


from PIL import Image







def        SCREAMING_SNAKE_CASE_						(			__A :			Image				)	->		Image:

       """simple docstring"""

       a_							,		a_					:		int							=  image.size
       a_					:		Any							=  0
       a_					:		List[str]							=  image.load()
       for i in range(__A				):
              for j in range(__A				):
                     a_					:		int							=  pixels[j, i]
                     mean += pixel
       mean //= width * height

       for j in range(__A				):
              for i in range(__A				):
                     a_					:		Union[str, Any]							=  2_55 if pixels[i, j] > mean else 0
       return image


if __name__ == "__main__":
   UpperCAmelCase_      : Optional[int]  					=       mean_threshold(Image.open('path_to_image').convert('L'))
   image.save('output_image_path')



import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('''dataset_size'''		,  [None, 400 * 2**20, 600 * 2**20]						)
@pytest.mark.parametrize('''input_in_memory_max_size'''		,  ['''default''', 0, 100 * 2**20, 900 * 2**20]						)
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						if input_in_memory_max_size != "default":
												monkeypatch.setattr(datasets.config		,  '''IN_MEMORY_MAX_SIZE'''		,  _lowercase						)
						SCREAMING_SNAKE_CASE							:    Tuple							=							datasets.config.IN_MEMORY_MAX_SIZE
						if input_in_memory_max_size == "default":
												assert in_memory_max_size == 0
						else:
												assert in_memory_max_size == input_in_memory_max_size
						if dataset_size and in_memory_max_size:
												SCREAMING_SNAKE_CASE							:    str							=							dataset_size < in_memory_max_size
						else:
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							False
						SCREAMING_SNAKE_CASE							:    Any							=							is_small_dataset(_lowercase						)
						assert result == expected





