"""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       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = 42






class 				snake_case       ( __UpperCAmelCase      ,    __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = True







					@register_to_config
					def __init__(				self   :	Dict    ,lowerCamelCase__   :	int = 3    ,lowerCamelCase__   :	int = 3    ,lowerCamelCase__   :	Tuple[str] = ("DownEncoderBlock2D",)    ,lowerCamelCase__   :	Tuple[str] = ("UpDecoderBlock2D",)    ,lowerCamelCase__   :	Tuple[int] = (64,)    ,lowerCamelCase__   :	int = 1    ,lowerCamelCase__   :	str = "silu"    ,lowerCamelCase__   :	int = 4    ,lowerCamelCase__   :	int = 32    ,lowerCamelCase__   :	int = 32    ,lowerCamelCase__   :	float = 0.1_8_2_1_5    ,):
									super().__init__()

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

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

									UpperCAmelCase__             =							nn.Convad(2 * latent_channels    ,2 * latent_channels    ,1			)
									UpperCAmelCase__             =							nn.Convad(lowerCamelCase__    ,lowerCamelCase__    ,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 		__lowerCAmelCase						(				self   :	int    ,lowerCamelCase__   :	Optional[Any]    ,lowerCamelCase__   :	int=False			):
									if isinstance(lowerCamelCase__    ,(Encoder, Decoder)			):
													UpperCAmelCase__             =							value







					def 		__lowerCAmelCase						(				self   :	Optional[int]    ,lowerCamelCase__   :	bool = True			):
									UpperCAmelCase__             =							use_tiling







					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									self.enable_tiling(lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Any			):
									UpperCAmelCase__             =							True







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									UpperCAmelCase__             =							False







					@property
					# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
					def 		__lowerCAmelCase						(				self   :	int			):
									UpperCAmelCase__             =							{}

									def fn_recursive_add_processors(lowerCamelCase__   :	str    ,lowerCamelCase__   :	torch.nn.Module    ,lowerCamelCase__   :	Dict[str, AttentionProcessor]			):
													if hasattr(lowerCamelCase__    ,'set_processor'			):
																	UpperCAmelCase__             =							module.processor

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

													return processors

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

									return processors







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]    ,lowerCamelCase__   :	Union[AttentionProcessor, Dict[str, AttentionProcessor]]			):
									UpperCAmelCase__             =							len(self.attn_processors.keys()			)

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

									def fn_recursive_attn_processor(lowerCamelCase__   :	str    ,lowerCamelCase__   :	torch.nn.Module    ,lowerCamelCase__   :	str			):
													if hasattr(lowerCamelCase__    ,'set_processor'			):
																	if not isinstance(lowerCamelCase__    ,lowerCamelCase__			):
																					module.set_processor(lowerCamelCase__			)
																	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}'''    ,lowerCamelCase__    ,lowerCamelCase__			)

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







					def 		__lowerCAmelCase						(				self   :	int			):
									self.set_attn_processor(AttnProcessor()			)







					@apply_forward_hook
					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	torch.FloatTensor    ,lowerCamelCase__   :	bool = 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(lowerCamelCase__    ,return_dict=lowerCamelCase__			)

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

									UpperCAmelCase__             =							self.quant_conv(lowerCamelCase__			)
									UpperCAmelCase__             =							DiagonalGaussianDistribution(lowerCamelCase__			)

									if not return_dict:
													return (posterior,)

									return AutoencoderKLOutput(latent_dist=lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	List[Any]    ,lowerCamelCase__   :	torch.FloatTensor    ,lowerCamelCase__   :	bool = 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(lowerCamelCase__    ,return_dict=lowerCamelCase__			)

									UpperCAmelCase__             =							self.post_quant_conv(lowerCamelCase__			)
									UpperCAmelCase__             =							self.decoder(lowerCamelCase__			)

									if not return_dict:
													return (dec,)

									return DecoderOutput(sample=lowerCamelCase__			)







					@apply_forward_hook
					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	torch.FloatTensor    ,lowerCamelCase__   :	bool = True			):
									if self.use_slicing and z.shape[0] > 1:
													UpperCAmelCase__             =							[self._decode(lowerCamelCase__			).sample for z_slice in z.split(1			)]
													UpperCAmelCase__             =							torch.cat(lowerCamelCase__			)
									else:
													UpperCAmelCase__             =							self._decode(lowerCamelCase__			).sample

									if not return_dict:
													return (decoded,)

									return DecoderOutput(sample=lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[int]    ,lowerCamelCase__   :	Optional[int]    ,lowerCamelCase__   :	List[Any]    ,lowerCamelCase__   :	Optional[int]			):
									UpperCAmelCase__             =							min(a.shape[2]    ,b.shape[2]    ,lowerCamelCase__			)
									for y in range(lowerCamelCase__			):
													UpperCAmelCase__             =							a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
									return b







					def 		__lowerCAmelCase						(				self   :	Any    ,lowerCamelCase__   :	int    ,lowerCamelCase__   :	Union[str, Any]    ,lowerCamelCase__   :	Union[str, Any]			):
									UpperCAmelCase__             =							min(a.shape[3]    ,b.shape[3]    ,lowerCamelCase__			)
									for x in range(lowerCamelCase__			):
													UpperCAmelCase__             =							a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
									return b







					def 		__lowerCAmelCase						(				self   :	str    ,lowerCamelCase__   :	torch.FloatTensor    ,lowerCamelCase__   :	bool = 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]    ,lowerCamelCase__			):
													UpperCAmelCase__             =							[]
													for j in range(0    ,x.shape[3]    ,lowerCamelCase__			):
																	UpperCAmelCase__             =							x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
																	UpperCAmelCase__             =							self.encoder(lowerCamelCase__			)
																	UpperCAmelCase__             =							self.quant_conv(lowerCamelCase__			)
																	row.append(lowerCamelCase__			)
													rows.append(lowerCamelCase__			)
									UpperCAmelCase__             =							[]
									for i, row in enumerate(lowerCamelCase__			):
													UpperCAmelCase__             =							[]
													for j, tile in enumerate(lowerCamelCase__			):
																	# 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]    ,lowerCamelCase__    ,lowerCamelCase__			)
																	if j > 0:
																					UpperCAmelCase__             =							self.blend_h(row[j - 1]    ,lowerCamelCase__    ,lowerCamelCase__			)
																	result_row.append(tile[:, :, :row_limit, :row_limit]			)
													result_rows.append(torch.cat(lowerCamelCase__    ,dim=3			)			)

									UpperCAmelCase__             =							torch.cat(lowerCamelCase__    ,dim=2			)
									UpperCAmelCase__             =							DiagonalGaussianDistribution(lowerCamelCase__			)

									if not return_dict:
													return (posterior,)

									return AutoencoderKLOutput(latent_dist=lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	List[Any]    ,lowerCamelCase__   :	torch.FloatTensor    ,lowerCamelCase__   :	bool = 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]    ,lowerCamelCase__			):
													UpperCAmelCase__             =							[]
													for j in range(0    ,z.shape[3]    ,lowerCamelCase__			):
																	UpperCAmelCase__             =							z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
																	UpperCAmelCase__             =							self.post_quant_conv(lowerCamelCase__			)
																	UpperCAmelCase__             =							self.decoder(lowerCamelCase__			)
																	row.append(lowerCamelCase__			)
													rows.append(lowerCamelCase__			)
									UpperCAmelCase__             =							[]
									for i, row in enumerate(lowerCamelCase__			):
													UpperCAmelCase__             =							[]
													for j, tile in enumerate(lowerCamelCase__			):
																	# 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]    ,lowerCamelCase__    ,lowerCamelCase__			)
																	if j > 0:
																					UpperCAmelCase__             =							self.blend_h(row[j - 1]    ,lowerCamelCase__    ,lowerCamelCase__			)
																	result_row.append(tile[:, :, :row_limit, :row_limit]			)
													result_rows.append(torch.cat(lowerCamelCase__    ,dim=3			)			)

									UpperCAmelCase__             =							torch.cat(lowerCamelCase__    ,dim=2			)
									if not return_dict:
													return (dec,)

									return DecoderOutput(sample=lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	List[Any]    ,lowerCamelCase__   :	torch.FloatTensor    ,lowerCamelCase__   :	bool = False    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	Optional[torch.Generator] = None    ,):
									UpperCAmelCase__             =							sample
									UpperCAmelCase__             =							self.encode(lowerCamelCase__			).latent_dist
									if sample_posterior:
													UpperCAmelCase__             =							posterior.sample(generator=lowerCamelCase__			)
									else:
													UpperCAmelCase__             =							posterior.mode()
									UpperCAmelCase__             =							self.decode(lowerCamelCase__			).sample

									if not return_dict:
													return (dec,)

									return DecoderOutput(sample=lowerCamelCase__			)




import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
   # Create universe of discourse in Python using linspace ()
   __lowerCamelCase			: List[Any]							   =		np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

   # Create two fuzzy sets by defining any membership function
   # (trapmf(), gbellmf(), gaussmf(), etc).
   __lowerCamelCase			: int							   =		[0, 25, 50]
   __lowerCamelCase			: Tuple							   =		[25, 50, 75]
   __lowerCamelCase			: List[str]							   =		fuzz.membership.trimf(X, abca)
   __lowerCamelCase			: Tuple							   =		fuzz.membership.trimf(X, abca)

   # Compute the different operations using inbuilt functions.
   __lowerCamelCase			: List[str]							   =		np.ones(75)
   __lowerCamelCase			: Tuple							   =		np.zeros((75,))
   # 1. Union = max(µA(x), µB(x))
   __lowerCamelCase			: str							   =		fuzz.fuzzy_or(X, young, X, middle_aged)[1]
   # 2. Intersection = min(µA(x), µB(x))
   __lowerCamelCase			: int							   =		fuzz.fuzzy_and(X, young, X, middle_aged)[1]
   # 3. Complement (A) = (1- min(µA(x))
   __lowerCamelCase			: Union[str, Any]							   =		fuzz.fuzzy_not(young)
   # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
   __lowerCamelCase			: Union[str, Any]							   =		fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
   # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
   __lowerCamelCase			: List[Any]							   =		young + middle_aged - (young * middle_aged)
   # 6. Algebraic Product = (µA(x) * µB(x))
   __lowerCamelCase			: int							   =		young * middle_aged
   # 7. Bounded Sum = min[1,(µA(x), µB(x))]
   __lowerCamelCase			: int							   =		fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
   # 8. Bounded difference = min[0,(µA(x), µB(x))]
   __lowerCamelCase			: str							   =		fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

   # max-min composition
   # max-product composition

   # Plot each set A, set B and each operation result using plot() and subplot().
   from matplotlib import pyplot as plt

   plt.figure()

   plt.subplot(4, 3, 1)
   plt.plot(X, young)
   plt.title('''Young''')
   plt.grid(True)

   plt.subplot(4, 3, 2)
   plt.plot(X, middle_aged)
   plt.title('''Middle aged''')
   plt.grid(True)

   plt.subplot(4, 3, 3)
   plt.plot(X, union)
   plt.title('''union''')
   plt.grid(True)

   plt.subplot(4, 3, 4)
   plt.plot(X, intersection)
   plt.title('''intersection''')
   plt.grid(True)

   plt.subplot(4, 3, 5)
   plt.plot(X, complement_a)
   plt.title('''complement_a''')
   plt.grid(True)

   plt.subplot(4, 3, 6)
   plt.plot(X, difference)
   plt.title('''difference a/b''')
   plt.grid(True)

   plt.subplot(4, 3, 7)
   plt.plot(X, alg_sum)
   plt.title('''alg_sum''')
   plt.grid(True)

   plt.subplot(4, 3, 8)
   plt.plot(X, alg_product)
   plt.title('''alg_product''')
   plt.grid(True)

   plt.subplot(4, 3, 9)
   plt.plot(X, bdd_sum)
   plt.title('''bdd_sum''')
   plt.grid(True)

   plt.subplot(4, 3, 10)
   plt.plot(X, bdd_difference)
   plt.title('''bdd_difference''')
   plt.grid(True)

   plt.subplots_adjust(hspace=0.5)
   plt.show()
