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 __snake_case    (						lowerCamelCase_		):




   @slow
   @require_torch
   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny"""	,     """prajjwal1/bert-tiny"""				)
     SCREAMING_SNAKE_CASE__      							=				BertTokenizer.from_pretrained("""bert-base-uncased"""				)

     SCREAMING_SNAKE_CASE__      							=				bertabert.config.encoder.vocab_size
     SCREAMING_SNAKE_CASE__      							=				tokenizer.sep_token_id
     SCREAMING_SNAKE_CASE__      							=				tokenizer.cls_token_id
     SCREAMING_SNAKE_CASE__      							=				1_28

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

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

     SCREAMING_SNAKE_CASE__      							=				4

     def _map_to_encoder_decoder_inputs(_lowercase      :    Tuple				):
       # Tokenizer will automatically set [BOS] <text> [EOS]
       SCREAMING_SNAKE_CASE__      							=				tokenizer(batch["""article"""]	,     padding="""max_length"""	,     truncation=_lowercase	,     max_length=5_12				)
       SCREAMING_SNAKE_CASE__      							=				tokenizer(batch["""highlights"""]	,     padding="""max_length"""	,     truncation=_lowercase	,     max_length=1_28				)
       SCREAMING_SNAKE_CASE__      							=				inputs.input_ids
       SCREAMING_SNAKE_CASE__      							=				inputs.attention_mask

       SCREAMING_SNAKE_CASE__      							=				outputs.input_ids
       SCREAMING_SNAKE_CASE__      							=				outputs.input_ids.copy()
       SCREAMING_SNAKE_CASE__      							=				[
           [-1_00 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
       ]
       SCREAMING_SNAKE_CASE__      							=				outputs.attention_mask

       assert all(len(_lowercase				) == 5_12 for x in inputs.input_ids				)
       assert all(len(_lowercase				) == 1_28 for x in outputs.input_ids				)

       return batch

     def _compute_metrics(_lowercase      :    List[Any]				):
       SCREAMING_SNAKE_CASE__      							=				pred.label_ids
       SCREAMING_SNAKE_CASE__      							=				pred.predictions

       # all unnecessary tokens are removed
       SCREAMING_SNAKE_CASE__      							=				tokenizer.batch_decode(_lowercase	,     skip_special_tokens=_lowercase				)
       SCREAMING_SNAKE_CASE__      							=				tokenizer.batch_decode(_lowercase	,     skip_special_tokens=_lowercase				)

       SCREAMING_SNAKE_CASE__      							=				sum([int(pred_str[i] == label_str[i]				) for i in range(len(_lowercase				)				)]				) / len(_lowercase				)

       return {"accuracy": accuracy}

     # map train dataset
     SCREAMING_SNAKE_CASE__      							=				train_dataset.map(
         _map_to_encoder_decoder_inputs	,     batched=_lowercase	,     batch_size=_lowercase	,     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
     SCREAMING_SNAKE_CASE__      							=				val_dataset.map(
         _map_to_encoder_decoder_inputs	,     batched=_lowercase	,     batch_size=_lowercase	,     remove_columns=["""article""", """highlights"""]	,     )
     val_dataset.set_format(
         type="""torch"""	,     columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]	,     )

     SCREAMING_SNAKE_CASE__      							=				self.get_auto_remove_tmp_dir()

     SCREAMING_SNAKE_CASE__      							=				SeqaSeqTrainingArguments(
         output_dir=_lowercase	,     per_device_train_batch_size=_lowercase	,     per_device_eval_batch_size=_lowercase	,     predict_with_generate=_lowercase	,     evaluation_strategy="""steps"""	,     do_train=_lowercase	,     do_eval=_lowercase	,     warmup_steps=0	,     eval_steps=2	,     logging_steps=2	,     )

     # instantiate trainer
     SCREAMING_SNAKE_CASE__      							=				SeqaSeqTrainer(
         model=_lowercase	,     args=_lowercase	,     compute_metrics=_compute_metrics	,     train_dataset=_lowercase	,     eval_dataset=_lowercase	,     tokenizer=_lowercase	,     )

     # start training
     trainer.train()

from __future__ import annotations

__lowerCamelCase      :			Dict							       =		[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__lowerCamelCase      :			Union[str, Any]							       =		[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	list[float]     )							-> list[float]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[]
  SCREAMING_SNAKE_CASE__      							=				len(__UpperCamelCase     )

  for i in range(__UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__      							=				-1
    for j in range(i + 1			,					__UpperCamelCase     ):
      if arr[i] < arr[j]:
        SCREAMING_SNAKE_CASE__      							=				arr[j]
        break
    result.append(__UpperCamelCase     )
  return result



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	list[float]     )							-> list[float]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[]
  for i, outer in enumerate(__UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__      							=				-1
    for inner in arr[i + 1 :]:
      if outer < inner:
        SCREAMING_SNAKE_CASE__      							=				inner
        break
    result.append(__UpperCamelCase     )
  return result



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	list[float]     )							-> list[float]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				len(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				[]
  SCREAMING_SNAKE_CASE__      							=				[-1] * arr_size

  for index in reversed(range(__UpperCamelCase     )     ):
    if stack:
      while stack[-1] <= arr[index]:
        stack.pop()
        if not stack:
          break
    if stack:
      SCREAMING_SNAKE_CASE__      							=				stack[-1]
    stack.append(arr[index]     )
  return result


if __name__ == "__main__":
   from doctest import testmod
   from timeit import timeit

   testmod()
   print(next_greatest_element_slow(arr))
   print(next_greatest_element_fast(arr))
   print(next_greatest_element(arr))

   __lowerCamelCase      :			List[Any]							       =		(
       '''from __main__ import arr, next_greatest_element_slow, '''
       '''next_greatest_element_fast, next_greatest_element'''
   )
   print(
       '''next_greatest_element_slow():''',
       timeit('''next_greatest_element_slow(arr)''', setup=setup),
   )
   print(
       '''next_greatest_element_fast():''',
       timeit('''next_greatest_element_fast(arr)''', setup=setup),
   )
   print(
       '''     next_greatest_element():''',
       timeit('''next_greatest_element(arr)''', setup=setup),
   )
