import os

import numpy as np

from fairseq.data import FairseqDataset
from .collaters import Seq2SeqCollater


class W2VAsrDataset(FairseqDataset):
    """
    A dataset representing speech and corresponding transcription.

    Args:
        emb_paths: (List[str]): A list of str with paths to audio embedding files.
        emb_num_tokens (List[int]): A list of int containing the number of audio embedding tokens per file.
        tgt (List[torch.LongTensor]): A list of LongTensors containing the indices
            of target transcriptions.
        tgt_dict (~fairseq.data.Dictionary): target vocabulary.
        ids (List[str]): A list of utterance IDs.
        speakers (List[str]): A list of speakers corresponding to utterances.
    """

    def __init__(
        self, emb_paths, emb_num_tokens, tgt, tgt_dict, ids, speakers
    ):
        assert len(emb_paths) > 0
        assert len(emb_paths) == len(emb_num_tokens)
        assert len(emb_paths) == len(tgt)
        assert len(emb_paths) == len(ids)
        assert len(emb_paths) == len(speakers)
        self.emb_paths = emb_paths
        self.emb_num_tokens = emb_num_tokens
        self.tgt_dict = tgt_dict
        self.tgt = tgt
        self.ids = ids
        self.speakers = speakers
        self.s2s_collater = Seq2SeqCollater(
            0, 1, pad_index=self.tgt_dict.pad(),
            eos_index=self.tgt_dict.eos(), move_eos_to_beginning=True
        )

    def __getitem__(self, index):
        import h5py
        tgt_item = self.tgt[index] if self.tgt is not None else None
        path = self.emb_paths[index]
        if not os.path.exists(path):
            raise FileNotFoundError("Audio file not found: {}".format(path))
        with h5py.File(self.fname, "r") as f:
            features = f["features"]
        return {"id": index, "data": [features, tgt_item]}

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

    def collater(self, samples):
        """Merge a list of samples to form a mini-batch.

        Args:
            samples (List[int]): sample indices to collate

        Returns:
            dict: a mini-batch suitable for forwarding with a Model
        """
        return self.s2s_collater.collate(samples)

    def num_tokens(self, index):
        return self.emb_num_tokens[index]

    def size(self, index):
        """Return an example's size as a float or tuple. This value is used when
        filtering a dataset with ``--max-positions``."""
        return (
            self.self.emb_num_tokens[index],
            len(self.tgt[index]) if self.tgt is not None else 0,
        )

    def ordered_indices(self):
        """Return an ordered list of indices. Batches will be constructed based
        on this order."""
        return np.arange(len(self))
import os

import numpy as np

from fairseq.data import FairseqDataset
from .collaters import Seq2SeqCollater


class W2VAsrDataset(FairseqDataset):
    """
    A dataset representing speech and corresponding transcription.

    Args:
        emb_paths: (List[str]): A list of str with paths to audio embedding files.
        emb_num_tokens (List[int]): A list of int containing the number of audio embedding tokens per file.
        tgt (List[torch.LongTensor]): A list of LongTensors containing the indices
            of target transcriptions.
        tgt_dict (~fairseq.data.Dictionary): target vocabulary.
        ids (List[str]): A list of utterance IDs.
        speakers (List[str]): A list of speakers corresponding to utterances.
    """

    def __init__(
        self, emb_paths, emb_num_tokens, tgt, tgt_dict, ids, speakers
    ):
        assert len(emb_paths) > 0
        assert len(emb_paths) == len(emb_num_tokens)
        assert len(emb_paths) == len(tgt)
        assert len(emb_paths) == len(ids)
        assert len(emb_paths) == len(speakers)
        self.emb_paths = emb_paths
        self.emb_num_tokens = emb_num_tokens
        self.tgt_dict = tgt_dict
        self.tgt = tgt
        self.ids = ids
        self.speakers = speakers
        self.s2s_collater = Seq2SeqCollater(
            0, 1, pad_index=self.tgt_dict.pad(),
            eos_index=self.tgt_dict.eos(), move_eos_to_beginning=True
        )

    def __getitem__(self, index):
        import h5py
        tgt_item = self.tgt[index] if self.tgt is not None else None
        path = self.emb_paths[index]
        if not os.path.exists(path):
            raise FileNotFoundError("Audio file not found: {}".format(path))
        with h5py.File(self.fname, "r") as f:
            features = f["features"]
        return {"id": index, "data": [features, tgt_item]}

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

    def collater(self, samples):
        """Merge a list of samples to form a mini-batch.

        Args:
            samples (List[int]): sample indices to collate

        Returns:
            dict: a mini-batch suitable for forwarding with a Model
        """
        return self.s2s_collater.collate(samples)

    def num_tokens(self, index):
        return self.emb_num_tokens[index]

    def size(self, index):
        """Return an example's size as a float or tuple. This value is used when
        filtering a dataset with ``--max-positions``."""
        return (
            self.self.emb_num_tokens[index],
            len(self.tgt[index]) if self.tgt is not None else 0,
        )

    def ordered_indices(self):
        """Return an ordered list of indices. Batches will be constructed based
        on this order."""
        return np.arange(len(self))
A dataset representing speech and corresponding transcription.      Args:         emb_paths: (List[str]): A list of str with paths to audio embedding files.         emb_num_tokens (List[int]): A list of int containing the number of audio embedding tokens per file.         tgt (List[torch.LongTensor]): A list of LongTensors containing the indices             of target transcriptions.         tgt_dict (~fairseq.data.Dictionary): target vocabulary.         ids (List[str]): A list of utterance IDs.         speakers (List[str]): A list of speakers corresponding to utterances. Merge a list of samples to form a mini-batch.          Args:             samples (List[int]): sample indices to collate          Returns:             dict: a mini-batch suitable for forwarding with a Model Return an example's size as a float or tuple. This value is used when         filtering a dataset with ``--max-positions``. Return an ordered list of indices. Batches will be constructed based         on this order.