import pandas as pd
import os
import numpy as np
import collections
import librosa
import torch
from torch.utils.data import Dataset

from TTS.utils.text import text_to_sequence
from TTS.utils.audio import AudioProcessor
from TTS.utils.data import prepare_data, pad_data, pad_per_step


class LJSpeechDataset(Dataset):

    def __init__(self, csv_file, root_dir, outputs_per_step, sample_rate,
                text_cleaner, num_mels, min_level_db, frame_shift_ms,
                frame_length_ms, preemphasis, ref_level_db, num_freq, power):
        self.frames = pd.read_csv(csv_file, sep='|', header=None)
        self.root_dir = root_dir
        self.outputs_per_step = outputs_per_step
        self.sample_rate = sample_rate
        self.cleaners = text_cleaner
        self.ap = AudioProcessor(sample_rate, num_mels, min_level_db, frame_shift_ms,
                                 frame_length_ms, preemphasis, ref_level_db, num_freq, power
                                )
        print(" > Reading LJSpeech from - {}".format(root_dir))
        print(" | > Number of instances : {}".format(len(self.frames)))

    def load_wav(self, filename):
        try:
            audio = librosa.core.load(filename, sr=self.sample_rate)
            return audio
        except RuntimeError as e:
            print(" !! Cannot read file : {}".format(filename))

    def __len__(self):
        return len(self.frames)

    def __getitem__(self, idx):
        wav_name = os.path.join(self.root_dir,
                                self.frames.ix[idx, 0]) + '.wav'
        text = self.frames.ix[idx, 1]
        text = np.asarray(text_to_sequence(text, [self.cleaners]), dtype=np.int32)
        wav = np.asarray(self.load_wav(wav_name)[0], dtype=np.float32)
        sample = {'text': text, 'wav': wav, 'item_idx': self.frames.ix[idx, 0]}
        return sample

    def get_dummy_data(self):
        return torch.autograd.Variable(torch.ones(16, 143)).type(torch.LongTensor)

    def collate_fn(self, batch):

        # Puts each data field into a tensor with outer dimension batch size
        if isinstance(batch[0], collections.Mapping):
            keys = list()

            wav = [d['wav'] for d in batch]
            item_idxs = [d['item_idx'] for d in batch]
            text = [d['text'] for d in batch]

            text_lenghts = np.array([len(x) for x in text])
            max_text_len = np.max(text_lenghts)

            # PAD sequences with largest length of the batch
            text = prepare_data(text).astype(np.int32)
            wav = prepare_data(wav)

            linear = np.array([self.ap.spectrogram(w).astype('float32') for w in wav])
            mel = np.array([self.ap.melspectrogram(w).astype('float32') for w in wav])
            assert mel.shape[2] == linear.shape[2]
            timesteps = mel.shape[2]

            # PAD with zeros that can be divided by outputs per step
            if timesteps % self.outputs_per_step != 0:
                linear = pad_per_step(linear, self.outputs_per_step)
                mel = pad_per_step(mel, self.outputs_per_step)

            # reshape jombo
            linear = linear.transpose(0, 2, 1)
            mel = mel.transpose(0, 2, 1)

            # convert things to pytorch
            text_lenghts = torch.LongTensor(text_lenghts)
            text = torch.LongTensor(text)
            linear = torch.FloatTensor(linear)
            mel = torch.FloatTensor(mel)
            return text, text_lenghts, linear, mel, item_idxs[0]

        raise TypeError(("batch must contain tensors, numbers, dicts or lists;\
                         found {}"
                         .format(type(batch[0]))))