




"""simple docstring"""






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

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
			from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
			lowercase__     						=						get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
			from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

lowercase__     						=						12_8022
lowercase__     						=						12_8028






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






			'''simple docstring'''



			lowerCamelCase__      =			MaMaaaTokenizer
			lowerCamelCase__      =			False
			lowerCamelCase__      =			False
			lowerCamelCase__      =			True
			def 					A_    (  self  ):
							super().setUp()

							_lowerCamelCase     :			Union[str, Any]    =    ["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
							_lowerCamelCase     :			str    =    dict(zip(UpperCamelCase__  ,      range(len(UpperCamelCase__  )  )  )  )
							_lowerCamelCase     :			List[str]    =    Path(self.tmpdirname  )
							save_json(UpperCamelCase__  ,      save_dir / VOCAB_FILES_NAMES['vocab_file']  )
							if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
											copyfile(UpperCamelCase__  ,      save_dir / VOCAB_FILES_NAMES['spm_file']  )

							_lowerCamelCase     :			Tuple    =    MaMaaaTokenizer.from_pretrained(self.tmpdirname  )
							tokenizer.save_pretrained(self.tmpdirname  )
			def 					A_    (  self  ,      **lowercase  ):
							return MaMaaaTokenizer.from_pretrained(self.tmpdirname  ,      **UpperCamelCase__  )
			def 					A_    (  self  ,      lowercase  ):
							return (
							    "This is a test",
							    "This is a test",
							)
			def 					A_    (  self  ):
							_lowerCamelCase     :			str    =    "</s>"
							_lowerCamelCase     :			int    =    0

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase__  )  ,      UpperCamelCase__  )
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase__  )  ,      UpperCamelCase__  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			Tuple    =    self.get_tokenizer()
							_lowerCamelCase     :			int    =    list(tokenizer.get_vocab().keys()  )

							self.assertEqual(vocab_keys[0]  ,      '</s>'  )
							self.assertEqual(vocab_keys[1]  ,      '<unk>'  )
							self.assertEqual(vocab_keys[-1]  ,      '<s>'  )
							self.assertEqual(len(UpperCamelCase__  )  ,      tokenizer.vocab_size + len(tokenizer.get_added_vocab()  )  )
			@unittest.skip('Skip this test while all models are still to be uploaded.'  )
			def 					A_    (  self  ):
							pass
			def 					A_    (  self  ):
							_lowerCamelCase     :			List[Any]    =    self.get_tokenizer()

							_lowerCamelCase     :			Optional[Any]    =    tokenizer.tokenize('This is a test'  )
							self.assertListEqual(UpperCamelCase__  ,      ['▁This', '▁is', '▁a', '▁t', 'est']  )

							self.assertListEqual(
							    tokenizer.convert_tokens_to_ids(UpperCamelCase__  )  ,      [2, 3, 4, 5, 6]  ,      )

							_lowerCamelCase     :			List[Any]    =    tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]  )
							self.assertListEqual(UpperCamelCase__  ,      ['▁This', '▁is', '▁a', '▁t', 'est']  )

							_lowerCamelCase     :			str    =    tokenizer.convert_tokens_to_string(UpperCamelCase__  )
							self.assertEqual(UpperCamelCase__  ,      'This is a test'  )






			@slow
			def 					A_    (  self  ):
							# fmt: off
							_lowerCamelCase     :			Optional[int]    =    {"input_ids": [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 5, 2, 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], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 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]], "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, 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], [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, 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=UpperCamelCase__  ,      model_name='facebook/m2m100_418M'  ,      revision='c168bae485c864188cf9aa0e4108b0b6934dc91e'  ,      )






@require_torch
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase__	(     unittest.TestCase						):






			'''simple docstring'''



			lowerCamelCase__      =			"""facebook/m2m100_418M"""
			lowerCamelCase__      =			[
			    """In my opinion, there are two levels of response from the French government.""",
			    """NSA Affair Emphasizes Complete Lack of Debate on Intelligence""",
			]
			lowerCamelCase__      =			[
			    """Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
			    """L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
			]

			# fmt: off
			lowerCamelCase__      =			[EN_CODE, 5_93, 19_49, 11_57_81, 4, 7_15_86, 42_34, 6_06_33, 12_62_33, 4_32, 12_38_08, 1_55_92, 11_97, 11_71_32, 12_06_18, 5, 2]
			@classmethod
			def 					A_    (  cls  ):
							_lowerCamelCase     :			MaMaaaTokenizer    =    MaMaaaTokenizer.from_pretrained(
							    cls.checkpoint_name  ,      src_lang='en'  ,      tgt_lang='fr'  )
							_lowerCamelCase     :			Dict    =    1
							return cls
			def 					A_    (  self  ):
							self.assertEqual(self.tokenizer.get_lang_id('ar'  )  ,      128006  )
							self.assertEqual(self.tokenizer.get_lang_id('en'  )  ,      128022  )
							self.assertEqual(self.tokenizer.get_lang_id('ro'  )  ,      128076  )
							self.assertEqual(self.tokenizer.get_lang_id('mr'  )  ,      128063  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			List[str]    =    self.tokenizer.get_vocab()
							self.assertEqual(len(UpperCamelCase__  )  ,      self.tokenizer.vocab_size  )
							self.assertEqual(vocab['<unk>']  ,      3  )
							self.assertIn(self.tokenizer.get_lang_token('en'  )  ,      UpperCamelCase__  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			Any    =    "en"
							_lowerCamelCase     :			Optional[Any]    =    self.tokenizer.batch_encode_plus(self.src_text  ).input_ids[0]
							self.assertListEqual(self.expected_src_tokens  ,      UpperCamelCase__  )
			def 					A_    (  self  ):
							self.assertIn(UpperCamelCase__  ,      self.tokenizer.all_special_ids  )
							# fmt: off
							_lowerCamelCase     :			Any    =    [FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2]
							# fmt: on
							_lowerCamelCase     :			List[str]    =    self.tokenizer.decode(UpperCamelCase__  ,      skip_special_tokens=UpperCamelCase__  )
							_lowerCamelCase     :			Any    =    self.tokenizer.decode(generated_ids[1:]  ,      skip_special_tokens=UpperCamelCase__  )
							self.assertEqual(UpperCamelCase__  ,      UpperCamelCase__  )
							self.assertNotIn(self.tokenizer.eos_token  ,      UpperCamelCase__  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			List[Any]    =    tempfile.mkdtemp()
							_lowerCamelCase     :			List[Any]    =    self.tokenizer.lang_token_to_id
							self.tokenizer.save_pretrained(UpperCamelCase__  )
							_lowerCamelCase     :			Tuple    =    MaMaaaTokenizer.from_pretrained(UpperCamelCase__  )
							self.assertDictEqual(new_tok.lang_token_to_id  ,      UpperCamelCase__  )
			@require_torch
			def 					A_    (  self  ):
							_lowerCamelCase     :			Any    =    "en"
							_lowerCamelCase     :			Optional[Any]    =    "fr"

							_lowerCamelCase     :			List[Any]    =    self.tokenizer(self.src_text  ,      text_target=self.tgt_text  ,      padding=UpperCamelCase__  ,      return_tensors='pt'  )

							_lowerCamelCase     :			Dict    =    shift_tokens_right(
							    batch['labels']  ,      self.tokenizer.pad_token_id  ,      self.tokenizer.eos_token_id  )

							for k in batch:
											_lowerCamelCase     :			Any    =    batch[k].tolist()
							# batch = {k: v.tolist() for k,v in batch.items()}
							# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
							# batch.decoder_inputs_ids[0][0] ==
							assert batch.input_ids[1][0] == EN_CODE
							assert batch.input_ids[1][-1] == 2
							assert batch.labels[1][0] == FR_CODE
							assert batch.labels[1][-1] == 2
							assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]
			@require_torch
			def 					A_    (  self  ):
							_lowerCamelCase     :			Union[str, Any]    =    "mr"
							self.assertListEqual(self.tokenizer.prefix_tokens  ,      [self.tokenizer.get_lang_id('mr'  )]  )
							self.assertListEqual(self.tokenizer.suffix_tokens  ,      [self.tokenizer.eos_token_id]  )

							_lowerCamelCase     :			List[str]    =    "zh"
							self.assertListEqual(self.tokenizer.prefix_tokens  ,      [self.tokenizer.get_lang_id('zh'  )]  )
							self.assertListEqual(self.tokenizer.suffix_tokens  ,      [self.tokenizer.eos_token_id]  )
			@require_torch
			def 					A_    (  self  ):
							_lowerCamelCase     :			List[Any]    =    "mr"
							self.tokenizer._switch_to_target_mode()
							self.assertListEqual(self.tokenizer.prefix_tokens  ,      [self.tokenizer.get_lang_id('mr'  )]  )
							self.assertListEqual(self.tokenizer.suffix_tokens  ,      [self.tokenizer.eos_token_id]  )
							self.tokenizer._switch_to_input_mode()
							self.assertListEqual(self.tokenizer.prefix_tokens  ,      [self.tokenizer.get_lang_id(self.tokenizer.src_lang  )]  )

							_lowerCamelCase     :			Dict    =    "zh"
							self.tokenizer._switch_to_target_mode()
							self.assertListEqual(self.tokenizer.prefix_tokens  ,      [self.tokenizer.get_lang_id('zh'  )]  )
							self.assertListEqual(self.tokenizer.suffix_tokens  ,      [self.tokenizer.eos_token_id]  )
							self.tokenizer._switch_to_input_mode()
							self.assertListEqual(self.tokenizer.prefix_tokens  ,      [self.tokenizer.get_lang_id(self.tokenizer.src_lang  )]  )






			@require_torch
			def 					A_    (  self  ):
							_lowerCamelCase     :			List[Any]    =    self.tokenizer._build_translation_inputs('A test'  ,      return_tensors='pt'  ,      src_lang='en'  ,      tgt_lang='ar'  )

							self.assertEqual(
							    nested_simplify(UpperCamelCase__  )  ,      {
							        # en_XX, A, test, EOS
							        'input_ids': [[128022, 58, 4183, 2]],
							        'attention_mask': [[1, 1, 1, 1]],
							        # ar_AR
							        'forced_bos_token_id': 128006,
							    }  ,      )





from math import sqrt

def      A      (      _SCREAMING_SNAKE_CASE = 100_0000    )	-> int:
       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(_SCREAMING_SNAKE_CASE ,sum_shortest_sides // 2    )
                                - max(1 ,sum_shortest_sides - max_cuboid_size    )
                                + 1
                            )

       return max_cuboid_size


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






