"""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	(						_UpperCAmelCase					):




    """simple docstring"""




    @slow
    @require_torch
    def  lowercase					(		self							:     Any    )    ->       Optional[Any]:
         __snake_case				=							EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny'''	,      '''prajjwal1/bert-tiny'''    )
         __snake_case				=							BertTokenizer.from_pretrained('''bert-base-uncased'''    )

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

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

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

         __snake_case				=							4

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

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

              # all unnecessary tokens are removed
              __snake_case				=							tokenizer.batch_decode(A_	,      skip_special_tokens=A_    )
              __snake_case				=							tokenizer.batch_decode(A_	,      skip_special_tokens=A_    )

              __snake_case				=							sum([int(pred_str[i] == label_str[i]    ) for i in range(len(A_    )    )]    ) / len(A_    )

              return {"accuracy": accuracy}

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

         __snake_case				=							self.get_auto_remove_tmp_dir()

         __snake_case				=							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
         __snake_case				=							SeqaSeqTrainer(
             model=A_	,      args=A_	,      compute_metrics=_compute_metrics	,      train_dataset=A_	,      eval_dataset=A_	,      tokenizer=A_	,      )

         # start training
         trainer.train()
"""simple docstring"""



from __future__ import annotations

from collections.abc import Iterator
from typing import Any



class      _A	:




    """simple docstring"""




    def __init__(		self							:     Tuple	,      A_							:     Any    )    ->       Union[str, Any]:
         __snake_case				=							data
         __snake_case				=							None






class      _A	:




    """simple docstring"""




    def __init__(		self							:     Union[str, Any]    )    ->       List[Any]:
         __snake_case				=							None
         __snake_case				=							None
    def __iter__(		self							:     Optional[Any]    )    ->       Iterator[Any]:
         __snake_case				=							self.head
         while self.head:
              yield node.data
              __snake_case				=							node.next
              if node == self.head:
                   break
    def __len__(		self							:     List[Any]    )    ->       int:
         return sum(1 for _ in self    )
    def __repr__(		self							:     Optional[int]    )    ->       Union[str, Any]:
         return "->".join(str(A_    ) for item in iter(self    )    )
    def  lowercase					(		self							:     int	,      A_							:     Any    )    ->       None:
         self.insert_nth(len(self    )	,      A_    )
    def  lowercase					(		self							:     List[str]	,      A_							:     Any    )    ->       None:
         self.insert_nth(0	,      A_    )
    def  lowercase					(		self							:     List[Any]	,      A_							:     int	,      A_							:     Any    )    ->       None:
         if index < 0 or index > len(self    ):
              raise IndexError('''list index out of range.'''    )
         __snake_case				=							Node(A_    )
         if self.head is None:
              __snake_case				=							new_node  # first node points itself
              __snake_case				=							__snake_case				=							new_node
         elif index == 0:  # insert at head
              __snake_case				=							self.head
              __snake_case				=							__snake_case				=							new_node
         else:
              __snake_case				=							self.head
              for _ in range(index - 1    ):
                   __snake_case				=							temp.next
              __snake_case				=							temp.next
              __snake_case				=							new_node
              if index == len(self    ) - 1:  # insert at tail
                   __snake_case				=							new_node
    def  lowercase					(		self							:     int    )    ->       str:
         return self.delete_nth(0    )
    def  lowercase					(		self							:     List[Any]    )    ->       Any:
         return self.delete_nth(len(self    ) - 1    )
    def  lowercase					(		self							:     List[str]	,      A_							:     int = 0    )    ->       Any:
         if not 0 <= index < len(self    ):
              raise IndexError('''list index out of range.'''    )
         __snake_case				=							self.head
         if self.head == self.tail:  # just one node
              __snake_case				=							__snake_case				=							None
         elif index == 0:  # delete head node
              __snake_case				=							self.tail.next.next
              __snake_case				=							self.head.next
         else:
              __snake_case				=							self.head
              for _ in range(index - 1    ):
                   __snake_case				=							temp.next
              __snake_case				=							temp.next
              __snake_case				=							temp.next.next
              if index == len(self    ) - 1:  # delete at tail
                   __snake_case				=							temp
         return delete_node.data







    def  lowercase					(		self							:     Dict    )    ->       bool:
         return len(self    ) == 0






def    SCREAMING_SNAKE_CASE       (     ):
     __snake_case				=							CircularLinkedList()
     assert len(snake_case) == 0
     assert circular_linked_list.is_empty() is True
     assert str(snake_case) == ""

     try:
          circular_linked_list.delete_front()
          raise AssertionError  # This should not happen
     except IndexError:
          assert True  # This should happen

     try:
          circular_linked_list.delete_tail()
          raise AssertionError  # This should not happen
     except IndexError:
          assert True  # This should happen

     try:
          circular_linked_list.delete_nth(-1)
          raise AssertionError
     except IndexError:
          assert True

     try:
          circular_linked_list.delete_nth(0)
          raise AssertionError
     except IndexError:
          assert True

     assert circular_linked_list.is_empty() is True
     for i in range(5):
          assert len(snake_case) == i
          circular_linked_list.insert_nth(snake_case,      i + 1)
     assert str(snake_case) == "->".join(str(snake_case) for i in range(1,      6))

     circular_linked_list.insert_tail(6)
     assert str(snake_case) == "->".join(str(snake_case) for i in range(1,      7))
     circular_linked_list.insert_head(0)
     assert str(snake_case) == "->".join(str(snake_case) for i in range(0,      7))

     assert circular_linked_list.delete_front() == 0
     assert circular_linked_list.delete_tail() == 6
     assert str(snake_case) == "->".join(str(snake_case) for i in range(1,      6))
     assert circular_linked_list.delete_nth(2) == 3

     circular_linked_list.insert_nth(2,      3)
     assert str(snake_case) == "->".join(str(snake_case) for i in range(1,      6))

     assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
       import doctest

       doctest.testmod()