


"""simple docstring"""







from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

lowerCAmelCase__				    =       TypeVar('''T''')
def  a__      (    SCREAMING_SNAKE_CASE   :   int    ):


  '''simple docstring'''







  return (position - 1) // 2
def  a__      (    SCREAMING_SNAKE_CASE   :   int    ):


  '''simple docstring'''







  return (2 * position) + 1
def  a__      (    SCREAMING_SNAKE_CASE   :   int    ):


  '''simple docstring'''







  return (2 * position) + 2


class 				SCREAMING_SNAKE_CASE__    (		Generic[T]    ):
       """simple docstring"""


       def __init__(						self						):






         """simple docstring"""





         lowerCAmelCase					:					list[tuple[T, int]]								=							[]
         lowerCAmelCase					:					dict[T, int]								=							{}
         lowerCAmelCase					:					int								=							0

       def __len__(						self						):






         """simple docstring"""





         return self.elements

       def __repr__(						self						):






         """simple docstring"""





         return str(self.heap						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         return self.elements == 0

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         self.heap.append((elem, weight)						)
         lowerCAmelCase					:					Tuple								=							self.elements
         self.elements += 1
         self._bubble_up(snake_case__						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         if self.elements > 1:
           self._swap_nodes(0		,					self.elements - 1						)
         lowerCAmelCase					:					Tuple								=							self.heap.pop()
         del self.position_map[elem]
         self.elements -= 1
         if self.elements > 0:
           lowerCAmelCase					:					Union[str, Any]								=							self.heap[0]
           self._bubble_down(snake_case__						)
         return elem

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					str								=							self.position_map[elem]
         lowerCAmelCase					:					Optional[int]								=							(elem, weight)
         if position > 0:
           lowerCAmelCase					:					Union[str, Any]								=							get_parent_position(snake_case__						)
           lowerCAmelCase					:					List[Any]								=							self.heap[parent_position]
           if parent_weight > weight:
             self._bubble_up(snake_case__						)
           else:
             self._bubble_down(snake_case__						)
         else:
           self._bubble_down(snake_case__						)

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					List[str]								=							self.position_map[elem]
         if curr_pos == 0:
           return None
         lowerCAmelCase					:					List[str]								=							get_parent_position(snake_case__						)
         lowerCAmelCase					:					str								=							self.heap[curr_pos]
         lowerCAmelCase					:					Tuple								=							self.heap[parent_position]
         if parent_weight > weight:
           self._swap_nodes(snake_case__		,					snake_case__						)
           return self._bubble_up(snake_case__						)
         return None

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Any								=							self.position_map[elem]
         lowerCAmelCase					:					List[str]								=							self.heap[curr_pos]
         lowerCAmelCase					:					Optional[Any]								=							get_child_left_position(snake_case__						)
         lowerCAmelCase					:					Union[str, Any]								=							get_child_right_position(snake_case__						)
         if child_left_position < self.elements and child_right_position < self.elements:
           lowerCAmelCase					:					Optional[Any]								=							self.heap[child_left_position]
           lowerCAmelCase					:					Dict								=							self.heap[child_right_position]
           if child_right_weight < child_left_weight and child_right_weight < weight:
             self._swap_nodes(snake_case__		,					snake_case__						)
             return self._bubble_down(snake_case__						)
         if child_left_position < self.elements:
           lowerCAmelCase					:					int								=							self.heap[child_left_position]
           if child_left_weight < weight:
             self._swap_nodes(snake_case__		,					snake_case__						)
             return self._bubble_down(snake_case__						)
         else:
           return None
         if child_right_position < self.elements:
           lowerCAmelCase					:					Optional[int]								=							self.heap[child_right_position]
           if child_right_weight < weight:
             self._swap_nodes(snake_case__		,					snake_case__						)
             return self._bubble_down(snake_case__						)
         return None







       def 	lowercase__							(						self		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Dict								=							self.heap[nodea_pos][0]
         lowerCAmelCase					:					Optional[Any]								=							self.heap[nodea_pos][0]
         lowerCAmelCase					:					Tuple								=							(
             self.heap[nodea_pos],
             self.heap[nodea_pos],
         )
         lowerCAmelCase					:					int								=							nodea_pos
         lowerCAmelCase					:					str								=							nodea_pos


class 				SCREAMING_SNAKE_CASE__    (		Generic[T]    ):
       """simple docstring"""


       def __init__(						self						):






         """simple docstring"""





         lowerCAmelCase					:					dict[T, dict[T, int]]								=							{}
         lowerCAmelCase					:					int								=							0

       def __repr__(						self						):






         """simple docstring"""





         return str(self.connections						)

       def __len__(						self						):






         """simple docstring"""





         return self.nodes

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         if node not in self.connections:
           lowerCAmelCase					:					List[Any]								=							{}
           self.nodes += 1







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






         """simple docstring"""





         self.add_node(snake_case__						)
         self.add_node(snake_case__						)
         lowerCAmelCase					:					List[Any]								=							weight
         lowerCAmelCase					:					Dict								=							weight

def  a__      (    SCREAMING_SNAKE_CASE   :   GraphUndirectedWeighted[T]    ,				):


  '''simple docstring'''







  lowerCAmelCase					:					dict[T, int]								=							{node: maxsize for node in graph.connections}
  lowerCAmelCase					:					dict[T, T | None]								=							{node: None for node in graph.connections}

  lowerCAmelCase					:					MinPriorityQueue[T]								=							MinPriorityQueue()
  for node, weight in dist.items():
    priority_queue.push(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )

  if priority_queue.is_empty():
    return dist, parent

  # initialization
  lowerCAmelCase					:					Optional[Any]								=							priority_queue.extract_min()
  lowerCAmelCase					:					Optional[int]								=							0
  for neighbour in graph.connections[node]:
    if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
      lowerCAmelCase					:					List[str]								=							dist[node] + graph.connections[node][neighbour]
      priority_queue.update_key(SCREAMING_SNAKE_CASE    ,				dist[neighbour]    )
      lowerCAmelCase					:					Optional[Any]								=							node

    # running prim's algorithm
  while not priority_queue.is_empty():
    lowerCAmelCase					:					List[str]								=							priority_queue.extract_min()
    for neighbour in graph.connections[node]:
      if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
        lowerCAmelCase					:					str								=							dist[node] + graph.connections[node][neighbour]
        priority_queue.update_key(SCREAMING_SNAKE_CASE    ,				dist[neighbour]    )
        lowerCAmelCase					:					Tuple								=							node
  return dist, parent






"""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}},
             ]		,					)





