


"""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 	lowercase__				(					A_				):





   @slow
   @require_torch
   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      _lowerCamelCase	:					int   =		EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny"""      ,   """prajjwal1/bert-tiny""")
      _lowerCamelCase	:					Dict   =		BertTokenizer.from_pretrained("""bert-base-uncased""")

      _lowerCamelCase	:					Tuple   =		bertabert.config.encoder.vocab_size
      _lowerCamelCase	:					int   =		tokenizer.sep_token_id
      _lowerCamelCase	:					List[str]   =		tokenizer.cls_token_id
      _lowerCamelCase	:					Optional[int]   =		128

      _lowerCamelCase	:					int   =		datasets.load_dataset("""cnn_dailymail"""      ,   """3.0.0"""      ,   split="""train[:1%]""")
      _lowerCamelCase	:					Union[str, Any]   =		datasets.load_dataset("""cnn_dailymail"""      ,   """3.0.0"""      ,   split="""validation[:1%]""")

      _lowerCamelCase	:					List[Any]   =		train_dataset.select(range(32))
      _lowerCamelCase	:					Optional[int]   =		val_dataset.select(range(16))

      _lowerCamelCase	:					Any   =		4

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

         _lowerCamelCase	:					Union[str, Any]   =		outputs.input_ids
         _lowerCamelCase	:					str   =		outputs.input_ids.copy()
         _lowerCamelCase	:					List[Any]   =		[
             [-100 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(SCREAMING_SNAKE_CASE) == 512 for x in inputs.input_ids)
         assert all(len(SCREAMING_SNAKE_CASE) == 128 for x in outputs.input_ids)

         return batch

      def _compute_metrics(SCREAMING_SNAKE_CASE):
         _lowerCamelCase	:					Any   =		pred.label_ids
         _lowerCamelCase	:					Optional[int]   =		pred.predictions

         # all unnecessary tokens are removed
         _lowerCamelCase	:					Tuple   =		tokenizer.batch_decode(SCREAMING_SNAKE_CASE      ,   skip_special_tokens=SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					Tuple   =		tokenizer.batch_decode(SCREAMING_SNAKE_CASE      ,   skip_special_tokens=SCREAMING_SNAKE_CASE)

         _lowerCamelCase	:					Tuple   =		sum([int(pred_str[i] == label_str[i]) for i in range(len(SCREAMING_SNAKE_CASE))]) / len(SCREAMING_SNAKE_CASE)

         return {"accuracy": accuracy}

      # map train dataset
      _lowerCamelCase	:					str   =		train_dataset.map(
          _map_to_encoder_decoder_inputs      ,   batched=SCREAMING_SNAKE_CASE      ,   batch_size=SCREAMING_SNAKE_CASE      ,   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	:					Union[str, Any]   =		val_dataset.map(
          _map_to_encoder_decoder_inputs      ,   batched=SCREAMING_SNAKE_CASE      ,   batch_size=SCREAMING_SNAKE_CASE      ,   remove_columns=["""article""", """highlights"""]      ,   )
      val_dataset.set_format(
          type="""torch"""      ,   columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]      ,   )

      _lowerCamelCase	:					Optional[Any]   =		self.get_auto_remove_tmp_dir()

      _lowerCamelCase	:					Union[str, Any]   =		SeqaSeqTrainingArguments(
          output_dir=SCREAMING_SNAKE_CASE      ,   per_device_train_batch_size=SCREAMING_SNAKE_CASE      ,   per_device_eval_batch_size=SCREAMING_SNAKE_CASE      ,   predict_with_generate=SCREAMING_SNAKE_CASE      ,   evaluation_strategy="""steps"""      ,   do_train=SCREAMING_SNAKE_CASE      ,   do_eval=SCREAMING_SNAKE_CASE      ,   warmup_steps=0      ,   eval_steps=2      ,   logging_steps=2      ,   )

      # instantiate trainer
      _lowerCamelCase	:					Optional[Any]   =		SeqaSeqTrainer(
          model=SCREAMING_SNAKE_CASE      ,   args=SCREAMING_SNAKE_CASE      ,   compute_metrics=_compute_metrics      ,   train_dataset=SCREAMING_SNAKE_CASE      ,   eval_dataset=SCREAMING_SNAKE_CASE      ,   tokenizer=SCREAMING_SNAKE_CASE      ,   )

      # start training
      trainer.train()





"""simple docstring"""
from math import isqrt, loga

def 				_snake_case      (   __snake_case							:      int   ):




   """simple docstring"""







   _lowerCamelCase	:					List[str]   =		[True] * max_number
   for i in range(2						, isqrt(max_number - 1   ) + 1   ):
      if is_prime[i]:
         for j in range(i**2						, __snake_case						, __snake_case   ):
            _lowerCamelCase	:					Optional[int]   =		False

   return [i for i in range(2						, __snake_case   ) if is_prime[i]]

def 				_snake_case      (   __snake_case							:      int = 800800						, __snake_case							:      int = 800800   ):




   """simple docstring"""







   _lowerCamelCase	:					Union[str, Any]   =		degree * loga(__snake_case   )
   _lowerCamelCase	:					Union[str, Any]   =		int(__snake_case   )
   _lowerCamelCase	:					Dict   =		calculate_prime_numbers(__snake_case   )

   _lowerCamelCase	:					Optional[int]   =		0
   _lowerCamelCase	:					Any   =		0
   _lowerCamelCase	:					Any   =		len(__snake_case   ) - 1
   while left < right:
      while (
          prime_numbers[right] * loga(prime_numbers[left]   )
          + prime_numbers[left] * loga(prime_numbers[right]   )
          > upper_bound
      ):
         right -= 1
      hybrid_integers_count += right - left
      left += 1

   return hybrid_integers_count


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

