import torch
from einops import rearrange, repeat
from PIL import Image

from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
from invokeai.app.invocations.fields import (
    ConditioningField,
    FieldDescriptions,
    Input,
    InputField,
    WithBoard,
    WithMetadata,
)
from invokeai.app.invocations.model import TransformerField, VAEField
from invokeai.app.invocations.primitives import ImageOutput
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.backend.flux.model import Flux
from invokeai.backend.flux.modules.autoencoder import AutoEncoder
from invokeai.backend.flux.sampling import denoise, get_noise, get_schedule, unpack
from invokeai.backend.stable_diffusion.diffusion.conditioning_data import FLUXConditioningInfo
from invokeai.backend.util.devices import TorchDevice
from invokeai.backend.model_manager.config import CheckpointConfigBase


@invocation(
    "flux_text_to_image",
    title="FLUX Text to Image",
    tags=["image"],
    category="image",
    version="1.0.0",
)
class FluxTextToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
    """Text-to-image generation using a FLUX model."""

    transformer: TransformerField = InputField(
        description=FieldDescriptions.unet,
        input=Input.Connection,
        title="Transformer",
    )
    vae: VAEField = InputField(
        description=FieldDescriptions.vae,
        input=Input.Connection,
    )
    positive_text_conditioning: ConditioningField = InputField(
        description=FieldDescriptions.positive_cond, input=Input.Connection
    )
    width: int = InputField(default=1024, multiple_of=16, description="Width of the generated image.")
    height: int = InputField(default=1024, multiple_of=16, description="Height of the generated image.")
    num_steps: int = InputField(default=4, description="Number of diffusion steps.")
    guidance: float = InputField(
        default=4.0,
        description="The guidance strength. Higher values adhere more strictly to the prompt, and will produce less diverse images.",
    )
    seed: int = InputField(default=0, description="Randomness seed for reproducibility.")

    @torch.no_grad()
    def invoke(self, context: InvocationContext) -> ImageOutput:
        # Load the conditioning data.
        cond_data = context.conditioning.load(self.positive_text_conditioning.conditioning_name)
        assert len(cond_data.conditionings) == 1
        flux_conditioning = cond_data.conditionings[0]
        assert isinstance(flux_conditioning, FLUXConditioningInfo)

        latents = self._run_diffusion(context, flux_conditioning.clip_embeds, flux_conditioning.t5_embeds)
        image = self._run_vae_decoding(context, latents)
        image_dto = context.images.save(image=image)
        return ImageOutput.build(image_dto)

    def _run_diffusion(
        self,
        context: InvocationContext,
        clip_embeddings: torch.Tensor,
        t5_embeddings: torch.Tensor,
    ):
        transformer_info = context.models.load(self.transformer.transformer)
        inference_dtype = torch.bfloat16

        # Prepare input noise.
        # TODO(ryand): Does the seed behave the same on different devices? Should we re-implement this to always use a
        # CPU RNG?
        x = get_noise(
            num_samples=1,
            height=self.height,
            width=self.width,
            device=TorchDevice.choose_torch_device(),
            dtype=inference_dtype,
            seed=self.seed,
        )

        img, img_ids = self._prepare_latent_img_patches(x)

        # HACK(ryand): Find a better way to determine if this is a schnell model or not.
        is_schnell = "schnell" in transformer_info.config.config_path if transformer_info.config and isinstance(transformer_info.config, CheckpointConfigBase) else ""
        timesteps = get_schedule(
            num_steps=self.num_steps,
            image_seq_len=img.shape[1],
            shift=not is_schnell,
        )

        bs, t5_seq_len, _ = t5_embeddings.shape
        txt_ids = torch.zeros(bs, t5_seq_len, 3, dtype=inference_dtype, device=TorchDevice.choose_torch_device())

        # HACK(ryand): Manually empty the cache. Currently we don't check the size of the model before loading it from
        # disk. Since the transformer model is large (24GB), there's a good chance that it will OOM on 32GB RAM systems
        # if the cache is not empty.
        context.models._services.model_manager.load.ram_cache.make_room(24 * 2**30)

        with transformer_info as transformer:
            assert isinstance(transformer, Flux)

            x = denoise(
                model=transformer,
                img=img,
                img_ids=img_ids,
                txt=t5_embeddings,
                txt_ids=txt_ids,
                vec=clip_embeddings,
                timesteps=timesteps,
                guidance=self.guidance,
            )

        x = unpack(x.float(), self.height, self.width)

        return x

    def _prepare_latent_img_patches(self, latent_img: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
        """Convert an input image in latent space to patches for diffusion.

        This implementation was extracted from:
        https://github.com/black-forest-labs/flux/blob/c00d7c60b085fce8058b9df845e036090873f2ce/src/flux/sampling.py#L32

        Returns:
            tuple[Tensor, Tensor]: (img, img_ids), as defined in the original flux repo.
        """
        bs, c, h, w = latent_img.shape

        # Pixel unshuffle with a scale of 2, and flatten the height/width dimensions to get an array of patches.
        img = rearrange(latent_img, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
        if img.shape[0] == 1 and bs > 1:
            img = repeat(img, "1 ... -> bs ...", bs=bs)

        # Generate patch position ids.
        img_ids = torch.zeros(h // 2, w // 2, 3, device=img.device)
        img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2, device=img.device)[:, None]
        img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2, device=img.device)[None, :]
        img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)

        return img, img_ids

    def _run_vae_decoding(
        self,
        context: InvocationContext,
        latents: torch.Tensor,
    ) -> Image.Image:
        vae_info = context.models.load(self.vae.vae)
        with vae_info as vae:
            assert isinstance(vae, AutoEncoder)
            # TODO(ryand): Test that this works with both float16 and bfloat16.
            # with torch.autocast(device_type=latents.device.type, dtype=torch.float32):
            vae.to(torch.float32)
            latents.to(torch.float32)
            img = vae.decode(latents)

        img.clamp(-1, 1)
        img = rearrange(img[0], "c h w -> h w c")
        img_pil = Image.fromarray((127.5 * (img + 1.0)).byte().cpu().numpy())

        return img_pil