



'''simple docstring'''






from typing import List

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


lowerCAmelCase: int							=						logging.get_logger(__name__)

lowerCAmelCase: List[Any]							=						{
    'snap-research/efficientformer-l1-300': (
        'https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'
    ),
}

class 							a__( lowerCamelCase__     ):
    lowercase__			=   """efficientformer"""







    def __init__(				self			:  List[str]						,      __snake_case			:  List[int] = [3, 2, 6, 4]						,      __snake_case			:  List[int] = [48, 96, 2_24, 4_48]						,      __snake_case			:  List[bool] = [True, True, True, True]						,      __snake_case			:  int = 4_48						,      __snake_case			:  int = 32						,      __snake_case			:  int = 4						,      __snake_case			:  int = 7						,      __snake_case			:  int = 5						,      __snake_case			:  int = 8						,      __snake_case			:  int = 4						,      __snake_case			:  float = 0.0						,      __snake_case			:  int = 16						,      __snake_case			:  int = 3						,      __snake_case			:  int = 3						,      __snake_case			:  int = 3						,      __snake_case			:  int = 2						,      __snake_case			:  int = 1						,      __snake_case			:  float = 0.0						,      __snake_case			:  int = 1						,      __snake_case			:  bool = True						,      __snake_case			:  bool = True						,      __snake_case			:  float = 1e-5						,      __snake_case			:  str = "gelu"						,      __snake_case			:  float = 0.02						,      __snake_case			:  float = 1e-1_2						,      __snake_case			:  int = 2_24						,      __snake_case			:  float = 1e-0_5						,      **__snake_case			:  List[Any]						,      ):
      super().__init__(**__snake_case     )

      a						: Optional[int]					       =					hidden_act
      a						: Dict					       =					hidden_dropout_prob
      a						: Optional[Any]					       =					hidden_sizes
      a						: List[Any]					       =					num_hidden_layers
      a						: Any					       =					num_attention_heads
      a						: Union[str, Any]					       =					initializer_range
      a						: Dict					       =					layer_norm_eps
      a						: Tuple					       =					patch_size
      a						: List[Any]					       =					num_channels
      a						: Any					       =					depths
      a						: List[str]					       =					mlp_expansion_ratio
      a						: Union[str, Any]					       =					downsamples
      a						: int					       =					dim
      a						: Dict					       =					key_dim
      a						: str					       =					attention_ratio
      a						: Any					       =					resolution
      a						: Any					       =					pool_size
      a						: Union[str, Any]					       =					downsample_patch_size
      a						: Optional[Any]					       =					downsample_stride
      a						: List[Any]					       =					downsample_pad
      a						: Optional[int]					       =					drop_path_rate
      a						: str					       =					num_metaad_blocks
      a						: int					       =					distillation
      a						: Dict					       =					use_layer_scale
      a						: List[str]					       =					layer_scale_init_value
      a						: List[str]					       =					image_size
      a						: Dict					       =					batch_norm_eps




'''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)