"""
Derived from Andrej Karpathy's nanochat project.

MIT License

Copyright (c) 2025 Andrej Karpathy

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
"""

from __future__ import annotations

from dataclasses import dataclass
import math
from typing import Callable

from dropout_decay.schedules import DropoutDecayConfig, DropoutDecayScheduler


@dataclass(frozen=True)
class DropoutCondition:
    name: str
    kind: str
    initial: float
    final: float
    schedule: str = "constant"
    decay_tokens: int | None = None
    anchors: tuple[tuple[int, float], ...] = ()

    def to_dict(self) -> dict:
        return {
            "name": self.name,
            "kind": self.kind,
            "initial": self.initial,
            "final": self.final,
            "schedule": self.schedule,
            "decay_tokens": self.decay_tokens,
            "anchors": [list(anchor) for anchor in self.anchors],
        }

    def make_fn(
        self,
        fallback_decay_tokens: int,
        unique_tokens: int | None = None,
    ) -> Callable[[int], float]:
        if self.kind == "static":
            return lambda _tokens_seen, p=self.initial: p
        if self.kind == "anchor_decay":
            if unique_tokens is None:
                raise ValueError("anchor_decay conditions require unique_tokens")
            p = anchor_dropout(unique_tokens, self.anchors)
            return lambda _tokens_seen, p=p: p
        scheduler = DropoutDecayScheduler(
            DropoutDecayConfig(
                initial_dropout=self.initial,
                final_dropout=self.final,
                decay_tokens=self.decay_tokens or fallback_decay_tokens,
                schedule=self.schedule,
            )
        )
        return scheduler.value


def anchor_dropout(unique_tokens: int, anchors: tuple[tuple[int, float], ...]) -> float:
    if not anchors:
        raise ValueError("anchor dropout schedule requires at least one anchor")
    ordered = sorted(anchors)
    if unique_tokens <= ordered[0][0]:
        return ordered[0][1]
    if unique_tokens >= ordered[-1][0]:
        return ordered[-1][1]
    log_unique = math.log(unique_tokens)
    for (left_tokens, left_dropout), (right_tokens, right_dropout) in zip(
        ordered, ordered[1:]
    ):
        if left_tokens <= unique_tokens <= right_tokens:
            left_log = math.log(left_tokens)
            right_log = math.log(right_tokens)
            mix = (log_unique - left_log) / (right_log - left_log)
            return left_dropout + mix * (right_dropout - left_dropout)
    return ordered[-1][1]