94 lines
3.6 KiB
Python
94 lines
3.6 KiB
Python
# Copyright 2025 The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from copy import deepcopy
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from typing import Any
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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from ..auto import CONFIG_MAPPING
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logger = logging.get_logger(__name__)
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class ColQwen2Config(PretrainedConfig):
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r"""
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Configuration class to store the configuration of a [`ColQ2en2ForRetrieval`]. It is used to instantiate an instance
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of `ColQwen2ForRetrieval` according to the specified arguments, defining the model architecture following the methodology
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from the "ColPali: Efficient Document Retrieval with Vision Language Models" paper.
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Instantiating a configuration with the defaults will yield a similar configuration to the vision encoder used by the pre-trained
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ColQwen2-v1.0 model, e.g. [vidore/colqwen2-v1.0-hf](https://huggingface.co/vidore/colqwen2-v1.0-hf).
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vlm_config (`PretrainedConfig`, *optional*):
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Configuration of the VLM backbone model.
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embedding_dim (`int`, *optional*, defaults to 128):
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Dimension of the multi-vector embeddings produced by the model.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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Example:
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```python
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from transformers.models.colqwen2 import ColQwen2Config, ColQwen2ForRetrieval
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config = ColQwen2Config()
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model = ColQwen2ForRetrieval(config)
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```
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"""
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model_type = "colqwen2"
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sub_configs: dict[str, Any] = {"vlm_config": PretrainedConfig}
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def __init__(
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self,
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vlm_config=None,
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embedding_dim: int = 128,
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initializer_range: float = 0.02,
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**kwargs,
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):
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if vlm_config is None:
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vlm_config = CONFIG_MAPPING["qwen2_vl"]()
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logger.info(
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"`vlm_config` is `None`. Initializing `vlm_config` with the `Qwen2VLConfig` with default values."
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)
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elif isinstance(vlm_config, dict):
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vlm_config = deepcopy(vlm_config)
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if "model_type" not in vlm_config:
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raise KeyError(
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"The `model_type` key is missing in the `vlm_config` dictionary. Please provide the model type."
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)
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vlm_config = CONFIG_MAPPING[vlm_config["model_type"]](**vlm_config)
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elif isinstance(vlm_config, PretrainedConfig):
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vlm_config = vlm_config
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else:
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raise TypeError(
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f"Invalid type for `vlm_config`. Expected `PretrainedConfig`, `dict`, or `None`, but got {type(vlm_config)}."
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)
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self.vlm_config = vlm_config
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self.embedding_dim = embedding_dim
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self.initializer_range = initializer_range
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super().__init__(**kwargs)
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def get_text_config(self, decoder=False) -> PretrainedConfig:
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return self.vlm_config.get_text_config(decoder=decoder)
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__all__ = ["ColQwen2Config"]
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