






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

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

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

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

__A		:       Any  				=		'''▁'''





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				Any							    =   VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE_		:				str							    =   PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE_		:				Optional[Any]							    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE_		:				Tuple							    =   ["input_ids", "attention_mask"]




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

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

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




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

							if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							lowercase_ : List[str]							       =   [self.cls_token_id]
							lowercase_ : Union[str, Any]							       =   [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep




					def   A  (				self     :						List[Any]    ,						A     :						List[int]    ,						A     :						Optional[List[int]] = None    ,						A     :						bool = False   )  ->  List[int]:
							if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=A    ,						token_ids_a=A    ,						already_has_special_tokens=A   )

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




					def   A  (				self     :						Union[str, Any]    ,						A     :						List[int]    ,						A     :						Optional[List[int]] = None   )  ->  List[int]:
							lowercase_ : List[Any]							       =   [self.sep_token_id]
							lowercase_ : 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   A  (				self     :						List[str]   )  ->  Union[str, Any]:
							return len(self.fairseq_tokens_to_ids   ) + len(self.sp_model   )




					def   A  (				self     :						Any   )  ->  Optional[Any]:
							lowercase_ : List[Any]							       =   {self.convert_ids_to_tokens(A   ): i for i in range(self.vocab_size   )}
							vocab.update(self.added_tokens_encoder   )
							return vocab




					def   A  (				self     :						Optional[int]    ,						A     :						str   )  ->  List[str]:
							return self.sp_model.encode(A    ,						out_type=A   )




					def   A  (				self     :						Union[str, Any]    ,						A     :						str   )  ->  Optional[Any]:
							if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
							elif self.sp_model.PieceToId(A   ) == 0:
									# Convert sentence piece unk token to fairseq unk token index
									return self.unk_token_id
							return self.fairseq_offset + self.sp_model.PieceToId(A   )




					def   A  (				self     :						int    ,						A     :						List[str]   )  ->  Dict:
							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   A  (				self     :						Tuple    ,						A     :						Tuple   )  ->  Optional[Any]:
							lowercase_ : Tuple							       =   []
							lowercase_ : int							       =   ''''''
							lowercase_ : 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(A   ) + token
											lowercase_ : Dict							       =   True
											lowercase_ : Any							       =   []
									else:
											current_sub_tokens.append(A   )
											lowercase_ : int							       =   False
							out_string += self.sp_model.decode(A   )
							return out_string.strip()




					def __getstate__(				self     :						Dict   )  ->  Union[str, Any]:
							lowercase_ : Optional[int]							       =   self.__dict__.copy()
							lowercase_ : Tuple							       =   None
							return state




					def __setstate__(				self     :						Optional[int]    ,						A     :						Optional[Any]   )  ->  Union[str, Any]:
							lowercase_ : Any							       =   d

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

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






					def   A  (				self     :						List[Any]    ,						A     :						str    ,						A     :						Optional[str] = None   )  ->  Tuple[str]:
							if not os.path.isdir(A   ):
									logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''   )
									return
							lowercase_ : Union[str, Any]							       =   os.path.join(
							    A    ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

							if os.path.abspath(self.vocab_file   ) != os.path.abspath(A   ) and os.path.isfile(self.vocab_file   ):
									copyfile(self.vocab_file    ,						A   )
							elif not os.path.isfile(self.vocab_file   ):
									with open(A    ,						'''wb'''   ) as fi:
											lowercase_ : Union[str, Any]							       =   self.sp_model.serialized_model_proto()
											fi.write(A   )

							return (out_vocab_file,)








"""simple docstring"""







from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
							from .features import FeatureType
@dataclass
class        __A       :







		'''simple docstring'''





		lowerCAmelCase			:					List[str]
		lowerCAmelCase			:					Optional[str]   	=				None
		# Automatically constructed
		lowerCAmelCase			:					ClassVar[str]   	=				"dict"
		lowerCAmelCase			:					ClassVar[Any]   	=				None
		lowerCAmelCase			:					str   	=				field(default="Translation"     ,init=A_     ,repr=A_    )







		def __call__(	self						:  List[str]						)					->		Any:






				"""simple docstring"""



				return pa.struct({lang: pa.string() for lang in sorted(self.languages						)}						)







		def 					UpperCAmelCase    (	self						:  List[str]						)					->		Union["FeatureType", Dict[str, "FeatureType"]]:






				"""simple docstring"""



				from .features import Value

				return {k: Value('''string'''						) for k in sorted(self.languages						)}





@dataclass
class        __A       :







		'''simple docstring'''





		lowerCAmelCase			:					Optional[List]   	=				None
		lowerCAmelCase			:					Optional[int]   	=				None
		lowerCAmelCase			:					Optional[str]   	=				None
		# Automatically constructed
		lowerCAmelCase			:					ClassVar[str]   	=				"dict"
		lowerCAmelCase			:					ClassVar[Any]   	=				None
		lowerCAmelCase			:					str   	=				field(default="TranslationVariableLanguages"     ,init=A_     ,repr=A_    )







		def 					UpperCAmelCase    (	self						:  List[Any]						)					->		Optional[int]:






				"""simple docstring"""



				lowercase__   :    Optional[int]				=			sorted(set(self.languages						)						) if self.languages else None
				lowercase__   :    Dict				=			len(self.languages						) if self.languages else None







		def __call__(	self						:  List[Any]						)					->		List[Any]:






				"""simple docstring"""



				return pa.struct({'''language''': pa.list_(pa.string()						), '''translation''': pa.list_(pa.string()						)}						)







		def 					UpperCAmelCase    (	self						:  Dict			,_snake_case						:  Tuple						)					->		int:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			set(self.languages						)
				if self.languages and set(_snake_case						) - lang_set:
						raise ValueError(
						    f"""Some languages in example ({", ".join(sorted(set(_snake_case						) - lang_set						)						)}) are not in valid set ({", ".join(_snake_case						)})."""						)

				# Convert dictionary into tuples, splitting out cases where there are
				# multiple translations for a single language.
				lowercase__   :    str				=			[]
				for lang, text in translation_dict.items():
						if isinstance(_snake_case			,_snake_case						):
								translation_tuples.append((lang, text)						)
						else:
								translation_tuples.extend([(lang, el) for el in text]						)

        # Ensure translations are in ascending order by language code.
				lowercase__		,			lowercase__   :    Optional[Any]				=			zip(*sorted(_snake_case						)						)

				return {"language": languages, "translation": translations}







		def 					UpperCAmelCase    (	self						:  List[Any]						)					->		Union["FeatureType", Dict[str, "FeatureType"]]:






				"""simple docstring"""



				from .features import Sequence, Value

				return {
				    "language": Sequence(Value('''string'''						)						),
				    "translation": Sequence(Value('''string'''						)						),
				}
