import os
import sys
import random
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from argparse import ArgumentParser

from core.registry import register_method
from core.base_method import BaseMethod

sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../../GaussianFocus_offy')))
from utils.loss_utils import l1_loss, ssim
from gaussian_renderer import render as native_render
from scene import Scene, GaussianModel
from arguments import ModelParams, PipelineParams, OptimizationParams

class PatchAttention(nn.Module):
    def __init__(self, channel):
        super(PatchAttention, self).__init__()
        self.query_conv = nn.Conv2d(channel, 1, kernel_size=1)
        self.key_conv = nn.Conv2d(channel, 1, kernel_size=1)
        self.value_conv = nn.Conv2d(channel, channel, kernel_size=1)
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, image, gt_image, block_size=64):
        batch_size, C, height, width = image.size()
        blocks = []
        for i in range(0, height, block_size):
            for j in range(0, width, block_size):
                block_image = image[:, :, i:i+block_size, j:j+block_size]
                block_gt_image = gt_image[:, :, i:i+block_size, j:j+block_size]

                pad_height = block_size - block_image.size(2)
                pad_width = block_size - block_image.size(3)
                if pad_height > 0 or pad_width > 0:
                    block_image = F.pad(block_image, (0, pad_width, 0, pad_height))
                    block_gt_image = F.pad(block_gt_image, (0, pad_width, 0, pad_height))

                query = self.query_conv(block_image).view(batch_size, -1, block_size * block_size).permute(0, 2, 1)
                key = self.key_conv(block_gt_image).view(batch_size, -1, block_size * block_size)
                value = self.value_conv(block_gt_image).view(batch_size, -1, block_size * block_size)

                attention = self.softmax(torch.bmm(query, key))
                out = torch.bmm(value, attention.permute(0, 2, 1))
                out = out.view(batch_size, C, block_size, block_size)
                blocks.append(out[:, :, :min(block_size, height - i), :min(block_size, width - j)])

        output = torch.cat([torch.cat(blocks[i * (width // block_size + (width % block_size > 0)):(i + 1) * (width // block_size + (width % block_size > 0))], dim=3) for i in range(height // block_size + (height % block_size > 0))], dim=2)
        return output

def edge_loss(input_img, target_img):
    edge_filter = torch.Tensor([[1, 0, -1], [2, 0, -2], [1, 0, -1]]).view(1, 1, 3, 3).to(input_img.device)
    edge_filter = edge_filter.repeat(input_img.size(1), 1, 1, 1)
    edge_filter = nn.Parameter(data=edge_filter, requires_grad=False)

    input_x = F.conv2d(input_img, edge_filter, padding=1, groups=input_img.size(1))
    input_y = F.conv2d(input_img, edge_filter.transpose(2, 3), padding=1, groups=input_img.size(1))
    target_x = F.conv2d(target_img, edge_filter, padding=1, groups=target_img.size(1))
    target_y = F.conv2d(target_img, edge_filter.transpose(2, 3), padding=1, groups=target_img.size(1))

    loss_x = F.l1_loss(input_x, target_x)
    loss_y = F.l1_loss(input_y, target_y)
    return (loss_x + loss_y) / 2

def frequency_loss(input_img, target_img):
    grad_x_input = input_img[:, :, 1:] - input_img[:, :, :-1]
    grad_y_input = input_img[:, 1:, :] - input_img[:, :-1, :]
    grad_x_target = target_img[:, :, 1:] - target_img[:, :, :-1]
    grad_y_target = target_img[:, 1:, :] - target_img[:, :-1, :]

    loss_x = F.l1_loss(grad_x_input, grad_x_target)
    loss_y = F.l1_loss(grad_y_input, grad_y_target)
    return (loss_x + loss_y) / 2

class CombinedLoss(nn.Module):
    def __init__(self):
        super(CombinedLoss, self).__init__()

    def forward(self, finals, gt_image, beta=1, eta=1):
        if len(finals.shape) == 3:
            finals = finals.unsqueeze(0)
        if len(gt_image.shape) == 3:
            gt_image = gt_image.unsqueeze(0)
        e_loss = edge_loss(finals, gt_image)
        f_loss = frequency_loss(finals, gt_image)
        return beta * e_loss + eta * f_loss

@register_method("gaussianfocus")
class GaussianFocusWrapper(BaseMethod):
    def __init__(self, dataset_config, hyperparams):
        self.parser = ArgumentParser()
        self.lp = ModelParams(self.parser)
        self.op = OptimizationParams(self.parser)
        self.pp = PipelineParams(self.parser)
        self.args = self.parser.parse_args([])

        self.args.source_path = dataset_config["source_path"]
        self.args.model_path = dataset_config["model_path"]
        self.args.eval = True
        self.args.resolution = dataset_config.get("resolution", 1)
        self.track_decoupling = hyperparams.get("track_decoupling", False)

        self.dataset = self.lp.extract(self.args)
        self.opt = self.op.extract(self.args)
        self.pipe = self.pp.extract(self.args)

        self.dataset.kernel_size = 0.1
        self.dataset.ray_jitter = False
        self.dataset.resample_gt_image = False
        self.dataset.sample_more_highres = False

        self.gaussians = GaussianModel(self.dataset.sh_degree)
        # INJECTED_RES_FIX begin
        import sys as _sys
        _scene, _explicit_res = None, None
        for _i, _a in enumerate(_sys.argv[:-1]):
            _v = _sys.argv[_i + 1]
            if _a == "--scene":          _scene = _v
            elif _a == "--source_path":  _scene = _v.rstrip("/").split("/")[-1]
            elif _a == "--resolution":
                try: _explicit_res = int(_v)
                except: pass
        _OUTDOOR_360 = {"bicycle", "flowers", "garden", "stump", "treehill"}
        if _explicit_res is not None and _explicit_res > 0:
            _res = _explicit_res
        elif _scene is not None:
            _res = 4 if _scene in _OUTDOOR_360 else 2
        else:
            _res = None
        try:
            if _res is not None:
                self.dataset.resolution = _res
                print("[res-fix] scene=%s explicit=%s -> res=%s  (%s)" % (_scene, _explicit_res, _res, __file__))
        except Exception as _e:
            print("[res-fix] FAILED:", _e)
        # INJECTED_RES_FIX end
        self.scene = Scene(self.dataset, self.gaussians)
        self.gaussians.training_setup(self.opt)

        bg_color = [1, 1, 1] if self.dataset.white_background else [0, 0, 0]
        self.background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")
        self.viewpoint_stack = self.scene.getTrainCameras().copy()
        self.gaussians.compute_3D_filter(cameras=self.scene.getTrainCameras())
        
        self.last_n_gaussians = len(self.gaussians.get_xyz)

        self.patch_attention = PatchAttention(channel=3).to("cuda")
        self.attn_optimizer = torch.optim.Adam(self.patch_attention.parameters(), lr=1e-3)
        self.combined_loss_fn = CombinedLoss()

    def train_iteration(self, step):
        self.gaussians.update_learning_rate(step)
        if step % 1000 == 0:
            self.gaussians.oneupSHdegree()
            
        if not self.viewpoint_stack:
            self.viewpoint_stack = self.scene.getTrainCameras().copy()
        self.gaussians.compute_3D_filter(cameras=self.scene.getTrainCameras())
            
        viewpoint_cam = self.viewpoint_stack.pop(random.randint(0, len(self.viewpoint_stack) - 1))
        
        render_pkg = native_render(viewpoint_cam, self.gaussians, self.pipe, self.background, kernel_size=self.dataset.kernel_size)
        image = render_pkg["render"]
        viewspace_point_tensor = render_pkg["viewspace_points"]
        visibility_filter = render_pkg["visibility_filter"]
        radii = render_pkg["radii"]
        
        gt_image = viewpoint_cam.original_image.cuda()
        
        patch_attn_active = 0
        finals = image
        if step % 50 == 0:
            patch_attn_active = 1
            image1 = image.unsqueeze(0)
            gt_image1 = gt_image.unsqueeze(0)
            output = self.patch_attention(image1, gt_image1)
            finals = output * image

        finals = torch.squeeze(finals, 0)
        finals_var = finals
        gt_image_var = gt_image

        Ll1 = l1_loss(image, gt_image)
        ssim_value = ssim(image, gt_image)
        loss_target = (1.0 - self.opt.lambda_dssim) * Ll1 + self.opt.lambda_dssim * (1.0 - ssim_value)
        loss_x = self.combined_loss_fn(finals_var, gt_image_var)
        loss = loss_target + loss_x

        grad_cos_sim = 0.0
        parasitic_ratio = 0.0
        stats = {
            "sti_spatial": 0.0,
            "sti_geometry": 0.0,
            "sti_opacity": 0.0,
            "sti_appearance": 0.0
        }

        if self.track_decoupling and step % 100 == 0:
            self.gaussians.optimizer.zero_grad(set_to_none=True)
            loss_target.backward(retain_graph=True)
            grad_target = self.gaussians._xyz.grad.clone() if self.gaussians._xyz.grad is not None else torch.zeros_like(self.gaussians._xyz)
            
            self.gaussians.optimizer.zero_grad(set_to_none=True)
            if getattr(loss_x, "requires_grad", False): loss_x.backward(retain_graph=True)
            grad_parasitic = self.gaussians._xyz.grad.clone() if self.gaussians._xyz.grad is not None else torch.zeros_like(self.gaussians._xyz)
            
            valid_mask = (torch.norm(grad_target, dim=1) > 0) & (torch.norm(grad_parasitic, dim=1) > 0)
            if valid_mask.any():
                grad_cos_sim = float(F.cosine_similarity(grad_target[valid_mask], grad_parasitic[valid_mask], dim=1).mean())
            parasitic_ratio = float(torch.norm(grad_parasitic, dim=1).mean() / (torch.norm(grad_target, dim=1).mean() + 1e-7))
            
            param_groups_map = {
                "spatial": [self.gaussians._xyz],
                "geometry": [self.gaussians._scaling, self.gaussians._rotation],
                "opacity": [self.gaussians._opacity],
                "appearance": [self.gaussians._features_dc, self.gaussians._features_rest],
            }

            self.gaussians.optimizer.zero_grad(set_to_none=True)
            loss_target.backward(retain_graph=True)
            grads_target = {}
            for group_name, params in param_groups_map.items():
                grads_target[group_name] = torch.cat([p.grad.clone().reshape(-1) for p in params if p.grad is not None])

            self.gaussians.optimizer.zero_grad(set_to_none=True)
            if getattr(loss_x, "requires_grad", False): loss_x.backward(retain_graph=True)
            grads_parasitic = {}
            for group_name, params in param_groups_map.items():
                grads_parasitic[group_name] = torch.cat([p.grad.clone().reshape(-1) for p in params if p.grad is not None])

            N_total = self.gaussians.get_xyz.shape[0]
            SAFE_N = 2_000_000  # above this, skip group-wise STI to avoid OOM
            for group_name in param_groups_map:
                if N_total > SAFE_N:
                    stats[f"sti_{group_name}"] = 0.0
                    continue
                gt, gp = grads_target.get(group_name), grads_parasitic.get(group_name)
                if gt is not None and gp is not None and gt.norm() > 0 and gp.norm() > 0:
                    # 1D dot product is memory-cheap; cosine_similarity on unsqueeze(0)
                    # allocates 2D intermediates that OOM at N>2M.
                    cos = float((gt @ gp) / (gt.norm() * gp.norm() + 1e-7))
                    r = float(gp.norm() / (gt.norm() + gp.norm() + 1e-7))
                    ti = r * max(0.0, -cos)
                else:
                    ti = 0.0
                stats[f"sti_{group_name}"] = ti

            self.gaussians.optimizer.zero_grad(set_to_none=True)
            self.attn_optimizer.zero_grad()
            loss.backward()
        else:
            self.attn_optimizer.zero_grad()
            loss.backward()

        with torch.no_grad():
            if step < self.opt.densify_until_iter:
                self.gaussians.max_radii2D[visibility_filter] = torch.max(self.gaussians.max_radii2D[visibility_filter], radii[visibility_filter])
                self.gaussians.add_densification_stats(viewspace_point_tensor, visibility_filter)
                
                if step > self.opt.densify_from_iter and step % self.opt.densification_interval == 0:
                    size_threshold = 0.02 if step > 1000 else None
                    self.gaussians.densify_and_prune(self.opt.densify_grad_threshold, 0.005, self.scene.cameras_extent, size_threshold, step)
                    self.gaussians.compute_3D_filter(cameras=self.scene.getTrainCameras())
                
                if step % self.opt.opacity_reset_interval == 0 or (self.dataset.white_background and step == self.opt.densify_from_iter):
                    self.gaussians.reset_opacity()

            if step % 100 == 0 and step > self.opt.densify_until_iter:
                if step < self.opt.iterations - 100:
                    self.gaussians.compute_3D_filter(cameras=self.scene.getTrainCameras())

            self.gaussians.optimizer.step()
            self.gaussians.optimizer.zero_grad(set_to_none=True)
            if patch_attn_active:
                self.attn_optimizer.step()

        num_gaussians = self.gaussians.get_xyz.shape[0]
        metrics = {
            "loss": float(loss), 
            "loss_l1": float(loss_target), 
            "loss_ssim": float(loss_x), 
            "loss_edge_freq": float(loss_x),
            "patch_attn_active": int(patch_attn_active),
            "num_gaussians": int(num_gaussians), 
            "delta_N": int(num_gaussians - self.last_n_gaussians),
            "peak_vram_GB": float(torch.cuda.max_memory_allocated() / (1024 ** 3)),
            "grad_cos_sim": float(grad_cos_sim), 
            "parasitic_ratio": float(parasitic_ratio)
        }
        metrics.update(stats)
        self.last_n_gaussians = num_gaussians
        
        histograms = {}
        if step % 1000 == 0:
            histograms["opacity"] = torch.sigmoid(self.gaussians._opacity).clone().detach()
            scales = torch.exp(self.gaussians._scaling).clone().detach()
            histograms["scaling"] = scales
            scales_2d = scales[:, :2] if scales.shape[1] >= 2 else scales
            gamma = scales_2d.max(dim=-1)[0] / (scales_2d.min(dim=-1)[0] + 1e-7)
            histograms["anisotropy"] = gamma
            histograms["sh_dc_mag"] = self.gaussians._features_dc.detach().norm(dim=-1)

        return metrics, histograms

    def render(self, camera):
        with torch.no_grad():
            render_pkg = native_render(camera, self.gaussians, self.pipe, self.background, kernel_size=self.dataset.kernel_size)
        return {"image": render_pkg["render"], "depth": render_pkg.get("depth", None)}

    def save(self, save_dir, step):
        self.scene.save(step)

    def load(self, model_path, iteration):
        self.gaussians.load_ply(os.path.join(model_path, 'point_cloud', f'iteration_{iteration}', 'point_cloud.ply'))

    def get_spatial_centers(self):
        return self.gaussians._xyz

    def compute_physical_metrics(self, cameras=None):
        metrics = {}
        with torch.no_grad():
            raw_scales = self.gaussians._scaling
            scales = torch.exp(raw_scales)
            
            scales_2d = scales[:, :2] if scales.dim() > 1 and scales.shape[1] >= 2 else scales.unsqueeze(-1).expand(-1, 2)
            
            max_S, _ = torch.max(scales_2d, dim=1)
            min_S, _ = torch.min(scales_2d, dim=1)
            gamma = max_S / (min_S + 1e-7)
            
            metrics["gamma_median"] = float(torch.median(gamma))
            metrics["gamma_90th_percentile"] = float(torch.quantile(gamma, 0.90))
            metrics["scale_mean"] = float(torch.mean(scales_2d))
            metrics["alpha_mean"] = float(torch.mean(torch.sigmoid(self.gaussians._opacity)))
            
            dc, rest = self.gaussians._features_dc, self.gaussians._features_rest
            if rest is not None and rest.shape[1] > 0:
                metrics["sh_energy_ratio"] = float(rest.norm(dim=-1).mean() / (dc.norm(dim=-1).mean() + 1e-7))
                
            if cameras is not None and len(cameras) > 0:
                view_dirs = []
                for c in cameras:
                    view_dirs.append(c.world_view_transform[:3, 2].tolist())
                view_dirs = F.normalize(torch.tensor(view_dirs, dtype=torch.float32, device="cuda"), dim=1)

                rots = F.normalize(self.gaussians._rotation.clone(), dim=1)
                w, x, y, z = rots.unbind(dim=-1)
                normals = F.normalize(torch.stack([2*(x*z + w*y), 2*(y*z - w*x), 1-2*(x*x + y*y)], dim=-1), dim=1)
                
                max_cos, _ = torch.max(torch.abs(torch.matmul(normals, view_dirs.T)), dim=1)
                metrics["billboard_bias_ratio"] = float((max_cos > 0.90).float().mean())

        return metrics

    def evaluate_spatial_field(self, query_points: torch.Tensor, cameras=None) -> torch.Tensor:
        with torch.no_grad():
            V = query_points.shape[0]
            densities = torch.zeros(V, device="cuda")
            xyz, opacities = self.gaussians._xyz, torch.sigmoid(self.gaussians._opacity).squeeze()
            scales = torch.exp(self.gaussians._scaling)
            sigma_sq = (scales[:, :2].max(dim=1)[0].pow(2)) if scales.shape[1] >= 2 else scales.squeeze().pow(2)
            
            N_gaussians = xyz.shape[0]
            chunk_size = max(1, 30_000_000 // (N_gaussians + 1))
            for i in range(0, V, chunk_size):
                end = min(i + chunk_size, V)
                dist_sq = torch.cdist(query_points[i:end], xyz, p=2).pow(2)
                weights = torch.exp(-0.5 * dist_sq / (sigma_sq.unsqueeze(0) + 1e-7))
                densities[i:end] = torch.sum(weights * opacities.unsqueeze(0), dim=1)
            return densities