code: 
  
  
  
  
  from string import ascii_uppercase
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a = False	,	__a = ascii_uppercase )       ->			str:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =keyword.upper()
  							SCREAMING_SNAKE_CASE			: str	     =plaintext.upper()
  							SCREAMING_SNAKE_CASE			: List[Any]	     =set(__a )
  
  							# create a list of unique characters in the keyword - their order matters
  							# it determines how we will map plaintext characters to the ciphertext
  							SCREAMING_SNAKE_CASE			: str	     =[]
  							for char in keyword:
  														if char in alphabet_set and char not in unique_chars:
  																					unique_chars.append(__a )
      # the number of those unique characters will determine the number of rows
  							SCREAMING_SNAKE_CASE			: Dict	     =len(__a )
  
  							# create a shifted version of the alphabet
  							SCREAMING_SNAKE_CASE			: 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
  							SCREAMING_SNAKE_CASE			: List[str]	     =[
  							    shifted_alphabet[k : k + num_unique_chars_in_keyword]
  							    for k in range(0	,	26	,	__a )
  							]
  
  							# map the alphabet characters to the modified alphabet characters
  							# going 'vertically' through the modified alphabet - consider columns first
  							SCREAMING_SNAKE_CASE			: Any	     ={}
  							SCREAMING_SNAKE_CASE			: int	     =0
  							for column in range(__a ):
  														for row in modified_alphabet:
  																					# if current row (the last one) is too short, break out of loop
  																					if len(__a ) <= column:
  																												break
  
  																					# map current letter to letter in modified alphabet
  																					SCREAMING_SNAKE_CASE			: List[Any]	     =row[column]
  																					letter_index += 1
  
  							if verbose:
  														print(__a )
  							# 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"""))
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  _A							    =	{"""configuration_regnet""": ["""REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RegNetConfig"""]}
  
  try:
  							if not is_torch_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	[
  							    """REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
  							    """RegNetForImageClassification""",
  							    """RegNetModel""",
  							    """RegNetPreTrainedModel""",
  							]
  
  try:
  							if not is_tf_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	[
  							    """TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
  							    """TFRegNetForImageClassification""",
  							    """TFRegNetModel""",
  							    """TFRegNetPreTrainedModel""",
  							]
  
  try:
  							if not is_flax_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	[
  							    """FlaxRegNetForImageClassification""",
  							    """FlaxRegNetModel""",
  							    """FlaxRegNetPreTrainedModel""",
  							]
  
  
  if TYPE_CHECKING:
  							from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig
  
  							try:
  														if not is_torch_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_regnet import (
  														    REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
  														    RegNetForImageClassification,
  														    RegNetModel,
  														    RegNetPreTrainedModel,
  														)
  
  							try:
  														if not is_tf_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_tf_regnet import (
  														    TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
  														    TFRegNetForImageClassification,
  														    TFRegNetModel,
  														    TFRegNetPreTrainedModel,
  														)
  
  							try:
  														if not is_flax_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_flax_regnet import (
  														    FlaxRegNetForImageClassification,
  														    FlaxRegNetModel,
  														    FlaxRegNetPreTrainedModel,
  														)
  
  
  else:
  							import sys
  
  							_A							    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure)
  
  
  
  
  
  
  