
"""simple docstring"""




import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase__   :					Optional[int]												=				logging.get_logger(__name__)

UpperCAmelCase__   :					Union[str, Any]												=				'▁'

UpperCAmelCase__   :					Tuple												=				{'vocab_file': 'prophetnet.tokenizer'}

UpperCAmelCase__   :					int												=				{
    'vocab_file': {
        'microsoft/xprophetnet-large-wiki100-cased': (
            'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'
        ),
    }
}

UpperCAmelCase__   :					Union[str, Any]												=				{
    'microsoft/xprophetnet-large-wiki100-cased': {'do_lower_case': False},
}

UpperCAmelCase__   :					Optional[int]												=				{
    'microsoft/xprophetnet-large-wiki100-cased': 5_1_2,
}

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: Dict     =     collections.OrderedDict()
							with open(_snake_case     ,"""r"""     ,encoding="""utf-8"""		) as reader:
														SCREAMING_SNAKE_CASE__		: Any     =     reader.readlines()
							for index, token in enumerate(_snake_case		):
														SCREAMING_SNAKE_CASE__		: str     =     token.rstrip("""\n"""		)
														SCREAMING_SNAKE_CASE__		: Dict     =     index
							return vocab



class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				__UpperCamelCase   :		Tuple       =     VOCAB_FILES_NAMES
				__UpperCamelCase   :		Union[str, Any]       =     PRETRAINED_VOCAB_FILES_MAP
				__UpperCamelCase   :		Any       =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__UpperCamelCase   :		Any       =     ['''input_ids''', '''attention_mask''']



				def __init__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__="[SEP]"     , SCREAMING_SNAKE_CASE__="[SEP]"     , SCREAMING_SNAKE_CASE__="[SEP]"     , SCREAMING_SNAKE_CASE__="[UNK]"     , SCREAMING_SNAKE_CASE__="[PAD]"     , SCREAMING_SNAKE_CASE__="[CLS]"     , SCREAMING_SNAKE_CASE__="[MASK]"     , SCREAMING_SNAKE_CASE__ = None     , **SCREAMING_SNAKE_CASE__     , )      ->	None:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     {} if sp_model_kwargs is None else sp_model_kwargs

											super().__init__(
											    bos_token=SCREAMING_SNAKE_CASE__     , eos_token=SCREAMING_SNAKE_CASE__     , sep_token=SCREAMING_SNAKE_CASE__     , unk_token=SCREAMING_SNAKE_CASE__     , pad_token=SCREAMING_SNAKE_CASE__     , cls_token=SCREAMING_SNAKE_CASE__     , mask_token=SCREAMING_SNAKE_CASE__     , sp_model_kwargs=self.sp_model_kwargs     , **SCREAMING_SNAKE_CASE__     , )

											try:
																		import sentencepiece as spm
											except ImportError:
																		logger.warning(
																		    """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
																		    """ pip install sentencepiece"""					)
																		raise

											SCREAMING_SNAKE_CASE__		: str     =     spm.SentencePieceProcessor(**self.sp_model_kwargs					)
											self.sp_model.Load(str(SCREAMING_SNAKE_CASE__					)					)
											SCREAMING_SNAKE_CASE__		: List[Any]     =     vocab_file

											# Original fairseq vocab and spm vocab must be "aligned":
											# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
											# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
											# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
											# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

											# put special tokens and [unused] tokens into the vocab
											SCREAMING_SNAKE_CASE__		: List[Any]     =     {"""[PAD]""": 0, """[CLS]""": 1, """[SEP]""": 2, """[UNK]""": 3, """[MASK]""": 4}

											for i in range(10					):
																		SCREAMING_SNAKE_CASE__		: Any     =     F'''[unused{i}]'''
																		SCREAMING_SNAKE_CASE__		: Tuple     =     5 + i

											# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     12
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     {v: k for k, v in self.fairseq_tokens_to_ids.items()}
											for k in self.fairseq_tokens_to_ids.keys():
																		self.unique_no_split_tokens.append(SCREAMING_SNAKE_CASE__					)



				def __getstate__(self					)      ->	List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: int     =     self.__dict__.copy()
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     None
											return state



				def __setstate__(self     , SCREAMING_SNAKE_CASE__					)      ->	List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[Any]     =     d
											try:
																		import sentencepiece as spm
											except ImportError:
																		logger.warning(
																		    """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
																		    """ pip install sentencepiece"""					)
																		raise

											# for backward compatibility
											if not hasattr(self     , """sp_model_kwargs"""					):
																		SCREAMING_SNAKE_CASE__		: Tuple     =     {}

											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     spm.SentencePieceProcessor(**self.sp_model_kwargs					)
											self.sp_model.Load(self.vocab_file					)



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



											"""simple docstring"""




											if already_has_special_tokens:
																		return super().get_special_tokens_mask(
																		    token_ids_a=SCREAMING_SNAKE_CASE__     , token_ids_a=SCREAMING_SNAKE_CASE__     , already_has_special_tokens=SCREAMING_SNAKE_CASE__					)

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



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     [self.sep_token_id]

											if token_ids_a is None:
																		return len(token_ids_a + sep					) * [0]
											return len(token_ids_a + sep + sep + token_ids_a + sep					) * [0]



				@property
				def 			__magic_name__					(self					)      ->	Optional[Any]:



											"""simple docstring"""




											return len(self.sp_model					) + self.fairseq_offset



				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: str     =     {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__					): i for i in range(self.vocab_size					)}
											vocab.update(self.added_tokens_encoder					)
											return vocab



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	str:



											"""simple docstring"""




											return self.sp_model.encode(SCREAMING_SNAKE_CASE__     , out_type=SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	List[Any]:



											"""simple docstring"""




											if token in self.fairseq_tokens_to_ids:
																		return self.fairseq_tokens_to_ids[token]
											SCREAMING_SNAKE_CASE__		: List[str]     =     self.sp_model.PieceToId(SCREAMING_SNAKE_CASE__					)

											# Need to return unknown token if the SP model returned 0
											return spm_id + self.fairseq_offset if spm_id else self.unk_token_id



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	Tuple:



											"""simple docstring"""




											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 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     """""".join(SCREAMING_SNAKE_CASE__					).replace(SCREAMING_SNAKE_CASE__     , """ """					).strip()
											return out_string



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__ = None					)      ->	Tuple[str]:



											"""simple docstring"""




											if not os.path.isdir(SCREAMING_SNAKE_CASE__					):
																		logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
																		return
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     os.path.join(
											    SCREAMING_SNAKE_CASE__     , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

											if os.path.abspath(self.vocab_file					) != os.path.abspath(SCREAMING_SNAKE_CASE__					) and os.path.isfile(self.vocab_file					):
																		copyfile(self.vocab_file     , SCREAMING_SNAKE_CASE__					)
											elif not os.path.isfile(self.vocab_file					):
																		with open(SCREAMING_SNAKE_CASE__     , """wb"""					) as fi:
																									SCREAMING_SNAKE_CASE__		: List[str]     =     self.sp_model.serialized_model_proto()
																									fi.write(SCREAMING_SNAKE_CASE__					)

											return (out_vocab_file,)



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



											"""simple docstring"""




											if token_ids_a is None:
																		return token_ids_a + [self.sep_token_id]
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     [self.sep_token_id]
											return token_ids_a + sep + token_ids_a + sep






"""simple docstring"""




def lowercase_						(     _snake_case		):
							if divisor % 5 == 0 or divisor % 2 == 0:
														return 0
							SCREAMING_SNAKE_CASE__		: List[str]     =     1
							SCREAMING_SNAKE_CASE__		: List[Any]     =     1
							while repunit:
														SCREAMING_SNAKE_CASE__		: Dict     =     (10 * repunit + 1) % divisor
														repunit_index += 1
							return repunit_index

def lowercase_						(     _snake_case = 1_000_000		):
							SCREAMING_SNAKE_CASE__		: int     =     limit - 1
							if divisor % 2 == 0:
														divisor += 1
							while least_divisible_repunit(_snake_case		) <= limit:
														divisor += 2
							return divisor


if __name__ == "__main__":
					print(f"""{solution() = }""")




