"""Native MLX schema25 confidence fusion and sample-independent commit policy."""

from __future__ import annotations

import math
from collections.abc import Sequence
from dataclasses import dataclass

import mlx.core as mx


JUMP_FAILURE_BUDGET = 2.0
FUSED_EPS = 1.0e-6
COMMIT_REASON_NONE = 0
COMMIT_REASON_NORMAL = 1
COMMIT_REASON_FORCED_JUMP = 2
COMMIT_REASON_TERMINAL = 3


def commit_target_confidence_bias(
    target_confidence: float | None,
    failure_budget: float = 0.2,
    budget_safety_ratio: float = 0.85,
) -> float:
    if target_confidence is None or target_confidence <= 0.0 or target_confidence >= 1.0:
        return 0.0
    target_failure = min(budget_safety_ratio * failure_budget, 1.0 - target_confidence)
    target_failure = max(target_failure, 1.0e-4)
    target_conf = 1.0 - target_failure
    logit_c = math.log(target_conf / (1.0 - target_conf))
    logit_p = math.log(target_confidence / (1.0 - target_confidence))
    return max(logit_c - logit_p, 0.0)


def fused_commit_confidence(
    proposal_confidence: mx.array,
    token_entropy: mx.array,
    *,
    eps: float = FUSED_EPS,
    entropy_weight: float = 1.0,
    confidence_power: float = 2.0,
    top_k: int | None = None,
    min_p: float | None = None,
    target_confidence: float | None = None,
    failure_budget: float = 0.2,
) -> mx.array:
    """Match the schema25 excess-entropy sigmoid and checkpoint power."""

    p = mx.clip(mx.nan_to_num(proposal_confidence.astype(mx.float32), nan=0.5), eps, 1.0 - eps)
    entropy = mx.maximum(mx.nan_to_num(token_entropy.astype(mx.float32), nan=0.0), 0.0)
    binary_entropy = -p * mx.log(p) - (1.0 - p) * mx.log1p(-p)
    excess = mx.maximum(entropy - binary_entropy, 0.0)
    k_eff = None
    if top_k is not None and top_k > 0:
        k_eff = mx.full(p.shape, float(top_k), dtype=mx.float32)
    if min_p is not None and min_p > 0:
        thresh = mx.maximum(float(min_p) * p, 1.0e-6)
        k_min_p = mx.maximum((1.0 - p) / thresh, 1.0)
        k_eff = k_min_p if k_eff is None else mx.minimum(k_eff, k_min_p)
    if k_eff is not None:
        max_excess = (1.0 - p) * mx.log(k_eff)
        excess = mx.minimum(excess, max_excess)
    bias = commit_target_confidence_bias(target_confidence, failure_budget=failure_budget)
    fused_logit = mx.log(p) - mx.log1p(-p) + bias - entropy_weight * excess
    return mx.clip(mx.sigmoid(fused_logit) ** confidence_power, eps, 1.0 - eps)


def fused_commit_failure_rate(
    proposal_confidence: mx.array, token_entropy: mx.array, **kwargs: object
) -> mx.array:
    return 1.0 - fused_commit_confidence(proposal_confidence, token_entropy, **kwargs)


def _terminal_mask(tokens: mx.array, stop_token_id: int | Sequence[int]) -> mx.array:
    ids = (stop_token_id,) if isinstance(stop_token_id, int) else tuple(dict.fromkeys(stop_token_id))
    if not ids:
        raise ValueError("At least one stop token ID is required.")
    matches = tokens == int(ids[0])
    for value in ids[1:]:
        matches = matches | (tokens == int(value))
    return matches


def prefix_failure_commit_lengths(
    failure_rate: mx.array,
    *,
    failure_budget: float,
    valid_mask: mx.array | None = None,
) -> mx.array:
    """Longest contiguous valid prefix whose cumulative failure stays below budget."""

    if failure_rate.ndim != 2:
        raise ValueError("Failure rate must have shape [batch, canvas].")
    if failure_budget <= 0:
        raise ValueError("Commit failure budget must be positive.")
    if valid_mask is None:
        valid_mask = mx.ones(failure_rate.shape, mx.bool_)
    if valid_mask.shape != failure_rate.shape:
        raise ValueError("Commit validity mask must match failure rate.")
    risk = mx.clip(failure_rate.astype(mx.float32), 0.0, 1.0) * valid_mask.astype(mx.float32)
    allowed = (mx.cumsum(risk, axis=-1) < failure_budget) & (
        mx.cumprod(valid_mask.astype(mx.int32), axis=-1).astype(mx.bool_)
    )
    return mx.sum(mx.cumprod(allowed.astype(mx.int32), axis=-1), axis=-1)


def first_committed_token_lengths(
    proposal: mx.array,
    commit_lengths: mx.array,
    token_id: int | Sequence[int],
    *,
    positions: mx.array | None = None,
) -> mx.array:
    if proposal.ndim != 2 or commit_lengths.shape != proposal.shape[:1]:
        raise ValueError("Proposal and commit lengths must share a batch dimension.")
    if positions is None:
        positions = mx.arange(proposal.shape[1])[None, :]
    elif positions.shape != (1, proposal.shape[1]):
        raise ValueError("Commit positions must have shape [1, canvas].")
    matches = _terminal_mask(proposal, token_id) & (positions < commit_lengths[:, None])
    first = mx.min(mx.where(matches, positions, proposal.shape[1]), axis=-1)
    return mx.minimum(mx.where(first < proposal.shape[1], first + 1, commit_lengths), commit_lengths)


def bounded_prefix_failure_commit_lengths(
    committed_token_ids: mx.array,
    failure_rate: mx.array,
    *,
    failure_budget: float,
    remaining_lengths: mx.array,
    stop_token_id: int | Sequence[int],
    valid_mask: mx.array | None = None,
    positions: mx.array | None = None,
) -> mx.array:
    if committed_token_ids.shape != failure_rate.shape:
        raise ValueError("Committed token IDs and failure rate must share [batch, canvas].")
    if remaining_lengths.shape != committed_token_ids.shape[:1]:
        raise ValueError("Remaining lengths must have shape [batch].")
    lengths = prefix_failure_commit_lengths(
        failure_rate, failure_budget=failure_budget, valid_mask=valid_mask
    )
    lengths = mx.minimum(lengths, mx.maximum(remaining_lengths, 0))
    return first_committed_token_lengths(
        committed_token_ids, lengths, stop_token_id, positions=positions
    )


@dataclass(frozen=True)
class MLXCommitPolicyDecision:
    normal_lengths: mx.array
    commit_lengths: mx.array
    commit_token_ids: mx.array
    jump_rows: mx.array
    ponder_steps: mx.array
    stagnation_steps: mx.array


def select_commit_lengths(
    sampled_token_ids: mx.array,
    normal_failure_rate: mx.array,
    previous_failure_rate: mx.array,
    greedy_token_ids: mx.array,
    jump_failure_rate: mx.array,
    *,
    ponder_steps: mx.array,
    stagnation_steps: mx.array,
    active_rows: mx.array,
    remaining_lengths: mx.array,
    failure_budget: float,
    stop_token_id: int | Sequence[int],
    stagnation_threshold: int,
    min_progress: float,
    max_ponder_steps: int | None = None,
    valid_mask: mx.array | None = None,
) -> MLXCommitPolicyDecision:
    """Select normal commits or bounded greedy JUMP independently per row."""

    if not (sampled_token_ids.shape == normal_failure_rate.shape
            == previous_failure_rate.shape == greedy_token_ids.shape
            == jump_failure_rate.shape):
        raise ValueError("Sampled and greedy statistics must share [batch, canvas].")
    if not (ponder_steps.shape == stagnation_steps.shape == active_rows.shape
            == remaining_lengths.shape == sampled_token_ids.shape[:1]):
        raise ValueError("Commit row inputs must share [batch].")
    if min_progress < 0:
        raise ValueError("Minimum progress must be nonnegative.")
    canvas = normal_failure_rate.shape[1]
    positions = mx.arange(canvas)[None, :]
    normal = bounded_prefix_failure_commit_lengths(
        sampled_token_ids, normal_failure_rate,
        failure_budget=failure_budget, remaining_lengths=remaining_lengths,
        stop_token_id=stop_token_id, valid_mask=valid_mask, positions=positions,
    )
    previous = prefix_failure_commit_lengths(
        previous_failure_rate, failure_budget=failure_budget, valid_mask=valid_mask
    )
    frontier = mx.maximum(previous, normal) + 1
    valid_lengths = (mx.sum(valid_mask.astype(mx.int32), axis=-1) if valid_mask is not None
                     else mx.full(frontier.shape, canvas, mx.int32))
    frontier = mx.minimum(frontier, valid_lengths)
    progress_mask = (positions < frontier[:, None]) & active_rows[:, None]
    if valid_mask is not None:
        progress_mask = progress_mask & valid_mask
    weights = progress_mask.astype(mx.float32)
    progress = mx.sum((previous_failure_rate.astype(mx.float32)
                       - normal_failure_rate.astype(mx.float32)) * weights, axis=-1) / mx.maximum(
                           mx.sum(weights, axis=-1), 1.0)
    waiting = active_rows & (normal == 0)
    next_ponder = mx.where(normal > 0, 0, ponder_steps + waiting.astype(mx.int32))
    next_stagnation = mx.where(normal > 0, 0, stagnation_steps + waiting.astype(mx.int32))
    jump = (normal == 0) & active_rows & (next_stagnation >= stagnation_threshold)
    jump = jump & (progress <= min_progress)
    if max_ponder_steps is not None and max_ponder_steps > 0:
        jump = jump | ((normal == 0) & active_rows & (next_ponder >= max_ponder_steps))
    jump_commit = bounded_prefix_failure_commit_lengths(
        greedy_token_ids, jump_failure_rate,
        failure_budget=JUMP_FAILURE_BUDGET, remaining_lengths=remaining_lengths,
        stop_token_id=stop_token_id, valid_mask=valid_mask, positions=positions,
    )
    commit_token_ids = mx.where(jump[:, None], greedy_token_ids, sampled_token_ids)
    committed = mx.where(active_rows, mx.where(jump, jump_commit, normal), 0)
    jump = jump & (committed > 0)
    next_ponder = mx.where(committed > 0, 0, next_ponder).astype(mx.int32)
    next_stagnation = mx.where(committed > 0, 0, next_stagnation).astype(mx.int32)
    return MLXCommitPolicyDecision(
        normal_lengths=normal,
        commit_lengths=committed,
        commit_token_ids=commit_token_ids,
        jump_rows=jump,
        ponder_steps=next_ponder,
        stagnation_steps=next_stagnation,
    )


def infer_commit_reason(
    commit_lengths: mx.array,
    *,
    jump_rows: mx.array | None = None,
    commit_token_ids: mx.array | None = None,
    terminal_token_ids: Sequence[int] = (),
) -> mx.array:
    """Reason codes consumed by the persistent writer at commit only."""

    committed = commit_lengths > 0
    reasons = mx.where(committed, COMMIT_REASON_NORMAL, COMMIT_REASON_NONE)
    if jump_rows is not None:
        reasons = mx.where(committed & jump_rows, COMMIT_REASON_FORCED_JUMP, reasons)
    if commit_token_ids is not None and terminal_token_ids:
        positions = mx.arange(commit_token_ids.shape[1])[None, :]
        terminal = mx.any(
            _terminal_mask(commit_token_ids, terminal_token_ids)
            & (positions < commit_lengths[:, None]), axis=-1,
        )
        reasons = mx.where(committed & terminal, COMMIT_REASON_TERMINAL, reasons)
    return reasons.astype(mx.int32)