import glob
import os
import sys
import copy
from typing import List

import numpy as np
import torch
from einops import rearrange
from omegaconf import OmegaConf
from PIL import Image
from pytorch_lightning import seed_everything
from pytorch3d.renderer.cameras import PerspectiveCameras
from pytorch3d.renderer import look_at_view_transform
from pytorch3d.renderer.camera_utils import join_cameras_as_batch

import json

sys.path.append('./')
from sgm.util import instantiate_from_config, load_safetensors

choices = []


def load_base_model(config, ckpt=None, verbose=True):
    config = OmegaConf.load(config)
    # load model
    config.model.params.network_config.params.far = 3
    config.model.params.first_stage_config.params.ckpt_path = "pretrained-models/sdxl_vae.safetensors"
    guider_config = {'target': 'sgm.modules.diffusionmodules.guiders.ScheduledCFGImgTextRef',
                        'params': {'scale': 7.5, 'scale_im': 3.5}
                        }
    config.model.params.sampler_config.params.guider_config = guider_config

    model = instantiate_from_config(config.model)

    if ckpt is not None:
        print(f"Loading model from {ckpt}")
        if ckpt.endswith("ckpt"):
            pl_sd = torch.load(ckpt, map_location="cpu")
            if "global_step" in pl_sd:
                print(f"Global Step: {pl_sd['global_step']}")
            sd = pl_sd["state_dict"]
        elif ckpt.endswith("safetensors"):
            sd = load_safetensors(ckpt)
            if 'modifier_token' in config.data.params:
                del sd['conditioner.embedders.0.transformer.text_model.embeddings.token_embedding.weight']
                del sd['conditioner.embedders.1.model.token_embedding.weight']
        else:
            raise NotImplementedError
        
    m, u = model.load_state_dict(sd, strict=False)

    model.cuda()
    model.eval()
    return model


def load_delta_model(model, delta_ckpt=None, verbose=True, freeze=True):
    """
    model is preloaded base stable diffusion model
    """

    msg = None
    if delta_ckpt is not None:
        pl_sd_delta = torch.load(delta_ckpt, map_location="cpu")
        sd_delta = pl_sd_delta["delta_state_dict"]

        # TODO: add new delta loading embedding stuff?

        for name, module in model.model.diffusion_model.named_modules():
            if len(name.split('.')) > 1 and name.split('.')[-2] == 'transformer_blocks':
                if hasattr(module, 'pose_emb_layers'):
                    module.register_buffer('references', sd_delta[f'model.diffusion_model.{name}.references'])
                    del sd_delta[f'model.diffusion_model.{name}.references']

        m, u = model.load_state_dict(sd_delta, strict=False)
    

    if len(m) > 0 and verbose:
        print("missing keys:")
    if len(u) > 0 and verbose:
        print("unexpected keys:")

    if freeze:
        for param in model.parameters():
            param.requires_grad = False

    model.cuda()
    model.eval()
    return model, msg


def get_unique_embedder_keys_from_conditioner(conditioner):
    p = [x.input_keys for x in conditioner.embedders]
    return list(set([item for sublist in p for item in sublist])) + ['jpg_ref']


def customforward(self, x, xr, context=None, contextr=None, pose=None, mask_ref=None, prev_weights=None, timesteps=None, drop_im=None):
    # note: if no context is given, cross-attention defaults to self-attention
    if not isinstance(context, list):
        context = [context]
    b, c, h, w = x.shape
    x_in = x
    fg_masks = []
    alphas = []
    rgbs = []

    x = self.norm(x)

    if not self.use_linear:
        x = self.proj_in(x)

    x = rearrange(x, "b c h w -> b (h w) c").contiguous()
    if self.use_linear:
        x = self.proj_in(x)

    prev_weights = None
    counter = 0
    for i, block in enumerate(self.transformer_blocks):
        if i > 0 and len(context) == 1:
            i = 0  # use same context for each block
        if self.image_cross and (counter % self.poscontrol_interval == 0):
            x, fg_mask, weights, alpha, rgb = block(x, context=context[i], context_ref=x, pose=pose, mask_ref=mask_ref, prev_weights=prev_weights, drop_im=drop_im)
            prev_weights = weights
            fg_masks.append(fg_mask)
            if alpha is not None:
                alphas.append(alpha)
            if rgb is not None:
                rgbs.append(rgb)
        else:
            x, _, _, _, _ = block(x, context=context[i], drop_im=drop_im)
        counter += 1
    if self.use_linear:
        x = self.proj_out(x)
    x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w).contiguous()
    if not self.use_linear:
        x = self.proj_out(x)
    if len(fg_masks) > 0:
        if len(rgbs) <= 0:
            rgbs = None
        if len(alphas) <= 0:
            alphas = None
        return x + x_in, None, fg_masks, prev_weights, alphas, rgbs
    else:
        return x + x_in, None, None, prev_weights, None, None


def _customforward(
        self, x, context=None, context_ref=None, pose=None, mask_ref=None, prev_weights=None, drop_im=None, additional_tokens=None, n_times_crossframe_attn_in_self=0
    ):
    if context_ref is not None:
        global choices
        batch_size = x.size(0)
        # IP2P like sampling or default sampling
        if batch_size % 3 == 0:
            batch_size = batch_size // 3
            context_ref = torch.stack([self.references[:-1][y] for y in choices]).unsqueeze(0).expand(batch_size, -1, -1, -1)
            context_ref = torch.cat([self.references[-1:].unsqueeze(0).expand(batch_size, context_ref.size(1), -1, -1), context_ref, context_ref], dim=0)
        else:
            batch_size = batch_size // 2
            context_ref = torch.stack([self.references[:-1][y] for y in choices]).unsqueeze(0).expand(batch_size, -1, -1, -1)
            context_ref = torch.cat([self.references[-1:].unsqueeze(0).expand(batch_size, context_ref.size(1), -1, -1), context_ref], dim=0)

    fg_mask = None
    weights = None
    alphas = None
    predicted_rgb = None

    x = (
        self.attn1(
            self.norm1(x),
            context=context if self.disable_self_attn else None,
            additional_tokens=additional_tokens,
            n_times_crossframe_attn_in_self=n_times_crossframe_attn_in_self
            if not self.disable_self_attn
            else 0,
        )
        + x
    )

    x = (
            self.attn2(
                self.norm2(x), context=context, additional_tokens=additional_tokens,
            )
            + x
        )

    if context_ref is not None:
        if self.rendered_feat is not None:
            x = self.pose_emb_layers(torch.cat([x, self.rendered_feat], dim=-1))
        else:
            xref, fg_mask, weights, alphas, predicted_rgb = self.reference_attn(x,
                                                                            context_ref, 
                                                                            context, 
                                                                            pose, 
                                                                            prev_weights, 
                                                                            mask_ref)
            self.rendered_feat = xref
            x = self.pose_emb_layers(torch.cat([x, xref], -1))

    x = self.ff(self.norm3(x)) + x
    return x, fg_mask, weights, alphas, predicted_rgb


def log_images(
        model,
        batch,
        N: int = 1,
        noise=None,
        scale_im=3.5,
        num_steps: int = 10,
        ucg_keys: List[str] = None,
        **kwargs,
        ):

    log = dict()
    conditioner_input_keys = [e.input_keys for e in model.conditioner.embedders]
    ucg_keys = conditioner_input_keys
    pose = batch['pose']

    c, uc = model.conditioner.get_unconditional_conditioning(
        batch,
        force_uc_zero_embeddings=ucg_keys
        if len(model.conditioner.embedders) > 0
        else [],
        force_ref_zero_embeddings=True
    )

    _, n = 1, len(pose)-1
    sampling_kwargs = {}

    if scale_im > 0:
        if uc is not None:
            if isinstance(pose, list):
                pose = pose[:N]*3
            else:
                pose = torch.cat([pose[:N]] * 3)
    else:
        if uc is not None:
            if isinstance(pose, list):
                pose = pose[:N]*2
            else:
                pose = torch.cat([pose[:N]] * 2)

    sampling_kwargs['pose'] = pose
    sampling_kwargs['drop_im'] = None
    sampling_kwargs['mask_ref'] = None

    for k in c:
        if isinstance(c[k], torch.Tensor):
            c[k], uc[k] = map(lambda y: y[k][:(n+1)*N].to('cuda'), (c, uc))

    import time
    st = time.time()
    with model.ema_scope("Plotting"):
        samples = model.sample(
                c, shape=noise.shape[1:], uc=uc, batch_size=N, num_steps=num_steps, noise=noise,  **sampling_kwargs
            )
        model.clear_rendered_feat()
    samples = model.decode_first_stage(samples)
    print("Time taken for sampling", time.time() - st)
    log["samples"] = samples.cpu()

    return log


def process_camera_json(camera_json, example_cam):
    # replace all single quotes in the camera_json with quotes quotes
    camera_json = camera_json.replace("'", "\"")
    print("input camera json")
    print(camera_json)
    
    camera_dict = json.loads(camera_json)["scene.camera"]
    eye = torch.tensor([camera_dict["eye"]["x"], camera_dict["eye"]["y"], camera_dict["eye"]["z"]], dtype=torch.float32).unsqueeze(0)
    up = torch.tensor([camera_dict["up"]["x"], camera_dict["up"]["y"], camera_dict["up"]["z"]], dtype=torch.float32).unsqueeze(0)
    center = torch.tensor([camera_dict["center"]["x"], camera_dict["center"]["y"], camera_dict["center"]["z"]], dtype=torch.float32).unsqueeze(0)
    new_R, new_T = look_at_view_transform(eye=eye, at=center, up=up)

    ## temp
    # new_R = torch.tensor([[[ 0.4988,  0.2666,  0.8247],
    #      [-0.1917, -0.8940,  0.4049],
    #      [ 0.8453, -0.3601, -0.3948]]], dtype=torch.float32)
    # new_T = torch.tensor([[ 0.0739, -0.0013,  0.9973]], dtype=torch.float32)


    # new_R = torch.tensor([[[ 0.2530,  0.2989,  0.9201],
    #                         [-0.2652, -0.8932,  0.3631],
    #                         [ 0.9304, -0.3359, -0.1467],]], dtype=torch.float32)
    # new_T = torch.tensor([[ 0.0081,  0.0337,  1.0452]], dtype=torch.float32)
    

    print("focal length", example_cam.focal_length)
    print("principal point", example_cam.principal_point)

    newcam = PerspectiveCameras(R=new_R, 
                                T=new_T,
                                focal_length=example_cam.focal_length,
                                principal_point=example_cam.principal_point,
                                image_size=512)
    
    print("input pose")
    print(newcam.get_world_to_view_transform().get_matrix())
    return newcam


def load_and_return_model_and_data(config, model,
        ckpt="/data/gdsu/customization3d/stable-diffusion-xl-base-1.0/sd_xl_base_1.0.safetensors",
        delta_ckpt=None,
        train=False,
        valid=False,
        far=3,
        num_images=1,
        num_ref=8,
        max_images=20,
):
    config = OmegaConf.load(config)
    # load data
    data = None
    # config.data.params.jitter = False
    # config.data.params.addreg = False
    # config.data.params.bbox = False

    # data = instantiate_from_config(config.data)
    # data = data.train_dataset

    # single_id = data.single_id

    # if hasattr(data, 'rotations'):
    #     total_images = len(data.rotations[data.sequence_list[single_id]])
    # else:
    #     total_images = len(data.annotations['chair'])
    # print(f"Total images in dataset: {total_images}")

    model, msg = load_delta_model(model, delta_ckpt,)

    # change forward methods to store rendered features and use the pre-calculated reference features
    def register_recr(net_):
        if net_.__class__.__name__ == 'SpatialTransformer':
            print(net_.__class__.__name__, "adding control")
            bound_method = customforward.__get__(net_, net_.__class__)
            setattr(net_, 'forward', bound_method)
            return
        elif hasattr(net_, 'children'):
            for net__ in net_.children():
                register_recr(net__)
        return

    def register_recr2(net_):
        if net_.__class__.__name__ == 'BasicTransformerBlock':
            print(net_.__class__.__name__, "adding control")
            bound_method = _customforward.__get__(net_, net_.__class__)
            setattr(net_, 'forward', bound_method)
            return
        elif hasattr(net_, 'children'):
            for net__ in net_.children():
                register_recr2(net__)
        return

    sub_nets = model.model.diffusion_model.named_children()
    for net in sub_nets:
        register_recr(net[1])
        register_recr2(net[1])

    # start sampling
    model.clear_rendered_feat()

    return model, data


def sample(model, data,
        num_images=1,
        prompt="",
        appendpath="",
        camera_json=None,
        train=False,
        scale=7.5,
        scale_im=3.5,
        beta=1.0,
        num_ref=8,
        skipreflater=False,
        num_steps=10,
        valid=False,
        max_images=20,
        seed=42,
        camera_path="pretrained-models/car0/camera.bin",
    ):

    """
    Only works with num_images=1 (because of camera_json processing)
    """
   
    if num_images != 1:
        print("forcing num_images to be 1")
        num_images = 1
    
    # set guidance scales
    model.sampler.guider.scale_im = scale_im
    model.sampler.guider.scale = scale

    seed_everything(seed)
  
    # load cameras
    cameras_val, cameras_train = torch.load(camera_path)
    global choices
    num_ref = 8
    max_diff = len(cameras_train)/num_ref
    choices = [int(x) for x in torch.linspace(0, len(cameras_train) - max_diff, num_ref)]
    cameras_train_final = [cameras_train[i] for i in choices]
    
    # start sampling
    model.clear_rendered_feat()

    if prompt == "":
        prompt = None

    noise = torch.randn(1, 4, 64, 64).to('cuda').repeat(num_images, 1, 1, 1)

    # random sample camera poses
    pose_ids = np.random.choice(len(cameras_val), num_images, replace=False)
    print(pose_ids)
    pose_ids[0] = 21

    pose = [cameras_val[i] for i in pose_ids]

    print("example camera")
    print(pose[0].R)
    print(pose[0].T)
    print(pose[0].focal_length)
    print(pose[0].principal_point)

    # prepare batches [if translating then call required functions on the target pose]
    batches = []
    for i in range(num_images):
        batch = {'pose': [pose[i]] + cameras_train_final,
                    "original_size_as_tuple": torch.tensor([512, 512]).reshape(-1, 2),
                    "target_size_as_tuple": torch.tensor([512, 512]).reshape(-1, 2),
                    "crop_coords_top_left": torch.tensor([0, 0]).reshape(-1, 2),
                    "original_size_as_tuple_ref": torch.tensor([512, 512]).reshape(-1, 2),
                    "target_size_as_tuple_ref": torch.tensor([512, 512]).reshape(-1, 2),
                    "crop_coords_top_left_ref": torch.tensor([0, 0]).reshape(-1, 2),
                    }
        batch_ = copy.deepcopy(batch)
        batch_["pose"][0] = process_camera_json(camera_json, pose[0])
        batch_["pose"] = [join_cameras_as_batch(batch_["pose"])]
        # print('batched')
        # print(batch_["pose"][0].get_world_to_view_transform().get_matrix())
        batches.append(batch_)

    print(f'len batches: {len(batches)}')

    image = None

    with torch.no_grad():
        for batch in batches:
            for key in batch.keys():
                if isinstance(batch[key], torch.Tensor):
                    batch[key] = batch[key].to('cuda')
                elif 'pose' in key:
                    batch[key] = [x.to('cuda') for x in batch[key]]
                else:
                    pass

            if prompt is not None:
                batch["txt"] = [prompt for _ in range(1)]
                batch["txt_ref"] = [prompt for _ in range(len(batch["pose"])-1)]

            print(batch["txt"])
            N = 1
            log_ = log_images(model, batch, N=N, noise=noise.clone()[:N], num_steps=num_steps, scale_im=scale_im)
            image = log_["samples"]
           
    torch.cuda.empty_cache()
    model.clear_rendered_feat()

    print("generation done")
    return image