



'''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  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		seed(1     )
		return [gauss(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ) for _ in range(SCREAMING_SNAKE_CASE     )]







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		return [k for k in range(SCREAMING_SNAKE_CASE     ) for _ in range(instance_count[k]     )]







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		return sum(SCREAMING_SNAKE_CASE     ) / instance_count







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		return instance_count / total_count







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Tuple										=      []  # An empty list to store all squared differences
		# iterate over number of elements in items
		for i in range(len(SCREAMING_SNAKE_CASE     )     ):
				# 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_						:Optional[Any]										=      len(SCREAMING_SNAKE_CASE     )  # Number of classes in dataset
		return 1 / (total_count - n_classes) * sum(SCREAMING_SNAKE_CASE     )







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      []
		# for loop iterates over number of elements in list
		for i in range(len(SCREAMING_SNAKE_CASE     )     ):
				# for loop iterates over number of inner items of each element
				for j in range(len(x_items[i]     )     ):
						SCREAMING_SNAKE_CASE_						: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(SCREAMING_SNAKE_CASE     )     ):
								# 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(SCREAMING_SNAKE_CASE     )

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







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Any										=      sum(1 for i, j in zip(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ) 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(SCREAMING_SNAKE_CASE     )) * 100


SCREAMING_SNAKE_CASE__     : List[Any]				=   TypeVar("""num""")







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE = lambda SCREAMING_SNAKE_CASE     : True ,		SCREAMING_SNAKE_CASE = None ,		):
		while True:
				try:
						SCREAMING_SNAKE_CASE_						:List[Any]										=      input_type(input(SCREAMING_SNAKE_CASE     ).strip() or default     )
						if condition(SCREAMING_SNAKE_CASE     ):
								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  SCREAMING_SNAKE_CASE__ (				):
		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_						:Optional[Any]										=      valid_input(
				    input_type=SCREAMING_SNAKE_CASE ,		condition=lambda SCREAMING_SNAKE_CASE     : 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_						:Optional[int]										=      valid_input(
				    input_type=SCREAMING_SNAKE_CASE ,		condition=lambda SCREAMING_SNAKE_CASE     : 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_						:Tuple										=      []  # An empty list to store instance counts of classes in dataset
				for i in range(SCREAMING_SNAKE_CASE     ):
						SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      valid_input(
						    input_type=SCREAMING_SNAKE_CASE ,		condition=lambda SCREAMING_SNAKE_CASE     : x > 0 ,		input_msg=(F'Enter The number of instances for class_{i+1}: ') ,		err_msg='Number of instances should be positive!' ,		)
						counts.append(SCREAMING_SNAKE_CASE     )
				print('-' * 100     )

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

				print('Standard deviation: ' ,		SCREAMING_SNAKE_CASE     )
				# print out the number of instances in classes in separated line
				for i, count in enumerate(SCREAMING_SNAKE_CASE ,		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(SCREAMING_SNAKE_CASE ,		1     ):
						print(F'Mean of class_{i} is: {user_mean}'     )
				print('-' * 100     )

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

				# Generating Ys to detecting corresponding classes
				SCREAMING_SNAKE_CASE_						:str										=      y_generator(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     )
				print('Generated Corresponding Ys: \n' ,		SCREAMING_SNAKE_CASE     )
				print('-' * 100     )

				# Calculating the value of actual mean for each class
				SCREAMING_SNAKE_CASE_						:Dict										=      [calculate_mean(counts[k] ,		x[k]     ) for k in range(SCREAMING_SNAKE_CASE     )]
				# for loop iterates over number of elements in 'actual_means' list and print
				# out them in separated line
				for i, actual_mean in enumerate(SCREAMING_SNAKE_CASE ,		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_						:Optional[Any]										=      [
				    calculate_probabilities(counts[i] ,		sum(SCREAMING_SNAKE_CASE     )     ) for i in range(SCREAMING_SNAKE_CASE     )
				]

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

				# Calculating the values of variance for each class
				SCREAMING_SNAKE_CASE_						:List[str]										=      calculate_variance(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		sum(SCREAMING_SNAKE_CASE     )     )
				print('Variance: ' ,		SCREAMING_SNAKE_CASE     )
				print('-' * 100     )

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

				# Calculating Accuracy of the model
				print(F'Accuracy: {accuracy(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     )}'     )
				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


snake_case_     			=						logging.get_logger(__name__)

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

snake_case_     			=						{
    """vocab_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/vocab.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/vocab.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/vocab.json""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/merges.txt""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/merges.txt""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/merges.txt""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"""
        ),
    },
}

snake_case_     			=						{
    """roberta-base""": 5_12,
    """roberta-large""": 5_12,
    """roberta-large-mnli""": 5_12,
    """distilroberta-base""": 5_12,
    """roberta-base-openai-detector""": 5_12,
    """roberta-large-openai-detector""": 5_12,
}
@lru_cache()
def 				_lowerCamelCase(	)		->	List[Any]:
    A							:							Tuple				=    (
        list(range(ord('''!'''		)     ,							ord('''~'''		) + 1		)		) + list(range(ord('''¡'''		)     ,							ord('''¬'''		) + 1		)		) + list(range(ord('''®'''		)     ,							ord('''ÿ'''		) + 1		)		)
    )
    A							:							List[Any]				=    bs[:]
    A							:							Tuple				=    0
    for b in range(2**8		):
        if b not in bs:
            bs.append(UpperCamelCase__		)
            cs.append(2**8 + n		)
            n += 1
    A							:							Optional[int]				=    [chr(UpperCamelCase__		) for n in cs]
    return dict(zip(UpperCamelCase__     ,							UpperCamelCase__		)		)
def 				_lowerCamelCase(	UpperCamelCase__     :			int		)		->	str:
    A							:							int				=    set()
    A							:							Any				=    word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)		)
        A							:							Tuple				=    char
    return pairs





class 	_lowercase    (				a					):
   _UpperCamelCase									= VOCAB_FILES_NAMES
   _UpperCamelCase									= PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase									= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase									= ["""input_ids""", """attention_mask"""]


   def __init__(			self     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase="replace"     , _UpperCAmelCase="<s>"     , _UpperCAmelCase="</s>"     , _UpperCAmelCase="</s>"     , _UpperCAmelCase="<s>"     , _UpperCAmelCase="<unk>"     , _UpperCAmelCase="<pad>"     , _UpperCAmelCase="<mask>"     , _UpperCAmelCase=False     , **_UpperCAmelCase     , ):
       A							:							Any				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else bos_token
       A							:							Optional[Any]				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else eos_token
       A							:							Any				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else sep_token
       A							:							List[Any]				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else cls_token
       A							:							List[Any]				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else unk_token
       A							:							List[Any]				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else pad_token

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

       super().__init__(
           errors=_UpperCAmelCase     , bos_token=_UpperCAmelCase     , eos_token=_UpperCAmelCase     , unk_token=_UpperCAmelCase     , sep_token=_UpperCAmelCase     , cls_token=_UpperCAmelCase     , pad_token=_UpperCAmelCase     , mask_token=_UpperCAmelCase     , add_prefix_space=_UpperCAmelCase     , **_UpperCAmelCase     , )

       with open(_UpperCAmelCase     , encoding='''utf-8'''       ) as vocab_handle:
           A							:							int				=    json.load(_UpperCAmelCase       )
       A							:							Union[str, Any]				=    {v: k for k, v in self.encoder.items()}
       A							:							Tuple				=    errors  # how to handle errors in decoding
       A							:							List[Any]				=    bytes_to_unicode()
       A							:							List[Any]				=    {v: k for k, v in self.byte_encoder.items()}
       with open(_UpperCAmelCase     , encoding='''utf-8'''       ) as merges_handle:
           A							:							Optional[int]				=    merges_handle.read().split('''\n'''       )[1:-1]
       A							:							int				=    [tuple(merge.split()       ) for merge in bpe_merges]
       A							:							Union[str, Any]				=    dict(zip(_UpperCAmelCase     , range(len(_UpperCAmelCase       )       )       )       )
       A							:							List[str]				=    {}
       A							:							List[Any]				=    add_prefix_space

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


   @property
   def        snake_case					(			self       ):
       return len(self.encoder       )


   def        snake_case					(			self       ):
       return dict(self.encoder     , **self.added_tokens_encoder       )


   def        snake_case					(			self     , _UpperCAmelCase       ):
       if token in self.cache:
           return self.cache[token]
       A							:							Union[str, Any]				=    tuple(_UpperCAmelCase       )
       A							:							Tuple				=    get_pairs(_UpperCAmelCase       )

       if not pairs:
           return token

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

               if word[i] == first and i < len(_UpperCAmelCase       ) - 1 and word[i + 1] == second:
                   new_word.append(first + second       )
                   i += 2
               else:
                   new_word.append(word[i]       )
                   i += 1
           A							:							Optional[Any]				=    tuple(_UpperCAmelCase       )
           A							:							Dict				=    new_word
           if len(_UpperCAmelCase       ) == 1:
               break
           else:
               A							:							int				=    get_pairs(_UpperCAmelCase       )
       A							:							Union[str, Any]				=    ''' '''.join(_UpperCAmelCase       )
       A							:							Tuple				=    word
       return word


   def        snake_case					(			self     , _UpperCAmelCase       ):
       A							:							Tuple				=    []
       for token in re.findall(self.pat     , _UpperCAmelCase       ):
           A							:							Tuple				=    ''''''.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(_UpperCAmelCase       ).split(''' '''       )       )
       return bpe_tokens


   def        snake_case					(			self     , _UpperCAmelCase       ):
       return self.encoder.get(_UpperCAmelCase     , self.encoder.get(self.unk_token       )       )


   def        snake_case					(			self     , _UpperCAmelCase       ):
       return self.decoder.get(_UpperCAmelCase       )


   def        snake_case					(			self     , _UpperCAmelCase       ):
       A							:							Any				=    ''''''.join(_UpperCAmelCase       )
       A							:							List[Any]				=    bytearray([self.byte_decoder[c] for c in text]       ).decode('''utf-8'''     , errors=self.errors       )
       return text


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None       ):
       if not os.path.isdir(_UpperCAmelCase       ):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''       )
           return
       A							:							Any				=    os.path.join(
           _UpperCAmelCase     , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )
       A							:							Any				=    os.path.join(
           _UpperCAmelCase     , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']       )

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

       A							:							Tuple				=    0
       with open(_UpperCAmelCase     , '''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 _UpperCAmelCase       : 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!'''       )
                   A							:							List[Any]				=    token_index
               writer.write(''' '''.join(_UpperCAmelCase       ) + '''\n'''       )
               index += 1

       return vocab_file, merge_file


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None       ):
       if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       A							:							Union[str, Any]				=    [self.cls_token_id]
       A							:							Dict				=    [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None     , _UpperCAmelCase = False       ):
       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 None:
           return [1] + ([0] * len(_UpperCAmelCase       )) + [1]
       return [1] + ([0] * len(_UpperCAmelCase       )) + [1, 1] + ([0] * len(_UpperCAmelCase       )) + [1]


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None       ):
       A							:							Dict				=    [self.sep_token_id]
       A							:							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 + sep + token_ids_a + sep       ) * [0]


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase=False     , **_UpperCAmelCase       ):
       A							:							str				=    kwargs.pop('''add_prefix_space'''     , self.add_prefix_space       )
       if (is_split_into_words or add_prefix_space) and (len(_UpperCAmelCase       ) > 0 and not text[0].isspace()):
           A							:							List[Any]				=    ''' ''' + text
       return (text, kwargs)
