audiocraft/lm.py

# ========================= From conditioners.py
import soundfile
from collections import defaultdict
from copy import deepcopy
from dataclasses import dataclass, field
from itertools import chain
import logging
import math
from pathlib import Path
import random
import re
import typing as tp
import warnings
import einops
from num2words import num2words
import spacy
from transformers import RobertaTokenizer, T5EncoderModel, T5Tokenizer  # type: ignore
import torch
import torch.nn.functional as F
from torch.nn.utils.rnn import pad_sequence
from audiocraft.streaming import StreamingModule
from audiocraft.transformer import create_sin_embedding
from audiocraft.utils.audio_utils import convert_audio
from audiocraft.utils.autocast import TorchAutocast
from audiocraft.utils.cache import EmbeddingCache
from audiocraft.utils.utils import collate, hash_trick, length_to_mask, load_clap_state_dict, warn_once
from audiocraft.transformer import StreamingTransformer, create_norm_fn
from dataclasses import dataclass
from functools import partial
import logging
import math
import typing as tp


from torch import nn

from audiocraft.utils import utils
from audiocraft.codebooks_patterns import CodebooksPatternProvider
from audiocraft.activations import get_activation_fn





logger = logging.getLogger(__name__)
TextCondition = tp.Optional[str]  # a text condition can be a string or None (if doesn't exist)
ConditionType = tp.Tuple[torch.Tensor, torch.Tensor]  # condition, mask


class WavCondition(tp.NamedTuple):
    wav: torch.Tensor
    length: torch.Tensor
    sample_rate: tp.List[int]
    path: tp.List[tp.Optional[str]] = []
    seek_time: tp.List[tp.Optional[float]] = []


class JointEmbedCondition(tp.NamedTuple):
    wav: torch.Tensor
    text: tp.List[tp.Optional[str]]
    length: torch.Tensor
    sample_rate: tp.List[int]
    path: tp.List[tp.Optional[str]] = []
    seek_time: tp.List[tp.Optional[float]] = []


@dataclass
class ConditioningAttributes:
    text: tp.Dict[str, tp.Optional[str]] = field(default_factory=dict)
    wav: tp.Dict[str, WavCondition] = field(default_factory=dict)
    joint_embed: tp.Dict[str, JointEmbedCondition] = field(default_factory=dict)

    def __getitem__(self, item):
        return getattr(self, item)

    @property
    def text_attributes(self):
        return self.text.keys()

    @property
    def wav_attributes(self):
        return self.wav.keys()

    @property
    def joint_embed_attributes(self):
        return self.joint_embed.keys()

    @property
    def attributes(self):
        return {
            "text": self.text_attributes,
            "wav": self.wav_attributes,
            "joint_embed": self.joint_embed_attributes,
        }

    def to_flat_dict(self):
        return {
            **{f"text.{k}": v for k, v in self.text.items()},
            **{f"wav.{k}": v for k, v in self.wav.items()},
            **{f"joint_embed.{k}": v for k, v in self.joint_embed.items()}
        }

    @classmethod
    def from_flat_dict(cls, x):
        out = cls()
        for k, v in x.items():
            kind, att = k.split(".")
            out[kind][att] = v
        return out





def nullify_condition(condition: ConditionType, dim: int = 1):
    """Transform an input condition to a null condition.
    The way it is done by converting it to a single zero vector similarly
    to how it is done inside WhiteSpaceTokenizer and NoopTokenizer.

    Args:
        condition (ConditionType): A tuple of condition and mask (tuple[torch.Tensor, torch.Tensor])
        dim (int): The dimension that will be truncated (should be the time dimension)
        WARNING!: dim should not be the batch dimension!
    Returns:
        ConditionType: A tuple of null condition and mask
    """
    assert dim != 0, "dim cannot be the batch dimension!"
    assert isinstance(condition, tuple) and \
        isinstance(condition[0], torch.Tensor) and \
        isinstance(condition[1], torch.Tensor), "'nullify_condition' got an unexpected input type!"
    cond, mask = condition
    B = cond.shape[0]
    last_dim = cond.dim() - 1
    out = cond.transpose(dim, last_dim)
    out = 0. * out[..., :1]
    out = out.transpose(dim, last_dim)
    mask = torch.zeros((B, 1), device=out.device).int()
    assert cond.dim() == out.dim()
    return out, mask


def nullify_wav(cond: WavCondition) -> WavCondition:
    """Transform a WavCondition to a nullified WavCondition.
    It replaces the wav by a null tensor, forces its length to 0, and replaces metadata by dummy attributes.

    Args:
        cond (WavCondition): Wav condition with wav, tensor of shape [B, T].
    Returns:
        WavCondition: Nullified wav condition.
    """
    null_wav, _ = nullify_condition((cond.wav, torch.zeros_like(cond.wav)), dim=cond.wav.dim() - 1)
    return WavCondition(
        wav=null_wav,
        length=torch.tensor([0] * cond.wav.shape[0], device=cond.wav.device),
        sample_rate=cond.sample_rate,
        path=[None] * cond.wav.shape[0],
        seek_time=[None] * cond.wav.shape[0],
    )


def nullify_joint_embed(embed: JointEmbedCondition) -> JointEmbedCondition:
    """Nullify the joint embedding condition by replacing it by a null tensor, forcing its length to 0,
    and replacing metadata by dummy attributes.

    Args:
        cond (JointEmbedCondition): Joint embedding condition with wav and text, wav tensor of shape [B, C, T].
    """
    null_wav, _ = nullify_condition((embed.wav, torch.zeros_like(embed.wav)), dim=embed.wav.dim() - 1)
    return JointEmbedCondition(
        wav=null_wav, text=[None] * len(embed.text),
        length=torch.LongTensor([0]).to(embed.wav.device),
        sample_rate=embed.sample_rate,
        path=[None] * embed.wav.shape[0],
        seek_time=[0] * embed.wav.shape[0],
    )


class Tokenizer:
    """Base tokenizer implementation
    (in case we want to introduce more advances tokenizers in the future).
    """
    def __call__(self, texts: tp.List[tp.Optional[str]]) -> tp.Tuple[torch.Tensor, torch.Tensor]:
        raise NotImplementedError()


class WhiteSpaceTokenizer(Tokenizer):
    """This tokenizer should be used for natural language descriptions.
    For example:
    ["he didn't, know he's going home.", 'shorter sentence'] =>
    [[78, 62, 31,  4, 78, 25, 19, 34],
    [59, 77,  0,  0,  0,  0,  0,  0]]
    """
    PUNCTUATION = "?:!.,;"

    def __init__(self, n_bins: int, pad_idx: int = 0, language: str = "en_core_web_sm",
                 lemma: bool = True, stopwords: bool = True) -> None:
        self.n_bins = n_bins
        self.pad_idx = pad_idx
        self.lemma = lemma
        self.stopwords = stopwords
        try:
            self.nlp = spacy.load(language)
        except IOError:
            spacy.cli.download(language)  # type: ignore
            self.nlp = spacy.load(language)

    @tp.no_type_check
    def __call__(self, texts: tp.List[tp.Optional[str]],
                 return_text: bool = False) -> tp.Tuple[torch.Tensor, torch.Tensor]:
        """Take a list of strings and convert them to a tensor of indices.

        Args:
            texts (list[str]): List of strings.
            return_text (bool, optional): Whether to return text as additional tuple item. Defaults to False.
        Returns:
            tuple[torch.Tensor, torch.Tensor]:
                - Indices of words in the LUT.
                - And a mask indicating where the padding tokens are
        """
        output, lengths = [], []
        texts = deepcopy(texts)
        for i, text in enumerate(texts):
            # if current sample doesn't have a certain attribute, replace with pad token
            if text is None:
                output.append(torch.Tensor([self.pad_idx]))
                lengths.append(0)
                continue

            # convert numbers to words
            text = re.sub(r"(\d+)", lambda x: num2words(int(x.group(0))), text)  # type: ignore
            # normalize text
            text = self.nlp(text)  # type: ignore
            # remove stopwords
            if self.stopwords:
                text = [w for w in text if not w.is_stop]  # type: ignore
            # remove punctuation
            text = [w for w in text if w.text not in self.PUNCTUATION]  # type: ignore
            # lemmatize if needed
            text = [getattr(t, "lemma_" if self.lemma else "text") for t in text]  # type: ignore

            texts[i] = " ".join(text)
            lengths.append(len(text))
            # convert to tensor
            tokens = torch.Tensor([hash_trick(w, self.n_bins) for w in text])
            output.append(tokens)

        mask = length_to_mask(torch.IntTensor(lengths)).int()
        padded_output = pad_sequence(output, padding_value=self.pad_idx).int().t()
        if return_text:
            return padded_output, mask, texts  # type: ignore
        return padded_output, mask


class NoopTokenizer(Tokenizer):
    """This tokenizer should be used for global conditioners such as: artist, genre, key, etc.
    The difference between this and WhiteSpaceTokenizer is that NoopTokenizer does not split
    strings, so "Jeff Buckley" will get it's own index. Whereas WhiteSpaceTokenizer will
    split it to ["Jeff", "Buckley"] and return an index per word.

    For example:
    ["Queen", "ABBA", "Jeff Buckley"] => [43, 55, 101]
    ["Metal", "Rock", "Classical"] => [0, 223, 51]
    """
    def __init__(self, n_bins: int, pad_idx: int = 0):
        self.n_bins = n_bins
        self.pad_idx = pad_idx

    def __call__(self, texts: tp.List[tp.Optional[str]]) -> tp.Tuple[torch.Tensor, torch.Tensor]:
        output, lengths = [], []
        for text in texts:
            # if current sample doesn't have a certain attribute, replace with pad token
            if text is None:
                output.append(self.pad_idx)
                lengths.append(0)
            else:
                output.append(hash_trick(text, self.n_bins))
                lengths.append(1)

        tokens = torch.LongTensor(output).unsqueeze(1)
        mask = length_to_mask(torch.IntTensor(lengths)).int()
        return tokens, mask


class BaseConditioner(nn.Module):
    """Base model for all conditioner modules.
    We allow the output dim to be different than the hidden dim for two reasons:
    1) keep our LUTs small when the vocab is large;
    2) make all condition dims consistent.

    Args:
        dim (int): Hidden dim of the model.
        output_dim (int): Output dim of the conditioner.
    """
    def __init__(self, dim: int, output_dim: int):
        super().__init__()
        self.dim = dim
        self.output_dim = output_dim
        self.output_proj = nn.Linear(dim, output_dim)

    def tokenize(self, *args, **kwargs) -> tp.Any:
        """Should be any part of the processing that will lead to a synchronization
        point, e.g. BPE tokenization with transfer to the GPU.

        The returned value will be saved and return later when calling forward().
        """
        raise NotImplementedError()

    def forward(self, inputs: tp.Any) -> ConditionType:
        """Gets input that should be used as conditioning (e.g, genre, description or a waveform).
        Outputs a ConditionType, after the input data was embedded as a dense vector.

        Returns:
            ConditionType:
                - A tensor of size [B, T, D] where B is the batch size, T is the length of the
                  output embedding and D is the dimension of the embedding.
                - And a mask indicating where the padding tokens.
        """
        raise NotImplementedError()


class TextConditioner(BaseConditioner):
    ...


class LUTConditioner(TextConditioner):
    """Lookup table TextConditioner.

    Args:
        n_bins (int): Number of bins.
        dim (int): Hidden dim of the model (text-encoder/LUT).
        output_dim (int): Output dim of the conditioner.
        tokenizer (str): Name of the tokenizer.
        pad_idx (int, optional): Index for padding token. Defaults to 0.
    """
    def __init__(self, n_bins: int, dim: int, output_dim: int, tokenizer: str, pad_idx: int = 0):
        super().__init__(dim, output_dim)
        self.embed = nn.Embedding(n_bins, dim)
        self.tokenizer: Tokenizer
        if tokenizer == 'whitespace':
            self.tokenizer = WhiteSpaceTokenizer(n_bins, pad_idx=pad_idx)
        elif tokenizer == 'noop':
            self.tokenizer = NoopTokenizer(n_bins, pad_idx=pad_idx)
        else:
            raise ValueError(f"unrecognized tokenizer `{tokenizer}`.")

    def tokenize(self, x: tp.List[tp.Optional[str]]) -> tp.Tuple[torch.Tensor, torch.Tensor]:
        device = self.embed.weight.device
        tokens, mask = self.tokenizer(x)
        tokens, mask = tokens.to(device), mask.to(device)
        return tokens, mask

    def forward(self, inputs: tp.Tuple[torch.Tensor, torch.Tensor]) -> ConditionType:
        tokens, mask = inputs
        embeds = self.embed(tokens)
        embeds = self.output_proj(embeds)
        embeds = (embeds * mask.unsqueeze(-1))
        return embeds, mask


class T5Conditioner(TextConditioner):
    """T5-based TextConditioner.

    Args:
        name (str): Name of the T5 model.
        output_dim (int): Output dim of the conditioner.
        finetune (bool): Whether to fine-tune T5 at train time.
        device (str): Device for T5 Conditioner.
        autocast_dtype (tp.Optional[str], optional): Autocast dtype.
        word_dropout (float, optional): Word dropout probability.
        normalize_text (bool, optional): Whether to apply text normalization.
    """
    MODELS = ["t5-small", "t5-base", "t5-large", "t5-3b", "t5-11b",
              "google/flan-t5-small", "google/flan-t5-base", "google/flan-t5-large",
              "google/flan-t5-xl", "google/flan-t5-xxl"]
    MODELS_DIMS = {
        "t5-small": 512,
        "t5-base": 768,
        "t5-large": 1024,
        "t5-3b": 1024,
        "t5-11b": 1024,
        "google/flan-t5-small": 512,
        "google/flan-t5-base": 768,
        "google/flan-t5-large": 1024,
        "google/flan-t5-3b": 1024,
        "google/flan-t5-11b": 1024,
    }

    def __init__(self, name: str, output_dim: int, finetune: bool, device: str,
                 autocast_dtype: tp.Optional[str] = 'float32', word_dropout: float = 0.,
                 normalize_text: bool = False):
        assert name in self.MODELS, f"Unrecognized t5 model name (should in {self.MODELS})"
        super().__init__(self.MODELS_DIMS[name], output_dim)
        self.device = device
        self.name = name
        self.finetune = finetune
        self.word_dropout = word_dropout
        if autocast_dtype is None or self.device == 'cpu':
            self.autocast = TorchAutocast(enabled=False)
            if self.device != 'cpu':
                logger.warning("T5 has no autocast, this might lead to NaN")
        else:
            dtype = getattr(torch, autocast_dtype)
            assert isinstance(dtype, torch.dtype)
            logger.info(f"T5 will be evaluated with autocast as {autocast_dtype}")
            self.autocast = TorchAutocast(enabled=True, device_type=self.device, dtype=dtype)
        # Let's disable logging temporarily because T5 will vomit some errors otherwise.
        # thanks https://gist.github.com/simon-weber/7853144
        previous_level = logging.root.manager.disable
        logging.disable(logging.ERROR)
        with warnings.catch_warnings():
            warnings.simplefilter("ignore")
            try:
                self.t5_tokenizer = T5Tokenizer.from_pretrained(name)
                t5 = T5EncoderModel.from_pretrained(name).train(mode=finetune)
            finally:
                logging.disable(previous_level)
        if finetune:
            self.t5 = t5
        else:
            # this makes sure that the t5 models is not part
            # of the saved checkpoint
            self.__dict__['t5'] = t5.to(device)

        self.normalize_text = normalize_text
        if normalize_text:
            self.text_normalizer = WhiteSpaceTokenizer(1, lemma=True, stopwords=True)

    def tokenize(self, x: tp.List[tp.Optional[str]]) -> tp.Dict[str, torch.Tensor]:
        # if current sample doesn't have a certain attribute, replace with empty string
        entries: tp.List[str] = [xi if xi is not None else "" for xi in x]
        if self.normalize_text:
            _, _, entries = self.text_normalizer(entries, return_text=True)
        if self.word_dropout > 0. and self.training:
            new_entries = []
            for entry in entries:
                words = [word for word in entry.split(" ") if random.random() >= self.word_dropout]
                new_entries.append(" ".join(words))
            entries = new_entries

        empty_idx = torch.LongTensor([i for i, xi in enumerate(entries) if xi == ""])

        inputs = self.t5_tokenizer(entries, return_tensors='pt', padding=True).to(self.device)
        mask = inputs['attention_mask']
        mask[empty_idx, :] = 0  # zero-out index where the input is non-existant
        return inputs

    def forward(self, inputs: tp.Dict[str, torch.Tensor]) -> ConditionType:
        mask = inputs['attention_mask']
        with torch.set_grad_enabled(self.finetune), self.autocast:
            embeds = self.t5(**inputs).last_hidden_state
        embeds = self.output_proj(embeds.to(self.output_proj.weight))
        embeds = (embeds * mask.unsqueeze(-1))
        return embeds, mask


class WaveformConditioner(BaseConditioner):
    """Base class for all conditioners that take a waveform as input.
    Classes that inherit must implement `_get_wav_embedding` that outputs
    a continuous tensor, and `_downsampling_factor` that returns the down-sampling
    factor of the embedding model.

    Args:
        dim (int): The internal representation dimension.
        output_dim (int): Output dimension.
        device (tp.Union[torch.device, str]): Device.
    """
    def __init__(self, dim: int, output_dim: int, device: tp.Union[torch.device, str]):
        super().__init__(dim, output_dim)
        self.device = device
        # if False no masking is done, used in ChromaStemConditioner when completing by periodicity a sample.
        self._use_masking = True

    def tokenize(self, x: WavCondition) -> WavCondition:
        wav, length, sample_rate, path, seek_time = x
        assert length is not None
        return WavCondition(wav.to(self.device), length.to(self.device), sample_rate, path, seek_time)

    def _get_wav_embedding(self, x: WavCondition) -> torch.Tensor:
        """Gets as input a WavCondition and returns a dense embedding."""
        raise NotImplementedError()

    def _downsampling_factor(self):
        """Returns the downsampling factor of the embedding model."""
        raise NotImplementedError()

    def forward(self, x: WavCondition) -> ConditionType:
        """Extract condition embedding and mask from a waveform and its metadata.
        Args:
            x (WavCondition): Waveform condition containing raw waveform and metadata.
        Returns:
            ConditionType: a dense vector representing the conditioning along with its mask
        """
        wav, lengths, *_ = x
        with torch.no_grad():
            embeds = self._get_wav_embedding(x)
        embeds = embeds.to(self.output_proj.weight)
        embeds = self.output_proj(embeds)

        if lengths is not None and self._use_masking:
            lengths = lengths / self._downsampling_factor()
            mask = length_to_mask(lengths, max_len=embeds.shape[1]).int()  # type: ignore
        else:
            mask = torch.ones_like(embeds[..., 0])
        embeds = (embeds * mask.unsqueeze(-1))
        return embeds, mask





class JointEmbeddingConditioner(BaseConditioner):
    """Joint embedding conditioning supporting both audio or text conditioning.

    Args:
        dim (int): Dimension.
        output_dim (int): Output dimension.
        device (str): Device.
        attribute (str): Attribute used by the conditioner.
        autocast_dtype (str): Autocast for the conditioner.
        quantize (bool): Whether to quantize the CLAP embedding.
        n_q (int): Number of residual quantizers (used if quantize is true).
        bins (int): Quantizers' codebooks size (used if quantize is true).
        kwargs: Additional parameters for residual vector quantizer.
    """
    def __init__(self, dim: int, output_dim: int, device: str, attribute: str,
                 autocast_dtype: tp.Optional[str] = 'float32', quantize: bool = True,
                 n_q: int = 12, bins: int = 1024, **kwargs):
        super().__init__(dim=dim, output_dim=output_dim)
        self.device = device
        self.attribute = attribute
        if autocast_dtype is None or device == 'cpu':
            self.autocast = TorchAutocast(enabled=False)
            logger.warning("JointEmbeddingConditioner has no autocast, this might lead to NaN.")
        else:
            dtype = getattr(torch, autocast_dtype)
            assert isinstance(dtype, torch.dtype)
            logger.info(f"JointEmbeddingConditioner will be evaluated with autocast as {autocast_dtype}.")
            self.autocast = TorchAutocast(enabled=True, device_type=self.device, dtype=dtype)
        # residual vector quantizer to discretize the conditioned embedding
        self.quantizer=None
        if quantize:
            print('\n\n\n\nWANTS TO QUANTIZE on Inference\n\n\n\n')
            # self.quantizer = ResidualVectorQuantizer(dim, n_q=n_q, bins=bins, **kwargs)

    def _get_embed(self, x: JointEmbedCondition) -> tp.Tuple[torch.Tensor, torch.Tensor]:
        """Get joint embedding in latent space from the inputs.

        Returns:
            tuple[torch.Tensor, torch.Tensor]: Tensor for the latent embedding
                and corresponding empty indexes.
        """
        raise NotImplementedError()

    def forward(self, x: JointEmbedCondition) -> ConditionType:
        with self.autocast:
            embed, empty_idx = self._get_embed(x)
            if self.quantizer is not None:
                embed = embed.view(-1, self.dim, 1)
                q_res = self.quantizer(embed, frame_rate=1)
                out_embed = q_res.x.view(-1, self.dim)
            else:
                out_embed = embed
            out_embed = self.output_proj(out_embed).view(-1, 1, self.output_dim)
            mask = torch.ones(*out_embed.shape[:2], device=out_embed.device)
            mask[empty_idx, :] = 0  # zero-out index where the input is non-existant
            out_embed = (out_embed * mask.unsqueeze(-1))
            return out_embed, mask

    def tokenize(self, x: JointEmbedCondition) -> JointEmbedCondition:
        return x


class CLAPEmbeddingConditioner(JointEmbeddingConditioner):
    """Joint Embedding conditioner based on pre-trained CLAP model.

    This CLAP-based conditioner supports a caching mechanism
    over the computed embeddings for faster training.

    Args:
        dim (int): Dimension.
        output_dim (int): Output dimension.
        device (str): Device.
        attribute (str): Attribute used by the conditioner.
        quantize (bool): Whether to quantize the CLAP embedding.
        n_q (int): Number of residual quantizers (used if quantize is true).
        bins (int): Quantizers' codebooks size (used if quantize is true).
        checkpoint (str): Path to CLAP checkpoint.
        model_arch (str): CLAP model architecture.
        enable_fusion (bool): Enable fusion for CLAP model.
        sample_rate (int): Sample rate used by CLAP model.
        max_audio_length (float): Maximum audio length for CLAP model.
        audio_stride (float): Stride to use for getting a CLAP embedding on the full sequence.
        normalize (bool): Whether to normalize the CLAP embedding.
        text_p (float): Probability of using text representation instead of audio at train time.
        batch_size (Optional[int]): Batch size for CLAP embedding computation.
        autocast_dtype (str): Autocast for the conditioner.
        cache_path (Optional[str]): Path for pre-computed embeddings caching.
        kwargs: Additional parameters for residual vector quantizer.
    """
    def __init__(self, dim: int, output_dim: int, device: str, attribute: str,
                 quantize: bool, n_q: int, bins: int, checkpoint: tp.Union[str, Path], model_arch: str,
                 enable_fusion: bool, sample_rate: int, max_audio_length: int, audio_stride: int,
                 normalize: bool, text_p: bool, batch_size: tp.Optional[int] = None,
                 autocast_dtype: tp.Optional[str] = 'float32', cache_path: tp.Optional[str] = None, **kwargs):
        try:
            import laion_clap  # type: ignore
        except ImportError:
            raise ImportError("Please install CLAP to use the CLAPEmbeddingConditioner: 'pip install laion_clap'")
        warnings.warn("Sample rate for CLAP conditioner was fixed in version v1.1.0, (from 44.1 to 48 kHz). "
                      "Please retrain all models.")
        checkpoint = AudioCraftEnvironment.resolve_reference_path(checkpoint)
        clap_tokenize = RobertaTokenizer.from_pretrained('roberta-base')
        clap_model = laion_clap.CLAP_Module(enable_fusion=enable_fusion, amodel=model_arch)
        load_clap_state_dict(clap_model, checkpoint)
        clap_model.eval()
        clap_model.to(device)
        super().__init__(dim=dim, output_dim=output_dim, device=device, attribute=attribute,
                         autocast_dtype=autocast_dtype, quantize=quantize, n_q=n_q, bins=bins,
                         **kwargs)
        self.checkpoint = checkpoint
        self.enable_fusion = enable_fusion
        self.model_arch = model_arch
        self.clap: laion_clap.CLAP_Module
        self.clap_tokenize: RobertaTokenizer
        self.clap_sample_rate = sample_rate
        self.clap_max_frames = int(self.clap_sample_rate * max_audio_length)
        self.clap_stride = int(self.clap_sample_rate * audio_stride)
        self.batch_size = batch_size or 1
        self.normalize = normalize
        self.text_p = text_p
        self.__dict__['clap_tokenize'] = clap_tokenize
        self.__dict__['clap'] = clap_model
        self.wav_cache, self.text_cache = None, None
        if cache_path is not None:
            self.wav_cache = EmbeddingCache(Path(cache_path) / 'wav', self.device,
                                            compute_embed_fn=self._get_wav_embedding_for_cache,
                                            extract_embed_fn=self._extract_wav_embedding_chunk)
            self.text_cache = EmbeddingCache(Path(cache_path) / 'text', self.device,
                                             compute_embed_fn=self._get_text_embedding_for_cache)

    def _tokenizer(self, texts: tp.Union[str, tp.List[str]]) -> dict:
        # we use the default params from CLAP module here as well
        return self.clap_tokenize(texts, padding="max_length", truncation=True, max_length=77, return_tensors="pt")

    def _compute_text_embedding(self, text: tp.List[str]) -> torch.Tensor:
        """Compute text embedding from CLAP model on a given a batch of text.

        Args:
            text (list[str]): List of text for the batch, with B items.
        Returns:
            torch.Tensor: CLAP embedding derived from text, of shape [B, 1, D], with D the CLAP embedding dimension.
        """
        with torch.no_grad():
            embed = self.clap.get_text_embedding(text, tokenizer=self._tokenizer, use_tensor=True)
            return embed.view(embed.size(0), 1, embed.size(-1))

    def _get_text_embedding_for_cache(self, path: tp.Union[Path, str],
                                      x: JointEmbedCondition, idx: int) -> torch.Tensor:
        """Get text embedding function for the cache."""
        text = x.text[idx]
        text = text if text is not None else ""
        return self._compute_text_embedding([text])[0]

    def _preprocess_wav(self, wav: torch.Tensor, length: torch.Tensor, sample_rates: tp.List[int]) -> torch.Tensor:
        """Preprocess wav to expected format by CLAP model.

        Args:
            wav (torch.Tensor): Audio wav, of shape [B, C, T].
            length (torch.Tensor): Actual length of the audio for each item in the batch, of shape [B].
            sample_rates (list[int]): Sample rates for each sample in the batch
        Returns:
            torch.Tensor: Audio wav of shape [B, T].
        """
        assert wav.dim() == 3, "Expecting wav to be [B, C, T]"
        if sample_rates is not None:
            _wav = []
            for i, audio in enumerate(wav):
                sr = sample_rates[i]
                audio = convert_audio(audio, from_rate=sr, to_rate=self.clap_sample_rate, to_channels=1)
                _wav.append(audio)
            wav = torch.stack(_wav, dim=0)
        wav = wav.mean(dim=1)
        return wav

    def _compute_wav_embedding(self, wav: torch.Tensor, length: torch.Tensor,
                               sample_rates: tp.List[int], reduce_mean: bool = False) -> torch.Tensor:
        """Compute audio wave embedding from CLAP model.

        Since CLAP operates on a fixed sequence length audio inputs and we need to process longer audio sequences,
        we calculate the wav embeddings on `clap_max_frames` windows with `clap_stride`-second stride and
        average the resulting embeddings.

        Args:
            wav (torch.Tensor): Audio wav, of shape [B, C, T].
            length (torch.Tensor): Actual length of the audio for each item in the batch, of shape [B].
            sample_rates (list[int]): Sample rates for each sample in the batch.
            reduce_mean (bool): Whether to get the average tensor.
        Returns:
            torch.Tensor: Audio embedding of shape [B, F, D], F being the number of chunks, D the dimension.
        """
        with torch.no_grad():
            wav = self._preprocess_wav(wav, length, sample_rates)
            B, T = wav.shape
            if T >= self.clap_max_frames:
                wav = wav.unfold(-1, self.clap_max_frames, self.clap_stride)  # [B, F, T]
            else:
                wav = wav.view(-1, 1, T)  # [B, F, T] with F=1
            wav = einops.rearrange(wav, 'b f t -> (b f) t')
            embed_list = []
            for i in range(0, wav.size(0), self.batch_size):
                _wav = wav[i:i+self.batch_size, ...]
                _embed = self.clap.get_audio_embedding_from_data(_wav, use_tensor=True)
                embed_list.append(_embed)
            embed = torch.cat(embed_list, dim=0)
            embed = einops.rearrange(embed, '(b f) d -> b f d', b=B)
            if reduce_mean:
                embed = embed.mean(dim=1, keepdim=True)
            return embed  # [B, F, D] with F=1 if reduce_mean is True

    def _get_wav_embedding_for_cache(self, path: tp.Union[str, Path],
                                     x: JointEmbedCondition, idx: int) -> torch.Tensor:
        """Compute audio wave embedding for the cache.
        The embedding is computed on a given audio read from file.

        Args:
            path (str or Path): Path to the full audio file.
        Returns:
            torch.Tensor: Single-item tensor of shape [F, D], F being the number of chunks, D the dimension.
        """
        wav, sr = soundfile.read(path)  # [C, T]
        wav = wav.unsqueeze(0).to(self.device)  # [1, C, T]
        wav_len = torch.LongTensor([wav.shape[-1]]).to(self.device)
        embed = self._compute_wav_embedding(wav, wav_len, [sr], reduce_mean=False)  # [B, F, D]
        return embed.squeeze(0)  # [F, D]

    def _extract_wav_embedding_chunk(self, full_embed: torch.Tensor, x: JointEmbedCondition, idx: int) -> torch.Tensor:
        """Extract the chunk of embedding matching the seek_time and length from the full CLAP audio embedding.

        Args:
            full_embed (torch.Tensor): CLAP embedding computed on the full wave, of shape [F, D].
            x (JointEmbedCondition): Joint embedding condition for the full batch.
            idx (int): Index considered for the given embedding to extract.
        Returns:
            torch.Tensor: Wav embedding averaged on sliding window, of shape [1, D].
        """
        sample_rate = x.sample_rate[idx]
        seek_time = x.seek_time[idx]
        seek_time = 0. if seek_time is None else seek_time
        clap_stride = int(self.clap_stride / self.clap_sample_rate) * sample_rate
        end_seek_time = seek_time + self.clap_max_frames / self.clap_sample_rate
        start_offset = int(seek_time * sample_rate // clap_stride)
        end_offset = int(end_seek_time * sample_rate // clap_stride)
        wav_embed = full_embed[start_offset:end_offset, ...]
        wav_embed = wav_embed.mean(dim=0, keepdim=True)
        return wav_embed.to(self.device)  # [F, D]

    def _get_text_embedding(self, x: JointEmbedCondition) -> torch.Tensor:
        """Get CLAP embedding from a batch of text descriptions."""
        no_nullified_cond = x.wav.shape[-1] > 1  # we don't want to read from cache when condition dropout
        if self.text_cache is not None and no_nullified_cond:
            assert all(p is not None for p in x.path), "Cache requires all JointEmbedCondition paths to be provided"
            paths = [Path(p) for p in x.path if p is not None]
            embed = self.text_cache.get_embed_from_cache(paths, x)
        else:
            text = [xi if xi is not None else "" for xi in x.text]
            embed = self._compute_text_embedding(text)
        if self.normalize:
            embed = torch.nn.functional.normalize(embed, p=2.0, dim=-1)
        return embed

    def _get_wav_embedding(self, x: JointEmbedCondition) -> torch.Tensor:
        """Get CLAP embedding from a batch of audio tensors (and corresponding sample rates)."""
        no_undefined_paths = all(p is not None for p in x.path)
        no_nullified_cond = x.wav.shape[-1] > 1  # we don't want to read from cache when condition dropout
        if self.wav_cache is not None and no_undefined_paths and no_nullified_cond:
            paths = [Path(p) for p in x.path if p is not None]
            embed = self.wav_cache.get_embed_from_cache(paths, x)
        else:
            embed = self._compute_wav_embedding(x.wav, x.length, x.sample_rate, reduce_mean=True)
        if self.normalize:
            embed = torch.nn.functional.normalize(embed, p=2.0, dim=-1)
        return embed

    def tokenize(self, x: JointEmbedCondition) -> JointEmbedCondition:
        # Trying to limit as much as possible sync points when the cache is warm.
        no_undefined_paths = all(p is not None for p in x.path)
        if self.wav_cache is not None and no_undefined_paths:
            assert all([p is not None for p in x.path]), "Cache requires all JointEmbedCondition paths to be provided"
            paths = [Path(p) for p in x.path if p is not None]
            self.wav_cache.populate_embed_cache(paths, x)
        if self.text_cache is not None and no_undefined_paths:
            assert all([p is not None for p in x.path]), "Cache requires all JointEmbedCondition paths to be provided"
            paths = [Path(p) for p in x.path if p is not None]
            self.text_cache.populate_embed_cache(paths, x)
        return x

    def _get_embed(self, x: JointEmbedCondition) -> tp.Tuple[torch.Tensor, torch.Tensor]:
        """Extract shared latent representation from either the wav or the text using CLAP."""
        # decide whether to use text embedding at train time or not
        use_text_embed = random.random() < self.text_p
        if self.training and not use_text_embed:
            embed = self._get_wav_embedding(x)
            empty_idx = torch.LongTensor([])  # we assume we always have the audio wav
        else:
            embed = self._get_text_embedding(x)
            empty_idx = torch.LongTensor([i for i, xi in enumerate(x.text) if xi is None or xi == ""])
        return embed, empty_idx


def dropout_condition(sample: ConditioningAttributes, condition_type: str, condition: str) -> ConditioningAttributes:
    """Utility function for nullifying an attribute inside an ConditioningAttributes object.
    If the condition is of type "wav", then nullify it using `nullify_condition` function.
    If the condition is of any other type, set its value to None.
    Works in-place.
    """
    if condition_type not in ['text', 'wav', 'joint_embed']:
        raise ValueError(
            "dropout_condition got an unexpected condition type!"
            f" expected 'text', 'wav' or 'joint_embed' but got '{condition_type}'"
        )

    if condition not in getattr(sample, condition_type):
        raise ValueError(
            "dropout_condition received an unexpected condition!"
            f" expected wav={sample.wav.keys()} and text={sample.text.keys()}"
            f" but got '{condition}' of type '{condition_type}'!"
        )

    if condition_type == 'wav':
        wav_cond = sample.wav[condition]
        sample.wav[condition] = nullify_wav(wav_cond)
    elif condition_type == 'joint_embed':
        embed = sample.joint_embed[condition]
        sample.joint_embed[condition] = nullify_joint_embed(embed)
    else:
        sample.text[condition] = None

    return sample


class DropoutModule(nn.Module):
    """Base module for all dropout modules."""
    def __init__(self, seed: int = 1234):
        super().__init__()
        self.rng = torch.Generator()
        self.rng.manual_seed(seed)


class AttributeDropout(DropoutModule):
    """Dropout with a given probability per attribute.
    This is different from the behavior of ClassifierFreeGuidanceDropout as this allows for attributes
    to be dropped out separately. For example, "artist" can be dropped while "genre" remains.
    This is in contrast to ClassifierFreeGuidanceDropout where if "artist" is dropped "genre"
    must also be dropped.

    Args:
        p (tp.Dict[str, float]): A dict mapping between attributes and dropout probability. For example:
            ...
            "genre": 0.1,
            "artist": 0.5,
            "wav": 0.25,
            ...
        active_on_eval (bool, optional): Whether the dropout is active at eval. Default to False.
        seed (int, optional): Random seed.
    """
    def __init__(self, p: tp.Dict[str, tp.Dict[str, float]], active_on_eval: bool = False, seed: int = 1234):
        super().__init__(seed=seed)
        self.active_on_eval = active_on_eval
        # construct dict that return the values from p otherwise 0
        self.p = {}
        for condition_type, probs in p.items():
            self.p[condition_type] = defaultdict(lambda: 0, probs)

    def forward(self, samples: tp.List[ConditioningAttributes]) -> tp.List[ConditioningAttributes]:
        """
        Args:
            samples (list[ConditioningAttributes]): List of conditions.
        Returns:
            list[ConditioningAttributes]: List of conditions after certain attributes were set to None.
        """
        if not self.training and not self.active_on_eval:
            return samples

        samples = deepcopy(samples)
        for condition_type, ps in self.p.items():  # for condition types [text, wav]
            for condition, p in ps.items():  # for attributes of each type (e.g., [artist, genre])
                if torch.rand(1, generator=self.rng).item() < p:
                    for sample in samples:
                        dropout_condition(sample, condition_type, condition)
        return samples

    def __repr__(self):
        return f"AttributeDropout({dict(self.p)})"


class ClassifierFreeGuidanceDropout(DropoutModule):
    """Classifier Free Guidance dropout.
    All attributes are dropped with the same probability.

    Args:
        p (float): Probability to apply condition dropout during training.
        seed (int): Random seed.
    """
    def __init__(self, p: float, seed: int = 1234):
        super().__init__(seed=seed)
        self.p = p

    def forward(self, samples: tp.List[ConditioningAttributes]) -> tp.List[ConditioningAttributes]:
        """
        Args:
            samples (list[ConditioningAttributes]): List of conditions.
        Returns:
            list[ConditioningAttributes]: List of conditions after all attributes were set to None.
        """
        if not self.training:
            return samples

        # decide on which attributes to drop in a batched fashion
        drop = torch.rand(1, generator=self.rng).item() < self.p
        if not drop:
            return samples

        # nullify conditions of all attributes
        samples = deepcopy(samples)
        for condition_type in ["wav", "text"]:
            for sample in samples:
                for condition in sample.attributes[condition_type]:
                    dropout_condition(sample, condition_type, condition)
        return samples

    def __repr__(self):
        return f"ClassifierFreeGuidanceDropout(p={self.p})"


class ConditioningProvider(nn.Module):
    """Prepare and provide conditions given all the supported conditioners.

    Args:
        conditioners (dict): Dictionary of conditioners.
        device (torch.device or str, optional): Device for conditioners and output condition types.
    """
    def __init__(self, conditioners: tp.Dict[str, BaseConditioner], device: tp.Union[torch.device, str] = "cpu"):
        super().__init__()
        self.device = device
        self.conditioners = nn.ModuleDict(conditioners)

    @property
    def joint_embed_conditions(self):
        return [m.attribute for m in self.conditioners.values() if isinstance(m, JointEmbeddingConditioner)]

    @property
    def has_joint_embed_conditions(self):
        return len(self.joint_embed_conditions) > 0

    @property
    def text_conditions(self):
        return [k for k, v in self.conditioners.items() if isinstance(v, TextConditioner)]

    @property
    def wav_conditions(self):
        return [k for k, v in self.conditioners.items() if isinstance(v, WaveformConditioner)]

    @property
    def has_wav_condition(self):
        return len(self.wav_conditions) > 0

    def tokenize(self, inputs: tp.List[ConditioningAttributes]) -> tp.Dict[str, tp.Any]:
        """Match attributes/wavs with existing conditioners in self, and compute tokenize them accordingly.
        This should be called before starting any real GPU work to avoid synchronization points.
        This will return a dict matching conditioner names to their arbitrary tokenized representations.

        Args:
            inputs (list[ConditioningAttributes]): List of ConditioningAttributes objects containing
                text and wav conditions.
        """
        assert all([isinstance(x, ConditioningAttributes) for x in inputs]), (
            "Got unexpected types input for conditioner! should be tp.List[ConditioningAttributes]",
            f" but types were {set([type(x) for x in inputs])}"
        )

        output = {}
        text = self._collate_text(inputs)
        wavs = self._collate_wavs(inputs)
        joint_embeds = self._collate_joint_embeds(inputs)

        assert set(text.keys() | wavs.keys() | joint_embeds.keys()).issubset(set(self.conditioners.keys())), (
            f"Got an unexpected attribute! Expected {self.conditioners.keys()}, ",
            f"got {text.keys(), wavs.keys(), joint_embeds.keys()}"
        )

        for attribute, batch in chain(text.items(), wavs.items(), joint_embeds.items()):
            output[attribute] = self.conditioners[attribute].tokenize(batch)
        return output

    def forward(self, tokenized: tp.Dict[str, tp.Any]) -> tp.Dict[str, ConditionType]:
        """Compute pairs of `(embedding, mask)` using the configured conditioners and the tokenized representations.
        The output is for example:
        {
            "genre": (torch.Tensor([B, 1, D_genre]), torch.Tensor([B, 1])),
            "description": (torch.Tensor([B, T_desc, D_desc]), torch.Tensor([B, T_desc])),
            ...
        }

        Args:
            tokenized (dict): Dict of tokenized representations as returned by `tokenize()`.
        """
        output = {}
        for attribute, inputs in tokenized.items():
            condition, mask = self.conditioners[attribute](inputs)
            output[attribute] = (condition, mask)
        return output

    def _collate_text(self, samples: tp.List[ConditioningAttributes]) -> tp.Dict[str, tp.List[tp.Optional[str]]]:
        """Given a list of ConditioningAttributes objects, compile a dictionary where the keys
        are the attributes and the values are the aggregated input per attribute.
        For example:
        Input:
        [
            ConditioningAttributes(text={"genre": "Rock", "description": "A rock song with a guitar solo"}, wav=...),
            ConditioningAttributes(text={"genre": "Hip-hop", "description": "A hip-hop verse"}, wav=...),
        ]
        Output:
        {
            "genre": ["Rock", "Hip-hop"],
            "description": ["A rock song with a guitar solo", "A hip-hop verse"]
        }

        Args:
            samples (list of ConditioningAttributes): List of ConditioningAttributes samples.
        Returns:
            dict[str, list[str, optional]]: A dictionary mapping an attribute name to text batch.
        """
        out: tp.Dict[str, tp.List[tp.Optional[str]]] = defaultdict(list)
        texts = [x.text for x in samples]
        for text in texts:
            for condition in self.text_conditions:
                out[condition].append(text[condition])
        return out

    def _collate_wavs(self, samples: tp.List[ConditioningAttributes]) -> tp.Dict[str, WavCondition]:
        """Generate a dict where the keys are attributes by which we fetch similar wavs,
        and the values are Tensors of wavs according to said attributes.

        *Note*: by the time the samples reach this function, each sample should have some waveform
        inside the "wav" attribute. It should be either:
        1. A real waveform
        2. A null waveform due to the sample having no similar waveforms (nullified by the dataset)
        3. A null waveform due to it being dropped in a dropout module (nullified by dropout)

        Args:
            samples (list of ConditioningAttributes): List of ConditioningAttributes samples.
        Returns:
            dict[str, WavCondition]: A dictionary mapping an attribute name to wavs.
        """
        wavs = defaultdict(list)
        lengths = defaultdict(list)
        sample_rates = defaultdict(list)
        paths = defaultdict(list)
        seek_times = defaultdict(list)
        out: tp.Dict[str, WavCondition] = {}

        for sample in samples:
            for attribute in self.wav_conditions:
                wav, length, sample_rate, path, seek_time = sample.wav[attribute]
                assert wav.dim() == 3, f"Got wav with dim={wav.dim()}, but expected 3 [1, C, T]"
                assert wav.size(0) == 1, f"Got wav [B, C, T] with shape={wav.shape}, but expected B == 1"
                # mono-channel conditioning
                wav = wav.mean(1, keepdim=True)  # [1, 1, T]
                wavs[attribute].append(wav.flatten())  # [T]
                lengths[attribute].append(length)
                sample_rates[attribute].extend(sample_rate)
                paths[attribute].extend(path)
                seek_times[attribute].extend(seek_time)

        # stack all wavs to a single tensor
        for attribute in self.wav_conditions:
            stacked_wav, _ = collate(wavs[attribute], dim=0)
            out[attribute] = WavCondition(
                stacked_wav.unsqueeze(1), torch.cat(lengths[attribute]), sample_rates[attribute],
                paths[attribute], seek_times[attribute])

        return out

    def _collate_joint_embeds(self, samples: tp.List[ConditioningAttributes]) -> tp.Dict[str, JointEmbedCondition]:
        """Generate a dict where the keys are attributes by which we compute joint embeddings,
        and the values are Tensors of pre-computed embeddings and the corresponding text attributes.

        Args:
            samples (list[ConditioningAttributes]): List of ConditioningAttributes samples.
        Returns:
            A dictionary mapping an attribute name to joint embeddings.
        """
        texts = defaultdict(list)
        wavs = defaultdict(list)
        lengths = defaultdict(list)
        sample_rates = defaultdict(list)
        paths = defaultdict(list)
        seek_times = defaultdict(list)
        channels: int = 0

        out = {}
        for sample in samples:
            for attribute in self.joint_embed_conditions:
                wav, text, length, sample_rate, path, seek_time = sample.joint_embed[attribute]
                assert wav.dim() == 3
                if channels == 0:
                    channels = wav.size(1)
                else:
                    assert channels == wav.size(1), "not all audio has same number of channels in batch"
                assert wav.size(0) == 1, "Expecting single-wav batch in the collate method"
                wav = einops.rearrange(wav, "b c t -> (b c t)")  # [1, C, T] => [C * T]
                wavs[attribute].append(wav)
                texts[attribute].extend(text)
                lengths[attribute].append(length)
                sample_rates[attribute].extend(sample_rate)
                paths[attribute].extend(path)
                seek_times[attribute].extend(seek_time)

        for attribute in self.joint_embed_conditions:
            stacked_texts = texts[attribute]
            stacked_paths = paths[attribute]
            stacked_seek_times = seek_times[attribute]
            stacked_wavs = pad_sequence(wavs[attribute]).to(self.device)
            stacked_wavs = einops.rearrange(stacked_wavs, "(c t) b -> b c t", c=channels)
            stacked_sample_rates = sample_rates[attribute]
            stacked_lengths = torch.cat(lengths[attribute]).to(self.device)
            assert stacked_lengths.size(0) == stacked_wavs.size(0)
            assert len(stacked_sample_rates) == stacked_wavs.size(0)
            assert len(stacked_texts) == stacked_wavs.size(0)
            out[attribute] = JointEmbedCondition(
                text=stacked_texts, wav=stacked_wavs,
                length=stacked_lengths, sample_rate=stacked_sample_rates,
                path=stacked_paths, seek_time=stacked_seek_times)

        return out


class ConditionFuser(StreamingModule):
    """Condition fuser handles the logic to combine the different conditions
    to the actual model input.

    Args:
        fuse2cond (tp.Dict[str, str]): A dictionary that says how to fuse
            each condition. For example:
            {
                "prepend": ["description"],
                "sum": ["genre", "bpm"],
                "cross": ["description"],
            }
        cross_attention_pos_emb (bool, optional): Use positional embeddings in cross attention.
        cross_attention_pos_emb_scale (int): Scale for positional embeddings in cross attention if used.
    """
    FUSING_METHODS = ["sum", "prepend", "cross", "input_interpolate"]

    def __init__(self, fuse2cond: tp.Dict[str, tp.List[str]], cross_attention_pos_emb: bool = False,
                 cross_attention_pos_emb_scale: float = 1.0):
        super().__init__()
        assert all(
            [k in self.FUSING_METHODS for k in fuse2cond.keys()]
        ), f"Got invalid fuse method, allowed methods: {self.FUSING_METHODS}"
        self.cross_attention_pos_emb = cross_attention_pos_emb
        self.cross_attention_pos_emb_scale = cross_attention_pos_emb_scale
        self.fuse2cond: tp.Dict[str, tp.List[str]] = fuse2cond
        self.cond2fuse: tp.Dict[str, str] = {}
        for fuse_method, conditions in fuse2cond.items():
            for condition in conditions:
                self.cond2fuse[condition] = fuse_method

    def forward(
        self,
        input: torch.Tensor,
        conditions: tp.Dict[str, ConditionType]
    ) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
        """Fuse the conditions to the provided model input.

        Args:
            input (torch.Tensor): Transformer input.
            conditions (dict[str, ConditionType]): Dict of conditions.
        Returns:
            tuple[torch.Tensor, torch.Tensor]: The first tensor is the transformer input
                after the conditions have been fused. The second output tensor is the tensor
                used for cross-attention or None if no cross attention inputs exist.
        """
        B, T, _ = input.shape

        if 'offsets' in self._streaming_state:
            first_step = False
            offsets = self._streaming_state['offsets']
        else:
            first_step = True
            offsets = torch.zeros(input.shape[0], dtype=torch.long, device=input.device)

        assert set(conditions.keys()).issubset(set(self.cond2fuse.keys())), \
            f"given conditions contain unknown attributes for fuser, " \
            f"expected {self.cond2fuse.keys()}, got {conditions.keys()}"
        cross_attention_output = None
        for cond_type, (cond, cond_mask) in conditions.items():
            op = self.cond2fuse[cond_type]
            if op == 'sum':
                input += cond
            elif op == 'input_interpolate':
                cond = einops.rearrange(cond, "b t d -> b d t")
                cond = F.interpolate(cond, size=input.shape[1])
                input += einops.rearrange(cond, "b d t -> b t d")
            elif op == 'prepend':
                if first_step:
                    input = torch.cat([cond, input], dim=1)
            elif op == 'cross':
                if cross_attention_output is not None:
                    cross_attention_output = torch.cat([cross_attention_output, cond], dim=1)
                else:
                    cross_attention_output = cond
            else:
                raise ValueError(f"unknown op ({op})")

        if self.cross_attention_pos_emb and cross_attention_output is not None:
            positions = torch.arange(
                cross_attention_output.shape[1],
                device=cross_attention_output.device
            ).view(1, -1, 1)
            pos_emb = create_sin_embedding(positions, cross_attention_output.shape[-1])
            cross_attention_output = cross_attention_output + self.cross_attention_pos_emb_scale * pos_emb

        if self._is_streaming:
            self._streaming_state['offsets'] = offsets + T

        return input, cross_attention_output



# ============================================== From LM.py



logger = logging.getLogger(__name__)
ConditionTensors = tp.Dict[str, ConditionType]
CFGConditions = tp.Union[ConditionTensors, tp.Tuple[ConditionTensors, ConditionTensors]]


def get_init_fn(method: str, input_dim: int, init_depth: tp.Optional[int] = None):
    """LM layer initialization.
    Inspired from xlformers: https://github.com/fairinternal/xlformers

    Args:
        method (str): Method name for init function. Valid options are:
            'gaussian', 'uniform'.
        input_dim (int): Input dimension of the initialized module.
        init_depth (int, optional): Optional init depth value used to rescale
            the standard deviation if defined.
    """
    # Compute std
    std = 1 / math.sqrt(input_dim)
    # Rescale with depth
    if init_depth is not None:
        std = std / math.sqrt(2 * init_depth)

    if method == 'gaussian':
        return partial(
            torch.nn.init.trunc_normal_, mean=0.0, std=std, a=-3 * std, b=3 * std
        )
    elif method == 'uniform':
        bound = math.sqrt(3) * std  # ensure the standard deviation is `std`
        return partial(torch.nn.init.uniform_, a=-bound, b=bound)
    else:
        raise ValueError("Unsupported layer initialization method")


def init_layer(m: nn.Module,
               method: str,
               init_depth: tp.Optional[int] = None,
               zero_bias_init: bool = False):
    """Wrapper around ``get_init_fn`` for proper initialization of LM modules.

    Args:
        m (nn.Module): Module to initialize.
        method (str): Method name for the init function.
        init_depth (int, optional): Optional init depth value used to rescale
            the standard deviation if defined.
        zero_bias_init (bool): Whether to initialize the bias to 0 or not.
    """
    if isinstance(m, nn.Linear):
        init_fn = get_init_fn(method, m.in_features, init_depth=init_depth)
        if m.weight.device.type == 'cpu' and m.weight.dtype == torch.float16:
            weight = m.weight.float()
            init_fn(weight)
            m.weight.data[:] = weight.half()
        else:
            init_fn(m.weight)
        if zero_bias_init and m.bias is not None:
            nn.init.constant_(m.bias, 0)
    elif isinstance(m, nn.Embedding):
        init_fn = get_init_fn(method, m.embedding_dim, init_depth=None)
        if m.weight.device.type == 'cpu' and m.weight.dtype == torch.float16:
            weight = m.weight.float()
            init_fn(weight)
            m.weight.data[:] = weight.half()
        else:
            init_fn(m.weight)


class ScaledEmbedding(nn.Embedding):
    """Boost learning rate for embeddings (with `scale`).
    """
    def __init__(self, *args, lr=None, **kwargs):
        super().__init__(*args, **kwargs)
        self.lr = lr

    def make_optim_group(self):
        group = {"params": list(self.parameters())}
        if self.lr is not None:
            group["lr"] = self.lr
        return group


@dataclass
class LMOutput:
    # The logits are already re-aligned with the input codes
    # hence no extra shift is required, e.g. when computing CE
    logits: torch.Tensor  # [B, K, T, card]
    mask: torch.Tensor  # [B, K, T]


class LMModel(StreamingModule):
    """Transformer-based language model on multiple streams of codes.

    Args:
        pattern_provider (CodebooksPatternProvider): Pattern provider for codebook interleaving.
        condition_provider (MusicConditioningProvider): Conditioning provider from metadata.
        fuser (ConditionFuser): Fuser handling the fusing of conditions with language model input.
        n_q (int): Number of parallel streams to model.
        card (int): Cardinality, vocabulary size.
        dim (int): Dimension of the transformer encoder.
        num_heads (int): Number of heads for the transformer encoder.
        hidden_scale (int): Scale for hidden feed forward dimension of the transformer encoder.
        norm (str): Normalization method.
        norm_first (bool): Use pre-norm instead of post-norm.
        emb_lr (float, optional): Embedding-specific learning rate.
        bias_proj (bool): Use bias for output projections.
        weight_init (str, optional): Method for weight initialization.
        depthwise_init (str, optional): Method for depthwise weight initialization.
        zero_bias_init (bool): If true and bias in Linears, initialize bias to zeros.
        cfg_dropout (float): Classifier-free guidance dropout.
        cfg_coef (float): Classifier-free guidance coefficient.
        attribute_dropout (dict): Attribute dropout probabilities.
        two_step_cfg (bool): Whether to run classifier free-guidance with 2 distinct steps.
        **kwargs: Additional parameters for the transformer encoder.
    """
    def __init__(self, pattern_provider: CodebooksPatternProvider, condition_provider: ConditioningProvider,
                 fuser: ConditionFuser, n_q: int = 8, card: int = 1024, dim: int = 128, num_heads: int = 8,
                 hidden_scale: int = 4, norm: str = 'layer_norm', norm_first: bool = False,
                 emb_lr: tp.Optional[float] = None, bias_proj: bool = True,
                 weight_init: tp.Optional[str] = None, depthwise_init: tp.Optional[str] = None,
                 zero_bias_init: bool = False, cfg_dropout: float = 0, cfg_coef: float = 1.0,
                 attribute_dropout: tp.Dict[str, tp.Dict[str, float]] = {}, two_step_cfg: bool = False,
                 **kwargs):
        super().__init__()
        self.cfg_coef = cfg_coef
        self.cfg_dropout = ClassifierFreeGuidanceDropout(p=cfg_dropout)
        self.att_dropout = AttributeDropout(p=attribute_dropout)
        self.condition_provider = condition_provider
        self.fuser = fuser
        self.card = card
        embed_dim = self.card + 1
        self.n_q = n_q
        self.dim = dim
        self.pattern_provider = pattern_provider
        self.two_step_cfg = two_step_cfg
        self.emb = nn.ModuleList([ScaledEmbedding(embed_dim, dim, lr=emb_lr) for _ in range(n_q)])
        if 'activation' in kwargs:
            kwargs['activation'] = get_activation_fn(kwargs['activation'])
        self.transformer = StreamingTransformer(
            d_model=dim, num_heads=num_heads, dim_feedforward=int(hidden_scale * dim),
            norm=norm, norm_first=norm_first, **kwargs)
        self.out_norm: tp.Optional[nn.Module] = None
        if norm_first:
            self.out_norm = create_norm_fn(norm, dim)
        self.linears = nn.ModuleList([nn.Linear(dim, self.card, bias=bias_proj) for _ in range(n_q)])
        self._init_weights(weight_init, depthwise_init, zero_bias_init)
        self._fsdp: tp.Optional[nn.Module]
        self.__dict__['_fsdp'] = None

    def _init_weights(self, weight_init: tp.Optional[str], depthwise_init: tp.Optional[str], zero_bias_init: bool):
        """Initialization of the transformer module weights.

        Args:
            weight_init (str, optional): Weight initialization strategy. See ``get_init_fn`` for valid options.
            depthwise_init (str, optional): Depthwise initialization strategy. The following options are valid:
                'current' where the depth corresponds to the current layer index or 'global' where the total number
                of layer is used as depth. If not set, no depthwise initialization strategy is used.
            zero_bias_init (bool): Whether to initialize bias to zero or not.
        """
        assert depthwise_init is None or depthwise_init in ['current', 'global']
        assert depthwise_init is None or weight_init is not None, \
            "If 'depthwise_init' is defined, a 'weight_init' method should be provided."
        assert not zero_bias_init or weight_init is not None, \
            "If 'zero_bias_init', a 'weight_init' method should be provided"

        if weight_init is None:
            return

        for emb_layer in self.emb:
            init_layer(emb_layer, method=weight_init, init_depth=None, zero_bias_init=zero_bias_init)

        for layer_idx, tr_layer in enumerate(self.transformer.layers):
            depth = None
            if depthwise_init == 'current':
                depth = layer_idx + 1
            elif depthwise_init == 'global':
                depth = len(self.transformer.layers)
            init_fn = partial(init_layer, method=weight_init, init_depth=depth, zero_bias_init=zero_bias_init)
            tr_layer.apply(init_fn)

        for linear in self.linears:
            init_layer(linear, method=weight_init, init_depth=None, zero_bias_init=zero_bias_init)

    @property
    def special_token_id(self) -> int:
        return self.card

    @property
    def num_codebooks(self) -> int:
        return self.n_q

    def forward(self, sequence: torch.Tensor,
                conditions: tp.List[ConditioningAttributes],
                condition_tensors: tp.Optional[ConditionTensors] = None,
                stage: int = -1) -> torch.Tensor:
        """Apply language model on sequence and conditions.
        Given a tensor of sequence of shape [B, K, S] with K the number of codebooks and
        S the sequence steps, return the logits with shape [B, card, K, S].

        Args:
            indices (torch.Tensor): Indices of the codes to model.
            conditions (list of ConditioningAttributes): Conditions to use when modeling
                the given codes. Note that when evaluating multiple time with the same conditioning
                you should pre-compute those and pass them as `condition_tensors`.
            condition_tensors (dict[str, ConditionType], optional): Pre-computed conditioning
                tensors, see `conditions`.
            stage (int): The codebook level that is being predicted. Relevant for MAGNeT
                in which prediction is done in a codebook-by-codebook manner.
                Takes values in range(n_q), and ignored by default.
        Returns:
            torch.Tensor: Logits.
        """
        B, K, S = sequence.shape
        assert K == self.num_codebooks, "Sequence shape must match the specified number of codebooks"
        input_ = sum([self.emb[k](sequence[:, k]) for k in range(K)])
        if condition_tensors is None:
            assert not self._is_streaming, "Conditions tensors should be precomputed when streaming."
            # apply dropout modules
            conditions = self.cfg_dropout(conditions)
            conditions = self.att_dropout(conditions)
            tokenized = self.condition_provider.tokenize(conditions)
            # encode conditions and fuse, both have a streaming cache to not recompute when generating.
            condition_tensors = self.condition_provider(tokenized)
        else:
            assert not conditions, "Shouldn't pass both conditions and condition_tensors."

        input_, cross_attention_input = self.fuser(input_, condition_tensors)

        out = self.transformer(input_, cross_attention_src=cross_attention_input,
                               src_mask=(self.attn_mask_per_stage[stage] if stage >= 0 else None))
        if self.out_norm:
            out = self.out_norm(out)
        logits = torch.stack([self.linears[k](out) for k in range(K)], dim=1)  # [B, K, S, card]

        # remove the prefix from the model outputs
        if len(self.fuser.fuse2cond['prepend']) > 0:
            logits = logits[:, :, -S:]

        return logits  # [B, K, S, card]

    def compute_predictions(
            self, codes: torch.Tensor,
            conditions: tp.List[ConditioningAttributes],
            condition_tensors: tp.Optional[ConditionTensors] = None,
            stage: int = -1,
            keep_only_valid_steps: bool = True) -> LMOutput:
        """Given an input tensor of codes [B, K, T] and list of conditions, runs the model
        forward using the specified codes interleaving pattern.

        Args:
            codes (torch.Tensor): Input codes of shape [B, K, T] with B the batch size,
                K the number of codebooks and T the number of timesteps.
            conditions (list of ConditioningAttributes): conditionings to use when modeling
                the given codes. Note that when evaluating multiple time with the same conditioning
                you should pre-compute those and pass them as `condition_tensors`.
            condition_tensors (dict[str, ConditionType], optional): pre-computed conditioning
                tensors, see `conditions`.
            stage (int): The codebook level that is being predicted. Relevant for MAGNeT
                in which prediction is done in a codebook-by-codebook manner.
                Takes values in range(n_q), and ignored by default.
            keep_only_valid_steps (bool): Build a sequence from the pattern up to valid (= fully defined) steps.
                Steps that are beyond valid steps will be replaced by the special_token in that case.
        Returns:
            LMOutput: Language model outputs
                logits (torch.Tensor) of shape [B, K, T, card] corresponding to the provided codes,
                    i.e. the first item corresponds to logits to predict the first code, meaning that
                    no additional shifting of codes and logits is required.
                mask (torch.Tensor) of shape [B, K, T], mask over valid and invalid positions.
                    Given the specified interleaving strategies, parts of the logits and codes should
                    not be considered as valid predictions because of invalid context.
        """
        B, K, T = codes.shape
        codes = codes.contiguous()
        # map codes [B, K, T] into pattern sequence [B, K, S] using special_token_id for masked tokens
        pattern = self.pattern_provider.get_pattern(T)
        sequence_codes, sequence_indexes, sequence_mask = pattern.build_pattern_sequence(
            codes, self.special_token_id, keep_only_valid_steps=keep_only_valid_steps,
        )

        # apply model on pattern sequence
        model = self if self._fsdp is None else self._fsdp
        logits = model(sequence_codes, conditions, condition_tensors, stage=stage)  # [B, K, S, card]
        # map back the logits on pattern sequence to logits on original codes: [B, K, S, card] -> [B, K, T, card]
        # and provide the corresponding mask over invalid positions of tokens
        logits = logits.permute(0, 3, 1, 2)  # [B, card, K, S]
        # note: we use nans as special token to make it obvious if we feed unexpected logits
        logits, logits_indexes, logits_mask = pattern.revert_pattern_logits(
            logits, float('nan'), keep_only_valid_steps=keep_only_valid_steps
        )
        logits = logits.permute(0, 2, 3, 1)  # [B, K, T, card]
        logits_mask = logits_mask[None, :, :].expand(B, -1, -1)  # [K, T] -> [B, K, T]
        return LMOutput(logits, logits_mask)

    def _sample_next_token(self,
                           sequence,
                           cfg_conditions,
                           unconditional_state,
                           use_sampling=False,
                           temp: float = 1.0,
                           top_k: int = 0,
                           top_p: float = 0.0,
                           cfg_coef: tp.Optional[float] = None,
                           two_step_cfg: tp.Optional[bool] = None) -> torch.Tensor:
        """Sample next token from the model given a sequence and a set of conditions. The model supports
        multiple sampling strategies (greedy sampling, softmax, top-k, top-p...).

        Args:
            sequence (torch.Tensor): Current sequence of shape [B, K, S]
                with K corresponding to the number of codebooks and S the number of sequence steps.
                S = 1 in streaming mode, except for the first step that contains a bigger prompt.
            condition_tensors (dict[str, ConditionType): Set of conditions. If CFG is used,
                should be twice the batch size, being the concatenation of the conditions + null conditions.
            use_sampling (bool): Whether to use a sampling strategy or not.
            temp (float): Sampling temperature.
            top_k (int): K for "top-k" sampling.
            top_p (float): P for "top-p" sampling.
            cfg_coef (float, optional): classifier free guidance coefficient
        Returns:
            next_token (torch.Tensor): Next token tensor of shape [B, K, 1].
        """
        B = sequence.shape[0]
        cfg_coef = self.cfg_coef if cfg_coef is None else cfg_coef
        model = self if self._fsdp is None else self._fsdp
        two_step_cfg = self.two_step_cfg if two_step_cfg is None else two_step_cfg
        if two_step_cfg and cfg_conditions != {}:
            assert isinstance(cfg_conditions, tuple), type(cfg_conditions)
            condition_tensors, null_condition_tensors = cfg_conditions
            cond_logits = model(sequence, conditions=[], condition_tensors=condition_tensors)
            state = self.get_streaming_state()
            self.set_streaming_state(unconditional_state)
            uncond_logits = model(sequence, conditions=[], condition_tensors=null_condition_tensors)
            unconditional_state.update(self.get_streaming_state())
            self.set_streaming_state(state)
            logits = uncond_logits + (cond_logits - uncond_logits) * self.cfg_coef
        else:
            assert isinstance(cfg_conditions, dict)
            condition_tensors = cfg_conditions
            if condition_tensors:
                # Preparing for CFG, predicting both conditional and unconditional logits.
                sequence = torch.cat([sequence, sequence], dim=0)
            all_logits = model(
                sequence,
                conditions=[], condition_tensors=condition_tensors)
            if condition_tensors:
                cond_logits, uncond_logits = all_logits.split(B, dim=0)  # [B, K, T, card]
                logits = uncond_logits + (cond_logits - uncond_logits) * cfg_coef
            else:
                logits = all_logits

        logits = logits.permute(0, 1, 3, 2)  # [B, K, card, T]
        logits = logits[..., -1]  # [B x K x card]

        # Apply softmax for sampling if temp > 0. Else, do greedy sampling to avoid zero division error.
        if use_sampling and temp > 0.0:
            probs = torch.softmax(logits / temp, dim=-1)
            if top_p > 0.0:
                next_token = utils.sample_top_p(probs, p=top_p)
            elif top_k > 0:
                next_token = utils.sample_top_k(probs, k=top_k)
            else:
                next_token = utils.multinomial(probs, num_samples=1)
        else:
            next_token = torch.argmax(logits, dim=-1, keepdim=True)

        return next_token

    @torch.no_grad()
    def generate(self,
                 prompt: tp.Optional[torch.Tensor] = None,
                 conditions: tp.List[ConditioningAttributes] = [],
                 num_samples: tp.Optional[int] = None,
                 max_gen_len: int = 256,
                 use_sampling: bool = True,
                 temp: float = 1.0,
                 top_k: int = 250,
                 top_p: float = 0.0,
                 cfg_coef: tp.Optional[float] = None,
                 two_step_cfg: tp.Optional[bool] = None,
                 remove_prompts: bool = False,
                 check: bool = False,
                 callback: tp.Optional[tp.Callable[[int, int], None]] = None,
                 **kwargs) -> torch.Tensor:
        """Generate tokens sampling from the model given a prompt or unconditionally. Generation can
        be performed in a greedy fashion or using sampling with top K and top P strategies.

        Args:
            prompt (torch.Tensor, optional): Prompt tokens of shape [B, K, T].
            conditions_tensors (list of ConditioningAttributes, optional): List of conditions.
            num_samples (int, optional): Number of samples to generate when no prompt and no conditions are given.
            max_gen_len (int): Maximum generation length.
            use_sampling (bool): Whether to use a sampling strategy or not.
            temp (float): Sampling temperature.
            top_k (int): K for "top-k" sampling.
            top_p (float): P for "top-p" sampling.
            cfg_coeff (float, optional): Classifier-free guidance coefficient.
            two_step_cfg (bool, optional): Whether to perform classifier-free guidance with two steps generation.
            remove_prompts (bool): Whether to remove prompts from generation or not.
            check (bool): Whether to apply further checks on generated sequence.
            callback (Callback, optional): Callback function to report generation progress.
        Returns:
            torch.Tensor: Generated tokens.
        """
        assert not self.training, "generation shouldn't be used in training mode."
        first_param = next(iter(self.parameters()))
        device = first_param.device

        # Checking all input shapes are consistent.
        possible_num_samples = []
        if num_samples is not None:
            possible_num_samples.append(num_samples)
        elif prompt is not None:
            possible_num_samples.append(prompt.shape[0])
        elif conditions:
            possible_num_samples.append(len(conditions))
        else:
            possible_num_samples.append(1)
        assert [x == possible_num_samples[0] for x in possible_num_samples], "Inconsistent inputs shapes"
        num_samples = possible_num_samples[0]

        # below we create set of conditions: one conditional and one unconditional
        # to do that we merge the regular condition together with the null condition
        # we then do 1 forward pass instead of 2.
        # the reason for that is two-fold:
        # 1. it is about x2 faster than doing 2 forward passes
        # 2. avoid the streaming API treating the 2 passes as part of different time steps
        # We also support doing two different passes, in particular to ensure that
        # the padding structure is exactly the same between train and test.
        # With a batch size of 1, this can be slower though.
        cfg_conditions: CFGConditions
        two_step_cfg = self.two_step_cfg if two_step_cfg is None else two_step_cfg
        if conditions:
            null_conditions = ClassifierFreeGuidanceDropout(p=1.0)(conditions)
            if two_step_cfg:
                cfg_conditions = (
                    self.condition_provider(self.condition_provider.tokenize(conditions)),
                    self.condition_provider(self.condition_provider.tokenize(null_conditions)),
                )
            else:
                conditions = conditions + null_conditions
                tokenized = self.condition_provider.tokenize(conditions)
                cfg_conditions = self.condition_provider(tokenized)
        else:
            cfg_conditions = {}

        if prompt is None:
            assert num_samples > 0
            prompt = torch.zeros((num_samples, self.num_codebooks, 0), dtype=torch.long, device=device)

        B, K, T = prompt.shape
        start_offset = T
        assert start_offset < max_gen_len

        pattern = self.pattern_provider.get_pattern(max_gen_len)
        # this token is used as default value for codes that are not generated yet
        unknown_token = -1

        # we generate codes up to the max_gen_len that will be mapped to the pattern sequence
        gen_codes = torch.full((B, K, max_gen_len), unknown_token, dtype=torch.long, device=device)
        # filling the gen_codes with the prompt if needed
        gen_codes[..., :start_offset] = prompt
        # create the gen_sequence with proper interleaving from the pattern: [B, K, S]
        gen_sequence, indexes, mask = pattern.build_pattern_sequence(gen_codes, self.special_token_id)
        # retrieve the start_offset in the sequence:
        # it is the first sequence step that contains the `start_offset` timestep
        start_offset_sequence = pattern.get_first_step_with_timesteps(start_offset)
        assert start_offset_sequence is not None

        with self.streaming():
            unconditional_state = self.get_streaming_state()
            prev_offset = 0
            gen_sequence_len = gen_sequence.shape[-1]  # gen_sequence shape is [B, K, S]
            for offset in range(start_offset_sequence, gen_sequence_len):
                # get current sequence (note that the streaming API is providing the caching over previous offsets)
                curr_sequence = gen_sequence[..., prev_offset:offset]
                curr_mask = mask[None, ..., prev_offset:offset].expand(B, -1, -1)
                if check:
                    # check coherence between mask and sequence
                    assert (curr_sequence == torch.where(curr_mask, curr_sequence, self.special_token_id)).all()
                    # should never happen as gen_sequence is filled progressively
                    assert not (curr_sequence == unknown_token).any()
                # sample next token from the model, next token shape is [B, K, 1]
                next_token = self._sample_next_token(
                    curr_sequence, cfg_conditions, unconditional_state, use_sampling, temp, top_k, top_p,
                    cfg_coef=cfg_coef, two_step_cfg=two_step_cfg)
                # ensure the tokens that should be masked are properly set to special_token_id
                # as the model never output special_token_id
                valid_mask = mask[..., offset:offset+1].expand(B, -1, -1)
                next_token[~valid_mask] = self.special_token_id
                # ensure we don't overwrite prompt tokens, we only write over unknown tokens
                # (then mask tokens should be left as is as well, which is correct)
                gen_sequence[..., offset:offset+1] = torch.where(
                    gen_sequence[..., offset:offset+1] == unknown_token,
                    next_token, gen_sequence[..., offset:offset+1]
                )
                prev_offset = offset
                if callback is not None:
                    callback(1 + offset - start_offset_sequence, gen_sequence_len - start_offset_sequence)
        unconditional_state.clear()

        # ensure sequence has been entirely filled
        assert not (gen_sequence == unknown_token).any()
        # ensure gen_sequence pattern and mask are matching
        # which means the gen_sequence is valid according to the pattern
        assert (
            gen_sequence == torch.where(mask[None, ...].expand(B, -1, -1), gen_sequence, self.special_token_id)
        ).all()
        # get back the codes, trimming the prompt if needed and cutting potentially incomplete timesteps
        out_codes, out_indexes, out_mask = pattern.revert_pattern_sequence(gen_sequence, special_token=unknown_token)

        # sanity checks over the returned codes and corresponding masks
        assert (out_codes[..., :max_gen_len] != unknown_token).all()
        assert (out_mask[..., :max_gen_len] == 1).all()

        out_start_offset = start_offset if remove_prompts else 0
        out_codes = out_codes[..., out_start_offset:max_gen_len]

        # ensure the returned codes are all valid
        assert (out_codes >= 0).all() and (out_codes <= self.card).all()
        return out_codes