import os
import torch
import soundfile as sf
import pandas as pd
from tqdm import tqdm
from utils import minmax_norm_diff, reverse_minmax_norm_diff
from spk_ext import se_extractor


def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
    """
    Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
    Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
    """
    std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
    # rescale the results from guidance (fixes overexposure)
    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
    # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
    noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
    return noise_cfg


@torch.no_grad()
def inference_timbre(gen_shape, text,
                     model, scheduler,
                     guidance_scale=5, guidance_rescale=0.7,
                     ddim_steps=50, eta=1, random_seed=2023,
                     device='cuda',
                     ):
    text, text_mask = text
    model.eval()
    generator = torch.Generator(device=device).manual_seed(random_seed)
    scheduler.set_timesteps(ddim_steps)

    # init noise
    noise = torch.randn(gen_shape, generator=generator, device=device)
    latents = noise

    for t in scheduler.timesteps:
        latents = scheduler.scale_model_input(latents, t)

        if guidance_scale:
            output_text = model(latents, t, text, text_mask, train_cfg=False)
            output_uncond = model(latents, t, text, text_mask, train_cfg=True, cfg_prob=1.0)

            output_pred = output_uncond + guidance_scale * (output_text - output_uncond)
            if guidance_rescale > 0.0:
                output_pred = rescale_noise_cfg(output_pred, output_text,
                                                guidance_rescale=guidance_rescale)
        else:
            output_pred = model(latents, t, text, text_mask, train_cfg=False)

        latents = scheduler.step(model_output=output_pred, timestep=t, sample=latents,
                                 eta=eta, generator=generator).prev_sample

    # pred = reverse_minmax_norm_diff(latents, vmin=0.0, vmax=0.5)
    # pred = torch.clip(pred, min=0.0, max=0.5)
    return latents


@torch.no_grad()
def eval_plugin_light(vc_model, text_model,
                      timbre_model, timbre_scheduler, timbre_shape,
                      val_meta, val_folder,
                      guidance_scale=3, guidance_rescale=0.7,
                      ddim_steps=50, eta=1, random_seed=2024,
                      device='cuda',
                      epoch=0, save_path='logs/eval/', val_num=10, sr=24000):

    tokenizer, text_encoder = text_model

    df = pd.read_csv(val_meta)

    save_path = save_path + str(epoch) + '/'
    os.makedirs(save_path, exist_ok=True)

    step = 0

    for i in range(len(df)):
        row = df.iloc[i]

        source_path = val_folder + row['path']
        prompt = [row['prompt']]

        with torch.no_grad():
            text_batch = tokenizer(prompt,
                                   max_length=32,
                                   padding='max_length', truncation=True, return_tensors="pt")
            text, text_mask = text_batch.input_ids.to(device), \
                text_batch.attention_mask.to(device)
            text = text_encoder(input_ids=text, attention_mask=text_mask)[0]

        spk_embed = inference_timbre(timbre_shape, [text, text_mask],
                                     timbre_model, timbre_scheduler,
                                     guidance_scale=guidance_scale, guidance_rescale=guidance_rescale,
                                     ddim_steps=ddim_steps, eta=eta, random_seed=random_seed,
                                     device=device)

        source_se = se_extractor(source_path, vc_model).to(device)
        # print(source_se.shape)
        # print(spk_embed.shape)

        encode_message = "@MyShell"
        vc_model.convert(
            audio_src_path=source_path,
            src_se=source_se,
            tgt_se=spk_embed,
            output_path=save_path + f'{step}_{prompt[0]}' + '.wav',
            message=encode_message)

        step += 1
        if step >= val_num:
            break