





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									=				logging.get_logger(__name__)

__A									=				"▁"

__A									=				{"vocab_file": "sentencepiece.bpe.model"}

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

__A									=				{
    "facebook/xglm-564M": 2048,
}



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			VOCAB_FILES_NAMES
 lowercase_		=			PRETRAINED_VOCAB_FILES_MAP
 lowercase_		=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_		=			["input_ids", "attention_mask"]

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



   '''simple docstring'''



   lowerCamelCase__:      Dict			  ={} if sp_model_kwargs is None else sp_model_kwargs

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

   lowerCamelCase__:      str			  =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_       , )

   lowerCamelCase__:      Any			  =spm.SentencePieceProcessor(**self.sp_model_kwargs)
   self.sp_model.Load(str(UpperCAmelCase_))
   lowerCamelCase__:      List[str]			  =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
   lowerCamelCase__:      Union[str, Any]			  =1

   # Mimic fairseq token-to-id alignment for the first 4 token
   lowerCamelCase__:      Union[str, Any]			  ={"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

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

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

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



   '''simple docstring'''



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

 def __setstate__(self				:   Any       , UpperCAmelCase_				:   Tuple)       ->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[Any]			  =d

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

   lowerCamelCase__:      str			  =spm.SentencePieceProcessor(**self.sp_model_kwargs)
   self.sp_model.LoadFromSerializedProto(self.sp_model_proto)

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str]       , UpperCAmelCase_				:   List[int]       , UpperCAmelCase_				:   Optional[List[int]] = None)       ->List[int]:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_					(self				:   int       , UpperCAmelCase_				:   List[int]       , UpperCAmelCase_				:   Optional[List[int]] = None       , UpperCAmelCase_				:   bool = False)       ->List[int]:



   '''simple docstring'''



   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 							SCREAMING_SNAKE_CASE_					(self				:   Dict       , UpperCAmelCase_				:   List[int]       , UpperCAmelCase_				:   Optional[List[int]] = None)       ->List[int]:



   '''simple docstring'''



   lowerCamelCase__:      List[str]			  =[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				:   List[Any])       ->Optional[int]:



   '''simple docstring'''



   return len(self.sp_model) + self.fairseq_offset + self.num_madeup_words

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->str:



   '''simple docstring'''



   lowerCamelCase__:      str			  ={self.convert_ids_to_tokens(UpperCAmelCase_): i for i in range(self.vocab_size)}
   vocab.update(self.added_tokens_encoder)
   return vocab

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any       , UpperCAmelCase_				:   str)       ->List[str]:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str]       , UpperCAmelCase_				:   Union[str, Any])       ->Dict:



   '''simple docstring'''



   if token in self.fairseq_tokens_to_ids:
     return self.fairseq_tokens_to_ids[token]
   lowerCamelCase__:      Optional[int]			  =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 							SCREAMING_SNAKE_CASE_					(self				:   List[str]       , UpperCAmelCase_				:   List[str])       ->Optional[Any]:



   '''simple docstring'''



   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				:   Any       , UpperCAmelCase_				:   Dict)       ->Dict:



   '''simple docstring'''



   lowerCamelCase__:      str			  ="".join(UpperCAmelCase_).replace(UpperCAmelCase_       , " ").strip()
   return out_string







 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   Optional[str] = None)       ->Tuple[str]:



   '''simple docstring'''



   if not os.path.isdir(UpperCAmelCase_):
     logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
     return
   lowerCamelCase__:      List[str]			  =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:
       lowerCamelCase__:      Optional[int]			  =self.sp_model.serialized_model_proto()
       fi.write(UpperCAmelCase_)

   return (out_vocab_file,)



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowerCamelCase							=       {
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       [
       """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """BioGptForCausalLM""",
       """BioGptForTokenClassification""",
       """BioGptForSequenceClassification""",
       """BioGptModel""",
       """BioGptPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
   from .tokenization_biogpt import BioGptTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_biogpt import (
          BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BioGptForCausalLM,
          BioGptForSequenceClassification,
          BioGptForTokenClassification,
          BioGptModel,
          BioGptPreTrainedModel,
      )


else:
   import sys

   __lowerCamelCase							=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





