





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  A__    :




   """simple docstring"""



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







          '''simple docstring'''

          a__						:	Optional[int] =				parent
          a__						:	Optional[int] =				batch_size
          a__						:	Any =				image_size
          a__						:	Optional[Any] =				patch_size
          a__						:	Optional[Any] =				num_channels
          a__						:	int =				is_training
          a__						:	List[str] =				use_labels
          a__						:	List[str] =				hidden_size
          a__						:	Tuple =				num_hidden_layers
          a__						:	Optional[Any] =				num_attention_heads
          a__						:	Union[str, Any] =				intermediate_size
          a__						:	Optional[int] =				hidden_act
          a__						:	Optional[Any] =				hidden_dropout_prob
          a__						:	Any =				attention_probs_dropout_prob
          a__						:	Any =				type_sequence_label_size
          a__						:	Tuple =				initializer_range
          a__						:	Tuple =				scope
          a__						:	int =				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__						:	Any =				(self.image_size // 32) ** 2
          a__						:	List[Any] =				num_patches + 1

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

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

          a__						:	int =				None
          if self.use_labels:
                 a__						:	int =				ids_tensor([self.batch_size]					,  self.type_sequence_label_size)

          a__						:	List[str] =				self.get_config()

          return config, pixel_values, labels

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__						:	List[str] =				{
              '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=lowercase					,  initializer_range=self.initializer_range					,  backbone_featmap_shape=self.backbone_featmap_shape					,  backbone_config=lowercase					,  )

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						List[str]:







          '''simple docstring'''

          a__						:	List[str] =				ViTHybridModel(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	Union[str, Any] =				model(lowercase)
          self.parent.assertEqual(result.last_hidden_state.shape					,  (self.batch_size, self.seq_length, self.hidden_size))

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







          '''simple docstring'''

          a__						:	Dict =				self.type_sequence_label_size
          a__						:	Union[str, Any] =				ViTHybridForImageClassification(lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	Tuple =				model(lowercase					,  labels=lowercase)
          self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.type_sequence_label_size))

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

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





@require_torch
class  A__    (							__UpperCAmelCase ,  __UpperCAmelCase ,  unittest.TestCase	):




   """simple docstring"""



   __A   :				Optional[Any]         =  (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
   __A   :				List[str]         =  (
       {'''feature-extraction''': ViTHybridModel, '''image-classification''': ViTHybridForImageClassification}
       if is_torch_available()
       else {}
   )
   __A   :				Any         =  False
   __A   :				Optional[int]         =  False
   __A   :				Optional[Any]         =  False

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







          '''simple docstring'''

          a__						:	Any =				ViTHybridModelTester(self)
          a__						:	Any =				ConfigTester(self					,  config_class=lowercase					,  has_text_modality=lowercase					,  hidden_size=37)

   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          self.config_tester.run_common_tests()

   @unittest.skip(reason='ViT does not use inputs_embeds')
   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          pass

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







          '''simple docstring'''

          a__ ,							a__						:	List[str] =				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 a__						:	str =				model_class(lowercase)
                 self.assertIsInstance(model.get_input_embeddings()					,  (nn.Module))
                 a__						:	str =				model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(lowercase					,  nn.Linear))

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          a__ ,							a__						:	str =				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 a__						:	Union[str, Any] =				model_class(lowercase)
                 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]					,  lowercase)

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	List[Any] =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase)

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







          '''simple docstring'''

          a__						:	Any =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowercase)

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__ ,							a__						:	Tuple =				self.model_tester.prepare_config_and_inputs_for_common()

          a__						:	Tuple =				_config_zero_init(lowercase)
          for model_class in self.all_model_classes:
                 a__						:	List[Any] =				model_class(config=lowercase)
                 # Skip the check for the backbone
                 for name, module in model.named_modules():
                        if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                               a__						:	Dict =				[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 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 a__						:	Optional[Any] =				ViTHybridModel.from_pretrained(lowercase)
                 self.assertIsNotNone(lowercase)



def A_	(		)  ->		int:
       a__						:	Dict =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
       return image





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




   """simple docstring"""



   @cached_property
   def 							__lowercase		(					self)			->						Optional[Any]:







          '''simple docstring'''

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

   @slow
   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	List[str] =				ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
              lowercase)

          a__						:	List[str] =				self.default_image_processor
          a__						:	List[Any] =				prepare_img()
          a__						:	Any =				image_processor(images=lowercase					,  return_tensors='pt').to(lowercase)

          # forward pass
          with torch.no_grad():
                 a__						:	Optional[Any] =				model(**lowercase)

          # verify the logits
          a__						:	Optional[Any] =				torch.Size((1, 1000))
          self.assertEqual(outputs.logits.shape					,  lowercase)

          a__						:	Any =				torch.tensor([-1.90_90, -0.49_93, -0.23_89]).to(lowercase)

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

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







          '''simple docstring'''

          a__						:	List[str] =				ViTHybridImageProcessor.from_pretrained('google/vit-hybrid-base-bit-384')
          a__						:	Union[str, Any] =				ViTHybridForImageClassification.from_pretrained('google/vit-hybrid-base-bit-384'					,  device_map='auto')

          a__						:	Any =				prepare_img()

          a__						:	str =				image_processor(images=lowercase					,  return_tensors='pt')
          a__						:	List[Any] =				model(**lowercase)
          a__						:	int =				outputs.logits
          # model predicts one of the 1000 ImageNet classes
          a__						:	List[str] =				logits.argmax(-1).item()

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





'''simple docstring'''



import unittest

from transformers import (
    MODEL_FOR_OBJECT_DETECTION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForObjectDetection,
    ObjectDetectionPipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_pytesseract,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
  from PIL import Image
else:







  class 						lowerCAmelCase__   :



   """simple docstring"""



   @staticmethod
   def       UpperCAmelCase__		(		*__SCREAMING_SNAKE_CASE					:       Tuple					,							**__SCREAMING_SNAKE_CASE					:       Union[str, Any]			)  -> List[str]:


        """simple docstring"""




        pass







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 						lowerCAmelCase__   (      unittest.TestCase					):



 """simple docstring"""



 lowerCAmelCase__  					=      MODEL_FOR_OBJECT_DETECTION_MAPPING
 def       UpperCAmelCase__		(		self					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       Any					,							__SCREAMING_SNAKE_CASE					:       int					,							__SCREAMING_SNAKE_CASE					:       Tuple			)  -> str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      ObjectDetectionPipeline(model=__SCREAMING_SNAKE_CASE					,							image_processor=__SCREAMING_SNAKE_CASE			)
      return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
 def       UpperCAmelCase__		(		self					:       Optional[int]					,							__SCREAMING_SNAKE_CASE					:       Dict					,							__SCREAMING_SNAKE_CASE					:       List[Any]			)  -> Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      object_detector("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					,							threshold=0.0			)

      self.assertGreater(len(__SCREAMING_SNAKE_CASE			)					,							0			)
      for detected_object in outputs:
           self.assertEqual(
               __SCREAMING_SNAKE_CASE					,							{
                   """score""": ANY(__SCREAMING_SNAKE_CASE			),
                   """label""": ANY(__SCREAMING_SNAKE_CASE			),
                   """box""": {"""xmin""": ANY(__SCREAMING_SNAKE_CASE			), """ymin""": ANY(__SCREAMING_SNAKE_CASE			), """xmax""": ANY(__SCREAMING_SNAKE_CASE			), """ymax""": ANY(__SCREAMING_SNAKE_CASE			)},
               }					,							)

      import datasets

      __SCREAMING_SNAKE_CASE     =      datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""					,							"""image"""					,							split="""test"""			)

      __SCREAMING_SNAKE_CASE     =      [
          Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			),
          """http://images.cocodataset.org/val2017/000000039769.jpg""",
          # RGBA
          dataset[0]["""file"""],
          # LA
          dataset[1]["""file"""],
          # L
          dataset[2]["""file"""],
      ]
      __SCREAMING_SNAKE_CASE     =      object_detector(__SCREAMING_SNAKE_CASE					,							threshold=0.0			)

      self.assertEqual(len(__SCREAMING_SNAKE_CASE			)					,							len(__SCREAMING_SNAKE_CASE			)			)
      for outputs in batch_outputs:
           self.assertGreater(len(__SCREAMING_SNAKE_CASE			)					,							0			)
           for detected_object in outputs:
                self.assertEqual(
                    __SCREAMING_SNAKE_CASE					,							{
                        """score""": ANY(__SCREAMING_SNAKE_CASE			),
                        """label""": ANY(__SCREAMING_SNAKE_CASE			),
                        """box""": {"""xmin""": ANY(__SCREAMING_SNAKE_CASE			), """ymin""": ANY(__SCREAMING_SNAKE_CASE			), """xmax""": ANY(__SCREAMING_SNAKE_CASE			), """ymax""": ANY(__SCREAMING_SNAKE_CASE			)},
                    }					,							)
 @require_tf
 @unittest.skip("""Object detection not implemented in TF"""			)
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> str:


      """simple docstring"""




      pass
 @require_torch
 def       UpperCAmelCase__		(		self					:       str			)  -> Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      """hf-internal-testing/tiny-detr-mobilenetsv3"""

      __SCREAMING_SNAKE_CASE     =      AutoModelForObjectDetection.from_pretrained(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      AutoFeatureExtractor.from_pretrained(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      ObjectDetectionPipeline(model=__SCREAMING_SNAKE_CASE					,							feature_extractor=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""					,							threshold=0.0			)

      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE					,							decimals=4			)					,							[
              {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
              {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
          ]					,							)

      __SCREAMING_SNAKE_CASE     =      object_detector(
          [
              """http://images.cocodataset.org/val2017/000000039769.jpg""",
              """http://images.cocodataset.org/val2017/000000039769.jpg""",
          ]					,							threshold=0.0					,							)

      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE					,							decimals=4			)					,							[
              [
                  {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
                  {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
              ],
              [
                  {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
                  {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
              ],
          ]					,							)
 @require_torch
 @slow
 def       UpperCAmelCase__		(		self					:       Optional[int]			)  -> Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      """facebook/detr-resnet-50"""

      __SCREAMING_SNAKE_CASE     =      AutoModelForObjectDetection.from_pretrained(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      AutoFeatureExtractor.from_pretrained(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      ObjectDetectionPipeline(model=__SCREAMING_SNAKE_CASE					,							feature_extractor=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""			)
      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE					,							decimals=4			)					,							[
              {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
              {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
              {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
              {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
              {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
          ]					,							)

      __SCREAMING_SNAKE_CASE     =      object_detector(
          [
              """http://images.cocodataset.org/val2017/000000039769.jpg""",
              """http://images.cocodataset.org/val2017/000000039769.jpg""",
          ]			)
      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE					,							decimals=4			)					,							[
              [
                  {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
                  {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
                  {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
                  {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
                  {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
              ],
              [
                  {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
                  {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
                  {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
                  {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
                  {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
              ],
          ]					,							)
 @require_torch
 @slow
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      """facebook/detr-resnet-50"""

      __SCREAMING_SNAKE_CASE     =      pipeline("""object-detection"""					,							model=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""			)
      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE					,							decimals=4			)					,							[
              {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
              {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
              {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
              {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
              {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
          ]					,							)

      __SCREAMING_SNAKE_CASE     =      object_detector(
          [
              """http://images.cocodataset.org/val2017/000000039769.jpg""",
              """http://images.cocodataset.org/val2017/000000039769.jpg""",
          ]			)
      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE					,							decimals=4			)					,							[
              [
                  {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
                  {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
                  {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
                  {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
                  {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
              ],
              [
                  {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
                  {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
                  {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
                  {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
                  {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
              ],
          ]					,							)
 @require_torch
 @slow
 def       UpperCAmelCase__		(		self					:       Dict			)  -> Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      0.9985
      __SCREAMING_SNAKE_CASE     =      """facebook/detr-resnet-50"""

      __SCREAMING_SNAKE_CASE     =      pipeline("""object-detection"""					,							model=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""					,							threshold=__SCREAMING_SNAKE_CASE			)
      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE					,							decimals=4			)					,							[
              {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
              {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
          ]					,							)


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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      """Narsil/layoutlmv3-finetuned-funsd"""
      __SCREAMING_SNAKE_CASE     =      0.9993

      __SCREAMING_SNAKE_CASE     =      pipeline("""object-detection"""					,							model=__SCREAMING_SNAKE_CASE					,							threshold=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      object_detector(
          """https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"""			)
      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE					,							decimals=4			)					,							[
              {"""score""": 0.9993, """label""": """I-ANSWER""", """box""": {"""xmin""": 294, """ymin""": 254, """xmax""": 343, """ymax""": 264}},
              {"""score""": 0.9993, """label""": """I-ANSWER""", """box""": {"""xmin""": 294, """ymin""": 254, """xmax""": 343, """ymax""": 264}},
          ]					,							)






