code: 
  
  
  
  
  
  '''simple docstring'''
  
  from __future__ import annotations
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      str		, _lowerCamelCase      :      dict[str, str] ):
      A__,			A__,			A__       =    "", "", ""
      A__       =    []
  
      for character in message_part:
          tmp.append(character_to_number[character] )
  
      for each in tmp:
          one += each[0]
          two += each[1]
          three += each[2]
  
      return one + two + three
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      str		, _lowerCamelCase      :      dict[str, str] ):
      A__,			A__       =    "", ""
      A__       =    []
  
      for character in message_part:
          this_part += character_to_number[character]
  
      for digit in this_part:
          tmp += digit
          if len(_lowerCamelCase ) == len(_lowerCamelCase ):
              result.append(_lowerCamelCase )
              A__       =    ""
  
      return result[0], result[1], result[2]
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      str		, _lowerCamelCase      :      str ):
      # Validate message and alphabet, set to upper and remove spaces
      A__       =    alphabet.replace(" "		, "" ).upper()
      A__       =    message.replace(" "		, "" ).upper()
  
      # Check length and characters
      if len(_lowerCamelCase ) != 27:
          raise KeyError("Length of alphabet has to be 27." )
      for each in message:
          if each not in alphabet:
              raise ValueError("Each message character has to be included in alphabet!" )
  
      # Generate dictionares
      A__       =    (
          "111",
          "112",
          "113",
          "121",
          "122",
          "123",
          "131",
          "132",
          "133",
          "211",
          "212",
          "213",
          "221",
          "222",
          "223",
          "231",
          "232",
          "233",
          "311",
          "312",
          "313",
          "321",
          "322",
          "323",
          "331",
          "332",
          "333",
      )
      A__       =    {}
      A__       =    {}
      for letter, number in zip(_lowerCamelCase		, _lowerCamelCase ):
          A__       =    number
          A__       =    letter
  
      return message, alphabet, character_to_number, number_to_character
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      str		, _lowerCamelCase      :      str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."		, _lowerCamelCase      :      int = 5 ):
      A__,			A__,			A__,			A__       =    __prepare(
          _lowerCamelCase		, _lowerCamelCase )
      A__,			A__       =    "", ""
  
      for i in range(0		, len(_lowerCamelCase ) + 1		, _lowerCamelCase ):
          encrypted_numeric += __encrypt_part(
              message[i : i + period]		, _lowerCamelCase )
  
      for i in range(0		, len(_lowerCamelCase )		, 3 ):
          encrypted += number_to_character[encrypted_numeric[i : i + 3]]
  
      return encrypted
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      str		, _lowerCamelCase      :      str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."		, _lowerCamelCase      :      int = 5 ):
      A__,			A__,			A__,			A__       =    __prepare(
          _lowerCamelCase		, _lowerCamelCase )
      A__       =    []
      A__       =    ""
  
      for i in range(0		, len(_lowerCamelCase ) + 1		, _lowerCamelCase ):
          A__,			A__,			A__       =    __decrypt_part(message[i : i + period]		, _lowerCamelCase )
  
          for j in range(0		, len(_lowerCamelCase ) ):
              decrypted_numeric.append(a[j] + b[j] + c[j] )
  
      for each in decrypted_numeric:
          decrypted += number_to_character[each]
  
      return decrypted
  
  
  if __name__ == "__main__":
    __lowerCAmelCase							:			Tuple				="DEFEND THE EAST WALL OF THE CASTLE."
    __lowerCAmelCase							:			List[Any]				=encrypt_message(msg, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
    __lowerCAmelCase							:			Dict				=decrypt_message(encrypted, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
    print(f"""Encrypted: {encrypted}\nDecrypted: {decrypted}""")
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  __lowerCAmelCase							:			int				={
      "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
      "tokenization_layoutlm": ["LayoutLMTokenizer"],
  }
  
  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __lowerCAmelCase							:			Any				=["LayoutLMTokenizerFast"]
  
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __lowerCAmelCase							:			Dict				=[
        "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
        "LayoutLMForMaskedLM",
        "LayoutLMForSequenceClassification",
        "LayoutLMForTokenClassification",
        "LayoutLMForQuestionAnswering",
        "LayoutLMModel",
        "LayoutLMPreTrainedModel",
    ]
  
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __lowerCAmelCase							:			str				=[
        "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFLayoutLMForMaskedLM",
        "TFLayoutLMForSequenceClassification",
        "TFLayoutLMForTokenClassification",
        "TFLayoutLMForQuestionAnswering",
        "TFLayoutLMMainLayer",
        "TFLayoutLMModel",
        "TFLayoutLMPreTrainedModel",
    ]
  
  
  if TYPE_CHECKING:
    from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
    from .tokenization_layoutlm import LayoutLMTokenizer
  
    try:
      if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .tokenization_layoutlm_fast import LayoutLMTokenizerFast
  
    try:
      if not is_torch_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .modeling_layoutlm import (
          LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          LayoutLMForMaskedLM,
          LayoutLMForQuestionAnswering,
          LayoutLMForSequenceClassification,
          LayoutLMForTokenClassification,
          LayoutLMModel,
          LayoutLMPreTrainedModel,
      )
    try:
      if not is_tf_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .modeling_tf_layoutlm import (
          TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLayoutLMForMaskedLM,
          TFLayoutLMForQuestionAnswering,
          TFLayoutLMForSequenceClassification,
          TFLayoutLMForTokenClassification,
          TFLayoutLMMainLayer,
          TFLayoutLMModel,
          TFLayoutLMPreTrainedModel,
      )
  
  else:
    import sys
  
    __lowerCAmelCase							:			int				=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  