



'''simple docstring'''

import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors

@require_tokenizers
class __snake_case( __snake_case						,				unittest.TestCase   ):


						'''simple docstring'''



						UpperCAmelCase				:       List[str] 	=						MvpTokenizer
						UpperCAmelCase				:       Tuple 	=						MvpTokenizerFast
						UpperCAmelCase				:       Tuple 	=						True
						UpperCAmelCase				:       str 	=						filter_roberta_detectors


						def 							__snake_case       (   self )      ->      Any:
							super().setUp()
							lowerCAmelCase       =      [
							    "l",
							    "o",
							    "w",
							    "e",
							    "r",
							    "s",
							    "t",
							    "i",
							    "d",
							    "n",
							    "\u0120",
							    "\u0120l",
							    "\u0120n",
							    "\u0120lo",
							    "\u0120low",
							    "er",
							    "\u0120lowest",
							    "\u0120newer",
							    "\u0120wider",
							    "<unk>",
							]
							lowerCAmelCase       =      dict(zip(A_      ,			range(len(A_ ) ) ) )
							lowerCAmelCase       =      ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
							lowerCAmelCase       =      {"unk_token": "<unk>"}

							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"""      ,			encoding="""utf-8""" ) as fp:
								fp.write(json.dumps(A_ ) + """\n""" )
							with open(self.merges_file      ,			"""w"""      ,			encoding="""utf-8""" ) as fp:
								fp.write("""\n""".join(A_ ) )


						def 							__snake_case       (   self      ,			**A_ )      ->      int:
							kwargs.update(self.special_tokens_map )
							return self.tokenizer_class.from_pretrained(self.tmpdirname      ,			**A_ )


						def 							__snake_case       (   self      ,			**A_ )      ->      str:
							kwargs.update(self.special_tokens_map )
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,			**A_ )


						def 							__snake_case       (   self      ,			A_ )      ->      Union[str, Any]:
							return "lower newer", "lower newer"


						@cached_property
						def 							__snake_case       (   self )      ->      Optional[int]:
							return MvpTokenizer.from_pretrained("""RUCAIBox/mvp""" )


						@cached_property
						def 							__snake_case       (   self )      ->      Any:
							return MvpTokenizerFast.from_pretrained("""RUCAIBox/mvp""" )


						@require_torch
						def 							__snake_case       (   self )      ->      List[Any]:
							lowerCAmelCase       =      ["A long paragraph for summarization.", "Another paragraph for summarization."]
							lowerCAmelCase       =      [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								lowerCAmelCase       =      tokenizer(A_      ,			max_length=len(A_ )      ,			padding=A_      ,			return_tensors="""pt""" )
								self.assertIsInstance(A_      ,			A_ )

								self.assertEqual((2, 9)      ,			batch.input_ids.shape )
								self.assertEqual((2, 9)      ,			batch.attention_mask.shape )
								lowerCAmelCase       =      batch.input_ids.tolist()[0]
								self.assertListEqual(A_      ,			A_ )
								# Test that special tokens are reset


						@require_torch
						def 							__snake_case       (   self )      ->      List[Any]:
							lowerCAmelCase       =      ["A long paragraph for summarization.", "Another paragraph for summarization."]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								lowerCAmelCase       =      tokenizer(A_      ,			padding=A_      ,			return_tensors="""pt""" )
								# check if input_ids are returned and no labels
								self.assertIn("""input_ids"""      ,			A_ )
								self.assertIn("""attention_mask"""      ,			A_ )
								self.assertNotIn("""labels"""      ,			A_ )
								self.assertNotIn("""decoder_attention_mask"""      ,			A_ )


						@require_torch
						def 							__snake_case       (   self )      ->      Any:
							lowerCAmelCase       =      [
							    "Summary of the text.",
							    "Another summary.",
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								lowerCAmelCase       =      tokenizer(text_target=A_      ,			max_length=32      ,			padding="""max_length"""      ,			return_tensors="""pt""" )
								self.assertEqual(32      ,			targets["""input_ids"""].shape[1] )


						@require_torch
						def 							__snake_case       (   self )      ->      Any:




							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								lowerCAmelCase       =      tokenizer(
								    ["""I am a small frog""" * 1024, """I am a small frog"""]      ,			padding=A_      ,			truncation=A_      ,			return_tensors="""pt""" )
								self.assertIsInstance(A_      ,			A_ )
								self.assertEqual(batch.input_ids.shape      ,			(2, 1024) )


						@require_torch
						def 							__snake_case       (   self )      ->      List[str]:
							lowerCAmelCase       =      ["A long paragraph for summarization."]
							lowerCAmelCase       =      [
							    "Summary of the text.",
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								lowerCAmelCase       =      tokenizer(A_      ,			text_target=A_      ,			return_tensors="""pt""" )
								lowerCAmelCase       =      inputs["input_ids"]
								lowerCAmelCase       =      inputs["labels"]
								self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
								self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
								self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
								self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )


						def 							__snake_case       (   self )      ->      Optional[int]:
							pass




						def 							__snake_case       (   self )      ->      Union[str, Any]:




							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
									lowerCAmelCase       =      self.rust_tokenizer_class.from_pretrained(A_      ,			**A_ )
									lowerCAmelCase       =      self.tokenizer_class.from_pretrained(A_      ,			**A_ )
									lowerCAmelCase       =      "A, <mask> AllenNLP sentence."
									lowerCAmelCase       =      tokenizer_r.encode_plus(A_      ,			add_special_tokens=A_      ,			return_token_type_ids=A_ )
									lowerCAmelCase       =      tokenizer_p.encode_plus(A_      ,			add_special_tokens=A_      ,			return_token_type_ids=A_ )

									# token_type_ids should put 0 everywhere
									self.assertEqual(sum(tokens_r["""token_type_ids"""] )      ,			sum(tokens_p["""token_type_ids"""] ) )

									# attention_mask should put 1 everywhere, so sum over length should be 1
									self.assertEqual(
									    sum(tokens_r["""attention_mask"""] ) / len(tokens_r["""attention_mask"""] )      ,			sum(tokens_p["""attention_mask"""] ) / len(tokens_p["""attention_mask"""] )      ,			)

									lowerCAmelCase       =      tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""] )
									lowerCAmelCase       =      tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""] )

									# Rust correctly handles the space before the mask while python doesnt
									self.assertSequenceEqual(tokens_p["""input_ids"""]      ,			[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2] )
									self.assertSequenceEqual(tokens_r["""input_ids"""]      ,			[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2] )

									self.assertSequenceEqual(
									    A_      ,			["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )
									self.assertSequenceEqual(
									    A_      ,			["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )






def 						A_							(	_lowerCAmelCase = 1000	)		->					int:
 UpperCamelCase      :   Optional[int]			      =  -1
 UpperCamelCase      :   int			      =  0
 for a in range(1				,  n // 3	):
  # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
  UpperCamelCase      :   Optional[Any]			      =  (n * n - 2 * a * n) // (2 * n - 2 * a)
  UpperCamelCase      :   List[str]			      =  n - a - b
  if c * c == (a * a + b * b):
   UpperCamelCase      :   Union[str, Any]			      =  a * b * c
   if candidate >= product:
    UpperCamelCase      :   Tuple			      =  candidate
 return product


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