






'''simple docstring'''







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


_A     :      int   					=logging.get_logger(__name__)

_A     :      List[Any]   					={
    '''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}

class _lowercase     (    _lowercase		):
							a    				=	"""transfo-xl"""
							a    				=	["""mems"""]
							a    				=	{
							    """n_token""": """vocab_size""",
							    """hidden_size""": """d_model""",
							    """num_attention_heads""": """n_head""",
							    """num_hidden_layers""": """n_layer""",
							}




							def __init__(							self:    str      ,							UpperCamelCase__:    Any=267_735      ,							UpperCamelCase__:    Union[str, Any]=[20_000, 40_000, 200_000]      ,							UpperCamelCase__:    Tuple=1_024      ,							UpperCamelCase__:    Union[str, Any]=1_024      ,							UpperCamelCase__:    Optional[Any]=16      ,							UpperCamelCase__:    List[Any]=64      ,							UpperCamelCase__:    Tuple=4_096      ,							UpperCamelCase__:    List[str]=4      ,							UpperCamelCase__:    Optional[int]=False      ,							UpperCamelCase__:    str=18      ,							UpperCamelCase__:    List[Any]=1_600      ,							UpperCamelCase__:    Union[str, Any]=1_000      ,							UpperCamelCase__:    Any=True      ,							UpperCamelCase__:    List[str]=True      ,							UpperCamelCase__:    List[str]=0      ,							UpperCamelCase__:    Optional[int]=-1      ,							UpperCamelCase__:    Union[str, Any]=True      ,							UpperCamelCase__:    str=0.1      ,							UpperCamelCase__:    int=0.0      ,							UpperCamelCase__:    Tuple=True      ,							UpperCamelCase__:    Optional[Any]="normal"      ,							UpperCamelCase__:    List[Any]=0.01      ,							UpperCamelCase__:    Any=0.01      ,							UpperCamelCase__:    Any=0.02      ,							UpperCamelCase__:    Any=1e-5      ,							UpperCamelCase__:    int=0      ,							**UpperCamelCase__:    Optional[Any]      ,							):
														lowerCamelCase__ :     Any	  =	vocab_size
														lowerCamelCase__ :     List[Any]	  =	[]
														self.cutoffs.extend(UpperCamelCase__					)
														if proj_share_all_but_first:
																					lowerCamelCase__ :     Optional[int]	  =	[False] + [True] * len(self.cutoffs					)
														else:
																					lowerCamelCase__ :     Dict	  =	[False] + [False] * len(self.cutoffs					)
														lowerCamelCase__ :     Optional[Any]	  =	d_model
														lowerCamelCase__ :     Dict	  =	d_embed
														lowerCamelCase__ :     List[str]	  =	d_head
														lowerCamelCase__ :     Tuple	  =	d_inner
														lowerCamelCase__ :     Optional[Any]	  =	div_val
														lowerCamelCase__ :     Union[str, Any]	  =	pre_lnorm
														lowerCamelCase__ :     List[str]	  =	n_layer
														lowerCamelCase__ :     Optional[int]	  =	n_head
														lowerCamelCase__ :     int	  =	mem_len
														lowerCamelCase__ :     List[str]	  =	same_length
														lowerCamelCase__ :     Optional[Any]	  =	attn_type
														lowerCamelCase__ :     Tuple	  =	clamp_len
														lowerCamelCase__ :     int	  =	sample_softmax
														lowerCamelCase__ :     Union[str, Any]	  =	adaptive
														lowerCamelCase__ :     List[Any]	  =	dropout
														lowerCamelCase__ :     Dict	  =	dropatt
														lowerCamelCase__ :     Optional[Any]	  =	untie_r
														lowerCamelCase__ :     List[str]	  =	init
														lowerCamelCase__ :     int	  =	init_range
														lowerCamelCase__ :     Optional[int]	  =	proj_init_std
														lowerCamelCase__ :     str	  =	init_std
														lowerCamelCase__ :     Optional[Any]	  =	layer_norm_epsilon
														super().__init__(eos_token_id=UpperCamelCase__      ,							**UpperCamelCase__					)




							@property
							def 		lowerCamelCase_    (							self:    int					):
														# Message copied from Transformer-XL documentation
														logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''					)
														return -1




							@max_position_embeddings.setter
							def 		lowerCamelCase_    (							self:    List[str]      ,							UpperCamelCase__:    Union[str, Any]					):
														# Message copied from Transformer-XL documentation
														raise NotImplementedError(
														    F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''					)









'''simple docstring'''







import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
			_A     :      List[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.''',
			)

			_A     :      Dict   					=parser.parse_args()

			_A     :      List[str]   					=UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

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

			_A     :      Union[str, Any]   					=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)

