





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			:		int ,  lowerCAmelCase_			:		List[str] ,  lowerCAmelCase_			:		str=2 ,  lowerCAmelCase_			:		str=3_2 ,  lowerCAmelCase_			:		List[str]=1_6 ,  lowerCAmelCase_			:		Any=3 ,  lowerCAmelCase_			:		Optional[Any]=True ,  lowerCAmelCase_			:		int=True ,  lowerCAmelCase_			:		Any=3_2 ,  lowerCAmelCase_			:		str=4 ,  lowerCAmelCase_			:		int=[0, 1, 2, 3] ,  lowerCAmelCase_			:		List[str]=4 ,  lowerCAmelCase_			:		Optional[Any]=3_7 ,  lowerCAmelCase_			:		List[Any]="gelu" ,  lowerCAmelCase_			:		int=0.1 ,  lowerCAmelCase_			:		Union[str, Any]=0.1 ,  lowerCAmelCase_			:		Optional[int]=0.02 ,  lowerCAmelCase_			:		Dict=3 ,  lowerCAmelCase_			:		Optional[int]=[1, 3_8_4, 2_4, 2_4] ,  lowerCAmelCase_			:		Optional[Any]=True ,  lowerCAmelCase_			:		List[Any]=None ,  )							->						str:
     __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							     =     backbone_featmap_shape
     __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     lowercase		(    self			:		str					)							->						List[Any]:
     __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     lowercase		(    self			:		Dict					)							->						int:
     __lowerCAmelCase							     =     {
         '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=lowerCAmelCase_ ,  initializer_range=self.initializer_range ,  is_hybrid=self.is_hybrid ,  backbone_config=lowerCAmelCase_ ,  backbone_featmap_shape=self.backbone_featmap_shape ,  )






  def     lowercase		(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		Dict ,  lowerCAmelCase_			:		Optional[int] ,  lowerCAmelCase_			:		Tuple					)							->						Any:
     __lowerCAmelCase							     =     DPTModel(config=lowerCAmelCase_					)
     model.to(lowerCAmelCase_					)
     model.eval()
     __lowerCAmelCase							     =     model(lowerCAmelCase_					)
     self.parent.assertEqual(result.last_hidden_state.shape ,  (self.batch_size, self.seq_length, self.hidden_size)					)






  def     lowercase		(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		Tuple ,  lowerCAmelCase_			:		Any ,  lowerCAmelCase_			:		int					)							->						Dict:
     __lowerCAmelCase							     =     self.num_labels
     __lowerCAmelCase							     =     DPTForDepthEstimation(lowerCAmelCase_					)
     model.to(lowerCAmelCase_					)
     model.eval()
     __lowerCAmelCase							     =     model(lowerCAmelCase_					)
     self.parent.assertEqual(result.predicted_depth.shape ,  (self.batch_size, self.image_size, self.image_size)					)






  def     lowercase		(    self			:		str ,  lowerCAmelCase_			:		str ,  lowerCAmelCase_			:		Optional[Any] ,  lowerCAmelCase_			:		Optional[Any]					)							->						str:
     __lowerCAmelCase							     =     self.num_labels
     __lowerCAmelCase							     =     DPTForSemanticSegmentation(lowerCAmelCase_					)
     model.to(lowerCAmelCase_					)
     model.eval()
     __lowerCAmelCase							     =     model(lowerCAmelCase_ ,  labels=lowerCAmelCase_					)
     self.parent.assertEqual(
         result.logits.shape ,  (self.batch_size, self.num_labels, self.image_size, self.image_size)					)






  def     lowercase		(    self			:		Optional[int]					)							->						Optional[Any]:
     __lowerCAmelCase							     =     self.prepare_config_and_inputs()
     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase							     =     config_and_inputs
     __lowerCAmelCase							     =     {'pixel_values': pixel_values}
     return config, inputs_dict





@require_torch
class     _UpperCAmelCase   (   _UpperCamelCase		,   _UpperCamelCase		,   unittest.TestCase   ):

  """simple docstring"""


  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     lowercase		(    self			:		str					)							->						Optional[Any]:
     __lowerCAmelCase							     =     DPTModelTester(self					)
     __lowerCAmelCase							     =     ConfigTester(self ,  config_class=lowerCAmelCase_ ,  has_text_modality=lowerCAmelCase_ ,  hidden_size=3_7					)






  def     lowercase		(    self			:		List[Any]					)							->						List[Any]:
     self.config_tester.run_common_tests()






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






  def     lowercase		(    self			:		int					)							->						str:
     __lowerCAmelCase     ,     __lowerCAmelCase							     =     self.model_tester.prepare_config_and_inputs_for_common()

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






  def     lowercase		(    self			:		List[str]					)							->						List[Any]:
     __lowerCAmelCase     ,     __lowerCAmelCase							     =     self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        __lowerCAmelCase							     =     model_class(lowerCAmelCase_					)
        __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] ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		List[str]					)							->						int:
     __lowerCAmelCase							     =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowerCAmelCase_					)






  def     lowercase		(    self			:		List[Any]					)							->						Any:
     __lowerCAmelCase							     =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_depth_estimation(*lowerCAmelCase_					)






  def     lowercase		(    self			:		Optional[Any]					)							->						Dict:
     __lowerCAmelCase							     =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase_					)






  def     lowercase		(    self			:		int					)							->						List[str]:
     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(lowerCAmelCase_					):
           continue

        __lowerCAmelCase							     =     model_class(lowerCAmelCase_					)
        model.to(lowerCAmelCase_					)
        model.train()
        __lowerCAmelCase							     =     self._prepare_for_class(lowerCAmelCase_ ,  lowerCAmelCase_ ,  return_labels=lowerCAmelCase_					)
        __lowerCAmelCase							     =     model(**lowerCAmelCase_					).loss
        loss.backward()






  def     lowercase		(    self			:		Tuple					)							->						List[str]:
     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(lowerCAmelCase_					) or not model_class.supports_gradient_checkpointing:
           continue
        __lowerCAmelCase							     =     model_class(lowerCAmelCase_					)
        model.to(lowerCAmelCase_					)
        model.gradient_checkpointing_enable()
        model.train()
        __lowerCAmelCase							     =     self._prepare_for_class(lowerCAmelCase_ ,  lowerCAmelCase_ ,  return_labels=lowerCAmelCase_					)
        __lowerCAmelCase							     =     model(**lowerCAmelCase_					).loss
        loss.backward()






  def     lowercase		(    self			:		List[str]					)							->						int:
     __lowerCAmelCase     ,     __lowerCAmelCase							     =     self.model_tester.prepare_config_and_inputs_for_common()

     __lowerCAmelCase							     =     _config_zero_init(lowerCAmelCase_					)
     for model_class in self.all_model_classes:
        __lowerCAmelCase							     =     model_class(config=lowerCAmelCase_					)
        # 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     lowercase		(    self			:		int					)							->						Tuple:
     pass






  @slow
  def     lowercase		(    self			:		Tuple					)							->						Optional[Any]:
     for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
        __lowerCAmelCase							     =     DPTModel.from_pretrained(lowerCAmelCase_					)
        self.assertIsNotNone(lowerCAmelCase_					)






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





def   a_   (			):
   __lowerCAmelCase							     =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
   return image




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

  """simple docstring"""


  def     lowercase		(    self			:		Any					)							->						Tuple:
     __lowerCAmelCase							     =     DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'					)
     __lowerCAmelCase							     =     DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'					).to(lowerCAmelCase_					)

     __lowerCAmelCase							     =     prepare_img()
     __lowerCAmelCase							     =     image_processor(images=lowerCAmelCase_ ,  return_tensors='pt'					).to(lowerCAmelCase_					)

     # forward pass
     with torch.no_grad():
        __lowerCAmelCase							     =     model(**lowerCAmelCase_					)
        __lowerCAmelCase							     =     outputs.predicted_depth

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

     __lowerCAmelCase							     =     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(lowerCAmelCase_					)

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








from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
_snake_case  :   Dict						   =  0

_snake_case  :   Dict						   =  [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

_snake_case  :   List[str]						   =  [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

_snake_case  :   Tuple						   =  tuple[int, int]



class     _UpperCAmelCase   :

  """simple docstring"""


  def __init__(    self			:		Dict ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		Node | None ,  )							->						None:
     __lowerCAmelCase							     =     pos_x
     __lowerCAmelCase							     =     pos_y
     __lowerCAmelCase							     =     (pos_y, pos_x)
     __lowerCAmelCase							     =     goal_x
     __lowerCAmelCase							     =     goal_y
     __lowerCAmelCase							     =     g_cost
     __lowerCAmelCase							     =     parent
     __lowerCAmelCase							     =     self.calculate_heuristic()
     __lowerCAmelCase							     =     self.g_cost + self.h_cost






  def     lowercase		(    self			:		Any					)							->						float:
     __lowerCAmelCase							     =     self.pos_x - self.goal_x
     __lowerCAmelCase							     =     self.pos_y - self.goal_y
     if HEURISTIC == 1:
        return abs(lowerCAmelCase_					) + abs(lowerCAmelCase_					)
     else:
        return sqrt(dy**2 + dx**2					)






  def __lt__(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		Node					)							->						bool:
     return self.f_cost < other.f_cost





class     _UpperCAmelCase   :

  """simple docstring"""


  def __init__(    self			:		List[str] ,  lowerCAmelCase_			:		TPosition ,  lowerCAmelCase_			:		TPosition					)							->						Tuple:
     __lowerCAmelCase							     =     Node(start[1] ,  start[0] ,  goal[1] ,  goal[0] ,  0 ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     Node(goal[1] ,  goal[0] ,  goal[1] ,  goal[0] ,  9_9_9_9_9 ,  lowerCAmelCase_					)

     __lowerCAmelCase							     =     [self.start]
     __lowerCAmelCase							     =     []

     __lowerCAmelCase							     =     False






  def     lowercase		(    self			:		str					)							->						list[TPosition]:
     while self.open_nodes:
        # Open Nodes are sorted using __lt__
        self.open_nodes.sort()
        __lowerCAmelCase							     =     self.open_nodes.pop(0					)

        if current_node.pos == self.target.pos:
           return self.retrace_path(lowerCAmelCase_					)

        self.closed_nodes.append(lowerCAmelCase_					)
        __lowerCAmelCase							     =     self.get_successors(lowerCAmelCase_					)

        for child_node in successors:
           if child_node in self.closed_nodes:
              continue

           if child_node not in self.open_nodes:
              self.open_nodes.append(lowerCAmelCase_					)
           else:
              # retrieve the best current path
              __lowerCAmelCase							     =     self.open_nodes.pop(self.open_nodes.index(lowerCAmelCase_					)					)

              if child_node.g_cost < better_node.g_cost:
                 self.open_nodes.append(lowerCAmelCase_					)
              else:
                 self.open_nodes.append(lowerCAmelCase_					)

     return [self.start.pos]






  def     lowercase		(    self			:		Optional[Any] ,  lowerCAmelCase_			:		Node					)							->						list[Node]:
     __lowerCAmelCase							     =     []
     for action in delta:
        __lowerCAmelCase							     =     parent.pos_x + action[1]
        __lowerCAmelCase							     =     parent.pos_y + action[0]
        if not (0 <= pos_x <= len(grid[0]					) - 1 and 0 <= pos_y <= len(lowerCAmelCase_					) - 1):
           continue

        if grid[pos_y][pos_x] != 0:
           continue

        successors.append(
            Node(
                lowerCAmelCase_ ,  lowerCAmelCase_ ,  self.target.pos_y ,  self.target.pos_x ,  parent.g_cost + 1 ,  lowerCAmelCase_ ,  )					)
     return successors






  def     lowercase		(    self			:		Tuple ,  lowerCAmelCase_			:		Node | None					)							->						list[TPosition]:
     __lowerCAmelCase							     =     node
     __lowerCAmelCase							     =     []
     while current_node is not None:
        path.append((current_node.pos_y, current_node.pos_x)					)
        __lowerCAmelCase							     =     current_node.parent
     path.reverse()
     return path





class     _UpperCAmelCase   :

  """simple docstring"""


  def __init__(    self			:		int ,  lowerCAmelCase_			:		TPosition ,  lowerCAmelCase_			:		TPosition					)							->						None:
     __lowerCAmelCase							     =     AStar(lowerCAmelCase_ ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     AStar(lowerCAmelCase_ ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     False






  def     lowercase		(    self			:		Dict					)							->						list[TPosition]:
     while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
        self.fwd_astar.open_nodes.sort()
        self.bwd_astar.open_nodes.sort()
        __lowerCAmelCase							     =     self.fwd_astar.open_nodes.pop(0					)
        __lowerCAmelCase							     =     self.bwd_astar.open_nodes.pop(0					)

        if current_bwd_node.pos == current_fwd_node.pos:
           return self.retrace_bidirectional_path(
               lowerCAmelCase_ ,  lowerCAmelCase_					)

        self.fwd_astar.closed_nodes.append(lowerCAmelCase_					)
        self.bwd_astar.closed_nodes.append(lowerCAmelCase_					)

        __lowerCAmelCase							     =     current_bwd_node
        __lowerCAmelCase							     =     current_fwd_node

        __lowerCAmelCase							     =     {
            self.fwd_astar: self.fwd_astar.get_successors(lowerCAmelCase_					),
            self.bwd_astar: self.bwd_astar.get_successors(lowerCAmelCase_					),
        }

        for astar in [self.fwd_astar, self.bwd_astar]:
           for child_node in successors[astar]:
              if child_node in astar.closed_nodes:
                 continue

              if child_node not in astar.open_nodes:
                 astar.open_nodes.append(lowerCAmelCase_					)
              else:
                 # retrieve the best current path
                 __lowerCAmelCase							     =     astar.open_nodes.pop(
                     astar.open_nodes.index(lowerCAmelCase_					)					)

                 if child_node.g_cost < better_node.g_cost:
                    astar.open_nodes.append(lowerCAmelCase_					)
                 else:
                    astar.open_nodes.append(lowerCAmelCase_					)

     return [self.fwd_astar.start.pos]






  def     lowercase		(    self			:		Dict ,  lowerCAmelCase_			:		Node ,  lowerCAmelCase_			:		Node					)							->						list[TPosition]:
     __lowerCAmelCase							     =     self.fwd_astar.retrace_path(lowerCAmelCase_					)
     __lowerCAmelCase							     =     self.bwd_astar.retrace_path(lowerCAmelCase_					)
     bwd_path.pop()
     bwd_path.reverse()
     __lowerCAmelCase							     =     fwd_path + bwd_path
     return path


if __name__ == "__main__":
     # all coordinates are given in format [y,x]
     _snake_case  :   List[Any]						   =  (0, 0)
     _snake_case  :   Union[str, Any]						   =  (len(grid) - 1, len(grid[0]) - 1)
     for elem in grid:
          print(elem)

     _snake_case  :   int						   =  time.time()
     _snake_case  :   Optional[int]						   =  AStar(init, goal)
     _snake_case  :   int						   =  a_star.search()
     _snake_case  :   Union[str, Any]						   =  time.time() - start_time
     print(F"""AStar execution time = {end_time:f} seconds""")

     _snake_case  :   Any						   =  time.time()
     _snake_case  :   Dict						   =  BidirectionalAStar(init, goal)
     _snake_case  :   Optional[int]						   =  time.time() - bd_start_time
     print(F"""BidirectionalAStar execution time = {bd_end_time:f} seconds""")

