#    Copyright 2024 Xi Zhang
#
#    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

from .multimodal_encoder.builder import build_vision_tower
from .multimodal_projector.builder import build_vision_projector

from libra.constants import (
    IGNORE_INDEX,  
    IMAGE_TOKEN_INDEX,  
    DEFAULT_IMAGE_PATCH_TOKEN,  
    DEFAULT_IM_START_TOKEN, 
    DEFAULT_IM_END_TOKEN, 
)


class LibraMetaModel:
    """
    LibraMetaModel is a class that initializes and manages a multi-modal model with vision and projection modules.

    Attributes:
        config (object): Configuration object containing model parameters.
        vision_tower (object): Vision model component.
        mm_projector (object): Multi-modal projection module.

    Methods:
        __init__(config):
            Initializes the LibraMetaModel with the given configuration.
        
        get_vision_tower():
            Retrieves the vision model component. If the vision model is a list, returns the first element.
        
        initialize_vision_modules(model_args, fsdp=None):
            Initializes the vision and projection modules based on the provided model arguments.
            Loads pre-trained weights for the multi-modal MLP adapter if available.
    """
    def __init__(self, config):
 
        super(LibraMetaModel, 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)

    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 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 

        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 

        if getattr(self, 'mm_projector', None) is None:   
            self.mm_projector = build_vision_projector(self.config)
        else:
            for p in self.mm_projector.parameters():
                p.requires_grad = True

        if pretrain_mm_mlp_adapter is not None:
            mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu') 

            def get_w(weights, keyword):
                return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword in k}

            self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector'))


class LibraMetaForCausalLM(ABC):

    @abstractmethod
    def get_model(self):
        pass

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

    def encode_images(self, images):
        image_features_temp = self.get_model().get_vision_tower()(images)
        image_features = self.get_model().mm_projector(image_features_temp)
        
        return image_features

    def prepare_inputs_labels_for_multimodal(
        self, input_ids, attention_mask, past_key_values, labels, images
    ):
        """
        Prepare inputs and labels for multimodal tasks, applying different logic based on training or inference phase.
        
        Args:
            input_ids (Tensor): IDs of the input token sequence.
            attention_mask (Tensor): Attention mask.
            past_key_values (Tensor): Cached key and value for attention mechanism.
            labels (Tensor): Target labels.
            images (Tensor): Image inputs.

        Returns:
            Tuple: Processed input_ids, attention_mask, past_key_values, multimodal_features, labels
        """

        vision_tower = self.get_vision_tower()

        if vision_tower is None or images is None or input_ids.shape[1] == 1:
            if past_key_values is not None and vision_tower is not None and images is not None and input_ids.shape[1] == 1:
                attention_mask = torch.ones(
                    (attention_mask.shape[0], past_key_values[-1][-1].shape[-2] + 1),
                    dtype=attention_mask.dtype,
                    device=attention_mask.device
                )
            return input_ids, attention_mask, past_key_values, None, labels
        
        if input_ids.size(0) != images.size(0) and input_ids.size(0) != images.size(1):
            # print(
            #     "Warning: Dimension mismatch detected. Adjust dimensions for beam-search.\n"
            #     "Program continues..."
            # )
            num_groups = input_ids.size(0)
            images_1 = images[:num_groups]
            images_2 = images[num_groups:]
            images = torch.cat((images_1, images_2), dim=1)
            images = images.permute(1, 0, 2, 3, 4).contiguous()
               
        image_features = self.encode_images(images)  
        
        new_input_embeds = []
        new_labels = [] if labels is not None else None
        cur_image_idx = 0
        for batch_idx, cur_input_ids in enumerate(input_ids):
            if (cur_input_ids == IMAGE_TOKEN_INDEX).sum() == 0:

                cur_image_features = image_features[cur_image_idx]
                cur_input_embeds_temp = self.get_model().embed_tokens(cur_input_ids)
                cur_input_embeds = torch.cat([cur_input_embeds_temp, cur_image_features[0:0]], dim=0)
            
                new_input_embeds.append(cur_input_embeds)
                if labels is not None:
                    new_labels.append(labels[batch_idx])
                cur_image_idx += 1

                continue
                
            image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0]
            cur_new_input_embeds = []
            
            if labels is not None:
                cur_labels = labels[batch_idx]
                cur_new_labels = [] 

                assert cur_labels.shape == cur_input_ids.shape
            
            while image_token_indices.numel() > 0:

                cur_image_features = image_features[cur_image_idx]
                image_token_start = image_token_indices[0]

                if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False):
                    cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[:image_token_start-1]).detach())
                    cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[image_token_start-1:image_token_start]))
                    cur_new_input_embeds.append(cur_image_features)
                    cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[image_token_start+1:image_token_start+2]))
                    if labels is not None:
                        cur_new_labels.append(cur_labels[:image_token_start])
                        cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype))
                        cur_new_labels.append(cur_labels[image_token_start:image_token_start+1])
                        cur_labels_temp = cur_labels[image_token_start+2:]
                else:
                    cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[:image_token_start])) 
                    cur_new_input_embeds.append(cur_image_features) 

                    if labels is not None:
                        cur_new_labels.append(cur_labels[:image_token_start]) 
                        cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype)) 
                        cur_labels_temp = cur_labels[image_token_start+1:] 
                
                cur_image_idx += 1
                
                if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False):
                    cur_input_ids = cur_input_ids[image_token_start+2:]
                else:
                    cur_input_ids = cur_input_ids[image_token_start+1:]
                image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0]

            if cur_input_ids.numel() > 0:
                if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False):
                    cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids).detach())
                else:
                    cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids))
                if labels is not None:
                    cur_new_labels.append(cur_labels_temp)
            
            cur_new_input_embeds = [x.to(device=self.device) for x in cur_new_input_embeds]

            cur_new_input_embeds = torch.cat(cur_new_input_embeds, dim=0) 

            new_input_embeds.append(cur_new_input_embeds) 

            if labels is not None:
                cur_new_labels = torch.cat(cur_new_labels, dim=0) 
                new_labels.append(cur_new_labels) 
       
                
        if any(x.shape != new_input_embeds[0].shape for x in new_input_embeds):
            max_len = max(x.shape[0] for x in new_input_embeds)

            new_input_embeds_align = []
            for cur_new_embed in new_input_embeds:
                cur_new_embed = torch.cat((cur_new_embed, torch.zeros((max_len - cur_new_embed.shape[0], cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device)), dim=0)
                new_input_embeds_align.append(cur_new_embed)
            new_input_embeds = torch.stack(new_input_embeds_align, dim=0)

            if labels is not None:
                new_labels_align = []
                _new_labels = new_labels
                for cur_new_label in new_labels:
                    cur_new_label = torch.cat((cur_new_label, torch.full((max_len - cur_new_label.shape[0],), IGNORE_INDEX, dtype=cur_new_label.dtype, device=cur_new_label.device)), dim=0)
                    new_labels_align.append(cur_new_label)
                new_labels = torch.stack(new_labels_align, dim=0)

            if attention_mask is not None:
                new_attention_mask = []
                for cur_attention_mask, cur_new_labels, cur_new_labels_align in zip(attention_mask, _new_labels, new_labels):
                    new_attn_mask_pad_left = torch.full((cur_new_labels.shape[0] - labels.shape[1],), True, dtype=attention_mask.dtype, device=attention_mask.device)
                    new_attn_mask_pad_right = torch.full((cur_new_labels_align.shape[0] - cur_new_labels.shape[0],), False, dtype=attention_mask.dtype, device=attention_mask.device)
                    cur_new_attention_mask = torch.cat((new_attn_mask_pad_left, cur_attention_mask, new_attn_mask_pad_right), dim=0)
                    new_attention_mask.append(cur_new_attention_mask)
                attention_mask = torch.stack(new_attention_mask, dim=0)
                assert attention_mask.shape == new_labels.shape
        else:
            
            new_input_embeds = torch.stack(new_input_embeds, dim=0) 
            
            if labels is not None:
                new_labels  = torch.stack(new_labels, dim=0)

            if attention_mask is not None:
                new_attn_mask_pad_left = torch.full((attention_mask.shape[0], new_input_embeds.shape[1] - input_ids.shape[1]), True, dtype=attention_mask.dtype, device=attention_mask.device)
                attention_mask = torch.cat((new_attn_mask_pad_left, attention_mask), dim=1)

                assert attention_mask.shape == new_input_embeds.shape[:2]
        
        return None, 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