





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 					lowercase__ (			__lowerCamelCase    ,       __lowerCamelCase    ,       unittest.TestCase       ):

   '''simple docstring'''


   a		:							Any     	=	AutoencoderKL
   a		:							str     	=	"sample"
   a		:							List[Any]     	=	1E-2




   @property
   def    UpperCamelCase__						(					self							)				->   Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:       str      =   4
       UpperCamelCase__							:       Tuple      =   3
       UpperCamelCase__							:       Tuple      =   (32, 32)

       UpperCamelCase__							:       Optional[int]      =   floats_tensor((batch_size, num_channels) + sizes							).to(__magic_name__							)

       return {"sample": image}




   @property
   def    UpperCamelCase__						(					self							)				->   Union[str, Any]:






       """simple docstring"""
       return (3, 32, 32)




   @property
   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       return (3, 32, 32)




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   {
           '''block_out_channels''': [32, 64],
           '''in_channels''': 3,
           '''out_channels''': 3,
           '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
           '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
           '''latent_channels''': 4,
       }
       UpperCamelCase__							:       Any      =   self.dummy_input
       return init_dict, inputs_dict




   def    UpperCamelCase__						(					self							)				->   Optional[int]:






       """simple docstring"""
       pass




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       pass




   @unittest.skipIf(torch_device == '''mps''',	'''Gradient checkpointing skipped on MPS'''							)
   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       # enable deterministic behavior for gradient checkpointing
       UpperCamelCase__     ,UpperCamelCase__							:       List[str]      =   self.prepare_init_args_and_inputs_for_common()
       UpperCamelCase__							:       int      =   self.model_class(**__magic_name__							)
       model.to(__magic_name__							)

       assert not model.is_gradient_checkpointing and model.training

       UpperCamelCase__							:       List[str]      =   model(**__magic_name__							).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()

       UpperCamelCase__							:       Optional[int]      =   torch.randn_like(__magic_name__							)
       UpperCamelCase__							:       Union[str, Any]      =   (out - labels).mean()
       loss.backward()

       # re-instantiate the model now enabling gradient checkpointing
       UpperCamelCase__							:       Union[str, Any]      =   self.model_class(**__magic_name__							)
       # clone model
       model_a.load_state_dict(model.state_dict()							)
       model_a.to(__magic_name__							)
       model_a.enable_gradient_checkpointing()

       assert model_a.is_gradient_checkpointing and model_a.training

       UpperCamelCase__							:       Optional[int]      =   model_a(**__magic_name__							).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()
       UpperCamelCase__							:       Dict      =   (out_a - labels).mean()
       loss_a.backward()

       # compare the output and parameters gradients
       self.assertTrue((loss - loss_a).abs() < 1E-5							)
       UpperCamelCase__							:       Dict      =   dict(model.named_parameters()							)
       UpperCamelCase__							:       Optional[Any]      =   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							)				->   Dict:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__							:       List[Any]      =   AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy''',	output_loading_info=__magic_name__							)
       self.assertIsNotNone(__magic_name__							)
       self.assertEqual(len(loading_info['''missing_keys''']							),	0							)

       model.to(__magic_name__							)
       UpperCamelCase__							:       str      =   model(**self.dummy_input							)

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




   def    UpperCamelCase__						(					self							)				->   Tuple:






       """simple docstring"""
       UpperCamelCase__							:       Union[str, Any]      =   AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''							)
       UpperCamelCase__							:       Any      =   model.to(__magic_name__							)
       model.eval()

       if torch_device == "mps":
           UpperCamelCase__							:       Union[str, Any]      =   torch.manual_seed(0							)
       else:
           UpperCamelCase__							:       str      =   torch.Generator(device=__magic_name__							).manual_seed(0							)

       UpperCamelCase__							:       Any      =   torch.randn(
           1,	model.config.in_channels,	model.config.sample_size,	model.config.sample_size,	generator=torch.manual_seed(0							),	)
       UpperCamelCase__							:       Optional[int]      =   image.to(__magic_name__							)
       with torch.no_grad():
           UpperCamelCase__							:       int      =   model(__magic_name__,	sample_posterior=__magic_name__,	generator=__magic_name__							).sample

       UpperCamelCase__							:       int      =   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":
           UpperCamelCase__							:       Any      =   torch.tensor(
               [
                   -4.00_78E-01,
                   -3.83_23E-04,
                   -1.26_81E-01,
                   -1.14_62E-01,
                   2.00_95E-01,
                   1.08_93E-01,
                   -8.82_47E-02,
                   -3.03_61E-01,
                   -9.86_44E-03,
               ]							)
       elif torch_device == "cpu":
           UpperCamelCase__							:       Optional[int]      =   torch.tensor(
               [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]							)
       else:
           UpperCamelCase__							:       Union[str, Any]      =   torch.tensor(
               [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]							)

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






@slow
class 					lowercase__ (			unittest.TestCase       ):

   '''simple docstring'''


   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__							)				->   List[Any]:






       """simple docstring"""
       return f"gaussian_noise_s={seed}_shape={'_'.join([str(__magic_name__							) for s in shape]							)}.npy"




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




   def    UpperCamelCase__						(					self,	__magic_name__=0,	__magic_name__=(4, 3, 512, 512),	__magic_name__=False							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       int      =   torch.floataa if fpaa else torch.floataa
       UpperCamelCase__							:       Any      =   torch.from_numpy(load_hf_numpy(self.get_file_format(__magic_name__,	__magic_name__							)							)							).to(__magic_name__							).to(__magic_name__							)
       return image




   def    UpperCamelCase__						(					self,	__magic_name__="CompVis/stable-diffusion-v1-4",	__magic_name__=False							)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       Optional[int]      =   '''fp16''' if fpaa else None
       UpperCamelCase__							:       List[Any]      =   torch.floataa if fpaa else torch.floataa

       UpperCamelCase__							:       int      =   AutoencoderKL.from_pretrained(
           __magic_name__,	subfolder='''vae''',	torch_dtype=__magic_name__,	revision=__magic_name__,	)
       model.to(__magic_name__							).eval()

       return model




   def    UpperCamelCase__						(					self,	__magic_name__=0							)				->   Optional[int]:






       """simple docstring"""
       if torch_device == "mps":
           return torch.manual_seed(__magic_name__							)
       return torch.Generator(device=__magic_name__							).manual_seed(__magic_name__							)




   @parameterized.expand(
       [
           # fmt: off
           [33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
           [47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
           # fmt: on
       ]							)
   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__							)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       Optional[Any]      =   self.get_sd_vae_model()
       UpperCamelCase__							:       Optional[int]      =   self.get_sd_image(__magic_name__							)
       UpperCamelCase__							:       Tuple      =   self.get_generator(__magic_name__							)

       with torch.no_grad():
           UpperCamelCase__							:       Optional[Any]      =   model(__magic_name__,	generator=__magic_name__,	sample_posterior=__magic_name__							).sample

       assert sample.shape == image.shape

       UpperCamelCase__							:       str      =   sample[-1, -2:, -2:, :2].flatten().float().cpu()
       UpperCamelCase__							:       int      =   torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice							)

       assert torch_all_close(__magic_name__,	__magic_name__,	atol=3E-3							)




   @parameterized.expand(
       [
           # fmt: off
           [33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
           [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
           # fmt: on
       ]							)
   @require_torch_gpu
   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__							)				->   Tuple:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   self.get_sd_vae_model(fpaa=__magic_name__							)
       UpperCamelCase__							:       str      =   self.get_sd_image(__magic_name__,	fpaa=__magic_name__							)
       UpperCamelCase__							:       Union[str, Any]      =   self.get_generator(__magic_name__							)

       with torch.no_grad():
           UpperCamelCase__							:       Union[str, Any]      =   model(__magic_name__,	generator=__magic_name__,	sample_posterior=__magic_name__							).sample

       assert sample.shape == image.shape

       UpperCamelCase__							:       List[Any]      =   sample[-1, -2:, :2, -2:].flatten().float().cpu()
       UpperCamelCase__							:       Dict      =   torch.tensor(__magic_name__							)

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




   @parameterized.expand(
       [
           # fmt: off
           [33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
           [47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
           # fmt: on
       ]							)
   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__							)				->   Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   self.get_sd_vae_model()
       UpperCamelCase__							:       Optional[int]      =   self.get_sd_image(__magic_name__							)

       with torch.no_grad():
           UpperCamelCase__							:       Union[str, Any]      =   model(__magic_name__							).sample

       assert sample.shape == image.shape

       UpperCamelCase__							:       List[Any]      =   sample[-1, -2:, -2:, :2].flatten().float().cpu()
       UpperCamelCase__							:       List[str]      =   torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice							)

       assert torch_all_close(__magic_name__,	__magic_name__,	atol=3E-3							)




   @parameterized.expand(
       [
           # fmt: off
           [13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
           [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
           # fmt: on
       ]							)
   @require_torch_gpu
   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__							)				->   Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:       Optional[int]      =   self.get_sd_vae_model()
       UpperCamelCase__							:       int      =   self.get_sd_image(__magic_name__,	shape=(3, 4, 64, 64)							)

       with torch.no_grad():
           UpperCamelCase__							:       Optional[Any]      =   model.decode(__magic_name__							).sample

       assert list(sample.shape							) == [3, 3, 512, 512]

       UpperCamelCase__							:       Optional[Any]      =   sample[-1, -2:, :2, -2:].flatten().cpu()
       UpperCamelCase__							:       Optional[Any]      =   torch.tensor(__magic_name__							)

       assert torch_all_close(__magic_name__,	__magic_name__,	atol=1E-3							)




   @parameterized.expand(
       [
           # fmt: off
           [27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
           [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
           # fmt: on
       ]							)
   @require_torch_gpu
   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__							)				->   Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:       Optional[int]      =   self.get_sd_vae_model(fpaa=__magic_name__							)
       UpperCamelCase__							:       Any      =   self.get_sd_image(__magic_name__,	shape=(3, 4, 64, 64),	fpaa=__magic_name__							)

       with torch.no_grad():
           UpperCamelCase__							:       List[Any]      =   model.decode(__magic_name__							).sample

       assert list(sample.shape							) == [3, 3, 512, 512]

       UpperCamelCase__							:       List[Any]      =   sample[-1, -2:, :2, -2:].flatten().float().cpu()
       UpperCamelCase__							:       Tuple      =   torch.tensor(__magic_name__							)

       assert torch_all_close(__magic_name__,	__magic_name__,	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,	__magic_name__							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       Optional[Any]      =   self.get_sd_vae_model(fpaa=__magic_name__							)
       UpperCamelCase__							:       List[str]      =   self.get_sd_image(__magic_name__,	shape=(3, 4, 64, 64),	fpaa=__magic_name__							)

       with torch.no_grad():
           UpperCamelCase__							:       int      =   model.decode(__magic_name__							).sample

       model.enable_xformers_memory_efficient_attention()
       with torch.no_grad():
           UpperCamelCase__							:       Optional[Any]      =   model.decode(__magic_name__							).sample

       assert list(sample.shape							) == [3, 3, 512, 512]

       assert torch_all_close(__magic_name__,	__magic_name__,	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,	__magic_name__							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   self.get_sd_vae_model()
       UpperCamelCase__							:       Tuple      =   self.get_sd_image(__magic_name__,	shape=(3, 4, 64, 64)							)

       with torch.no_grad():
           UpperCamelCase__							:       Any      =   model.decode(__magic_name__							).sample

       model.enable_xformers_memory_efficient_attention()
       with torch.no_grad():
           UpperCamelCase__							:       Optional[Any]      =   model.decode(__magic_name__							).sample

       assert list(sample.shape							) == [3, 3, 512, 512]

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




   @parameterized.expand(
       [
           # fmt: off
           [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
           [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
           # fmt: on
       ]							)
   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__							)				->   List[Any]:






       """simple docstring"""
       UpperCamelCase__							:       Optional[Any]      =   self.get_sd_vae_model()
       UpperCamelCase__							:       Tuple      =   self.get_sd_image(__magic_name__							)
       UpperCamelCase__							:       int      =   self.get_generator(__magic_name__							)

       with torch.no_grad():
           UpperCamelCase__							:       Tuple      =   model.encode(__magic_name__							).latent_dist
           UpperCamelCase__							:       str      =   dist.sample(generator=__magic_name__							)

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

       UpperCamelCase__							:       List[str]      =   sample[0, -1, -3:, -3:].flatten().cpu()
       UpperCamelCase__							:       int      =   torch.tensor(__magic_name__							)

       UpperCamelCase__							:       Tuple      =   3E-3 if torch_device != '''mps''' else 1E-2
       assert torch_all_close(__magic_name__,	__magic_name__,	atol=__magic_name__							)








# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput






@dataclass
class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							torch.FloatTensor
   a		:							torch.FloatTensor






class 					lowercase__ (			__lowerCamelCase    ,       __lowerCamelCase       ):

   '''simple docstring'''


   a		:							List[Any]     	=	1




   @register_to_config
   def __init__(					self,	__magic_name__ = 2000,	__magic_name__ = 0.15,	__magic_name__ = 0.01,	__magic_name__ = 1348.0,	__magic_name__ = 1E-5,	__magic_name__ = 1,	)				->   int:






       """simple docstring"""
       # standard deviation of the initial noise distribution
       UpperCamelCase__							:       int      =   sigma_max

       # setable values
       UpperCamelCase__							:       Optional[int]      =   None

       self.set_sigmas(__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None							)				->   torch.FloatTensor:






       """simple docstring"""
       return sample




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None,	__magic_name__ = None							)				->   int:






       """simple docstring"""
       UpperCamelCase__							:       str      =   sampling_eps if sampling_eps is not None else self.config.sampling_eps

       UpperCamelCase__							:       List[Any]      =   torch.linspace(1,	__magic_name__,	__magic_name__,	device=__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None,	__magic_name__ = None,	__magic_name__ = None							)				->   Dict:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   sigma_min if sigma_min is not None else self.config.sigma_min
       UpperCamelCase__							:       str      =   sigma_max if sigma_max is not None else self.config.sigma_max
       UpperCamelCase__							:       Optional[int]      =   sampling_eps if sampling_eps is not None else self.config.sampling_eps
       if self.timesteps is None:
           self.set_timesteps(__magic_name__,	__magic_name__							)

       UpperCamelCase__							:       str      =   sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
       UpperCamelCase__							:       Optional[Any]      =   torch.exp(torch.linspace(math.log(__magic_name__							),	math.log(__magic_name__							),	__magic_name__							)							)
       UpperCamelCase__							:       Any      =   torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__							)				->   str:






       """simple docstring"""
       return torch.where(
           timesteps == 0,	torch.zeros_like(t.to(timesteps.device							)							),	self.discrete_sigmas[timesteps - 1].to(timesteps.device							),	)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__ = None,	__magic_name__ = True,	)				->   Union[SdeVeOutput, Tuple]:






       """simple docstring"""
       if self.timesteps is None:
           raise ValueError(
               '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''							)

       UpperCamelCase__							:       Any      =   timestep * torch.ones(
           sample.shape[0],	device=sample.device							)  # torch.repeat_interleave(timestep, sample.shape[0])
       UpperCamelCase__							:       Any      =   (timestep * (len(self.timesteps							) - 1)).long()

       # mps requires indices to be in the same device, so we use cpu as is the default with cuda
       UpperCamelCase__							:       List[Any]      =   timesteps.to(self.discrete_sigmas.device							)

       UpperCamelCase__							:       str      =   self.discrete_sigmas[timesteps].to(sample.device							)
       UpperCamelCase__							:       List[Any]      =   self.get_adjacent_sigma(__magic_name__,	__magic_name__							).to(sample.device							)
       UpperCamelCase__							:       Optional[Any]      =   torch.zeros_like(__magic_name__							)
       UpperCamelCase__							:       str      =   (sigma**2 - adjacent_sigma**2) ** 0.5

       # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
       # also equation 47 shows the analog from SDE models to ancestral sampling methods
       UpperCamelCase__							:       Union[str, Any]      =   diffusion.flatten()
       while len(diffusion.shape							) < len(sample.shape							):
           UpperCamelCase__							:       Any      =   diffusion.unsqueeze(-1							)
       UpperCamelCase__							:       str      =   drift - diffusion**2 * model_output

       #  equation 6: sample noise for the diffusion term of
       UpperCamelCase__							:       Union[str, Any]      =   randn_tensor(
           sample.shape,	layout=sample.layout,	generator=__magic_name__,	device=sample.device,	dtype=sample.dtype							)
       UpperCamelCase__							:       Optional[Any]      =   sample - drift  # subtract because `dt` is a small negative timestep
       # TODO is the variable diffusion the correct scaling term for the noise?
       UpperCamelCase__							:       str      =   prev_sample_mean + diffusion * noise  # add impact of diffusion field g

       if not return_dict:
           return (prev_sample, prev_sample_mean)

       return SdeVeOutput(prev_sample=__magic_name__,	prev_sample_mean=__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__ = None,	__magic_name__ = True,	)				->   Union[SchedulerOutput, Tuple]:






       """simple docstring"""
       if self.timesteps is None:
           raise ValueError(
               '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''							)

       # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
       # sample noise for correction
       UpperCamelCase__							:       List[str]      =   randn_tensor(sample.shape,	layout=sample.layout,	generator=__magic_name__							).to(sample.device							)

       # compute step size from the model_output, the noise, and the snr
       UpperCamelCase__							:       str      =   torch.norm(model_output.reshape(model_output.shape[0],	-1							),	dim=-1							).mean()
       UpperCamelCase__							:       Tuple      =   torch.norm(noise.reshape(noise.shape[0],	-1							),	dim=-1							).mean()
       UpperCamelCase__							:       Union[str, Any]      =   (self.config.snr * noise_norm / grad_norm) ** 2 * 2
       UpperCamelCase__							:       Optional[Any]      =   step_size * torch.ones(sample.shape[0]							).to(sample.device							)
       # self.repeat_scalar(step_size, sample.shape[0])

       # compute corrected sample: model_output term and noise term
       UpperCamelCase__							:       Tuple      =   step_size.flatten()
       while len(step_size.shape							) < len(sample.shape							):
           UpperCamelCase__							:       int      =   step_size.unsqueeze(-1							)
       UpperCamelCase__							:       int      =   sample + step_size * model_output
       UpperCamelCase__							:       List[Any]      =   prev_sample_mean + ((step_size * 2) ** 0.5) * noise

       if not return_dict:
           return (prev_sample,)

       return SchedulerOutput(prev_sample=__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	)				->   torch.FloatTensor:






       """simple docstring"""
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       UpperCamelCase__							:       Any      =   timesteps.to(original_samples.device							)
       UpperCamelCase__							:       List[str]      =   self.discrete_sigmas.to(original_samples.device							)[timesteps]
       UpperCamelCase__							:       Any      =   (
           noise * sigmas[:, None, None, None]
           if noise is not None
           else torch.randn_like(__magic_name__							) * sigmas[:, None, None, None]
       )
       UpperCamelCase__							:       int      =   noise + original_samples
       return noisy_samples




   def __len__(					self							)				->   Union[str, Any]:






       """simple docstring"""
       return self.config.num_train_timesteps

