


"""simple docstring"""


import os

from ...utils import logging
from .utils import DataProcessor, InputExample


a_											=     logging.get_logger(__name__)
class snake_case   (	lowercase_):







		def __init__(       self   :							Any     , a__   :							str     , a__   :							Optional[Any]=None		)							->    Dict:





						'''simple docstring'''







						_A       	=      language
						_A       	=      train_language







		def 			a_			(       self   :							Optional[Any]     , a__   :							List[str]		)							->    Union[str, Any]:





						'''simple docstring'''







						_A       	=      self.language if self.train_language is None else self.train_language
						_A       	=      self._read_tsv(os.path.join(a__     , F"""XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv"""		)		)
						_A       	=      []
						for i, line in enumerate(a__		):
										if i == 0:
														continue
										_A       	=      F"""train-{i}"""
										_A       	=      line[0]
										_A       	=      line[1]
										_A       	=      "contradiction" if line[2] == "contradictory" else line[2]
										if not isinstance(a__     , a__		):
														raise ValueError(F"""Training input {text_a} is not a string"""		)
										if not isinstance(a__     , a__		):
														raise ValueError(F"""Training input {text_b} is not a string"""		)
										if not isinstance(a__     , a__		):
														raise ValueError(F"""Training label {label} is not a string"""		)
										examples.append(InputExample(guid=a__     , text_a=a__     , text_b=a__     , label=a__		)		)
						return examples







		def 			a_			(       self   :							Union[str, Any]     , a__   :							int		)							->    Any:





						'''simple docstring'''







						_A       	=      self._read_tsv(os.path.join(a__     , "XNLI-1.0/xnli.test.tsv"		)		)
						_A       	=      []
						for i, line in enumerate(a__		):
										if i == 0:
														continue
										_A       	=      line[0]
										if language != self.language:
														continue
										_A       	=      F"""test-{i}"""
										_A       	=      line[6]
										_A       	=      line[7]
										_A       	=      line[1]
										if not isinstance(a__     , a__		):
														raise ValueError(F"""Training input {text_a} is not a string"""		)
										if not isinstance(a__     , a__		):
														raise ValueError(F"""Training input {text_b} is not a string"""		)
										if not isinstance(a__     , a__		):
														raise ValueError(F"""Training label {label} is not a string"""		)
										examples.append(InputExample(guid=a__     , text_a=a__     , text_b=a__     , label=a__		)		)
						return examples







		def 			a_			(       self   :							Union[str, Any]		)							->    List[str]:





						'''simple docstring'''







						return ["contradiction", "entailment", "neutral"]





a_											=     {
    "xnli": XnliProcessor,
}

a_											=     {
    "xnli": "classification",
}

a_											=     {
    "xnli": 3,
}

import random

class 		__magic_name__				:


  """simple docstring"""





  def __init__(					self	,		a__	,		a__	,		a__ = 0.01	,		a__ = 10_00	,		a__ = -1	,		):
    _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(a__		)
    _lowerCamelCase      =						len(sample[0]		)  # number of columns in dataset
    _lowerCamelCase      =						[]


  def     _UpperCAmelCase  (					self		):
    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(a__		)
        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     _UpperCAmelCase  (					self	,		a__		):
    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(a__		)

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


  def     _UpperCAmelCase  (					self	,		a__		):
    return 1 if u >= 0 else -1







_UpperCAmelCase    			=					[
    [-0.6_5_0_8, 0.1_0_9_7, 4.0_0_0_9],
    [-1.4_4_9_2, 0.8_8_9_6, 4.4_0_0_5],
    [2.0_8_5_0, 0.6_8_7_6, 1_2.0_7_1_0],
    [0.2_6_2_6, 1.1_4_7_6, 7.7_9_8_5],
    [0.6_4_1_8, 1.0_2_3_4, 7.0_4_2_7],
    [0.2_5_6_9, 0.6_7_3_0, 8.3_2_6_5],
    [1.1_1_5_5, 0.6_0_4_3, 7.4_4_4_6],
    [0.0_9_1_4, 0.3_3_9_9, 7.0_6_7_7],
    [0.0_1_2_1, 0.5_2_5_6, 4.6_3_1_6],
    [-0.0_4_2_9, 0.4_6_6_0, 5.4_3_2_3],
    [0.4_3_4_0, 0.6_8_7_0, 8.2_2_8_7],
    [0.2_7_3_5, 1.0_2_8_7, 7.1_9_3_4],
    [0.4_8_3_9, 0.4_8_5_1, 7.4_8_5_0],
    [0.4_0_8_9, -0.1_2_6_7, 5.5_0_1_9],
    [1.4_3_9_1, 0.1_6_1_4, 8.5_8_4_3],
    [-0.9_1_1_5, -0.1_9_7_3, 2.1_9_6_2],
    [0.3_6_5_4, 1.0_4_7_5, 7.4_8_5_8],
    [0.2_1_4_4, 0.7_5_1_5, 7.1_6_9_9],
    [0.2_0_1_3, 1.0_0_1_4, 6.5_4_8_9],
    [0.6_4_8_3, 0.2_1_8_3, 5.8_9_9_1],
    [-0.1_1_4_7, 0.2_2_4_2, 7.2_4_3_5],
    [-0.7_9_7_0, 0.8_7_9_5, 3.8_7_6_2],
    [-1.0_6_2_5, 0.6_3_6_6, 2.4_7_0_7],
    [0.5_3_0_7, 0.1_2_8_5, 5.6_8_8_3],
    [-1.2_2_0_0, 0.7_7_7_7, 1.7_2_5_2],
    [0.3_9_5_7, 0.1_0_7_6, 5.6_6_2_3],
    [-0.1_0_1_3, 0.5_9_8_9, 7.1_8_1_2],
    [2.4_4_8_2, 0.9_4_5_5, 1_1.2_0_9_5],
    [2.0_1_4_9, 0.6_1_9_2, 1_0.9_2_6_3],
    [0.2_0_1_2, 0.2_6_1_1, 5.4_6_3_1],
]

_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=0.0_1, epoch_number=1000, 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)




