'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class  __A				( _SCREAMING_SNAKE_CASE    ,						unittest.TestCase			):
 lowerCamelCase				       =KandinskyVaaControlnetPipeline
 lowerCamelCase				       =['''image_embeds''', '''negative_image_embeds''', '''hint''']
 lowerCamelCase				       =['''image_embeds''', '''negative_image_embeds''', '''hint''']
 lowerCamelCase				       =[
     '''generator''',
     '''height''',
     '''width''',
     '''latents''',
     '''guidance_scale''',
     '''num_inference_steps''',
     '''return_dict''',
     '''guidance_scale''',
     '''num_images_per_prompt''',
     '''output_type''',
     '''return_dict''',
 ]
 lowerCamelCase				       =False
 @property
 def 		lowercase_(   self			:    Tuple       ):



   """simple docstring"""





   return 32
 @property
 def 		lowercase_(   self			:    Any       ):



   """simple docstring"""





   return 32
 @property
 def 		lowercase_(   self			:    str       ):



   """simple docstring"""





   return self.time_input_dim
 @property
 def 		lowercase_(   self			:    Union[str, Any]       ):



   """simple docstring"""





   return self.time_input_dim * 4
 @property
 def 		lowercase_(   self			:    List[str]       ):



   """simple docstring"""





   return 1_00
 @property
 def 		lowercase_(   self			:    List[Any]       ):



   """simple docstring"""





   torch.manual_seed(0       )

   __A		:     str												=		{
       """in_channels""": 8,
       # Out channels is double in channels because predicts mean and variance
       """out_channels""": 8,
       """addition_embed_type""": """image_hint""",
       """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
       """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
       """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
       """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
       """layers_per_block""": 1,
       """encoder_hid_dim""": self.text_embedder_hidden_size,
       """encoder_hid_dim_type""": """image_proj""",
       """cross_attention_dim""": self.cross_attention_dim,
       """attention_head_dim""": 4,
       """resnet_time_scale_shift""": """scale_shift""",
       """class_embed_type""": None,
   }

   __A		:     int												=		UNetaDConditionModel(**lowerCamelCase       )
   return model
 @property
 def 		lowercase_(   self			:    Tuple       ):



   """simple docstring"""





   return {
       "block_out_channels": [32, 32, 64, 64],
       "down_block_types": [
           "DownEncoderBlock2D",
           "DownEncoderBlock2D",
           "DownEncoderBlock2D",
           "AttnDownEncoderBlock2D",
       ],
       "in_channels": 3,
       "latent_channels": 4,
       "layers_per_block": 1,
       "norm_num_groups": 8,
       "norm_type": "spatial",
       "num_vq_embeddings": 12,
       "out_channels": 3,
       "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
       "vq_embed_dim": 4,
   }
 @property
 def 		lowercase_(   self			:    List[Any]       ):



   """simple docstring"""





   torch.manual_seed(0       )
   __A		:     int												=		VQModel(**self.dummy_movq_kwargs       )
   return model
 def 		lowercase_(   self			:    Tuple       ):



   """simple docstring"""





   __A		:     Optional[int]												=		self.dummy_unet
   __A		:     List[str]												=		self.dummy_movq

   __A		:     List[str]												=		DDIMScheduler(
       num_train_timesteps=10_00				,   beta_schedule="""linear"""				,   beta_start=0.00_085				,   beta_end=0.012				,   clip_sample=lowerCamelCase				,   set_alpha_to_one=lowerCamelCase				,   steps_offset=1				,   prediction_type="""epsilon"""				,   thresholding=lowerCamelCase				,   )

   __A		:     Union[str, Any]												=		{
       """unet""": unet,
       """scheduler""": scheduler,
       """movq""": movq,
   }
   return components
 def 		lowercase_(   self			:    Optional[int]				,   lowerCamelCase			:    str				,   lowerCamelCase			:    List[Any]=0       ):



   """simple docstring"""





   __A		:     Tuple												=		floats_tensor((1, self.text_embedder_hidden_size)				,   rng=random.Random(lowerCamelCase       )       ).to(lowerCamelCase       )
   __A		:     Tuple												=		floats_tensor((1, self.text_embedder_hidden_size)				,   rng=random.Random(seed + 1       )       ).to(
       lowerCamelCase       )

   # create hint
   __A		:     Any												=		floats_tensor((1, 3, 64, 64)				,   rng=random.Random(lowerCamelCase       )       ).to(lowerCamelCase       )

   if str(lowerCamelCase       ).startswith("""mps"""       ):
     __A		:     Optional[Any]												=		torch.manual_seed(lowerCamelCase       )
   else:
     __A		:     Optional[Any]												=		torch.Generator(device=lowerCamelCase       ).manual_seed(lowerCamelCase       )
   __A		:     str												=		{
       """image_embeds""": image_embeds,
       """negative_image_embeds""": negative_image_embeds,
       """hint""": hint,
       """generator""": generator,
       """height""": 64,
       """width""": 64,
       """guidance_scale""": 4.0,
       """num_inference_steps""": 2,
       """output_type""": """np""",
   }
   return inputs






 def 		lowercase_(   self			:    Dict       ):



   """simple docstring"""





   __A		:     str												=		"""cpu"""

   __A		:     str												=		self.get_dummy_components()

   __A		:     str												=		self.pipeline_class(**lowerCamelCase       )
   __A		:     Dict												=		pipe.to(lowerCamelCase       )

   pipe.set_progress_bar_config(disable=lowerCamelCase       )

   __A		:     int												=		pipe(**self.get_dummy_inputs(lowerCamelCase       )       )
   __A		:     List[str]												=		output.images

   __A		:     Optional[Any]												=		pipe(
       **self.get_dummy_inputs(lowerCamelCase       )				,   return_dict=lowerCamelCase				,   )[0]

   __A		:     Dict												=		image[0, -3:, -3:, -1]
   __A		:     List[str]												=		image_from_tuple[0, -3:, -3:, -1]

   assert image.shape == (1, 64, 64, 3)

   __A		:     List[str]												=		np.array(
       [0.6_959_826, 0.868_279, 0.7_558_092, 0.68_769_467, 0.85_805_804, 0.65_977_496, 0.44_885_302, 0.5_959_111, 0.4_251_595]       )

   assert (
       np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
   ), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"

   assert (
       np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2
   ), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"







@slow
@require_torch_gpu
class  __A				( unittest.TestCase			):
 def 		lowercase_(   self			:    List[str]       ):



   """simple docstring"""





   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()






 def 		lowercase_(   self			:    Optional[Any]       ):



   """simple docstring"""





   __A		:     List[str]												=		load_numpy(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"""       )

   __A		:     Dict												=		load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/kandinskyv22/hint_image_cat.png"""       )
   __A		:     Union[str, Any]												=		torch.from_numpy(np.array(lowerCamelCase       )       ).float() / 255.0
   __A		:     Dict												=		hint.permute(2				,   0				,   1       ).unsqueeze(0       )

   __A		:     Any												=		KandinskyVaaPriorPipeline.from_pretrained(
       """kandinsky-community/kandinsky-2-2-prior"""				,   torch_dtype=torch.floataa       )
   pipe_prior.to(lowerCamelCase       )

   __A		:     Optional[int]												=		KandinskyVaaControlnetPipeline.from_pretrained(
       """kandinsky-community/kandinsky-2-2-controlnet-depth"""				,   torch_dtype=torch.floataa       )
   __A		:     Union[str, Any]												=		pipeline.to(lowerCamelCase       )
   pipeline.set_progress_bar_config(disable=lowerCamelCase       )

   __A		:     Any												=		"""A robot, 4k photo"""

   __A		:     List[str]												=		torch.Generator(device="""cuda"""       ).manual_seed(0       )
   __A		:     Optional[Any]												=		pipe_prior(
       lowerCamelCase				,   generator=lowerCamelCase				,   num_inference_steps=5				,   negative_prompt=""""""				,   ).to_tuple()

   __A		:     Optional[Any]												=		torch.Generator(device="""cuda"""       ).manual_seed(0       )
   __A		:     Optional[int]												=		pipeline(
       image_embeds=lowerCamelCase				,   negative_image_embeds=lowerCamelCase				,   hint=lowerCamelCase				,   generator=lowerCamelCase				,   num_inference_steps=1_00				,   output_type="""np"""				,   )

   __A		:     int												=		output.images[0]

   assert image.shape == (5_12, 5_12, 3)

   assert_mean_pixel_difference(lowerCamelCase				,   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()



