

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_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						"DiagonalGaussianDistribution"



class 				UpperCAmelCase_		(			__lowercase				,       __lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						List[str]	    =    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 = 0.1_8215       ,							)    ->    int:
												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	:    List[Any]         =		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	:    Optional[Any]         =		nn.Convad(2 * latent_channels       ,							2 * latent_channels       ,							1       )
												_a	:    List[Any]         =		nn.Convad(_a       ,							_a       ,							1       )

												_a	:    Tuple         =		False
												_a	:    Optional[int]         =		False

												# only relevant if vae tiling is enabled
												_a	:    Optional[Any]         =		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	:    Tuple         =		int(sample_size / (2 ** (len(self.config.block_out_channels       ) - 1))       )
												_a	:    Tuple         =		0.25


							def 				__lowercase				(			self       ,							_a       ,							_a=False       )    ->    Dict:
												if isinstance(_a       ,							(Encoder, Decoder)       ):
																	_a	:    Union[str, Any]         =		value


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


							def 				__lowercase				(			self       )    ->    Any:
												self.enable_tiling(_a       )


							def 				__lowercase				(			self       )    ->    str:
												_a	:    Union[str, Any]         =		True


							def 				__lowercase				(			self       )    ->    int:
												_a	:    Any         =		False


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

												def fn_recursive_add_processors(_a       ,							_a       ,							_a       ):
																	if hasattr(_a       ,							'''set_processor'''       ):
																						_a	:    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 				__lowercase				(			self       ,							_a       )    ->    List[Any]:
												_a	:    Tuple         =		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 				__lowercase				(			self       )    ->    Dict:
												self.set_attn_processor(AttnProcessor()       )


							@apply_forward_hook
							def 				__lowercase				(			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:
																	_a	:    Union[str, Any]         =		[self.encoder(_a       ) for x_slice in x.split(1       )]
																	_a	:    List[Any]         =		torch.cat(_a       )
												else:
																	_a	:    Optional[int]         =		self.encoder(_a       )

												_a	:    Any         =		self.quant_conv(_a       )
												_a	:    Union[str, Any]         =		DiagonalGaussianDistribution(_a       )

												if not return_dict:
																	return (posterior,)

												return AutoencoderKLOutput(latent_dist=_a       )


							def 				__lowercase				(			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       )

												_a	:    Dict         =		self.post_quant_conv(_a       )
												_a	:    Optional[Any]         =		self.decoder(_a       )

												if not return_dict:
																	return (dec,)

												return DecoderOutput(sample=_a       )


							@apply_forward_hook
							def 				__lowercase				(			self       ,							_a       ,							_a = True       )    ->    Union[DecoderOutput, torch.FloatTensor]:
												if self.use_slicing and z.shape[0] > 1:
																	_a	:    List[str]         =		[self._decode(_a       ).sample for z_slice in z.split(1       )]
																	_a	:    Optional[int]         =		torch.cat(_a       )
												else:
																	_a	:    int         =		self._decode(_a       ).sample

												if not return_dict:
																	return (decoded,)

												return DecoderOutput(sample=_a       )


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       )    ->    Any:
												_a	:    int         =		min(a.shape[2]       ,							b.shape[2]       ,							_a       )
												for y in range(_a       ):
																	_a	:    str         =		a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
												return b


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       )    ->    Any:
												_a	:    Dict         =		min(a.shape[3]       ,							b.shape[3]       ,							_a       )
												for x in range(_a       ):
																	_a	:    List[str]         =		a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
												return b


							def 				__lowercase				(			self       ,							_a       ,							_a = True       )    ->    AutoencoderKLOutput:
												_a	:    str         =		int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)       )
												_a	:    Tuple         =		int(self.tile_latent_min_size * self.tile_overlap_factor       )
												_a	:    str         =		self.tile_latent_min_size - blend_extent

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

												if not return_dict:
																	return (posterior,)

												return AutoencoderKLOutput(latent_dist=_a       )


							def 				__lowercase				(			self       ,							_a       ,							_a = True       )    ->    Union[DecoderOutput, torch.FloatTensor]:
												_a	:    List[str]         =		int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)       )
												_a	:    Union[str, Any]         =		int(self.tile_sample_min_size * self.tile_overlap_factor       )
												_a	:    int         =		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	:    str         =		[]
												for i in range(0       ,							z.shape[2]       ,							_a       ):
																	_a	:    str         =		[]
																	for j in range(0       ,							z.shape[3]       ,							_a       ):
																						_a	:    str         =		z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
																						_a	:    Union[str, Any]         =		self.post_quant_conv(_a       )
																						_a	:    Optional[int]         =		self.decoder(_a       )
																						row.append(_a       )
																	rows.append(_a       )
												_a	:    Union[str, Any]         =		[]
												for i, row in enumerate(_a       ):
																	_a	:    List[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	:    Optional[Any]         =		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	:    int         =		torch.cat(_a       ,							dim=2       )
												if not return_dict:
																	return (dec,)

												return DecoderOutput(sample=_a       )




							def 				__lowercase				(			self       ,							_a       ,							_a = False       ,							_a = True       ,							_a = None       ,							)    ->    Union[DecoderOutput, torch.FloatTensor]:
												_a	:    List[str]         =		sample
												_a	:    List[Any]         =		self.encode(_a       ).latent_dist
												if sample_posterior:
																	_a	:    Optional[Any]         =		posterior.sample(generator=_a       )
												else:
																	_a	:    Optional[int]         =		posterior.mode()
												_a	:    Optional[Any]         =		self.decode(_a       ).sample

												if not return_dict:
																	return (dec,)

												return DecoderOutput(sample=_a       )






from math import ceil



def    __UpperCAmelCase (	__a      :			int = 1_001	)			->     int:

					"""simple docstring"""

					_a	:    List[Any]         =		1

					for i in range(1		,int(ceil(n / 2.0	)	)	):
										_a	:    Optional[Any]         =		2 * i + 1
										_a	:    Optional[int]         =		2 * i
										_a	:    str         =		total + 4 * odd**2 - 6 * even

					return total


if __name__ == "__main__":
			import sys

			if len(sys.argv) == 1:
						print(solution())
			else:
						try:
									a__				=		int(sys.argv[1])
									print(solution(n))
						except ValueError:
									print('''Invalid entry - please enter a number''')



