"""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  _snake_case     :






										@staticmethod
										def       lowerCamelCase__     (       *UpperCAmelCase      :			str							,					**UpperCAmelCase      :			Union[str, Any]    ):
																	pass
@is_pipeline_test
@require_vision
@require_torch
class  _snake_case     (					unittest.TestCase		):
						snake_case__			  =			MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING






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

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






						def       lowerCamelCase__     (       self      :			int							,					UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			List[str]    ):
													__lowerCamelCase					:		List[str]		   =     object_detector(examples[0]							,					threshold=0.0    )

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






						@require_tf
						@unittest.skip("Zero Shot Object Detection not implemented in TF"    )
						def       lowerCamelCase__     (       self      :			List[str]    ):
													pass






						@require_torch
						def       lowerCamelCase__     (       self      :			List[Any]    ):
													__lowerCamelCase					:		int		   =     pipeline(
													    "zero-shot-object-detection"							,					model="hf-internal-testing/tiny-random-owlvit-object-detection"    )

													__lowerCamelCase					:		List[str]		   =     object_detector(
													    "./tests/fixtures/tests_samples/COCO/000000039769.png"							,					candidate_labels=["cat", "remote", "couch"]							,					threshold=0.6_4							,					)

													self.assertEqual(
													    nested_simplify(UpperCAmelCase							,					decimals=4    )							,					[
													        {"score": 0.7_2_3_5, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
													        {"score": 0.7_2_1_8, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
													        {"score": 0.7_1_8_4, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
													        {"score": 0.6_7_4_8, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
													        {"score": 0.6_6_5_6, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
													        {"score": 0.6_6_1_4, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
													        {"score": 0.6_4_5_6, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
													        {"score": 0.6_4_2, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
													        {"score": 0.6_4_1_9, "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=0.6_4							,					)

													self.assertEqual(
													    nested_simplify(UpperCAmelCase							,					decimals=4    )							,					[
													        [
													            {"score": 0.7_2_3_5, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
													            {"score": 0.7_2_1_8, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
													            {"score": 0.7_1_8_4, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
													            {"score": 0.6_7_4_8, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
													            {"score": 0.6_6_5_6, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
													            {"score": 0.6_6_1_4, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
													            {"score": 0.6_4_5_6, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
													            {"score": 0.6_4_2, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
													            {"score": 0.6_4_1_9, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
													        ]
													    ]							,					)






						@require_torch
						@slow
						def       lowerCamelCase__     (       self      :			List[str]    ):
													__lowerCamelCase					:		Dict		   =     pipeline("zero-shot-object-detection"    )

													__lowerCamelCase					:		Any		   =     object_detector(
													    "http://images.cocodataset.org/val2017/000000039769.jpg"							,					candidate_labels=["cat", "remote", "couch"]							,					)
													self.assertEqual(
													    nested_simplify(UpperCAmelCase							,					decimals=4    )							,					[
													        {"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
													        {"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
													        {"score": 0.2_5_3_7, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
													        {"score": 0.1_4_7_4, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
													        {"score": 0.1_2_0_8, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
													    ]							,					)

													__lowerCamelCase					:		Tuple		   =     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(UpperCAmelCase							,					decimals=4    )							,					[
													        [
													            {"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
													            {"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
													            {"score": 0.2_5_3_7, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
													            {"score": 0.1_4_7_4, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
													            {"score": 0.1_2_0_8, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
													        ],
													        [
													            {"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
													            {"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
													            {"score": 0.2_5_3_7, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
													            {"score": 0.1_4_7_4, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
													            {"score": 0.1_2_0_8, "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      :			int    ):
													pass






						@require_torch
						@slow
						def       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase					:		Dict		   =     0.2
													__lowerCamelCase					:		List[str]		   =     pipeline("zero-shot-object-detection"    )

													__lowerCamelCase					:		Dict		   =     object_detector(
													    "http://images.cocodataset.org/val2017/000000039769.jpg"							,					candidate_labels=["cat", "remote", "couch"]							,					threshold=UpperCAmelCase							,					)
													self.assertEqual(
													    nested_simplify(UpperCAmelCase							,					decimals=4    )							,					[
													        {"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
													        {"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
													        {"score": 0.2_5_3_7, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
													    ]							,					)







						@require_torch
						@slow
						def       lowerCamelCase__     (       self      :			int    ):
													__lowerCamelCase					:		Any		   =     2
													__lowerCamelCase					:		int		   =     pipeline("zero-shot-object-detection"    )

													__lowerCamelCase					:		List[Any]		   =     object_detector(
													    "http://images.cocodataset.org/val2017/000000039769.jpg"							,					candidate_labels=["cat", "remote", "couch"]							,					top_k=UpperCAmelCase							,					)
													self.assertEqual(
													    nested_simplify(UpperCAmelCase							,					decimals=4    )							,					[
													        {"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
													        {"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
													    ]							,					)
"""simple docstring"""





from functools import lru_cache


def    lowercase_   (   _lowerCamelCase:	int      ) ->      set:



							'''simple docstring'''

							__lowerCamelCase					:		Optional[Any]		   =     2
							__lowerCamelCase					:		Tuple		   =     set()
							while i * i <= n:
														if n % i:
																					i += 1
														else:
																					n //= i
																					factors.add(_lowerCamelCase      )
							if n > 1:
														factors.add(_lowerCamelCase      )
							return factors


@lru_cache
def    lowercase_   (   _lowerCamelCase:	int      ) ->      int:



							'''simple docstring'''

							return len(unique_prime_factors(_lowerCamelCase      )      )


def    lowercase_   (   _lowerCamelCase:	list      ) ->      bool:



							'''simple docstring'''

							return len(set(_lowerCamelCase      )      ) in (0, 1)


def    lowercase_   (   _lowerCamelCase:	int      ) ->      list:



							'''simple docstring'''

							__lowerCamelCase					:		str		   =     2

							while True:
														# Increment each value of a generated range
														__lowerCamelCase					:		int		   =     [base + i for i in range(_lowerCamelCase      )]

														# Run elements through out unique_prime_factors function
														# Append our target number to the end.
														__lowerCamelCase					:		Dict		   =     [upf_len(_lowerCamelCase      ) for x in group]
														checker.append(_lowerCamelCase      )

														# If all numbers in the list are equal, return the group variable.
														if equality(_lowerCamelCase      ):
																					return group

														# Increment our base variable by 1
														base += 1


def    lowercase_   (   _lowerCamelCase:	int = 4      ) ->      int:



							'''simple docstring'''

							__lowerCamelCase					:		Any		   =     run(_lowerCamelCase      )
							return results[0] if len(_lowerCamelCase      ) else None


if __name__ == "__main__":
				print(solution())