
"""simple docstring"""




from typing import TYPE_CHECKING

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


A__ :       Optional[Any]				= {
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A__ :       List[str]				= [
      'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
      'AdaptiveEmbedding',
      'TransfoXLForSequenceClassification',
      'TransfoXLLMHeadModel',
      'TransfoXLModel',
      'TransfoXLPreTrainedModel',
      'load_tf_weights_in_transfo_xl',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A__ :       int				= [
      'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFAdaptiveEmbedding',
      'TFTransfoXLForSequenceClassification',
      'TFTransfoXLLMHeadModel',
      'TFTransfoXLMainLayer',
      'TFTransfoXLModel',
      'TFTransfoXLPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
  from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_transfo_xl import (
        TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
        AdaptiveEmbedding,
        TransfoXLForSequenceClassification,
        TransfoXLLMHeadModel,
        TransfoXLModel,
        TransfoXLPreTrainedModel,
        load_tf_weights_in_transfo_xl,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_transfo_xl import (
        TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFAdaptiveEmbedding,
        TFTransfoXLForSequenceClassification,
        TFTransfoXLLMHeadModel,
        TFTransfoXLMainLayer,
        TFTransfoXLModel,
        TFTransfoXLPreTrainedModel,
    )

else:
  import sys

  A__ :       Tuple				= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


"""simple docstring"""




from cva import destroyAllWindows, imread, imshow, waitKey




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase			,					_lowercase:      Union[str, Any]   			=  img.shape[0], img.shape[1]

  # converting each pixel's color to its negative
  for i in range(_UpperCamelCase    ):
    for j in range(_UpperCamelCase    ):
      _lowercase:      Dict   			=  [255, 255, 255] - img[i][j]

  return img


if __name__ == "__main__":
  # read original image
  A__ :       Optional[Any]				= imread('image_data/lena.jpg', 1)

  # convert to its negative
  A__ :       Dict				= convert_to_negative(img)

  # show result image
  imshow('negative of original image', img)
  waitKey(0)
  destroyAllWindows()
