



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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase__			= logging.get_logger(__name__)

lowerCamelCase__			= {"vocab_file": "spiece.model"}

lowerCamelCase__			= {
    "vocab_file": {
        "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
        ),
    }
}

lowerCamelCase__			= {
    "google/bigbird-roberta-base": 4096,
    "google/bigbird-roberta-large": 4096,
    "google/bigbird-base-trivia-itc": 4096,
}






class    SCREAMING_SNAKE_CASE			(							_UpperCAmelCase					):

							'''simple docstring'''





							__lowerCamelCase	:	List[str]    =VOCAB_FILES_NAMES
							__lowerCamelCase	:	str    =PRETRAINED_VOCAB_FILES_MAP
							__lowerCamelCase	:	Optional[int]    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__lowerCamelCase	:	Any    =['''input_ids''', '''attention_mask''']
							__lowerCamelCase	:	List[int]    =[]





							def __init__(   self		:		Optional[Any]      ,				__lowercase		:		List[Any]      ,				__lowercase		:		int="<unk>"      ,				__lowercase		:		Dict="<s>"      ,				__lowercase		:		Tuple="</s>"      ,				__lowercase		:		str="<pad>"      ,				__lowercase		:		Optional[Any]="[SEP]"      ,				__lowercase		:		List[str]="[MASK]"      ,				__lowercase		:		Union[str, Any]="[CLS]"      ,				__lowercase		:		Optional[Dict[str, Any]] = None      ,				**__lowercase		:		Any      ,				):

											'''simple docstring'''
											__a       =		AddedToken(A_      ,				lstrip=A_      ,				rstrip=A_	) if isinstance(A_      ,				A_	) else bos_token
											__a       =		AddedToken(A_      ,				lstrip=A_      ,				rstrip=A_	) if isinstance(A_      ,				A_	) else eos_token
											__a       =		AddedToken(A_      ,				lstrip=A_      ,				rstrip=A_	) if isinstance(A_      ,				A_	) else unk_token
											__a       =		AddedToken(A_      ,				lstrip=A_      ,				rstrip=A_	) if isinstance(A_      ,				A_	) else pad_token
											__a       =		AddedToken(A_      ,				lstrip=A_      ,				rstrip=A_	) if isinstance(A_      ,				A_	) else cls_token
											__a       =		AddedToken(A_      ,				lstrip=A_      ,				rstrip=A_	) if isinstance(A_      ,				A_	) else sep_token

											# 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

											__a       =		{} if sp_model_kwargs is None else sp_model_kwargs

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

											__a       =		vocab_file

											__a       =		spm.SentencePieceProcessor(**self.sp_model_kwargs	)
											self.sp_model.Load(A_	)





							@property
							def 						UpperCamelCase_					(   self		:		List[str]	):

											'''simple docstring'''
											return self.sp_model.get_piece_size()





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

											'''simple docstring'''
											__a       =		{self.convert_ids_to_tokens(A_	): i for i in range(self.vocab_size	)}
											vocab.update(self.added_tokens_encoder	)
											return vocab





							def __getstate__(   self		:		Tuple	):

											'''simple docstring'''
											__a       =		self.__dict__.copy()
											__a       =		None
											return state





							def __setstate__(   self		:		Dict      ,				__lowercase		:		Optional[int]	):

											'''simple docstring'''
											__a       =		d

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

											__a       =		spm.SentencePieceProcessor(**self.sp_model_kwargs	)
											self.sp_model.Load(self.vocab_file	)





							def 						UpperCamelCase_					(   self		:		Dict      ,				__lowercase		:		str	):

											'''simple docstring'''
											return self.sp_model.encode(A_      ,				out_type=A_	)





							def 						UpperCamelCase_					(   self		:		Dict      ,				__lowercase		:		int	):

											'''simple docstring'''
											return self.sp_model.piece_to_id(A_	)





							def 						UpperCamelCase_					(   self		:		Optional[Any]      ,				__lowercase		:		Optional[int]	):

											'''simple docstring'''
											__a       =		self.sp_model.IdToPiece(A_	)
											return token





							def 						UpperCamelCase_					(   self		:		Any      ,				__lowercase		:		List[str]	):

											'''simple docstring'''
											__a       =		[]
											__a       =		""""""
											__a       =		False
											for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																			if not prev_is_special:
																							out_string += " "
																			out_string += self.sp_model.decode(A_	) + token
																			__a       =		True
																			__a       =		[]
															else:
																			current_sub_tokens.append(A_	)
																			__a       =		False
											out_string += self.sp_model.decode(A_	)
											return out_string.strip()





							def 						UpperCamelCase_					(   self		:		int      ,				__lowercase		:		List[int]      ,				__lowercase		:		bool = False      ,				__lowercase		:		bool = None      ,				__lowercase		:		bool = True      ,				**__lowercase		:		List[Any]      ,				):

											'''simple docstring'''
											__a       =		kwargs.pop("""use_source_tokenizer"""      ,				A_	)

											__a       =		self.convert_ids_to_tokens(A_      ,				skip_special_tokens=A_	)

											# To avoid mixing byte-level and unicode for byte-level BPT
											# we need to build string separately for added tokens and byte-level tokens
											# cf. https://github.com/huggingface/transformers/issues/1133
											__a       =		[]
											__a       =		[]
											for token in filtered_tokens:
															if skip_special_tokens and token in self.all_special_ids:
																			continue
															if token in self.added_tokens_encoder:
																			if current_sub_text:
																							sub_texts.append(self.convert_tokens_to_string(A_	)	)
																							__a       =		[]
																			sub_texts.append(A_	)
															else:
																			current_sub_text.append(A_	)
											if current_sub_text:
															sub_texts.append(self.convert_tokens_to_string(A_	)	)

											# Mimic the behavior of the Rust tokenizer:
											# No space before [MASK] and [SEP]
											if spaces_between_special_tokens:
															__a       =		re.sub(r""" (\[(MASK|SEP)\])"""      ,				r"""\1"""      ,				""" """.join(A_	)	)
											else:
															__a       =		"""""".join(A_	)

											__a       =		(
											    clean_up_tokenization_spaces
											    if clean_up_tokenization_spaces is not None
											    else self.clean_up_tokenization_spaces
											)
											if clean_up_tokenization_spaces:
															__a       =		self.clean_up_tokenization(A_	)
															return clean_text
											else:
															return text





							def 						UpperCamelCase_					(   self		:		Any      ,				__lowercase		:		str      ,				__lowercase		:		Optional[str] = None	):

											'''simple docstring'''
											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_	) 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:
																			__a       =		self.sp_model.serialized_model_proto()
																			fi.write(A_	)

											return (out_vocab_file,)





							def 						UpperCamelCase_					(   self		:		Dict      ,				__lowercase		:		List[int]      ,				__lowercase		:		Optional[List[int]] = None	):

											'''simple docstring'''
											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 + token_ids_a + sep





							def 						UpperCamelCase_					(   self		:		Any      ,				__lowercase		:		List[int]      ,				__lowercase		:		Optional[List[int]] = None      ,				__lowercase		:		bool = False	):

											'''simple docstring'''
											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 [1] + ([0] * len(A_	)) + [1]
											return [1] + ([0] * len(A_	)) + [1] + ([0] * len(A_	)) + [1]







							def 						UpperCamelCase_					(   self		:		Tuple      ,				__lowercase		:		List[int]      ,				__lowercase		:		Optional[List[int]] = None	):

											'''simple docstring'''
											__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	) * [0] + len(token_ids_a + sep	) * [1]





from math import sqrt

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			int    ):
				"""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(sqrt(_SCREAMING_SNAKE_CASE    ) + 1    )				,     6    ):
								if number % i == 0 or number % (i + 2) == 0:
												return False
				return True

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			int = 1_0001    ):
				"""simple docstring"""







				__a       =		0
				__a       =		1
				while count != nth and number < 3:
								number += 1
								if is_prime(_SCREAMING_SNAKE_CASE    ):
												count += 1
				while count != nth:
								number += 2
								if is_prime(_SCREAMING_SNAKE_CASE    ):
												count += 1
				return number


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