
"""simple docstring"""


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 PreTrainedTokenizer
from ...utils import logging


_lowercase							      = logging.get_logger(__name__)

_lowercase							      = '''▁'''

_lowercase							      = {
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
}

_lowercase							      = {
    '''vocab_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'''
        ),
    },
    '''spm_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'''
        )
    },
}

_lowercase							      = {
    '''facebook/s2t-small-librispeech-asr''': 10_24,
}

_lowercase							      = ['''pt''', '''fr''', '''ru''', '''nl''', '''ro''', '''it''', '''es''', '''de''']

_lowercase							      = {'''mustc''': MUSTC_LANGS}







class  lowerCAmelCase_ (					_lowercase			):



						'''simple docstring'''


						_lowerCamelCase:			int					=			VOCAB_FILES_NAMES
						_lowerCamelCase:			List[Any]					=			PRETRAINED_VOCAB_FILES_MAP
						_lowerCamelCase:			Any					=			MAX_MODEL_INPUT_SIZES
						_lowerCamelCase:			Optional[Any]					=			['''input_ids''', '''attention_mask''']

						_lowerCamelCase:			List[int]					=			[]


						def __init__(       self   :					Optional[Any]     ,A_   :					int     ,A_   :					Optional[Any]     ,A_   :					List[str]="<s>"     ,A_   :					Union[str, Any]="</s>"     ,A_   :					Dict="<pad>"     ,A_   :					Dict="<unk>"     ,A_   :					List[str]=False     ,A_   :					Tuple=False     ,A_   :					Optional[int]=None     ,A_   :					Dict=None     ,A_   :					Optional[Dict[str, Any]] = None     ,**A_   :					Union[str, Any]     ,)   ->      None:
							A        =      {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=A_     ,eos_token=A_     ,unk_token=A_     ,pad_token=A_     ,do_upper_case=A_     ,do_lower_case=A_     ,tgt_lang=A_     ,lang_codes=A_     ,sp_model_kwargs=self.sp_model_kwargs     ,**A_     ,)
							A        =      do_upper_case
							A        =      do_lower_case

							A        =      load_json(A_					)
							A        =      {v: k for k, v in self.encoder.items()}
							A        =      spm_file
							A        =      load_spm(A_     ,self.sp_model_kwargs					)

							if lang_codes is not None:
								A        =      lang_codes
								A        =      LANGUAGES[lang_codes]
								A        =      [F'<lang:{lang}>' for lang in self.langs]
								A        =      {lang: self.sp_model.PieceToId(F'<lang:{lang}>'					) for lang in self.langs}

								A        =      self.lang_tokens
								A        =      tgt_lang if tgt_lang is not None else self.langs[0]

								self.set_tgt_lang_special_tokens(self._tgt_lang					)
							else:
								A        =      {}


						@property
						def   _SCREAMING_SNAKE_CASE					(       self   :					Any					)   ->      int:
							return len(self.encoder					)


						@property
						def   _SCREAMING_SNAKE_CASE					(       self   :					int					)   ->      str:
							return self._tgt_lang


						@tgt_lang.setter
						def   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]     ,A_   :					List[str]					)   ->      None:
							A        =      new_tgt_lang
							self.set_tgt_lang_special_tokens(A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]     ,A_   :					str					)   ->      None:
							A        =      self.lang_code_to_id[tgt_lang]
							A        =      [lang_code_id]


						def   _SCREAMING_SNAKE_CASE					(       self   :					Tuple     ,A_   :					str					)   ->      List[str]:
							return self.sp_model.encode(A_     ,out_type=A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[str]     ,A_   :					List[str]					)   ->      Union[str, Any]:
							return self.encoder.get(A_     ,self.encoder[self.unk_token]					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Tuple     ,A_   :					int					)   ->      str:
							return self.decoder.get(A_     ,self.unk_token					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					str     ,A_   :					List[str]					)   ->      str:
							A        =      []
							A        =      ''
							for token in tokens:
								# make sure that special tokens are not decoded using sentencepiece model
								if token in self.all_special_tokens:
									A        =      self.sp_model.decode(A_					)
									out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
									A        =      []
								else:
									current_sub_tokens.append(A_					)
							A        =      self.sp_model.decode(A_					)
							out_string += decoded.upper() if self.do_upper_case else decoded
							return out_string.strip()


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[str]     ,A_   :					List[str]     ,A_   :					Optional[Any]=None					)   ->      List[int]:
							if token_ids_a is None:
								return self.prefix_tokens + token_ids_a + [self.eos_token_id]
							# 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.eos_token_id]


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

							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        =      [1] * len(self.prefix_tokens					)
							A        =      [1]
							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   _SCREAMING_SNAKE_CASE					(       self   :					Any					)   ->      Dict:
							A        =      self.encoder.copy()
							vocab.update(self.added_tokens_encoder					)
							return vocab


						def __getstate__(       self   :					int					)   ->      Dict:
							A        =      self.__dict__.copy()
							A        =      None
							return state


						def __setstate__(       self   :					List[str]     ,A_   :					Dict					)   ->      None:
							A        =      d

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

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




						def   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]     ,A_   :					str     ,A_   :					Optional[str] = None					)   ->      Tuple[str]:
							A        =      Path(A_					)
							assert save_dir.is_dir(), F'{save_directory} should be a directory'
							A        =      save_dir / (
							    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
							)
							A        =      save_dir / (
							    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
							)

							save_json(self.encoder     ,A_					)

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

							return (str(A_					), str(A_					))






def 					_snake_case (						snake_case__      :   str		,					snake_case__      :   Dict[str, Any]   ):
	A        =      sentencepiece.SentencePieceProcessor(**snake_case__   )
	spm.Load(str(snake_case__   )   )
	return spm






def 					_snake_case (						snake_case__      :   str   ):
	with open(snake_case__		,					'r'   ) as f:
		return json.load(snake_case__   )






def 					_snake_case (						snake_case__      :   int		,					snake_case__      :   str   ):
	with open(snake_case__		,					'w'   ) as f:
		json.dump(snake_case__		,					snake_case__		,					indent=2   )



def 	lowerCAmelCase       (						lowerCAmelCase_ = 10**9       )->       int:
				lowerCAmelCase_       :					List[Any]			 = 1
				lowerCAmelCase_       :					Optional[int]			 = 2

				lowerCAmelCase_       :					Any			 = 0
				lowerCAmelCase_       :					str			 = 0
				lowerCAmelCase_       :					str			 = 0
				while perimeter <= max_perimeter:
								perimeters_sum += perimeter

								prev_value += 2 * value
								value += prev_value

								lowerCAmelCase_       :					Any			 = 2 * value + 2 if i % 2 == 0 else 2 * value - 2
								i += 1

				return perimeters_sum


if __name__ == "__main__":
						print(f"""{solution() = }""")