"""Sparse query-program compilation without free evidence-slot selection."""

from __future__ import annotations

from dataclasses import dataclass
from enum import IntEnum

import torch
from torch import nn

from strata.modeling.algebra.entmax import entmax_bisect, sparse_topk


class GraphOperation(IntEnum):
    NO_GRAPH_READ = 0
    READ_ENTITY = 1
    READ_EVENT_ARG0 = 2
    READ_EVENT_ARG1 = 3
    READ_EVENT_TIME = 4
    FOLLOW_COREF = 5
    FOLLOW_TEMPORAL_BEFORE = 6
    FOLLOW_SUPPORT = 7
    FOLLOW_CONTRADICTION = 8


class AnchorType(IntEnum):
    ENTITY = 0
    EVENT = 1
    SEGMENT = 2
    TEMPORAL = 3


@dataclass(frozen=True, slots=True)
class QueryProgramOutput:
    query_state: torch.Tensor
    anchor_type_logits: torch.Tensor
    anchor_scores: torch.Tensor
    operation_logits: torch.Tensor
    operation_probabilities: torch.Tensor
    reliability: torch.Tensor

    @property
    def hard_anchor(self) -> torch.Tensor:
        return self.anchor_scores.argmax(dim=-1)

    @property
    def hard_operations(self) -> torch.Tensor:
        return self.operation_logits.argmax(dim=-1)


class QueryProgramCompiler(nn.Module):
    """Compile frozen token states into an anchor type and short operation list.

    Candidate scoring is set-equivariant: candidate order is never embedded.
    The scorer predicts graph anchors only; evidence slots do not enter this
    module. Operation probabilities use alpha-entmax and are capped at two
    active operations per step.
    """

    def __init__(
        self,
        d_model: int,
        *,
        hidden_dim: int = 256,
        max_steps: int = 3,
        maximum_active_operations: int = 2,
        entmax_alpha: float = 1.5,
    ) -> None:
        super().__init__()
        if d_model <= 0 or hidden_dim <= 0 or max_steps <= 0:
            raise ValueError("dimensions and max_steps must be positive")
        if not 1 <= maximum_active_operations <= len(GraphOperation):
            raise ValueError("invalid maximum_active_operations")
        self.d_model = int(d_model)
        self.max_steps = int(max_steps)
        self.maximum_active_operations = int(maximum_active_operations)
        self.entmax_alpha = float(entmax_alpha)
        self.query_attention = nn.Linear(d_model, 1, bias=False)
        self.query_projection = nn.Linear(d_model, d_model, bias=False)
        self.candidate_projection = nn.Linear(d_model, d_model, bias=False)
        self.anchor_scorer = nn.Sequential(
            nn.Linear(4 * d_model, hidden_dim),
            nn.GELU(),
            nn.Linear(hidden_dim, 1),
        )
        self.anchor_type_head = nn.Linear(d_model, len(AnchorType))
        self.operation_head = nn.Sequential(
            nn.Linear(d_model, hidden_dim),
            nn.GELU(),
            nn.Linear(hidden_dim, max_steps * len(GraphOperation)),
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        *,
        query_mask: torch.Tensor,
        candidate_states: torch.Tensor,
        candidate_mask: torch.Tensor,
    ) -> QueryProgramOutput:
        if hidden_states.ndim != 3 or hidden_states.shape[-1] != self.d_model:
            raise ValueError("hidden_states must have shape [batch, sequence, d_model]")
        if query_mask.shape != hidden_states.shape[:2]:
            raise ValueError("query_mask must have shape [batch, sequence]")
        if candidate_states.ndim != 3 or candidate_states.shape[0] != hidden_states.shape[0]:
            raise ValueError("candidate_states must have shape [batch, candidates, d_model]")
        if candidate_states.shape[-1] != self.d_model or candidate_mask.shape != candidate_states.shape[:2]:
            raise ValueError("candidate state width or mask is incompatible")
        attention_logits = self.query_attention(hidden_states).squeeze(-1)
        attention_logits = attention_logits.masked_fill(~query_mask.bool(), -torch.inf)
        attention = torch.softmax(attention_logits.float(), dim=-1).to(hidden_states.dtype)
        attention = torch.where(query_mask.any(dim=-1, keepdim=True), attention, torch.zeros_like(attention))
        query = torch.einsum("bs,bsd->bd", attention, hidden_states)

        q = self.query_projection(query)
        candidates = self.candidate_projection(candidate_states)
        expanded = q.unsqueeze(1).expand_as(candidates)
        anchor_features = torch.cat(
            [expanded, candidates, expanded * candidates, candidates - expanded], dim=-1
        )
        anchor_scores = self.anchor_scorer(anchor_features).squeeze(-1)
        anchor_scores = anchor_scores.masked_fill(~candidate_mask.bool(), -torch.inf)

        operation_logits = self.operation_head(query).view(
            hidden_states.shape[0], self.max_steps, len(GraphOperation)
        )
        operation_probabilities = entmax_bisect(
            operation_logits.float(), alpha=self.entmax_alpha, dim=-1
        ).to(operation_logits.dtype)
        operation_probabilities = sparse_topk(
            operation_probabilities,
            k=self.maximum_active_operations,
            dim=-1,
        )
        anchor_confidence = torch.softmax(anchor_scores.float(), dim=-1).amax(dim=-1)
        operation_confidence = operation_probabilities.float().amax(dim=-1).mean(dim=-1)
        reliability = (anchor_confidence * operation_confidence).clamp(0, 1).detach()
        return QueryProgramOutput(
            query_state=query,
            anchor_type_logits=self.anchor_type_head(query),
            anchor_scores=anchor_scores,
            operation_logits=operation_logits,
            operation_probabilities=operation_probabilities,
            reliability=reliability,
        )


__all__ = [
    "AnchorType",
    "GraphOperation",
    "QueryProgramCompiler",
    "QueryProgramOutput",
]