





"""simple docstring"""





import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch





class        _lowerCamelCase      (       unittest.TestCase ):
						UpperCAmelCase_ =			JukeboxTokenizer
						UpperCAmelCase_ =			{
						    "artist": "Zac Brown Band",
						    "genres": "Country",
						    "lyrics": "I met a traveller from an antique land,\n        Who said \"Two vast and trunkless legs of stone\n        Stand in the desert. . . . Near them, on the sand,\n        Half sunk a shattered visage lies, whose frown,\n        And wrinkled lip, and sneer of cold command,\n        Tell that its sculptor well those passions read\n        Which yet survive, stamped on these lifeless things,\n        The hand that mocked them, and the heart that fed;\n        And on the pedestal, these words appear:\n        My name is Ozymandias, King of Kings;\n        Look on my Works, ye Mighty, and despair!\n        Nothing beside remains. Round the decay\n        Of that colossal Wreck, boundless and bare\n        The lone and level sands stretch far away\n        ",
						}


						@require_torch
						def     snake_case_   (self		) ->   List[Any]:
								import torch

								UpperCamelCase   =	JukeboxTokenizer.from_pretrained("openai/jukebox-1b-lyrics"		)
								UpperCamelCase   =	tokenizer(**self.metas		)["input_ids"]
								# fmt: off
								UpperCamelCase   =	[
								    torch.tensor([[
								        0, 0, 0, 71_69, 5_07, 9, 76, 39, 31, 46, 76, 27,
								        76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
								        44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
								        47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
								        76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
								        30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
								        27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
								        45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
								        41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
								        19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
								        76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
								        76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
								        64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
								        30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
								        27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
								        34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
								        27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
								        41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
								        76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
								        44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
								        76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
								        32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
								        40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
								        20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
								        45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
								        31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
								        45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
								        76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
								        34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
								        31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
								        40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
								        38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
								        78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
								        76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
								        41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
								        27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
								        46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
								        76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
								        41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
								        46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
								        41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
								        78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
								        40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
								        27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
								        76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
								        76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
								        41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
								        76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
								        27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
								        78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
								        34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
								        44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
								        40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
								        78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
								        46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
								        38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
								        40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
								        27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
								        20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
								        76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
								        76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
								        76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
								        76, 76]]		),
								    torch.tensor([[0, 0, 0, 10_69, 11]]		),
								    torch.tensor([[0, 0, 0, 10_69, 11]]		),
								]
								# fmt: on
								self.assertTrue(torch.allclose(tokens[0]   ,							EXPECTED_OUTPUT[0]		)		)
								self.assertTrue(torch.allclose(tokens[1]   ,							EXPECTED_OUTPUT[1]		)		)
								self.assertTrue(torch.allclose(tokens[2]   ,							EXPECTED_OUTPUT[2]		)		)




						@require_torch
						def     snake_case_   (self		) ->   Optional[int]:
								import torch

								UpperCamelCase   =	JukeboxTokenizer.from_pretrained("openai/jukebox-5b-lyrics"		)
								UpperCamelCase   =	tokenizer(**self.metas		)["input_ids"]
								# fmt: off
								UpperCamelCase   =	[
								    torch.tensor([[
								        0, 0, 0, 10_69, 11, -1, -1, -1, -1, 9, 77, 39,
								        31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
								        31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
								        40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
								        79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
								        77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
								        27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
								        37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
								        32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
								        77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
								        77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
								        77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
								        46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
								        77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
								        77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
								        77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
								        77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
								        64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
								        40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
								        40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
								        38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
								        31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
								        41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
								        77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
								        46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
								        41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
								        31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
								        31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
								        23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
								        44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
								        31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
								        38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
								        40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
								        77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
								        27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
								        31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
								        34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
								        31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
								        1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
								        31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
								        45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
								        31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
								        77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
								        15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
								        11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
								        45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
								        41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
								        44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
								        46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
								        27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
								        77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
								        35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
								        77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
								        31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
								        77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
								        41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
								        77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
								        40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
								        77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
								        77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
								        27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
								        77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
								        77, 77, 77, 77, 77, 77]]		),
								    torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]]		),
								    torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]]		),
								]
								# fmt: on
								self.assertTrue(torch.allclose(tokens[0]   ,							EXPECTED_OUTPUT[0]		)		)
								self.assertTrue(torch.allclose(tokens[1]   ,							EXPECTED_OUTPUT[1]		)		)
								self.assertTrue(torch.allclose(tokens[2]   ,							EXPECTED_OUTPUT[2]		)		)













"""simple docstring"""





import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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


enable_full_determinism()





@skip_mps
class        _lowerCamelCase      (       _lowercase  ,			_lowercase  ,			unittest.TestCase ):
						UpperCAmelCase_ =			StableDiffusionPanoramaPipeline
						UpperCAmelCase_ =			TEXT_TO_IMAGE_PARAMS
						UpperCAmelCase_ =			TEXT_TO_IMAGE_BATCH_PARAMS
						UpperCAmelCase_ =			TEXT_TO_IMAGE_IMAGE_PARAMS
						UpperCAmelCase_ =			TEXT_TO_IMAGE_IMAGE_PARAMS


						def     snake_case_   (self		) ->   int:
								torch.manual_seed(0		)
								UpperCamelCase   =	UNetaDConditionModel(
								    block_out_channels=(32, 64)   ,							layers_per_block=1   ,							sample_size=32   ,							in_channels=4   ,							out_channels=4   ,							down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")   ,							up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")   ,							cross_attention_dim=32   ,							)
								UpperCamelCase   =	DDIMScheduler()
								torch.manual_seed(0		)
								UpperCamelCase   =	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		)
								UpperCamelCase   =	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=10_00   ,							)
								UpperCamelCase   =	CLIPTextModel(__a		)
								UpperCamelCase   =	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

								UpperCamelCase   =	{
								    "unet": unet,
								    "scheduler": scheduler,
								    "vae": vae,
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "safety_checker": None,
								    "feature_extractor": None,
								}
								return components


						def     snake_case_   (self   ,							__a   ,							__a=0		) ->   List[str]:
								UpperCamelCase   =	torch.manual_seed(__a		)
								UpperCamelCase   =	{
								    "prompt": "a photo of the dolomites",
								    "generator": generator,
								    # Setting height and width to None to prevent OOMs on CPU.
								    "height": None,
								    "width": None,
								    "num_inference_steps": 1,
								    "guidance_scale": 6.0,
								    "output_type": "numpy",
								}
								return inputs


						def     snake_case_   (self		) ->   int:
								UpperCamelCase   =	"cpu"  # ensure determinism for the device-dependent torch.Generator
								UpperCamelCase   =	self.get_dummy_components()
								UpperCamelCase   =	StableDiffusionPanoramaPipeline(**__a		)
								UpperCamelCase   =	sd_pipe.to(__a		)
								sd_pipe.set_progress_bar_config(disable=__a		)

								UpperCamelCase   =	self.get_dummy_inputs(__a		)
								UpperCamelCase   =	sd_pipe(**__a		).images
								UpperCamelCase   =	image[0, -3:, -3:, -1]
								assert image.shape == (1, 64, 64, 3)

								UpperCamelCase   =	np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757]		)

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


						def     snake_case_   (self		) ->   Optional[int]:
								super().test_inference_batch_consistent(batch_sizes=[1, 2]		)


						def     snake_case_   (self		) ->   Tuple:
								super().test_inference_batch_single_identical(batch_size=2   ,							expected_max_diff=3.2_5e-3		)


						def     snake_case_   (self		) ->   Tuple:
								UpperCamelCase   =	"cpu"  # ensure determinism for the device-dependent torch.Generator
								UpperCamelCase   =	self.get_dummy_components()
								UpperCamelCase   =	StableDiffusionPanoramaPipeline(**__a		)
								UpperCamelCase   =	sd_pipe.to(__a		)
								sd_pipe.set_progress_bar_config(disable=__a		)

								UpperCamelCase   =	self.get_dummy_inputs(__a		)
								UpperCamelCase   =	"french fries"
								UpperCamelCase   =	sd_pipe(**__a   ,							negative_prompt=__a		)
								UpperCamelCase   =	output.images
								UpperCamelCase   =	image[0, -3:, -3:, -1]

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

								UpperCamelCase   =	np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]		)

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


						def     snake_case_   (self		) ->   Tuple:
								UpperCamelCase   =	"cpu"  # ensure determinism for the device-dependent torch.Generator
								UpperCamelCase   =	self.get_dummy_components()
								UpperCamelCase   =	StableDiffusionPanoramaPipeline(**__a		)
								UpperCamelCase   =	sd_pipe.to(__a		)
								sd_pipe.set_progress_bar_config(disable=__a		)

								UpperCamelCase   =	self.get_dummy_inputs(__a		)
								UpperCamelCase   =	sd_pipe(**__a   ,							view_batch_size=2		)
								UpperCamelCase   =	output.images
								UpperCamelCase   =	image[0, -3:, -3:, -1]

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

								UpperCamelCase   =	np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]		)

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


						def     snake_case_   (self		) ->   Optional[int]:
								UpperCamelCase   =	"cpu"  # ensure determinism for the device-dependent torch.Generator
								UpperCamelCase   =	self.get_dummy_components()
								UpperCamelCase   =	EulerAncestralDiscreteScheduler(
								    beta_start=0.00085   ,							beta_end=0.012   ,							beta_schedule="scaled_linear"		)
								UpperCamelCase   =	StableDiffusionPanoramaPipeline(**__a		)
								UpperCamelCase   =	sd_pipe.to(__a		)
								sd_pipe.set_progress_bar_config(disable=__a		)

								UpperCamelCase   =	self.get_dummy_inputs(__a		)
								UpperCamelCase   =	sd_pipe(**__a		).images
								UpperCamelCase   =	image[0, -3:, -3:, -1]

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

								UpperCamelCase   =	np.array([0.4024, 0.6510, 0.4901, 0.5378, 0.5813, 0.5622, 0.4795, 0.4467, 0.4952]		)

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




						def     snake_case_   (self		) ->   Optional[Any]:
								UpperCamelCase   =	"cpu"  # ensure determinism for the device-dependent torch.Generator
								UpperCamelCase   =	self.get_dummy_components()
								UpperCamelCase   =	PNDMScheduler(
								    beta_start=0.00085   ,							beta_end=0.012   ,							beta_schedule="scaled_linear"   ,							skip_prk_steps=__a		)
								UpperCamelCase   =	StableDiffusionPanoramaPipeline(**__a		)
								UpperCamelCase   =	sd_pipe.to(__a		)
								sd_pipe.set_progress_bar_config(disable=__a		)

								UpperCamelCase   =	self.get_dummy_inputs(__a		)
								UpperCamelCase   =	sd_pipe(**__a		).images
								UpperCamelCase   =	image[0, -3:, -3:, -1]

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

								UpperCamelCase   =	np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539]		)

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





@slow
@require_torch_gpu
class        _lowerCamelCase      (       unittest.TestCase ):


						def     snake_case_   (self		) ->   List[str]:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()


						def     snake_case_   (self   ,							__a=0		) ->   List[str]:
								UpperCamelCase   =	torch.manual_seed(__a		)
								UpperCamelCase   =	{
								    "prompt": "a photo of the dolomites",
								    "generator": generator,
								    "num_inference_steps": 3,
								    "guidance_scale": 7.5,
								    "output_type": "numpy",
								}
								return inputs


						def     snake_case_   (self		) ->   Union[str, Any]:
								UpperCamelCase   =	"stabilityai/stable-diffusion-2-base"
								UpperCamelCase   =	DDIMScheduler.from_pretrained(__a   ,							subfolder="scheduler"		)
								UpperCamelCase   =	StableDiffusionPanoramaPipeline.from_pretrained(__a   ,							scheduler=__a   ,							safety_checker=__a		)
								pipe.to(__a		)
								pipe.set_progress_bar_config(disable=__a		)
								pipe.enable_attention_slicing()

								UpperCamelCase   =	self.get_inputs()
								UpperCamelCase   =	pipe(**__a		).images
								UpperCamelCase   =	image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 5_12, 20_48, 3)

								UpperCamelCase   =	np.array(
								    [
								        0.36968392,
								        0.27025372,
								        0.32446766,
								        0.28379387,
								        0.36363274,
								        0.30733347,
								        0.27100027,
								        0.27054125,
								        0.25536096,
								    ]		)

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


						def     snake_case_   (self		) ->   Union[str, Any]:
								UpperCamelCase   =	StableDiffusionPanoramaPipeline.from_pretrained(
								    "stabilityai/stable-diffusion-2-base"   ,							safety_checker=__a		)
								UpperCamelCase   =	LMSDiscreteScheduler.from_config(pipe.scheduler.config		)
								pipe.to(__a		)
								pipe.set_progress_bar_config(disable=__a		)
								pipe.enable_attention_slicing()

								UpperCamelCase   =	self.get_inputs()
								UpperCamelCase   =	pipe(**__a		).images
								UpperCamelCase   =	image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 5_12, 20_48, 3)

								UpperCamelCase   =	np.array(
								    [
								        [
								            0.0,
								            0.0,
								            0.0,
								            0.0,
								            0.0,
								            0.0,
								            0.0,
								            0.0,
								            0.0,
								        ]
								    ]		)

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


						def     snake_case_   (self		) ->   Tuple:
								UpperCamelCase   =	0

								def callback_fn(__a   ,							__a   ,							__a		) -> None:
										UpperCamelCase   =	True
										nonlocal number_of_steps
										number_of_steps += 1
										if step == 1:
												UpperCamelCase   =	latents.detach().cpu().numpy()
												assert latents.shape == (1, 4, 64, 2_56)
												UpperCamelCase   =	latents[0, -3:, -3:, -1]

												UpperCamelCase   =	np.array(
												    [
												        0.18681869,
												        0.33907816,
												        0.5361276,
												        0.14432865,
												        -0.02856611,
												        -0.73941123,
												        0.23397987,
												        0.47322682,
												        -0.37823164,
												    ]		)
												assert np.abs(latents_slice.flatten() - expected_slice		).max() < 5e-2
										elif step == 2:
												UpperCamelCase   =	latents.detach().cpu().numpy()
												assert latents.shape == (1, 4, 64, 2_56)
												UpperCamelCase   =	latents[0, -3:, -3:, -1]

												UpperCamelCase   =	np.array(
												    [
												        0.18539645,
												        0.33987248,
												        0.5378559,
												        0.14437142,
												        -0.02455261,
												        -0.7338317,
												        0.23990755,
												        0.47356272,
												        -0.3786505,
												    ]		)

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

								UpperCamelCase   =	False

								UpperCamelCase   =	"stabilityai/stable-diffusion-2-base"
								UpperCamelCase   =	DDIMScheduler.from_pretrained(__a   ,							subfolder="scheduler"		)
								UpperCamelCase   =	StableDiffusionPanoramaPipeline.from_pretrained(__a   ,							scheduler=__a   ,							safety_checker=__a		)
								UpperCamelCase   =	pipe.to(__a		)
								pipe.set_progress_bar_config(disable=__a		)
								pipe.enable_attention_slicing()

								UpperCamelCase   =	self.get_inputs()
								pipe(**__a   ,							callback=__a   ,							callback_steps=1		)
								assert callback_fn.has_been_called
								assert number_of_steps == 3




						def     snake_case_   (self		) ->   List[Any]:
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()
								torch.cuda.reset_peak_memory_stats()

								UpperCamelCase   =	"stabilityai/stable-diffusion-2-base"
								UpperCamelCase   =	DDIMScheduler.from_pretrained(__a   ,							subfolder="scheduler"		)
								UpperCamelCase   =	StableDiffusionPanoramaPipeline.from_pretrained(__a   ,							scheduler=__a   ,							safety_checker=__a		)
								UpperCamelCase   =	pipe.to(__a		)
								pipe.set_progress_bar_config(disable=__a		)
								pipe.enable_attention_slicing(1		)
								pipe.enable_sequential_cpu_offload()

								UpperCamelCase   =	self.get_inputs()
								UpperCamelCase   =	pipe(**__a		)

								UpperCamelCase   =	torch.cuda.max_memory_allocated()
								# make sure that less than 5.2 GB is allocated
								assert mem_bytes < 5.5 * 10**9






