





import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image
else:


			class       __lowerCAmelCase					:


				'''simple docstring'''





				@staticmethod
				def    snake_case__(						*lowerCamelCase_:						Dict,					**lowerCamelCase_:						List[str]			):
						pass


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Image			)   ->							str:




		'''simple docstring'''
		lowercase__					:      Union[str, Any]      =		hashlib.mda(image.tobytes()			)
		return m.hexdigest()





@is_pipeline_test
@require_vision
@require_timm
@require_torch
class       __lowerCAmelCase					(       unittest.TestCase      ):


	'''simple docstring'''





	_A													=				MODEL_FOR_DEPTH_ESTIMATION_MAPPING

	def    snake_case__(						self:						Tuple,					lowerCamelCase_:						Dict,					lowerCamelCase_:						Union[str, Any],					lowerCamelCase_:						Dict			):
			lowercase__					:      Tuple      =		DepthEstimationPipeline(model=lowerCamelCase_,					image_processor=lowerCamelCase_			)
			return depth_estimator, [
			    "./tests/fixtures/tests_samples/COCO/000000039769.png",
			    "./tests/fixtures/tests_samples/COCO/000000039769.png",
			]

	def    snake_case__(						self:						List[Any],					lowerCamelCase_:						int,					lowerCamelCase_:						Union[str, Any]			):
			lowercase__					:      int      =		depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
			self.assertEqual({'predicted_depth': ANY(torch.Tensor			), 'depth': ANY(Image.Image			)},					lowerCamelCase_			)
			import datasets

			lowercase__					:      Dict      =		datasets.load_dataset('hf-internal-testing/fixtures_image_utils',					'image',					split='test'			)
			lowercase__					:      List[Any]      =		depth_estimator(
			    [
			        Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			),
			        'http://images.cocodataset.org/val2017/000000039769.jpg',
			        # RGBA
			        dataset[0]['file'],
			        # LA
			        dataset[1]['file'],
			        # L
			        dataset[2]['file'],
			    ]			)
			self.assertEqual(
			    [
			        {'predicted_depth': ANY(torch.Tensor			), 'depth': ANY(Image.Image			)},
			        {'predicted_depth': ANY(torch.Tensor			), 'depth': ANY(Image.Image			)},
			        {'predicted_depth': ANY(torch.Tensor			), 'depth': ANY(Image.Image			)},
			        {'predicted_depth': ANY(torch.Tensor			), 'depth': ANY(Image.Image			)},
			        {'predicted_depth': ANY(torch.Tensor			), 'depth': ANY(Image.Image			)},
			    ],					lowerCamelCase_,					)

	@require_tf
	@unittest.skip('Depth estimation is not implemented in TF'			)
	def    snake_case__(						self:						Tuple			):
			pass

	@slow
	@require_torch
	def    snake_case__(						self:						List[Any]			):
			lowercase__					:      Any      =		'Intel/dpt-large'
			lowercase__					:      Optional[Any]      =		pipeline('depth-estimation',					model=lowerCamelCase_			)
			lowercase__					:      List[Any]      =		depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'			)
			lowercase__					:      Any      =		hashimage(outputs['depth']			)

			# This seems flaky.
			# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
			self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()			),					2_9.3_0_4			)
			self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()			),					2.6_6_2			)





	@require_torch
	def    snake_case__(						self:						List[Any]			):
			# This is highly irregular to have no small tests.
			self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'			)









__UpperCamelCase:	str = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

__UpperCamelCase:	Tuple = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : dict[int, list[int]]   ,    _lowercase     : int   ,    _lowercase     : list[bool]			)   ->							list[int]:




		'''simple docstring'''
		lowercase__					:      str      =		True
		lowercase__					:      int      =		[]

		for neighbour in graph[vert]:
				if not visited[neighbour]:
						order += topology_sort(_lowercase   ,    _lowercase   ,    _lowercase			)

		order.append(_lowercase			)

		return order


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : dict[int, list[int]]   ,    _lowercase     : int   ,    _lowercase     : list[bool]			)   ->							list[int]:




		'''simple docstring'''
		lowercase__					:      Union[str, Any]      =		True
		lowercase__					:      Optional[Any]      =		[vert]

		for neighbour in reversed_graph[vert]:
				if not visited[neighbour]:
						component += find_components(_lowercase   ,    _lowercase   ,    _lowercase			)

		return component





def       SCREAMING_SNAKE_CASE__  (       _lowercase     : dict[int, list[int]]			)   ->							list[list[int]]:




		'''simple docstring'''
		lowercase__					:      Optional[int]      =		len(_lowercase			) * [False]
		lowercase__					:      dict[int, list[int]]      =		{vert: [] for vert in range(len(_lowercase			)			)}

		for vert, neighbours in graph.items():
				for neighbour in neighbours:
						reversed_graph[neighbour].append(_lowercase			)

		lowercase__					:      Dict      =		[]
		for i, was_visited in enumerate(_lowercase			):
				if not was_visited:
						order += topology_sort(_lowercase   ,    _lowercase   ,    _lowercase			)

		lowercase__					:      Dict      =		[]
		lowercase__					:      Optional[Any]      =		len(_lowercase			) * [False]

		for i in range(len(_lowercase			)			):
				lowercase__					:      List[Any]      =		order[len(_lowercase			) - i - 1]
				if not visited[vert]:
						lowercase__					:      Tuple      =		find_components(_lowercase   ,    _lowercase   ,    _lowercase			)
						components_list.append(_lowercase			)

		return components_list


