






"""simple docstring"""



# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


__A		:       List[str]  				=		re.compile(R'''^(?P<major>\d+)''' R'''\.(?P<minor>\d+)''' R'''\.(?P<patch>\d+)$''')





@total_ordering
@dataclass
class 					_UpperCAmelCase       :
					SCREAMING_SNAKE_CASE_		:				str
					SCREAMING_SNAKE_CASE_		:				Optional[str]							    =   None
					SCREAMING_SNAKE_CASE_		:				Optional[Union[str, int]]							    =   None
					SCREAMING_SNAKE_CASE_		:				Optional[Union[str, int]]							    =   None
					SCREAMING_SNAKE_CASE_		:				Optional[Union[str, int]]							    =   None




					def   A  (				self     :						Optional[int]   )  ->  Union[str, Any]:
							lowercase_       ,					lowercase_       ,					lowercase_ : Union[str, Any]							       =   _str_to_version_tuple(self.version_str   )




					def __repr__(				self     :						int   )  ->  List[Any]:
							return F'''{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}'''




					@property
					def   A  (				self     :						Any   )  ->  Union[str, Any]:
							return self.major, self.minor, self.patch




					def   A  (				self     :						Dict    ,						A     :						List[Any]   )  ->  Tuple:
							if isinstance(A    ,						A   ):
									return Version(A   )
							elif isinstance(A    ,						A   ):
									return other
							raise TypeError(F'''{other} (type {type(A   )}) cannot be compared to version.'''   )




					def __eq__(				self     :						Union[str, Any]    ,						A     :						str   )  ->  List[str]:
							try:
									lowercase_ : Optional[int]							       =   self._validate_operand(A   )
							except (TypeError, ValueError):
									return False
							else:
									return self.tuple == other.tuple




					def __lt__(				self     :						int    ,						A     :						str   )  ->  Union[str, Any]:
							lowercase_ : Optional[Any]							       =   self._validate_operand(A   )
							return self.tuple < other.tuple




					def __hash__(				self     :						Dict   )  ->  Optional[Any]:
							return hash(_version_tuple_to_str(self.tuple   )   )




					@classmethod
					def   A  (				cls     :						int    ,						A     :						List[Any]   )  ->  List[str]:
							lowercase_ : Dict							       =   {f.name for f in dataclasses.fields(cls   )}
							return cls(**{k: v for k, v in dic.items() if k in field_names}   )






					def   A  (				self     :						Optional[int]   )  ->  str:
							return self.version_str



def 						lowercase    (				__snake_case				: Any	):
		lowercase_ : int							       =   _VERSION_REG.match(__snake_case	)
		if not res:
				raise ValueError(F'''Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits.'''	)
		return tuple(int(__snake_case	) for v in [res.group('''major'''	), res.group('''minor'''	), res.group('''patch'''	)]	)






def 						lowercase    (				__snake_case				: int	):
		return ".".join(str(__snake_case	) for v in version_tuple	)











"""simple docstring"""



import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
						__A		:       str  				=		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.''',
						)

						__A		:       str  				=		parser.parse_args()

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

						__A		:       Dict  				=		CLIPImageProcessor()
						__A		:       Union[str, Any]  				=		CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

						__A		:       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)



