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from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from transformers.configuration_utils import PretrainedConfig
from transformers.onnx import OnnxConfigWithPast, PatchingSpec
from transformers.utils import logging
logger = logging.get_logger(__name__)
class MLEConfig(PretrainedConfig):
model_type = "mle"
def __init__(
self,
in_channels=1,
num_encoder_layers=[2, 3, 5, 7, 12],
num_decoder_layers=[7, 5, 3, 2, 2],
last_hidden_channels=16,
block_stride_size=4,
block_kernel_size=3,
block_patch_size=24,
upsample_ratio=2,
batch_norm_eps=1e-3,
hidden_act="leaky_relu",
negative_slope=0.2,
**kwargs,
):
self.in_channels = in_channels
self.num_encoder_layers = num_encoder_layers
self.num_decoder_layers = num_decoder_layers
self.last_hidden_channels = last_hidden_channels
self.block_stride_size = block_stride_size
# if isinstance(block_kernel_size, int):
# self.block_kernel_size = (block_kernel_size, block_kernel_size)
self.block_kernel_size = block_kernel_size
self.block_patch_size = block_patch_size
self.upsample_ratio = upsample_ratio
self.batch_norm_eps = batch_norm_eps
self.hidden_act = hidden_act
self.negative_slope = negative_slope
super().__init__(**kwargs)
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