





import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_    =				logging.get_logger(__name__)

lowerCAmelCase_    =				{'''vocab_file''': '''spm_char.model'''}

lowerCAmelCase_    =				{
    '''vocab_file''': {
        '''microsoft/speecht5_asr''': '''https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model''',
        '''microsoft/speecht5_tts''': '''https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model''',
        '''microsoft/speecht5_vc''': '''https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model''',
    }
}

lowerCAmelCase_    =				{
    '''microsoft/speecht5_asr''': 1_0_2_4,
    '''microsoft/speecht5_tts''': 1_0_2_4,
    '''microsoft/speecht5_vc''': 1_0_2_4,
}



class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     List[str]       			=      VOCAB_FILES_NAMES
			lowerCamelCase_     :     int       			=      PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase_     :     Dict       			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase_     :     Any       			=      ['''input_ids''', '''attention_mask''']






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


							'''simple docstring'''



							snake_case_						:    int				=  {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=__magic_name__					,							eos_token=__magic_name__					,							unk_token=__magic_name__					,							pad_token=__magic_name__					,							sp_model_kwargs=self.sp_model_kwargs					,							**__magic_name__					,							)

							snake_case_						:    str				=  vocab_file

							snake_case_						:    List[str]				=  spm.SentencePieceProcessor(**self.sp_model_kwargs    )
							self.sp_model.Load(__magic_name__    )






			@property
			def 			lowerCamelCase  (self    )     ->   Optional[Any]:


							'''simple docstring'''



							return self.sp_model.get_piece_size()






			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



							snake_case_						:    Tuple				=  {self.convert_ids_to_tokens(__magic_name__    ): i for i in range(self.vocab_size    )}
							vocab.update(self.added_tokens_encoder    )
							return vocab






			def __getstate__(self    )     ->   Dict:


							'''simple docstring'''



							snake_case_						:    Any				=  self.__dict__.copy()
							snake_case_						:    Dict				=  None
							return state






			def __setstate__(self					,							__magic_name__    )     ->   List[Any]:


							'''simple docstring'''



							snake_case_						:    Optional[Any]				=  d

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

							snake_case_						:    str				=  spm.SentencePieceProcessor(**self.sp_model_kwargs    )
							self.sp_model.Load(self.vocab_file    )






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   List[str]:


							'''simple docstring'''



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






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   Optional[int]:


							'''simple docstring'''



							return self.sp_model.piece_to_id(__magic_name__    )






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   List[Any]:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  self.sp_model.IdToPiece(__magic_name__    )
							return token






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   Any:


							'''simple docstring'''



							snake_case_						:    Dict				=  []
							snake_case_						:    Optional[int]				=  ''''''
							for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
															out_string += self.sp_model.decode(__magic_name__    ) + token
															snake_case_						:    int				=  []
											else:
															current_sub_tokens.append(__magic_name__    )
							out_string += self.sp_model.decode(__magic_name__    )
							return out_string.strip()






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__=None    )     ->   List[int]:


							'''simple docstring'''



							if token_ids_a is None:
											return token_ids_a + [self.eos_token_id]
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return token_ids_a + token_ids_a + [self.eos_token_id]






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__ = None					,							__magic_name__ = False    )     ->   List[int]:


							'''simple docstring'''



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

							snake_case_						:    Optional[Any]				=  [1]
							if token_ids_a is None:
											return ([0] * len(__magic_name__    )) + suffix_ones
							return ([0] * len(__magic_name__    )) + ([0] * len(__magic_name__    )) + suffix_ones






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__ = None    )     ->   Tuple[str]:


							'''simple docstring'''



							if not os.path.isdir(__magic_name__    ):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''    )
											return
							snake_case_						:    List[Any]				=  os.path.join(
							    __magic_name__					,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

							if os.path.abspath(self.vocab_file    ) != os.path.abspath(__magic_name__    ) and os.path.isfile(self.vocab_file    ):
											copyfile(self.vocab_file					,							__magic_name__    )
							elif not os.path.isfile(self.vocab_file    ):
											with open(__magic_name__					,							'''wb'''    ) as fi:
															snake_case_						:    str				=  self.sp_model.serialized_model_proto()
															fi.write(__magic_name__    )

							return (out_vocab_file,)







lowerCAmelCase_    =				{
    "joule": 1.0,
    "kilojoule": 1_0_0_0,
    "megajoule": 1_0_0_0_0_0_0,
    "gigajoule": 1_0_0_0_0_0_0_0_0_0,
    "wattsecond": 1.0,
    "watthour": 3_6_0_0,
    "kilowatthour": 3_6_0_0_0_0_0,
    "newtonmeter": 1.0,
    "calorie_nutr": 4_1_8_6.8,
    "kilocalorie_nutr": 4_1_8_6_8_0_0.0_0,
    "electronvolt": 1.602_176_634e-19,
    "britishthermalunit_it": 1_0_5_5.0_5_5_8_5,
    "footpound": 1.355_818,
}






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						float:






				"""simple docstring"""


				if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
								snake_case_						:    str				=  (
								    f'''Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
								    f'''Valid values are: {", ".join(_UpperCamelCase )}'''
								)
								raise ValueError(_UpperCamelCase )
				return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
	import doctest

	doctest.testmod()
