


'''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 _lowerCamelCase	(						unittest.TestCase							):



  '''simple docstring'''







  A_     :      Optional[int]				   =    MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING







  def        __lowerCAmelCase		( self		:						Dict  ,					_A		:						Optional[int]  ,					_A		:						List[str]  ,					_A		:						str      )  ->			Dict:
   __magic_name__	:   List[Any]					     =					hf_hub_download(
       repo_id='nateraw/video-demo'  ,					filename='archery.mp4'  ,					repo_type='dataset'      )
   __magic_name__	:   str					     =					VideoClassificationPipeline(model=_A  ,					image_processor=_A  ,					top_k=2      )
   __magic_name__	:   Optional[Any]					     =					[
       example_video_filepath,
       """https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4""",
   ]
   return video_classifier, examples







  def        __lowerCAmelCase		( self		:						List[str]  ,					_A		:						Any  ,					_A		:						List[str]      )  ->			List[Any]:
   for example in examples:
    __magic_name__	:   str					     =					video_classifier(_A      )

    self.assertEqual(
        _A  ,					[
            {'score': ANY(_A      ), 'label': ANY(_A      )},
            {'score': ANY(_A      ), 'label': ANY(_A      )},
        ]  ,					)







  @require_torch
  def        __lowerCAmelCase		( self		:						Optional[int]      )  ->			List[Any]:
   __magic_name__	:   Tuple					     =					"""hf-internal-testing/tiny-random-VideoMAEForVideoClassification"""
   __magic_name__	:   Dict					     =					VideoMAEFeatureExtractor(
       size={'shortest_edge': 10}  ,					crop_size={'height': 10, 'width': 10}      )
   __magic_name__	:   int					     =					pipeline(
       'video-classification'  ,					model=_A  ,					feature_extractor=_A  ,					frame_sampling_rate=4      )

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

   __magic_name__	:   Union[str, Any]					     =					video_classifier(
       [
           video_file_path,
           video_file_path,
       ]  ,					top_k=2  ,					)
   self.assertEqual(
       nested_simplify(_A  ,					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        __lowerCAmelCase		( self		:						Union[str, Any]      )  ->			Dict:
   pass



'''simple docstring'''

lowerCAmelCase				:Union[str, Any]        =      '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowerCAmelCase				:Union[str, Any]        =      [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowerCAmelCase				:Tuple        =      {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}