# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto.  Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.

"""Generate images using pretrained network pickle."""

import math
import legacy
import clip
import dnnlib
import numpy as np
import torch
import torch.nn.functional as F
from torchvision.transforms import Compose, Resize, CenterCrop
from PIL import Image
from torch_utils import misc
from torch_utils.ops import upfirdn2d
import id_loss
from copy import deepcopy


def block_forward(self, x, img, ws, shapes, force_fp32=False, fused_modconv=None, **layer_kwargs):
        misc.assert_shape(ws, [None, self.num_conv + self.num_torgb, self.w_dim])
        w_iter = iter(ws.unbind(dim=1))
        dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32
        memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format
        if fused_modconv is None:
            with misc.suppress_tracer_warnings(): # this value will be treated as a constant
                fused_modconv = (not self.training) and (dtype == torch.float32 or int(x.shape[0]) == 1)

        # Input.
        if self.in_channels == 0:
            x = self.const.to(dtype=dtype, memory_format=memory_format)
            x = x.unsqueeze(0).repeat([ws.shape[0], 1, 1, 1])
        else:
            misc.assert_shape(x, [None, self.in_channels, self.resolution // 2, self.resolution // 2])
            x = x.to(dtype=dtype, memory_format=memory_format)

        # Main layers.
        if self.in_channels == 0:
            x = self.conv1(x, next(w_iter)[...,:shapes[0]], fused_modconv=fused_modconv, **layer_kwargs)
        elif self.architecture == 'resnet':
            y = self.skip(x, gain=np.sqrt(0.5))
            x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs)
            x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, gain=np.sqrt(0.5), **layer_kwargs)
            x = y.add_(x)
        else:
            x = self.conv0(x, next(w_iter)[...,:shapes[0]], fused_modconv=fused_modconv, **layer_kwargs)
            x = self.conv1(x, next(w_iter)[...,:shapes[1]], fused_modconv=fused_modconv, **layer_kwargs)

        # ToRGB.
        if img is not None:
            misc.assert_shape(img, [None, self.img_channels, self.resolution // 2, self.resolution // 2])
            img = upfirdn2d.upsample2d(img, self.resample_filter)
        if self.is_last or self.architecture == 'skip':
            y = self.torgb(x, next(w_iter)[...,:shapes[2]], fused_modconv=fused_modconv)
            y = y.to(dtype=torch.float32, memory_format=torch.contiguous_format)
            img = img.add_(y) if img is not None else y

        assert x.dtype == dtype
        assert img is None or img.dtype == torch.float32
        return x, img

def unravel_index(index, shape):
    out = []
    for dim in reversed(shape):
        out.append(index % dim)
        index = index // dim
    return tuple(reversed(out))

def find_direction(
    GIn,
    text_prompt: str,
    truncation_psi: float = 0.7,
    noise_mode: str = "const",
    resolution: int = 256,
    identity_power: float = 0.5,
):
    G = deepcopy(GIn)
    seeds=np.random.randint(0, 1000, 128)

    batch_size=1
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

    # Labels
    class_idx=None
    label = torch.zeros([1, G.c_dim], device=device).requires_grad_()
    if G.c_dim != 0:
        label[:, class_idx] = 1

    model, preprocess = clip.load("ViT-B/32", device=device)
    text = clip.tokenize([text_prompt]).to(device)
    text_features = model.encode_text(text)

    # Generate images
    for i in G.parameters():
        i.requires_grad = True

    mean = torch.as_tensor((0.48145466, 0.4578275, 0.40821073), dtype=torch.float, device=device)
    std = torch.as_tensor((0.26862954, 0.26130258, 0.27577711), dtype=torch.float, device=device)
    if mean.ndim == 1:
        mean = mean.view(-1, 1, 1)
    if std.ndim == 1:
        std = std.view(-1, 1, 1)

    transf = Compose([Resize(224, interpolation=Image.BICUBIC), CenterCrop(224)])

    styles_array = []
    for seed_idx, seed in enumerate(seeds):
        if seed == seeds[-1]:
            print('Generating image for seed %d (%d/%d) ...' % (seed, seed_idx, len(seeds)))
        z = torch.from_numpy(np.random.RandomState(seed).randn(1, G.z_dim)).to(device)
        ws = G.mapping(z, label, truncation_psi=truncation_psi)

        block_ws = []
        with torch.autograd.profiler.record_function('split_ws'):
            misc.assert_shape(ws, [None, G.synthesis.num_ws, G.synthesis.w_dim])
            ws = ws.to(torch.float32)

            w_idx = 0
            for res in G.synthesis.block_resolutions:
                block = getattr(G.synthesis, f'b{res}')
                block_ws.append(ws.narrow(1, w_idx, block.num_conv + block.num_torgb))
                w_idx += block.num_conv

        styles = torch.zeros(1, 26, 512, device=device)
        styles_idx = 0
        temp_shapes = []
        for res, cur_ws in zip(G.synthesis.block_resolutions, block_ws):
            block = getattr(G.synthesis, f'b{res}')

            if res == 4:
                temp_shape = (block.conv1.affine.weight.shape[0], block.conv1.affine.weight.shape[0], block.torgb.affine.weight.shape[0])
                styles[0, :1, :] = block.conv1.affine(cur_ws[0, :1, :])
                styles[0, 1:2, :] = block.torgb.affine(cur_ws[0, 1:2, :])
                if seed_idx == (len(seeds) - 1):
                    block.conv1.affine = torch.nn.Identity()
                    block.torgb.affine = torch.nn.Identity()
                styles_idx += 2
            else:
                temp_shape = (block.conv0.affine.weight.shape[0], block.conv1.affine.weight.shape[0], block.torgb.affine.weight.shape[0])
                styles[0,styles_idx:styles_idx+1,:temp_shape[0]] = block.conv0.affine(cur_ws[0,:1,:])
                styles[0,styles_idx+1:styles_idx+2,:temp_shape[1]] = block.conv1.affine(cur_ws[0,1:2,:])
                styles[0,styles_idx+2:styles_idx+3,:temp_shape[2]] = block.torgb.affine(cur_ws[0,2:3,:])
                if seed_idx == (len(seeds) - 1):
                    block.conv0.affine = torch.nn.Identity()
                    block.conv1.affine = torch.nn.Identity()
                    block.torgb.affine = torch.nn.Identity()
                styles_idx += 3
            temp_shapes.append(temp_shape)

        styles = styles.detach()
        styles_array.append(styles)

    resolution_dict = {256: 6, 512: 7, 1024: 8}
    id_coeff = identity_power
    styles_direction = torch.zeros(1, 26, 512, device=device)
    styles_direction_grad_el2 = torch.zeros(1, 26, 512, device=device)
    styles_direction.requires_grad_()

    global id_loss2
    #id_loss = id_loss.IDLoss("a").to(device).eval()
    id_loss2 = id_loss.IDLoss("a").to(device).eval()

    temp_photos = []
    grads = []
    for i in range(math.ceil(len(seeds) / batch_size)):

        styles = torch.vstack(styles_array[i*batch_size:(i+1)*batch_size]).to(device)
        seed = seeds[i]

        styles_idx = 0
        x2 = img2 = None

        for k, (res, cur_ws) in enumerate(zip(G.synthesis.block_resolutions, block_ws)):
            block = getattr(G.synthesis, f'b{res}')
            if k > resolution_dict[resolution]:
                continue

            if res == 4:
                x2, img2 = block_forward(block, x2, img2, styles[:, styles_idx:styles_idx+2, :], temp_shapes[k], noise_mode=noise_mode, force_fp32=True)
                styles_idx += 2
            else:
                x2, img2 = block_forward(block, x2, img2, styles[:, styles_idx:styles_idx+3, :], temp_shapes[k], noise_mode=noise_mode, force_fp32=True)
                styles_idx += 3

        img2_cpu = img2.detach().cpu().numpy()
        temp_photos.append(img2_cpu)
        if i > 3:
            continue

        styles2 = styles + styles_direction

        styles_idx = 0
        x = img = None
        for k, (res, cur_ws) in enumerate(zip(G.synthesis.block_resolutions, block_ws)):
            block = getattr(G.synthesis, f'b{res}')
            if k > resolution_dict[resolution]:
                continue
            if res == 4:
                x, img = block_forward(block, x, img, styles2[:, styles_idx:styles_idx+2, :], temp_shapes[k], noise_mode=noise_mode, force_fp32=True)
                styles_idx += 2
            else:
                x, img = block_forward(block, x, img, styles2[:, styles_idx:styles_idx+3, :], temp_shapes[k], noise_mode=noise_mode, force_fp32=True)
                styles_idx += 3

        identity_loss, _ = id_loss2(img, img2)
        identity_loss *= id_coeff
        img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255)
        img = (transf(img.permute(0, 3, 1, 2)) / 255).sub_(mean).div_(std)
        image_features = model.encode_image(img)
        cos_sim = -1*F.cosine_similarity(image_features, (text_features[0]).unsqueeze(0))
        (identity_loss + cos_sim.sum()).backward(retain_graph=True)

    styles_direction.grad[:, list(range(26)), :] = 0
    with torch.no_grad():
        styles_direction *= 0

    for i in range(math.ceil(len(seeds) / batch_size)):

        seed = seeds[i]
        styles = torch.vstack(styles_array[i*batch_size:(i+1)*batch_size]).to(device)
        img2 = torch.tensor(temp_photos[i]).to(device)
        styles2 = styles + styles_direction

        styles_idx = 0
        x = img = None
        for k, (res, cur_ws) in enumerate(zip(G.synthesis.block_resolutions, block_ws)):
            block = getattr(G.synthesis, f'b{res}')
            if k > resolution_dict[resolution]:
                continue

            if res == 4:
                x, img = block_forward(block, x, img, styles2[:, styles_idx:styles_idx+2, :], temp_shapes[k], noise_mode=noise_mode, force_fp32=True)
                styles_idx += 2
            else:
                x, img = block_forward(block, x, img, styles2[:, styles_idx:styles_idx+3, :], temp_shapes[k], noise_mode=noise_mode, force_fp32=True)
                styles_idx += 3

        identity_loss, _ = id_loss2(img, img2)
        identity_loss *= id_coeff
        img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255)
        img = (transf(img.permute(0, 3, 1, 2)) / 255).sub_(mean).div_(std)
        image_features = model.encode_image(img)
        cos_sim = -1*F.cosine_similarity(image_features, (text_features[0]).unsqueeze(0))
        (identity_loss + cos_sim.sum()).backward(retain_graph=True)

        styles_direction.grad[:, [0, 1, 4, 7, 10, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25], :] = 0

        if i % 2 == 1:
            styles_direction.data = (styles_direction - styles_direction.grad * 5)
            grads.append(styles_direction.grad.clone())
            styles_direction.grad.data.zero_()
            if i > 3:
                styles_direction_grad_el2[grads[-2] * grads[-1] < 0] += 1

    styles_direction = styles_direction.detach()
    styles_direction[styles_direction_grad_el2 > (len(seeds) / batch_size) / 4] = 0

    return styles_direction.cpu().numpy()