertTrue(hasattr(_A     ,						'''do_normalize''' ) )
       self.assertTrue(hasattr(_A     ,						'''image_mean''' ) )
       self.assertTrue(hasattr(_A     ,						'''image_std''' ) )
       self.assertTrue(hasattr(_A     ,						'''do_convert_rgb''' ) )



     def  UpperCAmelCase__							(				self							:							str ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	List[Any]					=		self.image_processing_class.from_dict(self.image_processor_dict )
       self.assertEqual(image_processor.size     ,						{'''height''': 224, '''width''': 224} )
       self.assertEqual(image_processor.crop_size     ,						{'''height''': 18, '''width''': 18} )

       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		self.image_processing_class.from_dict(self.image_processor_dict     ,						size=42     ,						crop_size=84 )
       self.assertEqual(image_processor.size     ,						{'''shortest_edge''': 42} )
       self.assertEqual(image_processor.crop_size     ,						{'''height''': 84, '''width''': 84} )



     def  UpperCAmelCase__							(				self							:							int ):







       """simple docstring"""




       pass



     def  UpperCAmelCase__							(				self							:							Optional[int] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	List[Any]					=		self.image_processing_class(**self.image_processor_dict )
       # create random PIL images
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		self.image_processor_tester.prepare_inputs(equal_resolution=_A )
       for image in image_inputs:
         self.assertIsInstance(_A     ,						Image.Image )

       # Test not batched input
       __SCREAMING_SNAKE_CASE      :	str					=		image_processing(image_inputs[0]     ,						return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape     ,						(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )     ,						)

       # Test batched
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		image_processing(_A     ,						return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape     ,						(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )     ,						)



     def  UpperCAmelCase__							(				self							:							Optional[int] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Tuple					=		self.image_processing_class(**self.image_processor_dict )
       # create random numpy tensors
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		self.image_processor_tester.prepare_inputs(equal_resolution=_A     ,						numpify=_A )
       for image in image_inputs:
         self.assertIsInstance(_A     ,						np.ndarray )

       # Test not batched input
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		image_processing(image_inputs[0]     ,						return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape     ,						(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )     ,						)

       # Test batched
       __SCREAMING_SNAKE_CASE      :	int					=		image_processing(_A     ,						return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape     ,						(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )     ,						)




     def  UpperCAmelCase__							(				self							:							str ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		self.image_processing_class(**self.image_processor_dict )
       # create random PyTorch tensors
       __SCREAMING_SNAKE_CASE      :	str					=		self.image_processor_tester.prepare_inputs(equal_resolution=_A     ,						torchify=_A )
       for image in image_inputs:
         self.assertIsInstance(_A     ,						torch.Tensor )

       # Test not batched input
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		image_processing(image_inputs[0]     ,						return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape     ,						(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )     ,						)

       # Test batched
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		image_processing(_A     ,						return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape     ,						(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )     ,						)






@require_torch
@require_vision
class     __UpperCamelCase (							lowerCAmelCase__ ,						unittest.TestCase       ):






     """simple docstring"""







     lowerCAmelCase_							   =			ChineseCLIPImageProcessor if is_vision_available() else None



     def  UpperCAmelCase__							(				self							:							Union[str, Any] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		ChineseCLIPImageProcessingTester(self     ,						num_channels=4     ,						do_center_crop=_A )
       __SCREAMING_SNAKE_CASE      :	int					=		3



     @property
     def  UpperCAmelCase__							(				self							:							Optional[Any] ):







       """simple docstring"""




       return self.image_processor_tester.prepare_image_processor_dict()



     def  UpperCAmelCase__							(				self							:							int ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	List[str]					=		self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(_A     ,						'''do_resize''' ) )
       self.assertTrue(hasattr(_A     ,						'''size''' ) )
       self.assertTrue(hasattr(_A     ,						'''do_center_crop''' ) )
       self.assertTrue(hasattr(_A     ,						'''center_crop''' ) )
       self.assertTrue(hasattr(_A     ,						'''do_normalize''' ) )
       self.assertTrue(hasattr(_A     ,						'''image_mean''' ) )
       self.assertTrue(hasattr(_A     ,						'''image_std''' ) )
       self.assertTrue(hasattr(_A     ,						'''do_convert_rgb''' ) )



     def  UpperCAmelCase__							(				self							:							Optional[Any] ):







       """simple docstring"""




       pass




     def  UpperCAmelCase__							(				self							:							Optional[Any] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	str					=		self.image_processing_class(**self.image_processor_dict )
       # create random PIL images
       __SCREAMING_SNAKE_CASE      :	Tuple					=		self.image_processor_tester.prepare_inputs(equal_resolution=_A )
       for image in image_inputs:
         self.assertIsInstance(_A     ,						Image.Image )

       # Test not batched input
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		image_processing(image_inputs[0]     ,						return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape     ,						(
               1,
               self.expected_encoded_image_num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )     ,						)

       # Test batched
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		image_processing(_A     ,						return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape     ,						(
               self.image_processor_tester.batch_size,
               self.expected_encoded_image_num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )     ,						)




import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
		import tensorflow as tf

		from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments

@require_tf
class 						__a (							unittest.TestCase     ):
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			List[str]:




									"""simple docstring"""





									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(_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase											=     'sshleifer/tiny-gpt2'
									_UpperCAmelCase											=     TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,       training=_SCREAMING_SNAKE_CASE     ,       inference=_SCREAMING_SNAKE_CASE     ,       sequence_lengths=[8]     ,       batch_sizes=[1]     ,       eager_mode=_SCREAMING_SNAKE_CASE     ,       multi_process=_SCREAMING_SNAKE_CASE     ,       )
									_UpperCAmelCase											=     TensorFlowBenchmark(_SCREAMING_SNAKE_CASE		)
									_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:




									"""simple docstring"""





									_UpperCAmelCase											=     'sgugger/tiny-distilbert-classification'
									_UpperCAmelCase											=     TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,       training=_SCREAMING_SNAKE_CASE     ,       inference=_SCREAMING_SNAKE_CASE     ,       sequence_lengths=[8]     ,       batch_sizes=[1]     ,       multi_process=_SCREAMING_SNAKE_CASE     ,       only_pretrain_model=_SCREAMING_SNAKE_CASE     ,       )
									_UpperCAmelCase											=     TensorFlowBenchmark(_SCREAMING_SNAKE_CASE		)
									_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]:




									"""simple docstring"""





									_UpperCAmelCase											=     'sshleifer/tiny-gpt2'
									_UpperCAmelCase											=     TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,       training=_SCREAMING_SNAKE_CASE     ,       inference=_SCREAMING_SNAKE_CASE     ,       sequence_lengths=[8]     ,       batch_sizes=[1]     ,       multi_process=_SCREAMING_SNAKE_CASE     ,       )
									_UpperCAmelCase											=     TensorFlowBenchmark(_SCREAMING_SNAKE_CASE		)
									_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]:




									"""simple docstring"""





									_UpperCAmelCase											=     'sshleifer/tiny-gpt2'
									_UpperCAmelCase											=     AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,       training=_SCREAMING_SNAKE_CASE     ,       inference=_SCREAMING_SNAKE_CASE     ,       sequence_lengths=[8]     ,       batch_sizes=[1]     ,       eager_mode=_SCREAMING_SNAKE_CASE     ,       multi_process=_SCREAMING_SNAKE_CASE     ,       )
									_UpperCAmelCase											=     TensorFlowBenchmark(_SCREAMING_SNAKE_CASE     ,       [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		)					->			List[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     'sshleifer/tiny-gpt2'
									_UpperCAmelCase											=     AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,       training=_SCREAMING_SNAKE_CASE     ,       inference=_SCREAMING_SNAKE_CASE     ,       sequence_lengths=[8]     ,       batch_sizes=[1]     ,       multi_process=_SCREAMING_SNAKE_CASE     ,       )
									_UpperCAmelCase											=     TensorFlowBenchmark(_SCREAMING_SNAKE_CASE     ,       [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		)					->			List[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     'sshleifer/tiny-gpt2'
									_UpperCAmelCase											=     TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,       training=_SCREAMING_SNAKE_CASE     ,       inference=_SCREAMING_SNAKE_CASE     ,       sequence_lengths=[8]     ,       batch_sizes=[1]     ,       multi_process=_SCREAMING_SNAKE_CASE     ,       )
									_UpperCAmelCase											=     TensorFlowBenchmark(_SCREAMING_SNAKE_CASE		)
									_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]:




									"""simple docstring"""





									_UpperCAmelCase											=     'sshleifer/tiny-gpt2'
									_UpperCAmelCase											=     AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,       training=_SCREAMING_SNAKE_CASE     ,       inference=_SCREAMING_SNAKE_CASE     ,       sequence_lengths=[8]     ,       batch_sizes=[1]     ,       multi_process=_SCREAMING_SNAKE_CASE     ,       )
									_UpperCAmelCase											=     TensorFlowBenchmark(_SCREAMING_SNAKE_CASE     ,       [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		)					->			Any:




									"""simple docstring"""





									_UpperCAmelCase											=     'patrickvonplaten/t5-tiny-random'
									_UpperCAmelCase											=     AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,       training=_SCREAMING_SNAKE_CASE     ,       inference=_SCREAMING_SNAKE_CASE     ,       sequence_lengths=[8]     ,       batch_sizes=[1]     ,       multi_process=_SCREAMING_SNAKE_CASE     ,       )
									_UpperCAmelCase											=     TensorFlowBenchmark(_SCREAMING_SNAKE_CASE     ,       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		)
		@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('GPU'		)		) == 0     ,       'Cannot do xla on CPU.'		)
		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase											=     'sshleifer/tiny-gpt2'
									_UpperCAmelCase											=     TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,       training=_SCREAMING_SNAKE_CASE     ,       inference=_SCREAMING_SNAKE_CASE     ,       sequence_lengths=[8]     ,       batch_sizes=[1]     ,       use_xla=_SCREAMING_SNAKE_CASE     ,       multi_process=_SCREAMING_SNAKE_CASE     ,       )
									_UpperCAmelCase											=     TensorFlowBenchmark(_SCREAMING_SNAKE_CASE		)
									_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:




									"""simple docstring"""





									_UpperCAmelCase											=     'sshleifer/tiny-gpt2'
									with tempfile.TemporaryDirectory() as tmp_dir:
																_UpperCAmelCase											=     TensorFlowBenchmarkArguments(
																    models=[MODEL_ID]     ,       inference=_SCREAMING_SNAKE_CASE     ,       save_to_csv=_SCREAMING_SNAKE_CASE     ,       sequence_lengths=[8]     ,       batch_sizes=[1]     ,       inference_time_csv_file=os.path.join(_SCREAMING_SNAKE_CASE     ,       'inf_time.csv'		)     ,       inference_memory_csv_file=os.path.join(_SCREAMING_SNAKE_CASE     ,       'inf_mem.csv'		)     ,       env_info_csv_file=os.path.join(_SCREAMING_SNAKE_CASE     ,       'env.csv'		)     ,       multi_process=_SCREAMING_SNAKE_CASE     ,       )
																_UpperCAmelCase											=     TensorFlowBenchmark(_SCREAMING_SNAKE_CASE		)
																benchmark.run()
																self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE     ,       'inf_time.csv'		)		).exists()		)
																self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE     ,       'inf_mem.csv'		)		).exists()		)
																self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE     ,       'env.csv'		)		).exists()		)






		def       UpperCAmelCase__							(     self		)					->			Tuple:




									"""simple docstring"""





									_UpperCAmelCase											=     'sshleifer/tiny-gpt2'

									def _check_summary_is_not_empty(_SCREAMING_SNAKE_CASE		):
																self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,       'sequential'		)		)
																self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,       'cumulative'		)		)
																self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,       'current'		)		)
																self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,       'total'		)		)

									with tempfile.TemporaryDirectory() as tmp_dir:
																_UpperCAmelCase											=     TensorFlowBenchmarkArguments(
																    models=[MODEL_ID]     ,       inference=_SCREAMING_SNAKE_CASE     ,       sequence_lengths=[8]     ,       batch_sizes=[1]     ,       log_filename=os.path.join(_SCREAMING_SNAKE_CASE     ,       'log.txt'		)     ,       log_print=_SCREAMING_SNAKE_CASE     ,       trace_memory_line_by_line=_SCREAMING_SNAKE_CASE     ,       eager_mode=_SCREAMING_SNAKE_CASE     ,       multi_process=_SCREAMING_SNAKE_CASE     ,       )
																_UpperCAmelCase											=     TensorFlowBenchmark(_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase											=     benchmark.run()
																_check_summary_is_not_empty(result.inference_summary		)
																self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE     ,       'log.txt'		)		).exists()		)
