from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block



@dataclass
class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  42





class  UpperCamelCase				( __lowercase   ,  __lowercase ):

     '''simple docstring'''

     @register_to_config
     def __init__(					self				,				A_ = 6_55_36				,				A_ = None				,				A_ = 2				,				A_ = 2				,				A_ = 0				,				A_ = "fourier"				,				A_ = True				,				A_ = False				,				A_ = 0.0				,				A_ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")				,				A_ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")				,				A_ = "UNetMidBlock1D"				,				A_ = None				,				A_ = (32, 32, 64)				,				A_ = None				,				A_ = 8				,				A_ = 1				,				A_ = False				,				) ->				Dict:






           """simple docstring"""
           super().__init__()
           _lowerCamelCase			    =				sample_size

           # time
           if time_embedding_type == "fourier":
                 _lowerCamelCase			    =				GaussianFourierProjection(
                     embedding_size=8				,				set_W_to_weight=A_				,				log=A_				,				flip_sin_to_cos=A_						)
                 _lowerCamelCase			    =				2 * block_out_channels[0]
           elif time_embedding_type == "positional":
                 _lowerCamelCase			    =				Timesteps(
                     block_out_channels[0]				,				flip_sin_to_cos=A_				,				downscale_freq_shift=A_						)
                 _lowerCamelCase			    =				block_out_channels[0]

           if use_timestep_embedding:
                 _lowerCamelCase			    =				block_out_channels[0] * 4
                 _lowerCamelCase			    =				TimestepEmbedding(
                     in_channels=A_				,				time_embed_dim=A_				,				act_fn=A_				,				out_dim=block_out_channels[0]				,				)

           _lowerCamelCase			    =				nn.ModuleList([]						)
           _lowerCamelCase			    =				None
           _lowerCamelCase			    =				nn.ModuleList([]						)
           _lowerCamelCase			    =				None

           # down
           _lowerCamelCase			    =				in_channels
           for i, down_block_type in enumerate(A_						):
                 _lowerCamelCase			    =				output_channel
                 _lowerCamelCase			    =				block_out_channels[i]

                 if i == 0:
                       input_channel += extra_in_channels

                 _lowerCamelCase			    =				i == len(A_						) - 1

                 _lowerCamelCase			    =				get_down_block(
                     A_				,				num_layers=A_				,				in_channels=A_				,				out_channels=A_				,				temb_channels=block_out_channels[0]				,				add_downsample=not is_final_block or downsample_each_block				,				)
                 self.down_blocks.append(A_						)

           # mid
           _lowerCamelCase			    =				get_mid_block(
               A_				,				in_channels=block_out_channels[-1]				,				mid_channels=block_out_channels[-1]				,				out_channels=block_out_channels[-1]				,				embed_dim=block_out_channels[0]				,				num_layers=A_				,				add_downsample=A_				,				)

           # up
           _lowerCamelCase			    =				list(reversed(A_						)						)
           _lowerCamelCase			    =				reversed_block_out_channels[0]
           if out_block_type is None:
                 _lowerCamelCase			    =				out_channels
           else:
                 _lowerCamelCase			    =				block_out_channels[0]

           for i, up_block_type in enumerate(A_						):
                 _lowerCamelCase			    =				output_channel
                 _lowerCamelCase			    =				(
                     reversed_block_out_channels[i + 1] if i < len(A_						) - 1 else final_upsample_channels
                 )

                 _lowerCamelCase			    =				i == len(A_						) - 1

                 _lowerCamelCase			    =				get_up_block(
                     A_				,				num_layers=A_				,				in_channels=A_				,				out_channels=A_				,				temb_channels=block_out_channels[0]				,				add_upsample=not is_final_block				,				)
                 self.up_blocks.append(A_						)
                 _lowerCamelCase			    =				output_channel

           # out
           _lowerCamelCase			    =				norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4				,				32						)
           _lowerCamelCase			    =				get_out_block(
               out_block_type=A_				,				num_groups_out=A_				,				embed_dim=block_out_channels[0]				,				out_channels=A_				,				act_fn=A_				,				fc_dim=block_out_channels[-1] // 4				,				)





     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_ = True				,				) ->				Union[UNetaDOutput, Tuple]:






           """simple docstring"""
           _lowerCamelCase			    =				timestep
           if not torch.is_tensor(A_						):
                 _lowerCamelCase			    =				torch.tensor([timesteps]				,				dtype=torch.long				,				device=sample.device						)
           elif torch.is_tensor(A_						) and len(timesteps.shape						) == 0:
                 _lowerCamelCase			    =				timesteps[None].to(sample.device						)

           _lowerCamelCase			    =				self.time_proj(A_						)
           if self.config.use_timestep_embedding:
                 _lowerCamelCase			    =				self.time_mlp(A_						)
           else:
                 _lowerCamelCase			    =				timestep_embed[..., None]
                 _lowerCamelCase			    =				timestep_embed.repeat([1, 1, sample.shape[2]]						).to(sample.dtype						)
                 _lowerCamelCase			    =				timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])						)

           # 2. down
           _lowerCamelCase			    =				()
           for downsample_block in self.down_blocks:
                 _lowerCamelCase    ,					_lowerCamelCase			    =				downsample_block(hidden_states=A_				,				temb=A_						)
                 down_block_res_samples += res_samples

           # 3. mid
           if self.mid_block:
                 _lowerCamelCase			    =				self.mid_block(A_				,				A_						)

           # 4. up
           for i, upsample_block in enumerate(self.up_blocks						):
                 _lowerCamelCase			    =				down_block_res_samples[-1:]
                 _lowerCamelCase			    =				down_block_res_samples[:-1]
                 _lowerCamelCase			    =				upsample_block(A_				,				res_hidden_states_tuple=A_				,				temb=A_						)

           # 5. post-process
           if self.out_block:
                 _lowerCamelCase			    =				self.out_block(A_				,				A_						)

           if not return_dict:
                 return (sample,)

           return UNetaDOutput(sample=A_						)
import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
     # Create universe of discourse in Python using linspace ()
     snake_case__				= 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).
     snake_case__				= [0, 25, 50]
     snake_case__				= [25, 50, 75]
     snake_case__				= fuzz.membership.trimf(X, abca)
     snake_case__				= fuzz.membership.trimf(X, abca)

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