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from transformers import PretrainedConfig
from typing import List
import json
# from transformers import CONFIG_MAPPING
from peft import PeftConfig
from .configuration_vision import Idefics2VisionConfig
from .configuration_internlm2 import InternLM2Config
from .configuration_projector import ProjectorConfig
from .configuration_connector import Idefics2ConnectorConfig
from .image_processor import Idefics2ImageProcessor
from .configuration_downsampler import DownsamplerConfig
class WeMMConfig(PretrainedConfig):
model_type = "wemm_hf"
def __init__(
self,
vision_config = None,
text_config = None,
projector_config = None,
connector_config = None,
adapter_path = None,
image_processor = None,
do_image_splitting = False,
spliter_emb_config = None,
downsampler_config = None,
tokenizer_path = None,
**kwargs
):
# vision_config
if vision_config is not None:
self.vision_config = Idefics2VisionConfig(**vision_config)
# text_config
if text_config is not None:
self.text_config = InternLM2Config(**text_config)
# projector_config
if projector_config is not None:
self.projector_config = ProjectorConfig(**projector_config)
# connector_config
if connector_config is not None:
self.connector_config = Idefics2ConnectorConfig(**connector_config)
if image_processor is not None:
self.image_processor = image_processor
if adapter_path is not None:
self.adapter_path = adapter_path
self.do_image_splitting = do_image_splitting
if spliter_emb_config is not None:
self.spliter_emb_config = spliter_emb_config
if downsampler_config is not None:
self.downsampler_config = DownsamplerConfig(**downsampler_config)
if tokenizer_path is not None:
self.tokenizer_path = tokenizer_path
super().__init__(**kwargs)
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