

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  _A		:

  def __init__(				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=2 ,					_SCREAMING_SNAKE_CASE=32 ,					_SCREAMING_SNAKE_CASE=16 ,					_SCREAMING_SNAKE_CASE=3 ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE=32 ,					_SCREAMING_SNAKE_CASE=4 ,					_SCREAMING_SNAKE_CASE=[0, 1, 2, 3] ,					_SCREAMING_SNAKE_CASE=4 ,					_SCREAMING_SNAKE_CASE=37 ,					_SCREAMING_SNAKE_CASE="gelu" ,					_SCREAMING_SNAKE_CASE=0.1 ,					_SCREAMING_SNAKE_CASE=0.1 ,					_SCREAMING_SNAKE_CASE=0.02 ,					_SCREAMING_SNAKE_CASE=3 ,					_SCREAMING_SNAKE_CASE=[1, 384, 24, 24] ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE=None ,					):





        """simple docstring"""







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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  int           =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

        SCREAMING_SNAKE_CASE_				:  Dict           =     self.get_config()

        return config, pixel_values, labels

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[str]           =     {
            'global_padding': 'same',
            'layer_type': 'bottleneck',
            'depths': [3, 4, 9],
            'out_features': ['stage1', 'stage2', 'stage3'],
            'embedding_dynamic_padding': True,
            'hidden_sizes': [96, 192, 384, 768],
            '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  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     DPTModel(config=_SCREAMING_SNAKE_CASE					)
        model.to(_SCREAMING_SNAKE_CASE					)
        model.eval()
        SCREAMING_SNAKE_CASE_				:  str           =     model(_SCREAMING_SNAKE_CASE					)
        self.parent.assertEqual(result.last_hidden_state.shape ,					(self.batch_size, self.seq_length, self.hidden_size)					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.num_labels
        SCREAMING_SNAKE_CASE_				:  Tuple           =     DPTForDepthEstimation(_SCREAMING_SNAKE_CASE					)
        model.to(_SCREAMING_SNAKE_CASE					)
        model.eval()
        SCREAMING_SNAKE_CASE_				:  Tuple           =     model(_SCREAMING_SNAKE_CASE					)
        self.parent.assertEqual(result.predicted_depth.shape ,					(self.batch_size, self.image_size, self.image_size)					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.num_labels
        SCREAMING_SNAKE_CASE_				:  List[str]           =     DPTForSemanticSegmentation(_SCREAMING_SNAKE_CASE					)
        model.to(_SCREAMING_SNAKE_CASE					)
        model.eval()
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     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  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Any           =     self.prepare_config_and_inputs()
        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     config_and_inputs
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     {'pixel_values': pixel_values}
        return config, inputs_dict

@require_torch
class  _A		(  __magic_name__   ,     __magic_name__   ,     unittest.TestCase):
  SCREAMING_SNAKE_CASE    :      Optional[int]       =  (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE    :      Union[str, Any]       =  (
      {
          '''depth-estimation''': DPTForDepthEstimation,
          '''feature-extraction''': DPTModel,
          '''image-segmentation''': DPTForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  SCREAMING_SNAKE_CASE    :      Optional[Any]       =  False
  SCREAMING_SNAKE_CASE    :      Dict       =  False
  SCREAMING_SNAKE_CASE    :      List[Any]       =  False

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     DPTModelTester(self					)
        SCREAMING_SNAKE_CASE_				:  Tuple           =     ConfigTester(self ,					config_class=_SCREAMING_SNAKE_CASE ,					has_text_modality=_SCREAMING_SNAKE_CASE ,					hidden_size=37					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        self.config_tester.run_common_tests()

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





        """simple docstring"""







        pass

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.model_tester.prepare_config_and_inputs_for_common()

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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.model_tester.prepare_config_and_inputs_for_common()

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

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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  str           =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_depth_estimation(*_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







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

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

              if model_class in get_values(_SCREAMING_SNAKE_CASE					):
                    continue

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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







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

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

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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







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

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





        """simple docstring"""







        pass

  @slow
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
              SCREAMING_SNAKE_CASE_				:  List[str]           =     DPTModel.from_pretrained(_SCREAMING_SNAKE_CASE					)
              self.assertIsNotNone(_SCREAMING_SNAKE_CASE					)



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.model_tester.prepare_config_and_inputs_for_common()
        SCREAMING_SNAKE_CASE_				:  Tuple           =     'add'
        with self.assertRaises(_SCREAMING_SNAKE_CASE					):
              SCREAMING_SNAKE_CASE_				:  int           =     DPTForDepthEstimation(_SCREAMING_SNAKE_CASE					)

def    A_  (							):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  List[str]           =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
      return image



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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







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

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     prepare_img()
        SCREAMING_SNAKE_CASE_				:  Tuple           =     image_processor(images=_SCREAMING_SNAKE_CASE ,					return_tensors='pt'					).to(_SCREAMING_SNAKE_CASE					)

        # forward pass
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_				:  str           =     model(**_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  Any           =     outputs.predicted_depth

        # verify the predicted depth
        SCREAMING_SNAKE_CASE_				:  Tuple           =     torch.Size((1, 384, 384)					)
        self.assertEqual(predicted_depth.shape ,					_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     torch.tensor(
            [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]					).to(_SCREAMING_SNAKE_CASE					)

        self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100 ,					_SCREAMING_SNAKE_CASE ,					atol=1e-4					)					)









def    A_  (							a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  List[Any]           =     0
      while num > 0:
            digit_sum += num % 1_0
            num //= 1_0
      return digit_sum

def    A_  (							a = 1_0_0  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  List[str]           =     1
      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     2

      for i in range(2					,   max_n + 1  ):
            SCREAMING_SNAKE_CASE_				:  List[str]           =     pre_numerator
            SCREAMING_SNAKE_CASE_				:  str           =     2 * i // 3 if i % 3 == 0 else 1
            SCREAMING_SNAKE_CASE_				:  Tuple           =     cur_numerator
            SCREAMING_SNAKE_CASE_				:  Tuple           =     e_cont * pre_numerator + temp

      return sum_digits(a  )


if __name__ == "__main__":
    print(F'{solution() = }')






