
'''simple docstring'''

from typing import TYPE_CHECKING

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


snake_case									=    {
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case									=    [
         """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """RobertaPreLayerNormForCausalLM""",
         """RobertaPreLayerNormForMaskedLM""",
         """RobertaPreLayerNormForMultipleChoice""",
         """RobertaPreLayerNormForQuestionAnswering""",
         """RobertaPreLayerNormForSequenceClassification""",
         """RobertaPreLayerNormForTokenClassification""",
         """RobertaPreLayerNormModel""",
         """RobertaPreLayerNormPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case									=    [
         """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFRobertaPreLayerNormForCausalLM""",
         """TFRobertaPreLayerNormForMaskedLM""",
         """TFRobertaPreLayerNormForMultipleChoice""",
         """TFRobertaPreLayerNormForQuestionAnswering""",
         """TFRobertaPreLayerNormForSequenceClassification""",
         """TFRobertaPreLayerNormForTokenClassification""",
         """TFRobertaPreLayerNormMainLayer""",
         """TFRobertaPreLayerNormModel""",
         """TFRobertaPreLayerNormPreTrainedModel""",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case									=    [
         """FlaxRobertaPreLayerNormForCausalLM""",
         """FlaxRobertaPreLayerNormForMaskedLM""",
         """FlaxRobertaPreLayerNormForMultipleChoice""",
         """FlaxRobertaPreLayerNormForQuestionAnswering""",
         """FlaxRobertaPreLayerNormForSequenceClassification""",
         """FlaxRobertaPreLayerNormForTokenClassification""",
         """FlaxRobertaPreLayerNormModel""",
         """FlaxRobertaPreLayerNormPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_roberta_prelayernorm import (
         ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
         RobertaPreLayerNormConfig,
         RobertaPreLayerNormOnnxConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_roberta_prelayernorm import (
              ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
              RobertaPreLayerNormForCausalLM,
              RobertaPreLayerNormForMaskedLM,
              RobertaPreLayerNormForMultipleChoice,
              RobertaPreLayerNormForQuestionAnswering,
              RobertaPreLayerNormForSequenceClassification,
              RobertaPreLayerNormForTokenClassification,
              RobertaPreLayerNormModel,
              RobertaPreLayerNormPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_roberta_prelayernorm import (
              TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFRobertaPreLayerNormForCausalLM,
              TFRobertaPreLayerNormForMaskedLM,
              TFRobertaPreLayerNormForMultipleChoice,
              TFRobertaPreLayerNormForQuestionAnswering,
              TFRobertaPreLayerNormForSequenceClassification,
              TFRobertaPreLayerNormForTokenClassification,
              TFRobertaPreLayerNormMainLayer,
              TFRobertaPreLayerNormModel,
              TFRobertaPreLayerNormPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_roberta_prelayernorm import (
              FlaxRobertaPreLayerNormForCausalLM,
              FlaxRobertaPreLayerNormForMaskedLM,
              FlaxRobertaPreLayerNormForMultipleChoice,
              FlaxRobertaPreLayerNormForQuestionAnswering,
              FlaxRobertaPreLayerNormForSequenceClassification,
              FlaxRobertaPreLayerNormForTokenClassification,
              FlaxRobertaPreLayerNormModel,
              FlaxRobertaPreLayerNormPreTrainedModel,
          )

else:
     import sys

     snake_case									=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







'''simple docstring'''

from cva import destroyAllWindows, imread, imshow, waitKey


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


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

 # converting each pixel's color to its negative
 for i in range(lowerCamelCase_			):
  for j in range(lowerCamelCase_			):
   lowerCAmelCase__     :   Optional[int]     		=	[2_5_5, 2_5_5, 2_5_5] - img[i][j]

 return img


if __name__ == "__main__":
     # read original image
     snake_case									=    imread("""image_data/lena.jpg""", 1)

     # convert to its negative
     snake_case									=    convert_to_negative(img)

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





