
'''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


__lowercase							:   List[str]					=     logging.get_logger(__name__)

__lowercase							:   str					=     '''▁'''

__lowercase							:   Optional[int]					=     {'''vocab_file''': '''prophetnet.tokenizer'''}

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

__lowercase							:   Any					=     {
    '''microsoft/xprophetnet-large-wiki100-cased''': {'''do_lower_case''': False},
}

__lowercase							:   Optional[Any]					=     {
    '''microsoft/xprophetnet-large-wiki100-cased''': 512,
}




def lowercase_	(   _lowercase				)     ->							List[Any]:
					'''simple docstring'''

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






class 	__lowercase				(  _lowercase    ):
			lowerCamelCase			:		Any	      =			VOCAB_FILES_NAMES
			lowerCamelCase			:		Dict	      =			PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase			:		List[str]	      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase			:		Optional[int]	      =			["input_ids", "attention_mask"]

			def __init__(self			,   A			,   A="[SEP]"			,   A="[SEP]"			,   A="[SEP]"			,   A="[UNK]"			,   A="[PAD]"			,   A="[CLS]"			,   A="[MASK]"			,   A = None			,   **A			,   ):
								lowerCamelCase_					:      Dict     =		{} if sp_model_kwargs is None else sp_model_kwargs

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

								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

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

								for i in range(1_0      ):
													lowerCamelCase_					:      Tuple     =		F"""[unused{i}]"""
													lowerCamelCase_					:      Any     =		5 + i

								# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
								lowerCamelCase_					:      Optional[Any]     =		1_2
								lowerCamelCase_					:      Dict     =		{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(A      )

			def __getstate__(self      ):
								lowerCamelCase_					:      str     =		self.__dict__.copy()
								lowerCamelCase_					:      Dict     =		None
								return state

			def __setstate__(self			,   A      ):
								lowerCamelCase_					:      Tuple     =		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'''      ):
													lowerCamelCase_					:      Dict     =		{}

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

			def        UpperCAmelCase__ (self			,   A			,   A = None			,   A = False      ):

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

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

			def        UpperCAmelCase__ (self			,   A			,   A = None      ):
								lowerCamelCase_					:      Dict     =		[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        UpperCAmelCase__ (self      ):
								return len(self.sp_model      ) + self.fairseq_offset

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Optional[int]     =		{self.convert_ids_to_tokens(A      ): i for i in range(self.vocab_size      )}
								vocab.update(self.added_tokens_encoder      )
								return vocab

			def        UpperCAmelCase__ (self			,   A      ):
								return self.sp_model.encode(A			,   out_type=A      )

			def        UpperCAmelCase__ (self			,   A      ):
								if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
								lowerCamelCase_					:      List[Any]     =		self.sp_model.PieceToId(A      )

								# 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        UpperCAmelCase__ (self			,   A      ):
								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        UpperCAmelCase__ (self			,   A      ):
								lowerCamelCase_					:      Union[str, Any]     =		''''''.join(A      ).replace(A			,   ''' '''      ).strip()
								return out_string

			def        UpperCAmelCase__ (self			,   A			,   A = None      ):
								if not os.path.isdir(A      ):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
													return
								lowerCamelCase_					:      List[Any]     =		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      ) and os.path.isfile(self.vocab_file      ):
													copyfile(self.vocab_file			,   A      )
								elif not os.path.isfile(self.vocab_file      ):
													with open(A			,   '''wb'''      ) as fi:
																		lowerCamelCase_					:      List[str]     =		self.sp_model.serialized_model_proto()
																		fi.write(A      )

								return (out_vocab_file,)






			def        UpperCAmelCase__ (self			,   A			,   A = None      ):

								if token_ids_a is None:
													return token_ids_a + [self.sep_token_id]
								lowerCamelCase_					:      List[Any]     =		[self.sep_token_id]
								return token_ids_a + sep + token_ids_a + sep




'''simple docstring'''


import itertools
import math




def lowercase_	(   _lowercase				)     ->							bool:
					'''simple docstring'''

					if 1 < number < 4:
										# 2 and 3 are primes
										return True
					elif number < 2 or number % 2 == 0 or number % 3 == 0:
										# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
										return False

					# All primes number are in format of 6k +/- 1
					for i in range(5			,       int(math.sqrt(_lowercase				) + 1				)			,       6				):
										if number % i == 0 or number % (i + 2) == 0:
															return False
					return True




def lowercase_	(   )     ->							Optional[int]:
					'''simple docstring'''

					lowerCamelCase_					:      Union[str, Any]     =		2
					while True:
										if is_prime(_lowercase				):
															yield num
										num += 1




def lowercase_	(   _lowercase = 10_001				)     ->							int:
					'''simple docstring'''

					return next(itertools.islice(prime_generator()			,       nth - 1			,       _lowercase				)				)


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


