





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  lowerCAmelCase							:







  def __init__(      self  :Dict   ,							_lowercase  :Any   ,							_lowercase  :Optional[Any]=2   ,							_lowercase  :int=32   ,							_lowercase  :Any=16   ,							_lowercase  :Optional[int]=3   ,							_lowercase  :int=True   ,							_lowercase  :str=True   ,							_lowercase  :List[Any]=32   ,							_lowercase  :List[str]=4   ,							_lowercase  :Tuple=[0, 1, 2, 3]   ,							_lowercase  :str=4   ,							_lowercase  :str=37   ,							_lowercase  :int="gelu"   ,							_lowercase  :Tuple=0.1   ,							_lowercase  :Optional[Any]=0.1   ,							_lowercase  :Any=0.02   ,							_lowercase  :int=3   ,							_lowercase  :List[str]=[1, 3_84, 24, 24]   ,							_lowercase  :Optional[Any]=True   ,							_lowercase  :str=None   ,							):






     '''simple docstring'''





     lowercase__			       =  parent
     lowercase__			       =  batch_size
     lowercase__			       =  image_size
     lowercase__			       =  patch_size
     lowercase__			       =  num_channels
     lowercase__			       =  is_training
     lowercase__			       =  use_labels
     lowercase__			       =  hidden_size
     lowercase__			       =  num_hidden_layers
     lowercase__			       =  backbone_out_indices
     lowercase__			       =  num_attention_heads
     lowercase__			       =  intermediate_size
     lowercase__			       =  hidden_act
     lowercase__			       =  hidden_dropout_prob
     lowercase__			       =  attention_probs_dropout_prob
     lowercase__			       =  initializer_range
     lowercase__			       =  num_labels
     lowercase__			       =  backbone_featmap_shape
     lowercase__			       =  scope
     lowercase__			       =  is_hybrid
     # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
     lowercase__			       =  (image_size // patch_size) ** 2
     lowercase__			       =  num_patches + 1







  def   UpperCAmelCase					(      self  :Union[str, Any] ):






     '''simple docstring'''





     lowercase__			       =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

     lowercase__			       =  self.get_config()

     return config, pixel_values, labels







  def   UpperCAmelCase					(      self  :int ):






     '''simple docstring'''





     lowercase__			       =  {
         "global_padding": "same",
         "layer_type": "bottleneck",
         "depths": [3, 4, 9],
         "out_features": ["stage1", "stage2", "stage3"],
         "embedding_dynamic_padding": True,
         "hidden_sizes": [96, 1_92, 3_84, 7_68],
         "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=_lowercase   ,							initializer_range=self.initializer_range   ,							is_hybrid=self.is_hybrid   ,							backbone_config=_lowercase   ,							backbone_featmap_shape=self.backbone_featmap_shape   ,							)







  def   UpperCAmelCase					(      self  :Optional[int]   ,							_lowercase  :Union[str, Any]   ,							_lowercase  :str   ,							_lowercase  :List[str] ):






     '''simple docstring'''





     lowercase__			       =  DPTModel(config=_lowercase )
     model.to(_lowercase )
     model.eval()
     lowercase__			       =  model(_lowercase )
     self.parent.assertEqual(result.last_hidden_state.shape   ,							(self.batch_size, self.seq_length, self.hidden_size) )







  def   UpperCAmelCase					(      self  :List[str]   ,							_lowercase  :int   ,							_lowercase  :List[str]   ,							_lowercase  :List[str] ):






     '''simple docstring'''





     lowercase__			       =  self.num_labels
     lowercase__			       =  DPTForDepthEstimation(_lowercase )
     model.to(_lowercase )
     model.eval()
     lowercase__			       =  model(_lowercase )
     self.parent.assertEqual(result.predicted_depth.shape   ,							(self.batch_size, self.image_size, self.image_size) )







  def   UpperCAmelCase					(      self  :Optional[Any]   ,							_lowercase  :str   ,							_lowercase  :Optional[Any]   ,							_lowercase  :Dict ):






     '''simple docstring'''





     lowercase__			       =  self.num_labels
     lowercase__			       =  DPTForSemanticSegmentation(_lowercase )
     model.to(_lowercase )
     model.eval()
     lowercase__			       =  model(_lowercase   ,							labels=_lowercase )
     self.parent.assertEqual(
         result.logits.shape   ,							(self.batch_size, self.num_labels, self.image_size, self.image_size) )







  def   UpperCAmelCase					(      self  :int ):






     '''simple docstring'''





     lowercase__			       =  self.prepare_config_and_inputs()
     lowercase__       ,							lowercase__       ,							lowercase__			       =  config_and_inputs
     lowercase__			       =  {"pixel_values": pixel_values}
     return config, inputs_dict





@require_torch
class  lowerCAmelCase							(       lowercase_							,							lowercase_							,							unittest.TestCase ):
  __lowerCamelCase		=      (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
  __lowerCamelCase		=      (
      {
          'depth-estimation': DPTForDepthEstimation,
          'feature-extraction': DPTModel,
          'image-segmentation': DPTForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  __lowerCamelCase		=      False
  __lowerCamelCase		=      False
  __lowerCamelCase		=      False







  def   UpperCAmelCase					(      self  :int ):






     '''simple docstring'''





     lowercase__			       =  DPTModelTester(self )
     lowercase__			       =  ConfigTester(self   ,							config_class=_lowercase   ,							has_text_modality=_lowercase   ,							hidden_size=37 )







  def   UpperCAmelCase					(      self  :int ):






     '''simple docstring'''





     self.config_tester.run_common_tests()







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






     '''simple docstring'''





     pass







  def   UpperCAmelCase					(      self  :Dict ):






     '''simple docstring'''





     lowercase__       ,							lowercase__			       =  self.model_tester.prepare_config_and_inputs_for_common()

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







  def   UpperCAmelCase					(      self  :int ):






     '''simple docstring'''





     lowercase__       ,							lowercase__			       =  self.model_tester.prepare_config_and_inputs_for_common()

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

        lowercase__			       =  ["pixel_values"]
        self.assertListEqual(arg_names[:1]   ,							_lowercase )







  def   UpperCAmelCase					(      self  :Dict ):






     '''simple docstring'''





     lowercase__			       =  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowercase )







  def   UpperCAmelCase					(      self  :Dict ):






     '''simple docstring'''





     lowercase__			       =  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_depth_estimation(*_lowercase )







  def   UpperCAmelCase					(      self  :List[str] ):






     '''simple docstring'''





     lowercase__			       =  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_semantic_segmentation(*_lowercase )







  def   UpperCAmelCase					(      self  :Tuple ):






     '''simple docstring'''





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

        lowercase__       ,							lowercase__			       =  self.model_tester.prepare_config_and_inputs_for_common()
        lowercase__			       =  True

        if model_class in get_values(_lowercase ):
           continue

        lowercase__			       =  model_class(_lowercase )
        model.to(_lowercase )
        model.train()
        lowercase__			       =  self._prepare_for_class(_lowercase   ,							_lowercase   ,							return_labels=_lowercase )
        lowercase__			       =  model(**_lowercase ).loss
        loss.backward()







  def   UpperCAmelCase					(      self  :Tuple ):






     '''simple docstring'''





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

        lowercase__       ,							lowercase__			       =  self.model_tester.prepare_config_and_inputs_for_common()
        lowercase__			       =  False
        lowercase__			       =  True

        if model_class in get_values(_lowercase ) or not model_class.supports_gradient_checkpointing:
           continue
        lowercase__			       =  model_class(_lowercase )
        model.to(_lowercase )
        model.gradient_checkpointing_enable()
        model.train()
        lowercase__			       =  self._prepare_for_class(_lowercase   ,							_lowercase   ,							return_labels=_lowercase )
        lowercase__			       =  model(**_lowercase ).loss
        loss.backward()







  def   UpperCAmelCase					(      self  :Optional[Any] ):






     '''simple docstring'''





     lowercase__       ,							lowercase__			       =  self.model_tester.prepare_config_and_inputs_for_common()

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






     '''simple docstring'''





     pass







  @slow
  def   UpperCAmelCase					(      self  :Union[str, Any] ):






     '''simple docstring'''





     for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
        lowercase__			       =  DPTModel.from_pretrained(_lowercase )
        self.assertIsNotNone(_lowercase )







  def   UpperCAmelCase					(      self  :int ):






     '''simple docstring'''





     lowercase__       ,							lowercase__			       =  self.model_tester.prepare_config_and_inputs_for_common()
     lowercase__			       =  "add"
     with self.assertRaises(_lowercase ):
        lowercase__			       =  DPTForDepthEstimation(_lowercase )


def _A       (   ):
   lowercase__			       =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
   return image







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







  def   UpperCAmelCase					(      self  :Optional[int] ):






     '''simple docstring'''





     lowercase__			       =  DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas" )
     lowercase__			       =  DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas" ).to(_lowercase )

     lowercase__			       =  prepare_img()
     lowercase__			       =  image_processor(images=_lowercase   ,							return_tensors="pt" ).to(_lowercase )

     # forward pass
     with torch.no_grad():
        lowercase__			       =  model(**_lowercase )
        lowercase__			       =  outputs.predicted_depth

     # verify the predicted depth
     lowercase__			       =  torch.Size((1, 3_84, 3_84) )
     self.assertEqual(predicted_depth.shape   ,							_lowercase )

     lowercase__			       =  torch.tensor(
         [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]] ).to(_lowercase )

     self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_00   ,							_lowercase   ,							atol=1e-4 ) )













from __future__ import annotations


def _A       (   __magic_name__  , __magic_name__	):
   lowercase__			       =  []
   create_all_state(1  , __magic_name__  , __magic_name__  , []  , __magic_name__	)
   return result


def _A       (   __magic_name__  , __magic_name__  , __magic_name__  , __magic_name__  , __magic_name__  , ):
   if level == 0:
      total_list.append(current_list[:]	)
      return

   for i in range(__magic_name__  , total_number - level + 2	):
      current_list.append(__magic_name__	)
      create_all_state(i + 1  , __magic_name__  , level - 1  , __magic_name__  , __magic_name__	)
      current_list.pop()


def _A       (   __magic_name__	):
   for i in total_list:
      print(*__magic_name__	)


if __name__ == "__main__":
  _snake_case           =			4
  _snake_case           =			2
  _snake_case           =			generate_all_combinations(n, k)
  print_all_state(total_list)






