

"""simple docstring"""
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


A									=						logging.get_logger(__name__)

A									=						{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

A									=						{
    """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""",
    },
}

A									=						{
    """microsoft/mpnet-base""": 512,
}

A									=						{
    """microsoft/mpnet-base""": {"""do_lower_case""": True},
}

class        _UpperCamelCase	(						lowerCamelCase__	):


  """simple docstring"""





  snake_case_        =    VOCAB_FILES_NAMES
  snake_case_        =    PRETRAINED_VOCAB_FILES_MAP
  snake_case_        =    PRETRAINED_INIT_CONFIGURATION
  snake_case_        =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  snake_case_        =    MPNetTokenizer
  snake_case_        =    ['input_ids', 'attention_mask']



  def __init__( self					: Optional[Any]       ,       snake_case					: Union[str, Any]=None       ,       snake_case					: Dict=None       ,       snake_case					: Tuple=True       ,       snake_case					: str="<s>"       ,       snake_case					: Dict="</s>"       ,       snake_case					: Tuple="</s>"       ,       snake_case					: List[Any]="<s>"       ,       snake_case					: Dict="[UNK]"       ,       snake_case					: str="<pad>"       ,       snake_case					: Union[str, Any]="<mask>"       ,       snake_case					: int=True       ,       snake_case					: Union[str, Any]=None       ,       **snake_case					: Optional[Any]       ,       )	->			str:


         '''simple docstring'''





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

         __magic_name__			:		Tuple        =     json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
         if (
             pre_tok_state.get('''lowercase'''       ,       snake_case      ) != do_lower_case
             or pre_tok_state.get('''strip_accents'''       ,       snake_case      ) != strip_accents
         ):
                __magic_name__			:		str        =     getattr(snake_case       ,       pre_tok_state.pop('''type'''      )      )
                __magic_name__			:		Any        =     do_lower_case
                __magic_name__			:		Union[str, Any]        =     strip_accents
                __magic_name__			:		Tuple        =     pre_tok_class(**snake_case      )

         __magic_name__			:		Dict        =     do_lower_case



  @property
  def 							_UpperCAmelCase      ( self					: int      )	->			str:


         '''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 							_UpperCAmelCase      ( self					: List[str]       ,       snake_case					: Tuple      )	->			int:


         '''simple docstring'''





         __magic_name__			:		int        =     AddedToken(snake_case       ,       lstrip=snake_case       ,       rstrip=snake_case      ) if isinstance(snake_case       ,       snake_case      ) else value
         __magic_name__			:		Tuple        =     value



  def 							_UpperCAmelCase      ( self					: List[str]       ,       snake_case					: Optional[int]       ,       snake_case					: Dict=None      )	->			int:


         '''simple docstring'''





         __magic_name__			:		Union[str, 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 							_UpperCAmelCase      ( self					: Union[str, Any]       ,       snake_case					: List[int]       ,       snake_case					: Optional[List[int]] = None      )	->			List[int]:


         '''simple docstring'''





         __magic_name__			:		Optional[int]        =     [self.sep_token_id]
         __magic_name__			:		int        =     [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 							_UpperCAmelCase      ( self					: Optional[int]       ,       snake_case					: str       ,       snake_case					: Optional[str] = None      )	->			Tuple[str]:


         '''simple docstring'''





         __magic_name__			:		Optional[Any]        =     self._tokenizer.model.save(snake_case       ,       name=snake_case      )
         return tuple(snake_case      )



"""simple docstring"""
from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A									=						logging.get_logger(__name__)

A									=						{
    """microsoft/prophetnet-large-uncased""": (
        """https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"""
    ),
}

class        _UpperCamelCase	(						lowerCamelCase__	):


  """simple docstring"""





  snake_case_        =    'prophetnet'
  snake_case_        =    ['past_key_values']
  snake_case_        =    {
      'num_attention_heads': 'num_encoder_attention_heads',
  }



  def __init__( self					: Union[str, Any]       ,       snake_case					: Optional[float] = 0.1       ,       snake_case					: Optional[Union[str, Callable]] = "gelu"       ,       snake_case					: Optional[int] = 3_0522       ,       snake_case					: Optional[int] = 1024       ,       snake_case					: Optional[int] = 4096       ,       snake_case					: Optional[int] = 12       ,       snake_case					: Optional[int] = 16       ,       snake_case					: Optional[int] = 4096       ,       snake_case					: Optional[int] = 12       ,       snake_case					: Optional[int] = 16       ,       snake_case					: Optional[float] = 0.1       ,       snake_case					: Optional[float] = 0.1       ,       snake_case					: Optional[int] = 512       ,       snake_case					: Optional[float] = 0.02       ,       snake_case					: Optional[bool] = True       ,       snake_case					: Optional[bool] = True       ,       snake_case					: Optional[int] = 0       ,       snake_case					: Optional[int] = 2       ,       snake_case					: Optional[int] = 32       ,       snake_case					: Optional[int] = 128       ,       snake_case					: Optional[bool] = False       ,       snake_case					: Optional[float] = 0.0       ,       snake_case					: Optional[bool] = True       ,       snake_case					: Optional[int] = 0       ,       snake_case					: Optional[int] = 1       ,       snake_case					: Optional[int] = 2       ,       **snake_case					: Optional[int]       ,       )	->			List[Any]:


         '''simple docstring'''





         __magic_name__			:		Union[str, Any]        =     vocab_size
         __magic_name__			:		Tuple        =     hidden_size
         __magic_name__			:		str        =     encoder_ffn_dim
         __magic_name__			:		Tuple        =     num_encoder_layers
         __magic_name__			:		int        =     num_encoder_attention_heads
         __magic_name__			:		int        =     decoder_ffn_dim
         __magic_name__			:		Optional[int]        =     num_decoder_layers
         __magic_name__			:		str        =     num_decoder_attention_heads
         __magic_name__			:		Optional[Any]        =     max_position_embeddings
         __magic_name__			:		Union[str, Any]        =     init_std  # Normal(0, this parameter)
         __magic_name__			:		Tuple        =     activation_function

         # parameters for prophetnet
         __magic_name__			:		int        =     ngram
         __magic_name__			:		str        =     num_buckets
         __magic_name__			:		Any        =     relative_max_distance
         __magic_name__			:		Optional[int]        =     disable_ngram_loss
         __magic_name__			:		str        =     eps

         # 3 Types of Dropout
         __magic_name__			:		Dict        =     attention_dropout
         __magic_name__			:		Optional[int]        =     activation_dropout
         __magic_name__			:		List[Any]        =     dropout

         __magic_name__			:		str        =     use_cache

         super().__init__(
             pad_token_id=snake_case       ,       bos_token_id=snake_case       ,       eos_token_id=snake_case       ,       is_encoder_decoder=snake_case       ,       add_cross_attention=snake_case       ,       decoder_start_token_id=snake_case       ,       **snake_case       ,       )



  @property
  def 							_UpperCAmelCase      ( self					: str      )	->			int:


         '''simple docstring'''





         return self.num_encoder_layers + self.num_decoder_layers




  @num_hidden_layers.setter
  def 							_UpperCAmelCase      ( self					: Dict       ,       snake_case					: int      )	->			Optional[int]:


         '''simple docstring'''





         raise NotImplementedError(
             '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
             ''' `num_decoder_layers`.'''      )
