
'''simple docstring'''






import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


UpperCAmelCase_       :   Tuple   =       get_tests_dir() + '/test_data/fsmt/fsmt_val_data.json'
with io.open(filename, 'r', encoding='utf-8') as f:
  UpperCAmelCase_       :   Optional[Any]   =       json.load(f)

@require_torch
class  UpperCAmelCase__						(       unittest.TestCase ):



    def   lowerCamelCase_  (	self      :       int,__A      :       Optional[Any]     ):
          return FSMTTokenizer.from_pretrained(__A     )



    def   lowerCamelCase_  (	self      :       Tuple,__A      :       Tuple     ):
          _lowerCamelCase				:   Optional[Any]								=   FSMTForConditionalGeneration.from_pretrained(__A     ).to(__A     )
          if torch_device == "cuda":
                model.half()
          return model



    @parameterized.expand(
        [
            ["en-ru", 26.0],
            ["ru-en", 22.0],
            ["en-de", 22.0],
            ["de-en", 29.0],
        ]     )
    @slow
    def   lowerCamelCase_  (	self      :       int,__A      :       Optional[Any],__A      :       Any     ):
          _lowerCamelCase				:   Tuple								=   f'facebook/wmt19-{pair}'
          _lowerCamelCase				:   Optional[int]								=   self.get_tokenizer(__A     )
          _lowerCamelCase				:   Tuple								=   self.get_model(__A     )

          _lowerCamelCase				:   List[str]								=   bleu_data[pair]["src"]
          _lowerCamelCase				:   int								=   bleu_data[pair]["tgt"]

          _lowerCamelCase				:   Optional[Any]								=   tokenizer(__A,return_tensors="pt",truncation=__A,padding="longest"     ).to(__A     )
          _lowerCamelCase				:   List[str]								=   model.generate(
              input_ids=batch.input_ids,num_beams=8,)
          _lowerCamelCase				:   Any								=   tokenizer.batch_decode(
              __A,skip_special_tokens=__A,clean_up_tokenization_spaces=__A     )
          _lowerCamelCase				:   Dict								=   calculate_bleu(__A,__A     )
          print(__A     )
          self.assertGreaterEqual(scores["bleu"],__A     )

'''simple docstring'''






from math import sqrt







def 		A_       (	_lowerCAmelCase   :    int = 1000000							):



      """simple docstring"""




      _lowerCamelCase				:   int								=   0
      _lowerCamelCase				:   int								=   0
      _lowerCamelCase				:   int

      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(_lowerCAmelCase				,	sum_shortest_sides // 2							)
                            - max(1				,	sum_shortest_sides - max_cuboid_size							)
                            + 1
                        )

      return max_cuboid_size


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