






"""simple docstring"""





import os
import re
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


_SCREAMING_SNAKE_CASE :    List[str]    =						logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE :    Optional[Any]    =						{'''vocab_file''': '''spiece.model'''}

_SCREAMING_SNAKE_CASE :    Tuple    =						{
    '''vocab_file''': {
        '''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/spiece.model''',
        '''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/spiece.model''',
    },
}

_SCREAMING_SNAKE_CASE :    Any    =						{
    '''google/fnet-base''': 512,
    '''google/fnet-large''': 512,
}

_SCREAMING_SNAKE_CASE :    List[Any]    =						'''▁'''






class 					a						( _lowerCAmelCase		):
    SCREAMING_SNAKE_CASE						:					Optional[Any]						=			VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE						:					int						=			PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE						:					Union[str, Any]						=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE						:					List[str]						=			["input_ids", "token_type_ids"]






    def __init__(	self					:							str	,						__SCREAMING_SNAKE_CASE					:							Tuple	,						__SCREAMING_SNAKE_CASE					:							List[Any]=False	,						__SCREAMING_SNAKE_CASE					:							List[str]=True	,						__SCREAMING_SNAKE_CASE					:							Dict=True	,						__SCREAMING_SNAKE_CASE					:							Dict="<unk>"	,						__SCREAMING_SNAKE_CASE					:							Tuple="[SEP]"	,						__SCREAMING_SNAKE_CASE					:							Optional[Any]="<pad>"	,						__SCREAMING_SNAKE_CASE					:							List[Any]="[CLS]"	,						__SCREAMING_SNAKE_CASE					:							str="[MASK]"	,						__SCREAMING_SNAKE_CASE					:							Optional[Dict[str, Any]] = None	,						**__SCREAMING_SNAKE_CASE					:							Any	,						)					->		str:
      lowerCamelCase_   					=						(
          AddedToken(_lowerCamelCase	,						lstrip=_lowerCamelCase	,						rstrip=_lowerCamelCase	,						normalized=_lowerCamelCase )
          if isinstance(_lowerCamelCase	,						_lowerCamelCase )
          else mask_token
      )

      lowerCamelCase_   					=						{} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          do_lower_case=_lowerCamelCase	,						remove_space=_lowerCamelCase	,						keep_accents=_lowerCamelCase	,						unk_token=_lowerCamelCase	,						sep_token=_lowerCamelCase	,						pad_token=_lowerCamelCase	,						cls_token=_lowerCamelCase	,						mask_token=_lowerCamelCase	,						sp_model_kwargs=self.sp_model_kwargs	,						**_lowerCamelCase	,						)

      lowerCamelCase_   					=						do_lower_case
      lowerCamelCase_   					=						remove_space
      lowerCamelCase_   					=						keep_accents
      lowerCamelCase_   					=						vocab_file

      lowerCamelCase_   					=						spm.SentencePieceProcessor(**self.sp_model_kwargs )
      self.sp_model.Load(_lowerCamelCase )






    @property
    def        UpperCamelCase	(	self					:							Tuple )					->		Optional[int]:
      return len(self.sp_model )






    def        UpperCamelCase	(	self					:							List[str] )					->		Tuple:
      lowerCamelCase_   					=						{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					:							int )					->		List[Any]:
      lowerCamelCase_   					=						self.__dict__.copy()
      lowerCamelCase_   					=						None
      return state






    def __setstate__(	self					:							Dict	,						__SCREAMING_SNAKE_CASE					:							str )					->		List[Any]:
      lowerCamelCase_   					=						d

      # for backward compatibility
      if not hasattr(self	,						'sp_model_kwargs' ):
        lowerCamelCase_   					=						{}

      lowerCamelCase_   					=						spm.SentencePieceProcessor(**self.sp_model_kwargs )
      self.sp_model.Load(self.vocab_file )






    def        UpperCamelCase	(	self					:							List[Any]	,						__SCREAMING_SNAKE_CASE					:							Tuple )					->		Any:
      if self.remove_space:
        lowerCamelCase_   					=						' '.join(inputs.strip().split() )
      else:
        lowerCamelCase_   					=						inputs
      lowerCamelCase_   					=						outputs.replace('``'	,						'"' ).replace('\'\''	,						'"' )

      if not self.keep_accents:
        lowerCamelCase_   					=						unicodedata.normalize('NFKD'	,						_lowerCamelCase )
        lowerCamelCase_   					=						''.join([c for c in outputs if not unicodedata.combining(_lowerCamelCase )] )
      if self.do_lower_case:
        lowerCamelCase_   					=						outputs.lower()

      return outputs






    def        UpperCamelCase	(	self					:							Union[str, Any]	,						__SCREAMING_SNAKE_CASE					:							str )					->		Union[str, Any]:
      lowerCamelCase_   					=						self.preprocess_text(_lowerCamelCase )
      lowerCamelCase_   					=						self.sp_model.encode(_lowerCamelCase	,						out_type=_lowerCamelCase )
      lowerCamelCase_   					=						[]
      for piece in pieces:
        if len(_lowerCamelCase ) > 1 and piece[-1] == str(',' ) and piece[-2].isdigit():
          lowerCamelCase_   					=						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:
              lowerCamelCase_   					=						cur_pieces[1:]
            else:
              lowerCamelCase_   					=						cur_pieces[0][1:]
          cur_pieces.append(piece[-1] )
          new_pieces.extend(_lowerCamelCase )
        else:
          new_pieces.append(_lowerCamelCase )

      return new_pieces






    def        UpperCamelCase	(	self					:							Tuple	,						__SCREAMING_SNAKE_CASE					:							str )					->		int:
      return self.sp_model.PieceToId(_lowerCamelCase )






    def        UpperCamelCase	(	self					:							Tuple	,						__SCREAMING_SNAKE_CASE					:							int )					->		str:
      return self.sp_model.IdToPiece(_lowerCamelCase )






    def        UpperCamelCase	(	self					:							Any	,						__SCREAMING_SNAKE_CASE					:							Optional[int] )					->		Union[str, Any]:
      lowerCamelCase_   					=						[]
      lowerCamelCase_   					=						''
      lowerCamelCase_   					=						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
          lowerCamelCase_   					=						True
          lowerCamelCase_   					=						[]
        else:
          current_sub_tokens.append(_lowerCamelCase )
          lowerCamelCase_   					=						False
      out_string += self.sp_model.decode(_lowerCamelCase )
      return out_string.strip()






    def        UpperCamelCase	(	self					:							Any	,						__SCREAMING_SNAKE_CASE					:							List[int]	,						__SCREAMING_SNAKE_CASE					:							bool = False	,						__SCREAMING_SNAKE_CASE					:							bool = None	,						__SCREAMING_SNAKE_CASE					:							bool = True	,						**__SCREAMING_SNAKE_CASE					:							Optional[Any]	,						)					->		str:
      lowerCamelCase_   					=						kwargs.pop('use_source_tokenizer'	,						_lowerCamelCase )

      lowerCamelCase_   					=						self.convert_ids_to_tokens(_lowerCamelCase	,						skip_special_tokens=_lowerCamelCase )

      # To avoid mixing byte-level and unicode for byte-level BPT
      # we need to build string separately for added tokens and byte-level tokens
      # cf. https://github.com/huggingface/transformers/issues/1133
      lowerCamelCase_   					=						[]
      lowerCamelCase_   					=						[]
      for token in filtered_tokens:
        if skip_special_tokens and token in self.all_special_ids:
          continue
        if token in self.added_tokens_encoder:
          if current_sub_text:
            sub_texts.append(self.convert_tokens_to_string(_lowerCamelCase ) )
            lowerCamelCase_   					=						[]
          sub_texts.append(_lowerCamelCase )
        else:
          current_sub_text.append(_lowerCamelCase )
      if current_sub_text:
        sub_texts.append(self.convert_tokens_to_string(_lowerCamelCase ) )

      # Mimic the behavior of the Rust tokenizer:
      # No space after <unk>
      if spaces_between_special_tokens:
        lowerCamelCase_   					=						re.sub(r'(<unk>) '	,						r'\1'	,						' '.join(_lowerCamelCase ) )
      else:
        lowerCamelCase_   					=						''.join(_lowerCamelCase )

      lowerCamelCase_   					=						(
          clean_up_tokenization_spaces
          if clean_up_tokenization_spaces is not None
          else self.clean_up_tokenization_spaces
      )
      if clean_up_tokenization_spaces:
        lowerCamelCase_   					=						self.clean_up_tokenization(_lowerCamelCase )
        return clean_text
      else:
        return text






    def        UpperCamelCase	(	self					:							str	,						__SCREAMING_SNAKE_CASE					:							List[int]	,						__SCREAMING_SNAKE_CASE					:							Optional[List[int]] = None )					->		Any:
      lowerCamelCase_   					=						[self.sep_token_id]
      lowerCamelCase_   					=						[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        UpperCamelCase	(	self					:							Dict	,						__SCREAMING_SNAKE_CASE					:							List[int]	,						__SCREAMING_SNAKE_CASE					:							Optional[List[int]] = None	,						__SCREAMING_SNAKE_CASE					:							bool = False )					->		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        UpperCamelCase	(	self					:							List[str]	,						__SCREAMING_SNAKE_CASE					:							List[int]	,						__SCREAMING_SNAKE_CASE					:							Optional[List[int]] = None )					->		str:
      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 ) * [0] + len(token_ids_a + sep ) * [1]






    def        UpperCamelCase	(	self					:							List[str]	,						__SCREAMING_SNAKE_CASE					:							str	,						__SCREAMING_SNAKE_CASE					:							Optional[str] = None )					->		Tuple:
      if not os.path.isdir(_lowerCamelCase ):
        logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
        return
      lowerCamelCase_   					=						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:
          lowerCamelCase_   					=						self.sp_model.serialized_model_proto()
          fi.write(_lowerCamelCase )

      return (out_vocab_file,)












"""simple docstring"""





import numpy as np







def 					lowerCamelCase__	(							_lowerCamelCase	:		Dict		,    _lowerCamelCase	:		Any		,    _lowerCamelCase	:		Tuple			)				->    str:
  lowerCamelCase_   					=						n_draws * class_counts / class_counts.sum()
  # floored means we don't overshoot n_samples, but probably undershoot
  lowerCamelCase_   					=						np.floor(_lowerCamelCase			)
  # we add samples according to how much "left over" probability
  # they had, until we arrive at n_samples
  lowerCamelCase_   					=						int(n_draws - floored.sum()			)
  if need_to_add > 0:
    lowerCamelCase_   					=						continuous - floored
    lowerCamelCase_   					=						np.sort(np.unique(_lowerCamelCase			)			)[::-1]
    # add according to remainder, but break ties
    # randomly to avoid biases
    for value in values:
      ((lowerCamelCase_)					,     )   					=						np.where(remainder == value			)
      # if we need_to_add less than what's in inds
      # we draw randomly from them.
      # if we need to add more, we add them all and
      # go to the next value
      lowerCamelCase_   					=						min(len(_lowerCamelCase			)		,    _lowerCamelCase			)
      lowerCamelCase_   					=						rng.choice(_lowerCamelCase		,    size=_lowerCamelCase		,    replace=_lowerCamelCase			)
      floored[inds] += 1
      need_to_add -= add_now
      if need_to_add == 0:
        break
  return floored.astype(np.intaa			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		Any		,    _lowerCamelCase	:		int		,    _lowerCamelCase	:		Optional[Any]		,    _lowerCamelCase	:		List[str]		,    _lowerCamelCase	:		Optional[int]=10			)				->    Any:
  lowerCamelCase_					,     lowerCamelCase_   					=						np.unique(_lowerCamelCase		,    return_inverse=_lowerCamelCase			)
  lowerCamelCase_   					=						classes.shape[0]
  lowerCamelCase_   					=						np.bincount(_lowerCamelCase			)
  if np.min(_lowerCamelCase			) < 2:
    raise ValueError('Minimum class count error'			)
  if n_train < n_classes:
    raise ValueError(
        'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)			)
  if n_test < n_classes:
    raise ValueError(
        'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)			)
  lowerCamelCase_   					=						np.split(np.argsort(_lowerCamelCase		,    kind='mergesort'			)		,    np.cumsum(_lowerCamelCase			)[:-1]			)
  for _ in range(_lowerCamelCase			):
    lowerCamelCase_   					=						approximate_mode(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)
    lowerCamelCase_   					=						class_counts - n_i
    lowerCamelCase_   					=						approximate_mode(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)

    lowerCamelCase_   					=						[]
    lowerCamelCase_   					=						[]

    for i in range(_lowerCamelCase			):
      lowerCamelCase_   					=						rng.permutation(class_counts[i]			)
      lowerCamelCase_   					=						class_indices[i].take(_lowerCamelCase		,    mode='clip'			)
      train.extend(perm_indices_class_i[: n_i[i]]			)
      test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]			)
    lowerCamelCase_   					=						rng.permutation(_lowerCamelCase			)
    lowerCamelCase_   					=						rng.permutation(_lowerCamelCase			)

    yield train, test




