import copy
import os
import shutil
from dataclasses import dataclass
from typing import List, Union

import cv2
import numpy as np
from PIL import Image

import insightface
from insightface.app.common import Face
try:
    import torch.cuda as cuda
except:
    cuda = None

from scripts.reactor_logger import logger
from reactor_utils import move_path, get_image_md5hash
import folder_paths

import warnings

np.warnings = warnings
np.warnings.filterwarnings('ignore')

if cuda is not None:
    if cuda.is_available():
        providers = ["CUDAExecutionProvider"]
    else:
        providers = ["CPUExecutionProvider"]
else:
    providers = ["CPUExecutionProvider"]

models_path_old = os.path.join(os.path.dirname(os.path.dirname(__file__)), "models")
insightface_path_old = os.path.join(models_path_old, "insightface")
insightface_models_path_old = os.path.join(insightface_path_old, "models")

models_path = folder_paths.models_dir
insightface_path = os.path.join(models_path, "insightface")
insightface_models_path = os.path.join(insightface_path, "models")

if os.path.exists(models_path_old):
    move_path(insightface_models_path_old, insightface_models_path)
    move_path(insightface_path_old, insightface_path)
    move_path(models_path_old, models_path)
if os.path.exists(insightface_path) and os.path.exists(insightface_path_old):
    shutil.rmtree(insightface_path_old)
    shutil.rmtree(models_path_old)


FS_MODEL = None
CURRENT_FS_MODEL_PATH = None

ANALYSIS_MODEL = None

SOURCE_FACES = None
SOURCE_IMAGE_HASH = None
TARGET_FACES = None
TARGET_IMAGE_HASH = None

def get_current_faces_model():
    global SOURCE_FACES
    return SOURCE_FACES

def getAnalysisModel():
    global ANALYSIS_MODEL
    if ANALYSIS_MODEL is None:
        ANALYSIS_MODEL = insightface.app.FaceAnalysis(
            name="buffalo_l", providers=providers, root=insightface_path
        )
    return ANALYSIS_MODEL


def getFaceSwapModel(model_path: str):
    global FS_MODEL
    global CURRENT_FS_MODEL_PATH
    if CURRENT_FS_MODEL_PATH is None or CURRENT_FS_MODEL_PATH != model_path:
        CURRENT_FS_MODEL_PATH = model_path
        FS_MODEL = insightface.model_zoo.get_model(model_path, providers=providers)

    return FS_MODEL


def get_face_gender(
        face,
        face_index,
        gender_condition,
        operated: str
):
    gender = [
        x.sex
        for x in face
    ]
    gender.reverse()
    # If index is outside of bounds, return None, avoid exception
    if face_index >= len(gender):
        logger.status("Requested face index (%s) is out of bounds (max available index is %s)", face_index, len(gender))
        return None, 0
    face_gender = gender[face_index]
    logger.status("%s Face %s: Detected Gender -%s-", operated, face_index, face_gender)
    if (gender_condition == 1 and face_gender == "F") or (gender_condition == 2 and face_gender == "M"):
        logger.status("OK - Detected Gender matches Condition")
        try:
            return sorted(face, key=lambda x: x.bbox[0])[face_index], 0
        except IndexError:
            return None, 0
    else:
        logger.status("WRONG - Detected Gender doesn't match Condition")
        return sorted(face, key=lambda x: x.bbox[0])[face_index], 1


def half_det_size(det_size):
    logger.status("Trying to halve 'det_size' parameter")
    return (det_size[0] // 2, det_size[1] // 2)

def analyze_faces(img_data: np.ndarray, det_size=(640, 640)):
    face_analyser = copy.deepcopy(getAnalysisModel())
    face_analyser.prepare(ctx_id=0, det_size=det_size)
    return face_analyser.get(img_data)

def get_face_single(img_data: np.ndarray, face, face_index=0, det_size=(640, 640), gender_source=0, gender_target=0):

    buffalo_path = os.path.join(insightface_models_path, "buffalo_l.zip")
    if os.path.exists(buffalo_path):
        os.remove(buffalo_path)

    if gender_source != 0:
        if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320:
            det_size_half = half_det_size(det_size)
            return get_face_single(img_data, analyze_faces(img_data, det_size_half), face_index, det_size_half, gender_source, gender_target)
        return get_face_gender(face,face_index,gender_source,"Source")

    if gender_target != 0:
        if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320:
            det_size_half = half_det_size(det_size)
            return get_face_single(img_data, analyze_faces(img_data, det_size_half), face_index, det_size_half, gender_source, gender_target)
        return get_face_gender(face,face_index,gender_target,"Target")
    
    if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320:
        det_size_half = half_det_size(det_size)
        return get_face_single(img_data, analyze_faces(img_data, det_size_half), face_index, det_size_half, gender_source, gender_target)

    try:
        return sorted(face, key=lambda x: x.bbox[0])[face_index], 0
    except IndexError:
        return None, 0


def swap_face(
    source_img: Union[Image.Image, None],
    target_img: Image.Image,
    model: Union[str, None] = None,
    source_faces_index: List[int] = [0],
    faces_index: List[int] = [0],
    gender_source: int = 0,
    gender_target: int = 0,
    face_model: Union[Face, None] = None,
):
    global SOURCE_FACES, SOURCE_IMAGE_HASH, TARGET_FACES, TARGET_IMAGE_HASH
    result_image = target_img

    if model is not None:

        if isinstance(source_img, str):  # source_img is a base64 string
            import base64, io
            if 'base64,' in source_img:  # check if the base64 string has a data URL scheme
                # split the base64 string to get the actual base64 encoded image data
                base64_data = source_img.split('base64,')[-1]
                # decode base64 string to bytes
                img_bytes = base64.b64decode(base64_data)
            else:
                # if no data URL scheme, just decode
                img_bytes = base64.b64decode(source_img)
            
            source_img = Image.open(io.BytesIO(img_bytes))
            
        target_img = cv2.cvtColor(np.array(target_img), cv2.COLOR_RGB2BGR)

        if source_img is not None:

            source_img = cv2.cvtColor(np.array(source_img), cv2.COLOR_RGB2BGR)

            source_image_md5hash = get_image_md5hash(source_img)

            if SOURCE_IMAGE_HASH is None:
                SOURCE_IMAGE_HASH = source_image_md5hash
                source_image_same = False
            else:
                source_image_same = True if SOURCE_IMAGE_HASH == source_image_md5hash else False
                if not source_image_same:
                    SOURCE_IMAGE_HASH = source_image_md5hash

            logger.info("Source Image MD5 Hash = %s", SOURCE_IMAGE_HASH)
            logger.info("Source Image the Same? %s", source_image_same)

            if SOURCE_FACES is None or not source_image_same:
                logger.status("Analyzing Source Image...")
                source_faces = analyze_faces(source_img)
                SOURCE_FACES = source_faces
            elif source_image_same:
                logger.status("Using Hashed Source Face(s) Model...")
                source_faces = SOURCE_FACES

        elif face_model is not None:

            source_faces_index = [0]
            logger.status("Using Loaded Source Face Model...")
            source_face_model = [face_model]
            source_faces = source_face_model

        else:
            logger.error("Cannot detect any Source")

        if source_faces is not None:

            target_image_md5hash = get_image_md5hash(target_img)

            if TARGET_IMAGE_HASH is None:
                TARGET_IMAGE_HASH = target_image_md5hash
                target_image_same = False
            else:
                target_image_same = True if TARGET_IMAGE_HASH == target_image_md5hash else False
                if not target_image_same:
                    TARGET_IMAGE_HASH = target_image_md5hash

            logger.info("Target Image MD5 Hash = %s", TARGET_IMAGE_HASH)
            logger.info("Target Image the Same? %s", target_image_same)
            
            if TARGET_FACES is None or not target_image_same:
                logger.status("Analyzing Target Image...")
                target_faces = analyze_faces(target_img)
                TARGET_FACES = target_faces
            elif target_image_same:
                logger.status("Using Hashed Target Face(s) Model...")
                target_faces = TARGET_FACES

            # No use in trying to swap faces if no faces are found, enhancement
            if len(target_faces) == 0:
                logger.status("Cannot detect any Target, skipping swapping...")
                return result_image

            if source_img is not None:
                # separated management of wrong_gender between source and target, enhancement
                source_face, src_wrong_gender = get_face_single(source_img, source_faces, face_index=source_faces_index[0], gender_source=gender_source)
            else:
                source_face = sorted(source_faces, key=lambda x: x.bbox[0])[source_faces_index[0]]
                src_wrong_gender = 0

            if len(source_faces_index) != 0 and len(source_faces_index) != 1 and len(source_faces_index) != len(faces_index):
                logger.status(f'Source Faces must have no entries (default=0), one entry, or same number of entries as target faces.')
            elif source_face is not None:
                result = target_img
                model_path = model_path = os.path.join(insightface_path, model)
                face_swapper = getFaceSwapModel(model_path)

                source_face_idx = 0

                for face_num in faces_index:
                    # No use in trying to swap faces if no further faces are found, enhancement
                    if face_num >= len(target_faces):
                        logger.status("Checked all existing target faces, skipping swapping...")
                        break

                    if len(source_faces_index) > 1 and source_face_idx > 0:
                        source_face, src_wrong_gender = get_face_single(source_img, source_faces, face_index=source_faces_index[source_face_idx], gender_source=gender_source)
                    source_face_idx += 1

                    if source_face is not None and src_wrong_gender == 0:
                        target_face, wrong_gender = get_face_single(target_img, target_faces, face_index=face_num, gender_target=gender_target)
                        if target_face is not None and wrong_gender == 0:
                            logger.status(f"Swapping...")
                            result = face_swapper.get(result, target_face, source_face)
                        elif wrong_gender == 1:
                            wrong_gender = 0
                            # Keep searching for other faces if wrong gender is detected, enhancement
                            #if source_face_idx == len(source_faces_index):
                            #    result_image = Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
                            #    return result_image
                            logger.status("Wrong target gender detected")
                            continue
                        else:
                            logger.status(f"No target face found for {face_num}")
                    elif src_wrong_gender == 1:
                        src_wrong_gender = 0
                        # Keep searching for other faces if wrong gender is detected, enhancement
                        #if source_face_idx == len(source_faces_index):
                        #    result_image = Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
                        #    return result_image
                        logger.status("Wrong source gender detected")
                        continue
                    else:
                        logger.status(f"No source face found for face number {source_face_idx}.")

                result_image = Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))

            else:
                logger.status("No source face(s) in the provided Index")
        else:
            logger.status("No source face(s) found")
    return result_image