





'''simple docstring'''




from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder







@dataclass
class   A_	(  A_			):
 _lowerCamelCase  : "DiagonalGaussianDistribution"







class   A_	(  A_							,						A_			):
 _lowerCamelCase  : List[Any]           =							True




 @register_to_config
 def __init__(					self       :    Optional[Any]	,			snake_case_       :    Dict = 3	,			snake_case_       :    str = 3	,			snake_case_       :    Optional[int] = ("DownEncoderBlock2D",)	,			snake_case_       :    int = ("UpDecoderBlock2D",)	,			snake_case_       :    Optional[Any] = (6_4,)	,			snake_case_       :    int = 1	,			snake_case_       :    Optional[int] = "silu"	,			snake_case_       :    int = 4	,			snake_case_       :    List[str] = 3_2	,			snake_case_       :    Union[str, Any] = 3_2	,			snake_case_       :    Tuple = 0.1_8_2_1_5	,			):
      super().__init__()

      # pass init params to Encoder
      _UpperCAmelCase     							=   Encoder(
          in_channels=A_	,			out_channels=A_	,			down_block_types=A_	,			block_out_channels=A_	,			layers_per_block=A_	,			act_fn=A_	,			norm_num_groups=A_	,			double_z=A_	,			)

      # pass init params to Decoder
      _UpperCAmelCase     							=   Decoder(
          in_channels=A_	,			out_channels=A_	,			up_block_types=A_	,			block_out_channels=A_	,			layers_per_block=A_	,			norm_num_groups=A_	,			act_fn=A_	,			)

      _UpperCAmelCase     							=   nn.Convad(2 * latent_channels	,			2 * latent_channels	,			1    )
      _UpperCAmelCase     							=   nn.Convad(A_	,			A_	,			1    )

      _UpperCAmelCase     							=   False
      _UpperCAmelCase     							=   False

      # only relevant if vae tiling is enabled
      _UpperCAmelCase     							=   self.config.sample_size
      _UpperCAmelCase     							=   (
          self.config.sample_size[0]
          if isinstance(self.config.sample_size	,			(list, tuple)    )
          else self.config.sample_size
      )
      _UpperCAmelCase     							=   int(sample_size / (2 ** (len(self.config.block_out_channels    ) - 1))    )
      _UpperCAmelCase     							=   0.2_5




 def      lowercase	(					self       :    Tuple	,			snake_case_       :    str	,			snake_case_       :    Optional[int]=False    ):
      if isinstance(A_	,			(Encoder, Decoder)    ):
           _UpperCAmelCase     							=   value




 def      lowercase	(					self       :    Dict	,			snake_case_       :    str = True    ):
      _UpperCAmelCase     							=   use_tiling




 def      lowercase	(					self       :    Any    ):
      self.enable_tiling(A_    )




 def      lowercase	(					self       :    int    ):
      _UpperCAmelCase     							=   True




 def      lowercase	(					self       :    Optional[Any]    ):
      _UpperCAmelCase     							=   False




 @property
 # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
 def      lowercase	(					self       :    Tuple    ):
      _UpperCAmelCase     							=   {}

      def fn_recursive_add_processors(snake_case_       :    Any	,			snake_case_       :    Tuple	,			snake_case_       :    List[Any]    ):
           if hasattr(A_	,			"set_processor"    ):
                _UpperCAmelCase     							=   module.processor

           for sub_name, child in module.named_children():
                fn_recursive_add_processors(f'{name}.{sub_name}'	,			A_	,			A_    )

           return processors

      for name, module in self.named_children():
           fn_recursive_add_processors(A_	,			A_	,			A_    )

      return processors




 def      lowercase	(					self       :    Optional[int]	,			snake_case_       :    Dict    ):
      _UpperCAmelCase     							=   len(self.attn_processors.keys()    )

      if isinstance(A_	,			A_    ) and len(A_    ) != count:
           raise ValueError(
               f'A dict of processors was passed, but the number of processors {len(A_    )} does not match the'
               f' number of attention layers: {count}. Please make sure to pass {count} processor classes.'    )

      def fn_recursive_attn_processor(snake_case_       :    int	,			snake_case_       :    List[str]	,			snake_case_       :    Tuple    ):
           if hasattr(A_	,			"set_processor"    ):
                if not isinstance(A_	,			A_    ):
                     module.set_processor(A_    )
                else:
                     module.set_processor(processor.pop(f'{name}.processor'    )    )

           for sub_name, child in module.named_children():
                fn_recursive_attn_processor(f'{name}.{sub_name}'	,			A_	,			A_    )

      for name, module in self.named_children():
           fn_recursive_attn_processor(A_	,			A_	,			A_    )




 def      lowercase	(					self       :    Union[str, Any]    ):
      self.set_attn_processor(AttnProcessor()    )




 @apply_forward_hook
 def      lowercase	(					self       :    Dict	,			snake_case_       :    List[Any]	,			snake_case_       :    List[str] = True    ):
      if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
           return self.tiled_encode(A_	,			return_dict=A_    )

      if self.use_slicing and x.shape[0] > 1:
           _UpperCAmelCase     							=   [self.encoder(A_    ) for x_slice in x.split(1    )]
           _UpperCAmelCase     							=   torch.cat(A_    )
      else:
           _UpperCAmelCase     							=   self.encoder(A_    )

      _UpperCAmelCase     							=   self.quant_conv(A_    )
      _UpperCAmelCase     							=   DiagonalGaussianDistribution(A_    )

      if not return_dict:
           return (posterior,)

      return AutoencoderKLOutput(latent_dist=A_    )




 def      lowercase	(					self       :    int	,			snake_case_       :    Union[str, Any]	,			snake_case_       :    Optional[Any] = True    ):
      if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
           return self.tiled_decode(A_	,			return_dict=A_    )

      _UpperCAmelCase     							=   self.post_quant_conv(A_    )
      _UpperCAmelCase     							=   self.decoder(A_    )

      if not return_dict:
           return (dec,)

      return DecoderOutput(sample=A_    )




 @apply_forward_hook
 def      lowercase	(					self       :    Optional[int]	,			snake_case_       :    Union[str, Any]	,			snake_case_       :    List[Any] = True    ):
      if self.use_slicing and z.shape[0] > 1:
           _UpperCAmelCase     							=   [self._decode(A_    ).sample for z_slice in z.split(1    )]
           _UpperCAmelCase     							=   torch.cat(A_    )
      else:
           _UpperCAmelCase     							=   self._decode(A_    ).sample

      if not return_dict:
           return (decoded,)

      return DecoderOutput(sample=A_    )




 def      lowercase	(					self       :    Dict	,			snake_case_       :    int	,			snake_case_       :    Dict	,			snake_case_       :    Dict    ):
      _UpperCAmelCase     							=   min(a.shape[2]	,			b.shape[2]	,			A_    )
      for y in range(A_    ):
           _UpperCAmelCase     							=   a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
      return b




 def      lowercase	(					self       :    Tuple	,			snake_case_       :    Optional[Any]	,			snake_case_       :    str	,			snake_case_       :    List[Any]    ):
      _UpperCAmelCase     							=   min(a.shape[3]	,			b.shape[3]	,			A_    )
      for x in range(A_    ):
           _UpperCAmelCase     							=   a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
      return b




 def      lowercase	(					self       :    Tuple	,			snake_case_       :    int	,			snake_case_       :    Optional[int] = True    ):
      _UpperCAmelCase     							=   int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)    )
      _UpperCAmelCase     							=   int(self.tile_latent_min_size * self.tile_overlap_factor    )
      _UpperCAmelCase     							=   self.tile_latent_min_size - blend_extent

      # Split the image into 512x512 tiles and encode them separately.
      _UpperCAmelCase     							=   []
      for i in range(0	,			x.shape[2]	,			A_    ):
           _UpperCAmelCase     							=   []
           for j in range(0	,			x.shape[3]	,			A_    ):
                _UpperCAmelCase     							=   x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
                _UpperCAmelCase     							=   self.encoder(A_    )
                _UpperCAmelCase     							=   self.quant_conv(A_    )
                row.append(A_    )
           rows.append(A_    )
      _UpperCAmelCase     							=   []
      for i, row in enumerate(A_    ):
           _UpperCAmelCase     							=   []
           for j, tile in enumerate(A_    ):
                # blend the above tile and the left tile
                # to the current tile and add the current tile to the result row
                if i > 0:
                     _UpperCAmelCase     							=   self.blend_v(rows[i - 1][j]	,			A_	,			A_    )
                if j > 0:
                     _UpperCAmelCase     							=   self.blend_h(row[j - 1]	,			A_	,			A_    )
                result_row.append(tile[:, :, :row_limit, :row_limit]    )
           result_rows.append(torch.cat(A_	,			dim=3    )    )

      _UpperCAmelCase     							=   torch.cat(A_	,			dim=2    )
      _UpperCAmelCase     							=   DiagonalGaussianDistribution(A_    )

      if not return_dict:
           return (posterior,)

      return AutoencoderKLOutput(latent_dist=A_    )




 def      lowercase	(					self       :    Any	,			snake_case_       :    List[str]	,			snake_case_       :    str = True    ):
      _UpperCAmelCase     							=   int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)    )
      _UpperCAmelCase     							=   int(self.tile_sample_min_size * self.tile_overlap_factor    )
      _UpperCAmelCase     							=   self.tile_sample_min_size - blend_extent

      # Split z into overlapping 64x64 tiles and decode them separately.
      # The tiles have an overlap to avoid seams between tiles.
      _UpperCAmelCase     							=   []
      for i in range(0	,			z.shape[2]	,			A_    ):
           _UpperCAmelCase     							=   []
           for j in range(0	,			z.shape[3]	,			A_    ):
                _UpperCAmelCase     							=   z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
                _UpperCAmelCase     							=   self.post_quant_conv(A_    )
                _UpperCAmelCase     							=   self.decoder(A_    )
                row.append(A_    )
           rows.append(A_    )
      _UpperCAmelCase     							=   []
      for i, row in enumerate(A_    ):
           _UpperCAmelCase     							=   []
           for j, tile in enumerate(A_    ):
                # blend the above tile and the left tile
                # to the current tile and add the current tile to the result row
                if i > 0:
                     _UpperCAmelCase     							=   self.blend_v(rows[i - 1][j]	,			A_	,			A_    )
                if j > 0:
                     _UpperCAmelCase     							=   self.blend_h(row[j - 1]	,			A_	,			A_    )
                result_row.append(tile[:, :, :row_limit, :row_limit]    )
           result_rows.append(torch.cat(A_	,			dim=3    )    )

      _UpperCAmelCase     							=   torch.cat(A_	,			dim=2    )
      if not return_dict:
           return (dec,)

      return DecoderOutput(sample=A_    )




 def      lowercase	(					self       :    List[Any]	,			snake_case_       :    List[Any]	,			snake_case_       :    str = False	,			snake_case_       :    Dict = True	,			snake_case_       :    str = None	,			):
      _UpperCAmelCase     							=   sample
      _UpperCAmelCase     							=   self.encode(A_    ).latent_dist
      if sample_posterior:
           _UpperCAmelCase     							=   posterior.sample(generator=A_    )
      else:
           _UpperCAmelCase     							=   posterior.mode()
      _UpperCAmelCase     							=   self.decode(A_    ).sample

      if not return_dict:
           return (dec,)

      return DecoderOutput(sample=A_    )








from __future__ import annotations

import math






class     UpperCAmelCase__       :



    """simple docstring"""






    def __init__(			self					,   A_    )  ->       None:
         __UpperCamelCase    							=size
         # approximate the overall size of segment tree with given value
         __UpperCamelCase    							=[0 for i in range(0					,   4 * size    )]
         # create array to store lazy update
         __UpperCamelCase    							=[0 for i in range(0					,   4 * size    )]
         __UpperCamelCase    							=[0 for i in range(0					,   4 * size    )]  # flag for lazy update






    def _a					(			self					,   A_    )  ->       int:
         return idx * 2






    def _a					(			self					,   A_    )  ->       int:
         return idx * 2 + 1






    def _a					(			self					,   A_					,   A_					,   A_					,   A_    )  ->       None:
         if left_element == right_element:
              __UpperCamelCase    							=a[left_element - 1]
         else:
              __UpperCamelCase    							=(left_element + right_element) // 2
              self.build(self.left(A_    )					,   A_					,   A_					,   A_    )
              self.build(self.right(A_    )					,   mid + 1					,   A_					,   A_    )
              __UpperCamelCase    							=max(
                  self.segment_tree[self.left(A_    )]					,   self.segment_tree[self.right(A_    )]    )






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_    )  ->       bool:
         if self.flag[idx] is True:
              __UpperCamelCase    							=self.lazy[idx]
              __UpperCamelCase    							=False
              if left_element != right_element:
                   __UpperCamelCase    							=self.lazy[idx]
                   __UpperCamelCase    							=self.lazy[idx]
                   __UpperCamelCase    							=True
                   __UpperCamelCase    							=True

         if right_element < a or left_element > b:
              return True
         if left_element >= a and right_element <= b:
              __UpperCamelCase    							=val
              if left_element != right_element:
                   __UpperCamelCase    							=val
                   __UpperCamelCase    							=val
                   __UpperCamelCase    							=True
                   __UpperCamelCase    							=True
              return True
         __UpperCamelCase    							=(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_    )
         __UpperCamelCase    							=max(
             self.segment_tree[self.left(A_    )]					,   self.segment_tree[self.right(A_    )]    )
         return True






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_    )  ->       int | float:
         if self.flag[idx] is True:
              __UpperCamelCase    							=self.lazy[idx]
              __UpperCamelCase    							=False
              if left_element != right_element:
                   __UpperCamelCase    							=self.lazy[idx]
                   __UpperCamelCase    							=self.lazy[idx]
                   __UpperCamelCase    							=True
                   __UpperCamelCase    							=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]
         __UpperCamelCase    							=(left_element + right_element) // 2
         __UpperCamelCase    							=self.query(self.left(A_    )					,   A_					,   A_					,   A_					,   A_    )
         __UpperCamelCase    							=self.query(self.right(A_    )					,   mid + 1					,   A_					,   A_					,   A_    )
         return max(A_					,   A_    )






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


if __name__ == "__main__":
   _A		       =						[1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
   _A		       =						15
   _A		       =						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, 111)
   print(segt.query(1, 1, size, 1, 15))
   segt.update(1, 1, size, 7, 8, 235)
   print(segt)





