


'''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   :





  """simple docstring"""




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







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






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

   lowerCamelCase_								=  self.get_config()

   return config, pixel_values, labels






  def   snake_case		(   self					):







   """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=UpperCamelCase      ,  initializer_range=self.initializer_range      ,  is_hybrid=self.is_hybrid      ,  )






  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   lowerCamelCase_								=  DPTModel(config=UpperCamelCase					)
   model.to(UpperCamelCase					)
   model.eval()
   lowerCamelCase_								=  model(UpperCamelCase					)
   self.parent.assertEqual(result.last_hidden_state.shape      ,  (self.batch_size, self.seq_length, self.hidden_size)					)






  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   lowerCamelCase_								=  self.num_labels
   lowerCamelCase_								=  DPTForDepthEstimation(UpperCamelCase					)
   model.to(UpperCamelCase					)
   model.eval()
   lowerCamelCase_								=  model(UpperCamelCase					)
   self.parent.assertEqual(result.predicted_depth.shape      ,  (self.batch_size, self.image_size, self.image_size)					)






  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   lowerCamelCase_								=  self.num_labels
   lowerCamelCase_								=  DPTForSemanticSegmentation(UpperCamelCase					)
   model.to(UpperCamelCase					)
   model.eval()
   lowerCamelCase_								=  model(UpperCamelCase      ,  labels=UpperCamelCase					)
   self.parent.assertEqual(
       result.logits.shape      ,  (self.batch_size, self.num_labels, self.image_size, self.image_size)					)







  def   snake_case		(   self					):







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





@require_torch
class  snake_case   (   lowercase      ,						lowercase      ,						unittest.TestCase      ):





  """simple docstring"""




  _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   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  DPTModelTester(self					)
   lowerCamelCase_								=  ConfigTester(self      ,  config_class=UpperCamelCase      ,  has_text_modality=UpperCamelCase      ,  hidden_size=37					)






  def   snake_case		(   self					):







   """simple docstring"""
   self.config_tester.run_common_tests()






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







   """simple docstring"""
   pass






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_			,lowerCamelCase_								=  self.model_tester.prepare_config_and_inputs_for_common()

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






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_			,lowerCamelCase_								=  self.model_tester.prepare_config_and_inputs_for_common()

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

    lowerCamelCase_								=  ["pixel_values"]
    self.assertListEqual(arg_names[:1]      ,  UpperCamelCase					)






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*UpperCamelCase					)






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_depth_estimation(*UpperCamelCase					)






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase					)






  def   snake_case		(   self					):







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

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

    if model_class in get_values(UpperCamelCase					):
     continue

    lowerCamelCase_								=  model_class(UpperCamelCase					)
    model.to(UpperCamelCase					)
    model.train()
    lowerCamelCase_								=  self._prepare_for_class(UpperCamelCase      ,  UpperCamelCase      ,  return_labels=UpperCamelCase					)
    lowerCamelCase_								=  model(**UpperCamelCase					).loss
    loss.backward()






  def   snake_case		(   self					):







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

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

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






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_			,lowerCamelCase_								=  self.model_tester.prepare_config_and_inputs_for_common()

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







   """simple docstring"""
   pass







  @slow
  def   snake_case		(   self					):







   """simple docstring"""
   for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    lowerCamelCase_								=  DPTModel.from_pretrained(UpperCamelCase					)
    self.assertIsNotNone(UpperCamelCase					)





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







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





  """simple docstring"""




  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  DPTImageProcessor.from_pretrained("Intel/dpt-large"					)
   lowerCamelCase_								=  DPTForDepthEstimation.from_pretrained("Intel/dpt-large"					).to(UpperCamelCase					)

   lowerCamelCase_								=  prepare_img()
   lowerCamelCase_								=  image_processor(images=UpperCamelCase      ,  return_tensors="pt"					).to(UpperCamelCase					)

   # forward pass
   with torch.no_grad():
    lowerCamelCase_								=  model(**UpperCamelCase					)
    lowerCamelCase_								=  outputs.predicted_depth

   # verify the predicted depth
   lowerCamelCase_								=  torch.Size((1, 384, 384)					)
   self.assertEqual(predicted_depth.shape      ,  UpperCamelCase					)

   lowerCamelCase_								=  torch.tensor(
       [[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]]					).to(UpperCamelCase					)

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






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"					)
   lowerCamelCase_								=  DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"					).to(UpperCamelCase					)

   lowerCamelCase_								=  prepare_img()
   lowerCamelCase_								=  image_processor(images=UpperCamelCase      ,  return_tensors="pt"					).to(UpperCamelCase					)

   # forward pass
   with torch.no_grad():
    lowerCamelCase_								=  model(**UpperCamelCase					)

   # verify the logits
   lowerCamelCase_								=  torch.Size((1, 150, 480, 480)					)
   self.assertEqual(outputs.logits.shape      ,  UpperCamelCase					)

   lowerCamelCase_								=  torch.tensor(
       [[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]]					).to(UpperCamelCase					)

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







  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"					)
   lowerCamelCase_								=  DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"					).to(UpperCamelCase					)

   lowerCamelCase_								=  prepare_img()
   lowerCamelCase_								=  image_processor(images=UpperCamelCase      ,  return_tensors="pt"					).to(UpperCamelCase					)

   # forward pass
   with torch.no_grad():
    lowerCamelCase_								=  model(**UpperCamelCase					)

   lowerCamelCase_								=  outputs.logits.detach().cpu()

   lowerCamelCase_								=  image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase      ,  target_sizes=[(500, 300)]					)
   lowerCamelCase_								=  torch.Size((500, 300)					)
   self.assertEqual(segmentation[0].shape      ,  UpperCamelCase					)

   lowerCamelCase_								=  image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase					)
   lowerCamelCase_								=  torch.Size((480, 480)					)
   self.assertEqual(segmentation[0].shape      ,  UpperCamelCase					)







'''simple docstring'''

class 					snake_case   :





  """simple docstring"""




  def __init__(   self      ,  UpperCamelCase					):







   """simple docstring"""
   lowerCamelCase_								=  data
   lowerCamelCase_								=  None
   lowerCamelCase_								=  None





def __snake_case (				UpperCAmelCase_						:						BinaryTreeNode | None ,    UpperCAmelCase_						:						int		):
 if node is None:
  lowerCamelCase_								=  BinaryTreeNode(UpperCAmelCase_		)
  return node

 # binary search tree is not empty,
 # so we will insert it into the tree
 # if new_value is less than value of data in node,
 #  add it to left subtree and proceed recursively
 if new_value < node.data:
  lowerCamelCase_								=  insert(node.left_child ,    UpperCAmelCase_		)
 else:
  # if new_value is greater than value of data in node,
  #  add it to right subtree and proceed recursively
  lowerCamelCase_								=  insert(node.right_child ,    UpperCAmelCase_		)
 return node





def __snake_case (				UpperCAmelCase_						:						None | BinaryTreeNode		):  # if node is None,return
 if node:
  lowerCamelCase_								=  inorder(node.left_child		)
  lowerCamelCase_								=  [*inorder_array, node.data]
  lowerCamelCase_								=  inorder_array + inorder(node.right_child		)
 else:
  lowerCamelCase_								=  []
 return inorder_array





def __snake_case (				):
 lowerCamelCase_								=  insert(UpperCAmelCase_ ,    15		)
 insert(UpperCAmelCase_ ,    10		)
 insert(UpperCAmelCase_ ,    25		)
 insert(UpperCAmelCase_ ,    6		)
 insert(UpperCAmelCase_ ,    14		)
 insert(UpperCAmelCase_ ,    20		)
 insert(UpperCAmelCase_ ,    60		)
 return root





def __snake_case (				):
 # main function
 lowerCamelCase_								=  make_tree()
 print("Printing values of binary search tree in Inorder Traversal."		)
 inorder(UpperCAmelCase_		)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()



