
'''simple docstring'''






import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


UpperCAmelCase_       :   Dict   =       logging.get_logger(__name__)

class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		'AutoTokenizer'
    lowerCAmelCase_				    =		['tokenizer']

    lowerCAmelCase_				    =		{
        'semantic_prompt': 1,
        'coarse_prompt': 2,
        'fine_prompt': 2,
    }



    def __init__(	self      :       str,__A      :       int,__A      :       int=None     ):
          super().__init__(__A     )

          _lowerCamelCase				:   Optional[Any]								=   speaker_embeddings



    @classmethod
    def   lowerCamelCase_  (	cls      :       List[Any],__A      :       List[Any],__A      :       Optional[Any]="speaker_embeddings_path.json",**__A      :       Union[str, Any]     ):

          if speaker_embeddings_dict_path is not None:
                _lowerCamelCase				:   int								=   get_file_from_repo(
                    __A,__A,subfolder=kwargs.pop("subfolder",__A     ),cache_dir=kwargs.pop("cache_dir",__A     ),force_download=kwargs.pop("force_download",__A     ),proxies=kwargs.pop("proxies",__A     ),resume_download=kwargs.pop("resume_download",__A     ),local_files_only=kwargs.pop("local_files_only",__A     ),use_auth_token=kwargs.pop("use_auth_token",__A     ),revision=kwargs.pop("revision",__A     ),)
                if speaker_embeddings_path is None:
                      logger.warning(
                          f'`{os.path.join(__A,__A     )}` does not exists\n                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json\n                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.'     )
                      _lowerCamelCase				:   Dict								=   None
                else:
                      with open(__A     ) as speaker_embeddings_json:
                            _lowerCamelCase				:   Union[str, Any]								=   json.load(__A     )
          else:
                _lowerCamelCase				:   Optional[int]								=   None

          _lowerCamelCase				:   Any								=   AutoTokenizer.from_pretrained(__A,**__A     )

          return cls(tokenizer=__A,speaker_embeddings=__A     )



    def   lowerCamelCase_  (	self      :       Optional[Any],__A      :       List[str],__A      :       Optional[Any]="speaker_embeddings_path.json",__A      :       int="speaker_embeddings",__A      :       bool = False,**__A      :       Tuple,):
          if self.speaker_embeddings is not None:
                os.makedirs(os.path.join(__A,__A,"v2"     ),exist_ok=__A     )

                _lowerCamelCase				:   Dict								=   {}

                _lowerCamelCase				:   Any								=   save_directory

                for prompt_key in self.speaker_embeddings:
                      if prompt_key != "repo_or_path":
                            _lowerCamelCase				:   Union[str, Any]								=   self._load_voice_preset(__A     )

                            _lowerCamelCase				:   List[str]								=   {}
                            for key in self.speaker_embeddings[prompt_key]:
                                  np.save(
                                      os.path.join(
                                          embeddings_dict["repo_or_path"],__A,f'{prompt_key}_{key}'     ),voice_preset[key],allow_pickle=__A,)
                                  _lowerCamelCase				:   List[str]								=   os.path.join(__A,f'{prompt_key}_{key}.npy'     )

                            _lowerCamelCase				:   Any								=   tmp_dict

                with open(os.path.join(__A,__A     ),"w"     ) as fp:
                      json.dump(__A,__A     )

          super().save_pretrained(__A,__A,**__A     )



    def   lowerCamelCase_  (	self      :       Any,__A      :       str = None,**__A      :       Optional[int]     ):
          _lowerCamelCase				:   Optional[int]								=   self.speaker_embeddings[voice_preset]

          _lowerCamelCase				:   int								=   {}
          for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
                if key not in voice_preset_paths:
                      raise ValueError(
                          f'Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].'     )

                _lowerCamelCase				:   List[str]								=   get_file_from_repo(
                    self.speaker_embeddings.get("repo_or_path","/"     ),voice_preset_paths[key],subfolder=kwargs.pop("subfolder",__A     ),cache_dir=kwargs.pop("cache_dir",__A     ),force_download=kwargs.pop("force_download",__A     ),proxies=kwargs.pop("proxies",__A     ),resume_download=kwargs.pop("resume_download",__A     ),local_files_only=kwargs.pop("local_files_only",__A     ),use_auth_token=kwargs.pop("use_auth_token",__A     ),revision=kwargs.pop("revision",__A     ),)
                if path is None:
                      raise ValueError(
                          f'`{os.path.join(self.speaker_embeddings.get("repo_or_path","/"     ),voice_preset_paths[key]     )}` does not exists\n                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n                    embeddings.'     )

                _lowerCamelCase				:   int								=   np.load(__A     )

          return voice_preset_dict



    def   lowerCamelCase_  (	self      :       Any,__A      :       Optional[dict] = None     ):
          for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
                if key not in voice_preset:
                      raise ValueError(f'Voice preset unrecognized, missing {key} as a key.'     )

                if not isinstance(voice_preset[key],np.ndarray     ):
                      raise ValueError(f'{key} voice preset must be a {str(self.preset_shape[key]     )}D ndarray.'     )

                if len(voice_preset[key].shape     ) != self.preset_shape[key]:
                      raise ValueError(f'{key} voice preset must be a {str(self.preset_shape[key]     )}D ndarray.'     )



    def __call__(	self      :       Tuple,__A      :       List[Any]=None,__A      :       Optional[int]=None,__A      :       str="pt",__A      :       Tuple=2_5_6,__A      :       Dict=False,__A      :       List[Any]=True,__A      :       Dict=False,**__A      :       Tuple,):
          if voice_preset is not None and not isinstance(__A,__A     ):
                if (
                    isinstance(__A,__A     )
                    and self.speaker_embeddings is not None
                    and voice_preset in self.speaker_embeddings
                ):
                      _lowerCamelCase				:   Optional[Any]								=   self._load_voice_preset(__A     )

                else:
                      if isinstance(__A,__A     ) and not voice_preset.endswith(".npz"     ):
                            _lowerCamelCase				:   List[str]								=   voice_preset + ".npz"

                      _lowerCamelCase				:   List[Any]								=   np.load(__A     )

          if voice_preset is not None:
                self._validate_voice_preset_dict(__A,**__A     )
                _lowerCamelCase				:   List[str]								=   BatchFeature(data=__A,tensor_type=__A     )

          _lowerCamelCase				:   Dict								=   self.tokenizer(
              __A,return_tensors=__A,padding="max_length",max_length=__A,return_attention_mask=__A,return_token_type_ids=__A,add_special_tokens=__A,**__A,)

          if voice_preset is not None:
                _lowerCamelCase				:   Any								=   voice_preset

          return encoded_text

'''simple docstring'''






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_       :   Any   =       logging.get_logger(__name__)


UpperCAmelCase_       :   Any   =       {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

UpperCAmelCase_       :   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'
    },
}

UpperCAmelCase_       :   List[Any]   =       {'facebook/blenderbot-3B': 128}

class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		VOCAB_FILES_NAMES
    lowerCAmelCase_				    =		PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase_				    =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase_				    =		['input_ids', 'attention_mask']
    lowerCAmelCase_				    =		BlenderbotTokenizer



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

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

          _lowerCamelCase				:   Any								=   add_prefix_space

          _lowerCamelCase				:   Any								=   "post_processor"
          _lowerCamelCase				:   Optional[Any]								=   getattr(self.backend_tokenizer,__A,__A     )
          if tokenizer_component_instance:
                _lowerCamelCase				:   Tuple								=   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:
                      _lowerCamelCase				:   List[Any]								=   tuple(state["sep"]     )
                if "cls" in state:
                      _lowerCamelCase				:   Optional[int]								=   tuple(state["cls"]     )

                _lowerCamelCase				:   List[str]								=   False

                if state.get("add_prefix_space",__A     ) != add_prefix_space:
                      _lowerCamelCase				:   List[str]								=   add_prefix_space
                      _lowerCamelCase				:   int								=   True

                if state.get("trim_offsets",__A     ) != trim_offsets:
                      _lowerCamelCase				:   List[str]								=   trim_offsets
                      _lowerCamelCase				:   Dict								=   True

                if changes_to_apply:
                      _lowerCamelCase				:   Tuple								=   getattr(__A,state.pop("type"     )     )
                      _lowerCamelCase				:   Union[str, 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   lowerCamelCase_  (	self      :       Optional[int]     ):
          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      :       Optional[Any],__A      :       List[Any]     ):
          _lowerCamelCase				:   Any								=   AddedToken(__A,lstrip=__A,rstrip=__A     ) if isinstance(__A,__A     ) else value
          _lowerCamelCase				:   List[str]								=   value



    def   lowerCamelCase_  (	self      :       Optional[int],*__A      :       Optional[Any],**__A      :       List[str]     ):
          _lowerCamelCase				:   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()._batch_encode_plus(*__A,**__A     )



    def   lowerCamelCase_  (	self      :       str,*__A      :       Union[str, Any],**__A      :       List[Any]     ):
          _lowerCamelCase				:   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   lowerCamelCase_  (	self      :       str,__A      :       str,__A      :       Optional[str] = None     ):
          _lowerCamelCase				:   Optional[int]								=   self._tokenizer.model.save(__A,name=__A     )
          return tuple(__A     )



    def   lowerCamelCase_  (	self      :       Optional[int],__A      :       List[int],__A      :       Optional[List[int]] = None     ):
          _lowerCamelCase				:   int								=   [self.sep_token_id]
          _lowerCamelCase				:   str								=   [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      :       Dict,__A      :       List[int],__A      :       Optional[List[int]] = None     ):
          return token_ids_a + [self.eos_token_id]



    def   lowerCamelCase_  (	self      :       List[str],__A      :       "Conversation"     ):
          _lowerCamelCase				:   Dict								=   []
          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     )

          _lowerCamelCase				:   List[Any]								=   "  ".join(__A     )
          _lowerCamelCase				:   List[str]								=   self.encode(__A     )
          if len(__A     ) > self.model_max_length:
                _lowerCamelCase				:   Tuple								=   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