configurations_deepfake.py

from transformers import PretrainedConfig
from typing import List

class DeepFakeConfig(PretrainedConfig):
    model_type = "pulse2pulse-2"

    def __init__(self, architectures="AutoModle", **kwargs):
       # if block_type not in ["basic", "bottleneck"]:
       #     raise ValueError(f"`block_type` must be 'basic' or bottleneck', got {block_type}.")
       # if stem_type not in ["", "deep", "deep-tiered"]:
       #     raise ValueError(f"`stem_type` must be '', 'deep' or 'deep-tiered', got {stem_type}.")

        #self.architectures = "AutoModle"
        self.architectures = architectures
        
        super().__init__(**kwargs)