


"""simple docstring"""
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_mbartaa import MBartaaTokenizer
else:
			SCREAMING_SNAKE_CASE					:       int							   =							None


SCREAMING_SNAKE_CASE					:       Any							   =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE					:       Tuple							   =							{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							{
    """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"""
        ),
    },
    """tokenizer_file""": {
        """facebook/mbart-large-50-one-to-many-mmt""": (
            """https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json"""
        ),
    },
}

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

# fmt: off
SCREAMING_SNAKE_CASE					:       str							   =							["""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					(				lowerCAmelCase__							):






		'''simple docstring'''

		lowerCamelCase__           =VOCAB_FILES_NAMES
		lowerCamelCase__           =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCamelCase__           =PRETRAINED_VOCAB_FILES_MAP
		lowerCamelCase__           =['input_ids', 'attention_mask']
		lowerCamelCase__           =MBartaaTokenizer

		lowerCamelCase__           =[]
		lowerCamelCase__           =[]



		def __init__(self							,			a_=None							,			a_=None							,			a_=None							,			a_=None							,			a_="</s>"							,			a_="</s>"							,			a_="<s>"							,			a_="<unk>"							,			a_="<pad>"							,			a_="<mask>"							,			**a_							,			):





			'''simple docstring'''

			__snake_case      :		Dict         =							AddedToken(a_							,			lstrip=a_							,			rstrip=a_   ) if isinstance(a_							,			a_   ) else mask_token

			__snake_case      :		List[Any]         =							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__(
			    a_							,			src_lang=a_							,			tgt_lang=a_							,			tokenizer_file=a_							,			eos_token=a_							,			sep_token=a_							,			cls_token=a_							,			unk_token=a_							,			pad_token=a_							,			mask_token=a_							,			**a_							,			)

			__snake_case      :		Any         =							vocab_file
			__snake_case      :		int         =							False if not self.vocab_file else True

			__snake_case      :		Dict         =							{
			    lang_code: self.convert_tokens_to_ids(a_   ) for lang_code in FAIRSEQ_LANGUAGE_CODES
			}

			__snake_case      :		str         =							src_lang if src_lang is not None else 'en_XX'
			__snake_case      :		Dict         =							tgt_lang
			__snake_case      :		List[str]         =							self.lang_code_to_id[self._src_lang]
			self.set_src_lang_special_tokens(self._src_lang   )



		@property
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			return self._src_lang



		@src_lang.setter
		def 							SCREAMING_SNAKE_CASE    (self							,			a_   ):





			'''simple docstring'''

			__snake_case      :		Optional[int]         =							new_src_lang
			self.set_src_lang_special_tokens(self._src_lang   )



		def 							SCREAMING_SNAKE_CASE    (self							,			a_							,			a_ = 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 							SCREAMING_SNAKE_CASE    (self							,			a_							,			a_ = "en_XX"							,			a_ = None							,			a_ = "ro_RO"							,			**a_							,			):





			'''simple docstring'''

			__snake_case      :		Optional[int]         =							src_lang
			__snake_case      :		int         =							tgt_lang
			return super().prepare_seqaseq_batch(a_							,			a_							,			**a_   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			return self.set_src_lang_special_tokens(self.src_lang   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			return self.set_tgt_lang_special_tokens(self.tgt_lang   )



		def 							SCREAMING_SNAKE_CASE    (self							,			a_   ):





			'''simple docstring'''

			__snake_case      :		Union[str, Any]         =							self.convert_tokens_to_ids(a_   )
			__snake_case      :		List[str]         =							[self.cur_lang_code_id]
			__snake_case      :		List[Any]         =							[self.eos_token_id]

			__snake_case      :		Tuple         =							self.convert_ids_to_tokens(self.prefix_tokens   )
			__snake_case      :		Any         =							self.convert_ids_to_tokens(self.suffix_tokens   )

			__snake_case      :		Optional[Any]         =							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 							SCREAMING_SNAKE_CASE    (self							,			a_   ):





			'''simple docstring'''

			__snake_case      :		Optional[Any]         =							self.convert_tokens_to_ids(a_   )
			__snake_case      :		Dict         =							[self.cur_lang_code_id]
			__snake_case      :		Any         =							[self.eos_token_id]

			__snake_case      :		int         =							self.convert_ids_to_tokens(self.prefix_tokens   )
			__snake_case      :		List[Any]         =							self.convert_ids_to_tokens(self.suffix_tokens   )

			__snake_case      :		List[Any]         =							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 							SCREAMING_SNAKE_CASE    (self							,			a_							,			a_							,			a_							,			a_							,			**a_   ):





			'''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      :		int         =							src_lang
			__snake_case      :		Optional[int]         =							self(a_							,			add_special_tokens=a_							,			return_tensors=a_							,			**a_   )
			__snake_case      :		Tuple         =							self.convert_tokens_to_ids(a_   )
			__snake_case      :		Dict         =							tgt_lang_id
			return inputs



		def 							SCREAMING_SNAKE_CASE    (self							,			a_							,			a_ = 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      :		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_   ):
				copyfile(self.vocab_file							,			a_   )

			return (out_vocab_file,)










'''simple docstring'''







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE = 1000     ):
		return sum(i for i in range(SCREAMING_SNAKE_CASE     ) if i % 3 == 0 or i % 5 == 0     )


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