

'''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 					_snake_case		(unittest.TestCase):
			__A  :	List[str]        =MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING






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






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case     ):
				for example in examples:
					UpperCAmelCase_     :      Any						=  video_classifier(_snake_case     )

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






			@require_torch
			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      List[str]						=  "hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
				UpperCAmelCase_     :      Tuple						=  VideoMAEFeatureExtractor(
				    size={"shortest_edge": 10}			,crop_size={"height": 10, "width": 10}     )
				UpperCAmelCase_     :      str						=  pipeline(
				    "video-classification"			,model=_snake_case			,feature_extractor=_snake_case			,frame_sampling_rate=4     )

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

				UpperCAmelCase_     :      Optional[Any]						=  video_classifier(
				    [
				        video_file_path,
				        video_file_path,
				    ]			,top_k=2			,)
				self.assertEqual(
				    nested_simplify(_snake_case			,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











'''simple docstring'''






from __future__ import annotations

import numpy as np
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase	,				_lowerCAmelCase					=				np.shape(lowerCAmelCase    )
      if rows != columns:
            _lowerCAmelCase					=				(
                """'table' has to be of square shaped array but got a """
                f"{rows}x{columns} array:\n{table}"
            )
            raise ValueError(lowerCAmelCase    )

      _lowerCAmelCase					=				np.zeros((rows, columns)    )
      _lowerCAmelCase					=				np.zeros((rows, columns)    )
      for i in range(lowerCAmelCase    ):
            for j in range(lowerCAmelCase    ):
                  _lowerCAmelCase					=				sum(lower[i][k] * upper[k][j] for k in range(lowerCAmelCase    )    )
                  if upper[j][j] == 0:
                        raise ArithmeticError("""No LU decomposition exists"""    )
                  _lowerCAmelCase					=				(table[i][j] - total) / upper[j][j]
            _lowerCAmelCase					=				1
            for j in range(lowerCAmelCase				,      lowerCAmelCase    ):
                  _lowerCAmelCase					=				sum(lower[i][k] * upper[k][j] for k in range(lowerCAmelCase    )    )
                  _lowerCAmelCase					=				table[i][j] - total
      return lower, upper


if __name__ == "__main__":
      import doctest

      doctest.testmod()






