




"""simple docstring"""




import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
					# Create universe of discourse in Python using linspace ()
					UpperCAmelCase__   :					Union[str, Any]												=				np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)

					# Create two fuzzy sets by defining any membership function
					# (trapmf(), gbellmf(), gaussmf(), etc).
					UpperCAmelCase__   :					Any												=				[0, 2_5, 5_0]
					UpperCAmelCase__   :					Tuple												=				[2_5, 5_0, 7_5]
					UpperCAmelCase__   :					List[Any]												=				fuzz.membership.trimf(X, abca)
					UpperCAmelCase__   :					Optional[Any]												=				fuzz.membership.trimf(X, abca)

					# Compute the different operations using inbuilt functions.
					UpperCAmelCase__   :					int												=				np.ones(7_5)
					UpperCAmelCase__   :					str												=				np.zeros((7_5,))
					# 1. Union = max(µA(x), µB(x))
					UpperCAmelCase__   :					Union[str, Any]												=				fuzz.fuzzy_or(X, young, X, middle_aged)[1]
					# 2. Intersection = min(µA(x), µB(x))
					UpperCAmelCase__   :					Union[str, Any]												=				fuzz.fuzzy_and(X, young, X, middle_aged)[1]
					# 3. Complement (A) = (1- min(µA(x))
					UpperCAmelCase__   :					int												=				fuzz.fuzzy_not(young)
					# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
					UpperCAmelCase__   :					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))]
					UpperCAmelCase__   :					List[str]												=				young + middle_aged - (young * middle_aged)
					# 6. Algebraic Product = (µA(x) * µB(x))
					UpperCAmelCase__   :					str												=				young * middle_aged
					# 7. Bounded Sum = min[1,(µA(x), µB(x))]
					UpperCAmelCase__   :					int												=				fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
					# 8. Bounded difference = min[0,(µA(x), µB(x))]
					UpperCAmelCase__   :					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, 1_0)
					plt.plot(X, bdd_difference)
					plt.title('bdd_difference')
					plt.grid(True)

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






"""simple docstring"""




from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput



class 			lowerCAmelCase_		(nn.Module						):


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__ = 16     , SCREAMING_SNAKE_CASE__ = 88     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = 1     , SCREAMING_SNAKE_CASE__ = 0.0     , SCREAMING_SNAKE_CASE__ = 32     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = False     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = "geglu"     , SCREAMING_SNAKE_CASE__ = None     , )      ->	Optional[Any]:



											"""simple docstring"""




											super().__init__()
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     nn.ModuleList(
											    [
											        TransformeraDModel(
											            num_attention_heads=SCREAMING_SNAKE_CASE__     , attention_head_dim=SCREAMING_SNAKE_CASE__     , in_channels=SCREAMING_SNAKE_CASE__     , num_layers=SCREAMING_SNAKE_CASE__     , dropout=SCREAMING_SNAKE_CASE__     , norm_num_groups=SCREAMING_SNAKE_CASE__     , cross_attention_dim=SCREAMING_SNAKE_CASE__     , attention_bias=SCREAMING_SNAKE_CASE__     , sample_size=SCREAMING_SNAKE_CASE__     , num_vector_embeds=SCREAMING_SNAKE_CASE__     , activation_fn=SCREAMING_SNAKE_CASE__     , num_embeds_ada_norm=SCREAMING_SNAKE_CASE__     , )
											        for _ in range(2					)
											    ]					)

											# Variables that can be set by a pipeline:

											# The ratio of transformer1 to transformer2's output states to be combined during inference
											SCREAMING_SNAKE_CASE__		: Tuple     =     0.5

											# The shape of `encoder_hidden_states` is expected to be
											# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     [77, 2_57]

											# Which transformer to use to encode which condition.
											# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
											SCREAMING_SNAKE_CASE__		: int     =     [1, 0]



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__ = True     , )      ->	Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Any     =     hidden_states

											SCREAMING_SNAKE_CASE__		: Tuple     =     []
											SCREAMING_SNAKE_CASE__		: List[str]     =     0
											# attention_mask is not used yet
											for i in range(2					):
																		# for each of the two transformers, pass the corresponding condition tokens
																		SCREAMING_SNAKE_CASE__		: str     =     encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
																		SCREAMING_SNAKE_CASE__		: Any     =     self.transformer_index_for_condition[i]
																		SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     self.transformers[transformer_index](
																		    SCREAMING_SNAKE_CASE__     , encoder_hidden_states=SCREAMING_SNAKE_CASE__     , timestep=SCREAMING_SNAKE_CASE__     , cross_attention_kwargs=SCREAMING_SNAKE_CASE__     , return_dict=SCREAMING_SNAKE_CASE__     , )[0]
																		encoded_states.append(encoded_state - input_states					)
																		tokens_start += self.condition_lengths[i]

											SCREAMING_SNAKE_CASE__		: Tuple     =     encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     output_states + input_states

											if not return_dict:
																		return (output_states,)

											return TransformeraDModelOutput(sample=SCREAMING_SNAKE_CASE__					)




