




'''simple docstring'''





import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

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


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation

UpperCamelCase_						:      List[str]       =						logging.get_logger(__name__)


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

UpperCamelCase_						:      str       =						{
    '''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'''
    },
}

UpperCamelCase_						:      Union[str, Any]       =						{'''facebook/blenderbot-3B''': 128}





@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def      __a     (       )						->			Optional[int]:

     """simple docstring"""




     _snake_case							=				(
         list(range(ord("!"   ) ,						ord("~"   ) + 1   )   ) + list(range(ord("¡"   ) ,						ord("¬"   ) + 1   )   ) + list(range(ord("®"   ) ,						ord("ÿ"   ) + 1   )   )
     )
     _snake_case							=				bs[:]
     _snake_case							=				0
     for b in range(2**8   ):
          if b not in bs:
               bs.append(_UpperCamelCase   )
               cs.append(2**8 + n   )
               n += 1
     _snake_case							=				[chr(_UpperCamelCase   ) for n in cs]
     return dict(zip(_UpperCamelCase ,						_UpperCamelCase   )   )





def      __a     (       _UpperCamelCase:   str   )						->			Optional[int]:

     """simple docstring"""




     _snake_case							=				set()
     _snake_case							=				word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)   )
          _snake_case							=				char
     return pairs





class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   str       =						VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE_							:   Optional[Any]       =						PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE_							:   Dict       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE_							:   int       =						["""input_ids""", """attention_mask"""]






     def __init__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE="replace" ,_SCREAMING_SNAKE_CASE="<s>" ,_SCREAMING_SNAKE_CASE="</s>" ,_SCREAMING_SNAKE_CASE="</s>" ,_SCREAMING_SNAKE_CASE="<s>" ,_SCREAMING_SNAKE_CASE="<unk>" ,_SCREAMING_SNAKE_CASE="<pad>" ,_SCREAMING_SNAKE_CASE="<mask>" ,_SCREAMING_SNAKE_CASE=False ,**_SCREAMING_SNAKE_CASE ,)							->      List[str]:
          _snake_case							=				AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE   ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ) else bos_token
          _snake_case							=				AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE   ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ) else eos_token
          _snake_case							=				AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE   ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ) else sep_token
          _snake_case							=				AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE   ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ) else cls_token
          _snake_case							=				AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE   ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ) else unk_token
          _snake_case							=				AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE   ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ) else pad_token

          # Mask token behave like a normal word, i.e. include the space before it
          _snake_case							=				AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE   ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ) else mask_token

          super().__init__(
              errors=_SCREAMING_SNAKE_CASE ,bos_token=_SCREAMING_SNAKE_CASE ,eos_token=_SCREAMING_SNAKE_CASE ,unk_token=_SCREAMING_SNAKE_CASE ,sep_token=_SCREAMING_SNAKE_CASE ,cls_token=_SCREAMING_SNAKE_CASE ,pad_token=_SCREAMING_SNAKE_CASE ,mask_token=_SCREAMING_SNAKE_CASE ,add_prefix_space=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ,)

          with open(_SCREAMING_SNAKE_CASE ,encoding="utf-8"   ) as vocab_handle:
               _snake_case							=				json.load(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				{v: k for k, v in self.encoder.items()}
          _snake_case							=				errors  # how to handle errors in decoding
          _snake_case							=				bytes_to_unicode()
          _snake_case							=				{v: k for k, v in self.byte_encoder.items()}
          with open(_SCREAMING_SNAKE_CASE ,encoding="utf-8"   ) as merges_handle:
               _snake_case							=				merges_handle.read().split("\n"   )[1:-1]
          _snake_case							=				[tuple(merge.split()   ) for merge in bpe_merges]
          _snake_case							=				dict(zip(_SCREAMING_SNAKE_CASE ,range(len(_SCREAMING_SNAKE_CASE   )   )   )   )
          _snake_case							=				{}
          _snake_case							=				add_prefix_space

          # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
          _snake_case							=				re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"   )






     @property
     # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
     def     _lowercase     (			self   )							->      List[str]:
          return len(self.encoder   )






     def     _lowercase     (			self   )							->      int:
          return dict(self.encoder ,**self.added_tokens_encoder   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      Optional[int]:
          if token in self.cache:
               return self.cache[token]
          _snake_case							=				tuple(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				get_pairs(_SCREAMING_SNAKE_CASE   )

          if not pairs:
               return token

          while True:
               _snake_case							=				min(_SCREAMING_SNAKE_CASE ,key=lambda _SCREAMING_SNAKE_CASE   : self.bpe_ranks.get(_SCREAMING_SNAKE_CASE ,float("inf"   )   )   )
               if bigram not in self.bpe_ranks:
                    break
               _snake_case							, _snake_case							=				bigram
               _snake_case							=				[]
               _snake_case							=				0
               while i < len(_SCREAMING_SNAKE_CASE   ):
                    try:
                         _snake_case							=				word.index(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
                    except ValueError:
                         new_word.extend(word[i:]   )
                         break
                    else:
                         new_word.extend(word[i:j]   )
                         _snake_case							=				j

                    if word[i] == first and i < len(_SCREAMING_SNAKE_CASE   ) - 1 and word[i + 1] == second:
                         new_word.append(first + second   )
                         i += 2
                    else:
                         new_word.append(word[i]   )
                         i += 1
               _snake_case							=				tuple(_SCREAMING_SNAKE_CASE   )
               _snake_case							=				new_word
               if len(_SCREAMING_SNAKE_CASE   ) == 1:
                    break
               else:
                    _snake_case							=				get_pairs(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				" ".join(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				word
          return word






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      List[Any]:
          _snake_case							=				[]
          for token in re.findall(self.pat ,_SCREAMING_SNAKE_CASE   ):
               _snake_case							=				"".join(
                   self.byte_encoder[b] for b in token.encode("utf-8"   )   )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
               bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_SCREAMING_SNAKE_CASE   ).split(" "   )   )
          return bpe_tokens






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      List[str]:
          return self.encoder.get(_SCREAMING_SNAKE_CASE ,self.encoder.get(self.unk_token   )   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      Union[str, Any]:
          return self.decoder.get(_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      Tuple:
          _snake_case							=				"".join(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				bytearray([self.byte_decoder[c] for c in text]   ).decode("utf-8" ,errors=self.errors   )
          return text






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None   )							->      Tuple[str]:
          if not os.path.isdir(_SCREAMING_SNAKE_CASE   ):
               logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
               return
          _snake_case							=				os.path.join(
              _SCREAMING_SNAKE_CASE ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )
          _snake_case							=				os.path.join(
              _SCREAMING_SNAKE_CASE ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]   )

          with open(_SCREAMING_SNAKE_CASE ,"w" ,encoding="utf-8"   ) as f:
               f.write(json.dumps(self.encoder ,indent=2 ,sort_keys=_SCREAMING_SNAKE_CASE ,ensure_ascii=_SCREAMING_SNAKE_CASE   ) + "\n"   )

          _snake_case							=				0
          with open(_SCREAMING_SNAKE_CASE ,"w" ,encoding="utf-8"   ) as writer:
               writer.write("#version: 0.2\n"   )
               for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,key=lambda _SCREAMING_SNAKE_CASE   : kv[1]   ):
                    if index != token_index:
                         logger.warning(
                             f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                             " Please check that the tokenizer is not corrupted!"   )
                         _snake_case							=				token_index
                    writer.write(" ".join(_SCREAMING_SNAKE_CASE   ) + "\n"   )
                    index += 1

          return vocab_file, merge_file






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = False   )							->      List[int]:
          if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=_SCREAMING_SNAKE_CASE ,token_ids_a=_SCREAMING_SNAKE_CASE ,already_has_special_tokens=_SCREAMING_SNAKE_CASE   )

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






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None   )							->      List[int]:
          _snake_case							=				[self.sep_token_id]
          _snake_case							=				[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     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=False ,**_SCREAMING_SNAKE_CASE   )							->      Optional[int]:
          _snake_case							=				kwargs.pop("add_prefix_space" ,self.add_prefix_space   )
          if (is_split_into_words or add_prefix_space) and (len(_SCREAMING_SNAKE_CASE   ) > 0 and not text[0].isspace()):
               _snake_case							=				" " + text
          return (text, kwargs)






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None   )							->      Union[str, Any]:
          return token_ids_a + [self.eos_token_id]






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      List[int]:
          _snake_case							=				[]
          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(_SCREAMING_SNAKE_CASE   )

          _snake_case							=				"  ".join(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				self.encode(_SCREAMING_SNAKE_CASE   )
          if len(_SCREAMING_SNAKE_CASE   ) > self.model_max_length:
               _snake_case							=				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'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_						:      List[Any]       =						{
    '''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
    '''tokenization_convbert''': ['''ConvBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_						:      Optional[Any]       =						['''ConvBertTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_						:      Optional[int]       =						[
       '''CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ConvBertForMaskedLM''',
       '''ConvBertForMultipleChoice''',
       '''ConvBertForQuestionAnswering''',
       '''ConvBertForSequenceClassification''',
       '''ConvBertForTokenClassification''',
       '''ConvBertLayer''',
       '''ConvBertModel''',
       '''ConvBertPreTrainedModel''',
       '''load_tf_weights_in_convbert''',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_						:      Tuple       =						[
       '''TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFConvBertForMaskedLM''',
       '''TFConvBertForMultipleChoice''',
       '''TFConvBertForQuestionAnswering''',
       '''TFConvBertForSequenceClassification''',
       '''TFConvBertForTokenClassification''',
       '''TFConvBertLayer''',
       '''TFConvBertModel''',
       '''TFConvBertPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
   from .tokenization_convbert import ConvBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_convbert_fast import ConvBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_convbert import (
          CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ConvBertForMaskedLM,
          ConvBertForMultipleChoice,
          ConvBertForQuestionAnswering,
          ConvBertForSequenceClassification,
          ConvBertForTokenClassification,
          ConvBertLayer,
          ConvBertModel,
          ConvBertPreTrainedModel,
          load_tf_weights_in_convbert,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_convbert import (
          TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFConvBertForMaskedLM,
          TFConvBertForMultipleChoice,
          TFConvBertForQuestionAnswering,
          TFConvBertForSequenceClassification,
          TFConvBertForTokenClassification,
          TFConvBertLayer,
          TFConvBertModel,
          TFConvBertPreTrainedModel,
      )


else:
   import sys

   UpperCamelCase_						:      Tuple       =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


