


'''simple docstring'''






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_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    ):
 _lowerCAmelCase	   =		StableDiffusionInpaintPipeline
 _lowerCAmelCase	   =		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 _lowerCAmelCase	   =		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 _lowerCAmelCase	   =		frozenset(
     []    )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 _lowerCAmelCase	   =		frozenset([]    )
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      torch.manual_seed(0  )
      _a      =							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 ,			attention_head_dim=(2, 4) ,			use_linear_projection=__magic_name__ ,			)
      _a      =							PNDMScheduler(skip_prk_steps=__magic_name__  )
      torch.manual_seed(0  )
      _a      =							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 ,			sample_size=1_28 ,			)
      torch.manual_seed(0  )
      _a      =							CLIPTextConfig(
          bos_token_id=0 ,			eos_token_id=2 ,			hidden_size=32 ,			intermediate_size=37 ,			layer_norm_eps=1e-05 ,			num_attention_heads=4 ,			num_hidden_layers=5 ,			pad_token_id=1 ,			vocab_size=10_00 ,			hidden_act='gelu' ,			projection_dim=5_12 ,			)
      _a      =							CLIPTextModel(__magic_name__  )
      _a      =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

      _a      =							{
          'unet': unet,
          'scheduler': scheduler,
          'vae': vae,
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'safety_checker': None,
          'feature_extractor': None,
      }
      return components
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__=0  )   ->							List[Any]:
      # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
      _a      =							floats_tensor((1, 3, 32, 32) ,			rng=random.Random(__magic_name__  )  ).to(__magic_name__  )
      _a      =							image.cpu().permute(0 ,			2 ,			3 ,			1  )[0]
      _a      =							Image.fromarray(np.uinta(__magic_name__  )  ).convert('RGB'  ).resize((64, 64)  )
      _a      =							Image.fromarray(np.uinta(image + 4  )  ).convert('RGB'  ).resize((64, 64)  )
      if str(__magic_name__  ).startswith('mps'  ):
           _a      =							torch.manual_seed(__magic_name__  )
      else:
           _a      =							torch.Generator(device=__magic_name__  ).manual_seed(__magic_name__  )
      _a      =							{
          'prompt': 'A painting of a squirrel eating a burger',
          'image': init_image,
          'mask_image': mask_image,
          'generator': generator,
          'num_inference_steps': 2,
          'guidance_scale': 6.0,
          'output_type': 'numpy',
      }
      return inputs
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      _a      =							'cpu'  # ensure determinism for the device-dependent torch.Generator
      _a      =							self.get_dummy_components()
      _a      =							StableDiffusionInpaintPipeline(**__magic_name__  )
      _a      =							sd_pipe.to(__magic_name__  )
      sd_pipe.set_progress_bar_config(disable=__magic_name__  )

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

      assert image.shape == (1, 64, 64, 3)
      _a      =							np.array([0.4_7_2_7, 0.5_7_3_5, 0.3_9_4_1, 0.5_4_4_6, 0.5_9_2_6, 0.4_3_9_4, 0.5_0_6_2, 0.4_6_5_4, 0.4_4_7_6]  )

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




 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      super().test_inference_batch_single_identical(expected_max_diff=3e-3  )





@slow
@require_torch_gpu
class 	a       (       unittest.TestCase    ):
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      _a      =							load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/sd2-inpaint/init_image.png'  )
      _a      =							load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'  )
      _a      =							load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
          '/yellow_cat_sitting_on_a_park_bench.npy'  )

      _a      =							'stabilityai/stable-diffusion-2-inpainting'
      _a      =							StableDiffusionInpaintPipeline.from_pretrained(__magic_name__ ,			safety_checker=__magic_name__  )
      pipe.to(__magic_name__  )
      pipe.set_progress_bar_config(disable=__magic_name__  )
      pipe.enable_attention_slicing()

      _a      =							'Face of a yellow cat, high resolution, sitting on a park bench'

      _a      =							torch.manual_seed(0  )
      _a      =							pipe(
          prompt=__magic_name__ ,			image=__magic_name__ ,			mask_image=__magic_name__ ,			generator=__magic_name__ ,			output_type='np' ,			)
      _a      =							output.images[0]

      assert image.shape == (5_12, 5_12, 3)
      assert np.abs(expected_image - image  ).max() < 9e-3
 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      _a      =							load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/sd2-inpaint/init_image.png'  )
      _a      =							load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'  )
      _a      =							load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
          '/yellow_cat_sitting_on_a_park_bench_fp16.npy'  )

      _a      =							'stabilityai/stable-diffusion-2-inpainting'
      _a      =							StableDiffusionInpaintPipeline.from_pretrained(
          __magic_name__ ,			torch_dtype=torch.floataa ,			safety_checker=__magic_name__ ,			)
      pipe.to(__magic_name__  )
      pipe.set_progress_bar_config(disable=__magic_name__  )
      pipe.enable_attention_slicing()

      _a      =							'Face of a yellow cat, high resolution, sitting on a park bench'

      _a      =							torch.manual_seed(0  )
      _a      =							pipe(
          prompt=__magic_name__ ,			image=__magic_name__ ,			mask_image=__magic_name__ ,			generator=__magic_name__ ,			output_type='np' ,			)
      _a      =							output.images[0]

      assert image.shape == (5_12, 5_12, 3)
      assert np.abs(expected_image - image  ).max() < 5e-1




 def 							__UpperCAmelCase						(	self  )   ->							str:
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      _a      =							load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/sd2-inpaint/init_image.png'  )
      _a      =							load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'  )

      _a      =							'stabilityai/stable-diffusion-2-inpainting'
      _a      =							PNDMScheduler.from_pretrained(__magic_name__ ,			subfolder='scheduler'  )
      _a      =							StableDiffusionInpaintPipeline.from_pretrained(
          __magic_name__ ,			safety_checker=__magic_name__ ,			scheduler=__magic_name__ ,			torch_dtype=torch.floataa ,			)
      pipe.to(__magic_name__  )
      pipe.set_progress_bar_config(disable=__magic_name__  )
      pipe.enable_attention_slicing(1  )
      pipe.enable_sequential_cpu_offload()

      _a      =							'Face of a yellow cat, high resolution, sitting on a park bench'

      _a      =							torch.manual_seed(0  )
      _a      =							pipe(
          prompt=__magic_name__ ,			image=__magic_name__ ,			mask_image=__magic_name__ ,			generator=__magic_name__ ,			num_inference_steps=2 ,			output_type='np' ,			)

      _a      =							torch.cuda.max_memory_allocated()
      # make sure that less than 2.65 GB is allocated
      assert mem_bytes < 2.6_5 * 10**9





'''simple docstring'''






from __future__ import annotations






def 		_A			(lowerCAmelCase__					:int						)				->					list[int]:




     '''simple docstring'''



     _a      =							2
     _a      =							[]
     while i * i <= n:
          if n % i:
               i += 1
          else:
               n //= i
               factors.append(lowerCAmelCase__						)
     if n > 1:
          factors.append(lowerCAmelCase__						)
     return factors


if __name__ == "__main__":
 import doctest

 doctest.testmod()

