

'''simple docstring'''




import unittest

import numpy as np
from datasets import load_dataset

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 BeitImageProcessor






class     _SCREAMING_SNAKE_CASE       (       unittest.TestCase      ):



 def __init__(       self						:       int					, a__						:       Union[str, Any]					, a__						:       Dict=7					, a__						:       int=3					, a__						:       Union[str, Any]=18					, a__						:       int=30					, a__						:       Any=400					, a__						:       List[str]=True					, a__						:       Optional[int]=None					, a__						:       Optional[Any]=True					, a__						:       Optional[int]=None					, a__						:       Any=True					, a__						:       List[Any]=[0.5, 0.5, 0.5]					, a__						:       Union[str, Any]=[0.5, 0.5, 0.5]					, a__						:       List[str]=False					, ):
      __magic_name__		  =							size if size is not None else {'''height''': 20, '''width''': 20}
      __magic_name__		  =							crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
      __magic_name__		  =							parent
      __magic_name__		  =							batch_size
      __magic_name__		  =							num_channels
      __magic_name__		  =							image_size
      __magic_name__		  =							min_resolution
      __magic_name__		  =							max_resolution
      __magic_name__		  =							do_resize
      __magic_name__		  =							size
      __magic_name__		  =							do_center_crop
      __magic_name__		  =							crop_size
      __magic_name__		  =							do_normalize
      __magic_name__		  =							image_mean
      __magic_name__		  =							image_std
      __magic_name__		  =							do_reduce_labels



 def 					snake_case__    (       self						:       Dict     ):
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_reduce_labels": self.do_reduce_labels,
      }




def 		UpperCamelCase			(					) ->      Optional[Any]:






     '''simple docstring'''
     __magic_name__		  =							load_dataset('''hf-internal-testing/fixtures_ade20k'''						,		split='''test'''					)

     __magic_name__		  =							Image.open(dataset[0]['''file''']					)
     __magic_name__		  =							Image.open(dataset[1]['''file''']					)

     return image, map




def 		UpperCamelCase			(					) ->      Union[str, Any]:






     '''simple docstring'''
     __magic_name__		  =							load_dataset('''hf-internal-testing/fixtures_ade20k'''						,		split='''test'''					)

     __magic_name__		  =							Image.open(ds[0]['''file''']					)
     __magic_name__		  =							Image.open(ds[1]['''file''']					)
     __magic_name__		  =							Image.open(ds[2]['''file''']					)
     __magic_name__		  =							Image.open(ds[3]['''file''']					)

     return [imagea, imagea], [mapa, mapa]






@require_torch
@require_vision
class     _SCREAMING_SNAKE_CASE       (       lowercase_						,unittest.TestCase      ):
 __SCREAMING_SNAKE_CASE	:Union[str, Any]     			=	BeitImageProcessor if is_vision_available() else None



 def 					snake_case__    (       self						:       Union[str, Any]     ):
      __magic_name__		  =							BeitImageProcessingTester(self     )



 @property
 def 					snake_case__    (       self						:       Dict     ):
      return self.image_processor_tester.prepare_image_processor_dict()



 def 					snake_case__    (       self						:       Union[str, Any]     ):
      __magic_name__		  =							self.image_processing_class(**self.image_processor_dict     )
      self.assertTrue(hasattr(_lowercase					, '''do_resize'''     )     )
      self.assertTrue(hasattr(_lowercase					, '''size'''     )     )
      self.assertTrue(hasattr(_lowercase					, '''do_center_crop'''     )     )
      self.assertTrue(hasattr(_lowercase					, '''center_crop'''     )     )
      self.assertTrue(hasattr(_lowercase					, '''do_normalize'''     )     )
      self.assertTrue(hasattr(_lowercase					, '''image_mean'''     )     )
      self.assertTrue(hasattr(_lowercase					, '''image_std'''     )     )



 def 					snake_case__    (       self						:       Union[str, Any]     ):
      __magic_name__		  =							self.image_processing_class.from_dict(self.image_processor_dict     )
      self.assertEqual(image_processor.size					, {'''height''': 20, '''width''': 20}     )
      self.assertEqual(image_processor.crop_size					, {'''height''': 18, '''width''': 18}     )
      self.assertEqual(image_processor.do_reduce_labels					, _lowercase     )

      __magic_name__		  =							self.image_processing_class.from_dict(
          self.image_processor_dict					, size=42					, crop_size=84					, reduce_labels=_lowercase     )
      self.assertEqual(image_processor.size					, {'''height''': 42, '''width''': 42}     )
      self.assertEqual(image_processor.crop_size					, {'''height''': 84, '''width''': 84}     )
      self.assertEqual(image_processor.do_reduce_labels					, _lowercase     )



 def 					snake_case__    (       self						:       Optional[int]     ):
      pass



 def 					snake_case__    (       self						:       List[str]     ):
      # Initialize image_processing
      __magic_name__		  =							self.image_processing_class(**self.image_processor_dict     )
      # create random PIL images
      __magic_name__		  =							prepare_image_inputs(self.image_processor_tester					, equal_resolution=_lowercase     )
      for image in image_inputs:
           self.assertIsInstance(_lowercase					, Image.Image     )

      # Test not batched input
      __magic_name__		  =							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
      __magic_name__		  =							image_processing(_lowercase					, 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						:       Union[str, Any]     ):
      # Initialize image_processing
      __magic_name__		  =							self.image_processing_class(**self.image_processor_dict     )
      # create random numpy tensors
      __magic_name__		  =							prepare_image_inputs(self.image_processor_tester					, equal_resolution=_lowercase					, numpify=_lowercase     )
      for image in image_inputs:
           self.assertIsInstance(_lowercase					, np.ndarray     )

      # Test not batched input
      __magic_name__		  =							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
      __magic_name__		  =							image_processing(_lowercase					, 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						:       str     ):
      # Initialize image_processing
      __magic_name__		  =							self.image_processing_class(**self.image_processor_dict     )
      # create random PyTorch tensors
      __magic_name__		  =							prepare_image_inputs(self.image_processor_tester					, equal_resolution=_lowercase					, torchify=_lowercase     )
      for image in image_inputs:
           self.assertIsInstance(_lowercase					, torch.Tensor     )

      # Test not batched input
      __magic_name__		  =							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
      __magic_name__		  =							image_processing(_lowercase					, 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						:       Any     ):
      # Initialize image_processing
      __magic_name__		  =							self.image_processing_class(**self.image_processor_dict     )
      # create random PyTorch tensors
      __magic_name__		  =							prepare_image_inputs(self.image_processor_tester					, equal_resolution=_lowercase					, torchify=_lowercase     )
      __magic_name__		  =							[]
      for image in image_inputs:
           self.assertIsInstance(_lowercase					, torch.Tensor     )
           maps.append(torch.zeros(image.shape[-2:]     ).long()     )

      # Test not batched input
      __magic_name__		  =							image_processing(image_inputs[0]					, maps[0]					, return_tensors='''pt'''     )
      self.assertEqual(
          encoding['''pixel_values'''].shape					, (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )					, )
      self.assertEqual(
          encoding['''labels'''].shape					, (
              1,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )					, )
      self.assertEqual(encoding['''labels'''].dtype					, torch.long     )
      self.assertTrue(encoding['''labels'''].min().item() >= 0     )
      self.assertTrue(encoding['''labels'''].max().item() <= 255     )

      # Test batched
      __magic_name__		  =							image_processing(_lowercase					, _lowercase					, return_tensors='''pt'''     )
      self.assertEqual(
          encoding['''pixel_values'''].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'''],
          )					, )
      self.assertEqual(
          encoding['''labels'''].shape					, (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )					, )
      self.assertEqual(encoding['''labels'''].dtype					, torch.long     )
      self.assertTrue(encoding['''labels'''].min().item() >= 0     )
      self.assertTrue(encoding['''labels'''].max().item() <= 255     )

      # Test not batched input (PIL images)
      __magic_name__  ,    __magic_name__		  =							prepare_semantic_single_inputs()

      __magic_name__		  =							image_processing(_lowercase					, _lowercase					, return_tensors='''pt'''     )
      self.assertEqual(
          encoding['''pixel_values'''].shape					, (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )					, )
      self.assertEqual(
          encoding['''labels'''].shape					, (
              1,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )					, )
      self.assertEqual(encoding['''labels'''].dtype					, torch.long     )
      self.assertTrue(encoding['''labels'''].min().item() >= 0     )
      self.assertTrue(encoding['''labels'''].max().item() <= 255     )

      # Test batched input (PIL images)
      __magic_name__  ,    __magic_name__		  =							prepare_semantic_batch_inputs()

      __magic_name__		  =							image_processing(_lowercase					, _lowercase					, return_tensors='''pt'''     )
      self.assertEqual(
          encoding['''pixel_values'''].shape					, (
              2,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )					, )
      self.assertEqual(
          encoding['''labels'''].shape					, (
              2,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )					, )
      self.assertEqual(encoding['''labels'''].dtype					, torch.long     )
      self.assertTrue(encoding['''labels'''].min().item() >= 0     )
      self.assertTrue(encoding['''labels'''].max().item() <= 255     )



 def 					snake_case__    (       self						:       Union[str, Any]     ):
      # Initialize image_processing
      __magic_name__		  =							self.image_processing_class(**self.image_processor_dict     )

      # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
      __magic_name__  ,    __magic_name__		  =							prepare_semantic_single_inputs()
      __magic_name__		  =							image_processing(_lowercase					, _lowercase					, return_tensors='''pt'''     )
      self.assertTrue(encoding['''labels'''].min().item() >= 0     )
      self.assertTrue(encoding['''labels'''].max().item() <= 150     )

      __magic_name__		  =							True
      __magic_name__		  =							image_processing(_lowercase					, _lowercase					, return_tensors='''pt'''     )
      self.assertTrue(encoding['''labels'''].min().item() >= 0     )
      self.assertTrue(encoding['''labels'''].max().item() <= 255     )









'''simple docstring'''

def 							_UpperCamelCase				(   lowerCAmelCase__:					int		,lowerCAmelCase__:					bool = 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_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
							SCREAMING_SNAKE_CASE_				=				[
							    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,
							]

							SCREAMING_SNAKE_CASE_				=				[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
																					SCREAMING_SNAKE_CASE_				=				primes[:idx]
																					break
							SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				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:
														SCREAMING_SNAKE_CASE_				=				False
														for r in range(lowerCAmelCase__   ):
																					SCREAMING_SNAKE_CASE_				=				pow(lowerCAmelCase__		,d * 2**r		,lowerCAmelCase__   )
																					# see article for analysis explanation for m
																					if (r == 0 and m == 1) or ((m + 1) % n == 0):
																												SCREAMING_SNAKE_CASE_				=				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:
							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()





