
'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCAmelCase												=	{
    'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	['RemBertTokenizer']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	['RemBertTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'RemBertForCausalLM',
				    'RemBertForMaskedLM',
				    'RemBertForMultipleChoice',
				    'RemBertForQuestionAnswering',
				    'RemBertForSequenceClassification',
				    'RemBertForTokenClassification',
				    'RemBertLayer',
				    'RemBertModel',
				    'RemBertPreTrainedModel',
				    'load_tf_weights_in_rembert',
				]


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFRemBertForCausalLM',
				    'TFRemBertForMaskedLM',
				    'TFRemBertForMultipleChoice',
				    'TFRemBertForQuestionAnswering',
				    'TFRemBertForSequenceClassification',
				    'TFRemBertForTokenClassification',
				    'TFRemBertLayer',
				    'TFRemBertModel',
				    'TFRemBertPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_rembert import RemBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_rembert_fast import RemBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_rembert import (
								    REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RemBertForCausalLM,
								    RemBertForMaskedLM,
								    RemBertForMultipleChoice,
								    RemBertForQuestionAnswering,
								    RemBertForSequenceClassification,
								    RemBertForTokenClassification,
								    RemBertLayer,
								    RemBertModel,
								    RemBertPreTrainedModel,
								    load_tf_weights_in_rembert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_rembert import (
								    TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFRemBertForCausalLM,
								    TFRemBertForMaskedLM,
								    TFRemBertForMultipleChoice,
								    TFRemBertForQuestionAnswering,
								    TFRemBertForSequenceClassification,
								    TFRemBertForTokenClassification,
								    TFRemBertLayer,
								    TFRemBertModel,
								    TFRemBertPreTrainedModel,
								)


else:
				import sys

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

'''simple docstring'''
def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):

							if not (isinstance(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				) and isinstance(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)):
														raise ValueError("""longest_common_substring() takes two strings for inputs"""				)

							_snake_case       =  len(_SCREAMING_SNAKE_CASE				)
							_snake_case       =  len(_SCREAMING_SNAKE_CASE				)

							_snake_case       =  [[0] * (texta_length + 1) for _ in range(texta_length + 1				)]
							_snake_case       =  0
							_snake_case       =  0

							for i in range(1							, texta_length + 1				):
														for j in range(1							, texta_length + 1				):
																					if texta[i - 1] == texta[j - 1]:
																												_snake_case       =  1 + dp[i - 1][j - 1]
																												if dp[i][j] > ans_length:
																																			_snake_case       =  i
																																			_snake_case       =  dp[i][j]

							return texta[ans_index - ans_length : ans_index]


if __name__ == "__main__":
				import doctest

				doctest.testmod()