"""simple docstring"""




from __future__ import annotations

__lowerCamelCase											=  {
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}
class     UpperCamelCase__:





						def __init__(       self		,__UpperCAmelCase		,__UpperCAmelCase     )					->				None:
													A__	     =							graph
													# mapping node to its parent in resulting breadth first tree
													A__	     =							{}
													A__	     =							source_vertex





						def 				snake_case__	(       self     )					->				None:
													A__	     =							{self.source_vertex}
													A__	     =							None
													A__	     =							[self.source_vertex]  # first in first out queue

													while queue:
																				A__	     =							queue.pop(0     )
																				for adjacent_vertex in self.graph[vertex]:
																											if adjacent_vertex not in visited:
																																		visited.add(__UpperCAmelCase     )
																																		A__	     =							vertex
																																		queue.append(__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				str:
													if target_vertex == self.source_vertex:
																				return self.source_vertex

													A__	     =							self.parent.get(__UpperCAmelCase     )
													if target_vertex_parent is None:
																				A__	     =							(
																				    f'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
																				)
																				raise ValueError(__UpperCAmelCase     )

													return self.shortest_path(__UpperCAmelCase     ) + f'''->{target_vertex}'''


if __name__ == "__main__":
		__lowerCamelCase											=  Graph(graph, "G")
		g.breath_first_search()
		print(g.shortest_path("D"))
		print(g.shortest_path("G"))
		print(g.shortest_path("Foo"))






"""simple docstring"""




import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						Union[str, Any]													=		'M-CLIP'





						def __init__(       self		,__UpperCAmelCase=10_24		,__UpperCAmelCase=7_68		,**__UpperCAmelCase     )					->				List[str]:
													A__	     =							transformerDimSize
													A__	     =							imageDimSize
													super().__init__(**__UpperCAmelCase     )






class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						Union[str, Any]													=		MCLIPConfig





						def __init__(       self		,__UpperCAmelCase		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				List[Any]:
													super().__init__(__UpperCAmelCase		,*__UpperCAmelCase		,**__UpperCAmelCase     )
													A__	     =							XLMRobertaModel(__UpperCAmelCase     )
													A__	     =							torch.nn.Linear(
													    in_features=config.transformerDimensions		,out_features=config.numDims     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Tuple:
													A__	     =							self.transformer(input_ids=__UpperCAmelCase		,attention_mask=__UpperCAmelCase     )[0]
													A__	     =							(embs * attention_mask.unsqueeze(2     )).sum(dim=1     ) / attention_mask.sum(dim=1     )[:, None]
													return self.LinearTransformation(__UpperCAmelCase     ), embs





