from __future__ import annotations

import base64
import enum
import json
from dataclasses import dataclass, field
from typing import Any

from anthropic.types import Message as AnthropicMessage
from google.genai.types import GenerateContentResponse
from openai.types.chat.chat_completion import ChatCompletion

import astrbot.core.message.components as Comp
from astrbot import logger
from astrbot.core.agent.message import (
    AssistantMessageSegment,
    ContentPart,
    ToolCall,
    ToolCallMessageSegment,
)
from astrbot.core.agent.tool import ToolSet
from astrbot.core.db.po import Conversation
from astrbot.core.message.message_event_result import MessageChain
from astrbot.core.utils.io import download_image_by_url


class ProviderType(enum.Enum):
    CHAT_COMPLETION = "chat_completion"
    SPEECH_TO_TEXT = "speech_to_text"
    TEXT_TO_SPEECH = "text_to_speech"
    EMBEDDING = "embedding"
    RERANK = "rerank"


@dataclass
class ProviderMeta:
    """The basic metadata of a provider instance."""

    id: str
    """the unique id of the provider instance that user configured"""
    model: str | None
    """the model name of the provider instance currently used"""
    type: str
    """the name of the provider adapter, such as openai, ollama"""
    provider_type: ProviderType = ProviderType.CHAT_COMPLETION
    """the capability type of the provider adapter"""


@dataclass
class ProviderMetaData(ProviderMeta):
    """The metadata of a provider adapter for registration."""

    desc: str = ""
    """the short description of the provider adapter"""
    cls_type: Any = None
    """the class type of the provider adapter"""
    default_config_tmpl: dict | None = None
    """the default configuration template of the provider adapter"""
    provider_display_name: str | None = None
    """the display name of the provider shown in the WebUI configuration page; if empty, the type is used"""


@dataclass
class ToolCallsResult:
    """工具调用结果"""

    tool_calls_info: AssistantMessageSegment
    """函数调用的信息"""
    tool_calls_result: list[ToolCallMessageSegment]
    """函数调用的结果"""

    def to_openai_messages(self) -> list[dict]:
        ret = [
            self.tool_calls_info.model_dump(),
            *[item.model_dump() for item in self.tool_calls_result],
        ]
        return ret

    def to_openai_messages_model(
        self,
    ) -> list[AssistantMessageSegment | ToolCallMessageSegment]:
        return [
            self.tool_calls_info,
            *self.tool_calls_result,
        ]


@dataclass
class ProviderRequest:
    prompt: str | None = None
    """提示词"""
    session_id: str | None = ""
    """会话 ID"""
    image_urls: list[str] = field(default_factory=list)
    """图片 URL 列表"""
    extra_user_content_parts: list[ContentPart] = field(default_factory=list)
    """额外的用户消息内容部分列表，用于在用户消息后添加额外的内容块（如系统提醒、指令等）。支持 dict 或 ContentPart 对象"""
    func_tool: ToolSet | None = None
    """可用的函数工具"""
    contexts: list[dict] = field(default_factory=list)
    """
    OpenAI 格式上下文列表。
    参考 https://platform.openai.com/docs/api-reference/chat/create#chat-create-messages
    """
    system_prompt: str = ""
    """系统提示词"""
    conversation: Conversation | None = None
    """关联的对话对象"""
    tool_calls_result: list[ToolCallsResult] | ToolCallsResult | None = None
    """附加的上次请求后工具调用的结果。参考: https://platform.openai.com/docs/guides/function-calling#handling-function-calls"""
    model: str | None = None
    """模型名称，为 None 时使用提供商的默认模型"""

    def __repr__(self) -> str:
        return (
            f"ProviderRequest(prompt={self.prompt}, session_id={self.session_id}, "
            f"image_count={len(self.image_urls or [])}, "
            f"func_tool={self.func_tool}, "
            f"contexts={self._print_friendly_context()}, "
            f"system_prompt={self.system_prompt}, "
            f"conversation_id={self.conversation.cid if self.conversation else 'N/A'}, "
        )

    def __str__(self) -> str:
        return self.__repr__()

    def append_tool_calls_result(self, tool_calls_result: ToolCallsResult) -> None:
        """添加工具调用结果到请求中"""
        if not self.tool_calls_result:
            self.tool_calls_result = []
        if isinstance(self.tool_calls_result, ToolCallsResult):
            self.tool_calls_result = [self.tool_calls_result]
        self.tool_calls_result.append(tool_calls_result)

    def _print_friendly_context(self):
        """打印友好的消息上下文。将 image_url 的值替换为 <Image>"""
        if not self.contexts:
            return f"prompt: {self.prompt}, image_count: {len(self.image_urls or [])}"

        result_parts = []

        for ctx in self.contexts:
            role = ctx.get("role", "unknown")
            content = ctx.get("content", "")

            if isinstance(content, str):
                result_parts.append(f"{role}: {content}")
            elif isinstance(content, list):
                msg_parts = []
                image_count = 0

                for item in content:
                    item_type = item.get("type", "")

                    if item_type == "text":
                        msg_parts.append(item.get("text", ""))
                    elif item_type == "image_url":
                        image_count += 1

                if image_count > 0:
                    if msg_parts:
                        msg_parts.append(f"[+{image_count} images]")
                    else:
                        msg_parts.append(f"[{image_count} images]")

                result_parts.append(f"{role}: {''.join(msg_parts)}")

        return "\n".join(result_parts)

    async def assemble_context(self) -> dict:
        """将请求(prompt 和 image_urls)包装成 OpenAI 的消息格式。"""
        # 构建内容块列表
        content_blocks = []

        # 1. 用户原始发言（OpenAI 建议：用户发言在前）
        if self.prompt and self.prompt.strip():
            content_blocks.append({"type": "text", "text": self.prompt})
        elif self.image_urls:
            # 如果没有文本但有图片，添加占位文本
            content_blocks.append({"type": "text", "text": "[图片]"})

        # 2. 额外的内容块（系统提醒、指令等）
        if self.extra_user_content_parts:
            for part in self.extra_user_content_parts:
                content_blocks.append(part.model_dump())

        # 3. 图片内容
        if self.image_urls:
            for image_url in self.image_urls:
                if image_url.startswith("http"):
                    image_path = await download_image_by_url(image_url)
                    image_data = await self._encode_image_bs64(image_path)
                elif image_url.startswith("file:///"):
                    image_path = image_url.replace("file:///", "")
                    image_data = await self._encode_image_bs64(image_path)
                else:
                    image_data = await self._encode_image_bs64(image_url)
                if not image_data:
                    logger.warning(f"图片 {image_url} 得到的结果为空，将忽略。")
                    continue
                content_blocks.append(
                    {"type": "image_url", "image_url": {"url": image_data}},
                )

        # 只有当只有一个来自 prompt 的文本块且没有额外内容块时，才降级为简单格式以保持向后兼容
        if (
            len(content_blocks) == 1
            and content_blocks[0]["type"] == "text"
            and not self.extra_user_content_parts
            and not self.image_urls
        ):
            return {"role": "user", "content": content_blocks[0]["text"]}

        # 否则返回多模态格式
        return {"role": "user", "content": content_blocks}

    async def _encode_image_bs64(self, image_url: str) -> str:
        """将图片转换为 base64"""
        if image_url.startswith("base64://"):
            return image_url.replace("base64://", "data:image/jpeg;base64,")
        with open(image_url, "rb") as f:
            image_bs64 = base64.b64encode(f.read()).decode("utf-8")
            return "data:image/jpeg;base64," + image_bs64
        return ""


@dataclass
class TokenUsage:
    input_other: int = 0
    """The number of input tokens, excluding cached tokens."""
    input_cached: int = 0
    """The number of input cached tokens."""
    output: int = 0
    """The number of output tokens."""

    @property
    def total(self) -> int:
        return self.input_other + self.input_cached + self.output

    @property
    def input(self) -> int:
        return self.input_other + self.input_cached

    def __add__(self, other: TokenUsage) -> TokenUsage:
        return TokenUsage(
            input_other=self.input_other + other.input_other,
            input_cached=self.input_cached + other.input_cached,
            output=self.output + other.output,
        )

    def __sub__(self, other: TokenUsage) -> TokenUsage:
        return TokenUsage(
            input_other=self.input_other - other.input_other,
            input_cached=self.input_cached - other.input_cached,
            output=self.output - other.output,
        )


@dataclass
class LLMResponse:
    role: str
    """The role of the message, e.g., assistant, tool, err"""
    result_chain: MessageChain | None = None
    """A chain of message components representing the text completion from LLM."""
    tools_call_args: list[dict[str, Any]] = field(default_factory=list)
    """Tool call arguments."""
    tools_call_name: list[str] = field(default_factory=list)
    """Tool call names."""
    tools_call_ids: list[str] = field(default_factory=list)
    """Tool call IDs."""
    tools_call_extra_content: dict[str, dict[str, Any]] = field(default_factory=dict)
    """Tool call extra content. tool_call_id -> extra_content dict"""
    reasoning_content: str = ""
    """The reasoning content extracted from the LLM, if any."""
    reasoning_signature: str | None = None
    """The signature of the reasoning content, if any."""

    raw_completion: (
        ChatCompletion | GenerateContentResponse | AnthropicMessage | None
    ) = None
    """The raw completion response from the LLM provider."""

    _completion_text: str = ""
    """The plain text of the completion."""

    is_chunk: bool = False
    """Indicates if the response is a chunked response."""

    id: str | None = None
    """The ID of the response. For chunked responses, it's the ID of the chunk; for non-chunked responses, it's the ID of the response."""
    usage: TokenUsage | None = None
    """The usage of the response. For chunked responses, it's the usage of the chunk; for non-chunked responses, it's the usage of the response."""

    def __init__(
        self,
        role: str,
        completion_text: str | None = None,
        result_chain: MessageChain | None = None,
        tools_call_args: list[dict[str, Any]] | None = None,
        tools_call_name: list[str] | None = None,
        tools_call_ids: list[str] | None = None,
        tools_call_extra_content: dict[str, dict[str, Any]] | None = None,
        reasoning_content: str | None = None,
        reasoning_signature: str | None = None,
        raw_completion: ChatCompletion
        | GenerateContentResponse
        | AnthropicMessage
        | None = None,
        is_chunk: bool = False,
        id: str | None = None,
        usage: TokenUsage | None = None,
    ) -> None:
        """初始化 LLMResponse

        Args:
            role (str): 角色, assistant, tool, err
            completion_text (str, optional): 返回的结果文本，已经过时，推荐使用 result_chain. Defaults to "".
            result_chain (MessageChain, optional): 返回的消息链. Defaults to None.
            tools_call_args (List[Dict[str, any]], optional): 工具调用参数. Defaults to None.
            tools_call_name (List[str], optional): 工具调用名称. Defaults to None.
            raw_completion (ChatCompletion, optional): 原始响应, OpenAI 格式. Defaults to None.

        """
        if reasoning_content is None:
            reasoning_content = ""
        if tools_call_args is None:
            tools_call_args = []
        if tools_call_name is None:
            tools_call_name = []
        if tools_call_ids is None:
            tools_call_ids = []
        if tools_call_extra_content is None:
            tools_call_extra_content = {}

        self.role = role
        self.completion_text = completion_text
        self.result_chain = result_chain
        self.tools_call_args = tools_call_args
        self.tools_call_name = tools_call_name
        self.tools_call_ids = tools_call_ids
        self.tools_call_extra_content = tools_call_extra_content
        self.reasoning_content = reasoning_content
        self.reasoning_signature = reasoning_signature
        self.raw_completion = raw_completion
        self.is_chunk = is_chunk

        if id is not None:
            self.id = id
        if usage is not None:
            self.usage = usage

    @property
    def completion_text(self):
        if self.result_chain:
            return self.result_chain.get_plain_text()
        return self._completion_text

    @completion_text.setter
    def completion_text(self, value) -> None:
        if self.result_chain:
            self.result_chain.chain = [
                comp
                for comp in self.result_chain.chain
                if not isinstance(comp, Comp.Plain)
            ]  # 清空 Plain 组件
            self.result_chain.chain.insert(0, Comp.Plain(value))
        else:
            self._completion_text = value

    def to_openai_tool_calls(self) -> list[dict]:
        """Convert to OpenAI tool calls format. Deprecated, use to_openai_to_calls_model instead."""
        ret = []
        for idx, tool_call_arg in enumerate(self.tools_call_args):
            payload = {
                "id": self.tools_call_ids[idx],
                "function": {
                    "name": self.tools_call_name[idx],
                    "arguments": json.dumps(tool_call_arg),
                },
                "type": "function",
            }
            if self.tools_call_extra_content.get(self.tools_call_ids[idx]):
                payload["extra_content"] = self.tools_call_extra_content[
                    self.tools_call_ids[idx]
                ]
            ret.append(payload)
        return ret

    def to_openai_to_calls_model(self) -> list[ToolCall]:
        """The same as to_openai_tool_calls but return pydantic model."""
        ret = []
        for idx, tool_call_arg in enumerate(self.tools_call_args):
            ret.append(
                ToolCall(
                    id=self.tools_call_ids[idx],
                    function=ToolCall.FunctionBody(
                        name=self.tools_call_name[idx],
                        arguments=json.dumps(tool_call_arg),
                    ),
                    # the extra_content will not serialize if it's None when calling ToolCall.model_dump()
                    extra_content=self.tools_call_extra_content.get(
                        self.tools_call_ids[idx]
                    ),
                ),
            )
        return ret


@dataclass
class RerankResult:
    index: int
    """在候选列表中的索引位置"""
    relevance_score: float
    """相关性分数"""