


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

from collections import namedtuple
from dataclasses import dataclass





@dataclass
class     lowerCamelCase__:
      UpperCAmelCase__      :	int
      UpperCAmelCase__      :	TreeNode | None    		=   None
      UpperCAmelCase__      :	TreeNode | None    		=   None



UpperCAmelCase_									=	namedtuple('CoinsDistribResult', 'moves excess')




def 					lowerCamelCase__       (						A__       :					TreeNode | None	):





       '''simple docstring'''



       if root is None:
              return 0

       # Validation
       def count_nodes(A__       :					TreeNode | None	) -> int:
              if node is None:
                     return 0

              return count_nodes(node.left	) + count_nodes(node.right	) + 1

       def count_coins(A__       :					TreeNode | None	) -> int:
              if node is None:
                     return 0

              return count_coins(node.left	) + count_coins(node.right	) + node.data

       if count_nodes(A__	) != count_coins(A__	):
              raise ValueError("""The nodes number should be same as the number of coins"""	)

       # Main calculation
       def get_distrib(A__       :					TreeNode | None	) -> CoinsDistribResult:

              if node is None:
                     return CoinsDistribResult(0  ,			1	)

              __lowerCamelCase,   __lowerCamelCase   =						get_distrib(node.left	)
              __lowerCamelCase,   __lowerCamelCase   =						get_distrib(node.right	)

              __lowerCamelCase   =						1 - left_distrib_excess
              __lowerCamelCase   =						1 - right_distrib_excess

              __lowerCamelCase   =						(
                  left_distrib_moves
                  + right_distrib_moves
                  + abs(A__	)
                  + abs(A__	)
              )
              __lowerCamelCase   =						node.data - coins_to_left - coins_to_right

              return CoinsDistribResult(A__  ,			A__	)

       return get_distrib(A__	)[0]


if __name__ == "__main__":
       import doctest

       doctest.testmod()

