






import numpy as np
import skfuzzy as fuzz

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

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










import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


lowerCamelCase__							=     """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
"""




class 			snake_case__   (							unittest.TestCase	,    lowercase_):

       '''simple docstring'''



       def 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:int          =						load_tool("""text-question-answering"""    )
            self.tool.setup()
            __snake_case			:Tuple          =						load_tool("""text-question-answering"""			,						remote=a__    )



       def 					__lowercase						(							self    ) ->      Any:






            '''simple docstring'''





            __snake_case			:List[str]          =						self.tool(a__			,						"""What did Hugging Face do in April 2021?"""    )
            self.assertEqual(a__			,						"""launched the BigScience Research Workshop"""    )



       def 					__lowercase						(							self    ) ->      Any:






            '''simple docstring'''





            __snake_case			:List[str]          =						self.remote_tool(a__			,						"""What did Hugging Face do in April 2021?"""    )
            self.assertEqual(a__			,						"""launched the BigScience Research Workshop"""    )



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						self.tool(text=a__			,						question="""What did Hugging Face do in April 2021?"""    )
            self.assertEqual(a__			,						"""launched the BigScience Research Workshop"""    )



       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:List[str]          =						self.remote_tool(text=a__			,						question="""What did Hugging Face do in April 2021?"""    )
            self.assertEqual(a__			,						"""launched the BigScience Research Workshop"""    )


