




from string import ascii_uppercase




def      __magic_name__							(					A						:   Dict,    A						:   Optional[int],    A						:   Dict = False,    A						:   Tuple = ascii_uppercase  ):






			'''simple docstring'''







			a       =     keyword.upper()
			a       =     plaintext.upper()
			a       =     set(__UpperCamelCase  )

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

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

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

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

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

			if verbose:
						print(__UpperCamelCase  )
			# 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'))








"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__A					=			{
    'configuration_donut_swin': ['DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DonutSwinConfig'],
    'processing_donut': ['DonutProcessor'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A					=			[
			    'DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'DonutSwinModel',
			    'DonutSwinPreTrainedModel',
			]

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A					=			['DonutFeatureExtractor']
			__A					=			['DonutImageProcessor']


if TYPE_CHECKING:
			from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
			from .processing_donut import DonutProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_donut_swin import (
						    DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DonutSwinModel,
						    DonutSwinPreTrainedModel,
						)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_donut import DonutFeatureExtractor
						from .image_processing_donut import DonutImageProcessor

else:
			import sys

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



