



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 				A__   (    *__lowercase       :    List[Any],       **__lowercase       :    List[Any]    ):
														pass
@is_pipeline_test
@require_vision
@require_torch
class 					_snake_case      (       unittest.TestCase):
				UpperCamelCase__					:       List[Any]  		=MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING




				def 				A__   (    self       :    Any,       __lowercase       :    List[str],       __lowercase       :    int,       __lowercase       :    Optional[int]    ):
							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 				A__   (    self       :    List[Any],       __lowercase       :    Union[str, Any],       __lowercase       :    Union[str, Any]    ):
							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 				A__   (    self       :    Union[str, Any]    ):
							pass




				@require_torch
				def 				A__   (    self       :    Tuple    ):
							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 				A__   (    self       :    Optional[Any]    ):
							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 				A__   (    self       :    Union[str, Any]    ):
							pass




				@require_torch
				@slow
				def 				A__   (    self       :    Union[str, Any]    ):
							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 				A__   (    self       :    Tuple    ):
							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}},
							    ],       )










SCREAMING_SNAKE_CASE:     dict[tuple[int, int, int], int]									= {}


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )->	int:

		# if we are absent twice, or late 3 consecutive days,
		# no further prize strings are possible
		if late == 3 or absent == 2:
				return 0

		# if we have no days left, and have not failed any other rules,
		# we have a prize string
		if days == 0:
				return 1

		# No easy solution, so now we need to do the recursive calculation

		# First, check if the combination is already in the cache, and
		# if yes, return the stored value from there since we already
		# know the number of possible prize strings from this point on
		SCREAMING_SNAKE_CASE_					      =							(days, absent, late)
		if key in cache:
				return cache[key]

		# now we calculate the three possible ways that can unfold from
		# this point on, depending on our attendance today

		# 1) if we are late (but not absent), the "absent" counter stays as
		# it is, but the "late" counter increases by one
		SCREAMING_SNAKE_CASE_					      =							_calculate(days - 1    ,						lowerCAmelCase    ,						late + 1 )

		# 2) if we are absent, the "absent" counter increases by 1, and the
		# "late" counter resets to 0
		SCREAMING_SNAKE_CASE_					      =							_calculate(days - 1    ,						absent + 1    ,						0 )

		# 3) if we are on time, this resets the "late" counter and keeps the
		# absent counter
		SCREAMING_SNAKE_CASE_					      =							_calculate(days - 1    ,						lowerCAmelCase    ,						0 )

		SCREAMING_SNAKE_CASE_					      =							state_late + state_absent + state_ontime

		SCREAMING_SNAKE_CASE_					      =							prizestrings
		return prizestrings


def 					_a							(					lowerCAmelCase = 30 )->	int:
		return _calculate(lowerCAmelCase    ,						absent=0    ,						late=0 )


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