


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

A      = logging.get_logger(__name__)


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

A      = {
    "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"
    },
}

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


class     lowercase__  (					__SCREAMING_SNAKE_CASE		):
  A__=  VOCAB_FILES_NAMES
  A__=  PRETRAINED_VOCAB_FILES_MAP
  A__=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A__=  ['input_ids', 'attention_mask']
  A__=  BlenderbotTokenizer






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




         """simple docstring"""







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

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

         UpperCAmelCase__										=     add_prefix_space

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

                UpperCAmelCase__										=     False

                if state.get("add_prefix_space"		,						_lowercase ) != add_prefix_space:
                       UpperCAmelCase__										=     add_prefix_space
                       UpperCAmelCase__										=     True

                if state.get("trim_offsets"		,						_lowercase ) != trim_offsets:
                       UpperCAmelCase__										=     trim_offsets
                       UpperCAmelCase__										=     True

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






  @property
  # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
  def 			_UpperCAmelCase  (					self      :					Any ):




         """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 			_UpperCAmelCase  (					self      :					Any		,						_lowercase      :					int ):




         """simple docstring"""







         UpperCAmelCase__										=     AddedToken(_lowercase		,						lstrip=_lowercase		,						rstrip=_lowercase ) if isinstance(_lowercase		,						_lowercase ) else value
         UpperCAmelCase__										=     value






  def 			_UpperCAmelCase  (					self      :					Dict		,						*_lowercase      :					int		,						**_lowercase      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     kwargs.get("is_split_into_words"		,						_lowercase )
         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(*_lowercase		,						**_lowercase )






  def 			_UpperCAmelCase  (					self      :					Dict		,						*_lowercase      :					str		,						**_lowercase      :					Any ):




         """simple docstring"""







         UpperCAmelCase__										=     kwargs.get("is_split_into_words"		,						_lowercase )

         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(*_lowercase		,						**_lowercase )






  def 			_UpperCAmelCase  (					self      :					Union[str, Any]		,						_lowercase      :					str		,						_lowercase      :					Optional[str] = None ):




         """simple docstring"""







         UpperCAmelCase__										=     self._tokenizer.model.save(_lowercase		,						name=_lowercase )
         return tuple(_lowercase )






  def 			_UpperCAmelCase  (					self      :					Dict		,						_lowercase      :					List[int]		,						_lowercase      :					Optional[List[int]] = None ):




         """simple docstring"""







         UpperCAmelCase__										=     [self.sep_token_id]
         UpperCAmelCase__										=     [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 			_UpperCAmelCase  (					self      :					Any		,						_lowercase      :					List[int]		,						_lowercase      :					Optional[List[int]] = None ):




         """simple docstring"""







         return token_ids_a + [self.eos_token_id]






  def 			_UpperCAmelCase  (					self      :					Dict		,						_lowercase      :					"Conversation" ):




         """simple docstring"""







         UpperCAmelCase__										=     []
         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(_lowercase )

         UpperCAmelCase__										=     "  ".join(_lowercase )
         UpperCAmelCase__										=     self.encode(_lowercase )
         if len(_lowercase ) > self.model_max_length:
                UpperCAmelCase__										=     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
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A      = logging.getLogger(__name__)


@dataclass
class     lowercase__  (					__SCREAMING_SNAKE_CASE		):
  A__=  field(
      default=0.0					,							metadata={'help': 'The label smoothing epsilon to apply (if not zero).'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Whether to SortishSamler or not.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'whether to use adafactor'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Encoder layer dropout probability. Goes into model.config.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Decoder layer dropout probability. Goes into model.config.'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Dropout probability. Goes into model.config.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Attention dropout probability. Goes into model.config.'}		)
  A__=  field(
      default='linear'					,							metadata={'help': f'Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()		)}'}					,							)





