import os
import traceback
from typing import List, Optional

import pydash as _
import torch
from numpy import who

import internals.util.prompt as prompt_util
from internals.data.dataAccessor import update_db, update_db_source_failed
from internals.data.task import Task, TaskType
from internals.pipelines.commons import Img2Img, Text2Img
from internals.pipelines.controlnets import ControlNet
from internals.pipelines.high_res import HighRes
from internals.pipelines.img_classifier import ImageClassifier
from internals.pipelines.img_to_text import Image2Text
from internals.pipelines.inpainter import InPainter
from internals.pipelines.pose_detector import PoseDetector
from internals.pipelines.prompt_modifier import PromptModifier
from internals.pipelines.replace_background import ReplaceBackground
from internals.pipelines.safety_checker import SafetyChecker
from internals.util.args import apply_style_args
from internals.util.avatar import Avatar
from internals.util.cache import auto_clear_cuda_and_gc, clear_cuda, clear_cuda_and_gc
from internals.util.commons import download_image, upload_image, upload_images
from internals.util.config import (
    get_is_sdxl,
    get_model_dir,
    num_return_sequences,
    set_configs_from_task,
    set_model_config,
    set_root_dir,
)
from internals.util.failure_hander import FailureHandler
from internals.util.lora_style import LoraStyle
from internals.util.model_loader import load_model_from_config
from internals.util.slack import Slack

torch.backends.cudnn.benchmark = True
torch.backends.cuda.matmul.allow_tf32 = True

auto_mode = False

prompt_modifier = PromptModifier(num_of_sequences=num_return_sequences)
pose_detector = PoseDetector()
inpainter = InPainter()
high_res = HighRes()
img2text = Image2Text()
img_classifier = ImageClassifier()
replace_background = ReplaceBackground()
controlnet = ControlNet()
lora_style = LoraStyle()
text2img_pipe = Text2Img()
img2img_pipe = Img2Img()
safety_checker = SafetyChecker()
slack = Slack()
avatar = Avatar()

custom_scripts: List = []


def get_patched_prompt(task: Task):
    return prompt_util.get_patched_prompt(task, avatar, lora_style, prompt_modifier)


def get_patched_prompt_text2img(task: Task):
    return prompt_util.get_patched_prompt_text2img(
        task, avatar, lora_style, prompt_modifier
    )


def get_patched_prompt_tile_upscale(task: Task):
    return prompt_util.get_patched_prompt_tile_upscale(
        task, avatar, lora_style, img_classifier, img2text
    )


def get_intermediate_dimension(task: Task):
    if task.get_high_res_fix():
        return HighRes.get_intermediate_dimension(task.get_width(), task.get_height())
    else:
        return task.get_width(), task.get_height()


@update_db
@auto_clear_cuda_and_gc(controlnet)
@slack.auto_send_alert
def canny(task: Task):
    prompt, _ = get_patched_prompt(task)

    width, height = get_intermediate_dimension(task)

    controlnet.load_model("canny")

    # pipe2 is used for canny and pose
    lora_patcher = lora_style.get_patcher(
        [controlnet.pipe2, high_res.pipe], task.get_style()
    )
    lora_patcher.patch()

    kwargs = {
        "prompt": prompt,
        "imageUrl": task.get_imageUrl(),
        "seed": task.get_seed(),
        "num_inference_steps": task.get_steps(),
        "width": width,
        "height": height,
        "negative_prompt": [
            f"monochrome, neon, x-ray, negative image, oversaturated, {task.get_negative_prompt()}"
        ]
        * num_return_sequences,
        **task.cnc_kwargs(),
        **lora_patcher.kwargs(),
    }
    images, has_nsfw = controlnet.process(**kwargs)
    if task.get_high_res_fix():
        kwargs = {
            "prompt": prompt,
            "negative_prompt": [task.get_negative_prompt()] * num_return_sequences,
            "images": images,
            "width": task.get_width(),
            "height": task.get_height(),
            "num_inference_steps": task.get_steps(),
            **task.high_res_kwargs(),
        }
        images, _ = high_res.apply(**kwargs)

    generated_image_urls = upload_images(images, "_canny", task.get_taskId())

    lora_patcher.cleanup()
    controlnet.cleanup()

    return {
        "modified_prompts": prompt,
        "generated_image_urls": generated_image_urls,
        "has_nsfw": has_nsfw,
    }


@update_db
@auto_clear_cuda_and_gc(controlnet)
@slack.auto_send_alert
def tile_upscale(task: Task):
    output_key = "crecoAI/{}_tile_upscaler.png".format(task.get_taskId())

    prompt = get_patched_prompt_tile_upscale(task)

    controlnet.load_model("tile_upscaler")

    lora_patcher = lora_style.get_patcher(controlnet.pipe, task.get_style())
    lora_patcher.patch()

    kwargs = {
        "imageUrl": task.get_imageUrl(),
        "seed": task.get_seed(),
        "num_inference_steps": task.get_steps(),
        "negative_prompt": task.get_negative_prompt(),
        "width": task.get_width(),
        "height": task.get_height(),
        "prompt": prompt,
        "resize_dimension": task.get_resize_dimension(),
        **task.cnt_kwargs(),
    }
    images, has_nsfw = controlnet.process(**kwargs)

    generated_image_url = upload_image(images[0], output_key)

    lora_patcher.cleanup()
    controlnet.cleanup()

    return {
        "modified_prompts": prompt,
        "generated_image_url": generated_image_url,
        "has_nsfw": has_nsfw,
    }


@update_db
@auto_clear_cuda_and_gc(controlnet)
@slack.auto_send_alert
def scribble(task: Task):
    prompt, _ = get_patched_prompt(task)

    width, height = get_intermediate_dimension(task)

    controlnet.load_model("scribble")

    lora_patcher = lora_style.get_patcher(
        [controlnet.pipe2, high_res.pipe], task.get_style()
    )
    lora_patcher.patch()

    image = download_image(task.get_imageUrl()).resize((width, height))
    if get_is_sdxl():
        # We use sketch in SDXL
        image = ControlNet.pidinet_image(image)
    else:
        image = ControlNet.scribble_image(image)

    kwargs = {
        "image": [image] * num_return_sequences,
        "seed": task.get_seed(),
        "num_inference_steps": task.get_steps(),
        "width": width,
        "height": height,
        "prompt": prompt,
        "negative_prompt": [task.get_negative_prompt()] * num_return_sequences,
        **task.cns_kwargs(),
    }
    images, has_nsfw = controlnet.process(**kwargs)

    if task.get_high_res_fix():
        kwargs = {
            "prompt": prompt,
            "negative_prompt": [task.get_negative_prompt()] * num_return_sequences,
            "images": images,
            "width": task.get_width(),
            "height": task.get_height(),
            "num_inference_steps": task.get_steps(),
            **task.high_res_kwargs(),
        }
        images, _ = high_res.apply(**kwargs)

    generated_image_urls = upload_images(images, "_scribble", task.get_taskId())

    lora_patcher.cleanup()
    controlnet.cleanup()

    return {
        "modified_prompts": prompt,
        "generated_image_urls": generated_image_urls,
        "has_nsfw": has_nsfw,
    }


@update_db
@auto_clear_cuda_and_gc(controlnet)
@slack.auto_send_alert
def linearart(task: Task):
    prompt, _ = get_patched_prompt(task)

    width, height = get_intermediate_dimension(task)

    controlnet.load_model("linearart")

    lora_patcher = lora_style.get_patcher(
        [controlnet.pipe2, high_res.pipe], task.get_style()
    )
    lora_patcher.patch()

    kwargs = {
        "imageUrl": task.get_imageUrl(),
        "seed": task.get_seed(),
        "num_inference_steps": task.get_steps(),
        "width": width,
        "height": height,
        "prompt": prompt,
        "negative_prompt": [task.get_negative_prompt()] * num_return_sequences,
        **task.cnl_kwargs(),
    }
    images, has_nsfw = controlnet.process(**kwargs)

    if task.get_high_res_fix():
        kwargs = {
            "prompt": prompt,
            "negative_prompt": [task.get_negative_prompt()] * num_return_sequences,
            "images": images,
            "width": task.get_width(),
            "height": task.get_height(),
            "num_inference_steps": task.get_steps(),
            **task.high_res_kwargs(),
        }
        images, _ = high_res.apply(**kwargs)

    generated_image_urls = upload_images(images, "_linearart", task.get_taskId())

    lora_patcher.cleanup()
    controlnet.cleanup()

    return {
        "modified_prompts": prompt,
        "generated_image_urls": generated_image_urls,
        "has_nsfw": has_nsfw,
    }


@update_db
@auto_clear_cuda_and_gc(controlnet)
@slack.auto_send_alert
def pose(task: Task, s3_outkey: str = "_pose", poses: Optional[list] = None):
    prompt, _ = get_patched_prompt(task)

    width, height = get_intermediate_dimension(task)

    controlnet.load_model("pose")

    # pipe2 is used for canny and pose
    lora_patcher = lora_style.get_patcher(
        [controlnet.pipe2, high_res.pipe], task.get_style()
    )
    lora_patcher.patch()

    if not task.get_pose_estimation():
        print("Not detecting pose")
        pose = download_image(task.get_imageUrl()).resize(
            (task.get_width(), task.get_height())
        )
        poses = [pose] * num_return_sequences
    elif task.get_pose_coordinates():
        infered_pose = pose_detector.transform(
            image=task.get_imageUrl(),
            client_coordinates=task.get_pose_coordinates(),
            width=task.get_width(),
            height=task.get_height(),
        )
        poses = [infered_pose] * num_return_sequences
    else:
        poses = [controlnet.detect_pose(task.get_imageUrl())] * num_return_sequences

    if not get_is_sdxl():
        # in normal pipeline we use depth + pose controlnet
        depth = download_image(task.get_auxilary_imageUrl()).resize(
            (task.get_width(), task.get_height())
        )
        depth = ControlNet.depth_image(depth)
        images = [depth, poses[0]]

        upload_image(depth, "crecoAI/{}_depth.png".format(task.get_taskId()))

        kwargs = {
            "control_guidance_end": [0.5, 1.0],
        }
    else:
        images = poses[0]
        kwargs = {}

    kwargs = {
        "prompt": prompt,
        "image": images,
        "seed": task.get_seed(),
        "num_inference_steps": task.get_steps(),
        "negative_prompt": [task.get_negative_prompt()] * num_return_sequences,
        "width": width,
        "height": height,
        **kwargs,
        **task.cnp_kwargs(),
        **lora_patcher.kwargs(),
    }
    images, has_nsfw = controlnet.process(**kwargs)

    if task.get_high_res_fix():
        kwargs = {
            "prompt": prompt,
            "negative_prompt": [task.get_negative_prompt()] * num_return_sequences,
            "images": images,
            "width": task.get_width(),
            "height": task.get_height(),
            "num_inference_steps": task.get_steps(),
            **task.high_res_kwargs(),
        }
        images, _ = high_res.apply(**kwargs)

    upload_image(poses[0], "crecoAI/{}_pose.png".format(task.get_taskId()))

    generated_image_urls = upload_images(images, s3_outkey, task.get_taskId())

    lora_patcher.cleanup()
    controlnet.cleanup()

    return {
        "modified_prompts": prompt,
        "generated_image_urls": generated_image_urls,
        "has_nsfw": has_nsfw,
    }


@update_db
@auto_clear_cuda_and_gc(controlnet)
@slack.auto_send_alert
def text2img(task: Task):
    params = get_patched_prompt_text2img(task)

    width, height = get_intermediate_dimension(task)

    lora_patcher = lora_style.get_patcher(
        [text2img_pipe.pipe, high_res.pipe], task.get_style()
    )
    lora_patcher.patch()

    torch.manual_seed(task.get_seed())

    kwargs = {
        "params": params,
        "num_inference_steps": task.get_steps(),
        "height": height,
        "width": width,
        "negative_prompt": task.get_negative_prompt(),
        **task.t2i_kwargs(),
        **lora_patcher.kwargs(),
    }
    images, has_nsfw = text2img_pipe.process(**kwargs)

    if task.get_high_res_fix():
        kwargs = {
            "prompt": params.prompt if params.prompt else [""] * num_return_sequences,
            "negative_prompt": [task.get_negative_prompt()] * num_return_sequences,
            "images": images,
            "width": task.get_width(),
            "height": task.get_height(),
            "num_inference_steps": task.get_steps(),
            **task.high_res_kwargs(),
        }
        images, _ = high_res.apply(**kwargs)

    generated_image_urls = upload_images(images, "", task.get_taskId())

    lora_patcher.cleanup()

    return {
        **params.__dict__,
        "generated_image_urls": generated_image_urls,
        "has_nsfw": has_nsfw,
    }


@update_db
@auto_clear_cuda_and_gc(controlnet)
@slack.auto_send_alert
def img2img(task: Task):
    prompt, _ = get_patched_prompt(task)

    width, height = get_intermediate_dimension(task)

    lora_patcher = lora_style.get_patcher(
        [img2img_pipe.pipe, high_res.pipe], task.get_style()
    )
    lora_patcher.patch()

    torch.manual_seed(task.get_seed())

    kwargs = {
        "prompt": prompt,
        "imageUrl": task.get_imageUrl(),
        "negative_prompt": [task.get_negative_prompt()] * num_return_sequences,
        "num_inference_steps": task.get_steps(),
        "width": width,
        "height": height,
        **task.i2i_kwargs(),
        **lora_patcher.kwargs(),
    }
    images, has_nsfw = img2img_pipe.process(**kwargs)

    if task.get_high_res_fix():
        kwargs = {
            "prompt": prompt,
            "negative_prompt": [task.get_negative_prompt()] * num_return_sequences,
            "images": images,
            "width": task.get_width(),
            "height": task.get_height(),
            "num_inference_steps": task.get_steps(),
            **task.high_res_kwargs(),
        }
        images, _ = high_res.apply(**kwargs)

    generated_image_urls = upload_images(images, "_imgtoimg", task.get_taskId())

    lora_patcher.cleanup()

    return {
        "modified_prompts": prompt,
        "generated_image_urls": generated_image_urls,
        "has_nsfw": has_nsfw,
    }


@update_db
@slack.auto_send_alert
def inpaint(task: Task):
    prompt, _ = get_patched_prompt(task)

    print({"prompts": prompt})

    kwargs = {
        "prompt": prompt,
        "image_url": task.get_imageUrl(),
        "mask_image_url": task.get_maskImageUrl(),
        "width": task.get_width(),
        "height": task.get_height(),
        "seed": task.get_seed(),
        "negative_prompt": [task.get_negative_prompt()] * num_return_sequences,
        "num_inference_steps": task.get_steps(),
        **task.ip_kwargs(),
    }
    images = inpainter.process(**kwargs)

    generated_image_urls = upload_images(images, "_inpaint", task.get_taskId())

    clear_cuda_and_gc()

    return {"modified_prompts": prompt, "generated_image_urls": generated_image_urls}


@update_db
@slack.auto_send_alert
def replace_bg(task: Task):
    prompt = task.get_prompt()
    if task.is_prompt_engineering():
        prompt = prompt_modifier.modify(prompt)
    else:
        prompt = [prompt] * num_return_sequences

    lora_patcher = lora_style.get_patcher(replace_background.pipe, task.get_style())
    lora_patcher.patch()

    images, has_nsfw = replace_background.replace(
        image=task.get_imageUrl(),
        prompt=prompt,
        negative_prompt=[task.get_negative_prompt()] * num_return_sequences,
        seed=task.get_seed(),
        width=task.get_width(),
        height=task.get_height(),
        steps=task.get_steps(),
        apply_high_res=task.get_high_res_fix(),
        conditioning_scale=task.rbg_controlnet_conditioning_scale(),
        model_type=task.get_modelType(),
    )

    generated_image_urls = upload_images(images, "_replace_bg", task.get_taskId())

    lora_patcher.cleanup()
    clear_cuda_and_gc()

    return {
        "modified_prompts": prompt,
        "generated_image_urls": generated_image_urls,
        "has_nsfw": has_nsfw,
    }


def custom_action(task: Task):
    from external.scripts import __scripts__

    global custom_scripts
    kwargs = {
        "CONTROLNET": controlnet,
        "LORASTYLE": lora_style,
    }

    torch.manual_seed(task.get_seed())

    for script in __scripts__:
        script = script.Script(**kwargs)
        existing_script = _.find(
            custom_scripts, lambda x: x.__name__ == script.__name__
        )
        if existing_script:
            script = existing_script
        else:
            custom_scripts.append(script)

        data = task.get_action_data()
        if data["name"] == script.__name__:
            return script(task, data)


def load_model_by_task(task: Task):
    if not text2img_pipe.is_loaded():
        text2img_pipe.load(get_model_dir())
        img2img_pipe.create(text2img_pipe)
        high_res.load(img2img_pipe)

        inpainter.init(text2img_pipe)
        controlnet.init(text2img_pipe)

        safety_checker.apply(text2img_pipe)
        safety_checker.apply(img2img_pipe)

    if task.get_type() == TaskType.INPAINT:
        inpainter.load()
        safety_checker.apply(inpainter)
    elif task.get_type() == TaskType.REPLACE_BG:
        replace_background.load(base=text2img_pipe, high_res=high_res)
    else:
        if task.get_type() == TaskType.TILE_UPSCALE:
            controlnet.load_model("tile_upscaler")
        elif task.get_type() == TaskType.CANNY:
            controlnet.load_model("canny")
        elif task.get_type() == TaskType.SCRIBBLE:
            controlnet.load_model("scribble")
        elif task.get_type() == TaskType.LINEARART:
            controlnet.load_model("linearart")
        elif task.get_type() == TaskType.POSE:
            controlnet.load_model("pose")

        safety_checker.apply(controlnet)


def model_fn(model_dir):
    print("Logs: model loaded .... starts")

    config = load_model_from_config(model_dir)
    set_model_config(config)
    set_root_dir(__file__)

    FailureHandler.register()

    avatar.load_local(model_dir)

    lora_style.load(model_dir)

    print("Logs: model loaded ....")
    return


@FailureHandler.clear
def predict_fn(data, pipe):
    task = Task(data)
    print("task is ", data)

    clear_cuda_and_gc()

    FailureHandler.handle(task)

    try:
        # Set set_environment
        set_configs_from_task(task)

        # Load model based on task
        load_model_by_task(task)

        # Apply arguments
        apply_style_args(data)

        # Re-fetch styles
        lora_style.fetch_styles()

        # Fetch avatars
        avatar.fetch_from_network(task.get_model_id())

        task_type = task.get_type()

        if task_type == TaskType.TEXT_TO_IMAGE:
            # character sheet
            # if "character sheet" in task.get_prompt().lower():
            #     return pose(task, s3_outkey="", poses=pickPoses())
            # else:
            return text2img(task)
        elif task_type == TaskType.IMAGE_TO_IMAGE:
            return img2img(task)
        elif task_type == TaskType.CANNY:
            return canny(task)
        elif task_type == TaskType.POSE:
            return pose(task)
        elif task_type == TaskType.TILE_UPSCALE:
            return tile_upscale(task)
        elif task_type == TaskType.INPAINT:
            return inpaint(task)
        elif task_type == TaskType.SCRIBBLE:
            return scribble(task)
        elif task_type == TaskType.LINEARART:
            return linearart(task)
        elif task_type == TaskType.REPLACE_BG:
            return replace_bg(task)
        elif task_type == TaskType.CUSTOM_ACTION:
            return custom_action(task)
        elif task_type == TaskType.SYSTEM_CMD:
            os.system(task.get_prompt())
        else:
            raise Exception("Invalid task type")
    except Exception as e:
        slack.error_alert(task, e)
        controlnet.cleanup()
        traceback.print_exc()
        update_db_source_failed(task.get_sourceId(), task.get_userId())
        return None