
"""simple docstring"""




from __future__ import annotations

import math

class 		a__						:
   def __init__(			self	,    _a				):
          lowercase    :				List[Any]       		=							size
          # approximate the overall size of segment tree with given value
          lowercase    :				Tuple       		=							[0 for i in range(0	,    4 * size				)]
          # create array to store lazy update
          lowercase    :				Optional[Any]       		=							[0 for i in range(0	,    4 * size				)]
          lowercase    :				List[str]       		=							[0 for i in range(0	,    4 * size				)]  # flag for lazy update
   def 			__magic_name__			(			self	,    _a				):
          return idx * 2
   def 			__magic_name__			(			self	,    _a				):
          return idx * 2 + 1
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a	,    _a				):
          if left_element == right_element:
                 lowercase    :				List[Any]       		=							a[left_element - 1]
          else:
                 lowercase    :				Union[str, Any]       		=							(left_element + right_element) // 2
                 self.build(self.left(_a				)	,    _a	,    _a	,    _a				)
                 self.build(self.right(_a				)	,    mid + 1	,    _a	,    _a				)
                 lowercase    :				Optional[Any]       		=							max(
                     self.segment_tree[self.left(_a				)]	,    self.segment_tree[self.right(_a				)]				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a				):
          if self.flag[idx] is True:
                 lowercase    :				Optional[Any]       		=							self.lazy[idx]
                 lowercase    :				Dict       		=							False
                 if left_element != right_element:
                        lowercase    :				List[str]       		=							self.lazy[idx]
                        lowercase    :				Optional[Any]       		=							self.lazy[idx]
                        lowercase    :				Optional[int]       		=							True
                        lowercase    :				int       		=							True

          if right_element < a or left_element > b:
                 return True
          if left_element >= a and right_element <= b:
                 lowercase    :				Any       		=							val
                 if left_element != right_element:
                        lowercase    :				int       		=							val
                        lowercase    :				Optional[Any]       		=							val
                        lowercase    :				List[str]       		=							True
                        lowercase    :				Dict       		=							True
                 return True
          lowercase    :				Dict       		=							(left_element + right_element) // 2
          self.update(self.left(_a				)	,    _a	,    _a	,    _a	,    _a	,    _a				)
          self.update(self.right(_a				)	,    mid + 1	,    _a	,    _a	,    _a	,    _a				)
          lowercase    :				Dict       		=							max(
              self.segment_tree[self.left(_a				)]	,    self.segment_tree[self.right(_a				)]				)
          return True
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a	,    _a	,    _a				):
          if self.flag[idx] is True:
                 lowercase    :				Union[str, Any]       		=							self.lazy[idx]
                 lowercase    :				Optional[Any]       		=							False
                 if left_element != right_element:
                        lowercase    :				Any       		=							self.lazy[idx]
                        lowercase    :				Dict       		=							self.lazy[idx]
                        lowercase    :				Any       		=							True
                        lowercase    :				Dict       		=							True
          if right_element < a or left_element > b:
                 return -math.inf
          if left_element >= a and right_element <= b:
                 return self.segment_tree[idx]
          lowercase    :				Union[str, Any]       		=							(left_element + right_element) // 2
          lowercase    :				Optional[Any]       		=							self.query(self.left(_a				)	,    _a	,    _a	,    _a	,    _a				)
          lowercase    :				str       		=							self.query(self.right(_a				)	,    mid + 1	,    _a	,    _a	,    _a				)
          return max(_a	,    _a				)

   def __str__(			self				):
          return str([self.query(1	,    1	,    self.size	,    _a	,    _a				) for i in range(1	,    self.size + 1				)]				)


if __name__ == "__main__":
   _A			:       int   		=       [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
   _A			:       str   		=       15
   _A			:       List[Any]   		=       SegmentTree(size)
   segt.build(1, 1, size, A)
   print(segt.query(1, 1, size, 4, 6))
   print(segt.query(1, 1, size, 7, 11))
   print(segt.query(1, 1, size, 7, 12))
   segt.update(1, 1, size, 1, 3, 1_11)
   print(segt.query(1, 1, size, 1, 15))
   segt.update(1, 1, size, 7, 8, 2_35)
   print(segt)




"""simple docstring"""




import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def   __magic_name__	(      __snake_case				:       List[str]     , __snake_case				:       Tuple     , __snake_case				:       Tuple  ) ->				int:
       lowercase    :				Union[str, Any]       		=							OmegaConf.load(__snake_case  )
       lowercase    :				int       		=							torch.load(__snake_case     , map_location="cpu"  )["model"]
       lowercase    :				Optional[Any]       		=							list(state_dict.keys()  )

       # extract state_dict for VQVAE
       lowercase    :				Optional[int]       		=							{}
       lowercase    :				Union[str, Any]       		=							"first_stage_model."
       for key in keys:
              if key.startswith(__snake_case  ):
                     lowercase    :				List[Any]       		=							state_dict[key]

    # extract state_dict for UNetLDM
       lowercase    :				Optional[int]       		=							{}
       lowercase    :				List[Any]       		=							"model.diffusion_model."
       for key in keys:
              if key.startswith(__snake_case  ):
                     lowercase    :				Optional[Any]       		=							state_dict[key]

       lowercase    :				Dict       		=							config.model.params.first_stage_config.params
       lowercase    :				List[str]       		=							config.model.params.unet_config.params

       lowercase    :				Union[str, Any]       		=							VQModel(**__snake_case  ).eval()
       vqvae.load_state_dict(__snake_case  )

       lowercase    :				List[Any]       		=							UNetLDMModel(**__snake_case  ).eval()
       unet.load_state_dict(__snake_case  )

       lowercase    :				Dict       		=							DDIMScheduler(
           timesteps=config.model.params.timesteps     , beta_schedule="scaled_linear"     , beta_start=config.model.params.linear_start     , beta_end=config.model.params.linear_end     , clip_sample=__snake_case     , )

       lowercase    :				Optional[Any]       		=							LDMPipeline(__snake_case     , __snake_case     , __snake_case  )
       pipeline.save_pretrained(__snake_case  )


if __name__ == "__main__":
   _A			:       Optional[Any]   		=       argparse.ArgumentParser()
   parser.add_argument("""--checkpoint_path""", type=str, required=True)
   parser.add_argument("""--config_path""", type=str, required=True)
   parser.add_argument("""--output_path""", type=str, required=True)
   _A			:       Dict   		=       parser.parse_args()

   convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)


