from contextlib import nullcontext
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 save_samples


def textual_inversion_strategy(
    accelerator: Accelerator,
    unet: UNet2DConditionModel,
    text_encoder: CLIPTextModel,
    tokenizer: MultiCLIPTokenizer,
    vae: AutoencoderKL,
    sample_scheduler: DPMSolverMultistepScheduler,
    train_dataloader: DataLoader,
    val_dataloader: DataLoader,
    dtype: torch.dtype,
    output_dir: Path,
    seed: int,
    placeholder_tokens: list[str],
    placeholder_token_ids: list[list[int]],
    learning_rate: float,
    gradient_checkpointing: bool = False,
    use_emb_decay: bool = False,
    emb_decay_target: float = 0.4,
    emb_decay_factor: float = 1,
    emb_decay_start: float = 1e-4,
    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,
):
    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=dtype,
        output_dir=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.temp_token_embedding.parameters(),
            inv_gamma=ema_inv_gamma,
            power=ema_power,
            max_value=ema_max_decay,
        )
    else:
        ema_embeddings = None

    def on_prepare():
        text_encoder.text_model.embeddings.temp_token_embedding.requires_grad_(True)

        if use_ema:
            ema_embeddings.to(accelerator.device)

        if gradient_checkpointing:
            unet.train()

    @contextmanager
    def on_train(epoch: int):
        try:
            tokenizer.train()
            yield
        finally:
            pass

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

            ema_context = ema_embeddings.apply_temporary(
                text_encoder.text_model.embeddings.temp_token_embedding.parameters()) if use_ema else nullcontext()

            with ema_context:
                yield
        finally:
            pass

    @torch.no_grad()
    def on_after_optimize(lr: float):
        if use_emb_decay:
            text_encoder.text_model.embeddings.normalize(
                emb_decay_target,
                min(1.0, max(0.0, emb_decay_factor * ((lr - emb_decay_start) / (learning_rate - emb_decay_start))))
            )

        if use_ema:
            ema_embeddings.step(text_encoder.text_model.embeddings.temp_token_embedding.parameters())

    def on_log():
        if use_ema:
            return {"ema_decay": ema_embeddings.decay}
        return {}

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

        checkpoints_path = output_dir.joinpath("checkpoints")
        checkpoints_path.mkdir(parents=True, exist_ok=True)

        text_encoder = accelerator.unwrap_model(text_encoder)

        ema_context = ema_embeddings.apply_temporary(
            text_encoder.text_model.embeddings.temp_token_embedding.parameters()
        ) if ema_embeddings is not None else nullcontext()

        with ema_context:
            for (token, ids) in zip(placeholder_tokens, placeholder_token_ids):
                text_encoder.text_model.embeddings.save_embed(
                    ids,
                    checkpoints_path.joinpath(f"{slugify(token)}_{step}_{postfix}.bin")
                )

    @torch.no_grad()
    def on_sample(step):
        ema_context = ema_embeddings.apply_temporary(
            text_encoder.text_model.embeddings.temp_token_embedding.parameters()
        ) if ema_embeddings is not None else nullcontext()

        with ema_context:
            save_samples_(step=step)

    return {
        "on_prepare": on_prepare,
        "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,
    }