import os
import io
import json
import numpy as np
import cv2

import gradio as gr

import modules.scripts as scripts
from modules import script_callbacks
import modules.shared as shared
from modules import images

from scripts.td_abg import get_foreground
from scripts.convertor import pil2cv
try:
    from modules.paths_internal import extensions_dir
except Exception:
    from modules.extensions import extensions_dir

from collections import OrderedDict
from PIL import Image

model_cache = OrderedDict()
sam_model_dir = os.path.join(
    extensions_dir, "PBRemTools/models/")
model_list = [f for f in os.listdir(sam_model_dir) if os.path.isfile(
    os.path.join(sam_model_dir, f)) and f.split('.')[-1] != 'txt']

def processing(single_image, batch_image, input_dir, output_dir, show_result, input_tab_state, *rem_args):
    # 0: single
    if (input_tab_state == 0):
        processed = process_image(single_image, *rem_args)
        return processed
    # 1: batch
    elif (input_tab_state == 1):
        processed = []
        for i in batch_image:
            image = Image.open(i)
            base, mask = process_image(image, *rem_args)
            processed.append(base)
            processed.append(mask)
        return processed
    # 2: batch dir (or other)
    else:
        processed = []
        files = shared.listfiles(input_dir)
        for f in files:
            try:
                image = Image.open(f)
            except Exception:
                continue
            base, mask = process_image(image, *rem_args)
            processed.append(base)
            processed.append(mask)
            basename = os.path.splitext(os.path.basename(f))[0]
            ext = os.path.splitext(f)[1][1:]
            images.save_image(
                Image.fromarray(base),
                path=output_dir,
                basename=basename,
                extension=ext,
            )
            images.save_image(
                Image.fromarray(mask),
                path=output_dir,
                basename=basename+"_mask",
                extension=ext,
            )
        if (show_result):
            return processed
        else:
            return None

def process_image(target_image, *rem_args):
    image = pil2cv(target_image)
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    mask, image = get_foreground(image, *rem_args)
    return image, mask

class Script(scripts.Script):
  def __init__(self) -> None:
    super().__init__()

  def title(self):
    return "PBRemTools"

  def show(self, is_img2img):
    return scripts.AlwaysVisible

  def ui(self, is_img2img):
    return ()

def on_ui_tabs():
    with gr.Blocks(analytics_enabled=False) as PBRemTools:
        input_tab_state = gr.State(value=0)
        with gr.Row():
            with gr.Column():
                with gr.Tabs():
                    with gr.TabItem(label="Single") as input_tab_single:
                        single_image = gr.Image(type="pil")
                    with gr.TabItem(label="Batch") as input_tab_batch:
                        batch_image = gr.File(label="Batch Images", file_count="multiple", interactive=True, type="file")
                    with gr.TabItem(label="Batch from Dir") as input_tab_dir:
                        input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs)
                        output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs)
                        show_result = gr.Checkbox(label="Show result images", value=True)
                with gr.Accordion("Mask Setting", open=True):
                    with gr.Tab("Segment Anything & CLIP"):
                        sa_enabled = gr.Checkbox(label="enabled", show_label=True)
                        model_name = gr.Dropdown(label="Model", elem_id="sam_model", choices=model_list,
                                                 value=model_list[0] if len(model_list) > 0 else None)
                        seg_query = gr.Textbox(label = "segmentation prompt", show_label=True)
                        predicted_iou_threshold = gr.Slider(0, 1, value=0.9, step=0.01, label="predicted_iou_threshold", show_label=True)
                        stability_score_threshold = gr.Slider(0, 1, value=0.9, step=0.01, label="stability_score_threshold", show_label=True)
                        clip_threshold =  gr.Slider(0, 1, value=0.1, step=0.01, label="clip_threshold", show_label=True)
                with gr.Accordion("Post Processing", open=True):
                    with gr.Tab("tile division BG Removers"):
                        td_abg_enabled = gr.Checkbox(label="enabled", show_label=True)
                        h_split = gr.Slider(1, 2048, value=256, step=4, label="horizontal split num", show_label=True)
                        v_split = gr.Slider(1, 2048, value=256, step=4, label="vertical split num", show_label=True)
                        
                        n_cluster = gr.Slider(1, 1000, value=500, step=10, label="cluster num", show_label=True)
                        alpha = gr.Slider(1, 255, value=50, step=1, label="alpha threshold", show_label=True)
                        th_rate = gr.Slider(0, 1, value=0.1, step=0.01, label="mask content ratio", show_label=True)
                    with gr.Tab("cascadePSP"):
                        cascadePSP_enabled = gr.Checkbox(label="enabled", show_label=True)
                        fast = gr.Checkbox(label="fast", show_label=True)
                        psp_L = gr.Slider(1, 2048, value=900, step=1, label="Memory usage", show_label=True)

                submit = gr.Button(value="Submit")
            with gr.Row():
                with gr.Column():
                    gallery = gr.Gallery(label="outputs", show_label=True, elem_id="gallery").style(grid=2)

        # 0: single 1: batch 2: batch dir
        input_tab_single.select(fn=lambda: 0, inputs=[], outputs=[input_tab_state])
        input_tab_batch.select(fn=lambda: 1, inputs=[], outputs=[input_tab_state])
        input_tab_dir.select(fn=lambda: 2, inputs=[], outputs=[input_tab_state])
        submit.click(
            processing, 
            inputs=[single_image, batch_image, input_dir, output_dir, show_result, input_tab_state, td_abg_enabled, h_split, v_split, n_cluster, alpha, th_rate, cascadePSP_enabled, fast, psp_L, sa_enabled, seg_query, model_name, predicted_iou_threshold, stability_score_threshold, clip_threshold],
            outputs=gallery
        )

    return [(PBRemTools, "PBRemTools", "pbremtools")]

script_callbacks.on_ui_tabs(on_ui_tabs)