





import inspect
import unittest

from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import DecisionTransformerModel
		from transformers.models.decision_transformer.modeling_decision_transformer import (
		    DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		)


class 			lowercase				:



						"""simple docstring"""

						def __init__(    self			:		str ,				lowerCamelCase_			:		Optional[int] ,				lowerCamelCase_			:		Optional[int]=13 ,				lowerCamelCase_			:		Dict=7 ,				lowerCamelCase_			:		str=6 ,				lowerCamelCase_			:		Union[str, Any]=17 ,				lowerCamelCase_			:		Union[str, Any]=23 ,				lowerCamelCase_			:		Tuple=11 ,				lowerCamelCase_			:		Optional[Any]=True ,				):





										'''simple docstring'''







										_snake_case :			List[Any]  						=   parent
										_snake_case :			Union[str, Any]  						=   batch_size
										_snake_case :			int  						=   seq_length
										_snake_case :			Dict  						=   act_dim
										_snake_case :			Union[str, Any]  						=   state_dim
										_snake_case :			Optional[int]  						=   hidden_size
										_snake_case :			List[Any]  						=   max_length
										_snake_case :			Union[str, Any]  						=   is_training







						def 		__UpperCAmelCase       (    self			:		Optional[int]					):





										'''simple docstring'''







										_snake_case :			List[str]  						=   floats_tensor((self.batch_size, self.seq_length, self.state_dim)					)
										_snake_case :			Any  						=   floats_tensor((self.batch_size, self.seq_length, self.act_dim)					)
										_snake_case :			List[Any]  						=   floats_tensor((self.batch_size, self.seq_length, 1)					)
										_snake_case :			Any  						=   floats_tensor((self.batch_size, self.seq_length, 1)					)
										_snake_case :			List[str]  						=   ids_tensor((self.batch_size, self.seq_length) ,				vocab_size=10_00					)
										_snake_case :			int  						=   random_attention_mask((self.batch_size, self.seq_length)					)

										_snake_case :			Union[str, Any]  						=   self.get_config()

										return (
										    config,
										    states,
										    actions,
										    rewards,
										    returns_to_go,
										    timesteps,
										    attention_mask,
										)







						def 		__UpperCAmelCase       (    self			:		Optional[Any]					):





										'''simple docstring'''







										return DecisionTransformerConfig(
										    batch_size=self.batch_size ,				seq_length=self.seq_length ,				act_dim=self.act_dim ,				state_dim=self.state_dim ,				hidden_size=self.hidden_size ,				max_length=self.max_length ,				)







						def 		__UpperCAmelCase       (    self			:		Optional[Any] ,				lowerCamelCase_			:		Union[str, Any] ,				lowerCamelCase_			:		Optional[Any] ,				lowerCamelCase_			:		Optional[Any] ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		Union[str, Any] ,				lowerCamelCase_			:		List[Any] ,				lowerCamelCase_			:		List[Any] ,				):





										'''simple docstring'''







										_snake_case :			str  						=   DecisionTransformerModel(config=lowerCamelCase_					)
										model.to(lowerCamelCase_					)
										model.eval()
										_snake_case :			str  						=   model(lowerCamelCase_ ,				lowerCamelCase_ ,				lowerCamelCase_ ,				lowerCamelCase_ ,				lowerCamelCase_ ,				lowerCamelCase_					)

										self.parent.assertEqual(result.state_preds.shape ,				states.shape					)
										self.parent.assertEqual(result.action_preds.shape ,				actions.shape					)
										self.parent.assertEqual(result.return_preds.shape ,				returns_to_go.shape					)
										self.parent.assertEqual(
										    result.last_hidden_state.shape ,				(self.batch_size, self.seq_length * 3, self.hidden_size)					)  # seq length *3 as there are 3 modelities: states, returns and actions







						def 		__UpperCAmelCase       (    self			:		Union[str, Any]					):





										'''simple docstring'''







										_snake_case :			int  						=   self.prepare_config_and_inputs()
										(
										    _snake_case			
										) :			Union[str, Any]  						=   config_and_inputs
										_snake_case :			List[Any]  						=   {
										    'states': states,
										    'actions': actions,
										    'rewards': rewards,
										    'returns_to_go': returns_to_go,
										    'timesteps': timesteps,
										    'attention_mask': attention_mask,
										}
										return config, inputs_dict



@require_torch
class 			lowercase				(							a_		,			a_		,			a_		,			unittest.TestCase     ):



						"""simple docstring"""

						_UpperCamelCase  :	Any								=	(DecisionTransformerModel,) if is_torch_available() else ()
						_UpperCamelCase  :	Union[str, Any]								=	()
						_UpperCamelCase  :	Optional[int]								=	{"feature-extraction": DecisionTransformerModel} if is_torch_available() else {}

						# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
						_UpperCamelCase  :	int								=	False

						# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
						_UpperCamelCase  :	Dict								=	False
						_UpperCamelCase  :	str								=	False
						_UpperCamelCase  :	Optional[Any]								=	False
						_UpperCamelCase  :	str								=	False
						_UpperCamelCase  :	Optional[int]								=	False
						_UpperCamelCase  :	Tuple								=	False
						_UpperCamelCase  :	str								=	False
						_UpperCamelCase  :	List[Any]								=	False
						_UpperCamelCase  :	List[Any]								=	False







						def 		__UpperCAmelCase       (    self			:		Dict					):





										'''simple docstring'''







										_snake_case :			Union[str, Any]  						=   DecisionTransformerModelTester(self					)
										_snake_case :			Union[str, Any]  						=   ConfigTester(self ,				config_class=lowerCamelCase_ ,				hidden_size=37					)







						def 		__UpperCAmelCase       (    self			:		int					):





										'''simple docstring'''







										self.config_tester.run_common_tests()







						def 		__UpperCAmelCase       (    self			:		Union[str, Any]					):





										'''simple docstring'''







										_snake_case :			Optional[int]  						=   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCamelCase_					)







						@slow
						def 		__UpperCAmelCase       (    self			:		Any					):





										'''simple docstring'''







										for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_snake_case :			Dict  						=   DecisionTransformerModel.from_pretrained(lowerCamelCase_					)
														self.assertIsNotNone(lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		Tuple					):





										'''simple docstring'''







										_snake_case :			Optional[int]  						=   self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														_snake_case :			List[Any]  						=   model_class(lowerCamelCase_					)
														_snake_case :			Dict  						=   inspect.signature(model.forward					)
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														_snake_case :			Optional[Any]  						=   [*signature.parameters.keys()]

														_snake_case :			Optional[Any]  						=   [
														    'states',
														    'actions',
														    'rewards',
														    'returns_to_go',
														    'timesteps',
														    'attention_mask',
														]

														self.assertListEqual(arg_names[: len(lowerCamelCase_					)] ,				lowerCamelCase_					)



@require_torch
class 			lowercase				(							unittest.TestCase     ):



						"""simple docstring"""

						@slow
						def 		__UpperCAmelCase       (    self			:		Union[str, Any]					):





										'''simple docstring'''







										_snake_case :			Union[str, Any]  						=   2  # number of steps of autoregressive prediction we will perform
										_snake_case :			str  						=   10  # defined by the RL environment, may be normalized
										_snake_case :			Optional[Any]  						=   DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-expert'					)
										_snake_case :			int  						=   model.to(lowerCamelCase_					)
										_snake_case :			Any  						=   model.config
										torch.manual_seed(0					)
										_snake_case :			str  						=   torch.randn(1 ,				1 ,				config.state_dim					).to(device=lowerCamelCase_ ,				dtype=torch.floataa					)  # env.reset()

										_snake_case :			int  						=   torch.tensor(
										    [[0.24_2793, -0.2869_3074, 0.874_2613], [0.6781_5274, -0.0810_1085, -0.1295_2147]] ,				device=lowerCamelCase_					)

										_snake_case :			Optional[int]  						=   torch.tensor(lowerCamelCase_ ,				device=lowerCamelCase_ ,				dtype=torch.floataa					).reshape(1 ,				1 ,				1					)
										_snake_case :			Union[str, Any]  						=   state
										_snake_case :			int  						=   torch.zeros(1 ,				0 ,				config.act_dim ,				device=lowerCamelCase_ ,				dtype=torch.floataa					)
										_snake_case :			int  						=   torch.zeros(1 ,				0 ,				device=lowerCamelCase_ ,				dtype=torch.floataa					)
										_snake_case :			Optional[int]  						=   torch.tensor(0 ,				device=lowerCamelCase_ ,				dtype=torch.long					).reshape(1 ,				1					)

										for step in range(lowerCamelCase_					):
														_snake_case :			int  						=   torch.cat([actions, torch.zeros(1 ,				1 ,				config.act_dim ,				device=lowerCamelCase_					)] ,				dim=1					)
														_snake_case :			Any  						=   torch.cat([rewards, torch.zeros(1 ,				1 ,				device=lowerCamelCase_					)] ,				dim=1					)

														_snake_case :			int  						=   torch.ones(1 ,				states.shape[1]					).to(dtype=torch.long ,				device=states.device					)

														with torch.no_grad():
																		_snake_case :			Optional[Any]  						=   model(
																		    states=lowerCamelCase_ ,				actions=lowerCamelCase_ ,				rewards=lowerCamelCase_ ,				returns_to_go=lowerCamelCase_ ,				timesteps=lowerCamelCase_ ,				attention_mask=lowerCamelCase_ ,				return_dict=lowerCamelCase_ ,				)

														self.assertEqual(action_pred.shape ,				actions.shape					)
														self.assertTrue(torch.allclose(action_pred[0, -1] ,				expected_outputs[step] ,				atol=1e-4					)					)
														_snake_case :			Union[str, Any]  						=   (  # env.step(action)
														    torch.randn(1 ,				1 ,				config.state_dim					).to(device=lowerCamelCase_ ,				dtype=torch.floataa					),
														    1.0,
														    False,
														    {},
														)

														_snake_case :			Union[str, Any]  						=   action_pred[0, -1]
														_snake_case :			List[Any]  						=   torch.cat([states, state] ,				dim=1					)
														_snake_case :			Dict  						=   returns_to_go[0, -1] - reward
														_snake_case :			str  						=   torch.cat([returns_to_go, pred_return.reshape(1 ,				1 ,				1					)] ,				dim=1					)
														_snake_case :			Union[str, Any]  						=   torch.cat(
														    [timesteps, torch.ones((1, 1) ,				device=lowerCamelCase_ ,				dtype=torch.long					) * (step + 1)] ,				dim=1					)













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     ):



						"""simple docstring"""

						def 		__UpperCAmelCase       (    self			:		Optional[Any]					):





										'''simple docstring'''







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







						@property
						def 		__UpperCAmelCase       (    self			:		Optional[int]					):





										'''simple docstring'''







										_snake_case :			Tuple  						=   1
										_snake_case :			str  						=   3
										_snake_case :			List[str]  						=   (32, 32)

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







						@property
						def 		__UpperCAmelCase       (    self			:		str					):





										'''simple docstring'''







										torch.manual_seed(0					)
										_snake_case :			Union[str, 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			:		Dict					):





										'''simple docstring'''







										torch.manual_seed(0					)
										_snake_case :			Optional[Any]  						=   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			:		Tuple					):





										'''simple docstring'''







										torch.manual_seed(0					)
										_snake_case :			List[str]  						=   CLIPTextConfig(
										    bos_token_id=0 ,				eos_token_id=2 ,				hidden_size=32 ,				intermediate_size=37 ,				layer_norm_eps=1e-05 ,				num_attention_heads=4 ,				num_hidden_layers=5 ,				pad_token_id=1 ,				vocab_size=10_00 ,				)
										return CLIPTextModel(lowerCamelCase_					)







						@property
						def 		__UpperCAmelCase       (    self			:		Optional[Any]					):





										'''simple docstring'''







										def extract(*lowerCamelCase_			:		Optional[int] ,				**lowerCamelCase_			:		str					):


														class 			lowercase				:



																				"""simple docstring"""

																				def __init__(    self			:		Tuple					):





																								'''simple docstring'''







																								_snake_case :			List[str]  						=   torch.ones([0]					)







																				def 		__UpperCAmelCase       (    self			:		int ,				lowerCamelCase_			:		Tuple					):





																								'''simple docstring'''







																								self.pixel_values.to(lowerCamelCase_					)
																								return self



														return Out()

										return extract







						def 		__UpperCAmelCase       (    self			:		int					):





										'''simple docstring'''







										_snake_case :			Optional[int]  						=   'cpu'  # ensure determinism for the device-dependent torch.Generator
										_snake_case :			int  						=   self.dummy_cond_unet
										_snake_case :			str  						=   DDIMScheduler(
										    beta_start=0.0_0085 ,				beta_end=0.012 ,				beta_schedule='scaled_linear' ,				clip_sample=lowerCamelCase_ ,				set_alpha_to_one=lowerCamelCase_ ,				)

										_snake_case :			Union[str, Any]  						=   self.dummy_vae
										_snake_case :			Optional[Any]  						=   self.dummy_text_encoder
										_snake_case :			Optional[Any]  						=   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

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

										_snake_case :			List[str]  						=   'A painting of a squirrel eating a burger'

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

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

										_snake_case :			Tuple  						=   image[0, -3:, -3:, -1]
										_snake_case :			List[Any]  						=   image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)
										_snake_case :			Optional[int]  						=   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 		__UpperCAmelCase       (    self			:		List[str]					):





										'''simple docstring'''







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

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

										_snake_case :			str  						=   'A painting of a squirrel eating a burger'
										_snake_case :			Optional[int]  						=   torch.Generator(device=lowerCamelCase_					).manual_seed(0					)
										_snake_case :			Tuple  						=   sd_pipe([prompt] ,				generator=lowerCamelCase_ ,				guidance_scale=6.0 ,				num_inference_steps=2 ,				output_type='np'					)

										_snake_case :			Optional[Any]  						=   output.images

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

										_snake_case :			Dict  						=   image[0, -3:, -3:, -1]
										_snake_case :			Any  						=   image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)
										_snake_case :			str  						=   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 		__UpperCAmelCase       (    self			:		int					):





										'''simple docstring'''







										_snake_case :			Union[str, Any]  						=   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

										_snake_case :			Dict  						=   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_					)
														_snake_case :			Any  						=   StableDiffusionPipeline.from_pretrained(lowerCamelCase_					)

										# sanity check that the pipeline still works
										assert pipe.safety_checker is None
										_snake_case :			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 		__UpperCAmelCase       (    self			:		Optional[Any]					):





										'''simple docstring'''







										_snake_case :			Union[str, Any]  						=   self.dummy_cond_unet
										_snake_case :			Optional[Any]  						=   PNDMScheduler(skip_prk_steps=lowerCamelCase_					)
										_snake_case :			Any  						=   self.dummy_vae
										_snake_case :			Optional[Any]  						=   self.dummy_text_encoder
										_snake_case :			Optional[int]  						=   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

										# put models in fp16
										_snake_case :			str  						=   unet.half()
										_snake_case :			Union[str, Any]  						=   vae.half()
										_snake_case :			Dict  						=   bert.half()

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

										_snake_case :			Tuple  						=   'A painting of a squirrel eating a burger'
										_snake_case :			Optional[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     ):



						"""simple docstring"""

						def 		__UpperCAmelCase       (    self			:		Tuple					):





										'''simple docstring'''







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







						def 		__UpperCAmelCase       (    self			:		List[str]					):





										'''simple docstring'''







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

										_snake_case :			Optional[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 '
										)
										_snake_case :			List[str]  						=   40_03_66_03_46
										_snake_case :			int  						=   7

										# without safety guidance (sld_guidance_scale = 0)
										_snake_case :			Union[str, Any]  						=   torch.manual_seed(lowerCamelCase_					)
										_snake_case :			Union[str, Any]  						=   sd_pipe(
										    [prompt] ,				generator=lowerCamelCase_ ,				guidance_scale=lowerCamelCase_ ,				num_inference_steps=50 ,				output_type='np' ,				width=5_12 ,				height=5_12 ,				sld_guidance_scale=0 ,				)

										_snake_case :			str  						=   output.images
										_snake_case :			Dict  						=   image[0, -3:, -3:, -1]
										_snake_case :			Optional[int]  						=   [0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]

										assert image.shape == (1, 5_12, 5_12, 3)

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

										# without safety guidance (strong configuration)
										_snake_case :			Tuple  						=   torch.manual_seed(lowerCamelCase_					)
										_snake_case :			int  						=   sd_pipe(
										    [prompt] ,				generator=lowerCamelCase_ ,				guidance_scale=lowerCamelCase_ ,				num_inference_steps=50 ,				output_type='np' ,				width=5_12 ,				height=5_12 ,				sld_guidance_scale=20_00 ,				sld_warmup_steps=7 ,				sld_threshold=0.025 ,				sld_momentum_scale=0.5 ,				sld_mom_beta=0.7 ,				)

										_snake_case :			Tuple  						=   output.images
										_snake_case :			int  						=   image[0, -3:, -3:, -1]
										_snake_case :			List[Any]  						=   [0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]

										assert image.shape == (1, 5_12, 5_12, 3)

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







						def 		__UpperCAmelCase       (    self			:		int					):





										'''simple docstring'''







										_snake_case :			str  						=   StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5' ,				safety_checker=lowerCamelCase_					)
										_snake_case :			Tuple  						=   LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config					)
										_snake_case :			Any  						=   sd_pipe.to(lowerCamelCase_					)
										sd_pipe.set_progress_bar_config(disable=lowerCamelCase_					)

										_snake_case :			Union[str, Any]  						=   'padme amidala taking a bath artwork, safe for work, no nudity'
										_snake_case :			Optional[Any]  						=   27_34_97_17_55
										_snake_case :			Union[str, Any]  						=   7

										_snake_case :			Dict  						=   torch.manual_seed(lowerCamelCase_					)
										_snake_case :			Tuple  						=   sd_pipe(
										    [prompt] ,				generator=lowerCamelCase_ ,				guidance_scale=lowerCamelCase_ ,				num_inference_steps=50 ,				output_type='np' ,				width=5_12 ,				height=5_12 ,				sld_guidance_scale=0 ,				)

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

										assert image.shape == (1, 5_12, 5_12, 3)

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

										_snake_case :			Optional[Any]  						=   torch.manual_seed(lowerCamelCase_					)
										_snake_case :			Any  						=   sd_pipe(
										    [prompt] ,				generator=lowerCamelCase_ ,				guidance_scale=lowerCamelCase_ ,				num_inference_steps=50 ,				output_type='np' ,				width=5_12 ,				height=5_12 ,				sld_guidance_scale=20_00 ,				sld_warmup_steps=7 ,				sld_threshold=0.025 ,				sld_momentum_scale=0.5 ,				sld_mom_beta=0.7 ,				)

										_snake_case :			str  						=   output.images
										_snake_case :			List[str]  						=   image[0, -3:, -3:, -1]
										_snake_case :			Union[str, Any]  						=   [0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]

										assert image.shape == (1, 5_12, 5_12, 3)

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







						def 		__UpperCAmelCase       (    self			:		Tuple					):





										'''simple docstring'''







										_snake_case :			Optional[int]  						=   StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'					)
										_snake_case :			Optional[int]  						=   sd_pipe.to(lowerCamelCase_					)
										sd_pipe.set_progress_bar_config(disable=lowerCamelCase_					)

										_snake_case :			List[Any]  						=   (
										    'the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'
										    ' leyendecker'
										)
										_snake_case :			Union[str, Any]  						=   10_44_35_52_34
										_snake_case :			Dict  						=   12

										_snake_case :			Optional[int]  						=   torch.manual_seed(lowerCamelCase_					)
										_snake_case :			Any  						=   sd_pipe(
										    [prompt] ,				generator=lowerCamelCase_ ,				guidance_scale=lowerCamelCase_ ,				num_inference_steps=50 ,				output_type='np' ,				width=5_12 ,				height=5_12 ,				sld_guidance_scale=0 ,				)

										_snake_case :			Optional[int]  						=   output.images
										_snake_case :			int  						=   image[0, -3:, -3:, -1]
										_snake_case :			Optional[int]  						=   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_12, 5_12, 3)

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

										_snake_case :			List[Any]  						=   torch.manual_seed(lowerCamelCase_					)
										_snake_case :			Optional[int]  						=   sd_pipe(
										    [prompt] ,				generator=lowerCamelCase_ ,				guidance_scale=lowerCamelCase_ ,				num_inference_steps=50 ,				output_type='np' ,				width=5_12 ,				height=5_12 ,				sld_guidance_scale=20_00 ,				sld_warmup_steps=7 ,				sld_threshold=0.025 ,				sld_momentum_scale=0.5 ,				sld_mom_beta=0.7 ,				)

										_snake_case :			str  						=   output.images
										_snake_case :			List[str]  						=   image[0, -3:, -3:, -1]
										_snake_case :			int  						=   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_12, 5_12, 3)

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






