



"""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()








"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase       =     logging.get_logger(__name__)


_UpperCAmelCase       =     {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all MVP models at https://huggingface.co/models?filter=mvp
_UpperCAmelCase       =     {
    """vocab_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
    },
    """added_tokens.json""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
    },
    """merges_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
    },
}

_UpperCAmelCase       =     {
    """RUCAIBox/mvp""": 1_0_2_4,
}





@lru_cache()
def        __magic_name__			(							):
   SCREAMING_SNAKE_CASE_:	Optional[Any]            =(
       list(range(ord("""!"""	)							,     ord("""~"""	) + 1	)	) + list(range(ord("""¡"""	)							,     ord("""¬"""	) + 1	)	) + list(range(ord("""®"""	)							,     ord("""ÿ"""	) + 1	)	)
   )
   SCREAMING_SNAKE_CASE_:	Any            =bs[:]
   SCREAMING_SNAKE_CASE_:	str            =0
   for b in range(2**8	):
      if b not in bs:
         bs.append(lowercase	)
         cs.append(2**8 + n	)
         n += 1
   SCREAMING_SNAKE_CASE_:	List[Any]            =[chr(lowercase	) for n in cs]
   return dict(zip(lowercase							,     lowercase	)	)





def        __magic_name__			(							lowercase	):
   SCREAMING_SNAKE_CASE_:	Optional[int]            =set()
   SCREAMING_SNAKE_CASE_:	Tuple            =word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)	)
      SCREAMING_SNAKE_CASE_:	Dict            =char
   return pairs
class    a		(					UpperCAmelCase__    ):
  UpperCamelCase		:			str         =   VOCAB_FILES_NAMES
  UpperCamelCase		:			List[Any]         =   PRETRAINED_VOCAB_FILES_MAP
  UpperCamelCase		:			Optional[Any]         =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  UpperCamelCase		:			Optional[int]         =   ['input_ids', 'attention_mask']



  def __init__(  self	:   Tuple    ,						lowerCAmelCase	:   Dict    ,						lowerCAmelCase	:   str    ,						lowerCAmelCase	:   List[str]="replace"    ,						lowerCAmelCase	:   List[Any]="<s>"    ,						lowerCAmelCase	:   str="</s>"    ,						lowerCAmelCase	:   Dict="</s>"    ,						lowerCAmelCase	:   Optional[int]="<s>"    ,						lowerCAmelCase	:   int="<unk>"    ,						lowerCAmelCase	:   Optional[int]="<pad>"    ,						lowerCAmelCase	:   str="<mask>"    ,						lowerCAmelCase	:   Dict=False    ,						**lowerCAmelCase	:   Any    ,						)       ->   Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Any            =AddedToken(lowerCAmelCase    ,						lstrip=lowerCAmelCase    ,						rstrip=lowerCAmelCase						) if isinstance(lowerCAmelCase    ,						lowerCAmelCase						) else bos_token
     SCREAMING_SNAKE_CASE_:	int            =AddedToken(lowerCAmelCase    ,						lstrip=lowerCAmelCase    ,						rstrip=lowerCAmelCase						) if isinstance(lowerCAmelCase    ,						lowerCAmelCase						) else eos_token
     SCREAMING_SNAKE_CASE_:	int            =AddedToken(lowerCAmelCase    ,						lstrip=lowerCAmelCase    ,						rstrip=lowerCAmelCase						) if isinstance(lowerCAmelCase    ,						lowerCAmelCase						) else sep_token
     SCREAMING_SNAKE_CASE_:	Dict            =AddedToken(lowerCAmelCase    ,						lstrip=lowerCAmelCase    ,						rstrip=lowerCAmelCase						) if isinstance(lowerCAmelCase    ,						lowerCAmelCase						) else cls_token
     SCREAMING_SNAKE_CASE_:	str            =AddedToken(lowerCAmelCase    ,						lstrip=lowerCAmelCase    ,						rstrip=lowerCAmelCase						) if isinstance(lowerCAmelCase    ,						lowerCAmelCase						) else unk_token
     SCREAMING_SNAKE_CASE_:	int            =AddedToken(lowerCAmelCase    ,						lstrip=lowerCAmelCase    ,						rstrip=lowerCAmelCase						) if isinstance(lowerCAmelCase    ,						lowerCAmelCase						) else pad_token

     # Mask token behave like a normal word, i.e. include the space before it
     SCREAMING_SNAKE_CASE_:	Optional[int]            =AddedToken(lowerCAmelCase    ,						lstrip=lowerCAmelCase    ,						rstrip=lowerCAmelCase						) if isinstance(lowerCAmelCase    ,						lowerCAmelCase						) else mask_token

     super().__init__(
         errors=lowerCAmelCase    ,						bos_token=lowerCAmelCase    ,						eos_token=lowerCAmelCase    ,						unk_token=lowerCAmelCase    ,						sep_token=lowerCAmelCase    ,						cls_token=lowerCAmelCase    ,						pad_token=lowerCAmelCase    ,						mask_token=lowerCAmelCase    ,						add_prefix_space=lowerCAmelCase    ,						**lowerCAmelCase    ,						)
     with open(lowerCAmelCase    ,						encoding="""utf-8"""						) as vocab_handle:
        SCREAMING_SNAKE_CASE_:	Any            =json.load(lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	Dict            ={v: k for k, v in self.encoder.items()}
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =errors  # how to handle errors in decoding
     SCREAMING_SNAKE_CASE_:	Any            =bytes_to_unicode()
     SCREAMING_SNAKE_CASE_:	str            ={v: k for k, v in self.byte_encoder.items()}
     with open(lowerCAmelCase    ,						encoding="""utf-8"""						) as merges_handle:
        SCREAMING_SNAKE_CASE_:	Union[str, Any]            =merges_handle.read().split("""\n"""						)[1:-1]
     SCREAMING_SNAKE_CASE_:	Dict            =[tuple(merge.split()						) for merge in bpe_merges]
     SCREAMING_SNAKE_CASE_:	Any            =dict(zip(lowerCAmelCase    ,						range(len(lowerCAmelCase						)						)						)						)
     SCREAMING_SNAKE_CASE_:	int            ={}
     SCREAMING_SNAKE_CASE_:	Optional[int]            =add_prefix_space

     # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
     SCREAMING_SNAKE_CASE_:	Tuple            =re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""						)



  @property
  def 		lowerCamelCase__    (  self	:   int						)       ->   List[Any]:


     '''simple docstring'''




     return len(self.encoder						)



  def 		lowerCamelCase__    (  self	:   Dict						)       ->   Optional[Any]:


     '''simple docstring'''




     return dict(self.encoder    ,						**self.added_tokens_encoder						)



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


     '''simple docstring'''




     if token in self.cache:
        return self.cache[token]
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =tuple(lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =get_pairs(lowerCAmelCase						)

     if not pairs:
        return token

     while True:
        SCREAMING_SNAKE_CASE_:	int            =min(lowerCAmelCase    ,						key=lambda lowerCAmelCase						: self.bpe_ranks.get(lowerCAmelCase    ,						float("""inf"""						)						)						)
        if bigram not in self.bpe_ranks:
           break
        SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Optional[Any]            =bigram
        SCREAMING_SNAKE_CASE_:	List[Any]            =[]
        SCREAMING_SNAKE_CASE_:	int            =0
        while i < len(lowerCAmelCase						):
           try:
              SCREAMING_SNAKE_CASE_:	List[Any]            =word.index(lowerCAmelCase    ,						lowerCAmelCase						)
           except ValueError:
              new_word.extend(word[i:]						)
              break
           else:
              new_word.extend(word[i:j]						)
              SCREAMING_SNAKE_CASE_:	str            =j

           if word[i] == first and i < len(lowerCAmelCase						) - 1 and word[i + 1] == second:
              new_word.append(first + second						)
              i += 2
           else:
              new_word.append(word[i]						)
              i += 1
        SCREAMING_SNAKE_CASE_:	Any            =tuple(lowerCAmelCase						)
        SCREAMING_SNAKE_CASE_:	str            =new_word
        if len(lowerCAmelCase						) == 1:
           break
        else:
           SCREAMING_SNAKE_CASE_:	List[str]            =get_pairs(lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =""" """.join(lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	List[Any]            =word
     return word



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Tuple            =[]
     for token in re.findall(self.pat    ,						lowerCAmelCase						):
        SCREAMING_SNAKE_CASE_:	Union[str, Any]            ="""""".join(
            self.byte_encoder[b] for b in token.encode("""utf-8"""						)						)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
        bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCAmelCase						).split(""" """						)						)
     return bpe_tokens



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


     '''simple docstring'''




     return self.encoder.get(lowerCAmelCase    ,						self.encoder.get(self.unk_token						)						)



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


     '''simple docstring'''




     return self.decoder.get(lowerCAmelCase						)



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[int]            ="""""".join(lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	Dict            =bytearray([self.byte_decoder[c] for c in text]						).decode("""utf-8"""    ,						errors=self.errors						)
     return text



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


     '''simple docstring'''




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

     with open(lowerCAmelCase    ,						"""w"""    ,						encoding="""utf-8"""						) as f:
        f.write(json.dumps(self.encoder    ,						indent=2    ,						sort_keys=lowerCAmelCase    ,						ensure_ascii=lowerCAmelCase						) + """\n"""						)

     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =0
     with open(lowerCAmelCase    ,						"""w"""    ,						encoding="""utf-8"""						) as writer:
        writer.write("""#version: 0.2\n"""						)
        for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,						key=lambda lowerCAmelCase						: kv[1]						):
           if index != token_index:
              logger.warning(
                  f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                  """ Please check that the tokenizer is not corrupted!"""						)
              SCREAMING_SNAKE_CASE_:	Union[str, Any]            =token_index
           writer.write(""" """.join(lowerCAmelCase						) + """\n"""						)
           index += 1

     return vocab_file, merge_file



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


     '''simple docstring'''




     if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     SCREAMING_SNAKE_CASE_:	List[str]            =[self.cls_token_id]
     SCREAMING_SNAKE_CASE_:	List[str]            =[self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep



  def 		lowerCamelCase__    (  self	:   List[str]    ,						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=lowerCAmelCase    ,						token_ids_a=lowerCAmelCase    ,						already_has_special_tokens=lowerCAmelCase						)

     if token_ids_a is None:
        return [1] + ([0] * len(lowerCAmelCase						)) + [1]
     return [1] + ([0] * len(lowerCAmelCase						)) + [1, 1] + ([0] * len(lowerCAmelCase						)) + [1]



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[Any]            =[self.sep_token_id]
     SCREAMING_SNAKE_CASE_:	str            =[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 + sep + token_ids_a + sep						) * [0]





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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	int            =kwargs.pop("""add_prefix_space"""    ,						self.add_prefix_space						)
     if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase						) > 0 and not text[0].isspace()):
        SCREAMING_SNAKE_CASE_:	List[Any]            =""" """ + text
     return (text, kwargs)



