from __future__ import annotations

from typing import Dict, Optional

import torch


def sampled_token_opd_loss(
    student_logp: torch.Tensor,
    teacher_logp: torch.Tensor,
    valid_mask: Optional[torch.Tensor] = None,
    advantage_clip: float = 10.0,
) -> tuple[torch.Tensor, Dict[str, torch.Tensor]]:
    """Policy-gradient style sampled-token OPD loss.

    Teacher and student tensors must score the exact same response token IDs.
    The advantage is detached so gradients only flow through student_logp.
    """
    if student_logp.shape != teacher_logp.shape:
        raise ValueError(
            f"logp shape mismatch: student={tuple(student_logp.shape)} "
            f"teacher={tuple(teacher_logp.shape)}"
        )
    if valid_mask is None:
        valid_mask = torch.ones_like(student_logp, dtype=torch.bool)
    if valid_mask.shape != student_logp.shape:
        raise ValueError("valid_mask must have the same shape as log-probabilities")
    if not bool(valid_mask.any()):
        raise ValueError("sampled-token OPD received no valid response tokens")

    raw_advantage = teacher_logp.float() - student_logp.detach().float()
    advantage = raw_advantage.clamp(-advantage_clip, advantage_clip)
    token_loss = -(advantage * student_logp.float())
    loss = token_loss.masked_select(valid_mask).mean()

    selected_raw = raw_advantage.masked_select(valid_mask)
    selected_adv = advantage.masked_select(valid_mask)
    clip_fraction = (selected_raw.abs() > advantage_clip).float().mean()
    stats = {
        "advantage_mean": selected_adv.mean().detach(),
        "advantage_std": selected_adv.std(unbiased=False).detach(),
        "advantage_positive_fraction": (selected_adv > 0).float().mean().detach(),
        "advantage_clip_fraction": clip_fraction.detach(),
        "student_logp_mean": student_logp.float().masked_select(valid_mask).mean().detach(),
        "teacher_logp_mean": teacher_logp.float().masked_select(valid_mask).mean().detach(),
    }
    return loss, stats


def response_token_logps(
    logits: torch.Tensor,
    response_ids: torch.Tensor,
    response_start: int,
) -> torch.Tensor:
    """Gather next-token log-probabilities for a response appended to a prompt.

    logits has shape [B, prompt_len + response_len, vocab]. response_start is
    the prompt length. The returned tensor has shape [B, response_len].
    """
    if logits.dim() != 3 or response_ids.dim() != 2:
        raise ValueError("Expected logits [B,L,V] and response_ids [B,T]")
    response_len = response_ids.shape[1]
    if response_len < 1:
        raise ValueError("response_ids must not be empty")
    start = response_start - 1
    end = start + response_len
    if start < 0 or end > logits.shape[1]:
        raise ValueError(
            f"Invalid response slice start={start}, end={end}, logits_len={logits.shape[1]}"
        )
    prediction_logits = logits[:, start:end, :].float()
    log_probs = prediction_logits.log_softmax(dim=-1)
    return log_probs.gather(-1, response_ids.unsqueeze(-1)).squeeze(-1)