



'''simple docstring'''





from __future__ import annotations

from typing import Any



class     _UpperCAmelCase			:




     def __init__(				self ,						a__):
         A__		 =					num_of_nodes
         A__		 =					[]
         A__		 =					{}




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__):
         self.m_edges.append([u_node, v_node, weight])




     def 		snake_case_		(				self ,						a__):

         if self.m_component[u_node] == u_node:
             return u_node
         return self.find_component(self.m_component[u_node])




     def 		snake_case_		(				self ,						a__):

         if self.m_component[u_node] != u_node:
             for k in self.m_component:
                 A__		 =					self.find_component(a__)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__):

         if component_size[u_node] <= component_size[v_node]:
             A__		 =					v_node
             component_size[v_node] += component_size[u_node]
             self.set_component(a__)

         elif component_size[u_node] >= component_size[v_node]:
             A__		 =					self.find_component(a__)
             component_size[u_node] += component_size[v_node]
             self.set_component(a__)




     def 		snake_case_		(				self):
         A__		 =					[]
         A__		 =					0

         A__		 =					[-1] * self.m_num_of_nodes

         # A list of components (initialized to all of the nodes)
         for node in range(self.m_num_of_nodes):
             self.m_component.update({node: node})
             component_size.append(1)

         A__		 =					self.m_num_of_nodes

         while num_of_components > 1:
             for edge in self.m_edges:
                 A__ ,	A__ ,	A__		 =					edge

                 A__		 =					self.m_component[u]
                 A__		 =					self.m_component[v]

                 if u_component != v_component:

                     for component in (u_component, v_component):
                         if (
                             minimum_weight_edge[component] == -1
                             or minimum_weight_edge[component][2] > w
                         ):
                             A__		 =					[u, v, w]

             for edge in minimum_weight_edge:
                 if isinstance(a__ ,						a__):
                     A__ ,	A__ ,	A__		 =					edge

                     A__		 =					self.m_component[u]
                     A__		 =					self.m_component[v]

                     if u_component != v_component:
                         mst_weight += w
                         self.union(a__ ,						a__ ,						a__)
                         print(F"Added edge [{u} - {v}]\nAdded weight: {w}\n")
                         num_of_components -= 1

             A__		 =					[-1] * self.m_num_of_nodes
         print(F"The total weight of the minimal spanning tree is: {mst_weight}")







def 		lowerCAmelCase__    (					)->  None:
    pass


if __name__ == "__main__":
   import doctest

   doctest.testmod()





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     _UpperCAmelCase			:




        @staticmethod
        def 		snake_case_		(				*a__ ,						**a__):
            pass



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Image	)->  str:
    A__		 =					hashlib.mda(image.tobytes()	)
    return m.hexdigest()



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     _UpperCAmelCase			(       unittest.TestCase     ):
     UpperCamelCase__		    =				MODEL_FOR_DEPTH_ESTIMATION_MAPPING




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__):
         A__		 =					DepthEstimationPipeline(model=a__ ,						image_processor=a__)
         return depth_estimator, [
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
         ]




     def 		snake_case_		(				self ,						a__ ,						a__):
         A__		 =					depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
         self.assertEqual({'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)} ,						a__)
         import datasets

         A__		 =					datasets.load_dataset('''hf-internal-testing/fixtures_image_utils''' ,						'''image''' ,						split='''test''')
         A__		 =					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)},
             ] ,						a__ ,						)




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




     @slow
     @require_torch
     def 		snake_case_		(				self):
         A__		 =					'''Intel/dpt-large'''
         A__		 =					pipeline('''depth-estimation''' ,						model=a__)
         A__		 =					depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg''')
         A__		 =					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):
         # This is highly irregular to have no small tests.
         self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT''')
