





def _A   (    __magic_name__  ,					__magic_name__ = 1000       ):
   if (not isinstance(lowerCAmelCase__  ,					lowerCAmelCase__       )) or (digit_position <= 0):
      raise ValueError("Digit position must be a positive integer"       )
   elif (not isinstance(lowerCAmelCase__  ,					lowerCAmelCase__       )) or (precision < 0):
      raise ValueError("Precision must be a nonnegative integer"       )

   # compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
   # accurate
   lowercase__    =  (
       4 * _subsum(lowerCAmelCase__  ,					1  ,					lowerCAmelCase__       )
       - 2 * _subsum(lowerCAmelCase__  ,					4  ,					lowerCAmelCase__       )
       - _subsum(lowerCAmelCase__  ,					5  ,					lowerCAmelCase__       )
       - _subsum(lowerCAmelCase__  ,					6  ,					lowerCAmelCase__       )
   )

   # return the first hex digit of the fractional part of the result
   return hex(int((sum_result % 1) * 16       )       )[2:]


def _A   (    __magic_name__  ,					__magic_name__  ,					__magic_name__       ):
   lowercase__    =  0.0
   for sum_index in range(digit_pos_to_extract + precision       ):
      lowercase__    =  8 * sum_index + denominator_addend
      if sum_index < digit_pos_to_extract:
         # if the exponential term is an integer and we mod it by the denominator
         # before dividing, only the integer part of the sum will change;
         # the fractional part will not
         lowercase__    =  pow(
             16  ,					digit_pos_to_extract - 1 - sum_index  ,					lowerCAmelCase__       )
      else:
         lowercase__    =  pow(16  ,					digit_pos_to_extract - 1 - sum_index       )
      total += exponential_term / denominator
   return total


if __name__ == "__main__":
  import doctest

  doctest.testmod()







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    PNDMScheduler,
    StableDiffusionImageVariationPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class 				a_ (						SCREAMING_SNAKE_CASE       ,     SCREAMING_SNAKE_CASE       ,     SCREAMING_SNAKE_CASE       ,     unittest.TestCase						):






 '''simple docstring'''






 UpperCAmelCase_	 =					StableDiffusionImageVariationPipeline
 UpperCAmelCase_	 =					IMAGE_VARIATION_PARAMS
 UpperCAmelCase_	 =					IMAGE_VARIATION_BATCH_PARAMS
 UpperCAmelCase_	 =					frozenset([]						)
 # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 UpperCAmelCase_	 =					frozenset([]						)




 def 		__snake_case						(			self					:  Optional[Any]):







        '''simple docstring'''



        torch.manual_seed(0)
        lowerCAmelCase__     	=					UNetaDConditionModel(
            block_out_channels=(32, 64)			,    layers_per_block=2			,    sample_size=32			,    in_channels=4			,    out_channels=4			,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')			,    up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')			,    cross_attention_dim=32			,    )
        lowerCAmelCase__     	=					PNDMScheduler(skip_prk_steps=lowercase__)
        torch.manual_seed(0)
        lowerCAmelCase__     	=					AutoencoderKL(
            block_out_channels=[32, 64]			,    in_channels=3			,    out_channels=3			,    down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']			,    up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']			,    latent_channels=4			,    )
        torch.manual_seed(0)
        lowerCAmelCase__     	=					CLIPVisionConfig(
            hidden_size=32			,    projection_dim=32			,    intermediate_size=37			,    layer_norm_eps=1e-05			,    num_attention_heads=4			,    num_hidden_layers=5			,    image_size=32			,    patch_size=4			,    )
        lowerCAmelCase__     	=					CLIPVisionModelWithProjection(lowercase__)
        lowerCAmelCase__     	=					CLIPImageProcessor(crop_size=32			,    size=32)

        lowerCAmelCase__     	=					{
            'unet': unet,
            'scheduler': scheduler,
            'vae': vae,
            'image_encoder': image_encoder,
            'feature_extractor': feature_extractor,
            'safety_checker': None,
        }
        return components




 def 		__snake_case						(			self					:  Optional[int]			,    lowercase__					:  Dict			,    lowercase__					:  Tuple=0):







        '''simple docstring'''



        lowerCAmelCase__     	=					floats_tensor((1, 3, 32, 32)			,    rng=random.Random(lowercase__))
        lowerCAmelCase__     	=					image.cpu().permute(0			,    2			,    3			,    1)[0]
        lowerCAmelCase__     	=					Image.fromarray(np.uinta(lowercase__)).convert('RGB').resize((32, 32))
        if str(lowercase__).startswith('mps'):
               lowerCAmelCase__     	=					torch.manual_seed(lowercase__)
        else:
               lowerCAmelCase__     	=					torch.Generator(device=lowercase__).manual_seed(lowercase__)
        lowerCAmelCase__     	=					{
            'image': image,
            'generator': generator,
            'num_inference_steps': 2,
            'guidance_scale': 6.0,
            'output_type': 'numpy',
        }
        return inputs




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







        '''simple docstring'''



        lowerCAmelCase__     	=					'cpu'  # ensure determinism for the device-dependent torch.Generator
        lowerCAmelCase__     	=					self.get_dummy_components()
        lowerCAmelCase__     	=					StableDiffusionImageVariationPipeline(**lowercase__)
        lowerCAmelCase__     	=					sd_pipe.to(lowercase__)
        sd_pipe.set_progress_bar_config(disable=lowercase__)

        lowerCAmelCase__     	=					self.get_dummy_inputs(lowercase__)
        lowerCAmelCase__     	=					sd_pipe(**lowercase__).images
        lowerCAmelCase__     	=					image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        lowerCAmelCase__     	=					np.array([0.5_239, 0.5_723, 0.4_796, 0.5_049, 0.5_550, 0.4_685, 0.5_329, 0.4_891, 0.4_921])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3




 def 		__snake_case						(			self					:  Any):







        '''simple docstring'''



        lowerCAmelCase__     	=					'cpu'  # ensure determinism for the device-dependent torch.Generator
        lowerCAmelCase__     	=					self.get_dummy_components()
        lowerCAmelCase__     	=					StableDiffusionImageVariationPipeline(**lowercase__)
        lowerCAmelCase__     	=					sd_pipe.to(lowercase__)
        sd_pipe.set_progress_bar_config(disable=lowercase__)

        lowerCAmelCase__     	=					self.get_dummy_inputs(lowercase__)
        lowerCAmelCase__     	=					2 * [inputs['image']]
        lowerCAmelCase__     	=					sd_pipe(**lowercase__)

        lowerCAmelCase__     	=					output.images

        lowerCAmelCase__     	=					image[-1, -3:, -3:, -1]

        assert image.shape == (2, 64, 64, 3)
        lowerCAmelCase__     	=					np.array([0.6_892, 0.5_637, 0.5_836, 0.5_771, 0.6_254, 0.6_409, 0.5_580, 0.5_569, 0.5_289])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3




 def 		__snake_case						(			self					:  Optional[int]):







        '''simple docstring'''



        super().test_inference_batch_single_identical(expected_max_diff=3e-3)






@slow
@require_torch_gpu
class 				a_ (						unittest.TestCase						):






 '''simple docstring'''






 def 		__snake_case						(			self					:  List[Any]):







        '''simple docstring'''



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




 def 		__snake_case						(			self					:  List[Any]			,    lowercase__					:  List[str]			,    lowercase__					:  Optional[int]="cpu"			,    lowercase__					:  List[str]=torch.floataa			,    lowercase__					:  Dict=0):







        '''simple docstring'''



        lowerCAmelCase__     	=					torch.Generator(device=lowercase__).manual_seed(lowercase__)
        lowerCAmelCase__     	=					load_image(
            'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
            '/stable_diffusion_imgvar/input_image_vermeer.png')
        lowerCAmelCase__     	=					np.random.RandomState(lowercase__).standard_normal((1, 4, 64, 64))
        lowerCAmelCase__     	=					torch.from_numpy(lowercase__).to(device=lowercase__			,    dtype=lowercase__)
        lowerCAmelCase__     	=					{
            'image': init_image,
            'latents': latents,
            'generator': generator,
            'num_inference_steps': 3,
            'guidance_scale': 7.5,
            'output_type': 'numpy',
        }
        return inputs




 def 		__snake_case						(			self					:  List[str]):







        '''simple docstring'''



        lowerCAmelCase__     	=					StableDiffusionImageVariationPipeline.from_pretrained(
            'lambdalabs/sd-image-variations-diffusers'			,    safety_checker=lowercase__)
        lowerCAmelCase__     	=					sd_pipe.to(lowercase__)
        sd_pipe.set_progress_bar_config(disable=lowercase__)

        lowerCAmelCase__     	=					self.get_inputs(lowercase__)
        lowerCAmelCase__     	=					sd_pipe(**lowercase__).images
        lowerCAmelCase__     	=					image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 512, 512, 3)
        lowerCAmelCase__     	=					np.array([0.84_491, 0.90_789, 0.75_708, 0.78_734, 0.83_485, 0.70_099, 0.66_938, 0.68_727, 0.61_379])
        assert np.abs(image_slice - expected_slice).max() < 6e-3




 def 		__snake_case						(			self					:  Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__     	=					0

        def callback_fn(lowercase__					:  int			,    lowercase__					:  int			,    lowercase__					:  torch.FloatTensor) -> None:
               lowerCAmelCase__     	=					True
               nonlocal number_of_steps
               number_of_steps += 1
               if step == 1:
                      lowerCAmelCase__     	=					latents.detach().cpu().numpy()
                      assert latents.shape == (1, 4, 64, 64)
                      lowerCAmelCase__     	=					latents[0, -3:, -3:, -1]
                      lowerCAmelCase__     	=					np.array(
                          [-0.1_621, 0.2_837, -0.7_979, -0.1_221, -1.3_057, 0.7_681, -2.1_191, 0.0_464, 1.6_309])

                      assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
               elif step == 2:
                      lowerCAmelCase__     	=					latents.detach().cpu().numpy()
                      assert latents.shape == (1, 4, 64, 64)
                      lowerCAmelCase__     	=					latents[0, -3:, -3:, -1]
                      lowerCAmelCase__     	=					np.array([0.6_299, 1.7_500, 1.1_992, -2.1_582, -1.8_994, 0.7_334, -0.7_090, 1.0_137, 1.5_273])

                      assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2

        lowerCAmelCase__     	=					False

        lowerCAmelCase__     	=					StableDiffusionImageVariationPipeline.from_pretrained(
            'fusing/sd-image-variations-diffusers'			,    safety_checker=lowercase__			,    torch_dtype=torch.floataa			,    )
        pipe.to(lowercase__)
        pipe.set_progress_bar_config(disable=lowercase__)
        pipe.enable_attention_slicing()

        lowerCAmelCase__     	=					self.get_inputs(lowercase__			,    dtype=torch.floataa)
        pipe(**lowercase__			,    callback=lowercase__			,    callback_steps=1)
        assert callback_fn.has_been_called
        assert number_of_steps == inputs["num_inference_steps"]




 def 		__snake_case						(			self					:  str):







        '''simple docstring'''



        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        lowerCAmelCase__     	=					'fusing/sd-image-variations-diffusers'
        lowerCAmelCase__     	=					StableDiffusionImageVariationPipeline.from_pretrained(
            lowercase__			,    safety_checker=lowercase__			,    torch_dtype=torch.floataa)
        lowerCAmelCase__     	=					pipe.to(lowercase__)
        pipe.set_progress_bar_config(disable=lowercase__)
        pipe.enable_attention_slicing(1)
        pipe.enable_sequential_cpu_offload()

        lowerCAmelCase__     	=					self.get_inputs(lowercase__			,    dtype=torch.floataa)
        lowerCAmelCase__     	=					pipe(**lowercase__)

        lowerCAmelCase__     	=					torch.cuda.max_memory_allocated()
        # make sure that less than 2.6 GB is allocated
        assert mem_bytes < 2.6 * 10**9






@nightly
@require_torch_gpu
class 				a_ (						unittest.TestCase						):






 '''simple docstring'''






 def 		__snake_case						(			self					:  str):







        '''simple docstring'''



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




 def 		__snake_case						(			self					:  int			,    lowercase__					:  List[str]			,    lowercase__					:  Dict="cpu"			,    lowercase__					:  Optional[int]=torch.floataa			,    lowercase__					:  Optional[Any]=0):







        '''simple docstring'''



        lowerCAmelCase__     	=					torch.Generator(device=lowercase__).manual_seed(lowercase__)
        lowerCAmelCase__     	=					load_image(
            'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
            '/stable_diffusion_imgvar/input_image_vermeer.png')
        lowerCAmelCase__     	=					np.random.RandomState(lowercase__).standard_normal((1, 4, 64, 64))
        lowerCAmelCase__     	=					torch.from_numpy(lowercase__).to(device=lowercase__			,    dtype=lowercase__)
        lowerCAmelCase__     	=					{
            'image': init_image,
            'latents': latents,
            'generator': generator,
            'num_inference_steps': 50,
            'guidance_scale': 7.5,
            'output_type': 'numpy',
        }
        return inputs




 def 		__snake_case						(			self					:  Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__     	=					StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers')
        sd_pipe.to(lowercase__)
        sd_pipe.set_progress_bar_config(disable=lowercase__)

        lowerCAmelCase__     	=					self.get_inputs(lowercase__)
        lowerCAmelCase__     	=					sd_pipe(**lowercase__).images[0]

        lowerCAmelCase__     	=					load_numpy(
            'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
            '/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy')
        lowerCAmelCase__     	=					np.abs(expected_image - image).max()
        assert max_diff < 1e-3




 def 		__snake_case						(			self					:  Tuple):







        '''simple docstring'''



        lowerCAmelCase__     	=					StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers')
        lowerCAmelCase__     	=					DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config)
        sd_pipe.to(lowercase__)
        sd_pipe.set_progress_bar_config(disable=lowercase__)

        lowerCAmelCase__     	=					self.get_inputs(lowercase__)
        lowerCAmelCase__     	=					25
        lowerCAmelCase__     	=					sd_pipe(**lowercase__).images[0]

        lowerCAmelCase__     	=					load_numpy(
            'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
            '/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy')
        lowerCAmelCase__     	=					np.abs(expected_image - image).max()
        assert max_diff < 1e-3
