

"""simple docstring"""




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


SCREAMING_SNAKE_CASE_     :			Tuple	  =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_     :			Optional[Any]	  =			{'''vocab_file''': '''spiece.model'''}

SCREAMING_SNAKE_CASE_     :			Union[str, Any]	  =			{
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    }
}


class 		_A       (			__a					):





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=False    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=False    ,						SCREAMING_SNAKE_CASE__="<s>"    ,						SCREAMING_SNAKE_CASE__="</s>"    ,						SCREAMING_SNAKE_CASE__="<unk>"    ,						SCREAMING_SNAKE_CASE__="<sep>"    ,						SCREAMING_SNAKE_CASE__="<pad>"    ,						SCREAMING_SNAKE_CASE__="<cls>"    ,						SCREAMING_SNAKE_CASE__="<mask>"    ,						SCREAMING_SNAKE_CASE__=["<eop>", "<eod>"]    ,						SCREAMING_SNAKE_CASE__ = None    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					None:
         lowerCamelCase__						=			AddedToken(SCREAMING_SNAKE_CASE__    ,						lstrip=SCREAMING_SNAKE_CASE__    ,						rstrip=SCREAMING_SNAKE_CASE__		) if isinstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		) else mask_token

         lowerCamelCase__						=			{} if sp_model_kwargs is None else sp_model_kwargs

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

         lowerCamelCase__						=			3

         lowerCamelCase__						=			do_lower_case
         lowerCamelCase__						=			remove_space
         lowerCamelCase__						=			keep_accents
         lowerCamelCase__						=			vocab_file

         lowerCamelCase__						=			spm.SentencePieceProcessor(**self.sp_model_kwargs		)
         self.sp_model.Load(SCREAMING_SNAKE_CASE__		)

         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."		)
         lowerCamelCase__						=			jieba
         lowerCamelCase__						=			str.maketrans(" \n"    ,						"\u2582\u2583"		)





      @property
      # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
      def 					_lowerCamelCase  (  self		)   ->					int:
         return len(self.sp_model		)





      def 					_lowerCamelCase  (  self		)   ->					Dict:
         lowerCamelCase__						=			{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__		): i for i in range(self.vocab_size		)}
         vocab.update(self.added_tokens_encoder		)
         return vocab





      def __getstate__(  self		)   ->					Optional[int]:
         lowerCamelCase__						=			self.__dict__.copy()
         lowerCamelCase__						=			None
         return state





      def __setstate__(  self    ,						SCREAMING_SNAKE_CASE__		)   ->					List[str]:
         lowerCamelCase__						=			d

         # for backward compatibility
         if not hasattr(self    ,						"sp_model_kwargs"		):
            lowerCamelCase__						=			{}

         lowerCamelCase__						=			spm.SentencePieceProcessor(**self.sp_model_kwargs		)
         self.sp_model.Load(self.vocab_file		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					Tuple:
         if self.remove_space:
            lowerCamelCase__						=			" ".join(inputs.strip().split()		)
         else:
            lowerCamelCase__						=			inputs
         lowerCamelCase__						=			outputs.replace("``"    ,						"\""		).replace("''"    ,						"\""		)

         if not self.keep_accents:
            lowerCamelCase__						=			unicodedata.normalize("NFKD"    ,						SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			"".join([c for c in outputs if not unicodedata.combining(SCREAMING_SNAKE_CASE__		)]		)
         if self.do_lower_case:
            lowerCamelCase__						=			outputs.lower()

         return outputs





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					List[str]:
         lowerCamelCase__						=			self.preprocess_text(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			self.sp_model.encode(SCREAMING_SNAKE_CASE__    ,						out_type=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			[]
         for piece in pieces:
            if len(SCREAMING_SNAKE_CASE__		) > 1 and piece[-1] == str(","		) and piece[-2].isdigit():
               lowerCamelCase__						=			self.sp_model.EncodeAsPieces(piece[:-1].replace(SCREAMING_SNAKE_CASE__    ,						""		)		)
               if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                  if len(cur_pieces[0]		) == 1:
                     lowerCamelCase__						=			cur_pieces[1:]
                  else:
                     lowerCamelCase__						=			cur_pieces[0][1:]
               cur_pieces.append(piece[-1]		)
               new_pieces.extend(SCREAMING_SNAKE_CASE__		)
            else:
               new_pieces.append(SCREAMING_SNAKE_CASE__		)

         return new_pieces





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					Dict:
         return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					Any:
         return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					int:
         lowerCamelCase__						=			"".join(SCREAMING_SNAKE_CASE__		).replace(SCREAMING_SNAKE_CASE__    ,						" "		).strip()
         return out_string





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None		)   ->					List[int]:
         lowerCamelCase__						=			[self.sep_token_id]
         lowerCamelCase__						=			[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 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = False		)   ->					List[int]:

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

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





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None		)   ->					List[int]:
         lowerCamelCase__						=			[self.sep_token_id]
         lowerCamelCase__						=			[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 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None		)   ->					Tuple[str]:
         if not os.path.isdir(SCREAMING_SNAKE_CASE__		):
            logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
            return
         lowerCamelCase__						=			os.path.join(
             SCREAMING_SNAKE_CASE__    ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

         if os.path.abspath(self.vocab_file		) != os.path.abspath(SCREAMING_SNAKE_CASE__		) and os.path.isfile(self.vocab_file		):
            copyfile(self.vocab_file    ,						SCREAMING_SNAKE_CASE__		)
         elif not os.path.isfile(self.vocab_file		):
            with open(SCREAMING_SNAKE_CASE__    ,						"wb"		) as fi:
               lowerCamelCase__						=			self.sp_model.serialized_model_proto()
               fi.write(SCREAMING_SNAKE_CASE__		)

         return (out_vocab_file,)





      def 					_lowerCamelCase  (  self    ,						*SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)   ->					str:
         lowerCamelCase__						=			super()._decode(*SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			text.replace(" "    ,						""		).replace("\u2582"    ,						" "		).replace("\u2583"    ,						"\n"		)
         return text







"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_     :			List[Any]	  =			{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_     :			Union[str, Any]	  =			[
         '''RagModel''',
         '''RagPreTrainedModel''',
         '''RagSequenceForGeneration''',
         '''RagTokenForGeneration''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_     :			str	  =			[
         '''TFRagModel''',
         '''TFRagPreTrainedModel''',
         '''TFRagSequenceForGeneration''',
         '''TFRagTokenForGeneration''',
     ]


if TYPE_CHECKING:
     from .configuration_rag import RagConfig
     from .retrieval_rag import RagRetriever
     from .tokenization_rag import RagTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_rag import (
              TFRagModel,
              TFRagPreTrainedModel,
              TFRagSequenceForGeneration,
              TFRagTokenForGeneration,
          )

else:
     import sys

     SCREAMING_SNAKE_CASE_     :			List[str]	  =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




