





'''simple docstring'''




from queue import PriorityQueue
from typing import Any

import numpy as np







def _lowerCAmelCase  (   _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     )->  float | int:
 for nxt, d in graph[v]:
  if nxt in visited_forward:
   continue
  __UpperCAmelCase       = cst_fwd.get(__UpperCamelCase							,     np.inf     )
  __UpperCAmelCase       = cst_fwd[v] + d
  if new_cost_f < old_cost_f:
   queue.put((new_cost_f, nxt)     )
   __UpperCAmelCase       = new_cost_f
   __UpperCAmelCase       = v
  if nxt in visited_backward:
   if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
    __UpperCAmelCase       = cst_fwd[v] + d + cst_bwd[nxt]
 return shortest_distance







def _lowerCAmelCase  (   _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase     )->  int:
 __UpperCAmelCase       = -1

 __UpperCAmelCase       = set()
 __UpperCAmelCase       = set()
 __UpperCAmelCase       = {source: 0}
 __UpperCAmelCase       = {destination: 0}
 __UpperCAmelCase       = {source: None}
 __UpperCAmelCase       = {destination: None}
 __UpperCAmelCase       = PriorityQueue()
 __UpperCAmelCase       = PriorityQueue()

 __UpperCAmelCase       = np.inf

 queue_forward.put((0, source)     )
 queue_backward.put((0, destination)     )

 if source == destination:
  return 0

 while not queue_forward.empty() and not queue_backward.empty():
  __UpperCAmelCase       ,     __UpperCAmelCase       = queue_forward.get()
  visited_forward.add(__UpperCamelCase     )

  __UpperCAmelCase       ,     __UpperCAmelCase       = queue_backward.get()
  visited_backward.add(__UpperCamelCase     )

  __UpperCAmelCase       = pass_and_relaxation(
      __UpperCamelCase							,     __UpperCamelCase							,     __UpperCamelCase							,     __UpperCamelCase							,     __UpperCamelCase							,     __UpperCamelCase							,     __UpperCamelCase							,     __UpperCamelCase							,     __UpperCamelCase							,     )

  __UpperCAmelCase       = pass_and_relaxation(
      __UpperCamelCase							,     __UpperCamelCase							,     __UpperCamelCase							,     __UpperCamelCase							,     __UpperCamelCase							,     __UpperCamelCase							,     __UpperCamelCase							,     __UpperCamelCase							,     __UpperCamelCase							,     )

  if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
   break

 if shortest_distance != np.inf:
  __UpperCAmelCase       = shortest_distance
 return shortest_path_distance


_A:       int						    =					{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}
_A:       Optional[Any]						    =					{
    """B""": [["""E""", 1]],
    """C""": [["""B""", 1]],
    """D""": [["""C""", 1]],
    """F""": [["""D""", 1], ["""G""", 1]],
    """E""": [[None, np.inf]],
    """G""": [["""E""", 2]],
}

if __name__ == "__main__":
    import doctest

    doctest.testmod()










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   __lowerCAmelCase  :
												"""simple docstring"""






												@staticmethod
												def      _a				(  *_snake_case       :							int					,       **_snake_case       :							List[str]       ):






															"""simple docstring"""


															pass


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






					A__					:		List[str]      							=     MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING



					def      _a				(  self       :							Any					,       _snake_case       :							Union[str, Any]					,       _snake_case       :							Tuple					,       _snake_case       :							Optional[Any]       ):






								"""simple docstring"""


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

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



					def      _a				(  self       :							int					,       _snake_case       :							int					,       _snake_case       :							List[str]       ):






								"""simple docstring"""


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

								A__	  =     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      _a				(  self       :							List[str]       ):






								"""simple docstring"""


								pass



					@require_torch
					def      _a				(  self       :							Optional[int]       ):






								"""simple docstring"""


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

								A__	  =     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': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
								        {'score': 0.7218, 'label': 'remote', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
								        {'score': 0.7184, 'label': 'couch', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
								        {'score': 0.6748, 'label': 'remote', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
								        {'score': 0.6656, 'label': 'cat', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
								        {'score': 0.6614, 'label': 'couch', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
								        {'score': 0.6456, 'label': 'remote', 'box': {'xmin': 4_94, 'ymin': 1_05, 'xmax': 5_21, 'ymax': 1_27}},
								        {'score': 0.642, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 2_74, 'xmax': 93, 'ymax': 2_97}},
								        {'score': 0.6419, 'label': 'cat', 'box': {'xmin': 4_94, 'ymin': 1_05, 'xmax': 5_21, 'ymax': 1_27}},
								    ]					,       )

								A__	  =     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': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
								            {'score': 0.7218, 'label': 'remote', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
								            {'score': 0.7184, 'label': 'couch', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
								            {'score': 0.6748, 'label': 'remote', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
								            {'score': 0.6656, 'label': 'cat', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
								            {'score': 0.6614, 'label': 'couch', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
								            {'score': 0.6456, 'label': 'remote', 'box': {'xmin': 4_94, 'ymin': 1_05, 'xmax': 5_21, 'ymax': 1_27}},
								            {'score': 0.642, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 2_74, 'xmax': 93, 'ymax': 2_97}},
								            {'score': 0.6419, 'label': 'cat', 'box': {'xmin': 4_94, 'ymin': 1_05, 'xmax': 5_21, 'ymax': 1_27}},
								        ]
								    ]					,       )



					@require_torch
					@slow
					def      _a				(  self       :							int       ):






								"""simple docstring"""


								A__	  =     pipeline('zero-shot-object-detection'       )

								A__	  =     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': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
								        {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
								        {'score': 0.2537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 3_15, 'ymax': 4_72}},
								        {'score': 0.1474, 'label': 'remote', 'box': {'xmin': 3_35, 'ymin': 74, 'xmax': 3_71, 'ymax': 1_87}},
								        {'score': 0.1208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 6_42, 'ymax': 4_76}},
								    ]					,       )

								A__	  =     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': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
								            {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
								            {'score': 0.2537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 3_15, 'ymax': 4_72}},
								            {'score': 0.1474, 'label': 'remote', 'box': {'xmin': 3_35, 'ymin': 74, 'xmax': 3_71, 'ymax': 1_87}},
								            {'score': 0.1208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 6_42, 'ymax': 4_76}},
								        ],
								        [
								            {'score': 0.2868, 'label': 'cat', 'box': {'xmin': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
								            {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
								            {'score': 0.2537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 3_15, 'ymax': 4_72}},
								            {'score': 0.1474, 'label': 'remote', 'box': {'xmin': 3_35, 'ymin': 74, 'xmax': 3_71, 'ymax': 1_87}},
								            {'score': 0.1208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 6_42, 'ymax': 4_76}},
								        ],
								    ]					,       )



					@require_tf
					@unittest.skip('Zero Shot Object Detection not implemented in TF'       )
					def      _a				(  self       :							int       ):






								"""simple docstring"""


								pass



					@require_torch
					@slow
					def      _a				(  self       :							str       ):






								"""simple docstring"""


								A__	  =     0.2
								A__	  =     pipeline('zero-shot-object-detection'       )

								A__	  =     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': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
								        {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
								        {'score': 0.2537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 3_15, 'ymax': 4_72}},
								    ]					,       )



					@require_torch
					@slow
					def      _a				(  self       :							Any       ):






								"""simple docstring"""


								A__	  =     2
								A__	  =     pipeline('zero-shot-object-detection'       )

								A__	  =     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': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
								        {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
								    ]					,       )




