





'''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  lowerCAmelCase_		(		unittest.TestCase		):




 def 				_snake_case (						self		)     ->				int:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




 @property
 def 				_snake_case (						self		)     ->				Any:
     _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 				_snake_case (						self		)     ->				Tuple:
     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 				_snake_case (						self		)     ->				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 				_snake_case (						self		)     ->				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=1000      ,  )
     return CLIPTextModel(_lowerCAmelCase		)




 @property
 def 				_snake_case (						self		)     ->				Optional[int]:
     def extract(*_lowerCAmelCase      ,  **_lowerCAmelCase		):

         class  lowerCAmelCase_		:




          def __init__(						self		)     ->				Tuple:
              _lowerCAmelCase						      =   torch.ones([0]		)





          def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				str:
              self.pixel_values.to(_lowerCAmelCase		)
              return self







         return Out()

     return extract




 def 				_snake_case (						self		)     ->				Optional[int]:
     _lowerCAmelCase						      =   "cpu"  # ensure determinism for the device-dependent torch.Generator
     _lowerCAmelCase						      =   self.dummy_cond_unet
     _lowerCAmelCase						      =   DDIMScheduler(
         beta_start=0.00085      ,  beta_end=0.012      ,  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([0.5756, 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 				_snake_case (						self		)     ->				List[str]:
     _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([0.5125, 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 				_snake_case (						self		)     ->				Tuple:
     _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 				_snake_case (						self		)     ->				str:
     _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  lowerCAmelCase_		(		unittest.TestCase		):




 def 				_snake_case (						self		)     ->				Optional[Any]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




 def 				_snake_case (						self		)     ->				Optional[Any]:
     _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						      =   4003660346
     _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=512      ,  height=512      ,  sld_guidance_scale=0      ,  )

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

     assert image.shape == (1, 512, 512, 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=512      ,  height=512      ,  sld_guidance_scale=2000      ,  sld_warmup_steps=7      ,  sld_threshold=0.025      ,  sld_momentum_scale=0.5      ,  sld_mom_beta=0.7      ,  )

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

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

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




 def 				_snake_case (						self		)     ->				Optional[Any]:
     _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						      =   2734971755
     _lowerCAmelCase						      =   7

     _lowerCAmelCase						      =   torch.manual_seed(_lowerCAmelCase		)
     _lowerCAmelCase						      =   sd_pipe(
         [prompt]      ,  generator=_lowerCAmelCase      ,  guidance_scale=_lowerCAmelCase      ,  num_inference_steps=50      ,  output_type="np"      ,  width=512      ,  height=512      ,  sld_guidance_scale=0      ,  )

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

     assert image.shape == (1, 512, 512, 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=512      ,  height=512      ,  sld_guidance_scale=2000      ,  sld_warmup_steps=7      ,  sld_threshold=0.025      ,  sld_momentum_scale=0.5      ,  sld_mom_beta=0.7      ,  )

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

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

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





 def 				_snake_case (						self		)     ->				List[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						      =   1044355234
     _lowerCAmelCase						      =   12

     _lowerCAmelCase						      =   torch.manual_seed(_lowerCAmelCase		)
     _lowerCAmelCase						      =   sd_pipe(
         [prompt]      ,  generator=_lowerCAmelCase      ,  guidance_scale=_lowerCAmelCase      ,  num_inference_steps=50      ,  output_type="np"      ,  width=512      ,  height=512      ,  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, 512, 512, 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=512      ,  height=512      ,  sld_guidance_scale=2000      ,  sld_warmup_steps=7      ,  sld_threshold=0.025      ,  sld_momentum_scale=0.5      ,  sld_mom_beta=0.7      ,  )

     _lowerCAmelCase						      =   output.images
     _lowerCAmelCase						      =   image[0, -3:, -3:, -1]
     _lowerCAmelCase						      =   np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561]		)
     assert image.shape == (1, 512, 512, 3)

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









'''simple docstring'''


def 						__a(SCREAMING_SNAKE_CASE_	:       int		,							SCREAMING_SNAKE_CASE_	:       int     ):



    '''simple docstring'''





    return numa ^ numa < 0


if __name__ == "__main__":
       import doctest

       doctest.testmod()


