






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          = None


__A          = logging.get_logger(__name__)

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

__A          = {
    '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          = {
    '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   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							A_       =    VOCAB_FILES_NAMES
							A_       =    PRETRAINED_VOCAB_FILES_MAP
							A_       =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							A_       =    ["input_ids", "attention_mask"]
							A_       =    XLMRobertaTokenizer

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

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

												_A									=       vocab_file
												_A									=       False if not self.vocab_file else True

							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]
												_A									=       [self.cls_token_id]
												_A									=       [self.sep_token_id]
												return cls + token_ids_a + sep + sep + token_ids_a + sep

							def 					__A       (     self:      str						,   __A:      List[int]						,   __A:      Optional[List[int]] = None      )			-> List[int]:
												_A									=       [self.sep_token_id]
												_A									=       [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 					__A       (     self:      Tuple						,   __A:      str						,   __A:      Optional[str] = None      )			-> Tuple[str]:
												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(__A      ):
																	logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""      )
																	return
												_A									=       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      ):
																	copyfile(self.vocab_file						,   __A      )

												return (out_vocab_file,)











from __future__ import annotations

from collections.abc import Generator




def    __A    (   _lowercase       ):

					'''simple docstring'''







					if not isinstance(_lowercase    ,	_lowercase       ) or n < 1:
										raise Exception('''Sequence only defined for positive integers'''       )

					yield n
					while n != 1:
										if n % 2 == 0:
															n //= 2
										else:
															_A									=       3 * n + 1
										yield n




def    __A    (   ):

					'''simple docstring'''







					_A									=       43
					_A									=       tuple(collatz_sequence(_lowercase       )       )
					print(_lowercase       )
					print(f"""Collatz sequence from {n} took {len(_lowercase       )} steps."""       )


if __name__ == "__main__":
				main()



