
import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY





@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class 				UpperCAmelCase (    unittest.TestCase    ):




	'''simple docstring'''





	snake_case_          =	MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING






	def 							UpperCamelCase_    (			self   :					Tuple				,A   :					List[Any]				,A   :					int				,A   :					Any					):
					__A       =					hf_hub_download(
					    repo_id="nateraw/video-demo"				,filename="archery.mp4"				,repo_type="dataset"					)
					__A       =					VideoClassificationPipeline(model=A				,image_processor=A				,top_k=2					)
					__A       =					[
					    example_video_filepath,
					    "https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4",
					]
					return video_classifier, examples






	def 							UpperCamelCase_    (			self   :					str				,A   :					Union[str, Any]				,A   :					Dict					):
					for example in examples:
									__A       =					video_classifier(A					)

									self.assertEqual(
									    A				,[
									        {"score": ANY(A					), "label": ANY(A					)},
									        {"score": ANY(A					), "label": ANY(A					)},
									    ]				,)






	@require_torch
	def 							UpperCamelCase_    (			self   :					Tuple					):
					__A       =					"hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
					__A       =					VideoMAEFeatureExtractor(
					    size={"shortest_edge": 10}				,crop_size={"height": 10, "width": 10}					)
					__A       =					pipeline(
					    "video-classification"				,model=A				,feature_extractor=A				,frame_sampling_rate=4					)

					__A       =					hf_hub_download(repo_id="nateraw/video-demo"				,filename="archery.mp4"				,repo_type="dataset"					)
					__A       =					video_classifier(A				,top_k=2					)
					self.assertEqual(
					    nested_simplify(A				,decimals=4					)				,[{"score": 0.51_99, "label": "LABEL_0"}, {"score": 0.48_01, "label": "LABEL_1"}]				,)

					__A       =					video_classifier(
					    [
					        video_file_path,
					        video_file_path,
					    ]				,top_k=2				,)
					self.assertEqual(
					    nested_simplify(A				,decimals=4					)				,[
					        [{"score": 0.51_99, "label": "LABEL_0"}, {"score": 0.48_01, "label": "LABEL_1"}],
					        [{"score": 0.51_99, "label": "LABEL_0"}, {"score": 0.48_01, "label": "LABEL_1"}],
					    ]				,)







	@require_tf
	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					pass


"""simple docstring"""




def     __magic_name__      ( __snake_case							:			Any	,					__snake_case							:			Union[str, Any]	,					__snake_case							:			Union[str, Any]=False    )					->							Optional[Any]:

       if isinstance(__snake_case	,					__snake_case    ) and isinstance(__snake_case	,					__snake_case    ):
              lowercase       :							Tuple									=				len(set_a.intersection(__snake_case    )    )

              if alternative_union:
                     lowercase       :							Union[str, Any]									=				len(__snake_case    ) + len(__snake_case    )
              else:
                     lowercase       :							Optional[Any]									=				len(set_a.union(__snake_case    )    )

              return intersection / union

       if isinstance(__snake_case	,					(list, tuple)    ) and isinstance(__snake_case	,					(list, tuple)    ):
              lowercase       :							Tuple									=				[element for element in set_a if element in set_b]

              if alternative_union:
                     lowercase       :							List[str]									=				len(__snake_case    ) + len(__snake_case    )
                     return len(__snake_case    ) / union
              else:
                     lowercase       :							Dict									=				set_a + [element for element in set_b if element not in set_a]
                     return len(__snake_case    ) / len(__snake_case    )

              return len(__snake_case    ) / len(__snake_case    )
       return None


if __name__ == "__main__":
   _A     :			List[str]  						=			{"""a""", """b""", """c""", """d""", """e"""}
   _A     :			Dict  						=			{"""c""", """d""", """e""", """f""", """h""", """i"""}
   print(jaccard_similarity(set_a, set_b))


