
"""simple docstring"""



from typing import TYPE_CHECKING

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


UpperCamelCase__          =	{
    'configuration_roberta_prelayernorm': [
        'ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'RobertaPreLayerNormConfig',
        'RobertaPreLayerNormOnnxConfig',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__          =	[
        'ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST',
        'RobertaPreLayerNormForCausalLM',
        'RobertaPreLayerNormForMaskedLM',
        'RobertaPreLayerNormForMultipleChoice',
        'RobertaPreLayerNormForQuestionAnswering',
        'RobertaPreLayerNormForSequenceClassification',
        'RobertaPreLayerNormForTokenClassification',
        'RobertaPreLayerNormModel',
        'RobertaPreLayerNormPreTrainedModel',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__          =	[
        'TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFRobertaPreLayerNormForCausalLM',
        'TFRobertaPreLayerNormForMaskedLM',
        'TFRobertaPreLayerNormForMultipleChoice',
        'TFRobertaPreLayerNormForQuestionAnswering',
        'TFRobertaPreLayerNormForSequenceClassification',
        'TFRobertaPreLayerNormForTokenClassification',
        'TFRobertaPreLayerNormMainLayer',
        'TFRobertaPreLayerNormModel',
        'TFRobertaPreLayerNormPreTrainedModel',
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__          =	[
        'FlaxRobertaPreLayerNormForCausalLM',
        'FlaxRobertaPreLayerNormForMaskedLM',
        'FlaxRobertaPreLayerNormForMultipleChoice',
        'FlaxRobertaPreLayerNormForQuestionAnswering',
        'FlaxRobertaPreLayerNormForSequenceClassification',
        'FlaxRobertaPreLayerNormForTokenClassification',
        'FlaxRobertaPreLayerNormModel',
        'FlaxRobertaPreLayerNormPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_roberta_prelayernorm import (
        ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
        RobertaPreLayerNormConfig,
        RobertaPreLayerNormOnnxConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_roberta_prelayernorm import (
            ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
            RobertaPreLayerNormForCausalLM,
            RobertaPreLayerNormForMaskedLM,
            RobertaPreLayerNormForMultipleChoice,
            RobertaPreLayerNormForQuestionAnswering,
            RobertaPreLayerNormForSequenceClassification,
            RobertaPreLayerNormForTokenClassification,
            RobertaPreLayerNormModel,
            RobertaPreLayerNormPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_roberta_prelayernorm import (
            TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFRobertaPreLayerNormForCausalLM,
            TFRobertaPreLayerNormForMaskedLM,
            TFRobertaPreLayerNormForMultipleChoice,
            TFRobertaPreLayerNormForQuestionAnswering,
            TFRobertaPreLayerNormForSequenceClassification,
            TFRobertaPreLayerNormForTokenClassification,
            TFRobertaPreLayerNormMainLayer,
            TFRobertaPreLayerNormModel,
            TFRobertaPreLayerNormPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_roberta_prelayernorm import (
            FlaxRobertaPreLayerNormForCausalLM,
            FlaxRobertaPreLayerNormForMaskedLM,
            FlaxRobertaPreLayerNormForMultipleChoice,
            FlaxRobertaPreLayerNormForQuestionAnswering,
            FlaxRobertaPreLayerNormForSequenceClassification,
            FlaxRobertaPreLayerNormForTokenClassification,
            FlaxRobertaPreLayerNormModel,
            FlaxRobertaPreLayerNormPreTrainedModel,
        )

else:
    import sys

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



"""simple docstring"""



def 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    UpperCAmelCase__	:      Optional[int]     							=					''
    for i in table:
        res += inp[i - 1]
    return res
def 					lowerCamelCase   (     _snake_case ):
    return data[1:] + data[0]
def 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    UpperCAmelCase__	:      str     							=					''
    for i in range(len(_snake_case ) ):
        if a[i] == b[i]:
            res += "0"
        else:
            res += "1"
    return res
def 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    UpperCAmelCase__	:      str     							=					int('0b' + data[0] + data[-1]    ,2 )
    UpperCAmelCase__	:      Optional[Any]     							=					int('0b' + data[1:3]    ,2 )
    return bin(s[row][col] )[2:]
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case    ,_snake_case    ,_snake_case ):
    UpperCAmelCase__	:      Any     							=					message[:4]
    UpperCAmelCase__	:      Optional[Any]     							=					message[4:]
    UpperCAmelCase__	:      Dict     							=					apply_table(_snake_case    ,_snake_case )
    UpperCAmelCase__	:      Optional[Any]     							=					xor(_snake_case    ,_snake_case )
    UpperCAmelCase__	:      Dict     							=					apply_sbox(_snake_case    ,temp[:4] )  # noqa: E741
    UpperCAmelCase__	:      Tuple     							=					apply_sbox(_snake_case    ,temp[4:] )
    UpperCAmelCase__	:      List[str]     							=					'0' * (2 - len(_snake_case )) + l  # noqa: E741
    UpperCAmelCase__	:      Optional[Any]     							=					'0' * (2 - len(_snake_case )) + r
    UpperCAmelCase__	:      Optional[int]     							=					apply_table(l + r    ,_snake_case )
    UpperCAmelCase__	:      Any     							=					xor(_snake_case    ,_snake_case )
    return temp + right


if __name__ == "__main__":
    UpperCamelCase__          =	input('Enter 10 bit key: ')
    UpperCamelCase__          =	input('Enter 8 bit message: ')

    UpperCamelCase__          =	[6, 3, 7, 4, 8, 5, 10, 9]
    UpperCamelCase__          =	[3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
    UpperCamelCase__          =	[2, 4, 3, 1]
    UpperCamelCase__          =	[2, 6, 3, 1, 4, 8, 5, 7]
    UpperCamelCase__          =	[4, 1, 3, 5, 7, 2, 8, 6]
    UpperCamelCase__          =	[4, 1, 2, 3, 2, 3, 4, 1]
    UpperCamelCase__          =	[[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
    UpperCamelCase__          =	[[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]

    # key generation
    UpperCamelCase__          =	apply_table(key, paa_table)
    UpperCamelCase__          =	temp[:5]
    UpperCamelCase__          =	temp[5:]
    UpperCamelCase__          =	left_shift(left)
    UpperCamelCase__          =	left_shift(right)
    UpperCamelCase__          =	apply_table(left + right, pa_table)
    UpperCamelCase__          =	left_shift(left)
    UpperCamelCase__          =	left_shift(right)
    UpperCamelCase__          =	left_shift(left)
    UpperCamelCase__          =	left_shift(right)
    UpperCamelCase__          =	apply_table(left + right, pa_table)

    # encryption
    UpperCamelCase__          =	apply_table(message, IP)
    UpperCamelCase__          =	function(expansion, sa, sa, keya, temp)
    UpperCamelCase__          =	temp[4:] + temp[:4]
    UpperCamelCase__          =	function(expansion, sa, sa, keya, temp)
    UpperCamelCase__          =	apply_table(temp, IP_inv)
    print('Cipher text is:', CT)

    # decryption
    UpperCamelCase__          =	apply_table(CT, IP)
    UpperCamelCase__          =	function(expansion, sa, sa, keya, temp)
    UpperCamelCase__          =	temp[4:] + temp[:4]
    UpperCamelCase__          =	function(expansion, sa, sa, keya, temp)
    UpperCamelCase__          =	apply_table(temp, IP_inv)
    print('Plain text after decypting is:', PT)

