

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  A						(							unittest.TestCase ):







    """simple docstring"""





    lowerCamelCase			=							MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING


    def 						snake_case__     (      self :       Optional[int],lowercase_ :       Union[str, Any],lowercase_ :       Optional[Any],lowercase_ :       Optional[Any]		)->    Dict:





        '''simple docstring'''







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


    def 						snake_case__     (      self :       Any,lowercase_ :       Union[str, Any],lowercase_ :       Dict		)->    Tuple:





        '''simple docstring'''







        for example in examples:
            A__          =   video_classifier(_a		)

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


    @require_torch
    def 						snake_case__     (      self :       Dict		)->    Any:





        '''simple docstring'''







        A__          =   'hf-internal-testing/tiny-random-VideoMAEForVideoClassification'
        A__          =   VideoMAEFeatureExtractor(
            size={'shortest_edge': 1_0},crop_size={'height': 1_0, 'width': 1_0}		)
        A__          =   pipeline(
            'video-classification',model=_a,feature_extractor=_a,frame_sampling_rate=4		)

        A__          =   hf_hub_download(repo_id='nateraw/video-demo',filename='archery.mp4',repo_type='dataset'		)
        A__          =   video_classifier(_a,top_k=2		)
        self.assertEqual(
            nested_simplify(_a,decimals=4		),[{'score': 0.5_199, 'label': 'LABEL_0'}, {'score': 0.4_801, 'label': 'LABEL_1'}],)

        A__          =   video_classifier(
            [
                video_file_path,
                video_file_path,
            ],top_k=2,)
        self.assertEqual(
            nested_simplify(_a,decimals=4		),[
                [{'score': 0.5_199, 'label': 'LABEL_0'}, {'score': 0.4_801, 'label': 'LABEL_1'}],
                [{'score': 0.5_199, 'label': 'LABEL_0'}, {'score': 0.4_801, 'label': 'LABEL_1'}],
            ],)


    @require_tf
    def 						snake_case__     (      self :       str		)->    int:





        '''simple docstring'''







        pass







"""simple docstring"""




from math import ceil



def     a__ (				snake_case__       , snake_case__				)						->					Optional[int]:
			lowerCamelCase			      =					list(range(0       , snake_case__				)				)

			lowerCamelCase			      =					[item for sublist in list(device_map.values()				) for item in sublist]

			# Duplicate check
			lowerCamelCase			      =					[]
			for i in device_map_blocks:
						if device_map_blocks.count(snake_case__				) > 1 and i not in duplicate_blocks:
									duplicate_blocks.append(snake_case__				)
    # Missing blocks
			lowerCamelCase			      =					[i for i in blocks if i not in device_map_blocks]
			lowerCamelCase			      =					[i for i in device_map_blocks if i not in blocks]

			if len(snake_case__				) != 0:
						raise ValueError(
						    """Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."""
						    """ These attention blocks were specified more than once: """ + str(snake_case__				)				)
			if len(snake_case__				) != 0:
						raise ValueError(
						    """There are attention blocks for this model that are not specified in the device_map. Add these attention """
						    """blocks to a device on the device_map: """ + str(snake_case__				)				)
			if len(snake_case__				) != 0:
						raise ValueError(
						    """The device_map contains more attention blocks than this model has. Remove these from the device_map:"""
						    + str(snake_case__				)				)



def     a__ (				snake_case__       , snake_case__				)						->					List[Any]:
			lowerCamelCase			      =					list(range(snake_case__				)				)
			lowerCamelCase			      =					int(ceil(n_layers / len(snake_case__				)				)				)
			lowerCamelCase			      =					[layers[i : i + n_blocks] for i in range(0       , snake_case__       , snake_case__				)]

			return dict(zip(snake_case__       , snake_case__				)				)



