


"""simple docstring"""







import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()


class 				SCREAMING_SNAKE_CASE__    (		unittest.TestCase    ):
							"""simple docstring"""


							a  :    Any       =StableDiffusionLDMaDPipeline
							a  :    Union[str, Any]       =TEXT_TO_IMAGE_PARAMS
							a  :    Union[str, Any]       =TEXT_TO_IMAGE_BATCH_PARAMS
							a  :    Any       =TEXT_TO_IMAGE_IMAGE_PARAMS

							def 	lowercase__							(						self						):






									"""simple docstring"""





									torch.manual_seed(0						)
									lowerCAmelCase					:					str								=							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		,					)
									lowerCAmelCase					:					Dict								=							DDIMScheduler(
									    beta_start=0.00085		,					beta_end=0.012		,					beta_schedule="scaled_linear"		,					clip_sample=snake_case__		,					set_alpha_to_one=snake_case__		,					)
									torch.manual_seed(0						)
									lowerCAmelCase					:					Dict								=							AutoencoderKL(
									    block_out_channels=[32, 64]		,					in_channels=6		,					out_channels=6		,					down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]		,					up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]		,					latent_channels=4		,					)
									torch.manual_seed(0						)
									lowerCAmelCase					:					Dict								=							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=1_000		,					)
									lowerCAmelCase					:					int								=							CLIPTextModel(snake_case__						)
									lowerCAmelCase					:					Optional[int]								=							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

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

							def 	lowercase__							(						self		,					snake_case__		,					snake_case__=0						):






									"""simple docstring"""





									if str(snake_case__						).startswith("mps"						):
											lowerCAmelCase					:					int								=							torch.manual_seed(snake_case__						)
									else:
											lowerCAmelCase					:					Dict								=							torch.Generator(device=snake_case__						).manual_seed(snake_case__						)
									lowerCAmelCase					:					Dict								=							{
									    "prompt": "A painting of a squirrel eating a burger",
									    "generator": generator,
									    "num_inference_steps": 2,
									    "guidance_scale": 6.0,
									    "output_type": "numpy",
									}
									return inputs

							def 	lowercase__							(						self						):






									"""simple docstring"""





									lowerCAmelCase					:					Dict								=							"cpu"  # ensure determinism for the device-dependent torch.Generator

									lowerCAmelCase					:					Union[str, Any]								=							self.get_dummy_components()
									lowerCAmelCase					:					Tuple								=							StableDiffusionLDMaDPipeline(**snake_case__						)
									lowerCAmelCase					:					List[str]								=							ldmad_pipe.to(snake_case__						)
									ldmad_pipe.set_progress_bar_config(disable=snake_case__						)

									lowerCAmelCase					:					Optional[int]								=							self.get_dummy_inputs(snake_case__						)
									lowerCAmelCase					:					List[Any]								=							ldmad_pipe(**snake_case__						)
									lowerCAmelCase					:					Union[str, Any]								=							output.rgb, output.depth

									lowerCAmelCase					:					List[str]								=							rgb[0, -3:, -3:, -1]
									lowerCAmelCase					:					Optional[int]								=							depth[0, -3:, -1]

									assert rgb.shape == (1, 64, 64, 3)
									assert depth.shape == (1, 64, 64)

									lowerCAmelCase					:					Any								=							np.array(
									    [0.37338176, 0.70247, 0.74203193, 0.51643604, 0.58256793, 0.60932136, 0.4181095, 0.48355877, 0.46535262]						)
									lowerCAmelCase					:					Any								=							np.array([103.46727, 85.812004, 87.849236]						)

									assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb						).max() < 1e-2
									assert np.abs(image_slice_depth.flatten() - expected_slice_depth						).max() < 1e-2

							def 	lowercase__							(						self						):






									"""simple docstring"""





									lowerCAmelCase					:					int								=							self.get_dummy_components()
									lowerCAmelCase					:					int								=							StableDiffusionLDMaDPipeline(**snake_case__						)
									lowerCAmelCase					:					Union[str, Any]								=							ldmad_pipe.to(snake_case__						)
									ldmad_pipe.set_progress_bar_config(disable=snake_case__						)

									lowerCAmelCase					:					Optional[Any]								=							self.get_dummy_inputs(snake_case__						)
									lowerCAmelCase					:					int								=							3 * [inputs["prompt"]]

									# forward
									lowerCAmelCase					:					str								=							ldmad_pipe(**snake_case__						)
									lowerCAmelCase					:					Union[str, Any]								=							output.rgb, output.depth
									lowerCAmelCase					:					Any								=							rgb_slice_a[0, -3:, -3:, -1]
									lowerCAmelCase					:					str								=							depth_slice_a[0, -3:, -1]

									lowerCAmelCase					:					Optional[Any]								=							self.get_dummy_inputs(snake_case__						)
									lowerCAmelCase					:					Union[str, Any]								=							3 * [inputs.pop("prompt"						)]

									lowerCAmelCase					:					List[Any]								=							ldmad_pipe.tokenizer(
									    snake_case__		,					padding="max_length"		,					max_length=ldmad_pipe.tokenizer.model_max_length		,					truncation=snake_case__		,					return_tensors="pt"		,					)
									lowerCAmelCase					:					Tuple								=							text_inputs["input_ids"].to(snake_case__						)

									lowerCAmelCase					:					List[Any]								=							ldmad_pipe.text_encoder(snake_case__						)[0]

									lowerCAmelCase					:					Optional[int]								=							prompt_embeds

									# forward
									lowerCAmelCase					:					List[Any]								=							ldmad_pipe(**snake_case__						)
									lowerCAmelCase					:					Optional[int]								=							output.rgb, output.depth
									lowerCAmelCase					:					List[str]								=							rgb_slice_a[0, -3:, -3:, -1]
									lowerCAmelCase					:					Optional[int]								=							depth_slice_a[0, -3:, -1]

									assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()						).max() < 1e-4
									assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()						).max() < 1e-4







							def 	lowercase__							(						self						):






									"""simple docstring"""





									lowerCAmelCase					:					Union[str, Any]								=							"cpu"  # ensure determinism for the device-dependent torch.Generator
									lowerCAmelCase					:					Tuple								=							self.get_dummy_components()
									lowerCAmelCase					:					str								=							PNDMScheduler(skip_prk_steps=snake_case__						)
									lowerCAmelCase					:					Union[str, Any]								=							StableDiffusionLDMaDPipeline(**snake_case__						)
									lowerCAmelCase					:					Union[str, Any]								=							ldmad_pipe.to(snake_case__						)
									ldmad_pipe.set_progress_bar_config(disable=snake_case__						)

									lowerCAmelCase					:					Any								=							self.get_dummy_inputs(snake_case__						)
									lowerCAmelCase					:					Optional[Any]								=							"french fries"
									lowerCAmelCase					:					Optional[int]								=							ldmad_pipe(**snake_case__		,					negative_prompt=snake_case__						)

									lowerCAmelCase					:					Any								=							output.rgb, output.depth
									lowerCAmelCase					:					Optional[int]								=							rgb[0, -3:, -3:, -1]
									lowerCAmelCase					:					Optional[Any]								=							depth[0, -3:, -1]

									assert rgb.shape == (1, 64, 64, 3)
									assert depth.shape == (1, 64, 64)

									lowerCAmelCase					:					Dict								=							np.array(
									    [0.37044, 0.71811503, 0.7223251, 0.48603675, 0.5638391, 0.6364948, 0.42833704, 0.4901315, 0.47926217]						)
									lowerCAmelCase					:					List[str]								=							np.array([107.84738, 84.62802, 89.962135]						)
									assert np.abs(rgb_slice.flatten() - expected_slice_rgb						).max() < 1e-2
									assert np.abs(depth_slice.flatten() - expected_slice_depth						).max() < 1e-2


@slow
@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		,					snake_case__		,					snake_case__="cpu"		,					snake_case__=torch.floataa		,					snake_case__=0						):






									"""simple docstring"""





									lowerCAmelCase					:					int								=							torch.Generator(device=snake_case__						).manual_seed(snake_case__						)
									lowerCAmelCase					:					List[Any]								=							np.random.RandomState(snake_case__						).standard_normal((1, 4, 64, 64)						)
									lowerCAmelCase					:					str								=							torch.from_numpy(snake_case__						).to(device=snake_case__		,					dtype=snake_case__						)
									lowerCAmelCase					:					List[str]								=							{
									    "prompt": "a photograph of an astronaut riding a horse",
									    "latents": latents,
									    "generator": generator,
									    "num_inference_steps": 3,
									    "guidance_scale": 7.5,
									    "output_type": "numpy",
									}
									return inputs







							def 	lowercase__							(						self						):






									"""simple docstring"""





									lowerCAmelCase					:					Any								=							StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d"						)
									lowerCAmelCase					:					List[Any]								=							ldmad_pipe.to(snake_case__						)
									ldmad_pipe.set_progress_bar_config(disable=snake_case__						)

									lowerCAmelCase					:					Union[str, Any]								=							self.get_inputs(snake_case__						)
									lowerCAmelCase					:					int								=							ldmad_pipe(**snake_case__						)
									lowerCAmelCase					:					Tuple								=							output.rgb, output.depth
									lowerCAmelCase					:					Dict								=							rgb[0, -3:, -3:, -1].flatten()
									lowerCAmelCase					:					str								=							rgb[0, -3:, -1].flatten()

									assert rgb.shape == (1, 512, 512, 3)
									assert depth.shape == (1, 512, 512)

									lowerCAmelCase					:					Any								=							np.array(
									    [0.53805465, 0.56707305, 0.5486515, 0.57012236, 0.5814511, 0.56253487, 0.54843014, 0.55092263, 0.6459706]						)
									lowerCAmelCase					:					int								=							np.array(
									    [0.9263781, 0.6678672, 0.5486515, 0.92202145, 0.67831135, 0.56253487, 0.9241694, 0.7551478, 0.6459706]						)
									assert np.abs(rgb_slice - expected_slice_rgb						).max() < 3e-3
									assert np.abs(depth_slice - expected_slice_depth						).max() < 3e-3


@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		,					snake_case__		,					snake_case__="cpu"		,					snake_case__=torch.floataa		,					snake_case__=0						):






									"""simple docstring"""





									lowerCAmelCase					:					Optional[int]								=							torch.Generator(device=snake_case__						).manual_seed(snake_case__						)
									lowerCAmelCase					:					Tuple								=							np.random.RandomState(snake_case__						).standard_normal((1, 4, 64, 64)						)
									lowerCAmelCase					:					List[Any]								=							torch.from_numpy(snake_case__						).to(device=snake_case__		,					dtype=snake_case__						)
									lowerCAmelCase					:					Union[str, Any]								=							{
									    "prompt": "a photograph of an astronaut riding a horse",
									    "latents": latents,
									    "generator": generator,
									    "num_inference_steps": 50,
									    "guidance_scale": 7.5,
									    "output_type": "numpy",
									}
									return inputs

							def 	lowercase__							(						self						):






									"""simple docstring"""





									lowerCAmelCase					:					Any								=							StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d"						).to(snake_case__						)
									ldmad_pipe.set_progress_bar_config(disable=snake_case__						)

									lowerCAmelCase					:					Any								=							self.get_inputs(snake_case__						)
									lowerCAmelCase					:					Tuple								=							ldmad_pipe(**snake_case__						)
									lowerCAmelCase					:					Optional[int]								=							output.rgb, output.depth

									lowerCAmelCase					:					Union[str, Any]								=							0.495586
									lowerCAmelCase					:					Tuple								=							0.33795515
									lowerCAmelCase					:					int								=							112.48518
									lowerCAmelCase					:					Union[str, Any]								=							98.489746
									assert np.abs(expected_rgb_mean - rgb.mean()						) < 1e-3
									assert np.abs(expected_rgb_std - rgb.std()						) < 1e-3
									assert np.abs(expected_depth_mean - depth.mean()						) < 1e-3
									assert np.abs(expected_depth_std - depth.std()						) < 1e-3







							def 	lowercase__							(						self						):






									"""simple docstring"""





									lowerCAmelCase					:					Tuple								=							StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d-4c"						).to(snake_case__						)
									ldmad_pipe.set_progress_bar_config(disable=snake_case__						)

									lowerCAmelCase					:					Any								=							self.get_inputs(snake_case__						)
									lowerCAmelCase					:					List[str]								=							ldmad_pipe(**snake_case__						)
									lowerCAmelCase					:					int								=							output.rgb, output.depth

									lowerCAmelCase					:					int								=							0.4194127
									lowerCAmelCase					:					Dict								=							0.35375586
									lowerCAmelCase					:					List[Any]								=							0.5638502
									lowerCAmelCase					:					Union[str, Any]								=							0.34686103

									assert rgb.shape == (1, 512, 512, 3)
									assert depth.shape == (1, 512, 512, 1)
									assert np.abs(expected_rgb_mean - rgb.mean()						) < 1e-3
									assert np.abs(expected_rgb_std - rgb.std()						) < 1e-3
									assert np.abs(expected_depth_mean - depth.mean()						) < 1e-3
									assert np.abs(expected_depth_std - depth.std()						) < 1e-3









"""simple docstring"""







def  a__      (    SCREAMING_SNAKE_CASE   :   list[int]    ):


  '''simple docstring'''







  lowerCAmelCase					:					str								=							len(SCREAMING_SNAKE_CASE    )
  for i in range(SCREAMING_SNAKE_CASE    ):
    for j in range(i + 1    ,				SCREAMING_SNAKE_CASE    ):
      if numbers[j] < numbers[i]:
        lowerCAmelCase						,  lowerCAmelCase					:					Any								=							numbers[j], numbers[i]
  return numbers


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





