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 TrainingCallbacks, 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: Optional[DataLoader],
    sample_output_dir: Path,
    checkpoint_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 = 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,
):
    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_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 ema_context():
        if use_ema:
            return ema_embeddings.apply_temporary(
                text_encoder.text_model.embeddings.temp_token_embedding.parameters()
            )
        else:
            return nullcontext()

    def on_model():
        return text_encoder

    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):
        tokenizer.train()
        yield

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

        with ema_context():
            yield

    @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}...")

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

    @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_after_optimize=on_after_optimize,
        on_log=on_log,
        on_checkpoint=on_checkpoint,
        on_sample=on_sample,
    )