#    Copyright 2023 Haotian Liu & Qinghao Ye (Modified from LLaVA)
#
#    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
from typing import List, Optional, Tuple, Union

import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss

from transformers import AutoConfig, AutoModelForCausalLM, LlamaConfig, LlamaModel, LlamaForCausalLM
from transformers.modeling_outputs import CausalLMOutputWithPast

from .configuration_mplug_owl2 import MPLUGOwl2Config, MplugOwlVisionConfig, MplugOwlVisualAbstractorConfig, MPLUGOwl2QwenConfig
from .visual_encoder import MplugOwlVisionModel, MplugOwlVisualAbstractorModel
from .modeling_llama2 import replace_llama_modality_adaptive
from .modeling_qwen import QWenLMHeadModel, QWenModel
from mplug_owl2.constants import IMAGE_TOKEN_INDEX, IGNORE_INDEX
from icecream import ic
from transformers.modeling_utils import PreTrainedModel

class MPLUGOwl2MetaModel:
    def __init__(self, config):
        super(MPLUGOwl2MetaModel, self).__init__(config)
        self.vision_model = MplugOwlVisionModel(
            MplugOwlVisionConfig(**config.visual_config["visual_model"])
        )
        self.visual_abstractor = MplugOwlVisualAbstractorModel(
            MplugOwlVisualAbstractorConfig(**config.visual_config["visual_abstractor"]), config.hidden_size
        )
    
    def get_vision_tower(self):
        vision_model = getattr(self, 'vision_model', None)
        if type(vision_model) is list:
            vision_model = vision_model[0]
        return vision_model

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


class MPLUGOwl2MetaForCausalLM(ABC):
    @abstractmethod
    def get_model(self):
        pass

    def encode_images(self, images):
        image_features = self.get_model().vision_model(images).last_hidden_state
        image_features = self.get_model().visual_abstractor(encoder_hidden_states=image_features).last_hidden_state
        return image_features

    def prepare_inputs_labels_for_multimodal(
        self, input_ids, attention_mask, past_key_values, labels, images
    ):
        if images is None or input_ids.shape[1] == 1:
            if past_key_values is not None and images is not None and input_ids.shape[1] == 1:
                # print(attention_mask)
                if attention_mask is not None:
                    attention_mask = torch.ones((attention_mask.shape[0], past_key_values[-1][-1].shape[-2] + 1), dtype=attention_mask.dtype, device=attention_mask.device)
            multiway_indices = torch.zeros_like(input_ids).long().to(self.device)
            return input_ids, multiway_indices, attention_mask, past_key_values, None, labels
        
        if type(images) is list or images.ndim == 5:
            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)
            image_features = [x.flatten(0, 1) for x in image_features]
        else:
            image_features = self.encode_images(images)

        new_input_embeds = []
        new_modality_indicators = []
        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:
                # multimodal LLM, but the current sample is not multimodal
                # FIXME: this is a hacky fix, for deepspeed zero3 to work
                half_len = cur_input_ids.shape[0] // 2
                cur_image_features = image_features[cur_image_idx]
                cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids[:half_len])
                cur_input_embeds_2 = self.get_model().embed_tokens(cur_input_ids[half_len:])
                cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0], cur_input_embeds_2], dim=0)
                new_input_embeds.append(cur_input_embeds)
                
                cur_modality_indicators = torch.zeros(len(cur_input_embeds)).long().to(self.device)
                new_modality_indicators.append(cur_modality_indicators)
                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 = []
            cur_modality_indicators = []
            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]
                cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[:image_token_start]))
                cur_new_input_embeds.append(cur_image_features)
                
                # Add modality indicator
                assert image_token_start == len(cur_input_ids[:image_token_start])
                cur_modality_indicators.append(torch.zeros(len(cur_input_ids[:image_token_start])).long())
                cur_modality_indicators.append(torch.ones(len(cur_image_features)).long())
                
                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 = cur_labels[image_token_start+1:]
                cur_image_idx += 1
                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:
                cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids))
                cur_modality_indicators.append(torch.zeros(len(cur_input_ids)).long())
                if labels is not None:
                    cur_new_labels.append(cur_labels)
            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)
            
            # Modality
            cur_modality_indicators = [x.to(device=self.device) for x in cur_modality_indicators]
            cur_modality_indicators = torch.cat(cur_modality_indicators, dim=0)
            new_modality_indicators.append(cur_modality_indicators)
            
            
            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)
            
            # Embedding
            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)
            
            # Modality
            new_modality_indicators_align = []
            for cur_modality_indicator in new_modality_indicators:
                cur_new_embed = torch.cat((cur_modality_indicator, torch.zeros(max_len - cur_modality_indicator.shape[0], dtype=cur_modality_indicator.dtype, device=cur_modality_indicator.device)), dim=0)
                new_modality_indicators_align.append(cur_new_embed)
            new_modality_indicators = torch.stack(new_modality_indicators_align, dim=0)
            
            # Label
            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)
            
            # Attention Mask
            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)
            new_modality_indicators = torch.stack(new_modality_indicators, 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, new_modality_indicators, attention_mask, past_key_values, new_input_embeds, new_labels



class MPLUGOwl2LlamaModel(MPLUGOwl2MetaModel, LlamaModel):
    config_class = MPLUGOwl2Config

    def __init__(self, config: MPLUGOwl2Config):
        super(MPLUGOwl2LlamaModel, self).__init__(config)

class MPLUGOwl2QWenModel(MPLUGOwl2MetaModel, QWenModel):
    config_class = MPLUGOwl2QwenConfig

    def __init__(self, config: MPLUGOwl2QwenConfig):
        super(MPLUGOwl2QWenModel, self).__init__(config)

class MPLUGOwl2LlamaForCausalLM(LlamaForCausalLM, MPLUGOwl2MetaForCausalLM):
    config_class = MPLUGOwl2Config

    def __init__(self, config):
        super(LlamaForCausalLM, self).__init__(config)
        self.model = MPLUGOwl2LlamaModel(config)

        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

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

    def encode_images(self, images):
        image_features = self.get_model().vision_model(images).last_hidden_state
        image_features = self.get_model().visual_abstractor(encoder_hidden_states=image_features).last_hidden_state
        return image_features

    def get_model(self):
        return self.model

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = 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,
        images_cd: Optional[torch.FloatTensor] = None,
        cd_beta: Optional[torch.FloatTensor] = None,
        cd_alpha: Optional[torch.FloatTensor] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        input_ids, modality_indicators, attention_mask, past_key_values, inputs_embeds, labels = \
            self.prepare_inputs_labels_for_multimodal(input_ids, attention_mask, past_key_values, labels, images)

        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        outputs = self.model(
            input_ids=input_ids,
            modality_indicators=modality_indicators,
            attention_mask=attention_mask,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict
        )

        hidden_states = outputs[0]
        logits = self.lm_head(hidden_states)

        loss = None
        if labels is not None:
            # Shift so that tokens < n predict n
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            # Flatten the tokens
            loss_fct = CrossEntropyLoss()
            shift_logits = shift_logits.view(-1, self.config.vocab_size)
            shift_labels = shift_labels.view(-1)
            # Enable model/pipeline parallelism
            shift_labels = shift_labels.to(shift_logits.device)
            loss = loss_fct(shift_logits, shift_labels)

        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

    def prepare_inputs_for_generation(
        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
    ):
        if past_key_values:
            input_ids = input_ids[:, -1:]

        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
        if inputs_embeds is not None and past_key_values is None:
            model_inputs = {"inputs_embeds": inputs_embeds}
        else:
            model_inputs = {"input_ids": input_ids}

        model_inputs.update(
            {
                "past_key_values": past_key_values,
                "use_cache": kwargs.get("use_cache"),
                "attention_mask": attention_mask,
                "images": kwargs.get("images", None),
            }
        )
        return model_inputs

    def prepare_inputs_for_generation_cd(
        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
    ):
        if past_key_values:
            input_ids = input_ids[:, -1:]

        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
        if inputs_embeds is not None and past_key_values is None:
            model_inputs = {"inputs_embeds": inputs_embeds}
        else:
            model_inputs = {"input_ids": input_ids}

        model_inputs.update(
            {
                "past_key_values": past_key_values,
                "use_cache": kwargs.get("use_cache"),
                "attention_mask": attention_mask,
                "images": kwargs.get("images_cd", kwargs.get("images", None)),
            }
        )
        return model_inputs

class MPLUGOwl2QWenForCausalLM(QWenLMHeadModel, MPLUGOwl2MetaForCausalLM):
    config_class = MPLUGOwl2QwenConfig

    def __init__(self, config):
        super(QWenLMHeadModel, self).__init__(config)
        from .modeling_qwen import SUPPORT_BF16,logger,SUPPORT_FP16,SUPPORT_CUDA, _import_flash_attn
        autoset_precision = config.bf16 + config.fp16 + config.fp32 == 0

        if autoset_precision:
            if SUPPORT_BF16:
                logger.warn(
                    "The model is automatically converting to bf16 for faster inference. "
                    "If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\"."
                )
                config.bf16 = True
            elif SUPPORT_FP16:
                logger.warn(
                    "The model is automatically converting to fp16 for faster inference. "
                    "If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\"."
                )
                config.fp16 = True
            else:
                config.fp32 = True

        if config.bf16 and SUPPORT_CUDA and not SUPPORT_BF16:
            logger.warn("Your device does NOT seem to support bf16, you can switch to fp16 or fp32 by by passing fp16/fp32=True in \"AutoModelForCausalLM.from_pretrained\".")
        if config.fp16 and SUPPORT_CUDA and not SUPPORT_FP16:
            logger.warn("Your device does NOT support faster inference with fp16, please switch to fp32 which is likely to be faster")
        if config.fp32:
            if SUPPORT_BF16:
                logger.warn("Your device support faster inference by passing bf16=True in \"AutoModelForCausalLM.from_pretrained\".")
            elif SUPPORT_FP16:
                logger.warn("Your device support faster inference by passing fp16=True in \"AutoModelForCausalLM.from_pretrained\".")

        if config.use_flash_attn == "auto":
            if config.bf16 or config.fp16:
                logger.warn("Try importing flash-attention for faster inference...")
                config.use_flash_attn = True
            else:
                config.use_flash_attn = False
        if config.use_flash_attn and config.fp32:
            logger.warn("Flash attention will be disabled because it does NOT support fp32.")

        if config.use_flash_attn:
            _import_flash_attn()

        self.transformer = MPLUGOwl2QWenModel(config)
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        if config.bf16:
            self.transformer.bfloat16()
            self.lm_head.bfloat16()
        if config.fp16:
            self.transformer.half()
            self.lm_head.half()
        self.post_init()

    def get_model(self):
        return self.transformer

    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: 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=None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
      
        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )
        input_ids, modality_indicators, attention_mask, past_key_values, inputs_embeds, labels = \
            self.prepare_inputs_labels_for_multimodal(input_ids, attention_mask, past_key_values, labels, images)
        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        outputs = self.transformer(
            input_ids,
            modality_indicators=modality_indicators,
            past_key_values=past_key_values,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]
        logits = self.lm_head(hidden_states)

        loss = None
        if labels is not None:
            # Shift so that tokens < n predict n
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            # Flatten the tokens
            loss_fct = CrossEntropyLoss()
            shift_logits = shift_logits.view(-1, self.config.vocab_size)
            shift_labels = shift_labels.view(-1)
            # Enable model/pipeline parallelism
            shift_labels = shift_labels.to(shift_logits.device)
            loss = loss_fct(shift_logits, shift_labels)

        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


AutoConfig.register("mplug_owl2", MPLUGOwl2Config)
AutoModelForCausalLM.register(MPLUGOwl2Config, MPLUGOwl2LlamaForCausalLM)
AutoConfig.register("mplug_owl2_1", MPLUGOwl2QwenConfig)
AutoModelForCausalLM.register(MPLUGOwl2QwenConfig, MPLUGOwl2QWenForCausalLM)

replace_llama_modality_adaptive()

if __name__ == "__main__":
    config = MPLUGOwl2Config.from_pretrained('/cpfs01/shared/public/test/vicuna-7b-v1.5/')
    from icecream import ic
    # config = MPLUGOwl2Config()
    model =  MPLUGOwl2LlamaForCausalLM(config)
    
    images = torch.randn(2, 3, 448, 448)
    input_ids = torch.cat([
        torch.ones(8).long(), torch.tensor([-1]*1).long(), torch.ones(8).long(), torch.tensor([-1]*1).long(), torch.ones(8).long()
    ], dim=0).unsqueeze(0)
    labels = input_ids.clone()
    labels[labels < 0] = -100
    
    # image_feature = model.encode_images(images)
    # ic(image_feature.shape)
    
    output = model(images=images, input_ids=input_ids, labels=labels)
    ic(output.loss)
    ic(output.logits.shape)
    
    model.save_pretrained('/cpfs01/shared/public/test/tmp_owl')