
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 	A_       :




	'''simple docstring'''


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





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

		lowercase					  =   self.get_config()

		return config, pixel_values, labels





	def      SCREAMING_SNAKE_CASE__						(	self	):
		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=snake_case		,   initializer_range=self.initializer_range		,   is_hybrid=self.is_hybrid		,   )





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case	):
		lowercase					  =   DPTModel(config=snake_case	)
		model.to(snake_case	)
		model.eval()
		lowercase					  =   model(snake_case	)
		self.parent.assertEqual(result.last_hidden_state.shape		,   (self.batch_size, self.seq_length, self.hidden_size)	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case	):
		lowercase					  =   self.num_labels
		lowercase					  =   DPTForDepthEstimation(snake_case	)
		model.to(snake_case	)
		model.eval()
		lowercase					  =   model(snake_case	)
		self.parent.assertEqual(result.predicted_depth.shape		,   (self.batch_size, self.image_size, self.image_size)	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case	):
		lowercase					  =   self.num_labels
		lowercase					  =   DPTForSemanticSegmentation(snake_case	)
		model.to(snake_case	)
		model.eval()
		lowercase					  =   model(snake_case		,   labels=snake_case	)
		self.parent.assertEqual(
		    result.logits.shape		,   (self.batch_size, self.num_labels, self.image_size, self.image_size)	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   self.prepare_config_and_inputs()
		lowercase  , lowercase  , lowercase					  =   config_and_inputs
		lowercase					  =   {'pixel_values': pixel_values}
		return config, inputs_dict







@require_torch
class 	A_       (      __lowerCamelCase				,     __lowerCamelCase				,     unittest.TestCase       ):




	'''simple docstring'''


	_UpperCamelCase					:					Optional[int]										=	(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
	_UpperCamelCase					:					int										=	(
	    {
	        """depth-estimation""": DPTForDepthEstimation,
	        """feature-extraction""": DPTModel,
	        """image-segmentation""": DPTForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	_UpperCamelCase					:					Any										=	False
	_UpperCamelCase					:					Optional[int]										=	False
	_UpperCamelCase					:					Any										=	False





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   DPTModelTester(self	)
		lowercase					  =   ConfigTester(self		,   config_class=snake_case		,   has_text_modality=snake_case		,   hidden_size=37	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		self.config_tester.run_common_tests()





	@unittest.skip(reason='DPT does not use inputs_embeds'	)
	def      SCREAMING_SNAKE_CASE__						(	self	):
		pass





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase  , lowercase					  =   self.model_tester.prepare_config_and_inputs_for_common()

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





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase  , lowercase					  =   self.model_tester.prepare_config_and_inputs_for_common()

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

			lowercase					  =   ['pixel_values']
			self.assertListEqual(arg_names[:1]		,   snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_depth_estimation(*snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_semantic_segmentation(*snake_case	)





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

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

			if model_class in get_values(snake_case	):
				continue

			lowercase					  =   model_class(snake_case	)
			model.to(snake_case	)
			model.train()
			lowercase					  =   self._prepare_for_class(snake_case		,   snake_case		,   return_labels=snake_case	)
			lowercase					  =   model(**snake_case	).loss
			loss.backward()





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

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

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





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase  , lowercase					  =   self.model_tester.prepare_config_and_inputs_for_common()

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





	@slow
	def      SCREAMING_SNAKE_CASE__						(	self	):
		for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			lowercase					  =   DPTModel.from_pretrained(snake_case	)
			self.assertIsNotNone(snake_case	)






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







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




	'''simple docstring'''


	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   DPTImageProcessor.from_pretrained('Intel/dpt-large'	)
		lowercase					  =   DPTForDepthEstimation.from_pretrained('Intel/dpt-large'	).to(snake_case	)

		lowercase					  =   prepare_img()
		lowercase					  =   image_processor(images=snake_case		,   return_tensors='pt'	).to(snake_case	)

		# forward pass
		with torch.no_grad():
			lowercase					  =   model(**snake_case	)
			lowercase					  =   outputs.predicted_depth

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

		lowercase					  =   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(snake_case	)

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





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'	)
		lowercase					  =   DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'	).to(snake_case	)

		lowercase					  =   prepare_img()
		lowercase					  =   image_processor(images=snake_case		,   return_tensors='pt'	).to(snake_case	)

		# forward pass
		with torch.no_grad():
			lowercase					  =   model(**snake_case	)

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

		lowercase					  =   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(snake_case	)

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





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'	)
		lowercase					  =   DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'	).to(snake_case	)

		lowercase					  =   prepare_img()
		lowercase					  =   image_processor(images=snake_case		,   return_tensors='pt'	).to(snake_case	)

		# forward pass
		with torch.no_grad():
			lowercase					  =   model(**snake_case	)

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

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

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



from __future__ import annotations

from queue import Queue







class 	A_       :




	'''simple docstring'''


	def __init__(	self	):
		lowercase					  =   {}





	def      SCREAMING_SNAKE_CASE__						(	self	):
		for i in self.vertices:
			print(snake_case		,   ' : '		,   ' -> '.join([str(snake_case	) for j in self.vertices[i]]	)	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case	):
		if from_vertex in self.vertices:
			self.vertices[from_vertex].append(snake_case	)
		else:
			lowercase					  =   [to_vertex]





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case	):
		lowercase					  =   set()

		# create a first in first out queue to store all the vertices for BFS
		lowercase					  =   Queue()

		# mark the source node as visited and enqueue it
		visited.add(snake_case	)
		queue.put(snake_case	)

		while not queue.empty():
			lowercase					  =   queue.get()

			# loop through all adjacent vertex and enqueue it if not yet visited
			for adjacent_vertex in self.vertices[vertex]:
				if adjacent_vertex not in visited:
					queue.put(snake_case	)
					visited.add(snake_case	)
		return visited


if __name__ == "__main__":
	from doctest import testmod

	testmod(verbose=True)

	UpperCAmelCase     				=			Graph()
	g.add_edge(0, 1)
	g.add_edge(0, 2)
	g.add_edge(1, 2)
	g.add_edge(2, 0)
	g.add_edge(2, 3)
	g.add_edge(3, 3)

	g.print_graph()
	# 0  :  1 -> 2
	# 1  :  2
	# 2  :  0 -> 3
	# 3  :  3

	assert sorted(g.bfs(2)) == [0, 1, 2, 3]

