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Add Jupyter Notebook document change snapshot test (#11944)

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## Summary

Closes #11914.

This PR introduces a snapshot test that replays the LSP requests made
during a document formatting request, and confirms that the notebook
document is updated in the expected way.
This commit is contained in:
Jane Lewis 2024-06-20 22:29:27 -07:00 • committed by GitHub
parent 927069c12f
commit 3ab7a8da73
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373
crates/ruff_server/tests/notebook.rs Normal file
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use std::{
path::{Path, PathBuf},
str::FromStr,
};
use lsp_types::{
ClientCapabilities, LSPObject, NotebookDocumentCellChange, NotebookDocumentChangeTextContent,
Position, Range, TextDocumentContentChangeEvent, VersionedTextDocumentIdentifier,
};
use ruff_notebook::SourceValue;
use ruff_server::ClientSettings;
const SUPER_RESOLUTION_OVERVIEW_PATH: &str =
"./resources/test/fixtures/tensorflow_test_notebook.ipynb";
struct NotebookChange {
version: i32,
metadata: Option<LSPObject>,
updated_cells: lsp_types::NotebookDocumentCellChange,
}
#[test]
fn super_resolution_overview() {
let file_path =
std::path::absolute(PathBuf::from_str(SUPER_RESOLUTION_OVERVIEW_PATH).unwrap()).unwrap();
let file_url = lsp_types::Url::from_file_path(&file_path).unwrap();
let notebook = create_notebook(&file_path).unwrap();
insta::assert_snapshot!("initial_notebook", notebook_source(&notebook));
let mut session = ruff_server::Session::new(
&ClientCapabilities::default(),
ruff_server::PositionEncoding::UTF16,
ClientSettings::default(),
vec![(
lsp_types::Url::from_file_path(file_path.parent().unwrap()).unwrap(),
ClientSettings::default(),
)],
)
.unwrap();
session.open_notebook_document(file_url.clone(), notebook);
let changes = [NotebookChange {
version: 0,
metadata: None,
updated_cells: NotebookDocumentCellChange {
structure: None,
data: None,
text_content: Some(vec![NotebookDocumentChangeTextContent {
document: VersionedTextDocumentIdentifier {
uri: make_cell_uri(&file_path, 5),
version: 2,
},
changes: vec![
TextDocumentContentChangeEvent {
range: Some(Range {
start: Position {
line: 18,
character: 61,
},
end: Position {
line: 18,
character: 62,
},
}),
range_length: Some(1),
text: "\"".to_string(),
},
TextDocumentContentChangeEvent {
range: Some(Range {
start: Position {
line: 18,
character: 55,
},
end: Position {
line: 18,
character: 56,
},
}),
range_length: Some(1),
text: "\"".to_string(),
},
TextDocumentContentChangeEvent {
range: Some(Range {
start: Position {
line: 14,
character: 46,
},
end: Position {
line: 14,
character: 47,
},
}),
range_length: Some(1),
text: "\"".to_string(),
},
TextDocumentContentChangeEvent {
range: Some(Range {
start: Position {
line: 14,
character: 40,
},
end: Position {
line: 14,
character: 41,
},
}),
range_length: Some(1),
text: "\"".to_string(),
},
],
}]),
},
},
NotebookChange {
version: 1,
metadata: None,
updated_cells: NotebookDocumentCellChange {
structure: None,
data: None,
text_content: Some(vec![NotebookDocumentChangeTextContent {
document: VersionedTextDocumentIdentifier {
uri: make_cell_uri(&file_path, 4),
version: 2
},
changes: vec![TextDocumentContentChangeEvent {
range: Some(Range {
start: Position {
line: 0,
character: 0
},
end: Position {
line: 0,
character: 181
} }),
range_length: Some(181),
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()
}
]
}
]
)
}
},
NotebookChange {
version: 2,
metadata: None,
updated_cells: NotebookDocumentCellChange {
structure: None,
data: None,
text_content: Some(vec![NotebookDocumentChangeTextContent {
document: VersionedTextDocumentIdentifier {
uri: make_cell_uri(&file_path, 2),
version: 2,
},
changes: vec![TextDocumentContentChangeEvent {
range: Some(Range {
start: Position {
line: 3,
character: 0,
},
end: Position {
line: 3,
character: 21,
},
}),
range_length: Some(21),
text: "\nprint(tf.__version__)".to_string(),
}],
}]),
}
},
NotebookChange {
version: 3,
metadata: None,
updated_cells: NotebookDocumentCellChange {
structure: None,
data: None,
text_content: Some(vec![NotebookDocumentChangeTextContent {
document: VersionedTextDocumentIdentifier {
uri: make_cell_uri(&file_path, 1),
version: 2,
},
changes: vec![TextDocumentContentChangeEvent {
range: Some(Range {
start: Position {
line: 0,
character: 0,
},
end: Position {
line: 0,
character: 49,
},
}),
range_length: Some(49),
text: "!pip install matplotlib tensorflow tensorflow-hub".to_string(),
}],
}]),
},
},
NotebookChange {
version: 4,
metadata: None,
updated_cells: NotebookDocumentCellChange {
structure: None,
data: None,
text_content: Some(vec![NotebookDocumentChangeTextContent {
document: VersionedTextDocumentIdentifier {
uri: make_cell_uri(&file_path, 3),
version: 2,
},
changes: vec![TextDocumentContentChangeEvent {
range: Some(Range {
start: Position {
line: 3,
character: 0,
},
end: Position {
line: 15,
character: 37,
},
}),
range_length: Some(457),
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(),
}],
}]),
},
},
NotebookChange {
version: 5,
metadata: None,
updated_cells: NotebookDocumentCellChange {
structure: None,
data: None,
text_content: Some(vec![NotebookDocumentChangeTextContent {
document: VersionedTextDocumentIdentifier {
uri: make_cell_uri(&file_path, 0),
version: 2,
},
changes: vec![TextDocumentContentChangeEvent {
range: Some(Range {
start: Position {
line: 0,
character: 0,
},
end: Position {
line: 2,
character: 0,
},
}),
range_length: Some(139),
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(),
}],
}]),
},
},
NotebookChange {
version: 6,
metadata: None,
updated_cells: NotebookDocumentCellChange {
structure: None,
data: None,
text_content: Some(vec![NotebookDocumentChangeTextContent {
document: VersionedTextDocumentIdentifier {
uri: make_cell_uri(&file_path, 6),
version: 2,
},
changes: vec![TextDocumentContentChangeEvent {
range: Some(Range {
start: Position {
line: 1,
character: 0,
},
end: Position {
line: 14,
character: 28,
},
}),
range_length: Some(361),
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(),
}],
}]),
},
}
];
let key = session.key_from_url(file_url.clone());
for NotebookChange {
version,
metadata,
updated_cells,
} in changes
{
session
.update_notebook_document(&key, Some(updated_cells), metadata, version)
.unwrap();
}
let snapshot = session.take_snapshot(file_url.clone()).unwrap();
insta::assert_snapshot!(
"changed_notebook",
notebook_source(snapshot.query().as_notebook().unwrap())
);
}
fn notebook_source(notebook: &ruff_server::NotebookDocument) -> String {
notebook.make_ruff_notebook().source_code().to_string()
}
// produces an opaque URL based on a document path and a cell index
fn make_cell_uri(path: &Path, index: usize) -> lsp_types::Url {
lsp_types::Url::parse(&format!(
"notebook-cell:///Users/test/notebooks/{}.ipynb?cell={index}",
path.file_name().unwrap().to_string_lossy()
))
.unwrap()
}
fn create_notebook(file_path: &Path) -> anyhow::Result<ruff_server::NotebookDocument> {
let ruff_notebook = ruff_notebook::Notebook::from_path(file_path)?;
let mut cells = vec![];
let mut cell_documents = vec![];
for (i, cell) in ruff_notebook
.cells()
.iter()
.filter(|cell| cell.is_code_cell())
.enumerate()
{
let uri = make_cell_uri(file_path, i);
let (lsp_cell, cell_document) = cell_to_lsp_cell(cell, uri)?;
cells.push(lsp_cell);
cell_documents.push(cell_document);
}
let serde_json::Value::Object(metadata) = serde_json::to_value(ruff_notebook.metadata())?
else {
anyhow::bail!("Notebook metadata was not an object");
};
ruff_server::NotebookDocument::new(0, cells, metadata, cell_documents)
}
fn cell_to_lsp_cell(
cell: &ruff_notebook::Cell,
cell_uri: lsp_types::Url,
) -> anyhow::Result<(lsp_types::NotebookCell, lsp_types::TextDocumentItem)> {
let contents = match cell.source() {
SourceValue::String(string) => string.clone(),
SourceValue::StringArray(array) => array.join(""),
};
let metadata = match serde_json::to_value(cell.metadata())? {
serde_json::Value::Null => None,
serde_json::Value::Object(metadata) => Some(metadata),
_ => anyhow::bail!("Notebook cell metadata was not an object"),
};
Ok((
lsp_types::NotebookCell {
kind: match cell {
ruff_notebook::Cell::Code(_) => lsp_types::NotebookCellKind::Code,
ruff_notebook::Cell::Markdown(_) => lsp_types::NotebookCellKind::Markup,
ruff_notebook::Cell::Raw(_) => unreachable!(),
},
document: cell_uri.clone(),
metadata,
execution_summary: None,
},
lsp_types::TextDocumentItem::new(cell_uri, "python".to_string(), 1, contents),
))
}

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---
source: crates/ruff_server/tests/notebook.rs
expression: notebook_source(snapshot.query().as_notebook().unwrap())
---
# @title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
!pip install matplotlib tensorflow tensorflow-hub
import tensorflow as tf
import tensorflow_hub as hub
import matplotlib.pyplot as plt
print(tf.__version__)
model = hub.load("https://tfhub.dev/captain-pool/esrgan-tf2/1")
concrete_func = model.signatures[tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
@tf.function(input_signature=[tf.TensorSpec(shape=[1, 50, 50, 3], dtype=tf.float32)])
def f(input):
return concrete_func(input)
converter = tf.lite.TFLiteConverter.from_concrete_functions(
[f.get_concrete_function()], model
)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()
# Save the TF Lite model.
with tf.io.gfile.GFile("ESRGAN.tflite", "wb") as f:
f.write(tflite_model)
esrgan_model_path = "./ESRGAN.tflite"
test_img_path = tf.keras.utils.get_file(
"lr.jpg",
"https://raw.githubusercontent.com/tensorflow/examples/master/lite/examples/super_resolution/android/app/src/main/assets/lr-1.jpg",
)
lr = tf.io.read_file(test_img_path)
lr = tf.image.decode_jpeg(lr)
lr = tf.expand_dims(lr, axis=0)
lr = tf.cast(lr, tf.float32)
# Load TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path=esrgan_model_path)
interpreter.allocate_tensors()
# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
# Run the model
interpreter.set_tensor(input_details[0]["index"], lr)
interpreter.invoke()
# Extract the output and postprocess it
output_data = interpreter.get_tensor(output_details[0]["index"])
sr = tf.squeeze(output_data, axis=0)
sr = tf.clip_by_value(sr, 0, 255)
sr = tf.round(sr)
sr = tf.cast(sr, tf.uint8)
lr = tf.cast(tf.squeeze(lr, axis=0), tf.uint8)
plt.figure(figsize=(1, 1))
plt.title("LR")
plt.imshow(lr.numpy())
plt.figure(figsize=(10, 4))
plt.subplot(1, 2, 1)
plt.title(f"ESRGAN (x4)")
plt.imshow(sr.numpy())
bicubic = tf.image.resize(lr, [200, 200], tf.image.ResizeMethod.BICUBIC)
bicubic = tf.cast(bicubic, tf.uint8)
plt.subplot(1, 2, 2)
plt.title("Bicubic")
plt.imshow(bicubic.numpy());

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---
source: crates/ruff_server/tests/notebook.rs
expression: notebook_source(&notebook)
---
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
!pip install matplotlib tensorflow tensorflow-hub
import tensorflow as tf
import tensorflow_hub as hub
import matplotlib.pyplot as plt
print(tf.__version__)
model = hub.load("https://tfhub.dev/captain-pool/esrgan-tf2/1")
concrete_func = model.signatures[tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
@tf.function(input_signature=[tf.TensorSpec(shape=[1, 50, 50, 3], dtype=tf.float32)])
def f(input):
return concrete_func(input);
converter = tf.lite.TFLiteConverter.from_concrete_functions([f.get_concrete_function()], model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()
# Save the TF Lite model.
with tf.io.gfile.GFile('ESRGAN.tflite', 'wb') as f:
f.write(tflite_model)
esrgan_model_path = './ESRGAN.tflite'
test_img_path = tf.keras.utils.get_file('lr.jpg', 'https://raw.githubusercontent.com/tensorflow/examples/master/lite/examples/super_resolution/android/app/src/main/assets/lr-1.jpg')
lr = tf.io.read_file(test_img_path)
lr = tf.image.decode_jpeg(lr)
lr = tf.expand_dims(lr, axis=0)
lr = tf.cast(lr, tf.float32)
# Load TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path=esrgan_model_path)
interpreter.allocate_tensors()
# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
# Run the model
interpreter.set_tensor(input_details[0]['index'], lr)
interpreter.invoke()
# Extract the output and postprocess it
output_data = interpreter.get_tensor(output_details[0]['index'])
sr = tf.squeeze(output_data, axis=0)
sr = tf.clip_by_value(sr, 0, 255)
sr = tf.round(sr)
sr = tf.cast(sr, tf.uint8)
lr = tf.cast(tf.squeeze(lr, axis=0), tf.uint8)
plt.figure(figsize = (1, 1))
plt.title('LR')
plt.imshow(lr.numpy());
plt.figure(figsize=(10, 4))
plt.subplot(1, 2, 1)
plt.title(f'ESRGAN (x4)')
plt.imshow(sr.numpy());
bicubic = tf.image.resize(lr, [200, 200], tf.image.ResizeMethod.BICUBIC)
bicubic = tf.cast(bicubic, tf.uint8)
plt.subplot(1, 2, 2)
plt.title('Bicubic')
plt.imshow(bicubic.numpy());