





from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

UpperCamelCase__											= TypeVar("T")

class        __SCREAMING_SNAKE_CASE					(     Generic[T]		):






							def __init__(						self  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			None

														UpperCamelCase__				     =			len(__lowerCAmelCase  )
														UpperCamelCase__				     =			[any_type for _ in range(self.N  )] + arr
														UpperCamelCase__				     =			fnc
														self.build()






							def 				_lowerCamelCase		(						self  ):
														for p in range(self.N - 1  , 0  , -1  ):
																					UpperCamelCase__				     =			self.fn(self.st[p * 2]  , self.st[p * 2 + 1]  )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  ):
														p += self.N
														UpperCamelCase__				     =			v
														while p > 1:
																					UpperCamelCase__				     =			p // 2
																					UpperCamelCase__				     =			self.fn(self.st[p * 2]  , self.st[p * 2 + 1]  )







							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  ):  # noqa: E741
														UpperCamelCase__     ,     UpperCamelCase__				     =			l + self.N, r + self.N

														UpperCamelCase__				     =			None
														while l <= r:
																					if l % 2 == 1:
																												UpperCamelCase__				     =			self.st[l] if res is None else self.fn(__lowerCAmelCase  , self.st[l]  )
																					if r % 2 == 0:
																												UpperCamelCase__				     =			self.st[r] if res is None else self.fn(__lowerCAmelCase  , self.st[r]  )
																					UpperCamelCase__     ,     UpperCamelCase__				     =			(l + 1) // 2, (r - 1) // 2
														return res


if __name__ == "__main__":
					from functools import reduce

					UpperCamelCase__											= [1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]

					UpperCamelCase__											= {
					    0: 7,
					    1: 2,
					    2: 6,
					    3: -14,
					    4: 5,
					    5: 4,
					    6: 7,
					    7: -10,
					    8: 9,
					    9: 10,
					    10: 12,
					    11: 1,
					}

					UpperCamelCase__											= SegmentTree(test_array, min)
					UpperCamelCase__											= SegmentTree(test_array, max)
					UpperCamelCase__											= SegmentTree(test_array, lambda a, b: a + b)





					def     _UpperCamelCase     ():






												"""simple docstring"""





												for i in range(len(a__       )       ):
																			for j in range(a__   ,			len(a__       )       ):
																										UpperCamelCase__				     =			reduce(a__   ,			test_array[i : j + 1]       )
																										UpperCamelCase__				     =			reduce(a__   ,			test_array[i : j + 1]       )
																										UpperCamelCase__				     =			reduce(lambda a__   ,			a__       : a + b   ,			test_array[i : j + 1]       )
																										assert min_range == min_segment_tree.query(a__   ,			a__       )
																										assert max_range == max_segment_tree.query(a__   ,			a__       )
																										assert sum_range == sum_segment_tree.query(a__   ,			a__       )

					test_all_segments()

					for index, value in test_updates.items():
										UpperCamelCase__											= value
										min_segment_tree.update(index, value)
										max_segment_tree.update(index, value)
										sum_segment_tree.update(index, value)
										test_all_segments()











import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
					UpperCamelCase__											= 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__											= parser.parse_args()

					UpperCamelCase__											= UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

					UpperCamelCase__											= CLIPImageProcessor()
					UpperCamelCase__											= CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")

					UpperCamelCase__											= 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)




