import joblib
import numpy as np
from generate_synthetic_data_online import generate_synth_dataset_log_512, generate_synth_dataset_log_muted_512
from tools import show_spc, spc_to_VAE_input, VAE_out_put_to_spc, np_log10
import torch.utils.data as data


class Data_cache():
    """This is a class that stores synthetic data."""

    def __init__(self, synthetic_data, external_sources):
        self.n_synthetic = np.shape(synthetic_data)[0]
        self.synthetic_data = synthetic_data.astype(np.float32)
        self.external_sources = external_sources.astype(np.float32)
        self.epsilon = 1e-20
        
    def get_all_data(self):
        return np.vstack([self.synthetic_data, self.external_sources])
        
    def refresh(self):
        self.synthetic_data = generate_synth_dataset(self.n_synthetic, mute=True)

    def get_data_loader(self, shuffle=True, BATCH_SIZE=8, new_way=False):
        all_data = self.get_all_data()
        our_data = []
        for i in range(len(all_data)):
            if new_way:
                spectrogram = VAE_out_put_to_spc(np.reshape(all_data[i], (1, 512, 256)))
                log_spectrogram = np.log10(spectrogram + self.epsilon)
                our_data.append(log_spectrogram)
            else:
                our_data.append(np.reshape(all_data[i], (1, 512, 256)))

        iterator = data.DataLoader(our_data, shuffle=shuffle, batch_size=BATCH_SIZE)
        return iterator


def generate_synth_dataset(n_synthetic, mute=False):
    """Preprocessing for synthetic data"""
    n_synthetic_sample = n_synthetic
    if mute:
        Input0 = generate_synth_dataset_log_muted_512(n_synthetic_sample)
    else:
        Input0 = generate_synth_dataset_log_512(n_synthetic_sample)
    Input0 = spc_to_VAE_input(Input0)
    Input0 = Input0.reshape(Input0.shape[0], Input0.shape[1], Input0.shape[2], 1)
    return Input0


def read_data(data_path):
    """Read external sources"""
    
    data = np.array(joblib.load(data_path))
    data = spc_to_VAE_input(data)
    data = data.reshape(data.shape[0], data.shape[1], data.shape[2], 1)
    return data


def load_data(n_synthetic):
    """Generate the hybrid dataset."""
    Input_synthetic = generate_synth_dataset(n_synthetic)
    
    Input_AU = read_data("./data/external_data/ARTURIA_data")
    print("ARTURIA dataset loaded.")
    
    Input_NSynth = read_data("./data/external_data/NSynth_data")
    print("NSynth dataset loaded.")
    
    Input_SF = read_data("./data/external_data/soundfonts_data")
    Input_SF_256 = np.zeros((337, 512, 256, 1))
    Input_SF_256[:,:,:251,:] += Input_SF
    Input_SF =Input_SF_256
    print("SoundFonts dataset loaded.")

    Input_google = read_data("./data/external_data/WaveNet_samples")
    
    Input_external = np.vstack([Input_AU, Input_NSynth, Input_SF, Input_google])
    data_cache = Data_cache(Input_synthetic, Input_external)
    print(f"Data loaded, data shape: {np.shape(data_cache.get_all_data())}")
    return data_cache
    

def show_data(dataset_name, n_sample=3, index=-1, new_way=False):
    """Show and return a certain dataset.

    Parameters
    ----------
    dataset_name: String
        Name of the dataset to show.
    n_samples: int
        Number of samples to show.
    index: int
        Setting 'index' larger equal 0 shows the 'index'-th sample in the desired dataset.

    Returns
    -------
    np.ndarray:
        The showed dataset.
    """

    if dataset_name == "ARTURIA":
        data = read_data("./data/external_data/ARTURIA_data")
    elif dataset_name == "NSynth":
        data = read_data("./data/external_data/NSynth_data")
    elif dataset_name == "SoundFonts":
        data = read_data("./data/external_data/soundfonts_data")
    elif dataset_name == "Synthetic":
        data = generate_synth_dataset(int(n_sample * 3))
    else:
        print("Example command: \"!python thesis_main.py show_data -s [ARTURIA, NSynth, SoundFonts, Synthetic] -n 5\"")
        return
    
    if index >= 0:
        show_spc(VAE_out_put_to_spc(data[index]))
    else:
        for i in range(n_sample):
            index = np.random.randint(0,len(data))
            print(index)
            show_spc(VAE_out_put_to_spc(data[index]))
    return data
    

def show_data(tensor_batch, index=-1, new_way=False):
    if index < 0:
        index = np.random.randint(0, tensor_batch.shape[0])
        
    if new_way:
        sample = tensor_batch[index].detach().numpy()
        spectrogram = 10.0 ** sample
        print(f"The {index}-th sample:")
        show_spc(spectrogram)
    else:
        sample = tensor_batch[index].detach().numpy()
        show_spc(VAE_out_put_to_spc(sample))
    # return data