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import os
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from typing import Optional, Tuple, Union
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from collections import OrderedDict
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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class UniFlowVisionConfig(PretrainedConfig):
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model_type = 'uniflow'
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def __init__(
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self,
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num_channels=3,
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patch_size=14,
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image_size=224,
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qkv_bias=False,
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hidden_size=3200,
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num_attention_heads=25,
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intermediate_size=12800,
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qk_normalization=True,
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num_hidden_layers=48,
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use_flash_attn=True,
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hidden_act='gelu',
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norm_type='rms_norm',
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layer_norm_eps=1e-6,
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dropout=0.0,
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drop_path_rate=0.0,
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attention_dropout=0.0,
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initializer_range=0.02,
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initializer_factor=0.1,
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vit_hidden_size=1024,
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llm_hidden_size=1536,
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latent_ch=64,
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use_global_blocks=True,
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global_blocks_depth=6,
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num_decoder_layers=12,
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num_sampling_steps='100',
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use_disp_loss=False,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.dropout = dropout
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self.drop_path_rate = drop_path_rate
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.num_channels = num_channels
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self.patch_size = patch_size
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self.image_size = image_size
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self.initializer_range = initializer_range
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self.initializer_factor = initializer_factor
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self.attention_dropout = attention_dropout
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self.layer_norm_eps = layer_norm_eps
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self.hidden_act = hidden_act
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self.norm_type = norm_type
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self.qkv_bias = qkv_bias
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self.qk_normalization = qk_normalization
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self.use_flash_attn = use_flash_attn
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self.vit_hidden_size = vit_hidden_size
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self.llm_hidden_size = llm_hidden_size
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self.latent_ch = latent_ch
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self.use_disp_loss = use_disp_loss
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self.global_blocks_depth = global_blocks_depth
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self.num_decoder_layers = num_decoder_layers
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self.num_sampling_steps = num_sampling_steps
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> 'PretrainedConfig':
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config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
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if 'vision_config' in config_dict:
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config_dict = config_dict['vision_config']
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if 'model_type' in config_dict and hasattr(cls, 'model_type') and config_dict['model_type'] != cls.model_type:
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logger.warning(
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f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
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f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'
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)
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return cls.from_dict(config_dict, **kwargs)
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