# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.

"""Converting legacy network pickle into the new format."""

from pdb import set_trace as st
import click
import pickle
import re
import copy
import numpy as np
import torch
import dnnlib
from torch_utils import misc

#----------------------------------------------------------------------------

def load_network_pkl(f, device, force_fp16=False):
    data = _LegacyUnpickler(f).load()

    # Legacy TensorFlow pickle => convert.
    if isinstance(data, tuple) and len(data) == 3 and all(isinstance(net, _TFNetworkStub) for net in data):
        tf_G, tf_D, tf_Gs = data
        G = convert_tf_generator(tf_G)
        D = convert_tf_discriminator(tf_D)
        G_ema = convert_tf_generator(tf_Gs)
        data = dict(G=G, D=D, G_ema=G_ema)
    
    # for k, module in data.items():
    # for key in ['G', 'D', 'G_ema']:
    #     data[key].to(device)

    # Add missing fields.
    if 'training_set_kwargs' not in data:
        data['training_set_kwargs'] = None
    if 'augment_pipe' not in data:
        data['augment_pipe'] = None

    # Validate contents.
    assert isinstance(data['G'], torch.nn.Module)
    assert isinstance(data['D'], torch.nn.Module)
    assert isinstance(data['G_ema'], torch.nn.Module)
    assert isinstance(data['training_set_kwargs'], (dict, type(None)))
    assert isinstance(data['augment_pipe'], (torch.nn.Module, type(None)))

    # Force FP16.
    if force_fp16:
        for key in ['G', 'D', 'G_ema']:
            old = data[key]
            kwargs = copy.deepcopy(old.init_kwargs)
            fp16_kwargs = kwargs.get('synthesis_kwargs', kwargs)
            fp16_kwargs.num_fp16_res = 4
            fp16_kwargs.conv_clamp = 256
            if kwargs != old.init_kwargs:
                new = type(old)(**kwargs).eval().requires_grad_(False)
                misc.copy_params_and_buffers(old, new, require_all=True)
                data[key] = new
    return data

def load_network_pkl_E(f, force_fp16=False):
    data = _LegacyUnpickler(f).load()

    # Legacy TensorFlow pickle => convert.
    if isinstance(data, tuple) and len(data) == 3 and all(isinstance(net, _TFNetworkStub) for net in data):
        tf_E = data
        E = convert_tf_generator(tf_E)
        # D = convert_tf_discriminator(tf_D)
        # G_ema = convert_tf_generator(tf_Gs)
        data = dict(G=E)

    # Add missing fields.
    if 'training_set_kwargs' not in data:
        data['training_set_kwargs'] = None
    if 'augment_pipe' not in data:
        data['augment_pipe'] = None

    # Validate contents.
    assert isinstance(data['E'], torch.nn.Module)
    assert isinstance(data['training_set_kwargs'], (dict, type(None)))
    assert isinstance(data['augment_pipe'], (torch.nn.Module, type(None)))

    # Force FP16.
    if force_fp16:
        for key in ['E']:
            old = data[key]
            kwargs = copy.deepcopy(old.init_kwargs)
            fp16_kwargs = kwargs.get('synthesis_kwargs', kwargs)
            fp16_kwargs.num_fp16_res = 4
            fp16_kwargs.conv_clamp = 256
            if kwargs != old.init_kwargs:
                new = type(old)(**kwargs).eval().requires_grad_(False)
                misc.copy_params_and_buffers(old, new, require_all=True)
                data[key] = new
    return data
#----------------------------------------------------------------------------

class _TFNetworkStub(dnnlib.EasyDict):
    pass

class _LegacyUnpickler(pickle.Unpickler):
    def find_class(self, module, name):
        if module == 'dnnlib.tflib.network' and name == 'Network':
            return _TFNetworkStub
        if 'training' in module:
            module = module.replace('training', 'nsr') # map module position from eg3d repo

        return super().find_class(module, name)

#----------------------------------------------------------------------------

def _collect_tf_params(tf_net):
    # pylint: disable=protected-access
    tf_params = dict()
    def recurse(prefix, tf_net):
        for name, value in tf_net.variables:
            tf_params[prefix + name] = value
        for name, comp in tf_net.components.items():
            recurse(prefix + name + '/', comp)
    recurse('', tf_net)
    return tf_params

#----------------------------------------------------------------------------

def _populate_module_params(module, *patterns):
    for name, tensor in misc.named_params_and_buffers(module):
        found = False
        value = None
        for pattern, value_fn in zip(patterns[0::2], patterns[1::2]):
            match = re.fullmatch(pattern, name)
            if match:
                found = True
                if value_fn is not None:
                    value = value_fn(*match.groups())
                break
        try:
            assert found
            if value is not None:
                tensor.copy_(torch.from_numpy(np.array(value)))
        except:
            print(name, list(tensor.shape))
            raise

#----------------------------------------------------------------------------

def convert_tf_generator(tf_G):
    if tf_G.version < 4:
        raise ValueError('TensorFlow pickle version too low')

    # Collect kwargs.
    tf_kwargs = tf_G.static_kwargs
    known_kwargs = set()
    def kwarg(tf_name, default=None, none=None):
        known_kwargs.add(tf_name)
        val = tf_kwargs.get(tf_name, default)
        return val if val is not None else none

    # Convert kwargs.
    from training import networks_stylegan2
    network_class = networks_stylegan2.Generator
    kwargs = dnnlib.EasyDict(
        z_dim               = kwarg('latent_size',          512),
        c_dim               = kwarg('label_size',           0),
        w_dim               = kwarg('dlatent_size',         512),
        img_resolution      = kwarg('resolution',           1024),
        img_channels        = kwarg('num_channels',         3),
        channel_base        = kwarg('fmap_base',            16384) * 2,
        channel_max         = kwarg('fmap_max',             512),
        num_fp16_res        = kwarg('num_fp16_res',         0),
        conv_clamp          = kwarg('conv_clamp',           None),
        architecture        = kwarg('architecture',         'skip'),
        resample_filter     = kwarg('resample_kernel',      [1,3,3,1]),
        use_noise           = kwarg('use_noise',            True),
        activation          = kwarg('nonlinearity',         'lrelu'),
        mapping_kwargs      = dnnlib.EasyDict(
            num_layers      = kwarg('mapping_layers',       8),
            embed_features  = kwarg('label_fmaps',          None),
            layer_features  = kwarg('mapping_fmaps',        None),
            activation      = kwarg('mapping_nonlinearity', 'lrelu'),
            lr_multiplier   = kwarg('mapping_lrmul',        0.01),
            w_avg_beta      = kwarg('w_avg_beta',           0.995,  none=1),
        ),
    )

    # Check for unknown kwargs.
    kwarg('truncation_psi')
    kwarg('truncation_cutoff')
    kwarg('style_mixing_prob')
    kwarg('structure')
    kwarg('conditioning')
    kwarg('fused_modconv')
    unknown_kwargs = list(set(tf_kwargs.keys()) - known_kwargs)
    if len(unknown_kwargs) > 0:
        raise ValueError('Unknown TensorFlow kwarg', unknown_kwargs[0])

    # Collect params.
    tf_params = _collect_tf_params(tf_G)
    for name, value in list(tf_params.items()):
        match = re.fullmatch(r'ToRGB_lod(\d+)/(.*)', name)
        if match:
            r = kwargs.img_resolution // (2 ** int(match.group(1)))
            tf_params[f'{r}x{r}/ToRGB/{match.group(2)}'] = value
            kwargs.synthesis.kwargs.architecture = 'orig'
    #for name, value in tf_params.items(): print(f'{name:<50s}{list(value.shape)}')

    # Convert params.
    G = network_class(**kwargs).eval().requires_grad_(False)
    # pylint: disable=unnecessary-lambda
    # pylint: disable=f-string-without-interpolation
    _populate_module_params(G,
        r'mapping\.w_avg',                                  lambda:     tf_params[f'dlatent_avg'],
        r'mapping\.embed\.weight',                          lambda:     tf_params[f'mapping/LabelEmbed/weight'].transpose(),
        r'mapping\.embed\.bias',                            lambda:     tf_params[f'mapping/LabelEmbed/bias'],
        r'mapping\.fc(\d+)\.weight',                        lambda i:   tf_params[f'mapping/Dense{i}/weight'].transpose(),
        r'mapping\.fc(\d+)\.bias',                          lambda i:   tf_params[f'mapping/Dense{i}/bias'],
        r'synthesis\.b4\.const',                            lambda:     tf_params[f'synthesis/4x4/Const/const'][0],
        r'synthesis\.b4\.conv1\.weight',                    lambda:     tf_params[f'synthesis/4x4/Conv/weight'].transpose(3, 2, 0, 1),
        r'synthesis\.b4\.conv1\.bias',                      lambda:     tf_params[f'synthesis/4x4/Conv/bias'],
        r'synthesis\.b4\.conv1\.noise_const',               lambda:     tf_params[f'synthesis/noise0'][0, 0],
        r'synthesis\.b4\.conv1\.noise_strength',            lambda:     tf_params[f'synthesis/4x4/Conv/noise_strength'],
        r'synthesis\.b4\.conv1\.affine\.weight',            lambda:     tf_params[f'synthesis/4x4/Conv/mod_weight'].transpose(),
        r'synthesis\.b4\.conv1\.affine\.bias',              lambda:     tf_params[f'synthesis/4x4/Conv/mod_bias'] + 1,
        r'synthesis\.b(\d+)\.conv0\.weight',                lambda r:   tf_params[f'synthesis/{r}x{r}/Conv0_up/weight'][::-1, ::-1].transpose(3, 2, 0, 1),
        r'synthesis\.b(\d+)\.conv0\.bias',                  lambda r:   tf_params[f'synthesis/{r}x{r}/Conv0_up/bias'],
        r'synthesis\.b(\d+)\.conv0\.noise_const',           lambda r:   tf_params[f'synthesis/noise{int(np.log2(int(r)))*2-5}'][0, 0],
        r'synthesis\.b(\d+)\.conv0\.noise_strength',        lambda r:   tf_params[f'synthesis/{r}x{r}/Conv0_up/noise_strength'],
        r'synthesis\.b(\d+)\.conv0\.affine\.weight',        lambda r:   tf_params[f'synthesis/{r}x{r}/Conv0_up/mod_weight'].transpose(),
        r'synthesis\.b(\d+)\.conv0\.affine\.bias',          lambda r:   tf_params[f'synthesis/{r}x{r}/Conv0_up/mod_bias'] + 1,
        r'synthesis\.b(\d+)\.conv1\.weight',                lambda r:   tf_params[f'synthesis/{r}x{r}/Conv1/weight'].transpose(3, 2, 0, 1),
        r'synthesis\.b(\d+)\.conv1\.bias',                  lambda r:   tf_params[f'synthesis/{r}x{r}/Conv1/bias'],
        r'synthesis\.b(\d+)\.conv1\.noise_const',           lambda r:   tf_params[f'synthesis/noise{int(np.log2(int(r)))*2-4}'][0, 0],
        r'synthesis\.b(\d+)\.conv1\.noise_strength',        lambda r:   tf_params[f'synthesis/{r}x{r}/Conv1/noise_strength'],
        r'synthesis\.b(\d+)\.conv1\.affine\.weight',        lambda r:   tf_params[f'synthesis/{r}x{r}/Conv1/mod_weight'].transpose(),
        r'synthesis\.b(\d+)\.conv1\.affine\.bias',          lambda r:   tf_params[f'synthesis/{r}x{r}/Conv1/mod_bias'] + 1,
        r'synthesis\.b(\d+)\.torgb\.weight',                lambda r:   tf_params[f'synthesis/{r}x{r}/ToRGB/weight'].transpose(3, 2, 0, 1),
        r'synthesis\.b(\d+)\.torgb\.bias',                  lambda r:   tf_params[f'synthesis/{r}x{r}/ToRGB/bias'],
        r'synthesis\.b(\d+)\.torgb\.affine\.weight',        lambda r:   tf_params[f'synthesis/{r}x{r}/ToRGB/mod_weight'].transpose(),
        r'synthesis\.b(\d+)\.torgb\.affine\.bias',          lambda r:   tf_params[f'synthesis/{r}x{r}/ToRGB/mod_bias'] + 1,
        r'synthesis\.b(\d+)\.skip\.weight',                 lambda r:   tf_params[f'synthesis/{r}x{r}/Skip/weight'][::-1, ::-1].transpose(3, 2, 0, 1),
        r'.*\.resample_filter',                             None,
        r'.*\.act_filter',                                  None,
    )
    return G

#----------------------------------------------------------------------------

def convert_tf_discriminator(tf_D):
    if tf_D.version < 4:
        raise ValueError('TensorFlow pickle version too low')

    # Collect kwargs.
    tf_kwargs = tf_D.static_kwargs
    known_kwargs = set()
    def kwarg(tf_name, default=None):
        known_kwargs.add(tf_name)
        return tf_kwargs.get(tf_name, default)

    # Convert kwargs.
    kwargs = dnnlib.EasyDict(
        c_dim                   = kwarg('label_size',           0),
        img_resolution          = kwarg('resolution',           1024),
        img_channels            = kwarg('num_channels',         3),
        architecture            = kwarg('architecture',         'resnet'),
        channel_base            = kwarg('fmap_base',            16384) * 2,
        channel_max             = kwarg('fmap_max',             512),
        num_fp16_res            = kwarg('num_fp16_res',         0),
        conv_clamp              = kwarg('conv_clamp',           None),
        cmap_dim                = kwarg('mapping_fmaps',        None),
        block_kwargs = dnnlib.EasyDict(
            activation          = kwarg('nonlinearity',         'lrelu'),
            resample_filter     = kwarg('resample_kernel',      [1,3,3,1]),
            freeze_layers       = kwarg('freeze_layers',        0),
        ),
        mapping_kwargs = dnnlib.EasyDict(
            num_layers          = kwarg('mapping_layers',       0),
            embed_features      = kwarg('mapping_fmaps',        None),
            layer_features      = kwarg('mapping_fmaps',        None),
            activation          = kwarg('nonlinearity',         'lrelu'),
            lr_multiplier       = kwarg('mapping_lrmul',        0.1),
        ),
        epilogue_kwargs = dnnlib.EasyDict(
            mbstd_group_size    = kwarg('mbstd_group_size',     None),
            mbstd_num_channels  = kwarg('mbstd_num_features',   1),
            activation          = kwarg('nonlinearity',         'lrelu'),
        ),
    )

    # Check for unknown kwargs.
    kwarg('structure')
    kwarg('conditioning')
    unknown_kwargs = list(set(tf_kwargs.keys()) - known_kwargs)
    if len(unknown_kwargs) > 0:
        raise ValueError('Unknown TensorFlow kwarg', unknown_kwargs[0])

    # Collect params.
    tf_params = _collect_tf_params(tf_D)
    for name, value in list(tf_params.items()):
        match = re.fullmatch(r'FromRGB_lod(\d+)/(.*)', name)
        if match:
            r = kwargs.img_resolution // (2 ** int(match.group(1)))
            tf_params[f'{r}x{r}/FromRGB/{match.group(2)}'] = value
            kwargs.architecture = 'orig'
    #for name, value in tf_params.items(): print(f'{name:<50s}{list(value.shape)}')

    # Convert params.
    from training import networks_stylegan2
    D = networks_stylegan2.Discriminator(**kwargs).eval().requires_grad_(False)
    # pylint: disable=unnecessary-lambda
    # pylint: disable=f-string-without-interpolation
    _populate_module_params(D,
        r'b(\d+)\.fromrgb\.weight',     lambda r:       tf_params[f'{r}x{r}/FromRGB/weight'].transpose(3, 2, 0, 1),
        r'b(\d+)\.fromrgb\.bias',       lambda r:       tf_params[f'{r}x{r}/FromRGB/bias'],
        r'b(\d+)\.conv(\d+)\.weight',   lambda r, i:    tf_params[f'{r}x{r}/Conv{i}{["","_down"][int(i)]}/weight'].transpose(3, 2, 0, 1),
        r'b(\d+)\.conv(\d+)\.bias',     lambda r, i:    tf_params[f'{r}x{r}/Conv{i}{["","_down"][int(i)]}/bias'],
        r'b(\d+)\.skip\.weight',        lambda r:       tf_params[f'{r}x{r}/Skip/weight'].transpose(3, 2, 0, 1),
        r'mapping\.embed\.weight',      lambda:         tf_params[f'LabelEmbed/weight'].transpose(),
        r'mapping\.embed\.bias',        lambda:         tf_params[f'LabelEmbed/bias'],
        r'mapping\.fc(\d+)\.weight',    lambda i:       tf_params[f'Mapping{i}/weight'].transpose(),
        r'mapping\.fc(\d+)\.bias',      lambda i:       tf_params[f'Mapping{i}/bias'],
        r'b4\.conv\.weight',            lambda:         tf_params[f'4x4/Conv/weight'].transpose(3, 2, 0, 1),
        r'b4\.conv\.bias',              lambda:         tf_params[f'4x4/Conv/bias'],
        r'b4\.fc\.weight',              lambda:         tf_params[f'4x4/Dense0/weight'].transpose(),
        r'b4\.fc\.bias',                lambda:         tf_params[f'4x4/Dense0/bias'],
        r'b4\.out\.weight',             lambda:         tf_params[f'Output/weight'].transpose(),
        r'b4\.out\.bias',               lambda:         tf_params[f'Output/bias'],
        r'.*\.resample_filter',         None,
    )
    return D

#----------------------------------------------------------------------------

@click.command()
@click.option('--source', help='Input pickle', required=True, metavar='PATH')
@click.option('--dest', help='Output pickle', required=True, metavar='PATH')
@click.option('--force-fp16', help='Force the networks to use FP16', type=bool, default=False, metavar='BOOL', show_default=True)
def convert_network_pickle(source, dest, force_fp16):
    """Convert legacy network pickle into the native PyTorch format.

    The tool is able to load the main network configurations exported using the TensorFlow version of StyleGAN2 or StyleGAN2-ADA.
    It does not support e.g. StyleGAN2-ADA comparison methods, StyleGAN2 configs A-D, or StyleGAN1 networks.

    Example:

    \b
    python legacy.py \\
        --source=https://nvlabs-fi-cdn.nvidia.com/stylegan2/networks/stylegan2-cat-config-f.pkl \\
        --dest=stylegan2-cat-config-f.pkl
    """
    print(f'Loading "{source}"...')
    with dnnlib.util.open_url(source) as f:
        data = load_network_pkl(f, force_fp16=force_fp16)
    print(f'Saving "{dest}"...')
    with open(dest, 'wb') as f:
        pickle.dump(data, f)
    print('Done.')

#----------------------------------------------------------------------------

if __name__ == "__main__":
    convert_network_pickle() # pylint: disable=no-value-for-parameter

#----------------------------------------------------------------------------