
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 			_lowerCAmelCase      (			unittest.TestCase ):
				_lowercase    	=MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Dict:
							lowerCAmelCase_      			=			hf_hub_download(
							    repo_id="nateraw/video-demo"       ,       filename="archery.mp4"       ,       repo_type="dataset"  )
							lowerCAmelCase_      			=			VideoClassificationPipeline(model=_UpperCamelCase       ,       image_processor=_UpperCamelCase       ,       top_k=2  )
							lowerCAmelCase_      			=			[
							    example_video_filepath,
							    "https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4",
							]
							return video_classifier, examples




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Optional[Any]:
							for example in examples:
										lowerCAmelCase_      			=			video_classifier(_UpperCamelCase  )

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




				@require_torch
				def   __a		(      self  )       ->   List[str]:
							lowerCAmelCase_      			=			"hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
							lowerCAmelCase_      			=			VideoMAEFeatureExtractor(
							    size={"shortest_edge": 10}       ,       crop_size={"height": 10, "width": 10}  )
							lowerCAmelCase_      			=			pipeline(
							    "video-classification"       ,       model=_UpperCamelCase       ,       feature_extractor=_UpperCamelCase       ,       frame_sampling_rate=4  )

							lowerCAmelCase_      			=			hf_hub_download(repo_id="nateraw/video-demo"       ,       filename="archery.mp4"       ,       repo_type="dataset"  )
							lowerCAmelCase_      			=			video_classifier(_UpperCamelCase       ,       top_k=2  )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase       ,       decimals=4  )       ,       [{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}]       ,       )

							lowerCAmelCase_      			=			video_classifier(
							    [
							        video_file_path,
							        video_file_path,
							    ]       ,       top_k=2       ,       )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase       ,       decimals=4  )       ,       [
							        [{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}],
							        [{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}],
							    ]       ,       )




				@require_tf
				def   __a		(      self  )       ->   Dict:
							pass





import functools






def      lowerCamelCase__       (       __lowerCAmelCase						:						str					,							__lowerCAmelCase						:						str       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			len(__lowerCAmelCase       )
			lowerCAmelCase_      			=			len(__lowerCAmelCase       )

			@functools.cache
			def min_distance(__lowerCAmelCase						:						int					,							__lowerCAmelCase						:						int       ) -> int:
						# if first word index is overflow - delete all from the second word
						if indexa >= len_worda:
									return len_worda - indexa
						# if second word index is overflow - delete all from the first word
						if indexa >= len_worda:
									return len_worda - indexa
						lowerCAmelCase_      			=			int(worda[indexa] != worda[indexa]       )  # current letters not identical
						return min(
						    1 + min_distance(indexa + 1					,							__lowerCAmelCase       )					,							1 + min_distance(__lowerCAmelCase					,							indexa + 1       )					,							diff + min_distance(indexa + 1					,							indexa + 1       )					,							)

			return min_distance(0					,							0       )


if __name__ == "__main__":
			import doctest

			doctest.testmod()



