





import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
      # Create universe of discourse in Python using linspace ()
      SCREAMING_SNAKE_CASE_		     = 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).
      SCREAMING_SNAKE_CASE_		     = [0, 2_5, 5_0]
      SCREAMING_SNAKE_CASE_		     = [2_5, 5_0, 7_5]
      SCREAMING_SNAKE_CASE_		     = fuzz.membership.trimf(X, abca)
      SCREAMING_SNAKE_CASE_		     = fuzz.membership.trimf(X, abca)

      # Compute the different operations using inbuilt functions.
      SCREAMING_SNAKE_CASE_		     = np.ones(7_5)
      SCREAMING_SNAKE_CASE_		     = np.zeros((7_5,))
      # 1. Union = max(µA(x), µB(x))
      SCREAMING_SNAKE_CASE_		     = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
      # 2. Intersection = min(µA(x), µB(x))
      SCREAMING_SNAKE_CASE_		     = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
      # 3. Complement (A) = (1- min(µA(x))
      SCREAMING_SNAKE_CASE_		     = fuzz.fuzzy_not(young)
      # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
      SCREAMING_SNAKE_CASE_		     = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
      # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
      SCREAMING_SNAKE_CASE_		     = young + middle_aged - (young * middle_aged)
      # 6. Algebraic Product = (µA(x) * µB(x))
      SCREAMING_SNAKE_CASE_		     = young * middle_aged
      # 7. Bounded Sum = min[1,(µA(x), µB(x))]
      SCREAMING_SNAKE_CASE_		     = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
      # 8. Bounded difference = min[0,(µA(x), µB(x))]
      SCREAMING_SNAKE_CASE_		     = 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()









from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_		     = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_		     = {
    """google/pegasus-large""": """https://huggingface.co/google/pegasus-large/resolve/main/config.json""",
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     __snake_case					:			str				=    "pegasus"
     __snake_case					:			List[Any]				=    ["past_key_values"]
     __snake_case					:			Any				=    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






     def __init__(							self		:					Dict		,lowerCamelCase__		:					List[str]=50265		,lowerCamelCase__		:					int=1024		,lowerCamelCase__		:					Optional[Any]=12		,lowerCamelCase__		:					Tuple=4096		,lowerCamelCase__		:					Dict=16		,lowerCamelCase__		:					Optional[Any]=12		,lowerCamelCase__		:					str=4096		,lowerCamelCase__		:					Dict=16		,lowerCamelCase__		:					Tuple=0.0		,lowerCamelCase__		:					Dict=0.0		,lowerCamelCase__		:					str=True		,lowerCamelCase__		:					Any=True		,lowerCamelCase__		:					List[Any]="gelu"		,lowerCamelCase__		:					Tuple=1024		,lowerCamelCase__		:					Optional[int]=0.1		,lowerCamelCase__		:					Tuple=0.0		,lowerCamelCase__		:					Dict=0.0		,lowerCamelCase__		:					Optional[int]=0.02		,lowerCamelCase__		:					Optional[int]=0		,lowerCamelCase__		:					List[Any]=False		,lowerCamelCase__		:					Union[str, Any]=0		,lowerCamelCase__		:					Optional[int]=1		,lowerCamelCase__		:					Any=1		,**lowerCamelCase__		:					Dict		,)  ->	str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		vocab_size
           SCREAMING_SNAKE_CASE	=		max_position_embeddings
           SCREAMING_SNAKE_CASE	=		d_model
           SCREAMING_SNAKE_CASE	=		encoder_ffn_dim
           SCREAMING_SNAKE_CASE	=		encoder_layers
           SCREAMING_SNAKE_CASE	=		encoder_attention_heads
           SCREAMING_SNAKE_CASE	=		decoder_ffn_dim
           SCREAMING_SNAKE_CASE	=		decoder_layers
           SCREAMING_SNAKE_CASE	=		decoder_attention_heads
           SCREAMING_SNAKE_CASE	=		dropout
           SCREAMING_SNAKE_CASE	=		attention_dropout
           SCREAMING_SNAKE_CASE	=		activation_dropout
           SCREAMING_SNAKE_CASE	=		activation_function
           SCREAMING_SNAKE_CASE	=		init_std
           SCREAMING_SNAKE_CASE	=		encoder_layerdrop
           SCREAMING_SNAKE_CASE	=		decoder_layerdrop
           SCREAMING_SNAKE_CASE	=		use_cache
           SCREAMING_SNAKE_CASE	=		encoder_layers
           SCREAMING_SNAKE_CASE	=		scale_embedding  # scale factor will be sqrt(d_model) if True
           super().__init__(
               pad_token_id=lowerCamelCase__		,eos_token_id=lowerCamelCase__		,is_encoder_decoder=lowerCamelCase__		,decoder_start_token_id=lowerCamelCase__		,forced_eos_token_id=lowerCamelCase__		,**lowerCamelCase__		,)






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	int:




           '''simple docstring'''


           return self.encoder_attention_heads






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	int:




           '''simple docstring'''


           return self.d_model


