use crate::{    AgentTool, ContextServerRegistry, ListDirectoryTool, ListDirectoryToolInput, Template,    Templates, Thread, ToolCallEventStream, ToolInput, WriteFileTool, WriteFileToolInput,};use Role::*;use anyhow::{Context as _, Result};use client::{Client, RefreshLlmTokenListener, UserStore};use fs::FakeFs;use futures::{FutureExt as _, StreamExt};use gpui::{AppContext as _, AsyncApp, Entity, TestAppContext, UpdateGlobal as _};use http_client::StatusCode;use language::language_settings::FormatOnSave;use language_model::{    LanguageModel, LanguageModelCompletionError, LanguageModelCompletionEvent,    LanguageModelRegistry, LanguageModelRequest, LanguageModelRequestMessage,    LanguageModelToolResult, LanguageModelToolResultContent, LanguageModelToolUse,    LanguageModelToolUseId, MessageContent, Role, SelectedModel,};use project::Project;use prompt_store::{ProjectContext, WorktreeContext};use rand::prelude::*;use reqwest_client::ReqwestClient;use serde::Serialize;use settings::SettingsStore;use std::{    fmt::{self, Display},    path::{Path, PathBuf},    str::FromStr,    sync::Arc,    time::Duration,};use util::path; #[derive(Clone)]struct EvalInput {    conversation: Vec<LanguageModelRequestMessage>,    input_file_path: PathBuf,    input_content: Option<String>,    expected_output_content: String,} impl EvalInput {    fn new(        conversation: Vec<LanguageModelRequestMessage>,        input_file_path: impl Into<PathBuf>,        input_content: Option<String>,        expected_output_content: String,    ) -> Self {        Self {            conversation,            input_file_path: input_file_path.into(),            input_content,            expected_output_content,        }    }} struct WriteEvalOutput {    tool_input: WriteFileToolInput,    text_after: String,} impl Display for WriteEvalOutput {    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {        writeln!(f, "Tool Input:\n{:#?}", self.tool_input)?;        writeln!(f, "Text After:\n{}", self.text_after)?;        Ok(())    }} struct WriteToolTest {    fs: Arc<FakeFs>,    project: Entity<Project>,    model: Arc<dyn LanguageModel>,    model_thinking_effort: Option<String>,} impl WriteToolTest {    async fn new(cx: &mut TestAppContext) -> Self {        cx.executor().allow_parking();         let fs = FakeFs::new(cx.executor());        cx.update(|cx| {            let settings_store = SettingsStore::test(cx);            cx.set_global(settings_store);            SettingsStore::update_global(cx, |store: &mut SettingsStore, cx| {                store.update_user_settings(cx, |settings| {                    settings                        .project                        .all_languages                        .defaults                        .ensure_final_newline_on_save = Some(false);                    settings.project.all_languages.defaults.format_on_save =                        Some(FormatOnSave::Off);                });            });             gpui_tokio::init(cx);            let http_client = Arc::new(ReqwestClient::user_agent("agent tests").unwrap());            cx.set_http_client(http_client);            let client = Client::production(cx);            let user_store = cx.new(|cx| UserStore::new(client.clone(), cx));            language_model::init(cx);            RefreshLlmTokenListener::register(client.clone(), user_store.clone(), cx);            language_models::init(user_store, client, cx);        });         fs.insert_tree("/root", serde_json::json!({})).await;        let project = Project::test(fs.clone(), [path!("/root").as_ref()], cx).await;        let agent_model = SelectedModel::from_str(            &std::env::var("ZED_AGENT_MODEL")                .unwrap_or("anthropic/claude-sonnet-4-6-latest".into()),        )        .unwrap();         let authenticate_provider_tasks = cx.update(|cx| {            LanguageModelRegistry::global(cx).update(cx, |registry, cx| {                registry                    .providers()                    .iter()                    .map(|p| p.authenticate(cx))                    .collect::<Vec<_>>()            })        });        let model = cx            .update(|cx| {                cx.spawn(async move |cx| {                    futures::future::join_all(authenticate_provider_tasks).await;                    Self::load_model(&agent_model, cx).await.unwrap()                })            })            .await;         let model_thinking_effort = model            .default_effort_level()            .map(|effort_level| effort_level.value.to_string());         Self {            fs,            project,            model,            model_thinking_effort,        }    }     async fn load_model(        selected_model: &SelectedModel,        cx: &mut AsyncApp,    ) -> Result<Arc<dyn LanguageModel>> {        cx.update(|cx| {            let registry = LanguageModelRegistry::read_global(cx);            let provider = registry                .provider(&selected_model.provider)                .expect("Provider not found");            provider.authenticate(cx)        })        .await?;        Ok(cx.update(|cx| {            let models = LanguageModelRegistry::read_global(cx);            models                .available_models(cx)                .find(|model| {                    model.provider_id() == selected_model.provider                        && model.id() == selected_model.model                })                .unwrap_or_else(|| panic!("Model {} not found", selected_model.model.0))        }))    }     async fn eval(&self, mut eval: EvalInput, cx: &mut TestAppContext) -> Result<WriteEvalOutput> {        eval.conversation            .last_mut()            .context("Conversation must not be empty")?            .cache = true;         if let Some(input_content) = eval.input_content.as_deref() {            let abs_path = Path::new("/root").join(                eval.input_file_path                    .strip_prefix("root")                    .unwrap_or(&eval.input_file_path),            );            self.fs.insert_file(&abs_path, input_content.into()).await;            cx.run_until_parked();        }         let tools = crate::built_in_tools().collect::<Vec<_>>();         let system_prompt = {            let worktrees = vec![WorktreeContext {                root_name: "root".to_string(),                abs_path: Path::new("/path/to/root").into(),                rules_file: None,            }];            let project_context = ProjectContext::new(worktrees);            let tool_names = tools                .iter()                .map(|tool| tool.name.clone().into())                .collect::<Vec<_>>();            let template = crate::SystemPromptTemplate {                project: &project_context,                available_tools: tool_names,                model_name: None,                date: chrono::Local::now().format("%Y-%m-%d").to_string(),                user_agents_md: None,                sandboxing: false,                is_linux: cfg!(target_os = "linux"),                is_windows: cfg!(target_os = "windows"),            };            let templates = Templates::new();            template.render(&templates)?        };         let messages = [LanguageModelRequestMessage {            role: Role::System,            content: vec![MessageContent::Text(system_prompt)],            cache: true,            reasoning_details: None,        }]        .into_iter()        .chain(eval.conversation)        .collect::<Vec<_>>();         let request = LanguageModelRequest {            messages,            tools,            thinking_allowed: true,            thinking_effort: self.model_thinking_effort.clone(),            ..Default::default()        };         let tool_input =            retry_on_rate_limit(async || self.extract_tool_use(request.clone(), cx).await).await?;         let language_registry = self            .project            .read_with(cx, |project, _cx| project.languages().clone());         let context_server_registry = cx            .new(|cx| ContextServerRegistry::new(self.project.read(cx).context_server_store(), cx));        let thread = cx.new(|cx| {            Thread::new(                self.project.clone(),                cx.new(|_cx| ProjectContext::default()),                context_server_registry,                Templates::new(),                Some(self.model.clone()),                cx,            )        });        let action_log = thread.read_with(cx, |thread, _| thread.action_log().clone());         let tool = Arc::new(WriteFileTool::new(            self.project.clone(),            thread.downgrade(),            action_log,            language_registry,        ));         let result = cx            .update(|cx| {                tool.clone().run(                    ToolInput::resolved(tool_input.clone()),                    ToolCallEventStream::test().0,                    cx,                )            })            .await;         let output = match result {            Ok(output) => output,            Err(output) => anyhow::bail!("Tool returned error: {}", output),        };         let crate::EditFileToolOutput::Success { new_text, .. } = &output else {            anyhow::bail!("Tool returned error output: {}", output);        };         if tool_input.path != eval.input_file_path {            anyhow::bail!(                "Tool path mismatch. Expected {:?}, got {:?}",                eval.input_file_path,                tool_input.path,            );        }         if new_text != &eval.expected_output_content {            anyhow::bail!(                "Output content mismatch. Expected {:?}, got {:?}",                eval.expected_output_content,                new_text,            );        }         Ok(WriteEvalOutput {            tool_input,            text_after: new_text.clone(),        })    }     async fn extract_tool_use(        &self,        request: LanguageModelRequest,        cx: &mut TestAppContext,    ) -> Result<WriteFileToolInput> {        let model = self.model.clone();        let events = cx            .update(|cx| {                let async_cx = cx.to_async();                cx.foreground_executor()                    .spawn(async move { model.stream_completion(request, &async_cx).await })            })            .await            .map_err(|err| anyhow::anyhow!("completion error: {}", err))?;         let mut streamed_text = String::new();        let mut stop_reason = None;        let mut parse_errors = Vec::new();         let mut events = events.fuse();        while let Some(event) = events.next().await {            match event {                Ok(LanguageModelCompletionEvent::ToolUse(tool_use))                    if tool_use.is_input_complete                        && tool_use.name.as_ref() == WriteFileTool::NAME =>                {                    let input: WriteFileToolInput = tool_use                        .input                        .parse()                        .context("Failed to parse tool input as WriteFileToolInput")?;                    return Ok(input);                }                Ok(LanguageModelCompletionEvent::Text(text)) => {                    if streamed_text.len() < 2_000 {                        streamed_text.push_str(&text);                    }                }                Ok(LanguageModelCompletionEvent::Stop(reason)) => {                    stop_reason = Some(reason);                }                Ok(LanguageModelCompletionEvent::ToolUseJsonParseError {                    tool_name,                    raw_input,                    json_parse_error,                    ..                }) if tool_name.as_ref() == WriteFileTool::NAME => {                    parse_errors.push(format!("{json_parse_error}\nRaw input:\n{raw_input:?}"));                }                Err(err) => return Err(anyhow::anyhow!("completion error: {}", err)),                _ => {}            }        }         let streamed_text = streamed_text.trim();        let streamed_text_suffix = if streamed_text.is_empty() {            String::new()        } else {            format!("\nStreamed text:\n{streamed_text}")        };        let stop_reason_suffix = stop_reason            .map(|reason| format!("\nStop reason: {reason:?}"))            .unwrap_or_default();        let parse_errors_suffix = if parse_errors.is_empty() {            String::new()        } else {            format!("\nTool parse errors:\n{}", parse_errors.join("\n"))        };         anyhow::bail!(            "Stream ended without a write_file tool use{stop_reason_suffix}{parse_errors_suffix}{streamed_text_suffix}"        )    }} fn run_eval(eval: EvalInput) -> eval_utils::EvalOutput<()> {    super::run_gpui_eval(        |cx| {            async move {                let test = WriteToolTest::new(cx).await;                let result = test.eval(eval, cx).await;                drop(test);                cx.run_until_parked();                result            }            .boxed_local()        },        |_| eval_utils::OutcomeKind::Passed,    )} fn message(    role: Role,    content: impl IntoIterator<Item = MessageContent>,) -> LanguageModelRequestMessage {    LanguageModelRequestMessage {        role,        content: content.into_iter().collect(),        cache: false,        reasoning_details: None,    }} fn text(text: impl Into<String>) -> MessageContent {    MessageContent::Text(text.into())} fn tool_use(    id: impl Into<Arc<str>>,    name: impl Into<Arc<str>>,    input: impl Serialize,) -> MessageContent {    MessageContent::ToolUse(LanguageModelToolUse {        id: LanguageModelToolUseId::from(id.into()),        name: name.into(),        raw_input: serde_json::to_string_pretty(&input).unwrap(),        input: language_model::LanguageModelToolUseInput::Json(            serde_json::to_value(input).unwrap(),        ),        is_input_complete: true,        thought_signature: None,    })} fn tool_result(    id: impl Into<Arc<str>>,    name: impl Into<Arc<str>>,    result: impl Into<Arc<str>>,) -> MessageContent {    MessageContent::ToolResult(LanguageModelToolResult {        tool_use_id: LanguageModelToolUseId::from(id.into()),        tool_name: name.into(),        is_error: false,        content: vec![LanguageModelToolResultContent::Text(result.into())],        output: None,    })} async fn retry_on_rate_limit<R>(mut request: impl AsyncFnMut() -> Result<R>) -> Result<R> {    const MAX_RETRIES: usize = 20;    let mut attempt = 0;     loop {        attempt += 1;        let response = request().await;         if attempt >= MAX_RETRIES {            return response;        }         let retry_delay = match &response {            Ok(_) => None,            Err(err) => match err.downcast_ref::<LanguageModelCompletionError>() {                Some(err) => match &err {                    LanguageModelCompletionError::RateLimitExceeded { retry_after, .. }                    | LanguageModelCompletionError::ServerOverloaded { retry_after, .. } => {                        Some(retry_after.unwrap_or(Duration::from_secs(5)))                    }                    LanguageModelCompletionError::UpstreamProviderError {                        status,                        retry_after,                        ..                    } => {                        let should_retry = matches!(                            *status,                            StatusCode::TOO_MANY_REQUESTS | StatusCode::SERVICE_UNAVAILABLE                        ) || status.as_u16() == 529;                         if should_retry {                            Some(retry_after.unwrap_or(Duration::from_secs(5)))                        } else {                            None                        }                    }                    LanguageModelCompletionError::ApiReadResponseError { .. }                    | LanguageModelCompletionError::ApiInternalServerError { .. }                    | LanguageModelCompletionError::HttpSend { .. } => {                        Some(Duration::from_secs(2_u64.pow((attempt - 1) as u32).min(30)))                    }                    _ => None,                },                _ => None,            },        };         if let Some(retry_after) = retry_delay {            let jitter = retry_after.mul_f64(rand::rng().random_range(0.0..1.0));            eprintln!("Attempt #{attempt}: Retry after {retry_after:?} + jitter of {jitter:?}");            #[allow(clippy::disallowed_methods)]            async_io::Timer::after(retry_after + jitter).await;        } else {            return response;        }    }} #[test]#[cfg_attr(not(feature = "unit-eval"), ignore)]fn eval_create_file() {    let input_file_path = "root/TODO3";    let expected_output_content = "todo".to_string(); 

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