




'''simple docstring'''







import random


class       _a  :
       def __init__(     self						:     Dict       ,    lowercase						:     list[list[float]]       ,    lowercase						:     list[int]       ,    lowercase						:     float = 0.01       ,    lowercase						:     int = 1_000       ,    lowercase						:     float = -1       ,    ):



           '''simple docstring'''

           UpperCAmelCase      	=		sample
           if len(self.sample    ) == 0:
               raise ValueError('''Sample data can not be empty'''    )
           UpperCAmelCase      	=		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      	=		learning_rate
           UpperCAmelCase      	=		epoch_number
           UpperCAmelCase      	=		bias
           UpperCAmelCase      	=		len(lowercase    )
           UpperCAmelCase      	=		len(sample[0]    )  # number of columns in dataset
           UpperCAmelCase      	=		[]
       def    A				(     self						:     int    ):



           '''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      	=		0

           while True:
               UpperCAmelCase      	=		False
               for i in range(self.number_sample    ):
                   UpperCAmelCase      	=		0
                   for j in range(self.col_sample + 1    ):
                       UpperCAmelCase      	=		u + self.weight[j] * self.sample[i][j]
                   UpperCAmelCase      	=		self.sign(lowercase    )
                   if y != self.target[i]:
                       for j in range(self.col_sample + 1    ):
                           UpperCAmelCase      	=		(
                               self.weight[j]
                               + self.learning_rate
                               * (self.target[i] - y)
                               * self.sample[i][j]
                           )
                       UpperCAmelCase      	=		True
            # print('Epoch: \n',epoch_count)
               UpperCAmelCase      	=		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						:     Optional[int]       ,    lowercase						:     list[float]    ):



           '''simple docstring'''

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

           UpperCAmelCase      	=		self.sign(lowercase    )

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







       def    A				(     self						:     Optional[Any]       ,    lowercase						:     float    ):



           '''simple docstring'''

           return 1 if u >= 0 else -1






A			   =[
    [-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],
]

A			   =[
    -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()

   A			   =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:
      A			   =[]
      for i in range(len(samples[0])):
         A			   =input('value: ').strip()
         if user_input == "q":
            break
         A			   =float(user_input)
         sample.insert(i, observation)
      network.sort(sample)










'''simple docstring'''







import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class       _a  (     __a							,							unittest.TestCase			):
       __a    :					str           = HerbertTokenizer
       __a    :					Optional[Any]           = HerbertTokenizerFast
       __a    :					Any           = True
       def    A				(     self						:     Any    ):



           '''simple docstring'''

           super().setUp()

           # Use a simpler test file without japanese/chinese characters
           with open(f"{get_tests_dir()}/fixtures/sample_text_no_unicode.txt"       ,    encoding='''utf-8'''    ) as f_data:
               UpperCAmelCase      	=		f_data.read().replace('''\n\n'''       ,    '''\n'''    ).strip()

           UpperCAmelCase      	=		[
               '''<s>''',
               '''</s>''',
               '''l''',
               '''o''',
               '''w''',
               '''e''',
               '''r''',
               '''s''',
               '''t''',
               '''i''',
               '''d''',
               '''n''',
               '''w</w>''',
               '''r</w>''',
               '''t</w>''',
               '''lo''',
               '''low''',
               '''er</w>''',
               '''low</w>''',
               '''lowest</w>''',
               '''newer</w>''',
               '''wider</w>''',
               ''',</w>''',
               '''<unk>''',
           ]
           UpperCAmelCase      	=		dict(zip(lowercase       ,    range(len(lowercase    )    )    )    )
           UpperCAmelCase      	=		['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

           UpperCAmelCase      	=		os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES['''vocab_file''']    )
           UpperCAmelCase      	=		os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES['''merges_file''']    )
           with open(self.vocab_file       ,    '''w'''    ) as fp:
               fp.write(json.dumps(lowercase    )    )
           with open(self.merges_file       ,    '''w'''    ) as fp:
               fp.write('''\n'''.join(lowercase    )    )
       def    A				(     self						:     List[Any]       ,    lowercase						:     Optional[Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		'''lower newer'''
           UpperCAmelCase      	=		'''lower newer'''
           return input_text, output_text
       def    A				(     self						:     int    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tokenizer_class(vocab_file=self.vocab_file       ,    merges_file=self.merges_file    )

           UpperCAmelCase      	=		'''lower'''
           UpperCAmelCase      	=		['''low''', '''er</w>''']
           UpperCAmelCase      	=		tokenizer.tokenize(lowercase    )
           self.assertListEqual(lowercase       ,    lowercase    )

           UpperCAmelCase      	=		tokens + ['''<unk>''']
           UpperCAmelCase      	=		[16, 17, 23]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase    )       ,    lowercase    )
       def    A				(     self						:     str    ):



           '''simple docstring'''

           if not self.test_rust_tokenizer:
               return

           UpperCAmelCase      	=		self.get_tokenizer()
           UpperCAmelCase      	=		self.get_rust_tokenizer()

           UpperCAmelCase      	=		'''lower,newer'''

           UpperCAmelCase      	=		tokenizer.tokenize(lowercase    )
           UpperCAmelCase      	=		rust_tokenizer.tokenize(lowercase    )
           self.assertListEqual(lowercase       ,    lowercase    )

           UpperCAmelCase      	=		tokenizer.encode(lowercase       ,    add_special_tokens=lowercase    )
           UpperCAmelCase      	=		rust_tokenizer.encode(lowercase       ,    add_special_tokens=lowercase    )
           self.assertListEqual(lowercase       ,    lowercase    )

           UpperCAmelCase      	=		self.get_rust_tokenizer()
           UpperCAmelCase      	=		tokenizer.encode(lowercase    )
           UpperCAmelCase      	=		rust_tokenizer.encode(lowercase    )
           self.assertListEqual(lowercase       ,    lowercase    )







       @slow
       def    A				(     self						:     int    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tokenizer_class.from_pretrained('''allegro/herbert-base-cased'''    )

           UpperCAmelCase      	=		tokenizer.encode('''konstruowanie sekwencji'''       ,    add_special_tokens=lowercase    )
           UpperCAmelCase      	=		tokenizer.encode('''konstruowanie wielu sekwencji'''       ,    add_special_tokens=lowercase    )

           UpperCAmelCase      	=		tokenizer.build_inputs_with_special_tokens(lowercase    )
           UpperCAmelCase      	=		tokenizer.build_inputs_with_special_tokens(lowercase       ,    lowercase    )

           assert encoded_sentence == [0] + text + [2]
           assert encoded_pair == [0] + text + [2] + text_a + [2]




