






'''simple docstring'''




import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_=False)							->     List[str]:
		try:
				UpperCamelCase__				:					Union[str, Any]					=					os.environ[key]
		except KeyError:
				# KEY isn't set, default to `default`.
				UpperCamelCase__				:					Any					=					default
		else:
				# KEY is set, convert it to True or False.
				try:
						UpperCamelCase__				:					Union[str, Any]					=					strtobool(lowerCamelCase_)
				except ValueError:
						# More values are supported, but let's keep the message simple.
						raise ValueError(f'If set, {key} must be yes or no.')
		return _value


lowerCAmelCase__          =						parse_flag_from_env('RUN_SLOW', default=False)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Optional[Any]:
		return unittest.skip('Test was skipped')(lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     List[str]:
		return unittest.skipUnless(_run_slow_tests     ,      'test is slow')(lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Union[str, Any]:
		return unittest.skipUnless(not torch.cuda.is_available()     ,      'test requires only a CPU')(lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Any:
		return unittest.skipUnless(torch.cuda.is_available()     ,      'test requires a GPU')(lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Optional[int]:
		return unittest.skipUnless(is_xpu_available()     ,      'test requires a XPU')(lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     int:
		return unittest.skipUnless(is_mps_available()     ,      'test requires a `mps` backend support in `torch`')(lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Tuple:
		return unittest.skipUnless(
		    is_transformers_available() and is_datasets_available()     ,      'test requires the Hugging Face suite')(lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     str:
		return unittest.skipUnless(is_bnb_available()     ,      'test requires the bitsandbytes library')(lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Dict:
		return unittest.skipUnless(is_tpu_available()     ,      'test requires TPU')(lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Tuple:
		return unittest.skipUnless(torch.cuda.device_count() == 1     ,      'test requires a GPU')(lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Optional[Any]:
		return unittest.skipUnless(torch.xpu.device_count() == 1     ,      'test requires a XPU')(lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Union[str, Any]:
		return unittest.skipUnless(torch.cuda.device_count() > 1     ,      'test requires multiple GPUs')(lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Optional[int]:
		return unittest.skipUnless(torch.xpu.device_count() > 1     ,      'test requires multiple XPUs')(lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     List[str]:
		return unittest.skipUnless(is_safetensors_available()     ,      'test requires safetensors')(lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Tuple:
		return unittest.skipUnless(is_deepspeed_available()     ,      'test requires DeepSpeed')(lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Optional[int]:
		return unittest.skipUnless(is_torch_version('>='     ,      '1.12.0')     ,      'test requires torch version >= 1.12.0')(lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_=None     ,      lowerCamelCase_=None)							->     Any:
		if test_case is None:
				return partial(lowerCamelCase_     ,      version=lowerCamelCase_)
		return unittest.skipUnless(is_torch_version('>='     ,      lowerCamelCase_)     ,      f'test requires torch version >= {version}')(lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Optional[int]:
		return unittest.skipUnless(is_tensorboard_available()     ,      'test requires Tensorboard')(lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     int:
		return unittest.skipUnless(is_wandb_available()     ,      'test requires wandb')(lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Optional[Any]:
		return unittest.skipUnless(is_comet_ml_available()     ,      'test requires comet_ml')(lowerCamelCase_)


lowerCAmelCase__          =						(
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Optional[Any]:
		return unittest.skipUnless(
		    _atleast_one_tracker_available     ,      'test requires at least one tracker to be available and for `comet_ml` to not be installed'     ,      )(lowerCamelCase_)







class       __lowercase					(unittest.TestCase    ):
	_lowerCamelCase              =  True

	@classmethod
	def      __UpperCamelCase							(      cls   :						Tuple):
			UpperCamelCase__				:					Union[str, Any]					=					tempfile.mkdtemp()

	@classmethod
	def      __UpperCamelCase							(      cls   :						Any):
			if os.path.exists(cls.tmpdir):
					shutil.rmtree(cls.tmpdir)





	def      __UpperCamelCase							(      self   :						Dict):
			if self.clear_on_setup:
					for path in Path(self.tmpdir).glob('**/*'):
							if path.is_file():
									path.unlink()
							elif path.is_dir():
									shutil.rmtree(UpperCAmelCase_)







class       __lowercase					(unittest.TestCase    ):

	def      __UpperCamelCase							(      self   :						Dict):
			super().tearDown()
			# Reset the state of the AcceleratorState singleton.
			AcceleratorState._reset_state()
			PartialState._reset_state()







class       __lowercase					(unittest.TestCase    ):

	def      __UpperCamelCase							(      self   :						Dict  ,					UpperCAmelCase_   :						Union[mock.Mock, List[mock.Mock]]):
			UpperCamelCase__				:					Dict					=					mocks if isinstance(UpperCAmelCase_  ,					(tuple, list)) else [mocks]
			for m in self.mocks:
					m.start()
					self.addCleanup(m.stop)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     int:
		UpperCamelCase__				:					Any					=					AcceleratorState()
		UpperCamelCase__				:					List[Any]					=					tensor[None].clone().to(state.device)
		UpperCamelCase__				:					List[str]					=					gather(lowerCamelCase_).cpu()
		UpperCamelCase__				:					int					=					tensor[0].cpu()
		for i in range(tensors.shape[0]):
				if not torch.equal(tensors[i]     ,      lowerCamelCase_):
						return False
		return True







class       __lowercase					:

	def __init__(      self   :						str  ,					UpperCAmelCase_   :						List[str]  ,					UpperCAmelCase_   :						Optional[int]  ,					UpperCAmelCase_   :						Optional[int]):
			UpperCamelCase__				:					Optional[int]					=					returncode
			UpperCamelCase__				:					Any					=					stdout
			UpperCamelCase__				:					Union[str, Any]					=					stderr







async def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_)							->     Union[str, Any]:
		while True:
				UpperCamelCase__				:					Optional[int]					=					await stream.readline()
				if line:
						callback(lowerCamelCase_)
				else:
						break







async def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_=None     ,      lowerCamelCase_=None     ,      lowerCamelCase_=None     ,      lowerCamelCase_=False     ,      lowerCamelCase_=False)							->     _RunOutput:
		if echo:
				print('\nRunning: '     ,      ' '.join(lowerCamelCase_))

		UpperCamelCase__				:					Any					=					await asyncio.create_subprocess_exec(
		    cmd[0]     ,      *cmd[1:]     ,      stdin=lowerCamelCase_     ,      stdout=asyncio.subprocess.PIPE     ,      stderr=asyncio.subprocess.PIPE     ,      env=lowerCamelCase_     ,      )

		# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
		# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
		#
		# If it starts hanging, will need to switch to the following code. The problem is that no data
		# will be seen until it's done and if it hangs for example there will be no debug info.
		# out, err = await p.communicate()
		# return _RunOutput(p.returncode, out, err)

		UpperCamelCase__				:					Optional[int]					=					[]
		UpperCamelCase__				:					int					=					[]

		def tee(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_=""):
				UpperCamelCase__				:					Union[str, Any]					=					line.decode('utf-8').rstrip()
				sink.append(lowerCamelCase_)
				if not quiet:
						print(lowerCamelCase_     ,      lowerCamelCase_     ,      file=lowerCamelCase_)

    # XXX: the timeout doesn't seem to make any difference here
		await asyncio.wait(
		    [
		        asyncio.create_task(_read_stream(p.stdout     ,      lambda lowerCamelCase_: tee(lowerCamelCase_     ,      lowerCamelCase_     ,      sys.stdout     ,      label='stdout:'))),
		        asyncio.create_task(_read_stream(p.stderr     ,      lambda lowerCamelCase_: tee(lowerCamelCase_     ,      lowerCamelCase_     ,      sys.stderr     ,      label='stderr:'))),
		    ]     ,      timeout=lowerCamelCase_     ,      )
		return _RunOutput(await p.wait()     ,      lowerCamelCase_     ,      lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_=None     ,      lowerCamelCase_=None     ,      lowerCamelCase_=180     ,      lowerCamelCase_=False     ,      lowerCamelCase_=True)							->     _RunOutput:
		UpperCamelCase__				:					Union[str, Any]					=					asyncio.get_event_loop()
		UpperCamelCase__				:					Tuple					=					loop.run_until_complete(
		    _stream_subprocess(lowerCamelCase_     ,      env=lowerCamelCase_     ,      stdin=lowerCamelCase_     ,      timeout=lowerCamelCase_     ,      quiet=lowerCamelCase_     ,      echo=lowerCamelCase_))

		UpperCamelCase__				:					Union[str, Any]					=					' '.join(lowerCamelCase_)
		if result.returncode > 0:
				UpperCamelCase__				:					int					=					'\n'.join(result.stderr)
				raise RuntimeError(
				    f'\'{cmd_str}\' failed with returncode {result.returncode}\n\n'
				    f'The combined stderr from workers follows:\n{stderr}')

		return result







class       __lowercase					(__lowerCamelCase    ):
	pass







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_=False)							->     Dict:
		try:
				UpperCamelCase__				:					int					=					subprocess.check_output(lowerCamelCase_     ,      stderr=subprocess.STDOUT)
				if return_stdout:
						if hasattr(lowerCamelCase_     ,      'decode'):
								UpperCamelCase__				:					int					=					output.decode('utf-8')
						return output
		except subprocess.CalledProcessError as e:
				raise SubprocessCallException(
				    f'Command `{" ".join(lowerCamelCase_)}` failed with the following error:\n\n{e.output.decode()}') from e












'''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       __lowercase					(unittest.TestCase    ):

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

	@property
	def      __UpperCamelCase							(      self   :						List[Any]):
			UpperCamelCase__				:					Union[str, Any]					=					1
			UpperCamelCase__				:					Union[str, Any]					=					3
			UpperCamelCase__				:					Dict					=					(32, 32)

			UpperCamelCase__				:					int					=					floats_tensor((batch_size, num_channels) + sizes  ,					rng=random.Random(0)).to(UpperCAmelCase_)
			return image

	@property
	def      __UpperCamelCase							(      self   :						Any):
			torch.manual_seed(0)
			UpperCamelCase__				:					Any					=					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   :						Any):
			torch.manual_seed(0)
			UpperCamelCase__				:					List[str]					=					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   :						str):
			torch.manual_seed(0)
			UpperCamelCase__				:					Tuple					=					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(UpperCAmelCase_)

	@property
	def      __UpperCamelCase							(      self   :						Optional[Any]):
			def extract(*UpperCAmelCase_   :						List[Any]  ,					**UpperCAmelCase_   :						Dict):







					class       __lowercase					:

						def __init__(      self   :						List[Any]):
								UpperCamelCase__				:					Optional[Any]					=					torch.ones([0])





						def      __UpperCamelCase							(      self   :						Dict  ,					UpperCAmelCase_   :						int):
								self.pixel_values.to(UpperCAmelCase_)
								return self


					return Out()

			return extract

	def      __UpperCamelCase							(      self   :						str):
			UpperCamelCase__				:					Optional[Any]					=					'cpu'  # ensure determinism for the device-dependent torch.Generator
			UpperCamelCase__				:					Any					=					self.dummy_cond_unet
			UpperCamelCase__				:					Any					=					DDIMScheduler(
			    beta_start=0.0_00_85  ,					beta_end=0.0_12  ,					beta_schedule='scaled_linear'  ,					clip_sample=UpperCAmelCase_  ,					set_alpha_to_one=UpperCAmelCase_  ,					)

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

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

			UpperCamelCase__				:					Optional[Any]					=					'A painting of a squirrel eating a burger'

			UpperCamelCase__				:					Dict					=					torch.Generator(device=UpperCAmelCase_).manual_seed(0)
			UpperCamelCase__				:					List[Any]					=					sd_pipe([prompt]  ,					generator=UpperCAmelCase_  ,					guidance_scale=6.0  ,					num_inference_steps=2  ,					output_type='np')
			UpperCamelCase__				:					Tuple					=					output.images

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

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

			assert image.shape == (1, 64, 64, 3)
			UpperCamelCase__				:					List[Any]					=					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      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					List[Any]					=					'cpu'  # ensure determinism for the device-dependent torch.Generator
			UpperCamelCase__				:					int					=					self.dummy_cond_unet
			UpperCamelCase__				:					Dict					=					PNDMScheduler(skip_prk_steps=UpperCAmelCase_)
			UpperCamelCase__				:					Optional[int]					=					self.dummy_vae
			UpperCamelCase__				:					Optional[int]					=					self.dummy_text_encoder
			UpperCamelCase__				:					Union[str, Any]					=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

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

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

			UpperCamelCase__				:					List[str]					=					output.images

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

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

			assert image.shape == (1, 64, 64, 3)
			UpperCamelCase__				:					List[Any]					=					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      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					Dict					=					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__				:					List[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(UpperCAmelCase_)
					UpperCamelCase__				:					List[str]					=					StableDiffusionPipeline.from_pretrained(UpperCAmelCase_)

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

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

			# make sure here that pndm scheduler skips prk
			UpperCamelCase__				:					Optional[int]					=					StableDiffusionPipeline(
			    unet=UpperCAmelCase_  ,					scheduler=UpperCAmelCase_  ,					vae=UpperCAmelCase_  ,					text_encoder=UpperCAmelCase_  ,					tokenizer=UpperCAmelCase_  ,					safety_checker=UpperCAmelCase_  ,					feature_extractor=self.dummy_extractor  ,					)
			UpperCamelCase__				:					Dict					=					sd_pipe.to(UpperCAmelCase_)
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_)

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

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







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

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

	def      __UpperCamelCase							(      self   :						List[Any]):
			UpperCamelCase__				:					Optional[int]					=					StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'  ,					safety_checker=UpperCAmelCase_)
			UpperCamelCase__				:					Union[str, Any]					=					LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
			UpperCamelCase__				:					Optional[Any]					=					sd_pipe.to(UpperCAmelCase_)
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					List[Any]					=					(
			    '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__				:					Any					=					4_003_660_346
			UpperCamelCase__				:					Any					=					7

			# without safety guidance (sld_guidance_scale = 0)
			UpperCamelCase__				:					int					=					torch.manual_seed(UpperCAmelCase_)
			UpperCamelCase__				:					Optional[int]					=					sd_pipe(
			    [prompt]  ,					generator=UpperCAmelCase_  ,					guidance_scale=UpperCAmelCase_  ,					num_inference_steps=50  ,					output_type='np'  ,					width=512  ,					height=512  ,					sld_guidance_scale=0  ,					)

			UpperCamelCase__				:					str					=					output.images
			UpperCamelCase__				:					Union[str, Any]					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					Tuple					=					[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, 512, 512, 3)

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

			# without safety guidance (strong configuration)
			UpperCamelCase__				:					Tuple					=					torch.manual_seed(UpperCAmelCase_)
			UpperCamelCase__				:					str					=					sd_pipe(
			    [prompt]  ,					generator=UpperCAmelCase_  ,					guidance_scale=UpperCAmelCase_  ,					num_inference_steps=50  ,					output_type='np'  ,					width=512  ,					height=512  ,					sld_guidance_scale=2_000  ,					sld_warmup_steps=7  ,					sld_threshold=0.0_25  ,					sld_momentum_scale=0.5  ,					sld_mom_beta=0.7  ,					)

			UpperCamelCase__				:					Dict					=					output.images
			UpperCamelCase__				:					str					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					Tuple					=					[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, 512, 512, 3)

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

	def      __UpperCamelCase							(      self   :						Optional[Any]):
			UpperCamelCase__				:					Dict					=					StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'  ,					safety_checker=UpperCAmelCase_)
			UpperCamelCase__				:					str					=					LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
			UpperCamelCase__				:					Dict					=					sd_pipe.to(UpperCAmelCase_)
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					str					=					'padme amidala taking a bath artwork, safe for work, no nudity'
			UpperCamelCase__				:					Tuple					=					2_734_971_755
			UpperCamelCase__				:					Tuple					=					7

			UpperCamelCase__				:					Tuple					=					torch.manual_seed(UpperCAmelCase_)
			UpperCamelCase__				:					int					=					sd_pipe(
			    [prompt]  ,					generator=UpperCAmelCase_  ,					guidance_scale=UpperCAmelCase_  ,					num_inference_steps=50  ,					output_type='np'  ,					width=512  ,					height=512  ,					sld_guidance_scale=0  ,					)

			UpperCamelCase__				:					int					=					output.images
			UpperCamelCase__				:					Union[str, Any]					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					Any					=					[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, 512, 512, 3)

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

			UpperCamelCase__				:					List[str]					=					torch.manual_seed(UpperCAmelCase_)
			UpperCamelCase__				:					Union[str, Any]					=					sd_pipe(
			    [prompt]  ,					generator=UpperCAmelCase_  ,					guidance_scale=UpperCAmelCase_  ,					num_inference_steps=50  ,					output_type='np'  ,					width=512  ,					height=512  ,					sld_guidance_scale=2_000  ,					sld_warmup_steps=7  ,					sld_threshold=0.0_25  ,					sld_momentum_scale=0.5  ,					sld_mom_beta=0.7  ,					)

			UpperCamelCase__				:					Tuple					=					output.images
			UpperCamelCase__				:					List[str]					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					Union[str, Any]					=					[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, 512, 512, 3)

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





	def      __UpperCamelCase							(      self   :						Any):
			UpperCamelCase__				:					Optional[Any]					=					StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5')
			UpperCamelCase__				:					Optional[Any]					=					sd_pipe.to(UpperCAmelCase_)
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_)

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

			UpperCamelCase__				:					Optional[int]					=					torch.manual_seed(UpperCAmelCase_)
			UpperCamelCase__				:					str					=					sd_pipe(
			    [prompt]  ,					generator=UpperCAmelCase_  ,					guidance_scale=UpperCAmelCase_  ,					num_inference_steps=50  ,					output_type='np'  ,					width=512  ,					height=512  ,					sld_guidance_scale=0  ,					)

			UpperCamelCase__				:					List[str]					=					output.images
			UpperCamelCase__				:					Any					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					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

			UpperCamelCase__				:					int					=					torch.manual_seed(UpperCAmelCase_)
			UpperCamelCase__				:					List[str]					=					sd_pipe(
			    [prompt]  ,					generator=UpperCAmelCase_  ,					guidance_scale=UpperCAmelCase_  ,					num_inference_steps=50  ,					output_type='np'  ,					width=512  ,					height=512  ,					sld_guidance_scale=2_000  ,					sld_warmup_steps=7  ,					sld_threshold=0.0_25  ,					sld_momentum_scale=0.5  ,					sld_mom_beta=0.7  ,					)

			UpperCamelCase__				:					Optional[Any]					=					output.images
			UpperCamelCase__				:					List[Any]					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					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, 512, 512, 3)

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




