






import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


snake_case									=		logging.get_logger(__name__)

snake_case									=		{"""vocab_file""": """spiece.model"""}

snake_case									=		{
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    }
}


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


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

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           do_lower_case=UpperCAmelCase_					, remove_space=UpperCAmelCase_					, keep_accents=UpperCAmelCase_					, bos_token=UpperCAmelCase_					, eos_token=UpperCAmelCase_					, unk_token=UpperCAmelCase_					, sep_token=UpperCAmelCase_					, pad_token=UpperCAmelCase_					, cls_token=UpperCAmelCase_					, mask_token=UpperCAmelCase_					, additional_special_tokens=UpperCAmelCase_					, sp_model_kwargs=self.sp_model_kwargs					, **UpperCAmelCase_					, )

       SCREAMING_SNAKE_CASE       :      Tuple							=    3

       SCREAMING_SNAKE_CASE       :      Tuple							=    do_lower_case
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    remove_space
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    keep_accents
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    vocab_file

       SCREAMING_SNAKE_CASE       :      Tuple							=    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
       self.sp_model.Load(UpperCAmelCase_      )

       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."      )
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    jieba
       SCREAMING_SNAKE_CASE       :      List[Any]							=    str.maketrans(" \n"					, "\u2582\u2583"      )


     @property
     # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
     def 				_A       (				self       :	str      ):
       return len(self.sp_model      )


     def 				_A       (				self       :	Dict      ):
       SCREAMING_SNAKE_CASE       :      int							=    {self.convert_ids_to_tokens(UpperCAmelCase_      ): i for i in range(self.vocab_size      )}
       vocab.update(self.added_tokens_encoder      )
       return vocab


     def __getstate__(				self       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.__dict__.copy()
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    None
       return state


     def __setstate__(				self       :	List[str]					, UpperCAmelCase_       :	int      ):
       SCREAMING_SNAKE_CASE       :      Any							=    d

       # for backward compatibility
       if not hasattr(self					, "sp_model_kwargs"      ):
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    {}

       SCREAMING_SNAKE_CASE       :      List[Any]							=    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
       self.sp_model.Load(self.vocab_file      )


     def 				_A       (				self       :	Optional[Any]					, UpperCAmelCase_       :	Any      ):
       if self.remove_space:
         SCREAMING_SNAKE_CASE       :      List[str]							=    " ".join(inputs.strip().split()      )
       else:
         SCREAMING_SNAKE_CASE       :      int							=    inputs
       SCREAMING_SNAKE_CASE       :      int							=    outputs.replace("``"					, "\""      ).replace("''"					, "\""      )

       if not self.keep_accents:
         SCREAMING_SNAKE_CASE       :      Dict							=    unicodedata.normalize("NFKD"					, UpperCAmelCase_      )
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    "".join([c for c in outputs if not unicodedata.combining(UpperCAmelCase_      )]      )
       if self.do_lower_case:
         SCREAMING_SNAKE_CASE       :      List[Any]							=    outputs.lower()

       return outputs


     def 				_A       (				self       :	Tuple					, UpperCAmelCase_       :	str      ):
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.preprocess_text(UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.sp_model.encode(UpperCAmelCase_					, out_type=UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      str							=    []
       for piece in pieces:
         if len(UpperCAmelCase_      ) > 1 and piece[-1] == str(","      ) and piece[-2].isdigit():
           SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.sp_model.EncodeAsPieces(piece[:-1].replace(UpperCAmelCase_					, ""      )      )
           if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
             if len(cur_pieces[0]      ) == 1:
               SCREAMING_SNAKE_CASE       :      str							=    cur_pieces[1:]
             else:
               SCREAMING_SNAKE_CASE       :      str							=    cur_pieces[0][1:]
           cur_pieces.append(piece[-1]      )
           new_pieces.extend(UpperCAmelCase_      )
         else:
           new_pieces.append(UpperCAmelCase_      )

       return new_pieces


     def 				_A       (				self       :	Dict					, UpperCAmelCase_       :	str      ):
       return self.sp_model.PieceToId(UpperCAmelCase_      )


     def 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	Any      ):
       return self.sp_model.IdToPiece(UpperCAmelCase_      )


     def 				_A       (				self       :	List[Any]					, UpperCAmelCase_       :	Optional[Any]      ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    "".join(UpperCAmelCase_      ).replace(UpperCAmelCase_					, " "      ).strip()
       return out_string


     def 				_A       (				self       :	int					, UpperCAmelCase_       :	List[int]					, UpperCAmelCase_       :	Optional[List[int]] = None      ):
       SCREAMING_SNAKE_CASE       :      Any							=    [self.sep_token_id]
       SCREAMING_SNAKE_CASE       :      List[Any]							=    [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 				_A       (				self       :	Any					, UpperCAmelCase_       :	List[int]					, UpperCAmelCase_       :	Optional[List[int]] = None					, UpperCAmelCase_       :	bool = False      ):

       if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=UpperCAmelCase_					, token_ids_a=UpperCAmelCase_					, already_has_special_tokens=UpperCAmelCase_      )

       if token_ids_a is not None:
         return ([0] * len(UpperCAmelCase_      )) + [1] + ([0] * len(UpperCAmelCase_      )) + [1, 1]
       return ([0] * len(UpperCAmelCase_      )) + [1, 1]


     def 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	List[int]					, UpperCAmelCase_       :	Optional[List[int]] = None      ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    [self.sep_token_id]
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    [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 				_A       (				self       :	List[Any]					, UpperCAmelCase_       :	str					, UpperCAmelCase_       :	Optional[str] = None      ):
       if not os.path.isdir(UpperCAmelCase_      ):
         logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''      )
         return
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    os.path.join(
           UpperCAmelCase_					, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )

       if os.path.abspath(self.vocab_file      ) != os.path.abspath(UpperCAmelCase_      ) and os.path.isfile(self.vocab_file      ):
         copyfile(self.vocab_file					, UpperCAmelCase_      )
       elif not os.path.isfile(self.vocab_file      ):
         with open(UpperCAmelCase_					, "wb"      ) as fi:
           SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.sp_model.serialized_model_proto()
           fi.write(UpperCAmelCase_      )

       return (out_vocab_file,)






     def 				_A       (				self       :	Union[str, Any]					, *UpperCAmelCase_       :	Dict					, **UpperCAmelCase_       :	Union[str, Any]      ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    super()._decode(*UpperCAmelCase_					, **UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    text.replace(" "					, ""      ).replace("\u2582"					, " "      ).replace("\u2583"					, "\n"      )
       return text














from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case    				=		logging.get_logger(__name__)

snake_case    				=		{
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}


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


     UpperCamelCase_							:		Dict			=	'''camembert'''


     def __init__(				self       :	str					, UpperCAmelCase_       :	Optional[Any]=3_0522					, UpperCAmelCase_       :	Optional[int]=768					, UpperCAmelCase_       :	List[Any]=12					, UpperCAmelCase_       :	Union[str, Any]=12					, UpperCAmelCase_       :	Optional[Any]=3072					, UpperCAmelCase_       :	Optional[int]="gelu"					, UpperCAmelCase_       :	List[Any]=0.1					, UpperCAmelCase_       :	Dict=0.1					, UpperCAmelCase_       :	List[str]=512					, UpperCAmelCase_       :	List[Any]=2					, UpperCAmelCase_       :	Optional[int]=0.02					, UpperCAmelCase_       :	List[str]=1E-12					, UpperCAmelCase_       :	int=1					, UpperCAmelCase_       :	Union[str, Any]=0					, UpperCAmelCase_       :	List[str]=2					, UpperCAmelCase_       :	int="absolute"					, UpperCAmelCase_       :	Tuple=True					, UpperCAmelCase_       :	Optional[Any]=None					, **UpperCAmelCase_       :	Optional[int]					, ):
       super().__init__(pad_token_id=UpperCAmelCase_					, bos_token_id=UpperCAmelCase_					, eos_token_id=UpperCAmelCase_					, **UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      int							=    vocab_size
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    hidden_size
       SCREAMING_SNAKE_CASE       :      Any							=    num_hidden_layers
       SCREAMING_SNAKE_CASE       :      Dict							=    num_attention_heads
       SCREAMING_SNAKE_CASE       :      Dict							=    hidden_act
       SCREAMING_SNAKE_CASE       :      Tuple							=    intermediate_size
       SCREAMING_SNAKE_CASE       :      Any							=    hidden_dropout_prob
       SCREAMING_SNAKE_CASE       :      int							=    attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE       :      Dict							=    max_position_embeddings
       SCREAMING_SNAKE_CASE       :      List[Any]							=    type_vocab_size
       SCREAMING_SNAKE_CASE       :      List[Any]							=    initializer_range
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    layer_norm_eps
       SCREAMING_SNAKE_CASE       :      Dict							=    position_embedding_type
       SCREAMING_SNAKE_CASE       :      List[Any]							=    use_cache
       SCREAMING_SNAKE_CASE       :      str							=    classifier_dropout





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


     @property
     def 				_A       (				self       :	Optional[int]      ):
       if self.task == "multiple-choice":
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    {0: "batch", 1: "choice", 2: "sequence"}
       else:
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    {0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]      )






