



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 :     Optional[int]				,       SCREAMING_SNAKE_CASE :     List[Any]				,       SCREAMING_SNAKE_CASE :     Optional[Any]=2				,       SCREAMING_SNAKE_CASE :     Tuple=32				,       SCREAMING_SNAKE_CASE :     Tuple=16				,       SCREAMING_SNAKE_CASE :     Optional[Any]=3				,       SCREAMING_SNAKE_CASE :     Optional[int]=True				,       SCREAMING_SNAKE_CASE :     List[str]=True				,       SCREAMING_SNAKE_CASE :     Dict=32				,       SCREAMING_SNAKE_CASE :     List[str]=4				,       SCREAMING_SNAKE_CASE :     List[str]=[0, 1, 2, 3]				,       SCREAMING_SNAKE_CASE :     Optional[Any]=4				,       SCREAMING_SNAKE_CASE :     Dict=37				,       SCREAMING_SNAKE_CASE :     Union[str, Any]="gelu"				,       SCREAMING_SNAKE_CASE :     str=0.1				,       SCREAMING_SNAKE_CASE :     Optional[int]=0.1				,       SCREAMING_SNAKE_CASE :     int=0.02				,       SCREAMING_SNAKE_CASE :     Dict=3				,       SCREAMING_SNAKE_CASE :     List[str]=[1, 384, 24, 24]				,       SCREAMING_SNAKE_CASE :     List[str]=True				,       SCREAMING_SNAKE_CASE :     str=None				,       ):
									lowercase__					:   Dict													=					parent
									lowercase__					:   List[str]													=					batch_size
									lowercase__					:   Optional[Any]													=					image_size
									lowercase__					:   Tuple													=					patch_size
									lowercase__					:   Optional[int]													=					num_channels
									lowercase__					:   Optional[int]													=					is_training
									lowercase__					:   Dict													=					use_labels
									lowercase__					:   int													=					hidden_size
									lowercase__					:   Tuple													=					num_hidden_layers
									lowercase__					:   Dict													=					backbone_out_indices
									lowercase__					:   Tuple													=					num_attention_heads
									lowercase__					:   List[Any]													=					intermediate_size
									lowercase__					:   Optional[Any]													=					hidden_act
									lowercase__					:   Union[str, Any]													=					hidden_dropout_prob
									lowercase__					:   Optional[Any]													=					attention_probs_dropout_prob
									lowercase__					:   List[Any]													=					initializer_range
									lowercase__					:   str													=					num_labels
									lowercase__					:   Optional[int]													=					backbone_featmap_shape
									lowercase__					:   Tuple													=					scope
									lowercase__					:   int													=					is_hybrid
									# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
									lowercase__					:   Optional[int]													=					(image_size // patch_size) ** 2
									lowercase__					:   str													=					num_patches + 1







					def 			snake_case					(							self :     int					):
									lowercase__					:   Dict													=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

									lowercase__					:   Any													=					self.get_config()

									return config, pixel_values, labels







					def 			snake_case					(							self :     List[str]					):
									lowercase__					:   Any													=					{
									    """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 			snake_case					(							self :     Optional[int]				,       SCREAMING_SNAKE_CASE :     Tuple				,       SCREAMING_SNAKE_CASE :     Tuple				,       SCREAMING_SNAKE_CASE :     int					):
									lowercase__					:   Optional[int]													=					DPTModel(config=SCREAMING_SNAKE_CASE					)
									model.to(SCREAMING_SNAKE_CASE					)
									model.eval()
									lowercase__					:   List[str]													=					model(SCREAMING_SNAKE_CASE					)
									self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.hidden_size)					)







					def 			snake_case					(							self :     Dict				,       SCREAMING_SNAKE_CASE :     List[str]				,       SCREAMING_SNAKE_CASE :     List[str]				,       SCREAMING_SNAKE_CASE :     Optional[int]					):
									lowercase__					:   str													=					self.num_labels
									lowercase__					:   str													=					DPTForDepthEstimation(SCREAMING_SNAKE_CASE					)
									model.to(SCREAMING_SNAKE_CASE					)
									model.eval()
									lowercase__					:   int													=					model(SCREAMING_SNAKE_CASE					)
									self.parent.assertEqual(result.predicted_depth.shape				,       (self.batch_size, self.image_size, self.image_size)					)







					def 			snake_case					(							self :     Optional[int]				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     Any				,       SCREAMING_SNAKE_CASE :     Optional[Any]					):
									lowercase__					:   Tuple													=					self.num_labels
									lowercase__					:   Optional[Any]													=					DPTForSemanticSegmentation(SCREAMING_SNAKE_CASE					)
									model.to(SCREAMING_SNAKE_CASE					)
									model.eval()
									lowercase__					:   int													=					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 			snake_case					(							self :     Tuple					):
									lowercase__					:   Any													=					self.prepare_config_and_inputs()
									lowercase__					:   Optional[int]													=					config_and_inputs
									lowercase__					:   Tuple													=					{"""pixel_values""": pixel_values}
									return config, inputs_dict






@require_torch
class 				snake_case__(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE    ,      unittest.TestCase      ):

					"""simple docstring"""






					lowercase_								=		(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
					lowercase_								=		(
					    {
					        """depth-estimation""": DPTForDepthEstimation,
					        """feature-extraction""": DPTModel,
					        """image-segmentation""": DPTForSemanticSegmentation,
					    }
					    if is_torch_available()
					    else {}
					)

					lowercase_								=		False
					lowercase_								=		False
					lowercase_								=		False







					def 			snake_case					(							self :     List[Any]					):
									lowercase__					:   Union[str, Any]													=					DPTModelTester(self					)
									lowercase__					:   Tuple													=					ConfigTester(self				,       config_class=SCREAMING_SNAKE_CASE				,       has_text_modality=SCREAMING_SNAKE_CASE				,       hidden_size=37					)







					def 			snake_case					(							self :     Optional[int]					):
									self.config_tester.run_common_tests()







					@unittest.skip(reason="DPT does not use inputs_embeds"					)
					def 			snake_case					(							self :     Optional[int]					):
									pass







					def 			snake_case					(							self :     int					):
									lowercase__					:   Tuple													=					self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													lowercase__					:   List[str]													=					model_class(SCREAMING_SNAKE_CASE					)
													self.assertIsInstance(model.get_input_embeddings()				,       (nn.Module)					)
													lowercase__					:   Optional[int]													=					model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE				,       nn.Linear					)					)







					def 			snake_case					(							self :     Optional[int]					):
									lowercase__					:   Optional[int]													=					self.model_tester.prepare_config_and_inputs_for_common()

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

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







					def 			snake_case					(							self :     List[str]					):
									lowercase__					:   Any													=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     Optional[int]					):
									lowercase__					:   Dict													=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_depth_estimation(*SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     List[Any]					):
									lowercase__					:   Union[str, Any]													=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_semantic_segmentation(*SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     int					):
									for model_class in self.all_model_classes:
													if model_class.__name__ == "DPTForDepthEstimation":
																	continue

													lowercase__					:   List[str]													=					self.model_tester.prepare_config_and_inputs_for_common()
													lowercase__					:   Optional[int]													=					True

													if model_class in get_values(SCREAMING_SNAKE_CASE					):
																	continue

													lowercase__					:   Tuple													=					model_class(SCREAMING_SNAKE_CASE					)
													model.to(SCREAMING_SNAKE_CASE					)
													model.train()
													lowercase__					:   Dict													=					self._prepare_for_class(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       return_labels=SCREAMING_SNAKE_CASE					)
													lowercase__					:   Optional[int]													=					model(**SCREAMING_SNAKE_CASE					).loss
													loss.backward()







					def 			snake_case					(							self :     int					):
									for model_class in self.all_model_classes:
													if model_class.__name__ == "DPTForDepthEstimation":
																	continue

													lowercase__					:   int													=					self.model_tester.prepare_config_and_inputs_for_common()
													lowercase__					:   str													=					False
													lowercase__					:   Any													=					True

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







					def 			snake_case					(							self :     Tuple					):
									lowercase__					:   Optional[int]													=					self.model_tester.prepare_config_and_inputs_for_common()

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







					@slow
					def 			snake_case					(							self :     Tuple					):
									for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
													lowercase__					:   Dict													=					DPTModel.from_pretrained(SCREAMING_SNAKE_CASE					)
													self.assertIsNotNone(SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     str					):
									# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
									lowercase__					:   List[str]													=					self.model_tester.prepare_config_and_inputs_for_common()
									lowercase__					:   Dict													=					"""add"""
									with self.assertRaises(SCREAMING_SNAKE_CASE					):
													lowercase__					:   Tuple													=					DPTForDepthEstimation(SCREAMING_SNAKE_CASE					)






def  __lowerCamelCase (       ):

				"""simple docstring"""


				lowercase__					:   Union[str, Any]													=					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 :     int					):
									lowercase__					:   Optional[Any]													=					DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"					)
									lowercase__					:   Optional[int]													=					DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"					).to(SCREAMING_SNAKE_CASE					)

									lowercase__					:   Tuple													=					prepare_img()
									lowercase__					:   Dict													=					image_processor(images=SCREAMING_SNAKE_CASE				,       return_tensors="pt"					).to(SCREAMING_SNAKE_CASE					)

									# forward pass
									with torch.no_grad():
													lowercase__					:   List[str]													=					model(**SCREAMING_SNAKE_CASE					)
													lowercase__					:   Dict													=					outputs.predicted_depth

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

									lowercase__					:   Any													=					torch.tensor(
									    [[[5.6_437, 5.6_146, 5.6_511], [5.4_371, 5.5_649, 5.5_958], [5.5_215, 5.5_184, 5.5_293]]]					).to(SCREAMING_SNAKE_CASE					)

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











from __future__ import annotations

lowerCAmelCase__					  =  [
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			):

				"""simple docstring"""


				lowercase__					:   Optional[int]													=					[
				    [0 for col in range(len(grid[0]						)						)] for row in range(len(lowerCamelCase__						)						)
				]  # the reference grid
				lowercase__					:   List[Any]													=					1
				lowercase__					:   Tuple													=					[
				    [0 for col in range(len(grid[0]						)						)] for row in range(len(lowerCamelCase__						)						)
				]  # the action grid

				lowercase__					:   Union[str, Any]													=					init[0]
				lowercase__					:   List[str]													=					init[1]
				lowercase__					:   Optional[Any]													=					0
				lowercase__					:   Optional[int]													=					g + heuristic[x][y]  # cost from starting cell to destination cell
				lowercase__					:   Tuple													=					[[f, g, x, y]]

				lowercase__					:   Union[str, Any]													=					False  # flag that is set when search is complete
				lowercase__					:   Any													=					False  # flag set if we can't find expand

				while not found and not resign:
								if len(lowerCamelCase__						) == 0:
												raise ValueError("Algorithm is unable to find solution"						)
								else:  # to choose the least costliest action so as to move closer to the goal
												cell.sort()
												cell.reverse()
												lowercase__					:   Tuple													=					cell.pop()
												lowercase__					:   Optional[Any]													=					next_cell[2]
												lowercase__					:   int													=					next_cell[3]
												lowercase__					:   Union[str, Any]													=					next_cell[1]

												if x == goal[0] and y == goal[1]:
																lowercase__					:   Tuple													=					True
												else:
																for i in range(len(lowerCamelCase__						)						):  # to try out different valid actions
																				lowercase__					:   Tuple													=					x + DIRECTIONS[i][0]
																				lowercase__					:   str													=					y + DIRECTIONS[i][1]
																				if xa >= 0 and xa < len(lowerCamelCase__						) and ya >= 0 and ya < len(grid[0]						):
																								if closed[xa][ya] == 0 and grid[xa][ya] == 0:
																												lowercase__					:   List[Any]													=					g + cost
																												lowercase__					:   Tuple													=					ga + heuristic[xa][ya]
																												cell.append([fa, ga, xa, ya]						)
																												lowercase__					:   Dict													=					1
																												lowercase__					:   Union[str, Any]													=					i
				lowercase__					:   Optional[int]													=					[]
				lowercase__					:   List[Any]													=					goal[0]
				lowercase__					:   Optional[int]													=					goal[1]
				invpath.append([x, y]						)  # we get the reverse path from here
				while x != init[0] or y != init[1]:
								lowercase__					:   int													=					x - DIRECTIONS[action[x][y]][0]
								lowercase__					:   List[Any]													=					y - DIRECTIONS[action[x][y]][1]
								lowercase__					:   Optional[Any]													=					xa
								lowercase__					:   Dict													=					ya
								invpath.append([x, y]						)

				lowercase__					:   List[str]													=					[]
				for i in range(len(lowerCamelCase__						)						):
								path.append(invpath[len(lowerCamelCase__						) - 1 - i]						)
				return path, action


if __name__ == "__main__":
				lowerCAmelCase__					  =  [
				    [0, 1, 0, 0, 0, 0],
				    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
				    [0, 1, 0, 0, 0, 0],
				    [0, 1, 0, 0, 1, 0],
				    [0, 0, 0, 0, 1, 0],
				]

				lowerCAmelCase__					  =  [0, 0]
				# all coordinates are given in format [y,x]
				lowerCAmelCase__					  =  [len(grid) - 1, len(grid[0]) - 1]
				lowerCAmelCase__					  =  1

				# the cost map which pushes the path closer to the goal
				lowerCAmelCase__					  =  [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
				for i in range(len(grid)):
								for j in range(len(grid[0])):
												lowerCAmelCase__					  =  abs(i - goal[0]) + abs(j - goal[1])
												if grid[i][j] == 1:
																# added extra penalty in the heuristic map
																lowerCAmelCase__					  =  9_9

				lowerCAmelCase__  ,						lowerCAmelCase__					  =  search(grid, init, goal, cost, heuristic)

				print('''ACTION MAP''')
				for i in range(len(action)):
								print(action[i])

				for i in range(len(path)):
								print(path[i])




