import argparse

import time

from PIL import Image

import torch

from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaTokenizer

from transformers import StoppingCriteria, StoppingCriteriaList

import dataclasses

from enum import auto, Enum

from typing import List, Tuple, Any

from minigpt4.common.registry import registry

class SeparatorStyle(Enum):

    """Different separator style."""

    SINGLE = auto()

    TWO = auto()

@dataclasses.dataclass

class Conversation:

    """A class that keeps all conversation history."""

    system: str

    roles: List[str]

    messages: List[List[str]]

    offset: int

    # system_img: List[Image.Image] = []

    sep_style: SeparatorStyle = SeparatorStyle.SINGLE

    sep: str = "###"

    sep2: str = None

    skip_next: bool = False

    conv_id: Any = None

    def get_prompt(self):

        if self.sep_style == SeparatorStyle.SINGLE:

            ret = self.system + self.sep

            for role, message in self.messages:

                if message:

                    ret += role + ": " + message + self.sep

                else:

                    ret += role + ":"

            return ret

        elif self.sep_style == SeparatorStyle.TWO:

            seps = [self.sep, self.sep2]

            ret = self.system + seps[0]

            for i, (role, message) in enumerate(self.messages):

                if message:

                    ret += role + ": " + message + seps[i % 2]

                else:

                    ret += role + ":"

            return ret

        else:

            raise ValueError(f"Invalid style: {self.sep_style}")

    def append_message(self, role, message):

        self.messages.append([role, message])

    def to_gradio_chatbot(self):

        ret = []

        for i, (role, msg) in enumerate(self.messages[self.offset:]):

            if i % 2 == 0:

                ret.append([msg, None])

            else:

                ret[-1][-1] = msg

        return ret

    def copy(self):

        return Conversation(

            system=self.system,

            # system_img=self.system_img,

            roles=self.roles,

            messages=[[x, y] for x, y in self.messages],

            offset=self.offset,

            sep_style=self.sep_style,

            sep=self.sep,

            sep2=self.sep2,

            conv_id=self.conv_id)

    def dict(self):

        return {

            "system": self.system,

            # "system_img": self.system_img,

            "roles": self.roles,

            "messages": self.messages,

            "offset": self.offset,

            "sep": self.sep,

            "sep2": self.sep2,

            "conv_id": self.conv_id,

        }

class StoppingCriteriaSub(StoppingCriteria):

    def __init__(self, stops=[], encounters=1):

        super().__init__()

        self.stops = stops

    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor):

        for stop in self.stops:

            if torch.all((stop == input_ids[0][-len(stop):])).item():

                return True

        return False

CONV_VISION = Conversation(

    system="Give the following image: <Img>ImageContent</Img>. "

           "You will be able to see the image once I provide it to you. Please answer my questions.",

    roles=("Human", "Assistant"),

    messages=[],

    offset=2,

    sep_style=SeparatorStyle.SINGLE,

    sep="###",

)

class Chat:

    def __init__(self, model, vis_processor, device='cuda:0'):

        self.device = device

        self.model = model

        self.vis_processor = vis_processor

        stop_words_ids = [torch.tensor([835]).to(self.device),

                          torch.tensor([2277, 29937]).to(self.device)]  # '###' can be encoded in two different ways.

        self.stopping_criteria = StoppingCriteriaList([StoppingCriteriaSub(stops=stop_words_ids)])

    def ask(self, text, conv):

        if len(conv.messages) > 0 and conv.messages[-1][0] == conv.roles[0] \

                and conv.messages[-1][1][-6:] == '</Img>':  # last message is image.

            conv.messages[-1][1] = ' '.join([conv.messages[-1][1], text])

        else:

            conv.append_message(conv.roles[0], text)

    def answer(self, conv, img_list, max_new_tokens=300, num_beams=1, min_length=1, top_p=0.9,

               repetition_penalty=1.0, length_penalty=1, temperature=1.0, max_length=2000):

        conv.append_message(conv.roles[1], None)

        embs = self.get_context_emb(conv, img_list)

        current_max_len = embs.shape[1] + max_new_tokens

        if current_max_len - max_length > 0:

            print('Warning: The number of tokens in current conversation exceeds the max length. '

                  'The model will not see the contexts outside the range.')

        begin_idx = max(0, current_max_len - max_length)

        embs = embs[:, begin_idx:]

        outputs = self.model.llama_model.generate(

            inputs_embeds=embs,

            max_new_tokens=max_new_tokens,

            stopping_criteria=self.stopping_criteria,

            num_beams=num_beams,

            do_sample=True,

            min_length=min_length,

            top_p=top_p,

            repetition_penalty=repetition_penalty,

            length_penalty=length_penalty,

            temperature=temperature,

        )

        output_token = outputs[0]

        if output_token[0] == 0:  # the model might output a unknow token <unk> at the beginning. remove it

            output_token = output_token[1:]

        if output_token[0] == 1:  # some users find that there is a start token <s> at the beginning. remove it

            output_token = output_token[1:]

        output_text = self.model.llama_tokenizer.decode(output_token, add_special_tokens=False)

        output_text = output_text.split('###')[0]  # remove the stop sign '###'

        output_text = output_text.split('Assistant:')[-1].strip()

        conv.messages[-1][1] = output_text

        return output_text, output_token.cpu().numpy()

    def upload_img(self, image, conv, img_list):

        if isinstance(image, str):  # is a image path

            raw_image = Image.open(image).convert('RGB')

            image = self.vis_processor(raw_image).unsqueeze(0).to(self.device)

        elif isinstance(image, Image.Image):

            raw_image = image

            image = self.vis_processor(raw_image).unsqueeze(0).to(self.device)

        elif isinstance(image, torch.Tensor):

            if len(image.shape) == 3:

                image = image.unsqueeze(0)

            image = image.to(self.device)

        image_emb, _ = self.model.encode_img(image)

        img_list.append(image_emb)

        conv.append_message(conv.roles[0], "<Img><ImageHere></Img>")

        msg = "Received."

        # self.conv.append_message(self.conv.roles[1], msg)

        return msg

    def get_context_emb(self, conv, img_list):

        prompt = conv.get_prompt()

        prompt_segs = prompt.split('<ImageHere>')

        assert len(prompt_segs) == len(img_list) + 1, "Unmatched numbers of image placeholders and images."

        seg_tokens = [

            self.model.llama_tokenizer(

                seg, return_tensors="pt", add_special_tokens=i == 0).to(self.device).input_ids

            # only add bos to the first seg

            for i, seg in enumerate(prompt_segs)

        ]

        seg_embs = [self.model.llama_model.model.embed_tokens(seg_t) for seg_t in seg_tokens]

        mixed_embs = [emb for pair in zip(seg_embs[:-1], img_list) for emb in pair] + [seg_embs[-1]]

        mixed_embs = torch.cat(mixed_embs, dim=1)

        return mixed_embs