





'''simple docstring'''






from __future__ import annotations





def    lowercase_   (		lowercase__    ) ->list[int]:
						if not list_of_ints:
												return []

						if min(lowercase__    ) < 0:
												raise ValueError('All numbers must be positive'    )

						_snake_case: List[str]	       =		max(len(bin(lowercase__    )[2:]    ) for x in list_of_ints    )
						return _msd_radix_sort(lowercase__					,	lowercase__    )





def    lowercase_   (		lowercase__					,	lowercase__    ) ->list[int]:
						if bit_position == 0 or len(lowercase__    ) in [0, 1]:
												return list_of_ints

						_snake_case: Tuple	       =		[]
						_snake_case: Tuple	       =		[]
						# Split numbers based on bit at bit_position from the right
						for number in list_of_ints:
												if (number >> (bit_position - 1)) & 1:
																		# number has a one at bit bit_position
																		ones.append(lowercase__    )
												else:
																		# number has a zero at bit bit_position
																		zeros.append(lowercase__    )

    # recursively split both lists further
						_snake_case: Tuple	       =		_msd_radix_sort(lowercase__					,	bit_position - 1    )
						_snake_case: List[Any]	       =		_msd_radix_sort(lowercase__					,	bit_position - 1    )

						# recombine lists
						_snake_case: Any	       =		zeros
						res.extend(lowercase__    )

						return res





def    lowercase_   (		lowercase__    ) ->List[str]:
						_snake_case: Dict	       =		len(lowercase__    )
						if not list_of_ints or length == 1:
												return

						if min(lowercase__    ) < 0:
												raise ValueError('All numbers must be positive'    )

						_snake_case: Any	       =		max(len(bin(lowercase__    )[2:]    ) for x in list_of_ints    )
						_msd_radix_sort_inplace(lowercase__					,	lowercase__					,	0					,	lowercase__    )





def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__					,	lowercase__    ) ->str:
						if bit_position == 0 or end_index - begin_index <= 1:
												return

						bit_position -= 1

						_snake_case: Optional[int]	       =		begin_index
						_snake_case: Dict	       =		end_index - 1
						while i <= j:
												_snake_case: Optional[Any]	       =		False
												if not (list_of_ints[i] >> bit_position) & 1:
																		# found zero at the beginning
																		i += 1
																		_snake_case: List[str]	       =		True
												if (list_of_ints[j] >> bit_position) & 1:
																		# found one at the end
																		j -= 1
																		_snake_case: List[str]	       =		True

												if changed:
																		continue

												_snake_case       ,    _snake_case: Union[str, Any]	       =		list_of_ints[j], list_of_ints[i]
												j -= 1
												if j != i:
																		i += 1

						_msd_radix_sort_inplace(lowercase__					,	lowercase__					,	lowercase__					,	lowercase__    )
						_msd_radix_sort_inplace(lowercase__					,	lowercase__					,	lowercase__					,	lowercase__    )


if __name__ == "__main__":
		import doctest

		doctest.testmod()












'''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_xlm_roberta import XLMRobertaTokenizer
else:
		A :			Optional[Any]							   =      None


A :			Tuple							   =      logging.get_logger(__name__)

A :			Optional[int]							   =      {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

A :			Tuple							   =      {
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'
        ),
    },
}

A :			int							   =      {
    'xlm-roberta-base': 512,
    'xlm-roberta-large': 512,
    'xlm-roberta-large-finetuned-conll02-dutch': 512,
    'xlm-roberta-large-finetuned-conll02-spanish': 512,
    'xlm-roberta-large-finetuned-conll03-english': 512,
    'xlm-roberta-large-finetuned-conll03-german': 512,
}







class       lowerCamelCase       (    __UpperCAmelCase	):
					_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"]
					_SCREAMING_SNAKE_CASE				 =     XLMRobertaTokenizer


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





											'''simple docstring'''





											_snake_case: Optional[Any]	       =		AddedToken(__snake_case							,  lstrip=__snake_case							,  rstrip=__snake_case							) if isinstance(__snake_case							,  __snake_case							) else mask_token

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

											_snake_case: int	       =		vocab_file
											_snake_case: List[Any]	       =		False if not self.vocab_file else True


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							,  __snake_case						:	List[int]							,  __snake_case						:	Optional[List[int]] = None							):





											'''simple docstring'''





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


					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							,  __snake_case						:	List[int]							,  __snake_case						:	Optional[List[int]] = None							):





											'''simple docstring'''





											_snake_case: Union[str, Any]	       =		[self.sep_token_id]
											_snake_case: List[str]	       =		[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  SCREAMING_SNAKE_CASE_		(       self						:	Any							,  __snake_case						:	str							,  __snake_case						:	Optional[str] = None							):





											'''simple docstring'''





											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(__snake_case							):
																	logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''							)
																	return
											_snake_case: Optional[int]	       =		os.path.join(
											    __snake_case							,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)

											if os.path.abspath(self.vocab_file							) != os.path.abspath(__snake_case							):
																	copyfile(self.vocab_file							,  __snake_case							)

											return (out_vocab_file,)





