code: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  # Copyright 2023 The HuggingFace Team. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  from typing import TYPE_CHECKING
  
  # rely on isort to merge the imports
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
  )
  
  
  lowerCamelCase_     :     Tuple    		=						{
      """configuration_convnextv2""": [
          """CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
          """ConvNextV2Config""",
      ]
  }
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         lowerCamelCase_     :     List[str]    		=						[
             """CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
             """ConvNextV2ForImageClassification""",
             """ConvNextV2Model""",
             """ConvNextV2PreTrainedModel""",
             """ConvNextV2Backbone""",
         ]
  
  
  if TYPE_CHECKING:
         from .configuration_convnextva import (
             CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
             ConvNextVaConfig,
         )
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_convnextva import (
                    CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                    ConvNextVaBackbone,
                    ConvNextVaForImageClassification,
                    ConvNextVaModel,
                    ConvNextVaPreTrainedModel,
                )
  
  else:
         import sys
  
         lowerCamelCase_     :     Any    		=						_LazyModule(__name__, globals()["""__file__"""], _import_structure)
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  from cva import destroyAllWindows, imread, imshow, waitKey
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a	,   a					=img.shape[0], img.shape[1]
  
      def to_grayscale(lowercase ,					lowercase ,					lowercase	):
          return 0.2126 * red + 0.587 * green + 0.114 * blue
  
      def normalize(lowercase	):
          return min(lowercase ,					2_55	)
  
      for i in range(lowercase	):
          for j in range(lowercase	):
              a					=int(to_grayscale(*img[i][j]	)	)
              a					=[
                  normalize(lowercase	),
                  normalize(greyscale + factor	),
                  normalize(greyscale + 2 * factor	),
              ]
  
      return img
  
  
  if __name__ == "__main__":
         # read original image
         lowerCamelCase_     :     Any    		=						{
             percentage: imread("""image_data/lena.jpg""", 1) for percentage in (1_0, 2_0, 3_0, 4_0)
         }
  
         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()