import json
import os

import numpy as np
from PIL import Image
from PIL.PngImagePlugin import PngInfo

from comfy.cli_args import args
from comfy.cmd import folder_paths
from comfy.nodes.common import MAX_RESOLUTION
from comfy.utils import tensor2pil


class ImageCrop:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"image": ("IMAGE",),
                             "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
                             "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
                             "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
                             "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
                             }}

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "crop"

    CATEGORY = "image/transform"

    def crop(self, image, width, height, x, y):
        x = min(x, image.shape[2] - 1)
        y = min(y, image.shape[1] - 1)
        to_x = width + x
        to_y = height + y
        img = image[:, y:to_y, x:to_x, :]
        return (img,)


class RepeatImageBatch:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"image": ("IMAGE",),
                             "amount": ("INT", {"default": 1, "min": 1, "max": 4096}),
                             }}

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "repeat"

    CATEGORY = "image/batch"

    def repeat(self, image, amount):
        s = image.repeat((amount, 1, 1, 1))
        return (s,)


class ImageFromBatch:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"image": ("IMAGE",),
                             "batch_index": ("INT", {"default": 0, "min": 0, "max": 4095}),
                             "length": ("INT", {"default": 1, "min": 1, "max": 4096}),
                             }}

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "frombatch"

    CATEGORY = "image/batch"

    def frombatch(self, image, batch_index, length):
        s_in = image
        batch_index = min(s_in.shape[0] - 1, batch_index)
        length = min(s_in.shape[0] - batch_index, length)
        s = s_in[batch_index:batch_index + length].clone()
        return (s,)


class SaveAnimatedWEBP:
    def __init__(self):
        self.output_dir = folder_paths.get_output_directory()
        self.type = "output"
        self.prefix_append = ""

    methods = {"default": 4, "fastest": 0, "slowest": 6}

    @classmethod
    def INPUT_TYPES(s):
        return {"required":
                    {"images": ("IMAGE",),
                     "filename_prefix": ("STRING", {"default": "ComfyUI"}),
                     "fps": ("FLOAT", {"default": 6.0, "min": 0.01, "max": 1000.0, "step": 0.01}),
                     "lossless": ("BOOLEAN", {"default": True}),
                     "quality": ("INT", {"default": 80, "min": 0, "max": 100}),
                     "method": (list(s.methods.keys()),),
                     # "num_frames": ("INT", {"default": 0, "min": 0, "max": 8192}),
                     },
                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
                }

    RETURN_TYPES = ()
    FUNCTION = "save_images"

    OUTPUT_NODE = True

    CATEGORY = "image/animation"

    def save_images(self, images, fps, filename_prefix, lossless, quality, method, num_frames=0, prompt=None, extra_pnginfo=None):
        method = self.methods.get(method)
        filename_prefix += self.prefix_append
        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
        results = list()
        pil_images = []
        for image in images:
            i = 255. * image.float().cpu().numpy()
            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
            pil_images.append(img)

        metadata = pil_images[0].getexif()
        if not args.disable_metadata:
            if prompt is not None:
                metadata[0x0110] = "prompt:{}".format(json.dumps(prompt))
            if extra_pnginfo is not None:
                inital_exif = 0x010f
                for x in extra_pnginfo:
                    metadata[inital_exif] = "{}:{}".format(x, json.dumps(extra_pnginfo[x]))
                    inital_exif -= 1

        if num_frames == 0:
            num_frames = len(pil_images)

        c = len(pil_images)
        for i in range(0, c, num_frames):
            file = f"{filename}_{counter:05}_.webp"
            pil_images[i].save(os.path.join(full_output_folder, file), save_all=True, duration=int(1000.0 / fps), append_images=pil_images[i + 1:i + num_frames], exif=metadata, lossless=lossless, quality=quality, method=method)
            results.append({
                "filename": file,
                "subfolder": subfolder,
                "type": self.type
            })
            counter += 1

        animated = num_frames != 1
        return {"ui": {"images": results, "animated": (animated,)}}


class SaveAnimatedPNG:
    def __init__(self):
        self.output_dir = folder_paths.get_output_directory()
        self.type = "output"
        self.prefix_append = ""

    @classmethod
    def INPUT_TYPES(s):
        return {"required":
                    {"images": ("IMAGE",),
                     "filename_prefix": ("STRING", {"default": "ComfyUI"}),
                     "fps": ("FLOAT", {"default": 6.0, "min": 0.01, "max": 1000.0, "step": 0.01}),
                     "compress_level": ("INT", {"default": 4, "min": 0, "max": 9})
                     },
                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
                }

    RETURN_TYPES = ()
    FUNCTION = "save_images"

    OUTPUT_NODE = True

    CATEGORY = "image/animation"

    def save_images(self, images, fps, compress_level, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
        filename_prefix += self.prefix_append
        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
        results = list()
        pil_images = []
        for image in images:
            i = 255. * image.float().cpu().numpy()
            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
            pil_images.append(img)

        metadata = None
        if not args.disable_metadata:
            metadata = PngInfo()
            if prompt is not None:
                metadata.add(b"comf", "prompt".encode("latin-1", "strict") + b"\0" + json.dumps(prompt).encode("latin-1", "strict"), after_idat=True)
            if extra_pnginfo is not None:
                for x in extra_pnginfo:
                    metadata.add(b"comf", x.encode("latin-1", "strict") + b"\0" + json.dumps(extra_pnginfo[x]).encode("latin-1", "strict"), after_idat=True)

        file = f"{filename}_{counter:05}_.png"
        pil_images[0].save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=compress_level, save_all=True, duration=int(1000.0 / fps), append_images=pil_images[1:])
        results.append({
            "filename": file,
            "subfolder": subfolder,
            "type": self.type
        })

        return {"ui": {"images": results, "animated": (True,)}}


class ImageSizeToNumber:
    """
    By WASasquatch (Discord: WAS#0263)

    Copyright 2023 Jordan Thompson (WASasquatch)

    Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to
    deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
    and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

    The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

    THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
    THE SOFTWARE.
    """

    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "image": ("IMAGE",),
            }
        }

    RETURN_TYPES = ("*", "*", "FLOAT", "FLOAT", "INT", "INT")
    RETURN_NAMES = ("width_num", "height_num", "width_float", "height_float", "width_int", "height_int")
    FUNCTION = "image_width_height"

    CATEGORY = "image/operations"

    def image_width_height(self, image):
        image = tensor2pil(image)
        if image.size:
            return (
                image.size[0], image.size[1], float(image.size[0]), float(image.size[1]), image.size[0], image.size[1])
        return 0, 0, 0, 0, 0, 0


NODE_CLASS_MAPPINGS = {
    # From WAS Node Suite
    # Class mapping is kept for compatibility
    "Image Size to Number": ImageSizeToNumber,
    "ImageCrop": ImageCrop,
    "RepeatImageBatch": RepeatImageBatch,
    "ImageFromBatch": ImageFromBatch,
    "SaveAnimatedWEBP": SaveAnimatedWEBP,
    "SaveAnimatedPNG": SaveAnimatedPNG,
}