import functools
import gc
import logging
import math
import os
import random
import shutil
from pathlib import Path

import accelerate
import datasets
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
from accelerate.utils import InitProcessGroupKwargs
from datasets import load_dataset, DatasetDict, load_from_disk
from huggingface_hub import create_repo, upload_folder
from packaging import version
from torchvision import transforms
from torchvision.transforms.functional import crop
from tqdm.auto import tqdm
from transformers import AutoTokenizer, PretrainedConfig

import diffusers
from diffusers import (
    AutoencoderKL,
    DDPMScheduler,
    StableDiffusionXLPipeline,
    UNet2DConditionModel,
)
from diffusers.optimization import get_scheduler
from diffusers.training_utils import EMAModel, compute_snr
from diffusers.utils import check_min_version, is_wandb_available
from diffusers.utils.import_utils import is_xformers_available
from datetime import timedelta
from multiprocess import set_start_method


resolution = 128
dataset_name = "lambdalabs/pokemon-blip-captions"
cache_dir = "/workspace/dataset-cache"
pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"
pretrained_vae_model_name_or_path = "madebyollin/sdxl-vae-fp16-fix"

max_train_samples = None


check_min_version("0.24.0.dev0")
logger = get_logger(__name__)
DATASET_NAME_MAPPING = {
    "lambdalabs/pokemon-blip-captions": ("image", "text"),
}

dataset = load_dataset(
    dataset_name,
    cache_dir=cache_dir,
)


if max_train_samples is not None:
    dataset["train"] = dataset["train"].select(range(max_train_samples))


column_names = dataset["train"].column_names


train_resize = transforms.Resize(resolution, interpolation=transforms.InterpolationMode.BILINEAR)
train_transforms = transforms.Compose([transforms.ToTensor(), transforms.Normalize([0.5], [0.5])])
train_crop = transforms.RandomCrop(resolution)


def preprocess_train(examples):
    image_column = column_names[0]
    images = [image.convert("RGB") for image in examples[image_column]]
    # image aug
    original_sizes = []
    all_images = []
    crop_top_lefts = []
    for image in images:
        original_sizes.append((image.height, image.width))
        image = train_resize(image)

        y1, x1, h, w = train_crop.get_params(image, (resolution, resolution))
        image = crop(image, y1, x1, h, w)

        crop_top_left = (y1, x1)
        crop_top_lefts.append(crop_top_left)
        image = train_transforms(image)
        all_images.append(image)

    examples["original_sizes"] = original_sizes
    examples["crop_top_lefts"] = crop_top_lefts
    examples["pixel_values"] = all_images
    return examples


train_dataset = dataset["train"].with_transform(preprocess_train)
print(type(train_dataset))
train_dataset


def import_model_class_from_model_name_or_path(
    pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder"
):
    text_encoder_config = PretrainedConfig.from_pretrained(
        pretrained_model_name_or_path, subfolder=subfolder, revision=revision, device="cuda"
    )
    model_class = text_encoder_config.architectures[0]

    if model_class == "CLIPTextModel":
        from transformers import CLIPTextModel

        return CLIPTextModel
    elif model_class == "CLIPTextModelWithProjection":
        from transformers import CLIPTextModelWithProjection

        return CLIPTextModelWithProjection
    else:
        raise ValueError(f"{model_class} is not supported.")


text_encoder_cls_one = import_model_class_from_model_name_or_path(
    pretrained_model_name_or_path, None
)
text_encoder_cls_two = import_model_class_from_model_name_or_path(
    pretrained_model_name_or_path, None, subfolder="text_encoder_2"
)

text_encoder_one = text_encoder_cls_one.from_pretrained(
    pretrained_model_name_or_path, subfolder="text_encoder"
).to("cuda")
text_encoder_two = text_encoder_cls_two.from_pretrained(
    pretrained_model_name_or_path, subfolder="text_encoder_2"
).to("cuda")
vae = AutoencoderKL.from_pretrained(
    pretrained_model_name_or_path, subfolder="vae", device="cuda"
).to("cuda")

tokenizer_one = AutoTokenizer.from_pretrained(
    pretrained_model_name_or_path, subfolder="tokenizer", use_fast=False, device="cuda"
)
tokenizer_two = AutoTokenizer.from_pretrained(
    pretrained_model_name_or_path, subfolder="tokenizer_2", device="cuda"
)


text_encoders = [text_encoder_one, text_encoder_two]
tokenizers = [tokenizer_one, tokenizer_two]


def encode_prompt(batch, rank, text_encoders, tokenizers, proportion_empty_prompts, caption_column, is_train=True):
    print(rank)
    prompt_embeds_list = []
    prompt_batch = batch[caption_column]

    captions = []
    for caption in prompt_batch:
        if random.random() < proportion_empty_prompts:
            captions.append("")
        elif isinstance(caption, str):
            captions.append(caption)
        elif isinstance(caption, (list, np.ndarray)):
            # take a random caption if there are multiple
            captions.append(random.choice(caption) if is_train else caption[0])

    with torch.no_grad():
        for tokenizer, text_encoder in zip(tokenizers, text_encoders):
            text_inputs = tokenizer(
                captions,
                padding="max_length",
                max_length=tokenizer.model_max_length,
                truncation=True,
                return_tensors="pt",
            )
            text_input_ids = text_inputs.input_ids
            prompt_embeds = text_encoder(
                text_input_ids.to(text_encoder.device),
                output_hidden_states=True,
            )

            # We are only ALWAYS interested in the pooled output of the final text encoder
            pooled_prompt_embeds = prompt_embeds[0]
            prompt_embeds = prompt_embeds.hidden_states[-2]
            bs_embed, seq_len, _ = prompt_embeds.shape
            prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)
            prompt_embeds_list.append(prompt_embeds)

    prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
    pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)
    return {"prompt_embeds": prompt_embeds.cpu(), "pooled_prompt_embeds": pooled_prompt_embeds.cpu()}


def compute_vae_encodings(batch, vae):
    images = batch.pop("pixel_values")
    pixel_values = torch.stack(list(images))
    pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
    pixel_values = pixel_values.to(vae.device, dtype=vae.dtype)

    with torch.no_grad():
        model_input = vae.encode(pixel_values).latent_dist.sample()
    model_input = model_input * vae.config.scaling_factor
    return {"model_input": model_input.cpu()}


compute_embeddings_fn = functools.partial(
    encode_prompt,
    text_encoders=text_encoders,
    tokenizers=tokenizers,
    proportion_empty_prompts=0,
    caption_column="text",
)
compute_vae_encodings_fn = functools.partial(compute_vae_encodings, vae=vae)


set_start_method("spawn")


def map_train():
    return train_dataset.map(compute_embeddings_fn, batched=True, batch_size=4, with_rank=True, num_proc=2)