


'''simple docstring'''





import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler






class     UpperCamelCase:


					def __init__(						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Dict						)			->	str:




											'''simple docstring'''

											if isinstance(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						):
																	# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
																	# modified it, it will not be accepted here again, since `auto` values would have been overridden
																	__snake_case				=		deepcopy(SCREAMING_SNAKE_CASE						)
											elif os.path.exists(SCREAMING_SNAKE_CASE						):
																	with io.open(SCREAMING_SNAKE_CASE     ,    "r"     ,    encoding="utf-8"						) as f:
																							__snake_case				=		json.load(SCREAMING_SNAKE_CASE						)
											else:
																	try:
																							__snake_case				=		baseaa.urlsafe_baadecode(SCREAMING_SNAKE_CASE						).decode("utf-8"						)
																							__snake_case				=		json.loads(SCREAMING_SNAKE_CASE						)
																	except (UnicodeDecodeError, AttributeError, ValueError):
																							raise ValueError(
																							    f'''Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}'''						)

											__snake_case				=		config

											self.set_stage_and_offload()


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	List[str]:




											'''simple docstring'''

											__snake_case				=		self.get_value("zero_optimization.stage"     ,    -1						)

											# offload
											__snake_case				=		False
											if self.is_zeroa() or self.is_zeroa():
																	__snake_case				=		set(["cpu", "nvme"]						)
																	__snake_case				=		set(
																	    [
																	        self.get_value("zero_optimization.offload_optimizer.device"						),
																	        self.get_value("zero_optimization.offload_param.device"						),
																	    ]						)
																	if len(offload_devices & offload_devices_valid						) > 0:
																							__snake_case				=		True


					def 							SCREAMING_SNAKE_CASE_  (						self	:		int     ,    SCREAMING_SNAKE_CASE	:		List[Any]						)			->	str:




											'''simple docstring'''

											__snake_case				=		self.config

											# find the config node of interest if it exists
											__snake_case				=		ds_key_long.split("."						)
											__snake_case				=		nodes.pop()
											for node in nodes:
																	__snake_case				=		config.get(SCREAMING_SNAKE_CASE						)
																	if config is None:
																							return None, ds_key

											return config, ds_key


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		Tuple=None						)			->	List[str]:




											'''simple docstring'''

											__snake_case		,					__snake_case				=		self.find_config_node(SCREAMING_SNAKE_CASE						)
											if config is None:
																	return default
											return config.get(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Tuple=False						)			->	Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		self.config

											# find the config node of interest if it exists
											__snake_case				=		ds_key_long.split("."						)
											for node in nodes:
																	__snake_case				=		config
																	__snake_case				=		config.get(SCREAMING_SNAKE_CASE						)
																	if config is None:
																							if must_exist:
																													raise ValueError(f'''Can\'t find {ds_key_long} entry in the config: {self.config}'''						)
																							else:
																													return

        # if found remove it
											if parent_config is not None:
																	parent_config.pop(SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		Any						)			->	Optional[Any]:




											'''simple docstring'''

											__snake_case				=		self.get_value(SCREAMING_SNAKE_CASE						)
											return False if value is None else bool(SCREAMING_SNAKE_CASE						)


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




											'''simple docstring'''

											__snake_case				=		self.get_value(SCREAMING_SNAKE_CASE						)
											return False if value is None else not bool(SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	int:




											'''simple docstring'''

											return self._stage == 2


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	List[str]:




											'''simple docstring'''

											return self._stage == 3






					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]						)			->	Optional[Any]:




											'''simple docstring'''

											return self._offload






class     UpperCamelCase:


					def __init__(						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		Any						)			->	Optional[Any]:




											'''simple docstring'''

											__snake_case				=		engine






					def 							SCREAMING_SNAKE_CASE_  (						self	:		int     ,    SCREAMING_SNAKE_CASE	:		Tuple     ,    **SCREAMING_SNAKE_CASE	:		str						)			->	List[str]:




											'''simple docstring'''

											self.engine.backward(SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE						)

											# Deepspeed's `engine.step` performs the following operations:
											# - gradient accumulation check
											# - gradient clipping
											# - optimizer step
											# - zero grad
											# - checking overflow
											# - lr_scheduler step (only if engine.lr_scheduler is not None)
											self.engine.step()
											# and this plugin overrides the above calls with no-ops when Accelerate runs under
											# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
											# training loop that works transparently under many training regimes.






class     UpperCamelCase(  _a   ):


					def __init__(						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Dict						)			->	str:




											'''simple docstring'''

											super().__init__(SCREAMING_SNAKE_CASE     ,    device_placement=SCREAMING_SNAKE_CASE     ,    scaler=SCREAMING_SNAKE_CASE						)
											__snake_case				=		hasattr(self.optimizer     ,    "overflow"						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		List[Any]=None						)			->	str:




											'''simple docstring'''

											pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	Any:




											'''simple docstring'''

											pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	Any:




											'''simple docstring'''

											if self.__has_overflow__:
																	return self.optimizer.overflow
											return False






class     UpperCamelCase(  _a   ):


					def __init__(						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		str						)			->	Union[str, Any]:




											'''simple docstring'''

											super().__init__(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	Any:




											'''simple docstring'''

											pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






class     UpperCamelCase:


					def __init__(						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=0.001     ,    SCREAMING_SNAKE_CASE	:		Tuple=0     ,    **SCREAMING_SNAKE_CASE	:		Optional[int]						)			->	List[str]:




											'''simple docstring'''

											__snake_case				=		params
											__snake_case				=		lr
											__snake_case				=		weight_decay
											__snake_case				=		kwargs






class     UpperCamelCase:


					def __init__(						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Dict=None     ,    SCREAMING_SNAKE_CASE	:		Tuple=0     ,    **SCREAMING_SNAKE_CASE	:		Tuple						)			->	Tuple:




											'''simple docstring'''

											__snake_case				=		optimizer
											__snake_case				=		total_num_steps
											__snake_case				=		warmup_num_steps
											__snake_case				=		kwargs









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 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	Dict:




											'''simple docstring'''

											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()


					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple						)			->	Optional[Any]:




											'''simple docstring'''

											__snake_case				=		1
											__snake_case				=		3
											__snake_case				=		(3_2, 3_2)

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


					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	Any:




											'''simple docstring'''

											torch.manual_seed(0						)
											__snake_case				=		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 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	str:




											'''simple docstring'''

											torch.manual_seed(0						)
											__snake_case				=		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 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	int:




											'''simple docstring'''

											torch.manual_seed(0						)
											__snake_case				=		CLIPTextConfig(
											    bos_token_id=0     ,    eos_token_id=2     ,    hidden_size=3_2     ,    intermediate_size=3_7     ,    layer_norm_eps=1e-0_5     ,    num_attention_heads=4     ,    num_hidden_layers=5     ,    pad_token_id=1     ,    vocab_size=1_0_0_0     ,    )
											return CLIPTextModel(SCREAMING_SNAKE_CASE						)


					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]						)			->	str:




											'''simple docstring'''

											def extract(*SCREAMING_SNAKE_CASE	:		List[str]     ,    **SCREAMING_SNAKE_CASE	:		List[Any]						):






																	class     UpperCamelCase:


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




																												'''simple docstring'''

																												__snake_case				=		torch.ones([0]						)






																						def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]						)			->	List[Any]:




																												'''simple docstring'''

																												self.pixel_values.to(SCREAMING_SNAKE_CASE						)
																												return self




																	return Out()

											return extract


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		"cpu"  # ensure determinism for the device-dependent torch.Generator
											__snake_case				=		self.dummy_cond_unet
											__snake_case				=		DDIMScheduler(
											    beta_start=0.00085     ,    beta_end=0.012     ,    beta_schedule="scaled_linear"     ,    clip_sample=SCREAMING_SNAKE_CASE     ,    set_alpha_to_one=SCREAMING_SNAKE_CASE     ,    )

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

											# make sure here that pndm scheduler skips prk
											__snake_case				=		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     ,    )
											__snake_case				=		sd_pipe.to(SCREAMING_SNAKE_CASE						)
											sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

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

											__snake_case				=		torch.Generator(device=SCREAMING_SNAKE_CASE						).manual_seed(0						)
											__snake_case				=		sd_pipe([prompt]     ,    generator=SCREAMING_SNAKE_CASE     ,    guidance_scale=6.0     ,    num_inference_steps=2     ,    output_type="np"						)
											__snake_case				=		output.images

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

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

											assert image.shape == (1, 6_4, 6_4, 3)
											__snake_case				=		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 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	Tuple:




											'''simple docstring'''

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

											# make sure here that pndm scheduler skips prk
											__snake_case				=		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     ,    )
											__snake_case				=		sd_pipe.to(SCREAMING_SNAKE_CASE						)
											sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

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

											__snake_case				=		output.images

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

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

											assert image.shape == (1, 6_4, 6_4, 3)
											__snake_case				=		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 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	List[Any]:




											'''simple docstring'''

											__snake_case				=		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

											__snake_case				=		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						)
																	__snake_case				=		StableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE						)

											# sanity check that the pipeline still works
											assert pipe.safety_checker is None
											__snake_case				=		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 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	int:




											'''simple docstring'''

											__snake_case				=		self.dummy_cond_unet
											__snake_case				=		PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE						)
											__snake_case				=		self.dummy_vae
											__snake_case				=		self.dummy_text_encoder
											__snake_case				=		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

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

											# make sure here that pndm scheduler skips prk
											__snake_case				=		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     ,    )
											__snake_case				=		sd_pipe.to(SCREAMING_SNAKE_CASE						)
											sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

											__snake_case				=		"A painting of a squirrel eating a burger"
											__snake_case				=		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     UpperCamelCase(  unittest.TestCase   ):


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	Tuple:




											'''simple docstring'''

											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]						)			->	Optional[Any]:




											'''simple docstring'''

											__snake_case				=		StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"     ,    safety_checker=SCREAMING_SNAKE_CASE						)
											__snake_case				=		LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config						)
											__snake_case				=		sd_pipe.to(SCREAMING_SNAKE_CASE						)
											sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

											__snake_case				=		(
											    "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 "
											)
											__snake_case				=		4_0_0_3_6_6_0_3_4_6
											__snake_case				=		7

											# without safety guidance (sld_guidance_scale = 0)
											__snake_case				=		torch.manual_seed(SCREAMING_SNAKE_CASE						)
											__snake_case				=		sd_pipe(
											    [prompt]     ,    generator=SCREAMING_SNAKE_CASE     ,    guidance_scale=SCREAMING_SNAKE_CASE     ,    num_inference_steps=5_0     ,    output_type="np"     ,    width=5_1_2     ,    height=5_1_2     ,    sld_guidance_scale=0     ,    )

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

											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)
											__snake_case				=		torch.manual_seed(SCREAMING_SNAKE_CASE						)
											__snake_case				=		sd_pipe(
											    [prompt]     ,    generator=SCREAMING_SNAKE_CASE     ,    guidance_scale=SCREAMING_SNAKE_CASE     ,    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=0.025     ,    sld_momentum_scale=0.5     ,    sld_mom_beta=0.7     ,    )

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

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

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple						)			->	List[str]:




											'''simple docstring'''

											__snake_case				=		StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"     ,    safety_checker=SCREAMING_SNAKE_CASE						)
											__snake_case				=		LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config						)
											__snake_case				=		sd_pipe.to(SCREAMING_SNAKE_CASE						)
											sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

											__snake_case				=		"padme amidala taking a bath artwork, safe for work, no nudity"
											__snake_case				=		2_7_3_4_9_7_1_7_5_5
											__snake_case				=		7

											__snake_case				=		torch.manual_seed(SCREAMING_SNAKE_CASE						)
											__snake_case				=		sd_pipe(
											    [prompt]     ,    generator=SCREAMING_SNAKE_CASE     ,    guidance_scale=SCREAMING_SNAKE_CASE     ,    num_inference_steps=5_0     ,    output_type="np"     ,    width=5_1_2     ,    height=5_1_2     ,    sld_guidance_scale=0     ,    )

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

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

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

											__snake_case				=		torch.manual_seed(SCREAMING_SNAKE_CASE						)
											__snake_case				=		sd_pipe(
											    [prompt]     ,    generator=SCREAMING_SNAKE_CASE     ,    guidance_scale=SCREAMING_SNAKE_CASE     ,    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=0.025     ,    sld_momentum_scale=0.5     ,    sld_mom_beta=0.7     ,    )

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

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

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






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	int:




											'''simple docstring'''

											__snake_case				=		StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"						)
											__snake_case				=		sd_pipe.to(SCREAMING_SNAKE_CASE						)
											sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

											__snake_case				=		(
											    "the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."
											    " leyendecker"
											)
											__snake_case				=		1_0_4_4_3_5_5_2_3_4
											__snake_case				=		1_2

											__snake_case				=		torch.manual_seed(SCREAMING_SNAKE_CASE						)
											__snake_case				=		sd_pipe(
											    [prompt]     ,    generator=SCREAMING_SNAKE_CASE     ,    guidance_scale=SCREAMING_SNAKE_CASE     ,    num_inference_steps=5_0     ,    output_type="np"     ,    width=5_1_2     ,    height=5_1_2     ,    sld_guidance_scale=0     ,    )

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

											__snake_case				=		torch.manual_seed(SCREAMING_SNAKE_CASE						)
											__snake_case				=		sd_pipe(
											    [prompt]     ,    generator=SCREAMING_SNAKE_CASE     ,    guidance_scale=SCREAMING_SNAKE_CASE     ,    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=0.025     ,    sld_momentum_scale=0.5     ,    sld_mom_beta=0.7     ,    )

											__snake_case				=		output.images
											__snake_case				=		image[0, -3:, -3:, -1]
											__snake_case				=		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_1_2, 5_1_2, 3)

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





