


import numpy as np






def 		lowercase_(  SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     ):


  '''simple docstring'''

  lowerCamelCase			: List[Any]   =    n_draws * class_counts / class_counts.sum()
  # floored means we don't overshoot n_samples, but probably undershoot
  lowerCamelCase			: Any   =    np.floor(SCREAMING_SNAKE_CASE_     )
  # we add samples according to how much "left over" probability
  # they had, until we arrive at n_samples
  lowerCamelCase			: str   =    int(n_draws - floored.sum()     )
  if need_to_add > 0:
    lowerCamelCase			: List[str]   =    continuous - floored
    lowerCamelCase			: Optional[int]   =    np.sort(np.unique(SCREAMING_SNAKE_CASE_     )     )[::-1]
    # add according to remainder, but break ties
    # randomly to avoid biases
    for value in values:
      ((lowerCamelCase)   ,     )			: Union[str, Any]   =    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			: str   =    min(len(SCREAMING_SNAKE_CASE_     )				,						SCREAMING_SNAKE_CASE_     )
      lowerCamelCase			: Dict   =    rng.choice(SCREAMING_SNAKE_CASE_				,						size=SCREAMING_SNAKE_CASE_				,						replace=SCREAMING_SNAKE_CASE_     )
      floored[inds] += 1
      need_to_add -= add_now
      if need_to_add == 0:
        break
  return floored.astype(np.intaa     )







def 		lowercase_(  SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_=10     ):


  '''simple docstring'''

  lowerCamelCase   ,     lowerCamelCase			: int   =    np.unique(SCREAMING_SNAKE_CASE_				,						return_inverse=SCREAMING_SNAKE_CASE_     )
  lowerCamelCase			: int   =    classes.shape[0]
  lowerCamelCase			: int   =    np.bincount(SCREAMING_SNAKE_CASE_     )
  if np.min(SCREAMING_SNAKE_CASE_     ) < 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			: List[str]   =    np.split(np.argsort(SCREAMING_SNAKE_CASE_				,						kind="mergesort"     )				,						np.cumsum(SCREAMING_SNAKE_CASE_     )[:-1]     )
  for _ in range(SCREAMING_SNAKE_CASE_     ):
    lowerCamelCase			: str   =    approximate_mode(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     )
    lowerCamelCase			: Dict   =    class_counts - n_i
    lowerCamelCase			: Any   =    approximate_mode(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     )

    lowerCamelCase			: List[str]   =    []
    lowerCamelCase			: Any   =    []

    for i in range(SCREAMING_SNAKE_CASE_     ):
      lowerCamelCase			: int   =    rng.permutation(class_counts[i]     )
      lowerCamelCase			: Dict   =    class_indices[i].take(SCREAMING_SNAKE_CASE_				,						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			: int   =    rng.permutation(SCREAMING_SNAKE_CASE_     )
    lowerCamelCase			: Optional[int]   =    rng.permutation(SCREAMING_SNAKE_CASE_     )

    yield train, test







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


_snake_case					=					logging.get_logger(__name__)

_snake_case					=					{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_snake_case					=					{
    '''vocab_file''': {
        '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt''',
    },
    '''tokenizer_file''': {
        '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json''',
    },
}

_snake_case					=					{
    '''microsoft/mpnet-base''': 5_12,
}

_snake_case					=					{
    '''microsoft/mpnet-base''': {'''do_lower_case''': True},
}
class      UpperCAmelCase_ ( UpperCamelCase   ):

   '''simple docstring'''




   __A			:				List[str]							=		VOCAB_FILES_NAMES
   __A			:				int							=		PRETRAINED_VOCAB_FILES_MAP
   __A			:				str							=		PRETRAINED_INIT_CONFIGURATION
   __A			:				int							=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __A			:				Any							=		MPNetTokenizer
   __A			:				Dict							=		["input_ids", "attention_mask"]






   def __init__(		self						,    __A=None						,    __A=None						,    __A=True						,    __A="<s>"						,    __A="</s>"						,    __A="</s>"						,    __A="<s>"						,    __A="[UNK]"						,    __A="<pad>"						,    __A="<mask>"						,    __A=True						,    __A=None						,    **__A						,    ):






     """simple docstring"""





     super().__init__(
         __A						,    tokenizer_file=__A						,    do_lower_case=__A						,    bos_token=__A						,    eos_token=__A						,    sep_token=__A						,    cls_token=__A						,    unk_token=__A						,    pad_token=__A						,    mask_token=__A						,    tokenize_chinese_chars=__A						,    strip_accents=__A						,    **__A						,    )

     lowerCamelCase			: List[Any]   =    json.loads(self.backend_tokenizer.normalizer.__getstate__()			)
     if (
         pre_tok_state.get("lowercase"						,    __A			) != do_lower_case
         or pre_tok_state.get("strip_accents"						,    __A			) != strip_accents
     ):
       lowerCamelCase			: Dict   =    getattr(__A						,    pre_tok_state.pop("type"			)			)
       lowerCamelCase			: List[str]   =    do_lower_case
       lowerCamelCase			: Tuple   =    strip_accents
       lowerCamelCase			: int   =    pre_tok_class(**__A			)

     lowerCamelCase			: List[Any]   =    do_lower_case






   @property
   def _snake_case   (		self			):






     """simple docstring"""





     if self._mask_token is None:
       if self.verbose:
         logger.error("Using mask_token, but it is not set yet."			)
       return None
     return str(self._mask_token			)






   @mask_token.setter
   def _snake_case   (		self						,    __A			):






     """simple docstring"""





     lowerCamelCase			: Tuple   =    AddedToken(__A						,    lstrip=__A						,    rstrip=__A			) if isinstance(__A						,    __A			) else value
     lowerCamelCase			: Dict   =    value






   def _snake_case   (		self						,    __A						,    __A=None			):






     """simple docstring"""





     lowerCamelCase			: Optional[Any]   =    [self.bos_token_id] + token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
       return output

     return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






   def _snake_case   (		self						,    __A						,    __A = None			):






     """simple docstring"""





     lowerCamelCase			: Any   =    [self.sep_token_id]
     lowerCamelCase			: Union[str, Any]   =    [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						,    __A						,    __A = None			):






     """simple docstring"""





     lowerCamelCase			: int   =    self._tokenizer.model.save(__A						,    name=__A			)
     return tuple(__A			)



