code: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  snake_case_    						=				{"""configuration_qdqbert""": ["""QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """QDQBertConfig"""]}
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             """QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
             """QDQBertForMaskedLM""",
             """QDQBertForMultipleChoice""",
             """QDQBertForNextSentencePrediction""",
             """QDQBertForQuestionAnswering""",
             """QDQBertForSequenceClassification""",
             """QDQBertForTokenClassification""",
             """QDQBertLayer""",
             """QDQBertLMHeadModel""",
             """QDQBertModel""",
             """QDQBertPreTrainedModel""",
             """load_tf_weights_in_qdqbert""",
         ]
  
  
  if TYPE_CHECKING:
         from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_qdqbert import (
                    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                    QDQBertForMaskedLM,
                    QDQBertForMultipleChoice,
                    QDQBertForNextSentencePrediction,
                    QDQBertForQuestionAnswering,
                    QDQBertForSequenceClassification,
                    QDQBertForTokenClassification,
                    QDQBertLayer,
                    QDQBertLMHeadModel,
                    QDQBertModel,
                    QDQBertPreTrainedModel,
                    load_tf_weights_in_qdqbert,
                )
  
  
  else:
         import sys
  
         snake_case_    						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
style_context: 
  
  from cva import destroyAllWindows, imread, imshow, waitKey
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					int			,  SCREAMING_SNAKE_CASE_	:					int    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__   ,				lowercase__					:     Optional[int]   = img.shape[0], img.shape[1]
  
       def to_grayscale(SCREAMING_SNAKE_CASE_	:					Union[str, Any]			,  SCREAMING_SNAKE_CASE_	:					List[Any]			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]    ):
            return 0.2126 * red + 0.587 * green + 0.114 * blue
  
       def normalize(SCREAMING_SNAKE_CASE_	:					int    ):
            return min(SCREAMING_SNAKE_CASE_			,  255    )
  
       for i in range(SCREAMING_SNAKE_CASE_    ):
            for j in range(SCREAMING_SNAKE_CASE_    ):
                 lowercase__					:     str   = int(to_grayscale(*img[i][j]    )    )
                 lowercase__					:     Dict   = [
                     normalize(SCREAMING_SNAKE_CASE_    ),
                     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()
  
  