
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

snake_case__      :		List[Any]				=       logging.get_logger(__name__)


snake_case__      :		Union[str, Any]				=       {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

snake_case__      :		List[Any]				=       {
    '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'
    },
}

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





class 						_a   (						snake_case_       ):

       """simple docstring"""





       snake_case				=VOCAB_FILES_NAMES
       snake_case				=PRETRAINED_VOCAB_FILES_MAP
       snake_case				=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       snake_case				=['''input_ids''', '''attention_mask''']
       snake_case				=BlenderbotTokenizer
       def __init__(					self       ,					_snake_case=None       ,					_snake_case=None       ,					_snake_case=None       ,					_snake_case="replace"       ,					_snake_case="<s>"       ,					_snake_case="</s>"       ,					_snake_case="</s>"       ,					_snake_case="<s>"       ,					_snake_case="<unk>"       ,					_snake_case="<pad>"       ,					_snake_case="<mask>"       ,					_snake_case=False       ,					_snake_case=True       ,					**_snake_case       ,					):
        super().__init__(
            __lowercase       ,					__lowercase       ,					tokenizer_file=__lowercase       ,					errors=__lowercase       ,					bos_token=__lowercase       ,					eos_token=__lowercase       ,					sep_token=__lowercase       ,					cls_token=__lowercase       ,					unk_token=__lowercase       ,					pad_token=__lowercase       ,					mask_token=__lowercase       ,					add_prefix_space=__lowercase       ,					trim_offsets=__lowercase       ,					**__lowercase       ,					)

        _UpperCAmelCase 				=json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
        if pre_tok_state.get("add_prefix_space"       ,					__lowercase	) != add_prefix_space:
         _UpperCAmelCase 				=getattr(__lowercase       ,					pre_tok_state.pop("type"	)	)
         _UpperCAmelCase 				=add_prefix_space
         _UpperCAmelCase 				=pre_tok_class(**__lowercase	)

        _UpperCAmelCase 				=add_prefix_space

        _UpperCAmelCase 				='post_processor'
        _UpperCAmelCase 				=getattr(self.backend_tokenizer       ,					__lowercase       ,					__lowercase	)
        if tokenizer_component_instance:
         _UpperCAmelCase 				=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 				=tuple(state["sep"]	)
         if "cls" in state:
          _UpperCAmelCase 				=tuple(state["cls"]	)

         _UpperCAmelCase 				=False

         if state.get("add_prefix_space"       ,					__lowercase	) != add_prefix_space:
          _UpperCAmelCase 				=add_prefix_space
          _UpperCAmelCase 				=True

         if state.get("trim_offsets"       ,					__lowercase	) != trim_offsets:
          _UpperCAmelCase 				=trim_offsets
          _UpperCAmelCase 				=True

         if changes_to_apply:
          _UpperCAmelCase 				=getattr(__lowercase       ,					state.pop("type"	)	)
          _UpperCAmelCase 				=component_class(**__lowercase	)
          setattr(self.backend_tokenizer       ,					__lowercase       ,					__lowercase	)
       @property
       # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
       def     SCREAMING_SNAKE_CASE  (					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     SCREAMING_SNAKE_CASE  (					self       ,					_snake_case	):
        _UpperCAmelCase 				=AddedToken(__lowercase       ,					lstrip=__lowercase       ,					rstrip=__lowercase	) if isinstance(__lowercase       ,					__lowercase	) else value
        _UpperCAmelCase 				=value
       def     SCREAMING_SNAKE_CASE  (					self       ,					*_snake_case       ,					**_snake_case	):
        _UpperCAmelCase 				=kwargs.get("is_split_into_words"       ,					__lowercase	)
        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(*__lowercase       ,					**__lowercase	)
       def     SCREAMING_SNAKE_CASE  (					self       ,					*_snake_case       ,					**_snake_case	):
        _UpperCAmelCase 				=kwargs.get("is_split_into_words"       ,					__lowercase	)

        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(*__lowercase       ,					**__lowercase	)
       def     SCREAMING_SNAKE_CASE  (					self       ,					_snake_case       ,					_snake_case = None	):
        _UpperCAmelCase 				=self._tokenizer.model.save(__lowercase       ,					name=__lowercase	)
        return tuple(__lowercase	)
       def     SCREAMING_SNAKE_CASE  (					self       ,					_snake_case       ,					_snake_case = None	):
        _UpperCAmelCase 				=[self.sep_token_id]
        _UpperCAmelCase 				=[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     SCREAMING_SNAKE_CASE  (					self       ,					_snake_case       ,					_snake_case = None	):
        return token_ids_a + [self.eos_token_id]

       def     SCREAMING_SNAKE_CASE  (					self       ,					_snake_case	):
        _UpperCAmelCase 				=[]
        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(__lowercase	)

        _UpperCAmelCase 				='  '.join(__lowercase	)
        _UpperCAmelCase 				=self.encode(__lowercase	)
        if len(__lowercase	) > self.model_max_length:
         _UpperCAmelCase 				=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









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

from tokenizers import normalizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer


UpperCAmelCase_			:  int										=   logging.get_logger(__name__)

UpperCAmelCase_			:  int										=   {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase_			:  List[str]										=   {
    'vocab_file': {
        'google/realm-cc-news-pretrained-embedder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt'
        ),
        'google/realm-cc-news-pretrained-encoder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt'
        ),
        'google/realm-cc-news-pretrained-scorer': (
            'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt'
        ),
        'google/realm-cc-news-pretrained-openqa': (
            'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt'
        ),
        'google/realm-orqa-nq-openqa': 'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt',
        'google/realm-orqa-nq-reader': 'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt',
        'google/realm-orqa-wq-openqa': 'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt',
        'google/realm-orqa-wq-reader': 'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt',
    },
    'tokenizer_file': {
        'google/realm-cc-news-pretrained-embedder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont'
        ),
        'google/realm-cc-news-pretrained-encoder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json'
        ),
        'google/realm-cc-news-pretrained-scorer': (
            'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json'
        ),
        'google/realm-cc-news-pretrained-openqa': (
            'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json'
        ),
        'google/realm-orqa-nq-openqa': (
            'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json'
        ),
        'google/realm-orqa-nq-reader': (
            'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json'
        ),
        'google/realm-orqa-wq-openqa': (
            'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json'
        ),
        'google/realm-orqa-wq-reader': (
            'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json'
        ),
    },
}

UpperCAmelCase_			:  str										=   {
    'google/realm-cc-news-pretrained-embedder': 5_1_2,
    'google/realm-cc-news-pretrained-encoder': 5_1_2,
    'google/realm-cc-news-pretrained-scorer': 5_1_2,
    'google/realm-cc-news-pretrained-openqa': 5_1_2,
    'google/realm-orqa-nq-openqa': 5_1_2,
    'google/realm-orqa-nq-reader': 5_1_2,
    'google/realm-orqa-wq-openqa': 5_1_2,
    'google/realm-orqa-wq-reader': 5_1_2,
}

UpperCAmelCase_			:  str										=   {
    'google/realm-cc-news-pretrained-embedder': {'do_lower_case': True},
    'google/realm-cc-news-pretrained-encoder': {'do_lower_case': True},
    'google/realm-cc-news-pretrained-scorer': {'do_lower_case': True},
    'google/realm-cc-news-pretrained-openqa': {'do_lower_case': True},
    'google/realm-orqa-nq-openqa': {'do_lower_case': True},
    'google/realm-orqa-nq-reader': {'do_lower_case': True},
    'google/realm-orqa-wq-openqa': {'do_lower_case': True},
    'google/realm-orqa-wq-reader': {'do_lower_case': True},
}

class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		Union[str, Any]     				=    VOCAB_FILES_NAMES
 __lowercase					:		int     				=    PRETRAINED_VOCAB_FILES_MAP
 __lowercase					:		Union[str, Any]     				=    PRETRAINED_INIT_CONFIGURATION
 __lowercase					:		Union[str, Any]     				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase					:		List[str]     				=    RealmTokenizer


 def __init__(   self   ,   __lowercase=None   ,   __lowercase=None   ,   __lowercase=True   ,   __lowercase="[UNK]"   ,   __lowercase="[SEP]"   ,   __lowercase="[PAD]"   ,   __lowercase="[CLS]"   ,   __lowercase="[MASK]"   ,   __lowercase=True   ,   __lowercase=None   ,   **__lowercase   ,   ):

  """simple docstring"""







  super().__init__(
      __lowercase   ,   tokenizer_file=__lowercase   ,   do_lower_case=__lowercase   ,   unk_token=__lowercase   ,   sep_token=__lowercase   ,   pad_token=__lowercase   ,   cls_token=__lowercase   ,   mask_token=__lowercase   ,   tokenize_chinese_chars=__lowercase   ,   strip_accents=__lowercase   ,   **__lowercase   ,   )

  __A	:		Optional[int]	  =      json.loads(self.backend_tokenizer.normalizer.__getstate__()    )
  if (
      normalizer_state.get('lowercase'   ,   __lowercase    ) != do_lower_case
      or normalizer_state.get('strip_accents'   ,   __lowercase    ) != strip_accents
      or normalizer_state.get('handle_chinese_chars'   ,   __lowercase    ) != tokenize_chinese_chars
  ):
   __A	:		Tuple	  =      getattr(__lowercase   ,   normalizer_state.pop('type'    )    )
   __A	:		Optional[int]	  =      do_lower_case
   __A	:		List[str]	  =      strip_accents
   __A	:		Dict	  =      tokenize_chinese_chars
   __A	:		List[Any]	  =      normalizer_class(**__lowercase    )

  __A	:		int	  =      do_lower_case


 def 			snake_case__ (   self   ,   __lowercase   ,   **__lowercase    ):

  """simple docstring"""







  __A	:		Dict	  =      PaddingStrategy.MAX_LENGTH

  __A	:		Optional[int]	  =      text
  __A	:		Union[str, Any]	  =      kwargs.pop('text_pair'   ,   __lowercase    )
  __A	:		List[str]	  =      kwargs.pop('return_tensors'   ,   __lowercase    )

  __A	:		int	  =      {
      'input_ids': [],
      'attention_mask': [],
      'token_type_ids': [],
  }

  for idx, candidate_text in enumerate(__lowercase    ):
   if batch_text_pair is not None:
    __A	:		Tuple	  =      batch_text_pair[idx]
   else:
    __A	:		Union[str, Any]	  =      None

   __A	:		Optional[int]	  =      super().__call__(__lowercase   ,   __lowercase   ,   return_tensors=__lowercase   ,   **__lowercase    )

   __A	:		str	  =      encoded_candidates.get('input_ids'    )
   __A	:		Union[str, Any]	  =      encoded_candidates.get('attention_mask'    )
   __A	:		Optional[int]	  =      encoded_candidates.get('token_type_ids'    )

   if encoded_input_ids is not None:
    output_data["input_ids"].append(__lowercase    )
   if encoded_attention_mask is not None:
    output_data["attention_mask"].append(__lowercase    )
   if encoded_token_type_ids is not None:
    output_data["token_type_ids"].append(__lowercase    )

  __A	:		List[str]	  =      {key: item for key, item in output_data.items() if len(__lowercase    ) != 0}

  return BatchEncoding(__lowercase   ,   tensor_type=__lowercase    )


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase=None    ):

  """simple docstring"""







  __A	:		Union[str, Any]	  =      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

  if token_ids_a:
   output += token_ids_a + [self.sep_token_id]

  return output


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase = None    ):

  """simple docstring"""







  __A	:		Union[str, Any]	  =      [self.sep_token_id]
  __A	:		Tuple	  =      [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    ) * [0] + len(token_ids_a + sep    ) * [1]







 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase = None    ):

  """simple docstring"""







  __A	:		int	  =      self._tokenizer.model.save(__lowercase   ,   name=__lowercase    )
  return tuple(__lowercase    )






