





'''simple docstring'''




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_pegasus import PegasusTokenizer
else:
    _A:					List[str]							=				None


_A:					Dict							=				logging.get_logger(__name__)


_A:					str							=				"""▁"""

_A:					Optional[Any]							=				{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_A:					Union[str, Any]							=				{
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
    """tokenizer_file""": {
        """google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
    },
}

_A:					List[str]							=				{
    """google/pegasus-xsum""": 512,
}






class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			Tuple						   =       VOCAB_FILES_NAMES
    _A :			Optional[Any]						   =       PRETRAINED_VOCAB_FILES_MAP
    _A :			Optional[int]						   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _A :			Optional[int]						   =       PegasusTokenizer
    _A :			List[Any]						   =       ["""input_ids""", """attention_mask"""]



    def __init__(  self     ,			__A=None     ,			__A=None     ,			__A="<pad>"     ,			__A="</s>"     ,			__A="<unk>"     ,			__A="<mask_2>"     ,			__A="<mask_1>"     ,			__A=None     ,			__A=103     ,			**__A     ,			):
     __UpperCAmelCase					=   offset

     if additional_special_tokens is not None:
      if not isinstance(__A     ,			__A					):
       raise TypeError(
           f'additional_special_tokens should be of type {type(__A					)}, but is'
           f' {type(__A					)}'					)

      __UpperCAmelCase					=   (
          ([mask_token_sent] + additional_special_tokens)
          if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
          else additional_special_tokens
      )
      # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
      additional_special_tokens_extended += [
          f'<unk_{i}>' for i in range(len(__A					)     ,			self.offset - 1					)
      ]

      if len(set(__A					)					) != len(__A					):
       raise ValueError(
           'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
           f' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'					)
      __UpperCAmelCase					=   additional_special_tokens_extended
     else:
      __UpperCAmelCase					=   [mask_token_sent] if mask_token_sent is not None else []
      additional_special_tokens += [f'<unk_{i}>' for i in range(2     ,			self.offset					)]

     super().__init__(
         __A     ,			tokenizer_file=__A     ,			pad_token=__A     ,			eos_token=__A     ,			unk_token=__A     ,			mask_token=__A     ,			mask_token_sent=__A     ,			offset=__A     ,			additional_special_tokens=__A     ,			**__A     ,			)
     __UpperCAmelCase					=   vocab_file
     __UpperCAmelCase					=   False if not self.vocab_file else True



    def 					__lowerCamelCase						(  self     ,			__A					):
     __UpperCAmelCase					=   set(self.all_special_ids					)  # call it once instead of inside list comp
     all_special_ids.remove(self.unk_token_id					)  # <unk> is only sometimes special

     if all_special_ids != set(range(len(self.additional_special_tokens					) + 3					)					):
      raise ValueError(
          'There should be 3 special tokens: mask_token, pad_token, and eos_token +'
          f' {len(self.additional_special_tokens					)} additional_special_tokens, but got {all_special_ids}'					)

     return [1 if x in all_special_ids else 0 for x in seq]



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A = None     ,			__A = False					):
     if already_has_special_tokens:
      return self._special_token_mask(__A					)
     elif token_ids_a is None:
      return self._special_token_mask(__A					) + [1]
     else:
      return self._special_token_mask(token_ids_a + token_ids_a					) + [1]



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A=None					):
     if token_ids_a is None:
      return token_ids_a + [self.eos_token_id]
     # We don't expect to process pairs, but leave the pair logic for API consistency
     return token_ids_a + token_ids_a + [self.eos_token_id]




    def 					__lowerCamelCase						(  self     ,			__A     ,			__A = None					):
     if not self.can_save_slow_tokenizer:
      raise ValueError(
          'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
          'tokenizer.'					)

     if not os.path.isdir(__A					):
      logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
      return
     __UpperCAmelCase					=   os.path.join(
         __A     ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)

     if os.path.abspath(self.vocab_file					) != os.path.abspath(__A					):
      copyfile(self.vocab_file     ,			__A					)

     return (out_vocab_file,)












'''simple docstring'''




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


_A:					Union[str, Any]							=				logging.get_logger(__name__)

_A:					List[str]							=				{
    """weiweishi/roc-bert-base-zh""": """https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json""",
}






class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			int						   =       """roc_bert"""



    def __init__(  self     ,			__A=30_522     ,			__A=768     ,			__A=12     ,			__A=12     ,			__A=3_072     ,			__A="gelu"     ,			__A=0.1     ,			__A=0.1     ,			__A=512     ,			__A=2     ,			__A=0.0_2     ,			__A=1E-12     ,			__A=True     ,			__A=0     ,			__A="absolute"     ,			__A=None     ,			__A=True     ,			__A=True     ,			__A=768     ,			__A=910     ,			__A=512     ,			__A=24_858     ,			__A=True     ,			**__A     ,			):
     __UpperCAmelCase					=   vocab_size
     __UpperCAmelCase					=   max_position_embeddings
     __UpperCAmelCase					=   hidden_size
     __UpperCAmelCase					=   num_hidden_layers
     __UpperCAmelCase					=   num_attention_heads
     __UpperCAmelCase					=   intermediate_size
     __UpperCAmelCase					=   hidden_act
     __UpperCAmelCase					=   hidden_dropout_prob
     __UpperCAmelCase					=   attention_probs_dropout_prob
     __UpperCAmelCase					=   initializer_range
     __UpperCAmelCase					=   type_vocab_size
     __UpperCAmelCase					=   layer_norm_eps
     __UpperCAmelCase					=   use_cache
     __UpperCAmelCase					=   enable_pronunciation
     __UpperCAmelCase					=   enable_shape
     __UpperCAmelCase					=   pronunciation_embed_dim
     __UpperCAmelCase					=   pronunciation_vocab_size
     __UpperCAmelCase					=   shape_embed_dim
     __UpperCAmelCase					=   shape_vocab_size
     __UpperCAmelCase					=   concat_input
     __UpperCAmelCase					=   position_embedding_type
     __UpperCAmelCase					=   classifier_dropout
     super().__init__(pad_token_id=__A     ,			**__A					)





