


import numpy as np
import skfuzzy as fuzz

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

     # Compute the different operations using inbuilt functions.
     snake_case							:       List[Any]										=	np.ones(75)
     snake_case							:       Any										=	np.zeros((75,))
     # 1. Union = max(µA(x), µB(x))
     snake_case							:       List[str]										=	fuzz.fuzzy_or(X, young, X, middle_aged)[1]
     # 2. Intersection = min(µA(x), µB(x))
     snake_case							:       str										=	fuzz.fuzzy_and(X, young, X, middle_aged)[1]
     # 3. Complement (A) = (1- min(µA(x))
     snake_case							:       Tuple										=	fuzz.fuzzy_not(young)
     # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
     snake_case							:       List[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))]
     snake_case							:       List[str]										=	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							:       Tuple										=	fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
     # 8. Bounded difference = min[0,(µA(x), µB(x))]
     snake_case							:       Optional[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 dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput







@dataclass
class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			None
 SCREAMING_SNAKE_CASE__ =			None
 SCREAMING_SNAKE_CASE__ =			None
 SCREAMING_SNAKE_CASE__ =			None







class 						_snake_case    ( _snake_case				):




 def __init__(						self   ,    _lowerCamelCase=1   ,    _lowerCamelCase=0   ,    _lowerCamelCase=2   ,    _lowerCamelCase=512   ,    _lowerCamelCase="cls"   ,    _lowerCamelCase=False   ,    _lowerCamelCase=True   ,    **_lowerCamelCase   ,    ):
       super().__init__(pad_token_id=_lowerCamelCase   ,    bos_token_id=_lowerCamelCase   ,    eos_token_id=_lowerCamelCase   ,    **_lowerCamelCase       )
       a		:Tuple									=			project_dim
       a		:Optional[int]									=			pooler_fn
       a		:int									=			learn_encoder
       a		:int									=			use_attention_mask







class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			[r'pooler', r'logit_scale']
 SCREAMING_SNAKE_CASE__ =			[r'position_ids', r'predictions.decoder.bias']
 SCREAMING_SNAKE_CASE__ =			'roberta'
 SCREAMING_SNAKE_CASE__ =			RobertaSeriesConfig




 def __init__(						self   ,    _lowerCamelCase       ):
       super().__init__(_lowerCamelCase       )
       a		:Tuple									=			XLMRobertaModel(_lowerCamelCase       )
       a		:Optional[Any]									=			nn.Linear(config.hidden_size   ,    config.project_dim       )
       a		:Optional[int]									=			getattr(_lowerCamelCase   ,    '''has_pre_transformation'''   ,    _lowerCamelCase       )
       if self.has_pre_transformation:
             a		:Tuple									=			nn.Linear(config.hidden_size   ,    config.project_dim       )
             a		:Union[str, Any]									=			nn.LayerNorm(config.hidden_size   ,    eps=config.layer_norm_eps       )
       self.post_init()




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    _lowerCamelCase = None   ,    ):
       a		:Optional[int]									=			return_dict if return_dict is not None else self.config.use_return_dict

       a		:int									=			self.base_model(
           input_ids=_lowerCamelCase   ,    attention_mask=_lowerCamelCase   ,    token_type_ids=_lowerCamelCase   ,    position_ids=_lowerCamelCase   ,    head_mask=_lowerCamelCase   ,    inputs_embeds=_lowerCamelCase   ,    encoder_hidden_states=_lowerCamelCase   ,    encoder_attention_mask=_lowerCamelCase   ,    output_attentions=_lowerCamelCase   ,    output_hidden_states=True if self.has_pre_transformation else output_hidden_states   ,    return_dict=_lowerCamelCase   ,    )

       if self.has_pre_transformation:
             a		:Optional[int]									=			outputs['''hidden_states'''][-2]
             a		:List[Any]									=			self.pre_LN(_lowerCamelCase       )
             a		:Optional[Any]									=			self.transformation_pre(_lowerCamelCase       )

             return TransformationModelOutput(
                 projection_state=_lowerCamelCase   ,    last_hidden_state=outputs.last_hidden_state   ,    hidden_states=outputs.hidden_states   ,    attentions=outputs.attentions   ,    )
       else:
             a		:List[str]									=			self.transformation(outputs.last_hidden_state       )
             return TransformationModelOutput(
                 projection_state=_lowerCamelCase   ,    last_hidden_state=outputs.last_hidden_state   ,    hidden_states=outputs.hidden_states   ,    attentions=outputs.attentions   ,    )





