



'''simple docstring'''







from sklearn.neural_network import MLPClassifier

SCREAMING_SNAKE_CASE__     : Union[str, Any]				=   [[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
SCREAMING_SNAKE_CASE__     : int				=   [0, 1, 0, 0]


SCREAMING_SNAKE_CASE__     : int				=   MLPClassifier(
    solver="""lbfgs""", alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


SCREAMING_SNAKE_CASE__     : Any				=   [[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
SCREAMING_SNAKE_CASE__     : Any				=   clf.predict(test)







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE     ):
		return list(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
							import doctest

							doctest.testmod()







import os
from string import ascii_letters

SCREAMING_SNAKE_CASE_		  =			ascii_letters + """ \t\n"""



def   __lowercase  ( )					->  dict[str, None]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		os.path.split(os.path.realpath(_SCREAMING_SNAKE_CASE      )      )
      SCREAMING_SNAKE_CASE						=		{}
      with open(path[0] + """/dictionary.txt"""      ) as dictionary_file:
            for word in dictionary_file.read().split("""\n"""      ):
                  SCREAMING_SNAKE_CASE						=		None
      return english_words


SCREAMING_SNAKE_CASE_		  =			load_dictionary()



def   __lowercase  ( _SCREAMING_SNAKE_CASE      )					->  float:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		message.upper()
      SCREAMING_SNAKE_CASE						=		remove_non_letters(_SCREAMING_SNAKE_CASE      )
      SCREAMING_SNAKE_CASE						=		message.split()
      SCREAMING_SNAKE_CASE						=		len([word for word in possible_words if word in ENGLISH_WORDS]      )
      return float(_SCREAMING_SNAKE_CASE      ) / len(_SCREAMING_SNAKE_CASE      )



def   __lowercase  ( _SCREAMING_SNAKE_CASE      )					->  str:


      '''simple docstring'''


      return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE      )



def   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE = 20					,		_SCREAMING_SNAKE_CASE = 85      )					->  bool:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		get_english_count(_SCREAMING_SNAKE_CASE      ) * 1_00 >= word_percentage
      SCREAMING_SNAKE_CASE						=		len(remove_non_letters(_SCREAMING_SNAKE_CASE      )      )
      SCREAMING_SNAKE_CASE						=		(float(_SCREAMING_SNAKE_CASE      ) / len(_SCREAMING_SNAKE_CASE      )) * 1_00
      SCREAMING_SNAKE_CASE						=		message_letters_percentage >= letter_percentage
      return words_match and letters_match


if __name__ == "__main__":
      import doctest

      doctest.testmod()


