

def 						UpperCamelCase (lowercase_:			str   ,				lowercase_:			str )					->				str:

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

	A__							:   Tuple					       =		len(lowercase_ )
	A__							:   List[str]					       =		len(lowercase_ )

	A__							:   Optional[int]					       =		[[0] * (texta_length + 1) for _ in range(texta_length + 1 )]
	A__							:   int					       =		0
	A__							:   List[Any]					       =		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__							:   Optional[int]					       =		1 + dp[i - 1][j - 1]
				if dp[i][j] > ans_length:
					A__							:   Any					       =		i
					A__							:   Dict					       =		dp[i][j]

	return texta[ans_index - ans_length : ans_index]


if __name__ == "__main__":
	import doctest

	doctest.testmod()






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
	from transformers import AutoModelForSeqaSeqLM, AutoTokenizer



@require_torch
@require_sentencepiece
@require_tokenizers
class    _a						(unittest.TestCase    ):


					'''simple docstring'''






					@slow
					def   __A      (			self      ):
						A__							:   Optional[Any]					       =		AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""   ,	return_dict=A__      ).to(A__      )
						A__							:   str					       =		AutoTokenizer.from_pretrained("""google/mt5-small"""      )

						A__							:   int					       =		tokenizer("""Hello there"""   ,	return_tensors="""pt"""      ).input_ids
						A__							:   List[Any]					       =		tokenizer("""Hi I am"""   ,	return_tensors="""pt"""      ).input_ids

						A__							:   Union[str, Any]					       =		model(input_ids.to(A__      )   ,	labels=labels.to(A__      )      ).loss
						A__							:   Union[str, Any]					       =		-(labels.shape[-1] * loss.item())

						A__							:   Any					       =		-8_4.9_1_2_7
						self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 1e-4      )



