




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				:     Tuple					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     Dict=2					,      __lowerCamelCase				:     Dict=32					,      __lowerCamelCase				:     Tuple=16					,      __lowerCamelCase				:     Tuple=3					,      __lowerCamelCase				:     int=True					,      __lowerCamelCase				:     int=True					,      __lowerCamelCase				:     Any=32					,      __lowerCamelCase				:     List[Any]=4					,      __lowerCamelCase				:     Tuple=[0, 1, 2, 3]					,      __lowerCamelCase				:     Union[str, Any]=4					,      __lowerCamelCase				:     Union[str, Any]=37					,      __lowerCamelCase				:     Optional[int]="gelu"					,      __lowerCamelCase				:     str=0.1					,      __lowerCamelCase				:     List[str]=0.1					,      __lowerCamelCase				:     Optional[Any]=0.02					,      __lowerCamelCase				:     Optional[int]=3					,      __lowerCamelCase				:     List[Any]=[1, 3_84, 24, 24]					,      __lowerCamelCase				:     List[Any]=True					,      __lowerCamelCase				:     Optional[int]=None					,      )					->     Optional[int]:
							a    =			parent
							a    =			batch_size
							a    =			image_size
							a    =			patch_size
							a    =			num_channels
							a    =			is_training
							a    =			use_labels
							a    =			hidden_size
							a    =			num_hidden_layers
							a    =			backbone_out_indices
							a    =			num_attention_heads
							a    =			intermediate_size
							a    =			hidden_act
							a    =			hidden_dropout_prob
							a    =			attention_probs_dropout_prob
							a    =			initializer_range
							a    =			num_labels
							a    =			backbone_featmap_shape
							a    =			scope
							a    =			is_hybrid
							# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
							a    =			(image_size // patch_size) ** 2
							a    =			num_patches + 1





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     str:
							a    =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

							a    =			self.get_config()

							return config, pixel_values, labels





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Union[str, Any]:
							a    =			{
							    "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=__lowerCamelCase					,      initializer_range=self.initializer_range					,      is_hybrid=self.is_hybrid					,      backbone_config=__lowerCamelCase					,      backbone_featmap_shape=self.backbone_featmap_shape					,      )





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]					,      __lowerCamelCase				:     Any					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     Optional[Any]							)					->     Optional[Any]:
							a    =			DPTModel(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a    =			model(__lowerCamelCase							)
							self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)							)





				def 							__UpperCAmelCase							(      self				:     List[str]					,      __lowerCamelCase				:     List[str]					,      __lowerCamelCase				:     List[str]					,      __lowerCamelCase				:     Any							)					->     str:
							a    =			self.num_labels
							a    =			DPTForDepthEstimation(__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a    =			model(__lowerCamelCase							)
							self.parent.assertEqual(result.predicted_depth.shape					,      (self.batch_size, self.image_size, self.image_size)							)





				def 							__UpperCAmelCase							(      self				:     str					,      __lowerCamelCase				:     List[Any]					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     Tuple							)					->     Any:
							a    =			self.num_labels
							a    =			DPTForSemanticSegmentation(__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a    =			model(__lowerCamelCase					,      labels=__lowerCamelCase							)
							self.parent.assertEqual(
							    result.logits.shape					,      (self.batch_size, self.num_labels, self.image_size, self.image_size)							)





				def 							__UpperCAmelCase							(      self				:     str							)					->     str:
							a    =			self.prepare_config_and_inputs()
							a    ,		a    ,		a    =			config_and_inputs
							a    =			{"pixel_values": pixel_values}
							return config, inputs_dict




@require_torch
class     snake_case__							(_UpperCamelCase    ,       _UpperCamelCase    ,       unittest.TestCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Union[str, Any]							 =					(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[int]							 =					False
				SCREAMING_SNAKE_CASE_       :							str							 =					False
				SCREAMING_SNAKE_CASE_       :							str							 =					False





				def 							__UpperCAmelCase							(      self				:     int							)					->     List[str]:
							a    =			DPTModelTester(self							)
							a    =			ConfigTester(self					,      config_class=__lowerCamelCase					,      has_text_modality=__lowerCamelCase					,      hidden_size=37							)





				def 							__UpperCAmelCase							(      self				:     int							)					->     List[str]:
							self.config_tester.run_common_tests()





				@unittest.skip(reason="DPT does not use inputs_embeds"							)
				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     List[str]:
							pass





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Optional[Any]:
							a    ,		a    =			self.model_tester.prepare_config_and_inputs_for_common()

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





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     List[str]:
							a    ,		a    =			self.model_tester.prepare_config_and_inputs_for_common()

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

										a    =			["pixel_values"]
										self.assertListEqual(arg_names[:1]					,      __lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Any							)					->     Union[str, Any]:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Any							)					->     Dict:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_depth_estimation(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Optional[int]:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Optional[Any]:
							for model_class in self.all_model_classes:
										if model_class.__name__ == "DPTForDepthEstimation":
													continue

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

										if model_class in get_values(__lowerCamelCase							):
													continue

										a    =			model_class(__lowerCamelCase							)
										model.to(__lowerCamelCase							)
										model.train()
										a    =			self._prepare_for_class(__lowerCamelCase					,      __lowerCamelCase					,      return_labels=__lowerCamelCase							)
										a    =			model(**__lowerCamelCase							).loss
										loss.backward()





				def 							__UpperCAmelCase							(      self				:     Dict							)					->     List[str]:
							for model_class in self.all_model_classes:
										if model_class.__name__ == "DPTForDepthEstimation":
													continue

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

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





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Union[str, Any]:
							a    ,		a    =			self.model_tester.prepare_config_and_inputs_for_common()

							a    =			_config_zero_init(__lowerCamelCase							)
							for model_class in self.all_model_classes:
										a    =			model_class(config=__lowerCamelCase							)
										# Skip the check for the backbone
										a    =			[]
										for name, module in model.named_modules():
													if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																a    =			[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				:     int							)					->     Any:
							pass





				@slow
				def 							__UpperCAmelCase							(      self				:     str							)					->     Optional[int]:
							for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
										a    =			DPTModel.from_pretrained(__lowerCamelCase							)
										self.assertIsNotNone(__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Dict:
							# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
							a    ,		a    =			self.model_tester.prepare_config_and_inputs_for_common()
							a    =			"add"
							with self.assertRaises(__lowerCamelCase							):
										a    =			DPTForDepthEstimation(__lowerCamelCase							)




def 			__magic_name__				( ):






			'''simple docstring'''







			a    =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
			return image






@require_torch
@require_vision
@slow
class     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Optional[Any]:
							a    =			DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"							)
							a    =			DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"							).to(__lowerCamelCase							)

							a    =			prepare_img()
							a    =			image_processor(images=__lowerCamelCase					,      return_tensors="pt"							).to(__lowerCamelCase							)

							# forward pass
							with torch.no_grad():
										a    =			model(**__lowerCamelCase							)
										a    =			outputs.predicted_depth

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

							a    =			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(__lowerCamelCase							)

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













from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

__UpperCAmelCase       :   str				       =							TypeVar("T")







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    int:
  return (position - 1) // 2







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    int:
  return (2 * position) + 1







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    int:
  return (2 * position) + 2







class     __snake_case       (							Generic[T]			):





       '''simple docstring'''







       def __init__(  self :    Tuple		):
         __snake_case:						list[tuple[T, int]]   =						[]
         __snake_case:						dict[T, int]   =						{}
         __snake_case:						int   =						0




       def __len__(  self :    Tuple		):
         return self.elements




       def __repr__(  self :    Optional[int]		):
         return str(self.heap		)




       def 							UpperCAmelCase__ (  self :    List[Any]		):
         # Check if the priority queue is empty
         return self.elements == 0




       def 							UpperCAmelCase__ (  self :    List[Any]							,				A :    T							,				A :    int		):
         # Add an element with given priority to the queue
         self.heap.append((elem, weight)		)
         __snake_case:						Optional[Any]   =						self.elements
         self.elements += 1
         self._bubble_up(A		)




       def 							UpperCAmelCase__ (  self :    List[Any]		):
         # Remove and return the element with lowest weight (highest priority)
         if self.elements > 1:
           self._swap_nodes(0							,				self.elements - 1		)
         __snake_case  ,   __snake_case:						Union[str, Any]   =						self.heap.pop()
         del self.position_map[elem]
         self.elements -= 1
         if self.elements > 0:
           __snake_case  ,   __snake_case:						int   =						self.heap[0]
           self._bubble_down(A		)
         return elem




       def 							UpperCAmelCase__ (  self :    Any							,				A :    T							,				A :    int		):
         # Update the weight of the given key
         __snake_case:						Dict   =						self.position_map[elem]
         __snake_case:						Optional[Any]   =						(elem, weight)
         if position > 0:
           __snake_case:						Optional[int]   =						get_parent_position(A		)
           __snake_case  ,   __snake_case:						Dict   =						self.heap[parent_position]
           if parent_weight > weight:
             self._bubble_up(A		)
           else:
             self._bubble_down(A		)
         else:
           self._bubble_down(A		)




       def 							UpperCAmelCase__ (  self :    int							,				A :    T		):
         # Place a node at the proper position (upward movement) [to be used internally
         # only]
         __snake_case:						int   =						self.position_map[elem]
         if curr_pos == 0:
           return None
         __snake_case:						Optional[Any]   =						get_parent_position(A		)
         __snake_case  ,   __snake_case:						Tuple   =						self.heap[curr_pos]
         __snake_case  ,   __snake_case:						Optional[Any]   =						self.heap[parent_position]
         if parent_weight > weight:
           self._swap_nodes(A							,				A		)
           return self._bubble_up(A		)
         return None




       def 							UpperCAmelCase__ (  self :    List[str]							,				A :    T		):
         # Place a node at the proper position (downward movement) [to be used
         # internally only]
         __snake_case:						Any   =						self.position_map[elem]
         __snake_case  ,   __snake_case:						int   =						self.heap[curr_pos]
         __snake_case:						Union[str, Any]   =						get_child_left_position(A		)
         __snake_case:						Dict   =						get_child_right_position(A		)
         if child_left_position < self.elements and child_right_position < self.elements:
           __snake_case  ,   __snake_case:						Any   =						self.heap[child_left_position]
           __snake_case  ,   __snake_case:						List[str]   =						self.heap[child_right_position]
           if child_right_weight < child_left_weight and child_right_weight < weight:
             self._swap_nodes(A							,				A		)
             return self._bubble_down(A		)
         if child_left_position < self.elements:
           __snake_case  ,   __snake_case:						List[Any]   =						self.heap[child_left_position]
           if child_left_weight < weight:
             self._swap_nodes(A							,				A		)
             return self._bubble_down(A		)
         else:
           return None
         if child_right_position < self.elements:
           __snake_case  ,   __snake_case:						List[Any]   =						self.heap[child_right_position]
           if child_right_weight < weight:
             self._swap_nodes(A							,				A		)
             return self._bubble_down(A		)
         return None






       def 							UpperCAmelCase__ (  self :    int							,				A :    int							,				A :    int		):
         # Swap the nodes at the given positions
         __snake_case:						Optional[int]   =						self.heap[nodea_pos][0]
         __snake_case:						Optional[Any]   =						self.heap[nodea_pos][0]
         __snake_case  ,   __snake_case:						Tuple   =						(
             self.heap[nodea_pos],
             self.heap[nodea_pos],
         )
         __snake_case:						Any   =						nodea_pos
         __snake_case:						str   =						nodea_pos







class     __snake_case       (							Generic[T]			):





       '''simple docstring'''







       def __init__(  self :    Any		):
         __snake_case:						dict[T, dict[T, int]]   =						{}
         __snake_case:						int   =						0




       def __repr__(  self :    Dict		):
         return str(self.connections		)




       def __len__(  self :    List[str]		):
         return self.nodes




       def 							UpperCAmelCase__ (  self :    Optional[Any]							,				A :    T		):
         # Add a node in the graph if it is not in the graph
         if node not in self.connections:
           __snake_case:						List[str]   =						{}
           self.nodes += 1






       def 							UpperCAmelCase__ (  self :    Optional[Any]							,				A :    T							,				A :    T							,				A :    int		):
         # Add an edge between 2 nodes in the graph
         self.add_node(A		)
         self.add_node(A		)
         __snake_case:						Optional[Any]   =						weight
         __snake_case:						str   =						weight







def 			A__							(    SCREAMING_SNAKE_CASE__					,					)   ->    tuple[dict[T, int], dict[T, T | None]]:
  __snake_case:						dict[T, int]   =						{node: maxsize for node in graph.connections}
  __snake_case:						dict[T, T | None]   =						{node: None for node in graph.connections}

  __snake_case:						MinPriorityQueue[T]   =						MinPriorityQueue()
  for node, weight in dist.items():
    priority_queue.push(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)

  if priority_queue.is_empty():
    return dist, parent

  # initialization
  __snake_case:						Optional[int]   =						priority_queue.extract_min()
  __snake_case:						Any   =						0
  for neighbour in graph.connections[node]:
    if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
      __snake_case:						Tuple   =						dist[node] + graph.connections[node][neighbour]
      priority_queue.update_key(SCREAMING_SNAKE_CASE__					,					dist[neighbour])
      __snake_case:						Optional[int]   =						node

    # running prim's algorithm
  while not priority_queue.is_empty():
    __snake_case:						Optional[int]   =						priority_queue.extract_min()
    for neighbour in graph.connections[node]:
      if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
        __snake_case:						Tuple   =						dist[node] + graph.connections[node][neighbour]
        priority_queue.update_key(SCREAMING_SNAKE_CASE__					,					dist[neighbour])
        __snake_case:						List[Any]   =						node
  return dist, parent






