import random
from typing import Dict, Tuple, List, Union

import torch
import torch.nn as nn
import re
from torch import Tensor
from transformers import LlamaTokenizer
from omegaconf import DictConfig

from imagebind.models.image_bind import imagebind_huge, ImageBindJoiner, ModalityType, replace_joiner_vision
from bubogpt.common.registry import registry
from bubogpt.models.blip2 import BaseModel
from bubogpt.models.modeling_llama import LlamaForCausalLM


def filter_prompt(input_embeds: Dict[str, Tensor], prompt_list: List[str]) -> List[str]:
    if not prompt_list:
        return prompt_list
    input_modal_set = set([k.title() for k in input_embeds if input_embeds[k] is not None])
    prompt_modal_sets = [set(re.findall("<([^<>]+)><ModalityHere></\\1>", prompt)) for prompt in prompt_list]
    results = [prompt_list[i] for i, prompt_modal_set in enumerate(prompt_modal_sets) if
               prompt_modal_set == input_modal_set]
    return results


def arrange_modalities(input_embeds: Dict[str, Tensor], prompt: str) -> List[Tensor]:
    prompt_modalities = re.findall("<([^<>]+)><ModalityHere></\\1>", prompt)
    return [input_embeds[modality.lower()] for modality in prompt_modalities]


def concat_all_embeddings(input_embeds: Dict[str, Tensor], dim: int) -> Tensor:
    embeds = [input_embeds[key] for key in input_embeds if input_embeds[key] is not None]
    return torch.cat(embeds, dim=dim)


def filter_modalities(inputs):
    filtered_inputs = {}

    for k in ModalityType.__dict__.values():
        if k in inputs:
            filtered_inputs[k] = inputs[k]

    return filtered_inputs


@registry.register_model("mm_gpt4")
class MMGPT4(BaseModel):
    """
    ImageBind GPT-LLAMA model.
    """

    PRETRAINED_MODEL_CONFIG_DICT = {
        "pretrain_vicuna": "configs/models/mmgpt4.yaml",
    }

    def __init__(
            self,
            joiner_cfg: DictConfig,
            q_former_model="https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained_flant5xxl.pth",
            freeze_imagebind=True,
            freeze_qformer=False,
            num_query_token=32,
            llama_model="",
            prompt_path="",
            prompt_template="",
            max_txt_len=128,
            end_sym='\n',
            low_resource=False,  # use 8 bit and put vit in cpu
            device_8bit=0,  # the device of 8bit model should be set when loading and cannot be changed anymore.
            with_bind_head=False,
            freeze_llm=True,
            use_blip_vision=False,
            proj_model="",
    ):
        super().__init__()
        assert not low_resource, "Low Resource Mode is Currently Unavailable."

        self.low_resource = low_resource

        print('Loading ImageBind')
        self.multimodal_encoder = imagebind_huge(pretrained=True, freeze_imagebind=freeze_imagebind,
                                                 with_head=with_bind_head, use_blip_vision=use_blip_vision)
        print('Loading ImageBind Done')

        print(f'Loading LLAMA from {llama_model}')
        self.llama_tokenizer = LlamaTokenizer.from_pretrained('magicr/vicuna-7b', use_fast=False, use_auth_token=True)
        self.llama_tokenizer.pad_token = self.llama_tokenizer.eos_token

        self.llama_model = LlamaForCausalLM.from_pretrained('magicr/vicuna-7b', load_in_8bit=True,
                                                            torch_dtype=torch.float16, device_map="auto", use_auth_token=True)

        if freeze_llm:
            for name, param in self.llama_model.named_parameters():
                param.requires_grad = False
        print('Loading LLAMA Done')

        print('Loading Q-Former and Adapter/Projector')
        self.multimodal_joiner = ImageBindJoiner(joiner_cfg, output_dim=self.llama_model.config.hidden_size)
        if use_blip_vision:
            replace_joiner_vision(self.multimodal_joiner, q_former_model, proj_model)
        print('Loading Q-Former and Adapter/Projector Done')

        self.max_txt_len = max_txt_len
        self.end_sym = end_sym

        print("Preparing Prompts")
        self.prompt_template = prompt_template
        if prompt_path:
            with open(prompt_path, 'r') as f:
                raw_prompts = f.read().splitlines()
            self.prompt_list = [prompt_template.format(p) for p in raw_prompts]
            print('Load {} training prompts'.format(len(self.prompt_list)))
            print('Prompt Example \n{}'.format(random.choice(self.prompt_list)))
        else:
            self.prompt_list = []
        print("Preparing Prompts Done")

    def maybe_autocast(self, dtype=torch.float16):
        # if on cpu, don't use autocast
        # if on gpu, use autocast with dtype if provided, otherwise use torch.float16
        enable_autocast = self.device != torch.device("cpu")

        if enable_autocast:
            return torch.cuda.amp.autocast(dtype=dtype)
        else:
            import contextlib
            return contextlib.nullcontext()

    def encode_inputs(self, inputs: Dict[str, Tensor]) -> Dict[str, Tensor]:
        with self.maybe_autocast():
            imagebind_outputs = self.multimodal_encoder(inputs)
            llama_inputs = self.multimodal_joiner(imagebind_outputs)
        return llama_inputs

    def prompt_wrap(self, inputs: Dict[str, Tensor], prompt: Union[str, list]) -> Tuple[Tensor, Tensor]:
        if isinstance(prompt, (list, tuple)):
            bs = list(inputs.values())[0].shape[0]
            assert bs == len(prompt)

            return self.batch_prompt_wrap(inputs, prompt)
        elif isinstance(prompt, (str, type(None))):
            return self.single_prompt_wrap(inputs, prompt)
        else:
            raise NotImplementedError(f"Prompt type: {type(prompt)} not supported.")

    def single_prompt_wrap(self, inputs: Dict[str, Tensor], prompt: str) -> Tuple[Tensor, Tensor]:
        if not prompt:
            input_embeds = concat_all_embeddings(inputs, dim=1)
            attns_input = torch.ones(input_embeds.size()[:-1], dtype=torch.long).to(input_embeds.device)
            return input_embeds, attns_input
        input_embeds_list = arrange_modalities(inputs, prompt)
        batch_size = input_embeds_list[0].shape[0]
        prompt_slices = prompt.split('<ModalityHere>')
        prompt_tokens = [self.llama_tokenizer(prompt_slice, return_tensors="pt", add_special_tokens=False)
                         .to(input_embeds_list[0].device) for prompt_slice in prompt_slices]
        prompt_embeds = [self.llama_model.model.embed_tokens(prompt_token.input_ids).expand(batch_size, -1, -1)
                         for prompt_token in prompt_tokens]
        result_embeds = [emb for pair in zip(prompt_embeds[:-1], input_embeds_list)
                         for emb in pair] + [prompt_embeds[-1]]
        wrapped_input_embeds = torch.cat(result_embeds, dim=1)
        wrapped_atts_input = torch.ones(wrapped_input_embeds.size()[:-1],
                                        dtype=torch.long).to(wrapped_input_embeds.device)
        return wrapped_input_embeds, wrapped_atts_input

    def batch_prompt_wrap(self, inputs: Dict[str, Tensor], prompts: List[str]) -> Tuple[Tensor, Tensor]:
        device = list(inputs.values())[0].device
        # This one only works for visual prompting
        prompt_slices = [prompt.split('<ModalityHere>') for prompt in prompts]
        slice_batch = list(zip(*prompt_slices))

        prompt_tokens = [self.llama_tokenizer(slice,
                                              return_tensors="pt",
                                              add_special_tokens=False,
                                              padding="longest",
                                              truncation=True,
                                              max_length=self.max_txt_len).to(device)
                         for slice in slice_batch]
        prompt_embeds = [self.llama_model.model.embed_tokens(prompt_token.input_ids) for prompt_token in prompt_tokens]
        prompt_masks = [prompt_token.attention_mask for prompt_token in prompt_tokens]

        # NOTE: assuming moalities are the same within a batch
        input_embeds_list = arrange_modalities(inputs, prompts[0])
        input_mask_list = [torch.ones(input_embeds.size()[:-1], dtype=torch.long).to(device) for input_embeds in input_embeds_list]
        result_embeds = [emb for pair in zip(prompt_embeds[:-1], input_embeds_list) for emb in pair] + [prompt_embeds[-1]]
        result_masks = [mask for pair in zip(prompt_masks[:-1], input_mask_list) for mask in pair] + [prompt_masks[-1]]
        wrapped_input_embeds = torch.cat(result_embeds, dim=1)
        wrapped_atts_input = torch.cat(result_masks, dim=1)
        return wrapped_input_embeds, wrapped_atts_input

    def forward(self, inputs: Dict[str, Tensor]) -> Dict[str, Tensor]:
        # filter `inputs` as it may contain informatioins other than modalities
        modality_inputs = filter_modalities(inputs)
        embeds = self.encode_inputs(modality_inputs)
        filtered_prompts = filter_prompt(embeds, self.prompt_list)
        if "prompt" in inputs:
            assert isinstance(inputs["prompt"], (list, tuple))
            prompt = [self.prompt_template.format(p) for p in inputs["prompt"]]
        elif filtered_prompts:
            prompt = random.choice(filtered_prompts)
        else:
            prompt = None
        # NOTE&TODO: add support for a list of prompts
        input_embs, input_atts = self.prompt_wrap(embeds, prompt)

        # NOTE: No modifications from the next line to the end. Except for the autocast part.

        self.llama_tokenizer.padding_side = "right"

        text = [t + self.end_sym for t in inputs["text_input"]]

        to_regress_tokens = self.llama_tokenizer(
            text,
            return_tensors="pt",
            padding="longest",
            truncation=True,
            max_length=self.max_txt_len,
            add_special_tokens=False
        ).to(input_embs.device)

        targets = to_regress_tokens.input_ids.masked_fill(
            to_regress_tokens.input_ids == self.llama_tokenizer.pad_token_id, -100
        )

        empty_targets = (
            torch.ones([input_atts.shape[0], input_atts.shape[1] + 1],
                       dtype=torch.long).to(input_embs.device).fill_(-100)  # plus one for bos
        )
        targets = torch.cat([empty_targets, targets], dim=1)

        batch_size = input_embs.shape[0]
        bos = torch.ones([batch_size, 1],
                         dtype=to_regress_tokens.input_ids.dtype,
                         device=to_regress_tokens.input_ids.device) * self.llama_tokenizer.bos_token_id
        bos_embeds = self.llama_model.model.embed_tokens(bos)
        atts_bos = input_atts[:, :1]

        to_regress_embeds = self.llama_model.model.embed_tokens(to_regress_tokens.input_ids)
        inputs_embeds = torch.cat([bos_embeds, input_embs, to_regress_embeds], dim=1)
        attention_mask = torch.cat([atts_bos, input_atts, to_regress_tokens.attention_mask], dim=1)

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

        return {"loss": loss}

    @classmethod
    def from_config(cls, cfg):
        joiner_cfg = cfg.get("joiner_cfg")
        q_former_model = cfg.get(
            "q_former_model",
            "checkpoints/blip2_pretrained_flant5xxl.pth",
        )
        num_query_token = cfg.get("num_query_token")
        llama_model = cfg.get("llama_model")

        freeze_imagebind = cfg.get("freeze_imagebind", True)
        freeze_qformer = cfg.get("freeze_qformer", True)
        low_resource = cfg.get("low_resource", False)
        device_8bit = cfg.get("device_8bit", 0)

        prompt_path = cfg.get("prompt_path", "")
        prompt_template = cfg.get("prompt_template", "")
        max_txt_len = cfg.get("max_txt_len", 128)
        end_sym = cfg.get("end_sym", '\n')
        with_bind_head = cfg.get("with_bind_head", False)
        freeze_llm = cfg.get("freeze_llm", True)
        use_blip_vision = cfg.get("use_blip_vision", False)
        proj_model = cfg.get("proj_model", "checkpoints/prerained_minigpt4_7b.pth")

        model = cls(
            joiner_cfg=joiner_cfg,
            q_former_model=q_former_model,
            freeze_imagebind=freeze_imagebind,
            freeze_qformer=freeze_qformer,
            num_query_token=num_query_token,
            llama_model=llama_model,
            prompt_path=prompt_path,
            prompt_template=prompt_template,
            max_txt_len=max_txt_len,
            end_sym=end_sym,
            low_resource=low_resource,
            device_8bit=device_8bit,
            with_bind_head=with_bind_head,
            freeze_llm=freeze_llm,
            use_blip_vision=use_blip_vision,
            proj_model=proj_model,
        )

        ckpt_path = cfg.get("ckpt", "")  # load weights of MiniGPT-4
        if ckpt_path:
            if isinstance(ckpt_path, str):
                ckpt_path = [ckpt_path]
            for cur_ckpt_path in ckpt_path:
                print("Load ImageBind-LLM Checkpoint: {}".format(cur_ckpt_path))
                ckpt = torch.load(cur_ckpt_path, map_location="cpu")
                msg = model.load_state_dict(ckpt['model'], strict=False)

        return model