from contextlib import nullcontext
from typing import Optional
from functools import partial
from contextlib import contextmanager, nullcontext
from pathlib import Path
import itertools

import torch
import torch.nn as nn
from torch.utils.data import DataLoader

from accelerate import Accelerator
from transformers import CLIPTextModel
from diffusers import AutoencoderKL, UNet2DConditionModel, DPMSolverMultistepScheduler

from pipelines.stable_diffusion.vlpn_stable_diffusion import VlpnStableDiffusion
from models.clip.tokenizer import MultiCLIPTokenizer
from training.util import EMAModel
from training.functional import TrainingStrategy, TrainingCallbacks, save_samples


def dreambooth_strategy_callbacks(
    accelerator: Accelerator,
    unet: UNet2DConditionModel,
    text_encoder: CLIPTextModel,
    tokenizer: MultiCLIPTokenizer,
    vae: AutoencoderKL,
    sample_scheduler: DPMSolverMultistepScheduler,
    train_dataloader: DataLoader,
    val_dataloader: Optional[DataLoader],
    sample_output_dir: Path,
    checkpoint_output_dir: Path,
    seed: int,
    train_text_encoder_epochs: int,
    max_grad_norm: float = 1.0,
    use_ema: bool = False,
    ema_inv_gamma: float = 1.0,
    ema_power: int = 1,
    ema_max_decay: float = 0.9999,
    sample_batch_size: int = 1,
    sample_num_batches: int = 1,
    sample_num_steps: int = 20,
    sample_guidance_scale: float = 7.5,
    sample_image_size: Optional[int] = None,
):
    if accelerator.gradient_accumulation_steps > 1 and accelerator.num_processes > 1:
        raise ValueError(
            "Gradient accumulation is not supported when training the text encoder in distributed training. "
            "Please set gradient_accumulation_steps to 1. This feature will be supported in the future."
        )

    sample_output_dir.mkdir(parents=True, exist_ok=True)
    checkpoint_output_dir.mkdir(parents=True, exist_ok=True)

    weight_dtype = torch.float32
    if accelerator.state.mixed_precision == "fp16":
        weight_dtype = torch.float16
    elif accelerator.state.mixed_precision == "bf16":
        weight_dtype = torch.bfloat16

    save_samples_ = partial(
        save_samples,
        accelerator=accelerator,
        unet=unet,
        text_encoder=text_encoder,
        tokenizer=tokenizer,
        vae=vae,
        sample_scheduler=sample_scheduler,
        train_dataloader=train_dataloader,
        val_dataloader=val_dataloader,
        dtype=weight_dtype,
        output_dir=sample_output_dir,
        seed=seed,
        batch_size=sample_batch_size,
        num_batches=sample_num_batches,
        num_steps=sample_num_steps,
        guidance_scale=sample_guidance_scale,
        image_size=sample_image_size,
    )

    if use_ema:
        ema_unet = EMAModel(
            unet.parameters(),
            inv_gamma=ema_inv_gamma,
            power=ema_power,
            max_value=ema_max_decay,
        )
    else:
        ema_unet = None

    def ema_context():
        if ema_unet is not None:
            return ema_unet.apply_temporary(unet.parameters())
        else:
            return nullcontext()

    def on_model():
        return unet

    def on_prepare():
        unet.requires_grad_(True)
        text_encoder.requires_grad_(True)
        text_encoder.text_model.embeddings.requires_grad_(False)

        if ema_unet is not None:
            ema_unet.to(accelerator.device)

    @contextmanager
    def on_train(epoch: int):
        tokenizer.train()

        if epoch < train_text_encoder_epochs:
            text_encoder.train()
        elif epoch == train_text_encoder_epochs:
            text_encoder.requires_grad_(False)
            text_encoder.eval()

        yield

    @contextmanager
    def on_eval():
        tokenizer.eval()
        text_encoder.eval()

        with ema_context():
            yield

    def on_before_optimize(lr: float, epoch: int):
        if accelerator.sync_gradients:
            params_to_clip = [unet.parameters()]
            if epoch < train_text_encoder_epochs:
                params_to_clip.append(text_encoder.parameters())
            accelerator.clip_grad_norm_(itertools.chain(*params_to_clip), max_grad_norm)

    @torch.no_grad()
    def on_after_optimize(lr: float):
        if ema_unet is not None:
            ema_unet.step(unet.parameters())

    def on_log():
        if ema_unet is not None:
            return {"ema_decay": ema_unet.decay}
        return {}

    @torch.no_grad()
    def on_checkpoint(step, postfix):
        if postfix != "end":
            return

        print("Saving model...")

        unet_ = accelerator.unwrap_model(unet)
        text_encoder_ = accelerator.unwrap_model(text_encoder)

        with ema_context():
            pipeline = VlpnStableDiffusion(
                text_encoder=text_encoder_,
                vae=vae,
                unet=unet_,
                tokenizer=tokenizer,
                scheduler=sample_scheduler,
            )
            pipeline.save_pretrained(checkpoint_output_dir)

        del unet_
        del text_encoder_
        del pipeline

        if torch.cuda.is_available():
            torch.cuda.empty_cache()

    @torch.no_grad()
    def on_sample(step):
        with ema_context():
            save_samples_(step=step)

    return TrainingCallbacks(
        on_prepare=on_prepare,
        on_model=on_model,
        on_train=on_train,
        on_eval=on_eval,
        on_before_optimize=on_before_optimize,
        on_after_optimize=on_after_optimize,
        on_log=on_log,
        on_checkpoint=on_checkpoint,
        on_sample=on_sample,
    )


def dreambooth_prepare(
    accelerator: Accelerator,
    text_encoder: CLIPTextModel,
    unet: UNet2DConditionModel,
    *args
):
    prep = [text_encoder, unet] + list(args)
    text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler = accelerator.prepare(*prep)
    return text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler


dreambooth_strategy = TrainingStrategy(
    callbacks=dreambooth_strategy_callbacks,
    prepare=dreambooth_prepare
)