





'''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_       (    _lowerCAmelCase     ,						_lowerCAmelCase     ,						_lowerCAmelCase     ,						unittest.TestCase							):
     __A								=      StableDiffusionInpaintPipeline
     __A								=      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
     __A								=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     __A								=      frozenset(
         []							)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     __A								=      frozenset([]							)





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





        """simple docstring"""




        torch.manual_seed(0						)
        lowercase_      :str         =			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=lowercase     ,				)
        lowercase_      :str         =			PNDMScheduler(skip_prk_steps=lowercase						)
        torch.manual_seed(0						)
        lowercase_      :Optional[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     ,				sample_size=128     ,				)
        torch.manual_seed(0						)
        lowercase_      :Union[str, Any]         =			CLIPTextConfig(
            bos_token_id=0     ,				eos_token_id=2     ,				hidden_size=32     ,				intermediate_size=37     ,				layer_norm_eps=1e-0_5     ,				num_attention_heads=4     ,				num_hidden_layers=5     ,				pad_token_id=1     ,				vocab_size=1_000     ,				hidden_act="gelu"     ,				projection_dim=512     ,				)
        lowercase_      :Tuple         =			CLIPTextModel(lowercase						)
        lowercase_      :str         =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

        lowercase_      :Dict         =			{
            "unet": unet,
            "scheduler": scheduler,
            "vae": vae,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "safety_checker": None,
            "feature_extractor": None,
        }
        return components





     def 							lowercase__				(			self		: Tuple     ,				lowercase		: Optional[Any]     ,				lowercase		: str=0						):





        """simple docstring"""




        lowercase_      :List[Any]         =			floats_tensor((1, 3, 32, 32)     ,				rng=random.Random(lowercase						)						).to(lowercase						)
        lowercase_      :Optional[Any]         =			image.cpu().permute(0     ,				2     ,				3     ,				1						)[0]
        lowercase_      :int         =			Image.fromarray(np.uinta(lowercase						)						).convert("RGB"						).resize((64, 64)						)
        lowercase_      :Optional[int]         =			Image.fromarray(np.uinta(image + 4						)						).convert("RGB"						).resize((64, 64)						)
        if str(lowercase						).startswith("mps"						):
           lowercase_      :List[str]         =			torch.manual_seed(lowercase						)
        else:
           lowercase_      :Optional[int]         =			torch.Generator(device=lowercase						).manual_seed(lowercase						)
        lowercase_      :Optional[Any]         =			{
            "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 							lowercase__				(			self		: Optional[int]						):





        """simple docstring"""




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

        lowercase_      :List[Any]         =			self.get_dummy_inputs(lowercase						)
        lowercase_      :Optional[Any]         =			sd_pipe(**lowercase						).images
        lowercase_      :Any         =			image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        lowercase_      :int         =			np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76]						)

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







     def 							lowercase__				(			self		: List[str]						):





        """simple docstring"""




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







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





     def 							lowercase__				(			self		: Any						):





        """simple docstring"""




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





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





        """simple docstring"""




        lowercase_      :Optional[int]         =			load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/sd2-inpaint/init_image.png"						)
        lowercase_      :str         =			load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"						)
        lowercase_      :str         =			load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
            "/yellow_cat_sitting_on_a_park_bench.npy"						)

        lowercase_      :List[str]         =			"stabilityai/stable-diffusion-2-inpainting"
        lowercase_      :List[Any]         =			StableDiffusionInpaintPipeline.from_pretrained(lowercase     ,				safety_checker=lowercase						)
        pipe.to(lowercase						)
        pipe.set_progress_bar_config(disable=lowercase						)
        pipe.enable_attention_slicing()

        lowercase_      :Optional[Any]         =			"Face of a yellow cat, high resolution, sitting on a park bench"

        lowercase_      :int         =			torch.manual_seed(0						)
        lowercase_      :Optional[Any]         =			pipe(
            prompt=lowercase     ,				image=lowercase     ,				mask_image=lowercase     ,				generator=lowercase     ,				output_type="np"     ,				)
        lowercase_      :Optional[int]         =			output.images[0]

        assert image.shape == (512, 512, 3)
        assert np.abs(expected_image - image						).max() < 9e-3





     def 							lowercase__				(			self		: Optional[Any]						):





        """simple docstring"""




        lowercase_      :Optional[int]         =			load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/sd2-inpaint/init_image.png"						)
        lowercase_      :List[Any]         =			load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"						)
        lowercase_      :Any         =			load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
            "/yellow_cat_sitting_on_a_park_bench_fp16.npy"						)

        lowercase_      :Union[str, Any]         =			"stabilityai/stable-diffusion-2-inpainting"
        lowercase_      :List[Any]         =			StableDiffusionInpaintPipeline.from_pretrained(
            lowercase     ,				torch_dtype=torch.floataa     ,				safety_checker=lowercase     ,				)
        pipe.to(lowercase						)
        pipe.set_progress_bar_config(disable=lowercase						)
        pipe.enable_attention_slicing()

        lowercase_      :str         =			"Face of a yellow cat, high resolution, sitting on a park bench"

        lowercase_      :List[Any]         =			torch.manual_seed(0						)
        lowercase_      :Dict         =			pipe(
            prompt=lowercase     ,				image=lowercase     ,				mask_image=lowercase     ,				generator=lowercase     ,				output_type="np"     ,				)
        lowercase_      :Optional[Any]         =			output.images[0]

        assert image.shape == (512, 512, 3)
        assert np.abs(expected_image - image						).max() < 5e-1







     def 							lowercase__				(			self		: int						):





        """simple docstring"""




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

        lowercase_      :List[str]         =			load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/sd2-inpaint/init_image.png"						)
        lowercase_      :Any         =			load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"						)

        lowercase_      :Optional[Any]         =			"stabilityai/stable-diffusion-2-inpainting"
        lowercase_      :int         =			PNDMScheduler.from_pretrained(lowercase     ,				subfolder="scheduler"						)
        lowercase_      :List[Any]         =			StableDiffusionInpaintPipeline.from_pretrained(
            lowercase     ,				safety_checker=lowercase     ,				scheduler=lowercase     ,				torch_dtype=torch.floataa     ,				)
        pipe.to(lowercase						)
        pipe.set_progress_bar_config(disable=lowercase						)
        pipe.enable_attention_slicing(1						)
        pipe.enable_sequential_cpu_offload()

        lowercase_      :List[str]         =			"Face of a yellow cat, high resolution, sitting on a park bench"

        lowercase_      :Dict         =			torch.manual_seed(0						)
        lowercase_      :List[str]         =			pipe(
            prompt=lowercase     ,				image=lowercase     ,				mask_image=lowercase     ,				generator=lowercase     ,				num_inference_steps=2     ,				output_type="np"     ,				)

        lowercase_      :Optional[Any]         =			torch.cuda.max_memory_allocated()
        # make sure that less than 2.65 GB is allocated
        assert mem_bytes < 2.65 * 10**9









'''simple docstring'''


def 							UpperCAmelCase_  (						__lowerCamelCase : list			):
   if len(__lowerCamelCase			) <= 1:
      return lst

   lowercase_      :Optional[Any]         =			1

   while i < len(__lowerCamelCase			):
      if lst[i - 1] <= lst[i]:
         i += 1
      else:
         lowercase_      ,							lowercase_      :int         =			lst[i], lst[i - 1]
         i -= 1
         if i == 0:
            lowercase_      :Dict         =			1

   return lst


if __name__ == "__main__":
 lowerCAmelCase :					Any 							=input('''Enter numbers separated by a comma:\n''').strip()
 lowerCAmelCase :					List[str] 							=[int(item) for item in user_input.split(''',''')]
 print(gnome_sort(unsorted))


