
"""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

lowercase_         =       logging.get_logger(__name__)


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

lowercase_         =       {
    "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"
    },
}

lowercase_         =       {"facebook/blenderbot-3B": 128}




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


     """simple docstring"""







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




def       A_      (       lowercase   )  ->				Union[str, Any]:


     """simple docstring"""







     UpperCAmelCase_    :     List[Any]            =	set()
     UpperCAmelCase_    :     Union[str, Any]            =	word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)   )
          UpperCAmelCase_    :     int            =	char
     return pairs



class 		UpperCAmelCase_						(lowerCamelCase_    ):
 """simple docstring"""




 UpperCamelCase_    :  str					 =     VOCAB_FILES_NAMES
 UpperCamelCase_    :  str					 =     PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase_    :  Union[str, Any]					 =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCamelCase_    :  str					 =     ["""input_ids""", """attention_mask"""]




 def __init__(			self							:			List[str]  , a_							:			int  , a_							:			Tuple  , a_							:			str="replace"  , a_							:			Tuple="<s>"  , a_							:			List[str]="</s>"  , a_							:			Any="</s>"  , a_							:			Union[str, Any]="<s>"  , a_							:			Tuple="<unk>"  , a_							:			Optional[int]="<pad>"  , a_							:			Dict="<mask>"  , a_							:			Any=False  , **a_							:			Optional[int]  , )->       Tuple:



      """simple docstring"""

      UpperCAmelCase_    :     Tuple            =	AddedToken(a_  , lstrip=a_  , rstrip=a_			) if isinstance(a_  , a_			) else bos_token
      UpperCAmelCase_    :     Optional[Any]            =	AddedToken(a_  , lstrip=a_  , rstrip=a_			) if isinstance(a_  , a_			) else eos_token
      UpperCAmelCase_    :     Dict            =	AddedToken(a_  , lstrip=a_  , rstrip=a_			) if isinstance(a_  , a_			) else sep_token
      UpperCAmelCase_    :     List[Any]            =	AddedToken(a_  , lstrip=a_  , rstrip=a_			) if isinstance(a_  , a_			) else cls_token
      UpperCAmelCase_    :     Tuple            =	AddedToken(a_  , lstrip=a_  , rstrip=a_			) if isinstance(a_  , a_			) else unk_token
      UpperCAmelCase_    :     List[str]            =	AddedToken(a_  , lstrip=a_  , rstrip=a_			) if isinstance(a_  , a_			) else pad_token

      # Mask token behave like a normal word, i.e. include the space before it
      UpperCAmelCase_    :     Union[str, Any]            =	AddedToken(a_  , lstrip=a_  , rstrip=a_			) if isinstance(a_  , a_			) else mask_token

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

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

      # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
      UpperCAmelCase_    :     Tuple            =	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 	a   (			self							:			Any			)->       int:



      """simple docstring"""

      return len(self.encoder			)




 def 	a   (			self							:			int			)->       int:



      """simple docstring"""

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




 def 	a   (			self							:			str  , a_							:			Tuple			)->       List[Any]:



      """simple docstring"""

      if token in self.cache:
           return self.cache[token]
      UpperCAmelCase_    :     Dict            =	tuple(a_			)
      UpperCAmelCase_    :     Any            =	get_pairs(a_			)

      if not pairs:
           return token

      while True:
           UpperCAmelCase_    :     int            =	min(a_  , key=lambda a_			: self.bpe_ranks.get(a_  , float("""inf"""			)			)			)
           if bigram not in self.bpe_ranks:
                break
           UpperCAmelCase_       ,UpperCAmelCase_    :     List[Any]            =	bigram
           UpperCAmelCase_    :     Dict            =	[]
           UpperCAmelCase_    :     int            =	0
           while i < len(a_			):
                try:
                     UpperCAmelCase_    :     Tuple            =	word.index(a_  , a_			)
                except ValueError:
                     new_word.extend(word[i:]			)
                     break
                else:
                     new_word.extend(word[i:j]			)
                     UpperCAmelCase_    :     str            =	j

                if word[i] == first and i < len(a_			) - 1 and word[i + 1] == second:
                     new_word.append(first + second			)
                     i += 2
                else:
                     new_word.append(word[i]			)
                     i += 1
           UpperCAmelCase_    :     Optional[int]            =	tuple(a_			)
           UpperCAmelCase_    :     int            =	new_word
           if len(a_			) == 1:
                break
           else:
                UpperCAmelCase_    :     str            =	get_pairs(a_			)
      UpperCAmelCase_    :     List[str]            =	""" """.join(a_			)
      UpperCAmelCase_    :     List[Any]            =	word
      return word




 def 	a   (			self							:			List[Any]  , a_							:			Union[str, Any]			)->       Optional[Any]:



      """simple docstring"""

      UpperCAmelCase_    :     str            =	[]
      for token in re.findall(self.pat  , a_			):
           UpperCAmelCase_    :     Dict            =	"""""".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(a_			).split(""" """			)			)
      return bpe_tokens




 def 	a   (			self							:			List[Any]  , a_							:			List[str]			)->       Optional[Any]:



      """simple docstring"""

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




 def 	a   (			self							:			int  , a_							:			Dict			)->       Dict:



      """simple docstring"""

      return self.decoder.get(a_			)




 def 	a   (			self							:			Tuple  , a_							:			int			)->       Optional[Any]:



      """simple docstring"""

      UpperCAmelCase_    :     Tuple            =	"""""".join(a_			)
      UpperCAmelCase_    :     str            =	bytearray([self.byte_decoder[c] for c in text]			).decode("""utf-8"""  , errors=self.errors			)
      return text




 def 	a   (			self							:			Optional[int]  , a_							:			str  , a_							:			Optional[str] = None			)->       Tuple[str]:



      """simple docstring"""

      if not os.path.isdir(a_			):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
           return
      UpperCAmelCase_    :     Any            =	os.path.join(
          a_  , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]			)
      UpperCAmelCase_    :     Optional[Any]            =	os.path.join(
          a_  , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]			)

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

      UpperCAmelCase_    :     Union[str, Any]            =	0
      with open(a_  , """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 a_			: 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!"""			)
                     UpperCAmelCase_    :     Union[str, Any]            =	token_index
                writer.write(""" """.join(a_			) + """\n"""			)
                index += 1

      return vocab_file, merge_file




 def 	a   (			self							:			Tuple  , a_							:			List[int]  , a_							:			Optional[List[int]] = None  , a_							:			bool = False			)->       List[int]:



      """simple docstring"""

      if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=a_  , token_ids_a=a_  , already_has_special_tokens=a_			)

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




 def 	a   (			self							:			Optional[Any]  , a_							:			List[int]  , a_							:			Optional[List[int]] = None			)->       List[int]:



      """simple docstring"""

      UpperCAmelCase_    :     Tuple            =	[self.sep_token_id]
      UpperCAmelCase_    :     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 	a   (			self							:			Union[str, Any]  , a_							:			Union[str, Any]  , a_							:			str=False  , **a_							:			Dict			)->       List[Any]:



      """simple docstring"""

      UpperCAmelCase_    :     Optional[int]            =	kwargs.pop("""add_prefix_space"""  , self.add_prefix_space			)
      if (is_split_into_words or add_prefix_space) and (len(a_			) > 0 and not text[0].isspace()):
           UpperCAmelCase_    :     Tuple            =	""" """ + text
      return (text, kwargs)




 def 	a   (			self							:			List[str]  , a_							:			List[int]  , a_							:			Optional[List[int]] = None			)->       Dict:



      """simple docstring"""

      return token_ids_a + [self.eos_token_id]






 def 	a   (			self							:			Dict  , a_							:			"Conversation"			)->       List[int]:



      """simple docstring"""

      UpperCAmelCase_    :     int            =	[]
      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(a_			)

      UpperCAmelCase_    :     List[Any]            =	"""  """.join(a_			)
      UpperCAmelCase_    :     Optional[int]            =	self.encode(a_			)
      if len(a_			) > self.model_max_length:
           UpperCAmelCase_    :     Union[str, Any]            =	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"""







def       A_      (       lowercase = 1000   )  ->				int:


     """simple docstring"""







     return sum(e for e in range(3    ,			lowercase   ) if e % 3 == 0 or e % 5 == 0   )


if __name__ == "__main__":
       print(f"""{solution() = }""")

