




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 						lowercase							(   UpperCamelCase__		):

     @slow
     @require_torch
     def 					a__	(  self				)						->							Dict:
           _A							:     int			=						EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny"""		,     """prajjwal1/bert-tiny"""				)
           _A							:     Tuple			=						BertTokenizer.from_pretrained("""bert-base-uncased"""				)

           _A							:     Dict			=						bertabert.config.encoder.vocab_size
           _A							:     List[str]			=						tokenizer.sep_token_id
           _A							:     Dict			=						tokenizer.cls_token_id
           _A							:     List[Any]			=						128

           _A							:     Tuple			=						datasets.load_dataset("""cnn_dailymail"""		,     """3.0.0"""		,     split="""train[:1%]"""				)
           _A							:     List[str]			=						datasets.load_dataset("""cnn_dailymail"""		,     """3.0.0"""		,     split="""validation[:1%]"""				)

           _A							:     Union[str, Any]			=						train_dataset.select(range(32				)				)
           _A							:     str			=						val_dataset.select(range(16				)				)

           _A							:     Dict			=						4

           def _map_to_encoder_decoder_inputs(_a				):
                 # Tokenizer will automatically set [BOS] <text> [EOS]
                 _A							:     List[Any]			=						tokenizer(batch["""article"""]		,     padding="""max_length"""		,     truncation=_a		,     max_length=512				)
                 _A							:     int			=						tokenizer(batch["""highlights"""]		,     padding="""max_length"""		,     truncation=_a		,     max_length=128				)
                 _A							:     Union[str, Any]			=						inputs.input_ids
                 _A							:     List[Any]			=						inputs.attention_mask

                 _A							:     Dict			=						outputs.input_ids
                 _A							:     str			=						outputs.input_ids.copy()
                 _A							:     Union[str, Any]			=						[
                     [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
                 ]
                 _A							:     Dict			=						outputs.attention_mask

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

                 return batch

           def _compute_metrics(_a				):
                 _A							:     int			=						pred.label_ids
                 _A							:     str			=						pred.predictions

                 # all unnecessary tokens are removed
                 _A							:     Dict			=						tokenizer.batch_decode(_a		,     skip_special_tokens=_a				)
                 _A							:     Optional[Any]			=						tokenizer.batch_decode(_a		,     skip_special_tokens=_a				)

                 _A							:     Tuple			=						sum([int(pred_str[i] == label_str[i]				) for i in range(len(_a				)				)]				) / len(_a				)

                 return {"accuracy": accuracy}

           # map train dataset
           _A							:     Dict			=						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
           _A							:     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"""]		,     )

           _A							:     Optional[int]			=						self.get_auto_remove_tmp_dir()

           _A							:     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
           _A							:     Optional[Any]			=						SeqaSeqTrainer(
               model=_a		,     args=_a		,     compute_metrics=_compute_metrics		,     train_dataset=_a		,     eval_dataset=_a		,     tokenizer=_a		,     )

           # start training
           trainer.train()







def 				lowerCAmelCase_	(     snake_case_     ):
      if number < 0:
            raise ValueError("""number must not be negative"""     )
      return number & (number - 1) == 0


if __name__ == "__main__":
  import doctest

  doctest.testmod()

