




import random







def 			_a   (		SCREAMING_SNAKE_CASE_							:       int   ,   SCREAMING_SNAKE_CASE_							:       float   ,   SCREAMING_SNAKE_CASE_							:       bool = False  ):
					__lowerCAmelCase            = {i: [] for i in range(SCREAMING_SNAKE_CASE_  )}

					# if probability is greater or equal than 1, then generate a complete graph
					if probability >= 1:
										return complete_graph(SCREAMING_SNAKE_CASE_  )
					# if probability is lower or equal than 0, then return a graph without edges
					if probability <= 0:
										return graph

					# for each couple of nodes, add an edge from u to v
					# if the number randomly generated is greater than probability probability
					for i in range(SCREAMING_SNAKE_CASE_  ):
										for j in range(i + 1   ,   SCREAMING_SNAKE_CASE_  ):
															if random.random() < probability:
																				graph[i].append(SCREAMING_SNAKE_CASE_  )
																				if not directed:
																									# if the graph is undirected, add an edge in from j to i, either
																									graph[j].append(SCREAMING_SNAKE_CASE_  )
					return graph







def 			_a   (		SCREAMING_SNAKE_CASE_							:       int  ):
					return {
					    i: [j for j in range(SCREAMING_SNAKE_CASE_  ) if i != j] for i in range(SCREAMING_SNAKE_CASE_  )
					}


if __name__ == "__main__":
					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




