




'''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 =	None


_SCREAMING_SNAKE_CASE =	logging.get_logger(__name__)

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

_SCREAMING_SNAKE_CASE =	{
    "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 =	{
    "facebook/mbart-large-50-one-to-many-mmt": 10_24,
}

# fmt: off
_SCREAMING_SNAKE_CASE =	["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'''





					_UpperCamelCase  	=		VOCAB_FILES_NAMES
					_UpperCamelCase  	=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_UpperCamelCase  	=		PRETRAINED_VOCAB_FILES_MAP
					_UpperCamelCase  	=		["""input_ids""", """attention_mask"""]
					_UpperCamelCase  	=		MBartaaTokenizer

					_UpperCamelCase  	=		[]
					_UpperCamelCase  	=		[]



					def __init__(  self    :	Optional[int]			,					_lowercase    :	Union[str, Any]=None			,					_lowercase    :	Optional[int]=None			,					_lowercase    :	Dict=None			,					_lowercase    :	Optional[Any]=None			,					_lowercase    :	Dict="</s>"			,					_lowercase    :	Union[str, Any]="</s>"			,					_lowercase    :	int="<s>"			,					_lowercase    :	List[Any]="<unk>"			,					_lowercase    :	Dict="<pad>"			,					_lowercase    :	Dict="<mask>"			,					**_lowercase    :	List[Any]			,					)						->       Tuple:
								# Mask token behave like a normal word, i.e. include the space before it
								A_       		=   AddedToken(_lowercase			,					lstrip=_lowercase			,					rstrip=_lowercase) if isinstance(_lowercase			,					_lowercase) else mask_token

								A_       		=   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__(
								    _lowercase			,					src_lang=_lowercase			,					tgt_lang=_lowercase			,					tokenizer_file=_lowercase			,					eos_token=_lowercase			,					sep_token=_lowercase			,					cls_token=_lowercase			,					unk_token=_lowercase			,					pad_token=_lowercase			,					mask_token=_lowercase			,					**_lowercase			,					)

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

								A_       		=   {
								    lang_code: self.convert_tokens_to_ids(_lowercase) for lang_code in FAIRSEQ_LANGUAGE_CODES
								}

								A_       		=   src_lang if src_lang is not None else 'en_XX'
								A_       		=   tgt_lang
								A_       		=   self.lang_code_to_id[self._src_lang]
								self.set_src_lang_special_tokens(self._src_lang)



					@property
					def 						__snake_case	(  self    :	Any)						->       str:
								return self._src_lang



					@src_lang.setter
					def 						__snake_case	(  self    :	str			,					_lowercase    :	str)						->       None:
								A_       		=   new_src_lang
								self.set_src_lang_special_tokens(self._src_lang)



					def 						__snake_case	(  self    :	List[str]			,					_lowercase    :	List[int]			,					_lowercase    :	Optional[List[int]] = None)						->       List[int]:
								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 						__snake_case	(  self    :	Union[str, Any]			,					_lowercase    :	List[str]			,					_lowercase    :	str = "en_XX"			,					_lowercase    :	Optional[List[str]] = None			,					_lowercase    :	str = "ro_RO"			,					**_lowercase    :	Dict			,					)						->       BatchEncoding:
								A_       		=   src_lang
								A_       		=   tgt_lang
								return super().prepare_seqaseq_batch(_lowercase			,					_lowercase			,					**_lowercase)



					def 						__snake_case	(  self    :	Tuple)						->       List[str]:
								return self.set_src_lang_special_tokens(self.src_lang)



					def 						__snake_case	(  self    :	List[str])						->       Tuple:
								return self.set_tgt_lang_special_tokens(self.tgt_lang)



					def 						__snake_case	(  self    :	List[str]			,					_lowercase    :	str)						->       None:
								A_       		=   self.convert_tokens_to_ids(_lowercase)
								A_       		=   [self.cur_lang_code_id]
								A_       		=   [self.eos_token_id]

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

								A_       		=   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 						__snake_case	(  self    :	Dict			,					_lowercase    :	str)						->       None:
								A_       		=   self.convert_tokens_to_ids(_lowercase)
								A_       		=   [self.cur_lang_code_id]
								A_       		=   [self.eos_token_id]

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

								A_       		=   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 						__snake_case	(  self    :	Optional[int]			,					_lowercase    :	str			,					_lowercase    :	str			,					_lowercase    :	Optional[str]			,					_lowercase    :	Optional[str]			,					**_lowercase    :	Optional[Any])						->       Dict:
								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_       		=   src_lang
								A_       		=   self(_lowercase			,					add_special_tokens=_lowercase			,					return_tensors=_lowercase			,					**_lowercase)
								A_       		=   self.convert_tokens_to_ids(_lowercase)
								A_       		=   tgt_lang_id
								return inputs



					def 						__snake_case	(  self    :	List[Any]			,					_lowercase    :	str			,					_lowercase    :	Optional[str] = None)						->       Tuple[str]:
								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(_lowercase):
											logger.error(F'Vocabulary path ({save_directory}) should be a directory')
											return
								A_       		=   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):
											copyfile(self.vocab_file			,					_lowercase)

								return (out_vocab_file,)










'''simple docstring'''
from __future__ import annotations

import sys





def  lowerCamelCase(       SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    )		->		tuple[int, int]:
			# base cases
			if abs(SCREAMING_SNAKE_CASE_    ) == 1:
						return a, 0
			elif abs(SCREAMING_SNAKE_CASE_    ) == 1:
						return 0, b

			A_		,   A_       		=   a, b
			A_		,   A_       		=   1, 0
			A_		,   A_       		=   0, 1

			while remainder != 0:
						A_       		=   old_remainder // remainder
						A_		,   A_       		=   remainder, old_remainder - quotient * remainder
						A_		,   A_       		=   coeff_a, old_coeff_a - quotient * coeff_a
						A_		,   A_       		=   coeff_b, old_coeff_b - quotient * coeff_b

			# sign correction for negative numbers
			if a < 0:
						A_       		=   -old_coeff_a
			if b < 0:
						A_       		=   -old_coeff_b

			return old_coeff_a, old_coeff_b





def  lowerCamelCase(       )		->		Tuple:
			if len(sys.argv    ) < 3:
						print('2 integer arguments required'    )
						return 1
			A_       		=   int(sys.argv[1]    )
			A_       		=   int(sys.argv[2]    )
			print(extended_euclidean_algorithm(SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    )    )
			return 0


if __name__ == "__main__":
							raise SystemExit(main())




