



import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu






class 						__a (     unittest.TestCase				):



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



	@property
	def 		UpperCamelCase							(						self							:    Optional[Any])->							str:
						__lowerCAmelCase	   =1
						__lowerCAmelCase	   =3
						__lowerCAmelCase	   =(32, 32)

						__lowerCAmelCase	   =floats_tensor((batch_size, num_channels) + sizes				,	rng=random.Random(0)).to(lowerCamelCase_)
						return image



	@property
	def 		UpperCamelCase							(						self							:    Union[str, Any])->							Dict:
						torch.manual_seed(0)
						__lowerCAmelCase	   =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 		UpperCamelCase							(						self							:    Tuple)->							Union[str, Any]:
						torch.manual_seed(0)
						__lowerCAmelCase	   =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 		UpperCamelCase							(						self							:    Any)->							Union[str, Any]:
						torch.manual_seed(0)
						__lowerCAmelCase	   =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				,	)
						return CLIPTextModel(lowerCamelCase_)



	@property
	def 		UpperCamelCase							(						self							:    Optional[Any])->							str:




						def extract(*snake_case_							:    Union[str, Any]				,	**snake_case_							:    List[str]):






											class 						__a :



												def __init__(						self							:    Any)->							Union[str, Any]:
																	__lowerCAmelCase	   =torch.ones([0])



												def 		UpperCamelCase							(						self							:    Any				,	snake_case_							:    Tuple)->							int:
																	self.pixel_values.to(lowerCamelCase_)
																	return self


											return Out()

						return extract



	def 		UpperCamelCase							(						self							:    Union[str, Any])->							int:
						__lowerCAmelCase	   ='''cpu'''  # ensure determinism for the device-dependent torch.Generator
						__lowerCAmelCase	   =self.dummy_cond_unet
						__lowerCAmelCase	   =DDIMScheduler(
						    beta_start=0.0_0_0_8_5				,	beta_end=0.0_1_2				,	beta_schedule="""scaled_linear"""				,	clip_sample=lowerCamelCase_				,	set_alpha_to_one=lowerCamelCase_				,	)

						__lowerCAmelCase	   =self.dummy_vae
						__lowerCAmelCase	   =self.dummy_text_encoder
						__lowerCAmelCase	   =CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")

						# make sure here that pndm scheduler skips prk
						__lowerCAmelCase	   =StableDiffusionPipeline(
						    unet=lowerCamelCase_				,	scheduler=lowerCamelCase_				,	vae=lowerCamelCase_				,	text_encoder=lowerCamelCase_				,	tokenizer=lowerCamelCase_				,	safety_checker=lowerCamelCase_				,	feature_extractor=self.dummy_extractor				,	)
						__lowerCAmelCase	   =sd_pipe.to(lowerCamelCase_)
						sd_pipe.set_progress_bar_config(disable=lowerCamelCase_)

						__lowerCAmelCase	   ='''A painting of a squirrel eating a burger'''

						__lowerCAmelCase	   =torch.Generator(device=lowerCamelCase_).manual_seed(0)
						__lowerCAmelCase	   =sd_pipe([prompt]				,	generator=lowerCamelCase_				,	guidance_scale=6.0				,	num_inference_steps=2				,	output_type="""np""")
						__lowerCAmelCase	   =output.images

						__lowerCAmelCase	   =torch.Generator(device=lowerCamelCase_).manual_seed(0)
						__lowerCAmelCase	   =sd_pipe(
						    [prompt]				,	generator=lowerCamelCase_				,	guidance_scale=6.0				,	num_inference_steps=2				,	output_type="""np"""				,	return_dict=lowerCamelCase_				,	)[0]

						__lowerCAmelCase	   =image[0, -3:, -3:, -1]
						__lowerCAmelCase	   =image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						__lowerCAmelCase	   =np.array([0.5_7_5_6, 0.6_1_1_8, 0.5_0_0_5, 0.5_0_4_1, 0.5_4_7_1, 0.4_7_2_6, 0.4_9_7_6, 0.4_8_6_5, 0.4_8_6_4])

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



	def 		UpperCamelCase							(						self							:    int)->							Any:
						__lowerCAmelCase	   ='''cpu'''  # ensure determinism for the device-dependent torch.Generator
						__lowerCAmelCase	   =self.dummy_cond_unet
						__lowerCAmelCase	   =PNDMScheduler(skip_prk_steps=lowerCamelCase_)
						__lowerCAmelCase	   =self.dummy_vae
						__lowerCAmelCase	   =self.dummy_text_encoder
						__lowerCAmelCase	   =CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")

						# make sure here that pndm scheduler skips prk
						__lowerCAmelCase	   =StableDiffusionPipeline(
						    unet=lowerCamelCase_				,	scheduler=lowerCamelCase_				,	vae=lowerCamelCase_				,	text_encoder=lowerCamelCase_				,	tokenizer=lowerCamelCase_				,	safety_checker=lowerCamelCase_				,	feature_extractor=self.dummy_extractor				,	)
						__lowerCAmelCase	   =sd_pipe.to(lowerCamelCase_)
						sd_pipe.set_progress_bar_config(disable=lowerCamelCase_)

						__lowerCAmelCase	   ='''A painting of a squirrel eating a burger'''
						__lowerCAmelCase	   =torch.Generator(device=lowerCamelCase_).manual_seed(0)
						__lowerCAmelCase	   =sd_pipe([prompt]				,	generator=lowerCamelCase_				,	guidance_scale=6.0				,	num_inference_steps=2				,	output_type="""np""")

						__lowerCAmelCase	   =output.images

						__lowerCAmelCase	   =torch.Generator(device=lowerCamelCase_).manual_seed(0)
						__lowerCAmelCase	   =sd_pipe(
						    [prompt]				,	generator=lowerCamelCase_				,	guidance_scale=6.0				,	num_inference_steps=2				,	output_type="""np"""				,	return_dict=lowerCamelCase_				,	)[0]

						__lowerCAmelCase	   =image[0, -3:, -3:, -1]
						__lowerCAmelCase	   =image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						__lowerCAmelCase	   =np.array([0.5_1_2_5, 0.5_7_1_6, 0.4_8_2_8, 0.5_0_6_0, 0.5_6_5_0, 0.4_7_6_8, 0.5_1_8_5, 0.4_8_9_5, 0.4_9_9_3])

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



	def 		UpperCamelCase							(						self							:    str)->							List[Any]:
						__lowerCAmelCase	   =StableDiffusionPipeline.from_pretrained(
						    """hf-internal-testing/tiny-stable-diffusion-lms-pipe"""				,	safety_checker=lowerCamelCase_)
						assert isinstance(lowerCamelCase_				,	lowerCamelCase_)
						assert isinstance(pipe.scheduler				,	lowerCamelCase_)
						assert pipe.safety_checker is None

						__lowerCAmelCase	   =pipe("""example prompt"""				,	num_inference_steps=2).images[0]
						assert image is not None

						# check that there's no error when saving a pipeline with one of the models being None
						with tempfile.TemporaryDirectory() as tmpdirname:
											pipe.save_pretrained(lowerCamelCase_)
											__lowerCAmelCase	   =StableDiffusionPipeline.from_pretrained(lowerCamelCase_)

						# sanity check that the pipeline still works
						assert pipe.safety_checker is None
						__lowerCAmelCase	   =pipe("""example prompt"""				,	num_inference_steps=2).images[0]
						assert image is not None



	@unittest.skipIf(torch_device != """cuda"""				,	"""This test requires a GPU""")
	def 		UpperCamelCase							(						self							:    Tuple)->							int:
						__lowerCAmelCase	   =self.dummy_cond_unet
						__lowerCAmelCase	   =PNDMScheduler(skip_prk_steps=lowerCamelCase_)
						__lowerCAmelCase	   =self.dummy_vae
						__lowerCAmelCase	   =self.dummy_text_encoder
						__lowerCAmelCase	   =CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")

						# put models in fp16
						__lowerCAmelCase	   =unet.half()
						__lowerCAmelCase	   =vae.half()
						__lowerCAmelCase	   =bert.half()

						# make sure here that pndm scheduler skips prk
						__lowerCAmelCase	   =StableDiffusionPipeline(
						    unet=lowerCamelCase_				,	scheduler=lowerCamelCase_				,	vae=lowerCamelCase_				,	text_encoder=lowerCamelCase_				,	tokenizer=lowerCamelCase_				,	safety_checker=lowerCamelCase_				,	feature_extractor=self.dummy_extractor				,	)
						__lowerCAmelCase	   =sd_pipe.to(lowerCamelCase_)
						sd_pipe.set_progress_bar_config(disable=lowerCamelCase_)

						__lowerCAmelCase	   ='''A painting of a squirrel eating a burger'''
						__lowerCAmelCase	   =sd_pipe([prompt]				,	num_inference_steps=2				,	output_type="""np""").images

						assert image.shape == (1, 64, 64, 3)






@nightly
@require_torch_gpu
class 						__a (     unittest.TestCase				):



	def 		UpperCamelCase							(						self							:    int)->							List[str]:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



	def 		UpperCamelCase							(						self							:    List[str])->							str:
						__lowerCAmelCase	   =StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""				,	safety_checker=lowerCamelCase_)
						__lowerCAmelCase	   =LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
						__lowerCAmelCase	   =sd_pipe.to(lowerCamelCase_)
						sd_pipe.set_progress_bar_config(disable=lowerCamelCase_)

						__lowerCAmelCase	   =(
						    '''portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'''
						    ''' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'''
						    ''' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'''
						    ''' children from bahnhof zoo, detailed '''
						)
						__lowerCAmelCase	   =40_03_66_03_46
						__lowerCAmelCase	   =7

						# without safety guidance (sld_guidance_scale = 0)
						__lowerCAmelCase	   =torch.manual_seed(lowerCamelCase_)
						__lowerCAmelCase	   =sd_pipe(
						    [prompt]				,	generator=lowerCamelCase_				,	guidance_scale=lowerCamelCase_				,	num_inference_steps=50				,	output_type="""np"""				,	width=5_12				,	height=5_12				,	sld_guidance_scale=0				,	)

						__lowerCAmelCase	   =output.images
						__lowerCAmelCase	   =image[0, -3:, -3:, -1]
						__lowerCAmelCase	   =[0.2_2_7_8, 0.2_2_3_1, 0.2_2_4_9, 0.2_3_3_3, 0.2_3_0_3, 0.1_8_8_5, 0.2_2_7_3, 0.2_1_4_4, 0.2_1_7_6]

						assert image.shape == (1, 5_12, 5_12, 3)

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

						# without safety guidance (strong configuration)
						__lowerCAmelCase	   =torch.manual_seed(lowerCamelCase_)
						__lowerCAmelCase	   =sd_pipe(
						    [prompt]				,	generator=lowerCamelCase_				,	guidance_scale=lowerCamelCase_				,	num_inference_steps=50				,	output_type="""np"""				,	width=5_12				,	height=5_12				,	sld_guidance_scale=20_00				,	sld_warmup_steps=7				,	sld_threshold=0.0_2_5				,	sld_momentum_scale=0.5				,	sld_mom_beta=0.7				,	)

						__lowerCAmelCase	   =output.images
						__lowerCAmelCase	   =image[0, -3:, -3:, -1]
						__lowerCAmelCase	   =[0.2_3_8_3, 0.2_2_7_6, 0.2_3_6, 0.2_1_9_2, 0.2_1_8_6, 0.2_0_5_3, 0.1_9_7_1, 0.1_9_0_1, 0.1_7_1_9]

						assert image.shape == (1, 5_12, 5_12, 3)

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



	def 		UpperCamelCase							(						self							:    List[Any])->							Tuple:
						__lowerCAmelCase	   =StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""				,	safety_checker=lowerCamelCase_)
						__lowerCAmelCase	   =LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
						__lowerCAmelCase	   =sd_pipe.to(lowerCamelCase_)
						sd_pipe.set_progress_bar_config(disable=lowerCamelCase_)

						__lowerCAmelCase	   ='''padme amidala taking a bath artwork, safe for work, no nudity'''
						__lowerCAmelCase	   =27_34_97_17_55
						__lowerCAmelCase	   =7

						__lowerCAmelCase	   =torch.manual_seed(lowerCamelCase_)
						__lowerCAmelCase	   =sd_pipe(
						    [prompt]				,	generator=lowerCamelCase_				,	guidance_scale=lowerCamelCase_				,	num_inference_steps=50				,	output_type="""np"""				,	width=5_12				,	height=5_12				,	sld_guidance_scale=0				,	)

						__lowerCAmelCase	   =output.images
						__lowerCAmelCase	   =image[0, -3:, -3:, -1]
						__lowerCAmelCase	   =[0.3_5_0_2, 0.3_6_2_2, 0.3_3_9_6, 0.3_6_4_2, 0.3_4_7_8, 0.3_3_1_8, 0.3_5, 0.3_3_4_8, 0.3_2_9_7]

						assert image.shape == (1, 5_12, 5_12, 3)

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

						__lowerCAmelCase	   =torch.manual_seed(lowerCamelCase_)
						__lowerCAmelCase	   =sd_pipe(
						    [prompt]				,	generator=lowerCamelCase_				,	guidance_scale=lowerCamelCase_				,	num_inference_steps=50				,	output_type="""np"""				,	width=5_12				,	height=5_12				,	sld_guidance_scale=20_00				,	sld_warmup_steps=7				,	sld_threshold=0.0_2_5				,	sld_momentum_scale=0.5				,	sld_mom_beta=0.7				,	)

						__lowerCAmelCase	   =output.images
						__lowerCAmelCase	   =image[0, -3:, -3:, -1]
						__lowerCAmelCase	   =[0.5_5_3_1, 0.5_2_0_6, 0.4_8_9_5, 0.5_1_5_6, 0.5_1_8_2, 0.4_7_5_1, 0.4_8_0_2, 0.4_8_0_3, 0.4_4_4_3]

						assert image.shape == (1, 5_12, 5_12, 3)

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



	def 		UpperCamelCase							(						self							:    Any)->							Any:
						__lowerCAmelCase	   =StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""")
						__lowerCAmelCase	   =sd_pipe.to(lowerCamelCase_)
						sd_pipe.set_progress_bar_config(disable=lowerCamelCase_)

						__lowerCAmelCase	   =(
						    '''the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'''
						    ''' leyendecker'''
						)
						__lowerCAmelCase	   =10_44_35_52_34
						__lowerCAmelCase	   =12

						__lowerCAmelCase	   =torch.manual_seed(lowerCamelCase_)
						__lowerCAmelCase	   =sd_pipe(
						    [prompt]				,	generator=lowerCamelCase_				,	guidance_scale=lowerCamelCase_				,	num_inference_steps=50				,	output_type="""np"""				,	width=5_12				,	height=5_12				,	sld_guidance_scale=0				,	)

						__lowerCAmelCase	   =output.images
						__lowerCAmelCase	   =image[0, -3:, -3:, -1]
						__lowerCAmelCase	   =np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])

						assert image.shape == (1, 5_12, 5_12, 3)

						assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-7

						__lowerCAmelCase	   =torch.manual_seed(lowerCamelCase_)
						__lowerCAmelCase	   =sd_pipe(
						    [prompt]				,	generator=lowerCamelCase_				,	guidance_scale=lowerCamelCase_				,	num_inference_steps=50				,	output_type="""np"""				,	width=5_12				,	height=5_12				,	sld_guidance_scale=20_00				,	sld_warmup_steps=7				,	sld_threshold=0.0_2_5				,	sld_momentum_scale=0.5				,	sld_mom_beta=0.7				,	)

						__lowerCAmelCase	   =output.images
						__lowerCAmelCase	   =image[0, -3:, -3:, -1]
						__lowerCAmelCase	   =np.array([0.5_8_1_8, 0.6_2_8_5, 0.6_8_3_5, 0.6_0_1_9, 0.6_2_5, 0.6_7_5_4, 0.6_0_9_6, 0.6_3_3_4, 0.6_5_6_1])
						assert image.shape == (1, 5_12, 5_12, 3)

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









def 					__lowerCAmelCase		(  __lowerCamelCase	:    list	)  ->						list:
					__lowerCAmelCase	   =False
					while is_sorted is False:  # Until all the indices are traversed keep looping
										__lowerCAmelCase	   =True
										for i in range(0				,   len(__lowerCamelCase	) - 1				,   2	):  # iterating over all even indices
															if input_list[i] > input_list[i + 1]:
																				__lowerCAmelCase   ,	__lowerCAmelCase	   =input_list[i + 1], input_list[i]
																				# swapping if elements not in order
																				__lowerCAmelCase	   =False

										for i in range(1				,   len(__lowerCamelCase	) - 1				,   2	):  # iterating over all odd indices
															if input_list[i] > input_list[i + 1]:
																				__lowerCAmelCase   ,	__lowerCAmelCase	   =input_list[i + 1], input_list[i]
																				# swapping if elements not in order
																				__lowerCAmelCase	   =False
					return input_list


if __name__ == "__main__":
				print('''Enter list to be sorted''')
				lowercase_	       =			[int(x) for x in input().split()]
				# inputing elements of the list in one line
				lowercase_	       =			odd_even_sort(input_list)
				print('''The sorted list is''')
				print(sorted_list)




