"""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    a__    (   unittest.TestCase	):
   snake_case_							=      MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



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


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        for example in examples:
             lowercase__				 =							video_classifier(_UpperCAmelCase  )

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


   @require_torch
   def 	snake_case__	(     self  ):
        '''simple docstring'''



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

        lowercase__				 =							hf_hub_download(repo_id="nateraw/video-demo",				filename="archery.mp4",				repo_type="dataset"  )
        lowercase__				 =							video_classifier(_UpperCAmelCase,				top_k=2  )
        self.assertEqual(
            nested_simplify(_UpperCAmelCase,				decimals=4  ),				[{"score": 0.5_199, "label": "LABEL_0"}, {"score": 0.4_801, "label": "LABEL_1"}],				)

        lowercase__				 =							video_classifier(
            [
                video_file_path,
                video_file_path,
            ],				top_k=2,				)
        self.assertEqual(
            nested_simplify(_UpperCAmelCase,				decimals=4  ),				[
                [{"score": 0.5_199, "label": "LABEL_0"}, {"score": 0.4_801, "label": "LABEL_1"}],
                [{"score": 0.5_199, "label": "LABEL_0"}, {"score": 0.4_801, "label": "LABEL_1"}],
            ],				)


   @require_tf
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass





"""simple docstring"""


from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
 if not (is_transformers_available() and is_torch_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
 from .notes_encoder import SpectrogramNotesEncoder
 from .continous_encoder import SpectrogramContEncoder
 from .pipeline_spectrogram_diffusion import (
     SpectrogramContEncoder,
     SpectrogramDiffusionPipeline,
     TaFilmDecoder,
 )

try:
 if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
 from .midi_utils import MidiProcessor




