

from __future__ import annotations



def   __UpperCAmelCase					(      __a :     str      )     ->	list[int]:

					"""simple docstring"""

					return [ord(__a      ) - 96 for elem in plain]



def   __UpperCAmelCase					(      __a :     list[int]      )     ->	str:

					"""simple docstring"""

					return "".join(chr(elem + 96      ) for elem in encoded      )



def   __UpperCAmelCase					(      )     ->	None:

					"""simple docstring"""

					_a      :       Dict   =						encode(input('''-> '''      ).strip().lower()      )
					print('''Encoded: '''						,__a      )
					print('''Decoded:'''						,decode(__a      )      )


if __name__ == "__main__":
			main()







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase				={
    'configuration_table_transformer': [
        'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TableTransformerConfig',
        'TableTransformerOnnxConfig',
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase				=[
     'TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TableTransformerForObjectDetection',
     'TableTransformerModel',
     'TableTransformerPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_table_transformer import (
     TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     TableTransformerConfig,
     TableTransformerOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_table_transformer import (
      TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TableTransformerForObjectDetection,
      TableTransformerModel,
      TableTransformerPreTrainedModel,
  )

else:
 import sys

 lowercase				=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

