






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        _SCREAMING_SNAKE_CASE			:





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





	def 		_A					(						self							:   str      ):
		UpperCamelCase	:str		=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

		UpperCamelCase	:Optional[Any]		=      None
		if self.use_labels:
			UpperCamelCase	:List[Any]		=      ids_tensor([self.batch_size, self.image_size, self.image_size]					,    self.num_labels      )

		UpperCamelCase	:int		=      self.get_config()

		return config, pixel_values, labels





	def 		_A					(						self							:   Any      ):
		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					,    )





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





	def 		_A					(						self							:   Dict					,    __lowerCamelCase							:   str					,    __lowerCamelCase							:   Optional[int]					,    __lowerCamelCase							:   Tuple      ):
		UpperCamelCase	:int		=      self.num_labels
		UpperCamelCase	:Tuple		=      DPTForDepthEstimation(__lowerCamelCase      )
		model.to(__lowerCamelCase      )
		model.eval()
		UpperCamelCase	:str		=      model(__lowerCamelCase      )
		self.parent.assertEqual(result.predicted_depth.shape					,    (self.batch_size, self.image_size, self.image_size)      )





	def 		_A					(						self							:   List[str]					,    __lowerCamelCase							:   Tuple					,    __lowerCamelCase							:   Dict					,    __lowerCamelCase							:   Union[str, Any]      ):
		UpperCamelCase	:Dict		=      self.num_labels
		UpperCamelCase	:List[Any]		=      DPTForSemanticSegmentation(__lowerCamelCase      )
		model.to(__lowerCamelCase      )
		model.eval()
		UpperCamelCase	:Optional[int]		=      model(__lowerCamelCase					,    labels=__lowerCamelCase      )
		self.parent.assertEqual(
		    result.logits.shape					,    (self.batch_size, self.num_labels, self.image_size, self.image_size)      )





	def 		_A					(						self							:   Union[str, Any]      ):
		UpperCamelCase	:Union[str, Any]		=      self.prepare_config_and_inputs()
		UpperCamelCase	, UpperCamelCase	, UpperCamelCase	:Optional[int]		=      config_and_inputs
		UpperCamelCase	:str		=      {"""pixel_values""": pixel_values}
		return config, inputs_dict

@require_torch
class        _SCREAMING_SNAKE_CASE			(     _a						,  _a						,  unittest.TestCase ):
	snake_case__				:	Any	      =    (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
	snake_case__				:	Optional[int]	      =    (
	    {
	        """depth-estimation""": DPTForDepthEstimation,
	        """feature-extraction""": DPTModel,
	        """image-segmentation""": DPTForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	snake_case__				:	int	      =    False
	snake_case__				:	Optional[int]	      =    False
	snake_case__				:	List[Any]	      =    False





	def 		_A					(						self							:   str      ):
		UpperCamelCase	:List[str]		=      DPTModelTester(self      )
		UpperCamelCase	:Optional[Any]		=      ConfigTester(self					,    config_class=__lowerCamelCase					,    has_text_modality=__lowerCamelCase					,    hidden_size=37      )





	def 		_A					(						self							:   int      ):
		self.config_tester.run_common_tests()





	@unittest.skip(reason="""DPT does not use inputs_embeds"""      )
	def 		_A					(						self							:   Union[str, Any]      ):
		pass





	def 		_A					(						self							:   Optional[Any]      ):
		UpperCamelCase	, UpperCamelCase	:Optional[int]		=      self.model_tester.prepare_config_and_inputs_for_common()

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





	def 		_A					(						self							:   Any      ):
		UpperCamelCase	, UpperCamelCase	:Union[str, Any]		=      self.model_tester.prepare_config_and_inputs_for_common()

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

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





	def 		_A					(						self							:   Union[str, Any]      ):
		UpperCamelCase	:Optional[Any]		=      self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*__lowerCamelCase      )





	def 		_A					(						self							:   List[Any]      ):
		UpperCamelCase	:Optional[Any]		=      self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_depth_estimation(*__lowerCamelCase      )





	def 		_A					(						self							:   Optional[int]      ):
		UpperCamelCase	:str		=      self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCamelCase      )





	def 		_A					(						self							:   Optional[Any]      ):
		for model_class in self.all_model_classes:
			if model_class.__name__ == "DPTForDepthEstimation":
				continue

			UpperCamelCase	, UpperCamelCase	:Any		=      self.model_tester.prepare_config_and_inputs_for_common()
			UpperCamelCase	:List[str]		=      True

			if model_class in get_values(__lowerCamelCase      ):
				continue

			UpperCamelCase	:Any		=      model_class(__lowerCamelCase      )
			model.to(__lowerCamelCase      )
			model.train()
			UpperCamelCase	:Any		=      self._prepare_for_class(__lowerCamelCase					,    __lowerCamelCase					,    return_labels=__lowerCamelCase      )
			UpperCamelCase	:Tuple		=      model(**__lowerCamelCase      ).loss
			loss.backward()





	def 		_A					(						self							:   Optional[int]      ):
		for model_class in self.all_model_classes:
			if model_class.__name__ == "DPTForDepthEstimation":
				continue

			UpperCamelCase	, UpperCamelCase	:Dict		=      self.model_tester.prepare_config_and_inputs_for_common()
			UpperCamelCase	:List[Any]		=      False
			UpperCamelCase	:Dict		=      True

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





	def 		_A					(						self							:   Tuple      ):
		UpperCamelCase	, UpperCamelCase	:List[str]		=      self.model_tester.prepare_config_and_inputs_for_common()

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





	@slow
	def 		_A					(						self							:   int      ):
		for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			UpperCamelCase	:Union[str, Any]		=      DPTModel.from_pretrained(__lowerCamelCase      )
			self.assertIsNotNone(__lowerCamelCase      )

def   SCREAMING_SNAKE_CASE_				(				)						->     Any:






	"""simple docstring"""



	UpperCamelCase	:Optional[int]		=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
	return image

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





	def 		_A					(						self							:   Tuple      ):
		UpperCamelCase	:int		=      DPTImageProcessor.from_pretrained("""Intel/dpt-large"""      )
		UpperCamelCase	:Dict		=      DPTForDepthEstimation.from_pretrained("""Intel/dpt-large"""      ).to(__lowerCamelCase      )

		UpperCamelCase	:Dict		=      prepare_img()
		UpperCamelCase	:List[str]		=      image_processor(images=__lowerCamelCase					,    return_tensors="""pt"""      ).to(__lowerCamelCase      )

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

		# verify the predicted depth
		UpperCamelCase	:List[Any]		=      torch.Size((1, 384, 384)      )
		self.assertEqual(predicted_depth.shape					,    __lowerCamelCase      )

		UpperCamelCase	:Optional[int]		=      torch.tensor(
		    [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]      ).to(__lowerCamelCase      )

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





	def 		_A					(						self							:   Optional[int]      ):
		UpperCamelCase	:Any		=      DPTImageProcessor.from_pretrained("""Intel/dpt-large-ade"""      )
		UpperCamelCase	:Tuple		=      DPTForSemanticSegmentation.from_pretrained("""Intel/dpt-large-ade"""      ).to(__lowerCamelCase      )

		UpperCamelCase	:int		=      prepare_img()
		UpperCamelCase	:Optional[Any]		=      image_processor(images=__lowerCamelCase					,    return_tensors="""pt"""      ).to(__lowerCamelCase      )

		# forward pass
		with torch.no_grad():
			UpperCamelCase	:List[Any]		=      model(**__lowerCamelCase      )

		# verify the logits
		UpperCamelCase	:Optional[Any]		=      torch.Size((1, 150, 480, 480)      )
		self.assertEqual(outputs.logits.shape					,    __lowerCamelCase      )

		UpperCamelCase	:Any		=      torch.tensor(
		    [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]      ).to(__lowerCamelCase      )

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





	def 		_A					(						self							:   Any      ):
		UpperCamelCase	:str		=      DPTImageProcessor.from_pretrained("""Intel/dpt-large-ade"""      )
		UpperCamelCase	:Dict		=      DPTForSemanticSegmentation.from_pretrained("""Intel/dpt-large-ade"""      ).to(__lowerCamelCase      )

		UpperCamelCase	:Dict		=      prepare_img()
		UpperCamelCase	:int		=      image_processor(images=__lowerCamelCase					,    return_tensors="""pt"""      ).to(__lowerCamelCase      )

		# forward pass
		with torch.no_grad():
			UpperCamelCase	:Optional[Any]		=      model(**__lowerCamelCase      )

		UpperCamelCase	:List[Any]		=      outputs.logits.detach().cpu()

		UpperCamelCase	:Union[str, Any]		=      image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase					,    target_sizes=[(500, 300)]      )
		UpperCamelCase	:Dict		=      torch.Size((500, 300)      )
		self.assertEqual(segmentation[0].shape					,    __lowerCamelCase      )

		UpperCamelCase	:Tuple		=      image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase      )
		UpperCamelCase	:List[Any]		=      torch.Size((480, 480)      )
		self.assertEqual(segmentation[0].shape					,    __lowerCamelCase      )








from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar

class        _SCREAMING_SNAKE_CASE			(     _a ):





	@abstractmethod
	def __lt__(						self							:   T					,    __lowerCamelCase							:   T      ):
		pass





	@abstractmethod
	def __gt__(						self							:   T					,    __lowerCamelCase							:   T      ):
		pass





	@abstractmethod
	def __eq__(						self							:   T					,    __lowerCamelCase							:   object      ):
		pass
UpperCAmelCase_					:		str           = TypeVar('''T''', bound=Comparable)

class        _SCREAMING_SNAKE_CASE			(     Generic[T] ):





	def __init__(						self							:   Dict      ):
		UpperCamelCase	:list[T]		=      []
		UpperCamelCase	:int		=      0





	def __repr__(						self							:   List[str]      ):
		return str(self.h      )





	def 		_A					(						self							:   Tuple					,    __lowerCamelCase							:   int      ):
		if child_idx > 0:
			return (child_idx - 1) // 2
		return None





	def 		_A					(						self							:   List[str]					,    __lowerCamelCase							:   int      ):
		UpperCamelCase	:Optional[int]		=      2 * parent_idx + 1
		if left_child_index < self.heap_size:
			return left_child_index
		return None





	def 		_A					(						self							:   List[str]					,    __lowerCamelCase							:   int      ):
		UpperCamelCase	:Optional[Any]		=      2 * parent_idx + 2
		if right_child_index < self.heap_size:
			return right_child_index
		return None





	def 		_A					(						self							:   Tuple					,    __lowerCamelCase							:   int      ):
		if index < self.heap_size:
			UpperCamelCase	:int		=      index
			UpperCamelCase	:Dict		=      self.left_child_idx(__lowerCamelCase      )
			UpperCamelCase	:Any		=      self.right_child_idx(__lowerCamelCase      )
			# check which child is larger than its parent
			if left_child is not None and self.h[left_child] > self.h[violation]:
				UpperCamelCase	:Dict		=      left_child
			if right_child is not None and self.h[right_child] > self.h[violation]:
				UpperCamelCase	:Dict		=      right_child
			# if violation indeed exists
			if violation != index:
				# swap to fix the violation
				UpperCamelCase	, UpperCamelCase	:Optional[int]		=      self.h[index], self.h[violation]
				# fix the subsequent violation recursively if any
				self.max_heapify(__lowerCamelCase      )





	def 		_A					(						self							:   Any					,    __lowerCamelCase							:   Iterable[T]      ):
		UpperCamelCase	:Optional[Any]		=      list(__lowerCamelCase      )
		UpperCamelCase	:int		=      len(self.h      )
		if self.heap_size > 1:
			# max_heapify from right to left but exclude leaves (last level)
			for i in range(self.heap_size // 2 - 1					,    -1					,    -1      ):
				self.max_heapify(__lowerCamelCase      )





	def 		_A					(						self							:   Optional[int]      ):
		if self.heap_size >= 2:
			UpperCamelCase	:List[str]		=      self.h[0]
			UpperCamelCase	:str		=      self.h.pop(-1      )
			self.heap_size -= 1
			self.max_heapify(0      )
			return me
		elif self.heap_size == 1:
			self.heap_size -= 1
			return self.h.pop(-1      )
		else:
			raise Exception("""Empty heap"""      )





	def 		_A					(						self							:   Optional[Any]					,    __lowerCamelCase							:   T      ):
		self.h.append(__lowerCamelCase      )
		UpperCamelCase	:Any		=      (self.heap_size - 1) // 2
		self.heap_size += 1
		while idx >= 0:
			self.max_heapify(__lowerCamelCase      )
			UpperCamelCase	:List[str]		=      (idx - 1) // 2





	def 		_A					(						self							:   int      ):
		UpperCamelCase	:int		=      self.heap_size
		for j in range(size - 1					,    0					,    -1      ):
			UpperCamelCase	, UpperCamelCase	:Any		=      self.h[j], self.h[0]
			self.heap_size -= 1
			self.max_heapify(0      )
		UpperCamelCase	:List[str]		=      size


if __name__ == "__main__":
	import doctest

	# run doc test
	doctest.testmod()

	# demo
	for unsorted in [
	    [0],
	    [2],
	    [3, 5],
	    [5, 3],
	    [5, 5],
	    [0, 0, 0, 0],
	    [1, 1, 1, 1],
	    [2, 2, 3, 5],
	    [0, 2, 2, 3, 5],
	    [2, 5, 3, 0, 2, 3, 0, 3],
	    [6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
	    [1_03, 9, 1, 7, 11, 15, 25, 2_01, 2_09, 1_07, 5],
	    [-45, -2, -5],
	]:
		print(F'''unsorted array: {unsorted}''')

		UpperCAmelCase_					:		Heap[int]           = Heap()
		heap.build_max_heap(unsorted)
		print(F'''after build heap: {heap}''')

		print(F'''max value: {heap.extract_max()}''')
		print(F'''after max value removed: {heap}''')

		heap.insert(1_00)
		print(F'''after new value 100 inserted: {heap}''')

		heap.heap_sort()
		print(F'''heap-sorted array: {heap}\n''')
