# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)

from typing import Literal, Optional

import numpy
from PIL import Image, ImageFilter, ImageOps, ImageChops
from pydantic import BaseModel, Field
from pathlib import Path
from typing import Union
from invokeai.app.invocations.metadata import CoreMetadata
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from transformers import AutoFeatureExtractor
from ..models.image import ImageCategory, ImageField, ResourceOrigin
from .baseinvocation import (
    BaseInvocation,
    BaseInvocationOutput,
    InvocationContext,
    InvocationConfig,
)
from .image_defs import (
    PILInvocationConfig,
    ImageOutput,
    MaskOutput,
    )
from ..services.config import InvokeAIAppConfig
from invokeai.backend.util.devices import choose_torch_device
from invokeai.backend import SilenceWarnings

class LoadImageInvocation(BaseInvocation):
    """Load an image and provide it as output."""

    # fmt: off
    type: Literal["load_image"] = "load_image"

    # Inputs
    image: Optional[ImageField] = Field(
        default=None, description="The image to load"
    )
    # fmt: on

    class Config(InvocationConfig):
        schema_extra = {
            "ui": {
                "title": "Load Image",
                "tags": ["image", "load"]
            },
        }

    def invoke(self, context: InvocationContext) -> ImageOutput:
        image = context.services.images.get_pil_image(self.image.image_name)

        return ImageOutput(
            image=ImageField(image_name=self.image.image_name),
            width=image.width,
            height=image.height,
        )


class ShowImageInvocation(BaseInvocation):
    """Displays a provided image, and passes it forward in the pipeline."""

    type: Literal["show_image"] = "show_image"

    # Inputs
    image: Optional[ImageField] = Field(
        default=None, description="The image to show"
    )

    class Config(InvocationConfig):
        schema_extra = {
            "ui": {
                "title": "Show Image",
                "tags": ["image", "show"]
            },
        }

    def invoke(self, context: InvocationContext) -> ImageOutput:
        image = context.services.images.get_pil_image(self.image.image_name)
        if image:
            image.show()

        # TODO: how to handle failure?

        return ImageOutput(
            image=ImageField(image_name=self.image.image_name),
            width=image.width,
            height=image.height,
        )


class ImageCropInvocation(BaseInvocation, PILInvocationConfig):
    """Crops an image to a specified box. The box can be outside of the image."""

    # fmt: off
    type: Literal["img_crop"] = "img_crop"

    # Inputs
    image: Optional[ImageField]  = Field(default=None, description="The image to crop")
    x:      int = Field(default=0, description="The left x coordinate of the crop rectangle")
    y:      int = Field(default=0, description="The top y coordinate of the crop rectangle")
    width:  int = Field(default=512, gt=0, description="The width of the crop rectangle")
    height: int = Field(default=512, gt=0, description="The height of the crop rectangle")
    # fmt: on

    class Config(InvocationConfig):
        schema_extra = {
            "ui": {
                "title": "Crop Image",
                "tags": ["image", "crop"]
            },
        }

    def invoke(self, context: InvocationContext) -> ImageOutput:
        image = context.services.images.get_pil_image(self.image.image_name)

        image_crop = Image.new(
            mode="RGBA", size=(self.width, self.height), color=(0, 0, 0, 0)
        )
        image_crop.paste(image, (-self.x, -self.y))

        image_dto = context.services.images.create(
            image=image_crop,
            image_origin=ResourceOrigin.INTERNAL,
            image_category=ImageCategory.GENERAL,
            node_id=self.id,
            session_id=context.graph_execution_state_id,
            is_intermediate=self.is_intermediate,
        )

        return ImageOutput(
            image=ImageField(image_name=image_dto.image_name),
            width=image_dto.width,
            height=image_dto.height,
        )


class ImagePasteInvocation(BaseInvocation, PILInvocationConfig):
    """Pastes an image into another image."""

    # fmt: off
    type: Literal["img_paste"] = "img_paste"

    # Inputs
    base_image:     Optional[ImageField]  = Field(default=None, description="The base image")
    image:          Optional[ImageField]  = Field(default=None, description="The image to paste")
    mask: Optional[ImageField] = Field(default=None, description="The mask to use when pasting")
    x:                     int = Field(default=0, description="The left x coordinate at which to paste the image")
    y:                     int = Field(default=0, description="The top y coordinate at which to paste the image")
    # fmt: on

    class Config(InvocationConfig):
        schema_extra = {
            "ui": {
                "title": "Paste Image",
                "tags": ["image", "paste"]
            },
        }

    def invoke(self, context: InvocationContext) -> ImageOutput:
        base_image = context.services.images.get_pil_image(self.base_image.image_name)
        image = context.services.images.get_pil_image(self.image.image_name)
        mask = (
            None
            if self.mask is None
            else ImageOps.invert(
                context.services.images.get_pil_image(self.mask.image_name)
            )
        )
        # TODO: probably shouldn't invert mask here... should user be required to do it?

        min_x = min(0, self.x)
        min_y = min(0, self.y)
        max_x = max(base_image.width, image.width + self.x)
        max_y = max(base_image.height, image.height + self.y)

        new_image = Image.new(
            mode="RGBA", size=(max_x - min_x, max_y - min_y), color=(0, 0, 0, 0)
        )
        new_image.paste(base_image, (abs(min_x), abs(min_y)))
        new_image.paste(image, (max(0, self.x), max(0, self.y)), mask=mask)

        image_dto = context.services.images.create(
            image=new_image,
            image_origin=ResourceOrigin.INTERNAL,
            image_category=ImageCategory.GENERAL,
            node_id=self.id,
            session_id=context.graph_execution_state_id,
            is_intermediate=self.is_intermediate,
        )

        return ImageOutput(
            image=ImageField(image_name=image_dto.image_name),
            width=image_dto.width,
            height=image_dto.height,
        )


class MaskFromAlphaInvocation(BaseInvocation, PILInvocationConfig):
    """Extracts the alpha channel of an image as a mask."""

    # fmt: off
    type: Literal["tomask"] = "tomask"

    # Inputs
    image: Optional[ImageField]  = Field(default=None, description="The image to create the mask from")
    invert:      bool = Field(default=False, description="Whether or not to invert the mask")
    # fmt: on

    class Config(InvocationConfig):
        schema_extra = {
            "ui": {
                "title": "Mask From Alpha",
                "tags": ["image", "mask", "alpha"]
            },
        }

    def invoke(self, context: InvocationContext) -> MaskOutput:
        image = context.services.images.get_pil_image(self.image.image_name)

        image_mask = image.split()[-1]
        if self.invert:
            image_mask = ImageOps.invert(image_mask)

        image_dto = context.services.images.create(
            image=image_mask,
            image_origin=ResourceOrigin.INTERNAL,
            image_category=ImageCategory.MASK,
            node_id=self.id,
            session_id=context.graph_execution_state_id,
            is_intermediate=self.is_intermediate,
        )

        return MaskOutput(
            mask=ImageField(image_name=image_dto.image_name),
            width=image_dto.width,
            height=image_dto.height,
        )


class ImageMultiplyInvocation(BaseInvocation, PILInvocationConfig):
    """Multiplies two images together using `PIL.ImageChops.multiply()`."""

    # fmt: off
    type: Literal["img_mul"] = "img_mul"

    # Inputs
    image1: Optional[ImageField]  = Field(default=None, description="The first image to multiply")
    image2: Optional[ImageField]  = Field(default=None, description="The second image to multiply")
    # fmt: on

    class Config(InvocationConfig):
        schema_extra = {
            "ui": {
                "title": "Multiply Images",
                "tags": ["image", "multiply"]
            },
        }

    def invoke(self, context: InvocationContext) -> ImageOutput:
        image1 = context.services.images.get_pil_image(self.image1.image_name)
        image2 = context.services.images.get_pil_image(self.image2.image_name)

        multiply_image = ImageChops.multiply(image1, image2)

        image_dto = context.services.images.create(
            image=multiply_image,
            image_origin=ResourceOrigin.INTERNAL,
            image_category=ImageCategory.GENERAL,
            node_id=self.id,
            session_id=context.graph_execution_state_id,
            is_intermediate=self.is_intermediate,
        )

        return ImageOutput(
            image=ImageField(image_name=image_dto.image_name),
            width=image_dto.width,
            height=image_dto.height,
        )


IMAGE_CHANNELS = Literal["A", "R", "G", "B"]


class ImageChannelInvocation(BaseInvocation, PILInvocationConfig):
    """Gets a channel from an image."""

    # fmt: off
    type: Literal["img_chan"] = "img_chan"

    # Inputs
    image: Optional[ImageField]  = Field(default=None, description="The image to get the channel from")
    channel: IMAGE_CHANNELS  = Field(default="A", description="The channel to get")
    # fmt: on

    class Config(InvocationConfig):
        schema_extra = {
            "ui": {
                "title": "Image Channel",
                "tags": ["image", "channel"]
            },
        }

    def invoke(self, context: InvocationContext) -> ImageOutput:
        image = context.services.images.get_pil_image(self.image.image_name)

        channel_image = image.getchannel(self.channel)

        image_dto = context.services.images.create(
            image=channel_image,
            image_origin=ResourceOrigin.INTERNAL,
            image_category=ImageCategory.GENERAL,
            node_id=self.id,
            session_id=context.graph_execution_state_id,
            is_intermediate=self.is_intermediate,
        )

        return ImageOutput(
            image=ImageField(image_name=image_dto.image_name),
            width=image_dto.width,
            height=image_dto.height,
        )


IMAGE_MODES = Literal["L", "RGB", "RGBA", "CMYK", "YCbCr", "LAB", "HSV", "I", "F"]


class ImageConvertInvocation(BaseInvocation, PILInvocationConfig):
    """Converts an image to a different mode."""

    # fmt: off
    type: Literal["img_conv"] = "img_conv"

    # Inputs
    image: Optional[ImageField]  = Field(default=None, description="The image to convert")
    mode: IMAGE_MODES  = Field(default="L", description="The mode to convert to")
    # fmt: on

    class Config(InvocationConfig):
        schema_extra = {
            "ui": {
                "title": "Convert Image",
                "tags": ["image", "convert"]
            },
        }

    def invoke(self, context: InvocationContext) -> ImageOutput:
        image = context.services.images.get_pil_image(self.image.image_name)

        converted_image = image.convert(self.mode)

        image_dto = context.services.images.create(
            image=converted_image,
            image_origin=ResourceOrigin.INTERNAL,
            image_category=ImageCategory.GENERAL,
            node_id=self.id,
            session_id=context.graph_execution_state_id,
            is_intermediate=self.is_intermediate,
        )

        return ImageOutput(
            image=ImageField(image_name=image_dto.image_name),
            width=image_dto.width,
            height=image_dto.height,
        )

class ImageBlurInvocation(BaseInvocation, PILInvocationConfig):
    """Blurs an image"""

    # fmt: off
    type: Literal["img_blur"] = "img_blur"

    # Inputs
    image: Optional[ImageField]  = Field(default=None, description="The image to blur")
    radius:     float = Field(default=8.0, ge=0, description="The blur radius")
    blur_type: Literal["gaussian", "box"] = Field(default="gaussian", description="The type of blur")
    # fmt: on

    class Config(InvocationConfig):
        schema_extra = {
            "ui": {
                "title": "Blur Image",
                "tags": ["image", "blur"]
            },
        }

    def invoke(self, context: InvocationContext) -> ImageOutput:
        image = context.services.images.get_pil_image(self.image.image_name)

        blur = (
            ImageFilter.GaussianBlur(self.radius)
            if self.blur_type == "gaussian"
            else ImageFilter.BoxBlur(self.radius)
        )
        blur_image = image.filter(blur)

        image_dto = context.services.images.create(
            image=blur_image,
            image_origin=ResourceOrigin.INTERNAL,
            image_category=ImageCategory.GENERAL,
            node_id=self.id,
            session_id=context.graph_execution_state_id,
            is_intermediate=self.is_intermediate,
        )

        return ImageOutput(
            image=ImageField(image_name=image_dto.image_name),
            width=image_dto.width,
            height=image_dto.height,
        )


PIL_RESAMPLING_MODES = Literal[
    "nearest",
    "box",
    "bilinear",
    "hamming",
    "bicubic",
    "lanczos",
]


PIL_RESAMPLING_MAP = {
    "nearest": Image.Resampling.NEAREST,
    "box": Image.Resampling.BOX,
    "bilinear": Image.Resampling.BILINEAR,
    "hamming": Image.Resampling.HAMMING,
    "bicubic": Image.Resampling.BICUBIC,
    "lanczos": Image.Resampling.LANCZOS,
}


class ImageResizeInvocation(BaseInvocation, PILInvocationConfig):
    """Resizes an image to specific dimensions"""

    # fmt: off
    type: Literal["img_resize"] = "img_resize"

    # Inputs
    image: Optional[ImageField]  = Field(default=None, description="The image to resize")
    width:                         Union[int, None] = Field(ge=64, multiple_of=8, description="The width to resize to (px)")
    height:                        Union[int, None] = Field(ge=64, multiple_of=8, description="The height to resize to (px)")
    resample_mode:  PIL_RESAMPLING_MODES = Field(default="bicubic", description="The resampling mode")
    # fmt: on

    class Config(InvocationConfig):
        schema_extra = {
            "ui": {
                "title": "Resize Image",
                "tags": ["image", "resize"]
            },
        }

    def invoke(self, context: InvocationContext) -> ImageOutput:
        image = context.services.images.get_pil_image(self.image.image_name)

        resample_mode = PIL_RESAMPLING_MAP[self.resample_mode]

        resize_image = image.resize(
            (self.width, self.height),
            resample=resample_mode,
        )

        image_dto = context.services.images.create(
            image=resize_image,
            image_origin=ResourceOrigin.INTERNAL,
            image_category=ImageCategory.GENERAL,
            node_id=self.id,
            session_id=context.graph_execution_state_id,
            is_intermediate=self.is_intermediate,
        )

        return ImageOutput(
            image=ImageField(image_name=image_dto.image_name),
            width=image_dto.width,
            height=image_dto.height,
        )


class ImageScaleInvocation(BaseInvocation, PILInvocationConfig):
    """Scales an image by a factor"""

    # fmt: off
    type: Literal["img_scale"] = "img_scale"

    # Inputs
    image:          Optional[ImageField] = Field(default=None, description="The image to scale")
    scale_factor:        Optional[float] = Field(default=2.0, gt=0, description="The factor by which to scale the image")
    resample_mode:  PIL_RESAMPLING_MODES = Field(default="bicubic", description="The resampling mode")
    # fmt: on

    class Config(InvocationConfig):
        schema_extra = {
            "ui": {
                "title": "Scale Image",
                "tags": ["image", "scale"]
            },
        }

    def invoke(self, context: InvocationContext) -> ImageOutput:
        image = context.services.images.get_pil_image(self.image.image_name)

        resample_mode = PIL_RESAMPLING_MAP[self.resample_mode]
        width = int(image.width * self.scale_factor)
        height = int(image.height * self.scale_factor)

        resize_image = image.resize(
            (width, height),
            resample=resample_mode,
        )

        image_dto = context.services.images.create(
            image=resize_image,
            image_origin=ResourceOrigin.INTERNAL,
            image_category=ImageCategory.GENERAL,
            node_id=self.id,
            session_id=context.graph_execution_state_id,
            is_intermediate=self.is_intermediate,
        )

        return ImageOutput(
            image=ImageField(image_name=image_dto.image_name),
            width=image_dto.width,
            height=image_dto.height,
        )


class ImageLerpInvocation(BaseInvocation, PILInvocationConfig):
    """Linear interpolation of all pixels of an image"""

    # fmt: off
    type: Literal["img_lerp"] = "img_lerp"

    # Inputs
    image: Optional[ImageField]  = Field(default=None, description="The image to lerp")
    min: int = Field(default=0, ge=0, le=255, description="The minimum output value")
    max: int = Field(default=255, ge=0, le=255, description="The maximum output value")
    # fmt: on

    class Config(InvocationConfig):
        schema_extra = {
            "ui": {
                "title": "Image Linear Interpolation",
                "tags": ["image", "linear", "interpolation", "lerp"]
            },
        }

    def invoke(self, context: InvocationContext) -> ImageOutput:
        image = context.services.images.get_pil_image(self.image.image_name)

        image_arr = numpy.asarray(image, dtype=numpy.float32) / 255
        image_arr = image_arr * (self.max - self.min) + self.max

        lerp_image = Image.fromarray(numpy.uint8(image_arr))

        image_dto = context.services.images.create(
            image=lerp_image,
            image_origin=ResourceOrigin.INTERNAL,
            image_category=ImageCategory.GENERAL,
            node_id=self.id,
            session_id=context.graph_execution_state_id,
            is_intermediate=self.is_intermediate,
        )

        return ImageOutput(
            image=ImageField(image_name=image_dto.image_name),
            width=image_dto.width,
            height=image_dto.height,
        )

class ImageInverseLerpInvocation(BaseInvocation, PILInvocationConfig):
    """Inverse linear interpolation of all pixels of an image"""

    # fmt: off
    type: Literal["img_ilerp"] = "img_ilerp"

    # Inputs
    image: Optional[ImageField]  = Field(default=None, description="The image to lerp")
    min: int = Field(default=0, ge=0, le=255, description="The minimum input value")
    max: int = Field(default=255, ge=0, le=255, description="The maximum input value")
    # fmt: on

    class Config(InvocationConfig):
        schema_extra = {
            "ui": {
                "title": "Image Inverse Linear Interpolation",
                "tags": ["image", "linear", "interpolation", "inverse"]
            },
        }

    def invoke(self, context: InvocationContext) -> ImageOutput:
        image = context.services.images.get_pil_image(self.image.image_name)

        image_arr = numpy.asarray(image, dtype=numpy.float32)
        image_arr = (
            numpy.minimum(
                numpy.maximum(image_arr - self.min, 0) / float(self.max - self.min), 1
            )
            * 255
        )

        ilerp_image = Image.fromarray(numpy.uint8(image_arr))

        image_dto = context.services.images.create(
            image=ilerp_image,
            image_origin=ResourceOrigin.INTERNAL,
            image_category=ImageCategory.GENERAL,
            node_id=self.id,
            session_id=context.graph_execution_state_id,
            is_intermediate=self.is_intermediate,
        )

        return ImageOutput(
            image=ImageField(image_name=image_dto.image_name),
            width=image_dto.width,
            height=image_dto.height,
        )

class ImageNSFWBlurInvocation(BaseInvocation, PILInvocationConfig):
    """Add blur to NSFW-flagged images"""
    DEFAULT_ENABLED = InvokeAIAppConfig.get_config().nsfw_checker

    # fmt: off
    type: Literal["img_nsfw"] = "img_nsfw"

    # Inputs
    image: Optional[ImageField]  = Field(default=None, description="The image to check")
    enabled: bool = Field(default=DEFAULT_ENABLED, description="Whether the NSFW checker is enabled")
    metadata: Optional[CoreMetadata] = Field(default=None, description="Optional core metadata to be written to the image")    
    # fmt: on

    class Config(InvocationConfig):
        schema_extra = {
            "ui": {
                "title": "Blur NSFW Images",
                "tags": ["image", "nsfw", "checker"]
            },
        }

    def invoke(self, context: InvocationContext) -> ImageOutput:
        image = context.services.images.get_pil_image(self.image.image_name)
        
        config = context.services.configuration
        logger = context.services.logger
        device = choose_torch_device()
        
        if self.enabled:
            logger.info("Running NSFW checker")
            safety_checker = StableDiffusionSafetyChecker.from_pretrained(config.models_path / 'core/convert/stable-diffusion-safety-checker')
            feature_extractor = AutoFeatureExtractor.from_pretrained(config.models_path / 'core/convert/stable-diffusion-safety-checker')

            features = feature_extractor([image], return_tensors="pt")
            features.to(device)
            safety_checker.to(device)

            x_image = numpy.array(image).astype(numpy.float32) / 255.0
            x_image = x_image[None].transpose(0, 3, 1, 2)
            with SilenceWarnings():
                checked_image, has_nsfw_concept = safety_checker(images=x_image, clip_input=features.pixel_values)

            logger.info(f"NSFW scan result: {has_nsfw_concept[0]}")
            if has_nsfw_concept[0]:
                blurry_image = image.filter(filter=ImageFilter.GaussianBlur(radius=32))
                caution = self._get_caution_img()
                blurry_image.paste(caution,(0,0),caution)
                image = blurry_image

        image_dto = context.services.images.create(
            image=image,
            image_origin=ResourceOrigin.INTERNAL,
            image_category=ImageCategory.GENERAL,
            node_id=self.id,
            session_id=context.graph_execution_state_id,
            is_intermediate=self.is_intermediate,
            metadata=self.metadata.dict() if self.metadata else None,
        )
                
        return ImageOutput(
            image=ImageField(image_name=image_dto.image_name),
            width=image_dto.width,
            height=image_dto.height,
        )
    
    def _get_caution_img(self)->Image:
        import invokeai.assets.web as web_assets
        caution = Image.open(Path(web_assets.__path__[0]) / 'caution.png')
        return caution.resize((caution.width // 2, caution.height //2))

class ImageWatermarkInvocation(BaseInvocation, PILInvocationConfig):
    """ Add an invisible watermark to an image """

    # to avoid circular import
    DEFAULT_ENABLED = InvokeAIAppConfig.get_config().invisible_watermark
    
    # fmt: off
    type: Literal["img_watermark"] = "img_watermark"

    # Inputs
    image: Optional[ImageField]  = Field(default=None, description="The image to check")
    text: str = Field(default='InvokeAI', description="Watermark text")
    enabled: bool = Field(default=DEFAULT_ENABLED, description="Whether the invisible watermark is enabled")
    metadata: Optional[CoreMetadata] = Field(default=None, description="Optional core metadata to be written to the image")    
    # fmt: on

    class Config(InvocationConfig):
        schema_extra = {
            "ui": {
                "title": "Add Invisible Watermark",
                "tags": ["image", "watermark", "invisible"]
            },
        }

    def invoke(self, context: InvocationContext) -> ImageOutput:
        import cv2
        from imwatermark import WatermarkEncoder
        
        logger = context.services.logger
        image = context.services.images.get_pil_image(self.image.image_name)
        if self.enabled:
            logger.info("Running invisible watermarker")
            bgr = cv2.cvtColor(numpy.array(image.convert("RGB")), cv2.COLOR_RGB2BGR)
            wm = self.text
            encoder = WatermarkEncoder()
            encoder.set_watermark('bytes', wm.encode('utf-8'))
            bgr_encoded = encoder.encode(bgr, 'dwtDct')
            new_image = Image.fromarray(
                cv2.cvtColor(bgr_encoded, cv2.COLOR_BGR2RGB)
            ).convert("RGBA")
            image = new_image
        
        image_dto = context.services.images.create(
            image=image,
            image_origin=ResourceOrigin.INTERNAL,
            image_category=ImageCategory.GENERAL,
            node_id=self.id,
            session_id=context.graph_execution_state_id,
            is_intermediate=self.is_intermediate,
            metadata=self.metadata.dict() if self.metadata else None,
        )

        return ImageOutput(
            image=ImageField(image_name=image_dto.image_name),
            width=image_dto.width,
            height=image_dto.height,
        )