




'''simple docstring'''





def 			_a   (		SCREAMING_SNAKE_CASE_							:       str   ,   SCREAMING_SNAKE_CASE_							:       Any  ):
					assert x is not None
					assert y is not None

					__lowerCAmelCase            = len(UpperCAmelCase__  )
					__lowerCAmelCase            = len(UpperCAmelCase__  )

					# declaring the array for storing the dp values
					__lowerCAmelCase            = [[0] * (n + 1) for _ in range(m + 1  )]  # noqa: E741

					for i in range(1   ,   m + 1  ):
										for j in range(1   ,   n + 1  ):
															__lowerCAmelCase            = 1 if x[i - 1] == y[j - 1] else 0

															__lowerCAmelCase            = max(l[i - 1][j]   ,   l[i][j - 1]   ,   l[i - 1][j - 1] + match  )

					__lowerCAmelCase            = ""
					__lowerCAmelCase     ,							__lowerCAmelCase            = m, n
					while i > 0 and j > 0:
										__lowerCAmelCase            = 1 if x[i - 1] == y[j - 1] else 0

										if l[i][j] == l[i - 1][j - 1] + match:
															if match == 1:
																				__lowerCAmelCase            = x[i - 1] + seq
															i -= 1
															j -= 1
										elif l[i][j] == l[i - 1][j]:
															i -= 1
										else:
															j -= 1

					return l[m][n], seq


if __name__ == "__main__":
					UpperCamelCase__					    =    """AGGTAB"""
					UpperCamelCase__					    =    """GXTXAYB"""
					UpperCamelCase__					    =    4
					UpperCamelCase__					    =    """GTAB"""

					UpperCamelCase__    ,			UpperCamelCase__					    =    longest_common_subsequence(a, b)
					print("""len =""", ln, """, sub-sequence =""", subseq)
					import doctest

					doctest.testmod()










import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class     a__					(      snake_case__   ):
						_a							:					Optional[Any]      =					"""M-CLIP"""






						def __init__(     self	,    _A=1_0_2_4	,    _A=7_6_8	,    **_A    ):




											"""simple docstring"""



											__lowerCAmelCase            = transformerDimSize
											__lowerCAmelCase            = imageDimSize
											super().__init__(**_A    )






class     a__					(      snake_case__   ):
						_a							:					Tuple      =					MCLIPConfig






						def __init__(     self	,    _A	,    *_A	,    **_A    ):




											"""simple docstring"""



											super().__init__(_A	,    *_A	,    **_A    )
											__lowerCAmelCase            = XLMRobertaModel(_A    )
											__lowerCAmelCase            = torch.nn.Linear(
											    in_features=config.transformerDimensions	,    out_features=config.numDims    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.transformer(input_ids=_A	,    attention_mask=_A    )[0]
											__lowerCAmelCase            = (embs * attention_mask.unsqueeze(2    )).sum(dim=1    ) / attention_mask.sum(dim=1    )[:, None]
											return self.LinearTransformation(_A    ), embs




