
'''simple docstring'''

import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
		UpperCAmelCase__ :			Dict											=		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.",
		)

		UpperCAmelCase__ :			str											=		parser.parse_args()

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

		UpperCAmelCase__ :			str											=		CLIPImageProcessor()
		UpperCAmelCase__ :			List[Any]											=		CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")

		UpperCAmelCase__ :			List[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)






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


SCREAMING_SNAKE_CASE			:						int				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						Dict				  =		{
    """google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """canine"""

  def __init__(		self			:   int  ,				__SCREAMING_SNAKE_CASE			:   List[str]=7_68  ,				__SCREAMING_SNAKE_CASE			:   Any=12  ,				__SCREAMING_SNAKE_CASE			:   str=12  ,				__SCREAMING_SNAKE_CASE			:   int=30_72  ,				__SCREAMING_SNAKE_CASE			:   int="gelu"  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.1  ,				__SCREAMING_SNAKE_CASE			:   List[str]=0.1  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=1_63_84  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=16  ,				__SCREAMING_SNAKE_CASE			:   int=0.02  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=1E-12  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=0  ,				__SCREAMING_SNAKE_CASE			:   List[str]=0xE000  ,				__SCREAMING_SNAKE_CASE			:   int=0xE001  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=4  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=4  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=8  ,				__SCREAMING_SNAKE_CASE			:   List[str]=1_63_84  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=1_28  ,				**__SCREAMING_SNAKE_CASE			:   List[str]  ,				):
   super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE  ,				bos_token_id=__SCREAMING_SNAKE_CASE  ,				eos_token_id=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   __a							=	max_position_embeddings
   __a							=	hidden_size
   __a							=	num_hidden_layers
   __a							=	num_attention_heads
   __a							=	intermediate_size
   __a							=	hidden_act
   __a							=	hidden_dropout_prob
   __a							=	attention_probs_dropout_prob
   __a							=	initializer_range
   __a							=	type_vocab_size
   __a							=	layer_norm_eps

   # Character config:
   __a							=	downsampling_rate
   __a							=	upsampling_kernel_size
   __a							=	num_hash_functions
   __a							=	num_hash_buckets
   __a							=	local_transformer_stride


