
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"""







     SCREAMING_SNAKE_CASE_		 =	MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> Dict:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					hf_hub_download(
               repo_id='''nateraw/video-demo'''    ,    filename='''archery.mp4'''    ,    repo_type='''dataset'''	)
           UpperCamelCase__     : Any											=					VideoClassificationPipeline(model=__SCREAMING_SNAKE_CASE    ,    image_processor=__SCREAMING_SNAKE_CASE    ,    top_k=2	)
           UpperCamelCase__     : int											=					[
               example_video_filepath,
               '''https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4''',
           ]
           return video_classifier, examples






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> Any:




           """simple docstring"""







           for example in examples:
                 UpperCamelCase__     : Tuple											=					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  __SCREAMING_SNAKE_CASE      (				self	)	-> List[Any]:




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					'''hf-internal-testing/tiny-random-VideoMAEForVideoClassification'''
           UpperCamelCase__     : int											=					VideoMAEFeatureExtractor(
               size={'''shortest_edge''': 1_0}    ,    crop_size={'''height''': 1_0, '''width''': 1_0}	)
           UpperCamelCase__     : str											=					pipeline(
               '''video-classification'''    ,    model=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE    ,    frame_sampling_rate=4	)

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

           UpperCamelCase__     : str											=					video_classifier(
               [
                   video_file_path,
                   video_file_path,
               ]    ,    top_k=2    ,    )
           self.assertEqual(
               nested_simplify(__SCREAMING_SNAKE_CASE    ,    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  __SCREAMING_SNAKE_CASE      (				self	)	-> Tuple:




           """simple docstring"""







           pass




def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__ = " "		):
      UpperCamelCase__     : Optional[int]											=					[]

      UpperCamelCase__     : int											=					0
      for index, char in enumerate(UpperCamelCase__		):
            if char == separator:
                  split_words.append(string[last_index:index]		)
                  UpperCamelCase__     : int											=					index + 1
            elif index + 1 == len(UpperCamelCase__		):
                  split_words.append(string[last_index : index + 1]		)
      return split_words


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

    testmod()


