


'''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      snake_case    (    lowercase			):





  """simple docstring"""




  @slow
  @require_torch
  def   snake_case		(    self     ):







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

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

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

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

   lowerCamelCase_      =  4

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

    lowerCamelCase_      =  outputs.input_ids
    lowerCamelCase_      =  outputs.input_ids.copy()
    lowerCamelCase_      =  [
        [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
    ]
    lowerCamelCase_      =  outputs.attention_mask

    assert all(len(UpperCamelCase     ) == 512 for x in inputs.input_ids     )
    assert all(len(UpperCamelCase     ) == 128 for x in outputs.input_ids     )

    return batch

   def _compute_metrics(UpperCamelCase     ):
    lowerCamelCase_      =  pred.label_ids
    lowerCamelCase_      =  pred.predictions

    # all unnecessary tokens are removed
    lowerCamelCase_      =  tokenizer.batch_decode(UpperCamelCase			,  skip_special_tokens=UpperCamelCase     )
    lowerCamelCase_      =  tokenizer.batch_decode(UpperCamelCase			,  skip_special_tokens=UpperCamelCase     )

    lowerCamelCase_      =  sum([int(pred_str[i] == label_str[i]     ) for i in range(len(UpperCamelCase     )     )]     ) / len(UpperCamelCase     )

    return {"accuracy": accuracy}

   # map train dataset
   lowerCamelCase_      =  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_      =  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_      =  self.get_auto_remove_tmp_dir()

   lowerCamelCase_      =  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_      =  SeqaSeqTrainer(
       model=UpperCamelCase			,  args=UpperCamelCase			,  compute_metrics=_compute_metrics			,  train_dataset=UpperCamelCase			,  eval_dataset=UpperCamelCase			,  tokenizer=UpperCamelCase			,  )

   # start training
   trainer.train()




'''simple docstring'''




from math import sqrt






def 				snake_case__							(			_A:					int = 1000000 )    ->	int:

		'''simple docstring'''


		lowerCAmelCase   					=  0
		lowerCAmelCase   					=  0
		lowerCAmelCase   					=  42

		while num_cuboids <= limit:
				max_cuboid_size += 1
				for sum_shortest_sides in range(2      ,   2 * max_cuboid_size + 1 ):
						if sqrt(sum_shortest_sides**2 + max_cuboid_size**2 ).is_integer():
								num_cuboids += (
								    min(_A      ,   sum_shortest_sides // 2 )
								    - max(1      ,   sum_shortest_sides - max_cuboid_size )
								    + 1
								)

		return max_cuboid_size


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




