


"""simple docstring"""






import inspect
import unittest

from transformers import YolosConfig
from transformers.testing_utils import 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, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import YolosForObjectDetection, YolosModel
   from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor


class     __lowerCamelCase  :

     '''simple docstring'''


     def __init__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase=13    ,				__UpperCAmelCase=[30, 30]    ,				__UpperCAmelCase=2    ,				__UpperCAmelCase=3    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=32    ,				__UpperCAmelCase=5    ,				__UpperCAmelCase=4    ,				__UpperCAmelCase=37    ,				__UpperCAmelCase="gelu"    ,				__UpperCAmelCase=0.1    ,				__UpperCAmelCase=0.1    ,				__UpperCAmelCase=10    ,				__UpperCAmelCase=0.02    ,				__UpperCAmelCase=3    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=8    ,				__UpperCAmelCase=10    ,				)       ->							int:
          _a    	=				parent
          _a    	=				batch_size
          _a    	=				image_size
          _a    	=				patch_size
          _a    	=				num_channels
          _a    	=				is_training
          _a    	=				use_labels
          _a    	=				hidden_size
          _a    	=				num_hidden_layers
          _a    	=				num_attention_heads
          _a    	=				intermediate_size
          _a    	=				hidden_act
          _a    	=				hidden_dropout_prob
          _a    	=				attention_probs_dropout_prob
          _a    	=				type_sequence_label_size
          _a    	=				initializer_range
          _a    	=				num_labels
          _a    	=				scope
          _a    	=				n_targets
          _a    	=				num_detection_tokens
          # we set the expected sequence length (which is used in several tests)
          # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
          _a    	=				(image_size[1] // patch_size) * (image_size[0] // patch_size)
          _a    	=				num_patches + 1 + self.num_detection_tokens


     def 							_UpperCAmelCase      (      self							)       ->							List[Any]:
          _a    	=				floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]							)

          _a    	=				None
          if self.use_labels:
               # labels is a list of Dict (each Dict being the labels for a given example in the batch)
               _a    	=				[]
               for i in range(self.batch_size							):
                    _a    	=				{}
                    _a    	=				torch.randint(
                        high=self.num_labels    ,				size=(self.n_targets,)    ,				device=__UpperCAmelCase							)
                    _a    	=				torch.rand(self.n_targets    ,				4    ,				device=__UpperCAmelCase							)
                    labels.append(__UpperCAmelCase							)

          _a    	=				self.get_config()

          return config, pixel_values, labels


     def 							_UpperCAmelCase      (      self							)       ->							Tuple:
          return YolosConfig(
              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=__UpperCAmelCase    ,				initializer_range=self.initializer_range    ,				num_detection_tokens=self.num_detection_tokens    ,				num_labels=self.num_labels    ,				)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							Optional[int]:
          _a    	=				YolosModel(config=__UpperCAmelCase							)
          model.to(__UpperCAmelCase							)
          model.eval()
          _a    	=				model(__UpperCAmelCase							)
          self.parent.assertEqual(
              result.last_hidden_state.shape    ,				(self.batch_size, self.expected_seq_len, self.hidden_size)							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							Optional[int]:
          _a    	=				YolosForObjectDetection(__UpperCAmelCase							)
          model.to(__UpperCAmelCase							)
          model.eval()

          _a    	=				model(pixel_values=__UpperCAmelCase							)
          _a    	=				model(__UpperCAmelCase							)

          self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.num_detection_tokens, self.num_labels + 1)							)
          self.parent.assertEqual(result.pred_boxes.shape    ,				(self.batch_size, self.num_detection_tokens, 4)							)

          _a    	=				model(pixel_values=__UpperCAmelCase    ,				labels=__UpperCAmelCase							)

          self.parent.assertEqual(result.loss.shape    ,				()							)
          self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.num_detection_tokens, self.num_labels + 1)							)
          self.parent.assertEqual(result.pred_boxes.shape    ,				(self.batch_size, self.num_detection_tokens, 4)							)





     def 							_UpperCAmelCase      (      self							)       ->							str:
          _a    	=				self.prepare_config_and_inputs()
          _a	,      _a	,      _a    	=				config_and_inputs
          _a    	=				{'''pixel_values''': pixel_values}
          return config, inputs_dict







@require_torch
class     __lowerCamelCase  (					a__  ,					a__  ,					unittest.TestCase				):

     '''simple docstring'''
     A_       :	Tuple    =		(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
     A_       :	Optional[Any]    =		(
         {'feature-extraction': YolosModel, 'object-detection': YolosForObjectDetection} if is_torch_available() else {}
     )

     A_       :	str    =		False
     A_       :	str    =		False
     A_       :	List[str]    =		False
     A_       :	int    =		False


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase=False							)       ->							Optional[Any]:
          _a    	=				super()._prepare_for_class(__UpperCAmelCase    ,				__UpperCAmelCase    ,				return_labels=__UpperCAmelCase							)

          if return_labels:
               if model_class.__name__ == "YolosForObjectDetection":
                    _a    	=				[]
                    for i in range(self.model_tester.batch_size							):
                         _a    	=				{}
                         _a    	=				torch.ones(
                             size=(self.model_tester.n_targets,)    ,				device=__UpperCAmelCase    ,				dtype=torch.long							)
                         _a    	=				torch.ones(
                             self.model_tester.n_targets    ,				4    ,				device=__UpperCAmelCase    ,				dtype=torch.float							)
                         labels.append(__UpperCAmelCase							)
                    _a    	=				labels

          return inputs_dict


     def 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          _a    	=				YolosModelTester(self							)
          _a    	=				ConfigTester(self    ,				config_class=__UpperCAmelCase    ,				has_text_modality=__UpperCAmelCase    ,				hidden_size=37							)


     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          self.config_tester.run_common_tests()


     def 							_UpperCAmelCase      (      self							)       ->							List[Any]:
          # YOLOS does not use inputs_embeds
          pass


     def 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
          _a	,      _a    	=				self.model_tester.prepare_config_and_inputs_for_common()

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


     def 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
          _a	,      _a    	=				self.model_tester.prepare_config_and_inputs_for_common()

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

               _a    	=				['''pixel_values''']
               self.assertListEqual(arg_names[:1]    ,				__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          _a	,      _a    	=				self.model_tester.prepare_config_and_inputs_for_common()
          _a    	=				True

          # in YOLOS, the seq_len is different
          _a    	=				self.model_tester.expected_seq_len
          for model_class in self.all_model_classes:
               _a    	=				True
               _a    	=				False
               _a    	=				True
               _a    	=				model_class(__UpperCAmelCase							)
               model.to(__UpperCAmelCase							)
               model.eval()
               with torch.no_grad():
                    _a    	=				model(**self._prepare_for_class(__UpperCAmelCase    ,				__UpperCAmelCase							)							)
               _a    	=				outputs.attentions
               self.assertEqual(len(__UpperCAmelCase							)    ,				self.model_tester.num_hidden_layers							)

               # check that output_attentions also work using config
               del inputs_dict["output_attentions"]
               _a    	=				True
               _a    	=				model_class(__UpperCAmelCase							)
               model.to(__UpperCAmelCase							)
               model.eval()
               with torch.no_grad():
                    _a    	=				model(**self._prepare_for_class(__UpperCAmelCase    ,				__UpperCAmelCase							)							)
               _a    	=				outputs.attentions
               self.assertEqual(len(__UpperCAmelCase							)    ,				self.model_tester.num_hidden_layers							)

               self.assertListEqual(
                   list(attentions[0].shape[-3:]							)    ,				[self.model_tester.num_attention_heads, seq_len, seq_len]    ,				)
               _a    	=				len(__UpperCAmelCase							)

               # Check attention is always last and order is fine
               _a    	=				True
               _a    	=				True
               _a    	=				model_class(__UpperCAmelCase							)
               model.to(__UpperCAmelCase							)
               model.eval()
               with torch.no_grad():
                    _a    	=				model(**self._prepare_for_class(__UpperCAmelCase    ,				__UpperCAmelCase							)							)

               _a    	=				1
               self.assertEqual(out_len + added_hidden_states    ,				len(__UpperCAmelCase							)							)

               _a    	=				outputs.attentions

               self.assertEqual(len(__UpperCAmelCase							)    ,				self.model_tester.num_hidden_layers							)
               self.assertListEqual(
                   list(self_attentions[0].shape[-3:]							)    ,				[self.model_tester.num_attention_heads, seq_len, seq_len]    ,				)


     def 							_UpperCAmelCase      (      self							)       ->							str:
          def check_hidden_states_output(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							):
               _a    	=				model_class(__UpperCAmelCase							)
               model.to(__UpperCAmelCase							)
               model.eval()

               with torch.no_grad():
                    _a    	=				model(**self._prepare_for_class(__UpperCAmelCase    ,				__UpperCAmelCase							)							)

               _a    	=				outputs.hidden_states

               _a    	=				getattr(
                   self.model_tester    ,				'''expected_num_hidden_layers'''    ,				self.model_tester.num_hidden_layers + 1							)
               self.assertEqual(len(__UpperCAmelCase							)    ,				__UpperCAmelCase							)

               # YOLOS has a different seq_length
               _a    	=				self.model_tester.expected_seq_len

               self.assertListEqual(
                   list(hidden_states[0].shape[-2:]							)    ,				[seq_length, self.model_tester.hidden_size]    ,				)

          _a	,      _a    	=				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               _a    	=				True
               check_hidden_states_output(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               _a    	=				True

               check_hidden_states_output(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							str:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_object_detection(*__UpperCAmelCase							)





     @slow
     def 							_UpperCAmelCase      (      self							)       ->							List[Any]:
          for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _a    	=				YolosModel.from_pretrained(__UpperCAmelCase							)
               self.assertIsNotNone(__UpperCAmelCase							)







def       A_   (   ):






     """simple docstring"""

     _a    	=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
     return image


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

     '''simple docstring'''


     @cached_property
     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          return AutoImageProcessor.from_pretrained('''hustvl/yolos-small'''							) if is_vision_available() else None





     @slow
     def 							_UpperCAmelCase      (      self							)       ->							int:
          _a    	=				YolosForObjectDetection.from_pretrained('''hustvl/yolos-small'''							).to(__UpperCAmelCase							)

          _a    	=				self.default_image_processor
          _a    	=				prepare_img()
          _a    	=				image_processor(images=__UpperCAmelCase    ,				return_tensors='''pt'''							).to(__UpperCAmelCase							)

          # forward pass
          with torch.no_grad():
               _a    	=				model(inputs.pixel_values							)

          # verify outputs
          _a    	=				torch.Size((1, 100, 92)							)
          self.assertEqual(outputs.logits.shape    ,				__UpperCAmelCase							)

          _a    	=				torch.tensor(
              [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]    ,				device=__UpperCAmelCase    ,				)
          _a    	=				torch.tensor(
              [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]    ,				device=__UpperCAmelCase							)
          self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]    ,				__UpperCAmelCase    ,				atol=1e-4							)							)
          self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]    ,				__UpperCAmelCase    ,				atol=1e-4							)							)

          # verify postprocessing
          _a    	=				image_processor.post_process_object_detection(
              __UpperCAmelCase    ,				threshold=0.3    ,				target_sizes=[image.size[::-1]]							)[0]
          _a    	=				torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861]							).to(__UpperCAmelCase							)
          _a    	=				[75, 75, 17, 63, 17]
          _a    	=				torch.tensor([335.0609, 79.3848, 375.4216, 187.2495]							).to(__UpperCAmelCase							)

          self.assertEqual(len(results['''scores''']							)    ,				5							)
          self.assertTrue(torch.allclose(results['''scores''']    ,				__UpperCAmelCase    ,				atol=1e-4							)							)
          self.assertSequenceEqual(results['''labels'''].tolist()    ,				__UpperCAmelCase							)
          self.assertTrue(torch.allclose(results['''boxes'''][0, :]    ,				__UpperCAmelCase							)							)



"""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 DeformableDetrImageProcessor


class     __lowerCamelCase  (					unittest.TestCase				):

     '''simple docstring'''


     def __init__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase=7    ,				__UpperCAmelCase=3    ,				__UpperCAmelCase=30    ,				__UpperCAmelCase=400    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=[0.5, 0.5, 0.5]    ,				__UpperCAmelCase=[0.5, 0.5, 0.5]    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=1 / 255    ,				__UpperCAmelCase=True    ,				)       ->							Optional[int]:
          # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
          _a    	=				size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
          _a    	=				parent
          _a    	=				batch_size
          _a    	=				num_channels
          _a    	=				min_resolution
          _a    	=				max_resolution
          _a    	=				do_resize
          _a    	=				size
          _a    	=				do_normalize
          _a    	=				image_mean
          _a    	=				image_std
          _a    	=				do_rescale
          _a    	=				rescale_factor
          _a    	=				do_pad


     def 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          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    ,				__UpperCAmelCase    ,				__UpperCAmelCase=False							)       ->							List[str]:
          if not batched:
               _a    	=				image_inputs[0]
               if isinstance(__UpperCAmelCase    ,				Image.Image							):
                    _a	,      _a    	=				image.size
               else:
                    _a	,      _a    	=				image.shape[1], image.shape[2]
               if w < h:
                    _a    	=				int(self.size['''shortest_edge'''] * h / w							)
                    _a    	=				self.size['''shortest_edge''']
               elif w > h:
                    _a    	=				self.size['''shortest_edge''']
                    _a    	=				int(self.size['''shortest_edge'''] * w / h							)
               else:
                    _a    	=				self.size['''shortest_edge''']
                    _a    	=				self.size['''shortest_edge''']

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

          return expected_height, expected_width







@require_torch
@require_vision
class     __lowerCamelCase  (					a__  ,					unittest.TestCase				):

     '''simple docstring'''
     A_       :	List[str]    =		DeformableDetrImageProcessor if is_vision_available() else None


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          _a    	=				DeformableDetrImageProcessingTester(self							)


     @property
     def 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          return self.image_processor_tester.prepare_image_processor_dict()


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          _a    	=				self.image_processing_class(**self.image_processor_dict							)
          self.assertTrue(hasattr(__UpperCAmelCase    ,				'''image_mean'''							)							)
          self.assertTrue(hasattr(__UpperCAmelCase    ,				'''image_std'''							)							)
          self.assertTrue(hasattr(__UpperCAmelCase    ,				'''do_normalize'''							)							)
          self.assertTrue(hasattr(__UpperCAmelCase    ,				'''do_resize'''							)							)
          self.assertTrue(hasattr(__UpperCAmelCase    ,				'''do_rescale'''							)							)
          self.assertTrue(hasattr(__UpperCAmelCase    ,				'''do_pad'''							)							)
          self.assertTrue(hasattr(__UpperCAmelCase    ,				'''size'''							)							)


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          _a    	=				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    ,				__UpperCAmelCase							)

          _a    	=				self.image_processing_class.from_dict(
              self.image_processor_dict    ,				size=42    ,				max_size=84    ,				pad_and_return_pixel_mask=__UpperCAmelCase							)
          self.assertEqual(image_processor.size    ,				{'''shortest_edge''': 42, '''longest_edge''': 84}							)
          self.assertEqual(image_processor.do_pad    ,				__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							int:
          pass


     def 							_UpperCAmelCase      (      self							)       ->							List[Any]:
          # Initialize image_processing
          _a    	=				self.image_processing_class(**self.image_processor_dict							)
          # create random PIL images
          _a    	=				prepare_image_inputs(self.image_processor_tester    ,				equal_resolution=__UpperCAmelCase							)
          for image in image_inputs:
               self.assertIsInstance(__UpperCAmelCase    ,				Image.Image							)

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

          _a	,      _a    	=				self.image_processor_tester.get_expected_values(__UpperCAmelCase							)

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

          # Test batched
          _a	,      _a    	=				self.image_processor_tester.get_expected_values(__UpperCAmelCase    ,				batched=__UpperCAmelCase							)

          _a    	=				image_processing(__UpperCAmelCase    ,				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							)       ->							int:
          # Initialize image_processing
          _a    	=				self.image_processing_class(**self.image_processor_dict							)
          # create random numpy tensors
          _a    	=				prepare_image_inputs(self.image_processor_tester    ,				equal_resolution=__UpperCAmelCase    ,				numpify=__UpperCAmelCase							)
          for image in image_inputs:
               self.assertIsInstance(__UpperCAmelCase    ,				np.ndarray							)

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

          _a	,      _a    	=				self.image_processor_tester.get_expected_values(__UpperCAmelCase							)

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

          # Test batched
          _a    	=				image_processing(__UpperCAmelCase    ,				return_tensors='''pt'''							).pixel_values

          _a	,      _a    	=				self.image_processor_tester.get_expected_values(__UpperCAmelCase    ,				batched=__UpperCAmelCase							)

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


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          # Initialize image_processing
          _a    	=				self.image_processing_class(**self.image_processor_dict							)
          # create random PyTorch tensors
          _a    	=				prepare_image_inputs(self.image_processor_tester    ,				equal_resolution=__UpperCAmelCase    ,				torchify=__UpperCAmelCase							)
          for image in image_inputs:
               self.assertIsInstance(__UpperCAmelCase    ,				torch.Tensor							)

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

          _a	,      _a    	=				self.image_processor_tester.get_expected_values(__UpperCAmelCase							)

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

          # Test batched
          _a    	=				image_processing(__UpperCAmelCase    ,				return_tensors='''pt'''							).pixel_values

          _a	,      _a    	=				self.image_processor_tester.get_expected_values(__UpperCAmelCase    ,				batched=__UpperCAmelCase							)

          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							)       ->							Optional[Any]:
          # prepare image and target
          _a    	=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
          with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''    ,				'''r'''							) as f:
               _a    	=				json.loads(f.read()							)

          _a    	=				{'''image_id''': 39769, '''annotations''': target}

          # encode them
          _a    	=				DeformableDetrImageProcessor()
          _a    	=				image_processing(images=__UpperCAmelCase    ,				annotations=__UpperCAmelCase    ,				return_tensors='''pt'''							)

          # verify pixel values
          _a    	=				torch.Size([1, 3, 800, 1066]							)
          self.assertEqual(encoding['''pixel_values'''].shape    ,				__UpperCAmelCase							)

          _a    	=				torch.tensor([0.2796, 0.3138, 0.3481]							)
          self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]    ,				__UpperCAmelCase    ,				atol=1e-4							)							)

          # verify area
          _a    	=				torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,				__UpperCAmelCase							)							)
          # verify boxes
          _a    	=				torch.Size([6, 4]							)
          self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,				__UpperCAmelCase							)
          _a    	=				torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,				__UpperCAmelCase    ,				atol=1e-3							)							)
          # verify image_id
          _a    	=				torch.tensor([39769]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,				__UpperCAmelCase							)							)
          # verify is_crowd
          _a    	=				torch.tensor([0, 0, 0, 0, 0, 0]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,				__UpperCAmelCase							)							)
          # verify class_labels
          _a    	=				torch.tensor([75, 75, 63, 65, 17, 17]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,				__UpperCAmelCase							)							)
          # verify orig_size
          _a    	=				torch.tensor([480, 640]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,				__UpperCAmelCase							)							)
          # verify size
          _a    	=				torch.tensor([800, 1066]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,				__UpperCAmelCase							)							)





     @slow
     def 							_UpperCAmelCase      (      self							)       ->							str:
          # prepare image, target and masks_path
          _a    	=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
          with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''    ,				'''r'''							) as f:
               _a    	=				json.loads(f.read()							)

          _a    	=				{'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}

          _a    	=				pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''							)

          # encode them
          _a    	=				DeformableDetrImageProcessor(format='''coco_panoptic'''							)
          _a    	=				image_processing(images=__UpperCAmelCase    ,				annotations=__UpperCAmelCase    ,				masks_path=__UpperCAmelCase    ,				return_tensors='''pt'''							)

          # verify pixel values
          _a    	=				torch.Size([1, 3, 800, 1066]							)
          self.assertEqual(encoding['''pixel_values'''].shape    ,				__UpperCAmelCase							)

          _a    	=				torch.tensor([0.2796, 0.3138, 0.3481]							)
          self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]    ,				__UpperCAmelCase    ,				atol=1e-4							)							)

          # verify area
          _a    	=				torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,				__UpperCAmelCase							)							)
          # verify boxes
          _a    	=				torch.Size([6, 4]							)
          self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,				__UpperCAmelCase							)
          _a    	=				torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,				__UpperCAmelCase    ,				atol=1e-3							)							)
          # verify image_id
          _a    	=				torch.tensor([39769]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,				__UpperCAmelCase							)							)
          # verify is_crowd
          _a    	=				torch.tensor([0, 0, 0, 0, 0, 0]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,				__UpperCAmelCase							)							)
          # verify class_labels
          _a    	=				torch.tensor([17, 17, 63, 75, 75, 93]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,				__UpperCAmelCase							)							)
          # verify masks
          _a    	=				822873
          self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()    ,				__UpperCAmelCase							)
          # verify orig_size
          _a    	=				torch.tensor([480, 640]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,				__UpperCAmelCase							)							)
          # verify size
          _a    	=				torch.tensor([800, 1066]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,				__UpperCAmelCase							)							)