





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

import sentencepiece as spm

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


UpperCAmelCase						:   Union[str, Any]			  =     logging.get_logger(__name__)

UpperCAmelCase						:   Optional[int]			  =     '''▁'''

UpperCAmelCase						:   Tuple			  =     {'''vocab_file''': '''sentencepiece.bpe.model'''}

UpperCAmelCase						:   Optional[int]			  =     {
    '''vocab_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

UpperCAmelCase						:   Any			  =     {
    '''facebook/mbart-large-50-one-to-many-mmt''': 10_24,
}

# fmt: off
UpperCAmelCase						:   Dict			  =     ['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''', '''af_ZA''', '''az_AZ''', '''bn_IN''', '''fa_IR''', '''he_IL''', '''hr_HR''', '''id_ID''', '''ka_GE''', '''km_KH''', '''mk_MK''', '''ml_IN''', '''mn_MN''', '''mr_IN''', '''pl_PL''', '''ps_AF''', '''pt_XX''', '''sv_SE''', '''sw_KE''', '''ta_IN''', '''te_IN''', '''th_TH''', '''tl_XX''', '''uk_UA''', '''ur_PK''', '''xh_ZA''', '''gl_ES''', '''sl_SI''']





class 						_A(					snake_case__   ):


				"""simple docstring"""


				UpperCamelCase    :  List[Any]					    =	VOCAB_FILES_NAMES
				UpperCamelCase    :  Any					    =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCamelCase    :  Any					    =	PRETRAINED_VOCAB_FILES_MAP
				UpperCamelCase    :  Tuple					    =	['''input_ids''', '''attention_mask''']

				UpperCamelCase    :  List[int]					    =	[]
				UpperCamelCase    :  List[int]					    =	[]



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

									__A	:						Dict       =				{} if sp_model_kwargs is None else sp_model_kwargs

									__A	:						Optional[int]       =				kwargs.get('additional_special_tokens'     ,					[]     )
									kwargs["additional_special_tokens"] += [
									    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
									]

									super().__init__(
									    src_lang=_A     ,					tgt_lang=_A     ,					eos_token=_A     ,					unk_token=_A     ,					sep_token=_A     ,					cls_token=_A     ,					pad_token=_A     ,					mask_token=_A     ,					sp_model_kwargs=self.sp_model_kwargs     ,					**_A     ,					)

									__A	:						Union[str, Any]       =				spm.SentencePieceProcessor(**self.sp_model_kwargs     )
									self.sp_model.Load(str(_A     )     )
									__A	:						Optional[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'

									# Mimic fairseq token-to-id alignment for the first 4 token
									__A	:						Tuple       =				{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

									# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
									__A	:						str       =				1

									__A	:						List[Any]       =				len(self.sp_model     )
									__A	:						int       =				{
									    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_A     )
									}
									__A	:						int       =				{v: k for k, v in self.lang_code_to_id.items()}
									__A	:						Dict       =				len(self.sp_model     ) + len(self.lang_code_to_id     ) + self.fairseq_offset

									self.fairseq_tokens_to_ids.update(self.lang_code_to_id     )
									__A	:						List[Any]       =				{v: k for k, v in self.fairseq_tokens_to_ids.items()}

									__A	:						str       =				src_lang if src_lang is not None else 'en_XX'
									__A	:						Optional[Any]       =				self.lang_code_to_id[self._src_lang]
									__A	:						Any       =				tgt_lang
									self.set_src_lang_special_tokens(self._src_lang     )



				@property
				def 					UpperCAmelCase_		(			self     ):
									return len(self.sp_model     ) + len(self.lang_code_to_id     ) + self.fairseq_offset + 1  # Plus 1 for the mask token



				@property
				def 					UpperCAmelCase_		(			self     ):
									return self._src_lang



				@src_lang.setter
				def 					UpperCAmelCase_		(			self     ,					_A     ):
									__A	:						Optional[int]       =				new_src_lang
									self.set_src_lang_special_tokens(self._src_lang     )



				def __getstate__(			self     ):
									__A	:						Optional[int]       =				self.__dict__.copy()
									__A	:						int       =				None
									return state



				def __setstate__(			self     ,					_A     ):
									__A	:						Optional[Any]       =				d

									# for backward compatibility
									if not hasattr(self     ,					'sp_model_kwargs'     ):
														__A	:						Any       =				{}

									__A	:						Dict       =				spm.SentencePieceProcessor(**self.sp_model_kwargs     )
									self.sp_model.Load(self.vocab_file     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Optional[Any]       =				{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]
									__A	:						Union[str, 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     ):
									__A	:						Tuple       =				[]
									__A	:						Optional[Any]       =				''
									__A	:						Any       =				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	:						int       =				True
																			__A	:						Optional[int]       =				[]
														else:
																			current_sub_tokens.append(_A     )
																			__A	:						Any       =				False
									out_string += self.sp_model.decode(_A     )
									return out_string.strip()



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A = None     ):
									if not os.path.isdir(_A     ):
														logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""     )
														return
									__A	:						Optional[int]       =				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	:						Any       =				self.sp_model.serialized_model_proto()
																			fi.write(_A     )

									return (out_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     )

									__A	:						List[str]       =				[1] * len(self.prefix_tokens     )
									__A	:						int       =				[1] * len(self.suffix_tokens     )
									if token_ids_a is None:
														return prefix_ones + ([0] * len(_A     )) + suffix_ones
									return prefix_ones + ([0] * len(_A     )) + ([0] * len(_A     )) + suffix_ones



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A = None     ):
									if token_ids_a is None:
														return self.prefix_tokens + token_ids_a + self.suffix_tokens
									# We don't expect to process pairs, but leave the pair logic for API consistency
									return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ,					_A     ,					**_A     ):
									if src_lang is None or tgt_lang is None:
														raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'     )
									__A	:						List[str]       =				src_lang
									__A	:						int       =				self(_A     ,					add_special_tokens=_A     ,					return_tensors=_A     ,					**_A     )
									__A	:						str       =				self.convert_tokens_to_ids(_A     )
									__A	:						int       =				tgt_lang_id
									return inputs



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A = "en_XX"     ,					_A = None     ,					_A = "ro_RO"     ,					**_A     ,					):
									__A	:						Dict       =				src_lang
									__A	:						List[str]       =				tgt_lang
									return super().prepare_seqaseq_batch(_A     ,					_A     ,					**_A     )



				def 					UpperCAmelCase_		(			self     ):
									return self.set_src_lang_special_tokens(self.src_lang     )



				def 					UpperCAmelCase_		(			self     ):
									return self.set_tgt_lang_special_tokens(self.tgt_lang     )



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									__A	:						str       =				self.lang_code_to_id[src_lang]
									__A	:						List[Any]       =				[self.cur_lang_code_id]
									__A	:						List[str]       =				[self.eos_token_id]





				def 					UpperCAmelCase_		(			self     ,					_A     ):
									__A	:						Dict       =				self.lang_code_to_id[tgt_lang]
									__A	:						int       =				[self.cur_lang_code_id]
									__A	:						List[Any]       =				[self.eos_token_id]











import math







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      list[int]:
					__A	:						List[str]       =				[]
					__A	:						Any       =				2
					__A	:						Union[str, Any]       =				int(math.sqrt(a  )  )  # Size of every segment
					__A	:						Any       =				[True] * (end + 1)
					__A	:						List[Any]       =				[]

					while start <= end:
										if temp[start] is True:
															in_prime.append(a  )
															for i in range(start * start					,						end + 1					,						a  ):
																				__A	:						Optional[int]       =				False
										start += 1
					prime += in_prime

					__A	:						Any       =				end + 1
					__A	:						Any       =				min(2 * end					,						a  )

					while low <= n:
										__A	:						List[Any]       =				[True] * (high - low + 1)
										for each in in_prime:
															__A	:						List[str]       =				math.floor(low / each  ) * each
															if t < low:
																				t += each

															for j in range(a					,						high + 1					,						a  ):
																				__A	:						Optional[int]       =				False

										for j in range(len(a  )  ):
															if temp[j] is True:
																				prime.append(j + low  )

										__A	:						Optional[int]       =				high + 1
										__A	:						Tuple       =				min(high + end					,						a  )

					return prime




print(sieve(10**6))




