'''simple docstring'''
import inspect
import unittest

from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
     from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import MobileViTImageProcessor







class  __A				( _SCREAMING_SNAKE_CASE			):
 def 		lowercase_(   self			:    Optional[int]       ):



   """simple docstring"""





   __A		:     Union[str, Any]												=		self.config_class(**self.inputs_dict       )
   self.parent.assertTrue(hasattr(lowerCamelCase				,   """hidden_sizes"""       )       )
   self.parent.assertTrue(hasattr(lowerCamelCase				,   """neck_hidden_sizes"""       )       )
   self.parent.assertTrue(hasattr(lowerCamelCase				,   """num_attention_heads"""       )       )







class  __A				:
 def __init__(   self			:    List[Any]				,   lowerCamelCase			:    Any				,   lowerCamelCase			:    Dict=13				,   lowerCamelCase			:    Dict=32				,   lowerCamelCase			:    Union[str, Any]=2				,   lowerCamelCase			:    str=3				,   lowerCamelCase			:    Dict=6_40				,   lowerCamelCase			:    List[Any]=4				,   lowerCamelCase			:    Any="silu"				,   lowerCamelCase			:    Dict=3				,   lowerCamelCase			:    Any=32				,   lowerCamelCase			:    int=0.1				,   lowerCamelCase			:    List[Any]=0.1				,   lowerCamelCase			:    int=0.1				,   lowerCamelCase			:    Dict=0.02				,   lowerCamelCase			:    Union[str, Any]=True				,   lowerCamelCase			:    Union[str, Any]=True				,   lowerCamelCase			:    Optional[Any]=10				,   lowerCamelCase			:    str=None				,   ):



   """simple docstring"""





   __A		:     int												=		parent
   __A		:     List[Any]												=		batch_size
   __A		:     List[Any]												=		image_size
   __A		:     Any												=		patch_size
   __A		:     str												=		num_channels
   __A		:     str												=		last_hidden_size
   __A		:     Optional[int]												=		num_attention_heads
   __A		:     List[str]												=		hidden_act
   __A		:     Optional[Any]												=		conv_kernel_size
   __A		:     str												=		output_stride
   __A		:     List[str]												=		hidden_dropout_prob
   __A		:     int												=		attention_probs_dropout_prob
   __A		:     str												=		classifier_dropout_prob
   __A		:     Dict												=		use_labels
   __A		:     List[Any]												=		is_training
   __A		:     List[Any]												=		num_labels
   __A		:     str												=		initializer_range
   __A		:     Union[str, Any]												=		scope
 def 		lowercase_(   self			:    Tuple       ):



   """simple docstring"""





   __A		:     Any												=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

   __A		:     Dict												=		None
   __A		:     str												=		None
   if self.use_labels:
     __A		:     Dict												=		ids_tensor([self.batch_size]				,   self.num_labels       )
     __A		:     Optional[int]												=		ids_tensor([self.batch_size, self.image_size, self.image_size]				,   self.num_labels       )

   __A		:     Union[str, Any]												=		self.get_config()

   return config, pixel_values, labels, pixel_labels
 def 		lowercase_(   self			:    Optional[Any]       ):



   """simple docstring"""





   return MobileViTConfig(
       image_size=self.image_size				,   patch_size=self.patch_size				,   num_channels=self.num_channels				,   num_attention_heads=self.num_attention_heads				,   hidden_act=self.hidden_act				,   conv_kernel_size=self.conv_kernel_size				,   output_stride=self.output_stride				,   hidden_dropout_prob=self.hidden_dropout_prob				,   attention_probs_dropout_prob=self.attention_probs_dropout_prob				,   classifier_dropout_prob=self.classifier_dropout_prob				,   initializer_range=self.initializer_range				,   )
 def 		lowercase_(   self			:    List[str]				,   lowerCamelCase			:    Dict				,   lowerCamelCase			:    List[Any]				,   lowerCamelCase			:    Dict				,   lowerCamelCase			:    Optional[int]       ):



   """simple docstring"""





   __A		:     Optional[int]												=		MobileViTModel(config=lowerCamelCase       )
   model.to(lowerCamelCase       )
   model.eval()
   __A		:     Any												=		model(lowerCamelCase       )
   self.parent.assertEqual(
       result.last_hidden_state.shape				,   (
           self.batch_size,
           self.last_hidden_size,
           self.image_size // self.output_stride,
           self.image_size // self.output_stride,
       )				,   )
 def 		lowercase_(   self			:    List[str]				,   lowerCamelCase			:    Tuple				,   lowerCamelCase			:    Optional[Any]				,   lowerCamelCase			:    Any				,   lowerCamelCase			:    int       ):



   """simple docstring"""





   __A		:     Optional[int]												=		self.num_labels
   __A		:     Tuple												=		MobileViTForImageClassification(lowerCamelCase       )
   model.to(lowerCamelCase       )
   model.eval()
   __A		:     int												=		model(lowerCamelCase				,   labels=lowerCamelCase       )
   self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.num_labels)       )
 def 		lowercase_(   self			:    List[Any]				,   lowerCamelCase			:    Optional[Any]				,   lowerCamelCase			:    List[str]				,   lowerCamelCase			:    List[Any]				,   lowerCamelCase			:    Union[str, Any]       ):



   """simple docstring"""





   __A		:     Dict												=		self.num_labels
   __A		:     int												=		MobileViTForSemanticSegmentation(lowerCamelCase       )
   model.to(lowerCamelCase       )
   model.eval()
   __A		:     Optional[Any]												=		model(lowerCamelCase       )
   self.parent.assertEqual(
       result.logits.shape				,   (
           self.batch_size,
           self.num_labels,
           self.image_size // self.output_stride,
           self.image_size // self.output_stride,
       )				,   )
   __A		:     int												=		model(lowerCamelCase				,   labels=lowerCamelCase       )
   self.parent.assertEqual(
       result.logits.shape				,   (
           self.batch_size,
           self.num_labels,
           self.image_size // self.output_stride,
           self.image_size // self.output_stride,
       )				,   )






 def 		lowercase_(   self			:    Union[str, Any]       ):



   """simple docstring"""





   __A		:     Dict												=		self.prepare_config_and_inputs()
   __A		:     Union[str, Any]												=		config_and_inputs
   __A		:     int												=		{"""pixel_values""": pixel_values}
   return config, inputs_dict







@require_torch
class  __A				( _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						unittest.TestCase			):
 lowerCamelCase				       =(
     (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
     if is_torch_available()
     else ()
 )
 lowerCamelCase				       =(
     {
         '''feature-extraction''': MobileViTModel,
         '''image-classification''': MobileViTForImageClassification,
         '''image-segmentation''': MobileViTForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 lowerCamelCase				       =False
 lowerCamelCase				       =False
 lowerCamelCase				       =False
 lowerCamelCase				       =False
 def 		lowercase_(   self			:    Optional[int]       ):



   """simple docstring"""





   __A		:     List[str]												=		MobileViTModelTester(self       )
   __A		:     Any												=		MobileViTConfigTester(self				,   config_class=lowerCamelCase				,   has_text_modality=lowerCamelCase       )
 def 		lowercase_(   self			:    Any       ):



   """simple docstring"""





   self.config_tester.run_common_tests()
 @unittest.skip(reason="""MobileViT does not use inputs_embeds"""       )
 def 		lowercase_(   self			:    str       ):



   """simple docstring"""





   pass
 @unittest.skip(reason="""MobileViT does not support input and output embeddings"""       )
 def 		lowercase_(   self			:    List[str]       ):



   """simple docstring"""





   pass
 @unittest.skip(reason="""MobileViT does not output attentions"""       )
 def 		lowercase_(   self			:    Any       ):



   """simple docstring"""





   pass
 def 		lowercase_(   self			:    Tuple       ):



   """simple docstring"""





   __A		:     Dict												=		self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     __A		:     List[Any]												=		model_class(lowerCamelCase       )
     __A		:     Dict												=		inspect.signature(model.forward       )
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     __A		:     Tuple												=		[*signature.parameters.keys()]

     __A		:     Tuple												=		["""pixel_values"""]
     self.assertListEqual(arg_names[:1]				,   lowerCamelCase       )
 @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""       )
 def 		lowercase_(   self			:    Any       ):



   """simple docstring"""





   pass
 def 		lowercase_(   self			:    Tuple       ):



   """simple docstring"""





   __A		:     Any												=		self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowerCamelCase       )
 def 		lowercase_(   self			:    Optional[int]       ):



   """simple docstring"""





   def check_hidden_states_output(lowerCamelCase			:    List[Any]				,   lowerCamelCase			:    List[Any]				,   lowerCamelCase			:    Union[str, Any]       ):
     __A		:     Optional[int]												=		model_class(lowerCamelCase       )
     model.to(lowerCamelCase       )
     model.eval()

     with torch.no_grad():
       __A		:     List[Any]												=		model(**self._prepare_for_class(lowerCamelCase				,   lowerCamelCase       )       )

     __A		:     Dict												=		outputs.hidden_states

     __A		:     Tuple												=		5
     self.assertEqual(len(lowerCamelCase       )				,   lowerCamelCase       )

     # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
     # with the width and height being successively divided by 2.
     __A		:     Dict												=		2
     for i in range(len(lowerCamelCase       )       ):
       self.assertListEqual(
           list(hidden_states[i].shape[-2:]       )				,   [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]				,   )
       divisor *= 2

     self.assertEqual(self.model_tester.output_stride				,   divisor // 2       )

   __A		:     int												=		self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     __A		:     Union[str, Any]												=		True
     check_hidden_states_output(lowerCamelCase				,   lowerCamelCase				,   lowerCamelCase       )

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     __A		:     Any												=		True

     check_hidden_states_output(lowerCamelCase				,   lowerCamelCase				,   lowerCamelCase       )
 def 		lowercase_(   self			:    Tuple       ):



   """simple docstring"""





   __A		:     Dict												=		self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*lowerCamelCase       )
 def 		lowercase_(   self			:    Optional[Any]       ):



   """simple docstring"""





   __A		:     Union[str, Any]												=		self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase       )






 @slow
 def 		lowercase_(   self			:    Dict       ):



   """simple docstring"""





   for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     __A		:     List[Any]												=		MobileViTModel.from_pretrained(lowerCamelCase       )
     self.assertIsNotNone(lowerCamelCase       )







def        A_     (						)				->       List[str]:





  """simple docstring"""

  __A		:     Optional[Any]												=		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
  return image







@require_torch
@require_vision
class  __A				( unittest.TestCase			):
 @cached_property
 def 		lowercase_(   self			:    Dict       ):



   """simple docstring"""





   return MobileViTImageProcessor.from_pretrained("""apple/mobilevit-xx-small"""       ) if is_vision_available() else None
 @slow
 def 		lowercase_(   self			:    Any       ):



   """simple docstring"""





   __A		:     List[Any]												=		MobileViTForImageClassification.from_pretrained("""apple/mobilevit-xx-small"""       ).to(lowerCamelCase       )

   __A		:     Tuple												=		self.default_image_processor
   __A		:     List[Any]												=		prepare_img()
   __A		:     Tuple												=		image_processor(images=lowerCamelCase				,   return_tensors="""pt"""       ).to(lowerCamelCase       )

   # forward pass
   with torch.no_grad():
     __A		:     Dict												=		model(**lowerCamelCase       )

   # verify the logits
   __A		:     Union[str, Any]												=		torch.Size((1, 10_00)       )
   self.assertEqual(outputs.logits.shape				,   lowerCamelCase       )

   __A		:     Tuple												=		torch.tensor([-1.9_364, -1.2_327, -0.4_653]       ).to(lowerCamelCase       )

   self.assertTrue(torch.allclose(outputs.logits[0, :3]				,   lowerCamelCase				,   atol=1e-4       )       )
 @slow
 def 		lowercase_(   self			:    Union[str, Any]       ):



   """simple docstring"""





   __A		:     List[str]												=		MobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""       )
   __A		:     List[Any]												=		model.to(lowerCamelCase       )

   __A		:     List[str]												=		MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""       )

   __A		:     Dict												=		prepare_img()
   __A		:     Optional[Any]												=		image_processor(images=lowerCamelCase				,   return_tensors="""pt"""       ).to(lowerCamelCase       )

   # forward pass
   with torch.no_grad():
     __A		:     Dict												=		model(**lowerCamelCase       )
   __A		:     Optional[int]												=		outputs.logits

   # verify the logits
   __A		:     int												=		torch.Size((1, 21, 32, 32)       )
   self.assertEqual(logits.shape				,   lowerCamelCase       )

   __A		:     Any												=		torch.tensor(
       [
           [[6.9_713, 6.9_786, 7.2_422], [7.2_893, 7.2_825, 7.4_446], [7.6_580, 7.8_797, 7.9_420]],
           [[-10.6_869, -10.3_250, -10.3_471], [-10.4_228, -9.9_868, -9.7_132], [-11.0_405, -11.0_221, -10.7_318]],
           [[-3.3_089, -2.8_539, -2.6_740], [-3.2_706, -2.5_621, -2.5_108], [-3.2_534, -2.6_615, -2.6_651]],
       ]				,   device=lowerCamelCase				,   )

   self.assertTrue(torch.allclose(logits[0, :3, :3, :3]				,   lowerCamelCase				,   atol=1e-4       )       )






 @slow
 def 		lowercase_(   self			:    Tuple       ):



   """simple docstring"""





   __A		:     List[str]												=		MobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""       )
   __A		:     Optional[int]												=		model.to(lowerCamelCase       )

   __A		:     Optional[Any]												=		MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""       )

   __A		:     Tuple												=		prepare_img()
   __A		:     int												=		image_processor(images=lowerCamelCase				,   return_tensors="""pt"""       ).to(lowerCamelCase       )

   # forward pass
   with torch.no_grad():
     __A		:     Optional[int]												=		model(**lowerCamelCase       )

   __A		:     Optional[int]												=		outputs.logits.detach().cpu()

   __A		:     List[Any]												=		image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase				,   target_sizes=[(50, 60)]       )
   __A		:     int												=		torch.Size((50, 60)       )
   self.assertEqual(segmentation[0].shape				,   lowerCamelCase       )

   __A		:     Optional[Any]												=		image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase       )
   __A		:     str												=		torch.Size((32, 32)       )
   self.assertEqual(segmentation[0].shape				,   lowerCamelCase       )





'''simple docstring'''
def        A_     (						__SCREAMING_SNAKE_CASE						:  int			,     __SCREAMING_SNAKE_CASE						:  bool = 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
  __A		:     Optional[int]												=		[
      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,
  ]

  __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		:     Union[str, Any]												=		primes[:idx]
      break
  __A							,					__A		:     int												=		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		:     int												=		False
    for r in range(__SCREAMING_SNAKE_CASE ):
      __A		:     Optional[Any]												=		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		:     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        A_     (						)				->       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()



