


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

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


_SCREAMING_SNAKE_CASE :					List[str]			=	logging.get_logger(__name__)

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

_SCREAMING_SNAKE_CASE :					Union[str, Any]			=	{
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json""",
    },
}

class  UpperCamelCase__ ( __lowerCamelCase			):






      def __init__(     self					:   str,		__lowerCamelCase					:   Tuple=None,		__lowerCamelCase					:   Optional[int]=None,		__lowerCamelCase					:   Any=False,		__lowerCamelCase					:   Tuple=True,		__lowerCamelCase					:   str=False,		__lowerCamelCase					:   Tuple="<s>",		__lowerCamelCase					:   Any="</s>",		__lowerCamelCase					:   str="<unk>",		__lowerCamelCase					:   int="<sep>",		__lowerCamelCase					:   Union[str, Any]="<pad>",		__lowerCamelCase					:   Union[str, Any]="<cls>",		__lowerCamelCase					:   List[str]="<mask>",		__lowerCamelCase					:   Optional[Any]=["<eop>", "<eod>"],		**__lowerCamelCase					:   Tuple,		)  -> Tuple:
           UpperCamelCase__							:  Any								=     AddedToken(__lowerCamelCase,		lstrip=__lowerCamelCase,		rstrip=__lowerCamelCase   ) if isinstance(__lowerCamelCase,		__lowerCamelCase   ) else mask_token

           super().__init__(
               vocab_file=__lowerCamelCase,		tokenizer_file=__lowerCamelCase,		do_lower_case=__lowerCamelCase,		remove_space=__lowerCamelCase,		keep_accents=__lowerCamelCase,		bos_token=__lowerCamelCase,		eos_token=__lowerCamelCase,		unk_token=__lowerCamelCase,		sep_token=__lowerCamelCase,		pad_token=__lowerCamelCase,		cls_token=__lowerCamelCase,		mask_token=__lowerCamelCase,		additional_special_tokens=__lowerCamelCase,		**__lowerCamelCase,		)

           UpperCamelCase__							:  Optional[int]								=     3
           UpperCamelCase__							:  Optional[int]								=     do_lower_case
           UpperCamelCase__							:  int								=     remove_space
           UpperCamelCase__							:  Any								=     keep_accents
           UpperCamelCase__							:  List[Any]								=     vocab_file
           UpperCamelCase__							:  Union[str, Any]								=     False if not self.vocab_file else True

           try:
                import jieba
           except ModuleNotFoundError as error:
                raise error.__class__(
                    '''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
                    '''See https://pypi.org/project/jieba/ for installation.'''   )
           UpperCamelCase__							:  Dict								=     jieba
           UpperCamelCase__							:  Dict								=     str.maketrans(''' \n''',		'''\u2582\u2583'''   )






      def 	__lowercase(     self					:   Union[str, Any],		__lowerCamelCase					:   List[int],		__lowerCamelCase					:   Optional[List[int]] = None   )  -> List[int]:
           UpperCamelCase__							:  List[str]								=     [self.sep_token_id]
           UpperCamelCase__							:  Optional[int]								=     [self.cls_token_id]
           if token_ids_a is None:
                return token_ids_a + sep + cls
           return token_ids_a + sep + token_ids_a + sep + cls






      def 	__lowercase(     self					:   Tuple,		__lowerCamelCase					:   List[int],		__lowerCamelCase					:   Optional[List[int]] = None   )  -> List[int]:
           UpperCamelCase__							:  Tuple								=     [self.sep_token_id]
           UpperCamelCase__							:  Tuple								=     [2]

           if token_ids_a is None:
                return len(token_ids_a + sep   ) * [0] + cls_segment_id
           return len(token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1] + cls_segment_id






      def 	__lowercase(     self					:   str,		__lowerCamelCase					:   str,		__lowerCamelCase					:   Optional[str] = None   )  -> Tuple[str]:
           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(__lowerCamelCase   ):
                logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
                return
           UpperCamelCase__							:  Dict								=     os.path.join(
               __lowerCamelCase,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

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

           return (out_vocab_file,)






      def 	__lowercase(     self					:   Union[str, Any],		__lowerCamelCase					:   Tuple,		*__lowerCamelCase					:   Any,		**__lowerCamelCase					:   int   )  -> List[str]:
           UpperCamelCase__							:  Union[str, Any]								=     [
               ''' '''.join([x.translate(self.translator   ) for x in self.jieba.cut(__lowerCamelCase,		cut_all=__lowerCamelCase   )]   )
               for text in batch_text_or_text_pairs
           ]
           return super()._batch_encode_plus(__lowerCamelCase,		*__lowerCamelCase,		**__lowerCamelCase   )






      def 	__lowercase(     self					:   Optional[Any],		*__lowerCamelCase					:   int,		**__lowerCamelCase					:   Optional[int]   )  -> Optional[int]:
           UpperCamelCase__							:  Optional[Any]								=     super()._decode(*__lowerCamelCase,		**__lowerCamelCase   )
           UpperCamelCase__							:  Any								=     text.replace(''' ''',		''''''   ).replace('''\u2582''',		''' '''   ).replace('''\u2583''',		'''\n'''   )
           return text








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


_SCREAMING_SNAKE_CASE :					Tuple			=	logging.get_logger(__name__)

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

_SCREAMING_SNAKE_CASE :					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"""
        ),
    },
}

_SCREAMING_SNAKE_CASE :					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,
}

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

class  UpperCamelCase__ ( __lowerCamelCase			):
      a__   :     int								=						VOCAB_FILES_NAMES
      a__   :     Optional[Any]								=						PRETRAINED_VOCAB_FILES_MAP
      a__   :     int								=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a__   :     str								=						['input_ids', 'attention_mask', 'token_type_ids']
      a__   :     int								=						DebertaTokenizer






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

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

           UpperCamelCase__							:  Optional[int]								=     add_prefix_space






      @property
      def 	__lowercase(     self					:   Dict   )  -> 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					:   List[Any],		__lowerCamelCase					:   Union[str, Any]   )  -> List[str]:
           UpperCamelCase__							:  List[Any]								=     AddedToken(__lowerCamelCase,		lstrip=__lowerCamelCase,		rstrip=__lowerCamelCase   ) if isinstance(__lowerCamelCase,		__lowerCamelCase   ) else value
           UpperCamelCase__							:  List[str]								=     value






      def 	__lowercase(     self					:   List[str],		__lowerCamelCase					:   List[int],		__lowerCamelCase					:   Optional[List[int]] = None   )  -> List[int]:
           if token_ids_a is None:
                return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           UpperCamelCase__							:  Union[str, Any]								=     [self.cls_token_id]
           UpperCamelCase__							:  Union[str, Any]								=     [self.sep_token_id]
           return cls + token_ids_a + sep + token_ids_a + sep






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






      def 	__lowercase(     self					:   Union[str, Any],		*__lowerCamelCase					:   Any,		**__lowerCamelCase					:   List[Any]   )  -> BatchEncoding:
           UpperCamelCase__							:  str								=     kwargs.get('''is_split_into_words''',		__lowerCamelCase   )
           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(*__lowerCamelCase,		**__lowerCamelCase   )






      def 	__lowercase(     self					:   Optional[Any],		*__lowerCamelCase					:   str,		**__lowerCamelCase					:   int   )  -> BatchEncoding:
           UpperCamelCase__							:  str								=     kwargs.get('''is_split_into_words''',		__lowerCamelCase   )

           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(*__lowerCamelCase,		**__lowerCamelCase   )






      def 	__lowercase(     self					:   Union[str, Any],		__lowerCamelCase					:   str,		__lowerCamelCase					:   Optional[str] = None   )  -> Tuple[str]:
           UpperCamelCase__							:  Dict								=     self._tokenizer.model.save(__lowerCamelCase,		name=__lowerCamelCase   )
           return tuple(__lowerCamelCase   )






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

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




