"""https://github.com/facebookresearch/audiocraft/blob/main/audiocraft/models/multibanddiffusion.py"""
import logging
from typing import Optional, List
from math import ceil
import torch
import julius

from tqdm import tqdm
from audiocraft.models.encodec import CompressionModel
from audiocraft.solvers.compression import CompressionSolver


class BaseEncodecTokenizer:

    def __init__(self,
                 codec_model: CompressionModel,
                 sample_per_token: int = 320,
                 num_codebooks_encoder: Optional[int] = None) -> None:
        """Base class for multi-band diffusion.
        Args:
            codec_model (CompressionModel): Underlying compression model used to obtain discrete tokens.
            sample_per_token (int): Number of sample per token (320 for 24kHz encodec).
            num_codebooks_encoder (int): Number of codebook to use for encoder (default full code).
        """
        self.codec_model = codec_model
        self.device = next(self.codec_model.parameters()).device
        self.sample_per_token = sample_per_token
        self.num_codebooks_encoder = num_codebooks_encoder

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

    @torch.no_grad()
    def wav_to_tokens(self,
                      wav: torch.Tensor,
                      sample_rate: int,
                      cpu_offload: bool = True,
                      chunk_length: Optional[int] = None,
                      stride: Optional[int] = None,
                      concat_strategy: str = "first") -> torch.Tensor:
        """Get audio tokens from waveform in batch. Note that Encodec generates 75 tokens per second of audio at 24 kHz
        meaning 320 samples (13.333 msec) per tokens.
        Args:
            wav (torch.Tensor): The audio that we want to extract the conditioning from (batch, channel, wav).
            sample_rate (int): Sample rate of the audio.
            cpu_offload (bool): Move the output tokens to cpu on the fly to save cuda memory.
            chunk_length (int): Chunk length to split a long audio (sample size, must be divisible by sample_per_token).
            stride (int): Stride over chunked audio (sample size, must be divisible by sample_per_token).
            concat_strategy (str): "first" or "last" to indicate which chunk to use when consolidating the overlap.
        """
        # sanity check
        if wav.ndim != 3:
            raise ValueError(f"wav should be (batch, channel, time): {wav.ndim} dims")
        original_device = wav.device
        # sampling audio
        if sample_rate != self.sample_rate:
            wav = julius.resample_frac(wav, sample_rate, self.sample_rate)
        batch_size, channels, input_length = wav.shape
        if channels > 1:
            logging.warning("Audio has more than one channel but encoder takes the first channel only.")
        # validate chunk length and stride (if None, do one-shot process)
        if chunk_length:
            if chunk_length % self.sample_per_token != 0:
                raise ValueError(f"chunk_length must be divisible by {self.sample_per_token}: {chunk_length}")
        else:
            chunk_length = input_length
        chunk_length_latent = ceil(chunk_length / self.sample_per_token)
        if stride:
            if stride % self.sample_per_token != 0:
                raise ValueError(f"stride must be divisible by {self.sample_per_token}: {stride}")
        else:
            stride = chunk_length
        stride_latent = ceil(stride / self.sample_per_token)
        # initialize the token tensor
        num_tokens = ceil(input_length / self.sample_per_token)
        num_filters = self.codec_model.model.config.num_filters
        if self.num_codebooks_encoder is not None:
            if self.num_codebooks_encoder > num_filters:
                raise ValueError(f"num_codebooks_encoder must be smaller than {num_filters}")
            num_filters = self.num_codebooks_encoder
        tokens = torch.zeros(
            (batch_size, num_filters, num_tokens),
            device="cpu" if cpu_offload else original_device,
            dtype=torch.int64
        )
        # tokenize by chunk in a sequential manner
        for offset in tqdm(list(range(0, input_length - chunk_length + stride, stride))):
            frame = wav[:, :1, offset: offset + chunk_length]
            tmp_tokens, _ = self.codec_model.encode(frame.to(self.device))
            offset_latent = int(offset / self.sample_per_token)
            tmp_tokens = tmp_tokens.to("cpu") if cpu_offload else tmp_tokens.to(original_device)
            if concat_strategy == "last" or offset == 0:
                tokens[:, :, offset_latent: offset_latent + chunk_length_latent] = tmp_tokens[:, :num_filters, :]
            else:
                overlap_token = chunk_length_latent - stride_latent
                tokens[:, :, offset_latent + overlap_token: offset_latent + chunk_length_latent] \
                    = tmp_tokens[:, :num_filters, overlap_token:]
        return tokens


class EncodecTokenizer:

    @staticmethod
    def from_pretrained(num_codebooks_encoder: Optional[int] = None) -> BaseEncodecTokenizer:
        """Get the pretrained Models for MultiBandDiffusion.
        Args:
            num_codebooks_encoder (int): Number of codebook to use for encoder (default full code).
        """
        device = 'cuda' if torch.cuda.is_available() else 'cpu'
        codec_model = CompressionSolver.model_from_checkpoint(
            '//pretrained/facebook/encodec_24khz', device=device
        )
        codec_model = codec_model.to(device)
        return BaseEncodecTokenizer(
            codec_model=codec_model,
            num_codebooks_encoder=num_codebooks_encoder
        )