{"FFFFFFFFFFFFFFFF", 1<<64 - 1, ""},
		{"000000000000bogus", 0, "invalid byte in chunk length"},
		{"00000000000000000", 0, "http chunk length too large"}, // could accept if we wanted
		{"10000000000000000", 0, "http chunk length too large"},
		{"00000000000000001", 0, "http chunk length too large"}, // could accept if we wanted
	}
	for i := uint64(0); i <= 1234; i++ {
		tests = append(tests, testCase{in: fmt.Sprintf("%x", i), want: i})
	}

from fastapi import FastAPI
from fastapi.responses import FileResponse

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


@app.get("/", response_class=FileResponse)
async def main():

    # shellcheck disable=SC2319
    IN_PROW="$?"

    # Don't persist large diffs (30M+) on CI
    LARGE_FILE="$(find "${ARTIFACTS}" -name "${PATCH_NAME}" -type 'f' -size +30M)"
    if [ "${IN_PROW}" -eq 0 ] && [ -n "${LARGE_FILE}" ]; then
      rm "${PATCH_OUT}"
      echo "WARNING: patch file was too large to persist ($(du -h "${PATCH_OUT}"))"
      return 0
    fi

from docs_src.custom_response import tutorial008
from docs_src.custom_response.tutorial008 import app

client = TestClient(app)


def test_get(tmp_path: Path):
    file_path: Path = tmp_path / "large-video-file.mp4"
    tutorial008.some_file_path = str(file_path)
    test_content = b"Fake video bytes"
    file_path.write_bytes(test_content)
    response = client.get("/")

      assertThrows<IllegalArgumentException> {
        checkDuration(
          "timeout",
          1L + Int.MAX_VALUE.toLong(),
          TimeUnit.MILLISECONDS,
        )
      },
    ).hasMessage("timeout too large")
  }

  @Test
  fun testDurationDuration() {
    assertThat(checkDuration("timeout", 0.milliseconds)).isEqualTo(0)
    assertThat(checkDuration("timeout", 1.milliseconds)).isEqualTo(1)

    assertThat(

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

	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.

from docs_src.custom_response import tutorial009
from docs_src.custom_response.tutorial009 import app

client = TestClient(app)


def test_get(tmp_path: Path):
    file_path: Path = tmp_path / "large-video-file.mp4"
    tutorial009.some_file_path = str(file_path)
    test_content = b"Fake video bytes"
    file_path.write_bytes(test_content)
    response = client.get("/")

   * may trigger an [OutOfMemoryError]. Prefer to stream the response body if this is a
   * possibility for your response.
   */
  @Throws(IOException::class)
  fun bytes() = commonBytes()

  /**
   * Returns the response as a [ByteString].
   *
   * This method loads entire response body into memory. If the response body is very large this

# See https://pre-commit.com/hooks.html for more hooks
default_language_version:
    python: python3.10
repos:
-   repo: https://github.com/pre-commit/pre-commit-hooks
    rev: v5.0.0
    hooks:
    -   id: check-added-large-files
    -   id: check-toml
    -   id: check-yaml
        args:
        -   --unsafe
    -   id: end-of-file-fixer
    -   id: trailing-whitespace
-   repo: https://github.com/astral-sh/ruff-pre-commit