






"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE						:						List[Any]					   =       {
    '''configuration_time_series_transformer''': [
        '''TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''TimeSeriesTransformerConfig''',
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE						:						Optional[int]					   =       [
					    '''TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TimeSeriesTransformerForPrediction''',
					    '''TimeSeriesTransformerModel''',
					    '''TimeSeriesTransformerPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_time_series_transformer import (
					    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    TimeSeriesTransformerConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_time_series_transformer import (
										    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TimeSeriesTransformerForPrediction,
										    TimeSeriesTransformerModel,
										    TimeSeriesTransformerPreTrainedModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE						:						Any					   =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)










"""simple docstring"""






from PIL import Image



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

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


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


