



"""simple docstring"""







import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
			import torch

if is_datasets_available():
			from datasets import load_dataset

A		=						random.Random()

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[Any]     ,		lowerCamelCase_:							Dict=1.0     ,		lowerCamelCase_:							Any=None     ,		lowerCamelCase_:							Union[str, Any]=None ):
						"""simple docstring"""






						if rng is None:
												snake_case   :			Optional[Any]						    = global_rng

						snake_case   :			Tuple						    = []
						for batch_idx in range(shape[0] ):
												values.append([] )
												for _ in range(shape[1] ):
																		values[-1].append(rng.random() * scale )

						return values

class       _a  ( unittest.TestCase):



			def __init__(			self							:				Any   ,	_lowercase							:				List[Any]   ,	_lowercase							:				int=7   ,	_lowercase							:				Union[str, Any]=400   ,	_lowercase							:				List[Any]=2000   ,	_lowercase							:				Any=2048   ,	_lowercase							:				List[Any]=128   ,	_lowercase							:				Optional[int]=1   ,	_lowercase							:				Optional[Any]=512   ,	_lowercase							:				Optional[int]=30   ,	_lowercase							:				str=44100   ,	)							->					List[Any]:
									snake_case   :			str						    = parent
									snake_case   :			List[str]						    = batch_size
									snake_case   :			int						    = min_seq_length
									snake_case   :			Union[str, Any]						    = max_seq_length
									snake_case   :			Union[str, Any]						    = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									snake_case   :			List[Any]						    = spectrogram_length
									snake_case   :			Optional[int]						    = feature_size
									snake_case   :			Union[str, Any]						    = num_audio_channels
									snake_case   :			Optional[Any]						    = hop_length
									snake_case   :			List[str]						    = chunk_length
									snake_case   :			Tuple						    = sampling_rate



			def 				__lowercase					(			self							:				Tuple		)							->					Optional[int]:
									return {
									    "spectrogram_length": self.spectrogram_length,
									    "feature_size": self.feature_size,
									    "num_audio_channels": self.num_audio_channels,
									    "hop_length": self.hop_length,
									    "chunk_length": self.chunk_length,
									    "sampling_rate": self.sampling_rate,
									}



			def 				__lowercase					(			self							:				Tuple   ,	_lowercase							:				Dict=False   ,	_lowercase							:				List[str]=False		)							->					Optional[int]:
									def _flatten(_lowercase							:				str		):
															return list(itertools.chain(*_lowercase		)		)

									if equal_length:
															snake_case   :			List[Any]						    = [floats_list((self.max_seq_length, self.feature_size)		) for _ in range(self.batch_size		)]
									else:
															# make sure that inputs increase in size
															snake_case   :			int						    = [
															    floats_list((x, self.feature_size)		)
															    for x in range(self.min_seq_length   ,	self.max_seq_length   ,	self.seq_length_diff		)
															]
									if numpify:
															snake_case   :			List[Any]						    = [np.asarray(_lowercase		) for x in speech_inputs]
									return speech_inputs



@require_torch
@require_torchaudio
class       _a  ( SCREAMING_SNAKE_CASE__     ,	unittest.TestCase):
			__magic_name__     					=     TvltFeatureExtractor



			def 				__lowercase					(			self							:				Any		)							->					Optional[Any]:
									snake_case   :			List[str]						    = TvltFeatureExtractionTester(self		)



			def 				__lowercase					(			self							:				int		)							->					List[Any]:
									snake_case   :			List[str]						    = self.feature_extraction_class(**self.feat_extract_dict		)
									self.assertTrue(hasattr(_lowercase   ,	"spectrogram_length"		)		)
									self.assertTrue(hasattr(_lowercase   ,	"feature_size"		)		)
									self.assertTrue(hasattr(_lowercase   ,	"num_audio_channels"		)		)
									self.assertTrue(hasattr(_lowercase   ,	"hop_length"		)		)
									self.assertTrue(hasattr(_lowercase   ,	"chunk_length"		)		)
									self.assertTrue(hasattr(_lowercase   ,	"sampling_rate"		)		)



			def 				__lowercase					(			self							:				Any		)							->					str:
									snake_case   :			Optional[Any]						    = self.feature_extraction_class(**self.feat_extract_dict		)

									with tempfile.TemporaryDirectory() as tmpdirname:
															snake_case   :			Optional[int]						    = feat_extract_first.save_pretrained(_lowercase		)[0]
															check_json_file_has_correct_format(_lowercase		)
															snake_case   :			Optional[int]						    = self.feature_extraction_class.from_pretrained(_lowercase		)

									snake_case   :			Union[str, Any]						    = feat_extract_first.to_dict()
									snake_case   :			str						    = feat_extract_second.to_dict()
									snake_case   :			Optional[int]						    = dict_first.pop("mel_filters"		)
									snake_case   :			Dict						    = dict_second.pop("mel_filters"		)
									self.assertTrue(np.allclose(_lowercase   ,	_lowercase		)		)
									self.assertEqual(_lowercase   ,	_lowercase		)



			def 				__lowercase					(			self							:				Tuple		)							->					str:
									snake_case   :			str						    = self.feature_extraction_class(**self.feat_extract_dict		)

									with tempfile.TemporaryDirectory() as tmpdirname:
															snake_case   :			Tuple						    = os.path.join(_lowercase   ,	"feat_extract.json"		)
															feat_extract_first.to_json_file(_lowercase		)
															snake_case   :			Optional[int]						    = self.feature_extraction_class.from_json_file(_lowercase		)

									snake_case   :			Tuple						    = feat_extract_first.to_dict()
									snake_case   :			Union[str, Any]						    = feat_extract_second.to_dict()
									snake_case   :			int						    = dict_first.pop("mel_filters"		)
									snake_case   :			Union[str, Any]						    = dict_second.pop("mel_filters"		)
									self.assertTrue(np.allclose(_lowercase   ,	_lowercase		)		)
									self.assertEqual(_lowercase   ,	_lowercase		)



			def 				__lowercase					(			self							:				Tuple		)							->					Optional[Any]:
									# Initialize feature_extractor
									snake_case   :			Optional[int]						    = self.feature_extraction_class(**self.feat_extract_dict		)

									# create three inputs of length 800, 1000, and 1200
									snake_case   :			List[str]						    = [floats_list((1, x)		)[0] for x in range(800   ,	1400   ,	200		)]
									snake_case   :			Optional[int]						    = [np.asarray(_lowercase		) for speech_input in speech_inputs]

									# Test not batched input
									snake_case   :			int						    = feature_extractor(np_speech_inputs[0]   ,	return_tensors="np"   ,	sampling_rate=44100		).audio_values

									self.assertTrue(encoded_audios.ndim == 4		)
									self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
									self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
									self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)

									# Test batched
									snake_case   :			Tuple						    = feature_extractor(_lowercase   ,	return_tensors="np"   ,	sampling_rate=44100		).audio_values

									self.assertTrue(encoded_audios.ndim == 4		)
									self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
									self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
									self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)

									# Test audio masking
									snake_case   :			Optional[int]						    = feature_extractor(
									    _lowercase   ,	return_tensors="np"   ,	sampling_rate=44100   ,	mask_audio=_lowercase		).audio_values

									self.assertTrue(encoded_audios.ndim == 4		)
									self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
									self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
									self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)

									# Test 2-D numpy arrays are batched.
									snake_case   :			Tuple						    = [floats_list((1, x)		)[0] for x in (800, 800, 800)]
									snake_case   :			Optional[Any]						    = np.asarray(_lowercase		)
									snake_case   :			Union[str, Any]						    = feature_extractor(_lowercase   ,	return_tensors="np"   ,	sampling_rate=44100		).audio_values
									self.assertTrue(encoded_audios.ndim == 4		)
									self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
									self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
									self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)



			def 				__lowercase					(			self							:				int   ,	_lowercase							:				List[Any]		)							->					int:
									snake_case   :			Any						    = load_dataset("hf-internal-testing/librispeech_asr_dummy"   ,	"clean"   ,	split="validation"		)
									# automatic decoding with librispeech
									snake_case   :			List[Any]						    = ds.sort("id"		).select(range(_lowercase		)		)[:num_samples]["audio"]

									return [x["array"] for x in speech_samples]



			def 				__lowercase					(			self							:				Tuple		)							->					Union[str, Any]:
									snake_case   :			Optional[int]						    = self._load_datasamples(1		)
									snake_case   :			Optional[Any]						    = TvltFeatureExtractor()
									snake_case   :			List[Any]						    = feature_extractor(_lowercase   ,	return_tensors="pt"		).audio_values

									self.assertEquals(audio_values.shape   ,	(1, 1, 192, 128)		)

									snake_case   :			Dict						    = torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]		)
									self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]   ,	_lowercase   ,	atol=1E-4		)		)








"""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       _a  ( SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	unittest.TestCase):
			__magic_name__     					=     StableDiffusionPanoramaPipeline
			__magic_name__     					=     TEXT_TO_IMAGE_PARAMS
			__magic_name__     					=     TEXT_TO_IMAGE_BATCH_PARAMS
			__magic_name__     					=     TEXT_TO_IMAGE_IMAGE_PARAMS
			__magic_name__     					=     TEXT_TO_IMAGE_IMAGE_PARAMS



			def 				__lowercase					(			self							:				Optional[int]		)							->					int:
									torch.manual_seed(0		)
									snake_case   :			Optional[Any]						    = 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   ,	)
									snake_case   :			Tuple						    = DDIMScheduler()
									torch.manual_seed(0		)
									snake_case   :			List[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   ,	)
									torch.manual_seed(0		)
									snake_case   :			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=1000   ,	)
									snake_case   :			int						    = CLIPTextModel(_lowercase		)
									snake_case   :			Union[str, Any]						    = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

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



			def 				__lowercase					(			self							:				List[str]   ,	_lowercase							:				int   ,	_lowercase							:				Dict=0		)							->					Optional[int]:
									snake_case   :			Union[str, Any]						    = torch.manual_seed(_lowercase		)
									snake_case   :			str						    = {
									    "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 				__lowercase					(			self							:				str		)							->					List[str]:
									snake_case   :			Any						    = "cpu"  # ensure determinism for the device-dependent torch.Generator
									snake_case   :			Dict						    = self.get_dummy_components()
									snake_case   :			Any						    = StableDiffusionPanoramaPipeline(**_lowercase		)
									snake_case   :			List[Any]						    = sd_pipe.to(_lowercase		)
									sd_pipe.set_progress_bar_config(disable=_lowercase		)

									snake_case   :			Optional[Any]						    = self.get_dummy_inputs(_lowercase		)
									snake_case   :			Union[str, Any]						    = sd_pipe(**_lowercase		).images
									snake_case   :			List[Any]						    = image[0, -3:, -3:, -1]
									assert image.shape == (1, 64, 64, 3)

									snake_case   :			List[Any]						    = 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 				__lowercase					(			self							:				int		)							->					Union[str, Any]:
									super().test_inference_batch_consistent(batch_sizes=[1, 2]		)



			def 				__lowercase					(			self							:				Tuple		)							->					Tuple:
									super().test_inference_batch_single_identical(batch_size=2   ,	expected_max_diff=3.25E-3		)



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

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

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

									snake_case   :			Union[str, Any]						    = 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 				__lowercase					(			self							:				str		)							->					Any:
									snake_case   :			Optional[Any]						    = "cpu"  # ensure determinism for the device-dependent torch.Generator
									snake_case   :			List[Any]						    = self.get_dummy_components()
									snake_case   :			List[Any]						    = StableDiffusionPanoramaPipeline(**_lowercase		)
									snake_case   :			int						    = sd_pipe.to(_lowercase		)
									sd_pipe.set_progress_bar_config(disable=_lowercase		)

									snake_case   :			Tuple						    = self.get_dummy_inputs(_lowercase		)
									snake_case   :			str						    = sd_pipe(**_lowercase   ,	view_batch_size=2		)
									snake_case   :			Optional[Any]						    = output.images
									snake_case   :			int						    = image[0, -3:, -3:, -1]

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

									snake_case   :			str						    = 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 				__lowercase					(			self							:				int		)							->					Optional[Any]:
									snake_case   :			Dict						    = "cpu"  # ensure determinism for the device-dependent torch.Generator
									snake_case   :			List[str]						    = self.get_dummy_components()
									snake_case   :			Any						    = EulerAncestralDiscreteScheduler(
									    beta_start=0.00085   ,	beta_end=0.012   ,	beta_schedule="scaled_linear"		)
									snake_case   :			List[str]						    = StableDiffusionPanoramaPipeline(**_lowercase		)
									snake_case   :			List[str]						    = sd_pipe.to(_lowercase		)
									sd_pipe.set_progress_bar_config(disable=_lowercase		)

									snake_case   :			List[Any]						    = self.get_dummy_inputs(_lowercase		)
									snake_case   :			Optional[Any]						    = sd_pipe(**_lowercase		).images
									snake_case   :			int						    = image[0, -3:, -3:, -1]

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

									snake_case   :			Tuple						    = 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 				__lowercase					(			self							:				Tuple		)							->					Union[str, Any]:
									snake_case   :			str						    = "cpu"  # ensure determinism for the device-dependent torch.Generator
									snake_case   :			str						    = self.get_dummy_components()
									snake_case   :			Optional[int]						    = PNDMScheduler(
									    beta_start=0.00085   ,	beta_end=0.012   ,	beta_schedule="scaled_linear"   ,	skip_prk_steps=_lowercase		)
									snake_case   :			Dict						    = StableDiffusionPanoramaPipeline(**_lowercase		)
									snake_case   :			Dict						    = sd_pipe.to(_lowercase		)
									sd_pipe.set_progress_bar_config(disable=_lowercase		)

									snake_case   :			Dict						    = self.get_dummy_inputs(_lowercase		)
									snake_case   :			Optional[int]						    = sd_pipe(**_lowercase		).images
									snake_case   :			str						    = image[0, -3:, -3:, -1]

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

									snake_case   :			Optional[int]						    = 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       _a  ( unittest.TestCase):



			def 				__lowercase					(			self							:				Union[str, Any]		)							->					str:
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



			def 				__lowercase					(			self							:				Dict   ,	_lowercase							:				Any=0		)							->					Optional[Any]:
									snake_case   :			Any						    = torch.manual_seed(_lowercase		)
									snake_case   :			Any						    = {
									    "prompt": "a photo of the dolomites",
									    "generator": generator,
									    "num_inference_steps": 3,
									    "guidance_scale": 7.5,
									    "output_type": "numpy",
									}
									return inputs



			def 				__lowercase					(			self							:				Optional[Any]		)							->					Union[str, Any]:
									snake_case   :			List[Any]						    = "stabilityai/stable-diffusion-2-base"
									snake_case   :			Dict						    = DDIMScheduler.from_pretrained(_lowercase   ,	subfolder="scheduler"		)
									snake_case   :			Any						    = StableDiffusionPanoramaPipeline.from_pretrained(_lowercase   ,	scheduler=_lowercase   ,	safety_checker=_lowercase		)
									pipe.to(_lowercase		)
									pipe.set_progress_bar_config(disable=_lowercase		)
									pipe.enable_attention_slicing()

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

									assert image.shape == (1, 512, 2048, 3)

									snake_case   :			List[str]						    = 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 				__lowercase					(			self							:				Tuple		)							->					List[str]:
									snake_case   :			List[str]						    = StableDiffusionPanoramaPipeline.from_pretrained(
									    "stabilityai/stable-diffusion-2-base"   ,	safety_checker=_lowercase		)
									snake_case   :			int						    = LMSDiscreteScheduler.from_config(pipe.scheduler.config		)
									pipe.to(_lowercase		)
									pipe.set_progress_bar_config(disable=_lowercase		)
									pipe.enable_attention_slicing()

									snake_case   :			Dict						    = self.get_inputs()
									snake_case   :			int						    = pipe(**_lowercase		).images
									snake_case   :			Optional[Any]						    = image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 512, 2048, 3)

									snake_case   :			List[str]						    = 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 				__lowercase					(			self							:				str		)							->					Any:
									snake_case   :			Any						    = 0

									def callback_fn(_lowercase							:				int   ,	_lowercase							:				int   ,	_lowercase							:				torch.FloatTensor		) -> None:
															snake_case   :			List[Any]						    = True
															nonlocal number_of_steps
															number_of_steps += 1
															if step == 1:
																					snake_case   :			List[Any]						    = latents.detach().cpu().numpy()
																					assert latents.shape == (1, 4, 64, 256)
																					snake_case   :			int						    = latents[0, -3:, -3:, -1]

																					snake_case   :			Tuple						    = 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:
																					snake_case   :			Union[str, Any]						    = latents.detach().cpu().numpy()
																					assert latents.shape == (1, 4, 64, 256)
																					snake_case   :			Tuple						    = latents[0, -3:, -3:, -1]

																					snake_case   :			Tuple						    = 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

									snake_case   :			Dict						    = False

									snake_case   :			List[Any]						    = "stabilityai/stable-diffusion-2-base"
									snake_case   :			List[str]						    = DDIMScheduler.from_pretrained(_lowercase   ,	subfolder="scheduler"		)
									snake_case   :			Optional[Any]						    = StableDiffusionPanoramaPipeline.from_pretrained(_lowercase   ,	scheduler=_lowercase   ,	safety_checker=_lowercase		)
									snake_case   :			Any						    = pipe.to(_lowercase		)
									pipe.set_progress_bar_config(disable=_lowercase		)
									pipe.enable_attention_slicing()

									snake_case   :			Tuple						    = self.get_inputs()
									pipe(**_lowercase   ,	callback=_lowercase   ,	callback_steps=1		)
									assert callback_fn.has_been_called
									assert number_of_steps == 3



			def 				__lowercase					(			self							:				Any		)							->					Tuple:
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									snake_case   :			Optional[int]						    = "stabilityai/stable-diffusion-2-base"
									snake_case   :			Tuple						    = DDIMScheduler.from_pretrained(_lowercase   ,	subfolder="scheduler"		)
									snake_case   :			Any						    = StableDiffusionPanoramaPipeline.from_pretrained(_lowercase   ,	scheduler=_lowercase   ,	safety_checker=_lowercase		)
									snake_case   :			Tuple						    = pipe.to(_lowercase		)
									pipe.set_progress_bar_config(disable=_lowercase		)
									pipe.enable_attention_slicing(1		)
									pipe.enable_sequential_cpu_offload()

									snake_case   :			Any						    = self.get_inputs()
									snake_case   :			Optional[int]						    = pipe(**_lowercase		)

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



