


import random

class    __magic_name__						:


     def __init__(   self				:  Optional[Any]				,						lowerCamelCase__				:  Tuple				,						lowerCamelCase__				:  Optional[Any]				,						lowerCamelCase__				:  List[Any] = 0.01				,						lowerCamelCase__				:  Tuple = 1000				,						lowerCamelCase__				:  Optional[int] = -1				,						)	->   List[str]:





      '''simple docstring'''






      UpperCamelCase__       :     List[str] 							= sample
      if len(self.sample      ) == 0:
       raise ValueError('''Sample data can not be empty'''      )
      UpperCamelCase__       :     int 							= 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__       :     str 							= learning_rate
      UpperCamelCase__       :     List[str] 							= epoch_number
      UpperCamelCase__       :     Optional[int] 							= bias
      UpperCamelCase__       :     Optional[Any] 							= len(_lowercase      )
      UpperCamelCase__       :     Any 							= len(sample[0]      )  # number of columns in dataset
      UpperCamelCase__       :     list 							= []


     def   UpperCAmelCase__						(   self				:  Tuple      )	->   str:





      '''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__       :     int 							= False
       for i in range(self.number_sample      ):
        UpperCamelCase__       :     Dict 							= 0
        for j in range(self.col_sample + 1      ):
         UpperCamelCase__       :     str 							= u + self.weight[j] * self.sample[i][j]
        UpperCamelCase__       :     str 							= self.sign(_lowercase      )
        if y != self.target[i]:
         for j in range(self.col_sample + 1      ):
          UpperCamelCase__       :     Tuple 							= (
              self.weight[j]
              + self.learning_rate
              * (self.target[i] - y)
              * self.sample[i][j]
          )
         UpperCamelCase__       :     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				:  List[str]				,						lowerCamelCase__				:  Optional[Any]      )	->   Union[str, Any]:





      '''simple docstring'''






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

      UpperCamelCase__       :     Dict 							= self.sign(_lowercase      )

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


     def   UpperCAmelCase__						(   self				:  Optional[int]				,						lowerCamelCase__				:  int      )	->   Union[str, Any]:





      '''simple docstring'''






      return 1 if u >= 0 else -1



__UpperCamelCase							:							Optional[Any]    							=					[
    [-0.65_08, 0.10_97, 4.00_09],
    [-1.44_92, 0.88_96, 4.40_05],
    [2.08_50, 0.68_76, 12.07_10],
    [0.26_26, 1.14_76, 7.79_85],
    [0.64_18, 1.02_34, 7.04_27],
    [0.25_69, 0.67_30, 8.32_65],
    [1.11_55, 0.60_43, 7.44_46],
    [0.09_14, 0.33_99, 7.06_77],
    [0.01_21, 0.52_56, 4.63_16],
    [-0.04_29, 0.46_60, 5.43_23],
    [0.43_40, 0.68_70, 8.22_87],
    [0.27_35, 1.02_87, 7.19_34],
    [0.48_39, 0.48_51, 7.48_50],
    [0.40_89, -0.12_67, 5.50_19],
    [1.43_91, 0.16_14, 8.58_43],
    [-0.91_15, -0.19_73, 2.19_62],
    [0.36_54, 1.04_75, 7.48_58],
    [0.21_44, 0.75_15, 7.16_99],
    [0.20_13, 1.00_14, 6.54_89],
    [0.64_83, 0.21_83, 5.89_91],
    [-0.11_47, 0.22_42, 7.24_35],
    [-0.79_70, 0.87_95, 3.87_62],
    [-1.06_25, 0.63_66, 2.47_07],
    [0.53_07, 0.12_85, 5.68_83],
    [-1.22_00, 0.77_77, 1.72_52],
    [0.39_57, 0.10_76, 5.66_23],
    [-0.10_13, 0.59_89, 7.18_12],
    [2.44_82, 0.94_55, 11.20_95],
    [2.01_49, 0.61_92, 10.92_63],
    [0.20_12, 0.26_11, 5.46_31],
]

__UpperCamelCase							:							Tuple    							=					[
    -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							:							Dict    							=					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:
              __UpperCamelCase							:							list    							=					[]
              for i in range(len(samples[0])):
                     __UpperCamelCase							:							Any    							=					input("value: ").strip()
                     if user_input == "q":
                            break
                     __UpperCamelCase							:							int    							=					float(user_input)
                     sample.insert(i, observation)
              network.sort(sample)









from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_       : Union[str, Any]							=    logging.get_logger(__name__)

UpperCAmelCase_       : int							=    {
    """google/electra-small-generator""": """https://huggingface.co/google/electra-small-generator/resolve/main/config.json""",
    """google/electra-base-generator""": """https://huggingface.co/google/electra-base-generator/resolve/main/config.json""",
    """google/electra-large-generator""": """https://huggingface.co/google/electra-large-generator/resolve/main/config.json""",
    """google/electra-small-discriminator""": (
        """https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json"""
    ),
    """google/electra-base-discriminator""": (
        """https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json"""
    ),
    """google/electra-large-discriminator""": (
        """https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json"""
    ),
}





class   lowercase__     (				__A	):
  __UpperCamelCase						  =	"""electra"""







  def __init__(						self     ,    _lowercase=30_522     ,    _lowercase=128     ,    _lowercase=256     ,    _lowercase=12     ,    _lowercase=4     ,    _lowercase=1_024     ,    _lowercase="gelu"     ,    _lowercase=0.1     ,    _lowercase=0.1     ,    _lowercase=512     ,    _lowercase=2     ,    _lowercase=0.02     ,    _lowercase=1e-12     ,    _lowercase="first"     ,    _lowercase=True     ,    _lowercase="gelu"     ,    _lowercase=0.1     ,    _lowercase=0     ,    _lowercase="absolute"     ,    _lowercase=True     ,    _lowercase=None     ,    **_lowercase     ,    ):
       super().__init__(pad_token_id=_lowercase     ,    **_lowercase    )

       lowerCAmelCase_       :							Dict			 =		vocab_size
       lowerCAmelCase_       :							Tuple			 =		embedding_size
       lowerCAmelCase_       :							Any			 =		hidden_size
       lowerCAmelCase_       :							Optional[Any]			 =		num_hidden_layers
       lowerCAmelCase_       :							Union[str, Any]			 =		num_attention_heads
       lowerCAmelCase_       :							List[str]			 =		intermediate_size
       lowerCAmelCase_       :							Union[str, Any]			 =		hidden_act
       lowerCAmelCase_       :							Any			 =		hidden_dropout_prob
       lowerCAmelCase_       :							Optional[Any]			 =		attention_probs_dropout_prob
       lowerCAmelCase_       :							int			 =		max_position_embeddings
       lowerCAmelCase_       :							Optional[int]			 =		type_vocab_size
       lowerCAmelCase_       :							Dict			 =		initializer_range
       lowerCAmelCase_       :							Any			 =		layer_norm_eps

       lowerCAmelCase_       :							str			 =		summary_type
       lowerCAmelCase_       :							Optional[int]			 =		summary_use_proj
       lowerCAmelCase_       :							Optional[int]			 =		summary_activation
       lowerCAmelCase_       :							int			 =		summary_last_dropout
       lowerCAmelCase_       :							str			 =		position_embedding_type
       lowerCAmelCase_       :							Optional[int]			 =		use_cache
       lowerCAmelCase_       :							Tuple			 =		classifier_dropout





class   lowercase__     (				__A	):







  @property
  def 						UpperCAmelCase__		(						self    ):
       if self.task == "multiple-choice":
            lowerCAmelCase_       :							Optional[int]			 =		{0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
            lowerCAmelCase_       :							Any			 =		{0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""input_ids""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
               ("""token_type_ids""", dynamic_axis),
           ]    )
