from collections import defaultdict
import contextlib
import os
from absl import app, flags
from ml_collections import config_flags
from accelerate import Accelerator
from accelerate.utils import set_seed
from accelerate.logging import get_logger
from diffusers import StableDiffusionPipeline, DDIMScheduler
from diffusers.loaders import AttnProcsLayers
from diffusers.models.attention_processor import LoRAAttnProcessor
import numpy as np
import ddpo_pytorch.prompts
import ddpo_pytorch.rewards
from ddpo_pytorch.stat_tracking import PerPromptStatTracker
from ddpo_pytorch.diffusers_patch.pipeline_with_logprob import pipeline_with_logprob
from ddpo_pytorch.diffusers_patch.ddim_with_logprob import ddim_step_with_logprob
import torch
import wandb
from functools import partial
import tqdm
import tempfile
from PIL import Image

tqdm = partial(tqdm.tqdm, dynamic_ncols=True)


FLAGS = flags.FLAGS
config_flags.DEFINE_config_file("config", "config/base.py", "Training configuration.")

logger = get_logger(__name__)


def main(_):
    # basic Accelerate and logging setup
    config = FLAGS.config
    accelerator = Accelerator(
        log_with="wandb",
        mixed_precision=config.mixed_precision,
        project_dir=config.logdir,
        gradient_accumulation_steps=config.train.gradient_accumulation_steps * config.sample.num_steps,
    )
    if accelerator.is_main_process:
        accelerator.init_trackers(project_name="ddpo-pytorch", config=config.to_dict())
    logger.info(f"\n{config}")

    # set seed
    set_seed(config.seed, device_specific=True)

    # load scheduler, tokenizer and models.
    pipeline = StableDiffusionPipeline.from_pretrained(config.pretrained.model, revision=config.pretrained.revision)
    # freeze parameters of models to save more memory
    pipeline.vae.requires_grad_(False)
    pipeline.text_encoder.requires_grad_(False)
    pipeline.unet.requires_grad_(not config.use_lora)
    # disable safety checker
    pipeline.safety_checker = None
    # make the progress bar nicer
    pipeline.set_progress_bar_config(
        position=1,
        disable=not accelerator.is_local_main_process,
        leave=False,
        desc="Timestep",
        dynamic_ncols=True,
    )
    # switch to DDIM scheduler
    pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)

    # For mixed precision training we cast all non-trainable weigths (vae, non-lora text_encoder and non-lora unet) to half-precision
    # as these weights are only used for inference, keeping weights in full precision is not required.
    inference_dtype = torch.float32
    if accelerator.mixed_precision == "fp16":
        inference_dtype = torch.float16
    elif accelerator.mixed_precision == "bf16":
        inference_dtype = torch.bfloat16

    # Move unet, vae and text_encoder to device and cast to inference_dtype
    pipeline.vae.to(accelerator.device, dtype=inference_dtype)
    pipeline.text_encoder.to(accelerator.device, dtype=inference_dtype)
    if config.use_lora:
        pipeline.unet.to(accelerator.device, dtype=inference_dtype)

    if config.use_lora:
        # Set correct lora layers
        lora_attn_procs = {}
        for name in pipeline.unet.attn_processors.keys():
            cross_attention_dim = (
                None if name.endswith("attn1.processor") else pipeline.unet.config.cross_attention_dim
            )
            if name.startswith("mid_block"):
                hidden_size = pipeline.unet.config.block_out_channels[-1]
            elif name.startswith("up_blocks"):
                block_id = int(name[len("up_blocks.")])
                hidden_size = list(reversed(pipeline.unet.config.block_out_channels))[block_id]
            elif name.startswith("down_blocks"):
                block_id = int(name[len("down_blocks.")])
                hidden_size = pipeline.unet.config.block_out_channels[block_id]

            lora_attn_procs[name] = LoRAAttnProcessor(hidden_size=hidden_size, cross_attention_dim=cross_attention_dim)
        pipeline.unet.set_attn_processor(lora_attn_procs)
        trainable_layers = AttnProcsLayers(pipeline.unet.attn_processors)
    else:
        trainable_layers = pipeline.unet

    # Enable TF32 for faster training on Ampere GPUs,
    # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
    if config.allow_tf32:
        torch.backends.cuda.matmul.allow_tf32 = True

    # Initialize the optimizer
    if config.train.use_8bit_adam:
        try:
            import bitsandbytes as bnb
        except ImportError:
            raise ImportError(
                "Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
            )

        optimizer_cls = bnb.optim.AdamW8bit
    else:
        optimizer_cls = torch.optim.AdamW

    optimizer = optimizer_cls(
        trainable_layers.parameters(),
        lr=config.train.learning_rate,
        betas=(config.train.adam_beta1, config.train.adam_beta2),
        weight_decay=config.train.adam_weight_decay,
        eps=config.train.adam_epsilon,
    )

    # prepare prompt and reward fn
    prompt_fn = getattr(ddpo_pytorch.prompts, config.prompt_fn)
    reward_fn = getattr(ddpo_pytorch.rewards, config.reward_fn)()

    # generate negative prompt embeddings
    neg_prompt_embed = pipeline.text_encoder(
        pipeline.tokenizer(
            [""],
            return_tensors="pt",
            padding="max_length",
            truncation=True,
            max_length=pipeline.tokenizer.model_max_length,
        ).input_ids.to(accelerator.device)
    )[0]
    sample_neg_prompt_embeds = neg_prompt_embed.repeat(config.sample.batch_size, 1, 1)
    train_neg_prompt_embeds = neg_prompt_embed.repeat(config.train.batch_size, 1, 1)

    # initialize stat tracker
    if config.per_prompt_stat_tracking:
        stat_tracker = PerPromptStatTracker(
            config.per_prompt_stat_tracking.buffer_size,
            config.per_prompt_stat_tracking.min_count,
        )

    # for some reason, autocast is necessary for non-lora training but for lora training it uses more memory
    autocast = contextlib.nullcontext if config.use_lora else accelerator.autocast

    # Prepare everything with our `accelerator`.
    trainable_layers, optimizer = accelerator.prepare(trainable_layers, optimizer)

    # Train!
    samples_per_epoch = config.sample.batch_size * accelerator.num_processes * config.sample.num_batches_per_epoch
    total_train_batch_size = (
        config.train.batch_size * accelerator.num_processes * config.train.gradient_accumulation_steps
    )

    logger.info("***** Running training *****")
    logger.info(f"  Num Epochs = {config.num_epochs}")
    logger.info(f"  Sample batch size per device = {config.sample.batch_size}")
    logger.info(f"  Train batch size per device = {config.train.batch_size}")
    logger.info(f"  Gradient Accumulation steps = {config.train.gradient_accumulation_steps}")
    logger.info("")
    logger.info(f"  Total number of samples per epoch = {samples_per_epoch}")
    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_train_batch_size}")
    logger.info(f"  Number of gradient updates per inner epoch = {samples_per_epoch // total_train_batch_size}")
    logger.info(f"  Number of inner epochs = {config.train.num_inner_epochs}")

    assert config.sample.batch_size % config.train.batch_size == 0
    assert samples_per_epoch % total_train_batch_size == 0

    global_step = 0
    for epoch in range(config.num_epochs):
        #################### SAMPLING ####################
        pipeline.unet.eval()
        samples = []
        prompts = []
        for i in tqdm(
            range(config.sample.num_batches_per_epoch),
            desc=f"Epoch {epoch}: sampling",
            disable=not accelerator.is_local_main_process,
            position=0,
        ):
            # generate prompts
            prompts, prompt_metadata = zip(
                *[prompt_fn(**config.prompt_fn_kwargs) for _ in range(config.sample.batch_size)]
            )

            # encode prompts
            prompt_ids = pipeline.tokenizer(
                prompts,
                return_tensors="pt",
                padding="max_length",
                truncation=True,
                max_length=pipeline.tokenizer.model_max_length,
            ).input_ids.to(accelerator.device)
            prompt_embeds = pipeline.text_encoder(prompt_ids)[0]

            # sample
            with autocast():
                images, _, latents, log_probs = pipeline_with_logprob(
                    pipeline,
                    prompt_embeds=prompt_embeds,
                    negative_prompt_embeds=sample_neg_prompt_embeds,
                    num_inference_steps=config.sample.num_steps,
                    guidance_scale=config.sample.guidance_scale,
                    eta=config.sample.eta,
                    output_type="pt",
                )

            latents = torch.stack(latents, dim=1)  # (batch_size, num_steps + 1, 4, 64, 64)
            log_probs = torch.stack(log_probs, dim=1)  # (batch_size, num_steps, 1)
            timesteps = pipeline.scheduler.timesteps.repeat(config.sample.batch_size, 1)  # (batch_size, num_steps)

            # compute rewards
            rewards, reward_metadata = reward_fn(images, prompts, prompt_metadata)

            samples.append(
                {
                    "prompt_ids": prompt_ids,
                    "prompt_embeds": prompt_embeds,
                    "timesteps": timesteps,
                    "latents": latents[:, :-1],  # each entry is the latent before timestep t
                    "next_latents": latents[:, 1:],  # each entry is the latent after timestep t
                    "log_probs": log_probs,
                    "rewards": torch.as_tensor(rewards, device=accelerator.device),
                }
            )

        # collate samples into dict where each entry has shape (num_batches_per_epoch * sample.batch_size, ...)
        samples = {k: torch.cat([s[k] for s in samples]) for k in samples[0].keys()}

        # gather rewards across processes
        rewards = accelerator.gather(samples["rewards"]).cpu().numpy()

        # log rewards and images
        accelerator.log(
            {"reward": rewards, "epoch": epoch, "reward_mean": rewards.mean(), "reward_std": rewards.std()},
            step=global_step,
        )
        # this is a hack to force wandb to log the images as JPEGs instead of PNGs
        with tempfile.TemporaryDirectory() as tmpdir:
            for i, image in enumerate(images):
                pil = Image.fromarray((image.cpu().numpy().transpose(1, 2, 0) * 255).astype(np.uint8))
                pil = pil.resize((256, 256))
                pil.save(os.path.join(tmpdir, f"{i}.jpg"))
            accelerator.log(
                {
                    "images": [
                        wandb.Image(os.path.join(tmpdir, f"{i}.jpg"), caption=prompt)
                        for i, prompt in enumerate(prompts)
                    ],
                },
                step=global_step,
            )

        # per-prompt mean/std tracking
        if config.per_prompt_stat_tracking:
            # gather the prompts across processes
            prompt_ids = accelerator.gather(samples["prompt_ids"]).cpu().numpy()
            prompts = pipeline.tokenizer.batch_decode(prompt_ids, skip_special_tokens=True)
            advantages = stat_tracker.update(prompts, rewards)
        else:
            advantages = (rewards - rewards.mean()) / (rewards.std() + 1e-8)

        # ungather advantages; we only need to keep the entries corresponding to the samples on this process
        samples["advantages"] = (
            torch.as_tensor(advantages)
            .reshape(accelerator.num_processes, -1)[accelerator.process_index]
            .to(accelerator.device)
        )

        del samples["rewards"]
        del samples["prompt_ids"]

        total_batch_size, num_timesteps = samples["timesteps"].shape
        assert total_batch_size == config.sample.batch_size * config.sample.num_batches_per_epoch
        assert num_timesteps == config.sample.num_steps

        #################### TRAINING ####################
        for inner_epoch in range(config.train.num_inner_epochs):
            # shuffle samples along batch dimension
            indices = torch.randperm(total_batch_size, device=accelerator.device)
            samples = {k: v[indices] for k, v in samples.items()}

            # rebatch for training
            samples_batched = {k: v.reshape(-1, config.train.batch_size, *v.shape[1:]) for k, v in samples.items()}

            # dict of lists -> list of dicts for easier iteration
            samples_batched = [dict(zip(samples_batched, x)) for x in zip(*samples_batched.values())]

            # train
            pipeline.unet.train()
            for i, sample in tqdm(
                list(enumerate(samples_batched)),
                desc=f"Epoch {epoch}.{inner_epoch}: training",
                position=0,
                disable=not accelerator.is_local_main_process,
            ):
                if config.train.cfg:
                    # concat negative prompts to sample prompts to avoid two forward passes
                    embeds = torch.cat([train_neg_prompt_embeds, sample["prompt_embeds"]])
                else:
                    embeds = sample["prompt_embeds"]

                info = defaultdict(list)
                for j in tqdm(
                    range(num_timesteps),
                    desc="Timestep",
                    position=1,
                    leave=False,
                    disable=not accelerator.is_local_main_process,
                ):
                    with accelerator.accumulate(pipeline.unet):
                        with autocast():
                            if config.train.cfg:
                                noise_pred = pipeline.unet(
                                    torch.cat([sample["latents"][:, j]] * 2),
                                    torch.cat([sample["timesteps"][:, j]] * 2),
                                    embeds,
                                ).sample
                                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                                noise_pred = noise_pred_uncond + config.sample.guidance_scale * (
                                    noise_pred_text - noise_pred_uncond
                                )
                            else:
                                noise_pred = pipeline.unet(
                                    sample["latents"][:, j], sample["timesteps"][:, j], embeds
                                ).sample
                            _, log_prob = ddim_step_with_logprob(
                                pipeline.scheduler,
                                noise_pred,
                                sample["timesteps"][:, j],
                                sample["latents"][:, j],
                                eta=config.sample.eta,
                                prev_sample=sample["next_latents"][:, j],
                            )

                        # ppo logic
                        advantages = torch.clamp(
                            sample["advantages"], -config.train.adv_clip_max, config.train.adv_clip_max
                        )
                        ratio = torch.exp(log_prob - sample["log_probs"][:, j])
                        unclipped_loss = -advantages * ratio
                        clipped_loss = -advantages * torch.clamp(
                            ratio, 1.0 - config.train.clip_range, 1.0 + config.train.clip_range
                        )
                        loss = torch.mean(torch.maximum(unclipped_loss, clipped_loss))

                        # debugging values
                        # John Schulman says that (ratio - 1) - log(ratio) is a better
                        # estimator, but most existing code uses this so...
                        # http://joschu.net/blog/kl-approx.html
                        info["approx_kl"].append(0.5 * torch.mean((log_prob - sample["log_probs"][:, j]) ** 2))
                        info["clipfrac"].append(torch.mean((torch.abs(ratio - 1.0) > config.train.clip_range).float()))
                        info["loss"].append(loss)

                        # backward pass
                        accelerator.backward(loss)
                        if accelerator.sync_gradients:
                            accelerator.clip_grad_norm_(trainable_layers.parameters(), config.train.max_grad_norm)
                        optimizer.step()
                        optimizer.zero_grad()

                    if accelerator.sync_gradients:
                        # log training-related stuff
                        info = {k: torch.mean(torch.stack(v)) for k, v in info.items()}
                        info.update({"epoch": epoch, "inner_epoch": inner_epoch})
                        accelerator.log(info, step=global_step)
                        global_step += 1
                        info = defaultdict(list)


if __name__ == "__main__":
    app.run(main)