






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


a_							=			logging.get_logger(__name__)

a_							=			{
    '''facebook/convnextv2-tiny-1k-224''': '''https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json''',
}


class    lowercase__   (	lowerCamelCase__,    lowerCamelCase__		):
	a_   			='convnextv2'

	def __init__(							self						,			__UpperCAmelCase=3						,			__UpperCAmelCase=4						,			__UpperCAmelCase=4						,			__UpperCAmelCase=None						,			__UpperCAmelCase=None						,			__UpperCAmelCase="gelu"						,			__UpperCAmelCase=0.02						,			__UpperCAmelCase=1E-1_2						,			__UpperCAmelCase=0.0						,			__UpperCAmelCase=224						,			__UpperCAmelCase=None						,			__UpperCAmelCase=None						,			**__UpperCAmelCase						,			)->    List[Any]:







							'''simple docstring'''





							super().__init__(**lowercase__				)

							lowerCAmelCase__       					=  num_channels
							lowerCAmelCase__       					=  patch_size
							lowerCAmelCase__       					=  num_stages
							lowerCAmelCase__       					=  [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
							lowerCAmelCase__       					=  [3, 3, 9, 3] if depths is None else depths
							lowerCAmelCase__       					=  hidden_act
							lowerCAmelCase__       					=  initializer_range
							lowerCAmelCase__       					=  layer_norm_eps
							lowerCAmelCase__       					=  drop_path_rate
							lowerCAmelCase__       					=  image_size
							lowerCAmelCase__       					=  ["stem"] + [F"stage{idx}" for idx in range(1						,			len(self.depths				) + 1				)]
							lowerCAmelCase__					,							lowerCAmelCase__       					=  get_aligned_output_features_output_indices(
							    out_features=lowercase__						,			out_indices=lowercase__						,			stage_names=self.stage_names				)









'''simple docstring'''






import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
 lowerCAmelCase__											=	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__											=	parser.parse_args()

 lowerCAmelCase__											=	UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

 lowerCAmelCase__											=	CLIPImageProcessor()
 lowerCAmelCase__											=	CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

 lowerCAmelCase__											=	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)

