

from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	np.ndarray    ,			SCREAMING_SNAKE_CASE__			:	np.ndarray      )							->      float:





    '''simple docstring'''






    return math.sqrt(sum(pow(a - b    ,			2      ) for a, b in zip(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )      )      )
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	np.ndarray    ,			SCREAMING_SNAKE_CASE__			:	np.ndarray      )							->      list[list[list[float] | float]]:





    '''simple docstring'''






    if dataset.ndim != value_array.ndim:
        A__          =   (
            'Wrong input data\'s dimensions... '
            f'dataset : {dataset.ndim}, value_array : {value_array.ndim}'
        )
        raise ValueError(SCREAMING_SNAKE_CASE__      )

    try:
        if dataset.shape[1] != value_array.shape[1]:
            A__          =   (
                'Wrong input data\'s shape... '
                f'dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'
            )
            raise ValueError(SCREAMING_SNAKE_CASE__      )
    except IndexError:
        if dataset.ndim != value_array.ndim:
            raise TypeError('Wrong shape'      )

    if dataset.dtype != value_array.dtype:
        A__          =   (
            'Input data have different datatype... '
            f'dataset : {dataset.dtype}, value_array : {value_array.dtype}'
        )
        raise TypeError(SCREAMING_SNAKE_CASE__      )

    A__          =   []

    for value in value_array:
        A__          =   euclidean(SCREAMING_SNAKE_CASE__    ,			dataset[0]      )
        A__          =   dataset[0].tolist()

        for dataset_value in dataset[1:]:
            A__          =   euclidean(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )

            if dist > temp_dist:
                A__          =   temp_dist
                A__          =   dataset_value.tolist()

        answer.append([vector, dist]      )

    return answer
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	np.ndarray    ,			SCREAMING_SNAKE_CASE__			:	np.ndarray      )							->      float:





    '''simple docstring'''






    return np.dot(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      ) / (norm(SCREAMING_SNAKE_CASE__      ) * norm(SCREAMING_SNAKE_CASE__      ))


if __name__ == "__main__":
     import doctest

     doctest.testmod()



"""simple docstring"""



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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase__          = logging.get_logger(__name__)

UpperCAmelCase__          = {'vocab_file': 'sentencepiece.model'}

UpperCAmelCase__          = {
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
}

UpperCAmelCase__          = {
    'google/rembert': 256,
}

class       lowerCAmelCase__				(						A_	):
     __a						=					VOCAB_FILES_NAMES
     __a						=					PRETRAINED_VOCAB_FILES_MAP
     __a						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







     def __init__(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			Any    ,      _lowerCamelCase			:			Union[str, Any]=False    ,      _lowerCamelCase			:			Any=True    ,      _lowerCamelCase			:			Optional[Any]=True    ,      _lowerCamelCase			:			int="[CLS]"    ,      _lowerCamelCase			:			Optional[int]="[SEP]"    ,      _lowerCamelCase			:			Optional[int]="[UNK]"    ,      _lowerCamelCase			:			Optional[Any]="[SEP]"    ,      _lowerCamelCase			:			str="[PAD]"    ,      _lowerCamelCase			:			List[Any]="[CLS]"    ,      _lowerCamelCase			:			Any="[MASK]"    ,      **_lowerCamelCase			:			Optional[int]    ,      ):
       super().__init__(
           do_lower_case=_lowerCamelCase    ,      remove_space=_lowerCamelCase    ,      keep_accents=_lowerCamelCase    ,      bos_token=_lowerCamelCase    ,      eos_token=_lowerCamelCase    ,      unk_token=_lowerCamelCase    ,      sep_token=_lowerCamelCase    ,      pad_token=_lowerCamelCase    ,      cls_token=_lowerCamelCase    ,      mask_token=_lowerCamelCase    ,      **_lowerCamelCase    ,      )

       _snake_case					     =       do_lower_case
       _snake_case					     =       remove_space
       _snake_case					     =       keep_accents
       _snake_case					     =       vocab_file

       _snake_case					     =       spm.SentencePieceProcessor()
       self.sp_model.Load(_lowerCamelCase			)







     @property
     def      lowercase	(							self			:			int			):
       return len(self.sp_model			)







     def      lowercase	(							self			:			Any			):
       _snake_case					     =       {self.convert_ids_to_tokens(_lowerCamelCase			): i for i in range(self.vocab_size			)}
       vocab.update(self.added_tokens_encoder			)
       return vocab







     def __getstate__(							self			:			List[str]			):
       _snake_case					     =       self.__dict__.copy()
       _snake_case					     =       None
       return state







     def __setstate__(							self			:			List[str]    ,      _lowerCamelCase			:			Tuple			):
       _snake_case					     =       d
       _snake_case					     =       spm.SentencePieceProcessor()
       self.sp_model.Load(self.vocab_file			)







     def      lowercase	(							self			:			str    ,      _lowerCamelCase			:			List[str]    ,      _lowerCamelCase			:			Tuple=False			):
       _snake_case					     =       self.sp_model.EncodeAsPieces(_lowerCamelCase			)
       return pieces







     def      lowercase	(							self			:			str    ,      _lowerCamelCase			:			str			):
       return self.sp_model.PieceToId(_lowerCamelCase			)







     def      lowercase	(							self			:			List[str]    ,      _lowerCamelCase			:			int			):
       return self.sp_model.IdToPiece(_lowerCamelCase			)







     def      lowercase	(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			Any			):
       _snake_case					     =       self.sp_model.decode_pieces(_lowerCamelCase			)
       return out_string







     def      lowercase	(							self			:			Optional[Any]    ,      _lowerCamelCase			:			List[int]    ,      _lowerCamelCase			:			Optional[List[int]] = None			):
       _snake_case					     =       [self.sep_token_id]
       _snake_case					     =       [self.cls_token_id]
       if token_ids_a is None:
         return cls + token_ids_a + sep
       return cls + token_ids_a + sep + token_ids_a + sep







     def      lowercase	(							self			:			Tuple    ,      _lowerCamelCase			:			List[int]    ,      _lowerCamelCase			:			Optional[List[int]] = None    ,      _lowerCamelCase			:			bool = False			):

       if already_has_special_tokens:
         if token_ids_a is not None:
           raise ValueError(
               '''You should not supply a second sequence if the provided sequence of '''
               '''ids is already formatted with special tokens for the model.'''			)
         return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

       if token_ids_a is not None:
         return [1] + ([0] * len(_lowerCamelCase			)) + [1] + ([0] * len(_lowerCamelCase			)) + [1]
       return [1] + ([0] * len(_lowerCamelCase			)) + [1]







     def      lowercase	(							self			:			Optional[int]    ,      _lowerCamelCase			:			List[int]    ,      _lowerCamelCase			:			Optional[List[int]] = None			):
       _snake_case					     =       [self.sep_token_id]
       _snake_case					     =       [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			) * [0] + len(token_ids_a + sep			) * [1]







     def      lowercase	(							self			:			List[str]    ,      _lowerCamelCase			:			str    ,      _lowerCamelCase			:			Optional[str] = None			):
       if not os.path.isdir(_lowerCamelCase			):
         logger.error('''Vocabulary path ({}) should be a directory'''.format(_lowerCamelCase			)			)
         return
       _snake_case					     =       os.path.join(
           _lowerCamelCase    ,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

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

       return (out_vocab_file,)



