


"""simple docstring"""







from __future__ import annotations

from typing import Any


class 				SCREAMING_SNAKE_CASE__    :
       """simple docstring"""


       def __init__(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							num_of_nodes
         lowerCAmelCase					:					list[list[int]]								=							[]
         lowerCAmelCase					:					dict[int, int]								=							{}

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         self.m_edges.append([u_node, v_node, weight]						)

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





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

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         if self.m_component[u_node] != u_node:
           for k in self.m_component:
             lowerCAmelCase					:					Optional[int]								=							self.find_component(snake_case__						)

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         if component_size[u_node] <= component_size[v_node]:
           lowerCAmelCase					:					Any								=							v_node
           component_size[v_node] += component_size[u_node]
           self.set_component(snake_case__						)

         elif component_size[u_node] >= component_size[v_node]:
           lowerCAmelCase					:					str								=							self.find_component(snake_case__						)
           component_size[u_node] += component_size[v_node]
           self.set_component(snake_case__						)







       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Tuple								=							[]
         lowerCAmelCase					:					Optional[int]								=							0

         lowerCAmelCase					:					list[Any]								=							[-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						)

         lowerCAmelCase					:					List[Any]								=							self.m_num_of_nodes

         while num_of_components > 1:
           for edge in self.m_edges:
             lowerCAmelCase					:					Any								=							edge

             lowerCAmelCase					:					Optional[int]								=							self.m_component[u]
             lowerCAmelCase					:					List[Any]								=							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
                 ):
                   lowerCAmelCase					:					int								=							[u, v, w]

           for edge in minimum_weight_edge:
             if isinstance(snake_case__		,					snake_case__						):
               lowerCAmelCase					:					Optional[int]								=							edge

               lowerCAmelCase					:					List[str]								=							self.m_component[u]
               lowerCAmelCase					:					List[Any]								=							self.m_component[v]

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

           lowerCAmelCase					:					Union[str, Any]								=							[-1] * self.m_num_of_nodes
         print(f"""The total weight of the minimal spanning tree is: {mst_weight}"""						)

def  a__      (    ):


  '''simple docstring'''
















if __name__ == "__main__":
     import doctest

     doctest.testmod()






"""simple docstring"""







import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
     from PIL import Image
else:


     class 				SCREAMING_SNAKE_CASE__    :
            """simple docstring"""


            @staticmethod
            def 	lowercase__							(						*snake_case__		,					**snake_case__						):






              """simple docstring"""





              pass


@is_pipeline_test
@require_vision
@require_torch
class 				SCREAMING_SNAKE_CASE__    (		unittest.TestCase    ):
       """simple docstring"""


       a  :    Optional[Any]       =MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					str								=							pipeline(
             "zero-shot-object-detection"		,					model="hf-internal-testing/tiny-random-owlvit-object-detection"						)

         lowerCAmelCase					:					Dict								=							[
             {
                 "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
                 "candidate_labels": ["cat", "remote", "couch"],
             }
         ]
         return object_detector, examples

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[Any]								=							object_detector(examples[0]		,					threshold=0.0						)

         lowerCAmelCase					:					Dict								=							len(snake_case__						)
         self.assertGreater(snake_case__		,					0						)
         self.assertEqual(
             snake_case__		,					[
                 {
                     "score": ANY(snake_case__						),
                     "label": ANY(snake_case__						),
                     "box": {"xmin": ANY(snake_case__						), "ymin": ANY(snake_case__						), "xmax": ANY(snake_case__						), "ymax": ANY(snake_case__						)},
                 }
                 for i in range(snake_case__						)
             ]		,					)

       @require_tf
       @unittest.skip("Zero Shot Object Detection not implemented in TF"						)
       def 	lowercase__							(						self						):






         """simple docstring"""





         pass

       @require_torch
       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					str								=							pipeline(
             "zero-shot-object-detection"		,					model="hf-internal-testing/tiny-random-owlvit-object-detection"						)

         lowerCAmelCase					:					Tuple								=							object_detector(
             "./tests/fixtures/tests_samples/COCO/000000039769.png"		,					candidate_labels=["cat", "remote", "couch"]		,					threshold=0.64		,					)

         self.assertEqual(
             nested_simplify(snake_case__		,					decimals=4						)		,					[
                 {"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                 {"score": 0.7218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                 {"score": 0.7184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                 {"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                 {"score": 0.6656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                 {"score": 0.6614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                 {"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
                 {"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
                 {"score": 0.6419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
             ]		,					)

         lowerCAmelCase					:					Optional[Any]								=							object_detector(
             [
                 {
                     "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
                     "candidate_labels": ["cat", "remote", "couch"],
                 }
             ]		,					threshold=0.64		,					)

         self.assertEqual(
             nested_simplify(snake_case__		,					decimals=4						)		,					[
                 [
                     {"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                     {"score": 0.7218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                     {"score": 0.7184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                     {"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                     {"score": 0.6656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                     {"score": 0.6614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                     {"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
                     {"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
                     {"score": 0.6419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
                 ]
             ]		,					)

       @require_torch
       @slow
       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Dict								=							pipeline("zero-shot-object-detection"						)

         lowerCAmelCase					:					Dict								=							object_detector(
             "http://images.cocodataset.org/val2017/000000039769.jpg"		,					candidate_labels=["cat", "remote", "couch"]		,					)
         self.assertEqual(
             nested_simplify(snake_case__		,					decimals=4						)		,					[
                 {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                 {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                 {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                 {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                 {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
             ]		,					)

         lowerCAmelCase					:					Dict								=							object_detector(
             [
                 {
                     "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
                     "candidate_labels": ["cat", "remote", "couch"],
                 },
                 {
                     "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
                     "candidate_labels": ["cat", "remote", "couch"],
                 },
             ]		,					)
         self.assertEqual(
             nested_simplify(snake_case__		,					decimals=4						)		,					[
                 [
                     {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                     {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                     {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                     {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                     {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
                 ],
                 [
                     {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                     {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                     {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                     {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                     {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
                 ],
             ]		,					)

       @require_tf
       @unittest.skip("Zero Shot Object Detection not implemented in TF"						)
       def 	lowercase__							(						self						):






         """simple docstring"""





         pass

       @require_torch
       @slow
       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Dict								=							0.2
         lowerCAmelCase					:					List[Any]								=							pipeline("zero-shot-object-detection"						)

         lowerCAmelCase					:					Union[str, Any]								=							object_detector(
             "http://images.cocodataset.org/val2017/000000039769.jpg"		,					candidate_labels=["cat", "remote", "couch"]		,					threshold=snake_case__		,					)
         self.assertEqual(
             nested_simplify(snake_case__		,					decimals=4						)		,					[
                 {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                 {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                 {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
             ]		,					)







       @require_torch
       @slow
       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							2
         lowerCAmelCase					:					Any								=							pipeline("zero-shot-object-detection"						)

         lowerCAmelCase					:					Any								=							object_detector(
             "http://images.cocodataset.org/val2017/000000039769.jpg"		,					candidate_labels=["cat", "remote", "couch"]		,					top_k=snake_case__		,					)
         self.assertEqual(
             nested_simplify(snake_case__		,					decimals=4						)		,					[
                 {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                 {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
             ]		,					)





