from typing import Optional
from functools import partial
from contextlib import contextmanager
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.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]],
    gradient_checkpointing: bool = False,
    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,
    )

    def on_accum_model():
        return text_encoder.text_model.embeddings.overlay

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

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

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

        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_
        del text_encoder_

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

    return TrainingCallbacks(
        on_accum_model=on_accum_model,
        on_train=on_train,
        on_eval=on_eval,
        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.eval()

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


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