
'''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


UpperCAmelCase_       :   List[Any]   =       logging.get_logger(__name__)

UpperCAmelCase_       :   Tuple   =       '''▁'''

UpperCAmelCase_       :   int   =       {'''vocab_file''': '''sentencepiece.bpe.model'''}

UpperCAmelCase_       :   Optional[int]   =       {
    '''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'''
        ),
    }
}

UpperCAmelCase_       :   List[str]   =       {
    '''facebook/mbart-large-en-ro''': 1024,
    '''facebook/mbart-large-cc25''': 1024,
}

# fmt: off
UpperCAmelCase_       :   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  UpperCAmelCase__						(       _UpperCAmelCase ):
    lowerCAmelCase_				    =		VOCAB_FILES_NAMES
    lowerCAmelCase_				    =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase_				    =		PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase_				    =		['input_ids', 'attention_mask']

    lowerCAmelCase_				    =		[]
    lowerCAmelCase_				    =		[]



    def __init__(	self      :       Tuple,__A      :       Any,__A      :       Any="<s>",__A      :       Any="</s>",__A      :       Dict="</s>",__A      :       Optional[Any]="<s>",__A      :       str="<unk>",__A      :       Optional[Any]="<pad>",__A      :       List[Any]="<mask>",__A      :       Union[str, Any]=None,__A      :       Optional[int]=None,__A      :       Union[str, Any]=None,__A      :       Optional[Dict[str, Any]] = None,__A      :       Any=None,**__A      :       Tuple,):
          _lowerCamelCase				:   Any								=   AddedToken(__UpperCamelCase,lstrip=__UpperCamelCase,rstrip=__UpperCamelCase     ) if isinstance(__UpperCamelCase,__UpperCamelCase     ) else mask_token

          _lowerCamelCase				:   Dict								=   {} if sp_model_kwargs is None else sp_model_kwargs

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

          _lowerCamelCase				:   Union[str, Any]								=   spm.SentencePieceProcessor(**self.sp_model_kwargs     )
          self.sp_model.Load(str(__UpperCamelCase     )     )
          _lowerCamelCase				:   Dict								=   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
          _lowerCamelCase				:   int								=   {"<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
          _lowerCamelCase				:   Optional[int]								=   1

          _lowerCamelCase				:   Optional[int]								=   len(self.sp_model     )
          _lowerCamelCase				:   Dict								=   {
              code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__UpperCamelCase     )
          }
          _lowerCamelCase				:   Dict								=   {v: k for k, v in self.lang_code_to_id.items()}
          _lowerCamelCase				:   Tuple								=   len(self.sp_model     ) + len(self.lang_code_to_id     ) + self.fairseq_offset

          self.fairseq_tokens_to_ids.update(self.lang_code_to_id     )
          _lowerCamelCase				:   Any								=   {v: k for k, v in self.fairseq_tokens_to_ids.items()}
          _lowerCamelCase				:   List[str]								=   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]     )

          _lowerCamelCase				:   Optional[int]								=   src_lang if src_lang is not None else "en_XX"
          _lowerCamelCase				:   Optional[Any]								=   self.lang_code_to_id[self._src_lang]
          _lowerCamelCase				:   Optional[int]								=   tgt_lang
          self.set_src_lang_special_tokens(self._src_lang     )



    def __getstate__(	self      :       List[str]     ):
          _lowerCamelCase				:   Optional[int]								=   self.__dict__.copy()
          _lowerCamelCase				:   str								=   None
          _lowerCamelCase				:   Optional[int]								=   self.sp_model.serialized_model_proto()
          return state



    def __setstate__(	self      :       Union[str, Any],__A      :       Dict     ):
          _lowerCamelCase				:   Union[str, Any]								=   d

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

          _lowerCamelCase				:   Tuple								=   spm.SentencePieceProcessor(**self.sp_model_kwargs     )
          self.sp_model.LoadFromSerializedProto(self.sp_model_proto     )



    @property
    def   lowerCamelCase_  (	self      :       Any     ):
          return len(self.sp_model     ) + len(self.lang_code_to_id     ) + self.fairseq_offset + 1  # Plus 1 for the mask token



    @property
    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          return self._src_lang



    @src_lang.setter
    def   lowerCamelCase_  (	self      :       int,__A      :       str     ):
          _lowerCamelCase				:   str								=   new_src_lang
          self.set_src_lang_special_tokens(self._src_lang     )



    def   lowerCamelCase_  (	self      :       List[str],__A      :       List[int],__A      :       Optional[List[int]] = None,__A      :       bool = False     ):






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

          _lowerCamelCase				:   int								=   [1] * len(self.prefix_tokens     )
          _lowerCamelCase				:   Any								=   [1] * len(self.suffix_tokens     )
          if token_ids_a is None:
                return prefix_ones + ([0] * len(__UpperCamelCase     )) + suffix_ones
          return prefix_ones + ([0] * len(__UpperCamelCase     )) + ([0] * len(__UpperCamelCase     )) + suffix_ones



    def   lowerCamelCase_  (	self      :       str,__A      :       List[int],__A      :       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   lowerCamelCase_  (	self      :       List[Any],__A      :       List[int],__A      :       Optional[List[int]] = None     ):
          _lowerCamelCase				:   Any								=   [self.sep_token_id]
          _lowerCamelCase				:   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   lowerCamelCase_  (	self      :       Optional[Any],__A      :       Union[str, Any],__A      :       str,__A      :       Optional[str],__A      :       Optional[str],**__A      :       Tuple     ):






          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				:   int								=   src_lang
          _lowerCamelCase				:   List[str]								=   self(__UpperCamelCase,add_special_tokens=__UpperCamelCase,return_tensors=__UpperCamelCase,**__UpperCamelCase     )
          _lowerCamelCase				:   Union[str, Any]								=   self.convert_tokens_to_ids(__UpperCamelCase     )
          _lowerCamelCase				:   Tuple								=   tgt_lang_id
          return inputs



    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          _lowerCamelCase				:   Union[str, Any]								=   {self.convert_ids_to_tokens(__UpperCamelCase     ): i for i in range(self.vocab_size     )}
          vocab.update(self.added_tokens_encoder     )
          return vocab



    def   lowerCamelCase_  (	self      :       Tuple,__A      :       str     ):
          return self.sp_model.encode(__UpperCamelCase,out_type=__UpperCamelCase     )



    def   lowerCamelCase_  (	self      :       Dict,__A      :       Any     ):






          if token in self.fairseq_tokens_to_ids:
                return self.fairseq_tokens_to_ids[token]
          _lowerCamelCase				:   Tuple								=   self.sp_model.PieceToId(__UpperCamelCase     )

          # 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   lowerCamelCase_  (	self      :       int,__A      :       List[Any]     ):






          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   lowerCamelCase_  (	self      :       Dict,__A      :       Dict     ):
          _lowerCamelCase				:   str								=   "".join(__UpperCamelCase     ).replace(__UpperCamelCase," "     ).strip()
          return out_string



    def   lowerCamelCase_  (	self      :       Tuple,__A      :       str,__A      :       Optional[str] = None     ):






          if not os.path.isdir(__UpperCamelCase     ):
                logger.error(f'Vocabulary path ({save_directory}) should be a directory'     )
                return
          _lowerCamelCase				:   Optional[int]								=   os.path.join(
              __UpperCamelCase,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

          if os.path.abspath(self.vocab_file     ) != os.path.abspath(__UpperCamelCase     ) and os.path.isfile(self.vocab_file     ):
                copyfile(self.vocab_file,__UpperCamelCase     )
          elif not os.path.isfile(self.vocab_file     ):
                with open(__UpperCamelCase,"wb"     ) as fi:
                      _lowerCamelCase				:   Optional[int]								=   self.sp_model.serialized_model_proto()
                      fi.write(__UpperCamelCase     )

          return (out_vocab_file,)



    def   lowerCamelCase_  (	self      :       List[Any],__A      :       List[str],__A      :       str = "en_XX",__A      :       Optional[List[str]] = None,__A      :       str = "ro_RO",**__A      :       Tuple,):
          _lowerCamelCase				:   List[Any]								=   src_lang
          _lowerCamelCase				:   str								=   tgt_lang
          return super().prepare_seqaseq_batch(__UpperCamelCase,__UpperCamelCase,**__UpperCamelCase     )



    def   lowerCamelCase_  (	self      :       Tuple     ):
          return self.set_src_lang_special_tokens(self.src_lang     )



    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          return self.set_tgt_lang_special_tokens(self.tgt_lang     )



    def   lowerCamelCase_  (	self      :       List[str],__A      :       Tuple     ):
          _lowerCamelCase				:   Union[str, Any]								=   self.lang_code_to_id[src_lang]
          _lowerCamelCase				:   List[str]								=   []
          _lowerCamelCase				:   str								=   [self.eos_token_id, self.cur_lang_code]



    def   lowerCamelCase_  (	self      :       Optional[int],__A      :       str     ):
          _lowerCamelCase				:   Union[str, Any]								=   self.lang_code_to_id[lang]
          _lowerCamelCase				:   List[Any]								=   []
          _lowerCamelCase				:   int								=   [self.eos_token_id, self.cur_lang_code]







'''simple docstring'''






from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

UpperCAmelCase_       :   str   =       (3, 9, -11, 0, 7, 5, 1, -1)
UpperCAmelCase_       :   int   =       (4, 6, 2, 0, 8, 10, 3, -2)

@dataclass
class  UpperCAmelCase__						:
    lowerCAmelCase_				    =		42
    lowerCAmelCase_				    =		42


class  UpperCAmelCase__						:



    def __init__(	self      :       Optional[int],__A      :       Iterable[int]     ):
          _lowerCamelCase				:   Node | None								=   None
          for i in sorted(__A,reverse=__A     ):
                _lowerCamelCase				:   Dict								=   Node(__A,self.head     )



    def __iter__(	self      :       str     ):
          _lowerCamelCase				:   Dict								=   self.head
          while node:
                yield node.data
                _lowerCamelCase				:   Optional[Any]								=   node.next_node



    def __len__(	self      :       str     ):
          return sum(1 for _ in self     )



    def __str__(	self      :       str     ):
          return " -> ".join([str(__A     ) for node in self]     )







def 		A_       (	_lowerCAmelCase   :    SortedLinkedList				,	_lowerCAmelCase   :    SortedLinkedList							):



      """simple docstring"""




      return SortedLinkedList(list(_lowerCAmelCase							) + list(_lowerCAmelCase							)							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 UpperCAmelCase_       :   List[str]   =       SortedLinkedList
 print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))