'''simple docstring'''





import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


SCREAMING_SNAKE_CASE_:     Dict  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:     Optional[Any]  ={'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE_:     List[Any]  ={
    'tokenizer_file': {
        'bigscience/tokenizer': 'https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json',
        'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json',
        'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json',
        'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json',
        'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json',
        'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json',
        'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json',
    },
}




class        __A      ( UpperCamelCase__	):
    a__       :						int          =  VOCAB_FILES_NAMES
    a__       :						Dict          =  PRETRAINED_VOCAB_FILES_MAP
    a__       :						Any          =  ["""input_ids""", """attention_mask"""]
    a__       :						Any          =  None



    def __init__(self    :    Optional[int]   ,			__a    :    Optional[int]=None   ,			__a    :    Union[str, Any]=None   ,			__a    :    Dict=None   ,			__a    :    List[Any]="<unk>"   ,			__a    :    Union[str, Any]="<s>"   ,			__a    :    Any="</s>"   ,			__a    :    int="<pad>"   ,			__a    :    str=False   ,			__a    :    str=False   ,			**__a    :    int   ,			):
         super().__init__(
             __a   ,			__a   ,			tokenizer_file=__a   ,			unk_token=__a   ,			bos_token=__a   ,			eos_token=__a   ,			pad_token=__a   ,			add_prefix_space=__a   ,			clean_up_tokenization_spaces=__a   ,			**__a   ,			)
         UpperCAmelCase_           =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
         if pre_tok_state.get("add_prefix_space"   ,			__a ) != add_prefix_space:
              UpperCAmelCase_           =   getattr(__a   ,			pre_tok_state.pop("type" ) )
              UpperCAmelCase_           =   add_prefix_space
              UpperCAmelCase_           =   pre_tok_class(**__a )

         UpperCAmelCase_           =   add_prefix_space



    def 					_lowercase						(self    :    Tuple   ,			*__a    :    Optional[Any]   ,			**__a    :    str ):
         UpperCAmelCase_           =   kwargs.get("is_split_into_words"   ,			__a )
         if not (self.add_prefix_space or not is_split_into_words):
              raise Exception(
                  f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
                  " pretokenized inputs." )

         return super()._batch_encode_plus(*__a   ,			**__a )



    def 					_lowercase						(self    :    Tuple   ,			*__a    :    Tuple   ,			**__a    :    int ):
         UpperCAmelCase_           =   kwargs.get("is_split_into_words"   ,			__a )

         if not (self.add_prefix_space or not is_split_into_words):
              raise Exception(
                  f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
                  " pretokenized inputs." )

         return super()._encode_plus(*__a   ,			**__a )



    def 					_lowercase						(self    :    Optional[int]   ,			__a    :    str   ,			__a    :    Optional[str] = None ):
         UpperCAmelCase_           =   self._tokenizer.model.save(__a   ,			name=__a )
         return tuple(__a )






    def 					_lowercase						(self    :    Optional[int]   ,			__a    :    "Conversation" ):
         UpperCAmelCase_           =   []
         for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(__a   ,			add_special_tokens=__a ) + [self.eos_token_id] )

         if len(__a ) > self.model_max_length:
              UpperCAmelCase_           =   input_ids[-self.model_max_length :]
         return input_ids





'''simple docstring'''





from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest




@require_tokenizers
class        __A      ( UpperCamelCase__	):
    a__       :						Optional[Any]          =  DistilBertTokenizer
    a__       :						Any          =  DistilBertTokenizerFast
    a__       :						str          =  True



    @slow
    def 					_lowercase						(self    :    int ):
         UpperCAmelCase_           =   DistilBertTokenizer.from_pretrained("distilbert-base-uncased" )

         UpperCAmelCase_           =   tokenizer.encode("sequence builders"   ,			add_special_tokens=__a )
         UpperCAmelCase_           =   tokenizer.encode("multi-sequence build"   ,			add_special_tokens=__a )

         UpperCAmelCase_           =   tokenizer.build_inputs_with_special_tokens(__a )
         UpperCAmelCase_           =   tokenizer.build_inputs_with_special_tokens(__a   ,			__a )

         assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
         assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
             tokenizer.sep_token_id
         ]




