


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     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		"DiagonalGaussianDistribution"






class     UpperCAmelCase__       (					A_   ,  A_		):



    """simple docstring"""






    UpperCAmelCase__						:		List[Any]      =  True






    @register_to_config
    def __init__(			self					,   A_ = 3					,   A_ = 3					,   A_ = ("DownEncoderBlock2D",)					,   A_ = ("UpDecoderBlock2D",)					,   A_ = (64,)					,   A_ = 1					,   A_ = "silu"					,   A_ = 4					,   A_ = 32					,   A_ = 32					,   A_ = 0.1_8215					,   )  ->       Any:
         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    							=0.25






    def _a					(			self					,   A_					,   A_=False    )  ->       Any:
         if isinstance(A_					,   (Encoder, Decoder)    ):
              __UpperCamelCase    							=value






    def _a					(			self					,   A_ = True    )  ->       List[Any]:
         __UpperCamelCase    							=use_tiling






    def _a					(			self    )  ->       Union[str, Any]:
         self.enable_tiling(A_    )






    def _a					(			self    )  ->       List[Any]:
         __UpperCamelCase    							=True






    def _a					(			self    )  ->       Any:
         __UpperCamelCase    							=False






    @property
    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
    def _a					(			self    )  ->       Dict[str, AttentionProcessor]:
         __UpperCamelCase    							={}

         def fn_recursive_add_processors(A_					,   A_					,   A_    ):
              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 _a					(			self					,   A_    )  ->       Tuple:
         __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(A_					,   A_					,   A_    ):
              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 _a					(			self    )  ->       List[Any]:
         self.set_attn_processor(AttnProcessor()    )






    @apply_forward_hook
    def _a					(			self					,   A_					,   A_ = True    )  ->       AutoencoderKLOutput:
         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 _a					(			self					,   A_					,   A_ = True    )  ->       Union[DecoderOutput, torch.FloatTensor]:
         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 _a					(			self					,   A_					,   A_ = True    )  ->       Union[DecoderOutput, torch.FloatTensor]:
         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 _a					(			self					,   A_					,   A_					,   A_    )  ->       Optional[Any]:
         __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 _a					(			self					,   A_					,   A_					,   A_    )  ->       Dict:
         __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 _a					(			self					,   A_					,   A_ = True    )  ->       AutoencoderKLOutput:
         __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 _a					(			self					,   A_					,   A_ = True    )  ->       Union[DecoderOutput, torch.FloatTensor]:
         __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 _a					(			self					,   A_					,   A_ = False					,   A_ = True					,   A_ = None					,   )  ->       Union[DecoderOutput, torch.FloatTensor]:
         __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)





