'''simple docstring'''
import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format






class 			__lowercase		:







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						None







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Union[str, Any]		    =  self.feature_extraction_class(**self.feat_extract_dict    )
         __UpperCamelCase	:	str		    =  json.loads(feat_extract.to_json_string()    )
         for key, value in self.feat_extract_dict.items():
             self.assertEqual(obj[key]     ,			_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Dict		    =  self.feature_extraction_class(**self.feat_extract_dict    )

         with tempfile.TemporaryDirectory() as tmpdirname:
             __UpperCamelCase	:	Any		    =  os.path.join(_lowerCamelCase     ,			'feat_extract.json'    )
             feat_extract_first.to_json_file(_lowerCamelCase    )
             __UpperCamelCase	:	Optional[Any]		    =  self.feature_extraction_class.from_json_file(_lowerCamelCase    )

         self.assertEqual(feat_extract_second.to_dict()     ,			feat_extract_first.to_dict()    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Optional[int]		    =  self.feature_extraction_class(**self.feat_extract_dict    )

         with tempfile.TemporaryDirectory() as tmpdirname:
             __UpperCamelCase	:	Optional[int]		    =  feat_extract_first.save_pretrained(_lowerCamelCase    )[0]
             check_json_file_has_correct_format(_lowerCamelCase    )
             __UpperCamelCase	:	int		    =  self.feature_extraction_class.from_pretrained(_lowerCamelCase    )

         self.assertEqual(feat_extract_second.to_dict()     ,			feat_extract_first.to_dict()    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Optional[Any]		    =  self.feature_extraction_class()
         self.assertIsNotNone(_lowerCamelCase    )




'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation

a=  logging.get_logger(__name__)


a=  {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

a=  {
    '''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
    '''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
    '''tokenizer_config_file''': {
        '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
    },
}

a=  {'''facebook/blenderbot-3B''': 1_2_8}






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     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''']
     SCREAMING_SNAKE_CASE__		   =						BlenderbotTokenizer







     def __init__(		self     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase="replace"     ,			_lowerCamelCase="<s>"     ,			_lowerCamelCase="</s>"     ,			_lowerCamelCase="</s>"     ,			_lowerCamelCase="<s>"     ,			_lowerCamelCase="<unk>"     ,			_lowerCamelCase="<pad>"     ,			_lowerCamelCase="<mask>"     ,			_lowerCamelCase=False     ,			_lowerCamelCase=True     ,			**_lowerCamelCase     ,			):
         super().__init__(
             _lowerCamelCase     ,			_lowerCamelCase     ,			tokenizer_file=_lowerCamelCase     ,			errors=_lowerCamelCase     ,			bos_token=_lowerCamelCase     ,			eos_token=_lowerCamelCase     ,			sep_token=_lowerCamelCase     ,			cls_token=_lowerCamelCase     ,			unk_token=_lowerCamelCase     ,			pad_token=_lowerCamelCase     ,			mask_token=_lowerCamelCase     ,			add_prefix_space=_lowerCamelCase     ,			trim_offsets=_lowerCamelCase     ,			**_lowerCamelCase     ,			)

         __UpperCamelCase	:	str		    =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
         if pre_tok_state.get('add_prefix_space'     ,			_lowerCamelCase    ) != add_prefix_space:
             __UpperCamelCase	:	Any		    =  getattr(_lowerCamelCase     ,			pre_tok_state.pop('type'    )    )
             __UpperCamelCase	:	Dict		    =  add_prefix_space
             __UpperCamelCase	:	Optional[Any]		    =  pre_tok_class(**_lowerCamelCase    )

         __UpperCamelCase	:	str		    =  add_prefix_space

         __UpperCamelCase	:	Optional[int]		    =  'post_processor'
         __UpperCamelCase	:	Tuple		    =  getattr(self.backend_tokenizer     ,			_lowerCamelCase     ,			_lowerCamelCase    )
         if tokenizer_component_instance:
             __UpperCamelCase	:	str		    =  json.loads(tokenizer_component_instance.__getstate__()    )

             # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
             if "sep" in state:
                 __UpperCamelCase	:	List[Any]		    =  tuple(state['sep']    )
             if "cls" in state:
                 __UpperCamelCase	:	str		    =  tuple(state['cls']    )

             __UpperCamelCase	:	Tuple		    =  False

             if state.get('add_prefix_space'     ,			_lowerCamelCase    ) != add_prefix_space:
                 __UpperCamelCase	:	Dict		    =  add_prefix_space
                 __UpperCamelCase	:	str		    =  True

             if state.get('trim_offsets'     ,			_lowerCamelCase    ) != trim_offsets:
                 __UpperCamelCase	:	int		    =  trim_offsets
                 __UpperCamelCase	:	Any		    =  True

             if changes_to_apply:
                 __UpperCamelCase	:	Dict		    =  getattr(_lowerCamelCase     ,			state.pop('type'    )    )
                 __UpperCamelCase	:	Any		    =  component_class(**_lowerCamelCase    )
                 setattr(self.backend_tokenizer     ,			_lowerCamelCase     ,			_lowerCamelCase    )







     @property
     # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
     def lowerCAmelCase     (		self    ):
         if self._mask_token is None:
             if self.verbose:
                 logger.error('Using mask_token, but it is not set yet.'    )
             return None
         return str(self._mask_token    )







     @mask_token.setter
     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	int		    =  AddedToken(_lowerCamelCase     ,			lstrip=_lowerCamelCase     ,			rstrip=_lowerCamelCase    ) if isinstance(_lowerCamelCase     ,			_lowerCamelCase    ) else value
         __UpperCamelCase	:	Optional[Any]		    =  value







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	Dict		    =  kwargs.get('is_split_into_words'     ,			_lowerCamelCase    )
         assert self.add_prefix_space or not is_split_into_words, (
             f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
             "to use it with pretokenized inputs."
         )

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







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	Dict		    =  kwargs.get('is_split_into_words'     ,			_lowerCamelCase    )

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

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







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None    ):
         __UpperCamelCase	:	List[str]		    =  self._tokenizer.model.save(_lowerCamelCase     ,			name=_lowerCamelCase    )
         return tuple(_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None    ):
         __UpperCamelCase	:	Union[str, Any]		    =  [self.sep_token_id]
         __UpperCamelCase	:	List[Any]		    =  [self.cls_token_id]

         if token_ids_a is None:
             return len(cls + token_ids_a + sep    ) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None    ):
         return token_ids_a + [self.eos_token_id]







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	List[str]		    =  []
         for is_user, text in conversation.iter_texts():
             if is_user:
                 # We need to space prefix as it's being done within blenderbot
                 inputs.append(' ' + text    )
             else:
                 # Generated responses should contain them already.
                 inputs.append(_lowerCamelCase    )

         __UpperCamelCase	:	Union[str, Any]		    =  '  '.join(_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  self.encode(_lowerCamelCase    )
         if len(_lowerCamelCase    ) > self.model_max_length:
             __UpperCamelCase	:	Tuple		    =  input_ids[-self.model_max_length :]
             logger.warning(f"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens."""    )
         return input_ids



