Mj: VddlmZddlmZmZddlmZdZdZdZ dZ dZ d Z d Z y ) )Refine)unifyVar) TensorTypect|}|jt|j}t |j |y)z- Calls our symbolic inferencer once. N)rrefineunify_eq constraintssubstitute_all_typesgraphtracedrmgus f/var/www/html/ia-enhanced/venv/lib/python3.12/site-packages/torch/fx/experimental/unify_refinements.py infer_symbolic_types_single_passrs2 vAHHJ 1== !Cs+c<t|}|jt|j}t |j |t|}|jt|j}t |j ||j y)z Calls our symbolic inferencer twice. This is useful when one pass is not enough to infer all the information such as the case for broadcasting. N)rrr r r r symbolic_relationsr s rinfer_symbolic_typesrsl vAHHJ 1== !Cs+vAHHJ 1== !Cs+rcg}g}|D]8}|j|j|j|j:t|t|fS)za Convert equality constraints in the right format to be used by unification library. )appendlhsrhstuple) list_of_eqrreqs r convert_eqr%sR C C 266 266 :uSz !!rc6t|\}}t||S)z@ Apply unification to a set of equality constraints )rr)rrrs rr r 2s *%HC c?rct|tr ||vr||S|St|trQg}|jD],}||vr|j |||j |.tt |St|t r&g}|D]}|j t|||St|t r/g}|D]}|j t||t |S|S)z2 Apply the most general unifier to a type ) isinstancerr__args__rrlistsubstitute_solution_one_type)mappingtnew_typetyps rr$r$;s!S <1: H Az ":: %Cg~ -$  % %/** At  HC OO8#F G H Au  HC OO8#F G HXrcd}|r0d}|D]&}||}|||vr ||}||||<|||k7s%d}(|r0|jD]}t||j|_y)z Apply the most general unifier to all types in a graph till reaching a fixed point. If the input and output graph are the same, we converge. TFN)nodesr$type)r r%flagkold_mapping_valnew_keyns rr r ^s D  A%ajOqzW$!!*$W- '!*,   [[?-gqvv>?rct|j|jD] \}}|j|jk7s yy)zv A check equality to be used in fixed points. We do not use graph equality but instead type equality. FT)zipr*r+)g1g2r0ms rcheck_for_type_equalityr6ss> BHHbhh'1 66QVV  rN)/torch.fx.experimental.graph_gradual_typecheckerr!torch.fx.experimental.unificationrrtorch.fx.tensor_typerrrrr r$r r6rrr;s2B8+,( " F?* r