






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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__a   :							str						= logging.get_logger(__name__)

__a   :							Optional[Any]						= {"vocab_file": "sentencepiece.model"}

__a   :							Tuple						= {
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
}

__a   :							Union[str, Any]						= {
    "google/rembert": 256,
}


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE     =					PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




     def __init__(					self							:      int			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      Union[str, Any]=False			,						UpperCamelCase_							:      int=True			,						UpperCamelCase_							:      Union[str, Any]=True			,						UpperCamelCase_							:      List[str]="[CLS]"			,						UpperCamelCase_							:      Optional[Any]="[SEP]"			,						UpperCamelCase_							:      Optional[Any]="[UNK]"			,						UpperCamelCase_							:      str="[SEP]"			,						UpperCamelCase_							:      int="[PAD]"			,						UpperCamelCase_							:      Optional[Any]="[CLS]"			,						UpperCamelCase_							:      List[Any]="[MASK]"			,						**UpperCamelCase_							:      Any			,						):


         """simple docstring"""



         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_			,						**UpperCamelCase_			,						)

         __A       =						do_lower_case
         __A       =						remove_space
         __A       =						keep_accents
         __A       =						vocab_file

         __A       =						spm.SentencePieceProcessor()
         self.sp_model.Load(UpperCamelCase_					)




     @property
     def 		lowerCAmelCase_  (					self							:      Union[str, Any]					):


         """simple docstring"""



         return len(self.sp_model					)




     def 		lowerCAmelCase_  (					self							:      int					):


         """simple docstring"""



         __A       =						{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							:      str					):


         """simple docstring"""



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




     def __setstate__(					self							:      Optional[Any]			,						UpperCamelCase_							:      Any					):


         """simple docstring"""



         __A       =						d
         __A       =						spm.SentencePieceProcessor()
         self.sp_model.Load(self.vocab_file					)




     def 		lowerCAmelCase_  (					self							:      Optional[int]			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      Union[str, Any]=False					):


         """simple docstring"""



         __A       =						self.sp_model.EncodeAsPieces(UpperCamelCase_					)
         return pieces




     def 		lowerCAmelCase_  (					self							:      str			,						UpperCamelCase_							:      Tuple					):


         """simple docstring"""



         return self.sp_model.PieceToId(UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Optional[int]			,						UpperCamelCase_							:      List[Any]					):


         """simple docstring"""



         return self.sp_model.IdToPiece(UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Any			,						UpperCamelCase_							:      int					):


         """simple docstring"""



         __A       =						self.sp_model.decode_pieces(UpperCamelCase_					)
         return out_string




     def 		lowerCAmelCase_  (					self							:      List[str]			,						UpperCamelCase_							:      List[int]			,						UpperCamelCase_							:      Optional[List[int]] = None					):


         """simple docstring"""



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




     def 		lowerCAmelCase_  (					self							:      Any			,						UpperCamelCase_							:      List[int]			,						UpperCamelCase_							:      Optional[List[int]] = None			,						UpperCamelCase_							:      bool = False					):


         """simple docstring"""



         if already_has_special_tokens:
             if token_ids_a is not None:
                 raise ValueError(
                     """You should not supply a second sequence if the provided sequence of """
                     """ids is already formatted with special tokens for the model."""					)
             return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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




     def 		lowerCAmelCase_  (					self							:      Union[str, Any]			,						UpperCamelCase_							:      List[int]			,						UpperCamelCase_							:      Optional[List[int]] = None					):


         """simple docstring"""



         __A       =						[self.sep_token_id]
         __A       =						[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 		lowerCAmelCase_  (					self							:      List[Any]			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Optional[str] = None					):


         """simple docstring"""



         if not os.path.isdir(UpperCamelCase_					):
             logger.error("""Vocabulary path ({}) should be a directory""".format(UpperCamelCase_					)					)
             return
         __A       =						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_					):
             copyfile(self.vocab_file			,						UpperCamelCase_					)

         return (out_vocab_file,)












# 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   :							Tuple						= "\nHuman: <<task>>\n\nAssistant: "


__a   :							Union[str, Any]						= "huggingface-tools/default-prompts"
__a   :							Optional[int]						= {"chat": "chat_prompt_template.txt", "run": "run_prompt_template.txt"}



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Tuple      ,					__lowercase :      Optional[Any]      ,					__lowercase :      List[Any]="run"     )    ->   Optional[int]:





    """simple docstring"""

    if prompt_or_repo_id is None:
        __A       =						DEFAULT_PROMPTS_REPO

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

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




