"""Shared CDF/CST schedule helpers.

The helpers in this file are intentionally dependency-light so they can be
used by LDM, DiT, and Stable Diffusion training entrypoints.
"""

from __future__ import annotations

import math
from dataclasses import dataclass
from typing import Iterable, List, Sequence, Tuple


SUPPORTED_SCHEDULES = (
    "fixed",
    "curriculum",
    "linear",
    "cosine",
    "cst_v1",
    "large_bias",
    "cst_v2",
    "small_bias",
    "staircase",
)

SUPPORTED_TRAIN_MODES = (
    "junior",
    "small",
    "senior",
    "large",
    "joint",
    "fmgt",
)


@dataclass(frozen=True)
class CDFInterval:
    """Inclusive-exclusive timestep interval [start, end)."""

    start: int
    end: int

    def clamp_nonempty(self, max_timestep: int) -> "CDFInterval":
        start = max(0, min(int(self.start), int(max_timestep) - 1))
        end = max(start + 1, min(int(self.end), int(max_timestep)))
        return CDFInterval(start=start, end=end)


def clamp01(value: float) -> float:
    return max(0.0, min(1.0, float(value)))


def schedule_progress(
    progress: float,
    schedule: str,
    staircase_bins: int = 8,
) -> float:
    """Map training progress in [0, 1] to curriculum progress in [0, 1]."""

    progress = clamp01(progress)
    schedule = (schedule or "fixed").lower().replace("-", "_")

    if schedule in {"fixed"}:
        return 1.0
    if schedule in {"curriculum", "linear"}:
        return progress
    if schedule == "cosine":
        return 0.5 - 0.5 * math.cos(math.pi * progress)
    if schedule in {"cst_v1", "large_bias"}:
        return math.sqrt(progress)
    if schedule in {"cst_v2", "small_bias"}:
        return progress * progress
    if schedule == "staircase":
        bins = max(1, int(staircase_bins))
        return math.floor(progress * bins) / float(bins)

    raise ValueError(f"Unsupported CDF schedule: {schedule}. Supported: {SUPPORTED_SCHEDULES}")


def ratio_at_progress(
    progress: float,
    schedule: str,
    ratio_start: float,
    ratio_end: float,
    staircase_bins: int = 8,
) -> float:
    """Return the active junior ratio for a CDF/CST training step."""

    if (schedule or "fixed").lower().replace("-", "_") == "fixed":
        return clamp01(ratio_end)

    weight = schedule_progress(progress, schedule, staircase_bins=staircase_bins)
    return clamp01(float(ratio_start) + (float(ratio_end) - float(ratio_start)) * weight)


def boundary_timestep(ratio: float, num_timesteps: int) -> int:
    """Return t_zeta = floor((1-r)T)."""

    return int(math.floor((1.0 - clamp01(ratio)) * int(num_timesteps)))


def junior_interval(ratio: float, num_timesteps: int) -> CDFInterval:
    """Timesteps trained or sampled by the junior model."""

    return CDFInterval(boundary_timestep(ratio, num_timesteps), int(num_timesteps)).clamp_nonempty(num_timesteps)


def senior_interval(ratio: float, num_timesteps: int) -> CDFInterval:
    """Timesteps trained or sampled by the senior model."""

    return CDFInterval(0, boundary_timestep(ratio, num_timesteps)).clamp_nonempty(num_timesteps)


def interval_for_train_mode(
    train_mode: str,
    ratio: float,
    num_timesteps: int,
) -> CDFInterval:
    """Return the timestep interval for a CDF train mode.

    `joint`/`fmgt` return the full interval; routing is handled by the
    stitched model itself.
    """

    mode = (train_mode or "junior").lower().replace("-", "_")
    if mode in {"junior", "small"}:
        return junior_interval(ratio, num_timesteps)
    if mode in {"senior", "large"}:
        return senior_interval(ratio, num_timesteps)
    if mode in {"joint", "fmgt"}:
        return CDFInterval(0, int(num_timesteps)).clamp_nonempty(num_timesteps)

    raise ValueError(f"Unsupported CDF train mode: {train_mode}. Supported: {SUPPORTED_TRAIN_MODES}")


def boundary_interval(
    ratio: float,
    num_timesteps: int,
    boundary_width: int,
) -> CDFInterval:
    """Return a non-empty boundary window around t_zeta."""

    center = boundary_timestep(ratio, num_timesteps)
    width = max(0, int(boundary_width))
    return CDFInterval(center - width, center + width + 1).clamp_nonempty(num_timesteps)


def parse_ratio_list(value: str | Sequence[float]) -> List[float]:
    if isinstance(value, str):
        values = [item.strip() for item in value.split(",") if item.strip()]
        return [clamp01(float(item)) for item in values]
    return [clamp01(float(item)) for item in value]


def format_ratio(ratio: float) -> str:
    return f"{clamp01(ratio):.1f}"


def ratio_grid(start: float = 0.0, end: float = 1.0, step: float = 0.1) -> List[float]:
    values: List[float] = []
    n_steps = int(round((float(end) - float(start)) / float(step)))
    for idx in range(n_steps + 1):
        values.append(clamp01(float(start) + idx * float(step)))
    return values


def csv_join(values: Iterable[float]) -> str:
    return ",".join(format_ratio(value) for value in values)