from string import ascii_uppercase





def      __lowercase  (		lowerCamelCase      :  str						,						lowerCamelCase      :  str						,						lowerCamelCase      :  bool = False						,						lowerCamelCase      :  str = ascii_uppercase			):
 UpperCamelCase_      :		List[Any]				=      keyword.upper()
 UpperCamelCase_      :		int				=      plaintext.upper()
 UpperCamelCase_      :		int				=      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
 UpperCamelCase_      :		Optional[int]				=      []
 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
 UpperCamelCase_      :		int				=      len(lowerCamelCase			)

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

 # create a modified alphabet by splitting the shifted alphabet into rows
 UpperCamelCase_      :		Union[str, Any]				=      [
     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
 UpperCamelCase_      :		Dict				=      {}
 UpperCamelCase_      :		str				=      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
   UpperCamelCase_      :		Tuple				=      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'))




from typing import TYPE_CHECKING

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


a_					      = {
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_					      = ['MarianTokenizer']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_					      = [
       'MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST',
       'MarianForCausalLM',
       'MarianModel',
       'MarianMTModel',
       'MarianPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_					      = ['TFMarianModel', 'TFMarianMTModel', 'TFMarianPreTrainedModel']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_					      = ['FlaxMarianModel', 'FlaxMarianMTModel', 'FlaxMarianPreTrainedModel']

if TYPE_CHECKING:
   from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_marian import MarianTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_marian import (
          MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
          MarianForCausalLM,
          MarianModel,
          MarianMTModel,
          MarianPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
   import sys

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



