
'''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         =    logging.get_logger(__name__)

__lowerCAmelCase         =    """▁"""

__lowerCAmelCase         =    {"""vocab_file""": """sentencepiece.bpe.model"""}

__lowerCAmelCase         =    {
    """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         =    {
    """facebook/mbart-large-en-ro""": 1_0_2_4,
    """facebook/mbart-large-cc25""": 1_0_2_4,
}

# fmt: off
__lowerCAmelCase         =    ["""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__	( lowercase__	):





  """simple docstring"""

  __UpperCAmelCase				:				List[Any]   =					VOCAB_FILES_NAMES
  __UpperCAmelCase				:				List[Any]   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __UpperCAmelCase				:				Union[str, Any]   =					PRETRAINED_VOCAB_FILES_MAP
  __UpperCAmelCase				:				Tuple   =					['''input_ids''', '''attention_mask''']

  __UpperCAmelCase				:				List[int]   =					[]
  __UpperCAmelCase				:				List[int]   =					[]


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


     '''simple docstring'''






     _a				:	Dict			=   AddedToken(_a							,lstrip=_a							,rstrip=_a       ) if isinstance(_a							,_a       ) else mask_token

     _a				:	List[str]			=   {} 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							,)

     _a				:	Any			=   spm.SentencePieceProcessor(**self.sp_model_kwargs       )
     self.sp_model.Load(str(_a       )       )
     _a				:	str			=   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
     _a				:	Dict			=   {'<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
     _a				:	Optional[int]			=   1

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

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


  def __getstate__(		self      :     int       ):


     '''simple docstring'''






     _a				:	int			=   self.__dict__.copy()
     _a				:	Tuple			=   None
     _a				:	List[str]			=   self.sp_model.serialized_model_proto()
     return state


  def __setstate__(		self      :     int							,_a      :     List[str]       ):


     '''simple docstring'''






     _a				:	int			=   d

     # for backward compatibility
     if not hasattr(self							,'sp_model_kwargs'       ):
        _a				:	Dict			=   {}

     _a				:	Dict			=   spm.SentencePieceProcessor(**self.sp_model_kwargs       )
     self.sp_model.LoadFromSerializedProto(self.sp_model_proto       )


  @property
  def   __lowercase		(		self      :     List[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   __lowercase		(		self      :     int       ):


     '''simple docstring'''






     return self._src_lang


  @src_lang.setter
  def   __lowercase		(		self      :     List[str]							,_a      :     str       ):


     '''simple docstring'''






     _a				:	List[str]			=   new_src_lang
     self.set_src_lang_special_tokens(self._src_lang       )


  def   __lowercase		(		self      :     Optional[Any]							,_a      :     List[int]							,_a      :     Optional[List[int]] = None							,_a      :     bool = False       ):


     '''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       )

     _a				:	str			=   [1] * len(self.prefix_tokens       )
     _a				:	Dict			=   [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   __lowercase		(		self      :     Optional[int]							,_a      :     List[int]							,_a      :     Optional[List[int]] = None       ):


     '''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   __lowercase		(		self      :     List[Any]							,_a      :     List[int]							,_a      :     Optional[List[int]] = None       ):


     '''simple docstring'''






     _a				:	Optional[int]			=   [self.sep_token_id]
     _a				:	str			=   [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   __lowercase		(		self      :     Union[str, Any]							,_a      :     Union[str, Any]							,_a      :     str							,_a      :     Optional[str]							,_a      :     Optional[str]							,**_a      :     Any       ):


     '''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'       )
     _a				:	Dict			=   src_lang
     _a				:	str			=   self(_a							,add_special_tokens=_a							,return_tensors=_a							,**_a       )
     _a				:	int			=   self.convert_tokens_to_ids(_a       )
     _a				:	Any			=   tgt_lang_id
     return inputs


  def   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






     _a				:	Any			=   {self.convert_ids_to_tokens(_a       ): i for i in range(self.vocab_size       )}
     vocab.update(self.added_tokens_encoder       )
     return vocab


  def   __lowercase		(		self      :     Any							,_a      :     str       ):


     '''simple docstring'''






     return self.sp_model.encode(_a							,out_type=_a       )


  def   __lowercase		(		self      :     Tuple							,_a      :     Dict       ):


     '''simple docstring'''






     if token in self.fairseq_tokens_to_ids:
        return self.fairseq_tokens_to_ids[token]
     _a				:	str			=   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   __lowercase		(		self      :     int							,_a      :     int       ):


     '''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   __lowercase		(		self      :     Optional[int]							,_a      :     str       ):


     '''simple docstring'''






     _a				:	str			=   ''.join(_a       ).replace(_a							,' '       ).strip()
     return out_string


  def   __lowercase		(		self      :     Union[str, Any]							,_a      :     str							,_a      :     Optional[str] = None       ):


     '''simple docstring'''






     if not os.path.isdir(_a       ):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
        return
     _a				:	str			=   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:
           _a				:	Optional[int]			=   self.sp_model.serialized_model_proto()
           fi.write(_a       )

     return (out_vocab_file,)


  def   __lowercase		(		self      :     Union[str, Any]							,_a      :     List[str]							,_a      :     str = "en_XX"							,_a      :     Optional[List[str]] = None							,_a      :     str = "ro_RO"							,**_a      :     List[str]							,):


     '''simple docstring'''






     _a				:	Any			=   src_lang
     _a				:	int			=   tgt_lang
     return super().prepare_seqaseq_batch(_a							,_a							,**_a       )


  def   __lowercase		(		self      :     int       ):


     '''simple docstring'''






     return self.set_src_lang_special_tokens(self.src_lang       )


  def   __lowercase		(		self      :     List[Any]       ):


     '''simple docstring'''






     return self.set_tgt_lang_special_tokens(self.tgt_lang       )


  def   __lowercase		(		self      :     int							,_a      :     Optional[int]       ):


     '''simple docstring'''






     _a				:	Any			=   self.lang_code_to_id[src_lang]
     _a				:	List[Any]			=   []
     _a				:	Any			=   [self.eos_token_id, self.cur_lang_code]


  def   __lowercase		(		self      :     List[str]							,_a      :     str       ):


     '''simple docstring'''






     _a				:	Any			=   self.lang_code_to_id[lang]
     _a				:	Dict			=   []
     _a				:	Optional[int]			=   [self.eos_token_id, self.cur_lang_code]





'''simple docstring'''







from __future__ import annotations

__lowerCAmelCase         =    [-1_0, -5, 0, 5, 5.1, 1_1, 1_3, 2_1, 3, 4, -2_1, -1_0, -5, -1, 0]
__lowerCAmelCase         =    [-5, 0, 5, 5.1, 1_1, 1_3, 2_1, -1, 4, -1, -1_0, -5, -1, 0, -1]





def 					UpperCAmelCase_		(__a      :    list[float] ):






   """simple docstring"""

   _a				:	Optional[int]			=   []
   _a				:	int			=   len(__a )

   for i in range(__a ):
      _a				:	float			=   -1
      for j in range(i + 1 ,	__a ):
         if arr[i] < arr[j]:
            _a				:	Any			=   arr[j]
            break
      result.append(__a )
   return result





def 					UpperCAmelCase_		(__a      :    list[float] ):






   """simple docstring"""

   _a				:	Tuple			=   []
   for i, outer in enumerate(__a ):
      _a				:	float			=   -1
      for inner in arr[i + 1 :]:
         if outer < inner:
            _a				:	Dict			=   inner
            break
      result.append(__a )
   return result





def 					UpperCAmelCase_		(__a      :    list[float] ):






   """simple docstring"""

   _a				:	int			=   len(__a )
   _a				:	list[float]			=   []
   _a				:	list[float]			=   [-1] * arr_size

   for index in reversed(range(__a ) ):
      if stack:
         while stack[-1] <= arr[index]:
            stack.pop()
            if not stack:
               break
      if stack:
         _a				:	Dict			=   stack[-1]
      stack.append(arr[index] )
   return result


if __name__ == "__main__":
  from doctest import testmod
  from timeit import timeit

  testmod()
  print(next_greatest_element_slow(arr))
  print(next_greatest_element_fast(arr))
  print(next_greatest_element(arr))

  __lowerCAmelCase         =    (
      """from __main__ import arr, next_greatest_element_slow, """
      """next_greatest_element_fast, next_greatest_element"""
  )
  print(
      """next_greatest_element_slow():""",
      timeit("""next_greatest_element_slow(arr)""", setup=setup),
  )
  print(
      """next_greatest_element_fast():""",
      timeit("""next_greatest_element_fast(arr)""", setup=setup),
  )
  print(
      """     next_greatest_element():""",
      timeit("""next_greatest_element(arr)""", setup=setup),
  )



