





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


A   : Dict     =				logging.get_logger(__name__)

A   : Union[str, Any]     =				{'''vocab_file''': '''spm_char.model'''}

A   : Union[str, Any]     =				{
    '''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''',
    }
}

A   : Union[str, Any]     =				{
    '''microsoft/speecht5_asr''': 1_0_2_4,
    '''microsoft/speecht5_tts''': 1_0_2_4,
    '''microsoft/speecht5_vc''': 1_0_2_4,
}


class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			__lowerCamelCase     :	List[str]          =   VOCAB_FILES_NAMES
			__lowerCamelCase     :	str          =   PRETRAINED_VOCAB_FILES_MAP
			__lowerCamelCase     :	List[str]          =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__lowerCamelCase     :	Union[str, Any]          =   ['''input_ids''', '''attention_mask''']






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







					"""simple docstring"""






					A__  				=       {} if sp_model_kwargs is None else sp_model_kwargs

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

					A__  				=       vocab_file

					A__  				=       spm.SentencePieceProcessor(**self.sp_model_kwargs       )
					self.sp_model.Load(__lowerCAmelCase       )






			@property
			def        a_	(      self       :   Dict       )     ->     str:







					"""simple docstring"""






					return self.sp_model.get_piece_size()






			def        a_	(      self       :   Tuple       )     ->     Optional[Any]:







					"""simple docstring"""






					A__  				=       {self.convert_ids_to_tokens(__lowerCAmelCase       ): i for i in range(self.vocab_size       )}
					vocab.update(self.added_tokens_encoder       )
					return vocab






			def __getstate__(      self       :   List[str]       )     ->     List[str]:







					"""simple docstring"""






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






			def __setstate__(      self       :   List[str]							,     __lowerCAmelCase       :   Optional[Any]       )     ->     Dict:







					"""simple docstring"""






					A__  				=       d

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

					A__  				=       spm.SentencePieceProcessor(**self.sp_model_kwargs       )
					self.sp_model.Load(self.vocab_file       )






			def        a_	(      self       :   Optional[Any]							,     __lowerCAmelCase       :   str       )     ->     List[str]:







					"""simple docstring"""






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






			def        a_	(      self       :   List[str]							,     __lowerCAmelCase       :   Any       )     ->     List[Any]:







					"""simple docstring"""






					return self.sp_model.piece_to_id(__lowerCAmelCase       )






			def        a_	(      self       :   Any							,     __lowerCAmelCase       :   Optional[int]       )     ->     Optional[Any]:







					"""simple docstring"""






					A__  				=       self.sp_model.IdToPiece(__lowerCAmelCase       )
					return token






			def        a_	(      self       :   Optional[Any]							,     __lowerCAmelCase       :   List[Any]       )     ->     List[str]:







					"""simple docstring"""






					A__  				=       []
					A__  				=       """"""
					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(__lowerCAmelCase       ) + token
									A__  				=       []
							else:
									current_sub_tokens.append(__lowerCAmelCase       )
					out_string += self.sp_model.decode(__lowerCAmelCase       )
					return out_string.strip()






			def        a_	(      self       :   List[str]							,     __lowerCAmelCase       :   List[Any]							,     __lowerCAmelCase       :   Any=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        a_	(      self       :   Union[str, Any]							,     __lowerCAmelCase       :   List[int]							,     __lowerCAmelCase       :   Optional[List[int]] = None							,     __lowerCAmelCase       :   bool = False       )     ->     List[int]:







					"""simple docstring"""






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

					A__  				=       [1]
					if token_ids_a is None:
							return ([0] * len(__lowerCAmelCase       )) + suffix_ones
					return ([0] * len(__lowerCAmelCase       )) + ([0] * len(__lowerCAmelCase       )) + suffix_ones







			def        a_	(      self       :   Union[str, Any]							,     __lowerCAmelCase       :   str							,     __lowerCAmelCase       :   Optional[str] = None       )     ->     Tuple[str]:







					"""simple docstring"""






					if not os.path.isdir(__lowerCAmelCase       ):
							logger.error(f'Vocabulary path ({save_directory}) should be a directory'       )
							return
					A__  				=       os.path.join(
					    __lowerCAmelCase							,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )

					if os.path.abspath(self.vocab_file       ) != os.path.abspath(__lowerCAmelCase       ) and os.path.isfile(self.vocab_file       ):
							copyfile(self.vocab_file							,     __lowerCAmelCase       )
					elif not os.path.isfile(self.vocab_file       ):
							with open(__lowerCAmelCase							,     """wb"""       ) as fi:
									A__  				=       self.sp_model.serialized_model_proto()
									fi.write(__lowerCAmelCase       )

					return (out_vocab_file,)










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


A   : Optional[Any]			=				{'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A   : List[str]			=				[
					    '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''ViTMAEForPreTraining''',
					    '''ViTMAELayer''',
					    '''ViTMAEModel''',
					    '''ViTMAEPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A   : Any			=				[
					    '''TFViTMAEForPreTraining''',
					    '''TFViTMAEModel''',
					    '''TFViTMAEPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_vit_mae import (
										    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ViTMAEForPreTraining,
										    ViTMAELayer,
										    ViTMAEModel,
										    ViTMAEPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
					import sys

					A   : str			=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



