'''simple docstring'''


import random






class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  def __init__(					self ,						snake_case_ ,						snake_case_ ,						snake_case_ = 0.01 ,						snake_case_ = 1_0_0_0 ,						snake_case_ = -1 ,						):




       '''simple docstring'''

       UpperCAmelCase_				:  Tuple  = sample
       if len(self.sample		) == 0:
            raise ValueError('Sample data can not be empty'		)
       UpperCAmelCase_				:  str  = target
       if len(self.target		) == 0:
            raise ValueError('Target data can not be empty'		)
       if len(self.sample		) != len(self.target		):
            raise ValueError('Sample data and Target data do not have matching lengths'		)
       UpperCAmelCase_				:  Union[str, Any]  = learning_rate
       UpperCAmelCase_				:  List[str]  = epoch_number
       UpperCAmelCase_				:  Optional[Any]  = bias
       UpperCAmelCase_				:  Tuple  = len(snake_case_		)
       UpperCAmelCase_				:  Optional[int]  = len(sample[0]		)  # number of columns in dataset
       UpperCAmelCase_				:  int  = []


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       for sample in self.sample:
            sample.insert(0 ,						self.bias		)

       for _ in range(self.col_sample		):
            self.weight.append(random.random()		)

       self.weight.insert(0 ,						self.bias		)

       UpperCAmelCase_				:  int  = 0

       while True:
            UpperCAmelCase_				:  List[str]  = False
            for i in range(self.number_sample		):
                 UpperCAmelCase_				:  Tuple  = 0
                 for j in range(self.col_sample + 1		):
                      UpperCAmelCase_				:  Optional[int]  = u + self.weight[j] * self.sample[i][j]
                 UpperCAmelCase_				:  Optional[Any]  = self.sign(snake_case_		)
                 if y != self.target[i]:
                      for j in range(self.col_sample + 1		):
                           UpperCAmelCase_				:  str  = (
                               self.weight[j]
                               + self.learning_rate
                               * (self.target[i] - y)
                               * self.sample[i][j]
                           )
                      UpperCAmelCase_				:  List[str]  = True
                 # print('Epoch: \n',epoch_count)
            UpperCAmelCase_				:  Union[str, Any]  = epoch_count + 1
            # if you want control the epoch or just by error
            if not has_misclassified:
                 print(('\nEpoch:\n', epoch_count)		)
                 print('------------------------\n'		)
                 # if epoch_count > self.epoch_number or not error:
                 break


  def _UpperCamelCase	(					self ,						snake_case_		):




       '''simple docstring'''

       if len(self.sample		) == 0:
            raise ValueError('Sample data can not be empty'		)
       sample.insert(0 ,						self.bias		)
       UpperCAmelCase_				:  Optional[int]  = 0
       for i in range(self.col_sample + 1		):
            UpperCAmelCase_				:  Dict  = u + self.weight[i] * sample[i]

       UpperCAmelCase_				:  Optional[int]  = self.sign(snake_case_		)

       if y == -1:
            print(('Sample: ', sample)		)
            print('classification: P1'		)
       else:
            print(('Sample: ', sample)		)
            print('classification: P2'		)






  def _UpperCamelCase	(					self ,						snake_case_		):




       '''simple docstring'''

       return 1 if u >= 0 else -1







snake_case__	:     Optional[int]      =    [
    [-0.6_508, 0.1_097, 4.0_009],
    [-1.4_492, 0.8_896, 4.4_005],
    [2.0_850, 0.6_876, 12.0_710],
    [0.2_626, 1.1_476, 7.7_985],
    [0.6_418, 1.0_234, 7.0_427],
    [0.2_569, 0.6_730, 8.3_265],
    [1.1_155, 0.6_043, 7.4_446],
    [0.0_914, 0.3_399, 7.0_677],
    [0.0_121, 0.5_256, 4.6_316],
    [-0.0_429, 0.4_660, 5.4_323],
    [0.4_340, 0.6_870, 8.2_287],
    [0.2_735, 1.0_287, 7.1_934],
    [0.4_839, 0.4_851, 7.4_850],
    [0.4_089, -0.1_267, 5.5_019],
    [1.4_391, 0.1_614, 8.5_843],
    [-0.9_115, -0.1_973, 2.1_962],
    [0.3_654, 1.0_475, 7.4_858],
    [0.2_144, 0.7_515, 7.1_699],
    [0.2_013, 1.0_014, 6.5_489],
    [0.6_483, 0.2_183, 5.8_991],
    [-0.1_147, 0.2_242, 7.2_435],
    [-0.7_970, 0.8_795, 3.8_762],
    [-1.0_625, 0.6_366, 2.4_707],
    [0.5_307, 0.1_285, 5.6_883],
    [-1.2_200, 0.7_777, 1.7_252],
    [0.3_957, 0.1_076, 5.6_623],
    [-0.1_013, 0.5_989, 7.1_812],
    [2.4_482, 0.9_455, 11.2_095],
    [2.0_149, 0.6_192, 10.9_263],
    [0.2_012, 0.2_611, 5.4_631],
]

snake_case__	:     Dict      =    [
    -1,
    -1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    -1,
    -1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    -1,
    1,
    -1,
    1,
]


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 snake_case__	:     List[Any]      =    Perceptron(
     sample=samples, target=target, learning_rate=0.01, epoch_number=1000, bias=-1
 )
 network.training()
 print('''Finished training perceptron''')
 print('''Enter values to predict or q to exit''')
 while True:
  snake_case__	:     Optional[Any]      =    []
  for i in range(len(samples[0])):
   snake_case__	:     Optional[int]      =    input('''value: ''').strip()
   if user_input == "q":
    break
   snake_case__	:     List[str]      =    float(user_input)
   sample.insert(i, observation)
  network.sort(sample)









import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class 					__lowerCamelCase							(      __snake_case      ):
      lowerCamelCase_      : Dict  			=   'AutoFeatureExtractor'
      lowerCamelCase_      : Optional[int]  			=   'Speech2Text2Tokenizer'







      def __init__(	self				,    lowerCamelCase				,    lowerCamelCase  )		->			List[str]:
        super().__init__(lowerCamelCase				,    lowerCamelCase  )
        snake_case_					= self.feature_extractor
        snake_case_					= False







      def __call__(	self				,    *lowerCamelCase				,    **lowerCamelCase  )		->			Dict:
        # For backward compatibility
        if self._in_target_context_manager:
          return self.current_processor(*lowerCamelCase				,    **lowerCamelCase  )

        if "raw_speech" in kwargs:
          warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""  )
          snake_case_					= kwargs.pop("""raw_speech"""  )
        else:
          snake_case_					= kwargs.pop("""audio"""				,    lowerCamelCase  )
        snake_case_					= kwargs.pop("""sampling_rate"""				,    lowerCamelCase  )
        snake_case_					= kwargs.pop("""text"""				,    lowerCamelCase  )
        if len(lowerCamelCase  ) > 0:
          snake_case_					= args[0]
          snake_case_					= args[1:]

        if audio is None and text is None:
          raise ValueError("""You need to specify either an `audio` or `text` input to process."""  )

        if audio is not None:
          snake_case_					= self.feature_extractor(lowerCamelCase				,    *lowerCamelCase				,    sampling_rate=lowerCamelCase				,    **lowerCamelCase  )
        if text is not None:
          snake_case_					= self.tokenizer(lowerCamelCase				,    **lowerCamelCase  )

        if text is None:
          return inputs
        elif audio is None:
          return encodings
        else:
          snake_case_					= encodings["""input_ids"""]
          return inputs







      def       lowerCAmelCase_       (	self				,    *lowerCamelCase				,    **lowerCamelCase  )		->			int:
        return self.tokenizer.batch_decode(*lowerCamelCase				,    **lowerCamelCase  )







      def       lowerCAmelCase_       (	self				,    *lowerCamelCase				,    **lowerCamelCase  )		->			Union[str, Any]:
        return self.tokenizer.decode(*lowerCamelCase				,    **lowerCamelCase  )







      @contextmanager
      def       lowerCAmelCase_       (	self  )		->			Any:
        warnings.warn(
            """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
            """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
            """your audio inputs, or in a separate call."""  )
        snake_case_					= True
        snake_case_					= self.tokenizer
        yield
        snake_case_					= self.feature_extractor
        snake_case_					= False