

'''simple docstring'''


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


lowerCAmelCase__						:       int										=						logging.get_logger(__name__)

lowerCAmelCase__						:       Dict										=						{
    """naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json""",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}




class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    """donut-swin"""

						__UpperCAmelCase			     =    {
						    """num_attention_heads""": """num_heads""",
						    """num_hidden_layers""": """num_layers""",
						}







						def __init__(							self					:      Union[str, Any]			,			snake_case_					:      Any=2_2_4			,			snake_case_					:      Tuple=4			,			snake_case_					:      Any=3			,			snake_case_					:      str=9_6			,			snake_case_					:      Optional[int]=[2, 2, 6, 2]			,			snake_case_					:      Any=[3, 6, 1_2, 2_4]			,			snake_case_					:      List[Any]=7			,			snake_case_					:      Optional[int]=4.0			,			snake_case_					:      Union[str, Any]=True			,			snake_case_					:      List[Any]=0.0			,			snake_case_					:      int=0.0			,			snake_case_					:      Union[str, Any]=0.1			,			snake_case_					:      Optional[int]="gelu"			,			snake_case_					:      Any=False			,			snake_case_					:      Dict=0.0_2			,			snake_case_					:      Optional[int]=1e-5			,			**snake_case_					:      Dict			,			):



								'''simple docstring'''



								super().__init__(**snake_case_  )

								snake_case__						:       str       						=  image_size
								snake_case__						:       str       						=  patch_size
								snake_case__						:       str       						=  num_channels
								snake_case__						:       Dict       						=  embed_dim
								snake_case__						:       int       						=  depths
								snake_case__						:       List[str]       						=  len(snake_case_  )
								snake_case__						:       List[str]       						=  num_heads
								snake_case__						:       int       						=  window_size
								snake_case__						:       Union[str, Any]       						=  mlp_ratio
								snake_case__						:       int       						=  qkv_bias
								snake_case__						:       Optional[Any]       						=  hidden_dropout_prob
								snake_case__						:       Tuple       						=  attention_probs_dropout_prob
								snake_case__						:       List[Any]       						=  drop_path_rate
								snake_case__						:       Optional[int]       						=  hidden_act
								snake_case__						:       List[Any]       						=  use_absolute_embeddings
								snake_case__						:       Optional[int]       						=  layer_norm_eps
								snake_case__						:       Optional[Any]       						=  initializer_range
								# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								snake_case__						:       Optional[int]       						=  int(embed_dim * 2 ** (len(snake_case_  ) - 1)  )






'''simple docstring'''


import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
					lowerCAmelCase__						:       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.""",
					)

					lowerCAmelCase__						:       List[Any]										=						parser.parse_args()

					lowerCAmelCase__						:       Optional[Any]										=						UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

					lowerCAmelCase__						:       Optional[int]										=						CLIPImageProcessor()
					lowerCAmelCase__						:       str										=						CLIPVisionModelWithProjection.from_pretrained("""openai/clip-vit-large-patch14""")

					lowerCAmelCase__						:       Dict										=						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)



