"""simple docstring"""


def      __UpperCAmelCase       (      lowercase		):







  """simple docstring"""
  if not head:
    return True
  # split the list to two parts
  _UpperCAmelCase			,					_UpperCAmelCase    			=   head.next, head
  while fast and fast.next:
    _UpperCAmelCase    			=   fast.next.next
    _UpperCAmelCase    			=   slow.next
  _UpperCAmelCase    			=   slow.next
  _UpperCAmelCase    			=   None  # Don't forget here! But forget still works!
  # reverse the second part
  _UpperCAmelCase    			=   None
  while second:
    _UpperCAmelCase    			=   second.next
    _UpperCAmelCase    			=   node
    _UpperCAmelCase    			=   second
    _UpperCAmelCase    			=   nxt
  # compare two parts
  # second part has the same or one less node
  while node:
    if node.val != head.val:
      return False
    _UpperCAmelCase    			=   node.next
    _UpperCAmelCase    			=   head.next
  return True






def      __UpperCAmelCase       (      lowercase		):







  """simple docstring"""
  if not head or not head.next:
    return True

  # 1. Get the midpoint (slow)
  _UpperCAmelCase    			=   _UpperCAmelCase    			=   _UpperCAmelCase    			=   head
  while fast and fast.next:
    _UpperCAmelCase			,					_UpperCAmelCase    			=   fast.next.next, slow.next

  # 2. Push the second half into the stack
  _UpperCAmelCase    			=   [slow.val]
  while slow.next:
    _UpperCAmelCase    			=   slow.next
    stack.append(slow.val		)

  # 3. Comparison
  while stack:
    if stack.pop() != cur.val:
      return False
    _UpperCAmelCase    			=   cur.next

  return True






def      __UpperCAmelCase       (      lowercase		):







  """simple docstring"""
  if not head or not head.next:
    return True
  _UpperCAmelCase    			=   {}
  _UpperCAmelCase    			=   0
  while head:
    if head.val in d:
      d[head.val].append(lowercase		)
    else:
      _UpperCAmelCase    			=   [pos]
    _UpperCAmelCase    			=   head.next
    pos += 1
  _UpperCAmelCase    			=   pos - 1
  _UpperCAmelCase    			=   0
  for v in d.values():
    if len(lowercase		) % 2 != 0:
      middle += 1
    else:
      _UpperCAmelCase    			=   0
      for i in range(0 ,len(lowercase		)		):
        if v[i] + v[len(lowercase		) - 1 - step] != checksum:
          return False
        step += 1
    if middle > 1:
      return False
  return True

"""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()
