code: import unittest
  
  import numpy as np
  import torch
  
  from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
  
  
  enable_full_determinism()
  
  class 				_lowercase						(    unittest.TestCase ):
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      @property
      def     _a						(						self   ):
         torch.manual_seed(0   )
         lowerCAmelCase_:       Optional[int]         =  UNetaDModel(
             block_out_channels=(32, 64)					,	layers_per_block=2					,	sample_size=32					,	in_channels=3					,	out_channels=3					,	down_block_types=("DownBlock2D", "AttnDownBlock2D")					,	up_block_types=("AttnUpBlock2D", "UpBlock2D")					,	)
         return model
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       Tuple         =  "cpu"
         lowerCAmelCase_:       int         =  self.dummy_uncond_unet
         lowerCAmelCase_:       List[Any]         =  DDPMScheduler()
  
         lowerCAmelCase_:       Tuple         =  DDPMPipeline(unet=lowerCAmelCase__					,	scheduler=lowerCAmelCase__   )
         ddpm.to(lowerCAmelCase__   )
         ddpm.set_progress_bar_config(disable=lowerCAmelCase__   )
  
         lowerCAmelCase_:       List[str]         =  torch.Generator(device=lowerCAmelCase__   ).manual_seed(0   )
         lowerCAmelCase_:       Optional[int]         =  ddpm(generator=lowerCAmelCase__					,	num_inference_steps=2					,	output_type="numpy"   ).images
  
         lowerCAmelCase_:       Optional[Any]         =  torch.Generator(device=lowerCAmelCase__   ).manual_seed(0   )
         lowerCAmelCase_:       Dict         =  ddpm(generator=lowerCAmelCase__					,	num_inference_steps=2					,	output_type="numpy"					,	return_dict=lowerCAmelCase__   )[0]
  
         lowerCAmelCase_:       List[str]         =  image[0, -3:, -3:, -1]
         lowerCAmelCase_:       List[Any]         =  image_from_tuple[0, -3:, -3:, -1]
  
         assert image.shape == (1, 32, 32, 3)
         lowerCAmelCase_:       Optional[Any]         =  np.array(
             [9.956E-01, 5.785E-01, 4.675E-01, 9.930E-01, 0.0, 1.0_0_0, 1.199E-03, 2.648E-04, 5.101E-04]   )
  
         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       int         =  self.dummy_uncond_unet
         lowerCAmelCase_:       List[str]         =  DDPMScheduler(prediction_type="sample"   )
  
         lowerCAmelCase_:       Optional[int]         =  DDPMPipeline(unet=lowerCAmelCase__					,	scheduler=lowerCAmelCase__   )
         ddpm.to(lowerCAmelCase__   )
         ddpm.set_progress_bar_config(disable=lowerCAmelCase__   )
  
         lowerCAmelCase_:       str         =  torch.manual_seed(0   )
         lowerCAmelCase_:       List[str]         =  ddpm(generator=lowerCAmelCase__					,	num_inference_steps=2					,	output_type="numpy"   ).images
  
         lowerCAmelCase_:       Optional[Any]         =  torch.manual_seed(0   )
         lowerCAmelCase_:       Dict         =  ddpm(generator=lowerCAmelCase__					,	num_inference_steps=2					,	output_type="numpy"   )[0]
  
         lowerCAmelCase_:       Any         =  image[0, -3:, -3:, -1]
         lowerCAmelCase_:       Optional[Any]         =  image_eps[0, -3:, -3:, -1]
  
         assert image.shape == (1, 32, 32, 3)
         lowerCAmelCase_:       List[str]         =  1E-2 if torch_device != "mps" else 3E-2
         assert np.abs(image_slice.flatten() - image_eps_slice.flatten()   ).max() < tolerance
  
  
  
  
  
  @slow
  @require_torch_gpu
  class 				_lowercase						(    unittest.TestCase ):
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       int         =  "google/ddpm-cifar10-32"
  
         lowerCAmelCase_:       int         =  UNetaDModel.from_pretrained(lowerCAmelCase__   )
         lowerCAmelCase_:       Any         =  DDPMScheduler.from_pretrained(lowerCAmelCase__   )
  
         lowerCAmelCase_:       List[Any]         =  DDPMPipeline(unet=lowerCAmelCase__					,	scheduler=lowerCAmelCase__   )
         ddpm.to(lowerCAmelCase__   )
         ddpm.set_progress_bar_config(disable=lowerCAmelCase__   )
  
         lowerCAmelCase_:       List[Any]         =  torch.manual_seed(0   )
         lowerCAmelCase_:       Dict         =  ddpm(generator=lowerCAmelCase__					,	output_type="numpy"   ).images
  
         lowerCAmelCase_:       str         =  image[0, -3:, -3:, -1]
  
         assert image.shape == (1, 32, 32, 3)
         lowerCAmelCase_:       List[Any]         =  np.array([0.4_2_0_0, 0.3_5_8_8, 0.1_9_3_9, 0.3_8_4_7, 0.3_3_8_2, 0.2_6_4_7, 0.4_1_5_5, 0.3_5_8_2, 0.3_3_8_5]   )
         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
style_context: import random
  
  class 				_lowercase						:
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def __init__(						self					,	lowerCamelCase__					,	lowerCamelCase__   ):
         lowerCAmelCase_:       int         =  x
         lowerCAmelCase_:       List[str]         =  y
  
  
  
  
      def     _a						(						self   ):
         return (self.x**2 + self.y**2) <= 1
  
  
  
  
      @classmethod
      def     _a						(						cls   ):
         return cls(x=random.random()					,	y=random.random()   )
  
  
  
  
  
  def 		snake_case__  (		lowercase				):
     if number_of_simulations < 1:
        raise ValueError("At least one simulation is necessary to estimate PI."				)
  
     lowerCAmelCase_:       List[Any]         =  0
     for _ in range(lowercase				):
        lowerCAmelCase_:       Tuple         =  Point.random_unit_square()
  
        if random_point.is_in_unit_circle():
           number_in_unit_circle += 1
  
     return 4 * number_in_unit_circle / number_of_simulations
  
  
  if __name__ == "__main__":
   # import doctest
  
   # doctest.testmod()
   from math import pi
  
   a     :			Union[str, Any]					    =						"""Please enter the desired number of Monte Carlo simulations: """
   a     :			int					    =						estimate_pi(int(input(prompt).strip()))
   print(f'''An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}''')