





from cva import destroyAllWindows, imread, imshow, waitKey



def   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      )					->  Optional[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE,   SCREAMING_SNAKE_CASE						=		img.shape[0], img.shape[1]

      def to_grayscale(_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      ):
            return 0.2_126 * red + 0.587 * green + 0.114 * blue

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

      for i in range(_SCREAMING_SNAKE_CASE      ):
            for j in range(_SCREAMING_SNAKE_CASE      ):
                  SCREAMING_SNAKE_CASE						=		int(to_grayscale(*img[i][j]      )      )
                  SCREAMING_SNAKE_CASE						=		[
                      normalize(_SCREAMING_SNAKE_CASE      ),
                      normalize(greyscale + factor      ),
                      normalize(greyscale + 2 * factor      ),
                  ]

      return img


if __name__ == "__main__":
      # read original image
      SCREAMING_SNAKE_CASE_		  =			{
          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()









from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_		  =			{
    """configuration_markuplm""": ["""MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarkupLMConfig"""],
    """feature_extraction_markuplm""": ["""MarkupLMFeatureExtractor"""],
    """processing_markuplm""": ["""MarkupLMProcessor"""],
    """tokenization_markuplm""": ["""MarkupLMTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_		  =			["""MarkupLMTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_		  =			[
          """MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MarkupLMForQuestionAnswering""",
          """MarkupLMForSequenceClassification""",
          """MarkupLMForTokenClassification""",
          """MarkupLMModel""",
          """MarkupLMPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
      from .feature_extraction_markuplm import MarkupLMFeatureExtractor
      from .processing_markuplm import MarkupLMProcessor
      from .tokenization_markuplm import MarkupLMTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_markuplm_fast import MarkupLMTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_markuplm import (
                MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                MarkupLMForQuestionAnswering,
                MarkupLMForSequenceClassification,
                MarkupLMForTokenClassification,
                MarkupLMModel,
                MarkupLMPreTrainedModel,
            )


else:
      import sys

      SCREAMING_SNAKE_CASE_		  =			_LazyModule(__name__, globals()["""__file__"""], _import_structure)


