"""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   __snake_case			:



      def __init__(		self       ,     lowercase       ,     lowercase=2       ,     lowercase=32       ,     lowercase=16       ,     lowercase=3       ,     lowercase=True       ,     lowercase=True       ,     lowercase=32       ,     lowercase=4       ,     lowercase=[0, 1, 2, 3]       ,     lowercase=4       ,     lowercase=37       ,     lowercase="gelu"       ,     lowercase=0.1       ,     lowercase=0.1       ,     lowercase=0.02       ,     lowercase=3       ,     lowercase=False       ,     lowercase=None       ,     )      ->     List[Any]:





        '''simple docstring'''





        a__:	List[str]     				=      parent
        a__:	Tuple     				=      batch_size
        a__:	Tuple     				=      image_size
        a__:	Union[str, Any]     				=      patch_size
        a__:	str     				=      num_channels
        a__:	Any     				=      is_training
        a__:	List[Any]     				=      use_labels
        a__:	Dict     				=      hidden_size
        a__:	Optional[int]     				=      num_hidden_layers
        a__:	List[str]     				=      backbone_out_indices
        a__:	List[str]     				=      num_attention_heads
        a__:	Optional[Any]     				=      intermediate_size
        a__:	str     				=      hidden_act
        a__:	Any     				=      hidden_dropout_prob
        a__:	Optional[Any]     				=      attention_probs_dropout_prob
        a__:	Optional[Any]     				=      initializer_range
        a__:	List[str]     				=      num_labels
        a__:	List[str]     				=      scope
        a__:	Any     				=      is_hybrid
        # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
        a__:	Optional[int]     				=      (image_size // patch_size) ** 2
        a__:	Tuple     				=      num_patches + 1



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





        '''simple docstring'''





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

        a__:	str     				=      None
        if self.use_labels:
          a__:	Union[str, Any]     				=      ids_tensor([self.batch_size, self.image_size, self.image_size]       ,     self.num_labels)

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

        return config, pixel_values, labels



      def     lowerCamelCase_  (		self)      ->     int:





        '''simple docstring'''





        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=lowercase       ,     initializer_range=self.initializer_range       ,     is_hybrid=self.is_hybrid       ,     )



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase       ,     lowercase)      ->     Optional[int]:





        '''simple docstring'''





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



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase       ,     lowercase)      ->     int:





        '''simple docstring'''





        a__:	str     				=      self.num_labels
        a__:	Optional[int]     				=      DPTForDepthEstimation(lowercase)
        model.to(lowercase)
        model.eval()
        a__:	List[str]     				=      model(lowercase)
        self.parent.assertEqual(result.predicted_depth.shape       ,     (self.batch_size, self.image_size, self.image_size))



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase       ,     lowercase)      ->     Dict:





        '''simple docstring'''





        a__:	List[Any]     				=      self.num_labels
        a__:	Optional[Any]     				=      DPTForSemanticSegmentation(lowercase)
        model.to(lowercase)
        model.eval()
        a__:	str     				=      model(lowercase       ,     labels=lowercase)
        self.parent.assertEqual(
            result.logits.shape       ,     (self.batch_size, self.num_labels, self.image_size, self.image_size))



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





        '''simple docstring'''





        a__:	Tuple     				=      self.prepare_config_and_inputs()
        a__				,       a__				,       a__:	Any     				=      config_and_inputs
        a__:	str     				=      {'pixel_values': pixel_values}
        return config, inputs_dict







@require_torch
class   __snake_case			(	__lowerCAmelCase			, __lowerCAmelCase			, unittest.TestCase							):
      a__								=						(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
      a__								=						(
          {
              """depth-estimation""": DPTForDepthEstimation,
              """feature-extraction""": DPTModel,
              """image-segmentation""": DPTForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

      a__								=						False
      a__								=						False
      a__								=						False



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





        '''simple docstring'''





        a__:	Tuple     				=      DPTModelTester(self)
        a__:	Dict     				=      ConfigTester(self       ,     config_class=lowercase       ,     has_text_modality=lowercase       ,     hidden_size=37)



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





        '''simple docstring'''





        self.config_tester.run_common_tests()



      @unittest.skip(reason='DPT does not use inputs_embeds')
      def     lowerCamelCase_  (		self)      ->     Optional[int]:





        '''simple docstring'''





        pass



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





        '''simple docstring'''





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

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



      def     lowerCamelCase_  (		self)      ->     int:





        '''simple docstring'''





        a__				,       a__:	Optional[Any]     				=      self.model_tester.prepare_config_and_inputs_for_common()

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

          a__:	Any     				=      ['pixel_values']
          self.assertListEqual(arg_names[:1]       ,     lowercase)



      def     lowerCamelCase_  (		self)      ->     Dict:





        '''simple docstring'''





        a__:	Union[str, Any]     				=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase)



      def     lowerCamelCase_  (		self)      ->     List[str]:





        '''simple docstring'''





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



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





        '''simple docstring'''





        a__:	Union[str, Any]     				=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*lowercase)



      def     lowerCamelCase_  (		self)      ->     List[str]:





        '''simple docstring'''





        for model_class in self.all_model_classes:
          if model_class.__name__ == "DPTForDepthEstimation":
            continue

          a__				,       a__:	int     				=      self.model_tester.prepare_config_and_inputs_for_common()
          a__:	Dict     				=      True

          if model_class in get_values(lowercase):
            continue

          a__:	str     				=      model_class(lowercase)
          model.to(lowercase)
          model.train()
          a__:	Optional[int]     				=      self._prepare_for_class(lowercase       ,     lowercase       ,     return_labels=lowercase)
          a__:	Any     				=      model(**lowercase).loss
          loss.backward()



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





        '''simple docstring'''





        for model_class in self.all_model_classes:
          if model_class.__name__ == "DPTForDepthEstimation":
            continue

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

          if model_class in get_values(lowercase) or not model_class.supports_gradient_checkpointing:
            continue
          a__:	Optional[int]     				=      model_class(lowercase)
          model.to(lowercase)
          model.gradient_checkpointing_enable()
          model.train()
          a__:	str     				=      self._prepare_for_class(lowercase       ,     lowercase       ,     return_labels=lowercase)
          a__:	List[Any]     				=      model(**lowercase).loss
          loss.backward()



      def     lowerCamelCase_  (		self)      ->     str:





        '''simple docstring'''





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

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





        '''simple docstring'''





        pass



      @slow
      def     lowerCamelCase_  (		self)      ->     Dict:





        '''simple docstring'''





        for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          a__:	int     				=      DPTModel.from_pretrained(lowercase)
          self.assertIsNotNone(lowercase)







def 		__a  (      )      ->Tuple:
  a__:	Optional[int]     				=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
  return image




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



      def     lowerCamelCase_  (		self)      ->     Tuple:





        '''simple docstring'''





        a__:	Dict     				=      DPTImageProcessor.from_pretrained('Intel/dpt-large')
        a__:	Dict     				=      DPTForDepthEstimation.from_pretrained('Intel/dpt-large').to(lowercase)

        a__:	int     				=      prepare_img()
        a__:	Any     				=      image_processor(images=lowercase       ,     return_tensors='pt').to(lowercase)

        # forward pass
        with torch.no_grad():
          a__:	List[str]     				=      model(**lowercase)
          a__:	Any     				=      outputs.predicted_depth

        # verify the predicted depth
        a__:	List[Any]     				=      torch.Size((1, 3_84, 3_84))
        self.assertEqual(predicted_depth.shape       ,     lowercase)

        a__:	str     				=      torch.tensor(
            [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]).to(lowercase)

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



      def     lowerCamelCase_  (		self)      ->     List[str]:





        '''simple docstring'''





        a__:	Union[str, Any]     				=      DPTImageProcessor.from_pretrained('Intel/dpt-large-ade')
        a__:	Dict     				=      DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade').to(lowercase)

        a__:	int     				=      prepare_img()
        a__:	int     				=      image_processor(images=lowercase       ,     return_tensors='pt').to(lowercase)

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

        # verify the logits
        a__:	Optional[Any]     				=      torch.Size((1, 1_50, 4_80, 4_80))
        self.assertEqual(outputs.logits.shape       ,     lowercase)

        a__:	List[str]     				=      torch.tensor(
            [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]).to(lowercase)

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



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





        '''simple docstring'''





        a__:	Optional[int]     				=      DPTImageProcessor.from_pretrained('Intel/dpt-large-ade')
        a__:	List[Any]     				=      DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade').to(lowercase)

        a__:	Optional[int]     				=      prepare_img()
        a__:	Union[str, Any]     				=      image_processor(images=lowercase       ,     return_tensors='pt').to(lowercase)

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

        a__:	Optional[Any]     				=      outputs.logits.detach().cpu()

        a__:	Tuple     				=      image_processor.post_process_semantic_segmentation(outputs=lowercase       ,     target_sizes=[(5_00, 3_00)])
        a__:	List[Any]     				=      torch.Size((5_00, 3_00))
        self.assertEqual(segmentation[0].shape       ,     lowercase)

        a__:	Optional[int]     				=      image_processor.post_process_semantic_segmentation(outputs=lowercase)
        a__:	List[str]     				=      torch.Size((4_80, 4_80))
        self.assertEqual(segmentation[0].shape       ,     lowercase)








'''simple docstring'''







from ..utils import DummyObject, requires_backends


class      _a    (      metaclass=__a					):
       __a							:	str  							=   ["""torch""", """librosa"""]
       def __init__(      self		:      int  ,							*lowercase		:      Any  ,							**lowercase		:      Optional[int]							):



           '''simple docstring'''

           requires_backends(self  ,							['''torch''', '''librosa''']							)
       @classmethod
       def        A (      cls		:      Optional[int]  ,							*lowercase		:      List[str]  ,							**lowercase		:      str							):



           '''simple docstring'''

           requires_backends(cls  ,							['''torch''', '''librosa''']							)







       @classmethod
       def        A (      cls		:      Dict  ,							*lowercase		:      Optional[int]  ,							**lowercase		:      List[Any]							):



           '''simple docstring'''

           requires_backends(cls  ,							['''torch''', '''librosa''']							)






class      _a    (      metaclass=__a					):
       __a							:	List[Any]  							=   ["""torch""", """librosa"""]
       def __init__(      self		:      int  ,							*lowercase		:      Optional[int]  ,							**lowercase		:      Optional[int]							):



           '''simple docstring'''

           requires_backends(self  ,							['''torch''', '''librosa''']							)
       @classmethod
       def        A (      cls		:      Dict  ,							*lowercase		:      Tuple  ,							**lowercase		:      List[str]							):



           '''simple docstring'''

           requires_backends(cls  ,							['''torch''', '''librosa''']							)







       @classmethod
       def        A (      cls		:      Any  ,							*lowercase		:      Any  ,							**lowercase		:      Tuple							):



           '''simple docstring'''

           requires_backends(cls  ,							['''torch''', '''librosa''']							)




