





'''simple docstring'''


import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
				from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCamelCase :		int 			=				get_tests_dir('fixtures/test_sentencepiece.model')

_lowerCamelCase :		List[str] 			=				{'target_lang': 'fi', 'source_lang': 'en'}
_lowerCamelCase :		int 			=				'>>zh<<'
_lowerCamelCase :		Optional[int] 			=				'Helsinki-NLP/'

if is_torch_available():
				_lowerCamelCase :		List[str] 			=				'pt'
elif is_tf_available():
				_lowerCamelCase :		List[str] 			=				'tf'
else:
				_lowerCamelCase :		str 			=				'jax'



@require_sentencepiece
class 							__UpperCAmelCase     (				lowerCAmelCase__						,    unittest.TestCase  ):







		'''simple docstring'''







		__lowerCAmelCase        =			MarianTokenizer
		__lowerCAmelCase        =			False
		__lowerCAmelCase        =			True







		def 				A   (self  :					List[str]					):
			super().setUp()
			A							     =					["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
			A							     =					dict(zip(__lowerCAmelCase					,					range(len(__lowerCAmelCase					)					)					)					)
			A							     =					Path(self.tmpdirname					)
			save_json(__lowerCAmelCase					,					save_dir / VOCAB_FILES_NAMES["""vocab"""]					)
			save_json(__lowerCAmelCase					,					save_dir / VOCAB_FILES_NAMES["""tokenizer_config_file"""]					)
			if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
				copyfile(__lowerCAmelCase					,					save_dir / VOCAB_FILES_NAMES["""source_spm"""]					)
				copyfile(__lowerCAmelCase					,					save_dir / VOCAB_FILES_NAMES["""target_spm"""]					)

			A							     =					MarianTokenizer.from_pretrained(self.tmpdirname					)
			tokenizer.save_pretrained(self.tmpdirname					)







		def 				A   (self  :					List[Any]					,					**_lowerCAmelCase  :					List[Any]					):
			return MarianTokenizer.from_pretrained(self.tmpdirname					,					**__lowerCAmelCase					)







		def 				A   (self  :					Any					,					_lowerCAmelCase  :					Any					):
			return (
			    "This is a test",
			    "This is a test",
			)







		def 				A   (self  :					List[Any]					):
			A							     =					"""</s>"""
			A							     =					0

			self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCAmelCase					)					,					__lowerCAmelCase					)
			self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCAmelCase					)					,					__lowerCAmelCase					)







		def 				A   (self  :					Dict					):
			A							     =					list(self.get_tokenizer().get_vocab().keys()					)

			self.assertEqual(vocab_keys[0]					,					"""</s>"""					)
			self.assertEqual(vocab_keys[1]					,					"""<unk>"""					)
			self.assertEqual(vocab_keys[-1]					,					"""<pad>"""					)
			self.assertEqual(len(__lowerCAmelCase					)					,					9					)







		def 				A   (self  :					Tuple					):
			self.assertEqual(self.get_tokenizer().vocab_size					,					9					)







		def 				A   (self  :					List[str]					):
			A							     =					MarianTokenizer.from_pretrained(F"""{ORG_NAME}opus-mt-en-de"""					)
			A							     =					en_de_tokenizer(["""I am a small frog"""]					,					return_tensors=__lowerCAmelCase					)
			self.assertIsInstance(__lowerCAmelCase					,					__lowerCAmelCase					)
			A							     =					[38, 121, 14, 697, 3_8848, 0]
			self.assertListEqual(__lowerCAmelCase					,					batch.input_ids[0]					)

			A							     =					tempfile.mkdtemp()
			en_de_tokenizer.save_pretrained(__lowerCAmelCase					)
			A							     =					[x.name for x in Path(__lowerCAmelCase					).glob("""*"""					)]
			self.assertIn("""source.spm"""					,					__lowerCAmelCase					)
			MarianTokenizer.from_pretrained(__lowerCAmelCase					)







		def 				A   (self  :					Optional[int]					):
			A							     =					self.get_tokenizer()

			A							     =					tok(
			    ["""I am a small frog""" * 1000, """I am a small frog"""]					,					padding=__lowerCAmelCase					,					truncation=__lowerCAmelCase					,					return_tensors=__lowerCAmelCase					)
			self.assertIsInstance(__lowerCAmelCase					,					__lowerCAmelCase					)
			self.assertEqual(batch.input_ids.shape					,					(2, 512)					)







		def 				A   (self  :					Dict					):
			A							     =					self.get_tokenizer()
			A							     =					tok(["""I am a tiny frog""", """I am a small frog"""]					,					padding=__lowerCAmelCase					,					return_tensors=__lowerCAmelCase					)
			self.assertIsInstance(__lowerCAmelCase					,					__lowerCAmelCase					)
			self.assertEqual(batch_smaller.input_ids.shape					,					(2, 10)					)







		@slow
		def 				A   (self  :					Optional[Any]					):
			A							     =					{"""input_ids""": [[4_3495, 462, 20, 4_2164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 3_8999, 6, 8, 464, 132, 1703, 492, 13, 4669, 3_7867, 13, 7525, 27, 1593, 988, 13, 3_3972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 1_2338, 2, 1_3958, 387, 2, 3629, 6953, 188, 2900, 2, 1_3958, 8011, 1_1501, 23, 8460, 4073, 3_4009, 20, 435, 1_1439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 3_7867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 2_6453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 1_0767, 6, 316, 304, 4239, 3, 0], [148, 1_5722, 19, 1839, 12, 1350, 13, 2_2327, 5082, 5418, 4_7567, 3_5938, 59, 318, 1_9552, 108, 2183, 54, 1_4976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 1_9088, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100], [36, 6395, 1_2570, 3_9147, 1_1597, 6, 266, 4, 4_5405, 7296, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
			# fmt: on

			self.tokenizer_integration_test_util(
			    expected_encoding=__lowerCAmelCase					,					model_name="""Helsinki-NLP/opus-mt-en-de"""					,					revision="""1a8c2263da11e68e50938f97e10cd57820bd504c"""					,					decode_kwargs={"""use_source_tokenizer""": True}					,					)







		def 				A   (self  :					List[str]					):
			A							     =					MarianTokenizer.from_pretrained("""hf-internal-testing/test-marian-two-vocabs"""					)

			A							     =					"""Tämä on testi"""
			A							     =					"""This is a test"""

			A							     =					[76, 7, 2047, 2]
			A							     =					[69, 12, 11, 940, 2]

			A							     =					tokenizer(__lowerCAmelCase					).input_ids
			self.assertListEqual(__lowerCAmelCase					,					__lowerCAmelCase					)

			A							     =					tokenizer(text_target=__lowerCAmelCase					).input_ids
			self.assertListEqual(__lowerCAmelCase					,					__lowerCAmelCase					)

			A							     =					tokenizer.decode(__lowerCAmelCase					,					skip_special_tokens=__lowerCAmelCase					)
			self.assertEqual(__lowerCAmelCase					,					__lowerCAmelCase					)


'''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() = }')




