

'''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						:  int							       =				logging.get_logger(__name__)


_UpperCamelCase						:  Tuple							       =				{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

_UpperCamelCase						:  Tuple							       =				{
    "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						:  Optional[int]							       =				{"facebook/blenderbot-3B": 128}


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


    """simple docstring"""


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


def       snake_case   (	snake_case      : Any			) ->  Dict:


    """simple docstring"""


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



class 				_snake_case					(   a_			):
  SCREAMING_SNAKE_CASE			:       List[str]				=     VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE			:       Any				=     PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE			:       Dict				=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE			:       List[str]				=     ['''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      , ):





      '''simple docstring'''

      lowerCAmelCase 						=							AddedToken(_SCREAMING_SNAKE_CASE      , lstrip=_SCREAMING_SNAKE_CASE      , rstrip=_SCREAMING_SNAKE_CASE	) if isinstance(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	) else bos_token
      lowerCAmelCase 						=							AddedToken(_SCREAMING_SNAKE_CASE      , lstrip=_SCREAMING_SNAKE_CASE      , rstrip=_SCREAMING_SNAKE_CASE	) if isinstance(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	) else eos_token
      lowerCAmelCase 						=							AddedToken(_SCREAMING_SNAKE_CASE      , lstrip=_SCREAMING_SNAKE_CASE      , rstrip=_SCREAMING_SNAKE_CASE	) if isinstance(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	) else sep_token
      lowerCAmelCase 						=							AddedToken(_SCREAMING_SNAKE_CASE      , lstrip=_SCREAMING_SNAKE_CASE      , rstrip=_SCREAMING_SNAKE_CASE	) if isinstance(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	) else cls_token
      lowerCAmelCase 						=							AddedToken(_SCREAMING_SNAKE_CASE      , lstrip=_SCREAMING_SNAKE_CASE      , rstrip=_SCREAMING_SNAKE_CASE	) if isinstance(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	) else unk_token
      lowerCAmelCase 						=							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
      lowerCAmelCase 						=							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:
          lowerCAmelCase 						=							json.load(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							{v: k for k, v in self.encoder.items()}
      lowerCAmelCase 						=							errors  # how to handle errors in decoding
      lowerCAmelCase 						=							bytes_to_unicode()
      lowerCAmelCase 						=							{v: k for k, v in self.byte_encoder.items()}
      with open(_SCREAMING_SNAKE_CASE      , encoding='utf-8'	) as merges_handle:
          lowerCAmelCase 						=							merges_handle.read().split('\n'	)[1:-1]
      lowerCAmelCase 						=							[tuple(merge.split()	) for merge in bpe_merges]
      lowerCAmelCase 						=							dict(zip(_SCREAMING_SNAKE_CASE      , range(len(_SCREAMING_SNAKE_CASE	)	)	)	)
      lowerCAmelCase 						=							{}
      lowerCAmelCase 						=							add_prefix_space

      # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
      lowerCAmelCase 						=							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   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      return len(self.encoder	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      return dict(self.encoder      , **self.added_tokens_encoder	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      if token in self.cache:
          return self.cache[token]
      lowerCAmelCase 						=							tuple(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							get_pairs(_SCREAMING_SNAKE_CASE	)

      if not pairs:
          return token

      while True:
          lowerCAmelCase 						=							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
          lowerCAmelCase						,   lowerCAmelCase 						=							bigram
          lowerCAmelCase 						=							[]
          lowerCAmelCase 						=							0
          while i < len(_SCREAMING_SNAKE_CASE	):
              try:
                  lowerCAmelCase 						=							word.index(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)
              except ValueError:
                  new_word.extend(word[i:]	)
                  break
              else:
                  new_word.extend(word[i:j]	)
                  lowerCAmelCase 						=							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
          lowerCAmelCase 						=							tuple(_SCREAMING_SNAKE_CASE	)
          lowerCAmelCase 						=							new_word
          if len(_SCREAMING_SNAKE_CASE	) == 1:
              break
          else:
              lowerCAmelCase 						=							get_pairs(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							' '.join(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							word
      return word

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							[]
      for token in re.findall(self.pat      , _SCREAMING_SNAKE_CASE	):
          lowerCAmelCase 						=							''.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   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      return self.encoder.get(_SCREAMING_SNAKE_CASE      , self.encoder.get(self.unk_token	)	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      return self.decoder.get(_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							''.join(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							bytearray([self.byte_decoder[c] for c in text]	).decode('utf-8'      , errors=self.errors	)
      return text

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE = None	):





      '''simple docstring'''

      if not os.path.isdir(_SCREAMING_SNAKE_CASE	):
          logger.error(F'Vocabulary path ({save_directory}) should be a directory'	)
          return
      lowerCAmelCase 						=							os.path.join(
          _SCREAMING_SNAKE_CASE      , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)
      lowerCAmelCase 						=							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'	)

      lowerCAmelCase 						=							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!'	)
                  lowerCAmelCase 						=							token_index
              writer.write(' '.join(_SCREAMING_SNAKE_CASE	) + '\n'	)
              index += 1

      return vocab_file, merge_file

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE = None      , _SCREAMING_SNAKE_CASE = False	):





      '''simple docstring'''

      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   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE = None	):





      '''simple docstring'''

      lowerCAmelCase 						=							[self.sep_token_id]
      lowerCAmelCase 						=							[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      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE=False      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							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()):
          lowerCAmelCase 						=							' ' + text
      return (text, kwargs)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE = None	):





      '''simple docstring'''

      return token_ids_a + [self.eos_token_id]


  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							[]
      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	)

      lowerCAmelCase 						=							'  '.join(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							self.encode(_SCREAMING_SNAKE_CASE	)
      if len(_SCREAMING_SNAKE_CASE	) > self.model_max_length:
          lowerCAmelCase 						=							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 ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


_UpperCamelCase						:  str							       =				{
    "configuration_gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXJapaneseConfig"],
    "tokenization_gpt_neox_japanese": ["GPTNeoXJapaneseTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase						:  Dict							       =				[
         "GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GPTNeoXJapaneseForCausalLM",
         "GPTNeoXJapaneseLayer",
         "GPTNeoXJapaneseModel",
         "GPTNeoXJapanesePreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
     from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gpt_neox_japanese import (
              GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTNeoXJapaneseForCausalLM,
              GPTNeoXJapaneseLayer,
              GPTNeoXJapaneseModel,
              GPTNeoXJapanesePreTrainedModel,
          )


else:
     import sys

     _UpperCamelCase						:  List[Any]							       =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





