






import numpy as np
import requests






def    UpperCamelCase__  (   )     ->		Tuple:







							"""simple docstring"""





							_a  :    Tuple			=						requests.get(
							    '''https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/'''
							    '''master/Week1/ADRvsRating.csv'''     )
							_a  :    Tuple			=						response.text.splitlines()
							_a  :    Any			=						[]
							for item in lines:
														_a  :    Optional[int]			=						item.split(''','''     )
														data.append(lowerCAmelCase__     )
							data.pop(0     )  # This is for removing the labels from the list
							_a  :    Dict			=						np.matrix(lowerCAmelCase__     )
							return dataset






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )     ->		Optional[Any]:







							"""simple docstring"""





							_a  :    List[str]			=						len_data

							_a  :    List[str]			=						np.dot(lowerCAmelCase__    ,	data_x.transpose()     )
							prod -= data_y.transpose()
							_a  :    Any			=						np.dot(lowerCAmelCase__    ,	lowerCAmelCase__     )
							_a  :    List[Any]			=						theta - (alpha / n) * sum_grad
							return theta






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )     ->		int:







							"""simple docstring"""





							_a  :    Tuple			=						np.dot(lowerCAmelCase__    ,	data_x.transpose()     )
							prod -= data_y.transpose()
							_a  :    List[str]			=						np.sum(np.square(lowerCAmelCase__     )     )
							_a  :    Union[str, Any]			=						sum_elem / (2 * len_data)
							return error






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase     )     ->		Optional[int]:







							"""simple docstring"""





							_a  :    Optional[int]			=						100000
							_a  :    Dict			=						0.0_0_0_1_5_5_0

							_a  :    List[str]			=						data_x.shape[1]
							_a  :    Union[str, Any]			=						data_x.shape[0] - 1

							_a  :    Union[str, Any]			=						np.zeros((1, no_features)     )

							for i in range(0    ,	lowerCAmelCase__     ):
														_a  :    Any			=						run_steep_gradient_descent(lowerCAmelCase__    ,	lowerCAmelCase__    ,	lowerCAmelCase__    ,	lowerCAmelCase__    ,	lowerCAmelCase__     )
														_a  :    Optional[Any]			=						sum_of_square_error(lowerCAmelCase__    ,	lowerCAmelCase__    ,	lowerCAmelCase__    ,	lowerCAmelCase__     )
														print(F'At Iteration {i + 1} - Error is {error:.5f}'     )

							return theta






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase     )     ->		int:







							"""simple docstring"""





							_a  :    int			=						sum(abs(y - predicted_y[i]     ) for i, y in enumerate(lowerCAmelCase__     )     )
							return total / len(lowerCAmelCase__     )






def    UpperCamelCase__  (   )     ->		Optional[int]:







							"""simple docstring"""





							_a  :    Dict			=						collect_dataset()

							_a  :    List[Any]			=						data.shape[0]
							_a  :    List[Any]			=						np.c_[np.ones(lowerCAmelCase__     ), data[:, :-1]].astype(lowerCAmelCase__     )
							_a  :    List[Any]			=						data[:, -1].astype(lowerCAmelCase__     )

							_a  :    Tuple			=						run_linear_regression(lowerCAmelCase__    ,	lowerCAmelCase__     )
							_a  :    Union[str, Any]			=						theta.shape[1]
							print('''Resultant Feature vector : '''     )
							for i in range(0    ,	lowerCAmelCase__     ):
														print(F'{theta[0, i]:.5f}'     )


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:
					snake_case_   :	Optional[int]							      =None


snake_case_   :	List[str]							      =logging.get_logger(__name__)

snake_case_   :	Optional[int]							      ={'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case_   :	Union[str, Any]							      ={
    '''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'''
        ),
    },
}

snake_case_   :	int							      ={
    '''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__		):
		UpperCAmelCase_							:       int 					= VOCAB_FILES_NAMES
		UpperCAmelCase_							:       str 					= PRETRAINED_VOCAB_FILES_MAP
		UpperCAmelCase_							:       Union[str, Any] 					= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCAmelCase_							:       int 					= ['input_ids', 'attention_mask']
		UpperCAmelCase_							:       Optional[int] 					= XLMRobertaTokenizer






		def __init__(      self							, lowercase__=None							, lowercase__=None							, lowercase__="<s>"							, lowercase__="</s>"							, lowercase__="</s>"							, lowercase__="<s>"							, lowercase__="<unk>"							, lowercase__="<pad>"							, lowercase__="<mask>"							, **lowercase__							, )				->				List[str]:
				# Mask token behave like a normal word, i.e. include the space before it
				__A    =							AddedToken(lowercase__							, lstrip=lowercase__							, rstrip=lowercase__						) if isinstance(lowercase__							, lowercase__						) else mask_token

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

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






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__ = None						)				->				List[int]:

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






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__ = None						)				->				List[int]:
				__A    =							[self.sep_token_id]
				__A    =							[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							, lowercase__							, lowercase__ = None						)				->				Tuple[str]:
				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(lowercase__						):
						logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""						)
						return
				__A    =							os.path.join(
				    lowercase__							, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

				if os.path.abspath(self.vocab_file						) != os.path.abspath(lowercase__						):
						copyfile(self.vocab_file							, lowercase__						)

				return (out_vocab_file,)

