"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


_a				:		Tuple	   =       {
    'configuration_speecht5': [
        'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP',
        'SpeechT5Config',
        'SpeechT5HifiGanConfig',
    ],
    'feature_extraction_speecht5': ['SpeechT5FeatureExtractor'],
    'processing_speecht5': ['SpeechT5Processor'],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a				:		Any	   =       ['SpeechT5Tokenizer']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a				:		Optional[Any]	   =       [
         'SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST',
         'SpeechT5ForSpeechToText',
         'SpeechT5ForSpeechToSpeech',
         'SpeechT5ForTextToSpeech',
         'SpeechT5Model',
         'SpeechT5PreTrainedModel',
         'SpeechT5HifiGan',
     ]

if TYPE_CHECKING:
     from .configuration_speechta import (
         SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
         SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
         SpeechTaConfig,
         SpeechTaHifiGanConfig,
     )
     from .feature_extraction_speechta import SpeechTaFeatureExtractor
     from .processing_speechta import SpeechTaProcessor

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_speechta import SpeechTaTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_speechta import (
              SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
              SpeechTaForSpeechToSpeech,
              SpeechTaForSpeechToText,
              SpeechTaForTextToSpeech,
              SpeechTaHifiGan,
              SpeechTaModel,
              SpeechTaPreTrainedModel,
          )

else:
     import sys

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





"""simple docstring"""




import math





def 							SCREAMING_SNAKE_CASE					(      )	->				None:
  _lowerCAmelCase						:	Any        =					input("""Enter message: """     )
  _lowerCAmelCase						:	List[Any]        =					int(input(f"Enter key [2-{len(_lowerCamelCase     ) - 1}]: "     )     )
  _lowerCAmelCase						:	Optional[Any]        =					input("""Encryption/Decryption [e/d]: """     )

  if mode.lower().startswith("""e"""     ):
    _lowerCAmelCase						:	Tuple        =					encrypt_message(_lowerCamelCase      ,_lowerCamelCase     )
  elif mode.lower().startswith("""d"""     ):
    _lowerCAmelCase						:	Dict        =					decrypt_message(_lowerCamelCase      ,_lowerCamelCase     )

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





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	int      ,_lowerCamelCase						:	str     )	->				str:
  _lowerCAmelCase						:	Dict        =					[""""""] * key
  for col in range(_lowerCamelCase     ):
    _lowerCAmelCase						:	List[str]        =					col
    while pointer < len(_lowerCamelCase     ):
      cipher_text[col] += message[pointer]
      pointer += key
  return "".join(_lowerCamelCase     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	int      ,_lowerCamelCase						:	str     )	->				str:
  _lowerCAmelCase						:	str        =					math.ceil(len(_lowerCamelCase     ) / key     )
  _lowerCAmelCase						:	Union[str, Any]        =					key
  _lowerCAmelCase						:	Any        =					(num_cols * num_rows) - len(_lowerCamelCase     )
  _lowerCAmelCase						:	Dict        =					[""""""] * num_cols
  _lowerCAmelCase						:	Optional[Any]        =					0
  _lowerCAmelCase						:	Dict        =					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						:	str        =					0
      row += 1

  return "".join(_lowerCamelCase     )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()




