


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__							:						Optional[int]							     =   logging.get_logger(__name__)

lowerCamelCase__							:						List[str]							     =   """▁"""

lowerCamelCase__							:						Optional[int]							     =   {"""vocab_file""": """sentencepiece.bpe.model"""}

lowerCamelCase__							:						Optional[int]							     =   {
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    }
}

lowerCamelCase__							:						List[Any]							     =   {
    """facebook/xglm-564M""": 2_0_4_8,
}
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							Optional[Any]	   =       VOCAB_FILES_NAMES
     __lowercase				:							Optional[Any]	   =       PRETRAINED_VOCAB_FILES_MAP
     __lowercase				:							List[str]	   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __lowercase				:							List[Any]	   =       ['input_ids', 'attention_mask']

     def __init__(					self:int     ,		_a:List[Any]     ,		_a:List[str]="<s>"     ,		_a:str="</s>"     ,		_a:int="</s>"     ,		_a:Optional[int]="<s>"     ,		_a:Any="<unk>"     ,		_a:int="<pad>"     ,		_a:Optional[Dict[str, Any]] = None     ,		**_a:int     ,		):
       snake_case__						= {} if sp_model_kwargs is None else sp_model_kwargs

       # Compatibility with the original tokenizer
       snake_case__						= 7
       snake_case__						= [F"""<madeupword{i}>""" for i in range(self.num_madeup_words   )]

       snake_case__						= kwargs.get('''additional_special_tokens'''     ,		[]   )
       kwargs["additional_special_tokens"] += [
           word for word in madeup_words if word not in kwargs["additional_special_tokens"]
       ]

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

       snake_case__						= spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(str(_a   )   )
       snake_case__						= vocab_file

       # Original fairseq vocab and spm vocab must be "aligned":
       # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
       # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
       # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
       # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

       # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
       snake_case__						= 1

       # Mimic fairseq token-to-id alignment for the first 4 token
       snake_case__						= {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

       snake_case__						= len(self.sp_model   )
       snake_case__						= {F"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words   )}
       self.fairseq_tokens_to_ids.update(_a   )

       snake_case__						= {v: k for k, v in self.fairseq_tokens_to_ids.items()}

     def __getstate__(					self:Optional[int]   ):
       snake_case__						= self.__dict__.copy()
       snake_case__						= None
       snake_case__						= self.sp_model.serialized_model_proto()
       return state

     def __setstate__(					self:Optional[Any]     ,		_a:Union[str, Any]   ):
       snake_case__						= d

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

       snake_case__						= spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Any     ,		_a:List[int]     ,		_a:Optional[List[int]] = None   ):

       if token_ids_a is None:
         return [self.sep_token_id] + token_ids_a
       snake_case__						= [self.sep_token_id]
       return sep + token_ids_a + sep + sep + token_ids_a

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		_a:List[int]     ,		_a:Optional[List[int]] = None     ,		_a:bool = False   ):

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

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

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		_a:List[int]     ,		_a:Optional[List[int]] = None   ):
       snake_case__						= [self.sep_token_id]

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

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       return len(self.sp_model   ) + self.fairseq_offset + self.num_madeup_words

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__						= {self.convert_ids_to_tokens(_a   ): i for i in range(self.vocab_size   )}
       vocab.update(self.added_tokens_encoder   )
       return vocab

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		_a:str   ):
       return self.sp_model.encode(_a     ,		out_type=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str     ,		_a:Optional[Any]   ):
       if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
       snake_case__						= self.sp_model.PieceToId(_a   )

       # Need to return unknown token if the SP model returned 0
       return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple     ,		_a:List[Any]   ):
       if index in self.fairseq_ids_to_tokens:
         return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(index - self.fairseq_offset   )

     def 	SCREAMING_SNAKE_CASE__						(					self:int     ,		_a:Any   ):
       snake_case__						= ''''''.join(_a   ).replace(_a     ,		''' '''   ).strip()
       return out_string







     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple     ,		_a:str     ,		_a:Optional[str] = None   ):
       if not os.path.isdir(_a   ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
         return
       snake_case__						= os.path.join(
           _a     ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

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

       return (out_vocab_file,)





import numpy as np







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				np.ndarray:
  return 1 / (1 + np.exp(-vector				))







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				np.ndarray:
  return vector * sigmoid(__lowerCAmelCase				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()

