# Adopted from https://github.com/haotian-liu/LLaVA. Below is the original copyright:
#    Copyright 2023 Haotian Liu
#
#    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.

import dataclasses
from enum import auto, Enum
from typing import List, Any, Union, Tuple
import base64
from io import BytesIO
from PIL import Image


class SeparatorStyle(Enum):
    """Different separator style."""
    TWO = auto()
    PLAIN = auto()
    LLAMA_2 = auto()
    LLAMA_3 = auto()
    QWEN = auto()

@dataclasses.dataclass
class Conversation:
    """A class that keeps all conversation history."""
    system: str
    roles: List[str]
    messages: List[List[str]]
    offset: int
    sep_style: SeparatorStyle = SeparatorStyle.PLAIN
    sep: str = "###"
    sep2: str = None
    version: str = "Unknown"

    tokenizer_id: str = ""
    tokenizer: Any = None
    # Stop criteria (the default one is EOS token)
    stop_str: Union[str, List[str]] = None
    # Stops generation if meeting any token in this list
    stop_token_ids: List[int] = None

    skip_next: bool = False

    def get_prompt(self, tokenizer = None, system_prompt = None):
        self.tokenizer = tokenizer
        messages = self.messages

        if self.sep_style == SeparatorStyle.TWO:
            seps = [self.sep, self.sep2]
            ret = self.system + seps[0]
            for i, (role, message) in enumerate(messages):
                if message:
                    if type(message) is tuple:
                        message = message[0]
                    ret += role + ": " + message + seps[i % 2]
                else:
                    ret += role + ":"
        elif self.sep_style == SeparatorStyle.LLAMA_3:
            wrap_sys = lambda msg: f"<|start_header_id|>system<|end_header_id|>\n\n{msg}<|eot_id|>" if len(msg) > 0 else msg
            if system_prompt is not None:
                self.system = system_prompt
            chat_template_messages = [{"role": "system", "content": self.system}]
            for role, message in messages:
                if message:
                    if type(message) is tuple:
                        message = message[0]
                    chat_template_messages.append({"role": role, "content": message})

            # print(chat_template_messages)
            return self.tokenizer.apply_chat_template(chat_template_messages, tokenize=False, add_generation_prompt=True)

            # ret = "<|begin_of_text|>" + wrap_sys(self.system)
            # for i, (role, message) in enumerate(messages):
            #     if message:
            #         if type(message) is tuple:
            #             message = message[0]
            #         ret += f"<|start_header_id|>{role}<|end_header_id|>\n\n"
            #         ret += message.strip() + self.sep2
            #     else:
            #         ret += f"<|start_header_id|>{role}<|end_header_id|>\n\n"
            # return ret
        elif self.sep_style == SeparatorStyle.QWEN:
            # Define helper function for wrapping the system message
            wrap_sys = lambda msg: (
                f"<|im_start|>system\n{msg}<|im_end|>\n" 
                if len(msg) > 0 
                else "<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n"
            )
            if system_prompt is not None:
                self.system = system_prompt
            chat_template_messages = [{"role": "system", "content": self.system}]
            for role, message in messages:
                if message:
                    if type(message) is tuple:
                        message = message[0]
                    chat_template_messages.append({"role": role, "content": message})

            # print(chat_template_messages)
            return self.tokenizer.apply_chat_template(chat_template_messages, tokenize=False, add_generation_prompt=True)
            
            # ret = wrap_sys(self.system)
            # for i, (role, message) in enumerate(messages):
            #     if message:
            #         if type(message) is tuple:
            #             message = message[0]
            #         ret += f"<|im_start|>{role}\n"
            #         ret += message.strip() + "<|im_end|>\n"
            #     else:
            #         ret += f"<|im_start|>{role}\n"
            # return ret
        elif self.sep_style == SeparatorStyle.LLAMA_2:
            wrap_sys = lambda msg: f"<<SYS>>\n{msg}\n<</SYS>>\n\n" if len(msg) > 0 else msg
            wrap_inst = lambda msg: f"[INST] {msg} [/INST]"
            ret = ""

            for i, (role, message) in enumerate(messages):
                if i == 0:
                    assert message, "first message should not be none"
                    assert role == self.roles[0], "first message should come from user"
                if message:
                    if type(message) is tuple:
                        message, _, _ = message
                    if i == 0:
                        message = wrap_sys(self.system) + message
                    if i % 2 == 0:
                        message = wrap_inst(message)
                        ret += self.sep + message
                    else:
                        ret += " " + message + " " + self.sep2
                else:
                    ret += ""
            ret = ret.lstrip(self.sep)
        elif self.sep_style == SeparatorStyle.PLAIN:
            seps = [self.sep, self.sep2]
            ret = self.system
            for i, (role, message) in enumerate(messages):
                if message:
                    if type(message) is tuple:
                        message, _, _ = message
                    ret += message + seps[i % 2]
                else:
                    ret += ""
        else:
            raise ValueError(f"Invalid style: {self.sep_style}")

        return ret

    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:
                if type(msg) is tuple:
                    msg, speech = msg
                    ret.append([msg, None])
                else:
                    ret.append([msg, None])
            else:
                ret[-1][-1] = msg
        return ret

    def copy(self):
        return Conversation(
            system=self.system,
            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,
            version=self.version)

    def dict(self):
        if len(self.get_images()) > 0:
            return {
                "system": self.system,
                "roles": self.roles,
                "messages": [[x, y[0] if type(y) is tuple else y] for x, y in self.messages],
                "offset": self.offset,
                "sep": self.sep,
                "sep2": self.sep2,
            }
        return {
            "system": self.system,
            "roles": self.roles,
            "messages": self.messages,
            "offset": self.offset,
            "sep": self.sep,
            "sep2": self.sep2,
        }

conv_vicuna_v1 = Conversation(
    system="A chat between a curious user and an artificial intelligence assistant. " "The assistant gives helpful, detailed, and polite answers to the user's questions.",
    roles=("USER", "ASSISTANT"),
    version="v1",
    messages=[],
    offset=0,
    sep_style=SeparatorStyle.TWO,
    sep=" ",
    sep2="</s>",
)

conv_llama_2 = Conversation(
    system="You are a helpful language and speech assistant. " "You are able to understand the speech content that the user provides, " "and assist the user with a variety of tasks using natural language.",
    roles=("USER", "ASSISTANT"),
    version="llama_v2",
    messages=[],
    offset=0,
    sep_style=SeparatorStyle.LLAMA_2,
    sep="<s>",
    sep2="</s>",
)

conv_llama_3 = Conversation(
    system="You are a helpful language and speech assistant. " "You are able to understand the speech content that the user provides, " "and assist the user with a variety of tasks using natural language.",
    roles=("user", "assistant"),
    version="llama_v3",
    messages=[],
    offset=0,
    sep_style=SeparatorStyle.LLAMA_3,
    sep="",
    sep2="<|eot_id|>"
)

conv_qwen = Conversation(
    system="""You are a helpful language and speech assistant. You are able to understand the speech content that the user provides, and assist the user with a variety of tasks using natural language.""",
    roles=("user", "assistant"),
    version="qwen",
    messages=[],
    offset=0,
    sep_style=SeparatorStyle.QWEN,
    sep="<|im_end|>",
)

conv_plain = Conversation(
    system="",
    roles=("", ""),
    messages=(
    ),
    offset=0,
    sep_style=SeparatorStyle.PLAIN,
    sep="</s>",
)


default_conversation = conv_llama_3
conv_templates = {
    "v1": conv_vicuna_v1,
    "plain": conv_plain,
    "llama_2": conv_llama_2,
    "llama_3": conv_llama_3,
    "qwen": conv_qwen,
}


if __name__ == "__main__":
    print(default_conversation.get_prompt())