

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


A__						=							logging.get_logger(__name__)

A__						=							"▁"

A__						=							{
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
}

A__						=							{
    "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"
        )
    },
}

A__						=							{
    "facebook/s2t-small-librispeech-asr": 1024,
}

A__						=							["pt", "fr", "ru", "nl", "ro", "it", "es", "de"]

A__						=							{"mustc": MUSTC_LANGS}

class 				__UpperCamelCase				(					__lowerCamelCase			):
	_lowercase  :   Dict       				=  VOCAB_FILES_NAMES
	_lowercase  :   Optional[Any]       				=  PRETRAINED_VOCAB_FILES_MAP
	_lowercase  :   List[str]       				=  MAX_MODEL_INPUT_SIZES
	_lowercase  :   Optional[int]       				=  ['''input_ids''', '''attention_mask''']

	_lowercase  :   List[int]       				=  []







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

						'''simple docstring'''





						__magic_name__							=    {} 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_  , )
						__magic_name__							=    do_upper_case
						__magic_name__							=    do_lower_case

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

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

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

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







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						return len(self.encoder      )







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						return self._tgt_lang







	@tgt_lang.setter
	def 	_SCREAMING_SNAKE_CASE							(   self: Any  , __UpperCamelCase: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    new_tgt_lang
						self.set_tgt_lang_special_tokens(a_      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Any  , __UpperCamelCase: Dict      ):

						'''simple docstring'''





						__magic_name__							=    self.lang_code_to_id[tgt_lang]
						__magic_name__							=    [lang_code_id]







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict  , __UpperCamelCase: Optional[int]      ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]  , __UpperCamelCase: int      ):

						'''simple docstring'''





						return self.encoder.get(a_  , self.encoder[self.unk_token]      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict  , __UpperCamelCase: Optional[int]      ):

						'''simple docstring'''





						return self.decoder.get(a_  , self.unk_token      )







	def 	_SCREAMING_SNAKE_CASE							(   self: str  , __UpperCamelCase: Union[str, Any]      ):

						'''simple docstring'''





						__magic_name__							=    []
						__magic_name__							=    ""
						for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
																__magic_name__							=    self.sp_model.decode(a_      )
																out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
																__magic_name__							=    []
											else:
																current_sub_tokens.append(a_      )
						__magic_name__							=    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: Union[str, Any]  , __UpperCamelCase: Optional[Any]  , __UpperCamelCase: Any=None      ):

						'''simple docstring'''





						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]  , __UpperCamelCase: str  , __UpperCamelCase: int = None  , __UpperCamelCase: Tuple = 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_      )

						__magic_name__							=    [1] * len(self.prefix_tokens      )
						__magic_name__							=    [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: int      ):

						'''simple docstring'''





						__magic_name__							=    self.encoder.copy()
						vocab.update(self.added_tokens_encoder      )
						return vocab







	def __getstate__(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.__dict__.copy()
						__magic_name__							=    None
						return state







	def __setstate__(   self: Optional[Any]  , __UpperCamelCase: Tuple      ):

						'''simple docstring'''





						__magic_name__							=    d

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

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







	def 	_SCREAMING_SNAKE_CASE							(   self: Any  , __UpperCamelCase: Union[str, Any]  , __UpperCamelCase: Tuple = None      ):

						'''simple docstring'''





						__magic_name__							=    Path(a_      )
						assert save_dir.is_dir(), F'{save_directory} should be a directory'
						__magic_name__							=    save_dir / (
						    (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
						)
						__magic_name__							=    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:
																__magic_name__							=    self.sp_model.serialized_model_proto()
																fi.write(a_      )

						return (str(a_      ), str(a_      ))




def 					_lowercase     (				a_				:					List[Any]					,a_				:					Dict				)				->	Dict:




					'''simple docstring'''







					__magic_name__							=    sentencepiece.SentencePieceProcessor(**lowerCAmelCase_				)
					spm.Load(str(lowerCAmelCase_				)				)
					return spm




def 					_lowercase     (				a_				:					str				)				->	Optional[Any]:




					'''simple docstring'''







					with open(lowerCAmelCase_					,'r'				) as f:
										return json.load(lowerCAmelCase_				)




def 					_lowercase     (				a_				:					List[str]					,a_				:					str				)				->	List[str]:




					'''simple docstring'''







					with open(lowerCAmelCase_					,'w'				) as f:
										json.dump(lowerCAmelCase_					,lowerCAmelCase_					,indent=2				)







import os

A__						=							{"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}




def 					_lowercase     (				a_				:					str				)				->	int:




					'''simple docstring'''







					__magic_name__							=    0

					__magic_name__							=    0
					while index < len(a_				) - 1:
										__magic_name__							=    SYMBOLS[numerals[index]]
										__magic_name__							=    SYMBOLS[numerals[index + 1]]
										if current_value < next_value:
															total_value -= current_value
										else:
															total_value += current_value
										index += 1
					total_value += SYMBOLS[numerals[index]]

					return total_value




def 					_lowercase     (				a_				:					int				)				->	str:




					'''simple docstring'''







					__magic_name__							=    ''

					__magic_name__							=    num // 1_0_0_0
					numerals += m_count * "M"
					num %= 1_0_0_0

					__magic_name__							=    num // 1_0_0
					if c_count == 9:
										numerals += "CM"
										c_count -= 9
					elif c_count == 4:
										numerals += "CD"
										c_count -= 4
					if c_count >= 5:
										numerals += "D"
										c_count -= 5
					numerals += c_count * "C"
					num %= 1_0_0

					__magic_name__							=    num // 1_0
					if x_count == 9:
										numerals += "XC"
										x_count -= 9
					elif x_count == 4:
										numerals += "XL"
										x_count -= 4
					if x_count >= 5:
										numerals += "L"
										x_count -= 5
					numerals += x_count * "X"
					num %= 1_0

					if num == 9:
										numerals += "IX"
										num -= 9
					elif num == 4:
										numerals += "IV"
										num -= 4
					if num >= 5:
										numerals += "V"
										num -= 5
					numerals += num * "I"

					return numerals




def 					_lowercase     (				a_				:					str = "/p089_roman.txt"				)				->	int:




					'''simple docstring'''







					__magic_name__							=    0

					with open(os.path.dirname(a_				) + roman_numerals_filename				) as filea:
										__magic_name__							=    filea.readlines()

					for line in lines:
										__magic_name__							=    line.strip()
										__magic_name__							=    parse_roman_numerals(a_				)
										__magic_name__							=    generate_roman_numerals(a_				)
										savings += len(a_				) - len(a_				)

					return savings


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




