


"""simple docstring"""



import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile

import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load


try:
 import torch

 UpperCAmelCase      		=			True
except ImportError:
 UpperCAmelCase      		=			False


try:
 from torch.hub import _get_torch_home

 UpperCAmelCase      		=			_get_torch_home()
except ImportError:
 UpperCAmelCase      		=			os.path.expanduser(
     os.getenv('''TORCH_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''torch'''))
 )

UpperCAmelCase      		=			os.path.join(torch_cache_home, '''transformers''')

UpperCAmelCase      		=			'''https://cdn.huggingface.co'''
UpperCAmelCase      		=			'''https://s3.amazonaws.com/models.huggingface.co/bert'''
UpperCAmelCase      		=			'''/'''.join(str(Path(__file__).resolve()).split('''/''')[:-1])
UpperCAmelCase      		=			os.path.join(PATH, '''config.yaml''')
UpperCAmelCase      		=			os.path.join(PATH, '''attributes.txt''')
UpperCAmelCase      		=			os.path.join(PATH, '''objects.txt''')
UpperCAmelCase      		=			os.getenv('''PYTORCH_PRETRAINED_BERT_CACHE''', default_cache_path)
UpperCAmelCase      		=			os.getenv('''PYTORCH_TRANSFORMERS_CACHE''', PYTORCH_PRETRAINED_BERT_CACHE)
UpperCAmelCase      		=			os.getenv('''TRANSFORMERS_CACHE''', PYTORCH_TRANSFORMERS_CACHE)
UpperCAmelCase      		=			'''pytorch_model.bin'''
UpperCAmelCase      		=			'''config.yaml'''

def       lowerCamelCase					(a_						:int=OBJECTS				,				a_						:Optional[Any]=ATTRIBUTES)     ->							List[Any]:
       lowercase		:Any       							=				[]
       with open(a_) as f:
              for object in f.readlines():
                     vg_classes.append(object.split(''',''')[0].lower().strip())

       lowercase		:Any       							=				[]
       with open(a_) as f:
              for object in f.readlines():
                     vg_attrs.append(object.split(''',''')[0].lower().strip())
       return vg_classes, vg_attrs

def       lowerCamelCase					(a_						:int)     ->							Union[str, Any]:
       lowercase		:Any       							=				OrderedDict()
       with open(a_				,				'''rb''') as f:
              lowercase		:Tuple       							=				pkl.load(a_)['''model''']
       for k in copy.deepcopy(list(ckp.keys())):
              lowercase		:List[str]       							=				ckp.pop(a_)
              if isinstance(a_				,				np.ndarray):
                     lowercase		:str       							=				torch.tensor(a_)
              else:
                     assert isinstance(a_				,				torch.tensor), type(a_)
              lowercase		:Optional[int]       							=				v
       return r
class 					__magic_name__		:
       __A			:      List[str]        =   {}






       def __init__(   self  :    List[Any]  ,     snake_case__  :    dict  ,     snake_case__  :    str = "root"  ,     snake_case__  :    Tuple=0				):

              '''simple docstring'''






              lowercase		:int       							=				name
              lowercase		:Union[str, Any]       							=				level
              lowercase		:int       							=				{}
              for k, v in dictionary.items():
                     if v is None:
                            raise ValueError()
                     lowercase		:Any       							=				copy.deepcopy(snake_case__				)
                     lowercase		:Any       							=				copy.deepcopy(snake_case__				)
                     if isinstance(snake_case__  ,     snake_case__				):
                            lowercase		:List[str]       							=				Config(snake_case__  ,     name=snake_case__  ,     level=level + 1				)
                     lowercase		:Any       							=				v
                     setattr(self  ,     snake_case__  ,     snake_case__				)

              lowercase		:int       							=				d






       def __repr__(   self  :    str				):

              '''simple docstring'''






              return str(list((self._pointer.keys())				)				)






       def __setattr__(   self  :    Tuple  ,     snake_case__  :    List[str]  ,     snake_case__  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase		:Optional[Any]       							=				val
              lowercase		:List[Any]       							=				val
              lowercase		:Optional[Any]       							=				key.split('''.'''				)
              lowercase		:int       							=				len(snake_case__				) - 1
              lowercase		:Optional[Any]       							=				self._pointer
              if len(snake_case__				) > 1:
                     for i, l in enumerate(snake_case__				):
                            if hasattr(self  ,     snake_case__				) and isinstance(getattr(self  ,     snake_case__				)  ,     snake_case__				):
                                   setattr(getattr(self  ,     snake_case__				)  ,     '''.'''.join(levels[i:]				)  ,     snake_case__				)
                            if l == last_level:
                                   lowercase		:str       							=				val
                            else:
                                   lowercase		:Dict       							=				pointer[l]






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              return self._pointer






       def  __snake_case						(   self  :    Tuple  ,     snake_case__  :    str  ,     snake_case__  :    List[Any]				):

              '''simple docstring'''






              with open(f"""{file_name}"""  ,     '''w'''				) as stream:
                     dump(snake_case__  ,     snake_case__				)






       def  __snake_case						(   self  :    str  ,     snake_case__  :    Dict  ,     snake_case__  :    int				):

              '''simple docstring'''






              with open(f"""{file_name}"""  ,     '''w'''				) as stream:
                     json.dump(snake_case__  ,     snake_case__				)






       @staticmethod
       def  __snake_case						(   snake_case__  :    str				):

              '''simple docstring'''






              with open(snake_case__				) as stream:
                     lowercase		:str       							=				load(snake_case__  ,     Loader=snake_case__				)
              return data






       def __str__(   self  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase		:Optional[Any]       							=				'''    '''
              if self._name != "root":
                     lowercase		:Tuple       							=				f"""{t * (self._level-1)}{self._name}:\n"""
              else:
                     lowercase		:int       							=				''''''
              lowercase		:int       							=				self._level
              for i, (k, v) in enumerate(self._pointer.items()				):
                     if isinstance(snake_case__  ,     snake_case__				):
                            r += f"""{t * (self._level)}{v}\n"""
                            self._level += 1
                     else:
                            r += f"""{t * (self._level)}{k}: {v} ({type(snake_case__				).__name__})\n"""
                     lowercase		:str       							=				level
              return r[:-1]






       @classmethod
       def  __snake_case						(   cls  :    int  ,     snake_case__  :    str  ,     **snake_case__  :    List[Any]				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				cls.get_config_dict(snake_case__  ,     **snake_case__				)
              return cls(snake_case__				)







       @classmethod
       def  __snake_case						(   cls  :    Optional[Any]  ,     snake_case__  :    str  ,     **snake_case__  :    List[str]				):

              '''simple docstring'''






              lowercase		:Dict       							=				kwargs.pop('''cache_dir'''  ,     snake_case__				)
              lowercase		:List[Any]       							=				kwargs.pop('''force_download'''  ,     snake_case__				)
              lowercase		:List[str]       							=				kwargs.pop('''resume_download'''  ,     snake_case__				)
              lowercase		:int       							=				kwargs.pop('''proxies'''  ,     snake_case__				)
              lowercase		:Dict       							=				kwargs.pop('''local_files_only'''  ,     snake_case__				)

              if os.path.isdir(snake_case__				):
                     lowercase		:Dict       							=				os.path.join(snake_case__  ,     snake_case__				)
              elif os.path.isfile(snake_case__				) or is_remote_url(snake_case__				):
                     lowercase		:List[str]       							=				pretrained_model_name_or_path
              else:
                     lowercase		:Any       							=				hf_bucket_url(snake_case__  ,     filename=snake_case__  ,     use_cdn=snake_case__				)

              try:
                     # Load from URL or cache if already cached
                     lowercase		:List[str]       							=				cached_path(
                         snake_case__  ,     cache_dir=snake_case__  ,     force_download=snake_case__  ,     proxies=snake_case__  ,     resume_download=snake_case__  ,     local_files_only=snake_case__  ,     )
                     # Load config dict
                     if resolved_config_file is None:
                            raise EnvironmentError

                     lowercase		:List[str]       							=				Config.load_yaml(snake_case__				)

              except EnvironmentError:
                     lowercase		:Optional[int]       							=				'''Can\'t load config for'''
                     raise EnvironmentError(snake_case__				)

              if resolved_config_file == config_file:
                     print('''loading configuration file from path'''				)
              else:
                     print('''loading configuration file cache'''				)

              return Config.load_yaml(snake_case__				), kwargs







def       lowerCamelCase					(a_						:Union[str, Any])     ->							Optional[Any]:
       lowercase		:str       							=				torch.load('''dump.pt'''				,				map_location=in_tensor.device)
       lowercase		:List[str]       							=				in_tensor.numpy()
       lowercase		:Dict       							=				out_tensor.numpy()[0]
       print(na.shape				,				na[0, 0, :5])
       print(na.shape				,				na[0, 0, :5])
       assert np.allclose(a_				,				a_				,				rtol=0.01				,				atol=0.1), (
           F"""{sum([1 for x in np.isclose(a_				,				a_				,				rtol=0.01				,				atol=0.1).flatten() if x is False])/len(na.flatten())*100:.4f} %"""
           " element-wise mismatch"
       )
       raise Exception('''tensors are all good''')

       # Hugging face functions below

def       lowerCamelCase					(a_						:Tuple)     ->							Dict:
       lowercase		:List[str]       							=				urlparse(a_)
       return parsed.scheme in ("http", "https")

def       lowerCamelCase					(a_						:str				,				a_						:str				,				a_						:Any=True)     ->							str:
       lowercase		:Optional[Any]       							=				CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
       lowercase		:List[str]       							=				'''/''' not in model_id
       if legacy_format:
              return F"""{endpoint}/{model_id}-{filename}"""
       else:
              return F"""{endpoint}/{model_id}/{filename}"""

def       lowerCamelCase					(a_						:Any				,				a_						:Tuple				,				a_						:str=None				,				a_						:Dict=0				,				a_						:str=None				,				)     ->							Union[str, Any]:
       lowercase		:Dict       							=				'''python/{}'''.format(sys.version.split()[0])
       if _torch_available:
              ua += "; torch/{}".format(torch.__version__)
       if isinstance(a_				,				a_):
              ua += "; " + "; ".join('''{}/{}'''.format(a_				,				a_) for k, v in user_agent.items())
       elif isinstance(a_				,				a_):
              ua += "; " + user_agent
       lowercase		:List[str]       							=				{'''user-agent''': ua}
       if resume_size > 0:
              lowercase		:str       							=				'''bytes=%d-''' % (resume_size,)
       lowercase		:Dict       							=				requests.get(a_				,				stream=a_				,				proxies=a_				,				headers=a_)
       if response.status_code == 416:  # Range not satisfiable
              return
       lowercase		:Optional[int]       							=				response.headers.get('''Content-Length''')
       lowercase		:str       							=				resume_size + int(a_) if content_length is not None else None
       lowercase		:Dict       							=				tqdm(
           unit='''B'''				,				unit_scale=a_				,				total=a_				,				initial=a_				,				desc='''Downloading'''				,				)
       for chunk in response.iter_content(chunk_size=1024):
              if chunk:  # filter out keep-alive new chunks
                     progress.update(len(a_))
                     temp_file.write(a_)
       progress.close()

def       lowerCamelCase					(a_						:Tuple				,				a_						:Any=None				,				a_						:Tuple=False				,				a_						:Dict=None				,				a_						:int=10				,				a_						:Any=False				,				a_						:Any=None				,				a_						:Any=False				,				)     ->							Dict:
       if cache_dir is None:
              lowercase		:Tuple       							=				TRANSFORMERS_CACHE
       if isinstance(a_				,				a_):
              lowercase		:Dict       							=				str(a_)

       os.makedirs(a_				,				exist_ok=a_)

       lowercase		:List[str]       							=				None
       if not local_files_only:
              try:
                     lowercase		:Tuple       							=				requests.head(a_				,				allow_redirects=a_				,				proxies=a_				,				timeout=a_)
                     if response.status_code == 200:
                            lowercase		:Any       							=				response.headers.get('''ETag''')
              except (EnvironmentError, requests.exceptions.Timeout):
                     # etag is already None
                     pass

       lowercase		:Optional[int]       							=				url_to_filename(a_				,				a_)

       # get cache path to put the file
       lowercase		:Union[str, Any]       							=				os.path.join(a_				,				a_)

       # etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
       # try to get the last downloaded one
       if etag is None:
              if os.path.exists(a_):
                     return cache_path
              else:
                     lowercase		:Tuple       							=				[
                         file
                         for file in fnmatch.filter(os.listdir(a_)				,				filename + '''.*''')
                         if not file.endswith('''.json''') and not file.endswith('''.lock''')
                     ]
                     if len(a_) > 0:
                            return os.path.join(a_				,				matching_files[-1])
                     else:
                            # If files cannot be found and local_files_only=True,
                            # the models might've been found if local_files_only=False
                            # Notify the user about that
                            if local_files_only:
                                   raise ValueError(
                                       '''Cannot find the requested files in the cached path and outgoing traffic has been'''
                                       ''' disabled. To enable model look-ups and downloads online, set \'local_files_only\''''
                                       ''' to False.''')
                            return None

    # From now on, etag is not None.
       if os.path.exists(a_) and not force_download:
              return cache_path

       # Prevent parallel downloads of the same file with a lock.
       lowercase		:Optional[int]       							=				cache_path + '''.lock'''
       with FileLock(a_):
              # If the download just completed while the lock was activated.
              if os.path.exists(a_) and not force_download:
                     # Even if returning early like here, the lock will be released.
                     return cache_path

              if resume_download:
                     lowercase		:int       							=				cache_path + '''.incomplete'''

                     @contextmanager
                     def _resumable_file_manager():
                            with open(a_				,				'''a+b''') as f:
                                   yield f

                     lowercase		:List[Any]       							=				_resumable_file_manager
                     if os.path.exists(a_):
                            lowercase		:Any       							=				os.stat(a_).st_size
                     else:
                            lowercase		:Optional[Any]       							=				0
              else:
                     lowercase		:str       							=				partial(tempfile.NamedTemporaryFile				,				dir=a_				,				delete=a_)
                     lowercase		:int       							=				0

              # Download to temporary file, then copy to cache dir once finished.
              # Otherwise you get corrupt cache entries if the download gets interrupted.
              with temp_file_manager() as temp_file:
                     print(
                         '''%s not found in cache or force_download set to True, downloading to %s'''				,				a_				,				temp_file.name				,				)

                     http_get(
                         a_				,				a_				,				proxies=a_				,				resume_size=a_				,				user_agent=a_				,				)

              os.replace(temp_file.name				,				a_)

              lowercase		:List[str]       							=				{'''url''': url, '''etag''': etag}
              lowercase		:Optional[int]       							=				cache_path + '''.json'''
              with open(a_				,				'''w''') as meta_file:
                     json.dump(a_				,				a_)

       return cache_path

def       lowerCamelCase					(a_						:List[str]				,				a_						:Optional[int]=None)     ->							Any:
       lowercase		:List[Any]       							=				url.encode('''utf-8''')
       lowercase		:Dict       							=				shaaaa(a_)
       lowercase		:str       							=				url_hash.hexdigest()

       if etag:
              lowercase		:Optional[Any]       							=				etag.encode('''utf-8''')
              lowercase		:Optional[Any]       							=				shaaaa(a_)
              filename += "." + etag_hash.hexdigest()

       if url.endswith('''.h5'''):
              filename += ".h5"

       return filename

def       lowerCamelCase					(a_						:Tuple				,				a_						:Optional[Any]=None				,				a_						:List[Any]=False				,				a_						:List[Any]=None				,				a_						:List[Any]=False				,				a_						:str=None				,				a_						:Any=False				,				a_						:str=False				,				a_						:int=False				,				)     ->							List[Any]:
       if cache_dir is None:
              lowercase		:Optional[Any]       							=				TRANSFORMERS_CACHE
       if isinstance(a_				,				a_):
              lowercase		:int       							=				str(a_)
       if isinstance(a_				,				a_):
              lowercase		:int       							=				str(a_)

       if is_remote_url(a_):
              # URL, so get it from the cache (downloading if necessary)
              lowercase		:Union[str, Any]       							=				get_from_cache(
                  a_				,				cache_dir=a_				,				force_download=a_				,				proxies=a_				,				resume_download=a_				,				user_agent=a_				,				local_files_only=a_				,				)
       elif os.path.exists(a_):
              # File, and it exists.
              lowercase		:Dict       							=				url_or_filename
       elif urlparse(a_).scheme == "":
              # File, but it doesn't exist.
              raise EnvironmentError('''file {} not found'''.format(a_))
       else:
              # Something unknown
              raise ValueError('''unable to parse {} as a URL or as a local path'''.format(a_))

       if extract_compressed_file:
              if not is_zipfile(a_) and not tarfile.is_tarfile(a_):
                     return output_path

              # Path where we extract compressed archives
              # We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
              lowercase		:Any       							=				os.path.split(a_)
              lowercase		:int       							=				output_file.replace('''.'''				,				'''-''') + '''-extracted'''
              lowercase		:Union[str, Any]       							=				os.path.join(a_				,				a_)

              if os.path.isdir(a_) and os.listdir(a_) and not force_extract:
                     return output_path_extracted

              # Prevent parallel extractions
              lowercase		:Any       							=				output_path + '''.lock'''
              with FileLock(a_):
                     shutil.rmtree(a_				,				ignore_errors=a_)
                     os.makedirs(a_)
                     if is_zipfile(a_):
                            with ZipFile(a_				,				'''r''') as zip_file:
                                   zip_file.extractall(a_)
                                   zip_file.close()
                     elif tarfile.is_tarfile(a_):
                            lowercase		:List[Any]       							=				tarfile.open(a_)
                            tar_file.extractall(a_)
                            tar_file.close()
                     else:
                            raise EnvironmentError('''Archive format of {} could not be identified'''.format(a_))

              return output_path_extracted

       return output_path

def       lowerCamelCase					(a_						:int				,				a_						:int=",")     ->							List[str]:
       assert isinstance(a_				,				a_)
       if os.path.isfile(a_):
              with open(a_) as f:
                     lowercase		:Tuple       							=				eval(f.read())
       else:
              lowercase		:Tuple       							=				requests.get(a_)
              try:
                     lowercase		:Tuple       							=				requests.json()
              except Exception:
                     lowercase		:Any       							=				req.content.decode()
                     assert data is not None, "could not connect"
                     try:
                            lowercase		:Optional[int]       							=				eval(a_)
                     except Exception:
                            lowercase		:List[str]       							=				data.split('''\n''')
              req.close()
       return data

def       lowerCamelCase					(a_						:int)     ->							Tuple:
       lowercase		:Any       							=				requests.get(a_)
       lowercase		:List[str]       							=				np.array(Image.open(BytesIO(response.content)))
       return img

def       lowerCamelCase					(a_						:List[str])     ->							Dict:
       lowercase		:List[str]       							=				url.split('''/''')[-1]
       if fn not in os.listdir(os.getcwd()):
              wget.download(a_)
       with open(a_				,				'''rb''') as stream:
              lowercase		:Optional[int]       							=				pkl.load(a_)
       lowercase		:List[str]       							=				weights.pop('''model''')
       lowercase		:Dict       							=				{}
       for k, v in model.items():
              lowercase		:Optional[Any]       							=				torch.from_numpy(a_)
              if "running_var" in k:
                     lowercase		:Optional[Any]       							=				torch.tensor([0])
                     lowercase		:Optional[int]       							=				k.replace('''running_var'''				,				'''num_batches_tracked''')
                     lowercase		:List[str]       							=				zero
       return new

def       lowerCamelCase					()     ->							Dict:
       print(F"""{os.path.abspath(os.path.join(a_				,				os.pardir))}/demo.ipynb""")

def       lowerCamelCase					(a_						:Tuple				,				a_						:Any="RGB")     ->							Dict:
       assert isinstance(a_				,				a_)
       if os.path.isfile(a_):
              lowercase		:Optional[Any]       							=				cva.imread(a_)
       else:
              lowercase		:Tuple       							=				get_image_from_url(a_)
              assert img is not None, F"""could not connect to: {im}"""
       lowercase		:str       							=				cva.cvtColor(a_				,				cva.COLOR_BGR2RGB)
       if input_format == "RGB":
              lowercase		:str       							=				img[:, :, ::-1]
       return img




def       lowerCamelCase					(a_						:List[str]				,				a_						:Optional[int]=1)     ->							Tuple:
       return (images[i : i + batch] for i in range(0				,				len(a_)				,				a_))





"""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__		(			__UpperCAmelCase     ):
       __A			:      torch.FloatTensor
       __A			:      torch.FloatTensor







class 					__magic_name__		(			__UpperCAmelCase			,						__UpperCAmelCase     ):
       __A			:      Any        =   1






       @register_to_config
       def __init__(   self  :    int  ,     snake_case__  :    int = 2_0_0_0  ,     snake_case__  :    float = 0.15  ,     snake_case__  :    float = 0.01  ,     snake_case__  :    float = 13_48.0  ,     snake_case__  :    float = 1e-5  ,     snake_case__  :    int = 1  ,     ):

              '''simple docstring'''






              lowercase		:List[Any]       							=				sigma_max

              # setable values
              lowercase		:List[str]       							=				None

              self.set_sigmas(snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__				)






       def  __snake_case						(   self  :    Optional[Any]  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    Optional[int] = None				):

              '''simple docstring'''






              return sample






       def  __snake_case						(   self  :    Optional[int]  ,     snake_case__  :    int  ,     snake_case__  :    float = None  ,     snake_case__  :    Union[str, torch.device] = None				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				sampling_eps if sampling_eps is not None else self.config.sampling_eps

              lowercase		:Tuple       							=				torch.linspace(1  ,     snake_case__  ,     snake_case__  ,     device=snake_case__				)






       def  __snake_case						(   self  :    List[str]  ,     snake_case__  :    int  ,     snake_case__  :    float = None  ,     snake_case__  :    float = None  ,     snake_case__  :    float = None				):

              '''simple docstring'''






              lowercase		:int       							=				sigma_min if sigma_min is not None else self.config.sigma_min
              lowercase		:Any       							=				sigma_max if sigma_max is not None else self.config.sigma_max
              lowercase		:Tuple       							=				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__				)

              lowercase		:Union[str, Any]       							=				sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
              lowercase		:str       							=				torch.exp(torch.linspace(math.log(snake_case__				)  ,     math.log(snake_case__				)  ,     snake_case__				)				)
              lowercase		:List[str]       							=				torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]				)






       def  __snake_case						(   self  :    Optional[Any]  ,     snake_case__  :    Tuple  ,     snake_case__  :    int				):

              '''simple docstring'''






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






       def  __snake_case						(   self  :    List[Any]  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    int  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    Optional[torch.Generator] = None  ,     snake_case__  :    bool = True  ,     ):

              '''simple docstring'''






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

              lowercase		:Optional[int]       							=				timestep * torch.ones(
                  sample.shape[0]  ,     device=sample.device				)  # torch.repeat_interleave(timestep, sample.shape[0])
              lowercase		:Optional[int]       							=				(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
              lowercase		:Union[str, Any]       							=				timesteps.to(self.discrete_sigmas.device				)

              lowercase		:List[Any]       							=				self.discrete_sigmas[timesteps].to(sample.device				)
              lowercase		:str       							=				self.get_adjacent_sigma(snake_case__  ,     snake_case__				).to(sample.device				)
              lowercase		:Optional[Any]       							=				torch.zeros_like(snake_case__				)
              lowercase		:List[Any]       							=				(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
              lowercase		:Union[str, Any]       							=				diffusion.flatten()
              while len(diffusion.shape				) < len(sample.shape				):
                     lowercase		:Dict       							=				diffusion.unsqueeze(-1				)
              lowercase		:List[Any]       							=				drift - diffusion**2 * model_output

              #  equation 6: sample noise for the diffusion term of
              lowercase		:List[Any]       							=				randn_tensor(
                  sample.shape  ,     layout=sample.layout  ,     generator=snake_case__  ,     device=sample.device  ,     dtype=sample.dtype				)
              lowercase		:int       							=				sample - drift  # subtract because `dt` is a small negative timestep
              # TODO is the variable diffusion the correct scaling term for the noise?
              lowercase		:Any       							=				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  __snake_case						(   self  :    Union[str, Any]  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    Optional[torch.Generator] = None  ,     snake_case__  :    bool = True  ,     ):

              '''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
              lowercase		:Tuple       							=				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
              lowercase		:List[Any]       							=				torch.norm(model_output.reshape(model_output.shape[0]  ,     -1				)  ,     dim=-1				).mean()
              lowercase		:List[str]       							=				torch.norm(noise.reshape(noise.shape[0]  ,     -1				)  ,     dim=-1				).mean()
              lowercase		:int       							=				(self.config.snr * noise_norm / grad_norm) ** 2 * 2
              lowercase		: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
              lowercase		:Union[str, Any]       							=				step_size.flatten()
              while len(step_size.shape				) < len(sample.shape				):
                     lowercase		:Union[str, Any]       							=				step_size.unsqueeze(-1				)
              lowercase		:Union[str, Any]       							=				sample + step_size * model_output
              lowercase		:Any       							=				prev_sample_mean + ((step_size * 2) ** 0.5) * noise

              if not return_dict:
                     return (prev_sample,)

              return SchedulerOutput(prev_sample=snake_case__				)






       def  __snake_case						(   self  :    List[str]  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    torch.FloatTensor  ,     ):

              '''simple docstring'''






              lowercase		:List[Any]       							=				timesteps.to(original_samples.device				)
              lowercase		:str       							=				self.discrete_sigmas.to(original_samples.device				)[timesteps]
              lowercase		:Optional[int]       							=				(
                  noise * sigmas[:, None, None, None]
                  if noise is not None
                  else torch.randn_like(snake_case__				) * sigmas[:, None, None, None]
              )
              lowercase		:str       							=				noise + original_samples
              return noisy_samples







       def __len__(   self  :    Optional[Any]				):

              '''simple docstring'''






              return self.config.num_train_timesteps

