"""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

	a				=True
except ImportError:
	a				=False


try:
	from torch.hub import _get_torch_home

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

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

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





def      lowerCamelCase_					(   __lowerCAmelCase=OBJECTS  ,       __lowerCAmelCase=ATTRIBUTES							)				->   List[str]:




						'''simple docstring'''



						lowerCamelCase__   =[]
						with open(__a							) as f:
												for object in f.readlines():
																		vg_classes.append(object.split(","							)[0].lower().strip()							)

						lowerCamelCase__   =[]
						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_					(   __lowerCAmelCase							)				->   Tuple:




						'''simple docstring'''



						lowerCamelCase__   =OrderedDict()
						with open(__a  ,       "rb"							) as f:
												lowerCamelCase__   =pkl.load(__a							)["model"]
						for k in copy.deepcopy(list(ckp.keys()							)							):
												lowerCamelCase__   =ckp.pop(__a							)
												if isinstance(__a  ,       np.ndarray							):
																		lowerCamelCase__   =torch.tensor(__a							)
												else:
																		assert isinstance(__a  ,       torch.tensor							), type(__a							)
												lowerCamelCase__   =v
						return r






class   __UpperCAmelCase		:
				A__ :  int    				=			{}

				def __init__( self      ,					_lowerCamelCase      ,					_lowerCamelCase = "root"      ,					_lowerCamelCase=0 ):
										lowerCamelCase__   =name
										lowerCamelCase__   =level
										lowerCamelCase__   ={}
										for k, v in dictionary.items():
																if v is None:
																						raise ValueError()
																lowerCamelCase__   =copy.deepcopy(SCREAMING_SNAKE_CASE_ )
																lowerCamelCase__   =copy.deepcopy(SCREAMING_SNAKE_CASE_ )
																if isinstance(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ ):
																						lowerCamelCase__   =Config(SCREAMING_SNAKE_CASE_      ,					name=SCREAMING_SNAKE_CASE_      ,					level=level + 1 )
																lowerCamelCase__   =v
																setattr(self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ )

										lowerCamelCase__   =d

				def __repr__( self ):
										return str(list((self._pointer.keys()) ) )

				def __setattr__( self      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										lowerCamelCase__   =val
										lowerCamelCase__   =val
										lowerCamelCase__   =key.split("." )
										lowerCamelCase__   =len(SCREAMING_SNAKE_CASE_ ) - 1
										lowerCamelCase__   =self._pointer
										if len(SCREAMING_SNAKE_CASE_ ) > 1:
																for i, l in enumerate(SCREAMING_SNAKE_CASE_ ):
																						if hasattr(self      ,					SCREAMING_SNAKE_CASE_ ) and isinstance(getattr(self      ,					SCREAMING_SNAKE_CASE_ )      ,					SCREAMING_SNAKE_CASE_ ):
																												setattr(getattr(self      ,					SCREAMING_SNAKE_CASE_ )      ,					".".join(levels[i:] )      ,					SCREAMING_SNAKE_CASE_ )
																						if l == last_level:
																												lowerCamelCase__   =val
																						else:
																												lowerCamelCase__   =pointer[l]

				def 						_a      ( self ):
										return self._pointer

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase ):







										with open(F'''{file_name}'''      ,					"w" ) as stream:
																dump(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ )

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase ):







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

				@staticmethod
				def 						_a      ( _lowerCamelCase ):







										with open(SCREAMING_SNAKE_CASE_ ) as stream:
																lowerCamelCase__   =load(SCREAMING_SNAKE_CASE_      ,					Loader=SCREAMING_SNAKE_CASE_ )
										return data

				def __str__( self ):
										lowerCamelCase__   ="    "
										if self._name != "root":
																lowerCamelCase__   =F'''{t * (self._level-1)}{self._name}:\n'''
										else:
																lowerCamelCase__   =""
										lowerCamelCase__   =self._level
										for i, (k, v) in enumerate(self._pointer.items() ):
																if isinstance(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ ):
																						r += F'''{t * (self._level)}{v}\n'''
																						self._level += 1
																else:
																						r += F'''{t * (self._level)}{k}: {v} ({type(SCREAMING_SNAKE_CASE_ ).__name__})\n'''
																lowerCamelCase__   =level
										return r[:-1]

				@classmethod
				def 						_a      ( cls      ,					_lowerCamelCase      ,					**_lowerCamelCase ):
										lowerCamelCase__   , lowerCamelCase__   =cls.get_config_dict(SCREAMING_SNAKE_CASE_      ,					**SCREAMING_SNAKE_CASE_ )
										return cls(SCREAMING_SNAKE_CASE_ )






				@classmethod
				def 						_a      ( cls      ,					_lowerCamelCase      ,					**_lowerCamelCase ):
										lowerCamelCase__   =kwargs.pop("cache_dir"      ,					SCREAMING_SNAKE_CASE_ )
										lowerCamelCase__   =kwargs.pop("force_download"      ,					SCREAMING_SNAKE_CASE_ )
										lowerCamelCase__   =kwargs.pop("resume_download"      ,					SCREAMING_SNAKE_CASE_ )
										lowerCamelCase__   =kwargs.pop("proxies"      ,					SCREAMING_SNAKE_CASE_ )
										lowerCamelCase__   =kwargs.pop("local_files_only"      ,					SCREAMING_SNAKE_CASE_ )

										if os.path.isdir(SCREAMING_SNAKE_CASE_ ):
																lowerCamelCase__   =os.path.join(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ )
										elif os.path.isfile(SCREAMING_SNAKE_CASE_ ) or is_remote_url(SCREAMING_SNAKE_CASE_ ):
																lowerCamelCase__   =pretrained_model_name_or_path
										else:
																lowerCamelCase__   =hf_bucket_url(SCREAMING_SNAKE_CASE_      ,					filename=SCREAMING_SNAKE_CASE_      ,					use_cdn=SCREAMING_SNAKE_CASE_ )

										try:
																# Load from URL or cache if already cached
																lowerCamelCase__   =cached_path(
																    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_      ,					)
																# Load config dict
																if resolved_config_file is None:
																						raise EnvironmentError

																lowerCamelCase__   =Config.load_yaml(SCREAMING_SNAKE_CASE_ )

										except EnvironmentError:
																lowerCamelCase__   ="Can\'t load config for"
																raise EnvironmentError(SCREAMING_SNAKE_CASE_ )

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

										return Config.load_yaml(SCREAMING_SNAKE_CASE_ ), kwargs





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   Optional[int]:




						'''simple docstring'''



						lowerCamelCase__   =torch.load("dump.pt"  ,       map_location=in_tensor.device							)
						lowerCamelCase__   =in_tensor.numpy()
						lowerCamelCase__   =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_					(   __lowerCAmelCase							)				->   Optional[int]:




						'''simple docstring'''



						lowerCamelCase__   =urlparse(__a							)
						return parsed.scheme in ("http", "https")





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase=True							)				->   Dict:




						'''simple docstring'''



						lowerCamelCase__   =CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
						lowerCamelCase__   ="/" not in model_id
						if legacy_format:
												return F'''{endpoint}/{model_id}-{filename}'''
						else:
												return F'''{endpoint}/{model_id}/{filename}'''





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase=None  ,       __lowerCAmelCase=0  ,       __lowerCAmelCase=None  ,       )				->   List[str]:




						'''simple docstring'''



						lowerCamelCase__   ="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
						lowerCamelCase__   ={"user-agent": ua}
						if resume_size > 0:
												lowerCamelCase__   ="bytes=%d-" % (resume_size,)
						lowerCamelCase__   =requests.get(__a  ,       stream=__a  ,       proxies=__a  ,       headers=__a							)
						if response.status_code == 416:  # Range not satisfiable
												return
						lowerCamelCase__   =response.headers.get("Content-Length"							)
						lowerCamelCase__   =resume_size + int(__a							) if content_length is not None else None
						lowerCamelCase__   =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_					(   __lowerCAmelCase  ,       __lowerCAmelCase=None  ,       __lowerCAmelCase=False  ,       __lowerCAmelCase=None  ,       __lowerCAmelCase=10  ,       __lowerCAmelCase=False  ,       __lowerCAmelCase=None  ,       __lowerCAmelCase=False  ,       )				->   List[Any]:




						'''simple docstring'''



						if cache_dir is None:
												lowerCamelCase__   =TRANSFORMERS_CACHE
						if isinstance(__a  ,       __a							):
												lowerCamelCase__   =str(__a							)

						os.makedirs(__a  ,       exist_ok=__a							)

						lowerCamelCase__   =None
						if not local_files_only:
												try:
																		lowerCamelCase__   =requests.head(__a  ,       allow_redirects=__a  ,       proxies=__a  ,       timeout=__a							)
																		if response.status_code == 200:
																								lowerCamelCase__   =response.headers.get("ETag"							)
												except (EnvironmentError, requests.exceptions.Timeout):
																		# etag is already None
																		pass

						lowerCamelCase__   =url_to_filename(__a  ,       __a							)

						# get cache path to put the file
						lowerCamelCase__   =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:
																		lowerCamelCase__   =[
																		    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.
						lowerCamelCase__   =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:
																		lowerCamelCase__   =cache_path + ".incomplete"

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

																		lowerCamelCase__   =_resumable_file_manager
																		if os.path.exists(__a							):
																								lowerCamelCase__   =os.stat(__a							).st_size
																		else:
																								lowerCamelCase__   =0
												else:
																		lowerCamelCase__   =partial(tempfile.NamedTemporaryFile  ,       dir=__a  ,       delete=__a							)
																		lowerCamelCase__   =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							)

												lowerCamelCase__   ={"url": url, "etag": etag}
												lowerCamelCase__   =cache_path + ".json"
												with open(__a  ,       "w"							) as meta_file:
																		json.dump(__a  ,       __a							)

						return cache_path





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase=None							)				->   Optional[int]:




						'''simple docstring'''



						lowerCamelCase__   =url.encode("utf-8"							)
						lowerCamelCase__   =shaaaa(__a							)
						lowerCamelCase__   =url_hash.hexdigest()

						if etag:
												lowerCamelCase__   =etag.encode("utf-8"							)
												lowerCamelCase__   =shaaaa(__a							)
												filename += "." + etag_hash.hexdigest()

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

						return filename





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase=None  ,       __lowerCAmelCase=False  ,       __lowerCAmelCase=None  ,       __lowerCAmelCase=False  ,       __lowerCAmelCase=None  ,       __lowerCAmelCase=False  ,       __lowerCAmelCase=False  ,       __lowerCAmelCase=False  ,       )				->   Dict:




						'''simple docstring'''



						if cache_dir is None:
												lowerCamelCase__   =TRANSFORMERS_CACHE
						if isinstance(__a  ,       __a							):
												lowerCamelCase__   =str(__a							)
						if isinstance(__a  ,       __a							):
												lowerCamelCase__   =str(__a							)

						if is_remote_url(__a							):
												# URL, so get it from the cache (downloading if necessary)
												lowerCamelCase__   =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.
												lowerCamelCase__   =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/"
												lowerCamelCase__   , lowerCamelCase__   =os.path.split(__a							)
												lowerCamelCase__   =output_file.replace("."  ,       "-"							) + "-extracted"
												lowerCamelCase__   =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
												lowerCamelCase__   =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							):
																								lowerCamelCase__   =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_					(   __lowerCAmelCase  ,       __lowerCAmelCase=","							)				->   Tuple:




						'''simple docstring'''



						assert isinstance(__a  ,       __a							)
						if os.path.isfile(__a							):
												with open(__a							) as f:
																		lowerCamelCase__   =eval(f.read()							)
						else:
												lowerCamelCase__   =requests.get(__a							)
												try:
																		lowerCamelCase__   =requests.json()
												except Exception:
																		lowerCamelCase__   =req.content.decode()
																		assert data is not None, "could not connect"
																		try:
																								lowerCamelCase__   =eval(__a							)
																		except Exception:
																								lowerCamelCase__   =data.split("\n"							)
												req.close()
						return data





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   int:




						'''simple docstring'''



						lowerCamelCase__   =requests.get(__a							)
						lowerCamelCase__   =np.array(Image.open(BytesIO(response.content							)							)							)
						return img





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   Optional[Any]:




						'''simple docstring'''



						lowerCamelCase__   =url.split("/"							)[-1]
						if fn not in os.listdir(os.getcwd()							):
												wget.download(__a							)
						with open(__a  ,       "rb"							) as stream:
												lowerCamelCase__   =pkl.load(__a							)
						lowerCamelCase__   =weights.pop("model"							)
						lowerCamelCase__   ={}
						for k, v in model.items():
												lowerCamelCase__   =torch.from_numpy(__a							)
												if "running_var" in k:
																		lowerCamelCase__   =torch.tensor([0]							)
																		lowerCamelCase__   =k.replace("running_var"  ,       "num_batches_tracked"							)
																		lowerCamelCase__   =zero
						return new





def      lowerCamelCase_					(   )				->   List[Any]:




						'''simple docstring'''



						print(F'''{os.path.abspath(os.path.join(__a  ,       os.pardir							)							)}/demo.ipynb'''							)





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase="RGB"							)				->   str:




						'''simple docstring'''



						assert isinstance(__a  ,       __a							)
						if os.path.isfile(__a							):
												lowerCamelCase__   =cva.imread(__a							)
						else:
												lowerCamelCase__   =get_image_from_url(__a							)
												assert img is not None, F'''could not connect to: {im}'''
						lowerCamelCase__   =cva.cvtColor(__a  ,       cva.COLOR_BGR2RGB							)
						if input_format == "RGB":
												lowerCamelCase__   =img[:, :, ::-1]
						return img





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase=1							)				->   Tuple:




						'''simple docstring'''



						return (images[i : i + batch] for i in range(0  ,       len(__a							)  ,       __a							))




"""simple docstring"""




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest





class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   (UnCLIPScheduler,)

							def _lowercase       (self							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			{
									    '''num_train_timesteps''': 10_00,
									    '''variance_type''': '''fixed_small_log''',
									    '''clip_sample''': True,
									    '''clip_sample_range''': 1.0,
									    '''prediction_type''': '''epsilon''',
									}

									config.update(**SCREAMING_SNAKE_CASE_     )
									return config

							def _lowercase       (self     ):

									"""simple docstring"""







									for timesteps in [1, 5, 1_00, 10_00]:
											self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for variance in ["fixed_small_log", "learned_range"]:
											self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for clip_sample_range in [1, 5, 10, 20]:
											self.check_over_configs(clip_sample_range=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for prediction_type in ["epsilon", "sample"]:
											self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for time_step in [0, 5_00, 9_99]:
											for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
													if prev_timestep is not None and prev_timestep >= time_step:
															continue

													self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_							,							prev_timestep=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config(variance_type='''fixed_small_log'''     )
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 1.0_0_0_0e-1_0     )     ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_87     ) - 0.0_54_96_25     )     ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_99     ) - 0.9_99_49_87     )     ) < 1e-5

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config(variance_type='''learned_range'''     )
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			0.5

									assert scheduler._get_variance(1							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -10.1_71_27_90 < 1e-5
									assert scheduler._get_variance(4_87							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -5.7_99_80_52 < 1e-5
									assert scheduler._get_variance(9_99							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -0.0_01_00_11 < 1e-5

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			scheduler.timesteps

									SCREAMING_SNAKE_CASE_    			=			self.dummy_model()
									SCREAMING_SNAKE_CASE_    			=			self.dummy_sample_deter
									SCREAMING_SNAKE_CASE_    			=			torch.manual_seed(0     )

									for i, t in enumerate(SCREAMING_SNAKE_CASE_     ):
											# 1. predict noise residual
											SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

											# 2. predict previous mean of sample x_t-1
											SCREAMING_SNAKE_CASE_    			=			scheduler.step(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							generator=SCREAMING_SNAKE_CASE_     ).prev_sample

											SCREAMING_SNAKE_CASE_    			=			pred_prev_sample

									SCREAMING_SNAKE_CASE_    			=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_     )     )
									SCREAMING_SNAKE_CASE_    			=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_     )     )

									assert abs(result_sum.item() - 2_52.2_68_24_95     ) < 1e-2
									assert abs(result_mean.item() - 0.3_28_47_43     ) < 1e-3

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									scheduler.set_timesteps(25     )

									SCREAMING_SNAKE_CASE_    			=			scheduler.timesteps

									SCREAMING_SNAKE_CASE_    			=			self.dummy_model()
									SCREAMING_SNAKE_CASE_    			=			self.dummy_sample_deter
									SCREAMING_SNAKE_CASE_    			=			torch.manual_seed(0     )

									for i, t in enumerate(SCREAMING_SNAKE_CASE_     ):
											# 1. predict noise residual
											SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

											if i + 1 == timesteps.shape[0]:
													SCREAMING_SNAKE_CASE_    			=			None
											else:
													SCREAMING_SNAKE_CASE_    			=			timesteps[i + 1]

											# 2. predict previous mean of sample x_t-1
											SCREAMING_SNAKE_CASE_    			=			scheduler.step(
											    SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							prev_timestep=SCREAMING_SNAKE_CASE_							,							generator=SCREAMING_SNAKE_CASE_     ).prev_sample

											SCREAMING_SNAKE_CASE_    			=			pred_prev_sample

									SCREAMING_SNAKE_CASE_    			=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_     )     )
									SCREAMING_SNAKE_CASE_    			=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_     )     )

									assert abs(result_sum.item() - 2_58.2_04_49_83     ) < 1e-2
									assert abs(result_mean.item() - 0.3_36_20_38     ) < 1e-3

							def _lowercase       (self     ):

									"""simple docstring"""







									pass







							def _lowercase       (self     ):

									"""simple docstring"""







									pass