





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_					:					Any	      =				PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase_					:					Tuple	      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase_					:					List[str]	      =				['''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_						:   Tuple           =  {} 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_						:   Tuple           =  vocab_file

							snake_case_						:   Dict           =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.Load(__magic_name__   )






			@property
			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							return self.sp_model.get_piece_size()






			def       lowerCamelCase  (self   )					->       Union[str, Any]:


							'''simple docstring'''



							snake_case_						:   Union[str, Any]           =  {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_						:   Tuple           =  self.__dict__.copy()
							snake_case_						:   Optional[int]           =  None
							return state






			def __setstate__(self ,							__magic_name__   )					->       int:


							'''simple docstring'''



							snake_case_						:   Dict           =  d

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

							snake_case_						:   Optional[Any]           =  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[Any]:


							'''simple docstring'''



							return self.sp_model.piece_to_id(__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__   )					->       Dict:


							'''simple docstring'''



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






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


							'''simple docstring'''



							snake_case_						:   List[Any]           =  []
							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_						:   str           =  []
											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_						:   Dict           =  [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_						:   str           =  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,)







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_    =    logging.get_logger(__name__)

lowerCAmelCase_    =    {
    '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}



class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					Any	      =				'''biogpt'''






			def __init__(self ,							__magic_name__=4_2384 ,							__magic_name__=1024 ,							__magic_name__=24 ,							__magic_name__=16 ,							__magic_name__=4096 ,							__magic_name__="gelu" ,							__magic_name__=0.1 ,							__magic_name__=0.1 ,							__magic_name__=1024 ,							__magic_name__=0.02 ,							__magic_name__=1e-12 ,							__magic_name__=True ,							__magic_name__=True ,							__magic_name__=0.0 ,							__magic_name__=0.0 ,							__magic_name__=1 ,							__magic_name__=0 ,							__magic_name__=2 ,							**__magic_name__ ,							)					->       List[str]:


							'''simple docstring'''



							snake_case_						:   List[str]           =  vocab_size
							snake_case_						:   Dict           =  max_position_embeddings
							snake_case_						:   Optional[int]           =  hidden_size
							snake_case_						:   List[Any]           =  num_hidden_layers
							snake_case_						:   List[str]           =  num_attention_heads
							snake_case_						:   int           =  intermediate_size
							snake_case_						:   List[Any]           =  hidden_act
							snake_case_						:   List[Any]           =  hidden_dropout_prob
							snake_case_						:   Optional[int]           =  attention_probs_dropout_prob
							snake_case_						:   Optional[int]           =  initializer_range
							snake_case_						:   Optional[int]           =  layer_norm_eps
							snake_case_						:   str           =  scale_embedding
							snake_case_						:   Optional[Any]           =  use_cache
							snake_case_						:   Optional[Any]           =  layerdrop
							snake_case_						:   Optional[Any]           =  activation_dropout
							super().__init__(pad_token_id=__magic_name__ ,							bos_token_id=__magic_name__ ,							eos_token_id=__magic_name__ ,							**__magic_name__   )
