'''simple docstring'''

from typing import TYPE_CHECKING

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


lowercase_     		= {
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_     		= ['''RoFormerTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_     		= [
							    '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''RoFormerForCausalLM''',
							    '''RoFormerForMaskedLM''',
							    '''RoFormerForMultipleChoice''',
							    '''RoFormerForQuestionAnswering''',
							    '''RoFormerForSequenceClassification''',
							    '''RoFormerForTokenClassification''',
							    '''RoFormerLayer''',
							    '''RoFormerModel''',
							    '''RoFormerPreTrainedModel''',
							    '''load_tf_weights_in_roformer''',
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_     		= [
							    '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFRoFormerForCausalLM''',
							    '''TFRoFormerForMaskedLM''',
							    '''TFRoFormerForMultipleChoice''',
							    '''TFRoFormerForQuestionAnswering''',
							    '''TFRoFormerForSequenceClassification''',
							    '''TFRoFormerForTokenClassification''',
							    '''TFRoFormerLayer''',
							    '''TFRoFormerModel''',
							    '''TFRoFormerPreTrainedModel''',
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_     		= [
							    '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''FlaxRoFormerForMaskedLM''',
							    '''FlaxRoFormerForMultipleChoice''',
							    '''FlaxRoFormerForQuestionAnswering''',
							    '''FlaxRoFormerForSequenceClassification''',
							    '''FlaxRoFormerForTokenClassification''',
							    '''FlaxRoFormerModel''',
							    '''FlaxRoFormerPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
							from .tokenization_roformer import RoFormerTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_roformer_fast import RoFormerTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_roformer import (
														    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RoFormerForCausalLM,
														    RoFormerForMaskedLM,
														    RoFormerForMultipleChoice,
														    RoFormerForQuestionAnswering,
														    RoFormerForSequenceClassification,
														    RoFormerForTokenClassification,
														    RoFormerLayer,
														    RoFormerModel,
														    RoFormerPreTrainedModel,
														    load_tf_weights_in_roformer,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_roformer import (
														    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFRoFormerForCausalLM,
														    TFRoFormerForMaskedLM,
														    TFRoFormerForMultipleChoice,
														    TFRoFormerForQuestionAnswering,
														    TFRoFormerForSequenceClassification,
														    TFRoFormerForTokenClassification,
														    TFRoFormerLayer,
														    TFRoFormerModel,
														    TFRoFormerPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_roformer import (
														    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FlaxRoFormerForMaskedLM,
														    FlaxRoFormerForMultipleChoice,
														    FlaxRoFormerForQuestionAnswering,
														    FlaxRoFormerForSequenceClassification,
														    FlaxRoFormerForTokenClassification,
														    FlaxRoFormerModel,
														    FlaxRoFormerPreTrainedModel,
														)


else:
							import sys

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






'''simple docstring'''

from PIL import Image



def 							UpperCamelCase__			(			a__			,      a__      ):


						'''simple docstring'''






						def brightness(a__      ) -> float:
												return 1_2_8 + level + (c - 1_2_8)

						if not -255.0 <= level <= 255.0:
												raise ValueError('level must be between -255.0 (black) and 255.0 (white)'      )
						return img.point(a__      )


if __name__ == "__main__":
							# Load image
							with Image.open('''image_data/lena.jpg''') as img:
														# Change brightness to 100
														lowercase_     		= change_brightness(img, 100)
														brigt_img.save('''image_data/lena_brightness.png''', format='''png''')





