
"""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:
	snake_case    =		None


snake_case    =		logging.get_logger(__name__)

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

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'
        ),
    },
}

snake_case    =		{
    'facebook/mbart-large-50-one-to-many-mmt': 1_0_2_4,
}

# fmt: off
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	(       __magic_name__      ):

				"""simple docstring"""



				UpperCAmelCase_				:      Optional[int]						  =				VOCAB_FILES_NAMES
				UpperCAmelCase_				:      Optional[int]						  =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCAmelCase_				:      Dict						  =				PRETRAINED_VOCAB_FILES_MAP
				UpperCAmelCase_				:      List[str]						  =				["input_ids", "attention_mask"]
				UpperCAmelCase_				:      List[Any]						  =				MBartaaTokenizer

				UpperCAmelCase_				:      List[int]						  =				[]
				UpperCAmelCase_				:      List[int]						  =				[]


				def __init__(				self		,	lowercase__=None		,	lowercase__=None		,	lowercase__=None		,	lowercase__=None		,	lowercase__="</s>"		,	lowercase__="</s>"		,	lowercase__="<s>"		,	lowercase__="<unk>"		,	lowercase__="<pad>"		,	lowercase__="<mask>"		,	**lowercase__		,	)    ->    List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	AddedToken(lowercase__		,	lstrip=lowercase__		,	rstrip=lowercase__			) if isinstance(lowercase__		,	lowercase__			) else mask_token

									SCREAMING_SNAKE_CASE				=	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__		,	)

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

									SCREAMING_SNAKE_CASE				=	{
									    lang_code: self.convert_tokens_to_ids(lowercase__			) for lang_code in FAIRSEQ_LANGUAGE_CODES
									}

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


				@property
				def 							A					(				self			)    ->    str:
									"""simple docstring"""







									return self._src_lang


				@src_lang.setter
				def 							A					(				self		,	lowercase__			)    ->    None:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	new_src_lang
									self.set_src_lang_special_tokens(self._src_lang			)


				def 							A					(				self		,	lowercase__		,	lowercase__ = None			)    ->    List[int]:
									"""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		,	lowercase__		,	lowercase__ = "en_XX"		,	lowercase__ = None		,	lowercase__ = "ro_RO"		,	**lowercase__		,	)    ->    BatchEncoding:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	src_lang
									SCREAMING_SNAKE_CASE				=	tgt_lang
									return super().prepare_seqaseq_batch(lowercase__		,	lowercase__		,	**lowercase__			)


				def 							A					(				self			)    ->    Dict:
									"""simple docstring"""







									return self.set_src_lang_special_tokens(self.src_lang			)


				def 							A					(				self			)    ->    Optional[int]:
									"""simple docstring"""







									return self.set_tgt_lang_special_tokens(self.tgt_lang			)


				def 							A					(				self		,	lowercase__			)    ->    None:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	self.convert_tokens_to_ids(lowercase__			)
									SCREAMING_SNAKE_CASE				=	[self.cur_lang_code_id]
									SCREAMING_SNAKE_CASE				=	[self.eos_token_id]

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

									SCREAMING_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		,	lowercase__			)    ->    None:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	self.convert_tokens_to_ids(lowercase__			)
									SCREAMING_SNAKE_CASE				=	[self.cur_lang_code_id]
									SCREAMING_SNAKE_CASE				=	[self.eos_token_id]

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

									SCREAMING_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		,	lowercase__		,	lowercase__		,	lowercase__		,	lowercase__		,	**lowercase__			)    ->    Tuple:
									"""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'			)
									SCREAMING_SNAKE_CASE				=	src_lang
									SCREAMING_SNAKE_CASE				=	self(lowercase__		,	add_special_tokens=lowercase__		,	return_tensors=lowercase__		,	**lowercase__			)
									SCREAMING_SNAKE_CASE				=	self.convert_tokens_to_ids(lowercase__			)
									SCREAMING_SNAKE_CASE				=	tgt_lang_id
									return inputs




				def 							A					(				self		,	lowercase__		,	lowercase__ = None			)    ->    Tuple[str]:
									"""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(lowercase__			):
														logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
														return
									SCREAMING_SNAKE_CASE				=	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

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	m - 1, 0
					while i >= 0 and j < n:
										if key == mat[i][j]:
															print(f'''Key {key} found at row- {i + 1} column- {j + 1}'''			)
															return
										if key < mat[i][j]:
															i -= 1
										else:
															j += 1
					print(f'''Key {key} not found'''			)

def   UpperCamelCase_  (		):
					SCREAMING_SNAKE_CASE				=	[[2, 5, 7], [4, 8, 1_3], [9, 1_1, 1_5], [1_2, 1_7, 2_0]]
					SCREAMING_SNAKE_CASE				=	int(input('Enter the element to be searched:'			)			)
					print(SCREAMING_SNAKE_CASE_			)
					search_in_a_sorted_matrix(SCREAMING_SNAKE_CASE_,     len(SCREAMING_SNAKE_CASE_			),     len(mat[0]			),     SCREAMING_SNAKE_CASE_			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()
