





from collections import OrderedDict
from typing import Mapping

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


snake_case 				=       logging.get_logger(__name__)

snake_case 				=       {
    """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 				A_				(      UpperCAmelCase	):







							"""simple docstring"""





							SCREAMING_SNAKE_CASE_      : Optional[int]      =  '''roberta'''







							def __init__(    self	:	Optional[Any]						,__A	:	Tuple=5_0265						,__A	:	Optional[Any]=768						,__A	:	Any=12						,__A	:	Union[str, Any]=12						,__A	:	Optional[Any]=3072						,__A	:	List[str]="gelu"						,__A	:	str=0.1						,__A	:	Union[str, Any]=0.1						,__A	:	Dict=512						,__A	:	str=2						,__A	:	Optional[Any]=0.02						,__A	:	Union[str, Any]=1e-12						,__A	:	Any=1						,__A	:	Dict=0						,__A	:	List[Any]=2						,__A	:	Dict="absolute"						,__A	:	int=True						,__A	:	Union[str, Any]=None						,**__A	:	Union[str, Any]						,)						->					Tuple:
									super().__init__(pad_token_id=__A						,bos_token_id=__A						,eos_token_id=__A						,**__A  )

									_lowercase							= vocab_size
									_lowercase							= hidden_size
									_lowercase							= num_hidden_layers
									_lowercase							= num_attention_heads
									_lowercase							= hidden_act
									_lowercase							= intermediate_size
									_lowercase							= hidden_dropout_prob
									_lowercase							= attention_probs_dropout_prob
									_lowercase							= max_position_embeddings
									_lowercase							= type_vocab_size
									_lowercase							= initializer_range
									_lowercase							= layer_norm_eps
									_lowercase							= position_embedding_type
									_lowercase							= use_cache
									_lowercase							= classifier_dropout





class 				A_				(      UpperCAmelCase	):







							"""simple docstring"""





							@property
							def    __UpperCAmelCase (    self	:	Dict  )						->					Mapping[str, Mapping[int, str]]:
									if self.task == "multiple-choice":
											_lowercase							= {0: 'batch', 1: 'choice', 2: 'sequence'}
									else:
											_lowercase							= {0: 'batch', 1: 'sequence'}
									return OrderedDict(
									    [
									        ('input_ids', dynamic_axis),
									        ('attention_mask', dynamic_axis),
									    ]  )






import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

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


if is_torch_available():
      import torch


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation


snake_case 				=       logging.get_logger(__name__)
snake_case 				=       {"""vocab_file""": """spiece.model"""}

snake_case 				=       {
    """vocab_file""": {
        """AI-Sweden/gpt-sw3-126m""": """https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-350m""": """https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-1.6b""": """https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-6.7b""": """https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-20b""": """https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model""",
    }
}

snake_case 				=       {
    """AI-Sweden/gpt-sw3-126m""": 2_0_4_8,
    """AI-Sweden/gpt-sw3-350m""": 2_0_4_8,
    """AI-Sweden/gpt-sw3-1.6b""": 2_0_4_8,
    """AI-Sweden/gpt-sw3-6.7b""": 2_0_4_8,
    """AI-Sweden/gpt-sw3-20b""": 2_0_4_8,
}





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





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







       def __init__(    self	:	Any						,__A	:	Tuple						,__A	:	Any=False						,__A	:	int=False						,__A	:	List[str]=False						,__A	:	List[str]=None						,__A	:	Dict=None						,__A	:	Dict=None						,__A	:	Union[str, Any]=None						,__A	:	Optional[Dict[str, Any]] = None						,**__A	:	Tuple						,)						->					None:
         _lowercase							= {} if sp_model_kwargs is None else sp_model_kwargs

         _lowercase							= kwargs.get('name_or_path'  )
         if name_or_path is None:
           logger.warning(
               'name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'
               ' you are testing the model, this can safely be ignored'  )
           _lowercase							= 'None'

         # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
         _lowercase							= '<|endoftext|>' if eos_token is None else eos_token
         _lowercase							= '<unk>' if unk_token is None else unk_token
         if "gpt-sw3-7b" in name_or_path:
           _lowercase							= unk_token if pad_token is None else pad_token
           _lowercase							= eos_token if bos_token is None else bos_token
         else:
           _lowercase							= '<pad>' if pad_token is None else pad_token
           _lowercase							= '<s>' if bos_token is None else bos_token

         super().__init__(
             do_lower_case=__A						,remove_space=__A						,keep_accents=__A						,bos_token=__A						,eos_token=__A						,unk_token=__A						,pad_token=__A						,sp_model_kwargs=self.sp_model_kwargs						,**__A						,)

         _lowercase							= do_lower_case
         _lowercase							= remove_space
         _lowercase							= keep_accents
         _lowercase							= vocab_file

         _lowercase							= spm.SentencePieceProcessor(**self.sp_model_kwargs  )
         self.sp_model.Load(__A  )

         # Used for whitespace normalization in input texts
         # fmt : off
         _lowercase							= {' ', ' ', ' ', ' ', ' ', '　', ' ', ' ', ' ', ' ', '￼', ''}
         # fmt : on

         # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
         _lowercase							= re.compile(
             F"""[{"".join(map(__A						,list(range(0						,9  )  ) + list(range(11						,32  )  ) + list(range(127						,160  )  ) + [160, 173, 8203]  )  )}]"""  )







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







       def __setstate__(    self	:	Optional[Any]						,__A	:	Dict  )						->					str:
         _lowercase							= d

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

         _lowercase							= spm.SentencePieceProcessor(**self.sp_model_kwargs  )
         self.sp_model.Load(self.vocab_file  )







       @property
       # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
       def    __UpperCAmelCase (    self	:	List[Any]  )						->					int:
         return len(self.sp_model  )







       def    __UpperCAmelCase (    self	:	Optional[Any]						,__A	:	str  )						->					str:
         _lowercase							= self.non_printing_characters_re.sub(''						,__A  )

         # Normalize whitespaces
         _lowercase							= ''.join([char if char not in self.whitespaces else ' ' for char in text]  )

         # NFC Unicode normalization
         _lowercase							= unicodedata.normalize('NFC'						,__A  )
         return text







       def    __UpperCAmelCase (    self	:	Optional[Any]						,__A	:	str						,**__A	:	Optional[int]  )						->					List[str]:
         _lowercase							= self.preprocess_text(__A  )
         return self.sp_model.encode(__A						,out_type=__A  )







       def    __UpperCAmelCase (    self	:	List[Any]						,__A	:	str  )						->					int:
         return self.sp_model.PieceToId(__A  )







       def    __UpperCAmelCase (    self	:	Any						,__A	:	int  )						->					str:
         return self.sp_model.IdToPiece(__A  )







       @staticmethod
       def    __UpperCAmelCase (    __A	:	str  )						->					str:
         return out_string







       def    __UpperCAmelCase (    self	:	Tuple						,__A	:	List[str]  )						->					str:
         _lowercase							= []
         _lowercase							= ''
         _lowercase							= False
         for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
             # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
             if not prev_is_special:
               out_string += " "

             out_string += self.sp_model.decode(__A  ) + token
             _lowercase							= True
             _lowercase							= []
           else:
             current_sub_tokens.append(__A  )
             _lowercase							= False
         out_string += self.sp_model.decode(__A  )

         return out_string







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







       def    __UpperCAmelCase (    self	:	Optional[int]						,__A	:	str						,__A	:	Optional[str] = None  )						->					Tuple[str]:
         if not os.path.isdir(__A  ):
           logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""  )
           return
         _lowercase							= 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:
             _lowercase							= self.sp_model.serialized_model_proto()
             fi.write(__A  )

         return (out_vocab_file,)







       def    __UpperCAmelCase (    self	:	str						,__A	:	Union[str, List[str]]						,__A	:	Union[str, bool] = False  )						->					Union[List[int], List[List[int]], "torch.Tensor"]:

         if isinstance(__A						,__A  ):
           _lowercase							= self.preprocess_text(__A  )
           _lowercase							= self.sp_model.encode(__A  )
         else:
           _lowercase							= [self.preprocess_text(__A  ) for t in text]
           _lowercase							= self.sp_model.encode(__A  )

         if return_tensors is True or return_tensors == "pt":
           _lowercase							= torch.tensor(__A  )

         return token_ids







       def    __UpperCAmelCase (    self	:	Optional[Any]						,__A	:	Union[int, List[int]]  )						->					str:
         return self.sp_model.decode(__A  )







       def    __UpperCAmelCase (    self	:	str						,__A	:	"Conversation"  )						->					List[int]:
         _lowercase							= [F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
         _lowercase							= (
             F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(__A  ) + F"""{self.bos_token}Bot:"""
         )
         return self.encode(text=__A  )