import random
from enum import Enum
from pathlib import Path
from typing import Optional, Literal, List

from loguru import logger
from pydantic import BaseModel, Field, field_validator

from iopaint.const import Device, InteractiveSegModel, RealESRGANModel


class PluginInfo(BaseModel):
    name: str
    support_gen_image: bool = False
    support_gen_mask: bool = False


class CV2Flag(str, Enum):
    INPAINT_NS = "INPAINT_NS"
    INPAINT_TELEA = "INPAINT_TELEA"


class ModelType(str, Enum):
    INPAINT = "inpaint"  # LaMa, MAT...
    DIFFUSERS_SD = "diffusers_sd"
    DIFFUSERS_SD_INPAINT = "diffusers_sd_inpaint"
    DIFFUSERS_SDXL = "diffusers_sdxl"
    DIFFUSERS_SDXL_INPAINT = "diffusers_sdxl_inpaint"
    DIFFUSERS_OTHER = "diffusers_other"


class HDStrategy(str, Enum):
    # Use original image size
    ORIGINAL = "Original"
    # Resize the longer side of the image to a specific size(hd_strategy_resize_limit),
    # then do inpainting on the resized image. Finally, resize the inpainting result to the original size.
    # The area outside the mask will not lose quality.
    RESIZE = "Resize"
    # Crop masking area(with a margin controlled by hd_strategy_crop_margin) from the original image to do inpainting
    CROP = "Crop"


class LDMSampler(str, Enum):
    ddim = "ddim"
    plms = "plms"


class SDSampler(str, Enum):
    dpm_plus_plus_2m = "DPM++ 2M"
    dpm_plus_plus_2m_karras = "DPM++ 2M Karras"
    dpm_plus_plus_2m_sde = "DPM++ 2M SDE"
    dpm_plus_plus_2m_sde_karras = "DPM++ 2M SDE Karras"
    dpm_plus_plus_sde = "DPM++ SDE"
    dpm_plus_plus_sde_karras = "DPM++ SDE Karras"
    dpm2 = "DPM2"
    dpm2_karras = "DPM2 Karras"
    dpm2_a = "DPM2 a"
    dpm2_a_karras = "DPM2 a Karras"
    euler = "Euler"
    euler_a = "Euler a"
    heun = "Heun"
    lms = "LMS"
    lms_karras = "LMS Karras"

    ddim = "DDIM"
    pndm = "PNDM"
    uni_pc = "UniPC"
    lcm = "LCM"


class FREEUConfig(BaseModel):
    s1: float = 0.9
    s2: float = 0.2
    b1: float = 1.2
    b2: float = 1.4


class PowerPaintTask(str, Enum):
    text_guided = "text-guided"
    shape_guided = "shape-guided"
    object_remove = "object-remove"
    outpainting = "outpainting"


class ApiConfig(BaseModel):
    host: str
    port: int
    model: str
    no_half: bool
    low_mem: bool
    cpu_offload: bool
    disable_nsfw_checker: bool
    local_files_only: bool
    cpu_textencoder: bool
    device: Device
    gui: bool
    disable_model_switch: bool
    input: Optional[Path]
    output_dir: Optional[Path]
    quality: int
    enable_interactive_seg: bool
    interactive_seg_model: InteractiveSegModel
    interactive_seg_device: Device
    enable_remove_bg: bool
    enable_anime_seg: bool
    enable_realesrgan: bool
    realesrgan_device: Device
    realesrgan_model: RealESRGANModel
    enable_gfpgan: bool
    gfpgan_device: Device
    enable_restoreformer: bool
    restoreformer_device: Device


class InpaintRequest(BaseModel):
    image: Optional[str] = Field(None, description="base64 encoded image")
    mask: Optional[str] = Field(None, description="base64 encoded mask")

    ldm_steps: int = Field(20, description="Steps for ldm model.")
    ldm_sampler: str = Field(LDMSampler.plms, discription="Sampler for ldm model.")
    zits_wireframe: bool = Field(True, description="Enable wireframe for zits model.")

    hd_strategy: str = Field(
        HDStrategy.CROP,
        description="Different way to preprocess image, only used by erase models(e.g. lama/mat)",
    )
    hd_strategy_crop_trigger_size: int = Field(
        800,
        description="Crop trigger size for hd_strategy=CROP, if the longer side of the image is larger than this value, use crop strategy",
    )
    hd_strategy_crop_margin: int = Field(
        128, description="Crop margin for hd_strategy=CROP"
    )
    hd_strategy_resize_limit: int = Field(
        1280, description="Resize limit for hd_strategy=RESIZE"
    )

    prompt: str = Field("", description="Prompt for diffusion models.")
    negative_prompt: str = Field(
        "", description="Negative prompt for diffusion models."
    )
    use_croper: bool = Field(
        False, description="Crop image before doing diffusion inpainting"
    )
    croper_x: int = Field(0, description="Crop x for croper")
    croper_y: int = Field(0, description="Crop y for croper")
    croper_height: int = Field(512, description="Crop height for croper")
    croper_width: int = Field(512, description="Crop width for croper")

    use_extender: bool = Field(
        False, description="Extend image before doing sd outpainting"
    )
    extender_x: int = Field(0, description="Extend x for extender")
    extender_y: int = Field(0, description="Extend y for extender")
    extender_height: int = Field(640, description="Extend height for extender")
    extender_width: int = Field(640, description="Extend width for extender")

    sd_scale: float = Field(
        1.0,
        description="Resize the image before doing sd inpainting, the area outside the mask will not lose quality.",
        gt=0.0,
        le=1.0,
    )
    sd_mask_blur: int = Field(
        11,
        description="Blur the edge of mask area. The higher the number the smoother blend with the original image",
    )
    sd_strength: float = Field(
        1.0,
        description="Strength is a measure of how much noise is added to the base image, which influences how similar the output is to the base image. Higher value means more noise and more different from the base image",
        le=1.0,
    )
    sd_steps: int = Field(
        50,
        description="The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference.",
    )
    sd_guidance_scale: float = Field(
        7.5,
        help="Higher guidance scale encourages to generate images that are closely linked to the text prompt, usually at the expense of lower image quality.",
    )
    sd_sampler: str = Field(
        SDSampler.uni_pc, description="Sampler for diffusion model."
    )
    sd_seed: int = Field(
        42,
        description="Seed for diffusion model. -1 mean random seed",
        validate_default=True,
    )
    sd_match_histograms: bool = Field(
        False,
        description="Match histograms between inpainting area and original image.",
    )

    sd_outpainting_softness: float = Field(20.0)
    sd_outpainting_space: float = Field(20.0)

    sd_freeu: bool = Field(
        False,
        description="Enable freeu mode. https://huggingface.co/docs/diffusers/main/en/using-diffusers/freeu",
    )
    sd_freeu_config: FREEUConfig = FREEUConfig()

    sd_lcm_lora: bool = Field(
        False,
        description="Enable lcm-lora mode. https://huggingface.co/docs/diffusers/main/en/using-diffusers/inference_with_lcm#texttoimage",
    )

    sd_keep_unmasked_area: bool = Field(
        True, description="Keep unmasked area unchanged"
    )

    cv2_flag: CV2Flag = Field(
        CV2Flag.INPAINT_NS,
        description="Flag for opencv inpainting: https://docs.opencv.org/4.6.0/d7/d8b/group__photo__inpaint.html#gga8002a65f5a3328fbf15df81b842d3c3ca05e763003a805e6c11c673a9f4ba7d07",
    )
    cv2_radius: int = Field(
        4,
        description="Radius of a circular neighborhood of each point inpainted that is considered by the algorithm",
    )

    # Paint by Example
    paint_by_example_example_image: Optional[str] = Field(
        None, description="Base64 encoded example image for paint by example model"
    )

    # InstructPix2Pix
    p2p_image_guidance_scale: float = Field(1.5, description="Image guidance scale")

    # ControlNet
    enable_controlnet: bool = Field(False, description="Enable controlnet")
    controlnet_conditioning_scale: float = Field(
        0.4, description="Conditioning scale", ge=0.0, le=1.0
    )
    controlnet_method: str = Field(
        "lllyasviel/control_v11p_sd15_canny", description="Controlnet method"
    )

    # PowerPaint
    powerpaint_task: PowerPaintTask = Field(
        PowerPaintTask.text_guided, description="PowerPaint task"
    )
    fitting_degree: float = Field(
        1.0,
        description="Control the fitting degree of the generated objects to the mask shape.",
        gt=0.0,
        le=1.0,
    )

    @field_validator("sd_seed")
    @classmethod
    def sd_seed_validator(cls, v: int) -> int:
        if v == -1:
            return random.randint(1, 99999999)
        return v

    @field_validator("controlnet_conditioning_scale")
    @classmethod
    def validate_field(cls, v: float, values):
        use_extender = values.data["use_extender"]
        enable_controlnet = values.data["enable_controlnet"]
        if use_extender and enable_controlnet:
            logger.info(f"Extender is enabled, set controlnet_conditioning_scale=0")
            return 0
        return v


class RunPluginRequest(BaseModel):
    name: str
    image: str = Field(..., description="base64 encoded image")
    clicks: List[List[int]] = Field(
        [], description="Clicks for interactive seg, [[x,y,0/1], [x2,y2,0/1]]"
    )
    scale: float = Field(2.0, description="Scale for upscaling")


MediaTab = Literal["input", "output"]


class MediasResponse(BaseModel):
    name: str
    height: int
    width: int
    ctime: float
    mtime: float


class GenInfoResponse(BaseModel):
    prompt: str = ""
    negative_prompt: str = ""


class ServerConfigResponse(BaseModel):
    plugins: List[PluginInfo]
    enableFileManager: bool
    enableAutoSaving: bool
    enableControlnet: bool
    controlnetMethod: Optional[str]
    disableModelSwitch: bool
    isDesktop: bool
    samplers: List[str]


class SwitchModelRequest(BaseModel):
    name: str


AdjustMaskOperate = Literal["expand", "shrink"]


class AdjustMaskRequest(BaseModel):
    mask: str = Field(
        ..., description="base64 encoded mask. 255 means area to do inpaint"
    )
    operate: AdjustMaskOperate = Field(..., description="expand or shrink")
    kernel_size: int = Field(5, description="Kernel size for expanding mask")