
'''simple docstring'''






from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
 import tensorflow as tf


UpperCAmelCase_       :   int   =       logging.get_logger(__name__)

@dataclass
class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		[
        'no_inference',
        'no_cuda',
        'no_tpu',
        'no_speed',
        'no_memory',
        'no_env_print',
        'no_multi_process',
    ]



    def __init__(	self      :       Tuple,**__A      :       Optional[int]     ):
          for deprecated_arg in self.deprecated_args:
                if deprecated_arg in kwargs:
                      _lowerCamelCase				:   Tuple								=   deprecated_arg[3:]
                      _lowerCamelCase				:   Tuple								=   not kwargs.pop(__A     )
                      logger.warning(
                          f'{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'
                          f' {positive_arg}={kwargs[positive_arg]}'     )
          _lowerCamelCase				:   Dict								=   kwargs.pop("tpu_name",self.tpu_name     )
          _lowerCamelCase				:   List[Any]								=   kwargs.pop("device_idx",self.device_idx     )
          _lowerCamelCase				:   Optional[int]								=   kwargs.pop("eager_mode",self.eager_mode     )
          _lowerCamelCase				:   Optional[Any]								=   kwargs.pop("use_xla",self.use_xla     )
          super().__init__(**__A     )

    lowerCAmelCase_				    =		field(
        default=A						,	metadata={'help': 'Name of TPU'}						,	)
    lowerCAmelCase_				    =		field(
        default=0						,	metadata={'help': 'CPU / GPU device index. Defaults to 0.'}						,	)
    lowerCAmelCase_				    =		field(default=A						,	metadata={'help': 'Benchmark models in eager model.'} )
    lowerCAmelCase_				    =		field(
        default=A						,	metadata={
            'help': 'Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'
        }						,	)



    @cached_property
    def   lowerCamelCase_  (	self      :       List[str]     ):
          requires_backends(self,["tf"]     )
          _lowerCamelCase				:   Any								=   None
          if self.tpu:
                try:
                      if self.tpu_name:
                            _lowerCamelCase				:   Tuple								=   tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name     )
                      else:
                            _lowerCamelCase				:   Optional[int]								=   tf.distribute.cluster_resolver.TPUClusterResolver()
                except ValueError:
                      _lowerCamelCase				:   Tuple								=   None
          return tpu



    @cached_property
    def   lowerCamelCase_  (	self      :       Any     ):
          requires_backends(self,["tf"]     )
          if self.is_tpu:
                tf.config.experimental_connect_to_cluster(self._setup_tpu     )
                tf.tpu.experimental.initialize_tpu_system(self._setup_tpu     )

                _lowerCamelCase				:   int								=   tf.distribute.TPUStrategy(self._setup_tpu     )
          else:
                # currently no multi gpu is allowed
                if self.is_gpu:
                      # TODO: Currently only single GPU is supported
                      tf.config.set_visible_devices(self.gpu_list[self.device_idx],"GPU"     )
                      _lowerCamelCase				:   Tuple								=   tf.distribute.OneDeviceStrategy(device=f'/gpu:{self.device_idx}'     )
                else:
                      tf.config.set_visible_devices([],"GPU"     )  # disable GPU
                      _lowerCamelCase				:   Optional[int]								=   tf.distribute.OneDeviceStrategy(device=f'/cpu:{self.device_idx}'     )

          return strategy



    @property
    def   lowerCamelCase_  (	self      :       str     ):
          requires_backends(self,["tf"]     )
          return self._setup_tpu is not None



    @property
    def   lowerCamelCase_  (	self      :       Dict     ):
          requires_backends(self,["tf"]     )
          return self._setup_strategy



    @property
    def   lowerCamelCase_  (	self      :       Dict     ):
          requires_backends(self,["tf"]     )
          return tf.config.list_physical_devices("GPU"     )



    @property
    def   lowerCamelCase_  (	self      :       Dict     ):
          requires_backends(self,["tf"]     )
          if self.cuda:
                return len(self.gpu_list     )
          return 0



    @property
    def   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          return self.n_gpu > 0

'''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