
def 						UpperCAmelCase      (     a_		,       a_						)		->     str:







				"""simple docstring"""



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

				__A             =			len(a_						)
				__A             =			len(a_						)

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

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

				return texta[ans_index - ans_length : ans_index]


if __name__ == "__main__":
					import doctest

					doctest.testmod()


from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE							:Any										=    {
    'configuration_rag': ['RagConfig'],
    'retrieval_rag': ['RagRetriever'],
    'tokenization_rag': ['RagTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:List[Any]										=    [
					    'RagModel',
					    'RagPreTrainedModel',
					    'RagSequenceForGeneration',
					    'RagTokenForGeneration',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Any										=    [
					    'TFRagModel',
					    'TFRagPreTrainedModel',
					    'TFRagSequenceForGeneration',
					    'TFRagTokenForGeneration',
					]


if TYPE_CHECKING:
					from .configuration_rag import RagConfig
					from .retrieval_rag import RagRetriever
					from .tokenization_rag import RagTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_rag import (
										    TFRagModel,
										    TFRagPreTrainedModel,
										    TFRagSequenceForGeneration,
										    TFRagTokenForGeneration,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE							:int										=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
