from diffusers.modular_pipelines import (
    PipelineBlock,
    InputParam,
    OutputParam,
    ConfigSpec,
)

from diffusers.utils import load_image
from PIL import Image
from typing import Union, Tuple
import numpy as np



class Wan14BImageProcessor(PipelineBlock):

    @property
    def description(self):
        return "default Image Processor for wan14B i2v (for both Wan2.1 and Wan2.2, it resizes image"

    @property
    def inputs(self):
        return [
            InputParam(name="image", type_hint=Union[Image.Image, str], description= "the Image to process"),
            InputParam(name="max_area", type_hint=int, description= "the maximum area of the Image to process")
        ]
    
    @property
    def intermediate_outputs(self):
        return [
            OutputParam(name="processed_image", type_hint=Image.Image, description= "the processed Image"),
        ]
    
    @property
    def expected_configs(self):
        return [
            ConfigSpec(name="patch_size", default=(1, 2, 2)),
            ConfigSpec(name="vae_stride", default=(4, 8, 8)),
        ]
    
    def __call__(self, components, state):

        block_state = self.get_block_state(state)

        if isinstance(block_state.image, str):
            image = load_image(block_state.image).convert("RGB")
        elif isinstance(block_state.image, Image.Image):
            image = block_state.image
        else:
            raise ValueError(f"Invalid image type: {type(block_state.image)}; only support PIL Image or url string")

        if block_state.max_area is None:
            max_area = 480 * 832
        else:
            max_area = block_state.max_area

        aspect_ratio = image.height / image.width
        mod_value_height = components.vae_stride[1] * components.patch_size[1]
        mod_value_width = components.vae_stride[2] * components.patch_size[2]
        height = round(np.sqrt(max_area * aspect_ratio)) // mod_value_height * mod_value_height
        width = round(np.sqrt(max_area / aspect_ratio)) // mod_value_width * mod_value_width
        resized_image = image.resize((width, height))
        

        block_state.processed_image = resized_image

        print(f" initial image size: {image.size}")
        print(f" processed image size: {resized_image.size}")
        

        self.set_block_state(state, block_state)
        return components, state