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