





"""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    _lowerCAmelCase  (	a			):
       """simple docstring"""




       @slow
       @require_torch
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'       ,					'prajjwal1/bert-tiny' )
         lowerCAmelCase__     :Tuple											=				BertTokenizer.from_pretrained('bert-base-uncased' )

         lowerCAmelCase__     :str											=				bertabert.config.encoder.vocab_size
         lowerCAmelCase__     :Tuple											=				tokenizer.sep_token_id
         lowerCAmelCase__     :int											=				tokenizer.cls_token_id
         lowerCAmelCase__     :Any											=				1_2_8

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

         lowerCAmelCase__     :Optional[Any]											=				train_dataset.select(range(3_2 ) )
         lowerCAmelCase__     :Tuple											=				val_dataset.select(range(1_6 ) )

         lowerCAmelCase__     :str											=				4

         def _map_to_encoder_decoder_inputs(__UpperCAmelCase ):
           # Tokenizer will automatically set [BOS] <text> [EOS]
           lowerCAmelCase__     :int											=				tokenizer(batch['article']       ,					padding='max_length'       ,					truncation=__UpperCAmelCase       ,					max_length=5_1_2 )
           lowerCAmelCase__     :int											=				tokenizer(batch['highlights']       ,					padding='max_length'       ,					truncation=__UpperCAmelCase       ,					max_length=1_2_8 )
           lowerCAmelCase__     :Any											=				inputs.input_ids
           lowerCAmelCase__     :Union[str, Any]											=				inputs.attention_mask

           lowerCAmelCase__     :Dict											=				outputs.input_ids
           lowerCAmelCase__     :Optional[Any]											=				outputs.input_ids.copy()
           lowerCAmelCase__     :List[str]											=				[
               [-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
           ]
           lowerCAmelCase__     :Dict											=				outputs.attention_mask

           assert all(len(__UpperCAmelCase ) == 5_1_2 for x in inputs.input_ids )
           assert all(len(__UpperCAmelCase ) == 1_2_8 for x in outputs.input_ids )

           return batch

         def _compute_metrics(__UpperCAmelCase ):
           lowerCAmelCase__     :Optional[Any]											=				pred.label_ids
           lowerCAmelCase__     :List[Any]											=				pred.predictions

           # all unnecessary tokens are removed
           lowerCAmelCase__     :Tuple											=				tokenizer.batch_decode(__UpperCAmelCase       ,					skip_special_tokens=__UpperCAmelCase )
           lowerCAmelCase__     :Any											=				tokenizer.batch_decode(__UpperCAmelCase       ,					skip_special_tokens=__UpperCAmelCase )

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

           return {"accuracy": accuracy}

         # map train dataset
         lowerCAmelCase__     :str											=				train_dataset.map(
             _map_to_encoder_decoder_inputs       ,					batched=__UpperCAmelCase       ,					batch_size=__UpperCAmelCase       ,					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__     :List[Any]											=				val_dataset.map(
             _map_to_encoder_decoder_inputs       ,					batched=__UpperCAmelCase       ,					batch_size=__UpperCAmelCase       ,					remove_columns=['article', 'highlights']       ,					)
         val_dataset.set_format(
             type='torch'       ,					columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']       ,					)

         lowerCAmelCase__     :str											=				self.get_auto_remove_tmp_dir()

         lowerCAmelCase__     :List[str]											=				SeqaSeqTrainingArguments(
             output_dir=__UpperCAmelCase       ,					per_device_train_batch_size=__UpperCAmelCase       ,					per_device_eval_batch_size=__UpperCAmelCase       ,					predict_with_generate=__UpperCAmelCase       ,					evaluation_strategy='steps'       ,					do_train=__UpperCAmelCase       ,					do_eval=__UpperCAmelCase       ,					warmup_steps=0       ,					eval_steps=2       ,					logging_steps=2       ,					)

         # instantiate trainer
         lowerCAmelCase__     :Any											=				SeqaSeqTrainer(
             model=__UpperCAmelCase       ,					args=__UpperCAmelCase       ,					compute_metrics=_compute_metrics       ,					train_dataset=__UpperCAmelCase       ,					eval_dataset=__UpperCAmelCase       ,					tokenizer=__UpperCAmelCase       ,					)

         # start training
         trainer.train()







"""simple docstring"""





from __future__ import annotations

import math
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->bool:





  """simple docstring"""


  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5	,  int(math.sqrt(_SCREAMING_SNAKE_CASE     ) + 1     )	,  6     ):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True


__A											=						[num for num in range(3, 10_0001, 2) if not is_prime(num)]
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->list[int]:





  """simple docstring"""


  if not isinstance(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     ):
    raise ValueError('n must be an integer'     )
  if n <= 0:
    raise ValueError('n must be >= 0'     )

  lowerCAmelCase__     :Tuple											=				[]
  for num in range(len(_SCREAMING_SNAKE_CASE     )     ):
    lowerCAmelCase__     :int											=				0
    while 2 * i * i <= odd_composites[num]:
      lowerCAmelCase__     :int											=				odd_composites[num] - 2 * i * i
      if is_prime(_SCREAMING_SNAKE_CASE     ):
        break
      i += 1
    else:
      list_nums.append(odd_composites[num]     )
      if len(_SCREAMING_SNAKE_CASE     ) == n:
        return list_nums

  return []
def 					__A  ()   ->int:





  """simple docstring"""


  return compute_nums(1     )[0]


if __name__ == "__main__":
  print(F'''{solution() = }''')
