

'''simple docstring'''

from typing import TYPE_CHECKING

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


lowerCAmelCase_						   =			{
    "configuration_led": ["LED_PRETRAINED_CONFIG_ARCHIVE_MAP", "LEDConfig"],
    "tokenization_led": ["LEDTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase_						   =			["LEDTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase_						   =			[
						    "LED_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "LEDForConditionalGeneration",
						    "LEDForQuestionAnswering",
						    "LEDForSequenceClassification",
						    "LEDModel",
						    "LEDPreTrainedModel",
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase_						   =			["TFLEDForConditionalGeneration", "TFLEDModel", "TFLEDPreTrainedModel"]


if TYPE_CHECKING:
						from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
						from .tokenization_led import LEDTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_led_fast import LEDTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_led import (
												    LED_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LEDForConditionalGeneration,
												    LEDForQuestionAnswering,
												    LEDForSequenceClassification,
												    LEDModel,
												    LEDPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
						import sys

						lowerCAmelCase_						   =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








'''simple docstring'''

from cva import destroyAllWindows, imread, imshow, waitKey

def     __magic_name__       ( A					,   A							)			->		Union[str, Any]:
	snake_case , snake_case							 =				img.shape[0], img.shape[1]

	def to_grayscale(A					,   A					,   A							):
		return 0.2_126 * red + 0.587 * green + 0.114 * blue

	def normalize(A							):
		return min(A					,   2_5_5							)

	for i in range(A							):
		for j in range(A							):
			snake_case							 =				int(to_grayscale(*img[i][j]							)							)
			snake_case							 =				[
			    normalize(A							),
			    normalize(greyscale + factor							),
			    normalize(greyscale + 2 * factor							),
			]

	return img


if __name__ == "__main__":
						# read original image
						lowerCAmelCase_						   =			{
						    percentage: imread("image_data/lena.jpg", 1) for percentage in (1_0, 2_0, 3_0, 4_0)
						}

						for percentage, img in images.items():
												make_sepia(img, percentage)

						for percentage, img in images.items():
												imshow(f"Original image with sepia (factor: {percentage})", img)

						waitKey(0)
						destroyAllWindows()





