



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


lowercase__					:						Tuple												=			logging.get_logger(__name__)

lowercase__					:						List[str]												=			'''▁'''

lowercase__					:						Union[str, Any]												=			{'''vocab_file''': '''sentencepiece.bpe.model'''}

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

lowercase__					:						str												=			{
    '''facebook/xglm-564M''': 20_48,
}




class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    str	      =	VOCAB_FILES_NAMES
 _lowerCAmelCase     :    str	      =	PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase     :    Union[str, Any]	      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase     :    List[Any]	      =	["""input_ids""", """attention_mask"""]

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

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

     snake_case_				:  Union[str, Any]       =			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=lowercase_						,  eos_token=lowercase_						,  unk_token=lowercase_						,  sep_token=lowercase_						,  cls_token=lowercase_						,  pad_token=lowercase_						,  sp_model_kwargs=self.sp_model_kwargs						,  **lowercase_						,  )

     snake_case_				:  str       =			spm.SentencePieceProcessor(**self.sp_model_kwargs       )
     self.sp_model.Load(str(lowercase_       )       )
     snake_case_				:  Optional[int]       =			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_				:  Optional[Any]       =			1

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

     snake_case_				:  List[str]       =			len(self.sp_model       )
     snake_case_				:  Any       =			{f"<madeupword{i}>": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words       )}
     self.fairseq_tokens_to_ids.update(lowercase_       )

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

 def __getstate__(  self	: int       ):
     snake_case_				:  Dict       =			self.__dict__.copy()
     snake_case_				:  Tuple       =			None
     snake_case_				:  Dict       =			self.sp_model.serialized_model_proto()
     return state

 def __setstate__(  self	: Any						,  lowercase_	: List[Any]       ):
     snake_case_				:  Optional[Any]       =			d

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

     snake_case_				:  Optional[int]       =			spm.SentencePieceProcessor(**self.sp_model_kwargs       )
     self.sp_model.LoadFromSerializedProto(self.sp_model_proto       )

 def 		_snake_case      (  self	: Dict						,  lowercase_	: List[int]						,  lowercase_	: Optional[List[int]] = None       ):

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

 def 		_snake_case      (  self	: List[str]						,  lowercase_	: List[int]						,  lowercase_	: Optional[List[int]] = None						,  lowercase_	: bool = False       ):

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

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

 def 		_snake_case      (  self	: str						,  lowercase_	: List[int]						,  lowercase_	: Optional[List[int]] = None       ):
     snake_case_				:  Union[str, Any]       =			[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 		_snake_case      (  self	: Dict       ):
     return len(self.sp_model       ) + self.fairseq_offset + self.num_madeup_words

 def 		_snake_case      (  self	: Union[str, Any]       ):
     snake_case_				:  Union[str, Any]       =			{self.convert_ids_to_tokens(lowercase_       ): i for i in range(self.vocab_size       )}
     vocab.update(self.added_tokens_encoder       )
     return vocab

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

 def 		_snake_case      (  self	: int						,  lowercase_	: str       ):
     if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
     snake_case_				:  List[Any]       =			self.sp_model.PieceToId(lowercase_       )

     # 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 		_snake_case      (  self	: Any						,  lowercase_	: int       ):
     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 		_snake_case      (  self	: Tuple						,  lowercase_	: Union[str, Any]       ):
     snake_case_				:  str       =			''''''.join(lowercase_       ).replace(lowercase_						,  ''' '''       ).strip()
     return out_string






 def 		_snake_case      (  self	: Union[str, Any]						,  lowercase_	: str						,  lowercase_	: Optional[str] = None       ):
     if not os.path.isdir(lowercase_       ):
         logger.error(f"Vocabulary path ({save_directory}) should be a directory"       )
         return
     snake_case_				:  Union[str, Any]       =			os.path.join(
         lowercase_						,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )

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

     return (out_vocab_file,)










"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__					:						Any												=			logging.get_logger(__name__)

lowercase__					:						Tuple												=			{
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}




class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Dict	      =	"""roberta"""

 def __init__(  self	: int						,  lowercase_	: Any=50265						,  lowercase_	: Union[str, Any]=768						,  lowercase_	: Optional[int]=12						,  lowercase_	: List[str]=12						,  lowercase_	: Dict=3072						,  lowercase_	: Tuple="gelu"						,  lowercase_	: Optional[int]=0.1						,  lowercase_	: Tuple=0.1						,  lowercase_	: List[str]=512						,  lowercase_	: List[Any]=2						,  lowercase_	: Union[str, Any]=0.02						,  lowercase_	: List[Any]=1E-12						,  lowercase_	: Optional[int]=1						,  lowercase_	: Tuple=0						,  lowercase_	: str=2						,  lowercase_	: int="absolute"						,  lowercase_	: str=True						,  lowercase_	: Tuple=None						,  **lowercase_	: str						,  ):
     super().__init__(pad_token_id=lowercase_						,  bos_token_id=lowercase_						,  eos_token_id=lowercase_						,  **lowercase_       )

     snake_case_				:  str       =			vocab_size
     snake_case_				:  Optional[Any]       =			hidden_size
     snake_case_				:  Any       =			num_hidden_layers
     snake_case_				:  List[str]       =			num_attention_heads
     snake_case_				:  Dict       =			hidden_act
     snake_case_				:  List[Any]       =			intermediate_size
     snake_case_				:  Optional[int]       =			hidden_dropout_prob
     snake_case_				:  List[str]       =			attention_probs_dropout_prob
     snake_case_				:  Any       =			max_position_embeddings
     snake_case_				:  Optional[Any]       =			type_vocab_size
     snake_case_				:  Optional[int]       =			initializer_range
     snake_case_				:  Dict       =			layer_norm_eps
     snake_case_				:  Optional[int]       =			position_embedding_type
     snake_case_				:  str       =			use_cache
     snake_case_				:  Tuple       =			classifier_dropout





class  _UpperCAmelCase (    lowerCAmelCase__):

 @property
 def 		_snake_case      (  self	: Any       ):
     if self.task == "multiple-choice":
         snake_case_				:  List[Any]       =			{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
     else:
         snake_case_				:  Union[str, Any]       =			{0: '''batch''', 1: '''sequence'''}
     return OrderedDict(
         [
             ('''input_ids''', dynamic_axis),
             ('''attention_mask''', dynamic_axis),
         ]       )





