# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import random
from typing import Dict, Optional

logger = logging.getLogger(__name__)
import torch
import torch.nn as nn
from torch.nn import functional as F
from .utils.mask import make_pad_mask
from .configs import CFM_PARAMS


class MaskedDiffWithXvec(torch.nn.Module):
    def __init__(
        self,
        input_size: int = 512,
        output_size: int = 80,
        spk_embed_dim: int = 192,
        output_type: str = "mel",
        vocab_size: int = 4096,
        input_frame_rate: int = 50,
        only_mask_loss: bool = True,
        encoder: torch.nn.Module = None,
        length_regulator: torch.nn.Module = None,
        decoder: torch.nn.Module = None,
        decoder_conf: Dict = {
            'in_channels': 240,
            'out_channel': 80,
            'spk_emb_dim': 80,
            'n_spks': 1,
            'cfm_params': CFM_PARAMS,
            'decoder_params': {
                'channels': [256, 256],
                'dropout': 0.0,
                'attention_head_dim': 64,
                'n_blocks': 4,
                'num_mid_blocks': 12,
                'num_heads': 8,
                'act_fn': 'gelu',
            }
        },
        mel_feat_conf: Dict = {
            'n_fft': 1024,
            'num_mels': 80,
            'sampling_rate': 22050,
            'hop_size': 256,
            'win_size': 1024,
            'fmin': 0,
            'fmax': 8000
        }
    ):
        super().__init__()
        self.input_size = input_size
        self.output_size = output_size
        self.decoder_conf = decoder_conf
        self.mel_feat_conf = mel_feat_conf
        self.vocab_size = vocab_size
        self.output_type = output_type
        self.input_frame_rate = input_frame_rate
        logging.info(f"input frame rate={self.input_frame_rate}")
        self.input_embedding = nn.Embedding(vocab_size, input_size)
        self.spk_embed_affine_layer = torch.nn.Linear(spk_embed_dim, output_size)
        self.encoder = encoder
        self.encoder_proj = torch.nn.Linear(self.encoder.output_size(), output_size)
        self.decoder = decoder
        self.length_regulator = length_regulator
        self.only_mask_loss = only_mask_loss

    def forward(
            self,
            batch: dict,
            device: torch.device,
    ) -> Dict[str, Optional[torch.Tensor]]:
        token = batch['speech_token'].to(device)
        token_len = batch['speech_token_len'].to(device)
        feat = batch['speech_feat'].to(device)
        feat_len = batch['speech_feat_len'].to(device)
        embedding = batch['embedding'].to(device)

        # xvec projection
        embedding = F.normalize(embedding, dim=1)
        embedding = self.spk_embed_affine_layer(embedding)

        # concat text and prompt_text
        mask = (~make_pad_mask(token_len)).float().unsqueeze(-1).to(device)
        token = self.input_embedding(torch.clamp(token, min=0, max=self.input_embedding.num_embeddings-1)) * mask

        # text encode
        h, h_lengths = self.encoder(token, token_len)
        h = self.encoder_proj(h)
        h, h_lengths = self.length_regulator(h, feat_len)

        # get conditions
        conds = torch.zeros(feat.shape, device=token.device)
        for i, j in enumerate(feat_len):
            if random.random() < 0.5:
                continue
            index = random.randint(0, int(0.3 * j))
            conds[i, :index] = feat[i, :index]
        conds = conds.transpose(1, 2)

        mask = (~make_pad_mask(feat_len)).to(h)
        feat = F.interpolate(feat.unsqueeze(dim=1), size=h.shape[1:], mode="nearest").squeeze(dim=1)
        loss, _ = self.decoder.compute_loss(
            feat.transpose(1, 2).contiguous(),
            mask.unsqueeze(1),
            h.transpose(1, 2).contiguous(),
            embedding,
            cond=conds
        )
        return {'loss': loss}

    @torch.inference_mode()
    def inference(self,
                  token,
                  token_len,
                  prompt_token,
                  prompt_token_len,
                  prompt_feat,
                  prompt_feat_len,
                  embedding,
                  flow_cache):
        if self.fp16 is True:
            prompt_feat = prompt_feat.half()
            embedding = embedding.half()

        assert token.shape[0] == 1
        # xvec projection
        embedding = F.normalize(embedding, dim=1)
        embedding = self.spk_embed_affine_layer(embedding)

        # concat text and prompt_text
        token_len1, token_len2 = prompt_token.shape[1], token.shape[1]
        token, token_len = torch.concat([prompt_token, token], dim=1), prompt_token_len + token_len
        mask = (~make_pad_mask(token_len)).unsqueeze(-1).to(embedding)
        
        # Check for out-of-bounds token IDs
        vocab_size = self.input_embedding.num_embeddings
        if token.max() >= vocab_size or token.min() < 0:
            logging.warning(f"S3Gen: Token IDs out of bounds: min={token.min().item()}, max={token.max().item()}, vocab_size={vocab_size}")
        
        token = self.input_embedding(torch.clamp(token, min=0, max=vocab_size-1)) * mask

        # text encode
        h, h_lengths = self.encoder(token, token_len)
        h = self.encoder_proj(h)
        mel_len1, mel_len2 = prompt_feat.shape[1], int(token_len2 / self.input_frame_rate * 22050 / 256)
        h, h_lengths = self.length_regulator.inference(h[:, :token_len1], h[:, token_len1:], mel_len1, mel_len2, self.input_frame_rate)

        # get conditions
        conds = torch.zeros([1, mel_len1 + mel_len2, self.output_size], device=token.device).to(h.dtype)
        conds[:, :mel_len1] = prompt_feat
        conds = conds.transpose(1, 2)

        mask = (~make_pad_mask(torch.tensor([mel_len1 + mel_len2]))).to(h)
        feat, flow_cache = self.decoder(
            mu=h.transpose(1, 2).contiguous(),
            mask=mask.unsqueeze(1),
            spks=embedding,
            cond=conds,
            n_timesteps=10,
            prompt_len=mel_len1,
            flow_cache=flow_cache
        )
        feat = feat[:, :, mel_len1:]
        assert feat.shape[2] == mel_len2
        return feat.float(), flow_cache


class CausalMaskedDiffWithXvec(torch.nn.Module):
    def __init__(
        self,
        input_size: int = 512,
        output_size: int = 80,
        spk_embed_dim: int = 192,
        output_type: str = "mel",
        vocab_size: int = 6561,
        input_frame_rate: int = 25,
        only_mask_loss: bool = True,
        token_mel_ratio: int = 2,
        pre_lookahead_len: int = 3,
        encoder: torch.nn.Module = None,
        decoder: torch.nn.Module = None,
        decoder_conf: Dict = {
            'in_channels': 240,
            'out_channel': 80,
            'spk_emb_dim': 80,
            'n_spks': 1,
            'cfm_params': CFM_PARAMS,
            'decoder_params': {
                'channels': [256, 256],
                'dropout': 0.0,
                'attention_head_dim': 64,
                'n_blocks': 4,
                'num_mid_blocks': 12,
                'num_heads': 8,
                'act_fn': 'gelu',
            }
        },
        mel_feat_conf: Dict = {
            'n_fft': 1024,
            'num_mels': 80,
            'sampling_rate': 22050,
            'hop_size': 256,
            'win_size': 1024,
            'fmin': 0,
            'fmax': 8000
        }
    ):
        super().__init__()
        self.input_size = input_size
        self.output_size = output_size
        self.decoder_conf = decoder_conf
        self.mel_feat_conf = mel_feat_conf
        self.vocab_size = vocab_size
        self.output_type = output_type
        self.input_frame_rate = input_frame_rate
        logging.info(f"input frame rate={self.input_frame_rate}")
        self.input_embedding = nn.Embedding(vocab_size, input_size)
        self.spk_embed_affine_layer = torch.nn.Linear(spk_embed_dim, output_size)
        self.encoder = encoder
        self.encoder_proj = torch.nn.Linear(self.encoder.output_size(), output_size)
        self.decoder = decoder
        self.only_mask_loss = only_mask_loss
        self.token_mel_ratio = token_mel_ratio
        self.pre_lookahead_len = pre_lookahead_len

        # FIXME: this was missing - just putting it in as false
        self.fp16 = False

    @torch.inference_mode()
    def inference(self,
                  token,
                  token_len,
                  prompt_token,
                  prompt_token_len,
                  prompt_feat,
                  prompt_feat_len,
                  embedding,
                  finalize):
        if self.fp16 is True:
            prompt_feat = prompt_feat.half()
            embedding = embedding.half()

        assert token.shape[0] == 1
        # xvec projection
        embedding = F.normalize(embedding, dim=1)
        embedding = self.spk_embed_affine_layer(embedding)

        # concat text and prompt_text
        token, token_len = torch.concat([prompt_token, token], dim=1), prompt_token_len + token_len
        mask = (~make_pad_mask(token_len)).unsqueeze(-1).to(embedding)
        token = self.input_embedding(torch.clamp(token, min=0, max=self.input_embedding.num_embeddings-1)) * mask

        # text encode
        h, h_lengths = self.encoder(token, token_len)
        if finalize is False:
            h = h[:, :-self.pre_lookahead_len * self.token_mel_ratio]
        mel_len1, mel_len2 = prompt_feat.shape[1], h.shape[1] - prompt_feat.shape[1]
        h = self.encoder_proj(h)

        # get conditions
        conds = torch.zeros([1, mel_len1 + mel_len2, self.output_size], device=token.device).to(h.dtype)
        conds[:, :mel_len1] = prompt_feat
        conds = conds.transpose(1, 2)

        mask = (~make_pad_mask(torch.tensor([mel_len1 + mel_len2]))).to(h)
        feat, _ = self.decoder(
            mu=h.transpose(1, 2).contiguous(),
            mask=mask.unsqueeze(1),
            spks=embedding,
            cond=conds,
            n_timesteps=10
        )
        feat = feat[:, :, mel_len1:]
        assert feat.shape[2] == mel_len2
        return feat.float(), None  # NOTE jrm: why are they returning None here?
