
'''simple docstring'''






from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
 import datasets

class  UpperCAmelCase__						(       _snake_case ):



    @slow
    @require_torch
    def   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          _lowerCamelCase				:   int								=   EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny","prajjwal1/bert-tiny"     )
          _lowerCamelCase				:   List[Any]								=   BertTokenizer.from_pretrained("bert-base-uncased"     )

          _lowerCamelCase				:   Dict								=   bertabert.config.encoder.vocab_size
          _lowerCamelCase				:   List[str]								=   tokenizer.sep_token_id
          _lowerCamelCase				:   List[str]								=   tokenizer.cls_token_id
          _lowerCamelCase				:   Optional[int]								=   1_2_8

          _lowerCamelCase				:   Optional[Any]								=   datasets.load_dataset("cnn_dailymail","3.0.0",split="train[:1%]"     )
          _lowerCamelCase				:   Dict								=   datasets.load_dataset("cnn_dailymail","3.0.0",split="validation[:1%]"     )

          _lowerCamelCase				:   Optional[Any]								=   train_dataset.select(range(3_2     )     )
          _lowerCamelCase				:   str								=   val_dataset.select(range(1_6     )     )

          _lowerCamelCase				:   List[Any]								=   4

          def _map_to_encoder_decoder_inputs(__A      :       Any     ):
                # Tokenizer will automatically set [BOS] <text> [EOS]
                _lowerCamelCase				:   int								=   tokenizer(batch["article"],padding="max_length",truncation=__A,max_length=5_1_2     )
                _lowerCamelCase				:   Optional[int]								=   tokenizer(batch["highlights"],padding="max_length",truncation=__A,max_length=1_2_8     )
                _lowerCamelCase				:   Tuple								=   inputs.input_ids
                _lowerCamelCase				:   Any								=   inputs.attention_mask

                _lowerCamelCase				:   Any								=   outputs.input_ids
                _lowerCamelCase				:   Any								=   outputs.input_ids.copy()
                _lowerCamelCase				:   List[str]								=   [
                    [-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
                ]
                _lowerCamelCase				:   Tuple								=   outputs.attention_mask

                assert all(len(__A     ) == 5_1_2 for x in inputs.input_ids     )
                assert all(len(__A     ) == 1_2_8 for x in outputs.input_ids     )

                return batch

          def _compute_metrics(__A      :       Tuple     ):
                _lowerCamelCase				:   List[Any]								=   pred.label_ids
                _lowerCamelCase				:   Optional[int]								=   pred.predictions

                # all unnecessary tokens are removed
                _lowerCamelCase				:   int								=   tokenizer.batch_decode(__A,skip_special_tokens=__A     )
                _lowerCamelCase				:   List[str]								=   tokenizer.batch_decode(__A,skip_special_tokens=__A     )

                _lowerCamelCase				:   Optional[Any]								=   sum([int(pred_str[i] == label_str[i]     ) for i in range(len(__A     )     )]     ) / len(__A     )

                return {"accuracy": accuracy}

          # map train dataset
          _lowerCamelCase				:   List[Any]								=   train_dataset.map(
              _map_to_encoder_decoder_inputs,batched=__A,batch_size=__A,remove_columns=["article", "highlights"],)
          train_dataset.set_format(
              type="torch",columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"],)

          # same for validation dataset
          _lowerCamelCase				:   Optional[int]								=   val_dataset.map(
              _map_to_encoder_decoder_inputs,batched=__A,batch_size=__A,remove_columns=["article", "highlights"],)
          val_dataset.set_format(
              type="torch",columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"],)

          _lowerCamelCase				:   Optional[Any]								=   self.get_auto_remove_tmp_dir()

          _lowerCamelCase				:   Optional[Any]								=   SeqaSeqTrainingArguments(
              output_dir=__A,per_device_train_batch_size=__A,per_device_eval_batch_size=__A,predict_with_generate=__A,evaluation_strategy="steps",do_train=__A,do_eval=__A,warmup_steps=0,eval_steps=2,logging_steps=2,)

          # instantiate trainer
          _lowerCamelCase				:   Union[str, Any]								=   SeqaSeqTrainer(
              model=__A,args=__A,compute_metrics=_compute_metrics,train_dataset=__A,eval_dataset=__A,tokenizer=__A,)

          # start training
          trainer.train()

'''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