"""simple docstring"""


from __future__ import annotations

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

UpperCAmelCase__  		=						TypeVar("""T""")


class        a				(		Generic[T]   ):


    def __init__(					self	:  str   ,				__lowerCAmelCase	:  list[T]   ,				__lowerCAmelCase	:  Callable[[T, T], T]					):
      _UpperCAmelCase						 =      None

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


    def      lowerCAmelCase_  (					self	:  Union[str, Any]					):
      for p in range(self.N - 1   ,				0   ,				-1					):
        _UpperCAmelCase						 =      self.fn(self.st[p * 2]   ,				self.st[p * 2 + 1]					)


    def      lowerCAmelCase_  (					self	:  Any   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  T					):
      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	:  int   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  int					):  # 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, 1_0, -2, 9, -3, 8, 4, -7, 5, 6, 1_1, -1_2]

    UpperCAmelCase__  		=						{
        0: 7,
        1: 2,
        2: 6,
        3: -1_4,
        4: 5,
        5: 4,
        6: 7,
        7: -1_0,
        8: 9,
        9: 1_0,
        1_0: 1_2,
        1_1: 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(lowercase		)		):
        for j in range(lowercase   ,len(lowercase		)		):
          _UpperCAmelCase						 =      reduce(lowercase   ,test_array[i : j + 1]		)
          _UpperCAmelCase						 =      reduce(lowercase   ,test_array[i : j + 1]		)
          _UpperCAmelCase						 =      reduce(lambda lowercase   ,lowercase		: a + b   ,test_array[i : j + 1]		)
          assert min_range == min_segment_tree.query(lowercase   ,lowercase		)
          assert max_range == max_segment_tree.query(lowercase   ,lowercase		)
          assert sum_range == sum_segment_tree.query(lowercase   ,lowercase		)

    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()

"""simple docstring"""


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)
