

'''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


lowerCAmelCase__						:       str										=						logging.get_logger(__name__)

lowerCAmelCase__						:       List[str]										=						"""▁"""

lowerCAmelCase__						:       Optional[Any]										=						{"""vocab_file""": """prophetnet.tokenizer"""}

lowerCAmelCase__						:       Dict										=						{
    """vocab_file""": {
        """microsoft/xprophetnet-large-wiki100-cased""": (
            """https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"""
        ),
    }
}

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

lowerCAmelCase__						:       Tuple										=						{
    """microsoft/xprophetnet-large-wiki100-cased""": 512,
}




def 	_a       (				__lowerCAmelCase		:      List[Any]     ):

		"""simple docstring"""

		snake_case__						:       List[str]       						=  collections.OrderedDict()
		with open(__lowerCAmelCase   ,							'''r'''   ,							encoding='''utf-8'''     ) as reader:
				snake_case__						:       Any       						=  reader.readlines()
		for index, token in enumerate(__lowerCAmelCase     ):
				snake_case__						:       Union[str, Any]       						=  token.rstrip('''\n'''     )
				snake_case__						:       List[str]       						=  index
		return vocab





class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    VOCAB_FILES_NAMES
						__UpperCAmelCase			     =    PRETRAINED_VOCAB_FILES_MAP
						__UpperCAmelCase			     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__UpperCAmelCase			     =    ["""input_ids""", """attention_mask"""]







						def __init__(							self					:      int			,			snake_case_					:      str			,			snake_case_					:      Any="[SEP]"			,			snake_case_					:      Optional[Any]="[SEP]"			,			snake_case_					:      Union[str, Any]="[SEP]"			,			snake_case_					:      Union[str, Any]="[UNK]"			,			snake_case_					:      Tuple="[PAD]"			,			snake_case_					:      Optional[Any]="[CLS]"			,			snake_case_					:      Optional[Any]="[MASK]"			,			snake_case_					:      Optional[Dict[str, Any]] = None			,			**snake_case_					:      List[Any]			,			):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  {} if sp_model_kwargs is None else sp_model_kwargs

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

								snake_case__						:       Tuple       						=  spm.SentencePieceProcessor(**self.sp_model_kwargs  )
								self.sp_model.Load(str(snake_case_  )  )
								snake_case__						:       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
								snake_case__						:       List[str]       						=  {'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}

								for i in range(1_0  ):
										snake_case__						:       Union[str, Any]       						=  F"""[unused{i}]"""
										snake_case__						:       Optional[int]       						=  5 + i

								# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
								snake_case__						:       str       						=  1_2
								snake_case__						:       Union[str, Any]       						=  {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(snake_case_  )







						def __getstate__(							self					:      Dict  ):



								'''simple docstring'''



								snake_case__						:       Tuple       						=  self.__dict__.copy()
								snake_case__						:       int       						=  None
								return state







						def __setstate__(							self					:      Optional[Any]			,			snake_case_					:      str  ):



								'''simple docstring'''



								snake_case__						:       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'''  ):
										snake_case__						:       List[Any]       						=  {}

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







						def       __magic_name__							(							self					:      Dict			,			snake_case_					:      List[int]			,			snake_case_					:      Optional[List[int]] = None			,			snake_case_					:      bool = False  ):



								'''simple docstring'''



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

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







						def       __magic_name__							(							self					:      Optional[Any]			,			snake_case_					:      List[int]			,			snake_case_					:      Optional[List[int]] = None  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  [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[int]  ):



								'''simple docstring'''



								return len(self.sp_model  ) + self.fairseq_offset







						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  {self.convert_ids_to_tokens(snake_case_  ): i for i in range(self.vocab_size  )}
								vocab.update(self.added_tokens_encoder  )
								return vocab







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      str  ):



								'''simple docstring'''



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







						def       __magic_name__							(							self					:      Any			,			snake_case_					:      Tuple  ):



								'''simple docstring'''



								if token in self.fairseq_tokens_to_ids:
										return self.fairseq_tokens_to_ids[token]
								snake_case__						:       Union[str, Any]       						=  self.sp_model.PieceToId(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					:      Union[str, Any]			,			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					:      Tuple			,			snake_case_					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  ''''''.join(snake_case_  ).replace(snake_case_			,			''' '''  ).strip()
								return out_string







						def       __magic_name__							(							self					:      int			,			snake_case_					:      str			,			snake_case_					:      Optional[str] = None  ):



								'''simple docstring'''



								if not os.path.isdir(snake_case_  ):
										logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""  )
										return
								snake_case__						:       str       						=  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_  ) and os.path.isfile(self.vocab_file  ):
										copyfile(self.vocab_file			,			snake_case_  )
								elif not os.path.isfile(self.vocab_file  ):
										with open(snake_case_			,			'''wb'''  ) as fi:
												snake_case__						:       Any       						=  self.sp_model.serialized_model_proto()
												fi.write(snake_case_  )

								return (out_vocab_file,)







						def       __magic_name__							(							self					:      Optional[Any]			,			snake_case_					:      List[int]			,			snake_case_					:      Optional[List[int]] = None  ):



								'''simple docstring'''



								if token_ids_a is None:
										return token_ids_a + [self.sep_token_id]
								snake_case__						:       Union[str, Any]       						=  [self.sep_token_id]
								return token_ids_a + sep + token_ids_a + sep






'''simple docstring'''


def 	_a       (				__lowerCAmelCase		:      int     ):

		"""simple docstring"""

		if p < 2:
				raise ValueError('''p should not be less than 2!'''     )
		elif p == 2:
				return True

		snake_case__						:       Any       						=  4
		snake_case__						:       int       						=  (1 << p) - 1
		for _ in range(p - 2     ):
				snake_case__						:       Tuple       						=  ((s * s) - 2) % m
		return s == 0


if __name__ == "__main__":
					print(lucas_lehmer_test(7))
					print(lucas_lehmer_test(11))



