



import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class       _SCREAMING_SNAKE_CASE	(UpperCamelCase			,    unittest.TestCase ):
					lowerCAmelCase					=			BarthezTokenizer
					lowerCAmelCase					=			BarthezTokenizerFast
					lowerCAmelCase					=			True
					lowerCAmelCase					=			True





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

						__SCREAMING_SNAKE_CASE       :    int       =	BarthezTokenizerFast.from_pretrained("moussaKam/mbarthez"				)
						tokenizer.save_pretrained(self.tmpdirname				)
						tokenizer.save_pretrained(self.tmpdirname     ,  legacy_format=UpperCamelCase				)
						__SCREAMING_SNAKE_CASE       :    int       =	tokenizer





					def 	__snake_case    (       self :							Optional[Any]				)->Optional[Any]:
						__SCREAMING_SNAKE_CASE       :    Any       =	'<pad>'
						__SCREAMING_SNAKE_CASE       :    Tuple       =	1

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





					def 	__snake_case    (       self :							Optional[Any]				)->Optional[Any]:
						__SCREAMING_SNAKE_CASE       :    str       =	list(self.get_tokenizer().get_vocab().keys()				)

						self.assertEqual(vocab_keys[0]     ,  "<s>"				)
						self.assertEqual(vocab_keys[1]     ,  "<pad>"				)
						self.assertEqual(vocab_keys[-1]     ,  "<mask>"				)
						self.assertEqual(len(UpperCamelCase				)     ,  1_0_1_1_2_2				)





					def 	__snake_case    (       self :							Optional[Any]				)->Tuple:
						self.assertEqual(self.get_tokenizer().vocab_size     ,  1_0_1_1_2_2				)





					@require_torch
					def 	__snake_case    (       self :							List[str]				)->Dict:
						__SCREAMING_SNAKE_CASE       :    str       =	['A long paragraph for summarization.', 'Another paragraph for summarization.']
						__SCREAMING_SNAKE_CASE       :    List[str]       =	[0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]

						__SCREAMING_SNAKE_CASE       :    Any       =	self.tokenizer(
						    UpperCamelCase     ,  max_length=len(UpperCamelCase				)     ,  padding=UpperCamelCase     ,  truncation=UpperCamelCase     ,  return_tensors="pt"				)
						self.assertIsInstance(UpperCamelCase     ,  UpperCamelCase				)

						self.assertEqual((2, 6)     ,  batch.input_ids.shape				)
						self.assertEqual((2, 6)     ,  batch.attention_mask.shape				)
						__SCREAMING_SNAKE_CASE       :    int       =	batch.input_ids.tolist()[0]
						self.assertListEqual(UpperCamelCase     ,  UpperCamelCase				)





					def 	__snake_case    (       self :							str				)->str:







						if not self.test_rust_tokenizer:
							return

						__SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.get_tokenizer()
						__SCREAMING_SNAKE_CASE       :    List[Any]       =	self.get_rust_tokenizer()

						__SCREAMING_SNAKE_CASE       :    int       =	'I was born in 92000, and this is falsé.'

						__SCREAMING_SNAKE_CASE       :    List[str]       =	tokenizer.tokenize(UpperCamelCase				)
						__SCREAMING_SNAKE_CASE       :    List[Any]       =	rust_tokenizer.tokenize(UpperCamelCase				)
						self.assertListEqual(UpperCamelCase     ,  UpperCamelCase				)

						__SCREAMING_SNAKE_CASE       :    List[Any]       =	tokenizer.encode(UpperCamelCase     ,  add_special_tokens=UpperCamelCase				)
						__SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	rust_tokenizer.encode(UpperCamelCase     ,  add_special_tokens=UpperCamelCase				)
						self.assertListEqual(UpperCamelCase     ,  UpperCamelCase				)

						__SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.get_rust_tokenizer()
						__SCREAMING_SNAKE_CASE       :    Optional[int]       =	tokenizer.encode(UpperCamelCase				)
						__SCREAMING_SNAKE_CASE       :    List[Any]       =	rust_tokenizer.encode(UpperCamelCase				)
						self.assertListEqual(UpperCamelCase     ,  UpperCamelCase				)






					@slow
					def 	__snake_case    (       self :							Any				)->str:
						__SCREAMING_SNAKE_CASE       :    int       =	{'input_ids': [[0, 4_9_0, 1_4_3_2_8, 4_5_0_7, 3_5_4, 4_7, 4_3_6_6_9, 9_5, 2_5, 7_8_1_1_7, 2_0_2_1_5, 1_9_7_7_9, 1_9_0, 2_2, 4_0_0, 4, 3_5_3_4_3, 8_0_3_1_0, 6_0_3, 8_6, 2_4_9_3_7, 1_0_5, 3_3_4_3_8, 9_4_7_6_2, 1_9_6, 3_9_6_4_2, 7, 1_5, 1_5_9_3_3, 1_7_3, 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], [0, 1_0_5_3_4, 8_7, 2_5, 6_6, 3_3_5_8, 1_9_6, 5_5_2_8_9, 8, 8_2_9_6_1, 8_1, 2_2_0_4, 7_5_2_0_3, 7, 1_5, 7_6_3, 1_2_9_5_6, 2_1_6, 1_7_8, 1_4_3_2_8, 9_5_9_5, 1_3_7_7, 6_9_6_9_3, 7, 4_4_8, 7_1_0_2_1, 1_9_6, 1_8_1_0_6, 1_4_3_7, 1_3_9_7_4, 1_0_8, 9_0_8_3, 4, 4_9_3_1_5, 7, 3_9, 8_6, 1_3_2_6, 2_7_9_3, 4_6_3_3_3, 4, 4_4_8, 1_9_6, 7_4_5_8_8, 7, 4_9_3_1_5, 7, 3_9, 2_1, 8_2_2, 3_8_4_7_0, 7_4, 2_1, 6_6_7_2_3, 6_2_4_8_0, 8, 2_2_0_5_0, 5, 2]], '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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
						# fmt: on

						# moussaKam/mbarthez is a french model. So we also use french texts.
						__SCREAMING_SNAKE_CASE       :    int       =	[
						    'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
						    'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
						    'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
						    'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
						    'telles que la traduction et la synthèse de texte.',
						]

						self.tokenizer_integration_test_util(
						    expected_encoding=UpperCamelCase     ,  model_name="moussaKam/mbarthez"     ,  revision="c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"     ,  sequences=UpperCamelCase     ,  )











from math import sqrt






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int = 1000000     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    int       =	0
 __SCREAMING_SNAKE_CASE       :    int       =	0
 __SCREAMING_SNAKE_CASE       :    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() = }''')






