






import numpy as np
import skfuzzy as fuzz

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

	# Compute the different operations using inbuilt functions.
	UpperCAmelCase_ :						Tuple      =      np.ones(75)
	UpperCAmelCase_ :						Optional[int]      =      np.zeros((75,))
	# 1. Union = max(µA(x), µB(x))
	UpperCAmelCase_ :						Tuple      =      fuzz.fuzzy_or(X, young, X, middle_aged)[1]
	# 2. Intersection = min(µA(x), µB(x))
	UpperCAmelCase_ :						str      =      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_ :						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))]
	UpperCAmelCase_ :						Union[str, Any]      =      young + middle_aged - (young * middle_aged)
	# 6. Algebraic Product = (µA(x) * µB(x))
	UpperCAmelCase_ :						Optional[int]      =      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_ :						Tuple      =      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 __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


_UpperCAmelCase	:     Dict   ={
    """susnato/ernie-m-base_pytorch""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json""",
    """susnato/ernie-m-large_pytorch""": """https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json""",
}







class  snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__					:     Dict    =							"""ernie_m"""
						SCREAMING_SNAKE_CASE__					:     Dict[str, str]    =							{"dropout": "classifier_dropout", "num_classes": "num_labels"}




						def __init__( self      ,  __lowercase = 2_5_0_0_0_2      ,  __lowercase = 7_6_8      ,  __lowercase = 1_2      ,  __lowercase = 1_2      ,  __lowercase = 3_0_7_2      ,  __lowercase = "gelu"      ,  __lowercase = 0.1      ,  __lowercase = 0.1      ,  __lowercase = 5_1_4      ,  __lowercase = 0.02      ,  __lowercase = 1      ,  __lowercase = 1e-05      ,  __lowercase=None      ,  __lowercase=False      ,  __lowercase=0.0      ,  **__lowercase      ,  )    ->			Tuple:
										super().__init__(pad_token_id=__lowercase      ,  **__lowercase							)
										lowerCAmelCase_       :					Tuple			 = vocab_size
										lowerCAmelCase_       :					Dict			 = hidden_size
										lowerCAmelCase_       :					Tuple			 = num_hidden_layers
										lowerCAmelCase_       :					int			 = num_attention_heads
										lowerCAmelCase_       :					Dict			 = intermediate_size
										lowerCAmelCase_       :					int			 = hidden_act
										lowerCAmelCase_       :					Union[str, Any]			 = hidden_dropout_prob
										lowerCAmelCase_       :					Any			 = attention_probs_dropout_prob
										lowerCAmelCase_       :					Union[str, Any]			 = max_position_embeddings
										lowerCAmelCase_       :					str			 = initializer_range
										lowerCAmelCase_       :					List[str]			 = layer_norm_eps
										lowerCAmelCase_       :					List[Any]			 = classifier_dropout
										lowerCAmelCase_       :					Any			 = is_decoder
										lowerCAmelCase_       :					List[Any]			 = act_dropout