



'''simple docstring'''

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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_camembert import CamembertTokenizer
else:
							__lowerCAmelCase						      =       None


__lowerCAmelCase						      =       logging.get_logger(__name__)

__lowerCAmelCase						      =       {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

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

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

__lowerCAmelCase						      =       '''▁'''

class      __magic_name__						(   _UpperCamelCase       ):
							lowerCAmelCase	:   Optional[int]           = VOCAB_FILES_NAMES
							lowerCAmelCase	:   Optional[int]           = PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase	:   Optional[Any]           = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase	:   List[Any]           = ['input_ids', 'attention_mask']
							lowerCAmelCase	:   Tuple           = CamembertTokenizer







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

									super().__init__(
									    _UpperCAmelCase     ,tokenizer_file=_UpperCAmelCase     ,bos_token=_UpperCAmelCase     ,eos_token=_UpperCAmelCase     ,sep_token=_UpperCAmelCase     ,cls_token=_UpperCAmelCase     ,unk_token=_UpperCAmelCase     ,pad_token=_UpperCAmelCase     ,mask_token=_UpperCAmelCase     ,additional_special_tokens=_UpperCAmelCase     ,**_UpperCAmelCase     ,)

									_a					:				Tuple				=							vocab_file
									_a					:				List[str]				=							False if not self.vocab_file else True







							def 							__lowercase      (							self :	List[Any]     ,_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					:				str				=							[self.cls_token_id]
									_a					:				Dict				=							[self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep







							def 							__lowercase      (							self :	Any     ,_UpperCAmelCase :	List[int]     ,_UpperCAmelCase :	Optional[List[int]] = None		):
									_a					:				Dict				=							[self.sep_token_id]
									_a					:				Optional[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]







							def 							__lowercase      (							self :	Tuple     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	Optional[str] = None		):
									if not self.can_save_slow_tokenizer:
											raise ValueError(
											    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
											    'tokenizer.'		)

									if not os.path.isdir(_UpperCAmelCase		):
											logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
											return
									_a					:				Tuple				=							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		):
											copyfile(self.vocab_file     ,_UpperCAmelCase		)

									return (out_vocab_file,)









'''simple docstring'''

def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				list:
		if any(not isinstance(lowerCAmelCase_						,      lowerCAmelCase_  ) or x < 0 for x in sequence  ):
				raise TypeError('Sequence must be list of non-negative integers'  )
		for _ in range(len(lowerCAmelCase_  )  ):
				for i, (rod_upper, rod_lower) in enumerate(zip(lowerCAmelCase_						,      sequence[1:]  )  ):
						if rod_upper > rod_lower:
								sequence[i] -= rod_upper - rod_lower
								sequence[i + 1] += rod_upper - rod_lower
		return sequence


if __name__ == "__main__":
							assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
							assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]




