


"""simple docstring"""
import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:      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.',
				)

				__SCREAMING_SNAKE_CASE							:      Tuple						=      parser.parse_args()

				__SCREAMING_SNAKE_CASE							:      Optional[int]						=      UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

				__SCREAMING_SNAKE_CASE							:      Dict						=      CLIPImageProcessor()
				__SCREAMING_SNAKE_CASE							:      Optional[int]						=      CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

				__SCREAMING_SNAKE_CASE							:      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)







"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE							:      Union[str, Any]						=      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							:      int						=      {
    '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       (snake_case__):





							'''simple docstring'''

							__lowercase:						List[str]							 =      """canine"""



							def __init__(			self							: Union[str, Any]						,    UpperCAmelCase_							: str=768						,    UpperCAmelCase_							: Union[str, Any]=12						,    UpperCAmelCase_							: Optional[Any]=12						,    UpperCAmelCase_							: Optional[Any]=3_072						,    UpperCAmelCase_							: List[Any]="gelu"						,    UpperCAmelCase_							: int=0.1						,    UpperCAmelCase_							: Any=0.1						,    UpperCAmelCase_							: List[str]=16_384						,    UpperCAmelCase_							: Tuple=16						,    UpperCAmelCase_							: int=0.02						,    UpperCAmelCase_							: Tuple=1E-12						,    UpperCAmelCase_							: str=0						,    UpperCAmelCase_							: int=0XE000						,    UpperCAmelCase_							: Optional[int]=0XE001						,    UpperCAmelCase_							: Dict=4						,    UpperCAmelCase_							: List[Any]=4						,    UpperCAmelCase_							: List[Any]=8						,    UpperCAmelCase_							: Dict=16_384						,    UpperCAmelCase_							: Optional[int]=128						,    **UpperCAmelCase_							: Any						,    )   ->int:



														"""simple docstring"""



														super().__init__(pad_token_id=UpperCAmelCase_						,    bos_token_id=UpperCAmelCase_						,    eos_token_id=UpperCAmelCase_						,    **UpperCAmelCase_    )

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

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



