



'''simple docstring'''






import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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


enable_full_determinism()

class 							a__(      unittest.TestCase			):







    def     lowercase_ (   self				:						Optional[Any]			):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







    @property
    def     lowercase_ (   self				:						Dict			):
      a		:      Optional[Any]       =  1
      a		:      List[str]       =  3
      a		:      Optional[Any]       =  (32, 32)

      a		:      List[str]       =  floats_tensor((batch_size, num_channels) + sizes		,    rng=random.Random(0			)			).to(__snake_case			)
      return image







    @property
    def     lowercase_ (   self				:						int			):
      torch.manual_seed(0			)
      a		:      Tuple       =  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		,    )
      return model







    @property
    def     lowercase_ (   self				:						Dict			):
      torch.manual_seed(0			)
      a		:      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		,    )
      return model







    @property
    def     lowercase_ (   self				:						str			):
      torch.manual_seed(0			)
      a		:      Optional[Any]       =  RobertaSeriesConfig(
          hidden_size=32		,    project_dim=32		,    intermediate_size=37		,    layer_norm_eps=1e-0_5		,    num_attention_heads=4		,    num_hidden_layers=5		,    pad_token_id=1		,    vocab_size=50_06		,    )
      return RobertaSeriesModelWithTransformation(__snake_case			)







    @property
    def     lowercase_ (   self				:						Optional[Any]			):
      def extract(*__snake_case				:						Union[str, Any]		,    **__snake_case				:						List[Any]			):

        class 							a__:







            def __init__(   self				:						Optional[int]			):
              a		:      Dict       =  torch.ones([0]			)







            def     lowercase_ (   self				:						List[Any]		,    __snake_case				:						Tuple			):
              self.pixel_values.to(__snake_case			)
              return self
        return Out()

      return extract







    def     lowercase_ (   self				:						Dict			):
      a		:      Dict       =  'cpu'  # ensure determinism for the device-dependent torch.Generator
      a		:      Any       =  self.dummy_cond_unet
      a		:      str       =  PNDMScheduler(skip_prk_steps=__snake_case			)
      a		:      Any       =  self.dummy_vae
      a		:      List[str]       =  self.dummy_text_encoder
      a		:      List[str]       =  XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'			)
      a		:      Union[str, Any]       =  77

      a		:      List[Any]       =  self.dummy_image.to(__snake_case			)
      a		:      str       =  init_image / 2 + 0.5

      # make sure here that pndm scheduler skips prk
      a		:      Any       =  AltDiffusionImgaImgPipeline(
          unet=__snake_case		,    scheduler=__snake_case		,    vae=__snake_case		,    text_encoder=__snake_case		,    tokenizer=__snake_case		,    safety_checker=__snake_case		,    feature_extractor=self.dummy_extractor		,    )
      a		:      Optional[Any]       =  VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor		,    do_normalize=__snake_case			)
      a		:      Optional[Any]       =  alt_pipe.to(__snake_case			)
      alt_pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      Dict       =  'A painting of a squirrel eating a burger'
      a		:      Optional[int]       =  torch.Generator(device=__snake_case			).manual_seed(0			)
      a		:      int       =  alt_pipe(
          [prompt]		,    generator=__snake_case		,    guidance_scale=6.0		,    num_inference_steps=2		,    output_type='np'		,    image=__snake_case		,    )

      a		:      Optional[int]       =  output.images

      a		:      Optional[int]       =  torch.Generator(device=__snake_case			).manual_seed(0			)
      a		:      Union[str, Any]       =  alt_pipe(
          [prompt]		,    generator=__snake_case		,    guidance_scale=6.0		,    num_inference_steps=2		,    output_type='np'		,    image=__snake_case		,    return_dict=__snake_case		,    )[0]

      a		:      Optional[Any]       =  image[0, -3:, -3:, -1]
      a		:      Tuple       =  image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      a		:      str       =  np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]			)

      assert np.abs(image_slice.flatten() - expected_slice			).max() < 5e-3
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 5e-3







    @unittest.skipIf(torch_device != 'cuda'		,    'This test requires a GPU'			)
    def     lowercase_ (   self				:						Union[str, Any]			):
      a		:      int       =  self.dummy_cond_unet
      a		:      Optional[Any]       =  PNDMScheduler(skip_prk_steps=__snake_case			)
      a		:      Union[str, Any]       =  self.dummy_vae
      a		:      List[Any]       =  self.dummy_text_encoder
      a		:      Optional[int]       =  XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'			)
      a		:      int       =  77

      a		:      int       =  self.dummy_image.to(__snake_case			)

      # put models in fp16
      a		:      List[str]       =  unet.half()
      a		:      Optional[Any]       =  vae.half()
      a		:      int       =  bert.half()

      # make sure here that pndm scheduler skips prk
      a		:      List[str]       =  AltDiffusionImgaImgPipeline(
          unet=__snake_case		,    scheduler=__snake_case		,    vae=__snake_case		,    text_encoder=__snake_case		,    tokenizer=__snake_case		,    safety_checker=__snake_case		,    feature_extractor=self.dummy_extractor		,    )
      a		:      Union[str, Any]       =  VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor		,    do_normalize=__snake_case			)
      a		:      Tuple       =  alt_pipe.to(__snake_case			)
      alt_pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      Optional[Any]       =  'A painting of a squirrel eating a burger'
      a		:      Dict       =  torch.manual_seed(0			)
      a		:      Tuple       =  alt_pipe(
          [prompt]		,    generator=__snake_case		,    num_inference_steps=2		,    output_type='np'		,    image=__snake_case		,    ).images

      assert image.shape == (1, 32, 32, 3)







    @unittest.skipIf(torch_device != 'cuda'		,    'This test requires a GPU'			)
    def     lowercase_ (   self				:						Tuple			):
      a		:      Union[str, Any]       =  load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/img2img/sketch-mountains-input.jpg'			)
      # resize to resolution that is divisible by 8 but not 16 or 32
      a		:      str       =  init_image.resize((7_60, 5_04)			)

      a		:      Tuple       =  'BAAI/AltDiffusion'
      a		:      int       =  AltDiffusionImgaImgPipeline.from_pretrained(
          __snake_case		,    safety_checker=__snake_case		,    )
      pipe.to(__snake_case			)
      pipe.set_progress_bar_config(disable=__snake_case			)
      pipe.enable_attention_slicing()

      a		:      List[str]       =  'A fantasy landscape, trending on artstation'

      a		:      List[str]       =  torch.manual_seed(0			)
      a		:      List[str]       =  pipe(
          prompt=__snake_case		,    image=__snake_case		,    strength=0.75		,    guidance_scale=7.5		,    generator=__snake_case		,    output_type='np'		,    )
      a		:      Optional[int]       =  output.images[0]

      a		:      Any       =  image[2_55:2_58, 3_83:3_86, -1]

      assert image.shape == (5_04, 7_60, 3)
      a		:      int       =  np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]			)

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

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







    def     lowercase_ (   self				:						Optional[int]			):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







    def     lowercase_ (   self				:						int			):
      a		:      Optional[int]       =  load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/img2img/sketch-mountains-input.jpg'			)
      a		:      int       =  init_image.resize((7_68, 5_12)			)
      a		:      int       =  load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy'			)

      a		:      Dict       =  'BAAI/AltDiffusion'
      a		:      Optional[int]       =  AltDiffusionImgaImgPipeline.from_pretrained(
          __snake_case		,    safety_checker=__snake_case		,    )
      pipe.to(__snake_case			)
      pipe.set_progress_bar_config(disable=__snake_case			)
      pipe.enable_attention_slicing()

      a		:      str       =  'A fantasy landscape, trending on artstation'

      a		:      int       =  torch.manual_seed(0			)
      a		:      Tuple       =  pipe(
          prompt=__snake_case		,    image=__snake_case		,    strength=0.75		,    guidance_scale=7.5		,    generator=__snake_case		,    output_type='np'		,    )
      a		:      List[str]       =  output.images[0]

      assert image.shape == (5_12, 7_68, 3)
      # img2img is flaky across GPUs even in fp32, so using MAE here
      assert np.abs(expected_image - image			).max() < 1e-2




'''simple docstring'''






def    lowerCamelCase__ (		_A = 6008_5147_5143       ):

  try:
    a		:      Optional[int]       =  int(_A       )
  except (TypeError, ValueError):
    raise TypeError('Parameter n must be int or castable to int.'       )
  if n <= 0:
    raise ValueError('Parameter n must be greater than or equal to one.'       )
  a		:      Any       =  1
  a		:      Union[str, Any]       =  2
  while i * i <= n:
    while n % i == 0:
      a		:      str       =  i
      n //= i
    i += 1
  if n > 1:
    a		:      Any       =  n
  return int(_A       )


if __name__ == "__main__":
     print(F"{solution() = }")