"""Anchor-linking interfaces kept separate from graph-program compilation."""

from __future__ import annotations

from dataclasses import dataclass

import torch
from torch import nn

from strata.modeling.compose.ast import AnchorRef
from strata.modeling.compose.types import SemanticType
from strata.modeling.participant_late_interaction import QueryFacetCrossAttentionReader


@dataclass(frozen=True, slots=True)
class AnchorLinkOutput:
    scores: torch.Tensor
    selected_index: torch.Tensor
    anchor_type: SemanticType


class StructuredAnchorLinker(nn.Module):
    """Copy an externally verified episode-local anchor without classifying IDs."""

    def forward(self, local_indices: torch.Tensor, *, anchor_type: SemanticType) -> AnchorLinkOutput:
        if local_indices.ndim != 1:
            raise ValueError("local_indices must have shape [batch]")
        candidates = int(local_indices.max().item()) + 1
        scores = torch.full(
            (local_indices.shape[0], candidates),
            -torch.inf,
            device=local_indices.device,
        )
        scores.scatter_(1, local_indices.unsqueeze(1), 0.0)
        return AnchorLinkOutput(scores, local_indices, anchor_type)


class ParticipantFacetAnchorLinker(nn.Module):
    """Adapt the frozen four-facet participant primitive to entity anchors."""

    def __init__(self, reader: QueryFacetCrossAttentionReader) -> None:
        super().__init__()
        self.reader = reader

    def forward(
        self,
        query_tokens: torch.Tensor,
        query_mask: torch.Tensor,
        query_vector: torch.Tensor,
        participant_facets: torch.Tensor,
    ) -> AnchorLinkOutput:
        scores = self.reader(query_tokens, query_mask, query_vector, participant_facets)
        return AnchorLinkOutput(scores, scores.argmax(dim=-1), SemanticType.ENTITY)


class TypedCandidateAnchorLinker(nn.Module):
    """Set-equivariant event/claim linker; candidate IDs are never embedded."""

    def __init__(self, hidden_dim: int, anchor_type: SemanticType) -> None:
        super().__init__()
        self.anchor_type = anchor_type
        self.query = nn.Linear(hidden_dim, hidden_dim, bias=False)
        self.candidate = nn.Linear(hidden_dim, hidden_dim, bias=False)

    def forward(
        self,
        query: torch.Tensor,
        candidates: torch.Tensor,
        candidate_mask: torch.Tensor | None = None,
    ) -> AnchorLinkOutput:
        scores = torch.einsum("bd,bnd->bn", self.query(query), self.candidate(candidates))
        scores = scores / query.shape[-1] ** 0.5
        if candidate_mask is not None:
            scores = scores.masked_fill(~candidate_mask, -torch.inf)
        return AnchorLinkOutput(scores, scores.argmax(dim=-1), self.anchor_type)


def copied_anchor(output: AnchorLinkOutput, batch_index: int = 0) -> AnchorRef:
    return AnchorRef(int(output.selected_index[batch_index]), output.anchor_type)


__all__ = [
    "AnchorLinkOutput",
    "ParticipantFacetAnchorLinker",
    "StructuredAnchorLinker",
    "TypedCandidateAnchorLinker",
    "copied_anchor",
]