





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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_    =    logging.get_logger(__name__)

lowerCAmelCase_    =    '''▁'''

lowerCAmelCase_    =    {'''vocab_file''': '''sentencepiece.bpe.model''', '''monolingual_vocab_file''': '''dict.txt'''}

lowerCAmelCase_    =    {
    '''vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model''',
    },
    '''monolingual_vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt''',
    },
}

lowerCAmelCase_    =    {'''vinai/bartpho-syllable''': 1_0_2_4}



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






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


							'''simple docstring'''



							snake_case_						:   int           =  AddedToken(__magic_name__ ,							lstrip=__magic_name__ ,							rstrip=__magic_name__   ) if isinstance(__magic_name__ ,							__magic_name__   ) else mask_token

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

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

							snake_case_						:   Optional[int]           =  vocab_file
							snake_case_						:   Dict           =  monolingual_vocab_file
							snake_case_						:   int           =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.Load(str(__magic_name__   )   )

							# Load the reduced vocab

							# Keep order of special tokens for backward compatibility
							snake_case_						:   Optional[Any]           =  {}
							snake_case_						:   List[Any]           =  0
							for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
											if str(__magic_name__   ) not in self.fairseq_tokens_to_ids:
															snake_case_						:   List[Any]           =  cnt
															cnt += 1
							with open(__magic_name__ ,							'''r''' ,							encoding='''utf-8'''   ) as f:
											for line in f.readlines():
															snake_case_						:   Tuple           =  line.strip().split()[0]
															snake_case_						:   Union[str, Any]           =  len(self.fairseq_tokens_to_ids   )
							if str(__magic_name__   ) not in self.fairseq_tokens_to_ids:
											snake_case_						:   Union[str, Any]           =  len(self.fairseq_tokens_to_ids   )

							snake_case_						:   Optional[int]           =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}






			def __getstate__(self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Any           =  self.__dict__.copy()
							snake_case_						:   List[Any]           =  None
							snake_case_						:   Any           =  self.sp_model.serialized_model_proto()
							return state






			def __setstate__(self ,							__magic_name__   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  d

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

							snake_case_						:   Union[str, Any]           =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )






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


							'''simple docstring'''



							if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							snake_case_						:   str           =  [self.cls_token_id]
							snake_case_						:   List[str]           =  [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep






			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__   )

							if token_ids_a is None:
											return [1] + ([0] * len(__magic_name__   )) + [1]
							return [1] + ([0] * len(__magic_name__   )) + [1, 1] + ([0] * len(__magic_name__   )) + [1]






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


							'''simple docstring'''



							snake_case_						:   Dict           =  [self.sep_token_id]
							snake_case_						:   Tuple           =  [self.cls_token_id]

							if token_ids_a is None:
											return len(cls + token_ids_a + sep   ) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]






			@property
			def       lowerCamelCase  (self   )					->       Tuple:


							'''simple docstring'''



							return len(self.fairseq_ids_to_tokens   )






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


							'''simple docstring'''



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






			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__   )					->       str:


							'''simple docstring'''



							if token in self.fairseq_tokens_to_ids:
											return self.fairseq_tokens_to_ids[token]
							else:
											return self.unk_token_id






			def       lowerCamelCase  (self ,							__magic_name__   )					->       int:


							'''simple docstring'''



							return self.fairseq_ids_to_tokens[index]






			def       lowerCamelCase  (self ,							__magic_name__   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   List[str]           =  ''''''.join(__magic_name__   ).replace(__magic_name__ ,							''' '''   ).strip()
							return out_string






			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_						:   int           =  os.path.join(
							    __magic_name__ ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )
							snake_case_						:   str           =  os.path.join(
							    __magic_name__ ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_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_						:   List[str]           =  self.sp_model.serialized_model_proto()
															fi.write(__magic_name__   )

							if os.path.abspath(self.monolingual_vocab_file   ) != os.path.abspath(
							    __magic_name__   ) and os.path.isfile(self.monolingual_vocab_file   ):
											copyfile(self.monolingual_vocab_file ,							__magic_name__   )
							elif not os.path.isfile(self.monolingual_vocab_file   ):
											with open(__magic_name__ ,							'''w''' ,							encoding='''utf-8'''   ) as fp:
															for token in self.fairseq_tokens_to_ids:
																			if token not in self.all_special_tokens:
																							fp.write(F'''{str(__magic_name__   )} \n'''   )

							return out_vocab_file, out_monolingual_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__   )
