"""
SamplingRunner: wires model, tokenizer, schedule, and sampler together.
"""
from __future__ import annotations

import time
from dataclasses import dataclass
from typing import Callable, Optional

import torch
import yaml

from .loader import ModelWrapper, load_checkpoint
from .vocab import build_masked_input, decode_output, load_tokenizer
from ..samplers import get_sampler
from ..samplers.base import StepCallback


@dataclass
class RunConfig:
    """All settings for one generation run."""
    # Required
    ckpt_path: str
    config_path: str

    # Tokenizer
    tokenizer_path: str = "whale-tokenizer"

    # Sampler
    sampler: str = "standard"
    steps: int = 64
    max_new_tokens: int = 256
    temperature: float = 0.0
    top_k: int = 0
    p: float = 0.9

    # Jump sampler extras
    jump_last_steps: int = 10
    jump_frac: float = 0.10
    jump_min_tokens: int = 1
    no_mask_jump: bool = True

    # GIDD sampler extras
    gidd_eps: float = 1e-4
    gidd_min_p: float = 0.0
    posterior_temperature: float = 1.0
    suppress_mask_clean: bool = False
    rho_mode: str = "w1_train_like"
    gidd_exact_mode: bool = False
    fail_on_negative_mass: bool = False

    # Runtime
    device: str = "cuda"
    dtype: str = "bf16"
    use_ema: bool = True
    strict: bool = False
    seed: Optional[int] = 1234


@dataclass
class GenerationResult:
    full_text: str
    new_text: str
    prompt_tokens: int
    generated_tokens: int
    total_tokens: int
    elapsed_s: float
    sampler: str
    steps_run: int


class SamplingRunner:
    """
    Load once, call ``run()`` many times with different prompts.

    Usage::

        runner = SamplingRunner(cfg)
        result = runner.run("The quick brown")
    """

    def __init__(self, cfg: RunConfig):
        self.cfg = cfg

        config = _load_yaml(cfg.config_path)
        device = torch.device(
            cfg.device if cfg.device
            else ("cuda" if torch.cuda.is_available() else "cpu")
        )

        self.model_wrapper = load_checkpoint(
            ckpt_path=cfg.ckpt_path,
            config=config,
            device=device,
            dtype=cfg.dtype,
            use_ema=cfg.use_ema,
            strict=cfg.strict,
        )

        self.tokenizer = load_tokenizer(cfg.tokenizer_path)
        self.config = config
        self.device = device
        self.mask_token_id = self.model_wrapper.mask_token_id
        self.vocab_size = self.model_wrapper.vocab_size
        self.max_seq_len = int(config["model"]["max_seq_len"])

    def run(
        self,
        prompt: str = "",
        callback: Optional[StepCallback] = None,
    ) -> GenerationResult:
        cfg = self.cfg

        if cfg.seed is not None:
            torch.manual_seed(cfg.seed)
            if self.device.type == "cuda":
                torch.cuda.manual_seed_all(cfg.seed)

        x_init, prefix_len = build_masked_input(
            tokenizer=self.tokenizer,
            prompt=prompt,
            max_new_tokens=cfg.max_new_tokens,
            max_seq_len=self.max_seq_len,
            mask_token_id=self.mask_token_id,
            device=self.device,
        )
        total_len = x_init.shape[1]

        timesteps = torch.linspace(1.0, 1e-4, cfg.steps, device=self.device)

        sampler_cfg = {
            "temperature": cfg.temperature,
            "top_k": cfg.top_k,
            "p": cfg.p,
            "jump_last_steps": cfg.jump_last_steps,
            "jump_frac": cfg.jump_frac,
            "jump_min_tokens": cfg.jump_min_tokens,
            "no_mask_jump": cfg.no_mask_jump,
            "gidd_eps": cfg.gidd_eps,
            "gidd_min_p": cfg.gidd_min_p,
            "posterior_temperature": cfg.posterior_temperature,
            "suppress_mask_clean": cfg.suppress_mask_clean,
            "rho_mode": cfg.rho_mode,
            "gidd_exact_mode": cfg.gidd_exact_mode,
            "fail_on_negative_mass": cfg.fail_on_negative_mass,
        }

        sampler_fn = get_sampler(cfg.sampler)

        t0 = time.perf_counter()
        if self.device.type == "cuda":
            torch.cuda.synchronize(self.device)

        x_final = sampler_fn(
            model_fn=self.model_wrapper,
            x_init=x_init,
            prefix_len=prefix_len,
            vocab_size=self.vocab_size,
            mask_token_id=self.mask_token_id,
            timesteps=timesteps,
            cfg=sampler_cfg,
            callback=callback,
        )

        if self.device.type == "cuda":
            torch.cuda.synchronize(self.device)
        elapsed = time.perf_counter() - t0

        full_text, new_text = decode_output(self.tokenizer, x_final, prefix_len)
        generated_tokens = total_len - prefix_len

        return GenerationResult(
            full_text=full_text,
            new_text=new_text,
            prompt_tokens=prefix_len,
            generated_tokens=generated_tokens,
            total_tokens=total_len,
            elapsed_s=elapsed,
            sampler=cfg.sampler,
            steps_run=cfg.steps,
        )


def _load_yaml(path: str) -> dict:
    with open(path, "r", encoding="utf-8") as f:
        return yaml.safe_load(f) or {}