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      lowerCAmelCase						(					_a			):



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

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

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

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

            _A=	4

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

                   _A=	outputs.input_ids
                   _A=	outputs.input_ids.copy()
                   _A=	[
                       [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
                   ]
                   _A=	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__       ):
                   _A=	pred.label_ids
                   _A=	pred.predictions

                   # all unnecessary tokens are removed
                   _A=	tokenizer.batch_decode(lowerCAmelCase__				,      skip_special_tokens=lowerCAmelCase__       )
                   _A=	tokenizer.batch_decode(lowerCAmelCase__				,      skip_special_tokens=lowerCAmelCase__       )

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

                   return {"accuracy": accuracy}

            # map train dataset
            _A=	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
            _A=	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']				,      )

            _A=	self.get_auto_remove_tmp_dir()

            _A=	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
            _A=	SeqaSeqTrainer(
                model=lowerCAmelCase__				,      args=lowerCAmelCase__				,      compute_metrics=_compute_metrics				,      train_dataset=lowerCAmelCase__				,      eval_dataset=lowerCAmelCase__				,      tokenizer=lowerCAmelCase__				,      )

            # start training
            trainer.train()
from ..utils import DummyObject, requires_backends





class      lowerCAmelCase						(					metaclass=_a			):
     _SCREAMING_SNAKE_CASE     :     Optional[int]		=["""transformers""", """torch""", """note_seq"""]



     def __init__(					self				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            requires_backends(self				,      ['transformers', 'torch', 'note_seq']       )



     @classmethod
     def 			a__  (					cls				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            requires_backends(cls				,      ['transformers', 'torch', 'note_seq']       )





     @classmethod
     def 			a__  (					cls				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            requires_backends(cls				,      ['transformers', 'torch', 'note_seq']       )