

'''simple docstring'''


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 					lowercase						:


													@staticmethod
													def 						_snake_case							(			*_snake_case    ,			**_snake_case)							->				str:
														pass







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    str:
	UpperCAmelCase_		:      Optional[int]       	=					hashlib.mda(image.tobytes()  )
	return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 					lowercase						(  unittest.TestCase ):
							_lowerCamelCase		:      Tuple=   MODEL_FOR_DEPTH_ESTIMATION_MAPPING


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				Dict:
								UpperCAmelCase_		:      Optional[int]       	=					DepthEstimationPipeline(model=__UpperCamelCase    ,			image_processor=__UpperCamelCase)
								return depth_estimator, [
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								]


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case)							->				Union[str, Any]:
								UpperCAmelCase_		:      List[str]       	=					depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png')
								self.assertEqual({'predicted_depth': ANY(torch.Tensor), 'depth': ANY(Image.Image)}    ,			__UpperCamelCase)
								import datasets

								UpperCAmelCase_		:      int       	=					datasets.load_dataset('hf-internal-testing/fixtures_image_utils'    ,			'image'    ,			split='test')
								UpperCAmelCase_		:      Tuple       	=					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)},
								    ]    ,			__UpperCamelCase    ,			)


							@require_tf
							@unittest.skip('Depth estimation is not implemented in TF')
							def 						_snake_case							(			self)							->				str:
								pass


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

								# This seems flaky.
								# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
								self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item())    ,			29.304)
								self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item())    ,			2.662)





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









'''simple docstring'''


import math



class 					lowercase						:


							def __init__(			self    ,			_snake_case=0)							->				Union[str, Any]:  # a graph with Node 0,1,...,N-1
								UpperCAmelCase_		:      Tuple       	=					n
								UpperCAmelCase_		:      Optional[Any]       	=					[
								    [math.inf for j in range(0    ,			_snake_case)] for i in range(0    ,			_snake_case)
								]  # adjacency matrix for weight
								UpperCAmelCase_		:      Tuple       	=					[
								    [math.inf for j in range(0    ,			_snake_case)] for i in range(0    ,			_snake_case)
								]  # dp[i][j] stores minimum distance from i to j


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				List[Any]:
								UpperCAmelCase_		:      Union[str, Any]       	=					w


							def 						_snake_case							(			self)							->				str:
								for k in range(0    ,			self.n):
									for i in range(0    ,			self.n):
										for j in range(0    ,			self.n):
											UpperCAmelCase_		:      Optional[int]       	=					min(self.dp[i][j]    ,			self.dp[i][k] + self.dp[k][j])





							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case)							->				str:
								return self.dp[u][v]


if __name__ == "__main__":
						lowerCAmelCase__										= Graph(5)
						graph.add_edge(0, 2, 9)
						graph.add_edge(0, 4, 10)
						graph.add_edge(1, 3, 5)
						graph.add_edge(2, 3, 7)
						graph.add_edge(3, 0, 10)
						graph.add_edge(3, 1, 2)
						graph.add_edge(3, 2, 1)
						graph.add_edge(3, 4, 6)
						graph.add_edge(4, 1, 3)
						graph.add_edge(4, 2, 4)
						graph.add_edge(4, 3, 9)
						graph.floyd_warshall()
						graph.show_min(1, 4)
						graph.show_min(0, 3)






