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

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

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class _lowerCAmelCase			(	snake_case							,   unittest.TestCase					):
      UpperCAmelCase_	:						Tuple	 =       PaintByExamplePipeline
      UpperCAmelCase_	:						str	 =       IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
      UpperCAmelCase_	:						Any	 =       IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      UpperCAmelCase_	:						Optional[Any]	 =       frozenset([]					)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess







      def      lowerCamelCase__      (  self     ):
        torch.manual_seed(0     )
        lowercase				:							Union[str, Any]									=   UNetaDConditionModel(
            block_out_channels=(32, 64)  ,    layers_per_block=2  ,    sample_size=32  ,    in_channels=9  ,    out_channels=4  ,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')  ,    up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')  ,    cross_attention_dim=32  ,    )
        lowercase				:							Union[str, Any]									=   PNDMScheduler(skip_prk_steps=lowercase     )
        torch.manual_seed(0     )
        lowercase				:							Union[str, Any]									=   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     )
        lowercase				:							List[Any]									=   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  ,    )
        lowercase				:							Optional[int]									=   PaintByExampleImageEncoder(lowercase  ,    proj_size=32     )
        lowercase				:							Union[str, Any]									=   CLIPImageProcessor(crop_size=32  ,    size=32     )

        lowercase				:							Union[str, Any]									=   {
            '''unet''': unet,
            '''scheduler''': scheduler,
            '''vae''': vae,
            '''image_encoder''': image_encoder,
            '''safety_checker''': None,
            '''feature_extractor''': feature_extractor,
        }
        return components







      def      lowerCamelCase__      (  self  ,    lowercase     ):
        lowercase				:							Union[str, Any]									=   np.array(image.convert('''RGB'''     )     )
        lowercase				:							List[str]									=   image[None].transpose(0  ,    3  ,    1  ,    2     )
        lowercase				:							Any									=   torch.from_numpy(lowercase     ).to(dtype=torch.floataa     ) / 127.5 - 1.0
        return image







      def      lowerCamelCase__      (  self  ,    lowercase="cpu"  ,    lowercase=0     ):
        # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
        lowercase				:							Tuple									=   floats_tensor((1, 3, 32, 32)  ,    rng=random.Random(lowercase     )     ).to(lowercase     )
        lowercase				:							List[Any]									=   image.cpu().permute(0  ,    2  ,    3  ,    1     )[0]
        lowercase				:							List[Any]									=   Image.fromarray(np.uinta(lowercase     )     ).convert('''RGB'''     ).resize((64, 64)     )
        lowercase				:							List[str]									=   Image.fromarray(np.uinta(image + 4     )     ).convert('''RGB'''     ).resize((64, 64)     )
        lowercase				:							str									=   Image.fromarray(np.uinta(lowercase     )     ).convert('''RGB'''     ).resize((32, 32)     )

        if str(lowercase     ).startswith('''mps'''     ):
          lowercase				:							Optional[Any]									=   torch.manual_seed(lowercase     )
        else:
          lowercase				:							Optional[int]									=   torch.Generator(device=lowercase     ).manual_seed(lowercase     )
        lowercase				:							int									=   {
            '''example_image''': example_image,
            '''image''': init_image,
            '''mask_image''': mask_image,
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''guidance_scale''': 6.0,
            '''output_type''': '''numpy''',
        }
        return inputs







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Dict									=   self.get_dummy_components()

        # make sure here that pndm scheduler skips prk
        lowercase				:							int									=   PaintByExamplePipeline(**lowercase     )
        lowercase				:							List[Any]									=   pipe.to('''cpu'''     )
        pipe.set_progress_bar_config(disable=lowercase     )

        lowercase				:							Optional[int]									=   self.get_dummy_inputs()
        lowercase				:							Optional[int]									=   pipe(**lowercase     )
        lowercase				:							Optional[int]									=   output.images

        lowercase				:							str									=   image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        lowercase				:							Tuple									=   np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503]     )

        assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Optional[Any]									=   '''cpu'''
        lowercase				:							int									=   self.get_dummy_inputs()
        inputs.pop('''mask_image'''     )
        lowercase				:							int									=   self.convert_to_pt(inputs.pop('''image'''     )     )
        lowercase				:							Tuple									=   image.clamp(0  ,    1     ) / 2

        # make sure here that pndm scheduler skips prk
        lowercase				:							Optional[Any]									=   PaintByExamplePipeline(**self.get_dummy_components()     )
        lowercase				:							List[str]									=   pipe.to(lowercase     )
        pipe.set_progress_bar_config(disable=lowercase     )

        lowercase				:							Any									=   pipe(image=lowercase  ,    mask_image=mask_image[:, 0]  ,    **lowercase     )
        lowercase				:							Union[str, Any]									=   output.images

        lowercase				:							Tuple									=   image.cpu().permute(0  ,    2  ,    3  ,    1     )[0]
        lowercase				:							Dict									=   mask_image.cpu().permute(0  ,    2  ,    3  ,    1     )[0]

        lowercase				:							List[Any]									=   Image.fromarray(np.uinta(lowercase     )     ).convert('''RGB'''     )
        lowercase				:							Any									=   Image.fromarray(np.uinta(lowercase     )     ).convert('''RGB'''     )

        lowercase				:							Tuple									=   pipe(**self.get_dummy_inputs()     )
        lowercase				:							Optional[int]									=   output.images

        assert out_a.shape == (1, 64, 64, 3)
        assert np.abs(out_a.flatten() - out_a.flatten()     ).max() < 5e-2







      def      lowerCamelCase__      (  self     ):
        super().test_inference_batch_single_identical(expected_max_diff=3e-3     )





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







      def      lowerCamelCase__      (  self     ):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







      def      lowerCamelCase__      (  self     ):
        # make sure here that pndm scheduler skips prk
        lowercase				:							int									=   load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/paint_by_example/dog_in_bucket.png'''     )
        lowercase				:							Optional[int]									=   load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/paint_by_example/mask.png'''     )
        lowercase				:							List[Any]									=   load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/paint_by_example/panda.jpg'''     )

        lowercase				:							Any									=   PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example'''     )
        lowercase				:							Optional[int]									=   pipe.to(lowercase     )
        pipe.set_progress_bar_config(disable=lowercase     )

        lowercase				:							List[str]									=   torch.manual_seed(321     )
        lowercase				:							str									=   pipe(
            image=lowercase  ,    mask_image=lowercase  ,    example_image=lowercase  ,    generator=lowercase  ,    guidance_scale=5.0  ,    num_inference_steps=50  ,    output_type='''np'''  ,    )

        lowercase				:							Union[str, Any]									=   output.images

        lowercase				:							str									=   image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        lowercase				:							str									=   np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374]     )

        assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2
'''simple docstring'''
import math







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

  lowercase				:							Dict									=   3

  while i <= int(math.sqrt(__UpperCAmelCase			)			):
    if n % i == 0:
      count += 1
      while n % i == 0:
        lowercase				:							Dict									=   int(n / i			)
    lowercase				:							List[Any]									=   i + 2

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

  if n > 2:
    count += 1
  return count


if __name__ == "__main__":
 _A		:      List[Any]						   =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}""")