


"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class 		SCREAMING_SNAKE_CASE_							(    UpperCamelCase__       ,  UpperCamelCase__       ,  UpperCamelCase__       ,  unittest.TestCase   ):
				"""simple docstring"""
				__snake_case   :       Tuple	 =		StableDiffusionInstructPixaPixPipeline
				__snake_case   :       List[Any]	 =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
				__snake_case   :       Optional[Any]	 =		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
				__snake_case   :       Optional[Any]	 =		IMAGE_TO_IMAGE_IMAGE_PARAMS
				__snake_case   :       str	 =		IMAGE_TO_IMAGE_IMAGE_PARAMS




				def 					__lowercase  (     self  :Any			):
											torch.manual_seed(0			)
											__lowerCamelCase      :      Optional[int]         =UNetaDConditionModel(
											    block_out_channels=(32, 64)						,    layers_per_block=2						,    sample_size=32						,    in_channels=8						,    out_channels=4						,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')						,    up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')						,    cross_attention_dim=32						,    )
											__lowerCamelCase      :      Union[str, Any]         =PNDMScheduler(skip_prk_steps=_a			)
											torch.manual_seed(0			)
											__lowerCamelCase      :      Optional[int]         =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						,    )
											torch.manual_seed(0			)
											__lowerCamelCase      :      Any         =CLIPTextConfig(
											    bos_token_id=0						,    eos_token_id=2						,    hidden_size=32						,    intermediate_size=37						,    layer_norm_eps=1e-0_5						,    num_attention_heads=4						,    num_hidden_layers=5						,    pad_token_id=1						,    vocab_size=1000						,    )
											__lowerCamelCase      :      Union[str, Any]         =CLIPTextModel(_a			)
											__lowerCamelCase      :      Dict         =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

											__lowerCamelCase      :      List[str]         ={
											    """unet""": unet,
											    """scheduler""": scheduler,
											    """vae""": vae,
											    """text_encoder""": text_encoder,
											    """tokenizer""": tokenizer,
											    """safety_checker""": None,
											    """feature_extractor""": None,
											}
											return components




				def 					__lowercase  (     self  :int						,    __lowercase  :Dict						,    __lowercase  :Tuple=0			):
											__lowerCamelCase      :      Optional[int]         =floats_tensor((1, 3, 32, 32)						,    rng=random.Random(_a			)			).to(_a			)
											__lowerCamelCase      :      Union[str, Any]         =image.cpu().permute(0						,    2						,    3						,    1			)[0]
											__lowerCamelCase      :      Tuple         =Image.fromarray(np.uinta(_a			)			).convert('''RGB'''			)
											if str(_a			).startswith('''mps'''			):
																		__lowerCamelCase      :      str         =torch.manual_seed(_a			)
											else:
																		__lowerCamelCase      :      Optional[Any]         =torch.Generator(device=_a			).manual_seed(_a			)
											__lowerCamelCase      :      Union[str, Any]         ={
											    """prompt""": """A painting of a squirrel eating a burger""",
											    """image""": image,
											    """generator""": generator,
											    """num_inference_steps""": 2,
											    """guidance_scale""": 6.0,
											    """image_guidance_scale""": 1,
											    """output_type""": """numpy""",
											}
											return inputs




				def 					__lowercase  (     self  :List[Any]			):
											__lowerCamelCase      :      Dict         ="""cpu"""  # ensure determinism for the device-dependent torch.Generator
											__lowerCamelCase      :      Tuple         =self.get_dummy_components()
											__lowerCamelCase      :      Dict         =StableDiffusionInstructPixaPixPipeline(**_a			)
											__lowerCamelCase      :      Optional[Any]         =sd_pipe.to(_a			)
											sd_pipe.set_progress_bar_config(disable=_a			)

											__lowerCamelCase      :      Tuple         =self.get_dummy_inputs(_a			)
											__lowerCamelCase      :      Tuple         =sd_pipe(**_a			).images
											__lowerCamelCase      :      Any         =image[0, -3:, -3:, -1]
											assert image.shape == (1, 32, 32, 3)
											__lowerCamelCase      :      List[str]         =np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]			)

											assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3




				def 					__lowercase  (     self  :List[Any]			):
											__lowerCamelCase      :      Tuple         ="""cpu"""  # ensure determinism for the device-dependent torch.Generator
											__lowerCamelCase      :      Optional[Any]         =self.get_dummy_components()
											__lowerCamelCase      :      Optional[Any]         =StableDiffusionInstructPixaPixPipeline(**_a			)
											__lowerCamelCase      :      Union[str, Any]         =sd_pipe.to(_a			)
											sd_pipe.set_progress_bar_config(disable=_a			)

											__lowerCamelCase      :      List[str]         =self.get_dummy_inputs(_a			)
											__lowerCamelCase      :      Optional[int]         ="""french fries"""
											__lowerCamelCase      :      Optional[int]         =sd_pipe(**_a						,    negative_prompt=_a			)
											__lowerCamelCase      :      Optional[Any]         =output.images
											__lowerCamelCase      :      str         =image[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											__lowerCamelCase      :      str         =np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]			)

											assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3




				def 					__lowercase  (     self  :Optional[Any]			):
											__lowerCamelCase      :      Optional[int]         ="""cpu"""  # ensure determinism for the device-dependent torch.Generator
											__lowerCamelCase      :      int         =self.get_dummy_components()
											__lowerCamelCase      :      Any         =StableDiffusionInstructPixaPixPipeline(**_a			)
											__lowerCamelCase      :      Optional[Any]         =sd_pipe.to(_a			)
											sd_pipe.set_progress_bar_config(disable=_a			)

											__lowerCamelCase      :      int         =self.get_dummy_inputs(_a			)
											__lowerCamelCase      :      Dict         =[inputs["""prompt"""]] * 2

											__lowerCamelCase      :      Optional[int]         =np.array(inputs['''image''']			).astype(np.floataa			) / 255.0
											__lowerCamelCase      :      Any         =torch.from_numpy(_a			).unsqueeze(0			).to(_a			)
											__lowerCamelCase      :      Tuple         =image / 2 + 0.5
											__lowerCamelCase      :      int         =image.permute(0						,    3						,    1						,    2			)
											__lowerCamelCase      :      Optional[Any]         =image.repeat(2						,    1						,    1						,    1			)

											__lowerCamelCase      :      Optional[Any]         =sd_pipe(**_a			).images
											__lowerCamelCase      :      Tuple         =image[-1, -3:, -3:, -1]

											assert image.shape == (2, 32, 32, 3)
											__lowerCamelCase      :      Optional[int]         =np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]			)

											assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3




				def 					__lowercase  (     self  :int			):
											__lowerCamelCase      :      Dict         ="""cpu"""  # ensure determinism for the device-dependent torch.Generator
											__lowerCamelCase      :      Optional[Any]         =self.get_dummy_components()
											__lowerCamelCase      :      Union[str, Any]         =EulerAncestralDiscreteScheduler(
											    beta_start=0.00085						,    beta_end=0.012						,    beta_schedule='''scaled_linear'''			)
											__lowerCamelCase      :      Optional[int]         =StableDiffusionInstructPixaPixPipeline(**_a			)
											__lowerCamelCase      :      Tuple         =sd_pipe.to(_a			)
											sd_pipe.set_progress_bar_config(disable=_a			)

											__lowerCamelCase      :      Tuple         =self.get_dummy_inputs(_a			)
											__lowerCamelCase      :      List[str]         =sd_pipe(**_a			).images
											__lowerCamelCase      :      Dict         =image[0, -3:, -3:, -1]

											__lowerCamelCase      :      Any         =[round(_a						,    4			) for x in image_slice.flatten().tolist()]
											print(''','''.join([str(_a			) for x in slice]			)			)

											assert image.shape == (1, 32, 32, 3)
											__lowerCamelCase      :      Dict         =np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]			)

											assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3




				def 					__lowercase  (     self  :Tuple			):
											super().test_inference_batch_single_identical(expected_max_diff=3e-3			)




				def 					__lowercase  (     self  :int			):
											__lowerCamelCase      :      int         =self.get_dummy_components()
											__lowerCamelCase      :      List[str]         =StableDiffusionInstructPixaPixPipeline(**_a			)
											__lowerCamelCase      :      Optional[int]         =VaeImageProcessor(do_resize=_a						,    do_normalize=_a			)
											__lowerCamelCase      :      Optional[Any]         =pipe.to(_a			)
											pipe.set_progress_bar_config(disable=_a			)

											__lowerCamelCase      :      str         =pipe(**self.get_dummy_inputs_by_type(_a						,    input_image_type='''pt'''			)			)[0]

											__lowerCamelCase      :      Any         =components["""vae"""]
											__lowerCamelCase      :      Any         =self.get_dummy_inputs_by_type(_a						,    input_image_type='''pt'''			)

											for image_param in self.image_latents_params:
																		if image_param in inputs.keys():
																									__lowerCamelCase      :      List[str]         =vae.encode(inputs[image_param]			).latent_dist.mode()

											__lowerCamelCase      :      Dict         =pipe(**_a			)[0]

											__lowerCamelCase      :      Any         =np.abs(out - out_latents_inputs			).max()
											self.assertLess(_a						,    1e-4						,    '''passing latents as image input generate different result from passing image'''			)

@slow
@require_torch_gpu
class 		SCREAMING_SNAKE_CASE_							(    unittest.TestCase   ):
				"""simple docstring"""




				def 					__lowercase  (     self  :int			):
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()




				def 					__lowercase  (     self  :Optional[Any]						,    __lowercase  :Union[str, Any]=0			):
											__lowerCamelCase      :      Optional[int]         =torch.manual_seed(_a			)
											__lowerCamelCase      :      Dict         =load_image(
											    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''			)
											__lowerCamelCase      :      int         ={
											    """prompt""": """turn him into a cyborg""",
											    """image""": image,
											    """generator""": generator,
											    """num_inference_steps""": 3,
											    """guidance_scale""": 7.5,
											    """image_guidance_scale""": 1.0,
											    """output_type""": """numpy""",
											}
											return inputs




				def 					__lowercase  (     self  :Tuple			):
											__lowerCamelCase      :      List[Any]         =StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    '''timbrooks/instruct-pix2pix'''						,    safety_checker=_a			)
											pipe.to(_a			)
											pipe.set_progress_bar_config(disable=_a			)
											pipe.enable_attention_slicing()

											__lowerCamelCase      :      int         =self.get_inputs()
											__lowerCamelCase      :      Any         =pipe(**_a			).images
											__lowerCamelCase      :      List[str]         =image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 512, 512, 3)
											__lowerCamelCase      :      str         =np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]			)

											assert np.abs(expected_slice - image_slice			).max() < 1e-3




				def 					__lowercase  (     self  :str			):
											__lowerCamelCase      :      Optional[Any]         =StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    '''timbrooks/instruct-pix2pix'''						,    safety_checker=_a			)
											__lowerCamelCase      :      List[Any]         =LMSDiscreteScheduler.from_config(pipe.scheduler.config			)
											pipe.to(_a			)
											pipe.set_progress_bar_config(disable=_a			)
											pipe.enable_attention_slicing()

											__lowerCamelCase      :      Union[str, Any]         =self.get_inputs()
											__lowerCamelCase      :      int         =pipe(**_a			).images
											__lowerCamelCase      :      str         =image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 512, 512, 3)
											__lowerCamelCase      :      int         =np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]			)

											assert np.abs(expected_slice - image_slice			).max() < 1e-3




				def 					__lowercase  (     self  :List[str]			):
											__lowerCamelCase      :      Any         =StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    '''timbrooks/instruct-pix2pix'''						,    safety_checker=_a			)
											__lowerCamelCase      :      Tuple         =DDIMScheduler.from_config(pipe.scheduler.config			)
											pipe.to(_a			)
											pipe.set_progress_bar_config(disable=_a			)
											pipe.enable_attention_slicing()

											__lowerCamelCase      :      Optional[Any]         =self.get_inputs()
											__lowerCamelCase      :      Tuple         =pipe(**_a			).images
											__lowerCamelCase      :      Any         =image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 512, 512, 3)
											__lowerCamelCase      :      Any         =np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]			)

											assert np.abs(expected_slice - image_slice			).max() < 1e-3




				def 					__lowercase  (     self  :Tuple			):
											__lowerCamelCase      :      str         =0

											def callback_fn(__lowercase  :List[str]						,    __lowercase  :List[Any]						,    __lowercase  :Any			) -> None:
																		__lowerCamelCase      :      List[Any]         =True
																		nonlocal number_of_steps
																		number_of_steps += 1
																		if step == 1:
																									__lowerCamelCase      :      List[Any]         =latents.detach().cpu().numpy()
																									assert latents.shape == (1, 4, 64, 64)
																									__lowerCamelCase      :      Dict         =latents[0, -3:, -3:, -1]
																									__lowerCamelCase      :      Union[str, Any]         =np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]			)

																									assert np.abs(latents_slice.flatten() - expected_slice			).max() < 5e-2
																		elif step == 2:
																									__lowerCamelCase      :      List[Any]         =latents.detach().cpu().numpy()
																									assert latents.shape == (1, 4, 64, 64)
																									__lowerCamelCase      :      Any         =latents[0, -3:, -3:, -1]
																									__lowerCamelCase      :      Optional[int]         =np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]			)

																									assert np.abs(latents_slice.flatten() - expected_slice			).max() < 5e-2

											__lowerCamelCase      :      int         =False

											__lowerCamelCase      :      Any         =StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    '''timbrooks/instruct-pix2pix'''						,    safety_checker=_a						,    torch_dtype=torch.floataa			)
											__lowerCamelCase      :      Optional[Any]         =pipe.to(_a			)
											pipe.set_progress_bar_config(disable=_a			)
											pipe.enable_attention_slicing()

											__lowerCamelCase      :      List[str]         =self.get_inputs()
											pipe(**_a						,    callback=_a						,    callback_steps=1			)
											assert callback_fn.has_been_called
											assert number_of_steps == 3




				def 					__lowercase  (     self  :Optional[int]			):
											torch.cuda.empty_cache()
											torch.cuda.reset_max_memory_allocated()
											torch.cuda.reset_peak_memory_stats()

											__lowerCamelCase      :      List[str]         =StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    '''timbrooks/instruct-pix2pix'''						,    safety_checker=_a						,    torch_dtype=torch.floataa			)
											__lowerCamelCase      :      str         =pipe.to(_a			)
											pipe.set_progress_bar_config(disable=_a			)
											pipe.enable_attention_slicing(1			)
											pipe.enable_sequential_cpu_offload()

											__lowerCamelCase      :      Any         =self.get_inputs()
											__lowerCamelCase      :      Tuple         =pipe(**_a			)

											__lowerCamelCase      :      Any         =torch.cuda.max_memory_allocated()
											# make sure that less than 2.2 GB is allocated
											assert mem_bytes < 2.2 * 10**9




				def 					__lowercase  (     self  :List[Any]			):
											__lowerCamelCase      :      Tuple         =self.get_inputs()
											# resize to resolution that is divisible by 8 but not 16 or 32
											__lowerCamelCase      :      str         =inputs["""image"""].resize((504, 504)			)

											__lowerCamelCase      :      List[Any]         ="""timbrooks/instruct-pix2pix"""
											__lowerCamelCase      :      List[str]         =StableDiffusionInstructPixaPixPipeline.from_pretrained(
											    _a						,    safety_checker=_a						,    )
											pipe.to(_a			)
											pipe.set_progress_bar_config(disable=_a			)
											pipe.enable_attention_slicing()

											__lowerCamelCase      :      str         =pipe(**_a			)
											__lowerCamelCase      :      str         =output.images[0]

											__lowerCamelCase      :      Any         =image[255:258, 383:386, -1]

											assert image.shape == (504, 504, 3)
											__lowerCamelCase      :      List[str]         =np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]			)

											assert np.abs(image_slice.flatten() - expected_slice			).max() < 5e-3











"""simple docstring"""





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class 		SCREAMING_SNAKE_CASE_							(    snake_case__       ,  unittest.TestCase   ):
				"""simple docstring"""
				__snake_case   :       Union[str, Any]	 =		LDMTextToImagePipeline
				__snake_case   :       Optional[Any]	 =		TEXT_TO_IMAGE_PARAMS - {
				    """negative_prompt""",
				    """negative_prompt_embeds""",
				    """cross_attention_kwargs""",
				    """prompt_embeds""",
				}
				__snake_case   :       str	 =		PipelineTesterMixin.required_optional_params - {
				    """num_images_per_prompt""",
				    """callback""",
				    """callback_steps""",
				}
				__snake_case   :       Union[str, Any]	 =		TEXT_TO_IMAGE_BATCH_PARAMS
				__snake_case   :       Optional[Any]	 =		False




				def 					__lowercase  (     self  :List[str]			):
											torch.manual_seed(0			)
											__lowerCamelCase      :      str         =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						,    )
											__lowerCamelCase      :      str         =DDIMScheduler(
											    beta_start=0.00085						,    beta_end=0.012						,    beta_schedule='''scaled_linear'''						,    clip_sample=__lowercase						,    set_alpha_to_one=__lowercase						,    )
											torch.manual_seed(0			)
											__lowerCamelCase      :      Optional[int]         =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						,    )
											torch.manual_seed(0			)
											__lowerCamelCase      :      Any         =CLIPTextConfig(
											    bos_token_id=0						,    eos_token_id=2						,    hidden_size=32						,    intermediate_size=37						,    layer_norm_eps=1e-0_5						,    num_attention_heads=4						,    num_hidden_layers=5						,    pad_token_id=1						,    vocab_size=1000						,    )
											__lowerCamelCase      :      Optional[int]         =CLIPTextModel(__lowercase			)
											__lowerCamelCase      :      Dict         =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

											__lowerCamelCase      :      Optional[int]         ={
											    '''unet''': unet,
											    '''scheduler''': scheduler,
											    '''vqvae''': vae,
											    '''bert''': text_encoder,
											    '''tokenizer''': tokenizer,
											}
											return components




				def 					__lowercase  (     self  :int						,    __lowercase  :Optional[int]						,    __lowercase  :Optional[Any]=0			):
											if str(__lowercase			).startswith('''mps'''			):
																		__lowerCamelCase      :      Any         =torch.manual_seed(__lowercase			)
											else:
																		__lowerCamelCase      :      str         =torch.Generator(device=__lowercase			).manual_seed(__lowercase			)
											__lowerCamelCase      :      Any         ={
											    '''prompt''': '''A painting of a squirrel eating a burger''',
											    '''generator''': generator,
											    '''num_inference_steps''': 2,
											    '''guidance_scale''': 6.0,
											    '''output_type''': '''numpy''',
											}
											return inputs




				def 					__lowercase  (     self  :List[str]			):
											__lowerCamelCase      :      List[str]         ='''cpu'''  # ensure determinism for the device-dependent torch.Generator

											__lowerCamelCase      :      str         =self.get_dummy_components()
											__lowerCamelCase      :      Optional[int]         =LDMTextToImagePipeline(**__lowercase			)
											pipe.to(__lowercase			)
											pipe.set_progress_bar_config(disable=__lowercase			)

											__lowerCamelCase      :      str         =self.get_dummy_inputs(__lowercase			)
											__lowerCamelCase      :      List[Any]         =pipe(**__lowercase			).images
											__lowerCamelCase      :      Optional[int]         =image[0, -3:, -3:, -1]

											assert image.shape == (1, 16, 16, 3)
											__lowerCamelCase      :      Optional[Any]         =np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014]			)

											assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3

@slow
@require_torch_gpu
class 		SCREAMING_SNAKE_CASE_							(    unittest.TestCase   ):
				"""simple docstring"""




				def 					__lowercase  (     self  :Any			):
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()




				def 					__lowercase  (     self  :int						,    __lowercase  :Any						,    __lowercase  :Optional[int]=torch.floataa						,    __lowercase  :Dict=0			):
											__lowerCamelCase      :      List[str]         =torch.manual_seed(__lowercase			)
											__lowerCamelCase      :      List[str]         =np.random.RandomState(__lowercase			).standard_normal((1, 4, 32, 32)			)
											__lowerCamelCase      :      List[str]         =torch.from_numpy(__lowercase			).to(device=__lowercase						,    dtype=__lowercase			)
											__lowerCamelCase      :      Any         ={
											    '''prompt''': '''A painting of a squirrel eating a burger''',
											    '''latents''': latents,
											    '''generator''': generator,
											    '''num_inference_steps''': 3,
											    '''guidance_scale''': 6.0,
											    '''output_type''': '''numpy''',
											}
											return inputs




				def 					__lowercase  (     self  :Tuple			):
											__lowerCamelCase      :      int         =LDMTextToImagePipeline.from_pretrained('''CompVis/ldm-text2im-large-256'''			).to(__lowercase			)
											pipe.set_progress_bar_config(disable=__lowercase			)

											__lowerCamelCase      :      Tuple         =self.get_inputs(__lowercase			)
											__lowerCamelCase      :      Optional[Any]         =pipe(**__lowercase			).images
											__lowerCamelCase      :      Union[str, Any]         =image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 256, 256, 3)
											__lowerCamelCase      :      Union[str, Any]         =np.array([0.51825, 0.52850, 0.52543, 0.54258, 0.52304, 0.52569, 0.54363, 0.55276, 0.56878]			)
											__lowerCamelCase      :      Dict         =np.abs(expected_slice - image_slice			).max()
											assert max_diff < 1e-3

@nightly
@require_torch_gpu
class 		SCREAMING_SNAKE_CASE_							(    unittest.TestCase   ):
				"""simple docstring"""




				def 					__lowercase  (     self  :Any			):
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()




				def 					__lowercase  (     self  :Dict						,    __lowercase  :Optional[Any]						,    __lowercase  :int=torch.floataa						,    __lowercase  :Dict=0			):
											__lowerCamelCase      :      Any         =torch.manual_seed(__lowercase			)
											__lowerCamelCase      :      Dict         =np.random.RandomState(__lowercase			).standard_normal((1, 4, 32, 32)			)
											__lowerCamelCase      :      str         =torch.from_numpy(__lowercase			).to(device=__lowercase						,    dtype=__lowercase			)
											__lowerCamelCase      :      Dict         ={
											    '''prompt''': '''A painting of a squirrel eating a burger''',
											    '''latents''': latents,
											    '''generator''': generator,
											    '''num_inference_steps''': 50,
											    '''guidance_scale''': 6.0,
											    '''output_type''': '''numpy''',
											}
											return inputs




				def 					__lowercase  (     self  :Tuple			):
											__lowerCamelCase      :      Optional[int]         =LDMTextToImagePipeline.from_pretrained('''CompVis/ldm-text2im-large-256'''			).to(__lowercase			)
											pipe.set_progress_bar_config(disable=__lowercase			)

											__lowerCamelCase      :      List[Any]         =self.get_inputs(__lowercase			)
											__lowerCamelCase      :      Optional[int]         =pipe(**__lowercase			).images[0]

											__lowerCamelCase      :      Optional[int]         =load_numpy(
											    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy'''			)
											__lowerCamelCase      :      Dict         =np.abs(expected_image - image			).max()
											assert max_diff < 1e-3




