# Copyright (c) 2021 Graphcore Ltd. All rights reserved.
# This file has been modified by Graphcore Ltd
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#  Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
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from pathlib import Path
from typing import Optional
import numpy as np
import torch
from scipy.io.wavfile import read


def mask_from_lens(lens, max_len: Optional[int] = None):
    if max_len is None:
        max_len = lens.max()
    ids = torch.arange(0, max_len, device=lens.device, dtype=lens.dtype)
    mask = torch.lt(ids, lens.unsqueeze(1))
    return mask


def mask_from_lens_static(lens, max_len):
    # if max_len is None:
    #     max_len = lens.max()
    ids = torch.arange(start=0, end=max_len, dtype=lens.dtype)
    mask = torch.lt(ids, lens.unsqueeze(1))
    return mask


def load_wav_to_torch(full_path):
    sampling_rate, data = read(full_path)
    return torch.FloatTensor(data.astype(np.float32)), sampling_rate


def load_filepaths_and_text(dataset_path, fnames, has_speakers=False, split="|"):
    def split_line(root, line):
        parts = line.strip().split(split)
        if has_speakers:
            paths, non_paths = parts[:-2], parts[-2:]
        else:
            paths, non_paths = parts[:-1], parts[-1:]
        return tuple(str(Path(root, p)) for p in paths) + tuple(non_paths)

    fpaths_and_text = []
    for fname in fnames.split(","):
        with open(fname, encoding="utf-8") as f:
            fpaths_and_text += [split_line(dataset_path, line) for line in f]
    return fpaths_and_text


def stats_filename(dataset_path, filelist_path, feature_name):
    stem = Path(filelist_path).stem
    return Path(dataset_path, f"{feature_name}_stats__{stem}.json")


def to_gpu(x):
    return x.contiguous()


def to_device_async(tensor, device):
    return tensor.to(device, non_blocking=True)


def to_numpy(x):
    return x.cpu().numpy() if isinstance(x, torch.Tensor) else x