


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      ):





  """simple docstring"""



  a_       :Any												=MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING

  def __a						(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							Any				,							SCREAMING_SNAKE_CASE__     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							List[Any]						):





       '''simple docstring'''

       __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 __a						(					self     :							Optional[int]				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]						):





       '''simple docstring'''

       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 __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

       __a       =   """hf-internal-testing/tiny-random-VideoMAEForVideoClassification"""
       __a       =   VideoMAEFeatureExtractor(
           size={"""shortest_edge""": 1_0}				,							crop_size={"""height""": 1_0, """width""": 1_0}						)
       __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 __a						(					self     :							Optional[int]						):





       '''simple docstring'''

       pass










'''simple docstring'''







def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					str:
     """simple docstring"""




     __a       =   """"""
     for word_or_phrase in separated:
          if not isinstance(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		):
               raise Exception("""join() accepts only strings to be joined"""		)
          joined += word_or_phrase + separator
     return joined.strip(__SCREAMING_SNAKE_CASE		)


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

 testmod()






