from typing import Dict, List, Optional, Tuple

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


_SCREAMING_SNAKE_CASE   		=			logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_		(			__lowerCAmelCase		):
   __lowerCAmelCase											=							["""input_ids""", """attention_mask"""]







   def __init__(    self	:	List[str]      ,      lowerCamelCase_	:	Dict="[PAD]"      ,      lowerCamelCase_	:	Any="[BOS]"      ,      lowerCamelCase_	:	List[str]="[EOS]"      ,      lowerCamelCase_	:	Union[str, Any]="[MASK]"      ,      lowerCamelCase_	:	Tuple="[CLS]"      ,      lowerCamelCase_	:	Optional[int]="[SEP]"      ,      lowerCamelCase_	:	Dict=2048      ,      **lowerCamelCase_	:	Any      ,      ):





    """simple docstring"""


    UpperCamelCase      =			AddedToken(lowerCamelCase_      ,      lstrip=lowerCamelCase_      ,      rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_      ,      lowerCamelCase_							) else pad_token
    UpperCamelCase      =			AddedToken(lowerCamelCase_      ,      lstrip=lowerCamelCase_      ,      rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_      ,      lowerCamelCase_							) else bos_token
    UpperCamelCase      =			AddedToken(lowerCamelCase_      ,      lstrip=lowerCamelCase_      ,      rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_      ,      lowerCamelCase_							) else eos_token
    UpperCamelCase      =			AddedToken(lowerCamelCase_      ,      lstrip=lowerCamelCase_      ,      rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_      ,      lowerCamelCase_							) else mask_token
    UpperCamelCase      =			AddedToken(lowerCamelCase_      ,      lstrip=lowerCamelCase_      ,      rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_      ,      lowerCamelCase_							) else cls_token
    UpperCamelCase      =			AddedToken(lowerCamelCase_      ,      lstrip=lowerCamelCase_      ,      rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_      ,      lowerCamelCase_							) else sep_token

    super().__init__(
        pad_token=lowerCamelCase_      ,      bos_token=lowerCamelCase_      ,      eos_token=lowerCamelCase_      ,      mask_token=lowerCamelCase_      ,      cls_token=lowerCamelCase_      ,      sep_token=lowerCamelCase_      ,      model_max_length=lowerCamelCase_      ,      **lowerCamelCase_      ,      )

    UpperCamelCase      =			2**8  # utf is 8 bits

    # define special tokens dict
    UpperCamelCase      =			{
        self.pad_token: 0,
        self.bos_token: 1,
        self.eos_token: 2,
        self.mask_token: 3,
        self.cls_token: 4,
        self.sep_token: 5,
    }
    UpperCamelCase      =			len(self.special_tokens_encoder							)
    UpperCamelCase      =			{v: k for k, v in self.special_tokens_encoder.items()}







   def 	lowerCamelCase_  (    self	:	Tuple							):





    """simple docstring"""


    UpperCamelCase      =			self.special_tokens_encoder.copy()
    vocab.update(self.added_tokens_encoder							)
    for i in range(self._utf_vocab_size							):
     UpperCamelCase      =			chr(lowerCamelCase_							)
     UpperCamelCase      =			i + len(self.special_tokens_encoder							)
    return vocab







   @property
   def 	lowerCamelCase_  (    self	:	Any							):





    """simple docstring"""


    return self._utf_vocab_size + self._num_special_tokens







   def 	lowerCamelCase_  (    self	:	List[Any]      ,      lowerCamelCase_	:	List[int]      ,      lowerCamelCase_	:	Optional[List[int]] = None      ,      lowerCamelCase_	:	bool = False							):





    """simple docstring"""


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

    # normal case: some special tokens
    if token_ids_a is None:
     return [1] + [0] * len(lowerCamelCase_							) + [1]
    return [1] + ([0] * len(lowerCamelCase_							)) + [1] + ([0] * len(lowerCamelCase_							)) + [1]







   def 	lowerCamelCase_  (    self	:	List[str]      ,      lowerCamelCase_	:	List[int]      ,      lowerCamelCase_	:	Optional[List[int]] = None							):





    """simple docstring"""


    if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    else:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]







   def 	lowerCamelCase_  (    self	:	Union[str, Any]      ,      lowerCamelCase_	:	str							):





    """simple docstring"""


    UpperCamelCase      =			[chr(lowerCamelCase_							) for i in text.encode("""utf-8"""							)]
    return tokens







   def 	lowerCamelCase_  (    self	:	List[str]      ,      lowerCamelCase_	:	Optional[Any]							):





    """simple docstring"""


    if token in self.special_tokens_encoder:
     UpperCamelCase      =			self.special_tokens_encoder[token]
    elif token in self.added_tokens_encoder:
     UpperCamelCase      =			self.added_tokens_encoder[token]
    elif len(lowerCamelCase_							) != 1:
     UpperCamelCase      =			self.unk_token_id
    else:
     UpperCamelCase      =			ord(lowerCamelCase_							) + self._num_special_tokens
    return token_id







   def 	lowerCamelCase_  (    self	:	List[Any]      ,      lowerCamelCase_	:	Any							):





    """simple docstring"""


    if index in self.special_tokens_decoder:
     UpperCamelCase      =			self.special_tokens_decoder[index]
    elif index in self.added_tokens_decoder:
     UpperCamelCase      =			self.added_tokens_decoder[index]
    else:
     UpperCamelCase      =			chr(index - self._num_special_tokens							)
    return token







   def 	lowerCamelCase_  (    self	:	str      ,      lowerCamelCase_	:	Optional[Any]							):





    """simple docstring"""


    UpperCamelCase      =			B""""""
    for token in tokens:
     if token in self.special_tokens_decoder:
      UpperCamelCase      =			self.special_tokens_decoder[token].encode("""utf-8"""							)
     elif token in self.added_tokens_decoder:
      UpperCamelCase      =			self.special_tokens_decoder[token].encode("""utf-8"""							)
     elif token in self.special_tokens_encoder:
      UpperCamelCase      =			token.encode("""utf-8"""							)
     elif token in self.added_tokens_encoder:
      UpperCamelCase      =			token.encode("""utf-8"""							)
     else:
      UpperCamelCase      =			bytes([ord(lowerCamelCase_							)]							)
     bstring += tok_string
    UpperCamelCase      =			bstring.decode("""utf-8"""      ,      errors="""replace"""							)
    return string







   def 	lowerCamelCase_  (    self	:	Any      ,      lowerCamelCase_	:	str      ,      lowerCamelCase_	:	Optional[str] = None							):





    """simple docstring"""


    return ()

import numpy as np





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)		->  List[str]:







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





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_=10					)		->  Dict:







 '''simple docstring'''
 UpperCamelCase     ,    UpperCamelCase      =			np.unique(UpperCamelCase_       ,				return_inverse=UpperCamelCase_					)
 UpperCamelCase      =			classes.shape[0]
 UpperCamelCase      =			np.bincount(UpperCamelCase_					)
 if np.min(UpperCamelCase_					) < 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)					)
 UpperCamelCase      =			np.split(np.argsort(UpperCamelCase_       ,				kind="""mergesort"""					)       ,				np.cumsum(UpperCamelCase_					)[:-1]					)
 for _ in range(UpperCamelCase_					):
  UpperCamelCase      =			approximate_mode(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)
  UpperCamelCase      =			class_counts - n_i
  UpperCamelCase      =			approximate_mode(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)

  UpperCamelCase      =			[]
  UpperCamelCase      =			[]

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

  yield train, test
