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# coding=utf-8
# Copyright 2024 Microsoft and 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.
from typing import Optional
from transformers import AutoConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class DavitConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Florence2VisionModel`]. It is used to instantiate a Florence2VisionModel
according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the Florence2VisionModel architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
drop_path_rate (`float`, *optional*, defaults to 0.1):
The dropout rate of the drop path layer.
patch_size (`List[int]`, *optional*, defaults to [7, 3, 3, 3]):
The patch size of the image.
patch_stride (`List[int]`, *optional*, defaults to [4, 2, 2, 2]):
The patch stride of the image.
patch_padding (`List[int]`, *optional*, defaults to [3, 1, 1, 1]):
The patch padding of the image.
patch_prenorm (`List[bool]`, *optional*, defaults to [false, true, true, true]):
Whether to apply layer normalization before the patch embedding layer.
enable_checkpoint (`bool`, *optional*, defaults to False):
Whether to enable checkpointing.
dim_embed (`List[int]`, *optional*, defaults to [256, 512, 1024, 2048]):
The dimension of the embedding layer.
num_heads (`List[int]`, *optional*, defaults to [8, 16, 32, 64]):
The number of attention heads.
num_groups (`List[int]`, *optional*, defaults to [8, 16, 32, 64]):
The number of groups.
depths (`List[int]`, *optional*, defaults to [1, 1, 9, 1]):
The depth of the model.
window_size (`int`, *optional*, defaults to 12):
The window size of the model.
projection_dim (`int`, *optional*, defaults to 1024):
The dimension of the projection layer.
visual_temporal_embedding (`dict`, *optional*):
The configuration of the visual temporal embedding.
image_pos_embed (`dict`, *optional*):
The configuration of the image position embedding.
image_feature_source (`List[str]`, *optional*, defaults to ["spatial_avg_pool", "temporal_avg_pool"]):
The source of the image feature.
Example:
```python
>>> from transformers import Florence2VisionConfig, Florence2VisionModel
>>> # Initializing a Florence2 Vision style configuration
>>> configuration = Florence2VisionConfig()
>>> # Initializing a model (with random weights)
>>> model = Florence2VisionModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "florence2_vision"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
drop_path_rate=0.1,
patch_size=[7, 3, 3, 3],
patch_stride=[4, 2, 2, 2],
patch_padding=[3, 1, 1, 1],
patch_prenorm=[False, True, True, True],
enable_checkpoint=False,
dim_embed=[256, 512, 1024, 2048],
num_heads=[8, 16, 32, 64],
num_groups=[8, 16, 32, 64],
depths=[1, 1, 9, 1],
window_size=12,
projection_dim=1024,
visual_temporal_embedding=None,
image_pos_embed=None,
image_feature_source=["spatial_avg_pool", "temporal_avg_pool"],
**kwargs,
):
self.drop_path_rate = drop_path_rate
self.patch_size = patch_size
self.patch_stride = patch_stride
self.patch_padding = patch_padding
self.patch_prenorm = patch_prenorm
self.enable_checkpoint = enable_checkpoint
self.dim_embed = dim_embed
self.num_heads = num_heads
self.num_groups = num_groups
self.depths = depths
self.window_size = window_size
self.projection_dim = projection_dim
self.visual_temporal_embedding = visual_temporal_embedding
self.image_pos_embed = image_pos_embed
self.image_feature_source = image_feature_source
super().__init__(**kwargs)
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