

"""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


_UpperCamelCase		     =    logging.get_logger(__name__)

_UpperCamelCase		     =    """▁"""

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

_UpperCamelCase		     =    {
    """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"""
        )
    },
}

_UpperCamelCase		     =    {
    """facebook/s2t-small-librispeech-asr""": 1_0_2_4,
}

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

_UpperCamelCase		     =    {"""mustc""": MUSTC_LANGS}






class 			__a					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      Any    =						VOCAB_FILES_NAMES
       __UpperCamelCase					:      int    =						PRETRAINED_VOCAB_FILES_MAP
       __UpperCamelCase					:      str    =						MAX_MODEL_INPUT_SIZES
       __UpperCamelCase					:      List[Any]    =						['input_ids', 'attention_mask']

       __UpperCamelCase					:      List[int]    =						[]






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





        """simple docstring"""

        lowerCAmelCase__   :				Any 			=				{} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=snake_case			,					eos_token=snake_case			,					unk_token=snake_case			,					pad_token=snake_case			,					do_upper_case=snake_case			,					do_lower_case=snake_case			,					tgt_lang=snake_case			,					lang_codes=snake_case			,					sp_model_kwargs=self.sp_model_kwargs			,					**snake_case			,					)
        lowerCAmelCase__   :				Optional[int] 			=				do_upper_case
        lowerCAmelCase__   :				str 			=				do_lower_case

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

        if lang_codes is not None:
         lowerCAmelCase__   :				Optional[Any] 			=				lang_codes
         lowerCAmelCase__   :				Optional[int] 			=				LANGUAGES[lang_codes]
         lowerCAmelCase__   :				Optional[Any] 			=				[F"""<lang:{lang}>""" for lang in self.langs]
         lowerCAmelCase__   :				str 			=				{lang: self.sp_model.PieceToId(F"""<lang:{lang}>"""   ) for lang in self.langs}

         lowerCAmelCase__   :				List[Any] 			=				self.lang_tokens
         lowerCAmelCase__   :				Union[str, Any] 			=				tgt_lang if tgt_lang is not None else self.langs[0]

         self.set_tgt_lang_special_tokens(self._tgt_lang   )
        else:
         lowerCAmelCase__   :				Tuple 			=				{}






       @property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return len(self.encoder   )






       @property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return self._tgt_lang






       @tgt_lang.setter
       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[str] 			=				new_tgt_lang
        self.set_tgt_lang_special_tokens(snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				self.lang_code_to_id[tgt_lang]
        lowerCAmelCase__   :				str 			=				[lang_code_id]






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

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






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

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






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

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






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

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






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case=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			,					snake_case			,					snake_case = None			,					snake_case = False   ):





        """simple docstring"""

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

        lowerCAmelCase__   :				int 			=				[1] * len(self.prefix_tokens   )
        lowerCAmelCase__   :				Dict 			=				[1]
        if token_ids_a is None:
         return prefix_ones + ([0] * len(snake_case   )) + suffix_ones
        return prefix_ones + ([0] * len(snake_case   )) + ([0] * len(snake_case   )) + suffix_ones






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Dict 			=				self.encoder.copy()
        vocab.update(self.added_tokens_encoder   )
        return vocab






       def __getstate__(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				self.__dict__.copy()
        lowerCAmelCase__   :				Union[str, Any] 			=				None
        return state






       def __setstate__(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[int] 			=				d

        # for backward compatibility
        if not hasattr(self			,					"sp_model_kwargs"   ):
         lowerCAmelCase__   :				Union[str, Any] 			=				{}

        lowerCAmelCase__   :				Tuple 			=				load_spm(self.spm_file			,					self.sp_model_kwargs   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case = None   ):





        """simple docstring"""

        lowerCAmelCase__   :				Any 			=				Path(snake_case   )
        assert save_dir.is_dir(), F"""{save_directory} should be a directory"""
        lowerCAmelCase__   :				Tuple 			=				save_dir / (
            (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
        )
        lowerCAmelCase__   :				int 			=				save_dir / (
            (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
        )

        save_json(self.encoder			,					snake_case   )

        if os.path.abspath(self.spm_file   ) != os.path.abspath(snake_case   ) and os.path.isfile(self.spm_file   ):
         copyfile(self.spm_file			,					snake_case   )
        elif not os.path.isfile(self.spm_file   ):
         with open(snake_case			,					"wb"   ) as fi:
          lowerCAmelCase__   :				int 			=				self.sp_model.serialized_model_proto()
          fi.write(snake_case   )

        return (str(snake_case   ), str(snake_case   ))




def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						sentencepiece.SentencePieceProcessor:
 lowerCAmelCase__   :				List[Any] 			=				sentencepiece.SentencePieceProcessor(**lowercase__   )
 spm.Load(str(lowercase__   )   )
 return spm



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						Union[Dict, List]:
 with open(lowercase__					,					"r"   ) as f:
  return json.load(lowercase__   )






def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						None:
 with open(lowercase__					,					"w"   ) as f:
  json.dump(lowercase__					,					lowercase__					,					indent=2   )









"""simple docstring"""

def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						float:
 if digit_amount > 0:
  return round(number - int(lowercase__   )					,					lowercase__   )
 return number - int(lowercase__   )


if __name__ == "__main__":
    print(decimal_isolate(1.53, 0))
    print(decimal_isolate(35.345, 1))
    print(decimal_isolate(35.345, 2))
    print(decimal_isolate(35.345, 3))
    print(decimal_isolate(-14.789, 3))
    print(decimal_isolate(0, 2))
    print(decimal_isolate(-14.123, 1))
    print(decimal_isolate(-14.123, 2))
    print(decimal_isolate(-14.123, 3))






