from cva import destroyAllWindows, imread, imshow, waitKey





def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase						)->  str:






      '''simple docstring'''





      UpperCAmelCase  ,						UpperCAmelCase    : str      =img.shape[0], img.shape[1]

      def to_grayscale(__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase						):
            return 0.2126 * red + 0.587 * green + 0.114 * blue

      def normalize(__lowerCAmelCase						):
            return min(__lowerCAmelCase		,					2_55						)

      for i in range(__lowerCAmelCase						):
            for j in range(__lowerCAmelCase						):
                  UpperCAmelCase    : List[Any]      =int(to_grayscale(*img[i][j]						)						)
                  UpperCAmelCase    : Any      =[
                      normalize(__lowerCAmelCase						),
                      normalize(greyscale + factor						),
                      normalize(greyscale + 2 * factor						),
                  ]

      return img


if __name__ == "__main__":
     # read original image
     __snake_case			      =    {
         percentage: imread('''image_data/lena.jpg''', 1) for percentage in (10, 20, 30, 40)
     }

     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()

from typing import TYPE_CHECKING

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


__snake_case			      =    {
    '''configuration_esm''': ['''ESM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''EsmConfig'''],
    '''tokenization_esm''': ['''EsmTokenizer'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case			      =    [
         '''ESM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''EsmForMaskedLM''',
         '''EsmForSequenceClassification''',
         '''EsmForTokenClassification''',
         '''EsmModel''',
         '''EsmPreTrainedModel''',
     ]
     __snake_case			      =    ['''EsmForProteinFolding''', '''EsmFoldPreTrainedModel''']

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case			      =    [
         '''TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFEsmForMaskedLM''',
         '''TFEsmForSequenceClassification''',
         '''TFEsmForTokenClassification''',
         '''TFEsmModel''',
         '''TFEsmPreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
     from .tokenization_esm import EsmTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_esm import (
              ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
              EsmForMaskedLM,
              EsmForSequenceClassification,
              EsmForTokenClassification,
              EsmModel,
              EsmPreTrainedModel,
          )
          from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_esm import (
              TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFEsmForMaskedLM,
              TFEsmForSequenceClassification,
              TFEsmForTokenClassification,
              TFEsmModel,
              TFEsmPreTrainedModel,
          )


else:
     import sys

     __snake_case			      =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)
