import ast
import gc
import random

import cv2
import numpy as np
import torch
import torch.nn.functional as F

from diffusers.models.attention_processor import AttnProcessor2_0
from diffusers.models.attention import BasicTransformerBlock
from decord import VideoReader
import wandb


def extract_into_tensor(a, t, x_shape):
    b, *_ = t.shape
    out = a.gather(-1, t)
    return out.reshape(b, *((1,) * (len(x_shape) - 1)))


def is_attn(name):
    return "attn1" or "attn2" == name.split(".")[-1]


def set_processors(attentions):
    for attn in attentions:
        attn.set_processor(AttnProcessor2_0())


def set_torch_2_attn(unet):
    optim_count = 0

    for name, module in unet.named_modules():
        if is_attn(name):
            if isinstance(module, torch.nn.ModuleList):
                for m in module:
                    if isinstance(m, BasicTransformerBlock):
                        set_processors([m.attn1, m.attn2])
                        optim_count += 1
    if optim_count > 0:
        print(f"{optim_count} Attention layers using Scaled Dot Product Attention.")


# From LatentConsistencyModel.get_guidance_scale_embedding
def guidance_scale_embedding(w, embedding_dim=512, dtype=torch.float32):
    """
    See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298

    Args:
        timesteps (`torch.Tensor`):
            generate embedding vectors at these timesteps
        embedding_dim (`int`, *optional*, defaults to 512):
            dimension of the embeddings to generate
        dtype:
            data type of the generated embeddings

    Returns:
        `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
    """
    assert len(w.shape) == 1
    w = w * 1000.0

    half_dim = embedding_dim // 2
    emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
    emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
    emb = w.to(dtype)[:, None] * emb[None, :]
    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
    if embedding_dim % 2 == 1:  # zero pad
        emb = torch.nn.functional.pad(emb, (0, 1))
    assert emb.shape == (w.shape[0], embedding_dim)
    return emb


def append_dims(x, target_dims):
    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
    dims_to_append = target_dims - x.ndim
    if dims_to_append < 0:
        raise ValueError(
            f"input has {x.ndim} dims but target_dims is {target_dims}, which is less"
        )
    return x[(...,) + (None,) * dims_to_append]


# From LCMScheduler.get_scalings_for_boundary_condition_discrete
def scalings_for_boundary_conditions(timestep, sigma_data=0.5, timestep_scaling=10.0):
    scaled_timestep = timestep_scaling * timestep
    c_skip = sigma_data**2 / (scaled_timestep**2 + sigma_data**2)
    c_out = scaled_timestep / (scaled_timestep**2 + sigma_data**2) ** 0.5
    return c_skip, c_out


# Compare LCMScheduler.step, Step 4
def get_predicted_original_sample(
    model_output, timesteps, sample, prediction_type, alphas, sigmas
):
    alphas = extract_into_tensor(alphas, timesteps, sample.shape)
    sigmas = extract_into_tensor(sigmas, timesteps, sample.shape)
    if prediction_type == "epsilon":
        pred_x_0 = (sample - sigmas * model_output) / alphas
    elif prediction_type == "sample":
        pred_x_0 = model_output
    elif prediction_type == "v_prediction":
        pred_x_0 = alphas * sample - sigmas * model_output
    else:
        raise ValueError(
            f"Prediction type {prediction_type} is not supported; currently, `epsilon`, `sample`, and `v_prediction`"
            f" are supported."
        )

    return pred_x_0


# Based on step 4 in DDIMScheduler.step
def get_predicted_noise(
    model_output, timesteps, sample, prediction_type, alphas, sigmas
):
    alphas = extract_into_tensor(alphas, timesteps, sample.shape)
    sigmas = extract_into_tensor(sigmas, timesteps, sample.shape)
    if prediction_type == "epsilon":
        pred_epsilon = model_output
    elif prediction_type == "sample":
        pred_epsilon = (sample - alphas * model_output) / sigmas
    elif prediction_type == "v_prediction":
        pred_epsilon = alphas * model_output + sigmas * sample
    else:
        raise ValueError(
            f"Prediction type {prediction_type} is not supported; currently, `epsilon`, `sample`, and `v_prediction`"
            f" are supported."
        )

    return pred_epsilon


# From LatentConsistencyModel.get_guidance_scale_embedding
def guidance_scale_embedding(w, embedding_dim=512, dtype=torch.float32):
    """
    See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298

    Args:
        timesteps (`torch.Tensor`):
            generate embedding vectors at these timesteps
        embedding_dim (`int`, *optional*, defaults to 512):
            dimension of the embeddings to generate
        dtype:
            data type of the generated embeddings

    Returns:
        `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
    """
    assert len(w.shape) == 1
    w = w * 1000.0

    half_dim = embedding_dim // 2
    emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
    emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
    emb = w.to(dtype)[:, None] * emb[None, :]
    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
    if embedding_dim % 2 == 1:  # zero pad
        emb = torch.nn.functional.pad(emb, (0, 1))
    assert emb.shape == (w.shape[0], embedding_dim)
    return emb


def append_dims(x, target_dims):
    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
    dims_to_append = target_dims - x.ndim
    if dims_to_append < 0:
        raise ValueError(
            f"input has {x.ndim} dims but target_dims is {target_dims}, which is less"
        )
    return x[(...,) + (None,) * dims_to_append]


# From LCMScheduler.get_scalings_for_boundary_condition_discrete
def scalings_for_boundary_conditions(timestep, sigma_data=0.5, timestep_scaling=10.0):
    scaled_timestep = timestep_scaling * timestep
    c_skip = sigma_data**2 / (scaled_timestep**2 + sigma_data**2)
    c_out = scaled_timestep / (scaled_timestep**2 + sigma_data**2) ** 0.5
    return c_skip, c_out


# Compare LCMScheduler.step, Step 4
def get_predicted_original_sample(
    model_output, timesteps, sample, prediction_type, alphas, sigmas
):
    alphas = extract_into_tensor(alphas, timesteps, sample.shape)
    sigmas = extract_into_tensor(sigmas, timesteps, sample.shape)
    if prediction_type == "epsilon":
        pred_x_0 = (sample - sigmas * model_output) / alphas
    elif prediction_type == "sample":
        pred_x_0 = model_output
    elif prediction_type == "v_prediction":
        pred_x_0 = alphas * sample - sigmas * model_output
    else:
        raise ValueError(
            f"Prediction type {prediction_type} is not supported; currently, `epsilon`, `sample`, and `v_prediction`"
            f" are supported."
        )

    return pred_x_0


# Based on step 4 in DDIMScheduler.step
def get_predicted_noise(
    model_output, timesteps, sample, prediction_type, alphas, sigmas
):
    alphas = extract_into_tensor(alphas, timesteps, sample.shape)
    sigmas = extract_into_tensor(sigmas, timesteps, sample.shape)
    if prediction_type == "epsilon":
        pred_epsilon = model_output
    elif prediction_type == "sample":
        pred_epsilon = (sample - alphas * model_output) / sigmas
    elif prediction_type == "v_prediction":
        pred_epsilon = alphas * model_output + sigmas * sample
    else:
        raise ValueError(
            f"Prediction type {prediction_type} is not supported; currently, `epsilon`, `sample`, and `v_prediction`"
            f" are supported."
        )

    return pred_epsilon


def param_optim(model, condition, extra_params=None, is_lora=False, negation=None):
    extra_params = extra_params if len(extra_params.keys()) > 0 else None
    return {
        "model": model,
        "condition": condition,
        "extra_params": extra_params,
        "is_lora": is_lora,
        "negation": negation,
    }


def create_optim_params(name="param", params=None, lr=5e-6, extra_params=None):
    params = {"name": name, "params": params, "lr": lr}
    if extra_params is not None:
        for k, v in extra_params.items():
            params[k] = v

    return params


def create_optimizer_params(model_list, lr):
    import itertools

    optimizer_params = []

    for optim in model_list:
        model, condition, extra_params, is_lora, negation = optim.values()
        # Check if we are doing LoRA training.
        if is_lora and condition and isinstance(model, list):
            params = create_optim_params(
                params=itertools.chain(*model), extra_params=extra_params
            )
            optimizer_params.append(params)
            continue

        if is_lora and condition and not isinstance(model, list):
            for n, p in model.named_parameters():
                if "lora" in n:
                    params = create_optim_params(n, p, lr, extra_params)
                    optimizer_params.append(params)
            continue

        # If this is true, we can train it.
        if condition:
            for n, p in model.named_parameters():
                should_negate = "lora" in n and not is_lora
                if should_negate:
                    continue

                params = create_optim_params(n, p, lr, extra_params)
                optimizer_params.append(params)

    return optimizer_params


def handle_trainable_modules(
    model, trainable_modules=None, is_enabled=True, negation=None
):
    acc = []
    unfrozen_params = 0

    if trainable_modules is not None:
        unlock_all = any([name == "all" for name in trainable_modules])
        if unlock_all:
            model.requires_grad_(True)
            unfrozen_params = len(list(model.parameters()))
        else:
            model.requires_grad_(False)
            for name, param in model.named_parameters():
                for tm in trainable_modules:
                    if all([tm in name, name not in acc, "lora" not in name]):
                        param.requires_grad_(is_enabled)
                        acc.append(name)
                        unfrozen_params += 1


def huber_loss(pred, target, huber_c=0.001):
    loss = torch.sqrt((pred.float() - target.float()) ** 2 + huber_c**2) - huber_c
    return loss.mean()


@torch.no_grad()
def update_ema(target_params, source_params, rate=0.99):
    """
    Update target parameters to be closer to those of source parameters using
    an exponential moving average.

    :param target_params: the target parameter sequence.
    :param source_params: the source parameter sequence.
    :param rate: the EMA rate (closer to 1 means slower).
    """
    for targ, src in zip(target_params, source_params):
        src_to_dtype = src.to(targ.dtype)
        targ.detach().mul_(rate).add_(src_to_dtype, alpha=1 - rate)


def log_validation_video(pipeline, args, accelerator, save_fps):
    if args.seed is None:
        generator = None
    else:
        generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)

    validation_prompts = [
        "An astronaut riding a horse.",
        "Darth vader surfing in waves.",
        "Robot dancing in times square.",
        "Clown fish swimming through the coral reef.",
        "A child excitedly swings on a rusty swing set, laughter filling the air.",
        "With the style of van gogh, A young couple dances under the moonlight by the lake.",
        "A young woman with glasses is jogging in the park wearing a pink headband.",
        "Impressionist style, a yellow rubber duck floating on the wave on the sunset",
        "Wolf, turns its head, in the wild",
        "Iron man, walks, on the moon, 8k, high detailed, best quality",
        "With the style of low-poly game art, A majestic, white horse gallops gracefully",
        "a rabbit, low-poly game art style",
    ]

    video_logs = []

    if getattr(args, "use_motion_cond", False):
        use_motion_cond = True
    else:
        use_motion_cond = False

    for _, prompt in enumerate(validation_prompts):
        if use_motion_cond:
            motin_gs_unit = (args.motion_gs_max - args.motion_gs_min) / 2
            for i in range(3):
                with torch.autocast("cuda"):
                    videos = pipeline(
                        prompt=prompt,
                        frames=args.n_frames,
                        num_inference_steps=8,
                        num_videos_per_prompt=1,
                        fps=args.fps,
                        use_motion_cond=True,
                        motion_gs=motin_gs_unit * i,
                        lcm_origin_steps=args.num_ddim_timesteps,
                        generator=generator,
                    )
                    videos = (videos.clamp(-1.0, 1.0) + 1.0) / 2.0
                    videos = (
                        (videos * 255)
                        .to(torch.uint8)
                        .permute(0, 2, 1, 3, 4)
                        .cpu()
                        .numpy()
                    )
                video_logs.append(
                    {
                        "validation_prompt": f"GS={i * motin_gs_unit}, {prompt}",
                        "videos": videos,
                    }
                )
        else:
            for i in range(2):
                with torch.autocast("cuda"):
                    videos = pipeline(
                        prompt=prompt,
                        frames=args.n_frames,
                        num_inference_steps=4 * (i + 1),
                        num_videos_per_prompt=1,
                        fps=args.fps,
                        use_motion_cond=False,
                        lcm_origin_steps=args.num_ddim_timesteps,
                        generator=generator,
                    )
                    videos = (videos.clamp(-1.0, 1.0) + 1.0) / 2.0
                    videos = (
                        (videos * 255)
                        .to(torch.uint8)
                        .permute(0, 2, 1, 3, 4)
                        .cpu()
                        .numpy()
                    )
                video_logs.append(
                    {
                        "validation_prompt": f"Steps={4 * (i + 1)}, {prompt}",
                        "videos": videos,
                    }
                )

    for tracker in accelerator.trackers:
        if tracker.name == "wandb":
            formatted_videos = []
            for log in video_logs:
                videos = log["videos"]
                validation_prompt = log["validation_prompt"]
                for video in videos:
                    video = wandb.Video(video, caption=validation_prompt, fps=save_fps)
                    formatted_videos.append(video)

            tracker.log({f"validation": formatted_videos})

        del pipeline
        gc.collect()


def tuple_type(s):
    if isinstance(s, tuple):
        return s
    value = ast.literal_eval(s)
    if isinstance(value, tuple):
        return value
    raise TypeError("Argument must be a tuple")


def load_model_checkpoint(model, ckpt):
    def load_checkpoint(model, ckpt, full_strict):
        state_dict = torch.load(ckpt, map_location="cpu", weights_only=True)
        if "state_dict" in list(state_dict.keys()):
            state_dict = state_dict["state_dict"]
        model.load_state_dict(state_dict, strict=full_strict)
        del state_dict
        gc.collect()
        return model

    load_checkpoint(model, ckpt, full_strict=True)
    print(">>> model checkpoint loaded.")
    return model


def read_video_to_tensor(
    path_to_video, sample_fps, sample_frames, uniform_sampling=False
):
    video_reader = VideoReader(path_to_video)
    video_fps = video_reader.get_avg_fps()
    video_frames = video_reader._num_frame
    video_duration = video_frames / video_fps
    sample_duration = sample_frames / sample_fps
    stride = video_fps / sample_fps

    if uniform_sampling or video_duration <= sample_duration:
        index_range = np.linspace(0, video_frames - 1, sample_frames).astype(np.int32)
    else:
        max_start_frame = video_frames - np.ceil(sample_frames * stride).astype(
            np.int32
        )
        if max_start_frame > 0:
            start_frame = random.randint(0, max_start_frame)
        else:
            start_frame = 0

        index_range = start_frame + np.arange(sample_frames) * stride
        index_range = np.round(index_range).astype(np.int32)

    sampled_frames = video_reader.get_batch(index_range).asnumpy()
    pixel_values = torch.from_numpy(sampled_frames).permute(0, 3, 1, 2).contiguous()
    pixel_values = pixel_values / 255.0
    del video_reader

    return pixel_values


def calculate_motion_rank_new(tensor_ref, tensor_gen, rank_k=1):
    if rank_k == 0:
        loss = torch.tensor(0.0, device=tensor_ref.device)
    elif rank_k > tensor_ref.shape[-1]:
        raise ValueError(
            "The value of rank_k cannot be larger than the number of frames"
        )
    else:
        # Sort the reference tensor along the frames dimension
        _, sorted_indices = torch.sort(tensor_ref, dim=-1)
        # Create a mask to select the top rank_k frames
        mask = torch.zeros_like(tensor_ref, dtype=torch.bool)
        mask.scatter_(-1, sorted_indices[..., -rank_k:], True)
        # Compute the mean squared error loss only on the masked elements
        loss = F.mse_loss(tensor_ref[mask].detach(), tensor_gen[mask])
    return loss


def compute_temp_loss(attention_prob, attention_prob_example):
    temp_attn_prob_loss = []
    # 1. Loop though all layers to get the query, key, and Compute the PCA loss
    for name in attention_prob.keys():
        attn_prob_example = attention_prob_example[name]
        attn_prob = attention_prob[name]

        module_attn_loss = calculate_motion_rank_new(
            attn_prob_example.detach(), attn_prob, rank_k=1
        )
        temp_attn_prob_loss.append(module_attn_loss)

    loss_temp = torch.stack(temp_attn_prob_loss) * 100
    loss = loss_temp.mean()
    return loss