





"""simple docstring"""




import random




class 					_a		:






				def __init__(					self						:       Union[str, Any],					lowerCAmelCase__						:       list[list[float]],					lowerCAmelCase__						:       list[int],					lowerCAmelCase__						:       float = 0.01,					lowerCAmelCase__						:       int = 1_0_0_0,					lowerCAmelCase__						:       float = -1,					)      -> None:




										'''simple docstring'''



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






				def 							snake_case      (					self						:       int				)      -> 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				)

										_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     :   Dict      =       u + self.weight[j] * self.sample[i][j]
																						_UpperCamelCase     :   Union[str, Any]      =       self.sign(lowerCAmelCase__				)
																						if y != self.target[i]:
																												for j in range(self.col_sample + 1				):
																																		_UpperCamelCase     :   int      =       (
																																		    self.weight[j]
																																		    + self.learning_rate
																																		    * (self.target[i] - y)
																																		    * self.sample[i][j]
																																		)
																												_UpperCamelCase     :   Optional[int]      =       True
            # print('Epoch: \n',epoch_count)
																_UpperCamelCase     :   Dict      =       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 							snake_case      (					self						:       Tuple,					lowerCAmelCase__						:       list[float]				)      -> None:




										'''simple docstring'''



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

										_UpperCamelCase     :   Union[str, Any]      =       self.sign(lowerCAmelCase__				)

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






				def 							snake_case      (					self						:       str,					lowerCAmelCase__						:       float				)      -> int:




										'''simple docstring'''



										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=1_000, 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)





"""simple docstring"""






import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 							snake_case       (     SCREAMING_SNAKE_CASE_							,       unittest.TestCase		):
   a_					:		Optional[int]     					=     GPTNeoXJapaneseTokenizer
   a_					:		Dict     					=     False
   a_					:		Union[str, Any]     					=     {"""do_clean_text""": False, """add_prefix_space""": False}







   def    UpperCAmelCase__	( self)   ->Union[str, Any]:
      super().setUp()

      a_						   =     [
          "こん",
          "こんに",
          "にちは",
          "ばんは",
          "世界,㔺界",
          "、",
          "。",
          "<BR>",
          "<SP>",
          "<TAB>",
          "<URL>",
          "<EMAIL>",
          "<TEL>",
          "<DATE>",
          "<PRICE>",
          "<BLOCK>",
          "<KIGOU>",
          "<U2000U2BFF>",
          "<|emoji1|>",
          "<unk>",
          "<|startoftext|>",
          "<|endoftext|>",
      ]
      a_						   =     {"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}}  # 😀
      a_						   =     {"unk_token": "<unk>"}

      a_						   =     os.path.join(self.tmpdirname       ,							VOCAB_FILES_NAMES["vocab_file"])
      a_						   =     os.path.join(self.tmpdirname       ,							VOCAB_FILES_NAMES["emoji_file"])
      with open(self.vocab_file       ,							"w"       ,							encoding="utf-8") as vocab_writer:
         vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
      with open(self.emoji_file       ,							"w") as emoji_writer:
         emoji_writer.write(json.dumps(__UpperCAmelCase))







   def    UpperCAmelCase__	( self       ,							**__UpperCAmelCase)   ->int:
      kwargs.update(self.special_tokens_map)
      return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname       ,							**__UpperCAmelCase)







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase)   ->Dict:
      a_						   =     "こんにちは、世界。 \nこんばんは、㔺界。😀"
      a_						   =     "こんにちは、世界。 \nこんばんは、世界。😀"
      return input_text, output_text







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase)   ->Optional[Any]:
      a_   ,			a_						   =     self.get_input_output_texts(__UpperCAmelCase)
      a_						   =     tokenizer.encode(__UpperCAmelCase       ,							add_special_tokens=__UpperCAmelCase)
      a_						   =     tokenizer.decode(__UpperCAmelCase       ,							clean_up_tokenization_spaces=__UpperCAmelCase)
      return text, ids







   def    UpperCAmelCase__	( self)   ->Any:
      pass  # TODO add if relevant







   def    UpperCAmelCase__	( self)   ->int:
      pass  # TODO add if relevant







   def    UpperCAmelCase__	( self)   ->Optional[Any]:
      pass  # TODO add if relevant







   def    UpperCAmelCase__	( self)   ->List[str]:
      a_						   =     self.get_tokenizer()

      # Testing tokenization
      a_						   =     "こんにちは、世界。　こんばんは、㔺界。"
      a_						   =     ["こん", "にちは", "、", "世界", "。", "<SP>", "こん", "ばんは", "、", "㔺界", "。"]
      a_						   =     tokenizer.tokenize(__UpperCAmelCase)
      self.assertListEqual(__UpperCAmelCase       ,							__UpperCAmelCase)

      # Testing conversion to ids without special tokens
      a_						   =     [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
      a_						   =     tokenizer.convert_tokens_to_ids(__UpperCAmelCase)
      self.assertListEqual(__UpperCAmelCase       ,							__UpperCAmelCase)

      # Testing conversion to ids with special tokens
      a_						   =     tokens + [tokenizer.unk_token]
      a_						   =     [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
      a_						   =     tokenizer.convert_tokens_to_ids(__UpperCAmelCase)
      self.assertListEqual(__UpperCAmelCase       ,							__UpperCAmelCase)







   @slow
   def    UpperCAmelCase__	( self)   ->Tuple:
      a_						   =     self.tokenizer_class.from_pretrained("abeja/gpt-neox-japanese-2.7b")

      a_						   =     tokenizer.encode("ありがとう。"       ,							add_special_tokens=__UpperCAmelCase)
      a_						   =     tokenizer.encode("どういたしまして。"       ,							add_special_tokens=__UpperCAmelCase)

      a_						   =     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase)
      a_						   =     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase       ,							__UpperCAmelCase)

      assert encoded_sentence == ids_a
      assert encoded_pair == ids_a + ids_a







   def    UpperCAmelCase__	( self)   ->Tuple:
      # Intentionally convert some words to accommodate character fluctuations unique to Japanese
      pass







   def    UpperCAmelCase__	( self)   ->Union[str, Any]:
      # tokenizer has no padding token
      pass