import os
from typing import Dict, Optional, Tuple, List, Union

import torch

import modules.scripts as scripts
from modules import shared, script_callbacks, processing, masking, images
from modules.api.api import decode_base64_to_image
import gradio as gr

from lib_controlnet import global_state, external_code
from lib_controlnet.utils import align_dim_latent, image_dict_from_any, set_numpy_seed, crop_and_resize_image, \
    prepare_mask, judge_image_type
from lib_controlnet.controlnet_ui.controlnet_ui_group import ControlNetUiGroup, UiControlNetUnit
from lib_controlnet.controlnet_ui.photopea import Photopea
from lib_controlnet.logging import logger
from modules.processing import StableDiffusionProcessingImg2Img, StableDiffusionProcessingTxt2Img, \
    StableDiffusionProcessing
from lib_controlnet.infotext import Infotext
from modules_forge.forge_util import HWC3, numpy_to_pytorch

import numpy as np
import functools

from PIL import Image
from modules_forge.shared import try_load_supported_control_model
from modules_forge.supported_controlnet import ControlModelPatcher

# Gradio 3.32 bug fix
import tempfile

gradio_tempfile_path = os.path.join(tempfile.gettempdir(), 'gradio')
os.makedirs(gradio_tempfile_path, exist_ok=True)

global_state.update_controlnet_filenames()


@functools.lru_cache(maxsize=shared.opts.data.get("control_net_model_cache_size", 5))
def cached_controlnet_loader(filename):
    return try_load_supported_control_model(filename)


class ControlNetCachedParameters:
    def __init__(self):
        self.preprocessor = None
        self.model = None
        self.control_cond = None
        self.control_cond_for_hr_fix = None


class ControlNetForForgeOfficial(scripts.Script):
    def title(self):
        return "ControlNet"

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

    def uigroup(self, tabname: str, is_img2img: bool, elem_id_tabname: str, photopea: Optional[Photopea]) -> Tuple[ControlNetUiGroup, gr.State]:
        default_unit = UiControlNetUnit(enabled=False, module="None", model="None")
        group = ControlNetUiGroup(is_img2img, default_unit, photopea)
        return group, group.render(tabname, elem_id_tabname)

    def ui(self, is_img2img):
        infotext = Infotext()
        ui_groups = []
        controls = []
        max_models = shared.opts.data.get("control_net_unit_count", 3)
        elem_id_tabname = ("img2img" if is_img2img else "txt2img") + "_controlnet"
        with gr.Group(elem_id=elem_id_tabname):
            with gr.Accordion(f"ControlNet Integrated", open=False, elem_id="controlnet"):
                photopea = Photopea() if not shared.opts.data.get("controlnet_disable_photopea_edit", False) else None
                if max_models > 1:
                    with gr.Tabs(elem_id=f"{elem_id_tabname}_tabs"):
                        for i in range(max_models):
                            with gr.Tab(f"ControlNet Unit {i}",
                                        elem_classes=['cnet-unit-tab']):
                                group, state = self.uigroup(f"ControlNet-{i}", is_img2img, elem_id_tabname, photopea)
                                ui_groups.append(group)
                                controls.append(state)
                else:
                    with gr.Column():
                        group, state = self.uigroup(f"ControlNet", is_img2img, elem_id_tabname, photopea)
                        ui_groups.append(group)
                        controls.append(state)

        for i, ui_group in enumerate(ui_groups):
            infotext.register_unit(i, ui_group)
        if shared.opts.data.get("control_net_sync_field_args", True):
            self.infotext_fields = infotext.infotext_fields
            self.paste_field_names = infotext.paste_field_names
        return tuple(controls)

    def get_enabled_units(self, p):
        units = external_code.get_all_units_in_processing(p)
        enabled_units = [x for x in units if x.enabled]
        return enabled_units

    def choose_input_image(
            self,
            p: processing.StableDiffusionProcessing,
            unit: external_code.ControlNetUnit,
    ) -> Tuple[np.ndarray, external_code.ResizeMode]:
        """ Choose input image from following sources with descending priority:
         - p.image_control: [Deprecated] Lagacy way to pass image to controlnet.
         - p.control_net_input_image: [Deprecated] Lagacy way to pass image to controlnet.
         - unit.image: ControlNet tab input image.
         - p.init_images: A1111 img2img tab input image.

        Returns:
            - The input image in ndarray form.
            - The resize mode.
        """

        def parse_unit_image(unit: external_code.ControlNetUnit) -> Union[
            List[Dict[str, np.ndarray]], Dict[str, np.ndarray]]:
            unit_has_multiple_images = (
                    isinstance(unit.image, list) and
                    len(unit.image) > 0 and
                    "image" in unit.image[0]
            )
            if unit_has_multiple_images:
                return [
                    d
                    for img in unit.image
                    for d in (image_dict_from_any(img),)
                    if d is not None
                ]
            return image_dict_from_any(unit.image)

        def decode_image(img) -> np.ndarray:
            """Need to check the image for API compatibility."""
            if isinstance(img, str):
                return np.asarray(decode_base64_to_image(image['image']))
            else:
                assert isinstance(img, np.ndarray)
                return img

        # 4 input image sources.
        image = parse_unit_image(unit)
        a1111_image = getattr(p, "init_images", [None])[0]

        resize_mode = external_code.resize_mode_from_value(unit.resize_mode)

        if image is not None:
            if isinstance(image, list):
                # Add mask logic if later there is a processor that accepts mask
                # on multiple inputs.
                input_image = [HWC3(decode_image(img['image'])) for img in image]
            else:
                input_image = HWC3(decode_image(image['image']))
                if 'mask' in image and image['mask'] is not None:
                    while len(image['mask'].shape) < 3:
                        image['mask'] = image['mask'][..., np.newaxis]
                    if 'inpaint' in unit.module:
                        logger.info("using inpaint as input")
                        color = HWC3(image['image'])
                        alpha = image['mask'][:, :, 0:1]
                        input_image = np.concatenate([color, alpha], axis=2)
                    elif (
                            not shared.opts.data.get("controlnet_ignore_noninpaint_mask", False) and
                            # There is wield gradio issue that would produce mask that is
                            # not pure color when no scribble is made on canvas.
                            # See https://github.com/Mikubill/sd-webui-controlnet/issues/1638.
                            not (
                                    (image['mask'][:, :, 0] <= 5).all() or
                                    (image['mask'][:, :, 0] >= 250).all()
                            )
                    ):
                        logger.info("using mask as input")
                        input_image = HWC3(image['mask'][:, :, 0])
                        unit.module = 'none'  # Always use black bg and white line
        elif a1111_image is not None:
            input_image = HWC3(np.asarray(a1111_image))
            a1111_i2i_resize_mode = getattr(p, "resize_mode", None)
            assert a1111_i2i_resize_mode is not None
            resize_mode = external_code.resize_mode_from_value(a1111_i2i_resize_mode)

            a1111_mask_image: Optional[Image.Image] = getattr(p, "image_mask", None)
            if 'inpaint' in unit.module:
                if a1111_mask_image is not None:
                    a1111_mask = np.array(prepare_mask(a1111_mask_image, p))
                    assert a1111_mask.ndim == 2
                    assert a1111_mask.shape[0] == input_image.shape[0]
                    assert a1111_mask.shape[1] == input_image.shape[1]
                    input_image = np.concatenate([input_image[:, :, 0:3], a1111_mask[:, :, None]], axis=2)
                else:
                    input_image = np.concatenate([
                        input_image[:, :, 0:3],
                        np.zeros_like(input_image, dtype=np.uint8)[:, :, 0:1],
                    ], axis=2)
        else:
            raise ValueError("controlnet is enabled but no input image is given")

        assert isinstance(input_image, (np.ndarray, list))
        return input_image, resize_mode

    @staticmethod
    def try_crop_image_with_a1111_mask(
            p: StableDiffusionProcessing,
            unit: external_code.ControlNetUnit,
            input_image: np.ndarray,
            resize_mode: external_code.ResizeMode,
            preprocessor
    ) -> np.ndarray:
        """
        Crop ControlNet input image based on A1111 inpaint mask given.
        This logic is crutial in upscale scripts, as they use A1111 mask + inpaint_full_res
        to crop tiles.
        """
        # Note: The method determining whether the active script is an upscale script is purely
        # based on `extra_generation_params` these scripts attach on `p`, and subject to change
        # in the future.
        # TODO: Change this to a more robust condition once A1111 offers a way to verify script name.
        is_upscale_script = any("upscale" in k.lower() for k in getattr(p, "extra_generation_params", {}).keys())
        logger.debug(f"is_upscale_script={is_upscale_script}")
        # Note: `inpaint_full_res` is "inpaint area" on UI. The flag is `True` when "Only masked"
        # option is selected.
        a1111_mask_image: Optional[Image.Image] = getattr(p, "image_mask", None)
        is_only_masked_inpaint = (
                issubclass(type(p), StableDiffusionProcessingImg2Img) and
                p.inpaint_full_res and
                a1111_mask_image is not None
        )
        if (
                preprocessor.corp_image_with_a1111_mask_when_in_img2img_inpaint_tab
                and is_only_masked_inpaint
                and (is_upscale_script or unit.inpaint_crop_input_image)
        ):
            logger.debug("Crop input image based on A1111 mask.")
            input_image = [input_image[:, :, i] for i in range(input_image.shape[2])]
            input_image = [Image.fromarray(x) for x in input_image]

            mask = prepare_mask(a1111_mask_image, p)

            crop_region = masking.get_crop_region(np.array(mask), p.inpaint_full_res_padding)
            crop_region = masking.expand_crop_region(crop_region, p.width, p.height, mask.width, mask.height)

            input_image = [
                images.resize_image(resize_mode.int_value(), i, mask.width, mask.height)
                for i in input_image
            ]

            input_image = [x.crop(crop_region) for x in input_image]
            input_image = [
                images.resize_image(external_code.ResizeMode.OUTER_FIT.int_value(), x, p.width, p.height)
                for x in input_image
            ]

            input_image = [np.asarray(x)[:, :, 0] for x in input_image]
            input_image = np.stack(input_image, axis=2)
        return input_image

    @staticmethod
    def bound_check_params(unit: external_code.ControlNetUnit) -> None:
        """
        Checks and corrects negative parameters in ControlNetUnit 'unit'.
        Parameters 'processor_res', 'threshold_a', 'threshold_b' are reset to
        their default values if negative.

        Args:
            unit (external_code.ControlNetUnit): The ControlNetUnit instance to check.
        """
        preprocessor = global_state.get_preprocessor(unit.module)

        if unit.processor_res < 0:
            unit.processor_res = int(preprocessor.slider_resolution.gradio_update_kwargs.get('value', 512))

        if unit.threshold_a < 0:
            unit.threshold_a = int(preprocessor.slider_1.gradio_update_kwargs.get('value', 1.0))

        if unit.threshold_b < 0:
            unit.threshold_b = int(preprocessor.slider_2.gradio_update_kwargs.get('value', 1.0))

        return

    @staticmethod
    def get_target_dimensions(p: StableDiffusionProcessing) -> Tuple[int, int, int, int]:
        """Returns (h, w, hr_h, hr_w)."""
        h = align_dim_latent(p.height)
        w = align_dim_latent(p.width)

        high_res_fix = (
                isinstance(p, StableDiffusionProcessingTxt2Img)
                and getattr(p, 'enable_hr', False)
        )
        if high_res_fix:
            if p.hr_resize_x == 0 and p.hr_resize_y == 0:
                hr_y = int(p.height * p.hr_scale)
                hr_x = int(p.width * p.hr_scale)
            else:
                hr_y, hr_x = p.hr_resize_y, p.hr_resize_x
            hr_y = align_dim_latent(hr_y)
            hr_x = align_dim_latent(hr_x)
        else:
            hr_y = h
            hr_x = w

        return h, w, hr_y, hr_x

    def get_input_data(self, p, unit, preprocessor):
        mask = None
        input_image, resize_mode = self.choose_input_image(p, unit)
        assert isinstance(input_image, np.ndarray), 'Invalid input image!'
        input_image = self.try_crop_image_with_a1111_mask(p, unit, input_image, resize_mode, preprocessor)
        input_image = np.ascontiguousarray(input_image.copy()).copy()  # safe numpy
        return input_image, mask, resize_mode

    @torch.no_grad()
    def process_unit_after_click_generate(self,
                                          p: StableDiffusionProcessing,
                                          unit: external_code.ControlNetUnit,
                                          params: ControlNetCachedParameters,
                                          *args, **kwargs):

        h, w, hr_y, hr_x = self.get_target_dimensions(p)

        has_high_res_fix = (
                isinstance(p, StableDiffusionProcessingTxt2Img)
                and getattr(p, 'enable_hr', False)
        )

        preprocessor = global_state.get_preprocessor(unit.module)

        input_image, input_mask, resize_mode = self.get_input_data(p, unit, preprocessor)

        if unit.pixel_perfect:
            unit.processor_res = external_code.pixel_perfect_resolution(
                input_image,
                target_H=h,
                target_W=w,
                resize_mode=resize_mode,
            )

        seed = set_numpy_seed(p)
        logger.debug(f"Use numpy seed {seed}.")
        logger.info(f"Using preprocessor: {unit.module}")
        logger.info(f'preprocessor resolution = {unit.processor_res}')

        preprocessor_output = preprocessor(
            input_image=input_image,
            resolution=unit.processor_res,
            slider_1=unit.threshold_a,
            slider_2=unit.threshold_b,
        )

        preprocessor_output_is_image = judge_image_type(preprocessor_output)

        if preprocessor_output_is_image:
            params.control_cond = crop_and_resize_image(preprocessor_output, resize_mode, h, w)
            p.extra_result_images.append(external_code.visualize_inpaint_mask(params.control_cond))
            params.control_cond = numpy_to_pytorch(params.control_cond).movedim(-1, 1)

            if has_high_res_fix:
                params.control_cond_for_hr_fix = crop_and_resize_image(preprocessor_output, resize_mode, hr_y, hr_x)
                p.extra_result_images.append(external_code.visualize_inpaint_mask(params.control_cond_for_hr_fix))
                params.control_cond_for_hr_fix = numpy_to_pytorch(params.control_cond_for_hr_fix).movedim(-1, 1)
            else:
                params.control_cond_for_hr_fix = params.control_cond
        else:
            params.control_cond = preprocessor_output
            params.control_cond_for_hr_fix = preprocessor_output
            p.extra_result_images.append(input_image)

        if preprocessor.do_not_need_model:
            model_filename = 'Not Needed'
            params.model = ControlModelPatcher()
        else:
            model_filename = global_state.get_controlnet_filename(unit.model)
            params.model = cached_controlnet_loader(model_filename)
            assert params.model is not None, logger.error(f"Recognizing Control Model failed: {model_filename}")

        params.preprocessor = preprocessor

        params.preprocessor.process_after_running_preprocessors(process=p, params=params, **kwargs)
        params.model.process_after_running_preprocessors(process=p, params=params, **kwargs)

        logger.info(f"Current ControlNet {type(params.model).__name__}: {model_filename}")
        return

    @torch.no_grad()
    def process_unit_before_every_sampling(self,
                                           p: StableDiffusionProcessing,
                                           unit: external_code.ControlNetUnit,
                                           params: ControlNetCachedParameters,
                                           *args, **kwargs):

        is_hr_pass = getattr(p, 'is_hr_pass', False)

        if is_hr_pass:
            cond = params.control_cond_for_hr_fix
        else:
            cond = params.control_cond

        kwargs.update(dict(unit=unit, params=params))

        # CN inpaint fix
        if isinstance(cond, torch.Tensor) and cond.ndim == 4 and cond.shape[1] == 4:
            kwargs['cond_before_inpaint_fix'] = cond.clone()
            cond = cond[:, :3] * (1.0 - cond[:, 3:]) - cond[:, 3:]

        params.model.strength = float(unit.weight)
        params.model.start_percent = float(unit.guidance_start)
        params.model.end_percent = float(unit.guidance_end)
        params.model.positive_advanced_weighting = None
        params.model.negative_advanced_weighting = None
        params.model.advanced_frame_weighting = None
        params.model.advanced_sigma_weighting = None

        soft_weighting = {
            'input': [0.09941396206337118, 0.12050177219802567, 0.14606275417942507, 0.17704576264172736,
                      0.214600924414215,
                      0.26012233262329093, 0.3152997971191405, 0.3821815722656249, 0.4632503906249999, 0.561515625,
                      0.6806249999999999, 0.825],
            'middle': [1.0],
            'output': [0.09941396206337118, 0.12050177219802567, 0.14606275417942507, 0.17704576264172736,
                       0.214600924414215,
                       0.26012233262329093, 0.3152997971191405, 0.3821815722656249, 0.4632503906249999, 0.561515625,
                       0.6806249999999999, 0.825]
        }

        zero_weighting = {
            'input': [0.0] * 12,
            'middle': [0.0],
            'output': [0.0] * 12
        }

        if unit.control_mode == external_code.ControlMode.CONTROL.value:
            params.model.positive_advanced_weighting = soft_weighting.copy()
            params.model.negative_advanced_weighting = zero_weighting.copy()

        # high-ref fix pass always use softer injections
        if is_hr_pass or unit.control_mode == external_code.ControlMode.PROMPT.value:
            params.model.positive_advanced_weighting = soft_weighting.copy()
            params.model.negative_advanced_weighting = soft_weighting.copy()

        params.preprocessor.process_before_every_sampling(p, cond, *args, **kwargs)
        params.model.process_before_every_sampling(p, cond, *args, **kwargs)

        logger.info(f"ControlNet Method {params.preprocessor.name} patched.")
        return

    @torch.no_grad()
    def process_unit_after_every_sampling(self,
                                          p: StableDiffusionProcessing,
                                          unit: external_code.ControlNetUnit,
                                          params: ControlNetCachedParameters,
                                          *args, **kwargs):

        params.preprocessor.process_after_every_sampling(p, params, *args, **kwargs)
        params.model.process_after_every_sampling(p, params, *args, **kwargs)
        return

    def process(self, p, *args, **kwargs):
        self.current_params = {}
        for i, unit in enumerate(self.get_enabled_units(p)):
            self.bound_check_params(unit)
            params = ControlNetCachedParameters()
            self.process_unit_after_click_generate(p, unit, params, *args, **kwargs)
            self.current_params[i] = params
        return

    def process_before_every_sampling(self, p, *args, **kwargs):
        for i, unit in enumerate(self.get_enabled_units(p)):
            self.process_unit_before_every_sampling(p, unit, self.current_params[i], *args, **kwargs)
        return

    def postprocess_batch_list(self, p, *args, **kwargs):
        for i, unit in enumerate(self.get_enabled_units(p)):
            self.process_unit_after_every_sampling(p, unit, self.current_params[i], *args, **kwargs)
        self.current_params = {}
        return


def on_ui_settings():
    section = ('control_net', "ControlNet")
    shared.opts.add_option("control_net_detectedmap_dir", shared.OptionInfo(
        "detected_maps", "Directory for detected maps auto saving", section=section))
    shared.opts.add_option("control_net_models_path", shared.OptionInfo(
        "", "Extra path to scan for ControlNet models (e.g. training output directory)", section=section))
    shared.opts.add_option("control_net_modules_path", shared.OptionInfo(
        "",
        "Path to directory containing annotator model directories (requires restart, overrides corresponding command line flag)",
        section=section))
    shared.opts.add_option("control_net_unit_count", shared.OptionInfo(
        3, "Multi-ControlNet: ControlNet unit number (requires restart)", gr.Slider,
        {"minimum": 1, "maximum": 10, "step": 1}, section=section))
    shared.opts.add_option("control_net_model_cache_size", shared.OptionInfo(
        5, "Model cache size (requires restart)", gr.Slider, {"minimum": 1, "maximum": 10, "step": 1}, section=section))
    shared.opts.add_option("control_net_no_detectmap", shared.OptionInfo(
        False, "Do not append detectmap to output", gr.Checkbox, {"interactive": True}, section=section))
    shared.opts.add_option("control_net_detectmap_autosaving", shared.OptionInfo(
        False, "Allow detectmap auto saving", gr.Checkbox, {"interactive": True}, section=section))
    shared.opts.add_option("control_net_allow_script_control", shared.OptionInfo(
        False, "Allow other script to control this extension", gr.Checkbox, {"interactive": True}, section=section))
    shared.opts.add_option("control_net_sync_field_args", shared.OptionInfo(
        True, "Paste ControlNet parameters in infotext", gr.Checkbox, {"interactive": True}, section=section))
    shared.opts.add_option("controlnet_show_batch_images_in_ui", shared.OptionInfo(
        False, "Show batch images in gradio gallery output", gr.Checkbox, {"interactive": True}, section=section))
    shared.opts.add_option("controlnet_increment_seed_during_batch", shared.OptionInfo(
        False, "Increment seed after each controlnet batch iteration", gr.Checkbox, {"interactive": True},
        section=section))
    shared.opts.add_option("controlnet_disable_openpose_edit", shared.OptionInfo(
        False, "Disable openpose edit", gr.Checkbox, {"interactive": True}, section=section))
    shared.opts.add_option("controlnet_disable_photopea_edit", shared.OptionInfo(
        False, "Disable photopea edit", gr.Checkbox, {"interactive": True}, section=section))
    shared.opts.add_option("controlnet_photopea_warning", shared.OptionInfo(
        True, "Photopea popup warning", gr.Checkbox, {"interactive": True}, section=section))
    shared.opts.add_option("controlnet_ignore_noninpaint_mask", shared.OptionInfo(
        False, "Ignore mask on ControlNet input image if control type is not inpaint",
        gr.Checkbox, {"interactive": True}, section=section))
    shared.opts.add_option("controlnet_clip_detector_on_cpu", shared.OptionInfo(
        False, "Load CLIP preprocessor model on CPU",
        gr.Checkbox, {"interactive": True}, section=section))


script_callbacks.on_ui_settings(on_ui_settings)
script_callbacks.on_infotext_pasted(Infotext.on_infotext_pasted)
script_callbacks.on_after_component(ControlNetUiGroup.on_after_component)
script_callbacks.on_before_reload(ControlNetUiGroup.reset)