# *************************************************************************
# Copyright (2023) Bytedance Inc.
#
# Copyright (2023) DragDiffusion Authors 
#
# 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 copy
import torch
import torch.nn.functional as F


def point_tracking(F0,
                   F1,
                   handle_points,
                   handle_points_init,
                   args):
    with torch.no_grad():
        for i in range(len(handle_points)):
            pi0, pi = handle_points_init[i], handle_points[i]
            f0 = F0[:, :, int(pi0[0]), int(pi0[1])]

            r1, r2 = int(pi[0])-args.r_p, int(pi[0])+args.r_p+1
            c1, c2 = int(pi[1])-args.r_p, int(pi[1])+args.r_p+1
            F1_neighbor = F1[:, :, r1:r2, c1:c2]
            all_dist = (f0.unsqueeze(dim=-1).unsqueeze(dim=-1) - F1_neighbor).abs().sum(dim=1)
            all_dist = all_dist.squeeze(dim=0)
            # WARNING: no boundary protection right now
            row, col = divmod(all_dist.argmin().item(), all_dist.shape[-1])
            handle_points[i][0] = pi[0] - args.r_p + row
            handle_points[i][1] = pi[1] - args.r_p + col
        return handle_points

def check_handle_reach_target(handle_points,
                              target_points):
    # dist = (torch.cat(handle_points,dim=0) - torch.cat(target_points,dim=0)).norm(dim=-1)
    all_dist = list(map(lambda p,q: (p-q).norm(), handle_points, target_points))
    return (torch.tensor(all_dist) < 2.0).all()

# obtain the bilinear interpolated feature patch centered around (x, y) with radius r
def interpolate_feature_patch(feat,
                              y,
                              x,
                              r):
    x0 = torch.floor(x).long()
    x1 = x0 + 1

    y0 = torch.floor(y).long()
    y1 = y0 + 1

    wa = (x1.float() - x) * (y1.float() - y)
    wb = (x1.float() - x) * (y - y0.float())
    wc = (x - x0.float()) * (y1.float() - y)
    wd = (x - x0.float()) * (y - y0.float())

    Ia = feat[:, :, y0-r:y0+r+1, x0-r:x0+r+1]
    Ib = feat[:, :, y1-r:y1+r+1, x0-r:x0+r+1]
    Ic = feat[:, :, y0-r:y0+r+1, x1-r:x1+r+1]
    Id = feat[:, :, y1-r:y1+r+1, x1-r:x1+r+1]

    return Ia * wa + Ib * wb + Ic * wc + Id * wd

def drag_diffusion_update(model,
                          init_code,
                          t,
                          handle_points,
                          target_points,
                          mask,
                          args):

    assert len(handle_points) == len(target_points), \
        "number of handle point must equals target points"

    text_emb = model.get_text_embeddings(args.prompt).detach()
    # the init output feature of unet
    with torch.no_grad():
        unet_output, F0 = model.forward_unet_features(init_code, t, encoder_hidden_states=text_emb,
            layer_idx=args.unet_feature_idx, interp_res_h=args.sup_res, interp_res_w=args.sup_res)
        x_prev_0,_ = model.step(unet_output, t, init_code)
        # init_code_orig = copy.deepcopy(init_code)

    # prepare optimizable init_code and optimizer
    init_code.requires_grad_(True)
    optimizer = torch.optim.Adam([init_code], lr=args.lr)

    # prepare for point tracking and background regularization
    handle_points_init = copy.deepcopy(handle_points)
    interp_mask = F.interpolate(mask, (init_code.shape[2],init_code.shape[3]), mode='nearest')

    # prepare amp scaler for mixed-precision training
    scaler = torch.cuda.amp.GradScaler()
    gif_init_code = []
    for step_idx in range(args.n_pix_step):
        with torch.autocast(device_type='cuda', dtype=torch.float16):
            unet_output, F1 = model.forward_unet_features(init_code, t, encoder_hidden_states=text_emb,
                layer_idx=args.unet_feature_idx, interp_res_h=args.sup_res, interp_res_w=args.sup_res)
            x_prev_updated,_ = model.step(unet_output, t, init_code)

            # do point tracking to update handle points before computing motion supervision loss
            if step_idx != 0:
                handle_points = point_tracking(F0, F1, handle_points, handle_points_init, args)
                print('new handle points', handle_points)

            # break if all handle points have reached the targets
            if check_handle_reach_target(handle_points, target_points):
                break

            loss = 0.0
            for i in range(len(handle_points)):
                pi, ti = handle_points[i], target_points[i]
                # skip if the distance between target and source is less than 1
                if (ti - pi).norm() < 2.:
                    continue

                di = (ti - pi) / (ti - pi).norm()

                # motion supervision
                f0_patch = F1[:,:,int(pi[0])-args.r_m:int(pi[0])+args.r_m+1, int(pi[1])-args.r_m:int(pi[1])+args.r_m+1].detach()
                f1_patch = interpolate_feature_patch(F1, pi[0] + di[0], pi[1] + di[1], args.r_m)
                loss += ((2*args.r_m+1)**2)*F.l1_loss(f0_patch, f1_patch)

            # masked region must stay unchanged
            loss += args.lam * ((x_prev_updated-x_prev_0)*(1.0-interp_mask)).abs().sum()
            # loss += args.lam * ((init_code_orig-init_code)*(1.0-interp_mask)).abs().sum()
            print('loss total=%f'%(loss.item()))

        scaler.scale(loss).backward()
        scaler.step(optimizer)
        scaler.update()
        optimizer.zero_grad()
        if args.create_gif_checkbox:
            if step_idx % args.gif_interval == 0 or step_idx == args.n_pix_step - 1:
                gif_init_code.append(init_code)
    return init_code, gif_init_code

def drag_diffusion_update_gen(model,
                              init_code,
                              t,
                              handle_points,
                              target_points,
                              mask,
                              args):

    assert len(handle_points) == len(target_points), \
        "number of handle point must equals target points"

    # positive prompt embedding
    text_emb = model.get_text_embeddings(args.prompt).detach()
    if args.guidance_scale > 1.0:
        unconditional_input = model.tokenizer(
            [args.neg_prompt],
            padding="max_length",
            max_length=77,
            return_tensors="pt"
        )
        unconditional_emb = model.text_encoder(unconditional_input.input_ids.to(text_emb.device))[0].detach()
        text_emb = torch.cat([unconditional_emb, text_emb], dim=0)

    # the init output feature of unet
    with torch.no_grad():
        if args.guidance_scale > 1.:
            model_inputs_0 = copy.deepcopy(torch.cat([init_code] * 2))
        else:
            model_inputs_0 = copy.deepcopy(init_code)
        unet_output, F0 = model.forward_unet_features(model_inputs_0, t, encoder_hidden_states=text_emb,
            layer_idx=args.unet_feature_idx, interp_res_h=args.sup_res_h, interp_res_w=args.sup_res_w)
        if args.guidance_scale > 1.:
            # strategy 1: discard the unconditional branch feature maps
            # F0 = F0[1].unsqueeze(dim=0)
            # strategy 2: concat pos and neg branch feature maps for motion-sup and point tracking
            # F0 = torch.cat([F0[0], F0[1]], dim=0).unsqueeze(dim=0)
            # strategy 3: concat pos and neg branch feature maps with guidance_scale consideration
            coef = args.guidance_scale / (2*args.guidance_scale - 1.0)
            F0 = torch.cat([(1-coef)*F0[0], coef*F0[1]], dim=0).unsqueeze(dim=0)

            unet_output_uncon, unet_output_con = unet_output.chunk(2, dim=0)
            unet_output = unet_output_uncon + args.guidance_scale * (unet_output_con - unet_output_uncon)
        x_prev_0,_ = model.step(unet_output, t, init_code)
        # init_code_orig = copy.deepcopy(init_code)

    # prepare optimizable init_code and optimizer
    init_code.requires_grad_(True)
    optimizer = torch.optim.Adam([init_code], lr=args.lr)

    # prepare for point tracking and background regularization
    handle_points_init = copy.deepcopy(handle_points)
    interp_mask = F.interpolate(mask, (init_code.shape[2],init_code.shape[3]), mode='nearest')

    # prepare amp scaler for mixed-precision training
    scaler = torch.cuda.amp.GradScaler()
    
    gif_init_code = []
    handle_points_list = []
    for step_idx in range(args.n_pix_step):
        with torch.autocast(device_type='cuda', dtype=torch.float16):
            if args.guidance_scale > 1.:
                model_inputs = init_code.repeat(2,1,1,1)
            else:
                model_inputs = init_code
            unet_output, F1 = model.forward_unet_features(model_inputs, t, encoder_hidden_states=text_emb,
                layer_idx=args.unet_feature_idx, interp_res_h=args.sup_res_h, interp_res_w=args.sup_res_w)
            if args.guidance_scale > 1.:
                # strategy 1: discard the unconditional branch feature maps
                # F1 = F1[1].unsqueeze(dim=0)
                # strategy 2: concat positive and negative branch feature maps for motion-sup and point tracking
                # F1 = torch.cat([F1[0], F1[1]], dim=0).unsqueeze(dim=0)
                # strategy 3: concat pos and neg branch feature maps with guidance_scale consideration
                coef = args.guidance_scale / (2*args.guidance_scale - 1.0)
                F1 = torch.cat([(1-coef)*F1[0], coef*F1[1]], dim=0).unsqueeze(dim=0)

                unet_output_uncon, unet_output_con = unet_output.chunk(2, dim=0)
                unet_output = unet_output_uncon + args.guidance_scale * (unet_output_con - unet_output_uncon)
            x_prev_updated,_ = model.step(unet_output, t, init_code)

            # do point tracking to update handle points before computing motion supervision loss
            if step_idx != 0:
                handle_points = point_tracking(F0, F1, handle_points, handle_points_init, args)
                print('new handle points', handle_points)
                print('target points', target_points)
                

            # break if all handle points have reached the targets
            if check_handle_reach_target(handle_points, target_points):
                break

            loss = 0.0
            for i in range(len(handle_points)):
                pi, ti = handle_points[i], target_points[i]
                # skip if the distance between target and source is less than 1
                if (ti - pi).norm() < 2.:
                    continue

                di = (ti - pi) / (ti - pi).norm()

                # motion supervision
                f0_patch = F1[:,:,int(pi[0])-args.r_m:int(pi[0])+args.r_m+1, int(pi[1])-args.r_m:int(pi[1])+args.r_m+1].detach()
                f1_patch = interpolate_feature_patch(F1, pi[0] + di[0], pi[1] + di[1], args.r_m)
                loss += ((2*args.r_m+1)**2)*F.l1_loss(f0_patch, f1_patch)

            # masked region must stay unchanged
            loss += args.lam * ((x_prev_updated-x_prev_0)*(1.0-interp_mask)).abs().sum()
            # loss += args.lam * ((init_code_orig - init_code)*(1.0-interp_mask)).abs().sum()
            print('loss total=%f'%(loss.item()))
        if args.create_gif_checkbox:
            if step_idx % args.gif_interval == 0 or step_idx == args.n_pix_step - 1:
                gif_init_code.append(init_code)
                
        if args.create_tracking_points_checkbox:
            if step_idx % args.gif_interval == 0 or step_idx == args.n_pix_step - 1:
                handle_points_list.append(handle_points)
        scaler.scale(loss).backward()
        scaler.step(optimizer)
        scaler.update()
        optimizer.zero_grad()
        
    return init_code, gif_init_code, handle_points_list