




'''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 ViTImageProcessor




class    __lowerCAmelCase       (							unittest.TestCase):






       def __init__(   self:					Dict       ,					_lowerCAmelCase:					Tuple       ,					_lowerCAmelCase:					Optional[Any]=13       ,					_lowerCAmelCase:					int=3       ,					_lowerCAmelCase:					int=2_24       ,					_lowerCAmelCase:					Tuple=30       ,					_lowerCAmelCase:					List[str]=4_00       ,					_lowerCAmelCase:					Union[str, Any]=True       ,					_lowerCAmelCase:					Any=None       ,					_lowerCAmelCase:					Tuple=True       ,					_lowerCAmelCase:					str=[0.5, 0.5, 0.5]       ,					_lowerCAmelCase:					List[Any]=[0.5, 0.5, 0.5]       ,					):
         lowercase    :List[Any]      	=						size if size is not None else {"height": 18, "width": 18}
         lowercase    :Dict      	=						parent
         lowercase    :Dict      	=						batch_size
         lowercase    :Tuple      	=						num_channels
         lowercase    :Tuple      	=						image_size
         lowercase    :Union[str, Any]      	=						min_resolution
         lowercase    :Optional[Any]      	=						max_resolution
         lowercase    :List[Any]      	=						do_resize
         lowercase    :List[Any]      	=						size
         lowercase    :Tuple      	=						do_normalize
         lowercase    :List[str]      	=						image_mean
         lowercase    :List[str]      	=						image_std






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]				):
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
         }







@require_torch
@require_vision
class    __lowerCAmelCase       (							_a      , unittest.TestCase):
       _a 					=						ViTImageProcessor if is_vision_available() else None






       def 					SCREAMING_SNAKE_CASE    (   self:					Any				):
         lowercase    :Dict      	=						EfficientFormerImageProcessorTester(self				)






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         return self.image_proc_tester.prepare_image_processor_dict()






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]				):
         lowercase    :Dict      	=						self.image_processing_class(**self.image_processor_dict				)
         self.assertTrue(hasattr(_A       ,					"image_mean"				)				)
         self.assertTrue(hasattr(_A       ,					"image_std"				)				)
         self.assertTrue(hasattr(_A       ,					"do_normalize"				)				)
         self.assertTrue(hasattr(_A       ,					"do_resize"				)				)
         self.assertTrue(hasattr(_A       ,					"size"				)				)






       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         pass






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         # Initialize image_processor
         lowercase    :List[Any]      	=						self.image_processing_class(**self.image_processor_dict				)
         # create random PIL images
         lowercase    :Optional[int]      	=						prepare_image_inputs(self.image_proc_tester       ,					equal_resolution=_A				)
         for image in image_inputs:
           self.assertIsInstance(_A       ,					Image.Image				)

         # Test not batched input
         lowercase    :Dict      	=						image_processor(image_inputs[0]       ,					return_tensors="pt"				).pixel_values
         self.assertEqual(
             encoded_images.shape       ,					(
                 1,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size["height"],
                 self.image_proc_tester.size["width"],
             )       ,					)

         # Test batched
         lowercase    :Optional[Any]      	=						image_processor(_A       ,					return_tensors="pt"				).pixel_values
         self.assertEqual(
             encoded_images.shape       ,					(
                 self.image_proc_tester.batch_size,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size["height"],
                 self.image_proc_tester.size["width"],
             )       ,					)






       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         # Initialize image_processor
         lowercase    :Dict      	=						self.image_processing_class(**self.image_processor_dict				)
         # create random numpy tensors
         lowercase    :Union[str, Any]      	=						prepare_image_inputs(self.image_proc_tester       ,					equal_resolution=_A       ,					numpify=_A				)
         for image in image_inputs:
           self.assertIsInstance(_A       ,					np.ndarray				)

         # Test not batched input
         lowercase    :str      	=						image_processor(image_inputs[0]       ,					return_tensors="pt"				).pixel_values
         self.assertEqual(
             encoded_images.shape       ,					(
                 1,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size["height"],
                 self.image_proc_tester.size["width"],
             )       ,					)

         # Test batched
         lowercase    :Optional[int]      	=						image_processor(_A       ,					return_tensors="pt"				).pixel_values
         self.assertEqual(
             encoded_images.shape       ,					(
                 self.image_proc_tester.batch_size,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size["height"],
                 self.image_proc_tester.size["width"],
             )       ,					)






       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         # Initialize image_processor
         lowercase    :List[str]      	=						self.image_processing_class(**self.image_processor_dict				)
         # create random PyTorch tensors
         lowercase    :str      	=						prepare_image_inputs(self.image_proc_tester       ,					equal_resolution=_A       ,					torchify=_A				)
         for image in image_inputs:
           self.assertIsInstance(_A       ,					torch.Tensor				)

         # Test not batched input
         lowercase    :Dict      	=						image_processor(image_inputs[0]       ,					return_tensors="pt"				).pixel_values
         self.assertEqual(
             encoded_images.shape       ,					(
                 1,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size["height"],
                 self.image_proc_tester.size["width"],
             )       ,					)

         # Test batched
         lowercase    :List[str]      	=						image_processor(_A       ,					return_tensors="pt"				).pixel_values
         self.assertEqual(
             encoded_images.shape       ,					(
                 self.image_proc_tester.batch_size,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size["height"],
                 self.image_proc_tester.size["width"],
             )       ,					)









def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase = False   ):
  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 > 3317044064679887385961981 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
  lowercase    :Optional[Any]      	=						[
      2047,
      1373653,
      25326001,
      3215031751,
      2152302898747,
      3474749660383,
      341550071728321,
      1,
      3825123056546413051,
      1,
      1,
      318665857834031151167461,
      3317044064679887385961981,
  ]

  lowercase    :List[Any]      	=						[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
  for idx, _p in enumerate(lowerCamelCase,			1   ):
    if n < _p:
      # then we have our last prime to check
      lowercase    :Tuple      	=						primes[:idx]
      break
  lowercase	,    lowercase    :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:
    lowercase    :Tuple      	=						False
    for r in range(lowerCamelCase   ):
      lowercase    :Any      	=						pow(lowerCamelCase,			d * 2**r,			lowerCamelCase   )
      # see article for analysis explanation for m
      if (r == 0 and m == 1) or ((m + 1) % n == 0):
        lowercase    :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       UpperCAmelCase__  (     ):
  assert not miller_rabin(561   )
  assert miller_rabin(563   )
  # 2047

  assert not miller_rabin(838201   )
  assert miller_rabin(838207   )
  # 1_373_653

  assert not miller_rabin(17316001   )
  assert miller_rabin(17316017   )
  # 25_326_001

  assert not miller_rabin(3078386641   )
  assert miller_rabin(3078386653   )
  # 3_215_031_751

  assert not miller_rabin(1713045574801   )
  assert miller_rabin(1713045574819   )
  # 2_152_302_898_747

  assert not miller_rabin(2779799728307   )
  assert miller_rabin(2779799728327   )
  # 3_474_749_660_383

  assert not miller_rabin(113850023909441   )
  assert miller_rabin(113850023909527   )
  # 341_550_071_728_321

  assert not miller_rabin(1275041018848804351   )
  assert miller_rabin(1275041018848804391   )
  # 3_825_123_056_546_413_051

  assert not miller_rabin(79666464458507787791867   )
  assert miller_rabin(79666464458507787791951   )
  # 318_665_857_834_031_151_167_461

  assert not miller_rabin(552840677446647897660333   )
  assert miller_rabin(552840677446647897660359   )
  # 3_317_044_064_679_887_385_961_981
  # upper limit for probabilistic test


if __name__ == "__main__":
     test_miller_rabin()



