
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	:				float   ,							SCREAMING_SNAKE_CASE	:				float   ,							SCREAMING_SNAKE_CASE	:				int      )					->						list:
				seed(1      )
				return [gauss(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      ) for _ in range(SCREAMING_SNAKE_CASE      )]




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				int   ,							SCREAMING_SNAKE_CASE	:				list      )					->						list:
				return [k for k in range(SCREAMING_SNAKE_CASE      ) for _ in range(instance_count[k]      )]




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				int   ,							SCREAMING_SNAKE_CASE	:				list      )					->						float:
				return sum(SCREAMING_SNAKE_CASE      ) / instance_count




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				int   ,							SCREAMING_SNAKE_CASE	:				int      )					->						float:
				return instance_count / total_count




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				list   ,							SCREAMING_SNAKE_CASE	:				list   ,							SCREAMING_SNAKE_CASE	:				int      )					->						float:
				__lowercase								= []  # 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
				__lowercase								= 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	:				list   ,							SCREAMING_SNAKE_CASE	:				list   ,							SCREAMING_SNAKE_CASE	:				float   ,							SCREAMING_SNAKE_CASE	:				list      )					->						list:
				__lowercase								= []
				# 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]      )      ):
												__lowercase								= []  # 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	:				list   ,							SCREAMING_SNAKE_CASE	:				list      )					->						float:
				__lowercase								= 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__							=    TypeVar("""num""")




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Callable[[object], num]   ,							SCREAMING_SNAKE_CASE	:				str   ,							SCREAMING_SNAKE_CASE	:				str   ,							SCREAMING_SNAKE_CASE	:				Callable[[num], bool] = lambda SCREAMING_SNAKE_CASE      : True   ,							SCREAMING_SNAKE_CASE	:				str | None = None   ,							)					->						num:
				while True:
								try:
												__lowercase								= 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       (		)					->						Optional[Any]:
				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
								__lowercase								= 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
								__lowercase								= 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
								__lowercase								= []  # An empty list to store instance counts of classes in dataset
								for i in range(SCREAMING_SNAKE_CASE      ):
												__lowercase								= 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
								__lowercase								= []
								for a in range(SCREAMING_SNAKE_CASE      ):
												__lowercase								= 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
								__lowercase								= [
								    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
								__lowercase								= 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
								__lowercase								= [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
								__lowercase								= [
								    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
								__lowercase								= 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
								__lowercase								= 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()







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
							SCREAMING_SNAKE_CASE__							=    None


SCREAMING_SNAKE_CASE__							=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__							=    {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__							=    {
    """vocab_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json"""
        ),
    },
}

SCREAMING_SNAKE_CASE__							=    {
    """xlm-roberta-base""": 512,
    """xlm-roberta-large""": 512,
    """xlm-roberta-large-finetuned-conll02-dutch""": 512,
    """xlm-roberta-large-finetuned-conll02-spanish""": 512,
    """xlm-roberta-large-finetuned-conll03-english""": 512,
    """xlm-roberta-large-finetuned-conll03-german""": 512,
}
class      A__       (						lowerCAmelCase__    ):
							lowerCAmelCase__		:						Dict         =							VOCAB_FILES_NAMES
							lowerCAmelCase__		:						List[str]         =							PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase__		:						List[Any]         =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase__		:						Dict         =							["input_ids", "attention_mask"]
							lowerCAmelCase__		:						List[str]         =							XLMRobertaTokenizer





							def __init__(			self  :       Tuple					,			_UpperCAmelCase  :       Tuple=None					,			_UpperCAmelCase  :       int=None					,			_UpperCAmelCase  :       Tuple="<s>"					,			_UpperCAmelCase  :       Dict="</s>"					,			_UpperCAmelCase  :       Dict="</s>"					,			_UpperCAmelCase  :       List[Any]="<s>"					,			_UpperCAmelCase  :       List[Any]="<unk>"					,			_UpperCAmelCase  :       Optional[int]="<pad>"					,			_UpperCAmelCase  :       Union[str, Any]="<mask>"					,			**_UpperCAmelCase  :       Optional[int]					,			)						->		Dict:



											"""simple docstring"""


											__lowercase								= AddedToken(_UpperCAmelCase					,			lstrip=_UpperCAmelCase					,			rstrip=_UpperCAmelCase    ) if isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ) else mask_token

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

											__lowercase								= vocab_file
											__lowercase								= False if not self.vocab_file else True





							def       a__			(			self  :       Union[str, Any]					,			_UpperCAmelCase  :       List[int]					,			_UpperCAmelCase  :       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]
											__lowercase								= [self.cls_token_id]
											__lowercase								= [self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep





							def       a__			(			self  :       Union[str, Any]					,			_UpperCAmelCase  :       List[int]					,			_UpperCAmelCase  :       Optional[List[int]] = None    )						->		List[int]:



											"""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 + sep + token_ids_a + sep    ) * [0]





							def       a__			(			self  :       str					,			_UpperCAmelCase  :       str					,			_UpperCAmelCase  :       Optional[str] = None    )						->		Tuple[str]:



											"""simple docstring"""


											if not self.can_save_slow_tokenizer:
															raise ValueError(
															    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
															    'tokenizer.'    )

											if not os.path.isdir(_UpperCAmelCase    ):
															logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""    )
															return
											__lowercase								= 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    ):
															copyfile(self.vocab_file					,			_UpperCAmelCase    )

											return (out_vocab_file,)





