
"""simple docstring"""







import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


_SCREAMING_SNAKE_CASE	       =      get_logger(__name__)


_SCREAMING_SNAKE_CASE	       =      Path(__file__).parent / """model_card_template.md"""
_SCREAMING_SNAKE_CASE	       =      uuida().hex
_SCREAMING_SNAKE_CASE	       =      os.getenv("""HF_HUB_OFFLINE""", """""").upper() in ENV_VARS_TRUE_VALUES
_SCREAMING_SNAKE_CASE	       =      os.getenv("""DISABLE_TELEMETRY""", """""").upper() in ENV_VARS_TRUE_VALUES
_SCREAMING_SNAKE_CASE	       =      HUGGINGFACE_CO_RESOLVE_ENDPOINT + """/api/telemetry/"""
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE = None )  ->     str:







							"""simple docstring"""


							__snake_case     			=       F'''diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}'''
							if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
														return ua + "; telemetry/off"
							if is_torch_available():
														ua += F'''; torch/{_torch_version}'''
							if is_flax_available():
														ua += F'''; jax/{_jax_version}'''
														ua += F'''; flax/{_flax_version}'''
							if is_onnx_available():
														ua += F'''; onnxruntime/{_onnxruntime_version}'''
							# CI will set this value to True
							if os.environ.get("DIFFUSERS_IS_CI"							,    "" ).upper() in ENV_VARS_TRUE_VALUES:
														ua += "; is_ci/true"
							if isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ):
														ua += "; " + "; ".join(F'''{k}/{v}''' for k, v in user_agent.items() )
							elif isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ):
														ua += "; " + user_agent
							return ua
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE = None							,    SCREAMING_SNAKE_CASE = None )  ->     Tuple:







							"""simple docstring"""


							if token is None:
														__snake_case     			=       HfFolder.get_token()
							if organization is None:
														__snake_case     			=       whoami(SCREAMING_SNAKE_CASE )["name"]
														return F'''{username}/{model_id}'''
							else:
														return F'''{organization}/{model_id}'''
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     List[Any]:







							"""simple docstring"""


							if not is_jinja_available():
														raise ValueError(
														    "Modelcard rendering is based on Jinja templates."
														    " Please make sure to have `jinja` installed before using `create_model_card`."
														    " To install it, please run `pip install Jinja2`." )

							if hasattr(SCREAMING_SNAKE_CASE							,    "local_rank" ) and args.local_rank not in [-1, 0]:
														return

							__snake_case     			=       args.hub_token if hasattr(SCREAMING_SNAKE_CASE							,    "hub_token" ) else None
							__snake_case     			=       get_full_repo_name(SCREAMING_SNAKE_CASE							,    token=SCREAMING_SNAKE_CASE )

							__snake_case     			=       ModelCard.from_template(
							    card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
							        language="en"							,    license="apache-2.0"							,    library_name="diffusers"							,    tags=[]							,    datasets=args.dataset_name							,    metrics=[]							,    )							,    template_path=SCREAMING_SNAKE_CASE							,    model_name=SCREAMING_SNAKE_CASE							,    repo_name=SCREAMING_SNAKE_CASE							,    dataset_name=args.dataset_name if hasattr(SCREAMING_SNAKE_CASE							,    "dataset_name" ) else None							,    learning_rate=args.learning_rate							,    train_batch_size=args.train_batch_size							,    eval_batch_size=args.eval_batch_size							,    gradient_accumulation_steps=(
							        args.gradient_accumulation_steps if hasattr(SCREAMING_SNAKE_CASE							,    "gradient_accumulation_steps" ) else None
							    )							,    adam_betaa=args.adam_betaa if hasattr(SCREAMING_SNAKE_CASE							,    "adam_beta1" ) else None							,    adam_betaa=args.adam_betaa if hasattr(SCREAMING_SNAKE_CASE							,    "adam_beta2" ) else None							,    adam_weight_decay=args.adam_weight_decay if hasattr(SCREAMING_SNAKE_CASE							,    "adam_weight_decay" ) else None							,    adam_epsilon=args.adam_epsilon if hasattr(SCREAMING_SNAKE_CASE							,    "adam_epsilon" ) else None							,    lr_scheduler=args.lr_scheduler if hasattr(SCREAMING_SNAKE_CASE							,    "lr_scheduler" ) else None							,    lr_warmup_steps=args.lr_warmup_steps if hasattr(SCREAMING_SNAKE_CASE							,    "lr_warmup_steps" ) else None							,    ema_inv_gamma=args.ema_inv_gamma if hasattr(SCREAMING_SNAKE_CASE							,    "ema_inv_gamma" ) else None							,    ema_power=args.ema_power if hasattr(SCREAMING_SNAKE_CASE							,    "ema_power" ) else None							,    ema_max_decay=args.ema_max_decay if hasattr(SCREAMING_SNAKE_CASE							,    "ema_max_decay" ) else None							,    mixed_precision=args.mixed_precision							,    )

							__snake_case     			=       os.path.join(args.output_dir							,    "README.md" )
							model_card.save(SCREAMING_SNAKE_CASE )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE = None )  ->     Tuple:







							"""simple docstring"""


							if resolved_file is None or commit_hash is not None:
														return commit_hash
							__snake_case     			=       str(Path(SCREAMING_SNAKE_CASE ).as_posix() )
							__snake_case     			=       re.search(r"snapshots/([^/]+)/"							,    SCREAMING_SNAKE_CASE )
							if search is None:
														return None
							__snake_case     			=       search.groups()[0]
							return commit_hash if REGEX_COMMIT_HASH.match(SCREAMING_SNAKE_CASE ) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
_SCREAMING_SNAKE_CASE	       =      os.path.expanduser(
    os.getenv("""HF_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """huggingface"""))
)
_SCREAMING_SNAKE_CASE	       =      os.path.join(hf_cache_home, """diffusers""")
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE = None							,    SCREAMING_SNAKE_CASE = None )  ->     None:







							"""simple docstring"""


							if new_cache_dir is None:
														__snake_case     			=       DIFFUSERS_CACHE
							if old_cache_dir is None:
														__snake_case     			=       old_diffusers_cache

							__snake_case     			=       Path(SCREAMING_SNAKE_CASE ).expanduser()
							__snake_case     			=       Path(SCREAMING_SNAKE_CASE ).expanduser()
							for old_blob_path in old_cache_dir.glob("**/blobs/*" ):
														if old_blob_path.is_file() and not old_blob_path.is_symlink():
																					__snake_case     			=       new_cache_dir / old_blob_path.relative_to(SCREAMING_SNAKE_CASE )
																					new_blob_path.parent.mkdir(parents=SCREAMING_SNAKE_CASE							,    exist_ok=SCREAMING_SNAKE_CASE )
																					os.replace(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
																					try:
																												os.symlink(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
																					except OSError:
																												logger.warning(
																												    "Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded." )
							# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


_SCREAMING_SNAKE_CASE	       =      os.path.join(DIFFUSERS_CACHE, """version_diffusers_cache.txt""")
if not os.path.isfile(cache_version_file):
		_SCREAMING_SNAKE_CASE	       =      0
else:
		with open(cache_version_file) as f:
				try:
						_SCREAMING_SNAKE_CASE	       =      int(f.read())
				except ValueError:
						_SCREAMING_SNAKE_CASE	       =      0

if cache_version < 1:
		_SCREAMING_SNAKE_CASE	       =      os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
		if old_cache_is_not_empty:
				logger.warning(
				    """The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your """
				    """existing cached models. This is a one-time operation, you can interrupt it or run it """
				    """later by calling `diffusers.utils.hub_utils.move_cache()`."""
				)
				try:
						move_cache()
				except Exception as e:
						_SCREAMING_SNAKE_CASE	       =      """\n""".join(traceback.format_tb(e.__traceback__))
						logger.error(
						    F"""There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease """
						    """file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole """
						    """message and we will do our best to help."""
						)

if cache_version < 1:
		try:
				os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
				with open(cache_version_file, """w""") as f:
						f.write("""1""")
		except Exception:
				logger.warning(
				    F"""There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure """
				    """the directory exists and can be written to."""
				)
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE = None )  ->     str:







							"""simple docstring"""


							if variant is not None:
														__snake_case     			=       weights_name.split("." )
														__snake_case     			=       splits[:-1] + [variant] + splits[-1:]
														__snake_case     			=       ".".join(SCREAMING_SNAKE_CASE )

							return weights_name



def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    *,
    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=None							,    )  ->     Optional[int]:







							"""simple docstring"""


							__snake_case     			=       str(SCREAMING_SNAKE_CASE )
							if os.path.isfile(SCREAMING_SNAKE_CASE ):
														return pretrained_model_name_or_path
							elif os.path.isdir(SCREAMING_SNAKE_CASE ):
														if os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ) ):
																					# Load from a PyTorch checkpoint
																					__snake_case     			=       os.path.join(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
																					return model_file
														elif subfolder is not None and os.path.isfile(
														    os.path.join(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ) ):
																					__snake_case     			=       os.path.join(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
																					return model_file
														else:
																					raise EnvironmentError(
																					    F'''Error no file named {weights_name} found in directory {pretrained_model_name_or_path}.''' )
							else:
														# 1. First check if deprecated way of loading from branches is used
														if (
														    revision in DEPRECATED_REVISION_ARGS
														    and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
														    and version.parse(version.parse(SCREAMING_SNAKE_CASE ).base_version ) >= version.parse("0.20.0" )
														):
																					try:
																												__snake_case     			=       hf_hub_download(
																												    SCREAMING_SNAKE_CASE							,    filename=_add_variant(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )							,    cache_dir=SCREAMING_SNAKE_CASE							,    force_download=SCREAMING_SNAKE_CASE							,    proxies=SCREAMING_SNAKE_CASE							,    resume_download=SCREAMING_SNAKE_CASE							,    local_files_only=SCREAMING_SNAKE_CASE							,    use_auth_token=SCREAMING_SNAKE_CASE							,    user_agent=SCREAMING_SNAKE_CASE							,    subfolder=SCREAMING_SNAKE_CASE							,    revision=revision or commit_hash							,    )
																												warnings.warn(
																												    F'''Loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'` is deprecated. Loading instead from `revision=\'main\'` with `variant={revision}`. Loading model variants via `revision=\'{revision}\'` will be removed in diffusers v1. Please use `variant=\'{revision}\'` instead.'''							,    SCREAMING_SNAKE_CASE							,    )
																												return model_file
																					except:  # noqa: E722
																												warnings.warn(
																												    F'''You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant=\'{revision}\'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )} file in the \'main\' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title \'{pretrained_model_name_or_path} is missing {_add_variant(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )}\' so that the correct variant file can be added.'''							,    SCREAMING_SNAKE_CASE							,    )
														try:
																					# 2. Load model file as usual
																					__snake_case     			=       hf_hub_download(
																					    SCREAMING_SNAKE_CASE							,    filename=SCREAMING_SNAKE_CASE							,    cache_dir=SCREAMING_SNAKE_CASE							,    force_download=SCREAMING_SNAKE_CASE							,    proxies=SCREAMING_SNAKE_CASE							,    resume_download=SCREAMING_SNAKE_CASE							,    local_files_only=SCREAMING_SNAKE_CASE							,    use_auth_token=SCREAMING_SNAKE_CASE							,    user_agent=SCREAMING_SNAKE_CASE							,    subfolder=SCREAMING_SNAKE_CASE							,    revision=revision or commit_hash							,    )
																					return model_file

														except RepositoryNotFoundError:
																					raise EnvironmentError(
																					    F'''{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier '''
																					    "listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a "
																					    "token having permission to this repo with `use_auth_token` or log in with `huggingface-cli "
																					    "login`." )
														except RevisionNotFoundError:
																					raise EnvironmentError(
																					    F'''{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for '''
																					    "this model name. Check the model page at "
																					    F'''\'https://huggingface.co/{pretrained_model_name_or_path}\' for available revisions.''' )
														except EntryNotFoundError:
																					raise EnvironmentError(
																					    F'''{pretrained_model_name_or_path} does not appear to have a file named {weights_name}.''' )
														except HTTPError as err:
																					raise EnvironmentError(
																					    F'''There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}''' )
														except ValueError:
																					raise EnvironmentError(
																					    F'''We couldn\'t connect to \'{HUGGINGFACE_CO_RESOLVE_ENDPOINT}\' to load this model, couldn\'t find it'''
																					    F''' in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a'''
																					    F''' directory containing a file named {weights_name} or'''
																					    " \nCheckout your internet connection or see how to run the library in"
																					    " offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'." )
														except EnvironmentError:
																					raise EnvironmentError(
																					    F'''Can\'t load the model for \'{pretrained_model_name_or_path}\'. If you were trying to load it from '''
																					    "'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
																					    F'''Otherwise, make sure \'{pretrained_model_name_or_path}\' is the correct path to a directory '''
																					    F'''containing a file named {weights_name}''' )


"""simple docstring"""







# 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 							__magic_name__     (		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					torch.FloatTensor
					_SCREAMING_SNAKE_CASE       :					torch.FloatTensor







class 							__magic_name__     (		lowercase__      ,		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					Optional[int]     							=   1





					@register_to_config
					def __init__(				self   :							List[Any]							,						snake_case_   :							int = 2000							,						snake_case_   :							float = 0.15							,						snake_case_   :							float = 0.01							,						snake_case_   :							float = 1348.0							,						snake_case_   :							float = 1e-5							,						snake_case_   :							int = 1							,						):
												# standard deviation of the initial noise distribution
												__snake_case     			=       sigma_max

												# setable values
												__snake_case     			=       None

												self.set_sigmas(snake_case_							,						snake_case_							,						snake_case_							,						snake_case_    )





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							Optional[int] = None    ):
												return sample





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							int							,						snake_case_   :							float = None							,						snake_case_   :							Union[str, torch.device] = None    ):
												__snake_case     			=       sampling_eps if sampling_eps is not None else self.config.sampling_eps

												__snake_case     			=       torch.linspace(1							,						snake_case_							,						snake_case_							,						device=snake_case_    )





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							int							,						snake_case_   :							float = None							,						snake_case_   :							float = None							,						snake_case_   :							float = None    ):
												__snake_case     			=       sigma_min if sigma_min is not None else self.config.sigma_min
												__snake_case     			=       sigma_max if sigma_max is not None else self.config.sigma_max
												__snake_case     			=       sampling_eps if sampling_eps is not None else self.config.sampling_eps
												if self.timesteps is None:
																			self.set_timesteps(snake_case_							,						snake_case_    )

												__snake_case     			=       sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
												__snake_case     			=       torch.exp(torch.linspace(math.log(snake_case_    )							,						math.log(snake_case_    )							,						snake_case_    )    )
												__snake_case     			=       torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]    )





					def 							lowerCAmelCase      (				self   :							Optional[Any]							,						snake_case_   :							str							,						snake_case_   :							Dict    ):
												return torch.where(
												    timesteps == 0							,						torch.zeros_like(t.to(timesteps.device    )    )							,						self.discrete_sigmas[timesteps - 1].to(timesteps.device    )							,						)





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							int							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							Optional[torch.Generator] = None							,						snake_case_   :							bool = True							,						):
												if self.timesteps is None:
																			raise ValueError(
																			    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"    )

												__snake_case     			=       timestep * torch.ones(
												    sample.shape[0]							,						device=sample.device    )  # torch.repeat_interleave(timestep, sample.shape[0])
												__snake_case     			=       (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
												__snake_case     			=       timesteps.to(self.discrete_sigmas.device    )

												__snake_case     			=       self.discrete_sigmas[timesteps].to(sample.device    )
												__snake_case     			=       self.get_adjacent_sigma(snake_case_							,						snake_case_    ).to(sample.device    )
												__snake_case     			=       torch.zeros_like(snake_case_    )
												__snake_case     			=       (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
												__snake_case     			=       diffusion.flatten()
												while len(diffusion.shape    ) < len(sample.shape    ):
																			__snake_case     			=       diffusion.unsqueeze(-1    )
												__snake_case     			=       drift - diffusion**2 * model_output

												#  equation 6: sample noise for the diffusion term of
												__snake_case     			=       randn_tensor(
												    sample.shape							,						layout=sample.layout							,						generator=snake_case_							,						device=sample.device							,						dtype=sample.dtype    )
												__snake_case     			=       sample - drift  # subtract because `dt` is a small negative timestep
												# TODO is the variable diffusion the correct scaling term for the noise?
												__snake_case     			=       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=snake_case_							,						prev_sample_mean=snake_case_    )





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							Optional[torch.Generator] = None							,						snake_case_   :							bool = True							,						):
												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
												__snake_case     			=       randn_tensor(sample.shape							,						layout=sample.layout							,						generator=snake_case_    ).to(sample.device    )

												# compute step size from the model_output, the noise, and the snr
												__snake_case     			=       torch.norm(model_output.reshape(model_output.shape[0]							,						-1    )							,						dim=-1    ).mean()
												__snake_case     			=       torch.norm(noise.reshape(noise.shape[0]							,						-1    )							,						dim=-1    ).mean()
												__snake_case     			=       (self.config.snr * noise_norm / grad_norm) ** 2 * 2
												__snake_case     			=       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
												__snake_case     			=       step_size.flatten()
												while len(step_size.shape    ) < len(sample.shape    ):
																			__snake_case     			=       step_size.unsqueeze(-1    )
												__snake_case     			=       sample + step_size * model_output
												__snake_case     			=       prev_sample_mean + ((step_size * 2) ** 0.5) * noise

												if not return_dict:
																			return (prev_sample,)

												return SchedulerOutput(prev_sample=snake_case_    )





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							torch.FloatTensor							,						):
												# Make sure sigmas and timesteps have the same device and dtype as original_samples
												__snake_case     			=       timesteps.to(original_samples.device    )
												__snake_case     			=       self.discrete_sigmas.to(original_samples.device    )[timesteps]
												__snake_case     			=       (
												    noise * sigmas[:, None, None, None]
												    if noise is not None
												    else torch.randn_like(snake_case_    ) * sigmas[:, None, None, None]
												)
												__snake_case     			=       noise + original_samples
												return noisy_samples





					def __len__(				self   :							Tuple    ):
												return self.config.num_train_timesteps
