


from typing import TYPE_CHECKING

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


__lowerCAmelCase	: Optional[int]  				={
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase	: int  				=['RobertaTokenizerFast']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase	: List[Any]  				=[
						    'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'RobertaForCausalLM',
						    'RobertaForMaskedLM',
						    'RobertaForMultipleChoice',
						    'RobertaForQuestionAnswering',
						    'RobertaForSequenceClassification',
						    'RobertaForTokenClassification',
						    'RobertaModel',
						    'RobertaPreTrainedModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase	: Optional[int]  				=[
						    'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFRobertaForCausalLM',
						    'TFRobertaForMaskedLM',
						    'TFRobertaForMultipleChoice',
						    'TFRobertaForQuestionAnswering',
						    'TFRobertaForSequenceClassification',
						    'TFRobertaForTokenClassification',
						    'TFRobertaMainLayer',
						    'TFRobertaModel',
						    'TFRobertaPreTrainedModel',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase	: List[str]  				=[
						    'FlaxRobertaForCausalLM',
						    'FlaxRobertaForMaskedLM',
						    'FlaxRobertaForMultipleChoice',
						    'FlaxRobertaForQuestionAnswering',
						    'FlaxRobertaForSequenceClassification',
						    'FlaxRobertaForTokenClassification',
						    'FlaxRobertaModel',
						    'FlaxRobertaPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
						from .tokenization_roberta import RobertaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_roberta_fast import RobertaTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_roberta import (
												    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RobertaForCausalLM,
												    RobertaForMaskedLM,
												    RobertaForMultipleChoice,
												    RobertaForQuestionAnswering,
												    RobertaForSequenceClassification,
												    RobertaForTokenClassification,
												    RobertaModel,
												    RobertaPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_roberta import (
												    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRobertaForCausalLM,
												    TFRobertaForMaskedLM,
												    TFRobertaForMultipleChoice,
												    TFRobertaForQuestionAnswering,
												    TFRobertaForSequenceClassification,
												    TFRobertaForTokenClassification,
												    TFRobertaMainLayer,
												    TFRobertaModel,
												    TFRobertaPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_roberta import (
												    FlaxRobertaForCausalLM,
												    FlaxRobertaForMaskedLM,
												    FlaxRobertaForMultipleChoice,
												    FlaxRobertaForQuestionAnswering,
												    FlaxRobertaForSequenceClassification,
												    FlaxRobertaForTokenClassification,
												    FlaxRobertaModel,
												    FlaxRobertaPreTrainedModel,
												)

else:
						import sys

						__lowerCAmelCase	: Optional[int]  				=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

from PIL import Image

def 			lowercase_				(			_lowerCamelCase							:       Image		,	_lowerCamelCase							:       int):
    lowercase__ :   List[str]    					=				(259 * (level + 255)) / (255 * (259 - level))

    def contrast(_lowerCamelCase							:       int) -> 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
            UpperCamelCase  							=						change_contrast(img, 170)
            cont_img.save('''image_data/lena_high_contrast.png''', format='''png''')



