from __future__ import annotations

import datetime
import os
import pathlib
import shlex
import shutil
import subprocess

import gradio as gr
import PIL.Image
import slugify
import torch

from constants import UploadTarget


def pad_image(image: PIL.Image.Image) -> PIL.Image.Image:
    w, h = image.size
    if w == h:
        return image
    elif w > h:
        new_image = PIL.Image.new(image.mode, (w, w), (0, 0, 0))
        new_image.paste(image, (0, (w - h) // 2))
        return new_image
    else:
        new_image = PIL.Image.new(image.mode, (h, h), (0, 0, 0))
        new_image.paste(image, ((h - w) // 2, 0))
        return new_image


class Trainer:
    def prepare_dataset(self, instance_images: list, resolution: int,
                        instance_data_dir: pathlib.Path) -> None:
        shutil.rmtree(instance_data_dir, ignore_errors=True)
        instance_data_dir.mkdir(parents=True)
        for i, temp_path in enumerate(instance_images):
            image = PIL.Image.open(temp_path.name)
            image = pad_image(image)
            image = image.resize((resolution, resolution))
            image = image.convert('RGB')
            out_path = instance_data_dir / f'{i:03d}.jpg'
            image.save(out_path, format='JPEG', quality=100)

    def run(
        self,
        instance_images: list | None,
        instance_prompt: str,
        output_model_name: str,
        overwrite_existing_model: bool,
        validation_prompt: str,
        base_model: str,
        resolution_s: str,
        n_steps: int,
        learning_rate: float,
        gradient_accumulation: int,
        seed: int,
        fp16: bool,
        use_8bit_adam: bool,
        checkpointing_steps: int,
        use_wandb: bool,
        validation_epochs: int,
        upload_to_hub: bool,
        use_private_repo: bool,
        delete_existing_repo: bool,
        upload_to: str,
    ) -> str:
        if not torch.cuda.is_available():
            raise gr.Error('CUDA is not available.')
        if instance_images is None:
            raise gr.Error('You need to upload images.')
        if not instance_prompt:
            raise gr.Error('The instance prompt is missing.')
        if not validation_prompt:
            raise gr.Error('The validation prompt is missing.')

        resolution = int(resolution_s)

        if not output_model_name:
            output_model_name = datetime.datetime.now().strftime(
                '%Y-%m-%d-%H-%M-%S')
        output_model_name = slugify.slugify(output_model_name)

        repo_dir = pathlib.Path(__file__).parent
        output_dir = repo_dir / 'experiments' / output_model_name
        if overwrite_existing_model or upload_to_hub:
            shutil.rmtree(output_dir, ignore_errors=True)
        if not upload_to_hub:
            output_dir.mkdir(parents=True)

        instance_data_dir = repo_dir / 'training_data' / output_model_name
        self.prepare_dataset(instance_images, resolution, instance_data_dir)

        command = f'''
        accelerate launch train_dreambooth_lora.py \
          --pretrained_model_name_or_path={base_model}  \
          --instance_data_dir={instance_data_dir} \
          --output_dir={output_dir} \
          --instance_prompt="{instance_prompt}" \
          --resolution={resolution} \
          --train_batch_size=1 \
          --gradient_accumulation_steps={gradient_accumulation} \
          --learning_rate={learning_rate} \
          --lr_scheduler=constant \
          --lr_warmup_steps=0 \
          --max_train_steps={n_steps} \
          --checkpointing_steps={checkpointing_steps} \
          --validation_prompt="{validation_prompt}" \
          --validation_epochs={validation_epochs} \
          --seed={seed}
        '''
        if fp16:
            command += ' --mixed_precision fp16'
        if use_8bit_adam:
            command += ' --use_8bit_adam'
        if use_wandb:
            command += ' --report_to wandb'
        if upload_to_hub:
            hf_token = os.getenv('HF_TOKEN')
            command += f' --push_to_hub --hub_token {hf_token}'
            if use_private_repo:
                command += ' --private_repo'
            if delete_existing_repo:
                command += ' --delete_existing_repo'
            if upload_to == UploadTarget.LORA_LIBRARY.value:
                command += ' --upload_to_lora_library'

        subprocess.run(shlex.split(command))

        with open(output_dir / 'train.sh', 'w') as f:
            command_s = ' '.join(command.split())
            f.write(command_s)

        return 'Training completed!'