





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        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):







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







							@property
							def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->Dict:
										lowerCamelCase__       :						Any        =							1
										lowerCamelCase__       :						Tuple        =							3
										lowerCamelCase__       :						str        =							(3_2, 3_2)

										lowerCamelCase__       :						List[Any]        =							floats_tensor((batch_size, num_channels) + sizes		, rng=random.Random(0      )      ).to(A      )
										return image







							@property
							def 						__lowerCamelCase   (				self					:     Any      )       ->List[Any]:
										torch.manual_seed(0      )
										lowerCamelCase__       :						Tuple        =							UNetaDConditionModel(
										    block_out_channels=(3_2, 6_4)		, layers_per_block=2		, sample_size=3_2		, in_channels=4		, out_channels=4		, down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		, up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		, cross_attention_dim=3_2		, )
										return model







							@property
							def 						__lowerCamelCase   (				self					:     List[str]      )       ->str:
										torch.manual_seed(0      )
										lowerCamelCase__       :						List[str]        =							AutoencoderKL(
										    block_out_channels=[3_2, 6_4]		, in_channels=3		, out_channels=3		, down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		, up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		, latent_channels=4		, )
										return model







							@property
							def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Optional[int]:
										torch.manual_seed(0      )
										lowerCamelCase__       :						Dict        =							CLIPTextConfig(
										    bos_token_id=0		, eos_token_id=2		, hidden_size=3_2		, intermediate_size=3_7		, layer_norm_eps=1e-05		, num_attention_heads=4		, num_hidden_layers=5		, pad_token_id=1		, vocab_size=1_0_0_0		, )
										return CLIPTextModel(A      )







							@property
							def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->Optional[int]:
										def extract(*A					:     Union[str, Any]		, **A					:     Optional[Any]      ):


													class        __SCREAMING_SNAKE_CASE      :







																				def __init__(				self					:     int      )       ->int:
																							lowerCamelCase__       :						List[Any]        =							torch.ones([0]      )







																				def 						__lowerCamelCase   (				self					:     str		, A					:     Tuple      )       ->Dict:
																							self.pixel_values.to(A      )
																							return self


													return Out()

										return extract







							def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Any:
										lowerCamelCase__       :						Tuple        =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
										lowerCamelCase__       :						List[str]        =							self.dummy_cond_unet
										lowerCamelCase__       :						Optional[Any]        =							DDIMScheduler(
										    beta_start=0.0_00_85		, beta_end=0.0_12		, beta_schedule='''scaled_linear'''		, clip_sample=A		, set_alpha_to_one=A		, )

										lowerCamelCase__       :						str        =							self.dummy_vae
										lowerCamelCase__       :						List[Any]        =							self.dummy_text_encoder
										lowerCamelCase__       :						Tuple        =							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

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

										lowerCamelCase__       :						str        =							'''A painting of a squirrel eating a burger'''

										lowerCamelCase__       :						Tuple        =							torch.Generator(device=A      ).manual_seed(0      )
										lowerCamelCase__       :						Optional[int]        =							sd_pipe([prompt]		, generator=A		, guidance_scale=6.0		, num_inference_steps=2		, output_type='''np'''      )
										lowerCamelCase__       :						Optional[int]        =							output.images

										lowerCamelCase__       :						str        =							torch.Generator(device=A      ).manual_seed(0      )
										lowerCamelCase__       :						Tuple        =							sd_pipe(
										    [prompt]		, generator=A		, guidance_scale=6.0		, num_inference_steps=2		, output_type='''np'''		, return_dict=A		, )[0]

										lowerCamelCase__       :						str        =							image[0, -3:, -3:, -1]
										lowerCamelCase__       :						List[str]        =							image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 6_4, 6_4, 3)
										lowerCamelCase__       :						Tuple        =							np.array([0.57_56, 0.61_18, 0.50_05, 0.50_41, 0.54_71, 0.47_26, 0.49_76, 0.48_65, 0.48_64]      )

										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 						__lowerCamelCase   (				self					:     Dict      )       ->Optional[int]:
										lowerCamelCase__       :						int        =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
										lowerCamelCase__       :						List[str]        =							self.dummy_cond_unet
										lowerCamelCase__       :						List[Any]        =							PNDMScheduler(skip_prk_steps=A      )
										lowerCamelCase__       :						Optional[Any]        =							self.dummy_vae
										lowerCamelCase__       :						str        =							self.dummy_text_encoder
										lowerCamelCase__       :						List[str]        =							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

										# make sure here that pndm scheduler skips prk
										lowerCamelCase__       :						Tuple        =							StableDiffusionPipeline(
										    unet=A		, scheduler=A		, vae=A		, text_encoder=A		, tokenizer=A		, safety_checker=A		, feature_extractor=self.dummy_extractor		, )
										lowerCamelCase__       :						List[str]        =							sd_pipe.to(A      )
										sd_pipe.set_progress_bar_config(disable=A      )

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

										lowerCamelCase__       :						Any        =							output.images

										lowerCamelCase__       :						List[Any]        =							torch.Generator(device=A      ).manual_seed(0      )
										lowerCamelCase__       :						List[Any]        =							sd_pipe(
										    [prompt]		, generator=A		, guidance_scale=6.0		, num_inference_steps=2		, output_type='''np'''		, return_dict=A		, )[0]

										lowerCamelCase__       :						Optional[Any]        =							image[0, -3:, -3:, -1]
										lowerCamelCase__       :						Tuple        =							image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 6_4, 6_4, 3)
										lowerCamelCase__       :						int        =							np.array([0.51_25, 0.57_16, 0.48_28, 0.50_60, 0.56_50, 0.47_68, 0.51_85, 0.48_95, 0.49_93]      )

										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 						__lowerCamelCase   (				self					:     str      )       ->int:
										lowerCamelCase__       :						List[str]        =							StableDiffusionPipeline.from_pretrained(
										    '''hf-internal-testing/tiny-stable-diffusion-lms-pipe'''		, safety_checker=A      )
										assert isinstance(A		, A      )
										assert isinstance(pipe.scheduler		, A      )
										assert pipe.safety_checker is None

										lowerCamelCase__       :						Union[str, Any]        =							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(A      )
													lowerCamelCase__       :						Optional[Any]        =							StableDiffusionPipeline.from_pretrained(A      )

										# sanity check that the pipeline still works
										assert pipe.safety_checker is None
										lowerCamelCase__       :						Union[str, Any]        =							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 						__lowerCamelCase   (				self					:     int      )       ->str:
										lowerCamelCase__       :						Any        =							self.dummy_cond_unet
										lowerCamelCase__       :						Dict        =							PNDMScheduler(skip_prk_steps=A      )
										lowerCamelCase__       :						Union[str, Any]        =							self.dummy_vae
										lowerCamelCase__       :						int        =							self.dummy_text_encoder
										lowerCamelCase__       :						List[Any]        =							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

										# put models in fp16
										lowerCamelCase__       :						int        =							unet.half()
										lowerCamelCase__       :						List[Any]        =							vae.half()
										lowerCamelCase__       :						Tuple        =							bert.half()

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

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

										assert image.shape == (1, 6_4, 6_4, 3)


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







							def 						__lowerCamelCase   (				self					:     List[str]      )       ->Dict:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







							def 						__lowerCamelCase   (				self					:     List[Any]      )       ->Optional[int]:
										lowerCamelCase__       :						int        =							StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''		, safety_checker=A      )
										lowerCamelCase__       :						Any        =							LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config      )
										lowerCamelCase__       :						Any        =							sd_pipe.to(A      )
										sd_pipe.set_progress_bar_config(disable=A      )

										lowerCamelCase__       :						int        =							(
										    '''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__       :						List[str]        =							4_0_0_3_6_6_0_3_4_6
										lowerCamelCase__       :						List[str]        =							7

										# without safety guidance (sld_guidance_scale = 0)
										lowerCamelCase__       :						Dict        =							torch.manual_seed(A      )
										lowerCamelCase__       :						List[Any]        =							sd_pipe(
										    [prompt]		, generator=A		, guidance_scale=A		, num_inference_steps=5_0		, output_type='''np'''		, width=5_1_2		, height=5_1_2		, sld_guidance_scale=0		, )

										lowerCamelCase__       :						List[Any]        =							output.images
										lowerCamelCase__       :						str        =							image[0, -3:, -3:, -1]
										lowerCamelCase__       :						Any        =							[0.22_78, 0.22_31, 0.22_49, 0.23_33, 0.23_03, 0.18_85, 0.22_73, 0.21_44, 0.21_76]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

										# without safety guidance (strong configuration)
										lowerCamelCase__       :						str        =							torch.manual_seed(A      )
										lowerCamelCase__       :						int        =							sd_pipe(
										    [prompt]		, generator=A		, guidance_scale=A		, num_inference_steps=5_0		, output_type='''np'''		, width=5_1_2		, height=5_1_2		, sld_guidance_scale=2_0_0_0		, sld_warmup_steps=7		, sld_threshold=0.0_25		, sld_momentum_scale=0.5		, sld_mom_beta=0.7		, )

										lowerCamelCase__       :						Any        =							output.images
										lowerCamelCase__       :						List[Any]        =							image[0, -3:, -3:, -1]
										lowerCamelCase__       :						int        =							[0.23_83, 0.22_76, 0.2_36, 0.21_92, 0.21_86, 0.20_53, 0.19_71, 0.19_01, 0.17_19]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

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







							def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Any:
										lowerCamelCase__       :						List[Any]        =							StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''		, safety_checker=A      )
										lowerCamelCase__       :						List[Any]        =							LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config      )
										lowerCamelCase__       :						Optional[Any]        =							sd_pipe.to(A      )
										sd_pipe.set_progress_bar_config(disable=A      )

										lowerCamelCase__       :						str        =							'''padme amidala taking a bath artwork, safe for work, no nudity'''
										lowerCamelCase__       :						int        =							2_7_3_4_9_7_1_7_5_5
										lowerCamelCase__       :						List[str]        =							7

										lowerCamelCase__       :						Dict        =							torch.manual_seed(A      )
										lowerCamelCase__       :						Optional[int]        =							sd_pipe(
										    [prompt]		, generator=A		, guidance_scale=A		, num_inference_steps=5_0		, output_type='''np'''		, width=5_1_2		, height=5_1_2		, sld_guidance_scale=0		, )

										lowerCamelCase__       :						str        =							output.images
										lowerCamelCase__       :						int        =							image[0, -3:, -3:, -1]
										lowerCamelCase__       :						Tuple        =							[0.35_02, 0.36_22, 0.33_96, 0.36_42, 0.34_78, 0.33_18, 0.35, 0.33_48, 0.32_97]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

										lowerCamelCase__       :						Any        =							torch.manual_seed(A      )
										lowerCamelCase__       :						List[Any]        =							sd_pipe(
										    [prompt]		, generator=A		, guidance_scale=A		, num_inference_steps=5_0		, output_type='''np'''		, width=5_1_2		, height=5_1_2		, sld_guidance_scale=2_0_0_0		, sld_warmup_steps=7		, sld_threshold=0.0_25		, sld_momentum_scale=0.5		, sld_mom_beta=0.7		, )

										lowerCamelCase__       :						Union[str, Any]        =							output.images
										lowerCamelCase__       :						List[str]        =							image[0, -3:, -3:, -1]
										lowerCamelCase__       :						Optional[int]        =							[0.55_31, 0.52_06, 0.48_95, 0.51_56, 0.51_82, 0.47_51, 0.48_02, 0.48_03, 0.44_43]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

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







							def 						__lowerCamelCase   (				self					:     List[str]      )       ->int:
										lowerCamelCase__       :						Optional[Any]        =							StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''      )
										lowerCamelCase__       :						Union[str, Any]        =							sd_pipe.to(A      )
										sd_pipe.set_progress_bar_config(disable=A      )

										lowerCamelCase__       :						Any        =							(
										    '''the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'''
										    ''' leyendecker'''
										)
										lowerCamelCase__       :						Tuple        =							1_0_4_4_3_5_5_2_3_4
										lowerCamelCase__       :						Any        =							1_2

										lowerCamelCase__       :						int        =							torch.manual_seed(A      )
										lowerCamelCase__       :						Dict        =							sd_pipe(
										    [prompt]		, generator=A		, guidance_scale=A		, num_inference_steps=5_0		, output_type='''np'''		, width=5_1_2		, height=5_1_2		, sld_guidance_scale=0		, )

										lowerCamelCase__       :						Optional[Any]        =							output.images
										lowerCamelCase__       :						int        =							image[0, -3:, -3:, -1]
										lowerCamelCase__       :						Any        =							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_1_2, 5_1_2, 3)

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

										lowerCamelCase__       :						Union[str, Any]        =							torch.manual_seed(A      )
										lowerCamelCase__       :						List[Any]        =							sd_pipe(
										    [prompt]		, generator=A		, guidance_scale=A		, num_inference_steps=5_0		, output_type='''np'''		, width=5_1_2		, height=5_1_2		, sld_guidance_scale=2_0_0_0		, sld_warmup_steps=7		, sld_threshold=0.0_25		, sld_momentum_scale=0.5		, sld_mom_beta=0.7		, )

										lowerCamelCase__       :						Any        =							output.images
										lowerCamelCase__       :						List[Any]        =							image[0, -3:, -3:, -1]
										lowerCamelCase__       :						str        =							np.array([0.58_18, 0.62_85, 0.68_35, 0.60_19, 0.6_25, 0.67_54, 0.60_96, 0.63_34, 0.65_61]      )
										assert image.shape == (1, 5_1_2, 5_1_2, 3)

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










import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version



def 	_a						(   *UpperCAmelCase      ,  UpperCAmelCase = None      ,  UpperCAmelCase=True      ,  UpperCAmelCase=2					)    ->    str:







   """simple docstring"""
   from .. import __version__

   lowerCamelCase__       :						Optional[Any]        =							take_from
   lowerCamelCase__       :						Any        =							()
   if not isinstance(args[0]      ,  UpperCAmelCase					):
      lowerCamelCase__       :						Optional[int]        =							(args,)

   for attribute, version_name, message in args:
      if version.parse(version.parse(UpperCAmelCase					).base_version					) >= version.parse(UpperCAmelCase					):
         raise ValueError(
             f"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"
             f" version {__version__} is >= {version_name}"					)

      lowerCamelCase__       :						List[Any]        =							None
      if isinstance(UpperCAmelCase      ,  UpperCAmelCase					) and attribute in deprecated_kwargs:
         values += (deprecated_kwargs.pop(UpperCAmelCase					),)
         lowerCamelCase__       :						Tuple        =							f"The `{attribute}` argument is deprecated and will be removed in version {version_name}."
      elif hasattr(UpperCAmelCase      ,  UpperCAmelCase					):
         values += (getattr(UpperCAmelCase      ,  UpperCAmelCase					),)
         lowerCamelCase__       :						Union[str, Any]        =							f"The `{attribute}` attribute is deprecated and will be removed in version {version_name}."
      elif deprecated_kwargs is None:
         lowerCamelCase__       :						int        =							f"`{attribute}` is deprecated and will be removed in version {version_name}."

      if warning is not None:
         lowerCamelCase__       :						int        =							warning + ''' ''' if standard_warn else ''''''
         warnings.warn(warning + message      ,  UpperCAmelCase      ,  stacklevel=UpperCAmelCase					)

   if isinstance(UpperCAmelCase      ,  UpperCAmelCase					) and len(UpperCAmelCase					) > 0:
      lowerCamelCase__       :						Tuple        =							inspect.getouterframes(inspect.currentframe()					)[1]
      lowerCamelCase__       :						Optional[Any]        =							call_frame.filename
      lowerCamelCase__       :						List[Any]        =							call_frame.lineno
      lowerCamelCase__       :						Optional[Any]        =							call_frame.function
      lowerCamelCase__       ,							lowerCamelCase__       :						Union[str, Any]        =							next(iter(deprecated_kwargs.items()					)					)
      raise TypeError(f"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"					)

   if len(UpperCAmelCase					) == 0:
      return
   elif len(UpperCAmelCase					) == 1:
      return values[0]
   return values



