


'''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


lowerCAmelCase    :     Any					=						logging.get_logger(__name__)

lowerCAmelCase    :     Dict					=						"""▁"""

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

lowerCAmelCase    :     Tuple					=						{
    """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"""
        )
    },
}

lowerCAmelCase    :     str					=						{
    """facebook/s2t-small-librispeech-asr""": 10_24,
}

lowerCAmelCase    :     Any					=						["""pt""", """fr""", """ru""", """nl""", """ro""", """it""", """es""", """de"""]

lowerCAmelCase    :     List[str]					=						{"""mustc""": MUSTC_LANGS}
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    VOCAB_FILES_NAMES
     __magic_name__    			=    PRETRAINED_VOCAB_FILES_MAP
     __magic_name__    			=    MAX_MODEL_INPUT_SIZES
     __magic_name__    			=    ["input_ids", "attention_mask"]

     __magic_name__    			=    []







     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    :							Optional[int]   			=       {} 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[Any]   			=       do_upper_case
            _lowerCAmelCase    :							List[Any]   			=       do_lower_case

            _lowerCAmelCase    :							List[Any]   			=       load_json(snake_case__   )
            _lowerCAmelCase    :							Optional[Any]   			=       {v: k for k, v in self.encoder.items()}
            _lowerCAmelCase    :							str   			=       spm_file
            _lowerCAmelCase    :							List[str]   			=       load_spm(snake_case__      ,	self.sp_model_kwargs   )

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

                   _lowerCAmelCase    :							str   			=       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    :							Optional[int]   			=       {}







     @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      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       new_tgt_lang
            self.set_tgt_lang_special_tokens(snake_case__   )







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       self.lang_code_to_id[tgt_lang]
            _lowerCAmelCase    :							Optional[Any]   			=       [lang_code_id]







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

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







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

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







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

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







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       []
            _lowerCAmelCase    :							Dict   			=       ''
            for token in tokens:
                   # make sure that special tokens are not decoded using sentencepiece model
                   if token in self.all_special_tokens:
                          _lowerCAmelCase    :							Any   			=       self.sp_model.decode(snake_case__   )
                          out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
                          _lowerCAmelCase    :							List[str]   			=       []
                   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 a	( 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 a	( 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    :							List[Any]   			=       [1] * len(self.prefix_tokens   )
            _lowerCAmelCase    :							Optional[Any]   			=       [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 a	( self   ):

            '''simple docstring'''

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







     def __getstate__( self   ):

            '''simple docstring'''

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







     def __setstate__( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       d

            # for backward compatibility
            if not hasattr(self      ,	'sp_model_kwargs'   ):
                   _lowerCAmelCase    :							List[str]   			=       {}

            _lowerCAmelCase    :							List[str]   			=       load_spm(self.spm_file      ,	self.sp_model_kwargs   )







     def a	( self      ,	snake_case__      ,	snake_case__ = None   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       Path(snake_case__   )
            assert save_dir.is_dir(), F'{save_directory} should be a directory'
            _lowerCAmelCase    :							Union[str, Any]   			=       save_dir / (
                (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
            )
            _lowerCAmelCase    :							List[str]   			=       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    :							Optional[Any]   			=       self.sp_model.serialized_model_proto()
                          fi.write(snake_case__   )

            return (str(snake_case__   ), str(snake_case__   ))






def 			lowercase				(_A   ,						_A					):







       """simple docstring"""







       _lowerCAmelCase    :							Union[str, Any]   			=       sentencepiece.SentencePieceProcessor(**_A					)
       spm.Load(str(_A					)					)
       return spm






def 			lowercase				(_A					):







       """simple docstring"""







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






def 			lowercase				(_A   ,						_A					):







       """simple docstring"""







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





'''simple docstring'''







def 			lowercase				(_A   ,						_A					):







       """simple docstring"""







       _lowerCAmelCase    :							Optional[int]   			=       (boundary[1] - boundary[0]) / steps
       _lowerCAmelCase    :							Any   			=       boundary[0]
       _lowerCAmelCase    :							List[str]   			=       boundary[1]
       _lowerCAmelCase    :							Tuple   			=       make_points(_A   ,						_A   ,						_A					)
       _lowerCAmelCase    :							Tuple   			=       0.0
       y += (h / 2.0) * f(_A					)
       for i in x_i:
              # print(i)
              y += h * f(_A					)
       y += (h / 2.0) * f(_A					)
       return y






def 			lowercase				(_A   ,						_A   ,						_A					):







       """simple docstring"""







       _lowerCAmelCase    :							Tuple   			=       a + h
       while x < (b - h):
              yield x
              _lowerCAmelCase    :							Any   			=       x + h






def 			lowercase				(_A					):  # enter your function here







       """simple docstring"""







       _lowerCAmelCase    :							int   			=       (x - 0) * (x - 0)
       return y






def 			lowercase				():







       """simple docstring"""







       _lowerCAmelCase    :							Optional[Any]   			=       0.0  # Lower bound of integration
       _lowerCAmelCase    :							Dict   			=       1.0  # Upper bound of integration
       _lowerCAmelCase    :							Optional[Any]   			=       10.0  # define number of steps or resolution
       _lowerCAmelCase    :							Optional[int]   			=       [a, b]  # define boundary of integration
       _lowerCAmelCase    :							List[Any]   			=       method_a(_A   ,						_A					)
       print(f'y = {y}'					)


if __name__ == "__main__":
       main()

