






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					:       Optional[int]							       = MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING


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


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase      ):






											for example in examples:
																		_lowercase							:   List[Any] 							=   video_classifier(_lowerCAmelCase      )

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


				@require_torch
				def 						__a					(					self      ):
											_lowercase							:   str 							=   'hf-internal-testing/tiny-random-VideoMAEForVideoClassification'
											_lowercase							:   Union[str, Any] 							=   VideoMAEFeatureExtractor(
											    size={'shortest_edge': 1_0} ,						crop_size={'height': 1_0, 'width': 1_0}      )
											_lowercase							:   List[str] 							=   pipeline(
											    'video-classification' ,						model=_lowerCAmelCase ,						feature_extractor=_lowerCAmelCase ,						frame_sampling_rate=4      )

											_lowercase							:   Tuple 							=   hf_hub_download(repo_id='nateraw/video-demo' ,						filename='archery.mp4' ,						repo_type='dataset'      )
											_lowercase							:   Optional[int] 							=   video_classifier(_lowerCAmelCase ,						top_k=2      )
											self.assertEqual(
											    nested_simplify(_lowerCAmelCase ,						decimals=4      ) ,						[{'score': 0.51_99, 'label': 'LABEL_0'}, {'score': 0.48_01, 'label': 'LABEL_1'}] ,						)

											_lowercase							:   List[Any] 							=   video_classifier(
											    [
											        video_file_path,
											        video_file_path,
											    ] ,						top_k=2 ,						)
											self.assertEqual(
											    nested_simplify(_lowerCAmelCase ,						decimals=4      ) ,						[
											        [{'score': 0.51_99, 'label': 'LABEL_0'}, {'score': 0.48_01, 'label': 'LABEL_1'}],
											        [{'score': 0.51_99, 'label': 'LABEL_0'}, {'score': 0.48_01, 'label': 'LABEL_1'}],
											    ] ,						)


				@require_tf
				def 						__a					(					self      ):
											pass






from PIL import Image


def    a      ( a	)     ->Image:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= image.size
			SCREAMING_SNAKE_CASE     						= 0
			SCREAMING_SNAKE_CASE     						= image.load()
			for i in range(a	):
						for j in range(a	):
									SCREAMING_SNAKE_CASE     						= pixels[j, i]
									mean += pixel
			mean //= width * height

			for j in range(a	):
						for i in range(a	):
									SCREAMING_SNAKE_CASE     						= 255 if pixels[i, j] > mean else 0
			return image


if __name__ == "__main__":
							__lowerCAmelCase					   =       mean_threshold(Image.open('path_to_image').convert('L'))
							image.save('output_image_path')