






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_		:    List[str]   							=     {'vocab_file': 'sentencepiece.bpe.model'}

a_		:    str   							=     {
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
}

a_		:    str   							=     {
    'moussaKam/mbarthez': 10_24,
    'moussaKam/barthez': 10_24,
    'moussaKam/barthez-orangesum-title': 10_24,
}

a_		:    Tuple   							=     '▁'

class 		_snake_case	(	A__       ):
			_lowercase		:     Dict        =				VOCAB_FILES_NAMES
			_lowercase		:     Union[str, Any]        =				PRETRAINED_VOCAB_FILES_MAP
			_lowercase		:     Union[str, Any]        =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_lowercase		:     Optional[Any]        =				['''input_ids''', '''attention_mask''']






			def __init__(							self		,       a		,       a="<s>"		,       a="</s>"		,       a="</s>"		,       a="<s>"		,       a="<unk>"		,       a="<pad>"		,       a="<mask>"		,       a = None		,       **a		,       )   ->							None:
						# Mask token behave like a normal word, i.e. include the space before it
						SCREAMING_SNAKE_CASE							=			AddedToken(a		,       lstrip=a		,       rstrip=a) if isinstance(a		,       a) else mask_token

						SCREAMING_SNAKE_CASE							=			{} 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		,       sp_model_kwargs=self.sp_model_kwargs		,       **a		,       )

						SCREAMING_SNAKE_CASE							=			vocab_file
						SCREAMING_SNAKE_CASE							=			spm.SentencePieceProcessor(**self.sp_model_kwargs)
						self.sp_model.Load(str(a))

						SCREAMING_SNAKE_CASE							=			{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

						SCREAMING_SNAKE_CASE							=			len(self.sp_model) - 1
						SCREAMING_SNAKE_CASE							=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a = None)   ->							List[int]:

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






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a = None		,       a = 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  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a = None)   ->							List[int]:
						SCREAMING_SNAKE_CASE							=			[self.sep_token_id]
						SCREAMING_SNAKE_CASE							=			[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)   ->							Optional[Any]:
						return len(self.sp_model)






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






			def  SCREAMING_SNAKE_CASE__     (							self		,       a)   ->							List[str]:
						return self.sp_model.encode(a		,       out_type=a)






			def  SCREAMING_SNAKE_CASE__     (							self		,       a)   ->							int:
						if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
						SCREAMING_SNAKE_CASE							=			self.sp_model.PieceToId(a)

						return spm_id if spm_id else self.unk_token_id






			def  SCREAMING_SNAKE_CASE__     (							self		,       a)   ->							Union[str, Any]:
						if index in self.fairseq_ids_to_tokens:
									return self.fairseq_ids_to_tokens[index]
						return self.sp_model.IdToPiece(a)






			def  SCREAMING_SNAKE_CASE__     (							self		,       a)   ->							List[Any]:
						SCREAMING_SNAKE_CASE							=			[]
						SCREAMING_SNAKE_CASE							=			''
						SCREAMING_SNAKE_CASE							=			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
												SCREAMING_SNAKE_CASE							=			True
												SCREAMING_SNAKE_CASE							=			[]
									else:
												current_sub_tokens.append(a)
												SCREAMING_SNAKE_CASE							=			False
						out_string += self.sp_model.decode(a)
						return out_string.strip()






			def __getstate__(							self)   ->							Union[str, Any]:
						SCREAMING_SNAKE_CASE							=			self.__dict__.copy()
						SCREAMING_SNAKE_CASE							=			None
						return state






			def __setstate__(							self		,       a)   ->							int:
						SCREAMING_SNAKE_CASE							=			d

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

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







			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a = None)   ->							Tuple[str]:
						if not os.path.isdir(a):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory''')
									return
						SCREAMING_SNAKE_CASE							=			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:
												SCREAMING_SNAKE_CASE							=			self.sp_model.serialized_model_proto()
												fi.write(a)

						return (out_vocab_file,)












'''simple docstring'''







import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout





def 		_UpperCAmelCase (				__A				: int   ):
				a_		:	Optional[Any]									=   FileLock(str(tmpdir / '''foo.lock'''   )   )
				a_		:	Union[str, Any]									=   FileLock(str(tmpdir / '''foo.lock'''   )   )
				a_		:	List[Any]									=   0.01
				with locka.acquire():
								with pytest.raises(__A   ):
												a_		:	List[Any]									=   time.time()
												locka.acquire(__A   )
												assert time.time() - _start > timeout





def 		_UpperCAmelCase (				__A				: Tuple   ):
				a_		:	Tuple									=   '''a''' * 10_00 + '''.lock'''
				a_		:	List[Any]									=   FileLock(str(tmpdir / filename   )   )
				assert locka._lock_file.endswith('''.lock'''   )
				assert not locka._lock_file.endswith(__A   )
				assert len(os.path.basename(locka._lock_file   )   ) <= 2_55
				a_		:	Optional[int]									=   FileLock(tmpdir / filename   )
				with locka.acquire():
								with pytest.raises(__A   ):
												locka.acquire(0   )




