




"""simple docstring"""






import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


lowercase__     						=						get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
			from transformers.models.mbart.modeling_mbart import shift_tokens_right

lowercase__     						=						25_0004
lowercase__     						=						25_0020






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






			'''simple docstring'''



			lowerCamelCase__      =			MBartTokenizer
			lowerCamelCase__      =			MBartTokenizerFast
			lowerCamelCase__      =			True
			lowerCamelCase__      =			True
			def 					A_    (  self  ):
							super().setUp()

							# We have a SentencePiece fixture for testing
							_lowerCamelCase     :			Tuple    =    MBartTokenizer(UpperCamelCase__  ,      keep_accents=UpperCamelCase__  )
							tokenizer.save_pretrained(self.tmpdirname  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			Union[str, Any]    =    MBartTokenizer(UpperCamelCase__  ,      keep_accents=UpperCamelCase__  )

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

							self.assertListEqual(
							    tokenizer.convert_tokens_to_ids(UpperCamelCase__  )  ,      [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]  ,      )

							_lowerCamelCase     :			Tuple    =    tokenizer.tokenize('I was born in 92000, and this is falsé.'  )
							self.assertListEqual(
							    UpperCamelCase__  ,      [
							        SPIECE_UNDERLINE + 'I',
							        SPIECE_UNDERLINE + 'was',
							        SPIECE_UNDERLINE + 'b',
							        'or',
							        'n',
							        SPIECE_UNDERLINE + 'in',
							        SPIECE_UNDERLINE + '',
							        '9',
							        '2',
							        '0',
							        '0',
							        '0',
							        ',',
							        SPIECE_UNDERLINE + 'and',
							        SPIECE_UNDERLINE + 'this',
							        SPIECE_UNDERLINE + 'is',
							        SPIECE_UNDERLINE + 'f',
							        'al',
							        's',
							        'é',
							        '.',
							    ]  ,      )
							_lowerCamelCase     :			Dict    =    tokenizer.convert_tokens_to_ids(UpperCamelCase__  )
							self.assertListEqual(
							    UpperCamelCase__  ,      [
							        value + tokenizer.fairseq_offset
							        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
							        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
							    ]  ,      )

							_lowerCamelCase     :			List[str]    =    tokenizer.convert_ids_to_tokens(UpperCamelCase__  )
							self.assertListEqual(
							    UpperCamelCase__  ,      [
							        SPIECE_UNDERLINE + 'I',
							        SPIECE_UNDERLINE + 'was',
							        SPIECE_UNDERLINE + 'b',
							        'or',
							        'n',
							        SPIECE_UNDERLINE + 'in',
							        SPIECE_UNDERLINE + '',
							        '<unk>',
							        '2',
							        '0',
							        '0',
							        '0',
							        ',',
							        SPIECE_UNDERLINE + 'and',
							        SPIECE_UNDERLINE + 'this',
							        SPIECE_UNDERLINE + 'is',
							        SPIECE_UNDERLINE + 'f',
							        'al',
							        's',
							        '<unk>',
							        '.',
							    ]  ,      )






			def 					A_    (  self  ):
							if not self.test_slow_tokenizer:
											# as we don't have a slow version, we can't compare the outputs between slow and fast versions
											return

							_lowerCamelCase     :			List[str]    =    (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart", {})
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
															_lowerCamelCase     :			Dict    =    self.rust_tokenizer_class.from_pretrained(UpperCamelCase__  ,      **UpperCamelCase__  )
															_lowerCamelCase     :			int    =    self.tokenizer_class.from_pretrained(UpperCamelCase__  ,      **UpperCamelCase__  )

															_lowerCamelCase     :			str    =    tempfile.mkdtemp()

															_lowerCamelCase     :			Dict    =    tokenizer_r.save_pretrained(UpperCamelCase__  )
															_lowerCamelCase     :			Optional[int]    =    tokenizer_p.save_pretrained(UpperCamelCase__  )

															# Checks it save with the same files + the tokenizer.json file for the fast one
															self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files  )  )
															_lowerCamelCase     :			Optional[int]    =    tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f  )
															self.assertSequenceEqual(UpperCamelCase__  ,      UpperCamelCase__  )

															# Checks everything loads correctly in the same way
															_lowerCamelCase     :			Optional[Any]    =    tokenizer_r.from_pretrained(UpperCamelCase__  )
															_lowerCamelCase     :			Union[str, Any]    =    tokenizer_p.from_pretrained(UpperCamelCase__  )

															# Check special tokens are set accordingly on Rust and Python
															for key in tokenizer_pp.special_tokens_map:
																			self.assertTrue(hasattr(UpperCamelCase__  ,      UpperCamelCase__  )  )
																			# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																			# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

															shutil.rmtree(UpperCamelCase__  )

															# Save tokenizer rust, legacy_format=True
															_lowerCamelCase     :			Union[str, Any]    =    tempfile.mkdtemp()

															_lowerCamelCase     :			Tuple    =    tokenizer_r.save_pretrained(UpperCamelCase__  ,      legacy_format=UpperCamelCase__  )
															_lowerCamelCase     :			Dict    =    tokenizer_p.save_pretrained(UpperCamelCase__  )

															# Checks it save with the same files
															self.assertSequenceEqual(UpperCamelCase__  ,      UpperCamelCase__  )

															# Checks everything loads correctly in the same way
															_lowerCamelCase     :			Dict    =    tokenizer_r.from_pretrained(UpperCamelCase__  )
															_lowerCamelCase     :			Dict    =    tokenizer_p.from_pretrained(UpperCamelCase__  )

															# Check special tokens are set accordingly on Rust and Python
															for key in tokenizer_pp.special_tokens_map:
																			self.assertTrue(hasattr(UpperCamelCase__  ,      UpperCamelCase__  )  )

															shutil.rmtree(UpperCamelCase__  )

															# Save tokenizer rust, legacy_format=False
															_lowerCamelCase     :			List[Any]    =    tempfile.mkdtemp()

															_lowerCamelCase     :			str    =    tokenizer_r.save_pretrained(UpperCamelCase__  ,      legacy_format=UpperCamelCase__  )
															_lowerCamelCase     :			str    =    tokenizer_p.save_pretrained(UpperCamelCase__  )

															# Checks it saved the tokenizer.json file
															self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files  )  )

															# Checks everything loads correctly in the same way
															_lowerCamelCase     :			List[str]    =    tokenizer_r.from_pretrained(UpperCamelCase__  )
															_lowerCamelCase     :			List[str]    =    tokenizer_p.from_pretrained(UpperCamelCase__  )

															# Check special tokens are set accordingly on Rust and Python
															for key in tokenizer_pp.special_tokens_map:
																			self.assertTrue(hasattr(UpperCamelCase__  ,      UpperCamelCase__  )  )

															shutil.rmtree(UpperCamelCase__  )






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






			'''simple docstring'''



			lowerCamelCase__      =			"""facebook/mbart-large-en-ro"""
			lowerCamelCase__      =			[
			    """ UN Chief Says There Is No Military Solution in Syria""",
			    """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
			]
			lowerCamelCase__      =			[
			    """Şeful ONU declară că nu există o soluţie militară în Siria""",
			    """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
			    """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
			    """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
			]
			lowerCamelCase__      =			[82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2, EN_CODE]
			@classmethod
			def 					A_    (  cls  ):
							_lowerCamelCase     :			MBartTokenizer    =    MBartTokenizer.from_pretrained(
							    cls.checkpoint_name  ,      src_lang='en_XX'  ,      tgt_lang='ro_RO'  )
							_lowerCamelCase     :			Dict    =    1
							return cls
			def 					A_    (  self  ):
							self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR']  ,      250001  )
							self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN']  ,      250004  )
							self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO']  ,      250020  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			str    =    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  )
							_lowerCamelCase     :			List[Any]    =    [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
							_lowerCamelCase     :			Optional[Any]    =    self.tokenizer.decode(UpperCamelCase__  ,      skip_special_tokens=UpperCamelCase__  )
							_lowerCamelCase     :			Tuple    =    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     :			Any    =    ["this is gunna be a long sentence " * 20]
							assert isinstance(src_text[0]  ,      UpperCamelCase__  )
							_lowerCamelCase     :			Union[str, Any]    =    10
							_lowerCamelCase     :			List[str]    =    self.tokenizer(UpperCamelCase__  ,      max_length=UpperCamelCase__  ,      truncation=UpperCamelCase__  ).input_ids[0]
							self.assertEqual(ids[-2]  ,      2  )
							self.assertEqual(ids[-1]  ,      UpperCamelCase__  )
							self.assertEqual(len(UpperCamelCase__  )  ,      UpperCamelCase__  )
			def 					A_    (  self  ):
							self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']  )  ,      [250026, 250001]  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			Tuple    =    tempfile.mkdtemp()
							_lowerCamelCase     :			Union[str, Any]    =    self.tokenizer.fairseq_tokens_to_ids
							self.tokenizer.save_pretrained(UpperCamelCase__  )
							_lowerCamelCase     :			int    =    MBartTokenizer.from_pretrained(UpperCamelCase__  )
							self.assertDictEqual(new_tok.fairseq_tokens_to_ids  ,      UpperCamelCase__  )
			@require_torch
			def 					A_    (  self  ):
							_lowerCamelCase     :			Dict    =    self.tokenizer(self.src_text  ,      text_target=self.tgt_text  ,      padding=UpperCamelCase__  ,      return_tensors='pt'  )
							_lowerCamelCase     :			str    =    shift_tokens_right(batch['labels']  ,      self.tokenizer.pad_token_id  )

							# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
							assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
							assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
							assert batch.decoder_input_ids[1][-1] == 2
							assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
			@require_torch
			def 					A_    (  self  ):
							_lowerCamelCase     :			Optional[Any]    =    self.tokenizer(
							    self.src_text  ,      text_target=self.tgt_text  ,      padding=UpperCamelCase__  ,      truncation=UpperCamelCase__  ,      max_length=len(self.expected_src_tokens  )  ,      return_tensors='pt'  ,      )

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

							self.assertIsInstance(UpperCamelCase__  ,      UpperCamelCase__  )

							self.assertEqual((2, 14)  ,      batch.input_ids.shape  )
							self.assertEqual((2, 14)  ,      batch.attention_mask.shape  )
							_lowerCamelCase     :			str    =    batch.input_ids.tolist()[0]
							self.assertListEqual(self.expected_src_tokens  ,      UpperCamelCase__  )
							self.assertEqual(2  ,      batch.decoder_input_ids[0, -1]  )  # EOS
							# Test that special tokens are reset
							self.assertEqual(self.tokenizer.prefix_tokens  ,      []  )
							self.assertEqual(self.tokenizer.suffix_tokens  ,      [self.tokenizer.eos_token_id, EN_CODE]  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			int    =    self.tokenizer(self.src_text  ,      padding=UpperCamelCase__  ,      truncation=UpperCamelCase__  ,      max_length=3  ,      return_tensors='pt'  )
							_lowerCamelCase     :			List[str]    =    self.tokenizer(
							    text_target=self.tgt_text  ,      padding=UpperCamelCase__  ,      truncation=UpperCamelCase__  ,      max_length=10  ,      return_tensors='pt'  )
							_lowerCamelCase     :			Tuple    =    targets["input_ids"]
							_lowerCamelCase     :			List[Any]    =    shift_tokens_right(UpperCamelCase__  ,      self.tokenizer.pad_token_id  )

							self.assertEqual(batch.input_ids.shape[1]  ,      3  )
							self.assertEqual(batch.decoder_input_ids.shape[1]  ,      10  )






			@require_torch
			def 					A_    (  self  ):
							_lowerCamelCase     :			Union[str, Any]    =    self.tokenizer._build_translation_inputs(
							    'A test'  ,      return_tensors='pt'  ,      src_lang='en_XX'  ,      tgt_lang='ar_AR'  )

							self.assertEqual(
							    nested_simplify(UpperCamelCase__  )  ,      {
							        # A, test, EOS, en_XX
							        'input_ids': [[62, 3034, 2, 250004]],
							        'attention_mask': [[1, 1, 1, 1]],
							        # ar_AR
							        'forced_bos_token_id': 250001,
							    }  ,      )





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() = }''')






