



"""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 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					42




class 			snake_case_						(						a_     ,a_				):
							__lowerCAmelCase    					=					True






							@register_to_config
							def __init__(   self			,     a_ = 3			,     a_ = 3			,     a_ = ("DownEncoderBlock2D",)			,     a_ = ("UpDecoderBlock2D",)			,     a_ = (6_4,)			,     a_ = 1			,     a_ = "silu"			,     a_ = 4			,     a_ = 3_2			,     a_ = 3_2			,     a_ = 0.18_215			,     ):
									super().__init__()

									# pass init params to Encoder
									a_     :      Optional[Any] 						=     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
									a_     :      Optional[int] 						=     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_			,     )

									a_     :      Any 						=     nn.Convad(2 * latent_channels			,     2 * latent_channels			,     1      )
									a_     :      int 						=     nn.Convad(a_			,     a_			,     1      )

									a_     :      List[str] 						=     False
									a_     :      List[Any] 						=     False

									# only relevant if vae tiling is enabled
									a_     :      str 						=     self.config.sample_size
									a_     :      int 						=     (
									    self.config.sample_size[0]
									    if isinstance(self.config.sample_size			,     (list, tuple)      )
									    else self.config.sample_size
									)
									a_     :      Dict 						=     int(sample_size / (2 ** (len(self.config.block_out_channels      ) - 1))      )
									a_     :      str 						=     0.25






							def snake_case_  (   self			,     a_			,     a_=False      ):
									if isinstance(a_			,     (Encoder, Decoder)      ):
											a_     :      Tuple 						=     value






							def snake_case_  (   self			,     a_ = True      ):
									a_     :      Dict 						=     use_tiling






							def snake_case_  (   self      ):
									self.enable_tiling(a_      )






							def snake_case_  (   self      ):
									a_     :      Union[str, Any] 						=     True






							def snake_case_  (   self      ):
									a_     :      Any 						=     False






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

									def fn_recursive_add_processors(a_			,     a_			,     a_      ):
											if hasattr(a_			,     "set_processor"      ):
													a_     :      Optional[Any] 						=     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 snake_case_  (   self			,     a_      ):
									a_     :      int 						=     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 snake_case_  (   self      ):
									self.set_attn_processor(AttnProcessor()      )






							@apply_forward_hook
							def snake_case_  (   self			,     a_			,     a_ = 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:
											a_     :      str 						=     [self.encoder(a_      ) for x_slice in x.split(1      )]
											a_     :      int 						=     torch.cat(a_      )
									else:
											a_     :      Tuple 						=     self.encoder(a_      )

									a_     :      str 						=     self.quant_conv(a_      )
									a_     :      Optional[Any] 						=     DiagonalGaussianDistribution(a_      )

									if not return_dict:
											return (posterior,)

									return AutoencoderKLOutput(latent_dist=a_      )






							def snake_case_  (   self			,     a_			,     a_ = 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_      )

									a_     :      Dict 						=     self.post_quant_conv(a_      )
									a_     :      int 						=     self.decoder(a_      )

									if not return_dict:
											return (dec,)

									return DecoderOutput(sample=a_      )






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

									if not return_dict:
											return (decoded,)

									return DecoderOutput(sample=a_      )






							def snake_case_  (   self			,     a_			,     a_			,     a_      ):
									a_     :      Dict 						=     min(a.shape[2]			,     b.shape[2]			,     a_      )
									for y in range(a_      ):
											a_     :      Union[str, Any] 						=     a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
									return b






							def snake_case_  (   self			,     a_			,     a_			,     a_      ):
									a_     :      Dict 						=     min(a.shape[3]			,     b.shape[3]			,     a_      )
									for x in range(a_      ):
											a_     :      int 						=     a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
									return b






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

									# Split the image into 512x512 tiles and encode them separately.
									a_     :      Dict 						=     []
									for i in range(0			,     x.shape[2]			,     a_      ):
											a_     :      List[str] 						=     []
											for j in range(0			,     x.shape[3]			,     a_      ):
													a_     :      Any 						=     x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
													a_     :      Optional[Any] 						=     self.encoder(a_      )
													a_     :      Union[str, Any] 						=     self.quant_conv(a_      )
													row.append(a_      )
											rows.append(a_      )
									a_     :      Optional[int] 						=     []
									for i, row in enumerate(a_      ):
											a_     :      Optional[Any] 						=     []
											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:
															a_     :      int 						=     self.blend_v(rows[i - 1][j]			,     a_			,     a_      )
													if j > 0:
															a_     :      Any 						=     self.blend_h(row[j - 1]			,     a_			,     a_      )
													result_row.append(tile[:, :, :row_limit, :row_limit]      )
											result_rows.append(torch.cat(a_			,     dim=3      )      )

									a_     :      Dict 						=     torch.cat(a_			,     dim=2      )
									a_     :      str 						=     DiagonalGaussianDistribution(a_      )

									if not return_dict:
											return (posterior,)

									return AutoencoderKLOutput(latent_dist=a_      )






							def snake_case_  (   self			,     a_			,     a_ = True      ):
									a_     :      str 						=     int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)      )
									a_     :      List[str] 						=     int(self.tile_sample_min_size * self.tile_overlap_factor      )
									a_     :      Any 						=     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.
									a_     :      Optional[Any] 						=     []
									for i in range(0			,     z.shape[2]			,     a_      ):
											a_     :      str 						=     []
											for j in range(0			,     z.shape[3]			,     a_      ):
													a_     :      Union[str, Any] 						=     z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
													a_     :      Any 						=     self.post_quant_conv(a_      )
													a_     :      List[str] 						=     self.decoder(a_      )
													row.append(a_      )
											rows.append(a_      )
									a_     :      int 						=     []
									for i, row in enumerate(a_      ):
											a_     :      Tuple 						=     []
											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:
															a_     :      List[Any] 						=     self.blend_v(rows[i - 1][j]			,     a_			,     a_      )
													if j > 0:
															a_     :      Tuple 						=     self.blend_h(row[j - 1]			,     a_			,     a_      )
													result_row.append(tile[:, :, :row_limit, :row_limit]      )
											result_rows.append(torch.cat(a_			,     dim=3      )      )

									a_     :      Union[str, Any] 						=     torch.cat(a_			,     dim=2      )
									if not return_dict:
											return (dec,)

									return DecoderOutput(sample=a_      )






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

									if not return_dict:
											return (dec,)

									return DecoderOutput(sample=a_      )




"""simple docstring"""
def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      float:
		_validate_point(SCREAMING_SNAKE_CASE__	)
		_validate_point(SCREAMING_SNAKE_CASE__	)
		if len(SCREAMING_SNAKE_CASE__	) != len(SCREAMING_SNAKE_CASE__	):
				raise ValueError("Both points must be in the same n-dimensional space"	)

		return float(sum(abs(a - b	) for a, b in zip(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)	)	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      None:
		if point:
				if isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	):
						for item in point:
								if not isinstance(SCREAMING_SNAKE_CASE__, (int, float)	):
										a_     :      int 						=     (
										    "Expected a list of numbers as input, found "
										    F"""{type(SCREAMING_SNAKE_CASE__	).__name__}"""
										)
										raise TypeError(SCREAMING_SNAKE_CASE__	)
				else:
						a_     :      Dict 						=     F"""Expected a list of numbers as input, found {type(SCREAMING_SNAKE_CASE__	).__name__}"""
						raise TypeError(SCREAMING_SNAKE_CASE__	)
		else:
				raise ValueError("Missing an input"	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      float:
		_validate_point(SCREAMING_SNAKE_CASE__	)
		_validate_point(SCREAMING_SNAKE_CASE__	)
		if len(SCREAMING_SNAKE_CASE__	) != len(SCREAMING_SNAKE_CASE__	):
				raise ValueError("Both points must be in the same n-dimensional space"	)

		return float(sum(abs(x - y	) for x, y in zip(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)	)	)


if __name__ == "__main__":
					import doctest

					doctest.testmod()