





from typing import TYPE_CHECKING

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


a__:      Tuple									=		{'configuration_dpt': ['DPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPTConfig']}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__:      List[str]									=		['DPTFeatureExtractor']
       a__:      str									=		['DPTImageProcessor']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__:      Optional[Any]									=		[
           'DPT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'DPTForDepthEstimation',
           'DPTForSemanticSegmentation',
           'DPTModel',
           'DPTPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_dpt import DPTFeatureExtractor
              from .image_processing_dpt import DPTImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_dpt import (
                  DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DPTForDepthEstimation,
                  DPTForSemanticSegmentation,
                  DPTModel,
                  DPTPreTrainedModel,
              )


else:
       import sys

       a__:      Optional[int]									=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)












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      ):
 __SCREAMING_SNAKE_CASE							       =	MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING



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



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        for example in examples:
               A__											=						video_classifier(__lowerCamelCase )

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



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

        A__											=						hf_hub_download(repo_id='''nateraw/video-demo''',filename='''archery.mp4''',repo_type='''dataset''' )
        A__											=						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__											=						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 				UpperCamelCase			(							self ):
        pass





