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# coding=utf-8
# Copyright 2024 Google Inc. 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.models.gemma2.configuration_gemma2 import Gemma2Config
class CostWiseGemmaConfig(Gemma2Config):
r"""
This is the configuration class to store the configuration of a [`GemmaModel`]. It is used to instantiate an Gemma
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the Gemma-7B.
e.g. [google/gemma-7b](https://huggingface.co/google/gemma-7b)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
start_layer (`int`, *optional*, defaults to 28):
The start layer to output score.
layer_sep (`int`, *optional*, defaults to 28):
The sep layer from the start layer to output score.
layer_wise (`bool`, *optional*, defaults to `False`):
Whether or not the model should be layerwise.
```python
>>> from transformers import Gemma2Model, Gemma2Config
>>> # Initializing a Gemma2 gemma2-9b style configuration
>>> configuration = Gemma2Config()
>>> # Initializing a model from the gemma2-9b style configuration
>>> model = Gemma2Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "cost_wise_gemma"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
start_layer: int = 28,
layer_sep: int = 28,
layer_wise: bool = False,
**kwargs,
):
self.start_layer = start_layer
self.layer_sep = layer_sep
self.layer_wise = layer_wise
super().__init__(
**kwargs,
) |