




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


lowerCAmelCase__ :   Optional[Any]						=     logging.get_logger(__name__)

lowerCAmelCase__ :   Optional[int]						=     {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase__ :   str						=     {
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

lowerCAmelCase__ :   Union[str, Any]						=     {
    'microsoft/deberta-base': 512,
    'microsoft/deberta-large': 512,
    'microsoft/deberta-xlarge': 512,
    'microsoft/deberta-base-mnli': 512,
    'microsoft/deberta-large-mnli': 512,
    'microsoft/deberta-xlarge-mnli': 512,
}

lowerCAmelCase__ :   str						=     {
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}




class 	UpperCamelCase__			(lowerCAmelCase__  ):

    '''simple docstring'''



    lowerCamelCase_   :			Union[str, Any]			  =   VOCAB_FILES_NAMES
    lowerCamelCase_   :			List[Any]			  =   PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase_   :			Any			  =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase_   :			Union[str, Any]			  =   ["""input_ids""", """attention_mask""", """token_type_ids"""]
    lowerCamelCase_   :			str			  =   DebertaTokenizer







    def __init__(					self  ,						UpperCamelCase__=None  ,						UpperCamelCase__=None  ,						UpperCamelCase__=None  ,						UpperCamelCase__="replace"  ,						UpperCamelCase__="[CLS]"  ,						UpperCamelCase__="[SEP]"  ,						UpperCamelCase__="[SEP]"  ,						UpperCamelCase__="[CLS]"  ,						UpperCamelCase__="[UNK]"  ,						UpperCamelCase__="[PAD]"  ,						UpperCamelCase__="[MASK]"  ,						UpperCamelCase__=False  ,						**UpperCamelCase__  ,						)  ->   Optional[Any]:
           super().__init__(
               UpperCamelCase__  ,						UpperCamelCase__  ,						tokenizer_file=UpperCamelCase__  ,						errors=UpperCamelCase__  ,						bos_token=UpperCamelCase__  ,						eos_token=UpperCamelCase__  ,						unk_token=UpperCamelCase__  ,						sep_token=UpperCamelCase__  ,						cls_token=UpperCamelCase__  ,						pad_token=UpperCamelCase__  ,						mask_token=UpperCamelCase__  ,						add_prefix_space=UpperCamelCase__  ,						**UpperCamelCase__  ,						)
           lowerCamelCase     :   Union[str, Any]    				=      kwargs.pop("add_bos_token"  ,						UpperCamelCase__  )

           lowerCamelCase     :   Any    				=      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
           if pre_tok_state.get("add_prefix_space"  ,						UpperCamelCase__  ) != add_prefix_space:
                  lowerCamelCase     :   Dict    				=      getattr(UpperCamelCase__  ,						pre_tok_state.pop("type"  )  )
                  lowerCamelCase     :   Any    				=      add_prefix_space
                  lowerCamelCase     :   Any    				=      pre_tok_class(**UpperCamelCase__  )

           lowerCamelCase     :   Tuple    				=      add_prefix_space







    @property
    def 					_lowercase  (					self  )  ->   str:
           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 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   Tuple:
           lowerCamelCase     :   Optional[Any]    				=      AddedToken(UpperCamelCase__  ,						lstrip=UpperCamelCase__  ,						rstrip=UpperCamelCase__  ) if isinstance(UpperCamelCase__  ,						UpperCamelCase__  ) else value
           lowerCamelCase     :   List[Any]    				=      value







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__ = None  )  ->   List[int]:
           if token_ids_a is None:
                  return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           lowerCamelCase     :   Any    				=      [self.cls_token_id]
           lowerCamelCase     :   int    				=      [self.sep_token_id]
           return cls + token_ids_a + sep + token_ids_a + sep







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__ = None  )  ->   List[int]:
           lowerCamelCase     :   List[str]    				=      [self.sep_token_id]
           lowerCamelCase     :   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  ) * [0] + len(token_ids_a + sep  ) * [1]







    def 					_lowercase  (					self  ,						*UpperCamelCase__  ,						**UpperCamelCase__  )  ->   BatchEncoding:
           lowerCamelCase     :   Optional[int]    				=      kwargs.get("is_split_into_words"  ,						UpperCamelCase__  )
           assert self.add_prefix_space or not is_split_into_words, (
               F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
               "to use it with pretokenized inputs."
           )

           return super()._batch_encode_plus(*UpperCamelCase__  ,						**UpperCamelCase__  )







    def 					_lowercase  (					self  ,						*UpperCamelCase__  ,						**UpperCamelCase__  )  ->   BatchEncoding:
           lowerCamelCase     :   List[str]    				=      kwargs.get("is_split_into_words"  ,						UpperCamelCase__  )

           assert self.add_prefix_space or not is_split_into_words, (
               F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
               "to use it with pretokenized inputs."
           )

           return super()._encode_plus(*UpperCamelCase__  ,						**UpperCamelCase__  )







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__ = None  )  ->   Tuple[str]:
           lowerCamelCase     :   Dict    				=      self._tokenizer.model.save(UpperCamelCase__  ,						name=UpperCamelCase__  )
           return tuple(UpperCamelCase__  )







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   List[int]:
           lowerCamelCase     :   Optional[Any]    				=      []
           for is_user, text in conversation.iter_texts():
                  input_ids.extend(self.encode(UpperCamelCase__  ,						add_special_tokens=UpperCamelCase__  ) + [self.eos_token_id]  )

           if len(UpperCamelCase__  ) > self.model_max_length:
                  lowerCamelCase     :   Optional[Any]    				=      input_ids[-self.model_max_length :]
           return input_ids












from itertools import compress, repeat
from math import ceil, sqrt

def   A   (   _SCREAMING_SNAKE_CASE					)			->				list[int]:

       if num <= 2:
              return []
       if num == 3:
              return [2]

       # Odd sieve for numbers in range [3, num - 1]
       lowerCamelCase     :   str    				=      bytearray(B"\x01"					) * ((num >> 1) - 1)

       for i in range(3				,int(sqrt(_SCREAMING_SNAKE_CASE					)					) + 1				,2					):
              if sieve[(i >> 1) - 1]:
                     lowerCamelCase     :   Tuple    				=      i**2
                     lowerCamelCase     :   str    				=      repeat(
                         0				,ceil((num - i_squared) / (i << 1)					)					)

       return [2] + list(compress(range(3				,_SCREAMING_SNAKE_CASE				,2					)				,_SCREAMING_SNAKE_CASE					)					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()






