import torch
import torch.nn as nn
from transformers import SiglipVisionModel, SiglipImageProcessor, SiglipVisionConfig
class SiglipVisionTower(nn.Module):

    def __init__(self, vision_tower, args, delay_load=False):
        super().__init__()

        self.is_loaded = False

        self.vision_tower_name = vision_tower
        self.select_layer = args.mm_vision_select_layer
        self.select_feature = getattr(args, "mm_vision_select_feature", "patch")

        if not delay_load:
            self.load_model()
        else:
            self.cfg_only = SiglipVisionConfig.from_pretrained(self.vision_tower_name)

    def load_model(self, device_map=None):
        if self.is_loaded:
            print(
                "{} is already loaded, `load_model` called again, skipping.".format(
                    self.vision_tower_name
                )
            )
            return

        self.image_processor = SiglipImageProcessor.from_pretrained(
            self.vision_tower_name
        )
        self.vision_tower = SiglipVisionModel.from_pretrained(
            self.vision_tower_name, device_map=device_map
        )
        self.vision_tower.requires_grad_(False)

        self.is_loaded = True

    def feature_select(self, image_forward_outs):
        image_features = image_forward_outs.hidden_states[self.select_layer]
        if self.select_feature == "patch":
            image_features = image_features[:, 1:]
        elif self.select_feature == "cls_patch":
            image_features = image_features
        else:
            raise ValueError(f"Unexpected select feature: {self.select_feature}")
        return image_features

    @torch.no_grad()
    def forward(self, images):
        if type(images) is list:
            image_features = []
            for image in images:
                image_forward_out = self.vision_tower(
                    image.to(device=self.device, dtype=self.dtype).unsqueeze(0),
                    output_hidden_states=True,
                )
                image_feature = self.feature_select(image_forward_out).to(image.dtype)
                image_features.append(image_feature)
        else:
            image_forward_outs = self.vision_tower(
                images.to(device=self.device, dtype=self.dtype),
                output_hidden_states=True,
            )
            image_features = self.feature_select(image_forward_outs).to(images.dtype)

        return image_features

    @property
    def dummy_feature(self):
        return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)

    @property
    def dtype(self):
        return self.vision_tower.dtype

    @property
    def device(self):
        return self.vision_tower.device

    @property
    def config(self):
        if self.is_loaded:
            return self.vision_tower.config
        else:
            return self.cfg_only

    @property
    def hidden_size(self):
        return self.config.hidden_size

    @property
    def num_patches_per_side(self):
        return self.config.image_size // self.config.patch_size

    @property
    def num_patches(self):
        return (self.config.image_size // self.config.patch_size) ** 2

from abc import ABC, abstractmethod


IGNORE_INDEX = -100
IMAGE_TOKEN_INDEX = -200
DEFAULT_IMAGE_PATCH_TOKEN = "<im_patch>"
DEFAULT_IM_START_TOKEN = "<im_start>"
DEFAULT_IM_END_TOKEN = "<im_end>"


def build_vision_tower(vision_tower_cfg, **kwargs):
    vision_tower = getattr(
        vision_tower_cfg,
        "mm_vision_tower",
        getattr(vision_tower_cfg, "vision_tower", None),
    )
    return SiglipVisionTower(vision_tower, args=vision_tower_cfg, **kwargs)


import re
def build_vision_projector(config, delay_load=False, **kwargs):
    projector_type = getattr(config, "mm_projector_type", "linear")

    mlp_gelu_match = re.match(r"^mlp(\d+)x_gelu$", projector_type)
    if mlp_gelu_match:
        mlp_depth = int(mlp_gelu_match.group(1))
        modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)]
        for _ in range(1, mlp_depth):
            modules.append(nn.GELU())
            modules.append(nn.Linear(config.hidden_size, config.hidden_size))
        return nn.Sequential(*modules)


class MixsenseMetaModel:

    def __init__(self, config):
        super(MixsenseMetaModel, 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)

            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 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)

            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

        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 MixsenseMetaForCausalLM(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 = self.get_model().get_vision_tower()(images)
        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,
            )
        elif 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]
        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 = []
        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])
                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)
            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

from typing import List, Optional, Tuple, Union
from transformers import (
    AutoConfig,
    AutoModelForCausalLM,
    LlamaConfig,
    LlamaModel,
    LlamaForCausalLM,
)
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.generation.utils import GenerateOutput


class MixsenseConfig(LlamaConfig):
    model_type = "mixsense_llama"


class MixsenseLlamaModel(MixsenseMetaModel, LlamaModel):
    config_class = MixsenseConfig

    def __init__(self, config: LlamaConfig):
        super(MixsenseLlamaModel, self).__init__(config)


class MixsenseLlamaForCausalLM(LlamaForCausalLM, MixsenseMetaForCausalLM):
    config_class = MixsenseConfig

    def __init__(self, config):
        super(LlamaForCausalLM, self).__init__(config)
        self.model = MixsenseLlamaModel(config)
        self.pretraining_tp = config.pretraining_tp
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

    def get_model(self):
        return self.model

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        images: Optional[torch.FloatTensor] = None,
        image_sizes: Optional[List[List[int]]] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        if inputs_embeds is None:
            (
                input_ids,
                position_ids,
                attention_mask,
                past_key_values,
                inputs_embeds,
                labels,
            ) = self.prepare_inputs_labels_for_multimodal(
                input_ids,
                position_ids,
                attention_mask,
                past_key_values,
                labels,
                images,
                image_sizes,
            )
        return super().forward(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            labels=labels,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

    @torch.no_grad()
    def generate(
        self,
        inputs: Optional[torch.Tensor] = None,
        images: Optional[torch.Tensor] = None,
        image_sizes: Optional[torch.Tensor] = None,
        **kwargs,
    ) -> Union[GenerateOutput, torch.LongTensor]:
        position_ids = kwargs.pop("position_ids", None)
        attention_mask = kwargs.pop("attention_mask", None)
        if "inputs_embeds" in kwargs:
            raise NotImplementedError("`inputs_embeds` is not supported")
        if images is not None:
            (inputs, position_ids, attention_mask, _, inputs_embeds, _) = (
                self.prepare_inputs_labels_for_multimodal(
                    inputs,
                    position_ids,
                    attention_mask,
                    None,
                    None,
                    images,
                    image_sizes=image_sizes,
                )
            )
        else:
            inputs_embeds = self.get_model().embed_tokens(inputs)
        output = super().generate(
            position_ids=position_ids,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            **kwargs,
        )
        return output

    def prepare_inputs_for_generation(
        self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs
    ):
        images = kwargs.pop("images", None)
        image_sizes = kwargs.pop("image_sizes", None)
        inputs = super().prepare_inputs_for_generation(
            input_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            **kwargs,
        )
        if images is not None:
            inputs["images"] = images
        if image_sizes is not None:
            inputs["image_sizes"] = image_sizes
        return inputs
    def image_process(self,images):
        vision_tower = self.get_vision_tower()
        if not vision_tower.is_loaded:
            vision_tower.load_model()
        processor = vision_tower.image_processor
        def expand2square(pil_img, background_color):
            from PIL import Image
            width, height = pil_img.size
            if width == height:
                return pil_img
            elif width > height:
                result = Image.new(pil_img.mode, (width, width), background_color)
                result.paste(pil_img, (0, (width - height) // 2))
                return result
            else:
                result = Image.new(pil_img.mode, (height, height), background_color)
                result.paste(pil_img, ((height - width) // 2, 0))
                return result
        processed_images=[]
        for image in images:
            image = expand2square(image, tuple(int(x*255) for x in processor.image_mean))
            image = processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
            processed_images.append(image)
        if all(x.shape == processed_images[0].shape for x in processed_images):
            processed_images = torch.stack(processed_images, dim=0)
        return processed_images
    def text_process(self,text,tokenizer):
        prompt=f"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful language and vision assistant. You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n<image>\n{text}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
        text_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split('<image>')]
        input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1][1:], dtype=torch.long).unsqueeze(0)
        return input_ids
        

AutoConfig.register("mixsense_llama", MixsenseConfig)
AutoModelForCausalLM.register(MixsenseConfig, MixsenseLlamaForCausalLM)