



import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
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__       (A__      ,			unittest.TestCase	):







						"""simple docstring"""




						__lowerCAmelCase  :Any			      =  BartTokenizer
						__lowerCAmelCase  :Union[str, Any]			      =  BartTokenizerFast
						__lowerCAmelCase  :Union[str, Any]			      =  True
						__lowerCAmelCase  :Optional[Any]			      =  filter_roberta_detectors

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Tuple:
									"""simple docstring"""




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

									a__     :		Any		 =						os.path.join(self.tmpdirname      ,     VOCAB_FILES_NAMES["""vocab_file"""]						)
									a__     :		Optional[Any]		 =						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(__lowercase						) + """\n"""						)
									with open(self.merges_file      ,     """w"""      ,     encoding="""utf-8"""						) as fp:
												fp.write("""\n""".join(__lowercase						)						)

						def 							SCREAMING_SNAKE_CASE__(     self      ,     **__lowercase						)      ->   Optional[Any]:
									"""simple docstring"""




									kwargs.update(self.special_tokens_map						)
									return self.tokenizer_class.from_pretrained(self.tmpdirname      ,     **__lowercase						)

						def 							SCREAMING_SNAKE_CASE__(     self      ,     **__lowercase						)      ->   Union[str, Any]:
									"""simple docstring"""




									kwargs.update(self.special_tokens_map						)
									return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,     **__lowercase						)

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   List[Any]:
									"""simple docstring"""




									return "lower newer", "lower newer"

						@cached_property
						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   int:
									"""simple docstring"""




									return BartTokenizer.from_pretrained("""facebook/bart-large"""						)

						@cached_property
						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Dict:
									"""simple docstring"""




									return BartTokenizerFast.from_pretrained("""facebook/bart-large"""						)

						@require_torch
						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Dict:
									"""simple docstring"""




									a__     :		Dict		 =						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
									a__     :		Tuple		 =						[0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
												a__     :		Union[str, Any]		 =						tokenizer(__lowercase      ,     max_length=len(__lowercase						)      ,     padding=__lowercase      ,     return_tensors="""pt"""						)
												self.assertIsInstance(__lowercase      ,     __lowercase						)

												self.assertEqual((2, 9)      ,     batch.input_ids.shape						)
												self.assertEqual((2, 9)      ,     batch.attention_mask.shape						)
												a__     :		List[str]		 =						batch.input_ids.tolist()[0]
												self.assertListEqual(__lowercase      ,     __lowercase						)
												# Test that special tokens are reset

						@require_torch
						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   List[Any]:
									"""simple docstring"""




									a__     :		List[Any]		 =						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
												a__     :		List[str]		 =						tokenizer(__lowercase      ,     padding=__lowercase      ,     return_tensors="""pt"""						)
												# check if input_ids are returned and no labels
												self.assertIn("""input_ids"""      ,     __lowercase						)
												self.assertIn("""attention_mask"""      ,     __lowercase						)
												self.assertNotIn("""labels"""      ,     __lowercase						)
												self.assertNotIn("""decoder_attention_mask"""      ,     __lowercase						)

						@require_torch
						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   int:
									"""simple docstring"""




									a__     :		str		 =						[
									    """Summary of the text.""",
									    """Another summary.""",
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
												a__     :		int		 =						tokenizer(text_target=__lowercase      ,     max_length=3_2      ,     padding="""max_length"""      ,     return_tensors="""pt"""						)
												self.assertEqual(3_2      ,     targets["""input_ids"""].shape[1]						)

						@require_torch
						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Union[str, Any]:
									"""simple docstring"""




									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
												a__     :		Any		 =						tokenizer(
												    ["""I am a small frog""" * 1_0_2_4, """I am a small frog"""]      ,     padding=__lowercase      ,     truncation=__lowercase      ,     return_tensors="""pt"""						)
												self.assertIsInstance(__lowercase      ,     __lowercase						)
												self.assertEqual(batch.input_ids.shape      ,     (2, 1_0_2_4)						)

						@require_torch
						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   int:
									"""simple docstring"""




									a__     :		Optional[int]		 =						["""A long paragraph for summarization."""]
									a__     :		Union[str, Any]		 =						[
									    """Summary of the text.""",
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
												a__     :		int		 =						tokenizer(__lowercase      ,     return_tensors="""pt"""						)
												a__     :		Any		 =						tokenizer(text_target=__lowercase      ,     return_tensors="""pt"""						)
												a__     :		str		 =						inputs["""input_ids"""]
												a__     :		Tuple		 =						targets["""input_ids"""]
												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 							SCREAMING_SNAKE_CASE__(     self						)      ->   Optional[int]:
									"""simple docstring"""




									pass

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Dict:
									"""simple docstring"""




									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
												with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''						):
															a__     :		int		 =						self.rust_tokenizer_class.from_pretrained(__lowercase      ,     **__lowercase						)
															a__     :		List[Any]		 =						self.tokenizer_class.from_pretrained(__lowercase      ,     **__lowercase						)
															a__     :		Optional[Any]		 =						"""A, <mask> AllenNLP sentence."""
															a__     :		Union[str, Any]		 =						tokenizer_r.encode_plus(__lowercase      ,     add_special_tokens=__lowercase      ,     return_token_type_ids=__lowercase						)
															a__     :		Tuple		 =						tokenizer_p.encode_plus(__lowercase      ,     add_special_tokens=__lowercase      ,     return_token_type_ids=__lowercase						)

															# 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"""]						)      ,     )

															a__     :		int		 =						tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]						)
															a__     :		Optional[Any]		 =						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, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]						)
															self.assertSequenceEqual(tokens_r["""input_ids"""]      ,     [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]						)

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









import decimal

def 			lowerCAmelCase_  (						)							-> int:







			"""simple docstring"""
			a__     :		Optional[Any]		 =						0
			a__     :		Optional[int]		 =						decimal.Context(prec=105)
			for i in range(2		,     100):
						a__     :		int		 =						decimal.Decimal(_lowercase)
						a__     :		Optional[int]		 =						number.sqrt(_lowercase)
						if len(str(_lowercase)) > 1:
									answer += int(str(_lowercase)[0])
									a__     :		Tuple		 =						str(_lowercase)[2:101]
									answer += sum(int(_lowercase) for x in sqrt_number_str)
			return answer


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(f'{solution() = }')




