



import numpy as np
import skfuzzy as fuzz

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

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








from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


snake_case__					:     Any    		=   logging.get_logger(__name__)

class   _A   ( _lowercase  ,						_lowercase	):


						'''simple docstring'''





						_snake_case			:     Dict      							=      """maskformer-swin"""

						_snake_case			:     List[str]      							=      {
						    """num_attention_heads""": """num_heads""",
						    """num_hidden_layers""": """num_layers""",
						}



						def __init__(	self	:							List[str]							,      lowerCamelCase	:							Any=224							,      lowerCamelCase	:							Optional[Any]=4							,      lowerCamelCase	:							Dict=3							,      lowerCamelCase	:							Tuple=96							,      lowerCamelCase	:							str=[2, 2, 6, 2]							,      lowerCamelCase	:							Dict=[3, 6, 12, 24]							,      lowerCamelCase	:							Optional[Any]=7							,      lowerCamelCase	:							Any=4.0							,      lowerCamelCase	:							Union[str, Any]=True							,      lowerCamelCase	:							List[str]=0.0							,      lowerCamelCase	:							Optional[int]=0.0							,      lowerCamelCase	:							List[str]=0.1							,      lowerCamelCase	:							int="gelu"							,      lowerCamelCase	:							Optional[int]=False							,      lowerCamelCase	:							List[Any]=0.02							,      lowerCamelCase	:							Tuple=1e-5							,      lowerCamelCase	:							Dict=None							,      lowerCamelCase	:							Dict=None							,      **lowerCamelCase	:							int							,      ):






							'''simple docstring'''



							super().__init__(**lowerCamelCase	)

							__lowercase		       =			image_size
							__lowercase		       =			patch_size
							__lowercase		       =			num_channels
							__lowercase		       =			embed_dim
							__lowercase		       =			depths
							__lowercase		       =			len(lowerCamelCase	)
							__lowercase		       =			num_heads
							__lowercase		       =			window_size
							__lowercase		       =			mlp_ratio
							__lowercase		       =			qkv_bias
							__lowercase		       =			hidden_dropout_prob
							__lowercase		       =			attention_probs_dropout_prob
							__lowercase		       =			drop_path_rate
							__lowercase		       =			hidden_act
							__lowercase		       =			use_absolute_embeddings
							__lowercase		       =			layer_norm_eps
							__lowercase		       =			initializer_range
							# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
							# this indicates the channel dimension after the last stage of the model
							__lowercase		       =			int(embed_dim * 2 ** (len(lowerCamelCase	) - 1)	)
							__lowercase		       =			["stem"] + [f"""stage{idx}""" for idx in range(1							,      len(lowerCamelCase	) + 1	)]
							__lowercase       ,    __lowercase		       =			get_aligned_output_features_output_indices(
							    out_features=lowerCamelCase							,      out_indices=lowerCamelCase							,      stage_names=self.stage_names	)



