from transformers import PretrainedConfig
from typing import List
import warnings
warnings.filterwarnings("ignore")

class InceptionV3Config(PretrainedConfig):
	model_type = "inceptionv3"
	def __init__(self, model_name: str = "inception_v3", input_channels: int = 3, num_classes: int = 3, input_size: List[int] = [3, 299, 299], pool_size: List[int] = [8, 8, 2048], crop_pct: float = 0.875, interpolation: str = "bicubic", mean: List[float] = [0.5, 0.5, 0.5], std: List[float] = [0.5, 0.5, 0.5], first_conv: str = "Conv2d_1a_3x3.conv", classifier: str = "fc", has_aux: bool = True, label_offset: int = 1, classes: dict = { '0': 'nsfw_gore', '1': 'nsfw_suggestive', '2': 'safe' }, output_channels: int = 2048, use_jit=False, **kwargs):
		self.model_name = model_name
		self.input_channels = input_channels
		self.num_classes = num_classes
		self.input_size = input_size
		self.pool_size = pool_size
		self.crop_pct = crop_pct
		self.interpolation = interpolation
		self.mean = mean
		self.std = std
		self.first_conv = first_conv
		self.classifier = classifier
		self.has_aux = has_aux
		self.label_offset = label_offset
		self.classes = classes
		self.output_channels = output_channels
		self.use_jit = use_jit
		super().__init__(**kwargs)

"""

inceptionv3_config = InceptionV3Config()
inceptionv3_config.save_pretrained("inceptionv3_config")

"""