




import warnings

from ...utils import logging
from .image_processing_vit import ViTImageProcessor


__lowerCamelCase :    Tuple      						=      logging.get_logger(__name__)


class  lowerCamelCase     (		_UpperCamelCase    ):






			'''simple docstring'''





			def __init__(			self      : Optional[Any]							,    *lowerCamelCase_      : List[Any]							,    **lowerCamelCase_      : Optional[Any]	)    -> None:
								warnings.warn(
								    '''The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
								    ''' use ViTImageProcessor instead.'''							,    _SCREAMING_SNAKE_CASE							,    )
								super().__init__(*_SCREAMING_SNAKE_CASE							,    **_SCREAMING_SNAKE_CASE	)











"""simple docstring"""



import random




class 							__lowercase:






						def __init__(       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE = 0.01     ,    _SCREAMING_SNAKE_CASE = 10_00     ,    _SCREAMING_SNAKE_CASE = -1     ,    )      ->	None:




											"""simple docstring"""





											lowerCamelCase__					=sample
											if len(self.sample) == 0:
																raise ValueError('Sample data can not be empty')
											lowerCamelCase__					=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__					=learning_rate
											lowerCamelCase__					=epoch_number
											lowerCamelCase__					=bias
											lowerCamelCase__					=len(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__					=len(sample[0])  # number of columns in dataset
											lowerCamelCase__					=[]






						def 		lowerCAmelCase (       self)      ->	None:




											"""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)

											lowerCamelCase__					=0

											while True:
																lowerCamelCase__					=False
																for i in range(self.number_sample):
																					lowerCamelCase__					=0
																					for j in range(self.col_sample + 1):
																										lowerCamelCase__					=u + self.weight[j] * self.sample[i][j]
																					lowerCamelCase__					=self.sign(_SCREAMING_SNAKE_CASE)
																					if y != self.target[i]:
																										for j in range(self.col_sample + 1):
																															lowerCamelCase__					=(
																															    self.weight[j]
																															    + self.learning_rate
																															    * (self.target[i] - y)
																															    * self.sample[i][j]
																															)
																										lowerCamelCase__					=True
            # print('Epoch: \n',epoch_count)
																lowerCamelCase__					=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 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE)      ->	None:




											"""simple docstring"""





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

											lowerCamelCase__					=self.sign(_SCREAMING_SNAKE_CASE)

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






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE)      ->	int:




											"""simple docstring"""





											return 1 if u >= 0 else -1



_snake_case							:   int	  =       [
    [-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],
]

_snake_case							:   List[str]	  =       [
    -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							:   int	  =       Perceptron(
						    sample=samples, target=target, learning_rate=0.01, epoch_number=1_000, bias=-1
						)
						network.training()
						print("Finished training perceptron")
						print("Enter values to predict or q to exit")
						while True:
												_snake_case							:   list	  =       []
												for i in range(len(samples[0])):
																		_snake_case							:   List[str]	  =       input("value: ").strip()
																		if user_input == "q":
																								break
																		_snake_case							:   Optional[Any]	  =       float(user_input)
																		sample.insert(i, observation)
												network.sort(sample)