




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

_UpperCAmelCase	:						int			=     logging.get_logger(__name__)


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

_UpperCAmelCase	:						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"
    },
}

_UpperCAmelCase	:						List[str]			=     {"facebook/blenderbot-3B": 128}




class    __lowerCAmelCase       (							_UpperCAmelCase):

       '''simple docstring'''





       _a 					=						VOCAB_FILES_NAMES
       _a 					=						PRETRAINED_VOCAB_FILES_MAP
       _a 					=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _a 					=						['''input_ids''', '''attention_mask''']
       _a 					=						BlenderbotTokenizer






       def __init__(   self:					Union[str, Any]       ,					_lowerCAmelCase:					Any=None       ,					_lowerCAmelCase:					Optional[int]=None       ,					_lowerCAmelCase:					Optional[Any]=None       ,					_lowerCAmelCase:					Union[str, Any]="replace"       ,					_lowerCAmelCase:					Union[str, Any]="<s>"       ,					_lowerCAmelCase:					Union[str, Any]="</s>"       ,					_lowerCAmelCase:					Optional[int]="</s>"       ,					_lowerCAmelCase:					List[Any]="<s>"       ,					_lowerCAmelCase:					Union[str, Any]="<unk>"       ,					_lowerCAmelCase:					Any="<pad>"       ,					_lowerCAmelCase:					List[str]="<mask>"       ,					_lowerCAmelCase:					int=False       ,					_lowerCAmelCase:					Tuple=True       ,					**_lowerCAmelCase:					Optional[Any]       ,					):
         super().__init__(
             A_       ,					A_       ,					tokenizer_file=A_       ,					errors=A_       ,					bos_token=A_       ,					eos_token=A_       ,					sep_token=A_       ,					cls_token=A_       ,					unk_token=A_       ,					pad_token=A_       ,					mask_token=A_       ,					add_prefix_space=A_       ,					trim_offsets=A_       ,					**A_       ,					)

         lowercase    :Tuple      	=						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
         if pre_tok_state.get("add_prefix_space"       ,					A_				) != add_prefix_space:
           lowercase    :Optional[Any]      	=						getattr(A_       ,					pre_tok_state.pop("type"				)				)
           lowercase    :int      	=						add_prefix_space
           lowercase    :Tuple      	=						pre_tok_class(**A_				)

         lowercase    :List[str]      	=						add_prefix_space

         lowercase    :List[Any]      	=						"post_processor"
         lowercase    :int      	=						getattr(self.backend_tokenizer       ,					A_       ,					A_				)
         if tokenizer_component_instance:
           lowercase    :Optional[int]      	=						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:
             lowercase    :Union[str, Any]      	=						tuple(state["sep"]				)
           if "cls" in state:
             lowercase    :Any      	=						tuple(state["cls"]				)

           lowercase    :Tuple      	=						False

           if state.get("add_prefix_space"       ,					A_				) != add_prefix_space:
             lowercase    :Any      	=						add_prefix_space
             lowercase    :List[str]      	=						True

           if state.get("trim_offsets"       ,					A_				) != trim_offsets:
             lowercase    :List[Any]      	=						trim_offsets
             lowercase    :List[str]      	=						True

           if changes_to_apply:
             lowercase    :str      	=						getattr(A_       ,					state.pop("type"				)				)
             lowercase    :Optional[Any]      	=						component_class(**A_				)
             setattr(self.backend_tokenizer       ,					A_       ,					A_				)






       @property
       # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         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:					int       ,					_lowerCAmelCase:					Union[str, Any]				):
         lowercase    :Any      	=						AddedToken(A_       ,					lstrip=A_       ,					rstrip=A_				) if isinstance(A_       ,					A_				) else value
         lowercase    :Tuple      	=						value






       def 					SCREAMING_SNAKE_CASE    (   self:					Any       ,					*_lowerCAmelCase:					List[str]       ,					**_lowerCAmelCase:					Optional[int]				):
         lowercase    :Dict      	=						kwargs.get("is_split_into_words"       ,					A_				)
         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(*A_       ,					**A_				)






       def 					SCREAMING_SNAKE_CASE    (   self:					str       ,					*_lowerCAmelCase:					Tuple       ,					**_lowerCAmelCase:					str				):
         lowercase    :Optional[int]      	=						kwargs.get("is_split_into_words"       ,					A_				)

         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(*A_       ,					**A_				)






       def 					SCREAMING_SNAKE_CASE    (   self:					str       ,					_lowerCAmelCase:					str       ,					_lowerCAmelCase:					Optional[str] = None				):
         lowercase    :Optional[int]      	=						self._tokenizer.model.save(A_       ,					name=A_				)
         return tuple(A_				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple       ,					_lowerCAmelCase:					List[int]       ,					_lowerCAmelCase:					Optional[List[int]] = None				):
         lowercase    :Union[str, Any]      	=						[self.sep_token_id]
         lowercase    :Union[str, 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 					SCREAMING_SNAKE_CASE    (   self:					Tuple       ,					_lowerCAmelCase:					List[int]       ,					_lowerCAmelCase:					Optional[List[int]] = None				):
         return token_ids_a + [self.eos_token_id]






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]       ,					_lowerCAmelCase:					"Conversation"				):
         lowercase    :Optional[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_				)

         lowercase    :Tuple      	=						"  ".join(A_				)
         lowercase    :List[str]      	=						self.encode(A_				)
         if len(A_				) > self.model_max_length:
           lowercase    :List[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









import string



def       UpperCAmelCase__  (     lowerCamelCase   ):
  lowercase    :Optional[int]      	=						""
  for i in sequence:
    lowercase    :Optional[Any]      	=						ord(lowerCamelCase   )
    if 65 <= extract <= 90:
      output += chr(155 - extract   )
    elif 97 <= extract <= 122:
      output += chr(219 - extract   )
    else:
      output += i
  return output



def       UpperCAmelCase__  (     lowerCamelCase   ):
  lowercase    :Union[str, Any]      	=						string.ascii_letters
  lowercase    :Dict      	=						string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
  return "".join(
      letters_reversed[letters.index(lowerCamelCase   )] if c in letters else c for c in sequence   )



def       UpperCAmelCase__  (     ):
  from timeit import timeit

  print("Running performance benchmarks..."   )
  lowercase    :Dict      	=						"from string import printable ; from __main__ import atbash, atbash_slow"
  print(F"> atbash_slow(): {timeit('atbash_slow(printable)',			setup=lowerCamelCase   )} seconds"   )
  print(F">      atbash(): {timeit('atbash(printable)',			setup=lowerCamelCase   )} seconds"   )


if __name__ == "__main__":
     for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
          print(f'''{example} encrypted in atbash: {atbash(example)}''')
     benchmark()



