from typing import List, Optional

import numpy as np
from fastapi import FastAPI, Body
from fastapi.exceptions import HTTPException
from pydantic import BaseModel

from PIL import Image

import gradio as gr

from modules.api.models import *
from modules.api import api

from scripts import external_code, global_state
from scripts.processor import preprocessor_filters
from scripts.logging import logger
from annotator.openpose import draw_poses, decode_json_as_poses
from annotator.openpose.animalpose import draw_animalposes


def encode_to_base64(image):
    if isinstance(image, str):
        return image
    elif isinstance(image, Image.Image):
        return api.encode_pil_to_base64(image)
    elif isinstance(image, np.ndarray):
        return encode_np_to_base64(image)
    else:
        return ""


def encode_np_to_base64(image):
    pil = Image.fromarray(image)
    return api.encode_pil_to_base64(pil)


def controlnet_api(_: gr.Blocks, app: FastAPI):
    @app.get("/controlnet/version")
    async def version():
        return {"version": external_code.get_api_version()}

    @app.get("/controlnet/model_list")
    async def model_list(update: bool = True):
        up_to_date_model_list = external_code.get_models(update=update)
        logger.debug(up_to_date_model_list)
        return {"model_list": up_to_date_model_list}

    @app.get("/controlnet/module_list")
    async def module_list(alias_names: bool = False):
        _module_list = external_code.get_modules(alias_names)
        logger.debug(_module_list)

        return {
            "module_list": _module_list,
            "module_detail": external_code.get_modules_detail(alias_names),
        }

    @app.get("/controlnet/control_types")
    async def control_types():
        def format_control_type(
            filtered_preprocessor_list,
            filtered_model_list,
            default_option,
            default_model,
        ):
            return {
                "module_list": filtered_preprocessor_list,
                "model_list": filtered_model_list,
                "default_option": default_option,
                "default_model": default_model,
            }

        return {
            "control_types": {
                control_type: format_control_type(
                    *global_state.select_control_type(control_type)
                )
                for control_type in preprocessor_filters.keys()
            }
        }

    @app.get("/controlnet/settings")
    async def settings():
        max_models_num = external_code.get_max_models_num()
        return {"control_net_unit_count": max_models_num}

    cached_cn_preprocessors = global_state.cache_preprocessors(
        global_state.cn_preprocessor_modules
    )

    @app.post("/controlnet/detect")
    async def detect(
        controlnet_module: str = Body("none", title="Controlnet Module"),
        controlnet_input_images: List[str] = Body([], title="Controlnet Input Images"),
        controlnet_processor_res: int = Body(
            512, title="Controlnet Processor Resolution"
        ),
        controlnet_threshold_a: float = Body(64, title="Controlnet Threshold a"),
        controlnet_threshold_b: float = Body(64, title="Controlnet Threshold b"),
    ):
        controlnet_module = global_state.reverse_preprocessor_aliases.get(
            controlnet_module, controlnet_module
        )

        if controlnet_module not in cached_cn_preprocessors:
            raise HTTPException(status_code=422, detail="Module not available")

        if len(controlnet_input_images) == 0:
            raise HTTPException(status_code=422, detail="No image selected")

        logger.info(
            f"Detecting {str(len(controlnet_input_images))} images with the {controlnet_module} module."
        )

        results = []
        poses = []

        processor_module = cached_cn_preprocessors[controlnet_module]

        for input_image in controlnet_input_images:
            img = external_code.to_base64_nparray(input_image)

            class JsonAcceptor:
                def __init__(self) -> None:
                    self.value = None

                def accept(self, json_dict: dict) -> None:
                    self.value = json_dict

            json_acceptor = JsonAcceptor()

            results.append(
                processor_module(
                    img,
                    res=controlnet_processor_res,
                    thr_a=controlnet_threshold_a,
                    thr_b=controlnet_threshold_b,
                    json_pose_callback=json_acceptor.accept,
                )[0]
            )

            if "openpose" in controlnet_module:
                assert json_acceptor.value is not None
                poses.append(json_acceptor.value)

        global_state.cn_preprocessor_unloadable.get(controlnet_module, lambda: None)()
        results64 = list(map(encode_to_base64, results))
        res = {"images": results64, "info": "Success"}
        if poses:
            res["poses"] = poses

        return res

    class Person(BaseModel):
        pose_keypoints_2d: List[float]
        hand_right_keypoints_2d: Optional[List[float]]
        hand_left_keypoints_2d: Optional[List[float]]
        face_keypoints_2d: Optional[List[float]]

    class PoseData(BaseModel):
        people: List[Person]
        canvas_width: int
        canvas_height: int

    @app.post("/controlnet/render_openpose_json")
    async def render_openpose_json(
        pose_data: List[PoseData] = Body([], title="Pose json files to render.")
    ):
        if not pose_data:
            return {"info": "No pose data detected."}
        else:

            def draw(poses, animals, H, W):
                if poses:
                    assert len(animals) == 0
                    return draw_poses(poses, H, W)
                else:
                    return draw_animalposes(animals, H, W)

            return {
                "images": [
                    encode_to_base64(draw(*decode_json_as_poses(pose.dict())))
                    for pose in pose_data
                ],
                "info": "Success",
            }


try:
    import modules.script_callbacks as script_callbacks

    script_callbacks.on_app_started(controlnet_api)
except:
    pass