

import numpy as np





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





def 							__a       (					__A ,       __A ,       __A ,       __A ,       __A=10	):
    SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__					     =						np.unique(__A ,       return_inverse=__A	)
    SCREAMING_SNAKE_CASE__					     =						classes.shape[0]
    SCREAMING_SNAKE_CASE__					     =						np.bincount(__A	)
    if np.min(__A	) < 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)	)
    SCREAMING_SNAKE_CASE__					     =						np.split(np.argsort(__A ,       kind="mergesort"	) ,       np.cumsum(__A	)[:-1]	)
    for _ in range(__A	):
        SCREAMING_SNAKE_CASE__					     =						approximate_mode(__A ,       __A ,       __A	)
        SCREAMING_SNAKE_CASE__					     =						class_counts - n_i
        SCREAMING_SNAKE_CASE__					     =						approximate_mode(__A ,       __A ,       __A	)

        SCREAMING_SNAKE_CASE__					     =						[]
        SCREAMING_SNAKE_CASE__					     =						[]

        for i in range(__A	):
            SCREAMING_SNAKE_CASE__					     =						rng.permutation(class_counts[i]	)
            SCREAMING_SNAKE_CASE__					     =						class_indices[i].take(__A ,       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]]	)
        SCREAMING_SNAKE_CASE__					     =						rng.permutation(__A	)
        SCREAMING_SNAKE_CASE__					     =						rng.permutation(__A	)

        yield train, test





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_splinter import SplinterTokenizer


A__        =			logging.get_logger(__name__)

A__        =			{"vocab_file": "vocab.txt"}

A__        =			{
    "vocab_file": {
        "tau/splinter-base": "https://huggingface.co/tau/splinter-base/resolve/main/vocab.txt",
        "tau/splinter-base-qass": "https://huggingface.co/tau/splinter-base-qass/resolve/main/vocab.txt",
        "tau/splinter-large": "https://huggingface.co/tau/splinter-large/resolve/main/vocab.txt",
        "tau/splinter-large-qass": "https://huggingface.co/tau/splinter-large-qass/resolve/main/vocab.txt",
    }
}

A__        =			{
    "tau/splinter-base": 512,
    "tau/splinter-base-qass": 512,
    "tau/splinter-large": 512,
    "tau/splinter-large-qass": 512,
}

A__        =			{
    "tau/splinter-base": {"do_lower_case": False},
    "tau/splinter-base-qass": {"do_lower_case": False},
    "tau/splinter-large": {"do_lower_case": False},
    "tau/splinter-large-qass": {"do_lower_case": False},
}







class    UpperCamelCase_			(  _snake_case			):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__				: Dict										=      VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__				: Optional[int]										=      PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__				: List[Any]										=      PRETRAINED_INIT_CONFIGURATION
  SCREAMING_SNAKE_CASE__				: str										=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__				: Optional[Any]										=      SplinterTokenizer



  def __init__(	self    :				Tuple     ,  A_    :				Optional[Any]=None     ,  A_    :				Optional[int]=None     ,  A_    :				Optional[Any]=True     ,  A_    :				str="[UNK]"     ,  A_    :				Optional[Any]="[SEP]"     ,  A_    :				str="[PAD]"     ,  A_    :				List[Any]="[CLS]"     ,  A_    :				Union[str, Any]="[MASK]"     ,  A_    :				Any="[QUESTION]"     ,  A_    :				List[Any]=True     ,  A_    :				Union[str, Any]=None     ,  **A_    :				Optional[Any]     ,  )					-> Optional[Any]:
      super().__init__(
          A_     ,  tokenizer_file=A_     ,  do_lower_case=A_     ,  unk_token=A_     ,  sep_token=A_     ,  pad_token=A_     ,  cls_token=A_     ,  mask_token=A_     ,  tokenize_chinese_chars=A_     ,  strip_accents=A_     ,  additional_special_tokens=(question_token,)     ,  **A_     ,  )

      SCREAMING_SNAKE_CASE__					     =						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
      ):
          SCREAMING_SNAKE_CASE__					     =						getattr(A_     ,  pre_tok_state.pop("type"	)	)
          SCREAMING_SNAKE_CASE__					     =						do_lower_case
          SCREAMING_SNAKE_CASE__					     =						strip_accents
          SCREAMING_SNAKE_CASE__					     =						pre_tok_class(**A_	)

      SCREAMING_SNAKE_CASE__					     =						do_lower_case



  @property
  def      _UpperCamelCase    (	self    :				List[Any]	)					-> Optional[Any]:
      return self.convert_tokens_to_ids(self.question_token	)



  def      _UpperCamelCase    (	self    :				List[str]     ,  A_    :				List[int]     ,  A_    :				Optional[List[int]] = None	)					-> List[int]:
      if token_ids_a is None:
          return [self.cls_token_id] + token_ids_a + [self.sep_token_id]

      SCREAMING_SNAKE_CASE__					     =						[self.cls_token_id]
      SCREAMING_SNAKE_CASE__					     =						[self.sep_token_id]
      SCREAMING_SNAKE_CASE__					     =						[self.question_token_id] + [self.convert_tokens_to_ids("."	)]
      if self.padding_side == "right":
          # Input is question-then-context
          return cls + token_ids_a + question_suffix + sep + token_ids_a + sep
      else:
          # Input is context-then-question
          return cls + token_ids_a + sep + token_ids_a + question_suffix + sep



  def      _UpperCamelCase    (	self    :				Optional[int]     ,  A_    :				List[int]     ,  A_    :				Optional[List[int]] = None	)					-> List[int]:
      SCREAMING_SNAKE_CASE__					     =						[self.sep_token_id]
      SCREAMING_SNAKE_CASE__					     =						[self.cls_token_id]
      SCREAMING_SNAKE_CASE__					     =						[self.question_token_id] + [self.convert_tokens_to_ids("."	)]
      if token_ids_a is None:
          return len(cls + token_ids_a + sep	) * [0]

      if self.padding_side == "right":
          # Input is question-then-context
          return len(cls + token_ids_a + question_suffix + sep	) * [0] + len(token_ids_a + sep	) * [1]
      else:
          # Input is context-then-question
          return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + question_suffix + sep	) * [1]







  def      _UpperCamelCase    (	self    :				Optional[int]     ,  A_    :				str     ,  A_    :				Optional[str] = None	)					-> Tuple[str]:
      SCREAMING_SNAKE_CASE__					     =						self._tokenizer.model.save(A_     ,  name=A_	)
      return tuple(A_	)


