



"""simple docstring"""
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


_UpperCAmelCase       =     logging.get_logger(__name__)
_UpperCAmelCase       =     {'vocab_file': 'spiece.model'}

_UpperCAmelCase       =     {
    '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',
    },
}

_UpperCAmelCase       =     {
    'google/fnet-base': 5_1_2,
    'google/fnet-large': 5_1_2,
}

_UpperCAmelCase       =     '▁'
class    a		(					_A    ):
  UpperCamelCase		:			Dict         =   VOCAB_FILES_NAMES
  UpperCamelCase		:			Optional[int]         =   PRETRAINED_VOCAB_FILES_MAP
  UpperCamelCase		:			Optional[int]         =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  UpperCamelCase		:			Union[str, Any]         =   ['input_ids', 'token_type_ids']



  def __init__(  self	:   Optional[Any]    ,						lowerCAmelCase	:   Optional[int]    ,						lowerCAmelCase	:   Optional[Any]=False    ,						lowerCAmelCase	:   Dict=True    ,						lowerCAmelCase	:   Union[str, Any]=True    ,						lowerCAmelCase	:   List[Any]="<unk>"    ,						lowerCAmelCase	:   Union[str, Any]="[SEP]"    ,						lowerCAmelCase	:   Dict="<pad>"    ,						lowerCAmelCase	:   int="[CLS]"    ,						lowerCAmelCase	:   int="[MASK]"    ,						lowerCAmelCase	:   Optional[Dict[str, Any]] = None    ,						**lowerCAmelCase	:   List[Any]    ,						)       ->   None:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	int            =(
         AddedToken(UpperCamelCase__    ,						lstrip=UpperCamelCase__    ,						rstrip=UpperCamelCase__    ,						normalized=UpperCamelCase__						)
         if isinstance(UpperCamelCase__    ,						UpperCamelCase__						)
         else mask_token
     )

     SCREAMING_SNAKE_CASE_:	int            ={} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         do_lower_case=UpperCamelCase__    ,						remove_space=UpperCamelCase__    ,						keep_accents=UpperCamelCase__    ,						unk_token=UpperCamelCase__    ,						sep_token=UpperCamelCase__    ,						pad_token=UpperCamelCase__    ,						cls_token=UpperCamelCase__    ,						mask_token=UpperCamelCase__    ,						sp_model_kwargs=self.sp_model_kwargs    ,						**UpperCamelCase__    ,						)

     SCREAMING_SNAKE_CASE_:	Optional[int]            =do_lower_case
     SCREAMING_SNAKE_CASE_:	Optional[int]            =remove_space
     SCREAMING_SNAKE_CASE_:	int            =keep_accents
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =vocab_file

     SCREAMING_SNAKE_CASE_:	Tuple            =spm.SentencePieceProcessor(**self.sp_model_kwargs						)
     self.sp_model.Load(UpperCamelCase__						)



  @property
  def 		lowerCamelCase__    (  self	:   Union[str, Any]						)       ->   Optional[Any]:


     '''simple docstring'''




     return len(self.sp_model						)



  def 		lowerCamelCase__    (  self	:   Optional[int]						)       ->   int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	str            ={self.convert_ids_to_tokens(UpperCamelCase__						): i for i in range(self.vocab_size						)}
     vocab.update(self.added_tokens_encoder						)
     return vocab



  def __getstate__(  self	:   Any						)       ->   Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[Any]            =self.__dict__.copy()
     SCREAMING_SNAKE_CASE_:	int            =None
     return state



  def __setstate__(  self	:   Tuple    ,						lowerCAmelCase	:   int						)       ->   int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =d

     # for backward compatibility
     if not hasattr(self    ,						"""sp_model_kwargs"""						):
        SCREAMING_SNAKE_CASE_:	Optional[Any]            ={}

     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =spm.SentencePieceProcessor(**self.sp_model_kwargs						)
     self.sp_model.Load(self.vocab_file						)



  def 		lowerCamelCase__    (  self	:   Dict    ,						lowerCAmelCase	:   List[str]						)       ->   Union[str, Any]:


     '''simple docstring'''




     if self.remove_space:
        SCREAMING_SNAKE_CASE_:	Tuple            =""" """.join(inputs.strip().split()						)
     else:
        SCREAMING_SNAKE_CASE_:	Dict            =inputs
     SCREAMING_SNAKE_CASE_:	Any            =outputs.replace("""``"""    ,						"""\""""						).replace("""''"""    ,						"""\""""						)

     if not self.keep_accents:
        SCREAMING_SNAKE_CASE_:	List[Any]            =unicodedata.normalize("""NFKD"""    ,						UpperCamelCase__						)
        SCREAMING_SNAKE_CASE_:	int            ="""""".join([c for c in outputs if not unicodedata.combining(UpperCamelCase__						)]						)
     if self.do_lower_case:
        SCREAMING_SNAKE_CASE_:	List[Any]            =outputs.lower()

     return outputs



  def 		lowerCamelCase__    (  self	:   Tuple    ,						lowerCAmelCase	:   str						)       ->   List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	List[str]            =self.preprocess_text(UpperCamelCase__						)
     SCREAMING_SNAKE_CASE_:	List[Any]            =self.sp_model.encode(UpperCamelCase__    ,						out_type=UpperCamelCase__						)
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =[]
     for piece in pieces:
        if len(UpperCamelCase__						) > 1 and piece[-1] == str(""","""						) and piece[-2].isdigit():
           SCREAMING_SNAKE_CASE_:	List[Any]            =self.sp_model.EncodeAsPieces(piece[:-1].replace(UpperCamelCase__    ,						""""""						)						)
           if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
              if len(cur_pieces[0]						) == 1:
                 SCREAMING_SNAKE_CASE_:	Tuple            =cur_pieces[1:]
              else:
                 SCREAMING_SNAKE_CASE_:	Dict            =cur_pieces[0][1:]
           cur_pieces.append(piece[-1]						)
           new_pieces.extend(UpperCamelCase__						)
        else:
           new_pieces.append(UpperCamelCase__						)

     return new_pieces



  def 		lowerCamelCase__    (  self	:   Dict    ,						lowerCAmelCase	:   Optional[int]						)       ->   Optional[int]:


     '''simple docstring'''




     return self.sp_model.PieceToId(UpperCamelCase__						)



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


     '''simple docstring'''




     return self.sp_model.IdToPiece(UpperCamelCase__						)



  def 		lowerCamelCase__    (  self	:   Tuple    ,						lowerCAmelCase	:   int						)       ->   int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =[]
     SCREAMING_SNAKE_CASE_:	List[Any]            =""""""
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =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(UpperCamelCase__						) + token
           SCREAMING_SNAKE_CASE_:	str            =True
           SCREAMING_SNAKE_CASE_:	Dict            =[]
        else:
           current_sub_tokens.append(UpperCamelCase__						)
           SCREAMING_SNAKE_CASE_:	int            =False
     out_string += self.sp_model.decode(UpperCamelCase__						)
     return out_string.strip()



  def 		lowerCamelCase__    (  self	:   Optional[Any]    ,						lowerCAmelCase	:   List[int]    ,						lowerCAmelCase	:   bool = False    ,						lowerCAmelCase	:   bool = None    ,						lowerCAmelCase	:   bool = True    ,						**lowerCAmelCase	:   Optional[Any]    ,						)       ->   str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	List[Any]            =kwargs.pop("""use_source_tokenizer"""    ,						UpperCamelCase__						)

     SCREAMING_SNAKE_CASE_:	Dict            =self.convert_ids_to_tokens(UpperCamelCase__    ,						skip_special_tokens=UpperCamelCase__						)

     # 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
     SCREAMING_SNAKE_CASE_:	Any            =[]
     SCREAMING_SNAKE_CASE_:	List[Any]            =[]
     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(UpperCamelCase__						)						)
              SCREAMING_SNAKE_CASE_:	Optional[Any]            =[]
           sub_texts.append(UpperCamelCase__						)
        else:
           current_sub_text.append(UpperCamelCase__						)
     if current_sub_text:
        sub_texts.append(self.convert_tokens_to_string(UpperCamelCase__						)						)

     # Mimic the behavior of the Rust tokenizer:
     # No space after <unk>
     if spaces_between_special_tokens:
        SCREAMING_SNAKE_CASE_:	Tuple            =re.sub(R"""(<unk>) """    ,						R"""\1"""    ,						""" """.join(UpperCamelCase__						)						)
     else:
        SCREAMING_SNAKE_CASE_:	Tuple            ="""""".join(UpperCamelCase__						)

     SCREAMING_SNAKE_CASE_:	List[str]            =(
         clean_up_tokenization_spaces
         if clean_up_tokenization_spaces is not None
         else self.clean_up_tokenization_spaces
     )
     if clean_up_tokenization_spaces:
        SCREAMING_SNAKE_CASE_:	Optional[Any]            =self.clean_up_tokenization(UpperCamelCase__						)
        return clean_text
     else:
        return text



  def 		lowerCamelCase__    (  self	:   Optional[Any]    ,						lowerCAmelCase	:   List[int]    ,						lowerCAmelCase	:   Optional[List[int]] = None						)       ->   List[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[int]            =[self.sep_token_id]
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =[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 		lowerCamelCase__    (  self	:   Any    ,						lowerCAmelCase	:   List[int]    ,						lowerCAmelCase	:   Optional[List[int]] = None    ,						lowerCAmelCase	:   bool = False						)       ->   List[int]:


     '''simple docstring'''




     if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=UpperCamelCase__    ,						token_ids_a=UpperCamelCase__    ,						already_has_special_tokens=UpperCamelCase__						)

     if token_ids_a is not None:
        return [1] + ([0] * len(UpperCamelCase__						)) + [1] + ([0] * len(UpperCamelCase__						)) + [1]
     return [1] + ([0] * len(UpperCamelCase__						)) + [1]



  def 		lowerCamelCase__    (  self	:   int    ,						lowerCAmelCase	:   List[int]    ,						lowerCAmelCase	:   Optional[List[int]] = None						)       ->   List[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	int            =[self.sep_token_id]
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =[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 		lowerCamelCase__    (  self	:   int    ,						lowerCAmelCase	:   str    ,						lowerCAmelCase	:   Optional[str] = None						)       ->   Tuple[str]:


     '''simple docstring'''




     if not os.path.isdir(UpperCamelCase__						):
        logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
        return
     SCREAMING_SNAKE_CASE_:	Tuple            =os.path.join(
         UpperCamelCase__    ,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

     if os.path.abspath(self.vocab_file						) != os.path.abspath(UpperCamelCase__						) and os.path.isfile(self.vocab_file						):
        copyfile(self.vocab_file    ,						UpperCamelCase__						)
     elif not os.path.isfile(self.vocab_file						):
        with open(UpperCamelCase__    ,						"""wb"""						) as fi:
           SCREAMING_SNAKE_CASE_:	Union[str, Any]            =self.sp_model.serialized_model_proto()
           fi.write(UpperCamelCase__						)

     return (out_vocab_file,)









"""simple docstring"""
from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase	):
   seed(1	)
   return [gauss(lowercase							,     lowercase	) for _ in range(lowercase	)]





def        __magic_name__			(							lowercase							,     lowercase	):
   return [k for k in range(lowercase	) for _ in range(instance_count[k]	)]





def        __magic_name__			(							lowercase							,     lowercase	):
   return sum(lowercase	) / instance_count





def        __magic_name__			(							lowercase							,     lowercase	):
   return instance_count / total_count





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	Optional[Any]            =[]  # An empty list to store all squared differences
   # iterate over number of elements in items
   for i in range(len(lowercase	)	):
      # for loop iterates over number of elements in inner layer of items
      for j in range(len(items[i]	)	):
         # appending squared differences to 'squared_diff' list
         squared_diff.append((items[i][j] - means[i]) ** 2	)

    # one divided by (the number of all instances - number of classes) multiplied by
    # sum of all squared differences
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =len(lowercase	)  # Number of classes in dataset
   return 1 / (total_count - n_classes) * sum(lowercase	)





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	Optional[Any]            =[]
   # for loop iterates over number of elements in list
   for i in range(len(lowercase	)	):
      # for loop iterates over number of inner items of each element
      for j in range(len(x_items[i]	)	):
         SCREAMING_SNAKE_CASE_:	Optional[int]            =[]  # to store all discriminant values of each item as a list
         # for loop iterates over number of classes we have in our dataset
         for k in range(len(lowercase	)	):
            # appending values of discriminants for each class to 'temp' list
            temp.append(
                x_items[i][j] * (means[k] / variance)
                - (means[k] ** 2 / (2 * variance))
                + log(probabilities[k]	)	)
         # appending discriminant values of each item to 'results' list
         results.append(lowercase	)

   return [result.index(max(lowercase	)	) for result in results]





def        __magic_name__			(							lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	Dict            =sum(1 for i, j in zip(lowercase							,     lowercase	) if i == j	)
   # percentage of accuracy equals to number of correct predictions divided by number
   # of all data and multiplied by 100
   return (correct / len(lowercase	)) * 100


_UpperCAmelCase       =     TypeVar("""num""")





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase							,     lowercase = lambda lowercase	: True							,     lowercase = None							,     ):
   while True:
      try:
         SCREAMING_SNAKE_CASE_:	List[Any]            =input_type(input(lowercase	).strip() or default	)
         if condition(lowercase	):
            return user_input
         else:
            print(f'''{user_input}: {err_msg}'''	)
            continue
      except ValueError:
         print(
             f'''{user_input}: Incorrect input type, expected {input_type.__name__!r}'''	)





def        __magic_name__			(							):
   while True:
      print(""" Linear Discriminant Analysis """.center(50							,     """*"""	)	)
      print("""*""" * 50							,     """\n"""	)
      print("""First of all we should specify the number of classes that"""	)
      print("""we want to generate as training dataset"""	)
      # Trying to get number of classes
      SCREAMING_SNAKE_CASE_:	List[Any]            =valid_input(
          input_type=lowercase							,     condition=lambda lowercase	: x > 0							,     input_msg="""Enter the number of classes (Data Groupings): """							,     err_msg="""Number of classes should be positive!"""							,     )

      print("""-""" * 100	)

      # Trying to get the value of standard deviation
      SCREAMING_SNAKE_CASE_:	Union[str, Any]            =valid_input(
          input_type=lowercase							,     condition=lambda lowercase	: x >= 0							,     input_msg=(
              """Enter the value of standard deviation"""
              """(Default value is 1.0 for all classes): """
          )							,     err_msg="""Standard deviation should not be negative!"""							,     default="""1.0"""							,     )

      print("""-""" * 100	)

      # Trying to get number of instances in classes and theirs means to generate
      # dataset
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =[]  # An empty list to store instance counts of classes in dataset
      for i in range(lowercase	):
         SCREAMING_SNAKE_CASE_:	int            =valid_input(
             input_type=lowercase							,     condition=lambda lowercase	: x > 0							,     input_msg=(f'''Enter The number of instances for class_{i+1}: ''')							,     err_msg="""Number of instances should be positive!"""							,     )
         counts.append(lowercase	)
      print("""-""" * 100	)

      # An empty list to store values of user-entered means of classes
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =[]
      for a in range(lowercase	):
         SCREAMING_SNAKE_CASE_:	Tuple            =valid_input(
             input_type=lowercase							,     input_msg=(f'''Enter the value of mean for class_{a+1}: ''')							,     err_msg="""This is an invalid value."""							,     )
         user_means.append(lowercase	)
      print("""-""" * 100	)

      print("""Standard deviation: """							,     lowercase	)
      # print out the number of instances in classes in separated line
      for i, count in enumerate(lowercase							,     1	):
         print(f'''Number of instances in class_{i} is: {count}'''	)
      print("""-""" * 100	)

      # print out mean values of classes separated line
      for i, user_mean in enumerate(lowercase							,     1	):
         print(f'''Mean of class_{i} is: {user_mean}'''	)
      print("""-""" * 100	)

      # Generating training dataset drawn from gaussian distribution
      SCREAMING_SNAKE_CASE_:	Tuple            =[
          gaussian_distribution(user_means[j]							,     lowercase							,     counts[j]	)
          for j in range(lowercase	)
      ]
      print("""Generated Normal Distribution: \n"""							,     lowercase	)
      print("""-""" * 100	)

      # Generating Ys to detecting corresponding classes
      SCREAMING_SNAKE_CASE_:	int            =y_generator(lowercase							,     lowercase	)
      print("""Generated Corresponding Ys: \n"""							,     lowercase	)
      print("""-""" * 100	)

      # Calculating the value of actual mean for each class
      SCREAMING_SNAKE_CASE_:	str            =[calculate_mean(counts[k]							,     x[k]	) for k in range(lowercase	)]
      # for loop iterates over number of elements in 'actual_means' list and print
      # out them in separated line
      for i, actual_mean in enumerate(lowercase							,     1	):
         print(f'''Actual(Real) mean of class_{i} is: {actual_mean}'''	)
      print("""-""" * 100	)

      # Calculating the value of probabilities for each class
      SCREAMING_SNAKE_CASE_:	List[Any]            =[
          calculate_probabilities(counts[i]							,     sum(lowercase	)	) for i in range(lowercase	)
      ]

      # for loop iterates over number of elements in 'probabilities' list and print
      # out them in separated line
      for i, probability in enumerate(lowercase							,     1	):
         print(f'''Probability of class_{i} is: {probability}'''	)
      print("""-""" * 100	)

      # Calculating the values of variance for each class
      SCREAMING_SNAKE_CASE_:	str            =calculate_variance(lowercase							,     lowercase							,     sum(lowercase	)	)
      print("""Variance: """							,     lowercase	)
      print("""-""" * 100	)

      # Predicting Y values
      # storing predicted Y values in 'pre_indexes' variable
      SCREAMING_SNAKE_CASE_:	Dict            =predict_y_values(lowercase							,     lowercase							,     lowercase							,     lowercase	)
      print("""-""" * 100	)

      # Calculating Accuracy of the model
      print(f'''Accuracy: {accuracy(lowercase							,     lowercase	)}'''	)
      print("""-""" * 100	)
      print(""" DONE """.center(100							,     """+"""	)	)

      if input("""Press any key to restart or 'q' for quit: """	).strip().lower() == "q":
         print("""\n""" + """GoodBye!""".center(100							,     """-"""	) + """\n"""	)
         break
      system("""cls""" if name == """nt""" else """clear"""	)  # noqa: S605


if __name__ == "__main__":
   main()



