# 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.
"""Compression models or wrapper around existing models.
Also defines the main interface that a model must follow to be usable as an audio tokenizer.
"""

from abc import ABC, abstractmethod
import logging
import math
from pathlib import Path
import typing as tp

from einops import rearrange
import numpy as np
import torch
from torch import nn
from transformers import EncodecModel as HFEncodecModel

import audiocraft.quantization as qt


logger = logging.getLogger()


class CompressionModel(ABC, nn.Module):
    """Base API for all compression models that aim at being used as audio tokenizers
    with a language model.
    """

    @abstractmethod
    def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
        ...

    @abstractmethod
    def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
        """See `EncodecModel.encode`."""
        ...

    @abstractmethod
    def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
        """See `EncodecModel.decode`."""
        ...

    @abstractmethod
    def decode_latent(self, codes: torch.Tensor):
        """Decode from the discrete codes to continuous latent space."""
        ...

    @property
    @abstractmethod
    def channels(self) -> int:
        ...

    @property
    @abstractmethod
    def frame_rate(self) -> float:
        ...

    @property
    @abstractmethod
    def sample_rate(self) -> int:
        ...

    @property
    @abstractmethod
    def cardinality(self) -> int:
        ...

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

    @property
    @abstractmethod
    def total_codebooks(self) -> int:
        ...

    @abstractmethod
    def set_num_codebooks(self, n: int):
        """Set the active number of codebooks used by the quantizer."""
        ...

    @staticmethod
    def get_pretrained(
            name: str, device: tp.Union[torch.device, str] = 'cpu'
            ) -> 'CompressionModel':
        """Instantiate a CompressionModel from a given pretrained model.

        Args:
            name (Path or str): name of the pretrained model. See after.
            device (torch.device or str): Device on which the model is loaded.

        Pretrained models:
            - dac_44khz (https://github.com/descriptinc/descript-audio-codec)
            - dac_24khz (same)
            - facebook/encodec_24khz (https://huggingface.co/facebook/encodec_24khz)
            - facebook/encodec_32khz (https://huggingface.co/facebook/encodec_32khz)
            - your own model on Hugging Face. Export instructions to come...
        """

        from . import builders, loaders
        model: CompressionModel
        if name in ['dac_44khz', 'dac_24khz']:
            model_type = name.split('_')[1]
            logger.info("Getting pretrained compression model from DAC %s", model_type)
            model = DAC(model_type)
        elif name in ['debug_compression_model']:
            logger.info("Getting pretrained compression model for debug")
            model = builders.get_debug_compression_model()
        elif Path(name).exists():
            # We assume here if the path exists that it is in fact an AC checkpoint
            # that was exported using `audiocraft.utils.export` functions.
            model = loaders.load_compression_model(name, device=device)
        else:
            logger.info("Getting pretrained compression model from HF %s", name)
            hf_model = HFEncodecModel.from_pretrained(name)
            model = HFEncodecCompressionModel(hf_model).to(device)
        return model.to(device).eval()


class EncodecModel(CompressionModel):
    """Encodec model operating on the raw waveform.

    Args:
        encoder (nn.Module): Encoder network.
        decoder (nn.Module): Decoder network.
        quantizer (qt.BaseQuantizer): Quantizer network.
        frame_rate (int): Frame rate for the latent representation.
        sample_rate (int): Audio sample rate.
        channels (int): Number of audio channels.
        causal (bool): Whether to use a causal version of the model.
        renormalize (bool): Whether to renormalize the audio before running the model.
    """
    # we need assignment to override the property in the abstract class,
    # I couldn't find a better way...
    frame_rate: float = 0
    sample_rate: int = 0
    channels: int = 0

    def __init__(self,
                 encoder: nn.Module,
                 decoder: nn.Module,
                 quantizer: qt.BaseQuantizer,
                 frame_rate: int,
                 sample_rate: int,
                 channels: int,
                 causal: bool = False,
                 renormalize: bool = False):
        super().__init__()
        self.encoder = encoder
        self.decoder = decoder
        self.quantizer = quantizer
        self.frame_rate = frame_rate
        self.sample_rate = sample_rate
        self.channels = channels
        self.renormalize = renormalize
        self.causal = causal
        if self.causal:
            # we force disabling here to avoid handling linear overlap of segments
            # as supported in original EnCodec codebase.
            assert not self.renormalize, 'Causal model does not support renormalize'

    @property
    def total_codebooks(self):
        """Total number of quantizer codebooks available."""
        return self.quantizer.total_codebooks

    @property
    def num_codebooks(self):
        """Active number of codebooks used by the quantizer."""
        return self.quantizer.num_codebooks

    def set_num_codebooks(self, n: int):
        """Set the active number of codebooks used by the quantizer."""
        self.quantizer.set_num_codebooks(n)

    @property
    def cardinality(self):
        """Cardinality of each codebook."""
        return self.quantizer.bins

    def preprocess(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
        scale: tp.Optional[torch.Tensor]
        if self.renormalize:
            mono = x.mean(dim=1, keepdim=True)
            volume = mono.pow(2).mean(dim=2, keepdim=True).sqrt()
            scale = 1e-8 + volume
            x = x / scale
            scale = scale.view(-1, 1)
        else:
            scale = None
        return x, scale

    def postprocess(self,
                    x: torch.Tensor,
                    scale: tp.Optional[torch.Tensor] = None) -> torch.Tensor:
        if scale is not None:
            assert self.renormalize
            x = x * scale.view(-1, 1, 1)
        return x

    def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
        assert x.dim() == 3
        length = x.shape[-1]
        x, scale = self.preprocess(x)

        emb = self.encoder(x)
        q_res = self.quantizer(emb, self.frame_rate)
        out = self.decoder(q_res.x)

        # remove extra padding added by the encoder and decoder
        assert out.shape[-1] >= length, (out.shape[-1], length)
        out = out[..., :length]

        q_res.x = self.postprocess(out, scale)

        return q_res

    def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
        """Encode the given input tensor to quantized representation along with scale parameter.

        Args:
            x (torch.Tensor): Float tensor of shape [B, C, T]

        Returns:
            codes, scale (tuple of torch.Tensor, torch.Tensor): Tuple composed of:
                codes: a float tensor of shape [B, K, T] with K the number of codebooks used and T the timestep.
                scale: a float tensor containing the scale for audio renormalization.
        """
        assert x.dim() == 3
        x, scale = self.preprocess(x)
        emb = self.encoder(x)
        codes = self.quantizer.encode(emb)
        return codes, scale

    def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
        """Decode the given codes to a reconstructed representation, using the scale to perform
        audio denormalization if needed.

        Args:
            codes (torch.Tensor): Int tensor of shape [B, K, T]
            scale (torch.Tensor, optional): Float tensor containing the scale value.

        Returns:
            out (torch.Tensor): Float tensor of shape [B, C, T], the reconstructed audio.
        """
        emb = self.decode_latent(codes)
        out = self.decoder(emb)
        out = self.postprocess(out, scale)
        # out contains extra padding added by the encoder and decoder
        return out

    def decode_latent(self, codes: torch.Tensor):
        """Decode from the discrete codes to continuous latent space."""
        return self.quantizer.decode(codes)


class DAC(CompressionModel):
    def __init__(self, model_type: str = "44khz"):
        super().__init__()
        try:
            import dac.utils
        except ImportError:
            raise RuntimeError("Could not import dac, make sure it is installed, "
                               "please run `pip install descript-audio-codec`")
        self.model = dac.utils.load_model(model_type=model_type)
        self.n_quantizers = self.total_codebooks
        self.model.eval()

    def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
        # We don't support training with this.
        raise NotImplementedError("Forward and training with DAC not supported.")

    def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
        codes = self.model.encode(x, self.n_quantizers)[1]
        return codes[:, :self.n_quantizers], None

    def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
        assert scale is None
        z_q = self.decode_latent(codes)
        return self.model.decode(z_q)

    def decode_latent(self, codes: torch.Tensor):
        """Decode from the discrete codes to continuous latent space."""
        return self.model.quantizer.from_codes(codes)[0]

    @property
    def channels(self) -> int:
        return 1

    @property
    def frame_rate(self) -> float:
        return self.model.sample_rate / self.model.hop_length

    @property
    def sample_rate(self) -> int:
        return self.model.sample_rate

    @property
    def cardinality(self) -> int:
        return self.model.codebook_size

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

    @property
    def total_codebooks(self) -> int:
        return self.model.n_codebooks

    def set_num_codebooks(self, n: int):
        """Set the active number of codebooks used by the quantizer.
        """
        assert n >= 1
        assert n <= self.total_codebooks
        self.n_quantizers = n


class HFEncodecCompressionModel(CompressionModel):
    """Wrapper around HuggingFace Encodec.
    """
    def __init__(self, model: HFEncodecModel):
        super().__init__()
        self.model = model
        bws = self.model.config.target_bandwidths
        num_codebooks = [
            bw * 1000 / (self.frame_rate * math.log2(self.cardinality))
            for bw in bws
        ]
        deltas = [nc - int(nc) for nc in num_codebooks]
        # Checking we didn't do some bad maths and we indeed have integers!
        assert all(deltas) <= 1e-3, deltas
        self.possible_num_codebooks = [int(nc) for nc in num_codebooks]
        self.set_num_codebooks(max(self.possible_num_codebooks))

    def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
        # We don't support training with this.
        raise NotImplementedError("Forward and training with HF EncodecModel not supported.")

    def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
        bandwidth_index = self.possible_num_codebooks.index(self.num_codebooks)
        bandwidth = self.model.config.target_bandwidths[bandwidth_index]
        res = self.model.encode(x, None, bandwidth)
        assert len(res[0]) == 1
        assert len(res[1]) == 1
        return res[0][0], res[1][0]

    def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
        if scale is None:
            scales = [None]  # type: ignore
        else:
            scales = scale  # type: ignore
        res = self.model.decode(codes[None], scales)
        return res[0]

    def decode_latent(self, codes: torch.Tensor):
        """Decode from the discrete codes to continuous latent space."""
        return self.model.quantizer.decode(codes.transpose(0, 1))

    @property
    def channels(self) -> int:
        return self.model.config.audio_channels

    @property
    def frame_rate(self) -> float:
        hop_length = int(np.prod(self.model.config.upsampling_ratios))
        return self.sample_rate / hop_length

    @property
    def sample_rate(self) -> int:
        return self.model.config.sampling_rate

    @property
    def cardinality(self) -> int:
        return self.model.config.codebook_size

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

    @property
    def total_codebooks(self) -> int:
        return max(self.possible_num_codebooks)

    def set_num_codebooks(self, n: int):
        """Set the active number of codebooks used by the quantizer.
        """
        if n not in self.possible_num_codebooks:
            raise ValueError(f"Allowed values for num codebooks: {self.possible_num_codebooks}")
        self._num_codebooks = n


class InterleaveStereoCompressionModel(CompressionModel):
    """Wraps a CompressionModel to support stereo inputs. The wrapped model
    will be applied independently to the left and right channels, and both codebooks
    will be interleaved. If the wrapped model returns a representation `[B, K ,T]` per
    channel, then the output will be `[B, K * 2, T]`  or `[B, K, T * 2]` depending on
    `per_timestep`.

    Args:
        model (CompressionModel): Compression model to wrap.
        per_timestep (bool): Whether to interleave on the timestep dimension
            or on the codebooks dimension.
    """
    def __init__(self, model: CompressionModel, per_timestep: bool = False):
        super().__init__()
        self.model = model
        self.per_timestep = per_timestep
        assert self.model.channels == 1, "Wrapped model is expected to be for monophonic audio"

    @property
    def total_codebooks(self):
        return self.model.total_codebooks

    @property
    def num_codebooks(self):
        """Active number of codebooks used by the quantizer.

        ..Warning:: this reports the number of codebooks after the interleaving
        of the codebooks!
        """
        return self.model.num_codebooks if self.per_timestep else self.model.num_codebooks * 2

    def set_num_codebooks(self, n: int):
        """Set the active number of codebooks used by the quantizer.

        ..Warning:: this sets the number of codebooks before the interleaving!
        """
        self.model.set_num_codebooks(n)

    @property
    def num_virtual_steps(self) -> float:
        """Return the number of virtual steps, e.g. one real step
        will be split into that many steps.
        """
        return 2 if self.per_timestep else 1

    @property
    def frame_rate(self) -> float:
        return self.model.frame_rate * self.num_virtual_steps

    @property
    def sample_rate(self) -> int:
        return self.model.sample_rate

    @property
    def channels(self) -> int:
        return 2

    @property
    def cardinality(self):
        """Cardinality of each codebook.
        """
        return self.model.cardinality

    def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
        raise NotImplementedError("Not supported, use encode and decode.")

    def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
        B, C, T = x.shape
        assert C == self.channels, f"Expecting stereo audio but audio num channels is {C}"

        indices_c0, scales_c0 = self.model.encode(x[:, 0, ...].unsqueeze(1))
        indices_c1, scales_c1 = self.model.encode(x[:, 1, ...].unsqueeze(1))
        indices = torch.stack([indices_c0, indices_c1], dim=0)
        scales: tp.Optional[torch.Tensor] = None
        if scales_c0 is not None and scales_c1 is not None:
            scales = torch.stack([scales_c0, scales_c1], dim=1)

        if self.per_timestep:
            indices = rearrange(indices, 'c b k t -> b k (t c)', c=2)
        else:
            indices = rearrange(indices, 'c b k t -> b (k c) t', c=2)

        return (indices, scales)

    def get_left_right_codes(self, codes: torch.Tensor) -> tp.Tuple[torch.Tensor, torch.Tensor]:
        if self.per_timestep:
            codes = rearrange(codes, 'b k (t c) -> c b k t', c=2)
        else:
            codes = rearrange(codes, 'b (k c) t -> c b k t', c=2)
        return codes[0], codes[1]

    def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
        B, K, T = codes.shape
        assert T % self.num_virtual_steps == 0, "Provided codes' number of timesteps does not match"
        assert K == self.num_codebooks, "Provided codes' number of codebooks does not match"

        scale_c0, scale_c1 = None, None
        if scale is not None:
            assert scale.size(0) == B and scale.size(1) == 2, f"Scale has unexpected shape: {scale.shape}"
            scale_c0 = scale[0, ...]
            scale_c1 = scale[1, ...]

        codes_c0, codes_c1 = self.get_left_right_codes(codes)
        audio_c0 = self.model.decode(codes_c0, scale_c0)
        audio_c1 = self.model.decode(codes_c1, scale_c1)
        return torch.cat([audio_c0, audio_c1], dim=1)

    def decode_latent(self, codes: torch.Tensor):
        """Decode from the discrete codes to continuous latent space."""
        raise NotImplementedError("Not supported by interleaved stereo wrapped models.")