from typing import Optional
from functools import partial
from contextlib import contextmanager
from pathlib import Path
import itertools
import json

import torch
from torch.utils.data import DataLoader

from accelerate import Accelerator
from transformers import CLIPTextModel
from diffusers import AutoencoderKL, UNet2DConditionModel, DPMSolverMultistepScheduler
from peft import get_peft_model_state_dict
from safetensors.torch import save_file

from slugify import slugify

from models.clip.tokenizer import MultiCLIPTokenizer
from training.functional import TrainingStrategy, TrainingCallbacks, save_samples


def lora_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,
    placeholder_tokens: list[str],
    placeholder_token_ids: list[list[int]],
    pti_mode: bool = False,
    use_emb_decay: bool = False,
    emb_decay_target: float = 0.4,
    emb_decay: float = 1e-2,
    max_grad_norm: float = 1.0,
    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,
):
    sample_output_dir.mkdir(parents=True, exist_ok=True)
    checkpoint_output_dir.mkdir(parents=True, exist_ok=True)

    save_samples_ = partial(
        save_samples,
        accelerator=accelerator,
        tokenizer=tokenizer,
        vae=vae,
        sample_scheduler=sample_scheduler,
        train_dataloader=train_dataloader,
        val_dataloader=val_dataloader,
        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,
    )

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

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

    def on_before_optimize(epoch: int):
        if not pti_mode:
            accelerator.clip_grad_norm_(
                itertools.chain(
                    unet.parameters(),
                    text_encoder.text_model.encoder.parameters(),
                    text_encoder.text_model.final_layer_norm.parameters(),
                ),
                max_grad_norm
            )

        if use_emb_decay:
            params = [
                p
                for p in text_encoder.text_model.embeddings.parameters()
                if p.grad is not None
            ]
            return torch.stack(params) if len(params) != 0 else None

    @torch.no_grad()
    def on_after_optimize(w, lrs: dict[str, float]):
        if use_emb_decay and w is not None and "emb" in lrs:
            lr = lrs["emb"]
            lambda_ = emb_decay * lr

            if lambda_ != 0:
                norm = w[:, :].norm(dim=-1, keepdim=True)
                w[:].add_((w[:] / norm.clamp_min(1e-12)) * lambda_ * (emb_decay_target - norm))

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

        print(f"Saving checkpoint for step {step}...")

        unet_ = accelerator.unwrap_model(unet, keep_fp32_wrapper=False)
        text_encoder_ = accelerator.unwrap_model(text_encoder, keep_fp32_wrapper=False)

        for (token, ids) in zip(placeholder_tokens, placeholder_token_ids):
            text_encoder_.text_model.embeddings.save_embed(
                ids,
                checkpoint_output_dir / f"{slugify(token)}_{step}_{postfix}.bin"
            )

        if not pti_mode:
            lora_config = {}
            state_dict = get_peft_model_state_dict(unet_, state_dict=accelerator.get_state_dict(unet_))
            lora_config["peft_config"] = unet_.get_peft_config_as_dict(inference=True)

            text_encoder_state_dict = get_peft_model_state_dict(
                text_encoder_, state_dict=accelerator.get_state_dict(text_encoder_)
            )
            text_encoder_state_dict = {f"text_encoder_{k}": v for k, v in text_encoder_state_dict.items()}
            state_dict.update(text_encoder_state_dict)
            lora_config["text_encoder_peft_config"] = text_encoder_.get_peft_config_as_dict(inference=True)

            save_file(state_dict, checkpoint_output_dir / f"{step}_{postfix}.safetensors")
            with open(checkpoint_output_dir / "lora_config.json", "w") as f:
                json.dump(lora_config, f)

        del unet_, text_encoder_

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

    @torch.no_grad()
    def on_sample(cycle, step):
        unet_ = accelerator.unwrap_model(unet, keep_fp32_wrapper=True)
        text_encoder_ = accelerator.unwrap_model(text_encoder, keep_fp32_wrapper=True)

        save_samples_(cycle=cycle, step=step, unet=unet_, text_encoder=text_encoder_)

        del unet_, text_encoder_

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

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


def lora_prepare(
    accelerator: Accelerator,
    text_encoder: CLIPTextModel,
    unet: UNet2DConditionModel,
    optimizer: torch.optim.Optimizer,
    train_dataloader: DataLoader,
    val_dataloader: Optional[DataLoader],
    lr_scheduler: torch.optim.lr_scheduler._LRScheduler,
    **kwargs
):
    text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler = accelerator.prepare(
        text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler)

    # text_encoder.text_model.embeddings.token_embedding.requires_grad_(True)

    return text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler


lora_strategy = TrainingStrategy(
    callbacks=lora_strategy_callbacks,
    prepare=lora_prepare,
)