





import random

class 		__a							:



       def __init__(     self	:   str					,			UpperCAmelCase	:   list[list[float]]					,			UpperCAmelCase	:   list[int]					,			UpperCAmelCase	:   float = 0.01					,			UpperCAmelCase	:   int = 10_00					,			UpperCAmelCase	:   float = -1					,			):
           lowerCAmelCase_					:	int						=   sample
           if len(self.sample			) == 0:
               raise ValueError("""Sample data can not be empty"""			)
           lowerCAmelCase_					:	List[Any]						=   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"""			)
           lowerCAmelCase_					:	Dict						=   learning_rate
           lowerCAmelCase_					:	Tuple						=   epoch_number
           lowerCAmelCase_					:	Tuple						=   bias
           lowerCAmelCase_					:	Any						=   len(UpperCAmelCase			)
           lowerCAmelCase_					:	List[str]						=   len(sample[0]			)  # number of columns in dataset
           lowerCAmelCase_					:	list						=   []



       def  A							(     self	:   Tuple			):
           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			)

           lowerCAmelCase_					:	Optional[int]						=   0

           while True:
               lowerCAmelCase_					:	List[Any]						=   False
               for i in range(self.number_sample			):
                   lowerCAmelCase_					:	Union[str, Any]						=   0
                   for j in range(self.col_sample + 1			):
                       lowerCAmelCase_					:	Any						=   u + self.weight[j] * self.sample[i][j]
                   lowerCAmelCase_					:	List[Any]						=   self.sign(UpperCAmelCase			)
                   if y != self.target[i]:
                       for j in range(self.col_sample + 1			):
                           lowerCAmelCase_					:	List[Any]						=   (
                               self.weight[j]
                               + self.learning_rate
                               * (self.target[i] - y)
                               * self.sample[i][j]
                           )
                       lowerCAmelCase_					:	Tuple						=   True
            # print('Epoch: \n',epoch_count)
               lowerCAmelCase_					:	List[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  A							(     self	:   Dict					,			UpperCAmelCase	:   list[float]			):
           if len(self.sample			) == 0:
               raise ValueError("""Sample data can not be empty"""			)
           sample.insert(0					,			self.bias			)
           lowerCAmelCase_					:	Any						=   0
           for i in range(self.col_sample + 1			):
               lowerCAmelCase_					:	List[str]						=   u + self.weight[i] * sample[i]

           lowerCAmelCase_					:	List[Any]						=   self.sign(UpperCAmelCase			)

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



       def  A							(     self	:   Optional[int]					,			UpperCAmelCase	:   float			):
           return 1 if u >= 0 else -1






__UpperCAmelCase						=      [
    [-0.6508, 0.1097, 4.0009],
    [-1.4492, 0.8896, 4.4005],
    [2.0850, 0.6876, 12.0710],
    [0.2626, 1.1476, 7.7985],
    [0.6418, 1.0234, 7.0427],
    [0.2569, 0.6730, 8.3265],
    [1.1155, 0.6043, 7.4446],
    [0.0914, 0.3399, 7.0677],
    [0.0121, 0.5256, 4.6316],
    [-0.0429, 0.4660, 5.4323],
    [0.4340, 0.6870, 8.2287],
    [0.2735, 1.0287, 7.1934],
    [0.4839, 0.4851, 7.4850],
    [0.4089, -0.1267, 5.5019],
    [1.4391, 0.1614, 8.5843],
    [-0.9115, -0.1973, 2.1962],
    [0.3654, 1.0475, 7.4858],
    [0.2144, 0.7515, 7.1699],
    [0.2013, 1.0014, 6.5489],
    [0.6483, 0.2183, 5.8991],
    [-0.1147, 0.2242, 7.2435],
    [-0.7970, 0.8795, 3.8762],
    [-1.0625, 0.6366, 2.4707],
    [0.5307, 0.1285, 5.6883],
    [-1.2200, 0.7777, 1.7252],
    [0.3957, 0.1076, 5.6623],
    [-0.1013, 0.5989, 7.1812],
    [2.4482, 0.9455, 11.2095],
    [2.0149, 0.6192, 10.9263],
    [0.2012, 0.2611, 5.4631],
]

__UpperCAmelCase						=      [
    -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()

  __UpperCAmelCase						=      Perceptron(
      sample=samples, target=target, learning_rate=0.01, epoch_number=10_00, bias=-1
  )
  network.training()
  print('Finished training perceptron')
  print('Enter values to predict or q to exit')
  while True:
    __UpperCAmelCase						=      []
    for i in range(len(samples[0])):
      __UpperCAmelCase						=      input('value: ').strip()
      if user_input == "q":
        break
      __UpperCAmelCase						=      float(user_input)
      sample.insert(i, observation)
    network.sort(sample)









from ..utils import deprecate
from .attention_processor import (  # noqa: F401
    Attention,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRALinearLayer,
    LoRAXFormersAttnProcessor,
    SlicedAttnAddedKVProcessor,
    SlicedAttnProcessor,
    XFormersAttnProcessor,
)
from .attention_processor import AttnProcessor as AttnProcessorRename  # noqa: F401


deprecate(
    'cross_attention',
    '0.20.0',
    'Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.',
    standard_warn=False,
)


__UpperCAmelCase						=      AttentionProcessor

class 		__a							(	__UpperCamelCase      ):



       def __init__(     self	:   List[Any]					,			*UpperCAmelCase	:   List[Any]					,			**UpperCAmelCase	:   Any			):
           lowerCAmelCase_					:	str						=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"			)			)} instead.'
           deprecate("""cross_attention"""					,			"""0.20.0"""					,			UpperCAmelCase					,			standard_warn=UpperCAmelCase			)
           super().__init__(*UpperCAmelCase					,			**UpperCAmelCase			)






class 		__a							(	__UpperCamelCase      ):



       def __init__(     self	:   Optional[Any]					,			*UpperCAmelCase	:   List[Any]					,			**UpperCAmelCase	:   List[str]			):
           lowerCAmelCase_					:	List[Any]						=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"			)			)} instead.'
           deprecate("""cross_attention"""					,			"""0.20.0"""					,			UpperCAmelCase					,			standard_warn=UpperCAmelCase			)
           super().__init__(*UpperCAmelCase					,			**UpperCAmelCase			)






class 		__a							(	__UpperCamelCase      ):



       def __init__(     self	:   List[str]					,			*UpperCAmelCase	:   str					,			**UpperCAmelCase	:   Union[str, Any]			):
           lowerCAmelCase_					:	Tuple						=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"			)			)} instead.'
           deprecate("""cross_attention"""					,			"""0.20.0"""					,			UpperCAmelCase					,			standard_warn=UpperCAmelCase			)
           super().__init__(*UpperCAmelCase					,			**UpperCAmelCase			)






class 		__a							(	__UpperCamelCase      ):



       def __init__(     self	:   Optional[int]					,			*UpperCAmelCase	:   Union[str, Any]					,			**UpperCAmelCase	:   Any			):
           lowerCAmelCase_					:	Tuple						=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"			)			)} instead.'
           deprecate("""cross_attention"""					,			"""0.20.0"""					,			UpperCAmelCase					,			standard_warn=UpperCAmelCase			)
           super().__init__(*UpperCAmelCase					,			**UpperCAmelCase			)






class 		__a							(	__UpperCamelCase      ):



       def __init__(     self	:   str					,			*UpperCAmelCase	:   Any					,			**UpperCAmelCase	:   List[Any]			):
           lowerCAmelCase_					:	Union[str, Any]						=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"			)			)} instead.'
           deprecate("""cross_attention"""					,			"""0.20.0"""					,			UpperCAmelCase					,			standard_warn=UpperCAmelCase			)
           super().__init__(*UpperCAmelCase					,			**UpperCAmelCase			)






class 		__a							(	__UpperCamelCase      ):



       def __init__(     self	:   List[str]					,			*UpperCAmelCase	:   Union[str, Any]					,			**UpperCAmelCase	:   Tuple			):
           lowerCAmelCase_					:	Tuple						=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"			)			)} instead.'
           deprecate("""cross_attention"""					,			"""0.20.0"""					,			UpperCAmelCase					,			standard_warn=UpperCAmelCase			)
           super().__init__(*UpperCAmelCase					,			**UpperCAmelCase			)






class 		__a							(	__UpperCamelCase      ):



       def __init__(     self	:   Dict					,			*UpperCAmelCase	:   int					,			**UpperCAmelCase	:   int			):
           lowerCAmelCase_					:	List[str]						=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"			)			)} instead.'
           deprecate("""cross_attention"""					,			"""0.20.0"""					,			UpperCAmelCase					,			standard_warn=UpperCAmelCase			)
           super().__init__(*UpperCAmelCase					,			**UpperCAmelCase			)






class 		__a							(	__UpperCamelCase      ):



       def __init__(     self	:   Optional[Any]					,			*UpperCAmelCase	:   Any					,			**UpperCAmelCase	:   int			):
           lowerCAmelCase_					:	Union[str, Any]						=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"			)			)} instead.'
           deprecate("""cross_attention"""					,			"""0.20.0"""					,			UpperCAmelCase					,			standard_warn=UpperCAmelCase			)
           super().__init__(*UpperCAmelCase					,			**UpperCAmelCase			)


