






"""simple docstring"""


from typing import TYPE_CHECKING

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


_UpperCamelCase							:       List[str]       					=		{
    'configuration_deberta_v2': ['DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaV2Config', 'DebertaV2OnnxConfig'],
    'tokenization_deberta_v2': ['DebertaV2Tokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							:       List[Any]       					=		['DebertaV2TokenizerFast']

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							:       str       					=		[
       'TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFDebertaV2ForMaskedLM',
       'TFDebertaV2ForQuestionAnswering',
       'TFDebertaV2ForSequenceClassification',
       'TFDebertaV2ForTokenClassification',
       'TFDebertaV2Model',
       'TFDebertaV2PreTrainedModel',
   ]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							:       List[Any]       					=		[
       'DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
       'DebertaV2ForMaskedLM',
       'DebertaV2ForMultipleChoice',
       'DebertaV2ForQuestionAnswering',
       'DebertaV2ForSequenceClassification',
       'DebertaV2ForTokenClassification',
       'DebertaV2Model',
       'DebertaV2PreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_deberta_va import (
       DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DebertaVaConfig,
       DebertaVaOnnxConfig,
   )
   from .tokenization_deberta_va import DebertaVaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_deberta_va import (
          TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFDebertaVaForMaskedLM,
          TFDebertaVaForQuestionAnswering,
          TFDebertaVaForSequenceClassification,
          TFDebertaVaForTokenClassification,
          TFDebertaVaModel,
          TFDebertaVaPreTrainedModel,
      )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_deberta_va import (
          DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
          DebertaVaForMaskedLM,
          DebertaVaForMultipleChoice,
          DebertaVaForQuestionAnswering,
          DebertaVaForSequenceClassification,
          DebertaVaForTokenClassification,
          DebertaVaModel,
          DebertaVaPreTrainedModel,
      )

else:
   import sys

   _UpperCamelCase							:       int       					=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)












"""simple docstring"""


from cva import destroyAllWindows, imread, imshow, waitKey




def      snake_case    (A_ :Optional[int]      ,     A_ :int ):







  '''simple docstring'''

  a,	a	:     Tuple							=  img.shape[0], img.shape[1]

  def to_grayscale(A_ :Tuple      ,     A_ :List[str]      ,     A_ :Union[str, Any] ):
    return 0.2126 * red + 0.587 * green + 0.114 * blue

  def normalize(A_ :Union[str, Any] ):
    return min(A_      ,     2_5_5 )

  for i in range(A_ ):
    for j in range(A_ ):
      a	:     Union[str, Any]							=  int(to_grayscale(*img[i][j] ) )
      a	:     Any							=  [
          normalize(A_ ),
          normalize(greyscale + factor ),
          normalize(greyscale + 2 * factor ),
      ]

  return img


if __name__ == "__main__":
   # read original image
   _UpperCamelCase							:       List[str]       					=		{
       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()




