




import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


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

lowerCamelCase__ :   List[str]					=							'▁'

lowerCamelCase__ :   Tuple					=							{
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
    'tokenizer_config_file': 'tokenizer_config.json',
}

lowerCamelCase__ :   List[str]					=							{
    'vocab_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json',
    },
    'spm_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_config_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json',
    },
}

lowerCamelCase__ :   Optional[Any]					=							{
    'facebook/m2m100_418M': 1_024,
}

# fmt: off
lowerCamelCase__ :   Dict					=							{
    'm2m100': ['af', 'am', 'ar', 'ast', 'az', 'ba', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'ceb', 'cs', 'cy', 'da', 'de', 'el', 'en', 'es', 'et', 'fa', 'ff', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gu', 'ha', 'he', 'hi', 'hr', 'ht', 'hu', 'hy', 'id', 'ig', 'ilo', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'lb', 'lg', 'ln', 'lo', 'lt', 'lv', 'mg', 'mk', 'ml', 'mn', 'mr', 'ms', 'my', 'ne', 'nl', 'no', 'ns', 'oc', 'or', 'pa', 'pl', 'ps', 'pt', 'ro', 'ru', 'sd', 'si', 'sk', 'sl', 'so', 'sq', 'sr', 'ss', 'su', 'sv', 'sw', 'ta', 'th', 'tl', 'tn', 'tr', 'uk', 'ur', 'uz', 'vi', 'wo', 'xh', 'yi', 'yo', 'zh', 'zu'],
    'wmt21': ['en', 'ha', 'is', 'ja', 'cs', 'ru', 'zh', 'de']
}





class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE					):


 '''simple docstring'''







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

 lowercase_				    =    []
 lowercase_				    =    []



 def __init__(							self  :		Any				,				_lowerCAmelCase  :		List[str]				,				_lowerCAmelCase  :		Union[str, Any]				,				_lowerCAmelCase  :		Any=None				,				_lowerCAmelCase  :		Any=None				,				_lowerCAmelCase  :		Any="<s>"				,				_lowerCAmelCase  :		List[Any]="</s>"				,				_lowerCAmelCase  :		str="</s>"				,				_lowerCAmelCase  :		Union[str, Any]="<pad>"				,				_lowerCAmelCase  :		int="<unk>"				,				_lowerCAmelCase  :		Any="m2m100"				,				_lowerCAmelCase  :		Optional[Dict[str, Any]] = None				,				_lowerCAmelCase  :		Dict=8				,				**_lowerCAmelCase  :		List[Any]				,				):
       SCREAMING_SNAKE_CASE_							=			{} if sp_model_kwargs is None else sp_model_kwargs

       SCREAMING_SNAKE_CASE_							=			language_codes
       SCREAMING_SNAKE_CASE_							=			FAIRSEQ_LANGUAGE_CODES[language_codes]
       SCREAMING_SNAKE_CASE_							=			{lang_code: F"__{lang_code}__" for lang_code in fairseq_language_code}

       SCREAMING_SNAKE_CASE_							=			kwargs.get('additional_special_tokens'				,				[]						)
       kwargs["additional_special_tokens"] += [
           self.get_lang_token(_lowerCAmelCase						)
           for lang_code in fairseq_language_code
           if self.get_lang_token(_lowerCAmelCase						) not in kwargs["additional_special_tokens"]
       ]

       super().__init__(
           src_lang=_lowerCAmelCase				,				tgt_lang=_lowerCAmelCase				,				bos_token=_lowerCAmelCase				,				eos_token=_lowerCAmelCase				,				sep_token=_lowerCAmelCase				,				unk_token=_lowerCAmelCase				,				pad_token=_lowerCAmelCase				,				language_codes=_lowerCAmelCase				,				sp_model_kwargs=self.sp_model_kwargs				,				num_madeup_words=_lowerCAmelCase				,				**_lowerCAmelCase				,				)

       SCREAMING_SNAKE_CASE_							=			vocab_file
       SCREAMING_SNAKE_CASE_							=			load_json(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			{v: k for k, v in self.encoder.items()}
       SCREAMING_SNAKE_CASE_							=			spm_file
       SCREAMING_SNAKE_CASE_							=			load_spm(_lowerCAmelCase				,				self.sp_model_kwargs						)

       SCREAMING_SNAKE_CASE_							=			len(self.encoder						)

       SCREAMING_SNAKE_CASE_							=			{
           self.get_lang_token(_lowerCAmelCase						): self.encoder_size + i for i, lang_code in enumerate(_lowerCAmelCase						)
       }
       SCREAMING_SNAKE_CASE_							=			{lang_code: self.encoder_size + i for i, lang_code in enumerate(_lowerCAmelCase						)}
       SCREAMING_SNAKE_CASE_							=			{v: k for k, v in self.lang_token_to_id.items()}

       SCREAMING_SNAKE_CASE_							=			src_lang if src_lang is not None else 'en'
       SCREAMING_SNAKE_CASE_							=			tgt_lang
       SCREAMING_SNAKE_CASE_							=			self.get_lang_id(self._src_lang						)
       self.set_src_lang_special_tokens(self._src_lang						)

       SCREAMING_SNAKE_CASE_							=			num_madeup_words



 @property
 def       lowerCAmelCase_  (							self  :		Union[str, Any]						):
       return len(self.encoder						) + len(self.lang_token_to_id						)



 @property
 def       lowerCAmelCase_  (							self  :		Union[str, Any]						):
       return self._src_lang



 @src_lang.setter
 def       lowerCAmelCase_  (							self  :		Tuple				,				_lowerCAmelCase  :		str						):
       SCREAMING_SNAKE_CASE_							=			new_src_lang
       self.set_src_lang_special_tokens(self._src_lang						)



 def       lowerCAmelCase_  (							self  :		Optional[Any]				,				_lowerCAmelCase  :		str						):
       return self.sp_model.encode(_lowerCAmelCase				,				out_type=_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		Optional[Any]				,				_lowerCAmelCase  :		List[Any]						):
       if token in self.lang_token_to_id:
             return self.lang_token_to_id[token]
       return self.encoder.get(_lowerCAmelCase				,				self.encoder[self.unk_token]						)



 def       lowerCAmelCase_  (							self  :		str				,				_lowerCAmelCase  :		int						):
       if index in self.id_to_lang_token:
             return self.id_to_lang_token[index]
       return self.decoder.get(_lowerCAmelCase				,				self.unk_token						)



 def       lowerCAmelCase_  (							self  :		List[str]				,				_lowerCAmelCase  :		List[str]						):
       SCREAMING_SNAKE_CASE_							=			[]
       SCREAMING_SNAKE_CASE_							=			''
       for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                   out_string += self.sp_model.decode(_lowerCAmelCase						) + token
                   SCREAMING_SNAKE_CASE_							=			[]
             else:
                   current_sub_tokens.append(_lowerCAmelCase						)
       out_string += self.sp_model.decode(_lowerCAmelCase						)
       return out_string.strip()



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

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

       SCREAMING_SNAKE_CASE_							=			[1] * len(self.prefix_tokens						)
       SCREAMING_SNAKE_CASE_							=			[1] * len(self.suffix_tokens						)
       if token_ids_a is None:
             return prefix_ones + ([0] * len(_lowerCAmelCase						)) + suffix_ones
       return prefix_ones + ([0] * len(_lowerCAmelCase						)) + ([0] * len(_lowerCAmelCase						)) + suffix_ones



 def       lowerCAmelCase_  (							self  :		List[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       lowerCAmelCase_  (							self  :		int						):
       SCREAMING_SNAKE_CASE_							=			{self.convert_ids_to_tokens(_lowerCAmelCase						): i for i in range(self.vocab_size						)}
       vocab.update(self.added_tokens_encoder						)
       return vocab



 def __getstate__(							self  :		Optional[int]						):
       SCREAMING_SNAKE_CASE_							=			self.__dict__.copy()
       SCREAMING_SNAKE_CASE_							=			None
       return state



 def __setstate__(							self  :		Dict				,				_lowerCAmelCase  :		Dict						):
       SCREAMING_SNAKE_CASE_							=			d

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

       SCREAMING_SNAKE_CASE_							=			load_spm(self.spm_file				,				self.sp_model_kwargs						)



 def       lowerCAmelCase_  (							self  :		Tuple				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		Optional[str] = None						):
       SCREAMING_SNAKE_CASE_							=			Path(_lowerCAmelCase						)
       if not save_dir.is_dir():
             raise OSError(F"{save_directory} should be a directory"						)
       SCREAMING_SNAKE_CASE_							=			save_dir / (
           (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
       )
       SCREAMING_SNAKE_CASE_							=			save_dir / (
           (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
       )

       save_json(self.encoder				,				_lowerCAmelCase						)

       if os.path.abspath(self.spm_file						) != os.path.abspath(_lowerCAmelCase						) and os.path.isfile(self.spm_file						):
             copyfile(self.spm_file				,				_lowerCAmelCase						)
       elif not os.path.isfile(self.spm_file						):
             with open(_lowerCAmelCase				,				'wb'						) as fi:
                   SCREAMING_SNAKE_CASE_							=			self.sp_model.serialized_model_proto()
                   fi.write(_lowerCAmelCase						)

       return (str(_lowerCAmelCase						), str(_lowerCAmelCase						))



 def       lowerCAmelCase_  (							self  :		Tuple				,				_lowerCAmelCase  :		List[str]				,				_lowerCAmelCase  :		str = "en"				,				_lowerCAmelCase  :		Optional[List[str]] = None				,				_lowerCAmelCase  :		str = "ro"				,				**_lowerCAmelCase  :		List[str]				,				):
       SCREAMING_SNAKE_CASE_							=			src_lang
       SCREAMING_SNAKE_CASE_							=			tgt_lang
       self.set_src_lang_special_tokens(self.src_lang						)
       return super().prepare_seqaseq_batch(_lowerCAmelCase				,				_lowerCAmelCase				,				**_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		Union[str, Any]				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		Optional[str]				,				_lowerCAmelCase  :		Optional[str]				,				**_lowerCAmelCase  :		int						):
       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(_lowerCAmelCase				,				add_special_tokens=_lowerCAmelCase				,				**_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			self.get_lang_id(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			tgt_lang_id
       return inputs



 def       lowerCAmelCase_  (							self  :		Any						):
       self.set_src_lang_special_tokens(self.src_lang						)



 def       lowerCAmelCase_  (							self  :		List[str]						):
       self.set_tgt_lang_special_tokens(self.tgt_lang						)



 def       lowerCAmelCase_  (							self  :		List[Any]				,				_lowerCAmelCase  :		str						):
       SCREAMING_SNAKE_CASE_							=			self.get_lang_token(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			self.lang_token_to_id[lang_token]
       SCREAMING_SNAKE_CASE_							=			[self.cur_lang_id]
       SCREAMING_SNAKE_CASE_							=			[self.eos_token_id]



 def       lowerCAmelCase_  (							self  :		Optional[int]				,				_lowerCAmelCase  :		str						):
       SCREAMING_SNAKE_CASE_							=			self.get_lang_token(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			self.lang_token_to_id[lang_token]
       SCREAMING_SNAKE_CASE_							=			[self.cur_lang_id]
       SCREAMING_SNAKE_CASE_							=			[self.eos_token_id]



 def       lowerCAmelCase_  (							self  :		Any				,				_lowerCAmelCase  :		str						):
       return self.lang_code_to_token[lang]




 def       lowerCAmelCase_  (							self  :		Dict				,				_lowerCAmelCase  :		str						):
       SCREAMING_SNAKE_CASE_							=			self.get_lang_token(_lowerCAmelCase						)
       return self.lang_token_to_id[lang_token]


def 					UpperCAmelCase_							(      __UpperCAmelCase						:	str			,			__UpperCAmelCase						:	Dict[str, Any]							)					-> sentencepiece.SentencePieceProcessor:
      SCREAMING_SNAKE_CASE_							=			sentencepiece.SentencePieceProcessor(**__UpperCAmelCase							)
      spm.Load(str(__UpperCAmelCase							)							)
      return spm

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	str							)					-> Union[Dict, List]:
      with open(__UpperCAmelCase			,			'r'							) as f:
            return json.load(__UpperCAmelCase							)





def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Optional[Any]			,			__UpperCAmelCase						:	str							)					-> None:
      with open(__UpperCAmelCase			,			'w'							) as f:
            json.dump(__UpperCAmelCase			,			__UpperCAmelCase			,			indent=2							)





from __future__ import annotations

lowerCamelCase__ :   Optional[int]					=							[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
lowerCamelCase__ :   List[Any]					=							[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	list[float]							)					-> list[float]:
      SCREAMING_SNAKE_CASE_							=			[]
      SCREAMING_SNAKE_CASE_							=			len(__UpperCAmelCase							)

      for i in range(__UpperCAmelCase							):
            SCREAMING_SNAKE_CASE_							=			-1
            for j in range(i + 1			,			__UpperCAmelCase							):
                  if arr[i] < arr[j]:
                        SCREAMING_SNAKE_CASE_							=			arr[j]
                        break
            result.append(__UpperCAmelCase							)
      return result

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	list[float]							)					-> list[float]:
      SCREAMING_SNAKE_CASE_							=			[]
      for i, outer in enumerate(__UpperCAmelCase							):
            SCREAMING_SNAKE_CASE_							=			-1
            for inner in arr[i + 1 :]:
                  if outer < inner:
                        SCREAMING_SNAKE_CASE_							=			inner
                        break
            result.append(__UpperCAmelCase							)
      return result

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	list[float]							)					-> list[float]:
      SCREAMING_SNAKE_CASE_							=			len(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			[]
      SCREAMING_SNAKE_CASE_							=			[-1] * arr_size

      for index in reversed(range(__UpperCAmelCase							)							):
            if stack:
                  while stack[-1] <= arr[index]:
                        stack.pop()
                        if not stack:
                              break
            if stack:
                  SCREAMING_SNAKE_CASE_							=			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__ :   List[str]					=							(
       '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),
   )