



"""simple docstring"""







from PIL import Image

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							Image:
							A         =					(259 * (level + 255)) / (255 * (259 - level))

							def contrast(lowerCamelCase__			) -> int:
														return int(128 + factor * (c - 128)			)

							return img.point(lowerCamelCase__			)


if __name__ == "__main__":
							# Load image
							with Image.open('image_data/lena.jpg') as img:
														# Change contrast to 170
														A    =			change_contrast(img, 1_7_0)
														cont_img.save('image_data/lena_high_contrast.png', format='png')








"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


A    =			{
    'configuration_gpt_neo': ['GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoConfig', 'GPTNeoOnnxConfig'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			[
							    'GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'GPTNeoForCausalLM',
							    'GPTNeoForQuestionAnswering',
							    'GPTNeoForSequenceClassification',
							    'GPTNeoForTokenClassification',
							    'GPTNeoModel',
							    'GPTNeoPreTrainedModel',
							    'load_tf_weights_in_gpt_neo',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			[
							    'FlaxGPTNeoForCausalLM',
							    'FlaxGPTNeoModel',
							    'FlaxGPTNeoPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gpt_neo import (
														    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTNeoForCausalLM,
														    GPTNeoForQuestionAnswering,
														    GPTNeoForSequenceClassification,
														    GPTNeoForTokenClassification,
														    GPTNeoModel,
														    GPTNeoPreTrainedModel,
														    load_tf_weights_in_gpt_neo,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
							import sys

							A    =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



