from diffusers import AutoencoderOobleck
import torch
from transformers  import T5EncoderModel,T5TokenizerFast
from diffusers import   FluxTransformer2DModel
from torch import nn
from typing import List
from diffusers import FlowMatchEulerDiscreteScheduler
from diffusers.training_utils import compute_density_for_timestep_sampling
import copy
import torch.nn.functional as F
import numpy as np
from src.model import TangoFlux
from huggingface_hub import snapshot_download
from tqdm import tqdm
from typing import Optional,Union,List
from datasets import load_dataset, Audio
from math import pi
import json
import inspect
import yaml
from safetensors.torch import load_file


class TangoFluxInference:

    def __init__(self,name='declare-lab/TangoFlux',device="cuda"):
        

        self.vae = AutoencoderOobleck()

        paths = snapshot_download(repo_id=name) 
        vae_weights = load_file("{}/vae.safetensors".format(paths))
        self.vae.load_state_dict(vae_weights)
        weights = load_file("{}/tangoflux.safetensors".format(paths))

        with open('{}/config.json'.format(paths),'r') as f:
            config = json.load(f)
        self.model = TangoFlux(config)
        self.model.load_state_dict(weights,strict=False)
        # _IncompatibleKeys(missing_keys=['text_encoder.encoder.embed_tokens.weight'], unexpected_keys=[]) this behaviour is expected 
        self.vae.to(device)
        self.model.to(device)
    
    def generate(self,prompt,steps=25,duration=10,guidance_scale=4.5):
        
        with torch.no_grad():
            latents = self.model.inference_flow(prompt,
            duration=duration,
            num_inference_steps=steps,
            guidance_scale=guidance_scale)



            wave = self.vae.decode(latents.transpose(2,1)).sample.cpu()[0]
        waveform_end = int(duration * self.vae.config.sampling_rate)
        wave =  wave[:, :waveform_end]
        return wave