

"""simple docstring"""




from string import ascii_uppercase
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__ = False			,							lowercase__ = ascii_uppercase      ):
					"""simple docstring"""
					A			=					keyword.upper()
					A			=					plaintext.upper()
					A			=					set(lowercase__      )

					# 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(lowercase__      )
    # the number of those unique characters will determine the number of rows
					A			=					len(lowercase__      )

					# 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			,							lowercase__      )
					]

					# 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(lowercase__      ):
										for row in modified_alphabet:
															# if current row (the last one) is too short, break out of loop
															if len(lowercase__      ) <= column:
																				break

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

					if verbose:
										print(lowercase__      )
					# 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


_lowerCamelCase       =	{
    '''configuration_mobilenet_v1''': [
        '''MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileNetV1Config''',
        '''MobileNetV1OnnxConfig''',
    ],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowerCamelCase       =	['''MobileNetV1FeatureExtractor''']
    _lowerCamelCase       =	['''MobileNetV1ImageProcessor''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowerCamelCase       =	[
        '''MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''MobileNetV1ForImageClassification''',
        '''MobileNetV1Model''',
        '''MobileNetV1PreTrainedModel''',
        '''load_tf_weights_in_mobilenet_v1''',
    ]


if TYPE_CHECKING:
    from .configuration_mobilenet_va import (
        MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
        MobileNetVaConfig,
        MobileNetVaOnnxConfig,
    )

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
        from .image_processing_mobilenet_va import MobileNetVaImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mobilenet_va import (
            MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
            MobileNetVaForImageClassification,
            MobileNetVaModel,
            MobileNetVaPreTrainedModel,
            load_tf_weights_in_mobilenet_va,
        )


else:
    import sys

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

