

'''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 							_snake_case (       unittest.TestCase						):
   def     __UpperCamelCase						( self			:      Union[str, Any]							):
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()
   @property
   def     __UpperCamelCase						( self			:      Tuple							):
     SCREAMING_SNAKE_CASE:Optional[int]						      =					1
     SCREAMING_SNAKE_CASE:int						      =					3
     SCREAMING_SNAKE_CASE:List[Any]						      =					(32, 32)

     SCREAMING_SNAKE_CASE:str						      =					floats_tensor((batch_size, num_channels) + sizes     ,rng=random.Random(0							)							).to(SCREAMING_SNAKE_CASE__							)
     return image
   @property
   def     __UpperCamelCase						( self			:      int							):
     torch.manual_seed(0							)
     SCREAMING_SNAKE_CASE:int						      =					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			:      List[str]							):
     torch.manual_seed(0							)
     SCREAMING_SNAKE_CASE:Tuple						      =					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			:      Union[str, Any]							):
     torch.manual_seed(0							)
     SCREAMING_SNAKE_CASE:str						      =					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     ,)
     return CLIPTextModel(SCREAMING_SNAKE_CASE__							)
   @property
   def     __UpperCamelCase						( self			:      List[str]							):
     def extract(*SCREAMING_SNAKE_CASE__			:      Dict     ,**SCREAMING_SNAKE_CASE__			:      List[Any]							):





       class 							_snake_case :
          def __init__( self			:      int							):
            SCREAMING_SNAKE_CASE:int						      =					torch.ones([0]							)







          def     __UpperCamelCase						( self			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      Any							):
            self.pixel_values.to(SCREAMING_SNAKE_CASE__							)
            return self



       return Out()

     return extract
   def     __UpperCamelCase						( self			:      str							):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					"cpu"  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE:Tuple						      =					self.dummy_cond_unet
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					DDIMScheduler(
         beta_start=0.00_085     ,beta_end=0.012     ,beta_schedule="scaled_linear"     ,clip_sample=SCREAMING_SNAKE_CASE__     ,set_alpha_to_one=SCREAMING_SNAKE_CASE__     ,)

     SCREAMING_SNAKE_CASE:Dict						      =					self.dummy_vae
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self.dummy_text_encoder
     SCREAMING_SNAKE_CASE:Tuple						      =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE:List[Any]						      =					StableDiffusionPipeline(
         unet=SCREAMING_SNAKE_CASE__     ,scheduler=SCREAMING_SNAKE_CASE__     ,vae=SCREAMING_SNAKE_CASE__     ,text_encoder=SCREAMING_SNAKE_CASE__     ,tokenizer=SCREAMING_SNAKE_CASE__     ,safety_checker=SCREAMING_SNAKE_CASE__     ,feature_extractor=self.dummy_extractor     ,)
     SCREAMING_SNAKE_CASE:Dict						      =					sd_pipe.to(SCREAMING_SNAKE_CASE__							)
     sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:List[Any]						      =					"A painting of a squirrel eating a burger"

     SCREAMING_SNAKE_CASE:int						      =					torch.Generator(device=SCREAMING_SNAKE_CASE__							).manual_seed(0							)
     SCREAMING_SNAKE_CASE:str						      =					sd_pipe([prompt]     ,generator=SCREAMING_SNAKE_CASE__     ,guidance_scale=6.0     ,num_inference_steps=2     ,output_type="np"							)
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					output.images

     SCREAMING_SNAKE_CASE:Optional[int]						      =					torch.Generator(device=SCREAMING_SNAKE_CASE__							).manual_seed(0							)
     SCREAMING_SNAKE_CASE:str						      =					sd_pipe(
         [prompt]     ,generator=SCREAMING_SNAKE_CASE__     ,guidance_scale=6.0     ,num_inference_steps=2     ,output_type="np"     ,return_dict=SCREAMING_SNAKE_CASE__     ,)[0]

     SCREAMING_SNAKE_CASE:int						      =					image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE:Dict						      =					image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     SCREAMING_SNAKE_CASE:Any						      =					np.array([0.5_756, 0.6_118, 0.5_005, 0.5_041, 0.5_471, 0.4_726, 0.4_976, 0.4_865, 0.4_864]							)

     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			:      Union[str, Any]							):
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					"cpu"  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE:Tuple						      =					self.dummy_cond_unet
     SCREAMING_SNAKE_CASE:Tuple						      =					PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Any						      =					self.dummy_vae
     SCREAMING_SNAKE_CASE:Any						      =					self.dummy_text_encoder
     SCREAMING_SNAKE_CASE:Dict						      =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					StableDiffusionPipeline(
         unet=SCREAMING_SNAKE_CASE__     ,scheduler=SCREAMING_SNAKE_CASE__     ,vae=SCREAMING_SNAKE_CASE__     ,text_encoder=SCREAMING_SNAKE_CASE__     ,tokenizer=SCREAMING_SNAKE_CASE__     ,safety_checker=SCREAMING_SNAKE_CASE__     ,feature_extractor=self.dummy_extractor     ,)
     SCREAMING_SNAKE_CASE:Dict						      =					sd_pipe.to(SCREAMING_SNAKE_CASE__							)
     sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:str						      =					"A painting of a squirrel eating a burger"
     SCREAMING_SNAKE_CASE:Any						      =					torch.Generator(device=SCREAMING_SNAKE_CASE__							).manual_seed(0							)
     SCREAMING_SNAKE_CASE:List[Any]						      =					sd_pipe([prompt]     ,generator=SCREAMING_SNAKE_CASE__     ,guidance_scale=6.0     ,num_inference_steps=2     ,output_type="np"							)

     SCREAMING_SNAKE_CASE:List[str]						      =					output.images

     SCREAMING_SNAKE_CASE:Optional[int]						      =					torch.Generator(device=SCREAMING_SNAKE_CASE__							).manual_seed(0							)
     SCREAMING_SNAKE_CASE:Optional[int]						      =					sd_pipe(
         [prompt]     ,generator=SCREAMING_SNAKE_CASE__     ,guidance_scale=6.0     ,num_inference_steps=2     ,output_type="np"     ,return_dict=SCREAMING_SNAKE_CASE__     ,)[0]

     SCREAMING_SNAKE_CASE:Optional[int]						      =					image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE:str						      =					image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     SCREAMING_SNAKE_CASE:Any						      =					np.array([0.5_125, 0.5_716, 0.4_828, 0.5_060, 0.5_650, 0.4_768, 0.5_185, 0.4_895, 0.4_993]							)

     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			:      Dict							):
     SCREAMING_SNAKE_CASE:List[Any]						      =					StableDiffusionPipeline.from_pretrained(
         "hf-internal-testing/tiny-stable-diffusion-lms-pipe"     ,safety_checker=SCREAMING_SNAKE_CASE__							)
     assert isinstance(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
     assert isinstance(pipe.scheduler     ,SCREAMING_SNAKE_CASE__							)
     assert pipe.safety_checker is None

     SCREAMING_SNAKE_CASE:str						      =					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(SCREAMING_SNAKE_CASE__							)
       SCREAMING_SNAKE_CASE:Dict						      =					StableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE__							)

     # sanity check that the pipeline still works
     assert pipe.safety_checker is None
     SCREAMING_SNAKE_CASE:Optional[int]						      =					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							):
     SCREAMING_SNAKE_CASE:str						      =					self.dummy_cond_unet
     SCREAMING_SNAKE_CASE:List[Any]						      =					PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:str						      =					self.dummy_vae
     SCREAMING_SNAKE_CASE:List[str]						      =					self.dummy_text_encoder
     SCREAMING_SNAKE_CASE:Any						      =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

     # put models in fp16
     SCREAMING_SNAKE_CASE:Optional[int]						      =					unet.half()
     SCREAMING_SNAKE_CASE:Any						      =					vae.half()
     SCREAMING_SNAKE_CASE:Optional[int]						      =					bert.half()

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

     SCREAMING_SNAKE_CASE:Dict						      =					"A painting of a squirrel eating a burger"
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					sd_pipe([prompt]     ,num_inference_steps=2     ,output_type="np"							).images

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





@nightly
@require_torch_gpu
class 							_snake_case (       unittest.TestCase						):
   def     __UpperCamelCase						( self			:      str							):
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()
   def     __UpperCamelCase						( self			:      Tuple							):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"     ,safety_checker=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:List[Any]						      =					LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config							)
     SCREAMING_SNAKE_CASE:str						      =					sd_pipe.to(SCREAMING_SNAKE_CASE__							)
     sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Dict						      =					(
         "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 "
     )
     SCREAMING_SNAKE_CASE:List[str]						      =					4_003_660_346
     SCREAMING_SNAKE_CASE:int						      =					7

     # without safety guidance (sld_guidance_scale = 0)
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					torch.manual_seed(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Optional[int]						      =					sd_pipe(
         [prompt]     ,generator=SCREAMING_SNAKE_CASE__     ,guidance_scale=SCREAMING_SNAKE_CASE__     ,num_inference_steps=50     ,output_type="np"     ,width=512     ,height=512     ,sld_guidance_scale=0     ,)

     SCREAMING_SNAKE_CASE:Any						      =					output.images
     SCREAMING_SNAKE_CASE:Dict						      =					image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE:Tuple						      =					[0.2_278, 0.2_231, 0.2_249, 0.2_333, 0.2_303, 0.1_885, 0.2_273, 0.2_144, 0.2_176]

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

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

     # without safety guidance (strong configuration)
     SCREAMING_SNAKE_CASE:Tuple						      =					torch.manual_seed(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					sd_pipe(
         [prompt]     ,generator=SCREAMING_SNAKE_CASE__     ,guidance_scale=SCREAMING_SNAKE_CASE__     ,num_inference_steps=50     ,output_type="np"     ,width=512     ,height=512     ,sld_guidance_scale=2_000     ,sld_warmup_steps=7     ,sld_threshold=0.025     ,sld_momentum_scale=0.5     ,sld_mom_beta=0.7     ,)

     SCREAMING_SNAKE_CASE:str						      =					output.images
     SCREAMING_SNAKE_CASE:str						      =					image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE:int						      =					[0.2_383, 0.2_276, 0.236, 0.2_192, 0.2_186, 0.2_053, 0.1_971, 0.1_901, 0.1_719]

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

     assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
   def     __UpperCamelCase						( self			:      Union[str, Any]							):
     SCREAMING_SNAKE_CASE:str						      =					StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"     ,safety_checker=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Any						      =					LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config							)
     SCREAMING_SNAKE_CASE:int						      =					sd_pipe.to(SCREAMING_SNAKE_CASE__							)
     sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:str						      =					"padme amidala taking a bath artwork, safe for work, no nudity"
     SCREAMING_SNAKE_CASE:str						      =					2_734_971_755
     SCREAMING_SNAKE_CASE:Optional[int]						      =					7

     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					torch.manual_seed(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:str						      =					sd_pipe(
         [prompt]     ,generator=SCREAMING_SNAKE_CASE__     ,guidance_scale=SCREAMING_SNAKE_CASE__     ,num_inference_steps=50     ,output_type="np"     ,width=512     ,height=512     ,sld_guidance_scale=0     ,)

     SCREAMING_SNAKE_CASE:Optional[int]						      =					output.images
     SCREAMING_SNAKE_CASE:Optional[int]						      =					image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[0.3_502, 0.3_622, 0.3_396, 0.3_642, 0.3_478, 0.3_318, 0.35, 0.3_348, 0.3_297]

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

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

     SCREAMING_SNAKE_CASE:Optional[Any]						      =					torch.manual_seed(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					sd_pipe(
         [prompt]     ,generator=SCREAMING_SNAKE_CASE__     ,guidance_scale=SCREAMING_SNAKE_CASE__     ,num_inference_steps=50     ,output_type="np"     ,width=512     ,height=512     ,sld_guidance_scale=2_000     ,sld_warmup_steps=7     ,sld_threshold=0.025     ,sld_momentum_scale=0.5     ,sld_mom_beta=0.7     ,)

     SCREAMING_SNAKE_CASE:str						      =					output.images
     SCREAMING_SNAKE_CASE:List[Any]						      =					image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE:str						      =					[0.5_531, 0.5_206, 0.4_895, 0.5_156, 0.5_182, 0.4_751, 0.4_802, 0.4_803, 0.4_443]

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

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







   def     __UpperCamelCase						( self			:      Dict							):
     SCREAMING_SNAKE_CASE:Tuple						      =					StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"							)
     SCREAMING_SNAKE_CASE:List[Any]						      =					sd_pipe.to(SCREAMING_SNAKE_CASE__							)
     sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Optional[Any]						      =					(
         "the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."
         " leyendecker"
     )
     SCREAMING_SNAKE_CASE:Tuple						      =					1_044_355_234
     SCREAMING_SNAKE_CASE:str						      =					12

     SCREAMING_SNAKE_CASE:Optional[Any]						      =					torch.manual_seed(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					sd_pipe(
         [prompt]     ,generator=SCREAMING_SNAKE_CASE__     ,guidance_scale=SCREAMING_SNAKE_CASE__     ,num_inference_steps=50     ,output_type="np"     ,width=512     ,height=512     ,sld_guidance_scale=0     ,)

     SCREAMING_SNAKE_CASE:Tuple						      =					output.images
     SCREAMING_SNAKE_CASE:str						      =					image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE:List[str]						      =					np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]							)

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

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

     SCREAMING_SNAKE_CASE:Optional[int]						      =					torch.manual_seed(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:List[str]						      =					sd_pipe(
         [prompt]     ,generator=SCREAMING_SNAKE_CASE__     ,guidance_scale=SCREAMING_SNAKE_CASE__     ,num_inference_steps=50     ,output_type="np"     ,width=512     ,height=512     ,sld_guidance_scale=2_000     ,sld_warmup_steps=7     ,sld_threshold=0.025     ,sld_momentum_scale=0.5     ,sld_mom_beta=0.7     ,)

     SCREAMING_SNAKE_CASE:str						      =					output.images
     SCREAMING_SNAKE_CASE:Tuple						      =					image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE:Tuple						      =					np.array([0.5_818, 0.6_285, 0.6_835, 0.6_019, 0.625, 0.6_754, 0.6_096, 0.6_334, 0.6_561]							)
     assert image.shape == (1, 512, 512, 3)

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



'''simple docstring'''

def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:Dict						      =					(1 + 24 * n) ** 0.5
  return ((1 + root) / 6) % 1 == 0





def    A_  ( snake_case = 5000 ):
  SCREAMING_SNAKE_CASE:int						      =					[(i * (3 * i - 1)) // 2 for i in range(1      ,       snake_case )]
  for i, pentagonal_i in enumerate(snake_case ):
    for j in range(snake_case      ,       len(snake_case ) ):
      SCREAMING_SNAKE_CASE:int						      =					pentagonal_nums[j]
      SCREAMING_SNAKE_CASE:Any						      =					pentagonal_i + pentagonal_j
      SCREAMING_SNAKE_CASE:int						      =					pentagonal_j - pentagonal_i
      if is_pentagonal(snake_case ) and is_pentagonal(snake_case ):
        return b

  return -1


if __name__ == "__main__":
    print(f'''{solution() = }''')






