"""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__			=    logging.get_logger(__name__)


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

UpperCAmelCase__			=    {
    """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__			=    {"""facebook/blenderbot-3B""": 1_2_8}


class 							a					(		lowerCAmelCase_ ):
    _snake_case  :	Dict  				=				VOCAB_FILES_NAMES
    _snake_case  :	int  				=				PRETRAINED_VOCAB_FILES_MAP
    _snake_case  :	str  				=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _snake_case  :	Any  				=				['input_ids', 'attention_mask']
    _snake_case  :	List[str]  				=				BlenderbotTokenizer


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

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

      _UpperCAmelCase    			=   add_prefix_space

      _UpperCAmelCase    			=   """post_processor"""
      _UpperCAmelCase    			=   getattr(self.backend_tokenizer ,					__lowerCAmelCase ,					__lowerCAmelCase       )
      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""" ,					__lowerCAmelCase       ) != add_prefix_space:
          _UpperCAmelCase    			=   add_prefix_space
          _UpperCAmelCase    			=   True

        if state.get("""trim_offsets""" ,					__lowerCAmelCase       ) != trim_offsets:
          _UpperCAmelCase    			=   trim_offsets
          _UpperCAmelCase    			=   True

        if changes_to_apply:
          _UpperCAmelCase    			=   getattr(__lowerCAmelCase ,					state.pop("""type"""       )       )
          _UpperCAmelCase    			=   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						:	Any       ):
      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						:	Tuple ,					__lowerCAmelCase						:	List[Any]       ):
      _UpperCAmelCase    			=   AddedToken(__lowerCAmelCase ,					lstrip=__lowerCAmelCase ,					rstrip=__lowerCAmelCase       ) if isinstance(__lowerCAmelCase ,					__lowerCAmelCase       ) else value
      _UpperCAmelCase    			=   value


    def   lowerCAmelCase_	(       self						:	Optional[Any] ,					*__lowerCAmelCase						:	str ,					**__lowerCAmelCase						:	Optional[Any]       ):
      _UpperCAmelCase    			=   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						:	List[Any] ,					*__lowerCAmelCase						:	Any ,					**__lowerCAmelCase						:	List[str]       ):
      _UpperCAmelCase    			=   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[int] ,					__lowerCAmelCase						:	str ,					__lowerCAmelCase						:	Optional[str] = None       ):
      _UpperCAmelCase    			=   self._tokenizer.model.save(__lowerCAmelCase ,					name=__lowerCAmelCase       )
      return tuple(__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	Any ,					__lowerCAmelCase						:	List[int] ,					__lowerCAmelCase						:	Optional[List[int]] = None       ):
      _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   lowerCAmelCase_	(       self						:	Dict ,					__lowerCAmelCase						:	List[int] ,					__lowerCAmelCase						:	Optional[List[int]] = None       ):
      return token_ids_a + [self.eos_token_id]


    def   lowerCAmelCase_	(       self						:	Dict ,					__lowerCAmelCase						:	"Conversation"       ):
      _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(__lowerCAmelCase       )

      _UpperCAmelCase    			=   """  """.join(__lowerCAmelCase       )
      _UpperCAmelCase    			=   self.encode(__lowerCAmelCase       )
      if len(__lowerCAmelCase       ) > 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

"""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 							a					(		lowerCAmelCase_ ):


    @slow
    @require_torch
    def   lowerCAmelCase_	(       self						:	str       ):
      _UpperCAmelCase    			=   EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny""" ,					"""prajjwal1/bert-tiny"""       )
      _UpperCAmelCase    			=   BertTokenizer.from_pretrained("""bert-base-uncased"""       )

      _UpperCAmelCase    			=   bertabert.config.encoder.vocab_size
      _UpperCAmelCase    			=   tokenizer.sep_token_id
      _UpperCAmelCase    			=   tokenizer.cls_token_id
      _UpperCAmelCase    			=   128

      _UpperCAmelCase    			=   datasets.load_dataset("""cnn_dailymail""" ,					"""3.0.0""" ,					split="""train[:1%]"""       )
      _UpperCAmelCase    			=   datasets.load_dataset("""cnn_dailymail""" ,					"""3.0.0""" ,					split="""validation[:1%]"""       )

      _UpperCAmelCase    			=   train_dataset.select(range(32       )       )
      _UpperCAmelCase    			=   val_dataset.select(range(16       )       )

      _UpperCAmelCase    			=   4

      def _map_to_encoder_decoder_inputs(__lowerCAmelCase						:	Dict       ):
        # Tokenizer will automatically set [BOS] <text> [EOS]
        _UpperCAmelCase    			=   tokenizer(batch["""article"""] ,					padding="""max_length""" ,					truncation=__lowerCAmelCase ,					max_length=512       )
        _UpperCAmelCase    			=   tokenizer(batch["""highlights"""] ,					padding="""max_length""" ,					truncation=__lowerCAmelCase ,					max_length=128       )
        _UpperCAmelCase    			=   inputs.input_ids
        _UpperCAmelCase    			=   inputs.attention_mask

        _UpperCAmelCase    			=   outputs.input_ids
        _UpperCAmelCase    			=   outputs.input_ids.copy()
        _UpperCAmelCase    			=   [
            [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
        ]
        _UpperCAmelCase    			=   outputs.attention_mask

        assert all(len(__lowerCAmelCase       ) == 512 for x in inputs.input_ids       )
        assert all(len(__lowerCAmelCase       ) == 128 for x in outputs.input_ids       )

        return batch

      def _compute_metrics(__lowerCAmelCase						:	int       ):
        _UpperCAmelCase    			=   pred.label_ids
        _UpperCAmelCase    			=   pred.predictions

        # all unnecessary tokens are removed
        _UpperCAmelCase    			=   tokenizer.batch_decode(__lowerCAmelCase ,					skip_special_tokens=__lowerCAmelCase       )
        _UpperCAmelCase    			=   tokenizer.batch_decode(__lowerCAmelCase ,					skip_special_tokens=__lowerCAmelCase       )

        _UpperCAmelCase    			=   sum([int(pred_str[i] == label_str[i]       ) for i in range(len(__lowerCAmelCase       )       )]       ) / len(__lowerCAmelCase       )

        return {"accuracy": accuracy}

      # map train dataset
      _UpperCAmelCase    			=   train_dataset.map(
          _map_to_encoder_decoder_inputs ,					batched=__lowerCAmelCase ,					batch_size=__lowerCAmelCase ,					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
      _UpperCAmelCase    			=   val_dataset.map(
          _map_to_encoder_decoder_inputs ,					batched=__lowerCAmelCase ,					batch_size=__lowerCAmelCase ,					remove_columns=["""article""", """highlights"""] ,					)
      val_dataset.set_format(
          type="""torch""" ,					columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] ,					)

      _UpperCAmelCase    			=   self.get_auto_remove_tmp_dir()

      _UpperCAmelCase    			=   SeqaSeqTrainingArguments(
          output_dir=__lowerCAmelCase ,					per_device_train_batch_size=__lowerCAmelCase ,					per_device_eval_batch_size=__lowerCAmelCase ,					predict_with_generate=__lowerCAmelCase ,					evaluation_strategy="""steps""" ,					do_train=__lowerCAmelCase ,					do_eval=__lowerCAmelCase ,					warmup_steps=0 ,					eval_steps=2 ,					logging_steps=2 ,					)

      # instantiate trainer
      _UpperCAmelCase    			=   SeqaSeqTrainer(
          model=__lowerCAmelCase ,					args=__lowerCAmelCase ,					compute_metrics=_compute_metrics ,					train_dataset=__lowerCAmelCase ,					eval_dataset=__lowerCAmelCase ,					tokenizer=__lowerCAmelCase ,					)

      # start training
      trainer.train()
