import logging
import os
import html
import boto3
import time
import json
import gradio
import requests
import gradio as gr

from aws_extension.cloud_api_manager.api_logger import ApiLogger
from aws_extension.constant import MODEL_TYPE

import utils
from aws_extension.auth_service.simple_cloud_auth import cloud_auth_manager
from aws_extension.cloud_api_manager.api_manager import api_manager
from aws_extension.cloud_api_manager.api import api
from aws_extension.sagemaker_ui_utils import create_refresh_button_by_user
from modules.shared import opts
from modules.ui_components import FormRow
from utils import get_variable_from_json, upload_multipart_files_to_s3_by_signed_url, has_config
from requests.exceptions import JSONDecodeError
from datetime import datetime
import math
import re
from modules.ui_components import ToolButton
import asyncio
import nest_asyncio

from utils import cp, tar, rm

logger = logging.getLogger(__name__)
logger.setLevel(utils.LOGGING_LEVEL)

None_Option_For_On_Cloud_Model = "don't use on cloud inference"
None_Option_For_Infer_Job = "No Selected"

inference_job_dropdown = None
embedding_dropdown = None
hypernet_dropdown = None
lora_dropdown = None
# sagemaker_endpoint = None
modelmerger_merge_on_cloud = None
interrogate_clip_on_cloud_button = None
interrogate_deep_booru_on_cloud_button = None

primary_model_name = None
secondary_model_name = None
tertiary_model_name = None

# TODO: convert to dynamically init the following variables
txt2img_inference_job_ids = []

sd_checkpoints = []
textual_inversion_list = []
lora_list = []
hyperNetwork_list = []
ControlNet_model_list = []

# Initial checkpoints information
checkpoint_info = {}
checkpoint_type = ["Stable-diffusion", "embeddings", "Lora", "hypernetworks", "ControlNet", "VAE"]
checkpoint_name = ["stable_diffusion", "embeddings", "lora", "hypernetworks", "controlnet", "VAE"]
stable_diffusion_list = []
embeddings_list = []

hypernetworks_list = []
controlnet_list = []
for ckpt_type, ckpt_name in zip(checkpoint_type, checkpoint_name):
    checkpoint_info[ckpt_type] = {}
# get api_gateway_url
api_gateway_url = get_variable_from_json('api_gateway_url')
api_key = get_variable_from_json('api_token')

start_time_picker_img_value = None
end_time_picker_img_value = None
start_time_picker_txt_value = None
end_time_picker_txt_value = None

txt_task_type = None
txt_status = None
txt_endpoint = None
txt_checkpoint = None

img_task_type = None
img_status = None
img_endpoint = None
img_checkpoint = None

show_all_inference_job = False

modelTypeMap = {
    'SD Checkpoints': 'Stable-diffusion',
    'Textual Inversion': 'embeddings',
    'LoRA model': 'Lora',
    'ControlNet model': 'ControlNet',
    'Hypernetwork': 'hypernetworks',
    'VAE': 'VAE'
}


def plaintext_to_html(text):
    text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
    return text


def server_request_get(path, params):
    api_gateway_url = get_variable_from_json('api_gateway_url')
    # Check if api_url ends with '/', if not append it
    if not api_gateway_url.endswith('/'):
        api_gateway_url += '/'
    api_key = get_variable_from_json('api_token')
    headers = {
        "x-api-key": api_key,
        "Content-Type": "application/json"
    }
    list_endpoint_url = f'{api_gateway_url}{path}'
    response = requests.get(list_endpoint_url, params=params, headers=headers)
    return response


def get_s3_file_names(bucket, folder):
    """Get a list of file names from an S3 bucket and folder."""
    s3 = boto3.resource('s3')
    bucket = s3.Bucket(bucket)
    objects = bucket.objects.filter(Prefix=folder)
    names = [obj.key for obj in objects]
    return names


def get_current_date():
    today = datetime.today()
    formatted_date = today.strftime('%Y-%m-%d')
    return formatted_date


def server_request(path):
    api_gateway_url = get_variable_from_json('api_gateway_url')
    if not has_config():
        return []
    # Check if api_url ends with '/', if not append it
    if not api_gateway_url.endswith('/'):
        api_gateway_url += '/'
    api_key = get_variable_from_json('api_token')
    headers = {
        "x-api-key": api_key,
        "Content-Type": "application/json"
    }
    list_endpoint_url = f'{api_gateway_url}{path}'
    response = requests.get(list_endpoint_url, headers=headers)
    return response


def datetime_to_short_form(datetime_str):
    dt = get_strptime(datetime_str)
    short_form = dt.strftime("%Y-%m-%d-%H-%M-%S")
    return short_form


def query_page_inference_job_list(task_type: str, status: str, endpoint: str, checkpoint: str, is_img2img: bool,
                                  show_all: bool):
    global show_all_inference_job
    show_all_inference_job = show_all
    if is_img2img:
        global img_task_type
        img_task_type = task_type
        global img_status
        img_status = status
        global img_endpoint
        img_endpoint = endpoint
        global img_checkpoint
        img_checkpoint = checkpoint
        opt_type = 'img2img'
        logger.debug(f"{opt_type} {img_task_type} {img_status} {img_endpoint} {img_checkpoint} {show_all}")
        return query_inference_job_list(task_type, status, endpoint, checkpoint, opt_type)
    else:
        global txt_task_type
        txt_task_type = task_type
        global txt_status
        txt_status = txt_status
        global txt_endpoint
        txt_endpoint = endpoint
        global txt_checkpoint
        txt_checkpoint = checkpoint
        opt_type = 'txt2img'
        logger.debug(f"{opt_type} {txt_task_type} {txt_status} {txt_endpoint} {txt_checkpoint} {show_all}")
        return query_inference_job_list(task_type, status, endpoint, checkpoint, opt_type)


def query_img_inference_job_list(task_type: str, status: str, endpoint: str, checkpoint: str):
    opt_type = 'img2img'
    global img_task_type
    img_task_type = task_type
    global img_status
    img_status = status
    global img_endpoint
    img_endpoint = endpoint
    global img_checkpoint
    img_checkpoint = checkpoint
    return query_inference_job_list(task_type, status, endpoint, checkpoint, opt_type)


def query_txt_inference_job_list(task_type: str, status: str, endpoint: str, checkpoint: str):
    opt_type = 'txt2img'
    global txt_task_type
    txt_task_type = task_type
    global txt_status
    txt_status = status
    global txt_endpoint
    txt_endpoint = endpoint
    global txt_checkpoint
    txt_checkpoint = checkpoint

    return query_inference_job_list(task_type, status, endpoint, checkpoint, opt_type)


def query_inference_job_list(task_type: str = '', status: str = '',
                             endpoint: str = '', checkpoint: str = '', opt_type: str = ''):
    logger.debug(
        f"query_inference_job_list start！！{status},{task_type},{endpoint},{checkpoint},{start_time_picker_txt_value},{end_time_picker_txt_value} {show_all_inference_job}")
    try:
        body_params = {}
        if status:
            body_params['status'] = status
        if task_type:
            body_params['task_type'] = task_type
        if opt_type == 'txt2img':
            if start_time_picker_txt_value:
                body_params['start_time'] = start_time_picker_txt_value
            if end_time_picker_txt_value:
                body_params['end_time'] = end_time_picker_txt_value
        elif opt_type == 'img2img':
            if start_time_picker_img_value:
                body_params['start_time'] = start_time_picker_img_value
            if end_time_picker_img_value:
                body_params['end_time'] = end_time_picker_img_value
        if endpoint:
            endpoint_name_array = endpoint.split("+")
            if len(endpoint_name_array) > 0:
                body_params['endpoint'] = endpoint_name_array[0]
        if checkpoint:
            body_params['checkpoint'] = checkpoint
        body_params['limit'] = -1 if show_all_inference_job else 10
        response = server_request_get(f'inferences', body_params)
        r = response.json()['data']['inferences']
        logger.debug(r)
        if r:
            txt2img_inference_job_ids.clear()  # Clear the existing list before appending new values
            temp_list = []
            for obj in r:
                if obj.get('completeTime') is None:
                    complete_time = obj.get('startTime')
                else:
                    complete_time = obj.get('completeTime')
                status = obj.get('status')
                task_type = obj.get('taskType', 'txt2img')
                inference_job_id = obj.get('InferenceJobId')
                combined_string = f"{complete_time}-->{task_type}-->{status}-->{inference_job_id}"
                temp_list.append((complete_time, combined_string))
            # Sort the list based on completeTime in ascending order
            sorted_list = sorted(temp_list, key=lambda x: x[0], reverse=False)
            # Append the sorted combined strings to the txt2img_inference_job_ids list
            for item in sorted_list:
                txt2img_inference_job_ids.append(item[1])
            # inference_job_dropdown.update(choices=txt2img_inference_job_ids)
            return gr.Dropdown.update(choices=txt2img_inference_job_ids)
        else:
            logger.info("The API response is empty.")
            return gr.Dropdown.update(choices=[])
    except Exception as e:
        logger.error("Exception occurred when fetching inference_job_ids")
        logger.error(e)
        return gr.Dropdown.update(choices=[])


def get_inference_job(inference_job_id):
    url = f'inferences/{inference_job_id}'
    response = server_request(url)
    logger.debug(f"get_inference_job response {response}")
    infer_id = ""
    if 'data' in response.json():
        infer_id = response.json()['data']['InferenceJobId']
    api_logger = ApiLogger(
        action='inference',
        append=True,
        infer_id=infer_id
    )
    headers = {
        "x-api-key": get_variable_from_json('api_token'),
        "Content-Type": "application/json"
    }
    api_gateway_url = get_variable_from_json('api_gateway_url')
    api_logger.req_log(sub_action="GetInferenceJob",
                       method='GET',
                       path=f"{api_gateway_url}{url}",
                       headers=headers,
                       response=response,
                       desc=f"Get inference job detail from cloud by ID ({inference_job_id}), "
                            f"end request if data.status == succeed, "
                            f"ID from previous step: CreateInference -> data -> inference -> id")
    if 'data' not in response.json():
        raise Exception(response.json())
    return response.json()['data']


def download_images(image_urls: list, local_directory: str):
    if not os.path.exists(local_directory):
        os.makedirs(local_directory)

    image_list = []
    for url in image_urls:
        try:
            response = requests.get(url)
            response.raise_for_status()
            image_name = os.path.basename(url).split('?')[0]
            local_path = os.path.join(local_directory, image_name)

            with open(local_path, 'wb') as f:
                f.write(response.content)
            image_list.append(local_path)
        except requests.exceptions.RequestException as e:
            logger.error(f"Error downloading image {url}: {e}")
    return image_list


def download_images_to_json(image_urls: list):
    results = []
    for url in image_urls:
        try:
            response = requests.get(url)
            response.raise_for_status()
            json_resp = response.json()
            results.append(json_resp['info'])
        except requests.exceptions.RequestException as e:
            logger.error(f"Error downloading image {url}: {e}")
    return results


def download_images_to_pil(image_urls: list):
    image_list = []
    for url in image_urls:
        try:
            response = requests.get(url)
            response.raise_for_status()
            from PIL import Image
            import io
            pil_image = Image.open(io.BytesIO(response.content))
            image_list.append(pil_image)
        except requests.exceptions.RequestException as e:
            logger.error(f"Error downloading image {url}: {e}")
    return image_list


def get_model_list_by_type(model_type, username=""):
    api_gateway_url = get_variable_from_json('api_gateway_url')
    api_key = get_variable_from_json('api_token')

    # check if api_gateway_url and api_key are set
    if not has_config():
        logger.info("api_gateway_url or api_key is not set")
        return []

    # Check if api_url ends with '/', if not append it
    if not api_gateway_url.endswith('/'):
        api_gateway_url += '/'

    url = api_gateway_url + f"checkpoints?status=Active"
    if isinstance(model_type, list):
        url += "&types=" + "&types=".join(model_type)
    else:
        url += f"&types={model_type}"

    try:
        response = requests.get(url=url, headers={
            'x-api-key': api_key,
            'username': username
        })
        response.raise_for_status()
        json_response = response.json()

        if "checkpoints" not in json_response.keys():
            return []

        checkpoint_list = []
        for ckpt in json_response["checkpoints"]:
            if "name" not in ckpt:
                continue
            if ckpt["name"] is None:
                continue
            if ckpt["type"] is None:
                continue
            ckpt_type = ckpt["type"]
            for ckpt_name in ckpt["name"]:
                ckpt_s3_pos = f"{ckpt['s3Location']}/{ckpt_name}"
                checkpoint_info[ckpt_type][ckpt_name] = ckpt_s3_pos
                checkpoint_list.append(ckpt_name)

        unique_list = list(set(checkpoint_list))
        return unique_list
    except Exception as e:
        logger.error(f"Error fetching model list: {e}")
        return []


def get_checkpoints_by_type(model_type):
    url = "checkpoints?status=Active"
    if isinstance(model_type, list):
        url += "&types=" + "&types=".join(model_type)
    else:
        url += f"&types={model_type}"
    try:
        response = server_request(url)
        response.raise_for_status()
        json_response = response.json()
        if "checkpoints" not in json_response.keys():
            return []
        checkpoint_dict = {}
        for ckpt in json_response["checkpoints"]:
            if "name" not in ckpt:
                continue
            if ckpt["name"] is None:
                continue
            create_time = ckpt['created']
            created = datetime.fromtimestamp(create_time)
            for ckpt_name in ckpt["name"]:
                checkpoint = [ckpt_name, created]
                if ckpt_name not in checkpoint_dict:
                    checkpoint_dict[ckpt_name] = checkpoint
        checkpoint_list = list(checkpoint_dict.values())
        return checkpoint_list
    except Exception as e:
        logging.error(f"Error fetching checkpoints list: {e}")
        return []


def update_sd_checkpoints(username):
    model_type = ["Stable-diffusion"]
    return get_model_list_by_type(model_type)


def get_texual_inversion_list():
    model_type = "embeddings"
    return get_model_list_by_type(model_type)


def get_lora_list():
    model_type = "Lora"
    return get_model_list_by_type(model_type)


def get_hypernetwork_list():
    model_type = "hypernetworks"
    return get_model_list_by_type(model_type)


def get_controlnet_model_list():
    model_type = "ControlNet"
    return get_model_list_by_type(model_type)


def refresh_all_models(username):
    api_gateway_url = get_variable_from_json('api_gateway_url')
    api_key = get_variable_from_json('api_token')

    if not has_config():
        return []

    try:
        for rp, name in zip(checkpoint_type, checkpoint_name):
            url = api_gateway_url + f"checkpoints?status=Active&types={rp}"
            response = requests.get(url=url, headers={
                'x-api-key': api_key,
                'username': username,
            })
            json_response = response.json()
            logger.debug(f"response url json for model {rp} is {json_response}")
            checkpoint_info[rp] = {}
            if "checkpoints" not in json_response.keys():
                continue
            for ckpt in json_response["checkpoints"]:
                if "name" not in ckpt:
                    continue
                if ckpt["name"] is None:
                    continue
                ckpt_type = ckpt["type"]
                checkpoint_info[ckpt_type] = {}
                for ckpt_name in ckpt["name"]:
                    ckpt_s3_pos = f"{ckpt['s3Location']}/{ckpt_name.split(os.sep)[-1]}"
                    checkpoint_info[ckpt_type][ckpt_name] = ckpt_s3_pos
    except Exception as e:
        logger.error(f"Error refresh all models: {e}")


def sagemaker_upload_model_s3(sd_checkpoints_path, textual_inversion_path, lora_path, hypernetwork_path,
                              controlnet_model_path, vae_path, pr: gradio.Request):

    if not has_config():
        return "Please config api url and token", None, None, None, None, None, None

    log = "start upload model to s3:"

    local_paths = [sd_checkpoints_path, textual_inversion_path, lora_path, hypernetwork_path, controlnet_model_path,
                   vae_path]

    # check parameters
    params_empty = True
    for local_path in local_paths:
        if local_path:
            params_empty = False
            break
    if params_empty:
        return "Please choose at least one model to upload.", None, None, None, None, None, None

    logger.info(f"Refresh checkpoints before upload to get rid of duplicate uploads...")
    refresh_all_models(pr.username)

    for lp, rp in zip(local_paths, checkpoint_type):
        if lp == "" or not lp:
            continue
        logger.debug(f"lp is {lp}")
        model_name = lp.split(os.sep)[-1]

        exist_model_list = list(checkpoint_info[rp].keys())

        if model_name in exist_model_list:
            logger.info(f"!!!skip to upload duplicate model {model_name}")
            continue

        part_size = 1000 * 1024 * 1024
        file_size = os.stat(lp)
        parts_number = math.ceil(file_size.st_size / part_size)
        logger.info(f'!!!!!!!!!!{file_size} {parts_number}')

        # local_tar_path = f'{model_name}.tar'
        local_tar_path = model_name
        payload = {
            "checkpoint_type": rp,
            "filenames": [{
                "filename": local_tar_path,
                "parts_number": parts_number
            }],
            "params": {
                "message": "placeholder for chkpts upload test",
                "creator": pr.username
            }
        }
        api_gateway_url = get_variable_from_json('api_gateway_url')
        # Check if api_url ends with '/', if not append it
        if not api_gateway_url.endswith('/'):
            api_gateway_url += '/'
        api_key = get_variable_from_json('api_token')
        logger.info(f'!!!!!!api_gateway_url {api_gateway_url}')

        url = str(api_gateway_url) + "checkpoints"

        logger.debug(f"Post request for upload s3 presign url: {url}")

        response = requests.post(url=url, json=payload, headers={'x-api-key': api_key, "username": pr.username})

        if response.status_code not in [201, 202]:
            logger.error(f"create_checkpoint: {response.json()}")
            return response.json()['message'], None, None, None, None, None, None

        try:
            json_response = response.json()['data']
            logger.debug(f"Response json {json_response}")
            s3_base = json_response["checkpoint"]["s3_location"]
            checkpoint_id = json_response["checkpoint"]["id"]
            logger.debug(f"Upload to S3 {s3_base}")
            logger.debug(f"Checkpoint ID: {checkpoint_id}")

            s3_signed_urls_resp = json_response["s3PresignUrl"][local_tar_path]
            # Upload src model to S3.
            if rp != "embeddings":
                local_model_path_in_repo = os.sep.join(['models', rp, model_name])
            else:
                local_model_path_in_repo = os.sep.join([rp, model_name])
            logger.debug("Pack the model file.")
            cp(lp, local_model_path_in_repo, recursive=True)
            if rp == "Stable-diffusion":
                model_yaml_name = model_name.split('.')[0] + ".yaml"
                local_model_yaml_path = os.sep.join([*lp.split(os.sep)[:-1], model_yaml_name])
                local_model_yaml_path_in_repo = os.sep.join(["models", rp, model_yaml_name])
                if os.path.isfile(local_model_yaml_path):
                    cp(local_model_yaml_path, local_model_yaml_path_in_repo, recursive=True)
                    tar(mode='c', archive=local_tar_path,
                        sfiles=[local_model_path_in_repo, local_model_yaml_path_in_repo], verbose=True)
                else:
                    tar(mode='c', archive=local_tar_path, sfiles=[local_model_path_in_repo], verbose=True)
            else:
                tar(mode='c', archive=local_tar_path, sfiles=[local_model_path_in_repo], verbose=True)

            multiparts_tags = upload_multipart_files_to_s3_by_signed_url(
                local_tar_path,
                s3_signed_urls_resp,
                part_size
            )
            logger.debug(f"multiparts_tags {multiparts_tags}")

            payload = {
                "status": "Active",
                "multi_parts_tags": {local_tar_path: multiparts_tags}
            }
            # Start creating model on cloud.
            response = requests.put(url=f"{url}/{checkpoint_id}", json=payload, headers={'x-api-key': api_key})
            logger.debug(response)

            log = f"finish upload {local_tar_path} to {s3_base}"

            # os.system(f"rm {local_tar_path}")
            rm(local_tar_path, recursive=True)
        except Exception as e:
            logger.error(f"fail to upload model {lp}, error: {e}")
            refresh_all_models(pr.username)
            return str(e), None, None, None, None, None, None

    logger.debug(f"Refresh checkpoints after upload...")
    refresh_all_models(pr.username)
    return log, None, None, None, None, None, None


def sagemaker_upload_model_s3_local():
    log = "Start upload:"
    return log


def check_url(url: str):
    url = url.replace('\n', '')
    return url.strip()


def sagemaker_upload_model_s3_url(model_type: str, url_list: str, description: str, pr: gradio.Request):
    if not url_list:
        return "Please fill the url."

    model_type = modelTypeMap.get(model_type)
    if not model_type:
        return "Please choose the model type."

    if description:
        params_dict = {
            'message': description
        }
    else:
        params_dict = {}

    params_dict['creator'] = pr.username

    url_list = url_list.split(',')
    modified_urls = [check_url(url) for url in url_list]
    unique_urls = list(set(modified_urls))
    for url in unique_urls:
        url_pattern = r'(https?|ftp)://[^\s/$.?#].[^\s]*'
        if not re.match(f'^{url_pattern}$', url):
            return f"{url} is not a valid url."

    data = {'checkpoint_type': model_type, 'urls': unique_urls, 'params': params_dict}
    api.set_username(pr.username)
    response = api.create_checkpoint(data=data)
    return response.json()['message']


def generate_on_cloud(sagemaker_endpoint):
    logger.info(f"checkpoint_info {checkpoint_info}")
    logger.info(f"sagemaker endpoint {sagemaker_endpoint}")
    text = "failed to check endpoint"
    return plaintext_to_html(text)


# create a global event loop and apply the patch to allow nested event loops in single thread
loop = asyncio.get_event_loop()
nest_asyncio.apply()

MAX_RUNNING_LIMIT = 10


def async_loop_wrapper(f):
    global loop
    # check if there are any running or queued tasks inside the event loop
    if loop.is_running():
        # Calculate the number of running tasks
        while len([task for task in asyncio.all_tasks(loop) if not task.done()]) > MAX_RUNNING_LIMIT:
            logger.debug(f'Waiting for {MAX_RUNNING_LIMIT} running tasks to complete')
            time.sleep(1)
    else:
        # check if loop is closed and create a new one
        if loop.is_closed():
            loop = asyncio.new_event_loop()
            asyncio.set_event_loop(loop)
            # log this event since it should never happen
            logger.debug('Event loop was closed, created a new one')

    # Add new task to the event loop
    result = loop.run_until_complete(f())
    return result


def process_result_by_inference_id(inference_id_or_data, endpoint_type):
    print(f"process_result_by_inference_id {inference_id_or_data} {endpoint_type}")
    if endpoint_type == 'Async':
        resp = get_inference_job(inference_id_or_data)
        inference_id = inference_id_or_data
    else:
        resp = inference_id_or_data
        inference_id = resp['InferenceJobId']

    image_list = []  # Return an empty list if selected_value is None
    info_text = ''
    infotexts = f"Inference id is {inference_id}, please check all historical inference result in 'Inference Job' dropdown list"
    json_list = []
    prompt_txt = ''

    if resp is None:
        logger.info(f"get_inference_job resp is null")
        return image_list, info_text, plaintext_to_html(infotexts), infotexts
    else:
        logger.debug(f"get_inference_job resp is {resp}")
        if 'taskType' not in resp:
            raise Exception(resp)

        if resp['taskType'] in ['txt2img', 'img2img', 'interrogate_clip', 'interrogate_deepbooru']:
            while resp and resp['status'] == "inprogress":
                time.sleep(1)
                resp = get_inference_job(inference_id)
            if resp is None:
                logger.info(f"get_inference_job resp is null.")
                return image_list, info_text, plaintext_to_html(infotexts), infotexts
            if resp['status'] == "failed":
                if 'sagemakerRaw' in resp:
                    infotexts = f"Inference job {inference_id} is failed, error message: {resp['sagemakerRaw']}"
                    return image_list, info_text, plaintext_to_html(infotexts), infotexts
            elif resp['status'] == "succeed":
                if resp['taskType'] in ['interrogate_clip', 'interrogate_deepbooru']:
                    prompt_txt = resp['caption']
                    # return with default value, including image_list, info_text, infotexts
                    return image_list, info_text, plaintext_to_html(infotexts), prompt_txt
                images = resp['img_presigned_urls']
                inference_param_json_list = resp['output_presigned_urls']
                # todo: these not need anymore
                if resp['taskType'] in ['txt2img', 'img2img']:
                    image_list = download_images_to_pil(images)
                    json_file = download_images_to_json(inference_param_json_list)[0]

                if json_file:
                    info_text = json_file
                    infotexts = f"Inference id is {inference_id}\n{get_infer_job_time(resp)}" + \
                                json.loads(info_text)["infotexts"][0]
                else:
                    logger.debug(f"File {json_file} does not exist.")
                    info_text = 'something wrong when trying to download the inference parameters'
                    infotexts = info_text
                return image_list, info_text, plaintext_to_html(infotexts), infotexts
            else:
                logger.debug(f"inference job status is {resp['status']}")
                return image_list, info_text, plaintext_to_html(infotexts), infotexts
        else:
            return image_list, info_text, plaintext_to_html(infotexts), infotexts


def update_prompt_with_embedding(selected_items, prompt, lora_and_hypernet_models_state):
    if MODEL_TYPE.EMBEDDING.value in lora_and_hypernet_models_state:
        return update_prompt_with_selected_model(
            selected_items,
            prompt,
            MODEL_TYPE.EMBEDDING,
            lora_and_hypernet_models_state[MODEL_TYPE.EMBEDDING.value]
            )

    return update_prompt_with_selected_model(selected_items, prompt, MODEL_TYPE.EMBEDDING)


def update_prompt_with_hypernetwork(selected_items, prompt):
    return update_prompt_with_selected_model(selected_items, prompt, MODEL_TYPE.HYPER_NETWORK)


def update_prompt_with_lora(selected_items, prompt):
    return update_prompt_with_selected_model(selected_items, prompt, MODEL_TYPE.LORA)


def update_prompt_with_selected_model(selected_value, original_prompt, type, state_value = None):
    """Update txt2img or img2img prompt with selected model name

    Args:
        selected_value (gr.Dropdown): the selected dropdown
        original_prompt (gr.Textbox): the original prompt before updating
        type: the model type, embedding|lora|hypernetwork

    Returns:
        gr.Textbox: The updated prompt
    """

    def _remove_embedding_prompt(state_value, selected_value, prompt_txt):
        if state_value:
            for embedding in state_value:
                if embedding not in selected_value:
                    prompt_txt = prompt_txt.replace(embedding.split(".")[0], "")

        return prompt_txt

    def _remove_prompt_by_regex(pattern, prompt_txt):
        matches = re.findall(pattern, prompt_txt)
        for match in matches:
            if match not in existed_item:
                prompt_txt = prompt_txt.replace(match, "")

        return prompt_txt

    logger.info(f"Selected value is {selected_value}, \
                original prompt is {original_prompt}, \
                type is {type}")
    prompt_txt = original_prompt
    existed_item = []

    # Compose prompt for Embedding/Lora/Hypernetwork
    for item in selected_value:
        model_name = item.split(".")[0]
        if MODEL_TYPE.LORA == type:
            model_prompt = f"<lora:{model_name}:1>"
        elif MODEL_TYPE.HYPER_NETWORK == type:
            model_prompt = f"<hypernet:{model_name}:1>"
        elif MODEL_TYPE.EMBEDDING == type:
            model_prompt = model_name
        else:
            logger.warning(f"The type {type} is not supported, skip it")
            continue

        existed_item.append(model_prompt)
        if model_prompt not in original_prompt:
            if 0 == len(original_prompt.strip()):
                prompt_txt = model_prompt
            else:
                prompt_txt += f" {model_prompt}"

    # Remove Embedding/Lora/Hypernetwork string which is not selected
    pattern = ""
    if MODEL_TYPE.LORA == type:
        pattern = r"<lora:[^>]*:1>"
        prompt_txt = _remove_prompt_by_regex(pattern, prompt_txt)
    elif MODEL_TYPE.HYPER_NETWORK == type:
        pattern = r"<hypernet:[^>]*:1>"
        prompt_txt = _remove_prompt_by_regex(pattern, prompt_txt)
    elif MODEL_TYPE.EMBEDDING == type:
        prompt_txt = _remove_embedding_prompt(state_value, selected_value, prompt_txt)
    else:
        logger.warning(f"The type {type} is not supported, skip it")
        return prompt_txt

    return prompt_txt


def fake_gan(selected_value, original_prompt):
    logger.debug(f"selected value is {selected_value}")
    logger.debug(f"original prompt is {original_prompt}")
    if selected_value and selected_value != None_Option_For_On_Cloud_Model:
        delimiter = "-->"
        parts = selected_value.split(delimiter)
        # Extract the InferenceJobId value
        inference_job_id = parts[3].strip()
        inference_job_status = parts[2].strip()
        inference_job_taskType = parts[1].strip()
        if inference_job_status == 'failed':
            job = get_inference_job(inference_job_id)
            return [], [], plaintext_to_html(f"inference is failed: {job['sagemakerRaw']}"), original_prompt
        if inference_job_status != 'succeed':
            return [], [], plaintext_to_html(f'inference is {inference_job_status}'), original_prompt

        if inference_job_taskType in ["txt2img", "img2img"]:
            prompt_txt = original_prompt
            # output directory mapping to task type
            job = get_inference_job(inference_job_id)
            images = job['img_presigned_urls']
            inference_param_json_list = job['output_presigned_urls']
            image_list = download_images_to_pil(images)
            images_to_json = download_images_to_json(inference_param_json_list)
            # maybe param json was deleted
            if len(images_to_json) == 0:
                json_file = ""
            else:
                json_file = images_to_json[0]
            if json_file:
                info_text = json_file
                infotexts = f"Inference id is {inference_job_id}\n{get_infer_job_time(job)}" + \
                            json.loads(info_text)["infotexts"][0]
            else:
                logger.debug(f"File {json_file} does not exist.")
                info_text = 'something wrong when trying to download the inference parameters'
                infotexts = info_text
        elif inference_job_taskType in ["interrogate_clip", "interrogate_deepbooru"]:
            job_status = get_inference_job(inference_job_id)
            logger.debug(job_status)
            caption = job_status['caption']
            prompt_txt = caption
            image_list = []  # Return an empty list if selected_value is None
            json_list = []
            info_text = ''
            infotexts = ''
    else:
        prompt_txt = original_prompt
        image_list = []  # Return an empty list if selected_value is None
        json_list = []
        info_text = ''
        infotexts = ''
    return image_list, info_text, plaintext_to_html(infotexts), prompt_txt


def get_strptime(time_str):
    time_str = time_str.replace("T", " ")
    return datetime.strptime(time_str, '%Y-%m-%d %H:%M:%S.%f')


def get_infer_job_time(job):
    string_array = []

    inference_type = ""
    if 'inference_type' in job:
        inference_type = job['inference_type'] + " "

    if 'createTime' in job and 'completeTime' in job:
        complete_time = get_strptime(job['completeTime'])
        create_time = get_strptime(job['createTime'])
        duration = complete_time - create_time
        duration = round(duration.total_seconds(), 2)
        string = f"End-to-end API Duration: {duration} seconds"

        start_time = get_strptime(job['startTime'])
        duration = complete_time - start_time
        duration = round(duration.total_seconds(), 2)
        string_array.append(f"{string} (in which {inference_type}Inference: {duration} seconds)")
    else:
        if 'startTime' in job and 'completeTime' in job:
            complete_time = get_strptime(job['completeTime'])
            start_time = get_strptime(job['startTime'])
            duration = complete_time - start_time
            duration = round(duration.total_seconds(), 2)
            string_array.append(f"{inference_type}Inference Time: {duration} seconds")

    if 'params' in job:
        if 'sagemaker_inference_endpoint_name' in job['params']:
            endpoint_name = job['params']['sagemaker_inference_endpoint_name']
            infer_ep_name = f"Endpoint: {endpoint_name}"
            if 'sagemaker_inference_instance_type' in job['params']:
                instance_type = job['params']['sagemaker_inference_instance_type']
                infer_ep_name += f" ({instance_type})"
            string_array.append(infer_ep_name)

    if len(string_array) == 0:
        return ""

    return "\n".join(string_array) + "\n"


def delete_inference_job(selected_value):
    logger.debug(f"selected value is {selected_value}")
    if selected_value and selected_value != None_Option_For_Infer_Job:
        if selected_value == 'cancelled':
            return
        delimiter = "-->"
        parts = selected_value.split(delimiter)
        # Extract the InferenceJobId value
        inference_job_id = parts[3].strip()
        resp = api.delete_inferences(data={
            "inference_id_list": [inference_job_id],
        })
        if resp.status_code != 204:
            gr.Error(f"Error deleting inference: {resp.json()['message']}")
        gr.Info(f"{inference_job_id} deleted successfully")
        file_path = f"{os.getcwd()}/outputs/{inference_job_id}.md"
        if os.path.exists(file_path):
            os.remove(file_path)
        file_path = f"{os.getcwd()}/outputs/{inference_job_id}.html"
        if os.path.exists(file_path):
            os.remove(file_path)
    else:
        gr.Warning('Please select a inference job to delete')


def init_refresh_resource_list_from_cloud(username):
    logger.debug(f"start refreshing resource list from cloud")
    if get_variable_from_json('api_gateway_url') is not None:
        if not cloud_auth_manager.enableAuth:
            refresh_all_models(username)
        else:
            logger.debug('auth enabled, not preload load any model')
    else:
        logger.debug(f"there is no api-gateway url and token in local file,")


def on_txt_time_change(start_time, end_time):
    logger.debug(f"!!!!!!!!!on_txt_time_change!!!!!!{start_time},{end_time}")
    global start_time_picker_txt_value
    global end_time_picker_txt_value
    start_time_picker_txt_value = start_time
    end_time_picker_txt_value = end_time
    return query_inference_job_list(txt_task_type, txt_status, txt_endpoint, txt_checkpoint, "txt2img")


def on_img_time_change(start_time, end_time):
    logger.debug(f"!!!!!!!!!on_img_time_change!!!!!!{start_time},{end_time}")
    global start_time_picker_img_value
    global end_time_picker_img_value
    start_time_picker_img_value = start_time
    end_time_picker_img_value = end_time
    return query_inference_job_list(img_task_type, img_status, img_endpoint, img_checkpoint, "img2img")


def load_inference_job_list(target_task_type, username, first_load="first"):
    inference_jobs = [None_Option_For_On_Cloud_Model]
    inferences_jobs_list = api_manager.list_all_inference_jobs_on_cloud(target_task_type, username, first_load)

    temp_list = []
    for obj in inferences_jobs_list:
        if obj.get('createTime') is None:
            complete_time = obj.get('startTime')
        else:
            complete_time = obj.get('createTime')
        status = obj.get('status')
        task_type = obj.get('taskType', 'txt2img')
        inference_job_id = obj.get('InferenceJobId')
        # Compatible with lower versions of APIs without type filters
        if target_task_type == task_type:
            temp_list.append((complete_time, f"{complete_time}-->{task_type}-->{status}-->{inference_job_id}"))
    # Append the sorted combined strings to the txt2img_inference_job_ids list
    for item in temp_list:
        inference_jobs.append(item[1])

    return inference_jobs


def load_model_list(username):
    models_on_cloud = []
    if 'sd_model_checkpoint' in opts.quicksettings_list:
        models_on_cloud = [None_Option_For_On_Cloud_Model]

    models_on_cloud += list(set([model['name'] for model in api_manager.list_models_on_cloud(username)]))
    return models_on_cloud


def load_lora_models(username):
    data = list(set([model['name'] for model in api_manager.list_models_on_cloud(username, types='Lora')]))
    data.sort()
    return data


def load_hypernetworks_models(username):
    return list(set([model['name'] for model in api_manager.list_models_on_cloud(username, types='hypernetworks')]))


def load_vae_list(username):
    vae_model_on_cloud = ['Automatic', 'None']
    vae_model_on_cloud += list(set([model['name'] for model in api_manager.list_models_on_cloud(username, types='VAE')]))
    return vae_model_on_cloud


def load_controlnet_list(username):
    controlnet_model_on_cloud = ['None']
    controlnet_model_on_cloud += list(
        set([model['name'] for model in api_manager.list_models_on_cloud(username, types='ControlNet')]))
    return controlnet_model_on_cloud


def load_xyz_controlnet_list(username):
    controlnet_model_on_cloud = ['None']
    controlnet_model_on_cloud += list(
        set([os.path.splitext(model['name'])[0] for model in
             api_manager.list_models_on_cloud(username, types='ControlNet')]))
    return controlnet_model_on_cloud


def load_embeddings_list(username):
    embedding_model_on_cloud = list(
        set([model['name'] for model in api_manager.list_models_on_cloud(username, types='embeddings')]))
    return embedding_model_on_cloud


def create_ui(is_img2img):
    global txt2img_gallery, txt2img_generation_info
    import modules.ui

    # init_refresh_resource_list_from_cloud()

    with gr.Blocks() as sagemaker_inference_tab:
        gr.HTML('<h3>Amazon SageMaker Inference</h3>')
        sagemaker_html_log = gr.HTML(elem_id=f'html_log_sagemaker')
        inference_task_type = 'txt2img' if not is_img2img else 'img2img'
        with gr.Column():
            with gr.Row():
                global lora_and_hypernet_models_state
                lora_and_hypernet_models_state = gr.State({})
                sd_model_on_cloud_dropdown = gr.Dropdown(choices=[], value=None_Option_For_On_Cloud_Model,
                                                         label='Stable Diffusion Checkpoint Used on Cloud')

                create_refresh_button_by_user(sd_model_on_cloud_dropdown,
                                              lambda *args: None,
                                              lambda username: {
                                                  'choices': load_model_list(username)
                                              }, 'refresh_cloud_model_down')

                infer_endpoint_dropdown = gr.Dropdown(choices=["Async", "Real-time"],
                                                         value="Async",
                                                         label='Inference Endpoint Type')
            with gr.Row():
                with gr.Column():
                    with gr.Row():
                        sd_vae_on_cloud_dropdown = gr.Dropdown(choices=[], value='Automatic',
                                                            label='SD Vae on Cloud')

                        create_refresh_button_by_user(sd_vae_on_cloud_dropdown,
                                                    lambda *args: None,
                                                    lambda username: {
                                                        'choices': load_vae_list(username)
                                                    }, 'refresh_cloud_vae_down')
                with gr.Column():
                    with gr.Row():
                        # Lora model
                        global lora_dropdown
                        lora_dropdown_local = gr.Dropdown(choices=[],
                                                        label="Lora model on cloud",
                                                        multiselect=True)
                        create_refresh_button_by_user(lora_dropdown_local,
                                                    lambda *args: None,
                                                    lambda username: {
                                                        'choices': load_lora_models(username)
                                                    }, 'refresh_lora_dropdown')
                        lora_dropdown = lora_dropdown_local

            with gr.Row():
                with gr.Column():
                    with gr.Row():
                        # Embedding model
                        global embedding_dropdown
                        embedding_dropdown_local = gr.Dropdown(choices=[],
                                                        label="Embedding on cloud",
                                                        multiselect=True)
                        create_refresh_button_by_user(embedding_dropdown_local,
                                                    lambda *args: None,
                                                    lambda username: {
                                                        'choices': load_embeddings_list(username)
                                                    }, 'refresh_embedding_dropdown')
                        embedding_dropdown = embedding_dropdown_local
                with gr.Column():
                    with gr.Row():
                        # Hypernetwork model
                        global hypernet_dropdown
                        hypernet_dropdown_local = gr.Dropdown(choices=[],
                                                        label="Hypernetwork on cloud",
                                                        multiselect=True)
                        create_refresh_button_by_user(hypernet_dropdown_local,
                                                    lambda *args: None,
                                                    lambda username: {
                                                        'choices': load_hypernetworks_models(username)
                                                    }, 'refresh_hypernet_dropdown')
                        hypernet_dropdown = hypernet_dropdown_local

            with gr.Row(visible=is_img2img):
                gr.HTML('<br/>')

            with gr.Row():
                global inference_job_dropdown

                inference_job_dropdown = gr.Dropdown(choices=[], value=None_Option_For_Infer_Job,
                                                     label="Inference Job Histories: Time-Type-Status-UUID")
                create_refresh_button_by_user(inference_job_dropdown,
                                              lambda *args: None,
                                              lambda username: {
                                                  'choices': load_inference_job_list(inference_task_type,
                                                                                     username, "first")
                                              }, 'refresh_inference_job_down')

                create_refresh_button_by_user(inference_job_dropdown,
                                              lambda *args: None,
                                              lambda username: {
                                                  'choices': load_inference_job_list(inference_task_type,
                                                                                     username, "previous")
                                              }, 'refresh_inference_job_down_previous', '←')

                create_refresh_button_by_user(inference_job_dropdown,
                                              lambda *args: None,
                                              lambda username: {
                                                  'choices': load_inference_job_list(inference_task_type,
                                                                                     username, "next")
                                              }, 'refresh_inference_job_down_next', '→')

                delete_inference_job_button = ToolButton(value='\U0001F5D1', elem_id="delete_inference_job")
                delete_inference_job_button.click(
                    _js="delete_inference_job_confirm",
                    fn=delete_inference_job,
                    inputs=[inference_job_dropdown],
                    outputs=[]
                )

                api_inference_job_button = ToolButton(value='API', elem_id="api_inference_job")
                api_inference_job_cwd = ToolButton(value=os.getcwd(), elem_id="api_inference_job_path", visible=False)
                api_inference_job_button.click(
                    _js="download_inference_job_api_call",
                    fn=None,
                    inputs=[api_inference_job_cwd, inference_job_dropdown],
                    outputs=[]
                )

            with gr.Row():

                def setup_inference_for_plugin(pr: gr.Request):
                    models_on_cloud = load_model_list(pr.username)
                    vae_model_on_cloud = load_vae_list(pr.username)
                    lora_models_on_cloud = load_lora_models(username=pr.username)
                    hypernetworks_models_on_cloud = load_hypernetworks_models(pr.username)
                    embedding_model_on_cloud = load_embeddings_list(pr.username)
                    controlnet_list = load_controlnet_list(pr.username)
                    controlnet_xyz_list = load_xyz_controlnet_list(pr.username)

                    inference_jobs = load_inference_job_list(inference_task_type, pr.username)
                    lora_hypernets = {
                        "lora": lora_models_on_cloud,
                        "hypernet": hypernetworks_models_on_cloud,
                        "controlnet": controlnet_list,
                        "controlnet_xyz": controlnet_xyz_list,
                        "vae": vae_model_on_cloud,
                        "sd": models_on_cloud,
                        "embedding": embedding_model_on_cloud
                    }

                    return lora_hypernets, \
                        gr.update(choices=models_on_cloud, value=models_on_cloud[0] if models_on_cloud and len(models_on_cloud) > 0 else None_Option_For_On_Cloud_Model), \
                        gr.update(choices=inference_jobs), \
                        gr.update(choices=vae_model_on_cloud), \
                        gr.update(choices=lora_models_on_cloud), \
                        gr.update(choices=hypernetworks_models_on_cloud), \
                        gr.update(choices=embedding_model_on_cloud)

                sagemaker_inference_tab.load(fn=setup_inference_for_plugin, inputs=[],
                                             outputs=[
                                                 lora_and_hypernet_models_state,
                                                 sd_model_on_cloud_dropdown,
                                                 inference_job_dropdown,
                                                 sd_vae_on_cloud_dropdown,
                                                 lora_dropdown_local,
                                                 hypernet_dropdown_local,
                                                 embedding_dropdown_local
                                             ])
    with gr.Group():
        with gr.Accordion("Open for Checkpoint Merge in the Cloud!", visible=False, open=False):
            sagemaker_html_log = gr.HTML(elem_id=f'html_log_sagemaker')
            with FormRow(elem_id="modelmerger_models_in_the_cloud"):
                global primary_model_name
                primary_model_name = gr.Dropdown(elem_id="modelmerger_primary_model_name_in_the_cloud",
                                                 label="Primary model (A) in the cloud")
                create_refresh_button_by_user(primary_model_name, lambda *args: None,
                                      lambda username: {"choices": sorted(update_sd_checkpoints(username))},
                                      "refresh_checkpoint_A_in_the_cloud")

                global secondary_model_name
                secondary_model_name = gr.Dropdown(elem_id="modelmerger_secondary_model_name_in_the_cloud",
                                                   label="Secondary model (B) in the cloud")
                create_refresh_button_by_user(secondary_model_name, lambda *args: None,
                                      lambda username: {"choices": sorted(update_sd_checkpoints(username))},
                                      "refresh_checkpoint_B_in_the_cloud")

                global tertiary_model_name
                tertiary_model_name = gr.Dropdown(elem_id="modelmerger_tertiary_model_name_in_the_cloud",
                                                  label="Tertiary model (C) in the cloud")
                create_refresh_button_by_user(tertiary_model_name, lambda *args: None,
                                      lambda username: {"choices": sorted(update_sd_checkpoints(username))},
                                      "refresh_checkpoint_C_in_the_cloud")
            with gr.Row():
                global modelmerger_merge_on_cloud
                modelmerger_merge_on_cloud = gr.Button(elem_id="modelmerger_merge_in_the_cloud", value="Merge on Cloud",
                                                       variant='primary')

    return sd_model_on_cloud_dropdown, infer_endpoint_dropdown, sd_vae_on_cloud_dropdown, inference_job_dropdown, primary_model_name, secondary_model_name, tertiary_model_name, modelmerger_merge_on_cloud, lora_and_hypernet_models_state