use async_trait::async_trait;
use futures_util::StreamExt;
use reqwest::Client;
use serde::Deserialize;
use serde_json::{Value, json};
use std::collections::HashMap;
use std::time::Duration;

use super::traits::Usage;
use super::{ChatCompletionRequest, ChatCompletionResponse, LLMProvider, ToolCall};
use crate::domain::messages::ContentBlock;

const INTERNAL_MODEL_EXTRA_KEYS: &[&str] = &["tool_call_arguments_json", "mock_response_content", "supported_content_types"];

/// 流式响应中的工具调用增量
#[derive(Debug, Default)]
struct StreamingToolCall {
    id: String,
    name: String,
    arguments: String,
}

/// 流式响应累积器
#[derive(Debug, Default)]
struct StreamingAccumulator {
    content: String,
    reasoning_content: Option<String>,
    tool_calls: HashMap<usize, StreamingToolCall>,
    response_id: String,
}

impl StreamingAccumulator {
    fn new() -> Self {
        Self::default()
    }

    /// 添加内容增量
    fn add_content(&mut self, delta: &str) {
        self.content.push_str(delta);
    }

    /// 添加推理内容增量
    fn add_reasoning_content(&mut self, delta: &str) {
        if self.reasoning_content.is_none() {
            self.reasoning_content = Some(String::new());
        }
        self.reasoning_content.as_mut().unwrap().push_str(delta);
    }

    /// 添加工具调用增量
    fn add_tool_call(&mut self, index: usize, id: Option<&str>, name: Option<&str>, arguments: Option<&str>) {
        let entry = self.tool_calls.entry(index).or_insert_with(StreamingToolCall::default);

        // 只在 id 非空时才更新，防止流式响应中后续 chunk 的空 id 覆盖之前的值
        if let Some(id) = id {
            if !id.is_empty() {
                entry.id = id.to_string();
            }
        }
        // 只在 name 非空时才更新，防止流式响应中后续 chunk 的 None 覆盖之前的值
        if let Some(name) = name {
            if !name.is_empty() {
                entry.name = name.to_string();
            }
        }
        if let Some(args) = arguments {
            entry.arguments.push_str(args);
        }
    }

    /// 设置响应 ID
    fn set_response_id(&mut self, id: String) {
        if self.response_id.is_empty() {
            self.response_id = id;
        }
    }

    /// 构建最终的 ChatCompletionResponse
    fn build_response(self, model: String) -> ChatCompletionResponse {
        let tool_calls: Vec<ToolCall> = self.tool_calls
            .into_iter()
            .filter(|(_, call)| !call.id.is_empty() && !call.name.is_empty())
            .map(|(_, call)| {
                let arguments = serde_json::from_str(&call.arguments)
                    .unwrap_or_else(|_| serde_json::Value::Null);
                ToolCall {
                    id: call.id,
                    name: call.name,
                    arguments,
                }
            })
            .collect();

        ChatCompletionResponse {
            id: if self.response_id.is_empty() {
                format!("stream-{}", uuid::Uuid::new_v4())
            } else {
                self.response_id
            },
            model,
            content: self.content,
            reasoning_content: self.reasoning_content,
            tool_calls,
            usage: Usage {
                prompt_tokens: 0,
                completion_tokens: 0,
                total_tokens: 0,
            },
        }
    }
}

fn format_error_chain(error: &(dyn std::error::Error + 'static)) -> String {
    let mut details = vec![error.to_string()];
    let mut current = error.source();

    while let Some(source) = current {
        details.push(source.to_string());
        current = source.source();
    }

    details.join("\ncaused by: ")
}

fn format_transport_error_context(
    provider_name: &str,
    model_id: &str,
    url: &str,
    timeout_secs: u64,
    error: &(dyn std::error::Error + 'static),
) -> String {
    format!(
        "transport error: provider={} model={} url={} timeout_secs={} details={}",
        provider_name,
        model_id,
        url,
        timeout_secs,
        format_error_chain(error)
    )
}

fn convert_content_blocks(
    supports_images: bool,
    provider_name: &str,
    model_id: &str,
    blocks: &[ContentBlock],
    message_idx: usize,
) -> Value {
    // 检查是否有图片且模型不支持
    if !supports_images {
        let has_images = blocks.iter().any(|b| matches!(b, ContentBlock::ImageUrl { .. }));

        if has_images {
            let image_count = blocks.iter()
                .filter(|b| matches!(b, ContentBlock::ImageUrl { .. }))
                .count();

            tracing::warn!(
                provider = %provider_name,
                model = %model_id,
                filtered_images = image_count,
                message_idx,
                "模型不支持图片；将图片转换为通知文本"
            );

            // 复用通知格式，将图片转换为文本通知
            let mut converted_blocks: Vec<Value> = Vec::new();
            let mut notices: Vec<String> = Vec::new();
            let mut image_idx = 0;

            for block in blocks.iter() {
                match block {
                    ContentBlock::Text { text } => {
                        converted_blocks.push(json!({ "type": "text", "text": text }));
                    }
                    ContentBlock::ImageUrl { .. } => {
                        image_idx += 1;
                        notices.push(format!(
                            "- 第 {} 张图片：当前模型不支持图片输入，该图片未能成功入模，请直接告知用户。",
                            image_idx
                        ));
                    }
                }
            }

            // 添加通知文本块
            if !notices.is_empty() {
                let notice_text = format!(
                    "[系统提示] 以下图片未能成功入模：\n{}",
                    notices.join("\n")
                );
                converted_blocks.push(json!({ "type": "text", "text": notice_text }));
            }

            // 如果只有一个文本块且没有通知，返回字符串形式
            if converted_blocks.len() == 1 {
                if let Some(block) = converted_blocks.first() {
                    if block.get("type").and_then(|t| t.as_str()) == Some("text") {
                        if let Some(text) = block.get("text").and_then(|t| t.as_str()) {
                            return Value::String(text.to_string());
                        }
                    }
                }
            }

            return Value::Array(converted_blocks);
        }
    }

    // 原有逻辑 - 模型支持图片，正常转换
    if blocks.len() == 1 {
        if let ContentBlock::Text { text } = &blocks[0] {
            return Value::String(text.clone());
        }
    }
    Value::Array(
        blocks
            .iter()
            .map(|b| match b {
                ContentBlock::Text { text } => json!({ "type": "text", "text": text }),
                ContentBlock::ImageUrl { image_url } => {
                    json!({ "type": "image_url", "image_url": { "url": image_url.url } })
                }
            })
            .collect(),
    )
}

pub struct OpenAIProvider {
    client: Client,
    name: String,
    api_key: String,
    base_url: String,
    extra_headers: HashMap<String, String>,
    llm_timeout_secs: u64,
    model_id: String,
    temperature: Option<f32>,
    max_tokens: Option<u32>,
    model_extra: HashMap<String, serde_json::Value>,
}

#[derive(Deserialize)]
#[serde(untagged)]
enum OAIFunctionArguments {
    Json(Value),
    String(String),
}

impl OpenAIProvider {
    pub fn new(
        name: String,
        api_key: String,
        base_url: String,
        extra_headers: HashMap<String, String>,
        llm_timeout_secs: u64,
        model_id: String,
        temperature: Option<f32>,
        max_tokens: Option<u32>,
        model_extra: HashMap<String, serde_json::Value>,
    ) -> Self {
        let client = Client::builder()
            .timeout(Duration::from_secs(llm_timeout_secs))
            .build()
            .unwrap_or_else(|_| Client::new());

        Self {
            client,
            name,
            api_key,
            base_url,
            extra_headers,
            llm_timeout_secs,
            model_id,
            temperature,
            max_tokens,
            model_extra,
        }
    }

    fn uses_json_tool_arguments(&self) -> bool {
        self.model_extra
            .get("tool_call_arguments_json")
            .and_then(|value| value.as_bool())
            .unwrap_or(false)
    }

    /// 检查是否启用流式输出，默认启用
    fn is_streaming_enabled(&self) -> bool {
        self.model_extra
            .get("enable_streaming")
            .and_then(|value| value.as_bool())
            .unwrap_or(true)
    }

    /// 检查模型是否支持指定内容类型
    /// 默认支持所有类型（text, image）
    fn supports_content_type(&self, content_type: &str) -> bool {
        self.model_extra
            .get("supported_content_types")
            .and_then(|value| value.as_array())
            .map(|types| {
                types.iter().any(|t| t.as_str() == Some(content_type))
            })
            .unwrap_or(true)
    }

    /// 检查模型是否支持图片
    fn supports_images(&self) -> bool {
        self.supports_content_type("image")
    }

    fn normalize_tool_arguments(&self, arguments: &Value) -> Value {
        match arguments {
            Value::String(raw) => serde_json::from_str(raw).unwrap_or_else(|_| arguments.clone()),
            _ => arguments.clone(),
        }
    }

    fn serialize_tool_arguments(&self, arguments: &Value) -> Value {
        let normalized = self.normalize_tool_arguments(arguments);

        if self.uses_json_tool_arguments() {
            // Model expects JSON object format (e.g., some code models)
            normalized
        } else {
            // Standard OpenAI format: arguments as JSON string
            // But ensure we serialize valid JSON, not null
            match normalized {
                Value::Null => Value::String("{}".to_string()),
                Value::String(raw) => {
                    // If the string is already valid JSON, keep it as-is
                    // Otherwise, ensure it's a proper JSON string
                    if serde_json::from_str::<Value>(&raw).is_ok() {
                        Value::String(raw)
                    } else {
                        // Invalid JSON string - wrap it as a proper JSON string
                        Value::String(serde_json::to_string(&raw).unwrap_or_else(|_| "null".to_string()))
                    }
                }
                value => Value::String(
                    serde_json::to_string(&value).unwrap_or_else(|_| "{}".to_string()),
                ),
            }
        }
    }

    fn request_model_extra(&self) -> impl Iterator<Item = (&String, &Value)> {
        self.model_extra.iter().filter(|(key, _)| {
            !INTERNAL_MODEL_EXTRA_KEYS
                .iter()
                .any(|internal| internal == &key.as_str())
        })
    }

    /// 内部流式聊天实现
    async fn chat_streaming(
        &self,
        request: &ChatCompletionRequest,
    ) -> Result<ChatCompletionResponse, Box<dyn std::error::Error + Send + Sync>> {
        tracing::debug!(provider = %self.name, model = %self.model_id, "Starting streaming chat");

        let url = format!("{}/chat/completions", self.base_url);

        let mut body = self.build_request_body(request);
        // 启用流式输出
        body["stream"] = json!(true);

        let mut req_builder = self
            .client
            .post(&url)
            .header("Authorization", format!("Bearer {}", self.api_key))
            .header("Content-Type", "application/json")
            .header("Accept", "text/event-stream");

        for (key, value) in &self.extra_headers {
            req_builder = req_builder.header(key.as_str(), value.as_str());
        }

        let resp = req_builder.json(&body).send().await.map_err(|err| {
            format_transport_error_context(
                &self.name,
                &self.model_id,
                &url,
                self.llm_timeout_secs,
                &err,
            )
        })?;

        let status = resp.status();
        if !status.is_success() {
            let text = resp.text().await.unwrap_or_default();
            return Err(format!("API error {}: {}", status, text).into());
        }

        let mut accumulator = StreamingAccumulator::new();

        // 读取 SSE 流
        let mut stream = resp.bytes_stream();
        let mut buffer = String::new();
        let mut done_received = false;

        while let Some(chunk_result) = stream.next().await {
            let chunk = chunk_result?;
            let text = String::from_utf8_lossy(&chunk);
            buffer.push_str(&text);

            // 处理缓冲区中的完整行
            while let Some(newline_pos) = buffer.find('\n') {
                let line = buffer[..newline_pos].to_string();
                buffer = buffer[newline_pos + 1..].to_string();

                let line_trimmed = line.trim();

                if line_trimmed.is_empty() || line_trimmed.starts_with(':') {
                    continue;
                }

                // SSE 格式: data: {...} 或 data:{...}（某些 API 如 139 云没有空格）
                let data_opt = line_trimmed.strip_prefix("data: ")
                    .or_else(|| line_trimmed.strip_prefix("data:"));

                if let Some(data) = data_opt {
                    if data == "[DONE]" {
                        // 流结束
                        done_received = true;
                        break;
                    }

                    // 解析 JSON
                    match serde_json::from_str::<Value>(data) {
                        Ok(json) => {
                            // 提取响应 ID
                            if let Some(id) = json.get("id").and_then(|v| v.as_str()) {
                                accumulator.set_response_id(id.to_string());
                            }

                            // 提取 choices
                            if let Some(choices) = json.get("choices").and_then(|c| c.as_array()) {
                                for choice in choices {
                                    // 尝试从 delta 提取（标准 OpenAI 流式格式）
                                    if let Some(delta) = choice.get("delta") {
                                        // 提取内容增量
                                        if let Some(content) = delta.get("content").and_then(|c| c.as_str()) {
                                            accumulator.add_content(content);
                                        }

                                        // 提取推理内容增量
                                        if let Some(reasoning) = delta.get("reasoning_content").and_then(|r| r.as_str()) {
                                            accumulator.add_reasoning_content(reasoning);
                                        }

                                        // 提取工具调用增量
                                        if let Some(tool_calls) = delta.get("tool_calls").and_then(|t| t.as_array()) {
                                            for tool_call in tool_calls {
                                                let index = tool_call.get("index").and_then(|i| i.as_u64()).unwrap_or(0) as usize;

                                                let id = tool_call.get("id").and_then(|v| v.as_str());
                                                let name = tool_call.get("function")
                                                    .and_then(|f| f.get("name"))
                                                    .and_then(|n| n.as_str());
                                                let arguments = tool_call.get("function")
                                                    .and_then(|f| f.get("arguments"))
                                                    .and_then(|a| a.as_str());

                                                accumulator.add_tool_call(index, id, name, arguments);
                                            }
                                        }
                                    }
                                    // 尝试从 message 提取（某些非标准 API 格式）
                                    else if let Some(message) = choice.get("message") {
                                        if let Some(content) = message.get("content").and_then(|c| c.as_str()) {
                                            accumulator.add_content(content);
                                        }
                                        if let Some(reasoning) = message.get("reasoning_content").and_then(|r| r.as_str()) {
                                            accumulator.add_reasoning_content(reasoning);
                                        }
                                    }
                                }
                            }
                        }
                        Err(e) => {
                            tracing::debug!(
                                error = %e,
                                data = %data,
                                "Failed to parse SSE data"
                            );
                        }
                    }
                }
            }

            if done_received {
                break;
            }
        }

        // 处理缓冲区中剩余的内容
        for line in buffer.lines() {
            let line_trimmed = line.trim();
            if line_trimmed.is_empty() || line_trimmed.starts_with(':') {
                continue;
            }

            // 同样支持 data: {...} 和 data:{...} 两种格式
            let data_opt = line_trimmed.strip_prefix("data: ")
                .or_else(|| line_trimmed.strip_prefix("data:"));

            if let Some(data) = data_opt {
                if data == "[DONE]" {
                    break;
                }

                if let Ok(json) = serde_json::from_str::<Value>(data) {
                    if let Some(id) = json.get("id").and_then(|v| v.as_str()) {
                        accumulator.set_response_id(id.to_string());
                    }

                    if let Some(choices) = json.get("choices").and_then(|c| c.as_array()) {
                        for choice in choices {
                            // 尝试从 delta 提取
                            if let Some(delta) = choice.get("delta") {
                                if let Some(content) = delta.get("content").and_then(|c| c.as_str()) {
                                    accumulator.add_content(content);
                                }
                                if let Some(reasoning) = delta.get("reasoning_content").and_then(|r| r.as_str()) {
                                    accumulator.add_reasoning_content(reasoning);
                                }
                                if let Some(tool_calls) = delta.get("tool_calls").and_then(|t| t.as_array()) {
                                    for tool_call in tool_calls {
                                        let index = tool_call.get("index").and_then(|i| i.as_u64()).unwrap_or(0) as usize;
                                        let id = tool_call.get("id").and_then(|v| v.as_str());
                                        let name = tool_call.get("function")
                                            .and_then(|f| f.get("name"))
                                            .and_then(|n| n.as_str());
                                        let arguments = tool_call.get("function")
                                            .and_then(|f| f.get("arguments"))
                                            .and_then(|a| a.as_str());
                                        accumulator.add_tool_call(index, id, name, arguments);
                                    }
                                }
                            }
                            // 尝试从 message 提取（某些非标准 API 格式）
                            else if let Some(message) = choice.get("message") {
                                if let Some(content) = message.get("content").and_then(|c| c.as_str()) {
                                    accumulator.add_content(content);
                                }
                                if let Some(reasoning) = message.get("reasoning_content").and_then(|r| r.as_str()) {
                                    accumulator.add_reasoning_content(reasoning);
                                }
                            }
                        }
                    }
                }
            }
        }

        let response = accumulator.build_response(self.model_id.clone());
        tracing::debug!(
            content_len = response.content.len(),
            tool_calls_count = response.tool_calls.len(),
            has_reasoning = response.reasoning_content.is_some(),
            "Streaming response built"
        );
        Ok(response)
    }

    fn build_request_body(&self, request: &ChatCompletionRequest) -> Value {
        let supports_images = self.supports_images();
        let mut body = json!({
            "model": self.model_id,
            "messages": request.messages.iter().enumerate().map(|(i, m)| {
                if m.role == "tool" {
                    json!({
                        "role": m.role,
                        "content": convert_content_blocks(supports_images, &self.name, &self.model_id, &m.content, i),
                        "tool_call_id": m.tool_call_id,
                        "name": m.name,
                    })
                } else if m.role == "assistant" && m.tool_calls.is_some() {
                    let mut message = json!({
                        "role": m.role,
                        "content": convert_content_blocks(supports_images, &self.name, &self.model_id, &m.content, i),
                        "tool_calls": m.tool_calls.as_ref().map(|calls| {
                            calls.iter().map(|call| json!({
                                "id": call.id,
                                "type": "function",
                                "function": {
                                    "name": call.name,
                                    "arguments": self.serialize_tool_arguments(&call.arguments)
                                }
                            })).collect::<Vec<_>>()
                        })
                    });

                    if let Some(reasoning_content) = &m.reasoning_content {
                        message["reasoning_content"] = Value::String(reasoning_content.clone());
                    }

                    message
                } else {
                    let mut message = json!({
                        "role": m.role,
                        "content": convert_content_blocks(supports_images, &self.name, &self.model_id, &m.content, i)
                    });

                    if m.role == "assistant" {
                        if let Some(reasoning_content) = &m.reasoning_content {
                            message["reasoning_content"] = Value::String(reasoning_content.clone());
                        }
                    }

                    message
                }
            }).collect::<Vec<_>>(),
        });

        // 只有配置了才添加 temperature，否则让模型使用默认值
        if let Some(temp) = request.temperature.or(self.temperature) {
            body["temperature"] = json!(temp);
        }

        // 只有配置了才添加 max_tokens
        if let Some(tokens) = request.max_tokens.or(self.max_tokens) {
            body["max_tokens"] = json!(tokens);
        }

        for (key, value) in self.request_model_extra() {
            body[key] = value.clone();
        }

        if let Some(tools) = &request.tools {
            body["tools"] = json!(tools);
        }

        body
    }
}

#[derive(Deserialize)]
struct OpenAIResponse {
    id: String,
    model: String,
    choices: Vec<OpenAIChoice>,
    #[serde(default)]
    usage: OpenAIUsage,
}

#[derive(Deserialize)]
struct OpenAIChoice {
    message: OpenAIMessage,
}

#[derive(Deserialize)]
struct OpenAIMessage {
    #[serde(default)]
    content: Option<String>,
    #[serde(default)]
    reasoning_content: Option<String>,
    #[allow(dead_code)]
    #[serde(default)]
    name: Option<String>,
    #[serde(default)]
    tool_calls: Vec<OpenAIToolCall>,
}

#[derive(Deserialize)]
struct OpenAIToolCall {
    id: String,
    #[serde(rename = "function")]
    function: OAIFunction,
    #[allow(dead_code)]
    #[serde(default)]
    index: Option<u32>,
}

#[derive(Deserialize)]
struct OAIFunction {
    name: String,
    arguments: OAIFunctionArguments,
}

#[derive(Deserialize, Default)]
struct OpenAIUsage {
    #[serde(default)]
    prompt_tokens: u32,
    #[serde(default)]
    completion_tokens: u32,
    #[serde(default)]
    total_tokens: u32,
}

#[async_trait]
impl LLMProvider for OpenAIProvider {
    async fn chat(
        &self,
        request: ChatCompletionRequest,
    ) -> Result<ChatCompletionResponse, Box<dyn std::error::Error + Send + Sync>> {
        // 检查是否启用流式输出
        if self.is_streaming_enabled() {
            // 优先尝试流式输出
            match self.chat_streaming(&request).await {
                Ok(response) => return Ok(response),
                Err(e) => {
                    tracing::debug!(
                        provider = %self.name,
                        model = %self.model_id,
                        error = %e,
                        "Streaming failed, falling back to non-streaming"
                    );
                    // 流式失败，回退到非流式
                }
            }
        } else {
            tracing::debug!(provider = %self.name, model = %self.model_id, "Streaming disabled, using non-streaming");
        }

        // 非流式回退实现
        let url = format!("{}/chat/completions", self.base_url);

        let body = self.build_request_body(&request);

        // Debug: Log LLM request summary (only in debug builds)
        #[cfg(debug_assertions)]
        {
            // Log messages summary
            let msg_count = body["messages"].as_array().map(|a| a.len()).unwrap_or(0);
            tracing::debug!(msg_count = msg_count, "LLM request messages count");

            // Log first 20 bytes of base64 images (don't log full base64)
            if let Some(msgs) = body["messages"].as_array() {
                for (i, msg) in msgs.iter().enumerate() {
                    if let Some(content) = msg.get("content").and_then(|c| c.as_array()) {
                        for (j, item) in content.iter().enumerate() {
                            if item.get("type").and_then(|t| t.as_str()) == Some("image_url") {
                                if let Some(url_str) = item
                                    .get("image_url")
                                    .and_then(|u| u.get("url"))
                                    .and_then(|v| v.as_str())
                                {
                                    let prefix: String = url_str.chars().take(20).collect();
                                    tracing::debug!(msg_idx = i, item_idx = j, image_prefix = %prefix, image_url_len = %url_str.len(), "Image in LLM request (first 20 bytes shown)");
                                }
                            }
                        }
                    }
                }
            }
        }

        let mut req_builder = self
            .client
            .post(&url)
            .header("Authorization", format!("Bearer {}", self.api_key))
            .header("Content-Type", "application/json");

        for (key, value) in &self.extra_headers {
            req_builder = req_builder.header(key.as_str(), value.as_str());
        }

        let resp = req_builder.json(&body).send().await.map_err(|err| {
            let error_context = format_transport_error_context(
                &self.name,
                &self.model_id,
                &url,
                self.llm_timeout_secs,
                &err,
            );
            tracing::error!(
                provider = %self.name,
                model = %self.model_id,
                url = %url,
                base_url = %self.base_url,
                timeout_secs = self.llm_timeout_secs,
                error = %error_context,
                "OpenAI-compatible API transport request failed"
            );
            error_context
        })?;

        let status = resp.status();
        let text = resp.text().await?;

        // Debug: Log LLM response (only in debug builds)
        if !status.is_success() {
            tracing::error!(
                provider = %self.name,
                model = %self.model_id,
                url = %url,
                status = %status,
                response_len = text.len(),
                response_body = %text,
                "OpenAI-compatible API request failed"
            );
            return Err(format!("API error {}: {}", status, text).into());
        }

        #[cfg(debug_assertions)]
        {
            let resp_preview: String = text.chars().take(100).collect();
            tracing::debug!(status = %status, response_preview = %resp_preview, response_len = %text.len(), timeout_secs = self.llm_timeout_secs, "LLM response (first 100 chars shown)");
        }

        let openai_resp: OpenAIResponse = serde_json::from_str(&text).map_err(|e| {
            tracing::error!(
                provider = %self.name,
                model = %self.model_id,
                url = %url,
                error = %format_error_chain(&e),
                response_len = text.len(),
                response_body = %text,
                "Failed to decode OpenAI-compatible API response"
            );
            format!("decode error: {} | body: {}", e, &text)
        })?;

        let content = openai_resp.choices[0]
            .message
            .content
            .as_ref()
            .unwrap_or(&String::new())
            .clone();

        let tool_calls: Vec<ToolCall> = openai_resp.choices[0]
            .message
            .tool_calls
            .iter()
            .map(|tc| ToolCall {
                id: tc.id.clone(),
                name: tc.function.name.clone(),
                arguments: match &tc.function.arguments {
                    OAIFunctionArguments::Json(arguments) => arguments.clone(),
                    OAIFunctionArguments::String(arguments) => {
                        serde_json::from_str(arguments).unwrap_or(serde_json::Value::Null)
                    }
                },
            })
            .collect();

        Ok(ChatCompletionResponse {
            id: openai_resp.id,
            model: openai_resp.model,
            content,
            reasoning_content: openai_resp.choices[0].message.reasoning_content.clone(),
            tool_calls,
            usage: Usage {
                prompt_tokens: openai_resp.usage.prompt_tokens,
                completion_tokens: openai_resp.usage.completion_tokens,
                total_tokens: openai_resp.usage.total_tokens,
            },
        })
    }

    fn ptype(&self) -> &str {
        "openai"
    }

    fn name(&self) -> &str {
        &self.name
    }

    fn model_id(&self) -> &str {
        &self.model_id
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::providers::Message;

    #[test]
    fn test_build_request_body_includes_assistant_tool_calls() {
        let provider = OpenAIProvider::new(
            "test".to_string(),
            "key".to_string(),
            "https://example.com/v1".to_string(),
            HashMap::new(),
            120,
            "gpt-test".to_string(),
            None,
            None,
            HashMap::new(),
        );

        let request = ChatCompletionRequest {
            messages: vec![Message {
                role: "assistant".to_string(),
                content: vec![ContentBlock::text("calling tool")],
                reasoning_content: None,
                tool_call_id: None,
                name: None,
                tool_calls: Some(vec![ToolCall {
                    id: "call_1".to_string(),
                    name: "calculator".to_string(),
                    arguments: json!({"expression": "1+1"}),
                }]),
            }],
            temperature: None,
            max_tokens: None,
            tools: None,
        };

        let body = provider.build_request_body(&request);
        let messages = body["messages"].as_array().unwrap();
        let tool_calls = messages[0]["tool_calls"].as_array().unwrap();

        assert_eq!(tool_calls.len(), 1);
        assert_eq!(tool_calls[0]["id"], "call_1");
        assert_eq!(tool_calls[0]["type"], "function");
        assert_eq!(tool_calls[0]["function"]["name"], "calculator");
        assert_eq!(
            tool_calls[0]["function"]["arguments"],
            "{\"expression\":\"1+1\"}"
        );
    }

    #[test]
    fn test_build_request_body_uses_json_tool_arguments_when_enabled() {
        let provider = OpenAIProvider::new(
            "test".to_string(),
            "key".to_string(),
            "https://example.com/v1".to_string(),
            HashMap::new(),
            120,
            "gpt-test".to_string(),
            None,
            None,
            HashMap::from([("tool_call_arguments_json".to_string(), Value::Bool(true))]),
        );

        let request = ChatCompletionRequest {
            messages: vec![Message {
                role: "assistant".to_string(),
                content: vec![ContentBlock::text("calling tool")],
                reasoning_content: None,
                tool_call_id: None,
                name: None,
                tool_calls: Some(vec![ToolCall {
                    id: "call_1".to_string(),
                    name: "calculator".to_string(),
                    arguments: json!({"expression": "1+1"}),
                }]),
            }],
            temperature: None,
            max_tokens: None,
            tools: None,
        };

        let body = provider.build_request_body(&request);
        let messages = body["messages"].as_array().unwrap();
        let tool_calls = messages[0]["tool_calls"].as_array().unwrap();

        assert_eq!(
            tool_calls[0]["function"]["arguments"],
            json!({"expression": "1+1"})
        );
        assert!(body.get("tool_call_arguments_json").is_none());
    }

    #[test]
    fn test_build_request_body_preserves_raw_json_string_arguments() {
        let provider = OpenAIProvider::new(
            "test".to_string(),
            "key".to_string(),
            "https://example.com/v1".to_string(),
            HashMap::new(),
            120,
            "gpt-test".to_string(),
            None,
            None,
            HashMap::new(),
        );

        let request = ChatCompletionRequest {
            messages: vec![Message {
                role: "assistant".to_string(),
                content: vec![ContentBlock::text("calling tool")],
                reasoning_content: None,
                tool_call_id: None,
                name: None,
                tool_calls: Some(vec![ToolCall {
                    id: "call_1".to_string(),
                    name: "calculator".to_string(),
                    arguments: Value::String("{\"expression\":\"1+1\"}".to_string()),
                }]),
            }],
            temperature: None,
            max_tokens: None,
            tools: None,
        };

        let body = provider.build_request_body(&request);
        let messages = body["messages"].as_array().unwrap();
        let tool_calls = messages[0]["tool_calls"].as_array().unwrap();

        assert_eq!(
            tool_calls[0]["function"]["arguments"],
            "{\"expression\":\"1+1\"}"
        );
    }

    #[test]
    fn test_build_request_body_omits_internal_model_extra_keys() {
        let provider = OpenAIProvider::new(
            "test".to_string(),
            "key".to_string(),
            "https://example.com/v1".to_string(),
            HashMap::new(),
            120,
            "gpt-test".to_string(),
            None,
            None,
            HashMap::from([
                ("tool_call_arguments_json".to_string(), Value::Bool(true)),
                (
                    "mock_response_content".to_string(),
                    Value::String("stub".to_string()),
                ),
                ("parallel_tool_calls".to_string(), Value::Bool(true)),
            ]),
        );

        let request = ChatCompletionRequest {
            messages: vec![Message::user("hello")],
            temperature: None,
            max_tokens: None,
            tools: None,
        };

        let body = provider.build_request_body(&request);

        assert!(body.get("tool_call_arguments_json").is_none());
        assert!(body.get("mock_response_content").is_none());
        assert_eq!(body["parallel_tool_calls"], Value::Bool(true));
    }

    #[test]
    fn test_build_request_body_includes_assistant_reasoning_content() {
        let provider = OpenAIProvider::new(
            "test".to_string(),
            "key".to_string(),
            "https://example.com/v1".to_string(),
            HashMap::new(),
            120,
            "gpt-test".to_string(),
            None,
            None,
            HashMap::new(),
        );

        let request = ChatCompletionRequest {
            messages: vec![Message {
                role: "assistant".to_string(),
                content: vec![ContentBlock::text("final answer")],
                reasoning_content: Some("step by step".to_string()),
                tool_call_id: None,
                name: None,
                tool_calls: None,
            }],
            temperature: None,
            max_tokens: None,
            tools: None,
        };

        let body = provider.build_request_body(&request);
        let messages = body["messages"].as_array().unwrap();

        assert_eq!(messages[0]["reasoning_content"], "step by step");
    }

    #[test]
    fn test_openai_response_parses_reasoning_content() {
        let response: OpenAIResponse = serde_json::from_value(json!({
            "id": "resp_1",
            "model": "gpt-test",
            "choices": [{
                "message": {
                    "content": "final answer",
                    "reasoning_content": "hidden reasoning",
                    "tool_calls": []
                }
            }],
            "usage": {
                "prompt_tokens": 10,
                "completion_tokens": 5,
                "total_tokens": 15
            }
        }))
        .unwrap();

        assert_eq!(
            response.choices[0].message.reasoning_content.as_deref(),
            Some("hidden reasoning")
        );
    }

    #[test]
    fn test_openai_response_parses_json_tool_arguments() {
        let response: OpenAIResponse = serde_json::from_value(json!({
            "id": "resp_1",
            "model": "gpt-test",
            "choices": [{
                "message": {
                    "content": "",
                    "tool_calls": [{
                        "id": "call_1",
                        "function": {
                            "name": "scheduler_manage",
                            "arguments": {"action": "list"}
                        }
                    }]
                }
            }],
            "usage": {
                "prompt_tokens": 1,
                "completion_tokens": 1,
                "total_tokens": 2
            }
        }))
        .unwrap();

        match &response.choices[0].message.tool_calls[0].function.arguments {
            OAIFunctionArguments::Json(arguments) => {
                assert_eq!(arguments, &json!({"action": "list"}));
            }
            OAIFunctionArguments::String(_) => panic!("expected JSON tool arguments"),
        }
    }

    #[test]
    fn test_streaming_accumulator_preserves_tool_call_id_with_empty_subsequent_chunks() {
        // 模拟阿里云等云服务商的流式响应行为：
        // 第一个 chunk 包含 id 和 name，后续 chunk 的 id 为空字符串、name 为 None
        let mut accumulator = StreamingAccumulator::new();

        // 第一个 chunk：包含完整的 id 和 name
        accumulator.add_tool_call(0, Some("call_abc123"), Some("memory_search"), Some("{\"action\":\""));
        // 第二个 chunk：只有参数增量
        accumulator.add_tool_call(0, None, None, Some("list"));
        // 第三个 chunk：参数继续
        accumulator.add_tool_call(0, None, None, Some("\""));
        // 第四个 chunk：id 为空字符串（某些云服务商的行为）
        accumulator.add_tool_call(0, Some(""), None, Some(", \"limit\": 20"));
        // 最后一个 chunk：name 为 None
        accumulator.add_tool_call(0, None, None, Some("}"));

        let response = accumulator.build_response("test-model".to_string());

        // 验证工具调用被正确保留，id 没有被空字符串覆盖
        assert_eq!(response.tool_calls.len(), 1);
        assert_eq!(response.tool_calls[0].id, "call_abc123");
        assert_eq!(response.tool_calls[0].name, "memory_search");
        assert_eq!(response.tool_calls[0].arguments, json!({"action":"list", "limit": 20}));
    }

    #[test]
    fn test_streaming_accumulator_handles_multiple_tool_calls() {
        let mut accumulator = StreamingAccumulator::new();

        // 第一个工具调用
        accumulator.add_tool_call(0, Some("call_1"), Some("calculator"), Some("{\"expr\": \"1+1\"}"));
        // 第二个工具调用（id 和 name 只在第一个 chunk 出现）
        accumulator.add_tool_call(1, Some("call_2"), Some("get_time"), Some("{}"));

        let response = accumulator.build_response("test-model".to_string());

        assert_eq!(response.tool_calls.len(), 2);
        assert_eq!(response.tool_calls[0].id, "call_1");
        assert_eq!(response.tool_calls[0].name, "calculator");
        assert_eq!(response.tool_calls[1].id, "call_2");
        assert_eq!(response.tool_calls[1].name, "get_time");
    }

    #[test]
    fn test_supports_images_default_true() {
        let provider = OpenAIProvider::new(
            "test".to_string(),
            "key".to_string(),
            "https://example.com/v1".to_string(),
            HashMap::new(),
            120,
            "gpt-test".to_string(),
            None,
            None,
            HashMap::new(),
        );

        assert!(provider.supports_images());
    }

    #[test]
    fn test_supports_images_disabled_via_config() {
        let provider = OpenAIProvider::new(
            "test".to_string(),
            "key".to_string(),
            "https://example.com/v1".to_string(),
            HashMap::new(),
            120,
            "gpt-test".to_string(),
            None,
            None,
            HashMap::from([(
                "supported_content_types".to_string(),
                Value::Array(vec![Value::String("text".to_string())]),
            )]),
        );

        assert!(!provider.supports_images());
    }

    #[test]
    fn test_convert_content_blocks_converts_images_to_notice_when_disabled() {
        let blocks = vec![
            ContentBlock::text("hello"),
            ContentBlock::image_url("data:image/png;base64,abc123"),
            ContentBlock::text("world"),
        ];

        let result = convert_content_blocks(false, "test", "test-model", &blocks, 0);

        // 应该是数组形式
        let arr = result.as_array().unwrap();
        assert_eq!(arr.len(), 3); // 两个文本块 + 一个通知块

        // 检查通知内容
        let notice_block = arr[2].as_object().unwrap();
        assert_eq!(notice_block["type"], "text");
        let notice_text = notice_block["text"].as_str().unwrap();
        assert!(notice_text.contains("[系统提示] 以下图片未能成功入模"));
        assert!(notice_text.contains("第 1 张图片"));
        assert!(notice_text.contains("当前模型不支持图片输入"));
    }

    #[test]
    fn test_convert_content_blocks_keeps_images_when_enabled() {
        let blocks = vec![
            ContentBlock::text("hello"),
            ContentBlock::image_url("data:image/png;base64,abc123"),
        ];

        let result = convert_content_blocks(true, "test", "test-model", &blocks, 0);

        // 应该是数组形式，包含文本和图片
        let arr = result.as_array().unwrap();
        assert_eq!(arr.len(), 2);
        assert_eq!(arr[0]["type"], "text");
        assert_eq!(arr[1]["type"], "image_url");
    }

    #[test]
    fn test_build_request_body_omits_supported_content_types_from_api() {
        let provider = OpenAIProvider::new(
            "test".to_string(),
            "key".to_string(),
            "https://example.com/v1".to_string(),
            HashMap::new(),
            120,
            "gpt-test".to_string(),
            None,
            None,
            HashMap::from([
                (
                    "supported_content_types".to_string(),
                    Value::Array(vec![Value::String("text".to_string())]),
                ),
                ("custom_param".to_string(), Value::String("value".to_string())),
            ]),
        );

        let request = ChatCompletionRequest {
            messages: vec![Message::user("hello")],
            temperature: None,
            max_tokens: None,
            tools: None,
        };

        let body = provider.build_request_body(&request);

        // supported_content_types 不应该发送到 API
        assert!(body.get("supported_content_types").is_none());
        // custom_param 应该保留
        assert_eq!(body["custom_param"], Value::String("value".to_string()));
    }
}