






'''simple docstring'''

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

import sentencepiece as spm

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


UpperCamelCase							="▁"

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

UpperCamelCase							={
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

UpperCamelCase							={
    "google/pegasus-xsum": 512,
}


UpperCamelCase							=logging.get_logger(__name__)




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 __a			:						List[Any]      =						VOCAB_FILES_NAMES

 __a			:						Dict      =						VOCAB_FILES_NAMES
 __a			:						Optional[int]      =						PRETRAINED_VOCAB_FILES_MAP
 __a			:						List[Any]      =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __a			:						str      =						['''input_ids''', '''attention_mask''']
 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase="<pad>"			,     __lowerCAmelCase="</s>"			,     __lowerCAmelCase="<unk>"			,     __lowerCAmelCase="<mask_2>"			,     __lowerCAmelCase="<mask_1>"			,     __lowerCAmelCase=None			,     __lowerCAmelCase=1_03			,     __lowerCAmelCase = None			,     **__lowerCAmelCase			,     ):
       UpperCamelCase_				:   Any					    =							offset
       if additional_special_tokens is not None:
             if not isinstance(__lowerCAmelCase			,     __lowerCAmelCase ):
                   raise TypeError(
                       F"additional_special_tokens should be of type {type(__lowerCAmelCase )}, but is"
                       F" {type(__lowerCAmelCase )}" )

             UpperCamelCase_				:   List[Any]					    =							(
                 ([mask_token_sent] + additional_special_tokens)
                 if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                 else additional_special_tokens
             )
             # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
             additional_special_tokens_extended += [
                 F"<unk_{i}>" for i in range(len(__lowerCAmelCase )			,     self.offset - 1 )
             ]

             if len(set(__lowerCAmelCase ) ) != len(__lowerCAmelCase ):
                   raise ValueError(
                       """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
                       F" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}." )
             UpperCamelCase_				:   Dict					    =							additional_special_tokens_extended
       else:
             UpperCamelCase_				:   str					    =							[mask_token_sent] if mask_token_sent is not None else []
             additional_special_tokens += [F"<unk_{i}>" for i in range(2			,     self.offset )]

       UpperCamelCase_				:   Union[str, Any]					    =							{} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           eos_token=__lowerCAmelCase			,     unk_token=__lowerCAmelCase			,     mask_token=__lowerCAmelCase			,     pad_token=__lowerCAmelCase			,     mask_token_sent=__lowerCAmelCase			,     offset=__lowerCAmelCase			,     additional_special_tokens=__lowerCAmelCase			,     sp_model_kwargs=self.sp_model_kwargs			,     **__lowerCAmelCase			,     )
       UpperCamelCase_				:   Optional[int]					    =							mask_token_sent
       UpperCamelCase_				:   Any					    =							vocab_file
       UpperCamelCase_				:   Optional[int]					    =							spm.SentencePieceProcessor(**self.sp_model_kwargs )
       self.sp_model.Load(__lowerCAmelCase )

       # add special tokens to encoder dict
       UpperCamelCase_				:   Dict[int, str]					    =							{
           0: self.pad_token,
           1: self.eos_token,
       }

       if self.mask_token_sent is not None:
             self.encoder.update(
                 {
                     2: self.mask_token_sent,
                     3: self.mask_token,
                 } )

       if self.offset > 0:
             # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
             # mask_token_sent is already added to list -> so start at 1
             self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1			,     self.offset - 1 )} )

       UpperCamelCase_				:   Dict[str, int]					    =							{v: k for k, v in self.encoder.items()}
 @property
 def   _UpperCAmelCase       (							self ):
       return len(self.sp_model ) + self.offset
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   List[str]					    =							{self.convert_ids_to_tokens(__lowerCAmelCase ): i for i in range(self.vocab_size )}
       vocab.update(self.added_tokens_encoder )
       return vocab
 def __getstate__(							self ):
       UpperCamelCase_				:   Optional[int]					    =							self.__dict__.copy()
       UpperCamelCase_				:   Optional[int]					    =							None
       return state
 def __setstate__(							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Union[str, Any]					    =							d

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

       UpperCamelCase_				:   Optional[Any]					    =							spm.SentencePieceProcessor(**self.sp_model_kwargs )
       self.sp_model.Load(self.vocab_file )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       return self.sp_model.encode(__lowerCAmelCase			,     out_type=__lowerCAmelCase )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       if token in self.decoder:
             return self.decoder[token]
       elif token in self.added_tokens_decoder:
             return self.added_tokens_decoder[token]
       UpperCamelCase_				:   str					    =							self.sp_model.piece_to_id(__lowerCAmelCase )
       return sp_id + self.offset
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       if index in self.encoder:
             return self.encoder[index]
       elif index in self.added_tokens_encoder:
             return self.added_tokens_encoder[index]
       else:
             UpperCamelCase_				:   str					    =							self.sp_model.IdToPiece(index - self.offset )
       return token
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   List[Any]					    =							[]
       UpperCamelCase_				:   List[Any]					    =							""""""
       for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                   out_string += self.sp_model.decode(__lowerCAmelCase ) + token
                   UpperCamelCase_				:   List[Any]					    =							[]
             else:
                   current_sub_tokens.append(__lowerCAmelCase )
       out_string += self.sp_model.decode(__lowerCAmelCase )
       return out_string.strip()
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase=False ):
       return 1
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   int					    =							set(self.all_special_ids )  # call it once instead of inside list comp
       all_special_ids.remove(self.unk_token_id )  # <unk> is only sometimes special

       return [1 if x in all_special_ids else 0 for x in seq]
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase = None			,     __lowerCAmelCase = False ):
       if already_has_special_tokens:
             return self._special_token_mask(__lowerCAmelCase )
       elif token_ids_a is None:
             return self._special_token_mask(__lowerCAmelCase ) + [1]
       else:
             return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase=None ):
       if token_ids_a is None:
             return token_ids_a + [self.eos_token_id]
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return token_ids_a + token_ids_a + [self.eos_token_id]





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase = None ):
       if not os.path.isdir(__lowerCAmelCase ):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
             return
       UpperCamelCase_				:   Optional[int]					    =							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_				:   List[str]					    =							self.sp_model.serialized_model_proto()
                   fi.write(__lowerCAmelCase )

       return (out_vocab_file,)














'''simple docstring'''

import warnings

from .generation import TFGenerationMixin




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 warnings.warn(
     '''Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will '''
     '''be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead.''',   SCREAMING_SNAKE_CASE__,   )






