



"""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


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

__snake_case							:   Tuple      =    {'''vocab_file''': '''sentencepiece.bpe.model'''}

__snake_case							:   Optional[Any]      =    {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    }
}

__snake_case							:   str      =    {
    '''camembert-base''': 512,
}

__snake_case							:   Any      =    '''▁'''



class 			A__   (  _A							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE          =	PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE          =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE          =	["input_ids", "attention_mask"]

	def __init__(     self:		Any       ,			_SCREAMING_SNAKE_CASE:		Any       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]="<s>"       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]="</s>"       ,			_SCREAMING_SNAKE_CASE:		str="</s>"       ,			_SCREAMING_SNAKE_CASE:		Optional[int]="<s>"       ,			_SCREAMING_SNAKE_CASE:		List[str]="<unk>"       ,			_SCREAMING_SNAKE_CASE:		List[Any]="<pad>"       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]="<mask>"       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=["<s>NOTUSED", "</s>NOTUSED"]       ,			_SCREAMING_SNAKE_CASE:		Optional[Dict[str, Any]] = None       ,			**_SCREAMING_SNAKE_CASE:		List[Any]       ,			)				->       None:

				"""simple docstring"""

				__lowerCAmelCase      :  Union[str, Any]												=       AddedToken(_SCREAMING_SNAKE_CASE       ,			lstrip=_SCREAMING_SNAKE_CASE       ,			rstrip=_SCREAMING_SNAKE_CASE) if isinstance(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE) else mask_token

				__lowerCAmelCase      :  str												=       {} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    bos_token=_SCREAMING_SNAKE_CASE       ,			eos_token=_SCREAMING_SNAKE_CASE       ,			unk_token=_SCREAMING_SNAKE_CASE       ,			sep_token=_SCREAMING_SNAKE_CASE       ,			cls_token=_SCREAMING_SNAKE_CASE       ,			pad_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      :  Tuple												=       spm.SentencePieceProcessor(**self.sp_model_kwargs)
				self.sp_model.Load(str(_SCREAMING_SNAKE_CASE))
				__lowerCAmelCase      :  Union[str, Any]												=       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>
				__lowerCAmelCase      :  List[Any]												=       {'''<s>NOTUSED''': 0, '''<pad>''': 1, '''</s>NOTUSED''': 2, '''<unk>''': 3}
				__lowerCAmelCase      :  Dict												=       len(self.fairseq_tokens_to_ids)
				__lowerCAmelCase      :  List[Any]												=       len(self.sp_model) + len(self.fairseq_tokens_to_ids)
				__lowerCAmelCase      :  int												=       {v: k for k, v in self.fairseq_tokens_to_ids.items()}

	def 							_SCREAMING_SNAKE_CASE   (     self:		Any       ,			_SCREAMING_SNAKE_CASE:		List[int]       ,			_SCREAMING_SNAKE_CASE:		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]
				__lowerCAmelCase      :  List[str]												=       [self.cls_token_id]
				__lowerCAmelCase      :  Union[str, Any]												=       [self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any]       ,			_SCREAMING_SNAKE_CASE:		List[int]       ,			_SCREAMING_SNAKE_CASE:		Optional[List[int]] = None       ,			_SCREAMING_SNAKE_CASE:		bool = False)				->       List[int]:

				"""simple docstring"""

				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 None:
							return [1] + ([0] * len(_SCREAMING_SNAKE_CASE)) + [1]
				return [1] + ([0] * len(_SCREAMING_SNAKE_CASE)) + [1, 1] + ([0] * len(_SCREAMING_SNAKE_CASE)) + [1]

	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any]       ,			_SCREAMING_SNAKE_CASE:		List[int]       ,			_SCREAMING_SNAKE_CASE:		Optional[List[int]] = None)				->       List[int]:

				"""simple docstring"""

				__lowerCAmelCase      :  List[Any]												=       [self.sep_token_id]
				__lowerCAmelCase      :  int												=       [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 							_SCREAMING_SNAKE_CASE   (     self:		List[str])				->       Union[str, Any]:

				"""simple docstring"""

				return len(self.fairseq_tokens_to_ids) + len(self.sp_model)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Any)				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  List[Any]												=       {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 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		str)				->       List[str]:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any]       ,			_SCREAMING_SNAKE_CASE:		str)				->       Optional[Any]:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE   (     self:		int       ,			_SCREAMING_SNAKE_CASE:		List[str])				->       Dict:

				"""simple docstring"""

				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 							_SCREAMING_SNAKE_CASE   (     self:		Tuple       ,			_SCREAMING_SNAKE_CASE:		Tuple)				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       []
				__lowerCAmelCase      :  int												=       ''''''
				__lowerCAmelCase      :  int												=       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(_SCREAMING_SNAKE_CASE) + token
										__lowerCAmelCase      :  Dict												=       True
										__lowerCAmelCase      :  Any												=       []
							else:
										current_sub_tokens.append(_SCREAMING_SNAKE_CASE)
										__lowerCAmelCase      :  int												=       False
				out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE)
				return out_string.strip()

	def __getstate__(     self:		Dict)				->       Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[int]												=       self.__dict__.copy()
				__lowerCAmelCase      :  Tuple												=       None
				return state

	def __setstate__(     self:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		Optional[Any])				->       Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Any												=       d

				# for backward compatibility
				if not hasattr(self       ,			"sp_model_kwargs"):
							__lowerCAmelCase      :  List[str]												=       {}

				__lowerCAmelCase      :  Tuple												=       spm.SentencePieceProcessor(**self.sp_model_kwargs)
				self.sp_model.Load(self.vocab_file)







	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any]       ,			_SCREAMING_SNAKE_CASE:		str       ,			_SCREAMING_SNAKE_CASE:		Optional[str] = None)				->       Tuple[str]:

				"""simple docstring"""

				if not os.path.isdir(_SCREAMING_SNAKE_CASE):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
							return
				__lowerCAmelCase      :  Union[str, Any]												=       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      :  Union[str, Any]												=       self.sp_model.serialized_model_proto()
										fi.write(_SCREAMING_SNAKE_CASE)

				return (out_vocab_file,)







"""simple docstring"""



from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
						from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline



