






'''simple docstring'''







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


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

_A     :      Dict   					={
    '''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/config.json''',
    '''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/config.json'''
    # See all FNet models at https://huggingface.co/models?filter=fnet
}

class _lowercase     (    _lowercase		):
							a    				=	"""fnet"""




							def __init__(							self:    Any      ,							UpperCamelCase__:    Optional[Any]=32_000      ,							UpperCamelCase__:    Tuple=768      ,							UpperCamelCase__:    Dict=12      ,							UpperCamelCase__:    List[Any]=3_072      ,							UpperCamelCase__:    List[str]="gelu_new"      ,							UpperCamelCase__:    List[Any]=0.1      ,							UpperCamelCase__:    Any=512      ,							UpperCamelCase__:    int=4      ,							UpperCamelCase__:    Tuple=0.02      ,							UpperCamelCase__:    int=1e-12      ,							UpperCamelCase__:    List[Any]=False      ,							UpperCamelCase__:    Optional[Any]=512      ,							UpperCamelCase__:    str=3      ,							UpperCamelCase__:    Dict=1      ,							UpperCamelCase__:    Optional[Any]=2      ,							**UpperCamelCase__:    Tuple      ,							):
														super().__init__(pad_token_id=UpperCamelCase__      ,							bos_token_id=UpperCamelCase__      ,							eos_token_id=UpperCamelCase__      ,							**UpperCamelCase__					)

														lowerCamelCase__ :     int	  =	vocab_size
														lowerCamelCase__ :     Optional[int]	  =	max_position_embeddings
														lowerCamelCase__ :     Optional[int]	  =	hidden_size
														lowerCamelCase__ :     Any	  =	num_hidden_layers
														lowerCamelCase__ :     Optional[int]	  =	intermediate_size
														lowerCamelCase__ :     str	  =	hidden_act
														lowerCamelCase__ :     List[Any]	  =	hidden_dropout_prob
														lowerCamelCase__ :     Any	  =	initializer_range
														lowerCamelCase__ :     Dict	  =	type_vocab_size
														lowerCamelCase__ :     List[str]	  =	layer_norm_eps
														lowerCamelCase__ :     Union[str, Any]	  =	use_tpu_fourier_optimizations
														lowerCamelCase__ :     int	  =	tpu_short_seq_length









'''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)

