



"""simple docstring"""
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import MobileViTImageProcessor




class 			snake_case_						(						unittest.TestCase				):






							def __init__(   self			,     a_			,     a_=7			,     a_=3			,     a_=1_8			,     a_=3_0			,     a_=4_0_0			,     a_=True			,     a_=None			,     a_=True			,     a_=None			,     a_=True			,     ):
									a_     :      Optional[int] 						=     size if size is not None else {"shortest_edge": 2_0}
									a_     :      Optional[int] 						=     crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
									a_     :      int 						=     parent
									a_     :      Optional[Any] 						=     batch_size
									a_     :      Union[str, Any] 						=     num_channels
									a_     :      Optional[int] 						=     image_size
									a_     :      List[str] 						=     min_resolution
									a_     :      int 						=     max_resolution
									a_     :      Any 						=     do_resize
									a_     :      List[str] 						=     size
									a_     :      Any 						=     do_center_crop
									a_     :      Tuple 						=     crop_size
									a_     :      Optional[int] 						=     do_flip_channel_order






							def snake_case_  (   self      ):
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_center_crop": self.do_center_crop,
									    "crop_size": self.crop_size,
									    "do_flip_channel_order": self.do_flip_channel_order,
									}




@require_torch
@require_vision
class 			snake_case_						(						a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					MobileViTImageProcessor if is_vision_available() else None






							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     MobileViTImageProcessingTester(self      )






							@property
							def snake_case_  (   self      ):
									return self.image_processor_tester.prepare_image_processor_dict()






							def snake_case_  (   self      ):
									a_     :      int 						=     self.image_processing_class(**self.image_processor_dict      )
									self.assertTrue(hasattr(a_			,     "do_resize"      )      )
									self.assertTrue(hasattr(a_			,     "size"      )      )
									self.assertTrue(hasattr(a_			,     "do_center_crop"      )      )
									self.assertTrue(hasattr(a_			,     "center_crop"      )      )
									self.assertTrue(hasattr(a_			,     "do_flip_channel_order"      )      )






							def snake_case_  (   self      ):
									a_     :      str 						=     self.image_processing_class.from_dict(self.image_processor_dict      )
									self.assertEqual(image_processor.size			,     {"shortest_edge": 2_0}      )
									self.assertEqual(image_processor.crop_size			,     {"height": 1_8, "width": 1_8}      )

									a_     :      List[str] 						=     self.image_processing_class.from_dict(self.image_processor_dict			,     size=4_2			,     crop_size=8_4      )
									self.assertEqual(image_processor.size			,     {"shortest_edge": 4_2}      )
									self.assertEqual(image_processor.crop_size			,     {"height": 8_4, "width": 8_4}      )






							def snake_case_  (   self      ):
									pass






							def snake_case_  (   self      ):
									# Initialize image_processing
									a_     :      Tuple 						=     self.image_processing_class(**self.image_processor_dict      )
									# create random PIL images
									a_     :      int 						=     prepare_image_inputs(self.image_processor_tester			,     equal_resolution=a_      )
									for image in image_inputs:
											self.assertIsInstance(a_			,     Image.Image      )

									# Test not batched input
									a_     :      List[Any] 						=     image_processing(image_inputs[0]			,     return_tensors="pt"      ).pixel_values
									self.assertEqual(
									    encoded_images.shape			,     (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )			,     )

									# Test batched
									a_     :      Union[str, Any] 						=     image_processing(a_			,     return_tensors="pt"      ).pixel_values
									self.assertEqual(
									    encoded_images.shape			,     (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )			,     )






							def snake_case_  (   self      ):
									# Initialize image_processing
									a_     :      Dict 						=     self.image_processing_class(**self.image_processor_dict      )
									# create random numpy tensors
									a_     :      Tuple 						=     prepare_image_inputs(self.image_processor_tester			,     equal_resolution=a_			,     numpify=a_      )
									for image in image_inputs:
											self.assertIsInstance(a_			,     np.ndarray      )

									# Test not batched input
									a_     :      Optional[Any] 						=     image_processing(image_inputs[0]			,     return_tensors="pt"      ).pixel_values
									self.assertEqual(
									    encoded_images.shape			,     (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )			,     )

									# Test batched
									a_     :      str 						=     image_processing(a_			,     return_tensors="pt"      ).pixel_values
									self.assertEqual(
									    encoded_images.shape			,     (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )			,     )






							def snake_case_  (   self      ):
									# Initialize image_processing
									a_     :      int 						=     self.image_processing_class(**self.image_processor_dict      )
									# create random PyTorch tensors
									a_     :      int 						=     prepare_image_inputs(self.image_processor_tester			,     equal_resolution=a_			,     torchify=a_      )
									for image in image_inputs:
											self.assertIsInstance(a_			,     torch.Tensor      )

									# Test not batched input
									a_     :      Optional[Any] 						=     image_processing(image_inputs[0]			,     return_tensors="pt"      ).pixel_values
									self.assertEqual(
									    encoded_images.shape			,     (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )			,     )

									# Test batched
									a_     :      Union[str, Any] 						=     image_processing(a_			,     return_tensors="pt"      ).pixel_values
									self.assertEqual(
									    encoded_images.shape			,     (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )			,     )




"""simple docstring"""
def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ = False	)			->      bool:
		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_317_044_064_679_887_385_961_981 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
		a_     :      Optional[Any] 						=     [
		    2_047,
		    1_373_653,
		    25_326_001,
		    3_215_031_751,
		    2_152_302_898_747,
		    3_474_749_660_383,
		    341_550_071_728_321,
		    1,
		    3_825_123_056_546_413_051,
		    1,
		    1,
		    318_665_857_834_031_151_167_461,
		    3_317_044_064_679_887_385_961_981,
		]

		a_     :      Optional[int] 						=     [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
		for idx, _p in enumerate(SCREAMING_SNAKE_CASE__, 1	):
				if n < _p:
						# then we have our last prime to check
						a_     :      List[Any] 						=     primes[:idx]
						break
		a_						,				a_     :      Tuple 						=     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:
				a_     :      str 						=     False
				for r in range(SCREAMING_SNAKE_CASE__	):
						a_     :      List[str] 						=     pow(SCREAMING_SNAKE_CASE__, d * 2**r, SCREAMING_SNAKE_CASE__	)
						# see article for analysis explanation for m
						if (r == 0 and m == 1) or ((m + 1) % n == 0):
								a_     :      List[Any] 						=     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        lowerCAmelCase_						(							)			->      None:
		assert not miller_rabin(561	)
		assert miller_rabin(563	)
		# 2047

		assert not miller_rabin(838_201	)
		assert miller_rabin(838_207	)
		# 1_373_653

		assert not miller_rabin(17_316_001	)
		assert miller_rabin(17_316_017	)
		# 25_326_001

		assert not miller_rabin(3_078_386_641	)
		assert miller_rabin(3_078_386_653	)
		# 3_215_031_751

		assert not miller_rabin(1_713_045_574_801	)
		assert miller_rabin(1_713_045_574_819	)
		# 2_152_302_898_747

		assert not miller_rabin(2_779_799_728_307	)
		assert miller_rabin(2_779_799_728_327	)
		# 3_474_749_660_383

		assert not miller_rabin(113_850_023_909_441	)
		assert miller_rabin(113_850_023_909_527	)
		# 341_550_071_728_321

		assert not miller_rabin(1_275_041_018_848_804_351	)
		assert miller_rabin(1_275_041_018_848_804_391	)
		# 3_825_123_056_546_413_051

		assert not miller_rabin(79_666_464_458_507_787_791_867	)
		assert miller_rabin(79_666_464_458_507_787_791_951	)
		# 318_665_857_834_031_151_167_461

		assert not miller_rabin(552_840_677_446_647_897_660_333	)
		assert miller_rabin(552_840_677_446_647_897_660_359	)
		# 3_317_044_064_679_887_385_961_981
		# upper limit for probabilistic test


if __name__ == "__main__":
					test_miller_rabin()