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# coding=utf-8
# Copyright 2024 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.
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import (
logging, )
logger = logging.get_logger(__name__)
class BeeConfig(PretrainedConfig):
model_type = "Bee"
attribute_map = {
"image_token_id": "image_token_index",
}
def __init__(
self,
vision_config=None,
text_config=None,
image_token_index=151646,
projector_hidden_act="gelu",
vision_feature_select_strategy="full",
vision_feature_layer=-1,
vision_aspect_ratio="anyres_max_6",
image_grid_pinpoints=None,
tie_word_embeddings=False,
multimodal_projector_bias=True,
max_position_embeddings=32768,
**kwargs,
):
from transformers.models.auto import CONFIG_MAPPING
self.image_token_index = image_token_index
self.projector_hidden_act = projector_hidden_act
self.multimodal_projector_bias = multimodal_projector_bias
if vision_feature_select_strategy not in ["default", "full"]:
raise ValueError(
"vision_feature_select_strategy should be one of 'default', 'full'."
f"Got: {vision_feature_select_strategy}")
self.vision_feature_select_strategy = vision_feature_select_strategy
self.vision_feature_layer = vision_feature_layer
self.vision_aspect_ratio = vision_aspect_ratio
image_grid_pinpoints = (
image_grid_pinpoints if image_grid_pinpoints is not None else
[[384, 768], [768, 384], [768, 768], [1152, 384], [384, 1152]])
self.image_grid_pinpoints = image_grid_pinpoints
if isinstance(vision_config, dict):
vision_config["model_type"] = (vision_config["model_type"]
if "model_type" in vision_config
else "siglip_vision_model")
vision_config = CONFIG_MAPPING[vision_config["model_type"]](
**vision_config)
elif vision_config is None:
vision_config = CONFIG_MAPPING["siglip_vision_model"](
hidden_size=1152,
intermediate_size=4304,
patch_size=14,
image_size=384,
num_hidden_layers=26,
num_attention_heads=14,
vision_use_head=False,
)
self.vision_config = vision_config
if isinstance(text_config, dict):
text_config["model_type"] = text_config[
"model_type"] if "model_type" in text_config else "qwen2"
text_config = CONFIG_MAPPING[text_config["model_type"]](
**text_config)
elif text_config is None:
text_config = CONFIG_MAPPING["qwen2"]()
self.text_config = text_config
super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
__all__ = ["BeeConfig"]
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