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import transformers
from transformers.models.auto import CONFIG_MAPPING
class CXRMateEDConfig(transformers.PretrainedConfig):
model_type = 'cxrmate-ed'
def __init__(
self,
vision_config=None,
text_config=None,
index_value_encoder_intermediate_size: int = 2048,
include_time_delta: bool = True,
time_delta_monotonic_inversion: bool = True,
add_time_deltas: bool = True,
history: int = 0,
tables_filter: list = ['mimic_cxr_sectioned', 'triage', 'medrecon'],
prompt_report_sections_filter: list = ['indication', 'history'],
pad_token_id: int = 4,
**kwargs,
):
super().__init__(**kwargs)
self.vision_config = vision_config
self.index_value_encoder_intermediate_size = index_value_encoder_intermediate_size
self.include_time_delta = include_time_delta
self.time_delta_monotonic_inversion = time_delta_monotonic_inversion
self.add_time_deltas = add_time_deltas
self.history = history
self.tables_filter = tables_filter
self.prompt_report_sections_filter = prompt_report_sections_filter
self.pad_token_id = pad_token_id
if isinstance(vision_config, dict):
vision_config = transformers.AutoConfig.from_pretrained(
'aehrc/uniformer_base_tl_384',
trust_remote_code=True,
**vision_config,
)
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 'llama'
text_config = CONFIG_MAPPING[text_config['model_type']](**text_config)
self.text_config = text_config
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