





from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


a__: List[Any]							   =						logging.get_logger(__name__)




class        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__ ):
 __SCREAMING_SNAKE_CASE							=      ['''pixel_values''']



 def __init__(				self,__lowerCamelCase = True,__lowerCamelCase = 32,__lowerCamelCase=PILImageResampling.BILINEAR,__lowerCamelCase = True,**__lowerCamelCase,):
        A__		   =		do_resize
        A__		   =		do_rescale
        A__		   =		size_divisor
        A__		   =		resample
        super().__init__(**__lowerCamelCase					)



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = None,**__lowerCamelCase					):
        A__   ,      A__		   =		get_image_size(__lowerCamelCase					)
        # Rounds the height and width down to the closest multiple of size_divisor
        A__		   =		height // size_divisor * size_divisor
        A__		   =		width // size_divisor * size_divisor
        A__		   =		resize(__lowerCamelCase,(new_h, new_w),resample=__lowerCamelCase,data_format=__lowerCamelCase,**__lowerCamelCase					)
        return image



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = None,**__lowerCamelCase					):
        return rescale(image=__lowerCamelCase,scale=__lowerCamelCase,data_format=__lowerCamelCase,**__lowerCamelCase					)




 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase=None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = ChannelDimension.FIRST,**__lowerCamelCase,):
        A__		   =		do_resize if do_resize is not None else self.do_resize
        A__		   =		do_rescale if do_rescale is not None else self.do_rescale
        A__		   =		size_divisor if size_divisor is not None else self.size_divisor
        A__		   =		resample if resample is not None else self.resample

        if do_resize and size_divisor is None:
               raise ValueError('''size_divisor is required for resizing'''					)

        A__		   =		make_list_of_images(__lowerCamelCase					)

        if not valid_images(__lowerCamelCase					):
               raise ValueError('''Invalid image(s)'''					)

        # All transformations expect numpy arrays.
        A__		   =		[to_numpy_array(__lowerCamelCase					) for img in images]

        if do_resize:
               A__		   =		[self.resize(__lowerCamelCase,size_divisor=__lowerCamelCase,resample=__lowerCamelCase					) for image in images]

        if do_rescale:
               A__		   =		[self.rescale(__lowerCamelCase,scale=1 / 255					) for image in images]

        A__		   =		[to_channel_dimension_format(__lowerCamelCase,__lowerCamelCase					) for image in images]

        A__		   =		{'''pixel_values''': images}
        return BatchFeature(data=__lowerCamelCase,tensor_type=__lowerCamelCase					)












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





