


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 		_snake_case  (				unittest.TestCase							):
 SCREAMING_SNAKE_CASE__				=	MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING




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




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase   ):
       for example in examples:
             a						:str										=	video_classifier(_lowerCamelCase   )

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




 @require_torch
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Tuple										=	"""hf-internal-testing/tiny-random-VideoMAEForVideoClassification"""
       a						:Dict										=	VideoMAEFeatureExtractor(
           size={'''shortest_edge''': 10} ,  crop_size={'''height''': 10, '''width''': 10}   )
       a						:int										=	pipeline(
           '''video-classification''' ,  model=_lowerCamelCase ,  feature_extractor=_lowerCamelCase ,  frame_sampling_rate=4   )

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

       a						:Union[str, Any]										=	video_classifier(
           [
               video_file_path,
               video_file_path,
           ] ,  top_k=2 ,  )
       self.assertEqual(
           nested_simplify(_lowerCamelCase ,  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   ):
       pass









from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


snake_case    :   Any	    =					{'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case    :   Union[str, Any]	    =					[
         '''VAN_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''VanForImageClassification''',
         '''VanModel''',
         '''VanPreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_van import (
              VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
              VanForImageClassification,
              VanModel,
              VanPreTrainedModel,
          )

else:
     import sys

     snake_case    :   Optional[Any]	    =					_LazyModule(__name__, globals()['''__file__'''], _import_structure)





