# Copyright (c) 2024 NVIDIA CORPORATION. 
#   Licensed under the MIT license.

# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
#   LICENSE is in incl_licenses directory.

import os
import json
from pathlib import Path

from collections import namedtuple
from typing import Optional, List, Union, Dict

import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn import Conv1d, ConvTranspose1d
from torch.nn.utils import weight_norm, remove_weight_norm

import activations
from utils import init_weights, get_padding
from alias_free_torch.act import Activation1d as TorchActivation1d
from env import AttrDict

from huggingface_hub import PyTorchModelHubMixin, hf_hub_download

def load_hparams_from_json(path) -> AttrDict:
    with open(path) as f:
        data = f.read()
    h = json.loads(data)
    return AttrDict(h)

class AMPBlock1(torch.nn.Module):
    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5), activation=None):
        super(AMPBlock1, self).__init__()
        self.h = h

        self.convs1 = nn.ModuleList([
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
                               padding=get_padding(kernel_size, dilation[0]))),
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
                               padding=get_padding(kernel_size, dilation[1]))),
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
                               padding=get_padding(kernel_size, dilation[2])))
        ])
        self.convs1.apply(init_weights)

        self.convs2 = nn.ModuleList([
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
                               padding=get_padding(kernel_size, 1))),
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
                               padding=get_padding(kernel_size, 1))),
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
                               padding=get_padding(kernel_size, 1)))
        ])
        self.convs2.apply(init_weights)

        self.num_layers = len(self.convs1) + len(self.convs2) # total number of conv layers

        # select which Activation1d, lazy-load cuda version to ensure backward compatibility
        if self.h.get("use_cuda_kernel", False):
            # faster CUDA kernel implementation of Activation1d
            from alias_free_cuda.activation1d import Activation1d as CudaActivation1d
            Activation1d = CudaActivation1d
        else:
            Activation1d = TorchActivation1d

        if activation == 'snake': # periodic nonlinearity with snake function and anti-aliasing
            self.activations = nn.ModuleList([
                Activation1d(
                    activation=activations.Snake(channels, alpha_logscale=h.snake_logscale))
                for _ in range(self.num_layers)
            ])
        elif activation == 'snakebeta': # periodic nonlinearity with snakebeta function and anti-aliasing
            self.activations = nn.ModuleList([
                Activation1d(
                    activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale))
                 for _ in range(self.num_layers)
            ])
        else:
            raise NotImplementedError("activation incorrectly specified. check the config file and look for 'activation'.")

    def forward(self, x):
        acts1, acts2 = self.activations[::2], self.activations[1::2]
        for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2):
            xt = a1(x)
            xt = c1(xt)
            xt = a2(xt)
            xt = c2(xt)
            x = xt + x

        return x

    def remove_weight_norm(self):
        for l in self.convs1:
            remove_weight_norm(l)
        for l in self.convs2:
            remove_weight_norm(l)


class AMPBlock2(torch.nn.Module):
    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3), activation=None):
        super(AMPBlock2, self).__init__()
        self.h = h

        self.convs = nn.ModuleList([
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
                               padding=get_padding(kernel_size, dilation[0]))),
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
                               padding=get_padding(kernel_size, dilation[1])))
        ])
        self.convs.apply(init_weights)

        self.num_layers = len(self.convs) # total number of conv layers

        # select which Activation1d, lazy-load cuda version to ensure backward compatibility
        if self.h.get("use_cuda_kernel", False):
            # faster CUDA kernel implementation of Activation1d
            from alias_free_cuda.activation1d import Activation1d as CudaActivation1d
            Activation1d = CudaActivation1d
        else:
            Activation1d = TorchActivation1d

        if activation == 'snake': # periodic nonlinearity with snake function and anti-aliasing
            self.activations = nn.ModuleList([
                Activation1d(
                    activation=activations.Snake(channels, alpha_logscale=h.snake_logscale))
                for _ in range(self.num_layers)
            ])
        elif activation == 'snakebeta': # periodic nonlinearity with snakebeta function and anti-aliasing
            self.activations = nn.ModuleList([
                Activation1d(
                    activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale))
                 for _ in range(self.num_layers)
            ])
        else:
            raise NotImplementedError("activation incorrectly specified. check the config file and look for 'activation'.")

    def forward(self, x):
        for c, a in zip (self.convs, self.activations):
            xt = a(x)
            xt = c(xt)
            x = xt + x

        return x

    def remove_weight_norm(self):
        for l in self.convs:
            remove_weight_norm(l)


class BigVGAN(
    torch.nn.Module,
    PyTorchModelHubMixin,
    library_name="bigvgan",
    repo_url="https://github.com/NVIDIA/BigVGAN",
    docs_url="https://github.com/NVIDIA/BigVGAN/blob/main/README.md",
    pipeline_tag="audio-to-audio",
    license="mit",
    tags=["neural-vocoder", "audio-generation", "arxiv:2206.04658"]
):
    # this is our main BigVGAN model. Applies anti-aliased periodic activation for resblocks.
    # New in v2: if use_cuda_kernel is set to True, it loads optimized CUDA kernels for AMP.
    # NOTE: use_cuda_kernel=True should be used for inference only (training is not supported).
    def __init__(
        self,
        h,
        use_cuda_kernel: bool=False
    ):
        super(BigVGAN, self).__init__()
        self.h = h
        self.h["use_cuda_kernel"] = use_cuda_kernel # add it to global hyperparameters (h)

        self.num_kernels = len(h.resblock_kernel_sizes)
        self.num_upsamples = len(h.upsample_rates)

        # pre conv
        self.conv_pre = weight_norm(Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3))

        # define which AMPBlock to use. BigVGAN uses AMPBlock1 as default
        resblock = AMPBlock1 if h.resblock == '1' else AMPBlock2

        # transposed conv-based upsamplers. does not apply anti-aliasing
        self.ups = nn.ModuleList()
        for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
            self.ups.append(nn.ModuleList([
                weight_norm(ConvTranspose1d(h.upsample_initial_channel // (2 ** i),
                                            h.upsample_initial_channel // (2 ** (i + 1)),
                                            k, u, padding=(k - u) // 2))
            ]))

        # residual blocks using anti-aliased multi-periodicity composition modules (AMP)
        self.resblocks = nn.ModuleList()
        for i in range(len(self.ups)):
            ch = h.upsample_initial_channel // (2 ** (i + 1))
            for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)):
                self.resblocks.append(resblock(h, ch, k, d, activation=h.activation))
        
        # select which Activation1d, lazy-load cuda version to ensure backward compatibility
        if self.h.get("use_cuda_kernel", False):
            # faster CUDA kernel implementation of Activation1d
            from alias_free_cuda.activation1d import Activation1d as CudaActivation1d
            Activation1d = CudaActivation1d
        else:
            Activation1d = TorchActivation1d

        # post conv
        if h.activation == "snake": # periodic nonlinearity with snake function and anti-aliasing
            activation_post = activations.Snake(ch, alpha_logscale=h.snake_logscale)
            self.activation_post = Activation1d(activation=activation_post)
        elif h.activation == "snakebeta": # periodic nonlinearity with snakebeta function and anti-aliasing
            activation_post = activations.SnakeBeta(ch, alpha_logscale=h.snake_logscale)
            self.activation_post = Activation1d(activation=activation_post)
        else:
            raise NotImplementedError("activation incorrectly specified. check the config file and look for 'activation'.")
        
        # whether to use bias for the final conv_post. Defaults to True for backward compatibility
        self.use_bias_at_final = h.get("use_bias_at_final", True)
        self.conv_post = weight_norm(Conv1d(
            ch, 1, 7, 1, padding=3, bias=self.use_bias_at_final
        ))

        # weight initialization
        for i in range(len(self.ups)):
            self.ups[i].apply(init_weights)
        self.conv_post.apply(init_weights)
        
        # final tanh activation. Defaults to True for backward compatibility
        self.use_tanh_at_final = h.get("use_tanh_at_final", True)

    def forward(self, x):
        # pre conv
        x = self.conv_pre(x)

        for i in range(self.num_upsamples):
            # upsampling
            for i_up in range(len(self.ups[i])):
                x = self.ups[i][i_up](x)
            # AMP blocks
            xs = None
            for j in range(self.num_kernels):
                if xs is None:
                    xs = self.resblocks[i * self.num_kernels + j](x)
                else:
                    xs += self.resblocks[i * self.num_kernels + j](x)
            x = xs / self.num_kernels

        # post conv
        x = self.activation_post(x)
        x = self.conv_post(x)
        # final tanh activation
        if self.use_tanh_at_final:
            x = torch.tanh(x)
        else:
            x = torch.clamp(x, min=-1., max=1.) # bound the output to [-1, 1]

        return x

    def remove_weight_norm(self):
        print('Removing weight norm...')
        for l in self.ups:
            for l_i in l:
                remove_weight_norm(l_i)
        for l in self.resblocks:
            l.remove_weight_norm()
        remove_weight_norm(self.conv_pre)
        remove_weight_norm(self.conv_post)

    ##################################################################
    # additional methods for huggingface_hub support
    ##################################################################
    def _save_pretrained(self, save_directory: Path) -> None:
        """Save weights and config.json from a Pytorch model to a local directory."""

        model_path = save_directory / 'bigvgan_generator.pt'
        torch.save(
            {'generator': self.state_dict()},
            model_path
        )

        config_path = save_directory / 'config.json'
        with open(config_path, 'w') as config_file:
            json.dump(self.h, config_file, indent=4)

    @classmethod
    def _from_pretrained(
        cls,
        *,
        model_id: str,
        revision: str,
        cache_dir: str,
        force_download: bool,
        proxies: Optional[Dict],
        resume_download: bool,
        local_files_only: bool,
        token: Union[str, bool, None],
        map_location: str = "cpu", # additional argument
        strict: bool = False, # additional argument
        use_cuda_kernel: bool = False,
        **model_kwargs,
    ):
        """Load Pytorch pretrained weights and return the loaded model."""

        ##################################################################
        # download and load hyperparameters (h) used by BigVGAN
        ##################################################################
        config_file = hf_hub_download(
            repo_id=model_id,
            filename='config.json',
            revision=revision,
            cache_dir=cache_dir,
            force_download=force_download,
            proxies=proxies,
            resume_download=resume_download,
            token=token,
            local_files_only=local_files_only,
        )
        h = load_hparams_from_json(config_file)

        ##################################################################
        # instantiate BigVGAN using h
        ##################################################################
        if use_cuda_kernel:
            print(f"[WARNING] You have specified use_cuda_kernel=True during BigVGAN.from_pretrained(). Only inference is supported (training is not implemented)!")
            print(f"[WARNING] You need nvcc and ninja installed in your system to build the kernel. For detail, see the official GitHub repository: https://github.com/NVIDIA/BigVGAN?tab=readme-ov-file#using-custom-cuda-kernel-for-synthesis")
            model = cls(h, use_cuda_kernel=use_cuda_kernel)

        ##################################################################
        # download and load pretrained generator weight
        ##################################################################
        if os.path.isdir(model_id):
            print("Loading weights from local directory")
            model_file = os.path.join(model_id, 'bigvgan_generator.pt')
        else:
            print(f"Downloading weights from {model_id}")
            model_file = hf_hub_download(
                repo_id=model_id,
                filename='bigvgan_generator.pt',
                revision=revision,
                cache_dir=cache_dir,
                force_download=force_download,
                proxies=proxies,
                resume_download=resume_download,
                token=token,
                local_files_only=local_files_only,
                )
        
        checkpoint_dict = torch.load(model_file, map_location=map_location)
        model.load_state_dict(checkpoint_dict['generator'])

        return model