




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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_mbart import MBartTokenizer
else:
			_lowerCamelCase	:    Union[str, Any]          =      None


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


_lowerCamelCase	:    Dict          =      {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCamelCase	:    Union[str, Any]          =      {
    '''vocab_file''': {
        '''facebook/mbart-large-en-ro''': (
            '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'''
        ),
        '''facebook/mbart-large-cc25''': (
            '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/mbart-large-en-ro''': '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json''',
        '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json''',
    },
}

_lowerCamelCase	:    List[str]          =      {
    '''facebook/mbart-large-en-ro''': 1_024,
    '''facebook/mbart-large-cc25''': 1_024,
}

# fmt: off
_lowerCamelCase	:    Tuple          =      ['''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''']




class        SCREAMING_SNAKE_CASE__					(     __SCREAMING_SNAKE_CASE ):





	'''simple docstring'''



	_UpperCAmelCase	:		Tuple          = VOCAB_FILES_NAMES
	_UpperCAmelCase	:		str          = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_UpperCAmelCase	:		Tuple          = PRETRAINED_VOCAB_FILES_MAP
	_UpperCAmelCase	:		Tuple          = ['input_ids', 'attention_mask']
	_UpperCAmelCase	:		Optional[Any]          = MBartTokenizer

	_UpperCAmelCase	:		List[int]          = []
	_UpperCAmelCase	:		List[int]          = []



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


				'''simple docstring'''


				_snake_case          =      AddedToken(_a      ,  lstrip=_a      ,  rstrip=_a     ) if isinstance(_a      ,  _a     ) else mask_token

				super().__init__(
				    vocab_file=_a      ,  tokenizer_file=_a      ,  bos_token=_a      ,  eos_token=_a      ,  sep_token=_a      ,  cls_token=_a      ,  unk_token=_a      ,  pad_token=_a      ,  mask_token=_a      ,  src_lang=_a      ,  tgt_lang=_a      ,  additional_special_tokens=_a      ,  **_a      ,  )

				_snake_case          =      vocab_file
				_snake_case          =      False if not self.vocab_file else True

				_snake_case          =      FAIRSEQ_LANGUAGE_CODES.copy()

				if additional_special_tokens is not None:
							# Only add those special tokens if they are not already there.
							_additional_special_tokens.extend(
							    [t for t in additional_special_tokens if t not in _additional_special_tokens]     )

				self.add_special_tokens({'additional_special_tokens': _additional_special_tokens}     )
				_snake_case          =      {
				    lang_code: self.convert_tokens_to_ids(_a     ) for lang_code in FAIRSEQ_LANGUAGE_CODES
				}

				_snake_case          =      src_lang if src_lang is not None else 'en_XX'
				_snake_case          =      self.convert_tokens_to_ids(self._src_lang     )
				_snake_case          =      tgt_lang
				self.set_src_lang_special_tokens(self._src_lang     )



	@property
	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				return self._src_lang



	@src_lang.setter
	def 			A							(  self     :   List[str]      ,  lowercase     :   int     ):


				'''simple docstring'''


				_snake_case          =      new_src_lang
				self.set_src_lang_special_tokens(self._src_lang     )



	def 			A							(  self     :   str      ,  lowercase     :   str      ,  lowercase     :   List[Any] = 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 			A							(  self     :   Optional[int]      ,  lowercase     :   str      ,  lowercase     :   Optional[Any] = None     ):


				'''simple docstring'''


				_snake_case          =      [self.sep_token_id]
				_snake_case          =      [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 + sep + token_ids_a + sep     ) * [0]



	def 			A							(  self     :   List[str]      ,  lowercase     :   int      ,  lowercase     :   List[Any]      ,  lowercase     :   Union[str, Any]      ,  lowercase     :   Dict      ,  **lowercase     :   int     ):


				'''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'     )
				_snake_case          =      src_lang
				_snake_case          =      self(_a      ,  add_special_tokens=_a      ,  return_tensors=_a      ,  **_a     )
				_snake_case          =      self.convert_tokens_to_ids(_a     )
				_snake_case          =      tgt_lang_id
				return inputs



	def 			A							(  self     :   int      ,  lowercase     :   Any      ,  lowercase     :   List[str] = "en_XX"      ,  lowercase     :   str = None      ,  lowercase     :   Tuple = "ro_RO"      ,  **lowercase     :   Optional[Any]      ,  ):


				'''simple docstring'''


				_snake_case          =      src_lang
				_snake_case          =      tgt_lang
				return super().prepare_seqaseq_batch(_a      ,  _a      ,  **_a     )



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


				'''simple docstring'''


				return self.set_src_lang_special_tokens(self.src_lang     )



	def 			A							(  self     :   int     ):


				'''simple docstring'''


				return self.set_tgt_lang_special_tokens(self.tgt_lang     )



	def 			A							(  self     :   Optional[Any]      ,  lowercase     :   Optional[int]     ):


				'''simple docstring'''


				_snake_case          =      self.convert_tokens_to_ids(_a     )
				_snake_case          =      []
				_snake_case          =      [self.eos_token_id, self.cur_lang_code]

				_snake_case          =      self.convert_ids_to_tokens(self.prefix_tokens     )
				_snake_case          =      self.convert_ids_to_tokens(self.suffix_tokens     )

				_snake_case          =      processors.TemplateProcessing(
				    single=prefix_tokens_str + ['$A'] + suffix_tokens_str      ,  pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str      ,  special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str      ,  self.prefix_tokens + self.suffix_tokens     )     )      ,  )



	def 			A							(  self     :   Dict      ,  lowercase     :   Any     ):


				'''simple docstring'''


				_snake_case          =      self.convert_tokens_to_ids(_a     )
				_snake_case          =      []
				_snake_case          =      [self.eos_token_id, self.cur_lang_code]

				_snake_case          =      self.convert_ids_to_tokens(self.prefix_tokens     )
				_snake_case          =      self.convert_ids_to_tokens(self.suffix_tokens     )

				_snake_case          =      processors.TemplateProcessing(
				    single=prefix_tokens_str + ['$A'] + suffix_tokens_str      ,  pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str      ,  special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str      ,  self.prefix_tokens + self.suffix_tokens     )     )      ,  )





	def 			A							(  self     :   Optional[Any]      ,  lowercase     :   Optional[int]      ,  lowercase     :   Optional[Any] = None     ):


				'''simple docstring'''


				if not self.can_save_slow_tokenizer:
							raise ValueError(
							    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
							    'tokenizer.'     )

				if not os.path.isdir(_a     ):
							logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''     )
							return
				_snake_case          =      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     ):
							copyfile(self.vocab_file      ,  _a     )

				return (out_vocab_file,)

def 	a_		(	__lowercase       :     int = 50_000_000			)			->  int:
			_snake_case          =      set()
			_snake_case          =      int((limit - 24) ** (1 / 2)			)

			_snake_case          =      set(range(3	, prime_square_limit + 1	, 2			)			)
			primes.add(2			)
			for p in range(3	, prime_square_limit + 1	, 2			):
						if p not in primes:
									continue
						primes.difference_update(set(range(p * p	, prime_square_limit + 1	, __lowercase			)			)			)

			for primea in primes:
						_snake_case          =      primea * primea
						for primea in primes:
									_snake_case          =      primea * primea * primea
									if square + cube >= limit - 16:
												break
									for primea in primes:
												_snake_case          =      primea * primea * primea * primea
												_snake_case          =      square + cube + tetr
												if total >= limit:
															break
												ret.add(__lowercase			)

			return len(__lowercase			)


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