
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     _SCREAMING_SNAKE_CASE     :



				@staticmethod
				def     SCREAMING_SNAKE_CASE_(    *lowercase						,  **lowercase )				->       int:
								pass





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				lowerCamelCase_						=			hashlib.mda(image.tobytes()    )
				return m.hexdigest()





@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     _SCREAMING_SNAKE_CASE     (      unittest.TestCase						):
	lowerCAmelCase__         = MODEL_FOR_DEPTH_ESTIMATION_MAPPING



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase )				->       str:
					lowerCamelCase_						=			DepthEstimationPipeline(model=lowercase						,  image_processor=lowercase )
					return depth_estimator, [
					    "./tests/fixtures/tests_samples/COCO/000000039769.png",
					    "./tests/fixtures/tests_samples/COCO/000000039769.png",
					]



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase )				->       Optional[Any]:
					lowerCamelCase_						=			depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png" )
					self.assertEqual({"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )}						,  lowercase )
					import datasets

					lowerCamelCase_						=			datasets.load_dataset("hf-internal-testing/fixtures_image_utils"						,  "image"						,  split="test" )
					lowerCamelCase_						=			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 )},
					    ]						,  lowercase						,  )



	@require_tf
	@unittest.skip("Depth estimation is not implemented in TF" )
	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					pass



	@slow
	@require_torch
	def     SCREAMING_SNAKE_CASE_(    self )				->       List[Any]:
					lowerCamelCase_						=			"Intel/dpt-large"
					lowerCamelCase_						=			pipeline("depth-estimation"						,  model=lowercase )
					lowerCamelCase_						=			depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg" )
					lowerCamelCase_						=			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     SCREAMING_SNAKE_CASE_(    self )				->       Optional[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" )




from collections import deque





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				lowerCamelCase_						=			len(lowerCamelCase__    )
				lowerCamelCase_						=			deque()
				lowerCamelCase_						=			[False for _ in range(lowerCamelCase__    )]
				lowerCamelCase_						=			[-1 for _ in range(lowerCamelCase__    )]
				lowerCamelCase_						=			index_of[:]

				def strong_connect(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
								lowerCamelCase_						=			index  # the number when this node is seen
								lowerCamelCase_						=			index  # lowest rank node reachable from here
								index += 1
								stack.append(lowerCamelCase__    )
								lowerCamelCase_						=			True

								for w in g[v]:
												if index_of[w] == -1:
																lowerCamelCase_						=			strong_connect(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )
																lowerCamelCase_						=			(
																    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
																)
												elif on_stack[w]:
																lowerCamelCase_						=			(
																    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
																)

								if lowlink_of[v] == index_of[v]:
												lowerCamelCase_						=			[]
												lowerCamelCase_						=			stack.pop()
												lowerCamelCase_						=			False
												component.append(lowerCamelCase__    )
												while w != v:
																lowerCamelCase_						=			stack.pop()
																lowerCamelCase_						=			False
																component.append(lowerCamelCase__    )
												components.append(lowerCamelCase__    )
								return index

				lowerCamelCase_						=			[]
				for v in range(lowerCamelCase__    ):
								if index_of[v] == -1:
												strong_connect(lowerCamelCase__	, 0	, lowerCamelCase__    )

				return components





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			[[] for _ in range(lowerCamelCase__    )]
				for u, v in edges:
								g[u].append(lowerCamelCase__    )
				return g


if __name__ == "__main__":
			# Test
			__A				=7
			__A				=[0, 0, 1, 2, 3, 3, 4, 4, 6]
			__A				=[1, 3, 2, 0, 1, 4, 5, 6, 5]
			__A				=[(u, v) for u, v in zip(source, target)]
			__A				=create_graph(n_vertices, edges)

			assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)


