






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			):
         _lowerCAmelCase 			=  EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny'''       ,     '''prajjwal1/bert-tiny'''			)
         _lowerCAmelCase 			=  BertTokenizer.from_pretrained('''bert-base-uncased'''			)

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

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

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

         _lowerCAmelCase 			=  4

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

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

               # all unnecessary tokens are removed
               _lowerCAmelCase 			=  tokenizer.batch_decode(__A       ,     skip_special_tokens=__A			)
               _lowerCAmelCase 			=  tokenizer.batch_decode(__A       ,     skip_special_tokens=__A			)

               _lowerCAmelCase 			=  sum([int(pred_str[i] == label_str[i]			) for i in range(len(__A			)			)]			) / len(__A			)

               return {"accuracy": accuracy}

         # map train dataset
         _lowerCAmelCase 			=  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 			=  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 			=  self.get_auto_remove_tmp_dir()

         _lowerCAmelCase 			=  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 			=  SeqaSeqTrainer(
             model=__A       ,     args=__A       ,     compute_metrics=_compute_metrics       ,     train_dataset=__A       ,     eval_dataset=__A       ,     tokenizer=__A       ,     )

         # start training
         trainer.train()




def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Optional[Any]				)->Any:  # noqa: E741
      _lowerCAmelCase 			=  len(_SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase 			=  0
      _lowerCAmelCase 			=  [0] * n
      _lowerCAmelCase 			=  [False] * n
      _lowerCAmelCase 			=  [False] * n

      def dfs(_SCREAMING_SNAKE_CASE   :	List[str]     ,				_SCREAMING_SNAKE_CASE   :	List[Any]     ,				_SCREAMING_SNAKE_CASE   :	Optional[int]     ,				_SCREAMING_SNAKE_CASE   :	int				):
            if parent == root:
                  out_edge_count += 1
            _lowerCAmelCase 			=  True
            _lowerCAmelCase 			=  at

            for to in l[at]:
                  if to == parent:
                        pass
                  elif not visited[to]:
                        _lowerCAmelCase 			=  dfs(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)
                        _lowerCAmelCase 			=  min(low[at]     ,				low[to]				)

                        # AP found via bridge
                        if at < low[to]:
                              _lowerCAmelCase 			=  True
                        # AP found via cycle
                        if at == low[to]:
                              _lowerCAmelCase 			=  True
                  else:
                        _lowerCAmelCase 			=  min(low[at]     ,				_SCREAMING_SNAKE_CASE				)
            return out_edge_count

      for i in range(_SCREAMING_SNAKE_CASE				):
            if not visited[i]:
                  _lowerCAmelCase 			=  0
                  _lowerCAmelCase 			=  dfs(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				-1     ,				_SCREAMING_SNAKE_CASE				)
                  _lowerCAmelCase 			=  out_edge_count > 1

      for x in range(len(_SCREAMING_SNAKE_CASE				)				):
            if is_art[x] is True:
                  print(_SCREAMING_SNAKE_CASE				)


# Adjacency list of graph
UpperCAmelCase_									=							{
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}


compute_ap(data)