__lowercase							 =					{
    '''Pillow''': '''Pillow''',
    '''accelerate''': '''accelerate>=0.11.0''',
    '''compel''': '''compel==0.1.8''',
    '''black''': '''black~=23.1''',
    '''datasets''': '''datasets''',
    '''filelock''': '''filelock''',
    '''flax''': '''flax>=0.4.1''',
    '''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
    '''huggingface-hub''': '''huggingface-hub>=0.13.2''',
    '''requests-mock''': '''requests-mock==1.10.0''',
    '''importlib_metadata''': '''importlib_metadata''',
    '''invisible-watermark''': '''invisible-watermark''',
    '''isort''': '''isort>=5.5.4''',
    '''jax''': '''jax>=0.2.8,!=0.3.2''',
    '''jaxlib''': '''jaxlib>=0.1.65''',
    '''Jinja2''': '''Jinja2''',
    '''k-diffusion''': '''k-diffusion>=0.0.12''',
    '''torchsde''': '''torchsde''',
    '''note_seq''': '''note_seq''',
    '''librosa''': '''librosa''',
    '''numpy''': '''numpy''',
    '''omegaconf''': '''omegaconf''',
    '''parameterized''': '''parameterized''',
    '''protobuf''': '''protobuf>=3.20.3,<4''',
    '''pytest''': '''pytest''',
    '''pytest-timeout''': '''pytest-timeout''',
    '''pytest-xdist''': '''pytest-xdist''',
    '''ruff''': '''ruff>=0.0.241''',
    '''safetensors''': '''safetensors''',
    '''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
    '''scipy''': '''scipy''',
    '''onnx''': '''onnx''',
    '''regex''': '''regex!=2019.12.17''',
    '''requests''': '''requests''',
    '''tensorboard''': '''tensorboard''',
    '''torch''': '''torch>=1.4''',
    '''torchvision''': '''torchvision''',
    '''transformers''': '''transformers>=4.25.1''',
    '''urllib3''': '''urllib3<=2.0.0''',
}









"""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       SCREAMING_SNAKE_CASE__				(		unittest.TestCase				):
       """simple docstring"""


       a  :				int   =MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__						):






         """simple docstring"""





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

       def        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         for example in examples:
           lowerCAmelCase     :     str								=							video_classifier(snake_case__						)

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

       @require_torch
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Union[str, Any]								=							"hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
         lowerCAmelCase     :     str								=							VideoMAEFeatureExtractor(
             size={"shortest_edge": 10}		,     crop_size={"height": 10, "width": 10}						)
         lowerCAmelCase     :     int								=							pipeline(
             "video-classification"		,     model=snake_case__		,     feature_extractor=snake_case__		,     frame_sampling_rate=4						)

         lowerCAmelCase     :     Optional[int]								=							hf_hub_download(repo_id="nateraw/video-demo"		,     filename="archery.mp4"		,     repo_type="dataset"						)
         lowerCAmelCase     :     Union[str, Any]								=							video_classifier(snake_case__		,     top_k=2						)
         self.assertEqual(
             nested_simplify(snake_case__		,     decimals=4						)		,     [{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}]		,     )

         lowerCAmelCase     :     Tuple								=							video_classifier(
             [
                 video_file_path,
                 video_file_path,
             ]		,     top_k=2		,     )
         self.assertEqual(
             nested_simplify(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        lowercase__							(						self						):






         """simple docstring"""





         pass





