




"""simple docstring"""
import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class  _SCREAMING_SNAKE_CASE       (     SCREAMING_SNAKE_CASE__			,				unittest.TestCase				):



 '''simple docstring'''





 __UpperCAmelCase		   =							RePaintPipeline
 __UpperCAmelCase		   =							IMAGE_INPAINTING_PARAMS - {"""width""", """height""", """guidance_scale"""}
 __UpperCAmelCase		   =							PipelineTesterMixin.required_optional_params - {
     """latents""",
     """num_images_per_prompt""",
     """callback""",
     """callback_steps""",
 }
 __UpperCAmelCase		   =							IMAGE_INPAINTING_BATCH_PARAMS
 __UpperCAmelCase		   =							False






 def lowercase_      (self   ):

  '''simple docstring'''







  torch.manual_seed(0   )
  torch.manual_seed(0   )
  _UpperCamelCase      :	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")					,     )
  _UpperCamelCase      :	Dict					      =  RePaintScheduler()
  _UpperCamelCase      :	int					      =  {"unet": unet, "scheduler": scheduler}
  return components






 def lowercase_      (self					,     lowerCAmelCase__					,     lowerCAmelCase__=0   ):

  '''simple docstring'''







  if str(lowerCAmelCase__   ).startswith("mps"   ):
   _UpperCamelCase      :	List[str]					      =  torch.manual_seed(lowerCAmelCase__   )
  else:
   _UpperCamelCase      :	Dict					      =  torch.Generator(device=lowerCAmelCase__   ).manual_seed(lowerCAmelCase__   )
  _UpperCamelCase      :	int					      =  np.random.RandomState(lowerCAmelCase__   ).standard_normal((1, 3, 32, 32)   )
  _UpperCamelCase      :	int					      =  torch.from_numpy(lowerCAmelCase__   ).to(device=lowerCAmelCase__					,     dtype=torch.floataa   )
  _UpperCamelCase      :	Optional[Any]					      =  (image > 0).to(device=lowerCAmelCase__					,     dtype=torch.floataa   )
  _UpperCamelCase      :	Any					      =  {
      "image": image,
      "mask_image": mask,
      "generator": generator,
      "num_inference_steps": 5,
      "eta": 0.0,
      "jump_length": 2,
      "jump_n_sample": 2,
      "output_type": "numpy",
  }
  return inputs






 def lowercase_      (self   ):

  '''simple docstring'''







  _UpperCamelCase      :	int					      =  "cpu"  # ensure determinism for the device-dependent torch.Generator
  _UpperCamelCase      :	List[Any]					      =  self.get_dummy_components()
  _UpperCamelCase      :	List[Any]					      =  RePaintPipeline(**lowerCAmelCase__   )
  _UpperCamelCase      :	Any					      =  sd_pipe.to(lowerCAmelCase__   )
  sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__   )

  _UpperCamelCase      :	Union[str, Any]					      =  self.get_dummy_inputs(lowerCAmelCase__   )
  _UpperCamelCase      :	int					      =  sd_pipe(**lowerCAmelCase__   ).images
  _UpperCamelCase      :	Dict					      =  image[0, -3:, -3:, -1]

  assert image.shape == (1, 32, 32, 3)
  _UpperCamelCase      :	Tuple					      =  np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]   )

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






 @skip_mps
 def lowercase_      (self   ):

  '''simple docstring'''







  return super().test_save_load_local()






 @unittest.skip("non-deterministic pipeline"   )
 def lowercase_      (self   ):

  '''simple docstring'''







  return super().test_inference_batch_single_identical()






 @skip_mps
 def lowercase_      (self   ):

  '''simple docstring'''







  return super().test_dict_tuple_outputs_equivalent()






 @skip_mps
 def lowercase_      (self   ):

  '''simple docstring'''







  return super().test_save_load_optional_components()






 @skip_mps
 def lowercase_      (self   ):

  '''simple docstring'''







  return super().test_attention_slicing_forward_pass()







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



 '''simple docstring'''





 def lowercase_      (self   ):

  '''simple docstring'''







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






 def lowercase_      (self   ):

  '''simple docstring'''







  _UpperCamelCase      :	Union[str, Any]					      =  load_image(
      "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
      "repaint/celeba_hq_256.png"   )
  _UpperCamelCase      :	int					      =  load_image(
      "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"   )
  _UpperCamelCase      :	Tuple					      =  load_numpy(
      "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
      "repaint/celeba_hq_256_result.npy"   )

  _UpperCamelCase      :	Dict					      =  "google/ddpm-ema-celebahq-256"
  _UpperCamelCase      :	int					      =  UNetaDModel.from_pretrained(lowerCAmelCase__   )
  _UpperCamelCase      :	List[str]					      =  RePaintScheduler.from_pretrained(lowerCAmelCase__   )

  _UpperCamelCase      :	int					      =  RePaintPipeline(unet=lowerCAmelCase__					,     scheduler=lowerCAmelCase__   ).to(lowerCAmelCase__   )
  repaint.set_progress_bar_config(disable=lowerCAmelCase__   )
  repaint.enable_attention_slicing()

  _UpperCamelCase      :	str					      =  torch.manual_seed(0   )
  _UpperCamelCase      :	Optional[int]					      =  repaint(
      lowerCAmelCase__					,     lowerCAmelCase__					,     num_inference_steps=2_50					,     eta=0.0					,     jump_length=10					,     jump_n_sample=10					,     generator=lowerCAmelCase__					,     output_type="np"					,     )
  _UpperCamelCase      :	Optional[int]					      =  output.images[0]

  assert image.shape == (2_56, 2_56, 3)
  assert np.abs(expected_image - image   ).mean() < 1E-2









"""simple docstring"""
import math






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Tuple						)    ->       Any:
 _UpperCamelCase      :	str					      =  0
 if n % 2 == 0:
  count += 1
  while n % 2 == 0:
   _UpperCamelCase      :	List[Any]					      =  int(n / 2						)
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

 _UpperCamelCase      :	Tuple					      =  3

 while i <= int(math.sqrt(__lowerCAmelCase						)						):
  if n % i == 0:
   count += 1
   while n % i == 0:
    _UpperCamelCase      :	Any					      =  int(n / i						)
  _UpperCamelCase      :	Optional[Any]					      =  i + 2

 # this condition checks the prime
 # number n is greater than 2

 if n > 2:
  count += 1
 return count


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE						 =     5_1_2_4_2_1_8_3
    print(f'The number of distinct prime factors is/are {exact_prime_factor_count(n)}')
    print(f'The value of log(log(n)) is {math.log(math.log(n)):.4f}')



