


'''simple docstring'''



import random






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int   ):
					__a			:       Optional[Any]					    =  num - 1
					__a			:       List[str]					    =  0

					while s % 2 == 0:
										__a			:       Any					    =  s // 2
										t += 1

					for _ in range(5   ):
										__a			:       Tuple					    =  random.randrange(2						,      num - 1   )
										__a			:       int					    =  pow(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
										if v != 1:
															__a			:       List[str]					    =  0
															while v != (num - 1):
																				if i == t - 1:
																									return False
																				else:
																									__a			:       Union[str, Any]					    =  i + 1
																									__a			:       Union[str, Any]					    =  (v**2) % num
					return True






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int   ):
					if num < 2:
										return False

					__a			:       str					    =  [
					    2,
					    3,
					    5,
					    7,
					    11,
					    13,
					    17,
					    19,
					    23,
					    29,
					    31,
					    37,
					    41,
					    43,
					    47,
					    53,
					    59,
					    61,
					    67,
					    71,
					    73,
					    79,
					    83,
					    89,
					    97,
					    101,
					    103,
					    107,
					    109,
					    113,
					    127,
					    131,
					    137,
					    139,
					    149,
					    151,
					    157,
					    163,
					    167,
					    173,
					    179,
					    181,
					    191,
					    193,
					    197,
					    199,
					    211,
					    223,
					    227,
					    229,
					    233,
					    239,
					    241,
					    251,
					    257,
					    263,
					    269,
					    271,
					    277,
					    281,
					    283,
					    293,
					    307,
					    311,
					    313,
					    317,
					    331,
					    337,
					    347,
					    349,
					    353,
					    359,
					    367,
					    373,
					    379,
					    383,
					    389,
					    397,
					    401,
					    409,
					    419,
					    421,
					    431,
					    433,
					    439,
					    443,
					    449,
					    457,
					    461,
					    463,
					    467,
					    479,
					    487,
					    491,
					    499,
					    503,
					    509,
					    521,
					    523,
					    541,
					    547,
					    557,
					    563,
					    569,
					    571,
					    577,
					    587,
					    593,
					    599,
					    601,
					    607,
					    613,
					    617,
					    619,
					    631,
					    641,
					    643,
					    647,
					    653,
					    659,
					    661,
					    673,
					    677,
					    683,
					    691,
					    701,
					    709,
					    719,
					    727,
					    733,
					    739,
					    743,
					    751,
					    757,
					    761,
					    769,
					    773,
					    787,
					    797,
					    809,
					    811,
					    821,
					    823,
					    827,
					    829,
					    839,
					    853,
					    857,
					    859,
					    863,
					    877,
					    881,
					    883,
					    887,
					    907,
					    911,
					    919,
					    929,
					    937,
					    941,
					    947,
					    953,
					    967,
					    971,
					    977,
					    983,
					    991,
					    997,
					]

					if num in low_primes:
										return True

					for prime in low_primes:
										if (num % prime) == 0:
															return False

					return rabin_miller(_SCREAMING_SNAKE_CASE   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int = 1_024   ):
					while True:
										__a			:       Any					    =  random.randrange(2 ** (keysize - 1)						,      2 ** (keysize)   )
										if is_prime_low_num(_SCREAMING_SNAKE_CASE   ):
															return num


if __name__ == "__main__":
	__lowercase	:						List[str]       =	generate_large_prime()
	print(('Prime number:', num))
	print(('is_prime_low_num:', is_prime_low_num(num)))







'''simple docstring'''



import re
import string

import numpy as np

import datasets


__lowercase	:						Tuple       =	'\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'

__lowercase	:						List[str]       =	'\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results["exact_match"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]\n    >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    33.3\n\n'

__lowercase	:						Any       =	'\n'





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,      _KWARGS_DESCRIPTION					)
class 							__UpperCamelCase				(		datasets.Metric					):




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return datasets.MetricInfo(
						    description=_DESCRIPTION						,				citation=_CITATION						,				inputs_description=_KWARGS_DESCRIPTION						,				features=datasets.Features(
						        {
						            'predictions': datasets.Value('string'						,				id='sequence'							),
						            'references': datasets.Value('string'						,				id='sequence'							),
						        }							)						,				reference_urls=[]						,				)





	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a=None						,				__a=False						,				__a=False						,				__a=False						,				):

						'''simple docstring'''





						if regexes_to_ignore is not None:
											for s in regexes_to_ignore:
																__a			:       Tuple					    =  np.array([re.sub(__a						,				''						,				__a							) for x in predictions]							)
																__a			:       List[Any]					    =  np.array([re.sub(__a						,				''						,				__a							) for x in references]							)
						else:
											__a			:       int					    =  np.asarray(__a							)
											__a			:       str					    =  np.asarray(__a							)

						if ignore_case:
											__a			:       Dict					    =  np.char.lower(__a							)
											__a			:       List[str]					    =  np.char.lower(__a							)

						if ignore_punctuation:
											__a			:       Dict					    =  string.punctuation.maketrans(''						,				''						,				string.punctuation							)
											__a			:       Tuple					    =  np.char.translate(__a						,				table=__a							)
											__a			:       Dict					    =  np.char.translate(__a						,				table=__a							)

						if ignore_numbers:
											__a			:       Optional[int]					    =  string.digits.maketrans(''						,				''						,				string.digits							)
											__a			:       Tuple					    =  np.char.translate(__a						,				table=__a							)
											__a			:       Optional[int]					    =  np.char.translate(__a						,				table=__a							)

						__a			:       Any					    =  predictions == references

						return {"exact_match": np.mean(__a							) * 100}



