"""
Requires Transformer 4.28 and above, implementation may change according the Llama implementation
"""
import logging
import string
from packaging import version

import torch
from torch.cuda.amp import autocast as autocast
import torch.nn as nn

import transformers

from minigpt4.common.registry import registry
from minigpt4.models.blip2 import Blip2Base, disabled_train

transformers_version = version.parse(transformers.__version__)
assert transformers_version >= version.parse("4.28"), "BLIP-2 Vicuna requires transformers>=4.28"        
from minigpt4.models.modeling_llama import LlamaForCausalLM
from transformers import LlamaTokenizer


@registry.register_model("blip2_vicuna_instruct")
class Blip2VicunaInstruct(Blip2Base):
    """
    BLIP2 Vicuna model.
    Supported model types:
        - vicuna7b
        - vicuna13b
    Usage:
        >>> from minigpt4.models import load_model
        >>> model = load_model("blip2_vicuna_instruct", "vicuna7b")
    """

    PRETRAINED_MODEL_CONFIG_DICT = {
        "vicuna7b": "configs/models/blip2_instruct_vicuna7b.yaml",
        "vicuna13b": "configs/models/blip2_instruct_vicuna13b.yaml",
    }

    def __init__(
        self,
        vit_model="eva_clip_g",
        img_size=224,
        drop_path_rate=0,
        use_grad_checkpoint=False,
        vit_precision="fp16",
        freeze_vit=True,
        num_query_token=32,
        llm_model="",
        prompt="",
        max_txt_len=128,
        max_output_txt_len=256,
        apply_lemmatizer=False,
        qformer_text_input=True,
    ):
        super().__init__()
        
        self.tokenizer = self.init_tokenizer(truncation_side="left")

        self.visual_encoder, self.ln_vision = self.init_vision_encoder(
            vit_model, img_size, drop_path_rate, use_grad_checkpoint, vit_precision
        )
        if freeze_vit:
            for name, param in self.visual_encoder.named_parameters():
                param.requires_grad = False
            self.visual_encoder = self.visual_encoder.eval()
            self.visual_encoder.train = disabled_train
            logging.info("freeze vision encoder")

        self.Qformer, self.query_tokens = self.init_Qformer(
            num_query_token, self.visual_encoder.num_features
        )

        if not qformer_text_input:
            self.Qformer.bert.embeddings.word_embeddings = None
            self.Qformer.bert.embeddings.position_embeddings = None
            for layer in self.Qformer.bert.encoder.layer:
                layer.output = None
                layer.intermediate = None
        else:
            self.Qformer.resize_token_embeddings(len(self.tokenizer))
        self.Qformer.cls = None
        
        self.llm_tokenizer = LlamaTokenizer.from_pretrained(llm_model, use_fast=False, truncation_side="left")
        self.llm_model = LlamaForCausalLM.from_pretrained(
            llm_model, torch_dtype=torch.float16
        )
        self.llm_tokenizer.add_special_tokens({'pad_token': '[PAD]'})
        self.llm_tokenizer.add_special_tokens({'bos_token': '</s>'})
        self.llm_tokenizer.add_special_tokens({'eos_token': '</s>'})
        self.llm_tokenizer.add_special_tokens({'unk_token': '</s>'})
        # self.llm_tokenizer.pad_token = self.llm_tokenizer.unk_token

        self.llm_model.resize_token_embeddings(len(self.llm_tokenizer))

        # self.eos_token_id = self.llm_tokenizer(
        #     self.llm_tokenizer.eos_token, add_special_tokens=False
        # ).input_ids[0]

        for name, param in self.llm_model.named_parameters():
            param.requires_grad = False

        self.llm_proj = nn.Linear(
            self.Qformer.config.hidden_size, self.llm_model.config.hidden_size
        )

        self.max_txt_len = max_txt_len
        self.max_output_txt_len = max_output_txt_len
        self.prompt = prompt
        prompt_tokens = self.llm_tokenizer(self.prompt, return_tensors="pt")
        self.prompt_length = prompt_tokens.attention_mask.sum(1)

        self._lemmatizer = None

        self.qformer_text_input = qformer_text_input

    def concat_text_input_output(self, input_ids, input_atts, output_ids, output_atts):
        input_part_targets_len = []
        llm_tokens = {"input_ids": [], "attention_mask": []}
        for i in range(input_ids.size(0)):
            this_input_ones = input_atts[i].sum()
            input_part_targets_len.append(this_input_ones)
            llm_tokens['input_ids'].append(
                torch.cat([
                    input_ids[i][:this_input_ones],
                    output_ids[i][1:],
                    input_ids[i][this_input_ones:]
                ])
            )
            llm_tokens['attention_mask'].append(
                torch.cat([
                    input_atts[i][:this_input_ones],
                    output_atts[i][1:],
                    input_atts[i][this_input_ones:]
                ])
            )
        llm_tokens['input_ids'] = torch.stack(llm_tokens['input_ids'])
        llm_tokens['attention_mask'] = torch.stack(llm_tokens['attention_mask'])
        return llm_tokens, input_part_targets_len

    def forward(self, samples):
        # print('-----------------')
        # print(samples["text_input"])
        # print(samples["text_output"])
        # print('-----------------')

        image = samples["image"]
        with self.maybe_autocast():
            image_embeds = self.ln_vision(self.visual_encoder(image))
        image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(image.device)

        bs = image.size(0)

        query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
        if self.qformer_text_input:
            text_Qformer = self.tokenizer(
                samples["text_input"],
                padding='longest',
                truncation=True,
                max_length=self.max_txt_len,
                return_tensors="pt",
            ).to(image.device)
            query_atts = torch.ones(query_tokens.size()[:-1], dtype=torch.long).to(image.device)
            Qformer_atts = torch.cat([query_atts, text_Qformer.attention_mask],dim=1)

            query_output = self.Qformer.bert(
                text_Qformer.input_ids,
                attention_mask=Qformer_atts,
                query_embeds=query_tokens,
                encoder_hidden_states=image_embeds,
                encoder_attention_mask=image_atts,
                return_dict=True,
            )
        else:
            query_output = self.Qformer.bert(
                query_embeds=query_tokens,
                encoder_hidden_states=image_embeds,
                encoder_attention_mask=image_atts,
                return_dict=True,
            )

        inputs_llm = self.llm_proj(query_output.last_hidden_state[:,:query_tokens.size(1),:])
        atts_llm = torch.ones(inputs_llm.size()[:-1], dtype=torch.long).to(image.device)

        self.llm_tokenizer.padding_side = "right"
        self.llm_tokenizer.truncation_side = 'left'
        text_input_tokens = self.llm_tokenizer(
            samples['text_input'],
            return_tensors="pt",
            padding="longest",
            truncation=True,
            max_length=self.max_txt_len,
        ).to(image.device)

        self.llm_tokenizer.truncation_side = 'right'
        text_output_tokens = self.llm_tokenizer(
            [t + self.llm_tokenizer.eos_token for t in samples['text_output']],
            return_tensors="pt",
            padding="longest",
            truncation=True,
            max_length=self.max_output_txt_len,
        ).to(image.device)

        llm_tokens, input_part_targets_len = self.concat_text_input_output(
            text_input_tokens.input_ids,
            text_input_tokens.attention_mask,
            text_output_tokens.input_ids,
            text_output_tokens.attention_mask,
        )

        # do not apply loss to the padding
        targets = llm_tokens['input_ids'].masked_fill(
            llm_tokens['input_ids'] == self.llm_tokenizer.pad_token_id, -100
        )

        # do not apply loss to the text input (i.e., instruction)
        for i, l in enumerate(input_part_targets_len):
            targets[i][:l] = -100

        # do not apply loss to the query tokens
        empty_targets = (
            torch.ones(atts_llm.size(), dtype=torch.long).to(image.device).fill_(-100)
        )
        targets = torch.cat([empty_targets, targets], dim=1)

        inputs_embeds = self.llm_model.get_input_embeddings()(llm_tokens['input_ids'])
        inputs_embeds = torch.cat([inputs_llm, inputs_embeds], dim=1)
        attention_mask = torch.cat([atts_llm, llm_tokens['attention_mask']], dim=1)

        with self.maybe_autocast():
            outputs = self.llm_model(
                inputs_embeds=inputs_embeds,
                attention_mask=attention_mask,
                return_dict=True,
                labels=targets,
            )

        loss = outputs.loss

        return {"loss": loss}

    @torch.no_grad()
    def generate(
        self,
        samples,
        use_nucleus_sampling=False,
        num_beams=5,
        max_length=256,
        min_length=1,
        max_new_tokens=300,
        min_new_tokens=1,
        top_p=0.9,
        repetition_penalty=1.0,
        length_penalty=1,
        num_captions=1,
        temperature=1,
        output_attentions=False,
        return_dict_in_generate=False,
        # ours
        opera_decoding=False,
        key_position=None,
        scale_factor=1.0,
        threshold=1,
        num_attn_candidates=5,
        penalty_weights=1.0,
        kl_decoding=False,
        m3id=False,
    ):
        self.llm_tokenizer.padding_side = "left"

        if "prompt" in samples.keys():
            prompt = samples["prompt"]
        else:
            prompt = self.prompt

        image = samples["image"]

        bs = image.size(0)

        if isinstance(prompt, str):
            prompt = prompt.split("<ImageHere>")[-1]
            prompt = [prompt] * bs
        else:
            prompt = [p.split("<ImageHere>")[-1] for p in prompt]
            assert len(prompt) == bs, "The number of prompts must be equal to the batch size."

        # For TextCaps
        if "ocr_tokens" in samples.keys() and "{}" in prompt[0]:
            prompt = [p.format(', '.join(samples['ocr_tokens'][i][:30])) for i, p in enumerate(prompt)]

        query_tokens = self.query_tokens.expand(bs, -1, -1)
        if self.qformer_text_input:
            # remove ocr tokens in q_former (for eval textvqa)
            # qformer_prompt = prompt
            # qformer_prompt = ['Question: ' + qp.split(' Question: ')[1] for qp in qformer_prompt]

            text_Qformer = self.tokenizer(
                prompt,
                padding='longest',
                truncation=True,
                max_length=self.max_txt_len,
                return_tensors="pt",
            ).to(image.device)
            query_atts = torch.ones(query_tokens.size()[:-1], dtype=torch.long).to(image.device)
            Qformer_atts = torch.cat([query_atts, text_Qformer.attention_mask], dim=1)

        # For video data
        if image.dim() == 5:
            inputs_llm, atts_llm = [], []
            for j in range(image.size(2)):
                this_frame = image[:,:,j,:,:]
                with self.maybe_autocast():
                    frame_embeds = self.ln_vision(self.visual_encoder(this_frame))
                frame_atts = torch.ones(frame_embeds.size()[:-1], dtype=torch.long).to(image.device)

                if self.qformer_text_input:
                    frame_query_output = self.Qformer.bert(
                        text_Qformer.input_ids,
                        attention_mask=Qformer_atts,
                        query_embeds=query_tokens,
                        encoder_hidden_states=frame_embeds,
                        encoder_attention_mask=frame_atts,
                        return_dict=True,
                    )
                else:
                    frame_query_output = self.Qformer.bert(
                        query_embeds=query_tokens,
                        encoder_hidden_states=frame_embeds,
                        encoder_attention_mask=frame_atts,
                        return_dict=True,
                    )
                frame_inputs_llm = self.llm_proj(frame_query_output.last_hidden_state[:,:query_tokens.size(1),:])
                frame_atts_llm = torch.ones(frame_inputs_llm.size()[:-1], dtype=torch.long).to(image.device)
                inputs_llm.append(frame_inputs_llm)
                atts_llm.append(frame_atts_llm)
            inputs_llm = torch.cat(inputs_llm, dim=1)
            atts_llm = torch.cat(atts_llm, dim=1)
        else:
            
            with self.maybe_autocast():
                image_embeds = self.ln_vision(self.visual_encoder(image))
            image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(image.device)
            
            
            
            if self.qformer_text_input:
                query_output = self.Qformer.bert(
                    text_Qformer.input_ids,
                    attention_mask=Qformer_atts,
                    query_embeds=query_tokens,
                    encoder_hidden_states=image_embeds,
                    encoder_attention_mask=image_atts,
                    return_dict=True,
                )
            else:
                query_output = self.Qformer.bert(
                    query_embeds=query_tokens,
                    encoder_hidden_states=image_embeds,
                    encoder_attention_mask=image_atts,
                    return_dict=True,
                )
            

            
                
            # zero_tensor = torch.Size([1, 3, 224, 224])
            # # 모든 값을 0으로 채우기
            # zero_filled_tensor = torch.zeros(zero_tensor).to(image.device)

            

            # with self.maybe_autocast():
            #     zero_image_embeds = self.ln_vision(self.visual_encoder(zero_filled_tensor))
            # zero_image_atts = torch.ones(zero_image_embeds.size()[:-1], dtype=torch.long).to(image.device)
            # if self.qformer_text_input:
            #     zero_query_output = self.Qformer.bert(
            #         text_Qformer.input_ids,
            #         attention_mask=Qformer_atts,
            #         query_embeds=query_tokens,
            #         encoder_hidden_states=zero_image_embeds,
            #         encoder_attention_mask=zero_image_atts,
            #         return_dict=True,
            #     )
            # else:
            #     zero_query_output = self.Qformer.bert(
            #         query_embeds=query_tokens,
            #         encoder_hidden_states=zero_image_embeds,
            #         encoder_attention_mask=zero_image_atts,
            #         return_dict=True,
            #     )
                


            # zero_inputs_llm = self.llm_proj(zero_query_output.last_hidden_state[:,:query_tokens.size(1),:])
            # zero_atts_llm = torch.ones(zero_inputs_llm.size()[:-1], dtype=torch.long).to(image.device)

            inputs_llm = self.llm_proj(query_output.last_hidden_state[:,:query_tokens.size(1),:])#.index_select(dim=1, index=torch.tensor([4,31]).to(image.device))
            # inputs_llm = torch.cat([inputs_llm[:,-i,:].unsqueeze(1) for i in range(inputs_llm.shape[1])], dim=1)
            atts_llm = torch.ones(inputs_llm.size()[:-1], dtype=torch.long).to(image.device)


        llm_tokens = self.llm_tokenizer(
            prompt,
            padding="longest",
            return_tensors="pt"
        ).to(image.device)

        with self.maybe_autocast():
            inputs_embeds = self.llm_model.get_input_embeddings()(llm_tokens.input_ids)
            inputs_embeds_llm = self.llm_model.get_input_embeddings()(llm_tokens.input_ids)
            
            #print("llm_tokens.input_ids: ",llm_tokens.input_ids)
            masking_tensor = torch.tensor([[32000] * 32])
            masking_tensor = masking_tensor.to(image.device)
            masking_embeds = self.llm_model.get_input_embeddings()(masking_tensor)
            
            inputs_embeds_llm = torch.cat([masking_embeds, inputs_embeds_llm], dim=1)
            attention_mask_llm = torch.cat([atts_llm,llm_tokens.attention_mask], dim=1)
            inputs_embeds = torch.cat([inputs_llm, inputs_embeds], dim=1)
            attention_mask = torch.cat([atts_llm, llm_tokens.attention_mask], dim=1)

            if key_position is None:
                key_position = {
                    "image_start": 1, 
                    "image_end": inputs_llm.shape[1], 
                    "response_start": inputs_embeds.shape[1]
                }

            output_ids = self.llm_model.generate( #,jsd_list
                inputs_embeds=inputs_embeds,
                attention_mask=attention_mask,
                inputs_embeds_llm = inputs_embeds_llm,
                attention_mask_llm = attention_mask_llm,
                do_sample=use_nucleus_sampling,
                top_p=top_p,
                temperature=temperature,
                num_beams=num_beams,
                # max_length=max_length,
                #min_length=min_length,
                max_new_tokens=max_new_tokens,
                min_new_tokens=min_new_tokens,
                # eos_token_id=self.eos_token_id,
                repetition_penalty=repetition_penalty,
                length_penalty=length_penalty,
                num_return_sequences=num_captions,
                output_attentions=output_attentions,
                return_dict_in_generate=return_dict_in_generate,
                # opera
                opera_decoding=opera_decoding,
                key_position=key_position,
                scale_factor=scale_factor,
                threshold=threshold,
                num_attn_candidates=num_attn_candidates,
                penalty_weights=penalty_weights,
                kl_decoding=kl_decoding,
            )
        outputs=output_ids.tolist()[0]
        #print(outputs)
        outputs= outputs[1:]
        output_tokens = self.llm_tokenizer.convert_ids_to_tokens(outputs)
        # print(jsd_list)
        # print(output_tokens)
        # print(len(jsd_list))
        # print(len(output_tokens))
        

        output_ids[output_ids == 0] = 2 # convert output id 0 to 2 (eos_token_id)
        output_text = self.llm_tokenizer.batch_decode(output_ids, skip_special_tokens=True)
        output_text = [text.strip() for text in output_text]
        # print("output_tokens: ",len(output_tokens))
        # print("output_text: ",output_text)
        # print("len(jsd_list): ",len(jsd_list))
        # jsd_output_tokens = []
        # if len(output_tokens) != len(jsd_list):
        #     print("길이 안맞음")
        #     import sys
        #     sys.exit()
        # for _idx in range(len(output_tokens)):
        #     jsd_output_tokens.append([output_tokens[_idx],jsd_list[_idx]])
        return output_text#,jsd_list

    def predict_answers(
        self,
        samples,
        num_beams=5,
        inference_method="generate",
        max_len=10,
        min_len=1,
        num_ans_candidates=128,
        answer_list=None,
        prompt="",
        length_penalty=0,
        **kwargs
    ):
        if isinstance(samples["text_input"], str):
            samples["text_input"] = [samples["text_input"]]

        if prompt:
            if prompt.count("{}") == 2:
                if 'ocr_tokens' in samples:
                    text_input = [
                        prompt.format(', '.join(samples['ocr_tokens'][i][:30]), samples["text_input"][i])
                    for i in range(len(samples["text_input"]))]
                elif 'choices' in samples:
                    text_input = []
                    for i in range(len(samples["text_input"])):
                        this_choices = [f"({string.ascii_lowercase[j]}) {ch}" for j, ch in enumerate(samples["choices"][i])]
                        this_choices = " ".join(this_choices)
                        text_input.append(prompt.format(samples["text_input"][i], this_choices))
            else:
                text_input = [prompt.format(question) for question in samples["text_input"]]
        else:
            text_input = samples["text_input"]

        samples["prompt"] = text_input

        output_text = self.generate(
            samples,
            num_beams=num_beams,
            max_length=max_len,
            min_length=min_len,
            length_penalty=length_penalty
        )

        if "apply_lemmatizer" in samples.keys() and samples["apply_lemmatizer"]:
            output_text = self._lemmatize(output_text)

        return output_text

    def predict_class(
        self,
        samples,
        candidates,
        n_segments=1,
    ):
        self.llm_tokenizer.padding_side = "left"

        # If candidates is a list of lists, each sample has its candidates, then we need to iterate one by one
        if type(candidates[0]) == list:
            results = []

            for i in range(samples["image"].size(0)):
                this_sample = {
                    "image": samples["image"][i].unsqueeze(0),
                    "prompt": samples["prompt"],
                }

                if "text_input" in samples.keys():
                    this_sample["text_input"] = [samples["text_input"][i]]

                if 'context' in samples.keys():
                    this_sample['context'] = [samples["context"][i]]

                if 'history' in samples.keys():
                    this_sample['history'] = [samples["history"][i]]

                if 'caption' in samples.keys():
                    this_sample['caption'] = [samples["caption"][i]]

                this_result = self._predict_class(this_sample, candidates[i], n_segments)
                results.append(this_result)

            try:
                results = torch.cat(results, dim=0)
            except:
                results = [res.tolist()[0] for res in results]

            return results

        return self._predict_class(samples, candidates, n_segments)

    def _predict_class(
        self,
        samples,
        candidates,
        n_segments=1,
    ):
        image = samples["image"]
        prompt = samples["prompt"]

        bs = image.size(0)

        if isinstance(prompt, str):
            prompt = [prompt] * bs
        else:
            assert len(prompt) == bs, "The number of prompts must be equal to the batch size."

        if "text_input" in samples.keys():
            if type(samples["text_input"][0]) == list:
                prompt = [prompt[i].format(*samples["text_input"][i]) for i in range(len(prompt))]
            else:
                prompt = [prompt[i].format(samples["text_input"][i]) for i in range(len(prompt))]

        # scienceqa
        if 'context' in samples.keys() and samples['context'] != '':
            prompt = [f'context: {samples["context"][i]}. {prompt[i]}' for i in range(len(prompt))]

        # visual dialog
        if 'history' in samples.keys() and samples['history'][0] != '':
            prompt = [f'dialog history: {samples["history"][i]}\n{prompt[i]}' for i in range(len(prompt))]

        if 'caption' in samples.keys() and samples['caption'][0] != '':
            prompt = [f'This image has the caption "{samples["caption"][i]}". {prompt[i]}' for i in range(len(prompt))]

        query_tokens = self.query_tokens.expand(bs, -1, -1)
        if self.qformer_text_input:
            text_Qformer = self.tokenizer(
                prompt,
                padding='longest',
                truncation=True,
                max_length=self.max_txt_len,
                return_tensors="pt"
            ).to(image.device)
            query_atts = torch.ones(query_tokens.size()[:-1], dtype=torch.long).to(image.device)
            Qformer_atts = torch.cat([query_atts, text_Qformer.attention_mask], dim=1)

        if image.dim() == 5:
            inputs_llm, atts_llm = [], []
            for j in range(image.size(2)):
                this_frame = image[:,:,j,:,:]
                with self.maybe_autocast():
                    frame_embeds = self.ln_vision(self.visual_encoder(this_frame))
                    frame_atts = torch.ones(frame_embeds.size()[:-1], dtype=torch.long).to(image.device)

                if self.qformer_text_input:
                    frame_query_output = self.Qformer.bert(
                        text_Qformer.input_ids,
                        attention_mask=Qformer_atts,
                        query_embeds=query_tokens,
                        encoder_hidden_states=frame_embeds,
                        encoder_attention_mask=frame_atts,
                        return_dict=True,
                    )
                else:
                    frame_query_output = self.Qformer.bert(
                        query_embeds=query_tokens,
                        encoder_hidden_states=frame_embeds,
                        encoder_attention_mask=frame_atts,
                        return_dict=True,
                    )

                frame_inputs_llm = self.llm_proj(frame_query_output.last_hidden_state[:,:query_tokens.size(1),:])
                frame_atts_llm = torch.ones(frame_inputs_llm.size()[:-1], dtype=torch.long).to(image.device)
                inputs_llm.append(frame_inputs_llm)
                atts_llm.append(frame_atts_llm)
            inputs_llm = torch.cat(inputs_llm, dim=1)
            atts_llm = torch.cat(atts_llm, dim=1)
        else:
            with self.maybe_autocast():
                image_embeds = self.ln_vision(self.visual_encoder(image))
            image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(image.device)

            if self.qformer_text_input:
                query_output = self.Qformer.bert(
                    text_Qformer.input_ids,
                    attention_mask=Qformer_atts,
                    query_embeds=query_tokens,
                    encoder_hidden_states=image_embeds,
                    encoder_attention_mask=image_atts,
                    return_dict=True,
                )
            else:
                query_output = self.Qformer.bert(
                    query_embeds=query_tokens,
                    encoder_hidden_states=image_embeds,
                    encoder_attention_mask=image_atts,
                    return_dict=True,
                )

            inputs_llm = self.llm_proj(query_output.last_hidden_state[:,:query_tokens.size(1),:])
            atts_llm = torch.ones(inputs_llm.size()[:-1], dtype=torch.long).to(image.device)

        self.llm_tokenizer.padding_side = "right"
        self.llm_tokenizer.truncation_side = 'left'
        text_input_tokens = self.llm_tokenizer(
            prompt,
            return_tensors="pt",
            padding="longest",
            # truncation=True,
            # max_length=self.max_txt_len,
        ).to(image.device)

        empty_targets = torch.ones(atts_llm.size(), dtype=torch.long).to(image.device).fill_(-100)

        # self.llm_tokenizer.padding_side = "right"
        self.llm_tokenizer.truncation_side = 'right'
        n_cands = len(candidates)
        with self.maybe_autocast(dtype=torch.bfloat16):
            all_losses = []
            for n in range(n_segments):
                seg_len = n_cands // n_segments
                if n == (n_segments - 1):
                    seg_len = n_cands - seg_len * (n_segments - 1)

                start_i = n * (n_cands // n_segments)
                end_i = start_i + seg_len

                this_output_tokens = self.llm_tokenizer(
                    candidates[start_i:end_i],
                    return_tensors="pt",
                    padding="longest",
                    # truncation=True,
                    # max_length=self.max_output_txt_len,
                ).to(image.device)

                this_input_tokens_ids = text_input_tokens.input_ids.repeat_interleave(seg_len, dim=0)
                this_input_tokens_atts = text_input_tokens.attention_mask.repeat_interleave(seg_len, dim=0)

                this_output_tokens_ids = this_output_tokens.input_ids.repeat(bs, 1)
                this_output_tokens_atts = this_output_tokens.attention_mask.repeat(bs, 1)

                this_llm_tokens, this_input_targets_len = self.concat_text_input_output(
                    this_input_tokens_ids,
                    this_input_tokens_atts,
                    this_output_tokens_ids,
                    this_output_tokens_atts
                )

                this_llm_input_ids = this_llm_tokens['input_ids']
                this_llm_atts = this_llm_tokens['attention_mask']
                # this_llm_input_ids = torch.cat([this_input_tokens_ids, this_output_tokens_ids], dim=1)
                # this_llm_atts = torch.cat([this_input_tokens_atts, this_output_tokens_atts], dim=1)

                inputs_embeds = self.llm_model.get_input_embeddings()(this_llm_input_ids)
                inputs_embeds = torch.cat([inputs_llm.repeat_interleave(seg_len, dim=0), inputs_embeds], dim=1)
                attention_mask = torch.cat([atts_llm.repeat_interleave(seg_len, dim=0), this_llm_atts], dim=1)

                this_targets = this_llm_input_ids.masked_fill(this_llm_input_ids == self.llm_tokenizer.pad_token_id, -100)
                # this_targets[:, :this_input_tokens_ids.size(1)] = -100
                for i, l in enumerate(this_input_targets_len):
                    this_targets[i][:l] = -100

                this_targets = torch.cat([empty_targets.repeat_interleave(seg_len, dim=0), this_targets], dim=1)

                outputs = self.llm_model(
                    inputs_embeds=inputs_embeds,
                    attention_mask=attention_mask,
                    return_dict=True,
                    labels=this_targets,
                    reduction="none",
                )

                loss = outputs.loss

                loss = loss.reshape(bs, seg_len)
                # output_class_ranks = torch.argsort(loss, dim=-1)
                all_losses.append(loss)

            all_losses = torch.cat(all_losses, dim=-1)
            output_class_ranks = torch.argsort(all_losses, dim=-1)

        return output_class_ranks

    def _lemmatize(self, answers):
        def apply(answer):
            doc = self.lemmatizer(answer)

            words = []
            for token in doc:
                if token.pos_ in ["NOUN", "VERB"]:
                    words.append(token.lemma_)
                else:
                    words.append(token.text)
            answer = " ".join(words)

            return answer

        return [apply(answer) for answer in answers]

    @property
    def lemmatizer(self):
        if self._lemmatizer is None:
            try:
                import spacy

                self._lemmatizer = spacy.load("en_core_web_sm")
            except ImportError:
                logging.error(
                    """
                    Please install spacy and en_core_web_sm model to apply lemmatization.
                    python -m spacy download en_core_web_sm
                    OR
                    import spacy.cli
                    spacy.cli.download("en_core_web_sm")
                    """
                )
                exit(1)

        return self._lemmatizer

    @classmethod
    def from_config(cls, cfg):
        vit_model = cfg.get("vit_model", "eva_clip_g")
        img_size = cfg.get("image_size")
        num_query_token = cfg.get("num_query_token")
        llm_model = cfg.get("llm_model")

        drop_path_rate = cfg.get("drop_path_rate", 0)
        use_grad_checkpoint = cfg.get("use_grad_checkpoint", False)
        vit_precision = cfg.get("vit_precision", "fp16")
        freeze_vit = cfg.get("freeze_vit", True)

        prompt = cfg.get("prompt", "")
        max_txt_len = cfg.get("max_txt_len", 128)
        max_output_txt_len = cfg.get("max_output_txt_len", 256)

        apply_lemmatizer = cfg.get("apply_lemmatizer", False)

        qformer_text_input = cfg.get("qformer_text_input", True)

        model = cls(
            vit_model=vit_model,
            img_size=img_size,
            drop_path_rate=drop_path_rate,
            use_grad_checkpoint=use_grad_checkpoint,
            vit_precision=vit_precision,
            freeze_vit=freeze_vit,
            num_query_token=num_query_token,
            llm_model=llm_model,
            prompt=prompt,
            max_txt_len=max_txt_len,
            max_output_txt_len=max_output_txt_len,
            apply_lemmatizer=apply_lemmatizer,
            qformer_text_input=qformer_text_input,
        )

        # if qformer_text_input:
        #     # Hard-coded to load from BLIP-2 stage-1 pre-trained model (not ideal)
        #     model.load_from_pretrained(
        #         url_or_filename="https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth"
        #     )

        model.load_checkpoint_from_config(cfg)

        return model
