code: import os
  import re
  import unicodedata
  from shutil import copyfile
  from typing import Any, Dict, List, Optional, Tuple
  
  import sentencepiece as spm
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
  
  
  A_:      Optional[int]        =			logging.get_logger(__name__)
  A_:      Optional[Any]        =			{"""vocab_file""": """spiece.model"""}
  
  A_:      int        =			{
      """vocab_file""": {
          """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/spiece.model""",
          """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/spiece.model""",
      },
  }
  
  A_:      Union[str, Any]        =			{
      """google/fnet-base""": 512,
      """google/fnet-large""": 512,
  }
  
  A_:      Optional[Any]        =			"""▁"""
  
  
  
  
  
  
  class _lowercase					(       a__						):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							lowerCAmelCase__       =					VOCAB_FILES_NAMES
  							lowerCAmelCase__       =					PRETRAINED_VOCAB_FILES_MAP
  							lowerCAmelCase__       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  							lowerCAmelCase__       =					['input_ids', 'token_type_ids']
  
  
  
  							def __init__(    self     ,   UpperCAmelCase     ,   UpperCAmelCase=False     ,   UpperCAmelCase=True     ,   UpperCAmelCase=True     ,   UpperCAmelCase="<unk>"     ,   UpperCAmelCase="[SEP]"     ,   UpperCAmelCase="<pad>"     ,   UpperCAmelCase="[CLS]"     ,   UpperCAmelCase="[MASK]"     ,   UpperCAmelCase = None     ,   **UpperCAmelCase     ,   ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	(
  														    AddedToken(__A     ,   lstrip=__A     ,   rstrip=__A     ,   normalized=__A  )
  														    if isinstance(__A     ,   __A  )
  														    else mask_token
  														)
  
  														_lowercase				=	{} if sp_model_kwargs is None else sp_model_kwargs
  
  														super().__init__(
  														    do_lower_case=__A     ,   remove_space=__A     ,   keep_accents=__A     ,   unk_token=__A     ,   sep_token=__A     ,   pad_token=__A     ,   cls_token=__A     ,   mask_token=__A     ,   sp_model_kwargs=self.sp_model_kwargs     ,   **__A     ,   )
  
  														_lowercase				=	do_lower_case
  														_lowercase				=	remove_space
  														_lowercase				=	keep_accents
  														_lowercase				=	vocab_file
  
  														_lowercase				=	spm.SentencePieceProcessor(**self.sp_model_kwargs  )
  														self.sp_model.Load(__A  )
  
  
  
  							@property
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return len(self.sp_model  )
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	{self.convert_ids_to_tokens(__A  ): i for i in range(self.vocab_size  )}
  														vocab.update(self.added_tokens_encoder  )
  														return vocab
  
  
  
  							def __getstate__(    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	self.__dict__.copy()
  														_lowercase				=	None
  														return state
  
  
  
  							def __setstate__(    self     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	d
  
  														# for backward compatibility
  														if not hasattr(self     ,   """sp_model_kwargs"""  ):
  																					_lowercase				=	{}
  
  														_lowercase				=	spm.SentencePieceProcessor(**self.sp_model_kwargs  )
  														self.sp_model.Load(self.vocab_file  )
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														if self.remove_space:
  																					_lowercase				=	" ".join(inputs.strip().split()  )
  														else:
  																					_lowercase				=	inputs
  														_lowercase				=	outputs.replace("""``"""     ,   """\""""  ).replace("""''"""     ,   """\""""  )
  
  														if not self.keep_accents:
  																					_lowercase				=	unicodedata.normalize("""NFKD"""     ,   __A  )
  																					_lowercase				=	"".join([c for c in outputs if not unicodedata.combining(__A  )]  )
  														if self.do_lower_case:
  																					_lowercase				=	outputs.lower()
  
  														return outputs
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	self.preprocess_text(__A  )
  														_lowercase				=	self.sp_model.encode(__A     ,   out_type=__A  )
  														_lowercase				=	[]
  														for piece in pieces:
  																					if len(__A  ) > 1 and piece[-1] == str(""","""  ) and piece[-2].isdigit():
  																												_lowercase				=	self.sp_model.EncodeAsPieces(piece[:-1].replace(__A     ,   """"""  )  )
  																												if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
  																																			if len(cur_pieces[0]  ) == 1:
  																																										_lowercase				=	cur_pieces[1:]
  																																			else:
  																																										_lowercase				=	cur_pieces[0][1:]
  																												cur_pieces.append(piece[-1]  )
  																												new_pieces.extend(__A  )
  																					else:
  																												new_pieces.append(__A  )
  
  														return new_pieces
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return self.sp_model.PieceToId(__A  )
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return self.sp_model.IdToPiece(__A  )
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	[]
  														_lowercase				=	""
  														_lowercase				=	False
  														for token in tokens:
  																					# make sure that special tokens are not decoded using sentencepiece model
  																					if token in self.all_special_tokens:
  																												if not prev_is_special:
  																																			out_string += " "
  																												out_string += self.sp_model.decode(__A  ) + token
  																												_lowercase				=	True
  																												_lowercase				=	[]
  																					else:
  																												current_sub_tokens.append(__A  )
  																												_lowercase				=	False
  														out_string += self.sp_model.decode(__A  )
  														return out_string.strip()
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase = False     ,   UpperCAmelCase = None     ,   UpperCAmelCase = True     ,   **UpperCAmelCase     ,   ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	kwargs.pop("""use_source_tokenizer"""     ,   __A  )
  
  														_lowercase				=	self.convert_ids_to_tokens(__A     ,   skip_special_tokens=__A  )
  
  														# To avoid mixing byte-level and unicode for byte-level BPT
  														# we need to build string separately for added tokens and byte-level tokens
  														# cf. https://github.com/huggingface/transformers/issues/1133
  														_lowercase				=	[]
  														_lowercase				=	[]
  														for token in filtered_tokens:
  																					if skip_special_tokens and token in self.all_special_ids:
  																												continue
  																					if token in self.added_tokens_encoder:
  																												if current_sub_text:
  																																			sub_texts.append(self.convert_tokens_to_string(__A  )  )
  																																			_lowercase				=	[]
  																												sub_texts.append(__A  )
  																					else:
  																												current_sub_text.append(__A  )
  														if current_sub_text:
  																					sub_texts.append(self.convert_tokens_to_string(__A  )  )
  
  														# Mimic the behavior of the Rust tokenizer:
  														# No space after <unk>
  														if spaces_between_special_tokens:
  																					_lowercase				=	re.sub(r"""(<unk>) """     ,   r"""\1"""     ,   """ """.join(__A  )  )
  														else:
  																					_lowercase				=	"".join(__A  )
  
  														_lowercase				=	(
  														    clean_up_tokenization_spaces
  														    if clean_up_tokenization_spaces is not None
  														    else self.clean_up_tokenization_spaces
  														)
  														if clean_up_tokenization_spaces:
  																					_lowercase				=	self.clean_up_tokenization(__A  )
  																					return clean_text
  														else:
  																					return text
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase = None  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	[self.sep_token_id]
  														_lowercase				=	[self.cls_token_id]
  														if token_ids_a is None:
  																					return cls + token_ids_a + sep
  														return cls + token_ids_a + sep + token_ids_a + sep
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase = None     ,   UpperCAmelCase = False  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														if already_has_special_tokens:
  																					return super().get_special_tokens_mask(
  																					    token_ids_a=__A     ,   token_ids_a=__A     ,   already_has_special_tokens=__A  )
  
  														if token_ids_a is not None:
  																					return [1] + ([0] * len(__A  )) + [1] + ([0] * len(__A  )) + [1]
  														return [1] + ([0] * len(__A  )) + [1]
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase = None  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	[self.sep_token_id]
  														_lowercase				=	[self.cls_token_id]
  
  														if token_ids_a is None:
  																					return len(cls + token_ids_a + sep  ) * [0]
  														return len(cls + token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase = None  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														if not os.path.isdir(__A  ):
  																					logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
  																					return
  														_lowercase				=	os.path.join(
  														    __A     ,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )
  
  														if os.path.abspath(self.vocab_file  ) != os.path.abspath(__A  ) and os.path.isfile(self.vocab_file  ):
  																					copyfile(self.vocab_file     ,   __A  )
  														elif not os.path.isfile(self.vocab_file  ):
  																					with open(__A     ,   """wb"""  ) as fi:
  																												_lowercase				=	self.sp_model.serialized_model_proto()
  																												fi.write(__A  )
  
  														return (out_vocab_file,)
  
  
  
style_context: import random
  
  
  class _lowercase					:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							def __init__(    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase = 0.01     ,   UpperCAmelCase = 1000     ,   UpperCAmelCase = -1     ,   ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	sample
  														if len(self.sample  ) == 0:
  																					raise ValueError("""Sample data can not be empty"""  )
  														_lowercase				=	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"""  )
  														_lowercase				=	learning_rate
  														_lowercase				=	epoch_number
  														_lowercase				=	bias
  														_lowercase				=	len(UpperCAmelCase  )
  														_lowercase				=	len(sample[0]  )  # number of columns in dataset
  														_lowercase				=	[]
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''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  )
  
  														_lowercase				=	0
  
  														while True:
  																					_lowercase				=	False
  																					for i in range(self.number_sample  ):
  																												_lowercase				=	0
  																												for j in range(self.col_sample + 1  ):
  																																			_lowercase				=	u + self.weight[j] * self.sample[i][j]
  																												_lowercase				=	self.sign(UpperCAmelCase  )
  																												if y != self.target[i]:
  																																			for j in range(self.col_sample + 1  ):
  																																										_lowercase				=	(
  																																										    self.weight[j]
  																																										    + self.learning_rate
  																																										    * (self.target[i] - y)
  																																										    * self.sample[i][j]
  																																										)
  																																			_lowercase				=	True
              # print('Epoch: \n',epoch_count)
  																					_lowercase				=	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     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														if len(self.sample  ) == 0:
  																					raise ValueError("""Sample data can not be empty"""  )
  														sample.insert(0     ,   self.bias  )
  														_lowercase				=	0
  														for i in range(self.col_sample + 1  ):
  																					_lowercase				=	u + self.weight[i] * sample[i]
  
  														_lowercase				=	self.sign(UpperCAmelCase  )
  
  														if y == -1:
  																					print(("""Sample: """, sample)  )
  																					print("""classification: P1"""  )
  														else:
  																					print(("""Sample: """, sample)  )
  																					print("""classification: P2"""  )
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return 1 if u >= 0 else -1
  
  
  
  
  
  
  A_:      Optional[int]        =			[
      [-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],
  ]
  
  A_:      Optional[int]        =			[
      -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_:      str        =			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:
  								A_:      list        =			[]
  								for i in range(len(samples[0])):
  												A_:      Dict        =			input('value: ').strip()
  												if user_input == "q":
  																break
  												A_:      Union[str, Any]        =			float(user_input)
  												sample.insert(i, observation)
  								network.sort(sample)
  
  
  