




def 			_a   (		SCREAMING_SNAKE_CASE_							:       Optional[Any]  ):
					__lowerCAmelCase            = len(SCREAMING_SNAKE_CASE_  )
					__lowerCAmelCase            = sum(SCREAMING_SNAKE_CASE_  )

					__lowerCAmelCase            = [[False for x in range(s + 1  )] for y in range(n + 1  )]

					for i in range(1   ,   n + 1  ):
										__lowerCAmelCase            = True

					for i in range(1   ,   s + 1  ):
										__lowerCAmelCase            = False

					for i in range(1   ,   n + 1  ):
										for j in range(1   ,   s + 1  ):
															__lowerCAmelCase            = dp[i][j - 1]

															if arr[i - 1] <= j:
																				__lowerCAmelCase            = dp[i][j] or dp[i - 1][j - arr[i - 1]]

					for j in range(int(s / 2  )   ,   -1   ,   -1  ):
										if dp[n][j] is True:
															__lowerCAmelCase            = s - 2 * j
															break

					return diff










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




