



"""simple docstring"""







import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
     from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import DPTImageProcessor







class 				UpperCamelCase__       :




      """simple docstring"""




      def __init__(				self    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				int=2   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=3_2   ,      SCREAMING_SNAKE_CASE_    :				Dict=1_6   ,      SCREAMING_SNAKE_CASE_    :				int=3   ,      SCREAMING_SNAKE_CASE_    :				int=True   ,      SCREAMING_SNAKE_CASE_    :				Tuple=True   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=3_2   ,      SCREAMING_SNAKE_CASE_    :				Dict=4   ,      SCREAMING_SNAKE_CASE_    :				Any=[0, 1, 2, 3]   ,      SCREAMING_SNAKE_CASE_    :				Tuple=4   ,      SCREAMING_SNAKE_CASE_    :				List[str]=3_7   ,      SCREAMING_SNAKE_CASE_    :				Any="gelu"   ,      SCREAMING_SNAKE_CASE_    :				Any=0.1   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]=0.1   ,      SCREAMING_SNAKE_CASE_    :				int=0.02   ,      SCREAMING_SNAKE_CASE_    :				Any=3   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=[1, 3_8_4, 2_4, 2_4]   ,      SCREAMING_SNAKE_CASE_    :				List[str]=True   ,      SCREAMING_SNAKE_CASE_    :				str=None   ,      ):
        lowerCAmelCase_       :    Any								=			parent
        lowerCAmelCase_       :    Union[str, Any]								=			batch_size
        lowerCAmelCase_       :    Optional[int]								=			image_size
        lowerCAmelCase_       :    Tuple								=			patch_size
        lowerCAmelCase_       :    List[Any]								=			num_channels
        lowerCAmelCase_       :    Any								=			is_training
        lowerCAmelCase_       :    str								=			use_labels
        lowerCAmelCase_       :    Any								=			hidden_size
        lowerCAmelCase_       :    int								=			num_hidden_layers
        lowerCAmelCase_       :    List[Any]								=			backbone_out_indices
        lowerCAmelCase_       :    Any								=			num_attention_heads
        lowerCAmelCase_       :    Optional[Any]								=			intermediate_size
        lowerCAmelCase_       :    List[str]								=			hidden_act
        lowerCAmelCase_       :    str								=			hidden_dropout_prob
        lowerCAmelCase_       :    int								=			attention_probs_dropout_prob
        lowerCAmelCase_       :    Dict								=			initializer_range
        lowerCAmelCase_       :    List[Any]								=			num_labels
        lowerCAmelCase_       :    Any								=			backbone_featmap_shape
        lowerCAmelCase_       :    Optional[int]								=			scope
        lowerCAmelCase_       :    Union[str, Any]								=			is_hybrid
        # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
        lowerCAmelCase_       :    int								=			(image_size // patch_size) ** 2
        lowerCAmelCase_       :    Optional[int]								=			num_patches + 1



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]	):
        lowerCAmelCase_       :    Tuple								=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

        lowerCAmelCase_       :    int								=			None
        if self.use_labels:
          lowerCAmelCase_       :    str								=			ids_tensor([self.batch_size, self.image_size, self.image_size]   ,      self.num_labels	)

        lowerCAmelCase_       :    Union[str, Any]								=			self.get_config()

        return config, pixel_values, labels



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict	):
        lowerCAmelCase_       :    Dict								=			{
            'global_padding': 'same',
            'layer_type': 'bottleneck',
            'depths': [3, 4, 9],
            'out_features': ['stage1', 'stage2', 'stage3'],
            'embedding_dynamic_padding': True,
            'hidden_sizes': [9_6, 1_9_2, 3_8_4, 7_6_8],
            'num_groups': 2,
        }

        return DPTConfig(
            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   ,      backbone_out_indices=self.backbone_out_indices   ,      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=SCREAMING_SNAKE_CASE_   ,      initializer_range=self.initializer_range   ,      is_hybrid=self.is_hybrid   ,      backbone_config=SCREAMING_SNAKE_CASE_   ,      backbone_featmap_shape=self.backbone_featmap_shape   ,      )



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				Any   ,      SCREAMING_SNAKE_CASE_    :				List[str]   ,      SCREAMING_SNAKE_CASE_    :				Tuple	):
        lowerCAmelCase_       :    str								=			DPTModel(config=SCREAMING_SNAKE_CASE_	)
        model.to(SCREAMING_SNAKE_CASE_	)
        model.eval()
        lowerCAmelCase_       :    Optional[int]								=			model(SCREAMING_SNAKE_CASE_	)
        self.parent.assertEqual(result.last_hidden_state.shape   ,      (self.batch_size, self.seq_length, self.hidden_size)	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				Dict	):
        lowerCAmelCase_       :    str								=			self.num_labels
        lowerCAmelCase_       :    Any								=			DPTForDepthEstimation(SCREAMING_SNAKE_CASE_	)
        model.to(SCREAMING_SNAKE_CASE_	)
        model.eval()
        lowerCAmelCase_       :    List[Any]								=			model(SCREAMING_SNAKE_CASE_	)
        self.parent.assertEqual(result.predicted_depth.shape   ,      (self.batch_size, self.image_size, self.image_size)	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				int   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]	):
        lowerCAmelCase_       :    Tuple								=			self.num_labels
        lowerCAmelCase_       :    Dict								=			DPTForSemanticSegmentation(SCREAMING_SNAKE_CASE_	)
        model.to(SCREAMING_SNAKE_CASE_	)
        model.eval()
        lowerCAmelCase_       :    int								=			model(SCREAMING_SNAKE_CASE_   ,      labels=SCREAMING_SNAKE_CASE_	)
        self.parent.assertEqual(
            result.logits.shape   ,      (self.batch_size, self.num_labels, self.image_size, self.image_size)	)





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







@require_torch
class 				UpperCamelCase__       (     lowercase_, lowercase_, unittest.TestCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
      _SCREAMING_SNAKE_CASE      =					(
          {
              """depth-estimation""": DPTForDepthEstimation,
              """feature-extraction""": DPTModel,
              """image-segmentation""": DPTForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

      _SCREAMING_SNAKE_CASE      =					False
      _SCREAMING_SNAKE_CASE      =					False
      _SCREAMING_SNAKE_CASE      =					False



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict	):
        lowerCAmelCase_       :    List[Any]								=			DPTModelTester(self	)
        lowerCAmelCase_       :    Union[str, Any]								=			ConfigTester(self   ,      config_class=SCREAMING_SNAKE_CASE_   ,      has_text_modality=SCREAMING_SNAKE_CASE_   ,      hidden_size=3_7	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				int	):
        self.config_tester.run_common_tests()



      @unittest.skip(reason='DPT does not use inputs_embeds'	)
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any	):
        pass



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        lowerCAmelCase_						,lowerCAmelCase_       :    Union[str, Any]								=			self.model_tester.prepare_config_and_inputs_for_common()

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



      def 					SCREAMING_SNAKE_CASE__			(				self    :				int	):
        lowerCAmelCase_						,lowerCAmelCase_       :    List[str]								=			self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          lowerCAmelCase_       :    List[Any]								=			model_class(SCREAMING_SNAKE_CASE_	)
          lowerCAmelCase_       :    Optional[int]								=			inspect.signature(model.forward	)
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          lowerCAmelCase_       :    Optional[Any]								=			[*signature.parameters.keys()]

          lowerCAmelCase_       :    Union[str, Any]								=			['pixel_values']
          self.assertListEqual(arg_names[:1]   ,      SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict	):
        lowerCAmelCase_       :    Tuple								=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        lowerCAmelCase_       :    Optional[Any]								=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_depth_estimation(*SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]	):
        lowerCAmelCase_       :    List[str]								=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
        for model_class in self.all_model_classes:
          if model_class.__name__ == "DPTForDepthEstimation":
            continue

          lowerCAmelCase_						,lowerCAmelCase_       :    int								=			self.model_tester.prepare_config_and_inputs_for_common()
          lowerCAmelCase_       :    Union[str, Any]								=			True

          if model_class in get_values(SCREAMING_SNAKE_CASE_	):
            continue

          lowerCAmelCase_       :    Dict								=			model_class(SCREAMING_SNAKE_CASE_	)
          model.to(SCREAMING_SNAKE_CASE_	)
          model.train()
          lowerCAmelCase_       :    List[Any]								=			self._prepare_for_class(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      return_labels=SCREAMING_SNAKE_CASE_	)
          lowerCAmelCase_       :    int								=			model(**SCREAMING_SNAKE_CASE_	).loss
          loss.backward()



      def 					SCREAMING_SNAKE_CASE__			(				self    :				int	):
        for model_class in self.all_model_classes:
          if model_class.__name__ == "DPTForDepthEstimation":
            continue

          lowerCAmelCase_						,lowerCAmelCase_       :    Dict								=			self.model_tester.prepare_config_and_inputs_for_common()
          lowerCAmelCase_       :    Any								=			False
          lowerCAmelCase_       :    str								=			True

          if model_class in get_values(SCREAMING_SNAKE_CASE_	) or not model_class.supports_gradient_checkpointing:
            continue
          lowerCAmelCase_       :    Union[str, Any]								=			model_class(SCREAMING_SNAKE_CASE_	)
          model.to(SCREAMING_SNAKE_CASE_	)
          model.gradient_checkpointing_enable()
          model.train()
          lowerCAmelCase_       :    int								=			self._prepare_for_class(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      return_labels=SCREAMING_SNAKE_CASE_	)
          lowerCAmelCase_       :    Dict								=			model(**SCREAMING_SNAKE_CASE_	).loss
          loss.backward()



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
        lowerCAmelCase_						,lowerCAmelCase_       :    List[Any]								=			self.model_tester.prepare_config_and_inputs_for_common()

        lowerCAmelCase_       :    List[Any]								=			_config_zero_init(SCREAMING_SNAKE_CASE_	)
        for model_class in self.all_model_classes:
          lowerCAmelCase_       :    str								=			model_class(config=SCREAMING_SNAKE_CASE_	)
          # Skip the check for the backbone
          lowerCAmelCase_       :    List[str]								=			[]
          for name, module in model.named_modules():
            if module.__class__.__name__ == "DPTViTHybridEmbeddings":
              lowerCAmelCase_       :    Any								=			[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"   ,      )



      @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'	)
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        pass



      @slow
      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
          lowerCAmelCase_       :    Any								=			DPTModel.from_pretrained(SCREAMING_SNAKE_CASE_	)
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)





      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any	):
        # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
        lowerCAmelCase_						,lowerCAmelCase_       :    str								=			self.model_tester.prepare_config_and_inputs_for_common()
        lowerCAmelCase_       :    List[Any]								=			'add'
        with self.assertRaises(SCREAMING_SNAKE_CASE_	):
          lowerCAmelCase_       :    List[str]								=			DPTForDepthEstimation(SCREAMING_SNAKE_CASE_	)




def     UpperCamelCase_   (   )						->       int:

  """simple docstring"""




  lowerCAmelCase_       :    Union[str, Any]								=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
  return image







@require_torch
@require_vision
@slow
class 				UpperCamelCase__       (     unittest.TestCase  ):




      """simple docstring"""




      def 					SCREAMING_SNAKE_CASE__			(				self    :				int	):
        lowerCAmelCase_       :    Optional[Any]								=			DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'	)
        lowerCAmelCase_       :    Union[str, Any]								=			DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'	).to(SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    Any								=			prepare_img()
        lowerCAmelCase_       :    Union[str, Any]								=			image_processor(images=SCREAMING_SNAKE_CASE_   ,      return_tensors='pt'	).to(SCREAMING_SNAKE_CASE_	)

        # forward pass
        with torch.no_grad():
          lowerCAmelCase_       :    Union[str, Any]								=			model(**SCREAMING_SNAKE_CASE_	)
          lowerCAmelCase_       :    Dict								=			outputs.predicted_depth

        # verify the predicted depth
        lowerCAmelCase_       :    Dict								=			torch.Size((1, 3_8_4, 3_8_4)	)
        self.assertEqual(predicted_depth.shape   ,      SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    List[Any]								=			torch.tensor(
            [[[5.64_37, 5.61_46, 5.65_11], [5.43_71, 5.56_49, 5.59_58], [5.52_15, 5.51_84, 5.52_93]]]	).to(SCREAMING_SNAKE_CASE_	)

        self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_0_0   ,      SCREAMING_SNAKE_CASE_   ,      atol=1E-4	)	)










"""simple docstring"""







import baseaa

def     UpperCamelCase_   (   lowerCAmelCase__				:    str						)						->       bytes:

  """simple docstring"""




  return baseaa.aaaencode(string.encode('utf-8'						)						)

def     UpperCamelCase_   (   lowerCAmelCase__				:    bytes						)						->       str:

  """simple docstring"""




  return baseaa.aaadecode(lowerCAmelCase__						).decode('utf-8'						)


if __name__ == "__main__":
     import doctest

     doctest.testmod()





