


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


UpperCAmelCase__   	=							logging.get_logger(__name__)

UpperCAmelCase__   	=							'''▁'''

UpperCAmelCase__   	=							{'''vocab_file''': '''sentencepiece.bpe.model'''}

UpperCAmelCase__   	=							{
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    }
}

UpperCAmelCase__   	=							{
    '''facebook/xglm-564M''': 2048,
}







class       lowerCamelCase__   (       lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      			=			VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE__      			=			PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE__      			=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE__      			=			['''input_ids''', '''attention_mask''']





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase="<s>"		,     UpperCAmelCase="</s>"		,     UpperCAmelCase="</s>"		,     UpperCAmelCase="<s>"		,     UpperCAmelCase="<unk>"		,     UpperCAmelCase="<pad>"		,     UpperCAmelCase = None		,     **UpperCAmelCase		,     )     ->   None:
      _lowercase							      ={} if sp_model_kwargs is None else sp_model_kwargs

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

      _lowercase							      =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=UpperCAmelCase		,     eos_token=UpperCAmelCase		,     unk_token=UpperCAmelCase		,     sep_token=UpperCAmelCase		,     cls_token=UpperCAmelCase		,     pad_token=UpperCAmelCase		,     sp_model_kwargs=self.sp_model_kwargs		,     **UpperCAmelCase		,     )

      _lowercase							      =spm.SentencePieceProcessor(**self.sp_model_kwargs       )
      self.sp_model.Load(str(UpperCAmelCase       )       )
      _lowercase							      =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
      _lowercase							      =1

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

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

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





     def __getstate__(self       )     ->   List[str]:
      _lowercase							      =self.__dict__.copy()
      _lowercase							      =None
      _lowercase							      =self.sp_model.serialized_model_proto()
      return state





     def __setstate__(self		,     UpperCAmelCase       )     ->   Any:
      _lowercase							      =d

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

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





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase = None       )     ->   List[int]:

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





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase = None		,     UpperCAmelCase = False       )     ->   List[int]:

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

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





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase = None       )     ->   List[int]:
      _lowercase							      =[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      __A					(self       )     ->   int:
      return len(self.sp_model       ) + self.fairseq_offset + self.num_madeup_words





     def      __A					(self       )     ->   Union[str, Any]:
      _lowercase							      ={self.convert_ids_to_tokens(UpperCAmelCase       ): i for i in range(self.vocab_size       )}
      vocab.update(self.added_tokens_encoder       )
      return vocab





     def      __A					(self		,     UpperCAmelCase       )     ->   List[str]:
      return self.sp_model.encode(UpperCAmelCase		,     out_type=UpperCAmelCase       )





     def      __A					(self		,     UpperCAmelCase       )     ->   List[str]:
      if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
      _lowercase							      =self.sp_model.PieceToId(UpperCAmelCase       )

      # 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      __A					(self		,     UpperCAmelCase       )     ->   Tuple:
      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      __A					(self		,     UpperCAmelCase       )     ->   Dict:
      _lowercase							      =''''''.join(UpperCAmelCase       ).replace(UpperCAmelCase		,     ''' '''       ).strip()
      return out_string





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase = None       )     ->   Tuple[str]:
      if not os.path.isdir(UpperCAmelCase       ):
       logger.error(f"Vocabulary path ({save_directory}) should be a directory"       )
       return
      _lowercase							      =os.path.join(
          UpperCAmelCase		,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )

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

      return (out_vocab_file,)




"""simple docstring"""


from numpy import exp, pi, sqrt






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE = 0.0					, __SCREAMING_SNAKE_CASE = 1.0 )	->	int:
		return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )


if __name__ == "__main__":
			import doctest

			doctest.testmod()


