
'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowerCAmelCase					:    Dict           = {
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase					:    List[Any]           = [
						    'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'EncodecModel',
						    'EncodecPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_encodec import (
						    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    EncodecConfig,
						)
						from .feature_extraction_encodec import EncodecFeatureExtractor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_encodec import (
												    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
												    EncodecModel,
												    EncodecPreTrainedModel,
												)

else:
						import sys

						lowerCAmelCase					:    Dict           = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






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')