





"""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


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

__magic_name__       :					List[Any]						=			"""▁"""

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

__magic_name__       :					Optional[Any]						=			{
    """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"""
        )
    },
}

__magic_name__       :					Union[str, Any]						=			{
    """facebook/s2t-small-librispeech-asr""": 1_0_2_4,
}

__magic_name__       :					Optional[int]						=			["""pt""", """fr""", """ru""", """nl""", """ro""", """it""", """es""", """de"""]

__magic_name__       :					Tuple						=			{"""mustc""": MUSTC_LANGS}






class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				Any										=  VOCAB_FILES_NAMES
			__lowerCAmelCase     :				Tuple										=  PRETRAINED_VOCAB_FILES_MAP
			__lowerCAmelCase     :				List[Any]										=  MAX_MODEL_INPUT_SIZES
			__lowerCAmelCase     :				int										=  ["""input_ids""", """attention_mask"""]

			__lowerCAmelCase     :				List[int]										=  []







			def __init__(      self						,   _A						,   _A						,   _A="<s>"						,   _A="</s>"						,   _A="<pad>"						,   _A="<unk>"						,   _A=False						,   _A=False						,   _A=None						,   _A=None						,   _A = None						,   **_A						,   ):






									'''simple docstring'''







									UpperCamelCase				:		Union[str, Any]				   =			{} 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						,   )
									UpperCamelCase				:		Any				   =			do_upper_case
									UpperCamelCase				:		Tuple				   =			do_lower_case

									UpperCamelCase				:		Tuple				   =			load_json(_A    )
									UpperCamelCase				:		List[str]				   =			{v: k for k, v in self.encoder.items()}
									UpperCamelCase				:		Union[str, Any]				   =			spm_file
									UpperCamelCase				:		Any				   =			load_spm(_A						,   self.sp_model_kwargs    )

									if lang_codes is not None:
															UpperCamelCase				:		List[str]				   =			lang_codes
															UpperCamelCase				:		Tuple				   =			LANGUAGES[lang_codes]
															UpperCamelCase				:		List[str]				   =			[f"""<lang:{lang}>""" for lang in self.langs]
															UpperCamelCase				:		str				   =			{lang: self.sp_model.PieceToId(f"""<lang:{lang}>"""    ) for lang in self.langs}

															UpperCamelCase				:		Dict				   =			self.lang_tokens
															UpperCamelCase				:		Any				   =			tgt_lang if tgt_lang is not None else self.langs[0]

															self.set_tgt_lang_special_tokens(self._tgt_lang    )
									else:
															UpperCamelCase				:		List[Any]				   =			{}







			@property
			def   _a      (      self    ):






									'''simple docstring'''







									return len(self.encoder    )







			@property
			def   _a      (      self    ):






									'''simple docstring'''







									return self._tgt_lang







			@tgt_lang.setter
			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		List[Any]				   =			new_tgt_lang
									self.set_tgt_lang_special_tokens(_A    )







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			self.lang_code_to_id[tgt_lang]
									UpperCamelCase				:		int				   =			[lang_code_id]







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







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







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







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







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







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







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		Any				   =			[]
									UpperCamelCase				:		Any				   =			""""""
									for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																					UpperCamelCase				:		Any				   =			self.sp_model.decode(_A    )
																					out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
																					UpperCamelCase				:		List[Any]				   =			[]
															else:
																					current_sub_tokens.append(_A    )
									UpperCamelCase				:		List[Any]				   =			self.sp_model.decode(_A    )
									out_string += decoded.upper() if self.do_upper_case else decoded
									return out_string.strip()







			def   _a      (      self						,   _A						,   _A=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   _a      (      self						,   _A						,   _A = None						,   _A = 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    )

									UpperCamelCase				:		Tuple				   =			[1] * len(self.prefix_tokens    )
									UpperCamelCase				:		List[str]				   =			[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   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		List[Any]				   =			self.encoder.copy()
									vocab.update(self.added_tokens_encoder    )
									return vocab







			def __getstate__(      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[int]				   =			self.__dict__.copy()
									UpperCamelCase				:		Optional[Any]				   =			None
									return state







			def __setstate__(      self						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		List[str]				   =			d

									# for backward compatibility
									if not hasattr(self						,   """sp_model_kwargs"""    ):
															UpperCamelCase				:		Optional[int]				   =			{}

									UpperCamelCase				:		int				   =			load_spm(self.spm_file						,   self.sp_model_kwargs    )







			def   _a      (      self						,   _A						,   _A = None    ):






									'''simple docstring'''







									UpperCamelCase				:		int				   =			Path(_A    )
									assert save_dir.is_dir(), f"""{save_directory} should be a directory"""
									UpperCamelCase				:		Dict				   =			save_dir / (
									    (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
									)
									UpperCamelCase				:		Dict				   =			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:
																					UpperCamelCase				:		Tuple				   =			self.sp_model.serialized_model_proto()
																					fi.write(_A    )

									return (str(_A    ), str(_A    ))


def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Dict				   =			sentencepiece.SentencePieceProcessor(**SCREAMING_SNAKE_CASE     )
						spm.Load(str(SCREAMING_SNAKE_CASE     )     )
						return spm

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						with open(SCREAMING_SNAKE_CASE							,     """r"""     ) as f:
												return json.load(SCREAMING_SNAKE_CASE     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						with open(SCREAMING_SNAKE_CASE							,     """w"""     ) as f:
												json.dump(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     indent=2     )








'''simple docstring'''





def 							lowerCamelCase	(					_snake_case	:    str							):





    '''simple docstring'''


    return " ".join(
        "".join(word[::-1]							) if len(_snake_case							) > 4 else word for word in sentence.split()							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(reverse_long_words("Hey wollef sroirraw"))




