#    Copyright 2023 Haotian Liu
#
#    Licensed under the Apache License, Version 2.0 (the "License");
#    you may not use this file except in compliance with the License.
#    You may obtain a copy of the License at
#
#        http://www.apache.org/licenses/LICENSE-2.0
#
#    Unless required by applicable law or agreed to in writing, software
#    distributed under the License is distributed on an "AS IS" BASIS,
#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#    See the License for the specific language governing permissions and
#    limitations under the License.


from abc import ABC, abstractmethod

import torch
import torch.nn as nn
import torch.nn.functional as F

from .multimodal_encoder.builder import build_vision_tower
from .multimodal_projector.builder import build_vision_projector, build_vision_projector_baseline
from .multimodal_projector.builder import build_vision_projector_crosslayer

from llava.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN

from llava.mm_utils import get_anyres_image_grid_shape


class LlavaMetaModel:

    def __init__(self, config):
        super(LlavaMetaModel, self).__init__(config)

        if hasattr(config, "mm_vision_tower"):
            self.vision_tower = build_vision_tower(config, delay_load=True)
            self.mm_projector = build_vision_projector(config)
            self.mm_projector_crosslayer = build_vision_projector_crosslayer(config)

            if 'unpad' in getattr(config, 'mm_patch_merge_type', ''):
                self.image_newline = nn.Parameter(
                    torch.empty(config.hidden_size, dtype=self.dtype)
                )

    def get_vision_tower(self):
        vision_tower = getattr(self, 'vision_tower', None)
        if type(vision_tower) is list:
            vision_tower = vision_tower[0]
        return vision_tower

    def get_linear_projector(self):
        linear_proj = getattr(self, 'linear_projector', None)
        return linear_proj
    
    def get_local_attn(self):
        local_attn = getattr(self, 'local_attn', None)
        return local_attn
    
    def initialize_vision_modules(self, model_args, fsdp=None):
        vision_tower = model_args.vision_tower
        mm_vision_select_layer = model_args.mm_vision_select_layer
        mm_vision_select_feature = model_args.mm_vision_select_feature
        pretrain_mm_mlp_adapter = model_args.pretrain_mm_mlp_adapter
        mm_patch_merge_type = model_args.mm_patch_merge_type

        self.config.mm_vision_tower = vision_tower

        if self.get_vision_tower() is None:
            vision_tower = build_vision_tower(model_args)

            if fsdp is not None and len(fsdp) > 0:
                self.vision_tower = [vision_tower]
            else:
                self.vision_tower = vision_tower
        else:
            if fsdp is not None and len(fsdp) > 0:
                vision_tower = self.vision_tower[0]
            else:
                vision_tower = self.vision_tower
            vision_tower.load_model()

        self.config.use_mm_proj = True
        self.config.mm_projector_type = getattr(model_args, 'mm_projector_type', 'linear')
        self.config.mm_hidden_size = vision_tower.hidden_size
        self.config.mm_vision_select_layer = mm_vision_select_layer
        self.config.mm_vision_select_feature = mm_vision_select_feature
        self.config.mm_patch_merge_type = mm_patch_merge_type

        if getattr(self, 'mm_projector', None) is None:
            self.mm_projector = build_vision_projector(self.config)
            self.mm_projector_crosslayer = build_vision_projector_crosslayer(self.config)
            if 'unpad' in mm_patch_merge_type:
                embed_std = 1 / torch.sqrt(torch.tensor(self.config.hidden_size, dtype=self.dtype))
                self.image_newline = nn.Parameter(
                    torch.randn(self.config.hidden_size, dtype=self.dtype) * embed_std
                )
        else:
            # In case it is frozen by LoRA
            for p in self.mm_projector.parameters():
                p.requires_grad = True
            for p in self.mm_projector_crosslayer.parameters():
                p.requires_grad = True


        if pretrain_mm_mlp_adapter is not None:
            print("------------------------------------- load pretrained mlp --------------------------------------")
            mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')
            print(f'projector keys: {mm_projector_weights.keys()}')
            def get_w(weights, keyword):
                return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword in k}
            
            # ------------------------------------- baseline --------------------------------------
            # missing_keys, unexpected_keys = self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector'))
            # print('missing_keys student mm_projector:', missing_keys)
            # print('unexpected_keys student mm_projector:', unexpected_keys)

            # 
            missing_keys, unexpected_keys = self.load_state_dict(get_w(mm_projector_weights, 'model'), strict=False)
            # print(f"missing_keys: {missing_keys}")
            print(f"unexpected_keys: {unexpected_keys}")

def unpad_image(tensor, original_size):
    """
    Unpads a PyTorch tensor of a padded and resized image.

    Args:
    tensor (torch.Tensor): The image tensor, assumed to be in CxHxW format.
    original_size (tuple): The original size of PIL image (width, height).

    Returns:
    torch.Tensor: The unpadded image tensor.
    """
    original_width, original_height = original_size
    current_height, current_width = tensor.shape[1:]

    original_aspect_ratio = original_width / original_height
    current_aspect_ratio = current_width / current_height

    if original_aspect_ratio > current_aspect_ratio:
        scale_factor = current_width / original_width
        new_height = int(original_height * scale_factor)
        padding = (current_height - new_height) // 2
        unpadded_tensor = tensor[:, padding:current_height - padding, :]
    else:
        scale_factor = current_height / original_height
        new_width = int(original_width * scale_factor)
        padding = (current_width - new_width) // 2
        unpadded_tensor = tensor[:, :, padding:current_width - padding]

    return unpadded_tensor


class LlavaMetaForCausalLM(ABC):

    @abstractmethod
    def get_model(self):
        pass

    def get_vision_tower(self):
        return self.get_model().get_vision_tower()

    def split_image_features(self, image_features, window_size, concat=True):
        b, n, c = image_features.shape
        h = w = int(n ** 0.5) 
        ws = window_size
        dim_ratio = ws * ws
        image_features = image_features.view(b, h, w, c)
        image_features = image_features.unfold(1, ws, ws).unfold(2, ws, ws) 
        if concat:
            image_features = image_features.contiguous().view(b, h//ws, w//ws, dim_ratio*c)
            image_features = image_features.view(b, -1, dim_ratio*c)
        else:
            image_features = image_features.contiguous().view(b, h//ws, w//ws, ws*ws, c)
            image_features = image_features.view(b, -1, ws*ws, c)
        return image_features
    
    def k_split_image_features(self, image_features, window_size, group, concat=True):
        b, n, c = image_features.shape
        h = w = int(n ** 0.5) 
        ws = window_size
        dim_ratio = ws * ws
        image_features = image_features.view(b, h, w, c)
        image_features = image_features.unfold(1, ws, ws).unfold(2, ws, ws) 
        if concat:
            image_features = image_features.contiguous().view(b, h//ws, w//ws, ws*ws, c)
            image_features = image_features.view(b, h//ws, w//ws, group, (ws*ws//group), c)
            image_features = image_features.view(b, h//ws, w//ws, group, (ws*ws//group)*c)
            image_features = image_features.view(b, -1, group, (ws*ws//group)*c)
        else:
            image_features = image_features.contiguous().view(b, h//ws, w//ws, ws*ws, c)
            image_features = image_features.view(b, -1, ws*ws, c)
        return image_features
    
    def encode_images_video(self, images,labels=None,modal='image'):
        if modal=='video': 
            # Batch operations on video frames can further improve efficiency and will be implemented in the future.
            pass

        else:
            bsz,parts,rgb,height,width=images.size()
            print(f"image size: {images.size()}")
            if parts==1:
                images = images[:,0]
                image_features, image_features_l21, image_features_l18, image_features_l15, image_features_l12, image_features_l9, image_features_l6, image_features_l3 = self.get_model().get_vision_tower()(images)
                image_features = torch.cat((image_features,image_features_l21,image_features_l18,image_features_l15,image_features_l12,image_features_l9,image_features_l6,image_features_l3), dim=-1) #image_features_l8,image_features_l12,
                image_features = self.get_model().mm_projector_crosslayer(image_features)
                image_features = self.split_image_features(image_features, 2)
                image_features = self.get_model().mm_projector(image_features)
            else:
                images=images.view(bsz*parts,rgb,height,width)
        return image_features

    def encode_images(self, images):
        # --------------------------------------------- Cross Layer ------------------------------------
        image_features, image_features_l21, image_features_l18, image_features_l15, image_features_l12, image_features_l9, image_features_l6, image_features_l3 = self.get_model().get_vision_tower()(images)
        image_features = torch.cat((image_features,image_features_l21,image_features_l18,image_features_l15,image_features_l12,image_features_l9,image_features_l6,image_features_l3), dim=-1) #image_features_l8,image_features_l12,
        
        # image_features, image_features_l18, image_features_l12, image_features_l6 = self.get_model().get_vision_tower()(images)
        # image_features = torch.cat((image_features,image_features_l18,image_features_l12,image_features_l6), dim=-1) #image_features_l8,image_features_l12,

        image_features = self.get_model().mm_projector_crosslayer(image_features)
        image_features = self.split_image_features(image_features, 2)
        image_features = self.get_model().mm_projector(image_features)

        # --------------------------------------------- Baseline -----------------------------------------------------
        # image_features = self.get_model().get_vision_tower()(images)
        # # print(f'image shape: {image_features.shape}')
        # image_features = self.get_model().mm_projector(image_features)
        # ------------------------------------------------------------------------------------------------------------
        
        return image_features

    # def prepare_inputs_labels_for_multimodal(
    #     self, input_ids, position_ids, attention_mask, past_key_values, labels,
    #     images, image_sizes=None
    # ):
    #     vision_tower = self.get_vision_tower()
    #     if vision_tower is None or images is None or input_ids.shape[1] == 1:
    #         return input_ids, position_ids, attention_mask, past_key_values, None, labels

    #     if type(images) is list or images.ndim == 5:
    #         if type(images) is list:
    #             images = [x.unsqueeze(0) if x.ndim == 3 else x for x in images]
    #         concat_images = torch.cat([image for image in images], dim=0)
    #         image_features = self.encode_images(concat_images)
    #         split_sizes = [image.shape[0] for image in images]
    #         image_features = torch.split(image_features, split_sizes, dim=0)
    #         mm_patch_merge_type = getattr(self.config, 'mm_patch_merge_type', 'flat')
    #         image_aspect_ratio = getattr(self.config, 'image_aspect_ratio', 'square')
    #         if mm_patch_merge_type == 'flat':
    #             image_features = [x.flatten(0, 1) for x in image_features]
    #         elif mm_patch_merge_type.startswith('spatial'):
    #             new_image_features = []
    #             for image_idx, image_feature in enumerate(image_features):
    #                 if image_feature.shape[0] > 1:
    #                     base_image_feature = image_feature[0]
    #                     image_feature = image_feature[1:]
    #                     height = width = self.get_vision_tower().num_patches_per_side
    #                     assert height * width == base_image_feature.shape[0]
    #                     if image_aspect_ratio == 'anyres':
    #                         num_patch_width, num_patch_height = get_anyres_image_grid_shape(image_sizes[image_idx], self.config.image_grid_pinpoints, self.get_vision_tower().config.image_size)
    #                         image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1)
    #                     else:
    #                         raise NotImplementedError
    #                     if 'unpad' in mm_patch_merge_type:
    #                         image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
    #                         image_feature = image_feature.flatten(1, 2).flatten(2, 3)
    #                         image_feature = unpad_image(image_feature, image_sizes[image_idx])
    #                         image_feature = torch.cat((
    #                             image_feature,
    #                             self.model.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1).to(image_feature.device)
    #                         ), dim=-1)
    #                         image_feature = image_feature.flatten(1, 2).transpose(0, 1)
    #                     else:
    #                         image_feature = image_feature.permute(0, 2, 1, 3, 4).contiguous()
    #                         image_feature = image_feature.flatten(0, 3)
    #                     image_feature = torch.cat((base_image_feature, image_feature), dim=0)
    #                 else:
    #                     image_feature = image_feature[0]
    #                     if 'unpad' in mm_patch_merge_type:
    #                         image_feature = torch.cat((
    #                             image_feature,
    #                             self.model.image_newline[None].to(image_feature.device)
    #                         ), dim=0)
    #                 new_image_features.append(image_feature)
    #             image_features = new_image_features
    #         else:
    #             raise ValueError(f"Unexpected mm_patch_merge_type: {self.config.mm_patch_merge_type}")
    #     else:
    #         image_features = self.encode_images(images)

    #     # TODO: image start / end is not implemented here to support pretraining.
    #     if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False):
    #         raise NotImplementedError

    #     # Let's just add dummy tensors if they do not exist,
    #     # it is a headache to deal with None all the time.
    #     # But it is not ideal, and if you have a better idea,
    #     # please open an issue / submit a PR, thanks.
    #     _labels = labels
    #     _position_ids = position_ids
    #     _attention_mask = attention_mask
    #     if attention_mask is None:
    #         attention_mask = torch.ones_like(input_ids, dtype=torch.bool)
    #     else:
    #         attention_mask = attention_mask.bool()
    #     if position_ids is None:
    #         position_ids = torch.arange(0, input_ids.shape[1], dtype=torch.long, device=input_ids.device)
    #     if labels is None:
    #         labels = torch.full_like(input_ids, IGNORE_INDEX)

    #     # remove the padding using attention_mask -- FIXME
    #     _input_ids = input_ids
    #     input_ids = [cur_input_ids[cur_attention_mask] for cur_input_ids, cur_attention_mask in zip(input_ids, attention_mask)]
    #     labels = [cur_labels[cur_attention_mask] for cur_labels, cur_attention_mask in zip(labels, attention_mask)]

    #     new_input_embeds = []
    #     new_labels = []
    #     img_pos = []
    #     cur_image_idx = 0
    #     for batch_idx, cur_input_ids in enumerate(input_ids):
    #         num_images = (cur_input_ids == IMAGE_TOKEN_INDEX).sum()
    #         if num_images == 0:
    #             cur_image_features = image_features[cur_image_idx]
    #             cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids)
    #             cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0]], dim=0)
    #             new_input_embeds.append(cur_input_embeds)
    #             new_labels.append(labels[batch_idx])
    #             img_pos.append(2048)
    #             cur_image_idx += 1
    #             continue

    #         image_token_indices = [-1] + torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0].tolist() + [cur_input_ids.shape[0]]
    #         cur_input_ids_noim = []
    #         cur_labels = labels[batch_idx]
    #         cur_labels_noim = []
    #         for i in range(len(image_token_indices) - 1):
    #             cur_input_ids_noim.append(cur_input_ids[image_token_indices[i]+1:image_token_indices[i+1]])
    #             cur_labels_noim.append(cur_labels[image_token_indices[i]+1:image_token_indices[i+1]])
    #         split_sizes = [x.shape[0] for x in cur_labels_noim]
    #         cur_input_embeds = self.get_model().embed_tokens(torch.cat(cur_input_ids_noim))
    #         cur_input_embeds_no_im = torch.split(cur_input_embeds, split_sizes, dim=0)
    #         img_pos.append(cur_input_embeds_no_im[0].shape[0])
    #         cur_new_input_embeds = []
    #         cur_new_labels = []

    #         for i in range(num_images + 1):
    #             cur_new_input_embeds.append(cur_input_embeds_no_im[i])
    #             cur_new_labels.append(cur_labels_noim[i])
    #             if i < num_images:
    #                 cur_image_features = image_features[cur_image_idx]
    #                 cur_image_idx += 1
    #                 cur_new_input_embeds.append(cur_image_features)
    #                 cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=cur_labels.device, dtype=cur_labels.dtype))

    #         cur_new_input_embeds = [x.to(self.device) for x in cur_new_input_embeds]

    #         cur_new_input_embeds = torch.cat(cur_new_input_embeds)
    #         cur_new_labels = torch.cat(cur_new_labels)

    #         new_input_embeds.append(cur_new_input_embeds)
    #         new_labels.append(cur_new_labels)

    #     # Truncate sequences to max length as image embeddings can make the sequence longer
    #     tokenizer_model_max_length = getattr(self.config, 'tokenizer_model_max_length', None)
    #     if tokenizer_model_max_length is not None:
    #         new_input_embeds = [x[:tokenizer_model_max_length] for x in new_input_embeds]
    #         new_labels = [x[:tokenizer_model_max_length] for x in new_labels]

    #     # Combine them
    #     max_len = max(x.shape[0] for x in new_input_embeds)
    #     batch_size = len(new_input_embeds)

    #     new_input_embeds_padded = []
    #     new_labels_padded = torch.full((batch_size, max_len), IGNORE_INDEX, dtype=new_labels[0].dtype, device=new_labels[0].device)
    #     attention_mask = torch.zeros((batch_size, max_len), dtype=attention_mask.dtype, device=attention_mask.device)
    #     position_ids = torch.zeros((batch_size, max_len), dtype=position_ids.dtype, device=position_ids.device)

    #     for i, (cur_new_embed, cur_new_labels) in enumerate(zip(new_input_embeds, new_labels)):
    #         cur_len = cur_new_embed.shape[0]
    #         if getattr(self.config, 'tokenizer_padding_side', 'right') == "left":
    #             new_input_embeds_padded.append(torch.cat((
    #                 torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device),
    #                 cur_new_embed
    #             ), dim=0))
    #             if cur_len > 0:
    #                 new_labels_padded[i, -cur_len:] = cur_new_labels
    #                 attention_mask[i, -cur_len:] = True
    #                 position_ids[i, -cur_len:] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device)
    #         else:
    #             new_input_embeds_padded.append(torch.cat((
    #                 cur_new_embed,
    #                 torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device)
    #             ), dim=0))
    #             if cur_len > 0:
    #                 new_labels_padded[i, :cur_len] = cur_new_labels
    #                 attention_mask[i, :cur_len] = True
    #                 position_ids[i, :cur_len] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device)

    #     new_input_embeds = torch.stack(new_input_embeds_padded, dim=0)

    #     if _labels is None:
    #         new_labels = None
    #     else:
    #         new_labels = new_labels_padded

    #     if _attention_mask is None:
    #         attention_mask = None
    #     else:
    #         attention_mask = attention_mask.to(dtype=_attention_mask.dtype)

    #     if _position_ids is None:
    #         position_ids = None

    #     return None, position_ids, attention_mask, past_key_values, new_input_embeds, new_labels

    def prepare_inputs_labels_for_multimodal(
        self, input_ids, position_ids, attention_mask, past_key_values, labels,
        images, image_sizes=None
    ):
        vision_tower = self.get_vision_tower()
        if vision_tower is None or images is None or input_ids.shape[1] == 1:
            return input_ids, position_ids, attention_mask, past_key_values, None, labels

        if type(images) is list:
            # video
            image_features=[]
            # text_features=[]
            for i in range(len(images)):
                if images[i].ndim==5:

                    image=images[i].unsqueeze(0)
                    temporal_len=image.size(1)
                    image_features_list = []
                    # text_features_sum = 0
                    for frame_idx in range(temporal_len):
                        frame_image_features = self.encode_images_video(image[:,frame_idx],labels[i:i+1] if labels is not None else None)
                        image_features_list.append(frame_image_features)
                        # text_features_sum=text_features_sum+frame_text_features.float()
                    image_features.append(torch.cat(image_features_list,dim=1).squeeze(0))
                    # text_features.append((text_features_sum/temporal_len).squeeze(0).type_as(frame_text_features))


                else:
                    image_feature=self.encode_images_video(images[i].unsqueeze(0),labels[i:i+1] if labels is not None else None)
                    image_features.append(image_feature.squeeze(0))
                    # text_features.append(text_feature.squeeze(0))
        else:
            # image
            if images.ndim==6:
                temporal_len=images.size(1)
                image_features_list = []
                # text_features_sum = 0
                with  torch.no_grad():
                    for frame_idx in range(temporal_len):
                        frame_image_features = self.encode_images_video(images[:,frame_idx],labels=labels)
                        image_features_list.append(frame_image_features)
                        # text_features_sum=text_features_sum+frame_text_features.float()
                image_features=torch.cat(image_features_list,dim=1).requires_grad_()
                # text_features=(text_features_sum/temporal_len).type_as(frame_text_features).requires_grad_()

            else:
                image_features = self.encode_images_video(images,labels=labels)


        # TODO: image start / end is not implemented here to support pretraining.
        if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False):
            raise NotImplementedError

        # Let's just add dummy tensors if they do not exist,
        # it is a headache to deal with None all the time.
        # But it is not ideal, and if you have a better idea,
        # please open an issue / submit a PR, thanks.
        _labels = labels
        _position_ids = position_ids
        _attention_mask = attention_mask
        if attention_mask is None:
            attention_mask = torch.ones_like(input_ids, dtype=torch.bool)
        else:
            attention_mask = attention_mask.bool()
        if position_ids is None:
            position_ids = torch.arange(0, input_ids.shape[1], dtype=torch.long, device=input_ids.device)
        if labels is None:
            labels = torch.full_like(input_ids, IGNORE_INDEX)

        # remove the padding using attention_mask -- FIXME
        _input_ids = input_ids
        input_ids = [cur_input_ids[cur_attention_mask] for cur_input_ids, cur_attention_mask in zip(input_ids, attention_mask)]
        labels = [cur_labels[cur_attention_mask] for cur_labels, cur_attention_mask in zip(labels, attention_mask)]

        new_input_embeds = []
        new_labels = []
        img_pos = []
        cur_image_idx = 0
        for batch_idx, cur_input_ids in enumerate(input_ids):
            num_images = (cur_input_ids == IMAGE_TOKEN_INDEX).sum()
            if num_images == 0:
                cur_image_features = image_features[cur_image_idx]
                cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids)
                cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0]], dim=0)
                new_input_embeds.append(cur_input_embeds)
                new_labels.append(labels[batch_idx])
                img_pos.append(2048)
                cur_image_idx += 1
                continue

            image_token_indices = [-1] + torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0].tolist() + [cur_input_ids.shape[0]]
            cur_input_ids_noim = []
            cur_labels = labels[batch_idx]
            cur_labels_noim = []
            for i in range(len(image_token_indices) - 1):
                cur_input_ids_noim.append(cur_input_ids[image_token_indices[i]+1:image_token_indices[i+1]])
                cur_labels_noim.append(cur_labels[image_token_indices[i]+1:image_token_indices[i+1]])
            split_sizes = [x.shape[0] for x in cur_labels_noim]
            cur_input_embeds = self.get_model().embed_tokens(torch.cat(cur_input_ids_noim))
            cur_input_embeds_no_im = torch.split(cur_input_embeds, split_sizes, dim=0)
            img_pos.append(cur_input_embeds_no_im[0].shape[0])
            cur_new_input_embeds = []
            cur_new_labels = []

            for i in range(num_images + 1):
                cur_new_input_embeds.append(cur_input_embeds_no_im[i])
                cur_new_labels.append(cur_labels_noim[i])
                if i < num_images:
                    cur_image_features = image_features[cur_image_idx]
                    cur_image_idx += 1
                    cur_new_input_embeds.append(cur_image_features)
                    cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=cur_labels.device, dtype=cur_labels.dtype))

            cur_new_input_embeds = [x.to(self.device) for x in cur_new_input_embeds]

            cur_new_input_embeds = torch.cat(cur_new_input_embeds)
            cur_new_labels = torch.cat(cur_new_labels)

            new_input_embeds.append(cur_new_input_embeds)
            new_labels.append(cur_new_labels)

        # Truncate sequences to max length as image embeddings can make the sequence longer
        tokenizer_model_max_length = getattr(self.config, 'tokenizer_model_max_length', None)
        if tokenizer_model_max_length is not None:
            new_input_embeds = [x[:tokenizer_model_max_length] for x in new_input_embeds]
            new_labels = [x[:tokenizer_model_max_length] for x in new_labels]

        # Combine them
        max_len = max(x.shape[0] for x in new_input_embeds)
        batch_size = len(new_input_embeds)

        new_input_embeds_padded = []
        new_labels_padded = torch.full((batch_size, max_len), IGNORE_INDEX, dtype=new_labels[0].dtype, device=new_labels[0].device)
        attention_mask = torch.zeros((batch_size, max_len), dtype=attention_mask.dtype, device=attention_mask.device)
        position_ids = torch.zeros((batch_size, max_len), dtype=position_ids.dtype, device=position_ids.device)

        for i, (cur_new_embed, cur_new_labels) in enumerate(zip(new_input_embeds, new_labels)):
            cur_len = cur_new_embed.shape[0]
            if getattr(self.config, 'tokenizer_padding_side', 'right') == "left":
                new_input_embeds_padded.append(torch.cat((
                    torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device),
                    cur_new_embed
                ), dim=0))
                if cur_len > 0:
                    new_labels_padded[i, -cur_len:] = cur_new_labels
                    attention_mask[i, -cur_len:] = True
                    position_ids[i, -cur_len:] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device)
            else:
                new_input_embeds_padded.append(torch.cat((
                    cur_new_embed,
                    torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device)
                ), dim=0))
                if cur_len > 0:
                    new_labels_padded[i, :cur_len] = cur_new_labels
                    attention_mask[i, :cur_len] = True
                    position_ids[i, :cur_len] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device)

        new_input_embeds = torch.stack(new_input_embeds_padded, dim=0)

        if _labels is None:
            new_labels = None
        else:
            new_labels = new_labels_padded

        if _attention_mask is None:
            attention_mask = None
        else:
            attention_mask = attention_mask.to(dtype=_attention_mask.dtype)

        if _position_ids is None:
            position_ids = None

        return None, position_ids, attention_mask, past_key_values, new_input_embeds, new_labels

    def initialize_vision_tokenizer(self, model_args, tokenizer):
        if model_args.mm_use_im_patch_token:
            tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
            self.resize_token_embeddings(len(tokenizer))

        if model_args.mm_use_im_start_end:
            num_new_tokens = tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
            self.resize_token_embeddings(len(tokenizer))

            if num_new_tokens > 0:
                input_embeddings = self.get_input_embeddings().weight.data
                output_embeddings = self.get_output_embeddings().weight.data

                input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(
                    dim=0, keepdim=True)
                output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(
                    dim=0, keepdim=True)

                input_embeddings[-num_new_tokens:] = input_embeddings_avg
                output_embeddings[-num_new_tokens:] = output_embeddings_avg

            if model_args.tune_mm_mlp_adapter:
                for p in self.get_input_embeddings().parameters():
                    p.requires_grad = True
                for p in self.get_output_embeddings().parameters():
                    p.requires_grad = False

            if model_args.pretrain_mm_mlp_adapter:
                mm_projector_weights = torch.load(model_args.pretrain_mm_mlp_adapter, map_location='cpu')
                embed_tokens_weight = mm_projector_weights['model.embed_tokens.weight']
                assert num_new_tokens == 2
                if input_embeddings.shape == embed_tokens_weight.shape:
                    input_embeddings[-num_new_tokens:] = embed_tokens_weight[-num_new_tokens:]
                elif embed_tokens_weight.shape[0] == num_new_tokens:
                    input_embeddings[-num_new_tokens:] = embed_tokens_weight
                else:
                    raise ValueError(f"Unexpected embed_tokens_weight shape. Pretrained: {embed_tokens_weight.shape}. Current: {input_embeddings.shape}. Numer of new tokens: {num_new_tokens}.")
        elif model_args.mm_use_im_patch_token:
            if model_args.tune_mm_mlp_adapter:
                for p in self.get_input_embeddings().parameters():
                    p.requires_grad = False
                for p in self.get_output_embeddings().parameters():
                    p.requires_grad = False
