






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


snake_case			 =						logging.get_logger(__name__)

snake_case			 =						{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

snake_case			 =						{
    """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"""
        ),
    },
}

snake_case			 =						{
    """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,
}

snake_case			 =						{
    """microsoft/deberta-base""": {"""do_lower_case""": False},
    """microsoft/deberta-large""": {"""do_lower_case""": False},
}


class        SCREAMING_SNAKE_CASE	(	lowerCAmelCase    ):
     '''simple docstring'''


     UpperCamelCase_							:						Tuple								=		VOCAB_FILES_NAMES
     UpperCamelCase_							:						Any								=		PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase_							:						str								=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase_							:						str								=		['''input_ids''', '''attention_mask''', '''token_type_ids''']
     UpperCamelCase_							:						str								=		DebertaTokenizer


     def __init__( self				:		Dict    ,       UpperCAmelCase_				:		List[Any]=None    ,       UpperCAmelCase_				:		Union[str, Any]=None    ,       UpperCAmelCase_				:		Optional[Any]=None    ,       UpperCAmelCase_				:		List[Any]="replace"    ,       UpperCAmelCase_				:		Optional[Any]="[CLS]"    ,       UpperCAmelCase_				:		Optional[int]="[SEP]"    ,       UpperCAmelCase_				:		Optional[int]="[SEP]"    ,       UpperCAmelCase_				:		str="[CLS]"    ,       UpperCAmelCase_				:		Union[str, Any]="[UNK]"    ,       UpperCAmelCase_				:		str="[PAD]"    ,       UpperCAmelCase_				:		List[Any]="[MASK]"    ,       UpperCAmelCase_				:		List[str]=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_    ,       )
       SCREAMING_SNAKE_CASE				:					Tuple		  =			kwargs.pop("add_bos_token"    ,       UpperCAmelCase_					)

       SCREAMING_SNAKE_CASE				:					Any		  =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
       if pre_tok_state.get("add_prefix_space"    ,       UpperCAmelCase_					) != add_prefix_space:
         SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			getattr(UpperCAmelCase_    ,       pre_tok_state.pop("type"					)					)
         SCREAMING_SNAKE_CASE				:					List[str]		  =			add_prefix_space
         SCREAMING_SNAKE_CASE				:					Any		  =			pre_tok_class(**UpperCAmelCase_					)

       SCREAMING_SNAKE_CASE				:					Tuple		  =			add_prefix_space


     @property
     def  _A				( self				:		Union[str, Any]					):
       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  _A				( self				:		Dict    ,       UpperCAmelCase_				:		List[str]					):
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			AddedToken(UpperCAmelCase_    ,       lstrip=UpperCAmelCase_    ,       rstrip=UpperCAmelCase_					) if isinstance(UpperCAmelCase_    ,       UpperCAmelCase_					) else value
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			value


     def  _A				( self				:		Any    ,       UpperCAmelCase_				:		List[int]    ,       UpperCAmelCase_				:		Optional[List[int]] = None					):
       if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       SCREAMING_SNAKE_CASE				:					Dict		  =			[self.cls_token_id]
       SCREAMING_SNAKE_CASE				:					int		  =			[self.sep_token_id]
       return cls + token_ids_a + sep + token_ids_a + sep


     def  _A				( self				:		Optional[int]    ,       UpperCAmelCase_				:		List[int]    ,       UpperCAmelCase_				:		Optional[List[int]] = None					):
       SCREAMING_SNAKE_CASE				:					Tuple		  =			[self.sep_token_id]
       SCREAMING_SNAKE_CASE				:					Dict		  =			[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  _A				( self				:		str    ,       *UpperCAmelCase_				:		Optional[int]    ,       **UpperCAmelCase_				:		str					):
       SCREAMING_SNAKE_CASE				:					Tuple		  =			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  _A				( self				:		Union[str, Any]    ,       *UpperCAmelCase_				:		str    ,       **UpperCAmelCase_				:		List[Any]					):
       SCREAMING_SNAKE_CASE				:					Any		  =			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  _A				( self				:		Any    ,       UpperCAmelCase_				:		str    ,       UpperCAmelCase_				:		Optional[str] = None					):
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			self._tokenizer.model.save(UpperCAmelCase_    ,       name=UpperCAmelCase_					)
       return tuple(UpperCAmelCase_					)






     def  _A				( self				:		Optional[int]    ,       UpperCAmelCase_				:		"Conversation"					):
       SCREAMING_SNAKE_CASE				:					Dict		  =			[]
       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:
         SCREAMING_SNAKE_CASE				:					str		  =			input_ids[-self.model_max_length :]
       return input_ids














import re


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  def docstring_decorator(lowercase ):
    SCREAMING_SNAKE_CASE				:					List[Any]		  =			fn.__doc__
    SCREAMING_SNAKE_CASE				:					Tuple		  =			func_doc.split("\n" )
    SCREAMING_SNAKE_CASE				:					Tuple		  =			0
    while i < len(lowercase ) and re.search(R"^\s*Examples?:\s*$"  ,   lines[i] ) is None:
      i += 1
    if i < len(lowercase ):
      SCREAMING_SNAKE_CASE				:					int		  =			example_docstring
      SCREAMING_SNAKE_CASE				:					Tuple		  =			"\n".join(lowercase )
    else:
      raise ValueError(
          F'''The function {fn} should have an empty \'Examples:\' in its docstring as placeholder, '''
          F'''current docstring is:\n{func_doc}''' )
    SCREAMING_SNAKE_CASE				:					int		  =			func_doc
    return fn

  return docstring_decorator






