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385 lines
14 KiB
Rust
385 lines
14 KiB
Rust
use std::{
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path::{Path, PathBuf},
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str::FromStr,
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};
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use lsp_types::{
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ClientCapabilities, LSPObject, NotebookDocumentCellChange, NotebookDocumentChangeTextContent,
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Position, Range, TextDocumentContentChangeEvent, VersionedTextDocumentIdentifier,
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};
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use ruff_notebook::SourceValue;
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use ruff_server::{Client, ClientOptions, GlobalOptions, Workspace, Workspaces};
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const SUPER_RESOLUTION_OVERVIEW_PATH: &str =
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"./resources/test/fixtures/tensorflow_test_notebook.ipynb";
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struct NotebookChange {
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version: i32,
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metadata: Option<LSPObject>,
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updated_cells: lsp_types::NotebookDocumentCellChange,
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}
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#[test]
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fn super_resolution_overview() {
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let file_path =
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std::fs::canonicalize(PathBuf::from_str(SUPER_RESOLUTION_OVERVIEW_PATH).unwrap()).unwrap();
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let file_url = lsp_types::Url::from_file_path(&file_path).unwrap();
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let notebook = create_notebook(&file_path).unwrap();
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insta::assert_snapshot!("initial_notebook", notebook_source(¬ebook));
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let (main_loop_sender, main_loop_receiver) = crossbeam::channel::unbounded();
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let (client_sender, client_receiver) = crossbeam::channel::unbounded();
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let client = Client::new(main_loop_sender, client_sender);
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let options = GlobalOptions::default();
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let global = options.into_settings(client.clone());
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let mut session = ruff_server::Session::new(
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&ClientCapabilities::default(),
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ruff_server::PositionEncoding::UTF16,
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global,
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&Workspaces::new(vec![
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Workspace::new(lsp_types::Url::from_file_path(file_path.parent().unwrap()).unwrap())
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.with_options(ClientOptions::default()),
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]),
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&client,
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)
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.unwrap();
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session.open_notebook_document(file_url.clone(), notebook);
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let changes = [NotebookChange {
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version: 0,
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metadata: None,
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updated_cells: NotebookDocumentCellChange {
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structure: None,
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data: None,
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text_content: Some(vec![NotebookDocumentChangeTextContent {
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document: VersionedTextDocumentIdentifier {
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uri: make_cell_uri(&file_path, 5),
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version: 2,
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},
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changes: vec![
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TextDocumentContentChangeEvent {
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range: Some(Range {
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start: Position {
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line: 18,
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character: 61,
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},
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end: Position {
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line: 18,
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character: 62,
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},
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}),
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range_length: Some(1),
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text: "\"".to_string(),
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},
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TextDocumentContentChangeEvent {
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range: Some(Range {
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start: Position {
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line: 18,
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character: 55,
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},
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end: Position {
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line: 18,
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character: 56,
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},
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}),
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range_length: Some(1),
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text: "\"".to_string(),
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},
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TextDocumentContentChangeEvent {
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range: Some(Range {
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start: Position {
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line: 14,
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character: 46,
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},
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end: Position {
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line: 14,
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character: 47,
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},
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}),
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range_length: Some(1),
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text: "\"".to_string(),
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},
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TextDocumentContentChangeEvent {
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range: Some(Range {
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start: Position {
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line: 14,
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character: 40,
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},
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end: Position {
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line: 14,
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character: 41,
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},
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}),
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range_length: Some(1),
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text: "\"".to_string(),
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},
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],
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}]),
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},
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},
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NotebookChange {
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version: 1,
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metadata: None,
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updated_cells: NotebookDocumentCellChange {
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structure: None,
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data: None,
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text_content: Some(vec![NotebookDocumentChangeTextContent {
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document: VersionedTextDocumentIdentifier {
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uri: make_cell_uri(&file_path, 4),
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version: 2
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},
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changes: vec![TextDocumentContentChangeEvent {
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range: Some(Range {
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start: Position {
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line: 0,
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character: 0
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},
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end: Position {
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line: 0,
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character: 181
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} }),
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range_length: Some(181),
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text: "test_img_path = tf.keras.utils.get_file(\n \"lr.jpg\",\n \"https://raw.githubusercontent.com/tensorflow/examples/master/lite/examples/super_resolution/android/app/src/main/assets/lr-1.jpg\",\n)".to_string()
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}
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]
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}
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]
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)
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}
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},
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NotebookChange {
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version: 2,
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metadata: None,
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updated_cells: NotebookDocumentCellChange {
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structure: None,
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data: None,
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text_content: Some(vec![NotebookDocumentChangeTextContent {
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document: VersionedTextDocumentIdentifier {
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uri: make_cell_uri(&file_path, 2),
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version: 2,
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},
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changes: vec![TextDocumentContentChangeEvent {
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range: Some(Range {
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start: Position {
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line: 3,
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character: 0,
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},
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end: Position {
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line: 3,
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character: 21,
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},
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}),
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range_length: Some(21),
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text: "\nprint(tf.__version__)".to_string(),
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}],
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}]),
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}
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},
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NotebookChange {
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version: 3,
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metadata: None,
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updated_cells: NotebookDocumentCellChange {
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structure: None,
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data: None,
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text_content: Some(vec![NotebookDocumentChangeTextContent {
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document: VersionedTextDocumentIdentifier {
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uri: make_cell_uri(&file_path, 1),
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version: 2,
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},
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changes: vec![TextDocumentContentChangeEvent {
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range: Some(Range {
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start: Position {
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line: 0,
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character: 0,
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},
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end: Position {
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line: 0,
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character: 49,
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},
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}),
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range_length: Some(49),
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text: "!pip install matplotlib tensorflow tensorflow-hub".to_string(),
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}],
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}]),
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},
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},
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NotebookChange {
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version: 4,
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metadata: None,
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updated_cells: NotebookDocumentCellChange {
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structure: None,
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data: None,
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text_content: Some(vec![NotebookDocumentChangeTextContent {
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document: VersionedTextDocumentIdentifier {
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uri: make_cell_uri(&file_path, 3),
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version: 2,
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},
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changes: vec![TextDocumentContentChangeEvent {
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range: Some(Range {
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start: Position {
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line: 3,
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character: 0,
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},
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end: Position {
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line: 15,
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character: 37,
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},
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}),
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range_length: Some(457),
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text: "\n@tf.function(input_signature=[tf.TensorSpec(shape=[1, 50, 50, 3], dtype=tf.float32)])\ndef f(input):\n return concrete_func(input)\n\n\nconverter = tf.lite.TFLiteConverter.from_concrete_functions(\n [f.get_concrete_function()], model\n)\nconverter.optimizations = [tf.lite.Optimize.DEFAULT]\ntflite_model = converter.convert()\n\n# Save the TF Lite model.\nwith tf.io.gfile.GFile(\"ESRGAN.tflite\", \"wb\") as f:\n f.write(tflite_model)\n\nesrgan_model_path = \"./ESRGAN.tflite\"".to_string(),
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}],
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}]),
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},
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},
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NotebookChange {
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version: 5,
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metadata: None,
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updated_cells: NotebookDocumentCellChange {
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structure: None,
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data: None,
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text_content: Some(vec![NotebookDocumentChangeTextContent {
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document: VersionedTextDocumentIdentifier {
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uri: make_cell_uri(&file_path, 0),
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version: 2,
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},
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changes: vec![TextDocumentContentChangeEvent {
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range: Some(Range {
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start: Position {
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line: 0,
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character: 0,
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},
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end: Position {
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line: 2,
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character: 0,
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},
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}),
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range_length: Some(139),
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text: "# @title Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n".to_string(),
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}],
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}]),
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},
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},
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NotebookChange {
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version: 6,
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metadata: None,
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updated_cells: NotebookDocumentCellChange {
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structure: None,
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data: None,
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text_content: Some(vec![NotebookDocumentChangeTextContent {
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document: VersionedTextDocumentIdentifier {
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uri: make_cell_uri(&file_path, 6),
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version: 2,
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},
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changes: vec![TextDocumentContentChangeEvent {
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range: Some(Range {
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start: Position {
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line: 1,
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character: 0,
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},
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end: Position {
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line: 14,
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character: 28,
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},
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}),
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range_length: Some(361),
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text: "plt.figure(figsize=(1, 1))\nplt.title(\"LR\")\nplt.imshow(lr.numpy())\nplt.figure(figsize=(10, 4))\nplt.subplot(1, 2, 1)\nplt.title(f\"ESRGAN (x4)\")\nplt.imshow(sr.numpy())\nbicubic = tf.image.resize(lr, [200, 200], tf.image.ResizeMethod.BICUBIC)\nbicubic = tf.cast(bicubic, tf.uint8)\nplt.subplot(1, 2, 2)\nplt.title(\"Bicubic\")\nplt.imshow(bicubic.numpy());".to_string(),
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}],
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}]),
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},
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}
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];
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let key = session.key_from_url(file_url.clone());
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for NotebookChange {
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version,
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metadata,
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updated_cells,
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} in changes
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{
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session
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.update_notebook_document(&key, Some(updated_cells), metadata, version)
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.unwrap();
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}
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let snapshot = session.take_snapshot(file_url).unwrap();
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insta::assert_snapshot!(
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"changed_notebook",
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notebook_source(snapshot.query().as_notebook().unwrap())
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);
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assert!(client_receiver.is_empty());
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assert!(main_loop_receiver.is_empty());
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}
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fn notebook_source(notebook: &ruff_server::NotebookDocument) -> String {
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notebook.make_ruff_notebook().source_code().to_string()
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}
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// produces an opaque URL based on a document path and a cell index
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fn make_cell_uri(path: &Path, index: usize) -> lsp_types::Url {
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lsp_types::Url::parse(&format!(
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"notebook-cell:///Users/test/notebooks/{}.ipynb?cell={index}",
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path.file_name().unwrap().to_string_lossy()
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))
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.unwrap()
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}
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fn create_notebook(file_path: &Path) -> anyhow::Result<ruff_server::NotebookDocument> {
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let ruff_notebook = ruff_notebook::Notebook::from_path(file_path)?;
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let mut cells = vec![];
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let mut cell_documents = vec![];
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for (i, cell) in ruff_notebook
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.cells()
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.iter()
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.filter(|cell| cell.is_code_cell())
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.enumerate()
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{
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let uri = make_cell_uri(file_path, i);
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let (lsp_cell, cell_document) = cell_to_lsp_cell(cell, uri)?;
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cells.push(lsp_cell);
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cell_documents.push(cell_document);
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}
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let serde_json::Value::Object(metadata) = serde_json::to_value(ruff_notebook.metadata())?
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else {
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anyhow::bail!("Notebook metadata was not an object");
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};
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ruff_server::NotebookDocument::new(0, cells, metadata, cell_documents)
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}
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fn cell_to_lsp_cell(
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cell: &ruff_notebook::Cell,
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cell_uri: lsp_types::Url,
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) -> anyhow::Result<(lsp_types::NotebookCell, lsp_types::TextDocumentItem)> {
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let contents = match cell.source() {
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SourceValue::String(string) => string.clone(),
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SourceValue::StringArray(array) => array.join(""),
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};
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let metadata = match serde_json::to_value(cell.metadata())? {
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serde_json::Value::Null => None,
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serde_json::Value::Object(metadata) => Some(metadata),
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_ => anyhow::bail!("Notebook cell metadata was not an object"),
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};
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Ok((
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lsp_types::NotebookCell {
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kind: match cell {
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ruff_notebook::Cell::Code(_) => lsp_types::NotebookCellKind::Code,
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ruff_notebook::Cell::Markdown(_) => lsp_types::NotebookCellKind::Markup,
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ruff_notebook::Cell::Raw(_) => unreachable!(),
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},
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document: cell_uri.clone(),
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metadata,
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execution_summary: None,
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},
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lsp_types::TextDocumentItem::new(cell_uri, "python".to_string(), 1, contents),
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))
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}
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