





'''simple docstring'''

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 DetaImageProcessor







class __a (   unittest.TestCase			):





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

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
      __SCREAMING_SNAKE_CASE      							=       parent
      __SCREAMING_SNAKE_CASE      							=       batch_size
      __SCREAMING_SNAKE_CASE      							=       num_channels
      __SCREAMING_SNAKE_CASE      							=       min_resolution
      __SCREAMING_SNAKE_CASE      							=       max_resolution
      __SCREAMING_SNAKE_CASE      							=       do_resize
      __SCREAMING_SNAKE_CASE      							=       size
      __SCREAMING_SNAKE_CASE      							=       do_normalize
      __SCREAMING_SNAKE_CASE      							=       image_mean
      __SCREAMING_SNAKE_CASE      							=       image_std
      __SCREAMING_SNAKE_CASE      							=       do_rescale
      __SCREAMING_SNAKE_CASE      							=       rescale_factor
      __SCREAMING_SNAKE_CASE      							=       do_pad





   def      UpperCAmelCase__     (					self				:			Dict    ):

      '''simple docstring'''

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





   def      UpperCAmelCase__     (					self				:			int				,lowerCamelCase				:			List[Any]				,lowerCamelCase				:			Tuple=False    ):

      '''simple docstring'''

      if not batched:
         __SCREAMING_SNAKE_CASE      							=       image_inputs[0]
         if isinstance(lowerCamelCase				,Image.Image    ):
            __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       image.size
         else:
            __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       image.shape[1], image.shape[2]
         if w < h:
            __SCREAMING_SNAKE_CASE      							=       int(self.size["""shortest_edge"""] * h / w    )
            __SCREAMING_SNAKE_CASE      							=       self.size["""shortest_edge"""]
         elif w > h:
            __SCREAMING_SNAKE_CASE      							=       self.size["""shortest_edge"""]
            __SCREAMING_SNAKE_CASE      							=       int(self.size["""shortest_edge"""] * w / h    )
         else:
            __SCREAMING_SNAKE_CASE      							=       self.size["""shortest_edge"""]
            __SCREAMING_SNAKE_CASE      							=       self.size["""shortest_edge"""]

      else:
         __SCREAMING_SNAKE_CASE      							=       []
         for image in image_inputs:
            __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       self.get_expected_values([image]    )
            expected_values.append((expected_height, expected_width)    )
         __SCREAMING_SNAKE_CASE      							=       max(lowerCamelCase				,key=lambda lowerCamelCase    : item[0]    )[0]
         __SCREAMING_SNAKE_CASE      							=       max(lowerCamelCase				,key=lambda lowerCamelCase    : item[1]    )[1]

      return expected_height, expected_width







@require_torch
@require_vision
class __a (   _snake_case,   unittest.TestCase			):
   __UpperCamelCase   : Union[str, Any]	      =	DetaImageProcessor if is_vision_available() else None





   def      UpperCAmelCase__     (					self				:			int    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       DetaImageProcessingTester(self    )





   @property
   def      UpperCAmelCase__     (					self				:			int    ):

      '''simple docstring'''

      return self.image_processor_tester.prepare_image_processor_dict()





   def      UpperCAmelCase__     (					self				:			str    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.image_processing_class(**self.image_processor_dict    )
      self.assertTrue(hasattr(lowerCamelCase				,"""image_mean"""    )    )
      self.assertTrue(hasattr(lowerCamelCase				,"""image_std"""    )    )
      self.assertTrue(hasattr(lowerCamelCase				,"""do_normalize"""    )    )
      self.assertTrue(hasattr(lowerCamelCase				,"""do_resize"""    )    )
      self.assertTrue(hasattr(lowerCamelCase				,"""do_rescale"""    )    )
      self.assertTrue(hasattr(lowerCamelCase				,"""do_pad"""    )    )
      self.assertTrue(hasattr(lowerCamelCase				,"""size"""    )    )





   def      UpperCAmelCase__     (					self				:			str    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       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				,lowerCamelCase    )





   def      UpperCAmelCase__     (					self				:			Optional[int]    ):

      '''simple docstring'''

      pass





   def      UpperCAmelCase__     (					self				:			Tuple    ):

      '''simple docstring'''

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

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

      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       self.image_processor_tester.get_expected_values(lowerCamelCase    )

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

      # Test batched
      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       self.image_processor_tester.get_expected_values(lowerCamelCase				,batched=lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       image_processing(lowerCamelCase				,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				:			Union[str, Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.image_processing_class(**self.image_processor_dict    )
      # create random numpy tensors
      __SCREAMING_SNAKE_CASE      							=       prepare_image_inputs(self.image_processor_tester				,equal_resolution=lowerCamelCase				,numpify=lowerCamelCase    )
      for image in image_inputs:
         self.assertIsInstance(lowerCamelCase				,np.ndarray    )

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

      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       self.image_processor_tester.get_expected_values(lowerCamelCase    )

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

      # Test batched
      __SCREAMING_SNAKE_CASE      							=       image_processing(lowerCamelCase				,return_tensors="""pt"""    ).pixel_values

      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       self.image_processor_tester.get_expected_values(lowerCamelCase				,batched=lowerCamelCase    )

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





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

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.image_processing_class(**self.image_processor_dict    )
      # create random PyTorch tensors
      __SCREAMING_SNAKE_CASE      							=       prepare_image_inputs(self.image_processor_tester				,equal_resolution=lowerCamelCase				,torchify=lowerCamelCase    )
      for image in image_inputs:
         self.assertIsInstance(lowerCamelCase				,torch.Tensor    )

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

      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       self.image_processor_tester.get_expected_values(lowerCamelCase    )

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

      # Test batched
      __SCREAMING_SNAKE_CASE      							=       image_processing(lowerCamelCase				,return_tensors="""pt"""    ).pixel_values

      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       self.image_processor_tester.get_expected_values(lowerCamelCase				,batched=lowerCamelCase    )

      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				:			Dict    ):

      '''simple docstring'''

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

      __SCREAMING_SNAKE_CASE      							=       {"""image_id""": 3_9769, """annotations""": target}

      # encode them
      __SCREAMING_SNAKE_CASE      							=       DetaImageProcessor()
      __SCREAMING_SNAKE_CASE      							=       image_processing(images=lowerCamelCase				,annotations=lowerCamelCase				,return_tensors="""pt"""    )

      # verify pixel values
      __SCREAMING_SNAKE_CASE      							=       torch.Size([1, 3, 800, 1066]    )
      self.assertEqual(encoding["""pixel_values"""].shape				,lowerCamelCase    )

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

      # verify area
      __SCREAMING_SNAKE_CASE      							=       torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]    )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]				,lowerCamelCase    )    )
      # verify boxes
      __SCREAMING_SNAKE_CASE      							=       torch.Size([6, 4]    )
      self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape				,lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]    )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]				,lowerCamelCase				,atol=1E-3    )    )
      # verify image_id
      __SCREAMING_SNAKE_CASE      							=       torch.tensor([3_9769]    )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]				,lowerCamelCase    )    )
      # verify is_crowd
      __SCREAMING_SNAKE_CASE      							=       torch.tensor([0, 0, 0, 0, 0, 0]    )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]				,lowerCamelCase    )    )
      # verify class_labels
      __SCREAMING_SNAKE_CASE      							=       torch.tensor([75, 75, 63, 65, 17, 17]    )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]				,lowerCamelCase    )    )
      # verify orig_size
      __SCREAMING_SNAKE_CASE      							=       torch.tensor([480, 640]    )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]				,lowerCamelCase    )    )
      # verify size
      __SCREAMING_SNAKE_CASE      							=       torch.tensor([800, 1066]    )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]				,lowerCamelCase    )    )





   @slow
   def      UpperCAmelCase__     (					self				:			Dict    ):

      '''simple docstring'''

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

      __SCREAMING_SNAKE_CASE      							=       {"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

      __SCREAMING_SNAKE_CASE      							=       pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""    )

      # encode them
      __SCREAMING_SNAKE_CASE      							=       DetaImageProcessor(format="""coco_panoptic"""    )
      __SCREAMING_SNAKE_CASE      							=       image_processing(images=lowerCamelCase				,annotations=lowerCamelCase				,masks_path=lowerCamelCase				,return_tensors="""pt"""    )

      # verify pixel values
      __SCREAMING_SNAKE_CASE      							=       torch.Size([1, 3, 800, 1066]    )
      self.assertEqual(encoding["""pixel_values"""].shape				,lowerCamelCase    )

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

      # verify area
      __SCREAMING_SNAKE_CASE      							=       torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]    )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]				,lowerCamelCase    )    )
      # verify boxes
      __SCREAMING_SNAKE_CASE      							=       torch.Size([6, 4]    )
      self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape				,lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]    )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]				,lowerCamelCase				,atol=1E-3    )    )
      # verify image_id
      __SCREAMING_SNAKE_CASE      							=       torch.tensor([3_9769]    )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]				,lowerCamelCase    )    )
      # verify is_crowd
      __SCREAMING_SNAKE_CASE      							=       torch.tensor([0, 0, 0, 0, 0, 0]    )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]				,lowerCamelCase    )    )
      # verify class_labels
      __SCREAMING_SNAKE_CASE      							=       torch.tensor([17, 17, 63, 75, 75, 93]    )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]				,lowerCamelCase    )    )
      # verify masks
      __SCREAMING_SNAKE_CASE      							=       82_2873
      self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()				,lowerCamelCase    )
      # verify orig_size
      __SCREAMING_SNAKE_CASE      							=       torch.tensor([480, 640]    )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]				,lowerCamelCase    )    )
      # verify size
      __SCREAMING_SNAKE_CASE      							=       torch.tensor([800, 1066]    )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]				,lowerCamelCase    )    )












'''simple docstring'''

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 __a (   unittest.TestCase			):





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

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       parent
      __SCREAMING_SNAKE_CASE      							=       batch_size
      __SCREAMING_SNAKE_CASE      							=       image_size
      __SCREAMING_SNAKE_CASE      							=       patch_size
      __SCREAMING_SNAKE_CASE      							=       num_channels
      __SCREAMING_SNAKE_CASE      							=       is_training
      __SCREAMING_SNAKE_CASE      							=       use_labels
      __SCREAMING_SNAKE_CASE      							=       hidden_size
      __SCREAMING_SNAKE_CASE      							=       num_hidden_layers
      __SCREAMING_SNAKE_CASE      							=       num_attention_heads
      __SCREAMING_SNAKE_CASE      							=       intermediate_size
      __SCREAMING_SNAKE_CASE      							=       hidden_act
      __SCREAMING_SNAKE_CASE      							=       hidden_dropout_prob
      __SCREAMING_SNAKE_CASE      							=       attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE      							=       type_sequence_label_size
      __SCREAMING_SNAKE_CASE      							=       initializer_range

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





   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

      __SCREAMING_SNAKE_CASE      							=       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=lowerCamelCase				,initializer_range=self.initializer_range				,)

      return config, pixel_values





   def      UpperCAmelCase__     (					self				:			Tuple				,lowerCamelCase				:			int				,lowerCamelCase				:			Optional[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       FlaxViTModel(config=lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       model(lowerCamelCase    )
      # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
      __SCREAMING_SNAKE_CASE      							=       (self.image_size, self.image_size)
      __SCREAMING_SNAKE_CASE      							=       (self.patch_size, self.patch_size)
      __SCREAMING_SNAKE_CASE      							=       (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				:			Union[str, Any]				,lowerCamelCase				:			Optional[int]				,lowerCamelCase				:			Dict    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.type_sequence_label_size
      __SCREAMING_SNAKE_CASE      							=       FlaxViTForImageClassification(config=lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       model(lowerCamelCase    )
      self.parent.assertEqual(result.logits.shape				,(self.batch_size, self.type_sequence_label_size)    )

      # test greyscale images
      __SCREAMING_SNAKE_CASE      							=       1
      __SCREAMING_SNAKE_CASE      							=       FlaxViTForImageClassification(lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
      __SCREAMING_SNAKE_CASE      							=       model(lowerCamelCase    )





   def      UpperCAmelCase__     (					self				:			int    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.prepare_config_and_inputs()
      (
          (
          __SCREAMING_SNAKE_CASE
      )							,     (
          __SCREAMING_SNAKE_CASE
      )							,     
      )      							=       config_and_inputs
      __SCREAMING_SNAKE_CASE      							=       {"""pixel_values""": pixel_values}
      return config, inputs_dict







@require_flax
class __a (   _snake_case,   unittest.TestCase			):
   __UpperCamelCase   : Any	      =	(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()





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

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       FlaxViTModelTester(self    )
      __SCREAMING_SNAKE_CASE      							=       ConfigTester(self				,config_class=lowerCamelCase				,has_text_modality=lowerCamelCase				,hidden_size=37    )





   def      UpperCAmelCase__     (					self				:			Tuple    ):

      '''simple docstring'''

      self.config_tester.run_common_tests()





   def      UpperCAmelCase__     (					self				:			List[str]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase    )





   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*lowerCamelCase    )





   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       self.model_tester.prepare_config_and_inputs_for_common()

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

         __SCREAMING_SNAKE_CASE      							=       ["""pixel_values"""]
         self.assertListEqual(arg_names[:1]				,lowerCamelCase    )





   def      UpperCAmelCase__     (					self				:			Optional[int]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__    ):
            __SCREAMING_SNAKE_CASE      							=       self._prepare_for_class(lowerCamelCase				,lowerCamelCase    )
            __SCREAMING_SNAKE_CASE      							=       model_class(lowerCamelCase    )

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

            with self.subTest("""JIT Enabled"""    ):
               __SCREAMING_SNAKE_CASE      							=       model_jitted(**lowerCamelCase    ).to_tuple()

            with self.subTest("""JIT Disabled"""    ):
               with jax.disable_jit():
                  __SCREAMING_SNAKE_CASE      							=       model_jitted(**lowerCamelCase    ).to_tuple()

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





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

      '''simple docstring'''

      for model_class_name in self.all_model_classes:
         __SCREAMING_SNAKE_CASE      							=       model_class_name.from_pretrained("""google/vit-base-patch16-224"""    )
         __SCREAMING_SNAKE_CASE      							=       model(np.ones((1, 3, 224, 224)    )    )
         self.assertIsNotNone(lowerCamelCase    )





