





'''simple docstring'''

import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()






class   UpperCAmelCase      (			UpperCamelCase__     ,			UpperCamelCase__     ,			unittest.TestCase							):
 __lowercase  						=							AutoencoderKL
 __lowercase  						=							"""sample"""
 __lowercase  						=							1E-2
 @property
 def 	UpperCAmelCase_				(							self :Any				)->				int:
     A__							=     4
     A__							=     3
     A__							=     (32, 32)

     A__							=     floats_tensor((batch_size, num_channels) + sizes				).to(lowercase_				)

     return {"sample": image}
 @property
 def 	UpperCAmelCase_				(							self :int				)->				List[str]:
     return (3, 32, 32)
 @property
 def 	UpperCAmelCase_				(							self :List[str]				)->				Optional[int]:
     return (3, 32, 32)
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				str:
     A__							=     {
         "block_out_channels": [32, 64],
         "in_channels": 3,
         "out_channels": 3,
         "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
         "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
         "latent_channels": 4,
     }
     A__							=     self.dummy_input
     return init_dict, inputs_dict
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				Any:
     pass
 def 	UpperCAmelCase_				(							self :Optional[Any]				)->				Optional[int]:
     pass
 @unittest.skipIf(torch_device == "mps"		,			"Gradient checkpointing skipped on MPS"				)
 def 	UpperCAmelCase_				(							self :List[Any]				)->				Optional[int]:
     # enable deterministic behavior for gradient checkpointing
     A__,      A__							=     self.prepare_init_args_and_inputs_for_common()
     A__							=     self.model_class(**lowercase_				)
     model.to(lowercase_				)

     assert not model.is_gradient_checkpointing and model.training

     A__							=     model(**lowercase_				).sample
     # run the backwards pass on the model. For backwards pass, for simplicity purpose,
     # we won't calculate the loss and rather backprop on out.sum()
     model.zero_grad()

     A__							=     torch.randn_like(lowercase_				)
     A__							=     (out - labels).mean()
     loss.backward()

     # re-instantiate the model now enabling gradient checkpointing
     A__							=     self.model_class(**lowercase_				)
     # clone model
     model_a.load_state_dict(model.state_dict()				)
     model_a.to(lowercase_				)
     model_a.enable_gradient_checkpointing()

     assert model_a.is_gradient_checkpointing and model_a.training

     A__							=     model_a(**lowercase_				).sample
     # run the backwards pass on the model. For backwards pass, for simplicity purpose,
     # we won't calculate the loss and rather backprop on out.sum()
     model_a.zero_grad()
     A__							=     (out_a - labels).mean()
     loss_a.backward()

     # compare the output and parameters gradients
     self.assertTrue((loss - loss_a).abs() < 1E-5				)
     A__							=     dict(model.named_parameters()				)
     A__							=     dict(model_a.named_parameters()				)
     for name, param in named_params.items():
         self.assertTrue(torch_all_close(param.grad.data		,			named_params_a[name].grad.data		,			atol=5E-5				)				)
 def 	UpperCAmelCase_				(							self :Tuple				)->				Tuple:
     A__,      A__							=     AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"		,			output_loading_info=lowercase_				)
     self.assertIsNotNone(lowercase_				)
     self.assertEqual(len(loading_info["missing_keys"]				)		,			0				)

     model.to(lowercase_				)
     A__							=     model(**self.dummy_input				)

     assert image is not None, "Make sure output is not None"



 def 	UpperCAmelCase_				(							self :int				)->				Optional[int]:
     A__							=     AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"				)
     A__							=     model.to(lowercase_				)
     model.eval()

     if torch_device == "mps":
         A__							=     torch.manual_seed(0				)
     else:
         A__							=     torch.Generator(device=lowercase_				).manual_seed(0				)

     A__							=     torch.randn(
         1		,			model.config.in_channels		,			model.config.sample_size		,			model.config.sample_size		,			generator=torch.manual_seed(0				)		,			)
     A__							=     image.to(lowercase_				)
     with torch.no_grad():
         A__							=     model(lowercase_		,			sample_posterior=lowercase_		,			generator=lowercase_				).sample

     A__							=     output[0, -1, -3:, -3:].flatten().cpu()

     # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
     # the expected output slices are not the same for CPU and GPU.
     if torch_device == "mps":
         A__							=     torch.tensor(
             [
                 -4.0_078E-01,
                 -3.8_323E-04,
                 -1.2_681E-01,
                 -1.1_462E-01,
                 2.0_095E-01,
                 1.0_893E-01,
                 -8.8_247E-02,
                 -3.0_361E-01,
                 -9.8_644E-03,
             ]				)
     elif torch_device == "cpu":
         A__							=     torch.tensor(
             [-0.1_3_5_2, 0.0_8_7_8, 0.0_4_1_9, -0.0_8_1_8, -0.1_0_6_9, 0.0_6_8_8, -0.1_4_5_8, -0.4_4_4_6, -0.0_0_2_6]				)
     else:
         A__							=     torch.tensor(
             [-0.2_4_2_1, 0.4_6_4_2, 0.2_5_0_7, -0.0_4_3_8, 0.0_6_8_2, 0.3_1_6_0, -0.2_0_1_8, -0.0_7_2_7, 0.2_4_8_5]				)

     self.assertTrue(torch_all_close(lowercase_		,			lowercase_		,			rtol=1E-2				)				)






@slow
class   UpperCAmelCase      (			unittest.TestCase							):
 def 	UpperCAmelCase_				(							self :int		,			lowercase_ :Tuple		,			lowercase_ :Optional[int]				)->				str:
     return F"gaussian_noise_s={seed}_shape={'_'.join([str(lowercase_				) for s in shape]				)}.npy"
 def 	UpperCAmelCase_				(							self :List[Any]				)->				str:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()
 def 	UpperCAmelCase_				(							self :Union[str, Any]		,			lowercase_ :Optional[Any]=0		,			lowercase_ :List[Any]=(4, 3, 5_12, 5_12)		,			lowercase_ :Optional[int]=False				)->				Tuple:
     A__							=     torch.floataa if fpaa else torch.floataa
     A__							=     torch.from_numpy(load_hf_numpy(self.get_file_format(lowercase_		,			lowercase_				)				)				).to(lowercase_				).to(lowercase_				)
     return image
 def 	UpperCAmelCase_				(							self :Dict		,			lowercase_ :Tuple="CompVis/stable-diffusion-v1-4"		,			lowercase_ :Dict=False				)->				Tuple:
     A__							=     "fp16" if fpaa else None
     A__							=     torch.floataa if fpaa else torch.floataa

     A__							=     AutoencoderKL.from_pretrained(
         lowercase_		,			subfolder="vae"		,			torch_dtype=lowercase_		,			revision=lowercase_		,			)
     model.to(lowercase_				).eval()

     return model
 def 	UpperCAmelCase_				(							self :str		,			lowercase_ :List[Any]=0				)->				Union[str, Any]:
     if torch_device == "mps":
         return torch.manual_seed(lowercase_				)
     return torch.Generator(device=lowercase_				).manual_seed(lowercase_				)
 @parameterized.expand(
     [
         # fmt: off
         [33, [-0.1_6_0_3, 0.9_8_7_8, -0.0_4_9_5, -0.0_7_9_0, -0.2_7_0_9, 0.8_3_7_5, -0.2_0_6_0, -0.0_8_2_4], [-0.2_3_9_5, 0.0_0_9_8, 0.0_1_0_2, -0.0_7_0_9, -0.2_8_4_0, -0.0_2_7_4, -0.0_7_1_8, -0.1_8_2_4]],
         [47, [-0.2_3_7_6, 0.1_1_6_8, 0.1_3_3_2, -0.4_8_4_0, -0.2_5_0_8, -0.0_7_9_1, -0.0_4_9_3, -0.4_0_8_9], [0.0_3_5_0, 0.0_8_4_7, 0.0_4_6_7, 0.0_3_4_4, -0.0_8_4_2, -0.0_5_4_7, -0.0_6_3_3, -0.1_1_3_1]],
         # fmt: on
     ]				)
 def 	UpperCAmelCase_				(							self :Tuple		,			lowercase_ :Dict		,			lowercase_ :Any		,			lowercase_ :Union[str, Any]				)->				Any:
     A__							=     self.get_sd_vae_model()
     A__							=     self.get_sd_image(lowercase_				)
     A__							=     self.get_generator(lowercase_				)

     with torch.no_grad():
         A__							=     model(lowercase_		,			generator=lowercase_		,			sample_posterior=lowercase_				).sample

     assert sample.shape == image.shape

     A__							=     sample[-1, -2:, -2:, :2].flatten().float().cpu()
     A__							=     torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice				)

     assert torch_all_close(lowercase_		,			lowercase_		,			atol=3E-3				)
 @parameterized.expand(
     [
         # fmt: off
         [33, [-0.0_5_1_3, 0.0_2_8_9, 1.3_7_9_9, 0.2_1_6_6, -0.2_5_7_3, -0.0_8_7_1, 0.5_1_0_3, -0.0_9_9_9]],
         [47, [-0.4_1_2_8, -0.1_3_2_0, -0.3_7_0_4, 0.1_9_6_5, -0.4_1_1_6, -0.2_3_3_2, -0.3_3_4_0, 0.2_2_4_7]],
         # fmt: on
     ]				)
 @require_torch_gpu
 def 	UpperCAmelCase_				(							self :Optional[Any]		,			lowercase_ :List[Any]		,			lowercase_ :Union[str, Any]				)->				List[str]:
     A__							=     self.get_sd_vae_model(fpaa=lowercase_				)
     A__							=     self.get_sd_image(lowercase_		,			fpaa=lowercase_				)
     A__							=     self.get_generator(lowercase_				)

     with torch.no_grad():
         A__							=     model(lowercase_		,			generator=lowercase_		,			sample_posterior=lowercase_				).sample

     assert sample.shape == image.shape

     A__							=     sample[-1, -2:, :2, -2:].flatten().float().cpu()
     A__							=     torch.tensor(lowercase_				)

     assert torch_all_close(lowercase_		,			lowercase_		,			atol=1E-2				)
 @parameterized.expand(
     [
         # fmt: off
         [33, [-0.1_6_0_9, 0.9_8_6_6, -0.0_4_8_7, -0.0_7_7_7, -0.2_7_1_6, 0.8_3_6_8, -0.2_0_5_5, -0.0_8_1_4], [-0.2_3_9_5, 0.0_0_9_8, 0.0_1_0_2, -0.0_7_0_9, -0.2_8_4_0, -0.0_2_7_4, -0.0_7_1_8, -0.1_8_2_4]],
         [47, [-0.2_3_7_7, 0.1_1_4_7, 0.1_3_3_3, -0.4_8_4_1, -0.2_5_0_6, -0.0_8_0_5, -0.0_4_9_1, -0.4_0_8_5], [0.0_3_5_0, 0.0_8_4_7, 0.0_4_6_7, 0.0_3_4_4, -0.0_8_4_2, -0.0_5_4_7, -0.0_6_3_3, -0.1_1_3_1]],
         # fmt: on
     ]				)
 def 	UpperCAmelCase_				(							self :List[Any]		,			lowercase_ :int		,			lowercase_ :List[Any]		,			lowercase_ :Dict				)->				int:
     A__							=     self.get_sd_vae_model()
     A__							=     self.get_sd_image(lowercase_				)

     with torch.no_grad():
         A__							=     model(lowercase_				).sample

     assert sample.shape == image.shape

     A__							=     sample[-1, -2:, -2:, :2].flatten().float().cpu()
     A__							=     torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice				)

     assert torch_all_close(lowercase_		,			lowercase_		,			atol=3E-3				)
 @parameterized.expand(
     [
         # fmt: off
         [13, [-0.2_0_5_1, -0.1_8_0_3, -0.2_3_1_1, -0.2_1_1_4, -0.3_2_9_2, -0.3_5_7_4, -0.2_9_5_3, -0.3_3_2_3]],
         [37, [-0.2_6_3_2, -0.2_6_2_5, -0.2_1_9_9, -0.2_7_4_1, -0.4_5_3_9, -0.4_9_9_0, -0.3_7_2_0, -0.4_9_2_5]],
         # fmt: on
     ]				)
 @require_torch_gpu
 def 	UpperCAmelCase_				(							self :Optional[Any]		,			lowercase_ :Union[str, Any]		,			lowercase_ :Tuple				)->				Dict:
     A__							=     self.get_sd_vae_model()
     A__							=     self.get_sd_image(lowercase_		,			shape=(3, 4, 64, 64)				)

     with torch.no_grad():
         A__							=     model.decode(lowercase_				).sample

     assert list(sample.shape				) == [3, 3, 5_12, 5_12]

     A__							=     sample[-1, -2:, :2, -2:].flatten().cpu()
     A__							=     torch.tensor(lowercase_				)

     assert torch_all_close(lowercase_		,			lowercase_		,			atol=1E-3				)
 @parameterized.expand(
     [
         # fmt: off
         [27, [-0.0_3_6_9, 0.0_2_0_7, -0.0_7_7_6, -0.0_6_8_2, -0.1_7_4_7, -0.1_9_3_0, -0.1_4_6_5, -0.2_0_3_9]],
         [16, [-0.1_6_2_8, -0.2_1_3_4, -0.2_7_4_7, -0.2_6_4_2, -0.3_7_7_4, -0.4_4_0_4, -0.3_6_8_7, -0.4_2_7_7]],
         # fmt: on
     ]				)
 @require_torch_gpu
 def 	UpperCAmelCase_				(							self :Any		,			lowercase_ :Tuple		,			lowercase_ :Union[str, Any]				)->				Dict:
     A__							=     self.get_sd_vae_model(fpaa=lowercase_				)
     A__							=     self.get_sd_image(lowercase_		,			shape=(3, 4, 64, 64)		,			fpaa=lowercase_				)

     with torch.no_grad():
         A__							=     model.decode(lowercase_				).sample

     assert list(sample.shape				) == [3, 3, 5_12, 5_12]

     A__							=     sample[-1, -2:, :2, -2:].flatten().float().cpu()
     A__							=     torch.tensor(lowercase_				)

     assert torch_all_close(lowercase_		,			lowercase_		,			atol=5E-3				)
 @parameterized.expand([(13,), (16,), (27,)]				)
 @require_torch_gpu
 @unittest.skipIf(not is_xformers_available()		,			reason="xformers is not required when using PyTorch 2.0."				)
 def 	UpperCAmelCase_				(							self :int		,			lowercase_ :Any				)->				List[Any]:
     A__							=     self.get_sd_vae_model(fpaa=lowercase_				)
     A__							=     self.get_sd_image(lowercase_		,			shape=(3, 4, 64, 64)		,			fpaa=lowercase_				)

     with torch.no_grad():
         A__							=     model.decode(lowercase_				).sample

     model.enable_xformers_memory_efficient_attention()
     with torch.no_grad():
         A__							=     model.decode(lowercase_				).sample

     assert list(sample.shape				) == [3, 3, 5_12, 5_12]

     assert torch_all_close(lowercase_		,			lowercase_		,			atol=1E-1				)
 @parameterized.expand([(13,), (16,), (37,)]				)
 @require_torch_gpu
 @unittest.skipIf(not is_xformers_available()		,			reason="xformers is not required when using PyTorch 2.0."				)
 def 	UpperCAmelCase_				(							self :Tuple		,			lowercase_ :List[Any]				)->				Tuple:
     A__							=     self.get_sd_vae_model()
     A__							=     self.get_sd_image(lowercase_		,			shape=(3, 4, 64, 64)				)

     with torch.no_grad():
         A__							=     model.decode(lowercase_				).sample

     model.enable_xformers_memory_efficient_attention()
     with torch.no_grad():
         A__							=     model.decode(lowercase_				).sample

     assert list(sample.shape				) == [3, 3, 5_12, 5_12]

     assert torch_all_close(lowercase_		,			lowercase_		,			atol=1E-2				)



 @parameterized.expand(
     [
         # fmt: off
         [33, [-0.3_0_0_1, 0.0_9_1_8, -2.6_9_8_4, -3.9_7_2_0, -3.2_0_9_9, -5.0_3_5_3, 1.7_3_3_8, -0.2_0_6_5, 3.4_2_6_7]],
         [47, [-1.5_0_3_0, -4.3_8_7_1, -6.0_3_5_5, -9.1_1_5_7, -1.6_6_6_1, -2.7_8_5_3, 2.1_6_0_7, -5.0_8_2_3, 2.5_6_3_3]],
         # fmt: on
     ]				)
 def 	UpperCAmelCase_				(							self :List[Any]		,			lowercase_ :Dict		,			lowercase_ :Dict				)->				Any:
     A__							=     self.get_sd_vae_model()
     A__							=     self.get_sd_image(lowercase_				)
     A__							=     self.get_generator(lowercase_				)

     with torch.no_grad():
         A__							=     model.encode(lowercase_				).latent_dist
         A__							=     dist.sample(generator=lowercase_				)

     assert list(sample.shape				) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

     A__							=     sample[0, -1, -3:, -3:].flatten().cpu()
     A__							=     torch.tensor(lowercase_				)

     A__							=     3E-3 if torch_device != "mps" else 1E-2
     assert torch_all_close(lowercase_		,			lowercase_		,			atol=lowercase_				)










'''simple docstring'''

from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							["""vqvae"""]
 def __init__(							self :Dict		,			lowercase_ :AutoencoderKL		,			lowercase_ :UNetaDConditionModel		,			lowercase_ :Mel		,			lowercase_ :Union[DDIMScheduler, DDPMScheduler]		,			)->				Optional[Any]:
     super().__init__()
     self.register_modules(unet=lowercase_		,			scheduler=lowercase_		,			mel=lowercase_		,			vqvae=lowercase_				)
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				int:
     return 50 if isinstance(self.scheduler		,			lowercase_				) else 10_00
 @torch.no_grad()
 def __call__(							self :Optional[Any]		,			lowercase_ :int = 1		,			lowercase_ :str = None		,			lowercase_ :np.ndarray = None		,			lowercase_ :int = 0		,			lowercase_ :int = 0		,			lowercase_ :int = None		,			lowercase_ :torch.Generator = None		,			lowercase_ :float = 0		,			lowercase_ :float = 0		,			lowercase_ :torch.Generator = None		,			lowercase_ :float = 0		,			lowercase_ :torch.Tensor = None		,			lowercase_ :torch.Tensor = None		,			lowercase_ :Tuple=True		,			)->				Union[
     Union[AudioPipelineOutput, ImagePipelineOutput],
     Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
 ]:
     A__							=     steps or self.get_default_steps()
     self.scheduler.set_timesteps(lowercase_				)
     A__							=     step_generator or generator
     # For backwards compatibility
     if type(self.unet.config.sample_size				) == int:
         A__							=     (self.unet.config.sample_size, self.unet.config.sample_size)
     if noise is None:
         A__							=     randn_tensor(
             (
                 batch_size,
                 self.unet.config.in_channels,
                 self.unet.config.sample_size[0],
                 self.unet.config.sample_size[1],
             )		,			generator=lowercase_		,			device=self.device		,			)
     A__							=     noise
     A__							=     None

     if audio_file is not None or raw_audio is not None:
         self.mel.load_audio(lowercase_		,			lowercase_				)
         A__							=     self.mel.audio_slice_to_image(lowercase_				)
         A__							=     np.frombuffer(input_image.tobytes()		,			dtype="uint8"				).reshape(
             (input_image.height, input_image.width)				)
         A__							=     (input_image / 2_55) * 2 - 1
         A__							=     torch.tensor(input_image[np.newaxis, :, :]		,			dtype=torch.float				).to(self.device				)

         if self.vqvae is not None:
             A__							=     self.vqvae.encode(torch.unsqueeze(lowercase_		,			0				)				).latent_dist.sample(
                 generator=lowercase_				)[0]
             A__							=     self.vqvae.config.scaling_factor * input_images

         if start_step > 0:
             A__							=     self.scheduler.add_noise(lowercase_		,			lowercase_		,			self.scheduler.timesteps[start_step - 1]				)

         A__							=     (
             self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
         )
         A__							=     int(mask_start_secs * pixels_per_second				)
         A__							=     int(mask_end_secs * pixels_per_second				)
         A__							=     self.scheduler.add_noise(lowercase_		,			lowercase_		,			torch.tensor(self.scheduler.timesteps[start_step:]				)				)

     for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]				)				):
         if isinstance(self.unet		,			lowercase_				):
             A__							=     self.unet(lowercase_		,			lowercase_		,			lowercase_				)["sample"]
         else:
             A__							=     self.unet(lowercase_		,			lowercase_				)["sample"]

         if isinstance(self.scheduler		,			lowercase_				):
             A__							=     self.scheduler.step(
                 model_output=lowercase_		,			timestep=lowercase_		,			sample=lowercase_		,			eta=lowercase_		,			generator=lowercase_		,			)["prev_sample"]
         else:
             A__							=     self.scheduler.step(
                 model_output=lowercase_		,			timestep=lowercase_		,			sample=lowercase_		,			generator=lowercase_		,			)["prev_sample"]

         if mask is not None:
             if mask_start > 0:
                 A__							=     mask[:, step, :, :mask_start]
             if mask_end > 0:
                 A__							=     mask[:, step, :, -mask_end:]

     if self.vqvae is not None:
         # 0.18215 was scaling factor used in training to ensure unit variance
         A__							=     1 / self.vqvae.config.scaling_factor * images
         A__							=     self.vqvae.decode(lowercase_				)["sample"]

     A__							=     (images / 2 + 0.5).clamp(0		,			1				)
     A__							=     images.cpu().permute(0		,			2		,			3		,			1				).numpy()
     A__							=     (images * 2_55).round().astype("uint8"				)
     A__							=     list(
         (Image.fromarray(_[:, :, 0]				) for _ in images)
         if images.shape[3] == 1
         else (Image.fromarray(lowercase_		,			mode="RGB"				).convert("L"				) for _ in images)				)

     A__							=     [self.mel.image_to_audio(lowercase_				) for _ in images]
     if not return_dict:
         return images, (self.mel.get_sample_rate(), audios)

     return BaseOutput(**AudioPipelineOutput(np.array(lowercase_				)[:, np.newaxis, :]				)		,			**ImagePipelineOutput(lowercase_				)				)
 @torch.no_grad()
 def 	UpperCAmelCase_				(							self :Optional[Any]		,			lowercase_ :List[Image.Image]		,			lowercase_ :int = 50				)->				np.ndarray:
     assert isinstance(self.scheduler		,			lowercase_				)
     self.scheduler.set_timesteps(lowercase_				)
     A__							=     np.array(
         [np.frombuffer(image.tobytes()		,			dtype="uint8"				).reshape((1, image.height, image.width)				) for image in images]				)
     A__							=     (sample / 2_55) * 2 - 1
     A__							=     torch.Tensor(lowercase_				).to(self.device				)

     for t in self.progress_bar(torch.flip(self.scheduler.timesteps		,			(0,)				)				):
         A__							=     t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
         A__							=     self.scheduler.alphas_cumprod[t]
         A__							=     (
             self.scheduler.alphas_cumprod[prev_timestep]
             if prev_timestep >= 0
             else self.scheduler.final_alpha_cumprod
         )
         A__							=     1 - alpha_prod_t
         A__							=     self.unet(lowercase_		,			lowercase_				)["sample"]
         A__							=     (1 - alpha_prod_t_prev) ** 0.5 * model_output
         A__							=     (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
         A__							=     sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

     return sample



 @staticmethod
 def 	UpperCAmelCase_				(							lowercase_ :torch.Tensor		,			lowercase_ :torch.Tensor		,			lowercase_ :float				)->				torch.Tensor:
     A__							=     acos(torch.dot(torch.flatten(lowercase_				)		,			torch.flatten(lowercase_				)				) / torch.norm(lowercase_				) / torch.norm(lowercase_				)				)
     return sin((1 - alpha) * theta				) * xa / sin(lowercase_				) + sin(alpha * theta				) * xa / sin(lowercase_				)



