





"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__     			=			{'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__     			=			[
						    '''VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''ViTMSNModel''',
						    '''ViTMSNForImageClassification''',
						    '''ViTMSNPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_vit_msn import (
												    VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ViTMSNForImageClassification,
												    ViTMSNModel,
												    ViTMSNPreTrainedModel,
												)

else:
						import sys

						lowerCAmelCase__     			=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







"""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 ):
						UpperCAmelCase_ =			MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING


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


						def     snake_case_   (self   ,							__a   ,							__a		) ->   str:
								for example in examples:
										UpperCamelCase   =	video_classifier(__a		)

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


						@require_torch
						def     snake_case_   (self		) ->   int:
								UpperCamelCase   =	"hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
								UpperCamelCase   =	VideoMAEFeatureExtractor(
								    size={"shortest_edge": 10}   ,							crop_size={"height": 10, "width": 10}		)
								UpperCamelCase   =	pipeline(
								    "video-classification"   ,							model=__a   ,							feature_extractor=__a   ,							frame_sampling_rate=4		)

								UpperCamelCase   =	hf_hub_download(repo_id="nateraw/video-demo"   ,							filename="archery.mp4"   ,							repo_type="dataset"		)
								UpperCamelCase   =	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"}]   ,							)

								UpperCamelCase   =	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     snake_case_   (self		) ->   Optional[Any]:
								pass






