import math
from pathlib import Path
from typing import Dict, Iterator, List, Optional, Tuple, Union

import numpy as np
import torch
import torch.distributed as dist
import torchaudio
from torch import Tensor
from torch.utils.data import BatchSampler, Dataset, DistributedSampler

from ..lightning_modules import Batch


class BucketizeBatchSampler(BatchSampler):
    """Buketized BatchSampler for sequential data with different lengths to reduce number of paddings.

    Args:
        lengths (List[int]): The lengths of the samples in the dataset.
        num_buckets (int): The number of buckets to split the data samples.
        min_len (int, optional): The minimum sample lengths to keep.
            (Default: 0)
        max_len (int or None, optional): The maximum sample lengths to keep. Inferred if not provided.
            (Default ``None``)
        max_token_count (int or None, optional): The max number of tokens in one mini-batch.
            (Default: ``None``)
        batch_size (int or None, optional): The number of samples in one mini-batch.
            (Default: ``None``)
        shuffle (bool, optional): Whether to shuffle buckets for non-monotonic length sampling.
            (Default: True)
        seed (int, optional): The seed for initialzing RNG. Only used when `shuffle` is True. (Default: 0)
        drop_last (bool, optional): If ``True``, the sampler will drop the last batch if
            its size would be less than ``batch_size``
            (Default: False)

    Note:
        ``max_token_count`` and ``batch_size`` are mutually exclusive. Only one argument of the two
        should have value.

    Note:
        ``drop_last`` is only valid when ``batch_size`` argument is given.

    Note:
        if ``shuffle`` is True, it will only shuffle the data once. Please set ``reload_dataloaders_every_n_epochs=1``
        in pytorch_lightning Trainer and set ``seed`` to ``self.trainer.current_epoch`` to enable shuffling every epoch.
    """

    def __init__(
        self,
        lengths: List[int],
        num_buckets: int,
        min_len: int = 0,
        max_len: Optional[int] = None,
        max_token_count: Optional[int] = None,
        batch_size: Optional[int] = None,
        shuffle: bool = True,
        seed: int = 0,
        drop_last: bool = False,
    ) -> None:
        if max_len is None:
            max_len = max(lengths)

        if not (0 <= min_len <= max_len):
            raise AssertionError("``min_len`` should be non-negative and smaller than ``max_len``")
        if max_token_count is not None and batch_size is not None:
            raise AssertionError("The ``max_token_count`` and ``batch_size`` can't be both set.")
        if max_token_count is None and batch_size is None:
            raise AssertionError("One of ``max_token_count`` or ``batch_size`` must be set.")
        if max_token_count is not None:
            assert (
                max_len <= max_token_count
            ), "The  ``max_token_count`` must be greater than or equal to the maximum value of ``lengths``."
        # Filter out samples which are outside the bounds of [min_len, max_len]
        filtered_length_idx = [(length, i) for i, length in enumerate(lengths) if min_len <= length <= max_len]
        if len(filtered_length_idx) == 0:
            raise AssertionError("``lengths`` cannot be empty after filtering.")
        sorted_filtered_length_idx = sorted(filtered_length_idx, key=lambda x: x[0])
        self.lengths = [e[0] for e in sorted_filtered_length_idx]
        self.indices = [e[1] for e in sorted_filtered_length_idx]
        self.max_token_count = max_token_count
        self.batch_size = batch_size
        self.shuffle = shuffle
        self.seed = seed
        if self.shuffle:
            self.g = torch.Generator()
            self.g.manual_seed(self.seed)
        self.drop_last = drop_last
        self.buckets = self._get_buckets(self.lengths, num_buckets, min_len, max_len)
        self._update_iter_list()

    def _get_buckets(self, lengths: List[int], num_buckets: int, min_len: int, max_len: int) -> Dict[int, Tensor]:
        """Generate buckets based on the dataset.
        Args:
            lengths (List[int]): The lengths of the samples in the dataset.
            num_buckets (int): The number of buckets.
            min_len (int): The lower bound of the evenly spaced length intervals to determine bucket width.
            max_len (int): The upper bound of the evenly spaced length intervals to determine bucket width.

        Returns:
            (dict[int, Tensor]): A dictionary in which the key is the bucket index, the value is
                the Tensor of corresponding sample indices.
        """
        buckets = {}
        boundaries = torch.linspace(min_len - 1, max_len + 1, num_buckets + 1)
        bucket_ids = torch.bucketize(torch.tensor(lengths), boundaries)
        for i in range(bucket_ids.size(0)):
            bucket_id = int(bucket_ids[i])
            if bucket_id in buckets:
                buckets[bucket_id].append(i)
            else:
                buckets[bucket_id] = [i]
        for k in buckets:
            buckets[k] = torch.as_tensor(buckets[k], dtype=torch.int)
        buckets = {k: v for k, v in sorted(buckets.items())}
        return buckets

    def _update_iter_list(self) -> None:
        if self.shuffle:
            for k in self.buckets:
                self.buckets[k] = self.buckets[k][torch.randperm(self.buckets[k].size(0), generator=self.g)]
        self.iter_list = []
        total_len = 0
        batch = []
        max_batch_size = self.max_token_count if self.max_token_count else self.batch_size
        for k in self.buckets:
            for i in range(self.buckets[k].size(0)):
                index = int(self.buckets[k][i])
                sample_length = self.lengths[index] if self.max_token_count else 1
                if total_len + sample_length <= max_batch_size:
                    batch.append(self.indices[index])
                    total_len += sample_length
                else:
                    self.iter_list.append(batch)
                    batch = [self.indices[index]]
                    total_len = sample_length
        if len(batch) > 0 and (self.max_token_count or not self.drop_last):
            self.iter_list.append(batch)

    def __iter__(self) -> Iterator[List[int]]:
        return iter(self.iter_list)

    def __len__(self):
        if self.batch_size or (self.max_token_count and not self.shuffle):
            return len(self.iter_list)


class DistributedBatchSampler(DistributedSampler):
    """`BucketizeBatchSampler` wrapper that distributes across each processor.

    Args:
        batch_sampler (BucketizeBatchSampler): the initialized bucketize batch sampler.
        num_replicas (int, optional): Number of processes participating in
            distributed training. By default, :attr:`world_size` is retrieved from the
            current distributed group.
        rank (int, optional): Rank of the current process within :attr:`num_replicas`.
            By default, :attr:`rank` is retrieved from the current distributed
            group.
        shuffle (bool, optional): if ``True``, the list of batch indices will be shuffled.
            (Default: ``True``)
        seed (int, optional): random seed used to shuffle the batch_sampler if
            :attr:`shuffle=True`. This number should be identical across all
            processes in the distributed group. (Default: ``0``)
        drop_last (bool, optional): if ``True``, then the sampler will drop the
            tail of the data to make it evenly divisible across the number of
            replicas. If ``False``, the sampler will add extra indices to make
            the data evenly divisible across the replicas. (Default: ``False``)

    Note:
        if ``shuffle`` is True, it will only shuffle the data once. Please set ``reload_dataloaders_every_n_epochs=1``
        in pytorch_lightning Trainer, and set `sampler.set_epoch(self.current_epoch)` before DataLoader initialization
        in `train_dataloader` method to enable shuffling every epoch.
    """

    def __init__(
        self,
        batch_sampler: BucketizeBatchSampler,
        num_replicas: Optional[int] = None,
        rank: Optional[int] = None,
        shuffle: bool = True,
        seed: int = 0,
        drop_last: bool = False,
    ) -> None:
        self.batch_sampler = batch_sampler
        if num_replicas is None:
            if not dist.is_available():
                raise RuntimeError("Requires distributed package to be available")
            num_replicas = dist.get_world_size()
        if rank is None:
            if not dist.is_available():
                raise RuntimeError("Requires distributed package to be available")
            rank = dist.get_rank()
        self.num_replicas = num_replicas
        self.rank = rank
        self.shuffle = shuffle
        self.epoch = 0
        self.seed = seed
        self.drop_last = drop_last
        self.shuffle = shuffle
        indices = self.batch_sampler.iter_list
        if self.drop_last and len(indices) % self.num_replicas != 0:
            # Split to nearest available length that is evenly divisible.
            # This is to ensure each rank receives the same amount of data when
            # using this Sampler.
            self.num_samples = math.ceil((len(indices) - self.num_replicas) / self.num_replicas)
        else:
            self.num_samples = math.ceil(len(indices) / self.num_replicas)

    def __iter__(self):
        if self.shuffle:
            g = torch.Generator()
            g.manual_seed(self.seed + self.epoch)
            perm = torch.randperm(len(self.batch_sampler.iter_list), generator=g).tolist()
            indices = [self.batch_sampler.iter_list[i] for i in perm]
        else:
            indices = self.batch_sampler.iter_list
        if self.drop_last:
            self.total_size = len(indices) - len(indices) % self.num_replicas
        else:
            padding_size = self.num_replicas - len(indices) % self.num_replicas
            indices += indices[:padding_size]
            self.total_size = len(indices)
        self.num_samples = self.total_size // self.num_replicas
        self.subset = indices[self.rank : self.total_size : self.num_replicas]
        assert len(self.subset) == self.num_samples

        return iter(self.subset)

    def __len__(self):
        return self.num_samples


class HuBERTDataSet(Dataset):
    """Create a Dataset for HuBERT model training and fine-tuning.

    Args:
        exp_dir (str or Path): The root directory of the ``.tsv`` file list.
        dataset (str): The dataset for training. Options: [``librispeech``, ``librilight``].
        subset (str): The subset of the dataset. Options: [``train``, ``valid``].
    """

    def __init__(
        self,
        exp_dir: Union[str, Path],
        dataset: str,
        subset: str,
    ) -> None:
        self.exp_dir = Path(exp_dir)
        tsv_dir = self.exp_dir / "tsv"
        label_dir = self.exp_dir / "label"
        f_list, ind_list, len_list = self._get_lists(tsv_dir, dataset, subset)
        self.f_list, self.ind_list, self.len_list = f_list, ind_list, len_list
        self.labels = self._load_labels(label_dir, dataset, subset)

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

    def _get_lists(
        self,
        tsv_dir: Path,
        dataset: str,
        subset: str,
    ) -> Tuple[List[Path], List[int], List[int]]:
        """Get the list of paths for iteration.
        Args:
            tsv_dir (Path): The root directory of the ``.tsv`` file list.
            dataset (str): The dataset for training. Options: [``librispeech``, ``librilight``].
            subset (str): The subset of the dataset. Options: [``train``, ``valid``].

        Returns:
            (numpy.array) List of file paths.
            (numpy.array) List of indices.
            (numpy.array) List of waveform lengths.
        """
        f_ind_len_list = []
        with open(tsv_dir / f"{dataset}_{subset}.tsv") as f:
            root = f.readline().rstrip()
            for index, line in enumerate(f):
                path, nsample = line.split("\t")
                path = f"{root}/{path}"
                nsample = int(nsample)
                f_ind_len_list.append((path, index, nsample))
        f_list, ind_list, len_list = [], [], []
        for ele in f_ind_len_list:
            f_list.append(ele[0])
            ind_list.append(ele[1])
            len_list.append(ele[2])
        return np.asarray(f_list), np.asarray(ind_list), np.asarray(len_list)

    def _load_audio(self, index: int) -> Tensor:
        """Load waveform given the sample index of the dataset.
        Args:
            index (int): The sample index.

        Returns:
            (Tensor): The corresponding waveform Tensor.
        """
        wav_path = self.f_list[index]
        waveform, sample_rate = torchaudio.load(wav_path)
        assert waveform.shape[1] == self.len_list[index]
        return waveform

    def _load_labels(self, label_dir: Path, dataset: str, subset: str) -> np.array:
        """Load all labels to memory into a numpy array.
        Args:
            label_dir (Path): The directory that contains the label file.
            dataset (str): The dataset for training. Options: [``librispeech``, ``librilight``].
            subset (str): The subset of the dataset. Options: [``train``, ``valid``].

        Returns:
            (np.array): The numpy arrary that contains the labels for each audio file.
        """
        with open(label_dir / f"label_{subset}.pt") as f:
            labels = [line.rstrip() for line in f]
            labels = [labels[i] for i in self.ind_list]
        return np.asarray(labels, dtype=np.string_)

    def __getitem__(self, index):
        waveform = self._load_audio(index)
        length = waveform.shape[1]
        label = [int(ele) for ele in self.labels[index].split()]
        label = torch.tensor(label)
        return (waveform, label, length)


def _crop_audio_label(
    waveform: Tensor,
    label: Optional[Tensor],
    length: Tensor,
    num_frames: int,
    rand_crop: bool,
) -> Tuple[Tensor, Optional[Tensor], Tensor]:
    """Collate the audio and label at the same time.
    Args:
        waveform (Tensor): The waveform Tensor with dimensions `(1, time)`.
        label (Tensor, optional): The label Tensor with dimensions `(1, seq)`.
        length (Tensor): The length Tensor with dimension `(1,)`.
        num_frames (int): The final length of the waveform.
        rand_crop (bool): if ``rand_crop`` is True, the starting index of the
            waveform and label is random if the length is longer than the minimum
            length in the mini-batch.

    Returns:
        (Tuple(Tensor, (Tensor, optional), Tensor)): Returns the Tensors for the waveform,
            label, and the waveform length.
    """
    kernel_size = 25
    stride = 20
    sample_rate = 16  # 16 per millisecond
    frame_offset = 0
    waveform = waveform[0]
    if waveform.size(0) > num_frames and rand_crop:
        diff = waveform.size(0) - num_frames
        frame_offset = torch.randint(diff, size=(1,))
    elif waveform.size(0) < num_frames:
        num_frames = waveform.size(0)

    if label is not None:
        label_offset = max(
            math.floor((frame_offset - kernel_size * sample_rate) / (stride * sample_rate)) + 1,
            0,
        )
        num_label = math.floor((num_frames - kernel_size * sample_rate) / (stride * sample_rate)) + 1
        label = label[label_offset : label_offset + num_label]
    waveform = waveform[frame_offset : frame_offset + num_frames]
    length = num_frames

    return waveform, label, length


class CollateFnHubert:
    """The collate class for HuBERT pre-training and fine-tuning.
    Args:
        feature_type (str): The type of features for KMeans clustering.
            Options: [``mfcc``, ``hubert``].
        pad (bool): If ``True``, the waveforms and labels will be padded to the
            max length in the mini-batch. If ``pad`` is False, the waveforms
            and labels will be cropped to the minimum length in the mini-batch.
            (Default: False)
        rand_crop (bool): if ``True``, the starting index of the waveform
            and label is random if the length is longer than the minimum
            length in the mini-batch.
    """

    def __init__(
        self,
        feature_type: str,
        pad: bool = False,
        rand_crop: bool = True,
    ) -> None:
        self.feature_type = feature_type
        self.pad = pad
        self.rand_crop = rand_crop

    def __call__(self, batch: List[Tuple[Tensor, Tensor, int]]) -> Dict:
        """
        Args:
            batch (List[Tuple(Tensor, Tensor, int)]):
                The list of tuples that contains the waveforms, labels, and audio lengths.

        Returns:
            Dictionary
                "input": Tuple of waveforms and lengths.
                    waveforms Tensor with dimensions `(batch, time)`.
                    lengths Tensor with dimension `(batch,)`.
                "label": Tuple of label Tensor with dimensions `(batch, seq)`.
        """
        if self.pad:
            num_frames = max([sample[0].shape[1] for sample in batch])
        else:
            num_frames = min([sample[0].shape[1] for sample in batch])
        waveforms, labels, lengths = [], [], []
        for sample in batch:
            waveform, label, length = sample
            # The MFCC feature is 10ms per frame, while the HuBERT's transformer output
            # is 20ms per frame. Downsample the KMeans label if it's generated by MFCC features.
            if self.feature_type == "mfcc":
                label = label[::2]
            waveform, label, length = _crop_audio_label(waveform, label, length, num_frames, self.rand_crop)
            waveforms.append(waveform)
            lengths.append(length)
            labels.append(label)
        # make sure the shapes are the same if not apply zero-padding
        if not self.pad:
            assert all(
                [waveform.shape[0] == waveforms[0].shape[0] for waveform in waveforms]
            ), "The dimensions of the waveforms should be identical in the same batch."
            assert all(
                [label.shape[0] == labels[0].shape[0] for label in labels]
            ), "The dimensions of the labels should be identical in the same batch."
        waveforms = torch.nn.utils.rnn.pad_sequence(waveforms, batch_first=True)
        labels = torch.nn.utils.rnn.pad_sequence(labels, batch_first=True)
        lengths = torch.tensor(lengths)
        batch = Batch((waveforms, labels, lengths), (labels,))
        return batch


class CollateFnWav2Vec2:
    """The collate class for Wav2Vec2 pre-training and fine-tuning.
    Args:
        pad (bool): If ``True``, the waveforms and labels will be padded to the
            max length in the mini-batch. If ``pad`` is False, the waveforms
            and labels will be cropped to the minimum length in the mini-batch.
            (Default: False)
        rand_crop (bool): if ``True``, the starting index of the waveform
            and label is random if the length is longer than the minimum
            length in the mini-batch.
    """

    def __init__(
        self,
        pad: bool = False,
        rand_crop: bool = True,
    ) -> None:
        self.pad = pad
        self.rand_crop = rand_crop

    def __call__(self, batch: List[Tuple[Tensor, Tensor, int]]) -> Dict:
        """
        Args:
            batch (List[Tuple(Tensor, Tensor, int)]):
                The list of tuples that contains the waveforms, labels, and audio lengths.

        Returns:
            Dictionary
                "input": Tuple of waveforms and lengths.
                    waveforms Tensor with dimensions `(batch, time)`.
                    lengths Tensor with dimension `(batch,)`.
                "label": None
        """
        if self.pad:
            num_frames = max([sample[0].shape[1] for sample in batch])
        else:
            num_frames = min([sample[0].shape[1] for sample in batch])
        waveforms, lengths = [], []
        for sample in batch:
            waveform, length = sample
            waveform, _, length = _crop_audio_label(waveform, None, length, num_frames, self.rand_crop)
            waveforms.append(waveform)
            lengths.append(length)
        # make sure the shapes are the same if not apply zero-padding
        if not self.pad:
            assert all(
                [waveform.shape[0] == waveforms[0].shape[0] for waveform in waveforms]
            ), "The dimensions of the waveforms should be identical in the same batch."
        waveforms = torch.nn.utils.rnn.pad_sequence(waveforms, batch_first=True)
        lengths = torch.tensor(lengths)
        batch = Batch((waveforms, lengths), (None,))
        return batch
