
"""simple docstring"""





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    _UpperCAmelCase(   unittest.TestCase     ):



  def 						UpperCAmelCase							( self)	->	Dict:






       '''simple docstring'''
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()



  @property
  def 						UpperCAmelCase							( self)	->	List[str]:






       '''simple docstring'''
       _UpperCamelCase										=  1
       _UpperCamelCase										=  3
       _UpperCamelCase										=  (32, 32)

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



  @property
  def 						UpperCAmelCase							( self)	->	Any:






       '''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    ,		)
       return model



  @property
  def 						UpperCAmelCase							( self)	->	Dict:






       '''simple docstring'''
       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    ,		)
       return model



  @property
  def 						UpperCAmelCase							( self)	->	Dict:






       '''simple docstring'''
       torch.manual_seed(0)
       _UpperCamelCase										=  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(__UpperCAmelCase)



  @property
  def 						UpperCAmelCase							( self)	->	Tuple:






       '''simple docstring'''
       def extract(*__a    ,		**__a):






            class    _UpperCAmelCase:



              def __init__( self)	->	Any:






                   '''simple docstring'''
                   _UpperCamelCase										=  torch.ones([0])




              def 						UpperCAmelCase							( self    ,		__a)	->	Optional[int]:






                   '''simple docstring'''
                   self.pixel_values.to(__UpperCAmelCase)
                   return self





            return Out()

       return extract



  def 						UpperCAmelCase							( self)	->	Optional[int]:






       '''simple docstring'''
       _UpperCamelCase										=  """cpu"""  # ensure determinism for the device-dependent torch.Generator
       _UpperCamelCase										=  self.dummy_cond_unet
       _UpperCamelCase										=  DDIMScheduler(
           beta_start=0.0_0085    ,		beta_end=0.012    ,		beta_schedule='''scaled_linear'''    ,		clip_sample=__UpperCAmelCase    ,		set_alpha_to_one=__UpperCAmelCase    ,		)

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

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

       _UpperCamelCase										=  """A painting of a squirrel eating a burger"""

       _UpperCamelCase										=  torch.Generator(device=__UpperCAmelCase).manual_seed(0)
       _UpperCamelCase										=  sd_pipe([prompt]    ,		generator=__UpperCAmelCase    ,		guidance_scale=6.0    ,		num_inference_steps=2    ,		output_type='''np''')
       _UpperCamelCase										=  output.images

       _UpperCamelCase										=  torch.Generator(device=__UpperCAmelCase).manual_seed(0)
       _UpperCamelCase										=  sd_pipe(
           [prompt]    ,		generator=__UpperCAmelCase    ,		guidance_scale=6.0    ,		num_inference_steps=2    ,		output_type='''np'''    ,		return_dict=__UpperCAmelCase    ,		)[0]

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

       assert image.shape == (1, 64, 64, 3)
       _UpperCamelCase										=  np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864])

       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)	->	Optional[Any]:






       '''simple docstring'''
       _UpperCamelCase										=  """cpu"""  # ensure determinism for the device-dependent torch.Generator
       _UpperCamelCase										=  self.dummy_cond_unet
       _UpperCamelCase										=  PNDMScheduler(skip_prk_steps=__UpperCAmelCase)
       _UpperCamelCase										=  self.dummy_vae
       _UpperCamelCase										=  self.dummy_text_encoder
       _UpperCamelCase										=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

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

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

       _UpperCamelCase										=  output.images

       _UpperCamelCase										=  torch.Generator(device=__UpperCAmelCase).manual_seed(0)
       _UpperCamelCase										=  sd_pipe(
           [prompt]    ,		generator=__UpperCAmelCase    ,		guidance_scale=6.0    ,		num_inference_steps=2    ,		output_type='''np'''    ,		return_dict=__UpperCAmelCase    ,		)[0]

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

       assert image.shape == (1, 64, 64, 3)
       _UpperCamelCase										=  np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993])

       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)	->	List[str]:






       '''simple docstring'''
       _UpperCamelCase										=  StableDiffusionPipeline.from_pretrained(
           '''hf-internal-testing/tiny-stable-diffusion-lms-pipe'''    ,		safety_checker=__UpperCAmelCase)
       assert isinstance(__UpperCAmelCase    ,		__UpperCAmelCase)
       assert isinstance(pipe.scheduler    ,		__UpperCAmelCase)
       assert pipe.safety_checker is None

       _UpperCamelCase										=  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(__UpperCAmelCase)
            _UpperCamelCase										=  StableDiffusionPipeline.from_pretrained(__UpperCAmelCase)

       # sanity check that the pipeline still works
       assert pipe.safety_checker is None
       _UpperCamelCase										=  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)	->	Dict:






       '''simple docstring'''
       _UpperCamelCase										=  self.dummy_cond_unet
       _UpperCamelCase										=  PNDMScheduler(skip_prk_steps=__UpperCAmelCase)
       _UpperCamelCase										=  self.dummy_vae
       _UpperCamelCase										=  self.dummy_text_encoder
       _UpperCamelCase										=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

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

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

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

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






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



  def 						UpperCAmelCase							( self)	->	Optional[int]:






       '''simple docstring'''
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()



  def 						UpperCAmelCase							( self)	->	Union[str, Any]:






       '''simple docstring'''
       _UpperCamelCase										=  StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''    ,		safety_checker=__UpperCAmelCase)
       _UpperCamelCase										=  LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
       _UpperCamelCase										=  sd_pipe.to(__UpperCAmelCase)
       sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase)

       _UpperCamelCase										=  (
           """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 """
       )
       _UpperCamelCase										=  40_03_66_03_46
       _UpperCamelCase										=  7

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

       _UpperCamelCase										=  output.images
       _UpperCamelCase										=  image[0, -3:, -3:, -1]
       _UpperCamelCase										=  [0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]

       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)
       _UpperCamelCase										=  torch.manual_seed(__UpperCAmelCase)
       _UpperCamelCase										=  sd_pipe(
           [prompt]    ,		generator=__UpperCAmelCase    ,		guidance_scale=__UpperCAmelCase    ,		num_inference_steps=50    ,		output_type='''np'''    ,		width=5_12    ,		height=5_12    ,		sld_guidance_scale=20_00    ,		sld_warmup_steps=7    ,		sld_threshold=0.025    ,		sld_momentum_scale=0.5    ,		sld_mom_beta=0.7    ,		)

       _UpperCamelCase										=  output.images
       _UpperCamelCase										=  image[0, -3:, -3:, -1]
       _UpperCamelCase										=  [0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]

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

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



  def 						UpperCAmelCase							( self)	->	Optional[int]:






       '''simple docstring'''
       _UpperCamelCase										=  StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''    ,		safety_checker=__UpperCAmelCase)
       _UpperCamelCase										=  LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
       _UpperCamelCase										=  sd_pipe.to(__UpperCAmelCase)
       sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase)

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

       _UpperCamelCase										=  torch.manual_seed(__UpperCAmelCase)
       _UpperCamelCase										=  sd_pipe(
           [prompt]    ,		generator=__UpperCAmelCase    ,		guidance_scale=__UpperCAmelCase    ,		num_inference_steps=50    ,		output_type='''np'''    ,		width=5_12    ,		height=5_12    ,		sld_guidance_scale=0    ,		)

       _UpperCamelCase										=  output.images
       _UpperCamelCase										=  image[0, -3:, -3:, -1]
       _UpperCamelCase										=  [0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]

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

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

       _UpperCamelCase										=  torch.manual_seed(__UpperCAmelCase)
       _UpperCamelCase										=  sd_pipe(
           [prompt]    ,		generator=__UpperCAmelCase    ,		guidance_scale=__UpperCAmelCase    ,		num_inference_steps=50    ,		output_type='''np'''    ,		width=5_12    ,		height=5_12    ,		sld_guidance_scale=20_00    ,		sld_warmup_steps=7    ,		sld_threshold=0.025    ,		sld_momentum_scale=0.5    ,		sld_mom_beta=0.7    ,		)

       _UpperCamelCase										=  output.images
       _UpperCamelCase										=  image[0, -3:, -3:, -1]
       _UpperCamelCase										=  [0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]

       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]:






       '''simple docstring'''
       _UpperCamelCase										=  StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5''')
       _UpperCamelCase										=  sd_pipe.to(__UpperCAmelCase)
       sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase)

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

       _UpperCamelCase										=  torch.manual_seed(__UpperCAmelCase)
       _UpperCamelCase										=  sd_pipe(
           [prompt]    ,		generator=__UpperCAmelCase    ,		guidance_scale=__UpperCAmelCase    ,		num_inference_steps=50    ,		output_type='''np'''    ,		width=5_12    ,		height=5_12    ,		sld_guidance_scale=0    ,		)

       _UpperCamelCase										=  output.images
       _UpperCamelCase										=  image[0, -3:, -3:, -1]
       _UpperCamelCase										=  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

       _UpperCamelCase										=  torch.manual_seed(__UpperCAmelCase)
       _UpperCamelCase										=  sd_pipe(
           [prompt]    ,		generator=__UpperCAmelCase    ,		guidance_scale=__UpperCAmelCase    ,		num_inference_steps=50    ,		output_type='''np'''    ,		width=5_12    ,		height=5_12    ,		sld_guidance_scale=20_00    ,		sld_warmup_steps=7    ,		sld_threshold=0.025    ,		sld_momentum_scale=0.5    ,		sld_mom_beta=0.7    ,		)

       _UpperCamelCase										=  output.images
       _UpperCamelCase										=  image[0, -3:, -3:, -1]
       _UpperCamelCase										=  np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561])
       assert image.shape == (1, 5_12, 5_12, 3)

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








"""simple docstring"""





from __future__ import annotations



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Optional[int]:


					"""simple docstring"""





					if len(__snake_case     ) <= 1 or n <= 1:
										return

					insert_next(__snake_case,					n - 1     )
					rec_insertion_sort(__snake_case,					n - 1     )



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Dict:


					"""simple docstring"""





					if index >= len(__snake_case     ) or collection[index - 1] <= collection[index]:
										return

					# Swaps adjacent elements since they are not in ascending order
					_UpperCamelCase			,  _UpperCamelCase										=  (
					    collection[index],
					    collection[index - 1],
					)

					insert_next(__snake_case,					index + 1     )


if __name__ == "__main__":
				_a														=		input("""Enter integers separated by spaces: """)
				_a														=		[int(num) for num in numbers.split()]
				rec_insertion_sort(number_list, len(number_list))
				print(number_list)






