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            =		logging.get_logger(__name__)

_UpperCAmelCase            =		"""▁"""

_UpperCAmelCase            =		{"""vocab_file""": """sentencepiece.bpe.model"""}

_UpperCAmelCase            =		{
    """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            =		{
    """facebook/mbart-large-50-one-to-many-mmt""": 1024,
}

# fmt: off
_UpperCAmelCase            =		["""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 UpperCAmelCase						( __A  ):


				'''simple docstring'''






				lowerCamelCase_    = VOCAB_FILES_NAMES
				lowerCamelCase_    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCamelCase_    = PRETRAINED_VOCAB_FILES_MAP
				lowerCamelCase_    = ['''input_ids''', '''attention_mask''']

				lowerCamelCase_    = []
				lowerCamelCase_    = []
				def __init__(						self		,     lowercase		,     lowercase=None		,     lowercase=None		,     lowercase="</s>"		,     lowercase="</s>"		,     lowercase="<s>"		,     lowercase="<unk>"		,     lowercase="<pad>"		,     lowercase="<mask>"		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    List[str]         =				AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase ) if isinstance(lowercase		,     lowercase ) else mask_token

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

						A_		:    Dict         =				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=lowercase		,     tgt_lang=lowercase		,     eos_token=lowercase		,     unk_token=lowercase		,     sep_token=lowercase		,     cls_token=lowercase		,     pad_token=lowercase		,     mask_token=lowercase		,     sp_model_kwargs=self.sp_model_kwargs		,     **lowercase		,     )

						A_		:    Optional[int]         =				spm.SentencePieceProcessor(**self.sp_model_kwargs )
						self.sp_model.Load(str(lowercase ) )
						A_		:    Dict         =				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_		:    int         =				{'<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_		:    List[Any]         =				1

						A_		:    Union[str, Any]         =				len(self.sp_model )
						A_		:    List[str]         =				{
						    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowercase )
						}
						A_		:    Tuple         =				{v: k for k, v in self.lang_code_to_id.items()}
						A_		:    int         =				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_		:    Optional[Any]         =				{v: k for k, v in self.fairseq_tokens_to_ids.items()}

						A_		:    List[Any]         =				src_lang if src_lang is not None else 'en_XX'
						A_		:    Tuple         =				self.lang_code_to_id[self._src_lang]
						A_		:    Dict         =				tgt_lang
						self.set_src_lang_special_tokens(self._src_lang )
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





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



						"""simple docstring"""





						return self._src_lang
				@src_lang.setter
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_		:    Dict         =				new_src_lang
						self.set_src_lang_special_tokens(self._src_lang )
				def __getstate__(						self ):



						"""simple docstring"""





						A_		:    Dict         =				self.__dict__.copy()
						A_		:    Any         =				None
						return state
				def __setstate__(						self		,     lowercase ):



						"""simple docstring"""





						A_		:    Dict         =				d

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

						A_		:    Tuple         =				spm.SentencePieceProcessor(**self.sp_model_kwargs )
						self.sp_model.Load(self.vocab_file )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				{self.convert_ids_to_tokens(lowercase ): i for i in range(self.vocab_size )}
						vocab.update(self.added_tokens_encoder )
						return vocab
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						return self.sp_model.encode(lowercase		,     out_type=lowercase )
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						if token in self.fairseq_tokens_to_ids:
								return self.fairseq_tokens_to_ids[token]
						A_		:    Union[str, Any]         =				self.sp_model.PieceToId(lowercase )

						# 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   lowerCAmelCase_       (						self		,     lowercase ):



						"""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   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_		:    str         =				[]
						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(lowercase ) + token
										A_		:    Any         =				True
										A_		:    int         =				[]
								else:
										current_sub_tokens.append(lowercase )
										A_		:    Dict         =				False
						out_string += self.sp_model.decode(lowercase )
						return out_string.strip()
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





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

						if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase ) and os.path.isfile(self.vocab_file ):
								copyfile(self.vocab_file		,     lowercase )
						elif not os.path.isfile(self.vocab_file ):
								with open(lowercase		,     'wb' ) as fi:
										A_		:    Any         =				self.sp_model.serialized_model_proto()
										fi.write(lowercase )

						return (out_vocab_file,)
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None		,     lowercase = False ):



						"""simple docstring"""





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

						A_		:    str         =				[1] * len(self.prefix_tokens )
						A_		:    Any         =				[1] * len(self.suffix_tokens )
						if token_ids_a is None:
								return prefix_ones + ([0] * len(lowercase )) + suffix_ones
						return prefix_ones + ([0] * len(lowercase )) + ([0] * len(lowercase )) + suffix_ones
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						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   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase		,     lowercase		,     **lowercase ):



						"""simple docstring"""





						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_		:    str         =				src_lang
						A_		:    Dict         =				self(lowercase		,     add_special_tokens=lowercase		,     return_tensors=lowercase		,     **lowercase )
						A_		:    str         =				self.convert_tokens_to_ids(lowercase )
						A_		:    Optional[int]         =				tgt_lang_id
						return inputs
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = "en_XX"		,     lowercase = None		,     lowercase = "ro_RO"		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    str         =				src_lang
						A_		:    Dict         =				tgt_lang
						return super().prepare_seqaseq_batch(lowercase		,     lowercase		,     **lowercase )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





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



						"""simple docstring"""





						return self.set_tgt_lang_special_tokens(self.tgt_lang )
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_		:    List[Any]         =				self.lang_code_to_id[src_lang]
						A_		:    Tuple         =				[self.cur_lang_code_id]
						A_		:    int         =				[self.eos_token_id]







				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_		:    Any         =				self.lang_code_to_id[tgt_lang]
						A_		:    List[Any]         =				[self.cur_lang_code_id]
						A_		:    Dict         =				[self.eos_token_id]







from math import isqrt




def     UpperCamelCase						(	__lowercase						:			int     ):
		'''simple docstring'''




		return all(number % divisor != 0 for divisor in range(2						,isqrt(__lowercase     ) + 1     )     )




def     UpperCamelCase						(	__lowercase						:			int = 10**6     ):
		'''simple docstring'''




		A_		:    Optional[Any]         =				0
		A_		:    List[str]         =				1
		A_		:    Dict         =				7
		while prime_candidate < max_prime:
				primes_count += is_prime(__lowercase     )

				cube_index += 1
				prime_candidate += 6 * cube_index

		return primes_count


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






