//! OpenAI 兼容 Provider — 通过 /v1/chat/completions 端点实现
//!
//! 覆盖: OpenAI / GLM (open.bigmodel.cn) / DeepSeek / Claude OpenAI 兼容模式
//! 支持: 同步调用 + SSE 流式 + Function Calling / Tool Use

use std::time::Duration;

use async_trait::async_trait;
use eventsource_stream::Eventsource;
use futures::StreamExt;
use reqwest::Client;
use tracing::{debug, error, warn};

use crate::provider::{
    CompletionRequest, CompletionResponse, LlmProvider, StreamResult, TokenUsage, ToolCall,
};
// ChatMessage 仅单测构造 CompletionRequest 用，避免非 test 构建的 unused import 警告。
#[cfg(test)]
use crate::provider::ChatMessage;
use crate::retry::{
    retry_with_backoff, AttemptOutcome, is_reqwest_error_retryable, is_status_retryable,
};

// 协议数据结构（OpenAiRequest / OpenAiResponse / OpenAiStreamChunk …）与 SSE 解析纯函数
// apply_openai_sse 抽离到 openai_helpers.rs，结构对齐 anthropic_helpers.rs。
// Rust impl 块不可跨文件，Provider struct + impl 仍留本文件。
use crate::openai_helpers::{
    apply_openai_sse, OpenAiMessage, OpenAiRequest, OpenAiResponse, OpenAiToolCallResp,
};

// ============================================================
// OpenAI Compat Provider
// ============================================================

/// OpenAI 兼容 LLM Provider
pub struct OpenAICompatProvider {
    client: Client,
    api_key: String,
    base_url: String,
    default_model: String,
}

impl OpenAICompatProvider {
    /// 创建 Provider
    ///
    /// - `base_url`: 如 "https://api.openai.com", "https://open.bigmodel.cn/api/paas", "https://api.deepseek.com"
    /// - `api_key`: API 密钥
    /// - `default_model`: 默认模型名称
    pub fn new(base_url: impl Into<String>, api_key: impl Into<String>, default_model: impl Into<String>) -> Self {
        // SW-260618-10: reqwest Client 构建抽 crate::build_provider_client（与 Anthropic 共用 DRY）。
        // connect_timeout/回退策略集中此处，未来改一处即可（见 lib.rs::build_provider_client）。
        let client = crate::build_provider_client();
        Self {
            client,
            api_key: api_key.into(),
            base_url: base_url.into(),
            default_model: default_model.into(),
        }
    }

    /// 构建完整 API URL
    ///
    /// 智能拼接，兼容三种 base_url 约定：
    /// - 已含完整端点（…/chat/completions）→ 直接用
    /// - 已含版本段（…/v1 …/v4 等，如 GLM 的 /api/paas/v4）→ 补 /chat/completions
    /// - 仅域名无版本（如 api.openai.com / api.deepseek.com）→ 补 /v1/chat/completions（OpenAI 约定）
    fn chat_url(&self) -> String {
        let base = self.base_url.trim_end_matches('/');
        if base.ends_with("/chat/completions") {
            return base.to_string();
        }
        if Self::ends_with_version(base) {
            return format!("{}/chat/completions", base);
        }
        format!("{}/v1/chat/completions", base)
    }

    /// base_url 是否以 `/v<数字>` 结尾（如 /v1 /v4）
    fn ends_with_version(base: &str) -> bool {
        match base.rsplit_once('/') {
            Some((_, last)) if last.starts_with('v') && last.len() > 1 => {
                last[1..].bytes().all(|b| b.is_ascii_digit())
            }
            _ => false,
        }
    }

    /// 构建 embeddings API URL（与 chat_url 同套智能拼接规则）
    fn embed_url(&self) -> String {
        let base = self.base_url.trim_end_matches('/');
        if base.ends_with("/embeddings") {
            return base.to_string();
        }
        if Self::ends_with_version(base) {
            return format!("{}/embeddings", base);
        }
        format!("{}/v1/embeddings", base)
    }

    /// 将通用请求转换为 OpenAI 格式
    fn convert_request(&self, req: CompletionRequest) -> OpenAiRequest {
        let model = if req.model.is_empty() {
            self.default_model.clone()
        } else {
            req.model
        };

        let messages: Vec<OpenAiMessage> = req
            .messages
            .into_iter()
            .map(|m| {
                let role = match m.role {
                    crate::provider::MessageRole::System => "system",
                    crate::provider::MessageRole::User => "user",
                    crate::provider::MessageRole::Assistant => "assistant",
                    crate::provider::MessageRole::Tool => "tool",
                };
                // F-260614-05 Phase 2a: 多模态 content（须在 move m.tool_calls 之前算，借用 m）。
                // 含图消息走 content 数组（text/image_url）；纯文本走字符串简写
                // （保持与现有纯文本端点零回归）。image_url 支持 data URI（base64）与 http(s) URL。
                let content = if m.has_image() {
                    let parts: Vec<serde_json::Value> = m
                        .flattened_parts()
                        .into_iter()
                        .map(|p| match p {
                            crate::provider::ContentPart::Text { text } => serde_json::json!({
                                "type": "text",
                                "text": text,
                            }),
                            crate::provider::ContentPart::Image { url, base64, media_type, alt: _ } => {
                                let final_url = match (base64, url, media_type) {
                                    (Some(b), _, Some(mt)) => {
                                        format!("data:{};base64,{}", mt, b)
                                    }
                                    (None, Some(u), _) => u,
                                    // 完整性兜底：当前 image_base64 构造器强制 media_type:Some，
                                    // image_url 构造器提供 url:Some，二者分别命中上两个分支；
                                    // 此分支仅在 parts 来源被外部直接构造且字段均缺时才可达
                                    //（如 url:None+base64:None 或 base64:Some+media_type:None）。
                                    // 退化为空串（OpenAI 对空 image_url.url 会 400），
                                    // 由调用方保证 parts 合法性，provider 层不做静默伪造。
                                    _ => String::new(),
                                };
                                serde_json::json!({
                                    "type": "image_url",
                                    "image_url": { "url": final_url },
                                })
                            }
                        })
                        .collect();
                    serde_json::Value::Array(parts)
                } else {
                    serde_json::Value::String(m.content.clone())
                };
                let tool_calls = m.tool_calls.map(|calls| {
                    calls
                        .into_iter()
                        .map(|tc| {
                            serde_json::json!({
                                "id": tc.id,
                                "type": tc.call_type,
                                "function": {
                                    "name": tc.function.name,
                                    "arguments": tc.function.arguments,
                                }
                            })
                        })
                        .collect()
                });
                OpenAiMessage {
                    role: role.to_string(),
                    content,
                    tool_call_id: m.tool_call_id,
                    tool_calls,
                    reasoning_content: m.reasoning_content,
                }
            })
            .collect();

        let tools = req.tools.map(|defs| {
            defs.into_iter()
                .map(|d| serde_json::to_value(d).unwrap_or_default())
                .collect()
        });

        OpenAiRequest {
            model,
            messages,
            temperature: req.temperature,
            max_tokens: req.max_tokens,
            stream: req.stream,
            tools,
            tool_choice: req.tool_choice,
            reasoning_content: req.reasoning_content,
            // 流式请求末 chunk 带 usage（同步调用 complete 不需要）
            stream_options: if req.stream {
                Some(serde_json::json!({ "include_usage": true }))
            } else {
                None
            },
        }
    }

    /// 解析同步响应中的工具调用
    fn parse_tool_calls(calls: Vec<OpenAiToolCallResp>) -> Vec<ToolCall> {
        calls
            .into_iter()
            .map(|c| ToolCall::new(c.id, c.function.name, c.function.arguments))
            .collect()
    }
}

#[async_trait]
impl LlmProvider for OpenAICompatProvider {
    /// 文本嵌入: POST /v1/embeddings(OpenAI 兼容,智谱/阿里百炼/OpenAI 通用)
    async fn embed(&self, model: &str, texts: Vec<String>) -> anyhow::Result<Vec<Vec<f32>>> {
        #[derive(serde::Deserialize)]
        struct EmbedData { embedding: Vec<f32>, index: usize }
        #[derive(serde::Deserialize)]
        struct EmbedResponse { data: Vec<EmbedData> }

        let resp = self
            .client
            .post(self.embed_url())
            .header("Authorization", format!("Bearer {}", self.api_key))
            .header("Content-Type", "application/json")
            .json(&serde_json::json!({ "model": model, "input": texts }))
            .send()
            .await?;

        if !resp.status().is_success() {
            let status = resp.status();
            let body = resp.text().await.unwrap_or_default();
            anyhow::bail!("Embedding API 错误 {}: {}", status, body);
        }

        let mut body: EmbedResponse = resp.json().await?;
        // 按 index 排序保证与输入顺序一致(API 不保证返回顺序)
        body.data.sort_by_key(|d| d.index);
        Ok(body.data.into_iter().map(|d| d.embedding).collect())
    }

    async fn complete(&self, request: CompletionRequest) -> anyhow::Result<CompletionResponse> {
        let mut req = request;
        req.stream = false;
        let openai_req = self.convert_request(req);

        debug!(model = %openai_req.model, "OpenAI 同步调用");

        // 指数退避重试(B-260616-07): 包裹 send + 状态码判定。
        // 单请求 60s timeout 保持不变(FR-R4)，重试是额外层: 3 次 × 60s 最坏 180s，
        // 由 retry_with_backoff 内部 30s 总预算主动止损。
        let label = format!("OpenAI[{}]", openai_req.model);
        retry_with_backoff(&label, move |_| {
            let client = self.client.clone();
            let url = self.chat_url();
            let api_key = self.api_key.clone();
            let openai_req = openai_req.clone();
            async move {
                // send
                let resp = client
                    .post(url)
                    .header("Authorization", format!("Bearer {}", api_key))
                    .header("Content-Type", "application/json")
                    .timeout(Duration::from_secs(60))
                    .json(&openai_req)
                    .send()
                    .await;
                let resp = match resp {
                    Ok(r) => r,
                    Err(e) => {
                        if is_reqwest_error_retryable(&e) {
                            return AttemptOutcome::Retryable(format!("请求失败(可重试): {}", e));
                        }
                        return AttemptOutcome::Fatal(format!("请求失败(不可重试): {}", e));
                    }
                };
                // 状态码判定
                if !resp.status().is_success() {
                    let status = resp.status().as_u16();
                    let body = resp.text().await.unwrap_or_default();
                    let msg = format!("LLM API 错误 {}: {}", status, body);
                    if is_status_retryable(status) {
                        warn!(%status, "OpenAI 同步调用可重试状态码");
                        return AttemptOutcome::Retryable(msg);
                    }
                    error!(%status, %body, "LLM API 调用失败(不可重试)");
                    return AttemptOutcome::Fatal(msg);
                }
                // body 解析: 解析错属 Fatal(响应已成功送达，重试也会因同样格式失败)
                let body: OpenAiResponse = match resp.json().await {
                    Ok(b) => b,
                    Err(e) => return AttemptOutcome::Fatal(format!("响应解析失败: {}", e)),
                };
                let choice = match body.choices.into_iter().next() {
                    Some(c) => c,
                    None => return AttemptOutcome::Fatal("LLM 响应无 choices".to_string()),
                };
                let text = choice.message.content.unwrap_or_default();
                let tool_calls = choice.message.tool_calls.map(Self::parse_tool_calls);
                let usage = body.usage.map(|u| TokenUsage {
                    prompt_tokens: u.prompt_tokens,
                    completion_tokens: u.completion_tokens,
                    total_tokens: u.total_tokens,
                }).unwrap_or(TokenUsage {
                    prompt_tokens: 0,
                    completion_tokens: 0,
                    total_tokens: 0,
                });
                AttemptOutcome::Ok(CompletionResponse {
                    text,
                    model: body.model,
                    usage,
                    tool_calls,
                    reasoning_content: choice.message.reasoning_content,
                })
            }
        })
        .await
    }

    async fn stream(&self, request: CompletionRequest) -> anyhow::Result<StreamResult> {
        let mut req = request;
        req.stream = true;
        let openai_req = self.convert_request(req);

        debug!(model = %openai_req.model, "OpenAI 流式调用");

        let resp = self
            .client
            .post(self.chat_url())
            .header("Authorization", format!("Bearer {}", self.api_key))
            .header("Content-Type", "application/json")
            .json(&openai_req)
            .send()
            .await?;

        if !resp.status().is_success() {
            let status = resp.status();
            let body = resp.text().await.unwrap_or_default();
            error!(%status, %body, "LLM 流式 API 调用失败");
            anyhow::bail!("LLM 流式 API 错误 {}: {}", status, body);
        }

        // 累积流式 usage：开 include_usage 后，末段正常 chunk（finish_reason）及额外 usage-only chunk（choices=[]）都带 usage。
        // usage 解析/累积逻辑抽到 apply_openai_sse 纯函数，便于单测；此处闭包只负责传 data 与传递 last_usage。
        let mut last_usage: Option<TokenUsage> = None;

        let stream = resp
            .bytes_stream()
            .eventsource()
            .map(move |event| match event {
                Ok(event) => Ok(apply_openai_sse(&event.data, &mut last_usage)),
                Err(e) => {
                    // 保留 #[source] 因果链: anyhow!("...{}", e) 仅把 e 的 Display 塞进 message,
                    // 丢掉 source(无法 downcast/遍历)。改用 Error::from(e).context(...):
                    // Display 不变(仍为 "SSE 流错误: {e}"), 且 e 作为 .source() 可追溯。
                    // 顺序: 先 format(e) 构造 context 文案, 再 Error::from(e) move e 进 source。
                    let ctx = format!("SSE 流错误: {}", e);
                    error!("{}", ctx);
                    Err(anyhow::Error::from(e).context(ctx))
                }
            });

        Ok(Box::pin(stream))
    }

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

    fn endpoint(&self) -> String {
        self.chat_url()
    }
}

// ============================================================
// 单测（不发真实 HTTP，喂构造的 SSE data 字符串序列）
// ============================================================

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

    /// 辅助：构造普通文本 delta chunk 的 SSE data
    fn text_chunk(content: &str, finish_reason: Option<&str>) -> String {
        let fr = match finish_reason {
            Some(r) => format!(", \"finish_reason\": \"{}\"", r),
            None => String::from(", \"finish_reason\": null"),
        };
        format!(
            r#"{{"choices":[{{"delta":{{"content":"{}"}}{}}}]}}"#,
            content, fr
        )
    }

    /// 辅助：构造带 usage 的 chunk（choices 为空 → usage-only 末 chunk，对应 include_usage）
    fn usage_only_chunk(prompt: u32, completion: u32) -> String {
        format!(
            r#"{{"choices":[],"usage":{{"prompt_tokens":{},"completion_tokens":{},"total_tokens":{}}}}}"#,
            prompt,
            completion,
            prompt + completion
        )
    }

    /// 辅助：构造既有 content 又带 usage 的末段 chunk（部分兼容端点会把 usage 挂到正常末 chunk 上）
    fn text_chunk_with_usage(content: &str, finish_reason: &str, prompt: u32, completion: u32) -> String {
        format!(
            r#"{{"choices":[{{"delta":{{"content":"{}"}},"finish_reason":"{}"}}],"usage":{{"prompt_tokens":{},"completion_tokens":{},"total_tokens":{}}}}}"#,
            content,
            finish_reason,
            prompt,
            completion,
            prompt + completion
        )
    }

    /// 多 chunk 文本流后，末 chunk 携带 usage（include_usage 覆盖语义）
    #[test]
    fn openai_sse_multi_chunk_with_terminal_usage() {
        let mut acc: Option<TokenUsage> = None;

        // 1) 首个文本增量，无 usage
        let c = apply_openai_sse(&text_chunk("Hello", None), &mut acc);
        assert_eq!(c.delta, "Hello");
        assert!(!c.finished);
        assert!(c.usage.is_none());
        assert!(acc.is_none(), "无 usage 的 chunk 不应改累加器");

        // 2) 第二个文本增量
        let c = apply_openai_sse(&text_chunk(" world", None), &mut acc);
        assert_eq!(c.delta, " world");
        assert!(!c.finished);
        assert!(acc.is_none());

        // 3) 末段正常 chunk 带 finish_reason=stop（仍是文本 delta，不带 usage）
        let c = apply_openai_sse(&text_chunk("", Some("stop")), &mut acc);
        assert!(c.finished);
        assert_eq!(c.delta, "");
        assert!(acc.is_none(), "此 chunk 无 usage 字段，累加器仍为 None");

        // 4) usage-only chunk（choices=[]）携带累计 usage → 覆盖累加器
        let c = apply_openai_sse(&usage_only_chunk(12, 34), &mut acc);
        assert!(!c.finished);
        assert!(c.usage.is_none(), "非 [DONE] chunk 不带出 usage");
        let acc = acc.expect("累加器应已被 usage-only chunk 覆盖写入");
        assert_eq!(acc.prompt_tokens, 12);
        assert_eq!(acc.completion_tokens, 34);
        assert_eq!(acc.total_tokens, 46);
    }

    /// usage 挂在正常末段 chunk（含 finish_reason）上，而非独立 usage-only chunk
    #[test]
    fn openai_sse_usage_on_terminal_text_chunk() {
        let mut acc: Option<TokenUsage> = None;
        let c = apply_openai_sse(&text_chunk_with_usage("", "stop", 100, 200), &mut acc);
        assert!(c.finished);
        assert!(c.usage.is_none(), "非 [DONE] 不带出 usage，仅覆盖累加器");
        let acc = acc.expect("末段 chunk 的 usage 应已覆盖累加器");
        assert_eq!(acc.prompt_tokens, 100);
        assert_eq!(acc.completion_tokens, 200);
        assert_eq!(acc.total_tokens, 300);
    }

    /// [DONE] 时 take() 带出累积 usage，且取走后累加器清空
    #[test]
    fn openai_sse_done_takes_accumulated_usage() {
        let mut acc: Option<TokenUsage> = None;
        apply_openai_sse(&text_chunk("x", None), &mut acc);
        apply_openai_sse(&usage_only_chunk(5, 7), &mut acc);

        let c = apply_openai_sse("[DONE]", &mut acc);
        assert!(c.finished);
        let u = c.usage.expect("[DONE] 应带出累积 usage");
        assert_eq!(u.prompt_tokens, 5);
        assert_eq!(u.completion_tokens, 7);
        assert_eq!(u.total_tokens, 12);
        assert!(acc.is_none(), "take() 后累加器应清空");
    }

    /// 无 usage 的流：[DONE] 时 usage 字段为 None
    #[test]
    fn openai_sse_done_without_usage() {
        let mut acc: Option<TokenUsage> = None;
        apply_openai_sse(&text_chunk("hi", None), &mut acc);
        let c = apply_openai_sse("[DONE]", &mut acc);
        assert!(c.finished);
        assert!(c.usage.is_none(), "全程无 usage 时 [DONE] usage 应为 None");
        assert!(acc.is_none());
    }

    /// 后续 usage chunk 覆盖先前 usage（多轮 / 重发场景）
    #[test]
    fn openai_sse_later_usage_overrides_earlier() {
        let mut acc: Option<TokenUsage> = None;
        apply_openai_sse(&usage_only_chunk(1, 1), &mut acc);
        apply_openai_sse(&usage_only_chunk(50, 60), &mut acc);
        let c = apply_openai_sse("[DONE]", &mut acc);
        let u = c.usage.unwrap();
        assert_eq!(u.prompt_tokens, 50, "末 usage 应覆盖前值");
        assert_eq!(u.completion_tokens, 60);
        assert_eq!(u.total_tokens, 110);
    }

    /// finish_reason=length（max_tokens 截断）按正常终止处理
    #[test]
    fn openai_sse_length_finish_reason_treated_as_finished() {
        let mut acc: Option<TokenUsage> = None;
        let c = apply_openai_sse(&text_chunk("...", Some("length")), &mut acc);
        assert!(c.finished, "length 应视为正常终止");
        assert!(acc.is_none());
    }

    /// 非法 JSON data → 返回空 chunk，不 panic、不改累加器
    #[test]
    fn openai_sse_malformed_json_yields_empty_chunk() {
        let mut acc: Option<TokenUsage> = None;
        let c = apply_openai_sse("not a json", &mut acc);
        assert_eq!(c.delta, "");
        assert!(!c.finished);
        assert!(c.usage.is_none());
        assert!(acc.is_none());
    }

    /// tool_calls 增量解析
    #[test]
    fn openai_sse_tool_call_delta() {
        let mut acc: Option<TokenUsage> = None;
        let data = r#"{"choices":[{"delta":{"tool_calls":[{"index":0,"id":"call_1","function":{"name":"get_weather","arguments":"{\"q\":"}}]}}]}"#;
        let c = apply_openai_sse(data, &mut acc);
        assert!(acc.is_none());
        let tcs = c.tool_calls.expect("应有 tool_calls 增量");
        assert_eq!(tcs.len(), 1);
        assert_eq!(tcs[0].index, 0);
        assert_eq!(tcs[0].id.as_deref(), Some("call_1"));
        assert_eq!(tcs[0].function_name.as_deref(), Some("get_weather"));
        assert_eq!(tcs[0].function_arguments.as_deref(), Some("{\"q\":"));
        assert!(!c.finished);
    }

    // ---------- F-260614-05 Phase 2a 多模态 convert_request ----------

    /// 含图消息 → content 数组（text + image_url data URI）；纯文本 → 字符串简写
    #[test]
    fn openai_convert_multimodal_content() {
        let provider = OpenAICompatProvider::new("https://api.openai.com", "k", "gpt-4o");
        let req = CompletionRequest {
            model: "gpt-4o".into(),
            messages: vec![ChatMessage::user_parts(
                "看图",
                vec![
                    crate::provider::ContentPart::image_base64("image/png", "iVBOR"),
                    crate::provider::ContentPart::image_url("https://x/a.png"),
                ],
            )],
            temperature: None,
            max_tokens: None,
            stream: false,
            tools: None,
            tool_choice: None,
            reasoning_content: None,
        };
        let out = provider.convert_request(req);
        let msg = &out.messages[0];
        // content 是数组：[text "看图", image_url(data URI), image_url(http url)]
        let arr = msg.content.as_array().expect("含图 → content 数组");
        assert_eq!(arr.len(), 3);
        assert_eq!(arr[0]["type"], "text");
        assert_eq!(arr[0]["text"], "看图");
        assert_eq!(arr[1]["type"], "image_url");
        assert_eq!(
            arr[1]["image_url"]["url"],
            "data:image/png;base64,iVBOR"
        );
        assert_eq!(arr[2]["image_url"]["url"], "https://x/a.png");
    }

    /// 纯文本消息 → content 仍是字符串简写（无图不数组化，对齐纯文本端点兼容）
    #[test]
    fn openai_convert_text_only_remains_string() {
        let provider = OpenAICompatProvider::new("https://api.openai.com", "k", "gpt-4o");
        let req = CompletionRequest {
            model: "gpt-4o".into(),
            messages: vec![ChatMessage::user("hello")],
            temperature: None,
            max_tokens: None,
            stream: false,
            tools: None,
            tool_choice: None,
            reasoning_content: None,
        };
        let out = provider.convert_request(req);
        let msg = &out.messages[0];
        assert_eq!(msg.content, serde_json::Value::String("hello".into()));
    }
}