


import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__      		= logging.get_logger(__name__)
lowerCAmelCase__      		= {'vocab_file': 'spiece.model'}

lowerCAmelCase__      		= {
    'vocab_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
    }
}

lowerCAmelCase__      		= {
    'albert-base-v1': 5_12,
    'albert-large-v1': 5_12,
    'albert-xlarge-v1': 5_12,
    'albert-xxlarge-v1': 5_12,
    'albert-base-v2': 5_12,
    'albert-large-v2': 5_12,
    'albert-xlarge-v2': 5_12,
    'albert-xxlarge-v2': 5_12,
}

lowerCAmelCase__      		= '▁'







class      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= VOCAB_FILES_NAMES
     __SCREAMING_SNAKE_CASE  			= PRETRAINED_VOCAB_FILES_MAP
     __SCREAMING_SNAKE_CASE  			= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase=True ,						__lowerCamelCase=True ,						__lowerCamelCase=False ,						__lowerCamelCase="[CLS]" ,						__lowerCamelCase="[SEP]" ,						__lowerCamelCase="<unk>" ,						__lowerCamelCase="[SEP]" ,						__lowerCamelCase="<pad>" ,						__lowerCamelCase="[CLS]" ,						__lowerCamelCase="[MASK]" ,						__lowerCamelCase = None ,						**__lowerCamelCase ,						)			->				None:
            # Mask token behave like a normal word, i.e. include the space before it and
            # is included in the raw text, there should be a match in a non-normalized sentence.
            _A      :       Tuple				    = (
                AddedToken(__lowerCamelCase ,						lstrip=__lowerCamelCase ,						rstrip=__lowerCamelCase ,						normalized=__lowerCamelCase)
                if isinstance(__lowerCamelCase ,						__lowerCamelCase)
                else mask_token
            )

            _A      :       Union[str, Any]				    = {} if sp_model_kwargs is None else sp_model_kwargs

            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 ,						sp_model_kwargs=self.sp_model_kwargs ,						**__lowerCamelCase ,						)

            _A      :       Union[str, Any]				    = do_lower_case
            _A      :       List[str]				    = remove_space
            _A      :       str				    = keep_accents
            _A      :       List[str]				    = vocab_file

            _A      :       Optional[Any]				    = spm.SentencePieceProcessor(**self.sp_model_kwargs)
            self.sp_model.Load(__lowerCamelCase)


     @property
     def  _lowerCamelCase (						self)			->				Tuple:
            return len(self.sp_model)


     def  _lowerCamelCase (						self)			->				List[Any]:
            _A      :       Optional[Any]				    = {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)			->				Union[str, Any]:
            _A      :       int				    = self.__dict__.copy()
            _A      :       Optional[int]				    = None
            return state


     def __setstate__(						self ,						__lowerCamelCase)			->				int:
            _A      :       List[Any]				    = d

            # for backward compatibility
            if not hasattr(self ,						"sp_model_kwargs"):
                   _A      :       Tuple				    = {}

            _A      :       Tuple				    = spm.SentencePieceProcessor(**self.sp_model_kwargs)
            self.sp_model.Load(self.vocab_file)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				int:
            if self.remove_space:
                   _A      :       Optional[Any]				    = " ".join(inputs.strip().split())
            else:
                   _A      :       List[str]				    = inputs
            _A      :       int				    = outputs.replace("``" ,						"\"").replace("''" ,						"\"")

            if not self.keep_accents:
                   _A      :       Dict				    = unicodedata.normalize("NFKD" ,						__lowerCamelCase)
                   _A      :       Union[str, Any]				    = "".join([c for c in outputs if not unicodedata.combining(__lowerCamelCase)])
            if self.do_lower_case:
                   _A      :       Optional[Any]				    = outputs.lower()

            return outputs


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				List[str]:
            _A      :       Union[str, Any]				    = self.preprocess_text(__lowerCamelCase)
            _A      :       int				    = self.sp_model.encode(__lowerCamelCase ,						out_type=__lowerCamelCase)
            _A      :       Any				    = []
            for piece in pieces:
                   if len(__lowerCamelCase) > 1 and piece[-1] == str(",") and piece[-2].isdigit():
                          _A      :       Optional[Any]				    = self.sp_model.EncodeAsPieces(piece[:-1].replace(__lowerCamelCase ,						""))
                          if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                                 if len(cur_pieces[0]) == 1:
                                        _A      :       List[str]				    = cur_pieces[1:]
                                 else:
                                        _A      :       Tuple				    = cur_pieces[0][1:]
                          cur_pieces.append(piece[-1])
                          new_pieces.extend(__lowerCamelCase)
                   else:
                          new_pieces.append(__lowerCamelCase)

            return new_pieces


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Any:
            return self.sp_model.PieceToId(__lowerCamelCase)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Dict:
            return self.sp_model.IdToPiece(__lowerCamelCase)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Tuple:
            _A      :       int				    = []
            _A      :       str				    = ""
            _A      :       List[Any]				    = False
            for token in tokens:
                   # make sure that special tokens are not decoded using sentencepiece model
                   if token in self.all_special_tokens:
                          if not prev_is_special:
                                 out_string += " "
                          out_string += self.sp_model.decode(__lowerCamelCase) + token
                          _A      :       Any				    = True
                          _A      :       List[str]				    = []
                   else:
                          current_sub_tokens.append(__lowerCamelCase)
                          _A      :       Union[str, Any]				    = False
            out_string += self.sp_model.decode(__lowerCamelCase)
            return out_string.strip()


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				List[int]:
            _A      :       List[str]				    = [self.sep_token_id]
            _A      :       List[Any]				    = [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  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None ,						__lowerCamelCase = False)			->				List[int]:

            if already_has_special_tokens:
                   return super().get_special_tokens_mask(
                       token_ids_a=__lowerCamelCase ,						token_ids_a=__lowerCamelCase ,						already_has_special_tokens=__lowerCamelCase)

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


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				List[int]:
            _A      :       List[str]				    = [self.sep_token_id]
            _A      :       Optional[int]				    = [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  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				Tuple[str]:
            if not os.path.isdir(__lowerCamelCase):
                   logger.error(F"Vocabulary path ({save_directory}) should be a directory")
                   return
            _A      :       int				    = 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) and os.path.isfile(self.vocab_file):
                   copyfile(self.vocab_file ,						__lowerCamelCase)
            elif not os.path.isfile(self.vocab_file):
                   with open(__lowerCamelCase ,						"wb") as fi:
                          _A      :       List[str]				    = self.sp_model.serialized_model_proto()
                          fi.write(__lowerCamelCase)

            return (out_vocab_file,)




"""simple docstring"""





from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


def    _snake_case  (  _snake_case :	np.ndarray       ,      _snake_case :	np.ndarray	):
      return math.sqrt(sum(pow(a - b       ,      2	) for a, b in zip(_snake_case       ,      _snake_case	)	)	)


def    _snake_case  (  _snake_case :	np.ndarray       ,      _snake_case :	np.ndarray	):

      if dataset.ndim != value_array.ndim:
            lowerCAmelCase    :		List[Any]				=      (
                '''Wrong input data\'s dimensions... '''
                f'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
            )
            raise ValueError(_snake_case	)

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

      if dataset.dtype != value_array.dtype:
            lowerCAmelCase    :		Optional[Any]				=      (
                '''Input data have different datatype... '''
                f'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
            )
            raise TypeError(_snake_case	)

      lowerCAmelCase    :		str				=      []

      for value in value_array:
            lowerCAmelCase    :		int				=      euclidean(_snake_case       ,      dataset[0]	)
            lowerCAmelCase    :		Union[str, Any]				=      dataset[0].tolist()

            for dataset_value in dataset[1:]:
                  lowerCAmelCase    :		Any				=      euclidean(_snake_case       ,      _snake_case	)

                  if dist > temp_dist:
                        lowerCAmelCase    :		List[Any]				=      temp_dist
                        lowerCAmelCase    :		Tuple				=      dataset_value.tolist()

            answer.append([vector, dist]	)

      return answer


def    _snake_case  (  _snake_case :	np.ndarray       ,      _snake_case :	np.ndarray	):
      return np.dot(_snake_case       ,      _snake_case	) / (norm(_snake_case	) * norm(_snake_case	))


if __name__ == "__main__":
 import doctest

 doctest.testmod()


