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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 dataclasses import dataclass
	from functools import partial
	import logging
	import math
	import typing as tp

	import torch
	from torch import nn

	from ..utils import utils
	from ..modules.streaming import StreamingModule, State
	from ..modules.transformer import StreamingTransformer, create_norm_fn
	from ..modules.conditioners import (
	ConditionFuser,
	ClassifierFreeGuidanceDropout,
	AttributeDropout,
	ConditioningProvider,
	ConditioningAttributes,
	ConditionType,
	)
	from ..modules.codebooks_patterns import CodebooksPatternProvider
	from ..modules.activations import get_activation_fn


	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 (Optional[int]): 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 (Optional[int]): 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 (Optional[float]): Embedding-specific learning rate.
	bias_proj (bool): Use bias for output projections.
	weight_init (Optional[str]): Method for weight initialization.
	depthwise_init (Optional[str]): 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 (Optional[str]): Weight initialization strategy. See ``get_init_fn`` for valid options.
	depthwise_init (Optional[str]): Depwthwise 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 initalize 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) -> 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[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] or None): pre-computed conditioning
	tensors, see `conditions`.
	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)
	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) -> 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[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] or None): pre-computed conditioning
	tensors, see `conditions`.
	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=True
	)
	# apply model on pattern sequence
	model = self if self._fsdp is None else self._fsdp
	logits = model(sequence_codes, conditions, condition_tensors) # [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=True
	)
	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: torch.Tensor,
	cfg_conditions: CFGConditions,
	unconditional_state: State,
	use_sampling: bool = False,
	temp: float = 1.0,
	top_k: int = 0,
	top_p: float = 0.0,
	cfg_coef: tp.Optional[float] = 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): 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
	if self.two_step_cfg and cfg_conditions != {}:
	assert isinstance(cfg_conditions, tuple)
	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]

	if use_sampling:
	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: bool = False,
	remove_prompts: bool = False,
	check: bool = False,
	callback: tp.Optional[tp.Callable[[int, int], None]] = None) -> torch.Tensor:
	"""Generate tokens sampling from the model given a prompt or unconditionally. Generation can
	be perform in a greedy fashion or using sampling with top K and top P strategies.

	Args:
	prompt (Optional[torch.Tensor]): Prompt tokens of shape [B, K, T].
	conditions_tensors (Dict[str, torch.Tensor]): Set of conditions or None.
	num_samples (int or None): 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.
	remove_prompts (bool): Whether to remove prompts from generation or not.
	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 consistents.
	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], "Inconsitent 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 anf 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)
	# 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