# 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 pdb

import torch
import torch.nn.functional as F
from matcha.models.components.flow_matching import BASECFM


class ConditionalCFM(BASECFM):
    def __init__(self, in_channels, cfm_params, n_spks=1, spk_emb_dim=64, estimator: torch.nn.Module = None):
        super().__init__(
            n_feats=in_channels,
            cfm_params=cfm_params,
            n_spks=n_spks,
            spk_emb_dim=spk_emb_dim,
        )
        self.t_scheduler = cfm_params.t_scheduler
        self.training_cfg_rate = cfm_params.training_cfg_rate
        self.inference_cfg_rate = cfm_params.inference_cfg_rate
        in_channels = in_channels + (spk_emb_dim if n_spks > 0 else 0)
        # Just change the architecture of the estimator here

        io_channels = 80
        input_concat_dim = 80
        embed_dim = 768
        depth = 24
        num_heads = 24
        project_cond_tokens = False
        transformer_type = "continuous_transformer"
        self.estimator = estimator

    @torch.inference_mode()
    def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None):
        """Forward diffusion

        Args:
            mu (torch.Tensor): output of encoder
                shape: (batch_size, n_feats, mel_timesteps)
            mask (torch.Tensor): output_mask
                shape: (batch_size, 1, mel_timesteps)
            n_timesteps (int): number of diffusion steps
            temperature (float, optional): temperature for scaling noise. Defaults to 1.0.
            spks (torch.Tensor, optional): speaker ids. Defaults to None.
                shape: (batch_size, spk_emb_dim)
            cond: Not used but kept for future purposes

        Returns:
            sample: generated mel-spectrogram
                shape: (batch_size, n_feats, mel_timesteps)
        """
        z = torch.randn_like(mu) * temperature
        t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device)
        if self.t_scheduler == 'cosine':
            t_span = 1 - torch.cos(t_span * 0.5 * torch.pi)
        return self.solve_euler(z, t_span=t_span, mu=mu, mask=mask, spks=spks, cond=cond)

    def solve_euler(self, x, t_span, mu, mask, spks, cond):
        """
        Fixed euler solver for ODEs.
        Args:
            x (torch.Tensor): random noise  torch.Size([1, 80, 621])
            t_span (torch.Tensor): n_timesteps interpolated
                shape: (n_timesteps + 1,)
            mu (torch.Tensor): output of encoder
                shape: (batch_size, n_feats, mel_timesteps)
            mask (torch.Tensor): output_mask
                shape: (batch_size, 1, mel_timesteps)
            spks (torch.Tensor, optional): speaker ids. Defaults to None.
                shape: (batch_size, spk_emb_dim)
            cond: Not used but kept for future purposes
        """
        t, _, dt = t_span[0], t_span[-1], t_span[1] - t_span[0]

        # I am storing this because I can later plot it by putting a debugger here and saving it to a file
        # Or in future might add like a return_all_steps flag
        sol = []

        cfg_dropout_prob = 0.1
        cfg_scale = 1.0

        # cfg_dropout_prob = 0.0
        # cfg_scale = 3.0

        for step in range(1, len(t_span)):
            # dphi_dt = self.estimator(x, mask, mu, t, spks, cond)
            # pdb.set_trace()
            dphi_dt = self.estimator(x,  # [bs, 80, 229]
                                     t[None],  # (bs,)
                                     global_embed=spks,
                                     input_concat_cond=mu,
                                     mask=mask[0],  # [bs, 229]
                                     cfg_dropout_prob=cfg_dropout_prob, cfg_scale=cfg_scale)

            # Classifier-Free Guidance inference introduced in VoiceBox
            if self.inference_cfg_rate > 0:
                # cfg_dphi_dt = self.estimator(
                #     x, mask,
                #     torch.zeros_like(mu), t,
                #     torch.zeros_like(spks) if spks is not None else None,
                #     torch.zeros_like(cond)
                # )
                cfg_dphi_dt = self.estimator(x,  # [bs, 80, 229]
                                             t[None],  # (bs,)
                                             global_embed=torch.zeros_like(spks) if spks is not None else None,
                                             input_concat_cond=torch.zeros_like(mu),
                                             mask=mask[0],  # [bs, 229]
                                             cfg_dropout_prob=cfg_dropout_prob, cfg_scale=cfg_scale)

                dphi_dt = ((1.0 + self.inference_cfg_rate) * dphi_dt -
                           self.inference_cfg_rate * cfg_dphi_dt)
            x = x + dt * dphi_dt
            t = t + dt
            sol.append(x)
            if step < len(t_span) - 1:
                dt = t_span[step + 1] - t

        return sol[-1]

    def compute_loss(self, x1, mask, mu, spks=None, cond=None):
        """Computes diffusion loss

        Args:
            x1 (torch.Tensor): Target
                shape: (batch_size, n_feats, mel_timesteps)
            mask (torch.Tensor): target mask
                shape: (batch_size, 1, mel_timesteps)
            mu (torch.Tensor): output of encoder
                shape: (batch_size, n_feats, mel_timesteps)
            spks (torch.Tensor, optional): speaker embedding. Defaults to None.
                shape: (batch_size, spk_emb_dim)

        Returns:
            loss: conditional flow matching loss
            y: conditional flow
                shape: (batch_size, n_feats, mel_timesteps)
        """
        b, _, t = mu.shape

        # random timestep
        t = torch.rand([b, 1, 1], device=mu.device, dtype=mu.dtype)
        if self.t_scheduler == 'cosine':
            t = 1 - torch.cos(t * 0.5 * torch.pi)
        # sample noise p(x_0)
        z = torch.randn_like(x1)

        y = (1 - (1 - self.sigma_min) * t) * z + t * x1
        u = x1 - (1 - self.sigma_min) * z

        # during training, we randomly drop condition to trade off mode coverage and sample fidelity
        if self.training_cfg_rate > 0:
            cfg_mask = torch.rand(b, device=x1.device) > self.training_cfg_rate
            mu = mu * cfg_mask.view(-1, 1, 1)
            spks = spks * cfg_mask.view(-1, 1)
            cond = cond * cfg_mask.view(-1, 1, 1)

        # pred = self.estimator(y, mask, mu, t.squeeze(), spks, cond)
        pred = self.estimator(y,  # [bs, 80, 229]
                              t.squeeze(1, 2),  # (bs,)
                              global_embed=spks,
                              input_concat_cond=mu,
                              mask=mask.squeeze(1),  # [bs, 229]
                              cfg_dropout_prob=0.1)

        loss = F.mse_loss(pred * mask, u * mask, reduction="sum") / (torch.sum(mask) * u.shape[1])
        return loss, y

    # def estimator_trans(self):
    #     pass
