




"""simple docstring"""





import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin

class      a__				(	__magic_name__							,    unittest.TestCase   ):
			lowercase_    =	DebertaTokenizer
			lowercase_    =	True
			lowercase_    =	DebertaTokenizerFast


			def       a_     (      self      :    Tuple):


						"""simple docstring"""




						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						__UpperCAmelCase	: Any		=					[
						    "l",
						    "o",
						    "w",
						    "e",
						    "r",
						    "s",
						    "t",
						    "i",
						    "d",
						    "n",
						    "\u0120",
						    "\u0120l",
						    "\u0120n",
						    "\u0120lo",
						    "\u0120low",
						    "er",
						    "\u0120lowest",
						    "\u0120newer",
						    "\u0120wider",
						    "[UNK]",
						]
						__UpperCAmelCase	: Optional[int]		=					dict(zip(UpperCamelCase_      ,  range(len(UpperCamelCase_))))
						__UpperCAmelCase	: Union[str, Any]		=					["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
						__UpperCAmelCase	: Optional[Any]		=					{"unk_token": "[UNK]"}

						__UpperCAmelCase	: List[str]		=					os.path.join(self.tmpdirname      ,  VOCAB_FILES_NAMES["vocab_file"])
						__UpperCAmelCase	: List[str]		=					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(UpperCamelCase_) + "\n")
						with open(self.merges_file      ,  "w"      ,  encoding="utf-8") as fp:
									fp.write("\n".join(UpperCamelCase_))


			def       a_     (      self      :    Tuple      ,  **UpperCamelCase_      :    int):


						"""simple docstring"""




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


			def       a_     (      self      :    Dict      ,  UpperCamelCase_      :    List[str]):


						"""simple docstring"""




						__UpperCAmelCase	: int		=					"lower newer"
						__UpperCAmelCase	: Dict		=					"lower newer"
						return input_text, output_text


			def       a_     (      self      :    int):


						"""simple docstring"""




						__UpperCAmelCase	: List[str]		=					self.get_tokenizer()
						__UpperCAmelCase	: Tuple		=					"lower newer"
						__UpperCAmelCase	: int		=					["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
						__UpperCAmelCase	: List[Any]		=					tokenizer.tokenize(UpperCamelCase_)
						self.assertListEqual(UpperCamelCase_      ,  UpperCamelCase_)

						__UpperCAmelCase	: int		=					tokens + [tokenizer.unk_token]
						__UpperCAmelCase	: List[str]		=					[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_)      ,  UpperCamelCase_)


			def       a_     (      self      :    int):


						"""simple docstring"""




						__UpperCAmelCase	: Tuple		=					self.get_tokenizer()
						__UpperCAmelCase	: Tuple		=					tokenizer("Hello"      ,  "World")
						__UpperCAmelCase	: List[Any]		=					[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
						self.assertListEqual(tokd["token_type_ids"]      ,  UpperCamelCase_)


			@slow
			def       a_     (      self      :    Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase	: Any		=					self.tokenizer_class.from_pretrained("microsoft/deberta-base")

						__UpperCAmelCase	: Union[str, Any]		=					tokenizer.encode("sequence builders"      ,  add_special_tokens=UpperCamelCase_)
						__UpperCAmelCase	: str		=					tokenizer.encode("multi-sequence build"      ,  add_special_tokens=UpperCamelCase_)

						__UpperCAmelCase	: Optional[int]		=					tokenizer.encode(
						    "sequence builders"      ,  add_special_tokens=UpperCamelCase_      ,  add_prefix_space=UpperCamelCase_)
						__UpperCAmelCase	: Tuple		=					tokenizer.encode(
						    "sequence builders"      ,  "multi-sequence build"      ,  add_special_tokens=UpperCamelCase_      ,  add_prefix_space=UpperCamelCase_)

						__UpperCAmelCase	: List[Any]		=					tokenizer.build_inputs_with_special_tokens(UpperCamelCase_)
						__UpperCAmelCase	: Optional[int]		=					tokenizer.build_inputs_with_special_tokens(UpperCamelCase_      ,  UpperCamelCase_)

						assert encoded_sentence == encoded_text_from_decode
						assert encoded_pair == encoded_pair_from_decode


			@slow
			def       a_     (      self      :    Dict):


						"""simple docstring"""




						__UpperCAmelCase	: List[str]		=					[self.tokenizer_class]
						if self.test_rust_tokenizer:
									tokenizer_classes.append(self.rust_tokenizer_class)

						for tokenizer_class in tokenizer_classes:
									__UpperCAmelCase	: Dict		=					tokenizer_class.from_pretrained("microsoft/deberta-base")

									__UpperCAmelCase	: Any		=					[
									    "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
									    "ALBERT incorporates two parameter reduction techniques",
									    "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
									    " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
									    " vocabulary embedding.",
									]

									__UpperCAmelCase	: List[Any]		=					tokenizer(UpperCamelCase_      ,  padding=UpperCamelCase_)
									__UpperCAmelCase	: int		=					[tokenizer.decode(UpperCamelCase_      ,  skip_special_tokens=UpperCamelCase_) for seq in encoding["input_ids"]]

									# fmt: off
									__UpperCAmelCase	: Optional[Any]		=					{
									    "input_ids": [
									        [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 2, 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, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 2, 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, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 4, 2]
									    ],
									    "token_type_ids": [
									        [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, 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]
									    ],
									    "attention_mask": [
									        [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],
									        [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],
									        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
									    ]
									}
									# fmt: on

									__UpperCAmelCase	: Any		=					[
									    "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
									    "ALBERT incorporates two parameter reduction techniques",
									    "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
									    " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
									    " vocabulary embedding.",
									]

									self.assertDictEqual(encoding.data      ,  UpperCamelCase_)

									for expected, decoded in zip(UpperCamelCase_      ,  UpperCamelCase_):
												self.assertEqual(UpperCamelCase_      ,  UpperCamelCase_)








"""simple docstring"""





def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase = False							)			->			bool:


			"""simple docstring"""
			if n == 2:
						return True
			if not n % 2 or n < 2:
						return False
			if n > 5 and n % 10 not in (1, 3, 7, 9):  # can quickly check last digit
						return False
			if n > 3_3170_4406_4679_8873_8596_1981 and not allow_probable:
						raise ValueError(
						    "Warning: upper bound of deterministic test is exceeded. "
						    "Pass allow_probable=True to allow probabilistic test. "
						    "A return value of True indicates a probable prime."							)
			# array bounds provided by analysis
			__UpperCAmelCase	: List[str]		=					[
			    2047,
			    137_3653,
			    2532_6001,
			    32_1503_1751,
			    2_1523_0289_8747,
			    3_4747_4966_0383,
			    341_5500_7172_8321,
			    1,
			    382_5123_0565_4641_3051,
			    1,
			    1,
			    3186_6585_7834_0311_5116_7461,
			    3_3170_4406_4679_8873_8596_1981,
			]

			__UpperCAmelCase	: Tuple		=					[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
			for idx, _p in enumerate(UpperCamelCase				,       1							):
						if n < _p:
									# then we have our last prime to check
									__UpperCAmelCase	: Tuple		=					primes[:idx]
									break
			__UpperCAmelCase	,     __UpperCAmelCase	: List[str]		=					n - 1, 0
			# break up n -1 into a power of 2 (s) and
			# remaining odd component
			# essentially, solve for d * 2 ** s == n - 1
			while d % 2 == 0:
						d //= 2
						s += 1
			for prime in plist:
						__UpperCAmelCase	: Optional[int]		=					False
						for r in range(UpperCamelCase							):
									__UpperCAmelCase	: Dict		=					pow(UpperCamelCase				,       d * 2**r				,       UpperCamelCase							)
									# see article for analysis explanation for m
									if (r == 0 and m == 1) or ((m + 1) % n == 0):
												__UpperCAmelCase	: Optional[int]		=					True
												# this loop will not determine compositeness
												break
						if pr:
									continue
						# if pr is False, then the above loop never evaluated to true,
						# and the n MUST be composite
						return False
			return True

def 					_UpperCamelCase    (			)			->			None:


			"""simple docstring"""
			assert not miller_rabin(561							)
			assert miller_rabin(563							)
			# 2047

			assert not miller_rabin(83_8201							)
			assert miller_rabin(83_8207							)
			# 1_373_653

			assert not miller_rabin(1731_6001							)
			assert miller_rabin(1731_6017							)
			# 25_326_001

			assert not miller_rabin(30_7838_6641							)
			assert miller_rabin(30_7838_6653							)
			# 3_215_031_751

			assert not miller_rabin(1_7130_4557_4801							)
			assert miller_rabin(1_7130_4557_4819							)
			# 2_152_302_898_747

			assert not miller_rabin(2_7797_9972_8307							)
			assert miller_rabin(2_7797_9972_8327							)
			# 3_474_749_660_383

			assert not miller_rabin(113_8500_2390_9441							)
			assert miller_rabin(113_8500_2390_9527							)
			# 341_550_071_728_321

			assert not miller_rabin(127_5041_0188_4880_4351							)
			assert miller_rabin(127_5041_0188_4880_4391							)
			# 3_825_123_056_546_413_051

			assert not miller_rabin(796_6646_4458_5077_8779_1867							)
			assert miller_rabin(796_6646_4458_5077_8779_1951							)
			# 318_665_857_834_031_151_167_461

			assert not miller_rabin(5528_4067_7446_6478_9766_0333							)
			assert miller_rabin(5528_4067_7446_6478_9766_0359							)
			# 3_317_044_064_679_887_385_961_981
			# upper limit for probabilistic test


if __name__ == "__main__":
					test_miller_rabin()


