






from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__		:							Any 				=				{
    """configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PerceiverConfig""", """PerceiverOnnxConfig"""],
    """tokenization_perceiver""": ["""PerceiverTokenizer"""],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE__		:							Union[str, Any] 				=				["""PerceiverFeatureExtractor"""]
      SCREAMING_SNAKE_CASE__		:							Any 				=				["""PerceiverImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE__		:							int 				=				[
          """PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """PerceiverForImageClassificationConvProcessing""",
          """PerceiverForImageClassificationFourier""",
          """PerceiverForImageClassificationLearned""",
          """PerceiverForMaskedLM""",
          """PerceiverForMultimodalAutoencoding""",
          """PerceiverForOpticalFlow""",
          """PerceiverForSequenceClassification""",
          """PerceiverLayer""",
          """PerceiverModel""",
          """PerceiverPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
      from .tokenization_perceiver import PerceiverTokenizer

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_perceiver import PerceiverFeatureExtractor
            from .image_processing_perceiver import PerceiverImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_perceiver import (
                PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
                PerceiverForImageClassificationConvProcessing,
                PerceiverForImageClassificationFourier,
                PerceiverForImageClassificationLearned,
                PerceiverForMaskedLM,
                PerceiverForMultimodalAutoencoding,
                PerceiverForOpticalFlow,
                PerceiverForSequenceClassification,
                PerceiverLayer,
                PerceiverModel,
                PerceiverPreTrainedModel,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE__		:							Tuple 				=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)












'''simple docstring'''
from PIL import Image





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		Image:

			def brightness(SCREAMING_SNAKE_CASE_     ) -> float:
						return 128 + level + (c - 128)

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


if __name__ == "__main__":
							# Load image
							with Image.open("image_data/lena.jpg") as img:
														# Change brightness to 100
														_SCREAMING_SNAKE_CASE			=						change_brightness(img, 1_00)
														brigt_img.save("image_data/lena_brightness.png", format="png")




