"""
Credit: laksjdjf
https://github.com/laksjdjf/cgem156-ComfyUI/blob/main/scripts/attention_couple/node.py

Modified by. Haoming02 to work with Forge
"""

from scripts.attention_masks import get_mask, lcm_for_list
from modules.devices import get_optimal_device
import torch


class AttentionCouple:

    def patch_unet(self, model, base_mask, kwargs: dict):

        new_model = model.clone()
        dtype = new_model.model.diffusion_model.dtype
        device = get_optimal_device()

        num_conds = len(kwargs) // 2 + 1

        mask = [base_mask] + [kwargs[f"mask_{i}"] for i in range(1, num_conds)]
        mask = torch.stack(mask, dim=0).to(device, dtype=dtype)
        assert mask.sum(dim=0).min() > 0, "Masks must not contain zeroes..."
        mask = mask / mask.sum(dim=0, keepdim=True)

        conds = [
            kwargs[f"cond_{i}"][0][0].to(device, dtype=dtype)
            for i in range(1, num_conds)
        ]
        num_tokens = [cond.shape[1] for cond in conds]

        def attn2_patch(q, k, v, extra_options):
            assert k.mean() == v.mean(), "k and v must be the same."
            cond_or_unconds = extra_options["cond_or_uncond"]
            num_chunks = len(cond_or_unconds)
            self.batch_size = q.shape[0] // num_chunks
            q_chunks = q.chunk(num_chunks, dim=0)
            k_chunks = k.chunk(num_chunks, dim=0)
            lcm_tokens = lcm_for_list(num_tokens + [k.shape[1]])
            conds_tensor = torch.cat(
                [
                    cond.repeat(self.batch_size, lcm_tokens // num_tokens[i], 1)
                    for i, cond in enumerate(conds)
                ],
                dim=0,
            )

            qs, ks = [], []
            for i, cond_or_uncond in enumerate(cond_or_unconds):
                k_target = k_chunks[i].repeat(1, lcm_tokens // k.shape[1], 1)
                if cond_or_uncond == 1:  # uncond
                    qs.append(q_chunks[i])
                    ks.append(k_target)
                else:
                    qs.append(q_chunks[i].repeat(num_conds, 1, 1))
                    ks.append(torch.cat([k_target, conds_tensor], dim=0))

            qs = torch.cat(qs, dim=0)
            ks = torch.cat(ks, dim=0)

            return qs, ks, ks

        def attn2_output_patch(out, extra_options):
            cond_or_unconds = extra_options["cond_or_uncond"]
            mask_downsample = get_mask(
                mask, self.batch_size, out.shape[1], extra_options["original_shape"]
            )
            outputs = []
            pos = 0
            for cond_or_uncond in cond_or_unconds:
                if cond_or_uncond == 1:  # uncond
                    outputs.append(out[pos : pos + self.batch_size])
                    pos += self.batch_size
                else:
                    masked_output = (
                        out[pos : pos + num_conds * self.batch_size] * mask_downsample
                    ).view(num_conds, self.batch_size, out.shape[1], out.shape[2])
                    masked_output = masked_output.sum(dim=0)
                    outputs.append(masked_output)
                    pos += num_conds * self.batch_size
            return torch.cat(outputs, dim=0)

        new_model.set_model_attn2_patch(attn2_patch)
        new_model.set_model_attn2_output_patch(attn2_output_patch)

        return new_model