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"""
# Copyright 2025 The HuggingFace Inc. team.
#
# 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.
Configuration for ColQwen3, adapted to mirror the ColQwen2 structure.
"""
from copy import deepcopy
from typing import Any
from transformers.configuration_utils import PretrainedConfig
from transformers.models.auto import CONFIG_MAPPING
from transformers.models.qwen3_vl.configuration_qwen3_vl import Qwen3VLTextConfig, Qwen3VLVisionConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class ColQwen3Config(PretrainedConfig):
"""Configuration for ColQwen3 retrieval model."""
model_type = "colqwen3"
sub_configs: dict[str, Any] = {"vision_config": Qwen3VLVisionConfig, "text_config": Qwen3VLTextConfig}
def __init__(
self,
vision_config: Any = None,
text_config: Any = None,
embed_dim: int = 320,
padding_side: str = "left",
initializer_range: float = 0.02,
dtype: str | None = None,
**kwargs,
):
if vision_config is None or text_config is None:
base_vlm_config = CONFIG_MAPPING["qwen3_vl"]()
if vision_config is None:
vision_config = deepcopy(base_vlm_config.vision_config)
logger.info("`vision_config` is `None`. Initializing with the default `Qwen3VLVisionConfig`.")
if text_config is None:
text_config = deepcopy(base_vlm_config.text_config)
logger.info("`text_config` is `None`. Initializing with the default `Qwen3VLTextConfig`.")
if isinstance(vision_config, dict):
vision_config = Qwen3VLVisionConfig(**deepcopy(vision_config))
elif not isinstance(vision_config, PretrainedConfig):
raise TypeError(
f"Invalid type for `vision_config`. Expected `PretrainedConfig`, `dict`, or `None`, got {type(vision_config)}."
)
if isinstance(text_config, dict):
text_config = Qwen3VLTextConfig(**deepcopy(text_config))
elif not isinstance(text_config, PretrainedConfig):
raise TypeError(
f"Invalid type for `text_config`. Expected `PretrainedConfig`, `dict`, or `None`, got {type(text_config)}."
)
if embed_dim <= 0:
raise ValueError(f"`embed_dim` must be positive, got {embed_dim}.")
super().__init__(**kwargs)
self.vision_config = vision_config
self.text_config = text_config
self.embed_dim = embed_dim
self.padding_side = padding_side
self.initializer_range = initializer_range
# Preserve incoming dtype so downstream models avoid attribute errors
self.dtype = dtype or getattr(self, "dtype", None)
@classmethod
def from_base_config(cls, base_config: PretrainedConfig) -> "ColQwen3Config":
"""Upgrade a base Qwen3VLConfig-like config into ColQwen3Config."""
if isinstance(base_config, dict):
data = dict(base_config)
else:
data = base_config.to_dict()
vision_cfg = data.get("vision_config")
if isinstance(vision_cfg, dict):
data["vision_config"] = Qwen3VLVisionConfig.from_dict(vision_cfg)
text_cfg = data.get("text_config")
if isinstance(text_cfg, dict):
data["text_config"] = Qwen3VLTextConfig.from_dict(text_cfg)
data.setdefault("model_type", cls.model_type)
if hasattr(base_config, "dtype"):
data.setdefault("dtype", getattr(base_config, "dtype"))
elif hasattr(base_config, "torch_dtype") and base_config.torch_dtype is not None:
data.setdefault("dtype", str(base_config.torch_dtype))
return cls.from_dict(data)
def get_text_config(self, *args, **kwargs) -> PretrainedConfig:
return self.text_config
DEFAULT_CONFIG = ColQwen3Config()
__all__ = ["ColQwen3Config", "DEFAULT_CONFIG"]
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