import os
from typing import Dict, Iterator, List, Optional

import openai

from qwen_agent.llm.base import BaseChatModel

import re
import copy
import json
import time
from contextlib import asynccontextmanager
from typing import Dict, List, Literal, Optional, Union
import torch
from pydantic import BaseModel, Field
from sse_starlette.sse import EventSourceResponse
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers.generation import GenerationConfig


def _gc(forced: bool = False, disable_gc: bool = True):
    if disable_gc and not forced:
        return

    import gc
    gc.collect()
    if torch.cuda.is_available():
        torch.cuda.empty_cache()


class ChatMessage(BaseModel):
    role: Literal["user", "assistant", "system", "function"]
    content: Optional[str]
    function_call: Optional[Dict] = None


class DeltaMessage(BaseModel):
    role: Optional[Literal["user", "assistant", "system"]] = None
    content: Optional[str] = None


class ChatCompletionRequest(BaseModel):
    model: str
    messages: List[ChatMessage]
    functions: Optional[List[Dict]] = None
    temperature: Optional[float] = None
    top_p: Optional[float] = None
    max_length: Optional[int] = None
    stream: Optional[bool] = False
    stop: Optional[List[str]] = None


class ChatCompletionResponseChoice(BaseModel):
    index: int
    message: ChatMessage
    finish_reason: Literal["stop", "length", "function_call"]


class ChatCompletionResponseStreamChoice(BaseModel):
    index: int
    delta: DeltaMessage
    finish_reason: Optional[Literal["stop", "length"]]


class ChatCompletionResponse(BaseModel):
    model: str
    object: Literal["chat.completion", "chat.completion.chunk"]
    choices: List[
        Union[ChatCompletionResponseChoice, ChatCompletionResponseStreamChoice]
    ]
    created: Optional[int] = Field(default_factory=lambda: int(time.time()))


# To work around that unpleasant leading-\n tokenization issue!
def add_extra_stop_words(stop_words):
    if stop_words:
        _stop_words = []
        _stop_words.extend(stop_words)
        for x in stop_words:
            s = x.lstrip("\n")
            if s and (s not in _stop_words):
                _stop_words.append(s)
        return _stop_words
    return stop_words


def trim_stop_words(response, stop_words):
    if stop_words:
        for stop in stop_words:
            idx = response.find(stop)
            if idx != -1:
                response = response[:idx]
    return response


TOOL_DESC = """{name_for_model}: Call this tool to interact with the {name_for_human} API. What is the {name_for_human} API useful for? {description_for_model} Parameters: {parameters}"""

REACT_INSTRUCTION = """Answer the following questions as best you can. You have access to the following APIs:

{tools_text}

Use the following format:

Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tools_name_text}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can be repeated zero or more times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question

Begin!"""

_TEXT_COMPLETION_CMD = object()


#
# Temporarily, the system role does not work as expected.
# We advise that you write the setups for role-play in your query,
# i.e., use the user role instead of the system role.
#
# TODO: Use real system role when the model is ready.
#
def parse_messages(messages, functions):
    if all(m.role != "user" for m in messages):
        raise Exception(f"Invalid request: Expecting at least one user message.", )
    messages = copy.deepcopy(messages)
    default_system = "You are a helpful assistant."
    system = ""
    if messages[0].role == "system":
        system = messages.pop(0).content.lstrip("\n").rstrip()
        if system == default_system:
            system = ""

    if functions:
        tools_text = []
        tools_name_text = []
        for func_info in functions:
            name = func_info.get("name", "")
            name_m = func_info.get("name_for_model", name)
            name_h = func_info.get("name_for_human", name)
            desc = func_info.get("description", "")
            desc_m = func_info.get("description_for_model", desc)
            tool = TOOL_DESC.format(
                name_for_model=name_m,
                name_for_human=name_h,
                # Hint: You can add the following format requirements in description:
                #   "Format the arguments as a JSON object."
                #   "Enclose the code within triple backticks (`) at the beginning and end of the code."
                description_for_model=desc_m,
                parameters=json.dumps(func_info["parameters"], ensure_ascii=False),
            )
            tools_text.append(tool)
            tools_name_text.append(name_m)
        tools_text = "\n\n".join(tools_text)
        tools_name_text = ", ".join(tools_name_text)
        system += "\n\n" + REACT_INSTRUCTION.format(
            tools_text=tools_text,
            tools_name_text=tools_name_text,
        )
        system = system.lstrip("\n").rstrip()

    dummy_thought = {
        "en": "\nThought: I now know the final answer.\nFinal answer: ",
        "zh": "\nThought: 我会作答了。\nFinal answer: ",
    }

    _messages = messages
    messages = []
    for m_idx, m in enumerate(_messages):
        role, content, func_call = m.role, m.content, m.function_call
        if content:
            content = content.lstrip("\n").rstrip()
        if role == "function":
            if (len(messages) == 0) or (messages[-1].role != "assistant"):
                raise Exception("Invalid request: Expecting role assistant before role function.")
            messages[-1].content += f"\nObservation: {content}"
            if m_idx == len(_messages) - 1:
                messages[-1].content += "\nThought:"
        elif role == "assistant":
            if len(messages) == 0:
                raise Exception(f"Invalid request: Expecting role user before role assistant.")
            last_msg = messages[-1].content
            last_msg_has_zh = len(re.findall(r"[\u4e00-\u9fff]+", last_msg)) > 0
            if func_call is None:
                if functions:
                    content = dummy_thought["zh" if last_msg_has_zh else "en"] + content
            else:
                f_name, f_args = func_call["name"], func_call["arguments"]
                if not content:
                    if last_msg_has_zh:
                        content = f"Thought: 我可以使用 {f_name} API。"
                    else:
                        content = f"Thought: I can use {f_name}."
                content = f"\n{content}\nAction: {f_name}\nAction Input: {f_args}"
            if messages[-1].role == "user":
                messages.append(
                    ChatMessage(role="assistant", content=content.lstrip("\n").rstrip())
                )
            else:
                messages[-1].content += content
        elif role == "user":
            messages.append(
                ChatMessage(role="user", content=content.lstrip("\n").rstrip())
            )
        else:
            raise Exception(
                f"Invalid request: Incorrect role {role}."
            )

    query = _TEXT_COMPLETION_CMD
    if messages[-1].role == "user":
        query = messages[-1].content
        messages = messages[:-1]

    if len(messages) % 2 != 0:
        raise Exception("Invalid request")

    history = []  # [(Q1, A1), (Q2, A2), ..., (Q_last_turn, A_last_turn)]
    for i in range(0, len(messages), 2):
        if messages[i].role == "user" and messages[i + 1].role == "assistant":
            usr_msg = messages[i].content.lstrip("\n").rstrip()
            bot_msg = messages[i + 1].content.lstrip("\n").rstrip()
            if system and (i == len(messages) - 2):
                usr_msg = f"{system}\n\nQuestion: {usr_msg}"
                system = ""
            for t in dummy_thought.values():
                t = t.lstrip("\n")
                if bot_msg.startswith(t) and ("\nAction: " in bot_msg):
                    bot_msg = bot_msg[len(t):]
            history.append([usr_msg, bot_msg])
        else:
            raise Exception("Invalid request: Expecting exactly one user (or function) role before every assistant role.")
    if system:
        assert query is not _TEXT_COMPLETION_CMD
        query = f"{system}\n\nQuestion: {query}"
    return query, history


def parse_response(response):
    func_name, func_args = "", ""
    i = response.rfind("\nAction:")
    j = response.rfind("\nAction Input:")
    k = response.rfind("\nObservation:")
    if 0 <= i < j:  # If the text has `Action` and `Action input`,
        if k < j:  # but does not contain `Observation`,
            # then it is likely that `Observation` is omitted by the LLM,
            # because the output text may have discarded the stop word.
            response = response.rstrip() + "\nObservation:"  # Add it back.
        k = response.rfind("\nObservation:")
        func_name = response[i + len("\nAction:"): j].strip()
        func_args = response[j + len("\nAction Input:"): k].strip()
    if func_name:
        choice_data = ChatCompletionResponseChoice(
            index=0,
            message=ChatMessage(
                role="assistant",
                content=response[:i],
                function_call={"name": func_name, "arguments": func_args},
            ),
            finish_reason="function_call",
        )
        return choice_data
    z = response.rfind("\nFinal Answer: ")
    if z >= 0:
        response = response[z + len("\nFinal Answer: "):]
    choice_data = ChatCompletionResponseChoice(
        index=0,
        message=ChatMessage(role="assistant", content=response),
        finish_reason="stop",
    )
    return choice_data


# completion mode, not chat mode
def text_complete_last_message(history, stop_words_ids, gen_kwargs):
    im_start = "<|im_start|>"
    im_end = "<|im_end|>"
    prompt = f"{im_start}system\nYou are a helpful assistant.{im_end}"
    for i, (query, response) in enumerate(history):
        query = query.lstrip("\n").rstrip()
        response = response.lstrip("\n").rstrip()
        prompt += f"\n{im_start}user\n{query}{im_end}"
        prompt += f"\n{im_start}assistant\n{response}{im_end}"
    prompt = prompt[: -len(im_end)]

    _stop_words_ids = [tokenizer.encode(im_end)]
    if stop_words_ids:
        for s in stop_words_ids:
            _stop_words_ids.append(s)
    stop_words_ids = _stop_words_ids

    input_ids = torch.tensor([tokenizer.encode(prompt)]).to(qmodel.device)
    output = qmodel.generate(input_ids, stop_words_ids=stop_words_ids, **gen_kwargs).tolist()[0]
    output = tokenizer.decode(output, errors="ignore")
    assert output.startswith(prompt)
    output = output[len(prompt):]
    output = trim_stop_words(output, ["<|endoftext|>", im_end])
    print(f"<completion>\n{prompt}\n<!-- *** -->\n{output}\n</completion>")
    return output


def create_chat_completion(request: ChatCompletionRequest, qmodel, tokenizer):

    gen_kwargs = {}
    if request.temperature is not None:
        if request.temperature < 0.01:
            gen_kwargs['top_k'] = 1  # greedy decoding
        else:
            # Not recommended. Please tune top_p instead.
            gen_kwargs['temperature'] = request.temperature
    if request.top_p is not None:
        gen_kwargs['top_p'] = request.top_p

    stop_words = add_extra_stop_words(request.stop)
    if request.functions:
        stop_words = stop_words or []
        if "Observation:" not in stop_words:
            stop_words.append("Observation:")

    query, history = parse_messages(request.messages, request.functions)

    if request.stream:
        if request.functions:
            raise Exception("Invalid request: Function calling is not yet implemented for stream mode.")
        generate = predict(query, history, request.model, stop_words, gen_kwargs, qmodel, tokenizer)
        return generate
        # return EventSourceResponse(generate, media_type="text/event-stream")

    stop_words_ids = [tokenizer.encode(s) for s in stop_words] if stop_words else None
    if query is _TEXT_COMPLETION_CMD:
        response = text_complete_last_message(history, stop_words_ids=stop_words_ids, gen_kwargs=gen_kwargs)
    else:
        response, _ = qmodel.chat(
            tokenizer,
            query,
            history=history,
            stop_words_ids=stop_words_ids,
            **gen_kwargs
        )
        print(f"<chat>\n{history}\n{query}\n<!-- *** -->\n{response}\n</chat>")
    _gc()

    response = trim_stop_words(response, stop_words)
    if request.functions:
        choice_data = parse_response(response)
    else:
        choice_data = ChatCompletionResponseChoice(
            index=0,
            message=ChatMessage(role="assistant", content=response),
            finish_reason="stop",
        )
    return ChatCompletionResponse(
        model=request.model, choices=[choice_data], object="chat.completion"
    )


def _dump_json(data: BaseModel, *args, **kwargs) -> str:
    try:
        return data.model_dump_json(*args, **kwargs)
    except AttributeError:  # pydantic<2.0.0
        return data.json(*args, **kwargs)  # noqa


def predict(
        query: str, history: List[List[str]], model_id: str, stop_words: List[str], gen_kwargs: Dict, qmodel, tokenizer
):
    choice_data = ChatCompletionResponseStreamChoice(
        index=0, delta=DeltaMessage(role="assistant"), finish_reason=None
    )
    chunk = ChatCompletionResponse(
        model=model_id, choices=[choice_data], object="chat.completion.chunk"
    )
    # yield "{}".format(_dump_json(chunk, exclude_unset=True))
    yield chunk

    current_length = 0
    stop_words_ids = [tokenizer.encode(s) for s in stop_words] if stop_words else None
    if stop_words:
        # TODO: It's a little bit tricky to trim stop words in the stream mode.
        raise Exception("Invalid request: custom stop words are not yet supported for stream mode.", )
    response_generator = qmodel.chat_stream(
        tokenizer, query, history=history, stop_words_ids=stop_words_ids, **gen_kwargs
    )
    for new_response in response_generator:
        if len(new_response) == current_length:
            continue

        new_text = new_response[current_length:]
        current_length = len(new_response)

        choice_data = ChatCompletionResponseStreamChoice(
            index=0, delta=DeltaMessage(content=new_text), finish_reason=None
        )
        chunk = ChatCompletionResponse(
            model=model_id, choices=[choice_data], object="chat.completion.chunk"
        )
        # yield "{}".format(_dump_json(chunk, exclude_unset=True))
        yield chunk

    choice_data = ChatCompletionResponseStreamChoice(
        index=0, delta=DeltaMessage(), finish_reason="stop"
    )
    chunk = ChatCompletionResponse(
        model=model_id, choices=[choice_data], object="chat.completion.chunk"
    )
    # yield "{}".format(_dump_json(chunk, exclude_unset=True))
    yield chunk
    # yield "[DONE]"

    _gc()


class QwenChatAsOAI(BaseChatModel):

    def __init__(self, model: str, api_key: str, model_server: str):
        self.model = model
        super().__init__()
        tokenizer = AutoTokenizer.from_pretrained(
            self.model,
            trust_remote_code=True,
            resume_download=True,
        )
        device_map = "cpu"
        # device_map = "auto"
        qmodel = AutoModelForCausalLM.from_pretrained(
            self.model,
            device_map=device_map,
            trust_remote_code=True,
            resume_download=True,
        ).eval()

        qmodel.generation_config = GenerationConfig.from_pretrained(
            self.model,
            trust_remote_code=True,
            resume_download=True,
        )
        self.qmodel = qmodel
        self.tokenizer = tokenizer

    def _chat_stream(
            self,
            messages: List[Dict],
            stop: Optional[List[str]] = None,
    ) -> Iterator[str]:
        _request = ChatCompletionRequest(model=self.model,
                                         messages=messages,
                                         stop=stop,
                                         stream=True)
        response = create_chat_completion(_request, self.qmodel, self.tokenizer)
        # TODO: error handling
        for chunk in response:
            if hasattr(chunk.choices[0].delta, 'content'):
                yield chunk.choices[0].delta.content

    def _chat_no_stream(
            self,
            messages: List[Dict],
            stop: Optional[List[str]] = None,
    ) -> str:
        _request = ChatCompletionRequest(model=self.model, messages=messages, stop=stop, stream=False)
        response = create_chat_completion(_request, self.qmodel, self.tokenizer)
        # TODO: error handling
        return response.choices[0].message.content

    def chat_with_functions(self,
                            messages: List[Dict],
                            functions: Optional[List[Dict]] = None) -> Dict:
        if functions:
            _request = ChatCompletionRequest(model=self.model, messages=messages, functions=functions)
            response = create_chat_completion(_request, self.qmodel, self.tokenizer)
        else:
            _request = ChatCompletionRequest(model=self.model, messages=messages)
            response = create_chat_completion(_request, self.qmodel, self.tokenizer)
        # TODO: error handling
        return response.choices[0].message.model_dump()


class QwenChatAsOAI1(BaseChatModel):

    def __init__(self, model: str, api_key: str, model_server: str):
        super().__init__()
        if model_server.strip().lower() != 'openai':
            openai.api_base = model_server
        openai.api_key = api_key.strip() or os.getenv('OPENAI_API_KEY',
                                                      default='EMPTY')
        self.model = model

    def _chat_stream(
            self,
            messages: List[Dict],
            stop: Optional[List[str]] = None,
    ) -> Iterator[str]:
        response = openai.ChatCompletion.create(model=self.model,
                                                messages=messages,
                                                stop=stop,
                                                stream=True)
        # TODO: error handling
        for chunk in response:
            if hasattr(chunk.choices[0].delta, 'content'):
                yield chunk.choices[0].delta.content

    def _chat_no_stream(
            self,
            messages: List[Dict],
            stop: Optional[List[str]] = None,
    ) -> str:
        response = openai.ChatCompletion.create(model=self.model,
                                                messages=messages,
                                                stop=stop,
                                                stream=False)
        # TODO: error handling
        return response.choices[0].message.content

    def chat_with_functions(self,
                            messages: List[Dict],
                            functions: Optional[List[Dict]] = None) -> Dict:
        if functions:
            response = openai.ChatCompletion.create(model=self.model,
                                                    messages=messages,
                                                    functions=functions)
        else:
            response = openai.ChatCompletion.create(model=self.model,
                                                    messages=messages)
        # TODO: error handling
        return response.choices[0].message