

from math import factorial




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						int:



    """simple docstring"""






    if n < k or k < 0:
        raise ValueError("Please enter positive integers for n and k where n >= k"			)
    return factorial(SCREAMING_SNAKE_CASE_			) // (factorial(SCREAMING_SNAKE_CASE_			) * factorial(n - k			))


if __name__ == "__main__":
 print(
     """The number of five-card hands possible from a standard""",
     F'''fifty-two card deck is: {combinations(52, 5)}\n''',
 )

 print(
     """If a class of 40 students must be arranged into groups of""",
     F'''4 for group projects, there are {combinations(40, 4)} ways''',
     """to arrange them.\n""",
 )

 print(
     """If 10 teams are competing in a Formula One race, there""",
     F'''are {combinations(10, 3)} ways that first, second and''',
     """third place can be awarded.""",
 )



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 	UpperCAmelCase_				(				*_lowercase		,						**_lowercase	)->     Optional[int]:
          pass






@is_pipeline_test
@require_vision
@require_torch
class 						__magic_name__					(							unittest.TestCase				):
     UpperCamelCase_	:Optional[Any]										=      MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase		,						_lowercase	)->     Union[str, Any]:
         UpperCamelCase_			  =						pipeline(
             "zero-shot-object-detection"		,						model="hf-internal-testing/tiny-random-owlvit-object-detection"	)

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






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase	)->     Union[str, Any]:
         UpperCamelCase_			  =						object_detector(examples[0]		,						threshold=0.0	)

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






     @require_tf
     @unittest.skip("Zero Shot Object Detection not implemented in TF"	)
     def 	UpperCAmelCase_				(				self	)->     str:
         pass






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

         UpperCamelCase_			  =						object_detector(
             "./tests/fixtures/tests_samples/COCO/000000039769.png"		,						candidate_labels=["cat", "remote", "couch"]		,						threshold=0.64		,						)

         self.assertEqual(
             nested_simplify(_lowercase		,						decimals=4	)		,						[
                 {"score": 0.7_235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                 {"score": 0.7_218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                 {"score": 0.7_184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                 {"score": 0.6_748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                 {"score": 0.6_656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                 {"score": 0.6_614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                 {"score": 0.6_456, "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": 0.6_419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
             ]		,						)

         UpperCamelCase_			  =						object_detector(
             [
                 {
                     "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
                     "candidate_labels": ["cat", "remote", "couch"],
                 }
             ]		,						threshold=0.64		,						)

         self.assertEqual(
             nested_simplify(_lowercase		,						decimals=4	)		,						[
                 [
                     {"score": 0.7_235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                     {"score": 0.7_218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                     {"score": 0.7_184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                     {"score": 0.6_748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                     {"score": 0.6_656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                     {"score": 0.6_614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                     {"score": 0.6_456, "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": 0.6_419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
                 ]
             ]		,						)






     @require_torch
     @slow
     def 	UpperCAmelCase_				(				self	)->     Any:
         UpperCamelCase_			  =						pipeline("zero-shot-object-detection"	)

         UpperCamelCase_			  =						object_detector(
             "http://images.cocodataset.org/val2017/000000039769.jpg"		,						candidate_labels=["cat", "remote", "couch"]		,						)
         self.assertEqual(
             nested_simplify(_lowercase		,						decimals=4	)		,						[
                 {"score": 0.2_868, "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": 0.2_537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                 {"score": 0.1_474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                 {"score": 0.1_208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
             ]		,						)

         UpperCamelCase_			  =						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(_lowercase		,						decimals=4	)		,						[
                 [
                     {"score": 0.2_868, "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": 0.2_537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                     {"score": 0.1_474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                     {"score": 0.1_208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
                 ],
                 [
                     {"score": 0.2_868, "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": 0.2_537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                     {"score": 0.1_474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                     {"score": 0.1_208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
                 ],
             ]		,						)






     @require_tf
     @unittest.skip("Zero Shot Object Detection not implemented in TF"	)
     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         pass






     @require_torch
     @slow
     def 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         UpperCamelCase_			  =						0.2
         UpperCamelCase_			  =						pipeline("zero-shot-object-detection"	)

         UpperCamelCase_			  =						object_detector(
             "http://images.cocodataset.org/val2017/000000039769.jpg"		,						candidate_labels=["cat", "remote", "couch"]		,						threshold=_lowercase		,						)
         self.assertEqual(
             nested_simplify(_lowercase		,						decimals=4	)		,						[
                 {"score": 0.2_868, "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": 0.2_537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
             ]		,						)






     @require_torch
     @slow
     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         UpperCamelCase_			  =						2
         UpperCamelCase_			  =						pipeline("zero-shot-object-detection"	)

         UpperCamelCase_			  =						object_detector(
             "http://images.cocodataset.org/val2017/000000039769.jpg"		,						candidate_labels=["cat", "remote", "couch"]		,						top_k=_lowercase		,						)
         self.assertEqual(
             nested_simplify(_lowercase		,						decimals=4	)		,						[
                 {"score": 0.2_868, "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}},
             ]		,						)
