import os
import sys
#sys.path.append('.')
import cv2
import einops
import numpy as np
import torch
import random
import gradio as gr
import albumentations as A
from PIL import Image
import torchvision.transforms as T
from mydatasets.data_utils import * 
from cldm.model import create_model, load_state_dict
from cldm.ddim_hacked import DDIMSampler
from omegaconf import OmegaConf
from cldm.hack import disable_verbosity, enable_sliced_attention
from huggingface_hub import snapshot_download

snapshot_download(repo_id="onlineformapro/anydoor-models-ofp", local_dir="./anydoor-models-ofp")
snapshot_download(repo_id="onlineformapro/anydoor-refine-ofp", local_dir="./anydoor-refine-ofp")

cv2.setNumThreads(0)
cv2.ocl.setUseOpenCL(False)

save_memory = False
disable_verbosity()
if save_memory:
    enable_sliced_attention()


config = OmegaConf.load('./configs/demo.yaml')
model_ckpt =  config.pretrained_model
model_config = config.config_file
use_interactive_seg = config.config_file


model = create_model(model_config ).cpu()
model.load_state_dict(load_state_dict(model_ckpt, location='cuda'))
model = model.cuda()
ddim_sampler = DDIMSampler(model)

if use_interactive_seg:
    from iseg.coarse_mask_refine_util import BaselineModel
    model_path = './anydoor-refine-ofp/coarse_mask_refine.pth'
    iseg_model = BaselineModel().eval()
    weights = torch.load(model_path , map_location='cpu')['state_dict']
    iseg_model.load_state_dict(weights, strict= True)


def crop_back( pred, tar_image,  extra_sizes, tar_box_yyxx_crop):
    H1, W1, H2, W2 = extra_sizes
    y1,y2,x1,x2 = tar_box_yyxx_crop    
    pred = cv2.resize(pred, (W2, H2))
    m = 3 # maigin_pixel

    if W1 == H1:
        tar_image[y1+m :y2-m, x1+m:x2-m, :] =  pred[m:-m, m:-m]
        return tar_image

    if W1 < W2:
        pad1 = int((W2 - W1) / 2)
        pad2 = W2 - W1 - pad1
        pred = pred[:,pad1: -pad2, :]
    else:
        pad1 = int((H2 - H1) / 2)
        pad2 = H2 - H1 - pad1
        pred = pred[pad1: -pad2, :, :]
    tar_image[y1+m :y2-m, x1+m:x2-m, :] =  pred[m:-m, m:-m]
    return tar_image


def inference_single_image(ref_image, 
                           ref_mask, 
                           tar_image, 
                           tar_mask, 
                           strength, 
                           ddim_steps, 
                           scale, 
                           seed,
                           enable_shape_control 
                           ):
    raw_background = tar_image.copy()
    item = process_pairs(ref_image, ref_mask, tar_image, tar_mask, enable_shape_control = enable_shape_control)

    ref = item['ref']
    hint = item['hint']
    num_samples = 1

    control = torch.from_numpy(hint.copy()).float().cuda() 
    control = torch.stack([control for _ in range(num_samples)], dim=0)
    control = einops.rearrange(control, 'b h w c -> b c h w').clone()


    clip_input = torch.from_numpy(ref.copy()).float().cuda() 
    clip_input = torch.stack([clip_input for _ in range(num_samples)], dim=0)
    clip_input = einops.rearrange(clip_input, 'b h w c -> b c h w').clone()

    H,W = 512,512

    cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning( clip_input )]}
    un_cond = {"c_concat": [control], 
               "c_crossattn": [model.get_learned_conditioning([torch.zeros((1,3,224,224))] * num_samples)]}
    shape = (4, H // 8, W // 8)

    if save_memory:
        model.low_vram_shift(is_diffusing=True)

    model.control_scales = ([strength] * 13)
    samples, _ = ddim_sampler.sample(ddim_steps, num_samples,
                                     shape, cond, verbose=False, eta=0,
                                     unconditional_guidance_scale=scale,
                                     unconditional_conditioning=un_cond)

    if save_memory:
        model.low_vram_shift(is_diffusing=False)

    x_samples = model.decode_first_stage(samples)
    x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy()

    result = x_samples[0][:,:,::-1]
    result = np.clip(result,0,255)

    pred = x_samples[0]
    pred = np.clip(pred,0,255)[1:,:,:]
    sizes = item['extra_sizes']
    tar_box_yyxx_crop = item['tar_box_yyxx_crop'] 
    tar_image = crop_back(pred, tar_image, sizes, tar_box_yyxx_crop) 

    # keep background unchanged
    y1,y2,x1,x2 = item['tar_box_yyxx']
    raw_background[y1:y2, x1:x2, :] = tar_image[y1:y2, x1:x2, :]
    return raw_background


def process_pairs(ref_image, ref_mask, tar_image, tar_mask, max_ratio = 0.8, enable_shape_control = False):
    # ========= Reference ===========
    # ref expand 
    ref_box_yyxx = get_bbox_from_mask(ref_mask)

    # ref filter mask 
    ref_mask_3 = np.stack([ref_mask,ref_mask,ref_mask],-1)
    masked_ref_image = ref_image * ref_mask_3 + np.ones_like(ref_image) * 255 * (1-ref_mask_3)

    y1,y2,x1,x2 = ref_box_yyxx
    masked_ref_image = masked_ref_image[y1:y2,x1:x2,:]
    ref_mask = ref_mask[y1:y2,x1:x2]

    ratio = np.random.randint(11, 15) / 10 #11,13
    masked_ref_image, ref_mask = expand_image_mask(masked_ref_image, ref_mask, ratio=ratio)
    ref_mask_3 = np.stack([ref_mask,ref_mask,ref_mask],-1)

    # to square and resize
    masked_ref_image = pad_to_square(masked_ref_image, pad_value = 255, random = False)
    masked_ref_image = cv2.resize(masked_ref_image.astype(np.uint8), (224,224) ).astype(np.uint8)

    ref_mask_3 = pad_to_square(ref_mask_3 * 255, pad_value = 0, random = False)
    ref_mask_3 = cv2.resize(ref_mask_3.astype(np.uint8), (224,224) ).astype(np.uint8)
    ref_mask = ref_mask_3[:,:,0]

    # collage aug 
    masked_ref_image_compose, ref_mask_compose =  masked_ref_image, ref_mask
    ref_mask_3 = np.stack([ref_mask_compose,ref_mask_compose,ref_mask_compose],-1)
    ref_image_collage = sobel(masked_ref_image_compose, ref_mask_compose/255)

    # ========= Target ===========
    tar_box_yyxx = get_bbox_from_mask(tar_mask)
    tar_box_yyxx = expand_bbox(tar_mask, tar_box_yyxx, ratio=[1.1,1.2]) #1.1  1.3
    tar_box_yyxx_full = tar_box_yyxx
    
    # crop
    tar_box_yyxx_crop =  expand_bbox(tar_image, tar_box_yyxx, ratio=[1.3, 3.0])   
    tar_box_yyxx_crop = box2squre(tar_image, tar_box_yyxx_crop) # crop box
    y1,y2,x1,x2 = tar_box_yyxx_crop

    cropped_target_image = tar_image[y1:y2,x1:x2,:]
    cropped_tar_mask = tar_mask[y1:y2,x1:x2]

    tar_box_yyxx = box_in_box(tar_box_yyxx, tar_box_yyxx_crop)
    y1,y2,x1,x2 = tar_box_yyxx

    # collage
    ref_image_collage = cv2.resize(ref_image_collage.astype(np.uint8), (x2-x1, y2-y1))
    ref_mask_compose = cv2.resize(ref_mask_compose.astype(np.uint8), (x2-x1, y2-y1))
    ref_mask_compose = (ref_mask_compose > 128).astype(np.uint8)

    collage = cropped_target_image.copy() 
    collage[y1:y2,x1:x2,:] = ref_image_collage

    collage_mask = cropped_target_image.copy() * 0.0
    collage_mask[y1:y2,x1:x2,:] = 1.0
    if enable_shape_control:
        collage_mask = np.stack([cropped_tar_mask,cropped_tar_mask,cropped_tar_mask],-1)

    # the size before pad
    H1, W1 = collage.shape[0], collage.shape[1]

    cropped_target_image = pad_to_square(cropped_target_image, pad_value = 0, random = False).astype(np.uint8)
    collage = pad_to_square(collage, pad_value = 0, random = False).astype(np.uint8)
    collage_mask = pad_to_square(collage_mask, pad_value = 2, random = False).astype(np.uint8)

    # the size after pad
    H2, W2 = collage.shape[0], collage.shape[1]

    cropped_target_image = cv2.resize(cropped_target_image.astype(np.uint8), (512,512)).astype(np.float32)
    collage = cv2.resize(collage.astype(np.uint8), (512,512)).astype(np.float32)
    collage_mask  = cv2.resize(collage_mask.astype(np.uint8), (512,512),  interpolation = cv2.INTER_NEAREST).astype(np.float32)
    collage_mask[collage_mask == 2] = -1

    masked_ref_image = masked_ref_image  / 255 
    cropped_target_image = cropped_target_image / 127.5 - 1.0
    collage = collage / 127.5 - 1.0 
    collage = np.concatenate([collage, collage_mask[:,:,:1]  ] , -1)
    
    item = dict(ref=masked_ref_image.copy(), jpg=cropped_target_image.copy(), hint=collage.copy(), 
                extra_sizes=np.array([H1, W1, H2, W2]), 
                tar_box_yyxx_crop=np.array( tar_box_yyxx_crop ),
                tar_box_yyxx=np.array(tar_box_yyxx_full),
                 ) 
    return item


ref_dir='./examples/Gradio/FG'
image_dir='./examples/Gradio/BG'
ref_list=[os.path.join(ref_dir,file) for file in os.listdir(ref_dir) if '.jpg' in file or '.png' in file or '.jpeg' in file ]
ref_list.sort()
image_list=[os.path.join(image_dir,file) for file in os.listdir(image_dir) if '.jpg' in file or '.png' in file or '.jpeg' in file]
image_list.sort()

def mask_image(image, mask):
    blanc = np.ones_like(image) * 255
    mask = np.stack([mask,mask,mask],-1) / 255
    masked_image = mask * ( 0.5 * blanc + 0.5 * image) + (1-mask) * image
    return masked_image.astype(np.uint8)

def run_local(base,
              ref,
              *args):
    image = base["image"].convert("RGB")
    mask = base["mask"].convert("L")
    ref_image = ref["image"].convert("RGB")
    ref_mask = ref["mask"].convert("L")
    image = np.asarray(image)
    mask = np.asarray(mask)
    mask = np.where(mask > 128, 1, 0).astype(np.uint8)
    ref_image = np.asarray(ref_image)
    ref_mask = np.asarray(ref_mask)
    ref_mask = np.where(ref_mask > 128, 1, 0).astype(np.uint8)

    synthesis = inference_single_image(ref_image.copy(), ref_mask.copy(), image.copy(), mask.copy(), *args)
    synthesis = torch.from_numpy(synthesis).permute(2, 0, 1)
    synthesis = synthesis.permute(1, 2, 0).numpy()
    return [synthesis]

logo = r"""
<center style="background:white";><img src='https://www.onlineformapro.com/wp-content/uploads/2020/01/logo-03.svg' alt='Onlineformapro logo' style="width:280px; margin-bottom:10px"></center>
"""
title = r"""
<h1 align="center">ModaVirtuelle</h1>
"""

demo = gr.Blocks(
    css="css/style.css"
)

with demo:
    with gr.Column():
        # gr.Markdown("#  Play with AnyDoor to Teleport your Target Objects! ")
        gr.Markdown(logo)
        gr.Markdown(title)
        gr.Markdown("# Télécharger / sélectionner des images pour l'arrière-plan (à gauche) et l'objet de référence (à droite)")
        # gr.Markdown("### You could draw coarse masks on the background to indicate the desired location and shape.")
        # gr.Markdown("### <u>Do not forget</u> to annotate the target object on the reference image.")
        with gr.Row():
            base = gr.Image(label="Arrière-plan", source="upload", tool="sketch", type="pil", height=512, brush_color='#008000', mask_opacity=0.5)
            ref = gr.Image(label="Référence", source="upload", tool="sketch", type="pil", height=512, brush_color='#008000', mask_opacity=0.5)
        with gr.Row():
            with gr.Column():
                gr.Examples(image_list, inputs=[base],label="Exemples - Image d'arrière-plan",examples_per_page=16)
            with gr.Column():
                gr.Examples(ref_list, inputs=[ref],label="Exemples - Objet de référence",examples_per_page=16)
        run_local_button = gr.Button(label="Generate", value="Exécuter")

        with gr.Row():
            baseline_gallery = gr.Gallery(label='Sortie', show_label=True, elem_id="gallery", columns=1, height=768)
        with gr.Accordion("Advanced Option", open=False):
                num_samples = 1
                strength = gr.Slider(label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01)
                ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=30, step=1)
                scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=4.5, step=0.1)
                seed = gr.Slider(label="Seed", minimum=-1, maximum=999999999, step=1, value=-1)
                reference_mask_refine = gr.Checkbox(label='Reference Mask Refine', value=True, interactive = True)
                enable_shape_control = gr.Checkbox(label='Enable Shape Control', value=False, interactive = True)
                
                gr.Markdown("### Guidelines")
                gr.Markdown(" Higher guidance-scale makes higher fidelity, while lower one makes more harmonized blending.")
                gr.Markdown(" Users should annotate the mask of the target object, too coarse mask would lead to bad generation.\
                              Reference Mask Refine provides a segmentation model to refine the coarse mask. ")
                gr.Markdown(" Enable shape control means the generation results would consider user-drawn masks to control the shape & pose; otherwise it \
                              considers the location and size to adjust automatically.")      

    run_local_button.click(fn=run_local, 
                           inputs=[base, 
                                   ref, 
                                   strength, 
                                   ddim_steps, 
                                   scale, 
                                   seed,
                                   enable_shape_control, 
                                   ], 
                           outputs=[baseline_gallery]
                        )

def environ_auth(username, password):
    if username == os.environ["username"] and password == os.environ["password"]:
        return True
    else:
        return False

demo.launch(auth=environ_auth).launch()