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Convert formatting to use ruff instead of yapf + isort (#26247)

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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor
2025-10-05 15:06:22 +01:00
committed by GitHub
parent 17edd8a807
commit d6953beb91
1508 changed files with 115244 additions and 94146 deletions
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19
tests/models/multimodal/processing/test_nemotron_vl.py
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@@ -1,6 +1,7 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Tests for Nemotron-Nano-VL's multimodal preprocessing kwargs."""
from collections.abc import Mapping
from typing import Optional
@@ -24,7 +25,9 @@ def _get_expected_num_patches(
max_num: int,
):
from vllm.model_executor.models.nemotron_vl import (
calculate_nemotron_vl_targets, get_nemotron_vl_target_ratios)
calculate_nemotron_vl_targets,
get_nemotron_vl_target_ratios,
)
width, height = image.size
@@ -63,22 +66,21 @@ def _run_check(
total_expected_num_patches = sum(
_get_expected_num_patches(config, image, len(images), min_num, max_num)
for image in images)
for image in images
)
print(total_expected_num_patches)
processed_inputs = processor.apply(prompt, mm_data, mm_processor_kwargs)
# Ensure we have the right number of placeholders per num_crops size
image_token_id = tokenizer.convert_tokens_to_ids("<image>")
img_tok_count = processed_inputs["prompt_token_ids"].count(image_token_id)
pixel_shape = processed_inputs["mm_kwargs"].get_data(
)["pixel_values_flat"].shape
pixel_shape = processed_inputs["mm_kwargs"].get_data()["pixel_values_flat"].shape
print("Image token count:", img_tok_count, "Pixel shape:", pixel_shape)
assert img_tok_count == 256 * total_expected_num_patches
assert pixel_shape[0] == total_expected_num_patches
@pytest.mark.parametrize("model_id",
["nvidia/Llama-3.1-Nemotron-Nano-VL-8B-V1"])
@pytest.mark.parametrize("model_id", ["nvidia/Llama-3.1-Nemotron-Nano-VL-8B-V1"])
@pytest.mark.parametrize(
"size_factors",
[
@@ -125,10 +127,7 @@ def test_processor_override(
_run_check(
processor,
[
rescale_image_size(image_assets[0].pil_image, f)
for f in size_factors
],
[rescale_image_size(image_assets[0].pil_image, f) for f in size_factors],
min_num,
max_num,
hf_processor_mm_kwargs,