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support m18
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superhero-7
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73
configs/alt-diffusion-m18-inference.yaml
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73
configs/alt-diffusion-m18-inference.yaml
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@ -0,0 +1,73 @@
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model:
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base_learning_rate: 1.0e-04
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target: ldm.models.diffusion.ddpm.LatentDiffusion
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params:
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linear_start: 0.00085
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linear_end: 0.0120
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num_timesteps_cond: 1
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log_every_t: 200
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timesteps: 1000
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first_stage_key: "jpg"
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cond_stage_key: "txt"
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image_size: 64
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channels: 4
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cond_stage_trainable: false # Note: different from the one we trained before
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conditioning_key: crossattn
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monitor: val/loss_simple_ema
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scale_factor: 0.18215
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use_ema: False
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scheduler_config: # 10000 warmup steps
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target: ldm.lr_scheduler.LambdaLinearScheduler
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params:
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warm_up_steps: [ 10000 ]
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cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
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f_start: [ 1.e-6 ]
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f_max: [ 1. ]
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f_min: [ 1. ]
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unet_config:
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target: ldm.modules.diffusionmodules.openaimodel.UNetModel
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params:
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image_size: 32 # unused
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in_channels: 4
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out_channels: 4
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model_channels: 320
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attention_resolutions: [ 4, 2, 1 ]
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num_res_blocks: 2
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channel_mult: [ 1, 2, 4, 4 ]
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num_head_channels: 64
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use_spatial_transformer: True
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use_linear_in_transformer: True
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transformer_depth: 1
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context_dim: 1024
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use_checkpoint: True
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legacy: False
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first_stage_config:
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target: ldm.models.autoencoder.AutoencoderKL
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params:
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embed_dim: 4
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monitor: val/rec_loss
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ddconfig:
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double_z: true
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z_channels: 4
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resolution: 256
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in_channels: 3
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out_ch: 3
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ch: 128
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ch_mult:
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- 1
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- 2
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- 4
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- 4
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num_res_blocks: 2
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attn_resolutions: []
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dropout: 0.0
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lossconfig:
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target: torch.nn.Identity
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cond_stage_config:
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target: modules.xlmr_m18.BertSeriesModelWithTransformation
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params:
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name: "XLMR-Large"
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@ -5,7 +5,7 @@ from types import MethodType
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from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet
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from modules.hypernetworks import hypernetwork
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from modules.shared import cmd_opts
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from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr
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from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr, xlmr_m18
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import ldm.modules.attention
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import ldm.modules.diffusionmodules.model
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@ -208,11 +208,10 @@ class StableDiffusionModelHijack:
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else:
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m.cond_stage_model = conditioner
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if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
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if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation or type(m.cond_stage_model) == xlmr_m18.BertSeriesModelWithTransformation:
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model_embeddings = m.cond_stage_model.roberta.embeddings
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model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.word_embeddings, self)
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m.cond_stage_model = sd_hijack_xlmr.FrozenXLMREmbedderWithCustomWords(m.cond_stage_model, self)
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elif type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenCLIPEmbedder:
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model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
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model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
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@ -258,7 +257,6 @@ class StableDiffusionModelHijack:
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if hasattr(m, 'cond_stage_model'):
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delattr(m, 'cond_stage_model')
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elif type(m.cond_stage_model) == sd_hijack_xlmr.FrozenXLMREmbedderWithCustomWords:
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m.cond_stage_model = m.cond_stage_model.wrapped
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@ -21,7 +21,7 @@ config_unopenclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-h-inf
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config_inpainting = os.path.join(sd_configs_path, "v1-inpainting-inference.yaml")
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config_instruct_pix2pix = os.path.join(sd_configs_path, "instruct-pix2pix.yaml")
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config_alt_diffusion = os.path.join(sd_configs_path, "alt-diffusion-inference.yaml")
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config_alt_diffusion_m18 = os.path.join(sd_configs_path, "alt-diffusion-m18-inference.yaml")
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def is_using_v_parameterization_for_sd2(state_dict):
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"""
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@ -95,7 +95,11 @@ def guess_model_config_from_state_dict(sd, filename):
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if diffusion_model_input.shape[1] == 8:
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return config_instruct_pix2pix
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# import pdb; pdb.set_trace()
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if sd.get('cond_stage_model.roberta.embeddings.word_embeddings.weight', None) is not None:
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if sd.get('cond_stage_model.transformation.weight').size()[0] == 1024:
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return config_alt_diffusion_m18
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return config_alt_diffusion
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return config_default
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164
modules/xlmr_m18.py
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164
modules/xlmr_m18.py
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from transformers import BertPreTrainedModel,BertModel,BertConfig
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import torch.nn as nn
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import torch
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from transformers.models.xlm_roberta.configuration_xlm_roberta import XLMRobertaConfig
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from transformers import XLMRobertaModel,XLMRobertaTokenizer
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from typing import Optional
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class BertSeriesConfig(BertConfig):
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def __init__(self, vocab_size=30522, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, position_embedding_type="absolute", use_cache=True, classifier_dropout=None,project_dim=512, pooler_fn="average",learn_encoder=False,model_type='bert',**kwargs):
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super().__init__(vocab_size, hidden_size, num_hidden_layers, num_attention_heads, intermediate_size, hidden_act, hidden_dropout_prob, attention_probs_dropout_prob, max_position_embeddings, type_vocab_size, initializer_range, layer_norm_eps, pad_token_id, position_embedding_type, use_cache, classifier_dropout, **kwargs)
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self.project_dim = project_dim
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self.pooler_fn = pooler_fn
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self.learn_encoder = learn_encoder
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class RobertaSeriesConfig(XLMRobertaConfig):
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def __init__(self, pad_token_id=1, bos_token_id=0, eos_token_id=2,project_dim=512,pooler_fn='cls',learn_encoder=False, **kwargs):
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super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
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self.project_dim = project_dim
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self.pooler_fn = pooler_fn
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self.learn_encoder = learn_encoder
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class BertSeriesModelWithTransformation(BertPreTrainedModel):
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_keys_to_ignore_on_load_unexpected = [r"pooler"]
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_keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
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config_class = BertSeriesConfig
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def __init__(self, config=None, **kargs):
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# modify initialization for autoloading
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if config is None:
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config = XLMRobertaConfig()
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config.attention_probs_dropout_prob= 0.1
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config.bos_token_id=0
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config.eos_token_id=2
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config.hidden_act='gelu'
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config.hidden_dropout_prob=0.1
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config.hidden_size=1024
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config.initializer_range=0.02
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config.intermediate_size=4096
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config.layer_norm_eps=1e-05
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config.max_position_embeddings=514
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config.num_attention_heads=16
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config.num_hidden_layers=24
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config.output_past=True
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config.pad_token_id=1
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config.position_embedding_type= "absolute"
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config.type_vocab_size= 1
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config.use_cache=True
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config.vocab_size= 250002
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config.project_dim = 1024
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config.learn_encoder = False
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super().__init__(config)
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self.roberta = XLMRobertaModel(config)
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self.transformation = nn.Linear(config.hidden_size,config.project_dim)
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# self.pre_LN=nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.tokenizer = XLMRobertaTokenizer.from_pretrained('xlm-roberta-large')
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# self.pooler = lambda x: x[:,0]
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# self.post_init()
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self.has_pre_transformation = True
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if self.has_pre_transformation:
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self.transformation_pre = nn.Linear(config.hidden_size, config.project_dim)
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self.pre_LN = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.post_init()
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def encode(self,c):
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device = next(self.parameters()).device
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text = self.tokenizer(c,
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truncation=True,
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max_length=77,
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return_length=False,
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return_overflowing_tokens=False,
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padding="max_length",
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return_tensors="pt")
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text["input_ids"] = torch.tensor(text["input_ids"]).to(device)
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text["attention_mask"] = torch.tensor(
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text['attention_mask']).to(device)
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features = self(**text)
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return features['projection_state']
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def forward(
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self,
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input_ids: Optional[torch.Tensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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token_type_ids: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.Tensor] = None,
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head_mask: Optional[torch.Tensor] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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encoder_hidden_states: Optional[torch.Tensor] = None,
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encoder_attention_mask: Optional[torch.Tensor] = None,
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output_attentions: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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) :
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r"""
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"""
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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outputs = self.roberta(
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input_ids=input_ids,
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attention_mask=attention_mask,
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token_type_ids=token_type_ids,
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position_ids=position_ids,
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head_mask=head_mask,
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inputs_embeds=inputs_embeds,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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output_attentions=output_attentions,
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output_hidden_states=True,
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return_dict=return_dict,
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)
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# # last module outputs
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# sequence_output = outputs[0]
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# # project every module
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# sequence_output_ln = self.pre_LN(sequence_output)
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# # pooler
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# pooler_output = self.pooler(sequence_output_ln)
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# pooler_output = self.transformation(pooler_output)
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# projection_state = self.transformation(outputs.last_hidden_state)
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if self.has_pre_transformation:
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sequence_output2 = outputs["hidden_states"][-2]
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sequence_output2 = self.pre_LN(sequence_output2)
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projection_state2 = self.transformation_pre(sequence_output2)
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return {
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"projection_state": projection_state2,
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"last_hidden_state": outputs.last_hidden_state,
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"hidden_states": outputs.hidden_states,
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"attentions": outputs.attentions,
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}
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else:
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projection_state = self.transformation(outputs.last_hidden_state)
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return {
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"projection_state": projection_state,
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"last_hidden_state": outputs.last_hidden_state,
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"hidden_states": outputs.hidden_states,
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"attentions": outputs.attentions,
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}
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# return {
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# 'pooler_output':pooler_output,
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# 'last_hidden_state':outputs.last_hidden_state,
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# 'hidden_states':outputs.hidden_states,
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# 'attentions':outputs.attentions,
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# 'projection_state':projection_state,
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# 'sequence_out': sequence_output
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# }
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class RobertaSeriesModelWithTransformation(BertSeriesModelWithTransformation):
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base_model_prefix = 'roberta'
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config_class= RobertaSeriesConfig
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