


import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import 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
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 						__a (							UpperCAmelCase       , UpperCAmelCase       , unittest.TestCase     ):
		_a      :    Union[str, Any]           =      StableDiffusionSAGPipeline
		_a      :    Any           =      TEXT_TO_IMAGE_PARAMS
		_a      :    Union[str, Any]           =      TEXT_TO_IMAGE_BATCH_PARAMS
		_a      :    Dict           =      TEXT_TO_IMAGE_IMAGE_PARAMS
		_a      :    Optional[Any]           =      TEXT_TO_IMAGE_IMAGE_PARAMS
		_a      :    List[Any]           =      False
		def       UpperCAmelCase__							(     self		)					->			List[str]:




									"""simple docstring"""





									torch.manual_seed(0		)
									_UpperCAmelCase											=     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     ,       )
									_UpperCAmelCase											=     DDIMScheduler(
									    beta_start=0.00085     ,       beta_end=0.012     ,       beta_schedule='scaled_linear'     ,       clip_sample=_SCREAMING_SNAKE_CASE     ,       set_alpha_to_one=_SCREAMING_SNAKE_CASE     ,       )
									torch.manual_seed(0		)
									_UpperCAmelCase											=     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		)
									_UpperCAmelCase											=     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=1000     ,       )
									_UpperCAmelCase											=     CLIPTextModel(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

									_UpperCAmelCase											=     {
									    'unet': unet,
									    'scheduler': scheduler,
									    'vae': vae,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'safety_checker': None,
									    'feature_extractor': None,
									}
									return components
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE=0		)					->			Optional[int]:




									"""simple docstring"""





									if str(_SCREAMING_SNAKE_CASE		).startswith('mps'		):
																_UpperCAmelCase											=     torch.manual_seed(_SCREAMING_SNAKE_CASE		)
									else:
																_UpperCAmelCase											=     torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     {
									    'prompt': '.',
									    'generator': generator,
									    'num_inference_steps': 2,
									    'guidance_scale': 1.0,
									    'sag_scale': 1.0,
									    'output_type': 'numpy',
									}
									return inputs






		def       UpperCAmelCase__							(     self		)					->			List[Any]:




									"""simple docstring"""





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



@slow
@require_torch_gpu
class 						__a (							unittest.TestCase     ):
		def       UpperCAmelCase__							(     self		)					->			Tuple:




									"""simple docstring"""





									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()
		def       UpperCAmelCase__							(     self		)					->			List[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     StableDiffusionSAGPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'		)
									_UpperCAmelCase											=     sag_pipe.to(_SCREAMING_SNAKE_CASE		)
									sag_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     '.'
									_UpperCAmelCase											=     torch.manual_seed(0		)
									_UpperCAmelCase											=     sag_pipe(
									    [prompt]     ,       generator=_SCREAMING_SNAKE_CASE     ,       guidance_scale=7.5     ,       sag_scale=1.0     ,       num_inference_steps=20     ,       output_type='np'		)

									_UpperCAmelCase											=     output.images

									_UpperCAmelCase											=     image[0, -3:, -3:, -1]

									assert image.shape == (1, 512, 512, 3)
									_UpperCAmelCase											=     np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949]		)

									assert np.abs(image_slice.flatten() - expected_slice		).max() < 5e-2
		def       UpperCAmelCase__							(     self		)					->			Tuple:




									"""simple docstring"""





									_UpperCAmelCase											=     StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'		)
									_UpperCAmelCase											=     sag_pipe.to(_SCREAMING_SNAKE_CASE		)
									sag_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     '.'
									_UpperCAmelCase											=     torch.manual_seed(0		)
									_UpperCAmelCase											=     sag_pipe(
									    [prompt]     ,       generator=_SCREAMING_SNAKE_CASE     ,       guidance_scale=7.5     ,       sag_scale=1.0     ,       num_inference_steps=20     ,       output_type='np'		)

									_UpperCAmelCase											=     output.images

									_UpperCAmelCase											=     image[0, -3:, -3:, -1]

									assert image.shape == (1, 512, 512, 3)
									_UpperCAmelCase											=     np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371]		)

									assert np.abs(image_slice.flatten() - expected_slice		).max() < 5e-2






		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase											=     StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'		)
									_UpperCAmelCase											=     sag_pipe.to(_SCREAMING_SNAKE_CASE		)
									sag_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     '.'
									_UpperCAmelCase											=     torch.manual_seed(0		)
									_UpperCAmelCase											=     sag_pipe(
									    [prompt]     ,       width=768     ,       height=512     ,       generator=_SCREAMING_SNAKE_CASE     ,       guidance_scale=7.5     ,       sag_scale=1.0     ,       num_inference_steps=20     ,       output_type='np'     ,       )

									_UpperCAmelCase											=     output.images

									assert image.shape == (1, 512, 768, 3)




def 			lowerCAmelCase__					(     a__:						Optional[int]			)    ->						Optional[int]:


							'''simple docstring'''





							_UpperCAmelCase											=     len(a__			)
							for i in range(length - 1			):
														_UpperCAmelCase											=     i
														for k in range(i + 1     ,    a__			):
																					if collection[k] < collection[least]:
																												_UpperCAmelCase											=     k
														if least != i:
																					_UpperCAmelCase						,			_UpperCAmelCase											=     (collection[i], collection[least])
							return collection


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