
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     :				str             =       None


__SCREAMING_SNAKE_CASE     :				Optional[int]             =       logging.get_logger(__name__)

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

__SCREAMING_SNAKE_CASE     :				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     :				Optional[int]             =       {
    'facebook/mbart-large-50-one-to-many-mmt': 10_24,
}

# fmt: off
__SCREAMING_SNAKE_CASE     :				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', '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 						__magic_name__				(  snake_case      ):
		_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      :		Dict  ,					lowerCamelCase__      :		Optional[int]=None  ,					lowerCamelCase__      :		Optional[Any]=None  ,					lowerCamelCase__      :		Dict=None  ,					lowerCamelCase__      :		Tuple=None  ,					lowerCamelCase__      :		Tuple="</s>"  ,					lowerCamelCase__      :		List[Any]="</s>"  ,					lowerCamelCase__      :		List[str]="<s>"  ,					lowerCamelCase__      :		Tuple="<unk>"  ,					lowerCamelCase__      :		List[str]="<pad>"  ,					lowerCamelCase__      :		int="<mask>"  ,					**lowerCamelCase__      :		Optional[int]  ,					):
							# Mask token behave like a normal word, i.e. include the space before it
							lowerCAmelCase						:   Tuple							=       AddedToken(lowerCamelCase__  ,					lstrip=lowerCamelCase__  ,					rstrip=lowerCamelCase__			) if isinstance(lowerCamelCase__  ,					lowerCamelCase__			) else mask_token

							lowerCAmelCase						:   Tuple							=       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__(
							    lowerCamelCase__  ,					src_lang=lowerCamelCase__  ,					tgt_lang=lowerCamelCase__  ,					tokenizer_file=lowerCamelCase__  ,					eos_token=lowerCamelCase__  ,					sep_token=lowerCamelCase__  ,					cls_token=lowerCamelCase__  ,					unk_token=lowerCamelCase__  ,					pad_token=lowerCamelCase__  ,					mask_token=lowerCamelCase__  ,					**lowerCamelCase__  ,					)

							lowerCAmelCase						:   List[Any]							=       vocab_file
							lowerCAmelCase						:   List[str]							=       False if not self.vocab_file else True

							lowerCAmelCase						:   Dict							=       {
							    lang_code: self.convert_tokens_to_ids(lowerCamelCase__			) for lang_code in FAIRSEQ_LANGUAGE_CODES
							}

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





		@property
		def  _A							(							self      :		List[str]			):
							return self._src_lang





		@src_lang.setter
		def  _A							(							self      :		Tuple  ,					lowerCamelCase__      :		str			):
							lowerCAmelCase						:   Any							=       new_src_lang
							self.set_src_lang_special_tokens(self._src_lang			)





		def  _A							(							self      :		Union[str, Any]  ,					lowerCamelCase__      :		List[int]  ,					lowerCamelCase__      :		Optional[List[int]] = None			):
							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      :		Tuple  ,					lowerCamelCase__      :		List[str]  ,					lowerCamelCase__      :		str = "en_XX"  ,					lowerCamelCase__      :		Optional[List[str]] = None  ,					lowerCamelCase__      :		str = "ro_RO"  ,					**lowerCamelCase__      :		List[Any]  ,					):
							lowerCAmelCase						:   Union[str, Any]							=       src_lang
							lowerCAmelCase						:   Dict							=       tgt_lang
							return super().prepare_seqaseq_batch(lowerCamelCase__  ,					lowerCamelCase__  ,					**lowerCamelCase__			)





		def  _A							(							self      :		Dict			):
							return self.set_src_lang_special_tokens(self.src_lang			)





		def  _A							(							self      :		int			):
							return self.set_tgt_lang_special_tokens(self.tgt_lang			)





		def  _A							(							self      :		str  ,					lowerCamelCase__      :		str			):
							lowerCAmelCase						:   Optional[int]							=       self.convert_tokens_to_ids(lowerCamelCase__			)
							lowerCAmelCase						:   Optional[int]							=       [self.cur_lang_code_id]
							lowerCAmelCase						:   str							=       [self.eos_token_id]

							lowerCAmelCase						:   Union[str, Any]							=       self.convert_ids_to_tokens(self.prefix_tokens			)
							lowerCAmelCase						:   Union[str, Any]							=       self.convert_ids_to_tokens(self.suffix_tokens			)

							lowerCAmelCase						:   Optional[int]							=       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      :		List[str]  ,					lowerCamelCase__      :		str			):
							lowerCAmelCase						:   str							=       self.convert_tokens_to_ids(lowerCamelCase__			)
							lowerCAmelCase						:   List[Any]							=       [self.cur_lang_code_id]
							lowerCAmelCase						:   Dict							=       [self.eos_token_id]

							lowerCAmelCase						:   List[Any]							=       self.convert_ids_to_tokens(self.prefix_tokens			)
							lowerCAmelCase						:   Dict							=       self.convert_ids_to_tokens(self.suffix_tokens			)

							lowerCAmelCase						:   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  _A							(							self      :		int  ,					lowerCamelCase__      :		Optional[int]  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		Optional[str]  ,					lowerCamelCase__      :		Optional[str]  ,					**lowerCamelCase__      :		Union[str, Any]			):
							if src_lang is None or tgt_lang is None:
												raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''			)
							lowerCAmelCase						:   List[Any]							=       src_lang
							lowerCAmelCase						:   Dict							=       self(lowerCamelCase__  ,					add_special_tokens=lowerCamelCase__  ,					return_tensors=lowerCamelCase__  ,					**lowerCamelCase__			)
							lowerCAmelCase						:   Optional[int]							=       self.convert_tokens_to_ids(lowerCamelCase__			)
							lowerCAmelCase						:   Union[str, Any]							=       tgt_lang_id
							return inputs





		def  _A							(							self      :		List[Any]  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		Optional[str] = None			):
							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(lowerCamelCase__			):
												logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
												return
							lowerCAmelCase						:   List[Any]							=       os.path.join(
							    lowerCamelCase__  ,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

							if os.path.abspath(self.vocab_file			) != os.path.abspath(lowerCamelCase__			):
												copyfile(self.vocab_file  ,					lowerCamelCase__			)

							return (out_vocab_file,)


def       UpperCAmelCase__							(    __magic_name__  :       Union[str, Any]							,					__magic_name__  :       int					):


					'''simple docstring'''

					sequence.append(__magic_name__					)

					lowerCAmelCase						:   Tuple							=       0
					while sequence[index] != target:
										index += 1

					sequence.pop()

					if index == len(__magic_name__					):
										return None

					return index


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE     :				Dict             =       input('Enter numbers separated by comma:\n').strip()
		__SCREAMING_SNAKE_CASE     :				int             =       [int(item) for item in user_input.split(',')]

		__SCREAMING_SNAKE_CASE     :				str             =       input('Enter a single number to be found in the list:\n')
		__SCREAMING_SNAKE_CASE     :				Tuple             =       int(target_input)
		__SCREAMING_SNAKE_CASE     :				Union[str, Any]             =       sentinel_linear_search(sequence, target)
		if result is not None:
				print(f"""{target} found at positions: {result}""")
		else:
				print('Not found')
