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[Model] Cohere2 Vision (#39810)

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* Add cohere2_vision to support CohereLabs/command-a-vision-07-2025

* update and add modualr file

* update processors and check with orig impl later

* delete unused files

* image processor reduce LOC and re-use GotOCR2

* update the config to use modular

* model tests pass

* processor fixes

* check model outputs decorator

* address one more comment

* Update tokens. Temp - need to read from tokenizer'

* fix for multi-gpu

* Fix image token handling

* upadte image token expansion logic

* fix a few issues with remote code loading

* not related but modular forces us to change all files now

* Add overview and code sample to cohere vision docs

* add scripts. TMP.

* Update inference script

* Create script

* set dtype in export script

* TO revert: modular export fix

* Fix scripts

* Revert "TO revert: modular export fix"

This reverts commit bdb2f305b61027a05f0032ce70d6ca698879191c.

* Use modular weights

* Upload to hub

Removed OOD weights ad script

* Updated docs

* fix import error

Update docs

Added pipeline test

* Updated docs

* Run modular script

remove modular for config

Added patch_size

Added docstrings in modular

Fix OOM

Add docs, fixup integration tests. 8-gpu passing

* tiny updates

* address comments + fixup

* add test for chat template

* check model outputs workaround

* aya vision fix check model inputs

* Revert "add test for chat template"

This reverts commit 42c756e397f588d76b449ff1f93292d8ee0202d8.

* reveert more changes

* last revert

* skip and merge

* faulty copy from

---------

Co-authored-by: Julian Mack <julian.mack@cohere.com>
Co-authored-by: kyle-cohere <kyle@cohere.com>
This commit is contained in:
Raushan Turganbay
2025-07-31 12:57:34 +02:00
committed by GitHub
parent 6c3f27ba61
commit e1688d28d3
32 changed files with 2375 additions and 48 deletions
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0
tests/models/cohere2_vision/__init__.py Normal file
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tests/models/cohere2_vision/test_image_processing_cohere2_vision.py Normal file
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# Copyright 2025 HuggingFace Inc.
#
# 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
#
# http://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.
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
if is_torchvision_available():
from transformers import Cohere2VisionImageProcessorFast
class Cohere2VisionImageProcessingTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=7,
num_channels=3,
image_size=18,
min_resolution=30,
max_resolution=400,
do_resize=True,
size=None,
do_normalize=True,
image_mean=[0.48145466, 0.4578275, 0.40821073],
image_std=[0.26862954, 0.26130258, 0.27577711],
do_convert_rgb=True,
):
super().__init__()
size = size if size is not None else {"height": 30, "width": 30}
self.parent = parent
self.batch_size = batch_size
self.num_channels = num_channels
self.image_size = image_size
self.min_resolution = min_resolution
self.max_resolution = max_resolution
self.do_resize = do_resize
self.size = size
self.do_normalize = do_normalize
self.image_mean = image_mean
self.image_std = image_std
self.do_convert_rgb = do_convert_rgb
def prepare_image_processor_dict(self):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_convert_rgb": self.do_convert_rgb,
}
def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
return prepare_image_inputs(
batch_size=self.batch_size,
num_channels=self.num_channels,
min_resolution=self.min_resolution,
max_resolution=self.max_resolution,
equal_resolution=equal_resolution,
numpify=numpify,
torchify=torchify,
)
@require_torch
@require_vision
class Cohere2VisionProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
fast_image_processing_class = Cohere2VisionImageProcessorFast if is_torchvision_available() else None
test_slow_image_processor = False
def setUp(self):
super().setUp()
self.image_processor_tester = Cohere2VisionImageProcessingTester(self)
@property
def image_processor_dict(self):
return self.image_processor_tester.prepare_image_processor_dict()
def test_image_processor_properties(self):
for image_processing_class in self.image_processor_list:
image_processor = image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processor, "do_resize"))
self.assertTrue(hasattr(image_processor, "size"))
self.assertTrue(hasattr(image_processor, "do_normalize"))
self.assertTrue(hasattr(image_processor, "image_mean"))
self.assertTrue(hasattr(image_processor, "image_std"))
self.assertTrue(hasattr(image_processor, "do_convert_rgb"))
def test_call_pil(self):
for image_processing_class in self.image_processor_list:
# Initialize image_processing
image_processing = image_processing_class(**self.image_processor_dict)
# create random PIL images
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)
for image in image_inputs:
self.assertIsInstance(image, Image.Image)
# Test not batched input
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
self.assertEqual(tuple(encoded_images.shape), (10, 3, 30, 30))
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(tuple(encoded_images.shape), (70, 3, 30, 30))
def test_call_numpy(self):
for image_processing_class in self.image_processor_list:
# Initialize image_processing
image_processing = image_processing_class(**self.image_processor_dict)
# create random numpy tensors
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, numpify=True)
for image in image_inputs:
self.assertIsInstance(image, np.ndarray)
# Test not batched input
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
self.assertEqual(tuple(encoded_images.shape), (10, 3, 30, 30))
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(tuple(encoded_images.shape), (70, 3, 30, 30))
def test_call_pytorch(self):
for image_processing_class in self.image_processor_list:
# Initialize image_processing
image_processing = image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)
for image in image_inputs:
self.assertIsInstance(image, torch.Tensor)
# Test not batched input
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
self.assertEqual(tuple(encoded_images.shape), (10, 3, 30, 30))
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(tuple(encoded_images.shape), (70, 3, 30, 30))
def test_call_numpy_4_channels(self):
for image_processing_class in self.image_processor_list:
# Test that can process images which have an arbitrary number of channels
# Initialize image_processing
image_processor = image_processing_class(**self.image_processor_dict)
# create random numpy tensors
self.image_processor_tester.num_channels = 4
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, numpify=True)
# Test not batched input
encoded_images = image_processor(
image_inputs[0],
return_tensors="pt",
input_data_format="channels_last",
image_mean=0,
image_std=1,
).pixel_values
self.assertEqual(tuple(encoded_images.shape), (10, 4, 30, 30))
# Test batched
encoded_images = image_processor(
image_inputs,
return_tensors="pt",
input_data_format="channels_last",
image_mean=0,
image_std=1,
).pixel_values
self.assertEqual(tuple(encoded_images.shape), (70, 4, 30, 30))

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tests/models/cohere2_vision/test_modeling_cohere2_vision.py Normal file
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# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# 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
#
# http://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.
"""Testing suite for the PyTorch GotOcr2 model."""
import unittest
from transformers import (
AutoProcessor,
Cohere2VisionConfig,
is_torch_available,
)
from transformers.testing_utils import (
Expectations,
cleanup,
get_device_properties,
require_deterministic_for_xpu,
require_read_token,
require_torch,
require_torch_accelerator,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
Cohere2VisionForConditionalGeneration,
Cohere2VisionModel,
)
class Cohere2VisionText2TextModelTester:
def __init__(
self,
parent,
batch_size=3,
seq_length=7,
downsample_factor=2,
alignment_intermediate_size=32,
ignore_index=-100,
image_token_id=2,
num_channels=3,
image_size=64,
is_training=True,
text_config={
"model_type": "cohere2",
"vocab_size": 99,
"hidden_size": 128,
"intermediate_size": 37,
"num_hidden_layers": 4,
"num_attention_heads": 4,
"output_channels": 64,
"hidden_act": "silu",
"max_position_embeddings": 512,
"tie_word_embeddings": True,
"bos_token_id": 0,
"eos_token_id": 0,
"pad_token_id": 0,
},
vision_config={
"model_type": "siglip_vision_model",
"hidden_size": 32,
"num_hidden_layers": 2,
"num_attention_heads": 4,
"intermediate_size": 128,
"image_size": 64,
"patch_size": 8,
"vision_use_head": False,
},
):
self.parent = parent
self.ignore_index = ignore_index
self.bos_token_id = text_config["bos_token_id"]
self.eos_token_id = text_config["eos_token_id"]
self.pad_token_id = text_config["pad_token_id"]
self.image_token_id = image_token_id
self.text_config = text_config
self.vision_config = vision_config
self.batch_size = batch_size
self.downsample_factor = downsample_factor
self.alignment_intermediate_size = alignment_intermediate_size
self.is_training = is_training
self.num_channels = num_channels
self.image_size = image_size
self.image_seq_length = 16
self.seq_length = seq_length + self.image_seq_length
self.num_hidden_layers = text_config["num_hidden_layers"]
self.vocab_size = text_config["vocab_size"]
self.hidden_size = text_config["hidden_size"]
self.num_attention_heads = text_config["num_attention_heads"]
def get_config(self):
return Cohere2VisionConfig(
text_config=self.text_config,
vision_config=self.vision_config,
image_token_id=self.image_token_id,
downsample_factor=self.downsample_factor,
alignment_intermediate_size=self.alignment_intermediate_size,
)
def prepare_config_and_inputs(self):
config = self.get_config()
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
image_num_patches = torch.tensor([1] * self.batch_size).to(torch_device)
return config, pixel_values, image_num_patches
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, image_num_patches = config_and_inputs
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
input_ids[input_ids == self.image_token_id] = self.pad_token_id
input_ids[:, : self.image_seq_length] = self.image_token_id
inputs_dict = {
"pixel_values": pixel_values,
"input_ids": input_ids,
"attention_mask": attention_mask,
"image_num_patches": image_num_patches,
}
return config, inputs_dict
@require_torch
class Cohere2ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
Cohere2VisionModel,
Cohere2VisionForConditionalGeneration,
)
if is_torch_available()
else ()
)
all_generative_model_classes = (Cohere2VisionForConditionalGeneration,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"image-text-to-text": Cohere2VisionForConditionalGeneration,
}
if is_torch_available()
else {}
)
fx_compatible = False
test_pruning = False
test_torchscript = False
test_head_masking = False
_is_composite = True
def setUp(self):
self.model_tester = Cohere2VisionText2TextModelTester(self)
self.config_tester = ConfigTester(self, config_class=Cohere2VisionConfig, has_text_modality=False)
def test_config(self):
self.config_tester.run_common_tests()
@unittest.skip(reason="Siglip backbone uses the same initialization scheme as the Flax original implementation")
def test_initialization(self):
pass
@require_read_token
@require_torch
class Cohere2IntegrationTest(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.model_checkpoint = "CohereLabs/command-a-vision-07-2025"
cls.model = None
@classmethod
def tearDownClass(cls):
del cls.model
cleanup(torch_device, gc_collect=True)
def tearDown(self):
cleanup(torch_device, gc_collect=True)
@classmethod
def get_model(cls):
# Use 4-bit on T4
device_type, major, _ = get_device_properties()
load_in_4bit = (device_type == "cuda") and (major < 8)
torch_dtype = None if load_in_4bit else torch.float16
if cls.model is None:
cls.model = Cohere2VisionForConditionalGeneration.from_pretrained(
cls.model_checkpoint,
device_map="auto",
torch_dtype=torch_dtype,
load_in_4bit=load_in_4bit,
)
return cls.model
@slow
@require_torch_accelerator
def test_model_integration_forward(self):
processor = AutoProcessor.from_pretrained(self.model_checkpoint)
model = self.get_model()
messages = [
{
"role": "user",
"content": [
{"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
{"type": "text", "text": "Please describe the image explicitly."},
],
}
]
inputs = processor.apply_chat_template(
messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
).to(torch_device, dtype=torch.float16)
# Forward
with torch.inference_mode():
output = model(**inputs)
actual_logits = output.logits[0, -1, :5].cpu()
EXPECTED_LOGITS = Expectations(
{
("xpu", 3): [0.4109, 0.1532, 0.8018, 2.1328, 0.5483],
# 4-bit
("cuda", 7): [0.1097, 0.3481, 3.8340, 9.7969, 2.0488],
("cuda", 8): [2.4277, 1.6875, 1.8789, 2.1875, 1.9375],
}
) # fmt: skip
expected_logits = torch.tensor(EXPECTED_LOGITS.get_expectation(), dtype=torch.float16)
self.assertTrue(
torch.allclose(actual_logits, expected_logits, atol=0.1),
f"Actual logits: {actual_logits}"
f"\nExpected logits: {expected_logits}"
f"\nDifference: {torch.abs(actual_logits - expected_logits)}",
)
@slow
@require_torch_accelerator
@require_deterministic_for_xpu
def test_model_integration_generate_text_only(self):
processor = AutoProcessor.from_pretrained(self.model_checkpoint)
model = self.get_model()
messages = [
{
"role": "user",
"content": [
{"type": "text", "text": "Write a haiku"},
],
}
]
inputs = processor.apply_chat_template(
messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
).to(torch_device, dtype=torch.float16)
with torch.no_grad():
generate_ids = model.generate(**inputs, max_new_tokens=25, do_sample=False)
decoded_output = processor.decode(
generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True
)
expected_outputs = Expectations(
{
("xpu", 3): "Whispers on the breeze,\nLeaves dance under moonlit skies,\nNature's quiet song.",
# 4-bit
("cuda", 7): "Sure, here's a haiku for you:\n\nMorning dew sparkles,\nPetals unfold in sunlight,\n",
("cuda", 8): "**Haiku**\n\n*Softly falls the snow*\n*Blanketing the earth in white*\n*",
}
) # fmt: skip
expected_output = expected_outputs.get_expectation()
self.assertEqual(decoded_output, expected_output)
@slow
@require_torch_accelerator
@require_deterministic_for_xpu
def test_model_integration_generate_chat_template(self):
processor = AutoProcessor.from_pretrained(self.model_checkpoint)
model = self.get_model()
messages = [
{
"role": "user",
"content": [
{"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
{"type": "text", "text": "Please describe the image explicitly."},
],
}
]
inputs = processor.apply_chat_template(
messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
).to(torch_device, dtype=torch.float16)
with torch.no_grad():
generate_ids = model.generate(**inputs, max_new_tokens=20, do_sample=False)
decoded_output = processor.decode(
generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True
)
expected_outputs = Expectations(
{
("xpu", 3): 'The image depicts a cozy scene of two cats resting on a bright pink blanket. The cats,',
# 4-bit
("cuda", 7): 'The image depicts two cats comfortably resting on a pink blanket spread across a sofa. The cats,',
("cuda", 8): 'The image depicts two cats lying on a bright pink blanket that covers a red couch. The cat',
}
) # fmt: skip
expected_output = expected_outputs.get_expectation()
self.assertEqual(decoded_output, expected_output)
@slow
@require_torch_accelerator
def test_model_integration_batched_generate(self):
processor = AutoProcessor.from_pretrained(self.model_checkpoint)
model = self.get_model()
# Prepare inputs
messages = [
[
{
"role": "user",
"content": [
{"type": "image", "url": "https://llava-vl.github.io/static/images/view.jpg"},
{"type": "text", "text": "Write a haiku for this image"},
],
},
],
[
{
"role": "user",
"content": [
{"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"},
{"type": "text", "text": "Describe this image"},
],
},
],
]
inputs = processor.apply_chat_template(
messages, padding=True, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
).to(model.device, dtype=torch.float16)
output = model.generate(**inputs, do_sample=False, max_new_tokens=25)
# Check first output
decoded_output = processor.decode(output[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
expected_outputs = Expectations(
{
("xpu", 3): "Wooden path to water,\nMountains echo in stillness,\nPeaceful forest lake.",
# 4-bit
("cuda", 7): "Wooden bridge stretches\nMirrored lake below, mountains rise\nPeaceful, serene",
("cuda", 8): 'Dock stretches to calm, \nMountains whisper through the trees, \nLake mirrors the sky.',
}
) # fmt: skip
expected_output = expected_outputs.get_expectation()
self.assertEqual(
decoded_output,
expected_output,
f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
)
# Check second output
decoded_output = processor.decode(output[1, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
expected_outputs = Expectations(
{
("xpu", 3): 'This image captures a vibrant street scene in a bustling urban area, likely in an Asian city. The focal point is a',
# 4-bit
("cuda", 7): 'This vibrant image captures a bustling street scene in a multicultural urban area, featuring a traditional Chinese gate adorned with intricate red and',
("cuda", 8): 'The image depicts a vibrant street scene in what appears to be a Chinatown district, likely in an urban area. The focal',
}
) # fmt: skip
expected_output = expected_outputs.get_expectation()
self.assertEqual(
decoded_output,
expected_output,
f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
)
@slow
@require_torch_accelerator
@require_deterministic_for_xpu
def test_model_integration_batched_generate_multi_image(self):
processor = AutoProcessor.from_pretrained(self.model_checkpoint)
model = self.get_model()
# Prepare inputs
messages = [
[
{
"role": "user",
"content": [
{"type": "image", "url": "https://llava-vl.github.io/static/images/view.jpg"},
{"type": "text", "text": "Write a haiku for this image"},
],
},
],
[
{
"role": "user",
"content": [
{
"type": "image",
"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg",
},
{
"type": "image",
"url": "https://thumbs.dreamstime.com/b/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.jpg",
},
{
"type": "text",
"text": "These images depict two different landmarks. Can you identify them?",
},
],
},
],
]
inputs = processor.apply_chat_template(
messages, padding=True, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
).to(model.device, dtype=torch.float16)
output = model.generate(**inputs, do_sample=False, max_new_tokens=25)
# Check first output
decoded_output = processor.decode(output[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
# Batching seems to alter the output slightly, but it is also the case in the original implementation. This seems to be expected: https://github.com/huggingface/transformers/issues/23017#issuecomment-1649630232
expected_outputs = Expectations(
{
("xpu", 3): "Wooden path to water,\nMountains echo in stillness,\nPeaceful forest lake.",
("cuda", 7): 'Wooden bridge stretches\nMirrored lake below, mountains rise\nPeaceful, serene',
("cuda", 8): 'Dock stretches to calm, \nMountains whisper through the trees, \nLake mirrors the sky.',
}
) # fmt: skip
expected_output = expected_outputs.get_expectation()
self.assertEqual(
decoded_output,
expected_output,
f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
)
# Check second output
decoded_output = processor.decode(output[1, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
expected_outputs = Expectations(
{
("xpu", 3): "The first image showcases the Statue of Liberty, a colossal neoclassical sculpture on Liberty Island in New York Harbor. Standing at ",
("cuda", 7): 'The first image showcases the Statue of Liberty, a monumental sculpture located on Liberty Island in New York Harbor. Standing atop a',
("cuda", 8): 'The two landmarks depicted in the images are the Statue of Liberty and the Golden Gate Bridge. \n\n1. **Statue',
}
) # fmt: skip
expected_output = expected_outputs.get_expectation()
self.assertEqual(
decoded_output,
expected_output,
f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
)

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tests/models/cohere2_vision/test_processing_cohere2_vision.py Normal file
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@@ -0,0 +1,139 @@
# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# 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
#
# http://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.
import shutil
import tempfile
import unittest
from transformers import AutoProcessor, AutoTokenizer, Cohere2VisionProcessor
from transformers.testing_utils import require_read_token, require_torch, require_vision
from transformers.utils import is_torch_available, is_torchvision_available
from ...test_processing_common import ProcessorTesterMixin
if is_torch_available():
import torch
if is_torchvision_available():
from transformers import Cohere2VisionImageProcessorFast
@require_read_token
@require_vision
@unittest.skip("Model not released yet!")
class Cohere2VisionProcessorTest(ProcessorTesterMixin, unittest.TestCase):
processor_class = Cohere2VisionProcessor
@classmethod
def setUpClass(cls):
cls.tmpdirname = tempfile.mkdtemp()
image_processor = Cohere2VisionImageProcessorFast(
size={"height": 20, "width": 20},
max_patches=3,
)
tokenizer = AutoTokenizer.from_pretrained("CohereLabs/command-a-vision-07-2025")
processor_kwargs = cls.prepare_processor_dict()
processor = Cohere2VisionProcessor(
image_processor=image_processor,
tokenizer=tokenizer,
**processor_kwargs,
)
processor.save_pretrained(cls.tmpdirname)
cls.image_token = processor.image_token
@staticmethod
def prepare_processor_dict():
return {}
def get_tokenizer(self, **kwargs):
return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).tokenizer
def get_image_processor(self, **kwargs):
return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor
def get_processor(self, **kwargs):
return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs)
@classmethod
def tearDownClass(cls):
shutil.rmtree(cls.tmpdirname, ignore_errors=True)
@require_torch
def test_process_interleaved_images_videos(self):
processor = self.get_processor()
messages = [
[
{
"role": "user",
"content": [
{
"type": "image",
"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg",
},
{
"type": "image",
"url": "https://thumbs.dreamstime.com/b/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.jpg",
},
{"type": "text", "text": "What are the differences between these two images?"},
],
},
],
[
{
"role": "user",
"content": [
{
"type": "image",
"url": "https://llava-vl.github.io/static/images/view.jpg",
},
{"type": "text", "text": "Write a haiku for this image"},
],
}
],
]
inputs_batched = processor.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
padding=True,
)
# Process non batched inputs to check if the pixel_values and input_ids are reconstructed in the correct order when batched together
images_patches_index = 0
for i, message in enumerate(messages):
inputs = processor.apply_chat_template(
message,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
padding=True,
)
# We slice with [-inputs["input_ids"].shape[1] :] as the input_ids are left padded
torch.testing.assert_close(
inputs["input_ids"][0], inputs_batched["input_ids"][i][-inputs["input_ids"].shape[1] :]
)
torch.testing.assert_close(
inputs["pixel_values"],
inputs_batched["pixel_values"][
images_patches_index : images_patches_index + inputs["pixel_values"].shape[0]
],
)
images_patches_index += inputs["pixel_values"].shape[0]

10
tests/test_modeling_common.py
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@@ -4677,9 +4677,13 @@ class ModelTesterMixin:
sub_config = getattr(config, key)
update_config_for_flex(sub_config)
model = model_class(config).to(device=torch_device)
model.set_attn_implementation("flex_attention")
self.assertTrue(model.config._attn_implementation == "flex_attention")
if model_class._can_set_attn_implementation():
model = model_class(config).to(device=torch_device)
model.set_attn_implementation("flex_attention")
self.assertTrue(model.config._attn_implementation == "flex_attention")
else:
config._attn_implementation = "flex_attention"
model = model_class(config).to(device=torch_device)
# Elaborate workaround for encoder-decoder models as some do not specify their main input
dummy_inputs = {model.main_input_name: inputs_dict[model.main_input_name].to(torch_device)}