





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 ):

     @slow
     @require_torch
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _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(A      ):
                   # Tokenizer will automatically set [BOS] <text> [EOS]
                   _UpperCamelCase              =						tokenizer(batch['''article'''],       padding='''max_length''',       truncation=A,       max_length=512      )
                   _UpperCamelCase              =						tokenizer(batch['''highlights'''],       padding='''max_length''',       truncation=A,       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(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      ):
                   _UpperCamelCase              =						pred.label_ids
                   _UpperCamelCase              =						pred.predictions

                   # all unnecessary tokens are removed
                   _UpperCamelCase              =						tokenizer.batch_decode(A,       skip_special_tokens=A      )
                   _UpperCamelCase              =						tokenizer.batch_decode(A,       skip_special_tokens=A      )

                   _UpperCamelCase              =						sum([int(pred_str[i] == label_str[i]      ) for i in range(len(A      )      )]      ) / len(A      )

                   return {"accuracy": accuracy}

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

            _UpperCamelCase              =						self.get_auto_remove_tmp_dir()

            _UpperCamelCase              =						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
            _UpperCamelCase              =						SeqaSeqTrainer(
                model=A,       args=A,       compute_metrics=_compute_metrics,       train_dataset=A,       eval_dataset=A,       tokenizer=A,       )

            # start training
            trainer.train()













from __future__ import annotations

import math


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):

       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(lowerCAmelCase					) + 1					)    ,       6					):
              if number % i == 0 or number % (i + 2) == 0:
                     return False
       return True


lowercase    : Tuple    	=  [num for num in range(3, 10_0001, 2) if not is_prime(num)]


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

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

       return []


def     SCREAMING_SNAKE_CASE			(      ):
       return compute_nums(1					)[0]


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






