import yaml
import torch
from diffusers import StableDiffusionXLPipeline, DiffusionPipeline, AutoPipelineForText2Image, StableDiffusionPipeline
from .pipeline import PhotoMakerStableDiffusionXLPipeline
import os
import sys
import diffusers
dif_version = str(diffusers.__version__)
dif_version_int= int(dif_version.split(".")[1])
dir_path = os.path.dirname(os.path.abspath(__file__))
path_dir = os.path.dirname(dir_path)
file_path = os.path.dirname(path_dir)

def get_instance_path(path):
    instance_path = os.path.normpath(path)
    if sys.platform == 'win32':
        instance_path = path.replace('\\', "/")
    return instance_path

yaml_path = get_instance_path(os.path.join(path_dir ,'config','models.yaml'))
original_config_file=get_instance_path(os.path.join(path_dir,'config','sd_xl_base.yaml'))
loras_path = get_instance_path(os.path.join(path_dir,"config","lora.yaml"))

def get_lora_dict():
    # 打开并读取YAML文件
    with open(loras_path, 'r', encoding="UTF-8") as stream:
        try:
            # 解析YAML文件内容
            data = yaml.safe_load(stream)

            # 此时 'data' 是一个Python字典，里面包含了YAML文件的所有数据
            # print(data)
            return data

        except yaml.YAMLError as exc:
            # 如果在解析过程中发生了错误，打印异常信息
            print(exc)

datas = get_lora_dict()
lora_lightning_list = datas["lightning_xl_lora"]
#print(lora_lightning_list)
def get_models_dict():
    # 打开并读取YAML文件
    with open(yaml_path, 'r', encoding="UTF-8") as stream:
        try:
            # 解析YAML文件内容
            data = yaml.safe_load(stream)
            
            # 此时 'data' 是一个Python字典，里面包含了YAML文件的所有数据
            # print(data)
            return data
            
        except yaml.YAMLError as exc:
            # 如果在解析过程中发生了错误，打印异常信息
            print(exc)

def load_models(model_info,_sd_type,device,photomaker_path,lora,lora_path,trigger_words,lora_scale):
    path =  model_info["path"]
    single_files =  model_info["single_files"]
    use_safetensors = model_info["use_safetensors"]
    model_type = model_info["model_type"]

    if model_type == "txt2img":
        if single_files:
            if dif_version_int>=28:
                pipe = StableDiffusionXLPipeline.from_single_file(
                    pretrained_model_link_or_path=path, original_config=original_config_file, torch_dtype=torch.float16)

            else:
                pipe = StableDiffusionXLPipeline.from_single_file(
                    pretrained_model_link_or_path=path, original_config_file=original_config_file, torch_dtype=torch.float16
                )

        else:
            pipe = StableDiffusionXLPipeline.from_pretrained(
                path, torch_dtype=torch.float16,use_safetensors=use_safetensors
            )

        pipe = pipe.to(device)
        if lora != "none":
            if lora in lora_lightning_list:
                pipe.load_lora_weights(lora_path)
                pipe.fuse_lora()
            else:
                pipe.load_lora_weights(lora_path, adapter_name=trigger_words)
                pipe.fuse_lora(adapter_names=[trigger_words, ], lora_scale=lora_scale)

    elif model_type == "img2img":
        if single_files:
            #print("loading from a single_files")
            if dif_version_int>=28:
                pipe = PhotoMakerStableDiffusionXLPipeline.from_single_file(
                    pretrained_model_link_or_path=path, original_config=original_config_file, torch_dtype=torch.float16,use_safetensors=use_safetensors)
            else:
                pipe = PhotoMakerStableDiffusionXLPipeline.from_single_file(
                    pretrained_model_link_or_path=path, original_config_file=original_config_file, torch_dtype=torch.float16,use_safetensors=use_safetensors
                )
        else:
            pipe = PhotoMakerStableDiffusionXLPipeline.from_pretrained(
                path, torch_dtype=torch.float16,use_safetensors=use_safetensors
            )

        pipe = pipe.to(device)
        pipe.load_photomaker_adapter(
            photomaker_path,
            subfolder="",
            weight_name="photomaker-v1.bin",
            trigger_word="img"  # define the trigger word
        )
        if lora != "none":
            if lora in lora_lightning_list:
                pipe.load_lora_weights(lora_path)
                pipe.fuse_lora()
            else:
                pipe.load_lora_weights(lora_path, adapter_name=trigger_words)
                pipe.fuse_lora(adapter_names=[trigger_words, ], lora_scale=lora_scale)


    else:
        raise f"using{model_type}node,must choice{model_type}type in model_loader node"
    return pipe