# 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.

from dataclasses import dataclass
import logging
from pathlib import Path
from typing import Dict, List, Optional, Tuple

import torch
from fairseq.data import (
    ConcatDataset,
    data_utils as fairseq_data_utils,
    Dictionary,
)
from fairseq.data.audio.data_cfg import S2SDataConfig
from fairseq.data.audio.speech_to_text_dataset import (
    _collate_frames,
    get_features_or_waveform,
    SpeechToTextDataset,
    SpeechToTextDatasetCreator,
)


logger = logging.getLogger(__name__)


@dataclass
class SpeechToSpeechDatasetItem(object):
    index: int
    source: torch.Tensor
    target: Optional[torch.Tensor] = None
    target_speaker: Optional[torch.Tensor] = None


class SpeechToSpeechDataset(SpeechToTextDataset):
    def __init__(
        self,
        split: str,
        is_train_split: bool,
        data_cfg: S2SDataConfig,
        src_audio_paths: List[str],
        src_n_frames: List[int],
        tgt_audio_paths: List[str],
        tgt_n_frames: List[int],
        ids: Optional[List[str]] = None,
        target_is_code: bool = False,
        tgt_dict: Dictionary = None,
        n_frames_per_step: int = 1,
    ):
        tgt_texts = tgt_audio_paths if target_is_code else None
        super().__init__(
            split,
            is_train_split,
            data_cfg,
            src_audio_paths,
            src_n_frames,
            ids=ids,
            tgt_dict=tgt_dict,
            tgt_texts=tgt_texts,
            n_frames_per_step=n_frames_per_step,
        )

        self.tgt_audio_paths = tgt_audio_paths
        self.tgt_lens = [t // self.n_frames_per_step for t in tgt_n_frames]

        assert not target_is_code or tgt_dict is not None
        self.target_is_code = target_is_code

        assert len(tgt_audio_paths) == self.n_samples
        assert len(tgt_n_frames) == self.n_samples

        self.tgt_speakers = None
        if self.cfg.target_speaker_embed:
            samples = SpeechToTextDatasetCreator._load_samples_from_tsv(
                self.cfg.target_speaker_embed, split
            )
            spk_emb_dict = {s["id"]: s["speaker_embed"] for s in samples}
            self.tgt_speakers = [spk_emb_dict[id] for id in self.ids]
            assert len(self.tgt_speakers) == self.n_samples

        logger.info(self.__repr__())

    def pack_units(self, input: torch.Tensor) -> torch.Tensor:
        if self.n_frames_per_step <= 1:
            return input

        offset = 4
        vocab_size = (
            len(self.tgt_dict) - offset
        )  # remove offset from <bos>, <pad>, <eos>, <unk>, which is specific to fairseq dictionary

        assert input.dim() == 1
        stacked_input = (
            input[:-1].view(-1, self.n_frames_per_step) - offset
        )  # remove <eos>
        scale = [
            pow(vocab_size, self.n_frames_per_step - 1 - i)
            for i in range(self.n_frames_per_step)
        ]
        scale = torch.LongTensor(scale).squeeze(0)
        res = input.new((len(input) - 1) // self.n_frames_per_step + 1).fill_(input[-1])
        res[:-1] = (stacked_input * scale).sum(dim=1) + offset

        return res

    def __getitem__(self, index: int) -> SpeechToSpeechDatasetItem:
        source = self._get_source_audio(index)

        if not self.target_is_code:
            target = get_features_or_waveform(self.tgt_audio_paths[index])
            target = torch.from_numpy(target).float()
            target = self.pack_frames(target)
        else:
            target = self.tgt_dict.encode_line(
                self.tgt_audio_paths[index],
                add_if_not_exist=False,
                append_eos=True,
            ).long()
            if self.n_frames_per_step > 1:
                n_tgt_frame = target.size(0) - 1  # exclude <eos>
                keep_n_tgt_frame = n_tgt_frame - n_tgt_frame % self.n_frames_per_step
                target = torch.cat(
                    (
                        target[:keep_n_tgt_frame],
                        target.new_full((1,), self.tgt_dict.eos()),
                    ),
                    dim=0,
                )

        if self.tgt_speakers:
            tgt_spk = get_features_or_waveform(self.tgt_speakers[index])
            tgt_spk = torch.from_numpy(tgt_spk).float()
        else:
            tgt_spk = torch.FloatTensor([])

        return SpeechToSpeechDatasetItem(
            index=index, source=source, target=target, target_speaker=tgt_spk
        )

    def _collate_target(self, samples: List[SpeechToSpeechDatasetItem]) -> torch.Tensor:
        if self.target_is_code:
            target = fairseq_data_utils.collate_tokens(
                [x.target for x in samples],
                self.tgt_dict.pad(),
                self.tgt_dict.eos(),
                left_pad=False,
                move_eos_to_beginning=False,
            )
            # convert stacked units to a single id
            pack_targets = [self.pack_units(x.target) for x in samples]
            prev_output_tokens = fairseq_data_utils.collate_tokens(
                pack_targets,
                self.tgt_dict.pad(),
                self.tgt_dict.eos(),
                left_pad=False,
                move_eos_to_beginning=True,
            )
            target_lengths = torch.tensor(
                [x.size(0) for x in pack_targets], dtype=torch.long
            )
        else:
            target = _collate_frames([x.target for x in samples], is_audio_input=False)
            bsz, _, d = target.size()
            prev_output_tokens = torch.cat(
                (target.new_full((bsz, 1, d), 0.0), target[:, :-1, :]), dim=1
            )
            target_lengths = torch.tensor(
                [x.target.size(0) for x in samples], dtype=torch.long
            )

        return target, prev_output_tokens, target_lengths

    def collater(
        self, samples: List[SpeechToSpeechDatasetItem], return_order: bool = False
    ) -> Dict:
        if len(samples) == 0:
            return {}
        indices = torch.tensor([x.index for x in samples], dtype=torch.long)
        frames = _collate_frames([x.source for x in samples], self.cfg.use_audio_input)
        # sort samples by descending number of frames
        n_frames = torch.tensor([x.source.size(0) for x in samples], dtype=torch.long)
        n_frames, order = n_frames.sort(descending=True)
        indices = indices.index_select(0, order)
        frames = frames.index_select(0, order)

        target, prev_output_tokens, target_lengths = self._collate_target(samples)
        target = target.index_select(0, order)
        target_lengths = target_lengths.index_select(0, order)
        prev_output_tokens = prev_output_tokens.index_select(0, order)
        ntokens = sum(x.target.size(0) for x in samples)

        tgt_speakers = None
        if self.cfg.target_speaker_embed:
            tgt_speakers = _collate_frames(
                [x.target_speaker for x in samples], is_audio_input=True
            ).index_select(0, order)

        net_input = {
            "src_tokens": frames,
            "src_lengths": n_frames,
            "prev_output_tokens": prev_output_tokens,
            "tgt_speaker": tgt_speakers,  # TODO: unify "speaker" and "tgt_speaker"
        }
        out = {
            "id": indices,
            "net_input": net_input,
            "speaker": tgt_speakers,  # to support Tacotron2 loss for speech-to-spectrogram model
            "target": target,
            "target_lengths": target_lengths,
            "ntokens": ntokens,
            "nsentences": len(samples),
        }
        if return_order:
            out["order"] = order
        return out


class TextTargetMultitaskData(object):
    # mandatory columns
    KEY_ID, KEY_TEXT = "id", "tgt_text"

    def __init__(self, args, split, tgt_dict):
        samples = SpeechToTextDatasetCreator._load_samples_from_tsv(args.data, split)
        self.data = {s[self.KEY_ID]: s[self.KEY_TEXT] for s in samples}
        self.dict = tgt_dict
        self.append_eos = args.decoder_type != "ctc"

    def get(self, sample_id):
        if sample_id in self.data:
            return self.dict.encode_line(
                self.data[sample_id],
                add_if_not_exist=False,
                append_eos=self.append_eos,
            )
        else:
            logger.warning(f"no target for {sample_id}")
            return torch.IntTensor([])

    def collater(self, samples: List[torch.Tensor]) -> torch.Tensor:
        out = fairseq_data_utils.collate_tokens(
            samples,
            self.dict.pad(),
            self.dict.eos(),
            left_pad=False,
            move_eos_to_beginning=False,
        ).long()

        prev_out = fairseq_data_utils.collate_tokens(
            samples,
            self.dict.pad(),
            self.dict.eos(),
            left_pad=False,
            move_eos_to_beginning=True,
        ).long()

        target_lengths = torch.tensor([t.size(0) for t in samples], dtype=torch.long)
        ntokens = sum(t.size(0) for t in samples)

        output = {
            "prev_output_tokens": prev_out,
            "target": out,
            "target_lengths": target_lengths,
            "ntokens": ntokens,
        }

        return output


class SpeechToSpeechMultitaskDataset(SpeechToSpeechDataset):
    def __init__(self, *argv):
        super().__init__(*argv)
        self.multitask_data = {}

    def add_multitask_dataset(self, task_name, task_data):
        self.multitask_data[task_name] = task_data

    def __getitem__(
        self, index: int
    ) -> Tuple[SpeechToSpeechDatasetItem, Dict[str, torch.Tensor]]:
        s2s_data = super().__getitem__(index)

        multitask_target = {}
        sample_id = self.ids[index]
        for task_name, task_dataset in self.multitask_data.items():
            multitask_target[task_name] = task_dataset.get(sample_id)

        return s2s_data, multitask_target

    def collater(
        self, samples: List[Tuple[SpeechToSpeechDatasetItem, Dict[str, torch.Tensor]]]
    ) -> Dict:
        if len(samples) == 0:
            return {}

        out = super().collater([s for s, _ in samples], return_order=True)
        order = out["order"]
        del out["order"]

        for task_name, task_dataset in self.multitask_data.items():
            if "multitask" not in out:
                out["multitask"] = {}
            d = [s[task_name] for _, s in samples]
            task_target = task_dataset.collater(d)
            out["multitask"][task_name] = {
                "target": task_target["target"].index_select(0, order),
                "target_lengths": task_target["target_lengths"].index_select(0, order),
                "ntokens": task_target["ntokens"],
            }
            out["multitask"][task_name]["net_input"] = {
                "prev_output_tokens": task_target["prev_output_tokens"].index_select(
                    0, order
                ),
            }

        return out


class SpeechToSpeechDatasetCreator(object):
    # mandatory columns
    KEY_ID, KEY_SRC_AUDIO, KEY_SRC_N_FRAMES = "id", "src_audio", "src_n_frames"
    KEY_TGT_AUDIO, KEY_TGT_N_FRAMES = "tgt_audio", "tgt_n_frames"

    @classmethod
    def _from_list(
        cls,
        split_name: str,
        is_train_split,
        samples: List[Dict],
        data_cfg: S2SDataConfig,
        target_is_code: bool = False,
        target_dictionary: Dictionary = None,
        n_frames_per_step: int = 1,
        multitask: Optional[Dict] = None,
    ) -> SpeechToSpeechDataset:
        audio_root = Path(data_cfg.audio_root)
        ids = [s[cls.KEY_ID] for s in samples]
        src_audio_paths = [
            (audio_root / s[cls.KEY_SRC_AUDIO]).as_posix() for s in samples
        ]
        tgt_audio_paths = [
            s[cls.KEY_TGT_AUDIO]
            if target_is_code
            else (audio_root / s[cls.KEY_TGT_AUDIO]).as_posix()
            for s in samples
        ]
        src_n_frames = [int(s[cls.KEY_SRC_N_FRAMES]) for s in samples]
        tgt_n_frames = [int(s[cls.KEY_TGT_N_FRAMES]) for s in samples]

        has_multitask = len(multitask) > 0
        dataset_cls = (
            SpeechToSpeechMultitaskDataset if has_multitask else SpeechToSpeechDataset
        )

        ds = dataset_cls(
            split_name,
            is_train_split,
            data_cfg,
            src_audio_paths,
            src_n_frames,
            tgt_audio_paths,
            tgt_n_frames,
            ids,
            target_is_code,
            target_dictionary,
            n_frames_per_step,
        )

        if has_multitask:
            for task_name, task_obj in multitask.items():
                task_data = TextTargetMultitaskData(
                    task_obj.args, split_name, task_obj.target_dictionary
                )
                ds.add_multitask_dataset(task_name, task_data)
        return ds

    @classmethod
    def from_tsv(
        cls,
        root: str,
        data_cfg: S2SDataConfig,
        splits: str,
        is_train_split: bool,
        epoch: int,
        seed: int,
        target_is_code: bool = False,
        target_dictionary: Dictionary = None,
        n_frames_per_step: int = 1,
        multitask: Optional[Dict] = None,
    ) -> SpeechToSpeechDataset:
        datasets = []
        for split in splits.split(","):
            samples = SpeechToTextDatasetCreator._load_samples_from_tsv(root, split)
            ds = cls._from_list(
                split,
                is_train_split,
                samples,
                data_cfg,
                target_is_code,
                target_dictionary,
                n_frames_per_step,
                multitask,
            )
            datasets.append(ds)
        return ConcatDataset(datasets) if len(datasets) > 1 else datasets[0]
