



'''simple docstring'''

import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin

class __snake_case( _lowercase						,				unittest.TestCase   ):


						'''simple docstring'''



						UpperCAmelCase				:       Optional[int] 	=						BioGptTokenizer
						UpperCAmelCase				:       List[Any] 	=						False


						def 							__snake_case       (   self )      ->      Any:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							lowerCAmelCase       =      [
							    """l""",
							    """o""",
							    """w""",
							    """e""",
							    """r""",
							    """s""",
							    """t""",
							    """i""",
							    """d""",
							    """n""",
							    """w</w>""",
							    """r</w>""",
							    """t</w>""",
							    """lo""",
							    """low""",
							    """er</w>""",
							    """low</w>""",
							    """lowest</w>""",
							    """newer</w>""",
							    """wider</w>""",
							    """<unk>""",
							]
							lowerCAmelCase       =      dict(zip(__A      ,			range(len(__A ) ) ) )
							lowerCAmelCase       =      ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

							lowerCAmelCase       =      os.path.join(self.tmpdirname      ,			VOCAB_FILES_NAMES["""vocab_file"""] )
							lowerCAmelCase       =      os.path.join(self.tmpdirname      ,			VOCAB_FILES_NAMES["""merges_file"""] )
							with open(self.vocab_file      ,			"""w""" ) as fp:
								fp.write(json.dumps(__A ) )
							with open(self.merges_file      ,			"""w""" ) as fp:
								fp.write("""\n""".join(__A ) )


						def 							__snake_case       (   self      ,			A_ )      ->      Any:
							lowerCAmelCase       =      """lower newer"""
							lowerCAmelCase       =      """lower newer"""
							return input_text, output_text


						def 							__snake_case       (   self )      ->      List[Any]:
							lowerCAmelCase       =      BioGptTokenizer(self.vocab_file      ,			self.merges_file )

							lowerCAmelCase       =      """lower"""
							lowerCAmelCase       =      ["""low""", """er</w>"""]
							lowerCAmelCase       =      tokenizer.tokenize(__A )
							self.assertListEqual(__A      ,			__A )

							lowerCAmelCase       =      tokens + ["""<unk>"""]
							lowerCAmelCase       =      [14, 15, 20]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(__A )      ,			__A )




						@slow
						def 							__snake_case       (   self )      ->      int:
							lowerCAmelCase       =      BioGptTokenizer.from_pretrained("""microsoft/biogpt""" )

							lowerCAmelCase       =      tokenizer.encode("""sequence builders"""      ,			add_special_tokens=__A )
							lowerCAmelCase       =      tokenizer.encode("""multi-sequence build"""      ,			add_special_tokens=__A )

							lowerCAmelCase       =      tokenizer.build_inputs_with_special_tokens(__A )
							lowerCAmelCase       =      tokenizer.build_inputs_with_special_tokens(__A      ,			__A )

							self.assertTrue(encoded_sentence == [2] + text )
							self.assertTrue(encoded_pair == [2] + text + [2] + text_a )


from math import sqrt
def 							__SCREAMING_SNAKE_CASE     (   a__    :   int = 1000000       )					->				int:
	__A :			int		    =			0
	__A :			int		    =			0
	__A :			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(a__			,sum_shortest_sides // 2       )
				    - max(1			,sum_shortest_sides - max_cuboid_size       )
				    + 1
				)

	return max_cuboid_size


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





