




'''simple docstring'''

def __UpperCAmelCase	(   _UpperCAmelCase		:   str						)      ->      list:
			__snake_case			=		[0] * len(_UpperCAmelCase						)

			for i in range(1		,				len(_UpperCAmelCase						)						):
						# use last results for better performance - dynamic programming
						__snake_case			=		prefix_result[i - 1]
						while j > 0 and input_string[i] != input_string[j]:
									__snake_case			=		prefix_result[j - 1]

						if input_string[i] == input_string[j]:
									j += 1
						__snake_case			=		j

			return prefix_result


def __UpperCAmelCase	(   _UpperCAmelCase		:   str						)      ->      int:
			return max(prefix_function(_UpperCAmelCase						)						)


if __name__ == "__main__":
						import doctest

						doctest.testmod()









from typing import TYPE_CHECKING

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


__snake_case   							=						{'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case   							=						[
        '''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''UniSpeechForCTC''',
        '''UniSpeechForPreTraining''',
        '''UniSpeechForSequenceClassification''',
        '''UniSpeechModel''',
        '''UniSpeechPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_unispeech import (
            UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
            UniSpeechForCTC,
            UniSpeechForPreTraining,
            UniSpeechForSequenceClassification,
            UniSpeechModel,
            UniSpeechPreTrainedModel,
        )

else:
    import sys

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


