
"""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 							__magic_name__     :





							@staticmethod
							def 							lowerCAmelCase      (				*snake_case_   :							Any							,						**snake_case_   :							Any    ):
														pass







@is_pipeline_test
@require_vision
@require_torch
class 							__magic_name__     (		unittest.TestCase   ):
					_SCREAMING_SNAKE_CASE       :					Dict     							=   MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING





					def 							lowerCAmelCase      (				self   :							int							,						snake_case_   :							Optional[Any]							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							Tuple    ):
												__snake_case     			=       pipeline(
												    "zero-shot-object-detection"							,						model="hf-internal-testing/tiny-random-owlvit-object-detection"    )

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





					def 							lowerCAmelCase      (				self   :							int							,						snake_case_   :							Optional[Any]							,						snake_case_   :							Dict    ):
												__snake_case     			=       object_detector(examples[0]							,						threshold=0.0    )

												__snake_case     			=       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 							lowerCAmelCase      (				self   :							Tuple    ):
												pass





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

												__snake_case     			=       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}},
												    ]							,						)

												__snake_case     			=       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 							lowerCAmelCase      (				self   :							List[str]    ):
												__snake_case     			=       pipeline("zero-shot-object-detection"    )

												__snake_case     			=       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}},
												    ]							,						)

												__snake_case     			=       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 							lowerCAmelCase      (				self   :							Optional[int]    ):
												pass





					@require_torch
					@slow
					def 							lowerCAmelCase      (				self   :							List[Any]    ):
												__snake_case     			=       0.2
												__snake_case     			=       pipeline("zero-shot-object-detection"    )

												__snake_case     			=       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 							lowerCAmelCase      (				self   :							Tuple    ):
												__snake_case     			=       2
												__snake_case     			=       pipeline("zero-shot-object-detection"    )

												__snake_case     			=       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 heapq import heappop, heappush

import numpy as np
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    )  ->     tuple[float | int, list[tuple[int, int]]]:







							"""simple docstring"""


							__snake_case			,   __snake_case     			=       grid.shape
							__snake_case     			=       [-1, 1, 0, 0]
							__snake_case     			=       [0, 0, -1, 1]
							if allow_diagonal:
														dx += [-1, -1, 1, 1]
														dy += [-1, 1, -1, 1]

							__snake_case			,   __snake_case     			=       [(0, source)], set()
							__snake_case     			=       np.full((rows, cols)							,    np.inf )
							__snake_case     			=       0
							__snake_case     			=       np.empty((rows, cols)							,    dtype=SCREAMING_SNAKE_CASE )
							__snake_case     			=       None

							while queue:
														((__snake_case)			,   (__snake_case))     			=       heappop(SCREAMING_SNAKE_CASE )
														if (x, y) in visited:
																					continue
														visited.add((x, y) )

														if (x, y) == destination:
																					__snake_case     			=       []
																					while (x, y) != source:
																												path.append((x, y) )
																												__snake_case			,   __snake_case     			=       predecessors[x, y]
																					path.append(SCREAMING_SNAKE_CASE )  # add the source manually
																					path.reverse()
																					return matrix[destination], path

														for i in range(len(SCREAMING_SNAKE_CASE ) ):
																					__snake_case			,   __snake_case     			=       x + dx[i], y + dy[i]
																					if 0 <= nx < rows and 0 <= ny < cols:
																												__snake_case     			=       grid[nx][ny]
																												if next_node == 1 and matrix[nx, ny] > dist + 1:
																																			heappush(SCREAMING_SNAKE_CASE							,    (dist + 1, (nx, ny)) )
																																			__snake_case     			=       dist + 1
																																			__snake_case     			=       (x, y)

							return np.inf, []


if __name__ == "__main__":
		import doctest

		doctest.testmod()
