





'''simple docstring'''
import numpy as np
import skfuzzy as fuzz

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

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





"""simple docstring"""






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


__snake_case							:   Optional[int]      =    logging.get_logger(__name__)



class 			A__   (  __SCREAMING_SNAKE_CASE  ,		__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	'maskformer-swin'

	SCREAMING_SNAKE_CASE          =	{
	    'num_attention_heads': 'num_heads',
	    'num_hidden_layers': 'num_layers',
	}

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

				"""simple docstring"""

				super().__init__(**_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  int												=       image_size
				__lowerCAmelCase      :  Any												=       patch_size
				__lowerCAmelCase      :  Tuple												=       num_channels
				__lowerCAmelCase      :  Any												=       embed_dim
				__lowerCAmelCase      :  Any												=       depths
				__lowerCAmelCase      :  Dict												=       len(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  List[Any]												=       num_heads
				__lowerCAmelCase      :  Tuple												=       window_size
				__lowerCAmelCase      :  Dict												=       mlp_ratio
				__lowerCAmelCase      :  Any												=       qkv_bias
				__lowerCAmelCase      :  Union[str, Any]												=       hidden_dropout_prob
				__lowerCAmelCase      :  int												=       attention_probs_dropout_prob
				__lowerCAmelCase      :  Tuple												=       drop_path_rate
				__lowerCAmelCase      :  int												=       hidden_act
				__lowerCAmelCase      :  Optional[int]												=       use_absolute_embeddings
				__lowerCAmelCase      :  List[str]												=       layer_norm_eps
				__lowerCAmelCase      :  Any												=       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
				__lowerCAmelCase      :  Optional[Any]												=       int(embed_dim * 2 ** (len(_SCREAMING_SNAKE_CASE) - 1))
				__lowerCAmelCase      :  Any												=       ["stem"] + [F"""stage{idx}""" for idx in range(1       ,			len(_SCREAMING_SNAKE_CASE) + 1)]
				__lowerCAmelCase						,	__lowerCAmelCase      :  List[str]												=       get_aligned_output_features_output_indices(
				    out_features=_SCREAMING_SNAKE_CASE       ,			out_indices=_SCREAMING_SNAKE_CASE       ,			stage_names=self.stage_names)