


from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass





@dataclass
class        snake_case__	:
	UpperCAmelCase :				Any       =			42
	UpperCAmelCase :				str       =			None
	UpperCAmelCase :				int       =			None





SCREAMING_SNAKE_CASE			:			List[str]        =     namedtuple("CoinsDistribResult", "moves excess")





def     _lowerCamelCase		(				SCREAMING_SNAKE_CASE_					:			List[str]   ):






							"""simple docstring"""



							if root is None:
														return 0

							# Validation
							def count_nodes(SCREAMING_SNAKE_CASE_					:			Tuple   ) -> int:
														if node is None:
																					return 0

														return count_nodes(node.left   ) + count_nodes(node.right   ) + 1

							def count_coins(SCREAMING_SNAKE_CASE_					:			List[Any]   ) -> int:
														if node is None:
																					return 0

														return count_coins(node.left   ) + count_coins(node.right   ) + node.data

							if count_nodes(__a   ) != count_coins(__a   ):
														raise ValueError("""The nodes number should be same as the number of coins"""   )

							# Main calculation
							def get_distrib(SCREAMING_SNAKE_CASE_					:			int   ) -> CoinsDistribResult:

														if node is None:
																					return CoinsDistribResult(0						,						1   )

														a_							: Optional[Any]								=						get_distrib(node.left   )
														a_							: Union[str, Any]								=						get_distrib(node.right   )

														a_							: Tuple								=						1 - left_distrib_excess
														a_							: Optional[int]								=						1 - right_distrib_excess

														a_							: int								=						(
														    left_distrib_moves
														    + right_distrib_moves
														    + abs(__a   )
														    + abs(__a   )
														)
														a_							: Optional[Any]								=						node.data - coins_to_left - coins_to_right

														return CoinsDistribResult(__a						,						__a   )

							return get_distrib(__a   )[0]


if __name__ == "__main__":
	import doctest

	doctest.testmod()






'''simple docstring'''
import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


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

		__snake_case				 =	parser.parse_args()

		__snake_case				 =	UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

		__snake_case				 =	CLIPImageProcessor()
		__snake_case				 =	CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

		__snake_case				 =	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)