





import numpy as np
import requests





def    _lowercase    (  )  ->     Tuple:
				lowerCamelCase		       =requests.get(
				    """https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/"""
				    """master/Week1/ADRvsRating.csv""" )
				lowerCamelCase		       =response.text.splitlines()
				lowerCamelCase		       =[]
				for item in lines:
								lowerCamelCase		       =item.split(""",""" )
								data.append(_UpperCAmelCase )
				data.pop(0 )  # This is for removing the labels from the list
				lowerCamelCase		       =np.matrix(_UpperCAmelCase )
				return dataset





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase )  ->     List[str]:
				lowerCamelCase		       =len_data

				lowerCamelCase		       =np.dot(_UpperCAmelCase	,					data_x.transpose() )
				prod -= data_y.transpose()
				lowerCamelCase		       =np.dot(_UpperCAmelCase	,					_UpperCAmelCase )
				lowerCamelCase		       =theta - (alpha / n) * sum_grad
				return theta





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase )  ->     Any:
				lowerCamelCase		       =np.dot(_UpperCAmelCase	,					data_x.transpose() )
				prod -= data_y.transpose()
				lowerCamelCase		       =np.sum(np.square(_UpperCAmelCase ) )
				lowerCamelCase		       =sum_elem / (2 * len_data)
				return error





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase )  ->     Optional[int]:
				lowerCamelCase		       =10_00_00
				lowerCamelCase		       =0.0_0_0_1_5_5_0

				lowerCamelCase		       =data_x.shape[1]
				lowerCamelCase		       =data_x.shape[0] - 1

				lowerCamelCase		       =np.zeros((1, no_features) )

				for i in range(0	,					_UpperCAmelCase ):
								lowerCamelCase		       =run_steep_gradient_descent(_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase )
								lowerCamelCase		       =sum_of_square_error(_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase )
								print(F"""At Iteration {i + 1} - Error is {error:.5f}""" )

				return theta





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase )  ->     str:
				lowerCamelCase		       =sum(abs(y - predicted_y[i] ) for i, y in enumerate(_UpperCAmelCase ) )
				return total / len(_UpperCAmelCase )





def    _lowercase    (  )  ->     Optional[Any]:
				lowerCamelCase		       =collect_dataset()

				lowerCamelCase		       =data.shape[0]
				lowerCamelCase		       =np.c_[np.ones(_UpperCAmelCase ), data[:, :-1]].astype(_UpperCAmelCase )
				lowerCamelCase		       =data[:, -1].astype(_UpperCAmelCase )

				lowerCamelCase		       =run_linear_regression(_UpperCAmelCase	,					_UpperCAmelCase )
				lowerCamelCase		       =theta.shape[1]
				print("""Resultant Feature vector : """ )
				for i in range(0	,					_UpperCAmelCase ):
								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:
							UpperCAmelCase__  :	Union[str, Any]     =None


UpperCAmelCase__  :	str     =logging.get_logger(__name__)

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

UpperCAmelCase__  :	str     ={
    '''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'''
        ),
    },
}

UpperCAmelCase__  :	int     ={
    '''xlm-roberta-base''': 5_12,
    '''xlm-roberta-large''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-english''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-german''': 5_12,
}

class 					__A   (  a			):
				__A            = VOCAB_FILES_NAMES
				__A            = PRETRAINED_VOCAB_FILES_MAP
				__A            = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__A            = ["""input_ids""", """attention_mask"""]
				__A            = XLMRobertaTokenizer


				def __init__(						self      ,       UpperCAmelCase_=None      ,       UpperCAmelCase_=None      ,       UpperCAmelCase_="<s>"      ,       UpperCAmelCase_="</s>"      ,       UpperCAmelCase_="</s>"      ,       UpperCAmelCase_="<s>"      ,       UpperCAmelCase_="<unk>"      ,       UpperCAmelCase_="<pad>"      ,       UpperCAmelCase_="<mask>"      ,       **UpperCAmelCase_      ,       ):
								# Mask token behave like a normal word, i.e. include the space before it
								lowerCamelCase		       =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_      ,       )

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


				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]
								lowerCamelCase		       =[self.cls_token_id]
								lowerCamelCase		       =[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None							):
								lowerCamelCase		       =[self.sep_token_id]
								lowerCamelCase		       =[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_ = None							):
								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
								lowerCamelCase		       =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,)





