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

import torch
from torch.utils.data import DataLoader

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

from slugify import slugify

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


def textual_inversion_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]],
    use_emb_decay: bool = False,
    emb_decay_target: float = 0.4,
    emb_decay: float = 1e-2,
    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,
):
    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,
        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,
    )

    if use_ema:
        ema_embeddings = EMAModel(
            text_encoder.text_model.embeddings.token_embedding.parameters(),
            inv_gamma=ema_inv_gamma,
            power=ema_power,
            max_value=ema_max_decay,
        )
        ema_embeddings.to(accelerator.device)
    else:
        ema_embeddings = None

    def ema_context():
        if ema_embeddings is not None:
            return ema_embeddings.apply_temporary(
                text_encoder.text_model.embeddings.token_embedding.parameters()
            )
        else:
            return nullcontext()

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

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

        with ema_context():
            yield

    @torch.no_grad()
    def on_after_optimize(w, lrs: dict[str, float]):
        if ema_embeddings is not None:
            ema_embeddings.step(text_encoder.text_model.embeddings.token_embedding.parameters())

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

    @torch.no_grad()
    def on_checkpoint(step, postfix):
        print(f"Saving checkpoint for step {step}...")

        with ema_context():
            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"
                )

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

        orig_unet_dtype = unet_.dtype
        orig_text_encoder_dtype = text_encoder_.dtype

        unet_.to(dtype=weight_dtype)
        text_encoder_.to(dtype=weight_dtype)

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

        unet_.to(dtype=orig_unet_dtype)
        text_encoder_.to(dtype=orig_text_encoder_dtype)

        del unet_, text_encoder_

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

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


def textual_inversion_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,
    gradient_checkpointing: bool = False,
    **kwargs
):
    weight_dtype = torch.float32
    if accelerator.state.mixed_precision == "fp16":
        weight_dtype = torch.float16
    elif accelerator.state.mixed_precision == "bf16":
        weight_dtype = torch.bfloat16

    text_encoder, optimizer, train_dataloader, val_dataloader, lr_scheduler = accelerator.prepare(
        text_encoder, optimizer, train_dataloader, val_dataloader, lr_scheduler)

    unet.to(accelerator.device, dtype=weight_dtype)
    unet.requires_grad_(False)
    unet.eval()
    if gradient_checkpointing:
        unet.train()

    text_encoder.text_model.encoder.requires_grad_(False)
    text_encoder.text_model.final_layer_norm.requires_grad_(False)
    text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)

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


textual_inversion_strategy = TrainingStrategy(
    callbacks=textual_inversion_strategy_callbacks,
    prepare=textual_inversion_prepare,
)