# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import itertools
import logging
import os
import sys
from typing import Any, List, Optional, Union

import numpy as np

import torch
import torch.nn.functional as F
from fairseq.data import data_utils
from fairseq.data.fairseq_dataset import FairseqDataset

logger = logging.getLogger(__name__)


def load_audio(manifest_path, max_keep, min_keep):
    n_long, n_short = 0, 0
    names, inds, sizes = [], [], []
    with open(manifest_path) as f:
        root = f.readline().strip()
        for ind, line in enumerate(f):
            items = line.strip().split("\t")
            assert len(items) == 2, line
            sz = int(items[1])
            if min_keep is not None and sz < min_keep:
                n_short += 1
            elif max_keep is not None and sz > max_keep:
                n_long += 1
            else:
                names.append(items[0])
                inds.append(ind)
                sizes.append(sz)
    tot = ind + 1
    logger.info(
        (
            f"max_keep={max_keep}, min_keep={min_keep}, "
            f"loaded {len(names)}, skipped {n_short} short and {n_long} long, "
            f"longest-loaded={max(sizes)}, shortest-loaded={min(sizes)}"
        )
    )
    return root, names, inds, tot, sizes


def load_label(label_path, inds, tot):
    with open(label_path) as f:
        labels = [line.rstrip() for line in f]
        assert (
            len(labels) == tot
        ), f"number of labels does not match ({len(labels)} != {tot})"
        labels = [labels[i] for i in inds]
    return labels


def load_label_offset(label_path, inds, tot):
    with open(label_path) as f:
        code_lengths = [len(line.encode("utf-8")) for line in f]
        assert (
            len(code_lengths) == tot
        ), f"number of labels does not match ({len(code_lengths)} != {tot})"
        offsets = list(itertools.accumulate([0] + code_lengths))
        offsets = [(offsets[i], offsets[i + 1]) for i in inds]
    return offsets


def verify_label_lengths(
    audio_sizes,
    audio_rate,
    label_path,
    label_rate,
    inds,
    tot,
    tol=0.1,  # tolerance in seconds
):
    if label_rate < 0:
        logger.info(f"{label_path} is sequence label. skipped")
        return

    with open(label_path) as f:
        lengths = [len(line.rstrip().split()) for line in f]
        assert len(lengths) == tot
        lengths = [lengths[i] for i in inds]
    num_invalid = 0
    for i, ind in enumerate(inds):
        dur_from_audio = audio_sizes[i] / audio_rate
        dur_from_label = lengths[i] / label_rate
        if abs(dur_from_audio - dur_from_label) > tol:
            logger.warning(
                (
                    f"audio and label duration differ too much "
                    f"(|{dur_from_audio} - {dur_from_label}| > {tol}) "
                    f"in line {ind+1} of {label_path}. Check if `label_rate` "
                    f"is correctly set (currently {label_rate}). "
                    f"num. of samples = {audio_sizes[i]}; "
                    f"label length = {lengths[i]}"
                )
            )
            num_invalid += 1
    if num_invalid > 0:
        logger.warning(
            f"total {num_invalid} (audio, label) pairs with mismatched lengths"
        )


class HubertDataset(FairseqDataset):
    def __init__(
        self,
        manifest_path: str,
        sample_rate: float,
        label_paths: List[str],
        label_rates: Union[List[float], float],  # -1 for sequence labels
        pad_list: List[str],
        eos_list: List[str],
        label_processors: Optional[List[Any]] = None,
        max_keep_sample_size: Optional[int] = None,
        min_keep_sample_size: Optional[int] = None,
        max_sample_size: Optional[int] = None,
        shuffle: bool = True,
        pad_audio: bool = False,
        normalize: bool = False,
        store_labels: bool = True,
        random_crop: bool = False,
        single_target: bool = False,
    ):
        self.audio_root, self.audio_names, inds, tot, self.sizes = load_audio(
            manifest_path, max_keep_sample_size, min_keep_sample_size
        )
        self.sample_rate = sample_rate
        self.shuffle = shuffle
        self.random_crop = random_crop

        self.num_labels = len(label_paths)
        self.pad_list = pad_list
        self.eos_list = eos_list
        self.label_processors = label_processors
        self.single_target = single_target
        self.label_rates = (
            [label_rates for _ in range(len(label_paths))]
            if isinstance(label_rates, int)
            else label_rates
        )
        self.store_labels = store_labels
        if store_labels:
            self.label_list = [load_label(p, inds, tot) for p in label_paths]
        else:
            self.label_paths = label_paths
            self.label_offsets_list = [
                load_label_offset(p, inds, tot) for p in label_paths
            ]
        assert (
            label_processors is None
            or len(label_processors) == self.num_labels
        )
        for label_path, label_rate in zip(label_paths, self.label_rates):
            verify_label_lengths(
                self.sizes, sample_rate, label_path, label_rate, inds, tot
            )

        self.max_sample_size = (
            max_sample_size if max_sample_size is not None else sys.maxsize
        )
        self.pad_audio = pad_audio
        self.normalize = normalize
        logger.info(
            f"pad_audio={pad_audio}, random_crop={random_crop}, "
            f"normalize={normalize}, max_sample_size={self.max_sample_size}"
        )

    def get_audio(self, index):
        import soundfile as sf

        wav_path = os.path.join(self.audio_root, self.audio_names[index])
        wav, cur_sample_rate = sf.read(wav_path)
        wav = torch.from_numpy(wav).float()
        wav = self.postprocess(wav, cur_sample_rate)
        return wav

    def get_label(self, index, label_idx):
        if self.store_labels:
            label = self.label_list[label_idx][index]
        else:
            with open(self.label_paths[label_idx]) as f:
                offset_s, offset_e = self.label_offsets_list[label_idx][index]
                f.seek(offset_s)
                label = f.read(offset_e - offset_s)

        if self.label_processors is not None:
            label = self.label_processors[label_idx](label)
        return label

    def get_labels(self, index):
        return [self.get_label(index, i) for i in range(self.num_labels)]

    def __getitem__(self, index):
        wav = self.get_audio(index)
        labels = self.get_labels(index)
        return {"id": index, "source": wav, "label_list": labels}

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

    def crop_to_max_size(self, wav, target_size):
        size = len(wav)
        diff = size - target_size
        if diff <= 0:
            return wav, 0

        start, end = 0, target_size
        if self.random_crop:
            start = np.random.randint(0, diff + 1)
            end = size - diff + start
        return wav[start:end], start

    def collater(self, samples):
        # target = max(sizes) -> random_crop not used
        # target = max_sample_size -> random_crop used for long
        samples = [s for s in samples if s["source"] is not None]
        if len(samples) == 0:
            return {}

        audios = [s["source"] for s in samples]
        audio_sizes = [len(s) for s in audios]
        if self.pad_audio:
            audio_size = min(max(audio_sizes), self.max_sample_size)
        else:
            audio_size = min(min(audio_sizes), self.max_sample_size)
        collated_audios, padding_mask, audio_starts = self.collater_audio(
            audios, audio_size
        )

        targets_by_label = [
            [s["label_list"][i] for s in samples]
            for i in range(self.num_labels)
        ]
        targets_list, lengths_list, ntokens_list = self.collater_label(
            targets_by_label, audio_size, audio_starts
        )

        net_input = {"source": collated_audios, "padding_mask": padding_mask}
        batch = {
            "id": torch.LongTensor([s["id"] for s in samples]),
            "net_input": net_input,
        }

        if self.single_target:
            batch["target_lengths"] = lengths_list[0]
            batch["ntokens"] = ntokens_list[0]
            batch["target"] = targets_list[0]
        else:
            batch["target_lengths_list"] = lengths_list
            batch["ntokens_list"] = ntokens_list
            batch["target_list"] = targets_list
        return batch

    def collater_audio(self, audios, audio_size):
        collated_audios = audios[0].new_zeros(len(audios), audio_size)
        padding_mask = (
            torch.BoolTensor(collated_audios.shape).fill_(False)
            # if self.pad_audio else None
        )
        audio_starts = [0 for _ in audios]
        for i, audio in enumerate(audios):
            diff = len(audio) - audio_size
            if diff == 0:
                collated_audios[i] = audio
            elif diff < 0:
                assert self.pad_audio
                collated_audios[i] = torch.cat(
                    [audio, audio.new_full((-diff,), 0.0)]
                )
                padding_mask[i, diff:] = True
            else:
                collated_audios[i], audio_starts[i] = self.crop_to_max_size(
                    audio, audio_size
                )
        return collated_audios, padding_mask, audio_starts

    def collater_frm_label(
        self, targets, audio_size, audio_starts, label_rate, pad
    ):
        assert label_rate > 0
        s2f = label_rate / self.sample_rate
        frm_starts = [int(round(s * s2f)) for s in audio_starts]
        frm_size = int(round(audio_size * s2f))
        if not self.pad_audio:
            rem_size = [len(t) - s for t, s in zip(targets, frm_starts)]
            frm_size = min(frm_size, *rem_size)
        targets = [t[s: s + frm_size] for t, s in zip(targets, frm_starts)]
        logger.debug(f"audio_starts={audio_starts}")
        logger.debug(f"frame_starts={frm_starts}")
        logger.debug(f"frame_size={frm_size}")

        lengths = torch.LongTensor([len(t) for t in targets])
        ntokens = lengths.sum().item()
        targets = data_utils.collate_tokens(
            targets, pad_idx=pad, left_pad=False
        )
        return targets, lengths, ntokens

    def collater_seq_label(self, targets, pad):
        lengths = torch.LongTensor([len(t) for t in targets])
        ntokens = lengths.sum().item()
        targets = data_utils.collate_tokens(
            targets, pad_idx=pad, left_pad=False
        )
        return targets, lengths, ntokens

    def collater_label(self, targets_by_label, audio_size, audio_starts):
        targets_list, lengths_list, ntokens_list = [], [], []
        itr = zip(targets_by_label, self.label_rates, self.pad_list)
        for targets, label_rate, pad in itr:
            if label_rate == -1:
                targets, lengths, ntokens = self.collater_seq_label(
                    targets, pad
                )
            else:
                targets, lengths, ntokens = self.collater_frm_label(
                    targets, audio_size, audio_starts, label_rate, pad
                )
            targets_list.append(targets)
            lengths_list.append(lengths)
            ntokens_list.append(ntokens)
        return targets_list, lengths_list, ntokens_list

    def num_tokens(self, index):
        return self.size(index)

    def size(self, index):
        if self.pad_audio:
            return self.sizes[index]
        return min(self.sizes[index], self.max_sample_size)

    def ordered_indices(self):
        if self.shuffle:
            order = [np.random.permutation(len(self))]
        else:
            order = [np.arange(len(self))]

        order.append(self.sizes)
        return np.lexsort(order)[::-1]

    def postprocess(self, wav, cur_sample_rate):
        if wav.dim() == 2:
            wav = wav.mean(-1)
        assert wav.dim() == 1, wav.dim()

        if cur_sample_rate != self.sample_rate:
            raise Exception(f"sr {cur_sample_rate} != {self.sample_rate}")

        if self.normalize:
            with torch.no_grad():
                wav = F.layer_norm(wav, wav.shape)
        return wav
