





import json
import pathlib
import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import DetrImageProcessor






class        snake_case_    (		unittest.TestCase						):






		def __init__(							self			:					str       ,  _snake_case			:					int       ,  _snake_case			:					List[str]=7       ,  _snake_case			:					List[str]=3       ,  _snake_case			:					Optional[int]=30       ,  _snake_case			:					Optional[int]=400       ,  _snake_case			:					Optional[Any]=True       ,  _snake_case			:					Dict=None       ,  _snake_case			:					Any=True       ,  _snake_case			:					str=1 / 255       ,  _snake_case			:					str=True       ,  _snake_case			:					Union[str, Any]=[0.5, 0.5, 0.5]       ,  _snake_case			:					Optional[int]=[0.5, 0.5, 0.5]       ,  _snake_case			:					str=True       ,  )->int:






			'''simple docstring'''

			__lowerCAmelCase					:	str      =       size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
			__lowerCAmelCase					:	int      =       parent
			__lowerCAmelCase					:	str      =       batch_size
			__lowerCAmelCase					:	Dict      =       num_channels
			__lowerCAmelCase					:	Optional[Any]      =       min_resolution
			__lowerCAmelCase					:	List[str]      =       max_resolution
			__lowerCAmelCase					:	Dict      =       do_resize
			__lowerCAmelCase					:	Dict      =       size
			__lowerCAmelCase					:	Tuple      =       do_rescale
			__lowerCAmelCase					:	List[str]      =       rescale_factor
			__lowerCAmelCase					:	Any      =       do_normalize
			__lowerCAmelCase					:	Union[str, Any]      =       image_mean
			__lowerCAmelCase					:	List[Any]      =       image_std
			__lowerCAmelCase					:	Union[str, Any]      =       do_pad






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






			'''simple docstring'''

			return {
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "do_rescale": self.do_rescale,
			    "rescale_factor": self.rescale_factor,
			    "do_normalize": self.do_normalize,
			    "image_mean": self.image_mean,
			    "image_std": self.image_std,
			    "do_pad": self.do_pad,
			}






		def 				UpperCAmelCase__						(							self			:					Dict       ,  _snake_case			:					Tuple       ,  _snake_case			:					List[Any]=False      )->Optional[Any]:






			'''simple docstring'''

			if not batched:
				__lowerCAmelCase					:	Optional[Any]      =       image_inputs[0]
				if isinstance(_lowercase       ,  Image.Image      ):
					__lowerCAmelCase ,					__lowerCAmelCase					:	Any      =       image.size
				else:
					__lowerCAmelCase ,					__lowerCAmelCase					:	List[Any]      =       image.shape[1], image.shape[2]
				if w < h:
					__lowerCAmelCase					:	Any      =       int(self.size["""shortest_edge"""] * h / w      )
					__lowerCAmelCase					:	Any      =       self.size["""shortest_edge"""]
				elif w > h:
					__lowerCAmelCase					:	Dict      =       self.size["""shortest_edge"""]
					__lowerCAmelCase					:	Optional[Any]      =       int(self.size["""shortest_edge"""] * w / h      )
				else:
					__lowerCAmelCase					:	Tuple      =       self.size["""shortest_edge"""]
					__lowerCAmelCase					:	Optional[Any]      =       self.size["""shortest_edge"""]

			else:
				__lowerCAmelCase					:	Union[str, Any]      =       []
				for image in image_inputs:
					__lowerCAmelCase ,					__lowerCAmelCase					:	Optional[Any]      =       self.get_expected_values([image]      )
					expected_values.append((expected_height, expected_width)      )
				__lowerCAmelCase					:	Optional[int]      =       max(_lowercase       ,  key=lambda _snake_case      : item[0]      )[0]
				__lowerCAmelCase					:	int      =       max(_lowercase       ,  key=lambda _snake_case      : item[1]      )[1]

			return expected_height, expected_width






@require_torch
@require_vision
class        snake_case_    (		_lowerCAmelCase					,unittest.TestCase						):
		A_      							=   DetrImageProcessor if is_vision_available() else None






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






			'''simple docstring'''

			__lowerCAmelCase					:	Dict      =       DetrImageProcessingTester(self      )






		@property
		def 				UpperCAmelCase__						(							self			:					Optional[int]      )->Tuple:






			'''simple docstring'''

			return self.image_processor_tester.prepare_image_processor_dict()






		def 				UpperCAmelCase__						(							self			:					str      )->Dict:






			'''simple docstring'''

			__lowerCAmelCase					:	str      =       self.image_processing_class(**self.image_processor_dict      )
			self.assertTrue(hasattr(_lowercase       ,  """image_mean"""      )      )
			self.assertTrue(hasattr(_lowercase       ,  """image_std"""      )      )
			self.assertTrue(hasattr(_lowercase       ,  """do_normalize"""      )      )
			self.assertTrue(hasattr(_lowercase       ,  """do_rescale"""      )      )
			self.assertTrue(hasattr(_lowercase       ,  """rescale_factor"""      )      )
			self.assertTrue(hasattr(_lowercase       ,  """do_resize"""      )      )
			self.assertTrue(hasattr(_lowercase       ,  """size"""      )      )
			self.assertTrue(hasattr(_lowercase       ,  """do_pad"""      )      )






		def 				UpperCAmelCase__						(							self			:					Tuple      )->Optional[Any]:






			'''simple docstring'''

			__lowerCAmelCase					:	Tuple      =       self.image_processing_class.from_dict(self.image_processor_dict      )
			self.assertEqual(image_processor.size       ,  {"""shortest_edge""": 18, """longest_edge""": 1333}      )
			self.assertEqual(image_processor.do_pad       ,  _lowercase      )

			__lowerCAmelCase					:	str      =       self.image_processing_class.from_dict(
			    self.image_processor_dict       ,  size=42       ,  max_size=84       ,  pad_and_return_pixel_mask=_lowercase      )
			self.assertEqual(image_processor.size       ,  {"""shortest_edge""": 42, """longest_edge""": 84}      )
			self.assertEqual(image_processor.do_pad       ,  _lowercase      )






		def 				UpperCAmelCase__						(							self			:					List[str]      )->int:






			'''simple docstring'''

			pass






		def 				UpperCAmelCase__						(							self			:					Tuple      )->Dict:






			'''simple docstring'''

			__lowerCAmelCase					:	Optional[Any]      =       self.image_processing_class(**self.image_processor_dict      )
			# create random PIL images
			__lowerCAmelCase					:	Optional[Any]      =       prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=_lowercase      )
			for image in image_inputs:
				self.assertIsInstance(_lowercase       ,  Image.Image      )

			# Test not batched input
			__lowerCAmelCase					:	Any      =       image_processing(image_inputs[0]       ,  return_tensors="""pt"""      ).pixel_values

			__lowerCAmelCase ,					__lowerCAmelCase					:	Union[str, Any]      =       self.image_processor_tester.get_expected_values(_lowercase      )

			self.assertEqual(
			    encoded_images.shape       ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,  )

			# Test batched
			__lowerCAmelCase ,					__lowerCAmelCase					:	Optional[int]      =       self.image_processor_tester.get_expected_values(_lowercase       ,  batched=_lowercase      )

			__lowerCAmelCase					:	List[Any]      =       image_processing(_lowercase       ,  return_tensors="""pt"""      ).pixel_values
			self.assertEqual(
			    encoded_images.shape       ,  (
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        expected_height,
			        expected_width,
			    )       ,  )






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






			'''simple docstring'''

			__lowerCAmelCase					:	List[Any]      =       self.image_processing_class(**self.image_processor_dict      )
			# create random numpy tensors
			__lowerCAmelCase					:	List[Any]      =       prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=_lowercase       ,  numpify=_lowercase      )
			for image in image_inputs:
				self.assertIsInstance(_lowercase       ,  np.ndarray      )

			# Test not batched input
			__lowerCAmelCase					:	List[str]      =       image_processing(image_inputs[0]       ,  return_tensors="""pt"""      ).pixel_values

			__lowerCAmelCase ,					__lowerCAmelCase					:	str      =       self.image_processor_tester.get_expected_values(_lowercase      )

			self.assertEqual(
			    encoded_images.shape       ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,  )

			# Test batched
			__lowerCAmelCase					:	Tuple      =       image_processing(_lowercase       ,  return_tensors="""pt"""      ).pixel_values

			__lowerCAmelCase ,					__lowerCAmelCase					:	Any      =       self.image_processor_tester.get_expected_values(_lowercase       ,  batched=_lowercase      )

			self.assertEqual(
			    encoded_images.shape       ,  (
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        expected_height,
			        expected_width,
			    )       ,  )






		def 				UpperCAmelCase__						(							self			:					int      )->int:






			'''simple docstring'''

			__lowerCAmelCase					:	Optional[int]      =       self.image_processing_class(**self.image_processor_dict      )
			# create random PyTorch tensors
			__lowerCAmelCase					:	Optional[Any]      =       prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=_lowercase       ,  torchify=_lowercase      )
			for image in image_inputs:
				self.assertIsInstance(_lowercase       ,  torch.Tensor      )

			# Test not batched input
			__lowerCAmelCase					:	Optional[int]      =       image_processing(image_inputs[0]       ,  return_tensors="""pt"""      ).pixel_values

			__lowerCAmelCase ,					__lowerCAmelCase					:	Dict      =       self.image_processor_tester.get_expected_values(_lowercase      )

			self.assertEqual(
			    encoded_images.shape       ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,  )

			# Test batched
			__lowerCAmelCase					:	Tuple      =       image_processing(_lowercase       ,  return_tensors="""pt"""      ).pixel_values

			__lowerCAmelCase ,					__lowerCAmelCase					:	Optional[Any]      =       self.image_processor_tester.get_expected_values(_lowercase       ,  batched=_lowercase      )

			self.assertEqual(
			    encoded_images.shape       ,  (
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        expected_height,
			        expected_width,
			    )       ,  )






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






			'''simple docstring'''

			__lowerCAmelCase					:	str      =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
			with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""       ,  """r"""      ) as f:
				__lowerCAmelCase					:	Tuple      =       json.loads(f.read()      )

			__lowerCAmelCase					:	Any      =       {"""image_id""": 39769, """annotations""": target}

			# encode them
			__lowerCAmelCase					:	List[Any]      =       DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50"""      )
			__lowerCAmelCase					:	Optional[Any]      =       image_processing(images=_lowercase       ,  annotations=_lowercase       ,  return_tensors="""pt"""      )

			# verify pixel values
			__lowerCAmelCase					:	Dict      =       torch.Size([1, 3, 800, 1066]      )
			self.assertEqual(encoding["""pixel_values"""].shape       ,  _lowercase      )

			__lowerCAmelCase					:	Tuple      =       torch.tensor([0.2_796, 0.3_138, 0.3_481]      )
			self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]       ,  _lowercase       ,  atol=1E-4      )      )

			# verify area
			__lowerCAmelCase					:	Any      =       torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]      )
			self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]       ,  _lowercase      )      )
			# verify boxes
			__lowerCAmelCase					:	Tuple      =       torch.Size([6, 4]      )
			self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape       ,  _lowercase      )
			__lowerCAmelCase					:	Optional[Any]      =       torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]      )
			self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]       ,  _lowercase       ,  atol=1E-3      )      )
			# verify image_id
			__lowerCAmelCase					:	Optional[int]      =       torch.tensor([39769]      )
			self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]       ,  _lowercase      )      )
			# verify is_crowd
			__lowerCAmelCase					:	List[Any]      =       torch.tensor([0, 0, 0, 0, 0, 0]      )
			self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]       ,  _lowercase      )      )
			# verify class_labels
			__lowerCAmelCase					:	Tuple      =       torch.tensor([75, 75, 63, 65, 17, 17]      )
			self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]       ,  _lowercase      )      )
			# verify orig_size
			__lowerCAmelCase					:	Optional[Any]      =       torch.tensor([480, 640]      )
			self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]       ,  _lowercase      )      )
			# verify size
			__lowerCAmelCase					:	Any      =       torch.tensor([800, 1066]      )
			self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]       ,  _lowercase      )      )






		@slow
		def 				UpperCAmelCase__						(							self			:					int      )->Tuple:






			'''simple docstring'''

			__lowerCAmelCase					:	str      =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
			with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""       ,  """r"""      ) as f:
				__lowerCAmelCase					:	List[str]      =       json.loads(f.read()      )

			__lowerCAmelCase					:	Tuple      =       {"""file_name""": """000000039769.png""", """image_id""": 39769, """segments_info""": target}

			__lowerCAmelCase					:	Optional[int]      =       pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""      )

			# encode them
			__lowerCAmelCase					:	Union[str, Any]      =       DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50-panoptic"""      )
			__lowerCAmelCase					:	Tuple      =       image_processing(images=_lowercase       ,  annotations=_lowercase       ,  masks_path=_lowercase       ,  return_tensors="""pt"""      )

			# verify pixel values
			__lowerCAmelCase					:	int      =       torch.Size([1, 3, 800, 1066]      )
			self.assertEqual(encoding["""pixel_values"""].shape       ,  _lowercase      )

			__lowerCAmelCase					:	List[Any]      =       torch.tensor([0.2_796, 0.3_138, 0.3_481]      )
			self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]       ,  _lowercase       ,  atol=1E-4      )      )

			# verify area
			__lowerCAmelCase					:	List[str]      =       torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]      )
			self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]       ,  _lowercase      )      )
			# verify boxes
			__lowerCAmelCase					:	str      =       torch.Size([6, 4]      )
			self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape       ,  _lowercase      )
			__lowerCAmelCase					:	Dict      =       torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]      )
			self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]       ,  _lowercase       ,  atol=1E-3      )      )
			# verify image_id
			__lowerCAmelCase					:	Tuple      =       torch.tensor([39769]      )
			self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]       ,  _lowercase      )      )
			# verify is_crowd
			__lowerCAmelCase					:	Optional[int]      =       torch.tensor([0, 0, 0, 0, 0, 0]      )
			self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]       ,  _lowercase      )      )
			# verify class_labels
			__lowerCAmelCase					:	Dict      =       torch.tensor([17, 17, 63, 75, 75, 93]      )
			self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]       ,  _lowercase      )      )
			# verify masks
			__lowerCAmelCase					:	Dict      =       822873
			self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()       ,  _lowercase      )
			# verify orig_size
			__lowerCAmelCase					:	Union[str, Any]      =       torch.tensor([480, 640]      )
			self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]       ,  _lowercase      )      )
			# verify size
			__lowerCAmelCase					:	Tuple      =       torch.tensor([800, 1066]      )
			self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]       ,  _lowercase      )      )






import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
      import jax

      from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel






class        snake_case_    (		unittest.TestCase						):






  def __init__(							self			:					List[Any]       ,  _snake_case			:					List[Any]       ,  _snake_case			:					str=13       ,  _snake_case			:					int=30       ,  _snake_case			:					str=2       ,  _snake_case			:					int=3       ,  _snake_case			:					Optional[Any]=True       ,  _snake_case			:					str=True       ,  _snake_case			:					Optional[int]=32       ,  _snake_case			:					Dict=5       ,  _snake_case			:					Optional[int]=4       ,  _snake_case			:					List[Any]=37       ,  _snake_case			:					Union[str, Any]="gelu"       ,  _snake_case			:					str=0.1       ,  _snake_case			:					str=0.1       ,  _snake_case			:					str=10       ,  _snake_case			:					Any=0.02       ,  )->Tuple:






   '''simple docstring'''

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

   # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
   __lowerCAmelCase					:	Tuple      =       (image_size // patch_size) ** 2
   __lowerCAmelCase					:	Optional[Any]      =       num_patches + 1






  def 				UpperCAmelCase__						(							self			:					Any      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

   __lowerCAmelCase					:	Tuple      =       ViTConfig(
       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       ,  )

   return config, pixel_values






  def 				UpperCAmelCase__						(							self			:					List[Any]       ,  _snake_case			:					List[str]       ,  _snake_case			:					List[Any]      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	str      =       FlaxViTModel(config=_snake_case      )
   __lowerCAmelCase					:	int      =       model(_snake_case      )
   # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
   __lowerCAmelCase					:	Dict      =       (self.image_size, self.image_size)
   __lowerCAmelCase					:	Any      =       (self.patch_size, self.patch_size)
   __lowerCAmelCase					:	Any      =       (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
   self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, num_patches + 1, self.hidden_size)      )






  def 				UpperCAmelCase__						(							self			:					Dict       ,  _snake_case			:					str       ,  _snake_case			:					List[Any]      )->Union[str, Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Tuple      =       self.type_sequence_label_size
   __lowerCAmelCase					:	Tuple      =       FlaxViTForImageClassification(config=_snake_case      )
   __lowerCAmelCase					:	List[str]      =       model(_snake_case      )
   self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.type_sequence_label_size)      )

   # test greyscale images
   __lowerCAmelCase					:	str      =       1
   __lowerCAmelCase					:	Any      =       FlaxViTForImageClassification(_snake_case      )

   __lowerCAmelCase					:	int      =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
   __lowerCAmelCase					:	Dict      =       model(_snake_case      )






  def 				UpperCAmelCase__						(							self			:					str      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	Any      =       self.prepare_config_and_inputs()
   (
       (
       __lowerCAmelCase
   ) ,					(
       __lowerCAmelCase
   ) ,					
   )					:	Union[str, Any]      =       config_and_inputs
   __lowerCAmelCase					:	str      =       {"""pixel_values""": pixel_values}
   return config, inputs_dict






@require_flax
class        snake_case_    (		__lowercase					,unittest.TestCase						):
  A_      							=   (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()






  def 				UpperCAmelCase__						(							self			:					str      )->None:






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       FlaxViTModelTester(self      )
   __lowerCAmelCase					:	Optional[Any]      =       ConfigTester(self       ,  config_class=_snake_case       ,  has_text_modality=_snake_case       ,  hidden_size=37      )






  def 				UpperCAmelCase__						(							self			:					Any      )->Dict:






   '''simple docstring'''

   self.config_tester.run_common_tests()






  def 				UpperCAmelCase__						(							self			:					Any      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	Dict      =       self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*_snake_case      )






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






   '''simple docstring'''

   __lowerCAmelCase					:	Dict      =       self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*_snake_case      )






  def 				UpperCAmelCase__						(							self			:					int      )->int:






   '''simple docstring'''

   __lowerCAmelCase ,					__lowerCAmelCase					:	Any      =       self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __lowerCAmelCase					:	int      =       model_class(_snake_case      )
    __lowerCAmelCase					:	Optional[int]      =       inspect.signature(model.__call__      )
    # signature.parameters is an OrderedDict => so arg_names order is deterministic
    __lowerCAmelCase					:	Tuple      =       [*signature.parameters.keys()]

    __lowerCAmelCase					:	str      =       ["""pixel_values"""]
    self.assertListEqual(arg_names[:1]       ,  _snake_case      )






  def 				UpperCAmelCase__						(							self			:					str      )->str:






   '''simple docstring'''

   __lowerCAmelCase ,					__lowerCAmelCase					:	Any      =       self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    with self.subTest(model_class.__name__      ):
     __lowerCAmelCase					:	Optional[int]      =       self._prepare_for_class(_snake_case       ,  _snake_case      )
     __lowerCAmelCase					:	List[str]      =       model_class(_snake_case      )

     @jax.jit
     def model_jitted(_snake_case			:					Dict       ,  **_snake_case			:					Union[str, Any]      ):
      return model(pixel_values=_snake_case       ,  **_snake_case      )

     with self.subTest("""JIT Enabled"""      ):
      __lowerCAmelCase					:	List[Any]      =       model_jitted(**_snake_case      ).to_tuple()

     with self.subTest("""JIT Disabled"""      ):
      with jax.disable_jit():
       __lowerCAmelCase					:	str      =       model_jitted(**_snake_case      ).to_tuple()

     self.assertEqual(len(_snake_case      )       ,  len(_snake_case      )      )
     for jitted_output, output in zip(_snake_case       ,  _snake_case      ):
      self.assertEqual(jitted_output.shape       ,  output.shape      )






  @slow
  def 				UpperCAmelCase__						(							self			:					Dict      )->str:






   '''simple docstring'''

   for model_class_name in self.all_model_classes:
    __lowerCAmelCase					:	List[str]      =       model_class_name.from_pretrained("""google/vit-base-patch16-224"""      )
    __lowerCAmelCase					:	List[str]      =       model(np.ones((1, 3, 224, 224)      )      )
    self.assertIsNotNone(_snake_case      )