



"""simple docstring"""







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"""


			A__	:					Union[str, Any]							  =     MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Optional[int]:
				A__											=					hf_hub_download(
				    repo_id="nateraw/video-demo"						,	filename="archery.mp4"						,	repo_type="dataset"		)
				A__											=					VideoClassificationPipeline(model=SCREAMING_SNAKE_CASE_						,	image_processor=SCREAMING_SNAKE_CASE_						,	top_k=2		)
				A__											=					[
				    example_video_filepath,
				    'https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4',
				]
				return video_classifier, examples


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	str:
				for example in examples:
					A__											=					video_classifier(SCREAMING_SNAKE_CASE_		)

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


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

				A__											=					hf_hub_download(repo_id="nateraw/video-demo"						,	filename="archery.mp4"						,	repo_type="dataset"		)
				A__											=					video_classifier(SCREAMING_SNAKE_CASE_						,	top_k=2		)
				self.assertEqual(
				    nested_simplify(SCREAMING_SNAKE_CASE_						,	decimals=4		)						,	[{"score": 0.5_1_9_9, "label": "LABEL_0"}, {"score": 0.4_8_0_1, "label": "LABEL_1"}]						,	)

				A__											=					video_classifier(
				    [
				        video_file_path,
				        video_file_path,
				    ]						,	top_k=2						,	)
				self.assertEqual(
				    nested_simplify(SCREAMING_SNAKE_CASE_						,	decimals=4		)						,	[
				        [{"score": 0.5_1_9_9, "label": "LABEL_0"}, {"score": 0.4_8_0_1, "label": "LABEL_1"}],
				        [{"score": 0.5_1_9_9, "label": "LABEL_0"}, {"score": 0.4_8_0_1, "label": "LABEL_1"}],
				    ]						,	)







			@require_tf
			def   snake_case__	(      self		)      ->	str:
				pass








'''simple docstring'''



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



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							int				)	->						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
     __lowerCamelCase  :      List[Any]														=						(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
     __lowerCamelCase  :      Tuple														=						_calculate(days - 1 ,       UpperCAmelCase_ ,       late + 1				)

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

     # 3) if we are on time, this resets the "late" counter and keeps the
     # absent counter
     __lowerCamelCase  :      List[Any]														=						_calculate(days - 1 ,       UpperCAmelCase_ ,       0				)

     __lowerCamelCase  :      Optional[int]														=						state_late + state_absent + state_ontime

     __lowerCamelCase  :      Union[str, Any]														=						prizestrings
     return prizestrings



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int = 30				)	->						int:
     return _calculate(UpperCAmelCase_ ,       absent=0 ,       late=0				)


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






