from docs_src.custom_response import tutorial009b_py39
from docs_src.custom_response.tutorial009b_py39 import app

client = TestClient(app)


def test_get(tmp_path: Path):
    file_path: Path = tmp_path / "large-video-file.mp4"
    tutorial009b_py39.some_file_path = str(file_path)
    test_content = b"Fake video bytes"
    file_path.write_bytes(test_content)
    response = client.get("/")

	opReadRune4 readOp = 4  // Read rune of size 4.
)

// ErrTooLarge is passed to panic if memory cannot be allocated to store data in a buffer.
var ErrTooLarge = errors.New("bytes.Buffer: too large")
var errNegativeRead = errors.New("bytes.Buffer: reader returned negative count from Read")

const maxInt = int(^uint(0) >> 1)

// Bytes returns a slice of length b.Len() holding the unread portion of the buffer.

        SocketInputStream sis = new SocketInputStream(in);

        // Should throw IOException for unexpected EOF
        assertThrows(IOException.class, () -> sis.read());
    }

    @Test
    @DisplayName("Large message is read correctly")
    void largeMessageIsReadCorrectly() throws IOException {
        // Create a message with 1000 bytes
        int size = 1000;
        byte[] data = new byte[size];

from fastapi import FastAPI
from fastapi.responses import StreamingResponse

some_file_path = "large-video-file.mp4"
app = FastAPI()


@app.get("/")
def main():
    def iterfile():  # (1)
        with open(some_file_path, mode="rb") as file_like:  # (2)
            yield from file_like  # (3)

cff-version: 1.2.0
message: "If you use TensorFlow in your research, please cite it using these metadata. Software is available from tensorflow.org."
title: TensorFlow, Large-scale machine learning on heterogeneous systems

// A chunk has the following format:
//
//	<chunk-size-as-hex> + ";chunk-signature=" + <signature-as-hex> + "\r\n" + <payload> + "\r\n"
//
// First, we read the chunk size but fail if it is larger
// than 16 MiB. We must not accept arbitrary large chunks.
// One 16 MiB is a reasonable max limit.
//
// Then we read the signature and payload data. We compute the SHA256 checksum

* It uses a "spooled" file:
    * A file stored in memory up to a maximum size limit, and after passing this limit it will be stored in disk.
* This means that it will work well for large files like images, videos, large binaries, etc. without consuming all the memory.
* You can get metadata from the uploaded file.

Starten Sie Ihre Anwendung und gehen Sie auf <a href="http://127.0.0.1:8000/static/redoc.standalone.js" class="external-link" target="_blank">http://127.0.0.1:8000/static/redoc.standalone.js</a>.

Sie sollten eine sehr lange JavaScript-Datei für **ReDoc** sehen.

Sie könnte beginnen mit etwas wie:

```JavaScript
/*! For license information please see redoc.standalone.js.LICENSE.txt */

        assertNotNull(deserialized.getData());
    }

    /**
     * Test transformer with large data
     */
    public void test_transformerWithLargeData() {
        TestTransformer transformer = new TestTransformer("largeDataTransformer");

        // Create large response data
        byte[] largeBody = new byte[10 * 1024 * 1024]; // 10MB
        for (int i = 0; i < largeBody.length; i++) {

  "durations" : [ {
    "testProject" : "excludeRuleMergingBuild",
    "linux" : 233
  } ]
}, {
  "scenario" : "org.gradle.performance.regression.corefeature.LargeDependencyGraphPerformanceTest.resolve large dependency graph (parallel = false, locking = true)",
  "durations" : [ {