from __future__ import annotations

__SCREAMING_SNAKE_CASE						:   Optional[int]         =			{
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}



class        lowercase_   :



							def __init__(						self				,    lowercase_				,    lowercase_    ):
													_snake_case						:					str    =   graph
													# mapping node to its parent in resulting breadth first tree
													_snake_case						:					dict[str, str | None]    =   {}
													_snake_case						:					str    =   source_vertex



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[Any]    =   {self.source_vertex}
													_snake_case						:					Optional[int]    =   None
													_snake_case						:					Optional[Any]    =   [self.source_vertex]  # first in first out queue

													while queue:
																			_snake_case						:					Any    =   queue.pop(0    )
																			for adjacent_vertex in self.graph[vertex]:
																									if adjacent_vertex not in visited:
																															visited.add(lowercase_    )
																															_snake_case						:					Dict    =   vertex
																															queue.append(lowercase_    )



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													if target_vertex == self.source_vertex:
																			return self.source_vertex

													_snake_case						:					int    =   self.parent.get(lowercase_    )
													if target_vertex_parent is None:
																			_snake_case						:					List[str]    =   (
																			    f"""No path from vertex: {self.source_vertex} to vertex: {target_vertex}"""
																			)
																			raise ValueError(lowercase_    )

													return self.shortest_path(lowercase_    ) + f"""->{target_vertex}"""


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:   List[Any]         =			Graph(graph, 'G')
				g.breath_first_search()
				print(g.shortest_path('D'))
				print(g.shortest_path('G'))
				print(g.shortest_path('Foo'))
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel



class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		'M-CLIP'



							def __init__(						self				,    lowercase_=1_024				,    lowercase_=768				,    **lowercase_    ):
													_snake_case						:					str    =   transformerDimSize
													_snake_case						:					Union[str, Any]    =   imageDimSize
													super().__init__(**lowercase_    )




class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		MCLIPConfig



							def __init__(						self				,    lowercase_				,    *lowercase_				,    **lowercase_    ):
													super().__init__(lowercase_				,    *lowercase_				,    **lowercase_    )
													_snake_case						:					List[Any]    =   XLMRobertaModel(lowercase_    )
													_snake_case						:					int    =   torch.nn.Linear(
													    in_features=config.transformerDimensions				,    out_features=config.numDims    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_    ):
													_snake_case						:					Tuple    =   self.transformer(input_ids=lowercase_				,    attention_mask=lowercase_    )[0]
													_snake_case						:					Tuple    =   (embs * attention_mask.unsqueeze(2    )).sum(dim=1    ) / attention_mask.sum(dim=1    )[:, None]
													return self.LinearTransformation(lowercase_    ), embs