


'''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"""






    lowerCamelCase    :   Union[str, Any]					=     MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING







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







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	str:
         for example in examples:
              __lowerCamelCase  :      Optional[int]														=						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  lowercase_							(				self      )							->	List[Any]:
         __lowerCamelCase  :      Union[str, Any]														=						'hf-internal-testing/tiny-random-VideoMAEForVideoClassification'
         __lowerCamelCase  :      Union[str, Any]														=						VideoMAEFeatureExtractor(
             size={'shortest_edge': 10}     ,	crop_size={'height': 10, 'width': 10}      )
         __lowerCamelCase  :      Tuple														=						pipeline(
             'video-classification'     ,	model=SCREAMING_SNAKE_CASE_     ,	feature_extractor=SCREAMING_SNAKE_CASE_     ,	frame_sampling_rate=4      )

         __lowerCamelCase  :      int														=						hf_hub_download(repo_id='nateraw/video-demo'     ,	filename='archery.mp4'     ,	repo_type='dataset'      )
         __lowerCamelCase  :      str														=						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'}]     ,	)

         __lowerCamelCase  :      Dict														=						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  lowercase_							(				self      )							->	str:
         pass








"""simple docstring"""





import fire

from utils import calculate_rouge, save_json



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case=None,	**__snake_case      )				->						Optional[int]:


					"""simple docstring"""





					_UpperCamelCase									=    [x.strip() for x in open(__snake_case      ).readlines()]
					_UpperCamelCase									=    [x.strip() for x in open(__snake_case      ).readlines()][: len(__snake_case      )]
					_UpperCamelCase									=    calculate_rouge(__snake_case,	__snake_case,	**__snake_case      )
					if save_path is not None:
										save_json(__snake_case,	__snake_case,	indent=__snake_case      )
					return metrics  # these print nicely


if __name__ == "__main__":
				fire.Fire(calculate_rouge_path)






