


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

SCREAMING_SNAKE_CASE						:Any			=  logging.get_logger(__name__)


SCREAMING_SNAKE_CASE						:List[str]			=  {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

SCREAMING_SNAKE_CASE						:List[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'''
    },
}

SCREAMING_SNAKE_CASE						:Optional[Any]			=  {'''facebook/blenderbot-3B''': 1_28}


class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





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



  def __init__(  self							:							str     ,					_lowerCAmelCase							:							Any=None     ,					_lowerCAmelCase							:							List[str]=None     ,					_lowerCAmelCase							:							str=None     ,					_lowerCAmelCase							:							Optional[Any]="replace"     ,					_lowerCAmelCase							:							List[str]="<s>"     ,					_lowerCAmelCase							:							int="</s>"     ,					_lowerCAmelCase							:							Optional[Any]="</s>"     ,					_lowerCAmelCase							:							Optional[int]="<s>"     ,					_lowerCAmelCase							:							Union[str, Any]="<unk>"     ,					_lowerCAmelCase							:							Union[str, Any]="<pad>"     ,					_lowerCAmelCase							:							List[Any]="<mask>"     ,					_lowerCAmelCase							:							Tuple=False     ,					_lowerCAmelCase							:							Optional[Any]=True     ,					**_lowerCAmelCase							:							Any     ,					) ->			Union[str, Any]:




     """simple docstring"""
     super().__init__(
         _lowerCAmelCase     ,					_lowerCAmelCase     ,					tokenizer_file=_lowerCAmelCase     ,					errors=_lowerCAmelCase     ,					bos_token=_lowerCAmelCase     ,					eos_token=_lowerCAmelCase     ,					sep_token=_lowerCAmelCase     ,					cls_token=_lowerCAmelCase     ,					unk_token=_lowerCAmelCase     ,					pad_token=_lowerCAmelCase     ,					mask_token=_lowerCAmelCase     ,					add_prefix_space=_lowerCAmelCase     ,					trim_offsets=_lowerCAmelCase     ,					**_lowerCAmelCase     ,					)

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

     snake_case_            =       add_prefix_space

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

        snake_case_            =       False

        if state.get("add_prefix_space"     ,					_lowerCAmelCase	) != add_prefix_space:
           snake_case_            =       add_prefix_space
           snake_case_            =       True

        if state.get("trim_offsets"     ,					_lowerCAmelCase	) != trim_offsets:
           snake_case_            =       trim_offsets
           snake_case_            =       True

        if changes_to_apply:
           snake_case_            =       getattr(_lowerCAmelCase     ,					state.pop("type"	)	)
           snake_case_            =       component_class(**_lowerCAmelCase	)
           setattr(self.backend_tokenizer     ,					_lowerCAmelCase     ,					_lowerCAmelCase	)



  @property
  # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
  def        lowerCAmelCase__	(  self							:							Optional[int]	) ->			str:




     """simple docstring"""
     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        lowerCAmelCase__	(  self							:							Dict     ,					_lowerCAmelCase							:							Union[str, Any]	) ->			Optional[int]:




     """simple docstring"""
     snake_case_            =       AddedToken(_lowerCAmelCase     ,					lstrip=_lowerCAmelCase     ,					rstrip=_lowerCAmelCase	) if isinstance(_lowerCAmelCase     ,					_lowerCAmelCase	) else value
     snake_case_            =       value



  def        lowerCAmelCase__	(  self							:							List[Any]     ,					*_lowerCAmelCase							:							Dict     ,					**_lowerCAmelCase							:							List[str]	) ->			BatchEncoding:




     """simple docstring"""
     snake_case_            =       kwargs.get("is_split_into_words"     ,					_lowerCAmelCase	)
     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(*_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Dict     ,					*_lowerCAmelCase							:							Optional[int]     ,					**_lowerCAmelCase							:							Optional[Any]	) ->			BatchEncoding:




     """simple docstring"""
     snake_case_            =       kwargs.get("is_split_into_words"     ,					_lowerCAmelCase	)

     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(*_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							Optional[str] = None	) ->			Tuple[str]:




     """simple docstring"""
     snake_case_            =       self._tokenizer.model.save(_lowerCAmelCase     ,					name=_lowerCAmelCase	)
     return tuple(_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Tuple     ,					_lowerCAmelCase							:							List[int]     ,					_lowerCAmelCase							:							Optional[List[int]] = None	) ->			List[int]:




     """simple docstring"""
     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        lowerCAmelCase__	(  self							:							List[str]     ,					_lowerCAmelCase							:							List[int]     ,					_lowerCAmelCase							:							Optional[List[int]] = None	) ->			Optional[Any]:




     """simple docstring"""
     return token_ids_a + [self.eos_token_id]



  def        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							"Conversation"	) ->			List[int]:




     """simple docstring"""
     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(_lowerCAmelCase	)

     snake_case_            =       "  ".join(_lowerCAmelCase	)
     snake_case_            =       self.encode(_lowerCAmelCase	)
     if len(_lowerCAmelCase	) > 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








import logging
import os

from .state import PartialState


class      __lowerCAmelCase  (			logging.LoggerAdapter       ):






  """simple docstring"""





  @staticmethod
  def        lowerCAmelCase__	(  _lowerCAmelCase							:							int	) ->			Tuple:




     """simple docstring"""
     snake_case_            =       PartialState()
     return not main_process_only or (main_process_only and state.is_main_process)



  def        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							List[Any]     ,					_lowerCAmelCase							:							Tuple     ,					*_lowerCAmelCase							:							List[Any]     ,					**_lowerCAmelCase							:							Any	) ->			Optional[int]:




     """simple docstring"""
     if PartialState._shared_state == {}:
        raise RuntimeError(
            "You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility."	)
     snake_case_            =       kwargs.pop("main_process_only"     ,					_lowerCAmelCase	)
     snake_case_            =       kwargs.pop("in_order"     ,					_lowerCAmelCase	)

     if self.isEnabledFor(_lowerCAmelCase	):
        if self._should_log(_lowerCAmelCase	):
           snake_case_     ,     snake_case_            =       self.process(_lowerCAmelCase     ,					_lowerCAmelCase	)
           self.logger.log(_lowerCAmelCase     ,					_lowerCAmelCase     ,					*_lowerCAmelCase     ,					**_lowerCAmelCase	)

        elif in_order:
           snake_case_            =       PartialState()
           for i in range(state.num_processes	):
              if i == state.process_index:
                 snake_case_     ,     snake_case_            =       self.process(_lowerCAmelCase     ,					_lowerCAmelCase	)
                 self.logger.log(_lowerCAmelCase     ,					_lowerCAmelCase     ,					*_lowerCAmelCase     ,					**_lowerCAmelCase	)
              state.wait_for_everyone()






def       _lowerCAmelCase    (				lowerCAmelCase_				:str		,    lowerCAmelCase_				:str = None				)->Any:
   '''simple docstring'''






   if log_level is None:
      snake_case_            =       os.environ.get("ACCELERATE_LOG_LEVEL"		,    lowerCAmelCase_				)
   snake_case_            =       logging.getLogger(lowerCAmelCase_				)
   if log_level is not None:
      logger.setLevel(log_level.upper()				)
      logger.root.setLevel(log_level.upper()				)
   return MultiProcessAdapter(lowerCAmelCase_		,    {}				)




