# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py 

import torch
import torch.nn as nn
import ldm_patched.utils.path_utils
import ldm_patched.modules.clip_model
import ldm_patched.modules.clip_vision
import ldm_patched.modules.ops

# code for model from: https://github.com/TencentARC/PhotoMaker/blob/main/photomaker/model.py under Apache License Version 2.0
VISION_CONFIG_DICT = {
    "hidden_size": 1024,
    "image_size": 224,
    "intermediate_size": 4096,
    "num_attention_heads": 16,
    "num_channels": 3,
    "num_hidden_layers": 24,
    "patch_size": 14,
    "projection_dim": 768,
    "hidden_act": "quick_gelu",
}

class MLP(nn.Module):
    def __init__(self, in_dim, out_dim, hidden_dim, use_residual=True, operations=ldm_patched.modules.ops):
        super().__init__()
        if use_residual:
            assert in_dim == out_dim
        self.layernorm = operations.LayerNorm(in_dim)
        self.fc1 = operations.Linear(in_dim, hidden_dim)
        self.fc2 = operations.Linear(hidden_dim, out_dim)
        self.use_residual = use_residual
        self.act_fn = nn.GELU()

    def forward(self, x):
        residual = x
        x = self.layernorm(x)
        x = self.fc1(x)
        x = self.act_fn(x)
        x = self.fc2(x)
        if self.use_residual:
            x = x + residual
        return x


class FuseModule(nn.Module):
    def __init__(self, embed_dim, operations):
        super().__init__()
        self.mlp1 = MLP(embed_dim * 2, embed_dim, embed_dim, use_residual=False, operations=operations)
        self.mlp2 = MLP(embed_dim, embed_dim, embed_dim, use_residual=True, operations=operations)
        self.layer_norm = operations.LayerNorm(embed_dim)

    def fuse_fn(self, prompt_embeds, id_embeds):
        stacked_id_embeds = torch.cat([prompt_embeds, id_embeds], dim=-1)
        stacked_id_embeds = self.mlp1(stacked_id_embeds) + prompt_embeds
        stacked_id_embeds = self.mlp2(stacked_id_embeds)
        stacked_id_embeds = self.layer_norm(stacked_id_embeds)
        return stacked_id_embeds

    def forward(
        self,
        prompt_embeds,
        id_embeds,
        class_tokens_mask,
    ) -> torch.Tensor:
        # id_embeds shape: [b, max_num_inputs, 1, 2048]
        id_embeds = id_embeds.to(prompt_embeds.dtype)
        num_inputs = class_tokens_mask.sum().unsqueeze(0) # TODO: check for training case
        batch_size, max_num_inputs = id_embeds.shape[:2]
        # seq_length: 77
        seq_length = prompt_embeds.shape[1]
        # flat_id_embeds shape: [b*max_num_inputs, 1, 2048]
        flat_id_embeds = id_embeds.view(
            -1, id_embeds.shape[-2], id_embeds.shape[-1]
        )
        # valid_id_mask [b*max_num_inputs]
        valid_id_mask = (
            torch.arange(max_num_inputs, device=flat_id_embeds.device)[None, :]
            < num_inputs[:, None]
        )
        valid_id_embeds = flat_id_embeds[valid_id_mask.flatten()]

        prompt_embeds = prompt_embeds.view(-1, prompt_embeds.shape[-1])
        class_tokens_mask = class_tokens_mask.view(-1)
        valid_id_embeds = valid_id_embeds.view(-1, valid_id_embeds.shape[-1])
        # slice out the image token embeddings
        image_token_embeds = prompt_embeds[class_tokens_mask]
        stacked_id_embeds = self.fuse_fn(image_token_embeds, valid_id_embeds)
        assert class_tokens_mask.sum() == stacked_id_embeds.shape[0], f"{class_tokens_mask.sum()} != {stacked_id_embeds.shape[0]}"
        prompt_embeds.masked_scatter_(class_tokens_mask[:, None], stacked_id_embeds.to(prompt_embeds.dtype))
        updated_prompt_embeds = prompt_embeds.view(batch_size, seq_length, -1)
        return updated_prompt_embeds

class PhotoMakerIDEncoder(ldm_patched.modules.clip_model.CLIPVisionModelProjection):
    def __init__(self):
        self.load_device = ldm_patched.modules.model_management.text_encoder_device()
        offload_device = ldm_patched.modules.model_management.text_encoder_offload_device()
        dtype = ldm_patched.modules.model_management.text_encoder_dtype(self.load_device)

        super().__init__(VISION_CONFIG_DICT, dtype, offload_device, ldm_patched.modules.ops.manual_cast)
        self.visual_projection_2 = ldm_patched.modules.ops.manual_cast.Linear(1024, 1280, bias=False)
        self.fuse_module = FuseModule(2048, ldm_patched.modules.ops.manual_cast)

    def forward(self, id_pixel_values, prompt_embeds, class_tokens_mask):
        b, num_inputs, c, h, w = id_pixel_values.shape
        id_pixel_values = id_pixel_values.view(b * num_inputs, c, h, w)

        shared_id_embeds = self.vision_model(id_pixel_values)[2]
        id_embeds = self.visual_projection(shared_id_embeds)
        id_embeds_2 = self.visual_projection_2(shared_id_embeds)

        id_embeds = id_embeds.view(b, num_inputs, 1, -1)
        id_embeds_2 = id_embeds_2.view(b, num_inputs, 1, -1)

        id_embeds = torch.cat((id_embeds, id_embeds_2), dim=-1)
        updated_prompt_embeds = self.fuse_module(prompt_embeds, id_embeds, class_tokens_mask)

        return updated_prompt_embeds


class PhotoMakerLoader:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "photomaker_model_name": (ldm_patched.utils.path_utils.get_filename_list("photomaker"), )}}

    RETURN_TYPES = ("PHOTOMAKER",)
    FUNCTION = "load_photomaker_model"

    CATEGORY = "_for_testing/photomaker"

    def load_photomaker_model(self, photomaker_model_name):
        photomaker_model_path = ldm_patched.utils.path_utils.get_full_path("photomaker", photomaker_model_name)
        photomaker_model = PhotoMakerIDEncoder()
        data = ldm_patched.modules.utils.load_torch_file(photomaker_model_path, safe_load=True)
        if "id_encoder" in data:
            data = data["id_encoder"]
        photomaker_model.load_state_dict(data)
        return (photomaker_model,)


class PhotoMakerEncode:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "photomaker": ("PHOTOMAKER",),
                              "image": ("IMAGE",),
                              "clip": ("CLIP", ),
                              "text": ("STRING", {"multiline": True, "default": "photograph of photomaker"}),
                             }}

    RETURN_TYPES = ("CONDITIONING",)
    FUNCTION = "apply_photomaker"

    CATEGORY = "_for_testing/photomaker"

    def apply_photomaker(self, photomaker, image, clip, text):
        special_token = "photomaker"
        pixel_values = ldm_patched.modules.clip_vision.clip_preprocess(image.to(photomaker.load_device)).float()
        try:
            index = text.split(" ").index(special_token) + 1
        except ValueError:
            index = -1
        tokens = clip.tokenize(text, return_word_ids=True)
        out_tokens = {}
        for k in tokens:
            out_tokens[k] = []
            for t in tokens[k]:
                f = list(filter(lambda x: x[2] != index, t))
                while len(f) < len(t):
                    f.append(t[-1])
                out_tokens[k].append(f)

        cond, pooled = clip.encode_from_tokens(out_tokens, return_pooled=True)

        if index > 0:
            token_index = index - 1
            num_id_images = 1
            class_tokens_mask = [True if token_index <= i < token_index+num_id_images else False for i in range(77)]
            out = photomaker(id_pixel_values=pixel_values.unsqueeze(0), prompt_embeds=cond.to(photomaker.load_device),
                            class_tokens_mask=torch.tensor(class_tokens_mask, dtype=torch.bool, device=photomaker.load_device).unsqueeze(0))
        else:
            out = cond

        return ([[out, {"pooled_output": pooled}]], )


NODE_CLASS_MAPPINGS = {
    "PhotoMakerLoader": PhotoMakerLoader,
    "PhotoMakerEncode": PhotoMakerEncode,
}

