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 __snake_case       (		unittest.TestCase	):
   lowerCAmelCase_														=							MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING




   def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       List[Any] ,			_lowercase    :       Any ,			_lowercase    :       Optional[int]   ):



     """simple docstring"""






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




   def 			__a		(					self    :       List[str] ,			_lowercase    :       Optional[Any] ,			_lowercase    :       int   ):



     """simple docstring"""






     for example in examples:
       SCREAMING_SNAKE_CASE__    							=   video_classifier(_lowercase   )

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




   @require_torch
   def 			__a		(					self    :       str   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   """hf-internal-testing/tiny-random-VideoMAEForVideoClassification"""
     SCREAMING_SNAKE_CASE__    							=   VideoMAEFeatureExtractor(
         size={"""shortest_edge""": 10} ,			crop_size={"""height""": 10, """width""": 10}   )
     SCREAMING_SNAKE_CASE__    							=   pipeline(
         """video-classification""" ,			model=_lowercase ,			feature_extractor=_lowercase ,			frame_sampling_rate=4   )

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

     SCREAMING_SNAKE_CASE__    							=   video_classifier(
         [
             video_file_path,
             video_file_path,
         ] ,			top_k=2 ,			)
     self.assertEqual(
         nested_simplify(_lowercase ,			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 			__a		(					self    :       int   ):



     """simple docstring"""






     pass

def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int			)							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   [1]

  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   0, 0, 0
  SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 2
  SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 3
  SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 5

  for _ in range(1				,  __UpperCamelCase			):
    SCREAMING_SNAKE_CASE__    							=   min(__UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase			)
    ugly_nums.append(__UpperCamelCase			)
    if next_num == next_a:
      ia += 1
      SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 2
    if next_num == next_a:
      ia += 1
      SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 3
    if next_num == next_a:
      ia += 1
      SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 5
  return ugly_nums[-1]


if __name__ == "__main__":
   from doctest import testmod

   testmod(verbose=True)
   print(F"""{ugly_numbers(200) = }""")
