



from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase: Optional[int]							=						logging.get_logger(__name__)

lowerCAmelCase: Any							=						{
    'SCUT-DLVCLab/lilt-roberta-en-base': (
        'https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'
    ),
}

class 							a__( _lowerCAmelCase     ):
    lowercase__			=   "lilt"







    def __init__(				self			:  Any						,      __snake_case			:  Union[str, Any]=3_05_22						,      __snake_case			:  Any=7_68						,      __snake_case			:  str=12						,      __snake_case			:  List[str]=12						,      __snake_case			:  Tuple=30_72						,      __snake_case			:  str="gelu"						,      __snake_case			:  Optional[Any]=0.1						,      __snake_case			:  List[str]=0.1						,      __snake_case			:  Any=5_12						,      __snake_case			:  str=2						,      __snake_case			:  Any=0.02						,      __snake_case			:  Any=1e-1_2						,      __snake_case			:  Dict=0						,      __snake_case			:  List[str]="absolute"						,      __snake_case			:  List[Any]=None						,      __snake_case			:  Union[str, Any]=4						,      __snake_case			:  Dict=10_24						,      **__snake_case			:  Union[str, Any]						,      ):
      super().__init__(pad_token_id=_lowercase						,      **_lowercase     )

      a						: List[Any]					       =					vocab_size
      a						: Dict					       =					hidden_size
      a						: Optional[int]					       =					num_hidden_layers
      a						: Optional[Any]					       =					num_attention_heads
      a						: Optional[Any]					       =					hidden_act
      a						: Union[str, Any]					       =					intermediate_size
      a						: str					       =					hidden_dropout_prob
      a						: List[Any]					       =					attention_probs_dropout_prob
      a						: Tuple					       =					max_position_embeddings
      a						: Tuple					       =					type_vocab_size
      a						: Optional[int]					       =					initializer_range
      a						: Optional[Any]					       =					layer_norm_eps
      a						: Optional[Any]					       =					position_embedding_type
      a						: Optional[Any]					       =					classifier_dropout
      a						: Optional[int]					       =					channel_shrink_ratio
      a						: List[str]					       =					max_ad_position_embeddings




'''simple docstring'''






import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
     lowerCAmelCase: Any							=						argparse.ArgumentParser()

     parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

     parser.add_argument(
         '--txt2img_unclip',
         default='kakaobrain/karlo-v1-alpha',
         type=str,
         required=False,
         help='The pretrained txt2img unclip.',
     )

     lowerCAmelCase: Optional[int]							=						parser.parse_args()

     lowerCAmelCase: List[Any]							=						UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

     lowerCAmelCase: Optional[Any]							=						CLIPImageProcessor()
     lowerCAmelCase: Tuple							=						CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

     lowerCAmelCase: List[str]							=						UnCLIPImageVariationPipeline(
         decoder=txtaimg.decoder,
         text_encoder=txtaimg.text_encoder,
         tokenizer=txtaimg.tokenizer,
         text_proj=txtaimg.text_proj,
         feature_extractor=feature_extractor,
         image_encoder=image_encoder,
         super_res_first=txtaimg.super_res_first,
         super_res_last=txtaimg.super_res_last,
         decoder_scheduler=txtaimg.decoder_scheduler,
         super_res_scheduler=txtaimg.super_res_scheduler,
     )

     imgaimg.save_pretrained(args.dump_path)