



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 	_A :


							'''simple docstring'''





							@staticmethod
							def      _snake_case						(						*lowerCamelCase						:				Optional[int]				,   **lowerCamelCase						:				int						):






								'''simple docstring'''



								pass

@is_pipeline_test
@require_vision
@require_torch
class 	_A (			unittest.TestCase						):


						'''simple docstring'''





						_snake_case      :  Any   				=   MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING



						def      _snake_case						(						self						:				Optional[Any]				,   lowerCamelCase						:				List[str]				,   lowerCamelCase						:				List[Any]				,   lowerCamelCase						:				Any						):






							'''simple docstring'''



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

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



						def      _snake_case						(						self						:				str				,   lowerCamelCase						:				Tuple				,   lowerCamelCase						:				Optional[int]						):






							'''simple docstring'''



							__lowercase									=      object_detector(examples[0]				,   threshold=0.0						)

							__lowercase									=      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      _snake_case						(						self						:				Optional[int]						):






							'''simple docstring'''



							pass



						@require_torch
						def      _snake_case						(						self						:				List[Any]						):






							'''simple docstring'''



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

							__lowercase									=      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}},
							    ]				,   )

							__lowercase									=      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      _snake_case						(						self						:				str						):






							'''simple docstring'''



							__lowercase									=      pipeline("zero-shot-object-detection"						)

							__lowercase									=      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}},
							    ]				,   )

							__lowercase									=      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      _snake_case						(						self						:				Any						):






							'''simple docstring'''



							pass



						@require_torch
						@slow
						def      _snake_case						(						self						:				Tuple						):






							'''simple docstring'''



							__lowercase									=      0.2
							__lowercase									=      pipeline("zero-shot-object-detection"						)

							__lowercase									=      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      _snake_case						(						self						:				Optional[int]						):






							'''simple docstring'''



							__lowercase									=      2
							__lowercase									=      pipeline("zero-shot-object-detection"						)

							__lowercase									=      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}},
							    ]				,   )










'''simple docstring'''




from __future__ import annotations

import os
from collections.abc import Mapping

_SCREAMING_SNAKE_CASE							     =			tuple[int, int]
class 			_lowerCAmelCase		:






     """simple docstring"""






     def __init__(   self							:			List[Any]   ,       __snake_case							:			set[int]   ,       __snake_case							:			Mapping[EdgeT, int]						)->	None:
        snake_case 						=   vertices
        snake_case 						=   {
            (min(__snake_case						), max(__snake_case						)): weight for edge, weight in edges.items()
        }



     def      lowerCAmelCase			(   self							:			Optional[Any]   ,       __snake_case							:			EdgeT   ,       __snake_case							:			int						)->	None:
        self.vertices.add(edge[0]						)
        self.vertices.add(edge[1]						)
        snake_case 						=   weight



     def      lowerCAmelCase			(   self							:			str						)->	Graph:
        snake_case 						=   Graph({min(self.vertices						)}   ,       {}						)
        snake_case 						=   42
        snake_case 						=   42
        snake_case 						=   42
        snake_case 						=   42

        while len(subgraph.vertices						) < len(self.vertices						):
           snake_case 						=   max(self.edges.values()						) + 1
           for edge, weight in self.edges.items():
              if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
                 if weight < min_weight:
                    snake_case 						=   edge
                    snake_case 						=   weight

           subgraph.add_edge(__snake_case   ,       __snake_case						)

        return subgraph





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str = "p107_network.txt"		)		->  int:
   snake_case 						=   os.path.abspath(os.path.dirname(__lowerCAmelCase		)		)
   snake_case 						=   os.path.join(__lowerCAmelCase      ,    __lowerCAmelCase		)
   snake_case 						=   {}
   snake_case 						=   42
   snake_case 						=   42
   snake_case 						=   42

   with open(__lowerCAmelCase		) as f:
      snake_case 						=   f.read().strip().split("""\n"""		)

   snake_case 						=   [line.split(""","""		) for line in data]

   for edgea in range(1      ,    len(__lowerCAmelCase		)		):
      for edgea in range(__lowerCAmelCase		):
         if adjaceny_matrix[edgea][edgea] != "-":
            snake_case 						=   int(adjaceny_matrix[edgea][edgea]		)

   snake_case 						=   Graph(set(range(len(__lowerCAmelCase		)		)		)      ,    __lowerCAmelCase		)

   snake_case 						=   graph.prims_algorithm()

   snake_case 						=   sum(graph.edges.values()		)
   snake_case 						=   sum(subgraph.edges.values()		)

   return initial_total - optimal_total


if __name__ == "__main__":
 print(F"""{solution() = }""")

