




"""simple docstring"""

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


a         =      logging.get_logger(__name__)

a         =      {'''vocab_file''': '''sentencepiece.bpe.model'''}

a         =      {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    }
}

a         =      {
    '''camembert-base''': 512,
}

a         =      '''▁'''





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					int				  =     VOCAB_FILES_NAMES
 UpperCAmelCase   :					List[str]				  =     PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase   :					Any				  =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase   :					Dict				  =     ['''input_ids''', '''attention_mask''']

 def __init__(			self	:	Any   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	Dict="<s>"   ,							_UpperCAmelCase	:	List[Any]="</s>"   ,							_UpperCAmelCase	:	Tuple="</s>"   ,							_UpperCAmelCase	:	int="<s>"   ,							_UpperCAmelCase	:	Optional[Any]="<unk>"   ,							_UpperCAmelCase	:	List[str]="<pad>"   ,							_UpperCAmelCase	:	Dict="<mask>"   ,							_UpperCAmelCase	:	str=["<s>NOTUSED", "</s>NOTUSED"]   ,							_UpperCAmelCase	:	Optional[Dict[str, Any]] = None   ,							**_UpperCAmelCase	:	List[str]   ,							):
       # Mask token behave like a normal word, i.e. include the space before it
       _A        =       AddedToken(_UpperCAmelCase   ,							lstrip=_UpperCAmelCase   ,							rstrip=_UpperCAmelCase		) if isinstance(_UpperCAmelCase   ,							_UpperCAmelCase		) else mask_token

       _A        =       {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           bos_token=_UpperCAmelCase   ,							eos_token=_UpperCAmelCase   ,							unk_token=_UpperCAmelCase   ,							sep_token=_UpperCAmelCase   ,							cls_token=_UpperCAmelCase   ,							pad_token=_UpperCAmelCase   ,							mask_token=_UpperCAmelCase   ,							additional_special_tokens=_UpperCAmelCase   ,							sp_model_kwargs=self.sp_model_kwargs   ,							**_UpperCAmelCase   ,							)
       _A        =       spm.SentencePieceProcessor(**self.sp_model_kwargs		)
       self.sp_model.Load(str(_UpperCAmelCase		)		)
       _A        =       vocab_file
       # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
       # sentencepiece vocabulary (this is the case for <s> and </s>
       _A        =       {'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
       _A        =       len(self.fairseq_tokens_to_ids		)
       _A        =       len(self.sp_model		) + len(self.fairseq_tokens_to_ids		)
       _A        =       {v: k for k, v in self.fairseq_tokens_to_ids.items()}

 def       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	List[int]   ,							_UpperCAmelCase	:	Optional[List[int]] = None		):

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

 def       lowerCAmelCase_							(			self	:	Optional[int]   ,							_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 None:
             return [1] + ([0] * len(_UpperCAmelCase		)) + [1]
       return [1] + ([0] * len(_UpperCAmelCase		)) + [1, 1] + ([0] * len(_UpperCAmelCase		)) + [1]

 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	List[int]   ,							_UpperCAmelCase	:	Optional[List[int]] = None		):
       _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 + sep + token_ids_a + sep		) * [0]

 @property
 def       lowerCAmelCase_							(			self	:	str		):
       return len(self.fairseq_tokens_to_ids		) + len(self.sp_model		)

 def       lowerCAmelCase_							(			self	:	Any		):
       _A        =       {self.convert_ids_to_tokens(_UpperCAmelCase		): i for i in range(self.vocab_size		)}
       vocab.update(self.added_tokens_encoder		)
       return vocab

 def       lowerCAmelCase_							(			self	:	int   ,							_UpperCAmelCase	:	str		):
       return self.sp_model.encode(_UpperCAmelCase   ,							out_type=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Optional[int]   ,							_UpperCAmelCase	:	Optional[int]		):
       if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
       elif self.sp_model.PieceToId(_UpperCAmelCase		) == 0:
             # Convert sentence piece unk token to fairseq unk token index
             return self.unk_token_id
       return self.fairseq_offset + self.sp_model.PieceToId(_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Any   ,							_UpperCAmelCase	:	List[str]		):
       if index in self.fairseq_ids_to_tokens:
             return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(index - self.fairseq_offset		)

 def       lowerCAmelCase_							(			self	:	Optional[int]   ,							_UpperCAmelCase	:	List[str]		):
       _A        =       []
       _A        =       ''
       _A        =       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(_UpperCAmelCase		) + token
                   _A        =       True
                   _A        =       []
             else:
                   current_sub_tokens.append(_UpperCAmelCase		)
                   _A        =       False
       out_string += self.sp_model.decode(_UpperCAmelCase		)
       return out_string.strip()

 def __getstate__(			self	:	str		):
       _A        =       self.__dict__.copy()
       _A        =       None
       return state

 def __setstate__(			self	:	str   ,							_UpperCAmelCase	:	Optional[int]		):
       _A        =       d

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

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



 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	Optional[str] = None		):
       if not os.path.isdir(_UpperCAmelCase		):
             logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
             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		) 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:
                   _A        =       self.sp_model.serialized_model_proto()
                   fi.write(_UpperCAmelCase		)

       return (out_vocab_file,)







"""simple docstring"""

import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


a         =      HUGGINGFACE_HUB_CACHE


a         =      '''config.json'''
a         =      '''diffusion_pytorch_model.bin'''
a         =      '''diffusion_flax_model.msgpack'''
a         =      '''model.onnx'''
a         =      '''diffusion_pytorch_model.safetensors'''
a         =      '''weights.pb'''
a         =      '''https://huggingface.co'''
a         =      default_cache_path
a         =      '''diffusers_modules'''
a         =      os.getenv('''HF_MODULES_CACHE''', os.path.join(hf_cache_home, '''modules'''))
a         =      ['''fp16''', '''non-ema''']
a         =      '''.self_attn'''

