




from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE     : Any      ={
    """configuration_gptsan_japanese""": ["""GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTSanJapaneseConfig"""],
    """tokenization_gptsan_japanese""": ["""GPTSanJapaneseTokenizer"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE     : Union[str, Any]      =[
          """GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """GPTSanJapaneseForConditionalGeneration""",
          """GPTSanJapaneseModel""",
          """GPTSanJapanesePreTrainedModel""",
      ]
      __SCREAMING_SNAKE_CASE     : int      =[
          """GPTSanJapaneseTokenizer""",
      ]


if TYPE_CHECKING:
      from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
      from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_gptsan_japanese import (
                GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
                GPTSanJapaneseForConditionalGeneration,
                GPTSanJapaneseModel,
                GPTSanJapanesePreTrainedModel,
            )
            from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
      import sys

      __SCREAMING_SNAKE_CASE     : Optional[Any]      =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





from string import ascii_uppercase

def       lowerCAmelCase_      (	lowerCamelCase:							str  ,					lowerCamelCase:							str  ,					lowerCamelCase:							bool = False  ,					lowerCamelCase:							str = ascii_uppercase    )       ->      str:


      '''simple docstring'''




      lowerCamelCase__   	=					keyword.upper()
      lowerCamelCase__   	=					plaintext.upper()
      lowerCamelCase__   	=					set(lowerCamelCase    )

      # create a list of unique characters in the keyword - their order matters
      # it determines how we will map plaintext characters to the ciphertext
      lowerCamelCase__   	=					[]
      for char in keyword:
            if char in alphabet_set and char not in unique_chars:
                  unique_chars.append(lowerCamelCase    )
    # the number of those unique characters will determine the number of rows
      lowerCamelCase__   	=					len(lowerCamelCase    )

      # create a shifted version of the alphabet
      lowerCamelCase__   	=					unique_chars + [
          char for char in alphabet if char not in unique_chars
      ]

      # create a modified alphabet by splitting the shifted alphabet into rows
      lowerCamelCase__   	=					[
          shifted_alphabet[k : k + num_unique_chars_in_keyword]
          for k in range(0  ,					26  ,					lowerCamelCase    )
      ]

      # map the alphabet characters to the modified alphabet characters
      # going 'vertically' through the modified alphabet - consider columns first
      lowerCamelCase__   	=					{}
      lowerCamelCase__   	=					0
      for column in range(lowerCamelCase    ):
            for row in modified_alphabet:
                  # if current row (the last one) is too short, break out of loop
                  if len(lowerCamelCase    ) <= column:
                        break

                  # map current letter to letter in modified alphabet
                  lowerCamelCase__   	=					row[column]
                  letter_index += 1

      if verbose:
            print(lowerCamelCase    )
      # create the encrypted text by mapping the plaintext to the modified alphabet
      return "".join(mapping[char] if char in mapping else char for char in plaintext    )


if __name__ == "__main__":
      # example use
      print(mixed_keyword("""college""", """UNIVERSITY"""))