


'''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 YolosImageProcessor







class 							__magic_name__	(       unittest.TestCase ):

     """simple docstring"""






     def __init__(   self			,     lowerCamelCase			,     lowerCamelCase=7			,     lowerCamelCase=3			,     lowerCamelCase=30			,     lowerCamelCase=400			,     lowerCamelCase=True			,     lowerCamelCase=None			,     lowerCamelCase=True			,     lowerCamelCase=[0.5, 0.5, 0.5]			,     lowerCamelCase=[0.5, 0.5, 0.5]			,     lowerCamelCase=True			,     lowerCamelCase=1 / 255			,     lowerCamelCase=True			,     ):


          '''simple docstring'''





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


     def lowerCAmelCase__  (   self		):


          '''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 lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase=False		):


          '''simple docstring'''





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

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

          return expected_height, expected_width







@require_torch
@require_vision
class 							__magic_name__	(       lowerCAmelCase    ,		unittest.TestCase ):

     """simple docstring"""






     lowerCamelCase__       						=				YolosImageProcessor if is_vision_available() else None


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Any      =							YolosImageProcessingTester(self		)


     @property
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return self.image_processor_tester.prepare_image_processor_dict()


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       int      =							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			,     "size"		)		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Dict      =							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		)

          __A	:       int      =							self.image_processing_class.from_dict(
              self.image_processor_dict			,     size=42			,     max_size=84			,     pad_and_return_pixel_mask=lowerCamelCase		)
          self.assertEqual(image_processor.size			,     {"shortest_edge": 42, "longest_edge": 84}		)
          self.assertEqual(image_processor.do_pad			,     lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          pass


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





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

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

          __A  ,__A	:       Tuple      =							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
          __A  ,__A	:       str      =							self.image_processor_tester.get_expected_values(lowerCamelCase			,     batched=lowerCamelCase		)

          __A	:       Optional[Any]      =							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 lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       List[str]      =							self.image_processing_class(**self.image_processor_dict		)
          # create random numpy tensors
          __A	:       Dict      =							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
          __A	:       Any      =							image_processing(image_inputs[0]			,     return_tensors="pt"		).pixel_values

          __A  ,__A	:       Any      =							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
          __A	:       Optional[int]      =							image_processing(lowerCamelCase			,     return_tensors="pt"		).pixel_values

          __A  ,__A	:       Optional[Any]      =							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 lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Union[str, Any]      =							self.image_processing_class(**self.image_processor_dict		)
          # create random PyTorch tensors
          __A	:       Any      =							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
          __A	:       Optional[int]      =							image_processing(image_inputs[0]			,     return_tensors="pt"		).pixel_values

          __A  ,__A	:       List[Any]      =							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
          __A	:       Dict      =							image_processing(lowerCamelCase			,     return_tensors="pt"		).pixel_values

          __A  ,__A	:       List[Any]      =							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 lowerCAmelCase__  (   self		):


          '''simple docstring'''





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

          # Test whether the method "pad" and calling the image processor return the same tensors
          __A	:       str      =							image_processing_a.pad(lowerCamelCase			,     return_tensors="pt"		)
          __A	:       Union[str, Any]      =							image_processing_a(lowerCamelCase			,     return_tensors="pt"		)

          self.assertTrue(
              torch.allclose(encoded_images_with_method["pixel_values"]			,     encoded_images["pixel_values"]			,     atol=1E-4		)		)


     @slow
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Dict      =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
          with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"			,     "r"		) as f:
               __A	:       Optional[Any]      =							json.loads(f.read()		)

          __A	:       Optional[Any]      =							{"image_id": 3_9769, "annotations": target}

          # encode them
          __A	:       Optional[Any]      =							YolosImageProcessor.from_pretrained("hustvl/yolos-small"		)
          __A	:       Dict      =							image_processing(images=lowerCamelCase			,     annotations=lowerCamelCase			,     return_tensors="pt"		)

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

          __A	:       Union[str, Any]      =							torch.tensor([0.2796, 0.3138, 0.3481]		)
          self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]			,     lowerCamelCase			,     atol=1E-4		)		)

          # verify area
          __A	:       Union[str, Any]      =							torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["area"]			,     lowerCamelCase		)		)
          # verify boxes
          __A	:       Optional[int]      =							torch.Size([6, 4]		)
          self.assertEqual(encoding["labels"][0]["boxes"].shape			,     lowerCamelCase		)
          __A	:       Union[str, Any]      =							torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]			,     lowerCamelCase			,     atol=1E-3		)		)
          # verify image_id
          __A	:       Any      =							torch.tensor([3_9769]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]			,     lowerCamelCase		)		)
          # verify is_crowd
          __A	:       Tuple      =							torch.tensor([0, 0, 0, 0, 0, 0]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]			,     lowerCamelCase		)		)
          # verify class_labels
          __A	:       str      =							torch.tensor([75, 75, 63, 65, 17, 17]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]			,     lowerCamelCase		)		)
          # verify orig_size
          __A	:       Optional[Any]      =							torch.tensor([480, 640]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]			,     lowerCamelCase		)		)
          # verify size
          __A	:       int      =							torch.tensor([800, 1066]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["size"]			,     lowerCamelCase		)		)


     @slow
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





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

          __A	:       List[Any]      =							{"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

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

          # encode them
          __A	:       List[Any]      =							YolosImageProcessor(format="coco_panoptic"		)
          __A	:       int      =							image_processing(images=lowerCamelCase			,     annotations=lowerCamelCase			,     masks_path=lowerCamelCase			,     return_tensors="pt"		)

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

          __A	:       Dict      =							torch.tensor([0.2796, 0.3138, 0.3481]		)
          self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]			,     lowerCamelCase			,     atol=1E-4		)		)

          # verify area
          __A	:       Optional[Any]      =							torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["area"]			,     lowerCamelCase		)		)
          # verify boxes
          __A	:       Dict      =							torch.Size([6, 4]		)
          self.assertEqual(encoding["labels"][0]["boxes"].shape			,     lowerCamelCase		)
          __A	:       Union[str, Any]      =							torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]			,     lowerCamelCase			,     atol=1E-3		)		)
          # verify image_id
          __A	:       Tuple      =							torch.tensor([3_9769]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]			,     lowerCamelCase		)		)
          # verify is_crowd
          __A	:       Optional[Any]      =							torch.tensor([0, 0, 0, 0, 0, 0]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]			,     lowerCamelCase		)		)
          # verify class_labels
          __A	:       Optional[int]      =							torch.tensor([17, 17, 63, 75, 75, 93]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]			,     lowerCamelCase		)		)
          # verify masks
          __A	:       Union[str, Any]      =							82_2873
          self.assertEqual(encoding["labels"][0]["masks"].sum().item()			,     lowerCamelCase		)
          # verify orig_size
          __A	:       List[str]      =							torch.tensor([480, 640]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]			,     lowerCamelCase		)		)
          # verify size
          __A	:       Optional[Any]      =							torch.tensor([800, 1066]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["size"]			,     lowerCamelCase		)		)





'''simple docstring'''


import inspect
import unittest

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

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


if is_torch_available():
      import torch
      from torch import nn

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


if is_vision_available():
      from PIL import Image







class 							__magic_name__	:

     """simple docstring"""






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


          '''simple docstring'''





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

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


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       str      =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

          __A	:       Any      =							None
          if self.use_labels:
               __A	:       Optional[Any]      =							ids_tensor([self.batch_size]			,     self.type_sequence_label_size		)

          __A	:       Any      =							self.get_config()

          return config, pixel_values, labels


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





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

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


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





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


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       Any      =							self.type_sequence_label_size
          __A	:       int      =							ViTHybridForImageClassification(lowerCamelCase		)
          model.to(lowerCamelCase		)
          model.eval()
          __A	:       Any      =							model(lowerCamelCase			,     labels=lowerCamelCase		)
          self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.type_sequence_label_size)		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Dict      =							self.prepare_config_and_inputs()
          __A  ,__A  ,__A	:       Optional[int]      =							config_and_inputs
          __A	:       Dict      =							{"pixel_values": pixel_values}
          return config, inputs_dict







@require_torch
class 							__magic_name__	(       lowerCAmelCase    ,		lowerCAmelCase    ,		unittest.TestCase ):

     """simple docstring"""






     lowerCamelCase__       						=				(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
     lowerCamelCase__       						=				(
         {'feature-extraction': ViTHybridModel, 'image-classification': ViTHybridForImageClassification}
         if is_torch_available()
         else {}
     )
     lowerCamelCase__       						=				False
     lowerCamelCase__       						=				False
     lowerCamelCase__       						=				False


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       str      =							ViTHybridModelTester(self		)
          __A	:       Optional[Any]      =							ConfigTester(self			,     config_class=lowerCamelCase			,     has_text_modality=lowerCamelCase			,     hidden_size=37		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          self.config_tester.run_common_tests()


     @unittest.skip(reason="ViT does not use inputs_embeds"		)
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          pass


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A  ,__A	:       Dict      =							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __A	:       List[str]      =							model_class(lowerCamelCase		)
               self.assertIsInstance(model.get_input_embeddings()			,     (nn.Module)		)
               __A	:       int      =							model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowerCamelCase			,     nn.Linear		)		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





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

          for model_class in self.all_model_classes:
               __A	:       Union[str, Any]      =							model_class(lowerCamelCase		)
               __A	:       Union[str, Any]      =							inspect.signature(model.forward		)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               __A	:       Optional[Any]      =							[*signature.parameters.keys()]

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


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       int      =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[int]      =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





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

          __A	:       Dict      =							_config_zero_init(lowerCamelCase		)
          for model_class in self.all_model_classes:
               __A	:       List[str]      =							model_class(config=lowerCamelCase		)
               # Skip the check for the backbone
               for name, module in model.named_modules():
                    if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                         __A	:       int      =							[f"{name}.{key}" for key in module.state_dict().keys()]
                         break

               for name, param in model.named_parameters():
                    if param.requires_grad:
                         if name in backbone_params:
                              continue
                         self.assertIn(
                             ((param.data.mean() * 1E9).round() / 1E9).item()			,     [0.0, 1.0]			,     msg=f"Parameter {name} of model {model_class} seems not properly initialized"			,     )


     @slow
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __A	:       Tuple      =							ViTHybridModel.from_pretrained(lowerCamelCase		)
               self.assertIsNotNone(lowerCamelCase		)


def 		_lowercase ():




     '''simple docstring'''





     __A	:       Optional[Any]      =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
     return image







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

     """simple docstring"""






     @cached_property
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





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


     @slow
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       str      =							ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(
              lowerCamelCase		)

          __A	:       str      =							self.default_image_processor
          __A	:       Union[str, Any]      =							prepare_img()
          __A	:       List[str]      =							image_processor(images=lowerCamelCase			,     return_tensors="pt"		).to(lowerCamelCase		)

          # forward pass
          with torch.no_grad():
               __A	:       Any      =							model(**lowerCamelCase		)

          # verify the logits
          __A	:       List[str]      =							torch.Size((1, 1000)		)
          self.assertEqual(outputs.logits.shape			,     lowerCamelCase		)

          __A	:       Optional[int]      =							torch.tensor([-1.9090, -0.4993, -0.2389]		).to(lowerCamelCase		)

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


     @slow
     @require_accelerate
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





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

          __A	:       Union[str, Any]      =							prepare_img()

          __A	:       Tuple      =							image_processor(images=lowerCamelCase			,     return_tensors="pt"		)
          __A	:       Optional[Any]      =							model(**lowerCamelCase		)
          __A	:       List[Any]      =							outputs.logits
          # model predicts one of the 1000 ImageNet classes
          __A	:       Tuple      =							logits.argmax(-1		).item()

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

