






'''simple docstring'''

from typing import TYPE_CHECKING

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


UpperCAmelCase__						:Optional[Any]          =			{
    """configuration_resnet""": ["""RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ResNetConfig""", """ResNetOnnxConfig"""]
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:Tuple          =			[
       """RESNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """ResNetForImageClassification""",
       """ResNetModel""",
       """ResNetPreTrainedModel""",
       """ResNetBackbone""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:List[str]          =			[
       """TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFResNetForImageClassification""",
       """TFResNetModel""",
       """TFResNetPreTrainedModel""",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:Tuple          =			[
       """FlaxResNetForImageClassification""",
       """FlaxResNetModel""",
       """FlaxResNetPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_resnet import (
          RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          ResNetBackbone,
          ResNetForImageClassification,
          ResNetModel,
          ResNetPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_resnet import (
          TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFResNetForImageClassification,
          TFResNetModel,
          TFResNetPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
   import sys

   UpperCAmelCase__						:Any          =			_LazyModule(__name__, globals()["""__file__"""], _import_structure)











'''simple docstring'''

import math
def     __lowercase  ()   ->     None:


     """simple docstring"""
     __lowerCamelCase						:       Optional[int]				   =				input("""Enter message: """     )
     __lowerCamelCase						:       Optional[Any]				   =				int(input(f"Enter key [2-{len(_lowercase     ) - 1}]: "     )     )
     __lowerCamelCase						:       Dict				   =				input("""Encryption/Decryption [e/d]: """     )

     if mode.lower().startswith("""e"""     ):
          __lowerCamelCase						:       Any				   =				encrypt_message(_lowercase,						_lowercase     )
     elif mode.lower().startswith("""d"""     ):
          __lowerCamelCase						:       int				   =				decrypt_message(_lowercase,						_lowercase     )

     # Append pipe symbol (vertical bar) to identify spaces at the end.
     print(f"Output:\n{text + '|'}"     )
def     __lowercase  (_lowercase,						_lowercase     )   ->     str:


     """simple docstring"""
     __lowerCamelCase						:       int				   =				[""""""] * key
     for col in range(_lowercase     ):
          __lowerCamelCase						:       Optional[Any]				   =				col
          while pointer < len(_lowercase     ):
               cipher_text[col] += message[pointer]
               pointer += key
     return "".join(_lowercase     )
def     __lowercase  (_lowercase,						_lowercase     )   ->     str:


     """simple docstring"""
     __lowerCamelCase						:       List[str]				   =				math.ceil(len(_lowercase     ) / key     )
     __lowerCamelCase						:       List[str]				   =				key
     __lowerCamelCase						:       Tuple				   =				(num_cols * num_rows) - len(_lowercase     )
     __lowerCamelCase						:       List[str]				   =				[""""""] * num_cols
     __lowerCamelCase						:       List[str]				   =				0
     __lowerCamelCase						:       List[Any]				   =				0

     for symbol in message:
          plain_text[col] += symbol
          col += 1

          if (
              (col == num_cols)
              or (col == num_cols - 1)
              and (row >= num_rows - num_shaded_boxes)
          ):
               __lowerCamelCase						:       int				   =				0
               row += 1

     return "".join(_lowercase     )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()



