





import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union

from .. import config
from .filelock import FileLock
from .logging import get_logger


UpperCAmelCase_  							=				get_logger(__name__)






class 					lowercase__ :

   '''simple docstring'''


   def __init__(					self,	__magic_name__ = None							)				->   Tuple:






       """simple docstring"""
       UpperCamelCase__							:       Any      =   (
           os.path.join(lowerCamelCase__,	config.EXTRACTED_DATASETS_DIR							) if cache_dir else config.EXTRACTED_DATASETS_PATH
       )
       UpperCamelCase__							:       Tuple      =   Extractor




   def    UpperCamelCase__						(					self,	__magic_name__							)				->   str:






       """simple docstring"""
       from .file_utils import hash_url_to_filename

       # Path where we extract compressed archives
       # We extract in the cache dir, and get the extracted path name by hashing the original path"
       UpperCamelCase__							:       Dict      =   os.path.abspath(lowerCamelCase__							)
       return os.path.join(self.extract_dir,	hash_url_to_filename(lowerCamelCase__							)							)




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






       """simple docstring"""
       return force_extract or (
           not os.path.isfile(lowerCamelCase__							) and not (os.path.isdir(lowerCamelCase__							) and os.listdir(lowerCamelCase__							))
       )




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






       """simple docstring"""
       UpperCamelCase__							:       Union[str, Any]      =   self.extractor.infer_extractor_format(lowerCamelCase__							)
       if not extractor_format:
           return input_path
       UpperCamelCase__							:       List[Any]      =   self._get_output_path(lowerCamelCase__							)
       if self._do_extract(lowerCamelCase__,	lowerCamelCase__							):
           self.extractor.extract(lowerCamelCase__,	lowerCamelCase__,	lowerCamelCase__							)
       return output_path






class 					lowercase__ (			UpperCAmelCase__       ):

   '''simple docstring'''


   @classmethod
   @abstractmethod
   def    UpperCamelCase__						(					cls,	__magic_name__,	**__magic_name__							)				->   bool:






       """simple docstring"""
       ...




   @staticmethod
   @abstractmethod
   def    UpperCamelCase__						(					__magic_name__,	__magic_name__							)				->   None:






       """simple docstring"""
       ...






class 					lowercase__ (			UpperCAmelCase__    ,       UpperCAmelCase__       ):

   '''simple docstring'''


   a		:							List[bytes]     	=	[]




   @staticmethod
   def    UpperCamelCase__						(					__magic_name__,	__magic_name__							)				->   Any:






       """simple docstring"""







       with open(lowerCamelCase__,	'''rb'''							) as f:
           return f.read(lowerCamelCase__							)




   @classmethod
   def    UpperCamelCase__						(					cls,	__magic_name__,	__magic_name__ = b""							)				->   bool:






       """simple docstring"""







       if not magic_number:
           UpperCamelCase__							:       Dict      =   max(len(lowerCamelCase__							) for cls_magic_number in cls.magic_numbers							)
           try:
               UpperCamelCase__							:       Dict      =   cls.read_magic_number(lowerCamelCase__,	lowerCamelCase__							)
           except OSError:
               return False
       return any(magic_number.startswith(lowerCamelCase__							) for cls_magic_number in cls.magic_numbers							)






class 					lowercase__ (			UpperCAmelCase__       ):

   '''simple docstring'''


   @classmethod
   def    UpperCamelCase__						(					cls,	__magic_name__,	**__magic_name__							)				->   bool:






       """simple docstring"""
       return tarfile.is_tarfile(lowerCamelCase__							)




   @staticmethod
   def    UpperCamelCase__						(					__magic_name__,	__magic_name__							)				->   Optional[int]:






       """simple docstring"""







       def resolved(__magic_name__							) -> str:
           return os.path.realpath(os.path.abspath(lowerCamelCase__							)							)

       def badpath(__magic_name__,	__magic_name__							) -> bool:
           # joinpath will ignore base if path is absolute
           return not resolved(os.path.join(lowerCamelCase__,	lowerCamelCase__							)							).startswith(lowerCamelCase__							)

       def badlink(__magic_name__,	__magic_name__							) -> bool:
           # Links are interpreted relative to the directory containing the link
           UpperCamelCase__							:       List[Any]      =   resolved(os.path.join(lowerCamelCase__,	os.path.dirname(info.name							)							)							)
           return badpath(info.linkname,	base=lowerCamelCase__							)

       UpperCamelCase__							:       int      =   resolved(lowerCamelCase__							)

       for finfo in members:
           if badpath(finfo.name,	lowerCamelCase__							):
               logger.error(f"Extraction of {finfo.name} is blocked (illegal path)"							)
           elif finfo.issym() and badlink(lowerCamelCase__,	lowerCamelCase__							):
               logger.error(f"Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}"							)
           elif finfo.islnk() and badlink(lowerCamelCase__,	lowerCamelCase__							):
               logger.error(f"Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}"							)
           else:
               yield finfo




   @staticmethod
   def    UpperCamelCase__						(					__magic_name__,	__magic_name__							)				->   None:






       """simple docstring"""
       os.makedirs(lowerCamelCase__,	exist_ok=lowerCamelCase__							)
       UpperCamelCase__							:       Any      =   tarfile.open(lowerCamelCase__							)
       tar_file.extractall(lowerCamelCase__,	members=TarExtractor.safemembers(lowerCamelCase__,	lowerCamelCase__							)							)
       tar_file.close()






class 					lowercase__ (			UpperCAmelCase__       ):

   '''simple docstring'''


   a		:							Any     	=	[b"\x1F\x8B"]




   @staticmethod
   def    UpperCamelCase__						(					__magic_name__,	__magic_name__							)				->   None:






       """simple docstring"""







       with gzip.open(lowerCamelCase__,	'''rb'''							) as gzip_file:
           with open(lowerCamelCase__,	'''wb'''							) as extracted_file:
               shutil.copyfileobj(lowerCamelCase__,	lowerCamelCase__							)






class 					lowercase__ (			UpperCAmelCase__       ):

   '''simple docstring'''


   a		:							List[Any]     	=	[
       b"PK\x03\x04",
       b"PK\x05\x06",  # empty archive
       b"PK\x07\x08",  # spanned archive
   ]




   @classmethod
   def    UpperCamelCase__						(					cls,	__magic_name__,	__magic_name__ = b""							)				->   bool:






       """simple docstring"""







       if super().is_extractable(lowerCamelCase__,	magic_number=lowerCamelCase__							):
           return True
       try:
           # Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
           # From: https://github.com/python/cpython/pull/5053
           from zipfile import (
               _CD_SIGNATURE,
               _ECD_DISK_NUMBER,
               _ECD_DISK_START,
               _ECD_ENTRIES_TOTAL,
               _ECD_OFFSET,
               _ECD_SIZE,
               _EndRecData,
               sizeCentralDir,
               stringCentralDir,
               structCentralDir,
           )

           with open(lowerCamelCase__,	'''rb'''							) as fp:
               UpperCamelCase__							:       List[Any]      =   _EndRecData(lowerCamelCase__							)
               if endrec:
                   if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
                       return True  # Empty zipfiles are still zipfiles
                   elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
                       fp.seek(endrec[_ECD_OFFSET]							)  # Central directory is on the same disk
                       if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
                           UpperCamelCase__							:       Tuple      =   fp.read(lowerCamelCase__							)  # CD is where we expect it to be
                           if len(lowerCamelCase__							) == sizeCentralDir:
                               UpperCamelCase__							:       List[str]      =   struct.unpack(lowerCamelCase__,	lowerCamelCase__							)  # CD is the right size
                               if centdir[_CD_SIGNATURE] == stringCentralDir:
                                   return True  # First central directory entry  has correct magic number
           return False
       except Exception:  # catch all errors in case future python versions change the zipfile internals
           return False




   @staticmethod
   def    UpperCamelCase__						(					__magic_name__,	__magic_name__							)				->   None:






       """simple docstring"""
       os.makedirs(lowerCamelCase__,	exist_ok=lowerCamelCase__							)
       with zipfile.ZipFile(lowerCamelCase__,	'''r'''							) as zip_file:
           zip_file.extractall(lowerCamelCase__							)
           zip_file.close()






class 					lowercase__ (			UpperCAmelCase__       ):

   '''simple docstring'''


   a		:							List[str]     	=	[b"\xFD\x37\x7A\x58\x5A\x00"]




   @staticmethod
   def    UpperCamelCase__						(					__magic_name__,	__magic_name__							)				->   None:






       """simple docstring"""







       with lzma.open(lowerCamelCase__							) as compressed_file:
           with open(lowerCamelCase__,	'''wb'''							) as extracted_file:
               shutil.copyfileobj(lowerCamelCase__,	lowerCamelCase__							)






class 					lowercase__ (			UpperCAmelCase__       ):

   '''simple docstring'''


   a		:							str     	=	[b"Rar!\x1a\x07\x00", b"Rar!\x1a\x07\x01\x00"]  # RAR_ID  # RAR5_ID




   @staticmethod
   def    UpperCamelCase__						(					__magic_name__,	__magic_name__							)				->   None:






       """simple docstring"""







       if not config.RARFILE_AVAILABLE:
           raise ImportError('''Please pip install rarfile'''							)
       import rarfile

       os.makedirs(lowerCamelCase__,	exist_ok=lowerCamelCase__							)
       UpperCamelCase__							:       int      =   rarfile.RarFile(lowerCamelCase__							)
       rf.extractall(lowerCamelCase__							)
       rf.close()






class 					lowercase__ (			UpperCAmelCase__       ):

   '''simple docstring'''


   a		:							Dict     	=	[b"\x28\xb5\x2F\xFD"]




   @staticmethod
   def    UpperCamelCase__						(					__magic_name__,	__magic_name__							)				->   None:






       """simple docstring"""







       if not config.ZSTANDARD_AVAILABLE:
           raise ImportError('''Please pip install zstandard'''							)
       import zstandard as zstd

       UpperCamelCase__							:       Tuple      =   zstd.ZstdDecompressor()
       with open(lowerCamelCase__,	'''rb'''							) as ifh, open(lowerCamelCase__,	'''wb'''							) as ofh:
           dctx.copy_stream(lowerCamelCase__,	lowerCamelCase__							)






class 					lowercase__ (			UpperCAmelCase__       ):

   '''simple docstring'''


   a		:							str     	=	[b"\x42\x5A\x68"]




   @staticmethod
   def    UpperCamelCase__						(					__magic_name__,	__magic_name__							)				->   None:






       """simple docstring"""







       with bza.open(lowerCamelCase__,	'''rb'''							) as compressed_file:
           with open(lowerCamelCase__,	'''wb'''							) as extracted_file:
               shutil.copyfileobj(lowerCamelCase__,	lowerCamelCase__							)






class 					lowercase__ (			UpperCAmelCase__       ):

   '''simple docstring'''


   a		:							List[str]     	=	[b"\x37\x7A\xBC\xAF\x27\x1C"]




   @staticmethod
   def    UpperCamelCase__						(					__magic_name__,	__magic_name__							)				->   None:






       """simple docstring"""







       if not config.PY7ZR_AVAILABLE:
           raise ImportError('''Please pip install py7zr'''							)
       import pyazr

       os.makedirs(lowerCamelCase__,	exist_ok=lowerCamelCase__							)
       with pyazr.SevenZipFile(lowerCamelCase__,	'''r'''							) as archive:
           archive.extractall(lowerCamelCase__							)






class 					lowercase__ (			UpperCAmelCase__       ):

   '''simple docstring'''


   a		:							List[Any]     	=	[b"\x04\x22\x4D\x18"]




   @staticmethod
   def    UpperCamelCase__						(					__magic_name__,	__magic_name__							)				->   None:






       """simple docstring"""







       if not config.LZ4_AVAILABLE:
           raise ImportError('''Please pip install lz4'''							)
       import lza.frame

       with lza.frame.open(lowerCamelCase__,	'''rb'''							) as compressed_file:
           with open(lowerCamelCase__,	'''wb'''							) as extracted_file:
               shutil.copyfileobj(lowerCamelCase__,	lowerCamelCase__							)






class 					lowercase__ :

   '''simple docstring'''


   a		:							Dict[str, Type[BaseExtractor]]     	=	{
       "tar": TarExtractor,
       "gzip": GzipExtractor,
       "zip": ZipExtractor,
       "xz": XzExtractor,
       "rar": RarExtractor,
       "zstd": ZstdExtractor,
       "bz2": BzipaExtractor,
       "7z": SevenZipExtractor,  # <Added version="2.4.0"/>
       "lz4": LzaExtractor,  # <Added version="2.4.0"/>
   }




   @classmethod
   def    UpperCamelCase__						(					cls							)				->   Dict:






       """simple docstring"""
       return max(
           len(lowerCamelCase__							)
           for extractor in cls.extractors.values()
           if issubclass(lowerCamelCase__,	lowerCamelCase__							)
           for extractor_magic_number in extractor.magic_numbers							)




   @staticmethod
   def    UpperCamelCase__						(					__magic_name__,	__magic_name__							)				->   Any:






       """simple docstring"""







       try:
           return MagicNumberBaseExtractor.read_magic_number(lowerCamelCase__,	magic_number_length=lowerCamelCase__							)
       except OSError:
           return b""




   @classmethod
   def    UpperCamelCase__						(					cls,	__magic_name__,	__magic_name__ = False							)				->   bool:






       """simple docstring"""
       warnings.warn(
           '''Method \'is_extractable\' was deprecated in version 2.4.0 and will be removed in 3.0.0. '''
           '''Use \'infer_extractor_format\' instead.''',	category=lowerCamelCase__,	)
       UpperCamelCase__							:       Tuple      =   cls.infer_extractor_format(lowerCamelCase__							)
       if extractor_format:
           return True if not return_extractor else (True, cls.extractors[extractor_format])
       return False if not return_extractor else (False, None)




   @classmethod
   def    UpperCamelCase__						(					cls,	__magic_name__							)				->   str:  # <Added version="2.4.0"/>






       """simple docstring"""
       UpperCamelCase__							:       Union[str, Any]      =   cls._get_magic_number_max_length()
       UpperCamelCase__							:       Optional[Any]      =   cls._read_magic_number(lowerCamelCase__,	lowerCamelCase__							)
       for extractor_format, extractor in cls.extractors.items():
           if extractor.is_extractable(lowerCamelCase__,	magic_number=lowerCamelCase__							):
               return extractor_format




   @classmethod
   def    UpperCamelCase__						(					cls,	__magic_name__,	__magic_name__,	__magic_name__ = None,	__magic_name__ = "deprecated",	)				->   None:






       """simple docstring"""
       os.makedirs(os.path.dirname(lowerCamelCase__							),	exist_ok=lowerCamelCase__							)
       # Prevent parallel extractions
       UpperCamelCase__							:       List[str]      =   str(Path(lowerCamelCase__							).with_suffix('''.lock'''							)							)
       with FileLock(lowerCamelCase__							):
           shutil.rmtree(lowerCamelCase__,	ignore_errors=lowerCamelCase__							)
           if extractor_format or extractor != "deprecated":
               if extractor != "deprecated" or not isinstance(lowerCamelCase__,	lowerCamelCase__							):  # passed as positional arg
                   warnings.warn(
                       '''Parameter \'extractor\' was deprecated in version 2.4.0 and will be removed in 3.0.0. '''
                       '''Use \'extractor_format\' instead.''',	category=lowerCamelCase__,	)
                   UpperCamelCase__							:       Optional[Any]      =   extractor if extractor != "deprecated" else extractor_format
               else:
                   UpperCamelCase__							:       List[str]      =   cls.extractors[extractor_format]
               return extractor.extract(lowerCamelCase__,	lowerCamelCase__							)
           else:
               warnings.warn(
                   '''Parameter \'extractor_format\' was made required in version 2.4.0 and not passing it will raise an '''
                   '''exception in 3.0.0.''',	category=lowerCamelCase__,	)
               for extractor in cls.extractors.values():
                   if extractor.is_extractable(lowerCamelCase__							):
                       return extractor.extract(lowerCamelCase__,	lowerCamelCase__							)








# 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

