import torch

from audio_diffusion_pytorch import DiffusionModel, UNetV0, VDiffusion, VSampler
from module.models_s1 import DiT



latent_dim = 4
cond_channels = 192

net =DiT(latent_dim, cond_channels)

model = DiffusionModel(
    net=net, # The model type used for diffusion (U-Net V0 in this case)
    in_channels=2, # U-Net: number of input/output (audio) channels
    channels=[8, 32], # U-Net: channels at each layer
    factors=[1, 4,], # U-Net: downsampling and upsampling factors at each layer
    items=[1, 2], # U-Net: number of repeating items at each layer
    attentions=[0, 0, ], # U-Net: attention enabled/disabled at each layer
    attention_heads=8, # U-Net: number of attention heads per attention item
    attention_features=64, # U-Net: number of attention features per attention item
    diffusion_t=VDiffusion, # The diffusion method used
    sampler_t=VSampler, # The diffusion sampler used
)
print('n params:', sum(p.numel() for p in model.parameters())/1e6, 'M')


audio = torch.randn(2, 4, 111)
cond = torch.randn(2, 192, 111)
loss = model(audio, cond=cond)
loss.backward()
print(loss)
# # Turn noise into new audio sample with diffusion
noise = torch.randn(2, 4, 111)
sample = model.sample(noise, num_steps=10,cond=cond, show_progress=True) # Suggested num_steps 10-100
print(sample.shape)