





import os
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 logging


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

__lowercase	: Optional[int]       ={"""vocab_file""": """sentencepiece.bpe.model"""}

__lowercase	: Any       ={
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
}

__lowercase	: Union[str, Any]       ={
    """moussaKam/mbarthez""": 1024,
    """moussaKam/barthez""": 1024,
    """moussaKam/barthez-orangesum-title""": 1024,
}

__lowercase	: Dict       ="""▁"""

class   A							(						__lowercase    ):
      _snake_case				  =VOCAB_FILES_NAMES
      _snake_case				  =PRETRAINED_VOCAB_FILES_MAP
      _snake_case				  =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _snake_case				  =['''input_ids''', '''attention_mask''']



      def __init__(						self:    Optional[int]		,	_lowerCAmelCase:    List[Any]		,	_lowerCAmelCase:    Any="<s>"		,	_lowerCAmelCase:    int="</s>"		,	_lowerCAmelCase:    List[Any]="</s>"		,	_lowerCAmelCase:    List[Any]="<s>"		,	_lowerCAmelCase:    List[str]="<unk>"		,	_lowerCAmelCase:    str="<pad>"		,	_lowerCAmelCase:    Tuple="<mask>"		,	_lowerCAmelCase:    Optional[Dict[str, Any]] = None		,	**_lowerCAmelCase:    Optional[Any]		,	)    ->     None:
         '''simple docstring'''

         UpperCAmelCase_           =AddedToken(_lowerCAmelCase		,	lstrip=_lowerCAmelCase		,	rstrip=_lowerCAmelCase							) if isinstance(_lowerCAmelCase		,	_lowerCAmelCase							) else mask_token

         UpperCAmelCase_           ={} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=_lowerCAmelCase		,	eos_token=_lowerCAmelCase		,	unk_token=_lowerCAmelCase		,	sep_token=_lowerCAmelCase		,	cls_token=_lowerCAmelCase		,	pad_token=_lowerCAmelCase		,	mask_token=_lowerCAmelCase		,	sp_model_kwargs=self.sp_model_kwargs		,	**_lowerCAmelCase		,	)

         UpperCAmelCase_           =vocab_file
         UpperCAmelCase_           =spm.SentencePieceProcessor(**self.sp_model_kwargs							)
         self.sp_model.Load(str(_lowerCAmelCase							)							)

         UpperCAmelCase_           ={"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

         UpperCAmelCase_           =len(self.sp_model							) - 1
         UpperCAmelCase_           ={v: k for k, v in self.fairseq_tokens_to_ids.items()}



      def 						lowerCAmelCase__     (						self:    str		,	_lowerCAmelCase:    List[int]		,	_lowerCAmelCase:    Optional[List[int]] = None							)    ->     List[int]:
         '''simple docstring'''

         if token_ids_a is None:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         UpperCAmelCase_           =[self.cls_token_id]
         UpperCAmelCase_           =[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep



      def 						lowerCAmelCase__     (						self:    Tuple		,	_lowerCAmelCase:    List[int]		,	_lowerCAmelCase:    Optional[List[int]] = None		,	_lowerCAmelCase:    bool = False							)    ->     List[int]:
         '''simple docstring'''

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

         if token_ids_a is None:
            return [1] + ([0] * len(_lowerCAmelCase							)) + [1]
         return [1] + ([0] * len(_lowerCAmelCase							)) + [1, 1] + ([0] * len(_lowerCAmelCase							)) + [1]



      def 						lowerCAmelCase__     (						self:    Any		,	_lowerCAmelCase:    List[int]		,	_lowerCAmelCase:    Optional[List[int]] = None							)    ->     List[int]:
         '''simple docstring'''

         UpperCAmelCase_           =[self.sep_token_id]
         UpperCAmelCase_           =[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 + sep + token_ids_a + sep							) * [0]



      @property
      def 						lowerCAmelCase__     (						self:    Dict							)    ->     Dict:
         '''simple docstring'''

         return len(self.sp_model							)



      def 						lowerCAmelCase__     (						self:    List[str]							)    ->     str:
         '''simple docstring'''

         UpperCAmelCase_           ={self.convert_ids_to_tokens(_lowerCAmelCase							): i for i in range(self.vocab_size							)}
         vocab.update(self.added_tokens_encoder							)
         return vocab



      def 						lowerCAmelCase__     (						self:    Optional[int]		,	_lowerCAmelCase:    str							)    ->     List[str]:
         '''simple docstring'''

         return self.sp_model.encode(_lowerCAmelCase		,	out_type=_lowerCAmelCase							)



      def 						lowerCAmelCase__     (						self:    Union[str, Any]		,	_lowerCAmelCase:    List[Any]							)    ->     Optional[Any]:
         '''simple docstring'''

         if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
         UpperCAmelCase_           =self.sp_model.PieceToId(_lowerCAmelCase							)

         return spm_id if spm_id else self.unk_token_id



      def 						lowerCAmelCase__     (						self:    Union[str, Any]		,	_lowerCAmelCase:    Any							)    ->     str:
         '''simple docstring'''

         if index in self.fairseq_ids_to_tokens:
            return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(_lowerCAmelCase							)



      def 						lowerCAmelCase__     (						self:    Optional[int]		,	_lowerCAmelCase:    Dict							)    ->     int:
         '''simple docstring'''

         UpperCAmelCase_           =[]
         UpperCAmelCase_           =""
         UpperCAmelCase_           =False
         for token in tokens:
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
               if not prev_is_special:
                  out_string += " "
               out_string += self.sp_model.decode(_lowerCAmelCase							) + token
               UpperCAmelCase_           =True
               UpperCAmelCase_           =[]
            else:
               current_sub_tokens.append(_lowerCAmelCase							)
               UpperCAmelCase_           =False
         out_string += self.sp_model.decode(_lowerCAmelCase							)
         return out_string.strip()



      def __getstate__(						self:    Tuple							)    ->     List[str]:
         '''simple docstring'''

         UpperCAmelCase_           =self.__dict__.copy()
         UpperCAmelCase_           =None
         return state



      def __setstate__(						self:    Optional[int]		,	_lowerCAmelCase:    str							)    ->     Any:
         '''simple docstring'''

         UpperCAmelCase_           =d

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

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






      def 						lowerCAmelCase__     (						self:    Dict		,	_lowerCAmelCase:    str		,	_lowerCAmelCase:    Optional[str] = None							)    ->     Tuple[str]:
         '''simple docstring'''

         if not os.path.isdir(_lowerCAmelCase							):
            logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
            return
         UpperCAmelCase_           =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							) and os.path.isfile(self.vocab_file							):
            copyfile(self.vocab_file		,	_lowerCAmelCase							)
         elif not os.path.isfile(self.vocab_file							):
            with open(_lowerCAmelCase		,	"wb"							) as fi:
               UpperCAmelCase_           =self.sp_model.serialized_model_proto()
               fi.write(_lowerCAmelCase							)

         return (out_vocab_file,)









"""simple docstring"""







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..utils import cached_file


# docstyle-ignore
A		=						'\nHuman: <<task>>\n\nAssistant: '


A		=						'huggingface-tools/default-prompts'
A		=						{'chat': 'chat_prompt_template.txt', 'run': 'run_prompt_template.txt'}

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[int]     ,		lowerCamelCase_:							str     ,		lowerCamelCase_:							Tuple="run" ):
						"""simple docstring"""






						if prompt_or_repo_id is None:
												snake_case   :			Any						    = DEFAULT_PROMPTS_REPO

						# prompt is considered a repo ID when it does not contain any kind of space
						if re.search("\\s"     ,		lowerCamelCase_ ) is not None:
												return prompt_or_repo_id

						snake_case   :			Optional[int]						    = cached_file(
						    lowerCamelCase_     ,		PROMPT_FILES[mode]     ,		repo_type="dataset"     ,		user_agent={"agent": agent_name} )
						with open(lowerCamelCase_     ,		"r"     ,		encoding="utf-8" ) as f:
												return f.read()



