





"""simple docstring"""




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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase :    str  							= logging.get_logger(__name__)

_lowerCamelCase :    Dict  							= '''▁'''

_lowerCamelCase :    Tuple  							= {'''vocab_file''': '''sentencepiece.bpe.model'''}

_lowerCamelCase :    Optional[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'''
        ),
    }
}

_lowerCamelCase :    Union[str, Any]  							= {
    '''facebook/mbart-large-en-ro''': 1_0_2_4,
    '''facebook/mbart-large-cc25''': 1_0_2_4,
}

# fmt: off
_lowerCamelCase :    List[Any]  							= ['''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 						lowerCamelCase       (snake_case_      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   VOCAB_FILES_NAMES
							UpperCAmelCase_      						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase_      						=   PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase_      						=   ["input_ids", "attention_mask"]

							UpperCAmelCase_      						=   []
							UpperCAmelCase_      						=   []






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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						AddedToken(_A,			lstrip=_A,			rstrip=_A						) if isinstance(_A,			_A						) else mask_token

														SCREAMING_SNAKE_CASE__       :      Any     =						{} if sp_model_kwargs is None else sp_model_kwargs

														super().__init__(
														    bos_token=_A,			eos_token=_A,			unk_token=_A,			sep_token=_A,			cls_token=_A,			pad_token=_A,			mask_token=_A,			tokenizer_file=_A,			src_lang=_A,			tgt_lang=_A,			additional_special_tokens=_A,			sp_model_kwargs=self.sp_model_kwargs,			**_A,			)

														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						spm.SentencePieceProcessor(**self.sp_model_kwargs						)
														self.sp_model.Load(str(_A						)						)
														SCREAMING_SNAKE_CASE__       :      int     =						vocab_file

														# Original fairseq vocab and spm vocab must be "aligned":
														# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
														# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
														# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
														# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

														# Mimic fairseq token-to-id alignment for the first 4 token
														SCREAMING_SNAKE_CASE__       :      Any     =						{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

														# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						1

														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						len(self.sp_model						)
														SCREAMING_SNAKE_CASE__       :      str     =						{
														    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_A						)
														}
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						{v: k for k, v in self.lang_code_to_id.items()}
														SCREAMING_SNAKE_CASE__       :      str     =						len(self.sp_model						) + len(self.lang_code_to_id						) + self.fairseq_offset

														self.fairseq_tokens_to_ids.update(self.lang_code_to_id						)
														SCREAMING_SNAKE_CASE__       :      int     =						{v: k for k, v in self.fairseq_tokens_to_ids.items()}
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						list(self.lang_code_to_id.keys()						)

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

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






							def __getstate__( self     :					int						)				->			Tuple:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Dict     =						self.__dict__.copy()
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						None
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.sp_model.serialized_model_proto()
														return state






							def __setstate__( self     :					Optional[Any],			_UpperCAmelCase     :					str						)				->			int:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						d

														# for backward compatibility
														if not hasattr(self,			"sp_model_kwargs"						):
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						{}

														SCREAMING_SNAKE_CASE__       :      List[str]     =						spm.SentencePieceProcessor(**self.sp_model_kwargs						)
														self.sp_model.LoadFromSerializedProto(self.sp_model_proto						)






							@property
							def  A_     ( self     :					List[Any]						)				->			Union[str, Any]:





														"""simple docstring"""
														return len(self.sp_model						) + len(self.lang_code_to_id						) + self.fairseq_offset + 1  # Plus 1 for the mask token






							@property
							def  A_     ( self     :					Optional[int]						)				->			str:





														"""simple docstring"""
														return self._src_lang






							@src_lang.setter
							def  A_     ( self     :					Tuple,			_UpperCAmelCase     :					str						)				->			None:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Any     =						new_src_lang
														self.set_src_lang_special_tokens(self._src_lang						)






							def  A_     ( self     :					Dict,			_UpperCAmelCase     :					List[int],			_UpperCAmelCase     :					Optional[List[int]] = None,			_UpperCAmelCase     :					bool = False						)				->			List[int]:





														"""simple docstring"""


														if already_has_special_tokens:
																					return super().get_special_tokens_mask(
																					    token_ids_a=_A,			token_ids_a=_A,			already_has_special_tokens=_A						)

														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						[1] * len(self.prefix_tokens						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						[1] * len(self.suffix_tokens						)
														if token_ids_a is None:
																					return prefix_ones + ([0] * len(_A						)) + suffix_ones
														return prefix_ones + ([0] * len(_A						)) + ([0] * len(_A						)) + suffix_ones






							def  A_     ( self     :					List[Any],			_UpperCAmelCase     :					List[int],			_UpperCAmelCase     :					Optional[List[int]] = 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     :					List[Any],			_UpperCAmelCase     :					List[int],			_UpperCAmelCase     :					Optional[List[int]] = None						)				->			List[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						[self.sep_token_id]
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						[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],			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					str,			_UpperCAmelCase     :					Optional[str],			_UpperCAmelCase     :					Optional[str],			**_UpperCAmelCase     :					Optional[int]						)				->			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"						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						src_lang
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						self(_A,			add_special_tokens=_A,			return_tensors=_A,			**_A						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						self.convert_tokens_to_ids(_A						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						tgt_lang_id
														return inputs






							def  A_     ( self     :					Dict						)				->			Optional[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						{self.convert_ids_to_tokens(_A						): i for i in range(self.vocab_size						)}
														vocab.update(self.added_tokens_encoder						)
														return vocab






							def  A_     ( self     :					Optional[int],			_UpperCAmelCase     :					str						)				->			List[str]:





														"""simple docstring"""
														return self.sp_model.encode(_A,			out_type=_A						)






							def  A_     ( self     :					Optional[Any],			_UpperCAmelCase     :					List[str]						)				->			Tuple:





														"""simple docstring"""


														if token in self.fairseq_tokens_to_ids:
																					return self.fairseq_tokens_to_ids[token]
														SCREAMING_SNAKE_CASE__       :      Dict     =						self.sp_model.PieceToId(_A						)

														# Need to return unknown token if the SP model returned 0
														return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






							def  A_     ( self     :					Union[str, Any],			_UpperCAmelCase     :					List[str]						)				->			List[Any]:





														"""simple docstring"""


														if index in self.fairseq_ids_to_tokens:
																					return self.fairseq_ids_to_tokens[index]
														return self.sp_model.IdToPiece(index - self.fairseq_offset						)






							def  A_     ( self     :					List[str],			_UpperCAmelCase     :					Union[str, Any]						)				->			Union[str, Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      int     =						''.join(_A						).replace(_A,			" "						).strip()
														return out_string






							def  A_     ( self     :					Tuple,			_UpperCAmelCase     :					str,			_UpperCAmelCase     :					Optional[str] = None						)				->			Tuple[str]:





														"""simple docstring"""


														if not os.path.isdir(_A						):
																					logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
																					return
														SCREAMING_SNAKE_CASE__       :      List[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						) and os.path.isfile(self.vocab_file						):
																					copyfile(self.vocab_file,			_A						)
														elif not os.path.isfile(self.vocab_file						):
																					with open(_A,			"wb"						) as fi:
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						self.sp_model.serialized_model_proto()
																												fi.write(_A						)

														return (out_vocab_file,)






							def  A_     ( self     :					Optional[int],			_UpperCAmelCase     :					List[str],			_UpperCAmelCase     :					str = "en_XX",			_UpperCAmelCase     :					Optional[List[str]] = None,			_UpperCAmelCase     :					str = "ro_RO",			**_UpperCAmelCase     :					str,			)				->			BatchEncoding:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      int     =						src_lang
														SCREAMING_SNAKE_CASE__       :      Any     =						tgt_lang
														return super().prepare_seqaseq_batch(_A,			_A,			**_A						)






							def  A_     ( self     :					Optional[Any]						)				->			str:





														"""simple docstring"""
														return self.set_src_lang_special_tokens(self.src_lang						)






							def  A_     ( self     :					Optional[Any]						)				->			Any:





														"""simple docstring"""
														return self.set_tgt_lang_special_tokens(self.tgt_lang						)






							def  A_     ( self     :					List[str],			_UpperCAmelCase     :					Optional[Any]						)				->			None:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      int     =						self.lang_code_to_id[src_lang]
														SCREAMING_SNAKE_CASE__       :      List[str]     =						[]
														SCREAMING_SNAKE_CASE__       :      int     =						[self.eos_token_id, self.cur_lang_code]






							def  A_     ( self     :					str,			_UpperCAmelCase     :					str						)				->			None:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      int     =						self.lang_code_to_id[lang]
														SCREAMING_SNAKE_CASE__       :      Tuple     =						[]
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						[self.eos_token_id, self.cur_lang_code]













from __future__ import annotations




def _a (  SCREAMING_SNAKE_CASE__      :  list[float]			,				SCREAMING_SNAKE_CASE__      :  list[float]       )	->    float:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      List[Any]     =						sorted(numsa + numsa       )
							SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Dict     =						divmod(len(SCREAMING_SNAKE_CASE__       )			,				2       )
							if mod == 1:
														return all_numbers[div]
							else:
														return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		_lowerCamelCase :    List[str]  							= [float(x) for x in input('''Enter the elements of first array: ''').split()]
		_lowerCamelCase :    Any  							= [float(x) for x in input('''Enter the elements of second array: ''').split()]
		print(f"The median of two arrays is: {median_of_two_arrays(array_a, array_a)}")






