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MusicGen / audiocraft /modules /conditioners.py

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	# Copyright (c) Meta Platforms, Inc. and affiliates.
	# All rights reserved.
	#
	# This source code is licensed under the license found in the
	# LICENSE file in the root directory of this source tree.

	from collections import defaultdict
	from copy import deepcopy
	from dataclasses import dataclass, field
	from itertools import chain
	import logging
	import random
	import re
	import typing as tp
	import warnings

	from einops import rearrange
	from num2words import num2words
	import spacy
	from transformers import T5EncoderModel, T5Tokenizer # type: ignore
	import torchaudio
	import torch
	from torch import nn
	from torch import Tensor
	import torch.nn.functional as F
	from torch.nn.utils.rnn import pad_sequence

	from .streaming import StreamingModule
	from .transformer import create_sin_embedding
	from ..data.audio_dataset import SegmentInfo
	from ..utils.autocast import TorchAutocast
	from ..utils.utils import hash_trick, length_to_mask, collate


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


	class WavCondition(tp.NamedTuple):
	wav: Tensor
	length: Tensor
	path: tp.List[tp.Optional[str]] = []


	def nullify_condition(condition: ConditionType, dim: int = 1):
	"""This function transforms 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 (tp.Tuple[Tensor, 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 type(condition) == tuple and \
	type(condition[0]) == Tensor and \
	type(condition[1]) == 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(wav: Tensor) -> WavCondition:
	"""Create a nullified WavCondition from a wav tensor with appropriate shape.

	Args:
	wav (Tensor): tensor of shape [B, T]
	Returns:
	WavCondition: wav condition with nullified wav.
	"""
	null_wav, _ = nullify_condition((wav, torch.zeros_like(wav)), dim=wav.dim() - 1)
	return WavCondition(
	wav=null_wav,
	length=torch.tensor([0] * wav.shape[0], device=wav.device),
	path=['null_wav'] * wav.shape[0]
	)


	@dataclass
	class ConditioningAttributes:
	text: tp.Dict[str, tp.Optional[str]] = field(default_factory=dict)
	wav: tp.Dict[str, WavCondition] = 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 attributes(self):
	return {"text": self.text_attributes, "wav": self.wav_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()},
	}

	@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


	class SegmentWithAttributes(SegmentInfo):
	"""Base class for all dataclasses that are used for conditioning.
	All child classes should implement `to_condition_attributes` that converts
	the existing attributes to a dataclass of type ConditioningAttributes.
	"""
	def to_condition_attributes(self) -> ConditioningAttributes:
	raise NotImplementedError()


	class Tokenizer:
	"""Base class for all tokenizers
	(in case we want to introduce more advances tokenizers in the future).
	"""
	def __call__(self, texts: tp.List[tp.Optional[str]]) -> tp.Tuple[Tensor, 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]]
	"""
	PUNCTUATIONS = "?:!.,;"

	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[Tensor, Tensor]:
	"""Take a list of strings and convert them to a tensor of indices.

	Args:
	texts (tp.List[str]): List of strings.
	return_text (bool, optional): Whether to return text as additional tuple item. Defaults to False.
	Returns:
	tp.Tuple[Tensor, 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(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 punctuations
	text = [w for w in text if w.text not in self.PUNCTUATIONS] # 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 = 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[Tensor, 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 (text-encoder/LUT).
	output_dim (int): Output dim of the conditioner.
	"""
	def __init__(self, dim, output_dim):
	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

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

	def _get_wav_embedding(self, wav: Tensor) -> Tensor:
	"""Gets as input a wav and returns a dense vector of conditions."""
	raise NotImplementedError()

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

	def forward(self, inputs: WavCondition) -> ConditionType:
	"""
	Args:
	input (WavCondition): Tuple of (waveform, lengths).
	Returns:
	ConditionType: Dense vector representing the conditioning along with its' mask.
	"""
	wav, lengths, path = inputs
	with torch.no_grad():
	embeds = self._get_wav_embedding(wav)
	embeds = embeds.to(self.output_proj.weight)
	embeds = self.output_proj(embeds)

	if lengths is not None:
	lengths = lengths / self._downsampling_factor()
	mask = length_to_mask(lengths, max_len=embeds.shape[1]).int() # type: ignore
	else:
	mask = torch.ones_like(embeds)
	embeds = (embeds * mask.unsqueeze(2).to(self.device))

	return embeds, mask


	class ChromaStemConditioner(WaveformConditioner):
	"""Chroma conditioner that uses DEMUCS to first filter out drums and bass. The is followed by
	the insight the drums and bass often dominate the chroma, leading to the chroma not containing the
	information about melody.

	Args:
	output_dim (int): Output dimension for the conditioner.
	sample_rate (int): Sample rate for the chroma extractor.
	n_chroma (int): Number of chroma for the chroma extractor.
	radix2_exp (int): Radix2 exponent for the chroma extractor.
	duration (float): Duration used during training. This is later used for correct padding
	in case we are using chroma as prefix.
	match_len_on_eval (bool, optional): If True then all chromas are padded to the training
	duration. Defaults to False.
	eval_wavs (str, optional): Path to a json egg with waveform, this waveforms are used as
	conditions during eval (for cases where we don't want to leak test conditions like MusicCaps).
	Defaults to None.
	n_eval_wavs (int, optional): Limits the number of waveforms used for conditioning. Defaults to 0.
	device (tp.Union[torch.device, str], optional): Device for the conditioner.
	**kwargs: Additional parameters for the chroma extractor.
	"""
	def __init__(self, output_dim: int, sample_rate: int, n_chroma: int, radix2_exp: int,
	duration: float, match_len_on_eval: bool = False, eval_wavs: tp.Optional[str] = None,
	n_eval_wavs: int = 0, device: tp.Union[torch.device, str] = "cpu", **kwargs):
	from demucs import pretrained
	super().__init__(dim=n_chroma, output_dim=output_dim, device=device)
	self.autocast = TorchAutocast(enabled=device != "cpu", device_type=self.device, dtype=torch.float32)
	self.sample_rate = sample_rate
	self.match_len_on_eval = match_len_on_eval
	self.duration = duration
	self.__dict__["demucs"] = pretrained.get_model('htdemucs').to(device)
	self.stem2idx = {'drums': 0, 'bass': 1, 'other': 2, 'vocal': 3}
	self.stem_idx = torch.LongTensor([self.stem2idx['vocal'], self.stem2idx['other']]).to(device)
	self.chroma = ChromaExtractor(sample_rate=sample_rate, n_chroma=n_chroma, radix2_exp=radix2_exp,
	device=device, **kwargs)
	self.chroma_len = self._get_chroma_len()

	def _downsampling_factor(self):
	return self.chroma.winhop

	def _get_chroma_len(self):
	"""Get length of chroma during training"""
	dummy_wav = torch.zeros((1, self.sample_rate * self.duration), device=self.device)
	dummy_chr = self.chroma(dummy_wav)
	return dummy_chr.shape[1]

	@torch.no_grad()
	def _get_filtered_wav(self, wav):
	from demucs.apply import apply_model
	from demucs.audio import convert_audio
	with self.autocast:
	wav = convert_audio(wav, self.sample_rate, self.demucs.samplerate, self.demucs.audio_channels)
	stems = apply_model(self.demucs, wav, device=self.device)
	stems = stems[:, self.stem_idx] # extract stem
	stems = stems.sum(1) # merge extracted stems
	stems = stems.mean(1, keepdim=True) # mono
	stems = convert_audio(stems, self.demucs.samplerate, self.sample_rate, 1)
	return stems

	@torch.no_grad()
	def _get_wav_embedding(self, wav):
	# avoid 0-size tensors when we are working with null conds
	if wav.shape[-1] == 1:
	return self.chroma(wav)
	stems = self._get_filtered_wav(wav)
	chroma = self.chroma(stems)

	if self.match_len_on_eval:
	b, t, c = chroma.shape
	if t > self.chroma_len:
	chroma = chroma[:, :self.chroma_len]
	logger.debug(f'chroma was truncated! ({t} -> {chroma.shape[1]})')
	elif t < self.chroma_len:
	chroma = F.pad(chroma, (0, 0, 0, self.chroma_len - t))
	logger.debug(f'chroma was zero-padded! ({t} -> {chroma.shape[1]})')
	return chroma


	class ChromaExtractor(nn.Module):
	"""Chroma extraction class, handles chroma extraction and quantization.

	Args:
	sample_rate (int): Sample rate.
	n_chroma (int): Number of chroma to consider.
	radix2_exp (int): Radix2 exponent.
	nfft (tp.Optional[int], optional): Number of FFT.
	winlen (tp.Optional[int], optional): Window length.
	winhop (tp.Optional[int], optional): Window hop size.
	argmax (bool, optional): Whether to use argmax. Defaults to False.
	norm (float, optional): Norm for chroma normalization. Defaults to inf.
	device (tp.Union[torch.device, str], optional): Device to use. Defaults to cpu.
	"""
	def __init__(self, sample_rate: int, n_chroma: int = 12, radix2_exp: int = 12,
	nfft: tp.Optional[int] = None, winlen: tp.Optional[int] = None, winhop: tp.Optional[int] = None,
	argmax: bool = False, norm: float = torch.inf, device: tp.Union[torch.device, str] = "cpu"):
	super().__init__()
	from librosa import filters
	self.device = device
	self.autocast = TorchAutocast(enabled=device != "cpu", device_type=self.device, dtype=torch.float32)
	self.winlen = winlen or 2 ** radix2_exp
	self.nfft = nfft or self.winlen
	self.winhop = winhop or (self.winlen // 4)
	self.sr = sample_rate
	self.n_chroma = n_chroma
	self.norm = norm
	self.argmax = argmax
	self.window = torch.hann_window(self.winlen).to(device)
	self.fbanks = torch.from_numpy(filters.chroma(sr=sample_rate, n_fft=self.nfft, tuning=0,
	n_chroma=self.n_chroma)).to(device)
	self.spec = torchaudio.transforms.Spectrogram(n_fft=self.nfft, win_length=self.winlen,
	hop_length=self.winhop, power=2, center=True,
	pad=0, normalized=True).to(device)

	def forward(self, wav):
	with self.autocast:
	T = wav.shape[-1]
	# in case we are getting a wav that was dropped out (nullified)
	# make sure wav length is no less that nfft
	if T < self.nfft:
	pad = self.nfft - T
	r = 0 if pad % 2 == 0 else 1
	wav = F.pad(wav, (pad // 2, pad // 2 + r), 'constant', 0)
	assert wav.shape[-1] == self.nfft, f'expected len {self.nfft} but got {wav.shape[-1]}'
	spec = self.spec(wav).squeeze(1)
	raw_chroma = torch.einsum("cf,...ft->...ct", self.fbanks, spec)
	norm_chroma = torch.nn.functional.normalize(raw_chroma, p=self.norm, dim=-2, eps=1e-6)
	norm_chroma = rearrange(norm_chroma, "b d t -> b t d")

	if self.argmax:
	idx = norm_chroma.argmax(-1, keepdims=True)
	norm_chroma[:] = 0
	norm_chroma.scatter_(dim=-1, index=idx, value=1)

	return norm_chroma


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

	return sample


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


	class AttributeDropout(DropoutModule):
	"""Applies 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 (tp.List[ConditioningAttributes]): List of conditions.
	Returns:
	tp.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):
	"""Applies Classifier Free Guidance dropout, meaning 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 (tp.List[ConditioningAttributes]): List of conditions.
	Returns:
	tp.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):
	"""Main class to provide conditions given all the supported conditioners.

	Args:
	conditioners (dict): Dictionary of conditioners.
	merge_text_conditions_p (float, optional): Probability to merge all text sources
	into a single text condition. Defaults to 0.
	drop_desc_p (float, optional): Probability to drop the original description
	when merging all text sources into a single text condition. Defaults to 0.
	device (tp.Union[torch.device, str], optional): Device for conditioners and output condition types.
	"""
	def __init__(
	self,
	conditioners: tp.Dict[str, BaseConditioner],
	merge_text_conditions_p: float = 0,
	drop_desc_p: float = 0,
	device: tp.Union[torch.device, str] = "cpu",
	):
	super().__init__()
	self.device = device
	self.merge_text_conditions_p = merge_text_conditions_p
	self.drop_desc_p = drop_desc_p
	self.conditioners = nn.ModuleDict(conditioners)

	@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[ConditioningAttribres]): List of ConditioningAttributes objects containing
	text and wav conditions.
	"""
	assert all([type(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)

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

	for attribute, batch in chain(text.items(), wavs.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"]
	}
	"""
	batch_per_attribute: tp.Dict[str, tp.List[tp.Optional[str]]] = defaultdict(list)

	def _merge_conds(cond, merge_text_conditions_p=0, drop_desc_p=0):
	def is_valid(k, v):
	k_valid = k in ['key', 'bpm', 'genre', 'moods', 'instrument']
	v_valid = v is not None and isinstance(v, (int, float, str, list))
	return k_valid and v_valid

	def process_value(v):
	if isinstance(v, (int, float, str)):
	return v
	if isinstance(v, list):
	return ", ".join(v)
	else:
	RuntimeError(f"unknown type for text value! ({type(v), v})")

	desc = cond.text['description']
	meta_data = ""
	if random.uniform(0, 1) < merge_text_conditions_p:
	meta_pairs = [f'{k}: {process_value(v)}' for k, v in cond.text.items() if is_valid(k, v)]
	random.shuffle(meta_pairs)
	meta_data = ". ".join(meta_pairs)
	desc = desc if not random.uniform(0, 1) < drop_desc_p else None

	if desc is None:
	desc = meta_data if len(meta_data) > 1 else None
	else:
	desc = desc.rstrip('.') + ". " + meta_data
	cond.text['description'] = desc.strip() if desc else None

	if self.training and self.merge_text_conditions_p:
	for sample in samples:
	_merge_conds(sample, self.merge_text_conditions_p, self.drop_desc_p)

	texts = [x.text for x in samples]
	for text in texts:
	for condition in self.text_conditions:
	batch_per_attribute[condition].append(text[condition])

	return batch_per_attribute

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

	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 (tp.List[ConditioningAttributes]): List of ConditioningAttributes samples.
	Returns:
	dict: A dicionary mapping an attribute name to wavs.
	"""
	wavs = defaultdict(list)
	lens = defaultdict(list)
	paths = defaultdict(list)
	out = {}

	for sample in samples:
	for attribute in self.wav_conditions:
	wav, length, path = sample.wav[attribute]
	wavs[attribute].append(wav.flatten())
	lens[attribute].append(length)
	paths[attribute].append(path)

	# 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(lens['self_wav']), paths[attribute]) # type: ignore

	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_MEHTODS}"
	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: Tensor,
	conditions: tp.Dict[str, ConditionType]
	) -> tp.Tuple[Tensor, tp.Optional[Tensor]]:
	"""Fuse the conditions to the provided model input.

	Args:
	input (Tensor): Transformer input.
	conditions (tp.Dict[str, ConditionType]): Dict of conditions.
	Returns:
	tp.Tuple[Tensor, 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 = rearrange(cond, "b t d -> b d t")
	cond = F.interpolate(cond, size=input.shape[1])
	input += 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