





'''simple docstring'''




import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


__UpperCAmelCase         =   logging.get_logger(__name__)




@dataclass
class       UpperCamelCase__			:




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__						=					42
					SCREAMING_SNAKE_CASE__						=					42
					SCREAMING_SNAKE_CASE__						=					None
					SCREAMING_SNAKE_CASE__						=					None


					def lowerCamelCase_	(		self							:	List[Any]		):

							'''simple docstring'''



							return json.dumps(dataclasses.asdict(self		)     ,		indent=2		) + "\n"







@dataclass(frozen=lowercase_	)
class       UpperCamelCase__			:




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__						=					42
					SCREAMING_SNAKE_CASE__						=					None
					SCREAMING_SNAKE_CASE__						=					None
					SCREAMING_SNAKE_CASE__						=					None


					def lowerCamelCase_	(		self							:	Optional[Any]		):

							'''simple docstring'''



							return json.dumps(dataclasses.asdict(self		)		) + "\n"







class       UpperCamelCase__			:




					"""simple docstring"""


					def lowerCamelCase_	(		self							:	int     ,		lowerCamelCase_							:	List[str]		):

							'''simple docstring'''



							raise NotImplementedError()


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

							'''simple docstring'''



							raise NotImplementedError()


					def lowerCamelCase_	(		self							:	Optional[int]     ,		lowerCamelCase_							:	str		):

							'''simple docstring'''



							raise NotImplementedError()


					def lowerCamelCase_	(		self							:	str     ,		lowerCamelCase_							:	str		):

							'''simple docstring'''



							raise NotImplementedError()


					def lowerCamelCase_	(		self							:	List[Any]		):

							'''simple docstring'''



							raise NotImplementedError()


					def lowerCamelCase_	(		self							:	Optional[int]     ,		lowerCamelCase_							:	Optional[Any]		):

							'''simple docstring'''



							if len(self.get_labels()		) > 1:
									SCREAMING_SNAKE_CASE				:							Dict						=	self.get_labels()[int(example.label		)]
							return example



					@classmethod
					def lowerCamelCase_	(		cls							:	Optional[Any]     ,		lowerCamelCase_							:	Tuple     ,		lowerCamelCase_							:	str=None		):

							'''simple docstring'''



							with open(lowerCamelCase_     ,		"""r"""     ,		encoding="""utf-8-sig"""		) as f:
									return list(csv.reader(lowerCamelCase_     ,		delimiter="""\t"""     ,		quotechar=lowerCamelCase_		)		)







class       UpperCamelCase__			(			lowercase_	):




					"""simple docstring"""


					def __init__(		self							:	List[Any]     ,		lowerCamelCase_							:	Any=None     ,		lowerCamelCase_							:	str=None     ,		lowerCamelCase_							:	str="classification"     ,		lowerCamelCase_							:	List[str]=False		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[Any]						=	[] if labels is None else labels
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	[] if examples is None else examples
							SCREAMING_SNAKE_CASE				:							Any						=	mode
							SCREAMING_SNAKE_CASE				:							str						=	verbose


					def __len__(		self							:	Dict		):

							'''simple docstring'''



							return len(self.examples		)


					def __getitem__(		self							:	Union[str, Any]     ,		lowerCamelCase_							:	Union[str, Any]		):

							'''simple docstring'''



							if isinstance(lowerCamelCase_     ,		lowerCamelCase_		):
									return SingleSentenceClassificationProcessor(labels=self.labels     ,		examples=self.examples[idx]		)
							return self.examples[idx]


					@classmethod
					def lowerCamelCase_	(		cls							:	Dict     ,		lowerCamelCase_							:	Dict     ,		lowerCamelCase_							:	Optional[int]=""     ,		lowerCamelCase_							:	List[str]=0     ,		lowerCamelCase_							:	Dict=1     ,		lowerCamelCase_							:	List[Any]=None     ,		lowerCamelCase_							:	Optional[Any]=False     ,		**lowerCamelCase_							:	Tuple		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							str						=	cls(**lowerCamelCase_		)
							processor.add_examples_from_csv(
							    lowerCamelCase_     ,		split_name=lowerCamelCase_     ,		column_label=lowerCamelCase_     ,		column_text=lowerCamelCase_     ,		column_id=lowerCamelCase_     ,		skip_first_row=lowerCamelCase_     ,		overwrite_labels=lowerCamelCase_     ,		overwrite_examples=lowerCamelCase_     ,		)
							return processor


					@classmethod
					def lowerCamelCase_	(		cls							:	Tuple     ,		lowerCamelCase_							:	Any     ,		lowerCamelCase_							:	int=None     ,		**lowerCamelCase_							:	Union[str, Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[Any]						=	cls(**lowerCamelCase_		)
							processor.add_examples(lowerCamelCase_     ,		labels=lowerCamelCase_		)
							return processor


					def lowerCamelCase_	(		self							:	int     ,		lowerCamelCase_							:	Union[str, Any]     ,		lowerCamelCase_							:	Dict=""     ,		lowerCamelCase_							:	Tuple=0     ,		lowerCamelCase_							:	Dict=1     ,		lowerCamelCase_							:	int=None     ,		lowerCamelCase_							:	Optional[int]=False     ,		lowerCamelCase_							:	Dict=False     ,		lowerCamelCase_							:	List[Any]=False     ,		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[Any]						=	self._read_tsv(lowerCamelCase_		)
							if skip_first_row:
									SCREAMING_SNAKE_CASE				:							Dict						=	lines[1:]
							SCREAMING_SNAKE_CASE				:							Tuple						=	[]
							SCREAMING_SNAKE_CASE				:							List[Any]						=	[]
							SCREAMING_SNAKE_CASE				:							Tuple						=	[]
							for i, line in enumerate(lowerCamelCase_		):
									texts.append(line[column_text]		)
									labels.append(line[column_label]		)
									if column_id is not None:
											ids.append(line[column_id]		)
									else:
											SCREAMING_SNAKE_CASE				:							List[str]						=	f'''{split_name}-{i}''' if split_name else str(lowerCamelCase_		)
											ids.append(lowerCamelCase_		)

							return self.add_examples(
							    lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		overwrite_labels=lowerCamelCase_     ,		overwrite_examples=lowerCamelCase_		)


					def lowerCamelCase_	(		self							:	Union[str, Any]     ,		lowerCamelCase_							:	List[str]     ,		lowerCamelCase_							:	Optional[Any]=None     ,		lowerCamelCase_							:	Optional[int]=None     ,		lowerCamelCase_							:	Optional[int]=False     ,		lowerCamelCase_							:	Optional[Any]=False		):

							'''simple docstring'''



							if labels is not None and len(lowerCamelCase_		) != len(lowerCamelCase_		):
									raise ValueError(
									    f'''Text and labels have mismatched lengths {len(lowerCamelCase_		)} and {len(lowerCamelCase_		)}'''		)
							if ids is not None and len(lowerCamelCase_		) != len(lowerCamelCase_		):
									raise ValueError(f'''Text and ids have mismatched lengths {len(lowerCamelCase_		)} and {len(lowerCamelCase_		)}'''		)
							if ids is None:
									SCREAMING_SNAKE_CASE				:							List[Any]						=	[None] * len(lowerCamelCase_		)
							if labels is None:
									SCREAMING_SNAKE_CASE				:							str						=	[None] * len(lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	[]
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	set()
							for text_or_text_and_label, label, guid in zip(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_		):
									if isinstance(lowerCamelCase_     ,		(tuple, list)		) and label is None:
											SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							int						=	text_or_text_and_label
									else:
											SCREAMING_SNAKE_CASE				:							Dict						=	text_or_text_and_label
									added_labels.add(lowerCamelCase_		)
									examples.append(InputExample(guid=lowerCamelCase_     ,		text_a=lowerCamelCase_     ,		text_b=lowerCamelCase_     ,		label=lowerCamelCase_		)		)

							# Update examples
							if overwrite_examples:
									SCREAMING_SNAKE_CASE				:							int						=	examples
							else:
									self.examples.extend(lowerCamelCase_		)

							# Update labels
							if overwrite_labels:
									SCREAMING_SNAKE_CASE				:							Optional[Any]						=	list(lowerCamelCase_		)
							else:
									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	list(set(self.labels		).union(lowerCamelCase_		)		)

							return self.examples



					def lowerCamelCase_	(		self							:	Dict     ,		lowerCamelCase_							:	List[str]     ,		lowerCamelCase_							:	int=None     ,		lowerCamelCase_							:	str=False     ,		lowerCamelCase_							:	List[str]=0     ,		lowerCamelCase_							:	Dict=True     ,		lowerCamelCase_							:	str=None     ,		):

							'''simple docstring'''



							if max_length is None:
									SCREAMING_SNAKE_CASE				:							Optional[Any]						=	tokenizer.max_len

							SCREAMING_SNAKE_CASE				:							Tuple						=	{label: i for i, label in enumerate(self.labels		)}

							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	[]
							for ex_index, example in enumerate(self.examples		):
									if ex_index % 1_00_00 == 0:
											logger.info(f'''Tokenizing example {ex_index}'''		)

									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	tokenizer.encode(
									    example.text_a     ,		add_special_tokens=lowerCamelCase_     ,		max_length=min(lowerCamelCase_     ,		tokenizer.max_len		)     ,		)
									all_input_ids.append(lowerCamelCase_		)

							SCREAMING_SNAKE_CASE				:							List[Any]						=	max(len(lowerCamelCase_		) for input_ids in all_input_ids		)

							SCREAMING_SNAKE_CASE				:							Dict						=	[]
							for ex_index, (input_ids, example) in enumerate(zip(lowerCamelCase_     ,		self.examples		)		):
									if ex_index % 1_00_00 == 0:
											logger.info(f'''Writing example {ex_index}/{len(self.examples		)}'''		)
									# The mask has 1 for real tokens and 0 for padding tokens. Only real
									# tokens are attended to.
									SCREAMING_SNAKE_CASE				:							Dict						=	[1 if mask_padding_with_zero else 0] * len(lowerCamelCase_		)

									# Zero-pad up to the sequence length.
									SCREAMING_SNAKE_CASE				:							int						=	batch_length - len(lowerCamelCase_		)
									if pad_on_left:
											SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	([pad_token] * padding_length) + input_ids
											SCREAMING_SNAKE_CASE				:							int						=	([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
									else:
											SCREAMING_SNAKE_CASE				:							Optional[Any]						=	input_ids + ([pad_token] * padding_length)
											SCREAMING_SNAKE_CASE				:							List[str]						=	attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

									if len(lowerCamelCase_		) != batch_length:
											raise ValueError(f'''Error with input length {len(lowerCamelCase_		)} vs {batch_length}'''		)
									if len(lowerCamelCase_		) != batch_length:
											raise ValueError(f'''Error with input length {len(lowerCamelCase_		)} vs {batch_length}'''		)

									if self.mode == "classification":
											SCREAMING_SNAKE_CASE				:							Tuple						=	label_map[example.label]
									elif self.mode == "regression":
											SCREAMING_SNAKE_CASE				:							List[str]						=	float(example.label		)
									else:
											raise ValueError(self.mode		)

									if ex_index < 5 and self.verbose:
											logger.info("""*** Example ***"""		)
											logger.info(f'''guid: {example.guid}'''		)
											logger.info(f'''input_ids: {" ".join([str(lowerCamelCase_		) for x in input_ids]		)}'''		)
											logger.info(f'''attention_mask: {" ".join([str(lowerCamelCase_		) for x in attention_mask]		)}'''		)
											logger.info(f'''label: {example.label} (id = {label})'''		)

									features.append(InputFeatures(input_ids=lowerCamelCase_     ,		attention_mask=lowerCamelCase_     ,		label=lowerCamelCase_		)		)

							if return_tensors is None:
									return features
							elif return_tensors == "tf":
									if not is_tf_available():
											raise RuntimeError("""return_tensors set to 'tf' but TensorFlow 2.0 can't be imported"""		)
									import tensorflow as tf

									def gen():
											for ex in features:
													yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

									SCREAMING_SNAKE_CASE				:							List[Any]						=	tf.data.Dataset.from_generator(
									    lowerCamelCase_     ,		({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)     ,		({"""input_ids""": tf.TensorShape([None]		), """attention_mask""": tf.TensorShape([None]		)}, tf.TensorShape([]		))     ,		)
									return dataset
							elif return_tensors == "pt":
									if not is_torch_available():
											raise RuntimeError("""return_tensors set to 'pt' but PyTorch can't be imported"""		)
									import torch
									from torch.utils.data import TensorDataset

									SCREAMING_SNAKE_CASE				:							Optional[Any]						=	torch.tensor([f.input_ids for f in features]     ,		dtype=torch.long		)
									SCREAMING_SNAKE_CASE				:							str						=	torch.tensor([f.attention_mask for f in features]     ,		dtype=torch.long		)
									if self.mode == "classification":
											SCREAMING_SNAKE_CASE				:							List[Any]						=	torch.tensor([f.label for f in features]     ,		dtype=torch.long		)
									elif self.mode == "regression":
											SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	torch.tensor([f.label for f in features]     ,		dtype=torch.float		)

									SCREAMING_SNAKE_CASE				:							Optional[int]						=	TensorDataset(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_		)
									return dataset
							else:
									raise ValueError("""return_tensors should be one of 'tf' or 'pt'"""		)










'''simple docstring'''




import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler




class       UpperCamelCase__			(			unittest.TestCase	):




					"""simple docstring"""
					# TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
					SCREAMING_SNAKE_CASE__						=					(ScoreSdeVeScheduler,)
					SCREAMING_SNAKE_CASE__						=					()


					@property
					def lowerCamelCase_	(		self							:	Optional[int]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Any						=	4
							SCREAMING_SNAKE_CASE				:							int						=	3
							SCREAMING_SNAKE_CASE				:							Any						=	8
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	8

							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	torch.rand((batch_size, num_channels, height, width)		)

							return sample


					@property
					def lowerCamelCase_	(		self							:	Optional[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[str]						=	4
							SCREAMING_SNAKE_CASE				:							Tuple						=	3
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	8
							SCREAMING_SNAKE_CASE				:							List[str]						=	8

							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	batch_size * num_channels * height * width
							SCREAMING_SNAKE_CASE				:							Dict						=	torch.arange(lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	sample.reshape(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							Tuple						=	sample / num_elems
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	sample.permute(3     ,		0     ,		1     ,		2		)

							return sample


					def lowerCamelCase_	(		self							:	List[str]		):

							'''simple docstring'''



							def model(lowerCamelCase_							:	List[str]     ,		lowerCamelCase_							:	int     ,		*lowerCamelCase_							:	int		):
									return sample * t / (t + 1)

							return model


					def lowerCamelCase_	(		self							:	str     ,		**lowerCamelCase_							:	List[str]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	{
							    """num_train_timesteps""": 20_00,
							    """snr""": 0.15,
							    """sigma_min""": 0.01,
							    """sigma_max""": 13_48,
							    """sampling_eps""": 1e-5,
							}

							config.update(**lowerCamelCase_		)
							return config


					def lowerCamelCase_	(		self							:	List[str]     ,		lowerCamelCase_							:	List[Any]=0     ,		**lowerCamelCase_							:	Optional[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[Any]						=	dict(self.forward_default_kwargs		)

							for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE				:							Optional[int]						=	self.dummy_sample
									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	0.1 * sample

									SCREAMING_SNAKE_CASE				:							Tuple						=	self.get_scheduler_config(**lowerCamelCase_		)
									SCREAMING_SNAKE_CASE				:							int						=	scheduler_class(**lowerCamelCase_		)

									with tempfile.TemporaryDirectory() as tmpdirname:
											scheduler.save_config(lowerCamelCase_		)
											SCREAMING_SNAKE_CASE				:							int						=	scheduler_class.from_pretrained(lowerCamelCase_		)

									SCREAMING_SNAKE_CASE				:							Any						=	scheduler.step_pred(
									    lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample
									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	new_scheduler.step_pred(
									    lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample

									assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"

									SCREAMING_SNAKE_CASE				:							Any						=	scheduler.step_correct(lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample
									SCREAMING_SNAKE_CASE				:							int						=	new_scheduler.step_correct(
									    lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample

									assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler correction are not identical"


					def lowerCamelCase_	(		self							:	str     ,		lowerCamelCase_							:	List[Any]=0     ,		**lowerCamelCase_							:	Tuple		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[Any]						=	dict(self.forward_default_kwargs		)
							kwargs.update(lowerCamelCase_		)

							for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE				:							List[str]						=	self.dummy_sample
									SCREAMING_SNAKE_CASE				:							Tuple						=	0.1 * sample

									SCREAMING_SNAKE_CASE				:							int						=	self.get_scheduler_config()
									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	scheduler_class(**lowerCamelCase_		)

									with tempfile.TemporaryDirectory() as tmpdirname:
											scheduler.save_config(lowerCamelCase_		)
											SCREAMING_SNAKE_CASE				:							Tuple						=	scheduler_class.from_pretrained(lowerCamelCase_		)

									SCREAMING_SNAKE_CASE				:							Tuple						=	scheduler.step_pred(
									    lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample
									SCREAMING_SNAKE_CASE				:							Optional[int]						=	new_scheduler.step_pred(
									    lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample

									assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"

									SCREAMING_SNAKE_CASE				:							List[str]						=	scheduler.step_correct(lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample
									SCREAMING_SNAKE_CASE				:							Optional[int]						=	new_scheduler.step_correct(
									    lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample

									assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler correction are not identical"


					def lowerCamelCase_	(		self							:	List[Any]		):

							'''simple docstring'''



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


					def lowerCamelCase_	(		self							:	Dict		):

							'''simple docstring'''



							for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]     ,		[1, 1_00, 10_00]		):
									self.check_over_configs(sigma_min=lowerCamelCase_     ,		sigma_max=lowerCamelCase_		)


					def lowerCamelCase_	(		self							:	str		):

							'''simple docstring'''



							for t in [0.1, 0.5, 0.75]:
									self.check_over_forward(time_step=lowerCamelCase_		)


					def lowerCamelCase_	(		self							:	Any		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Tuple						=	dict(self.forward_default_kwargs		)

							SCREAMING_SNAKE_CASE				:							str						=	self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE				:							Dict						=	self.get_scheduler_config()
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	scheduler_class(**lowerCamelCase_		)

							SCREAMING_SNAKE_CASE				:							Optional[int]						=	3

							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	self.dummy_model()
							SCREAMING_SNAKE_CASE				:							Dict						=	self.dummy_sample_deter

							scheduler.set_sigmas(lowerCamelCase_		)
							scheduler.set_timesteps(lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							str						=	torch.manual_seed(0		)

							for i, t in enumerate(scheduler.timesteps		):
									SCREAMING_SNAKE_CASE				:							int						=	scheduler.sigmas[i]

									for _ in range(scheduler.config.correct_steps		):
											with torch.no_grad():
													SCREAMING_SNAKE_CASE				:							List[Any]						=	model(lowerCamelCase_     ,		lowerCamelCase_		)
											SCREAMING_SNAKE_CASE				:							List[str]						=	scheduler.step_correct(lowerCamelCase_     ,		lowerCamelCase_     ,		generator=lowerCamelCase_     ,		**lowerCamelCase_		).prev_sample

									with torch.no_grad():
											SCREAMING_SNAKE_CASE				:							Dict						=	model(lowerCamelCase_     ,		lowerCamelCase_		)

									SCREAMING_SNAKE_CASE				:							List[Any]						=	scheduler.step_pred(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		generator=lowerCamelCase_     ,		**lowerCamelCase_		)
									SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							List[Any]						=	output.prev_sample, output.prev_sample_mean

							SCREAMING_SNAKE_CASE				:							List[Any]						=	torch.sum(torch.abs(lowerCamelCase_		)		)
							SCREAMING_SNAKE_CASE				:							Any						=	torch.mean(torch.abs(lowerCamelCase_		)		)

							assert np.isclose(result_sum.item()     ,		14_372_758_528.0		)
							assert np.isclose(result_mean.item()     ,		18_714_530.0		)



					def lowerCamelCase_	(		self							:	Optional[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Optional[int]						=	dict(self.forward_default_kwargs		)

							SCREAMING_SNAKE_CASE				:							Any						=	kwargs.pop("""num_inference_steps"""     ,		lowerCamelCase_		)

							for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE				:							Tuple						=	self.get_scheduler_config()
									SCREAMING_SNAKE_CASE				:							int						=	scheduler_class(**lowerCamelCase_		)

									SCREAMING_SNAKE_CASE				:							Optional[Any]						=	self.dummy_sample
									SCREAMING_SNAKE_CASE				:							List[str]						=	0.1 * sample

									if num_inference_steps is not None and hasattr(lowerCamelCase_     ,		"""set_timesteps"""		):
											scheduler.set_timesteps(lowerCamelCase_		)
									elif num_inference_steps is not None and not hasattr(lowerCamelCase_     ,		"""set_timesteps"""		):
											SCREAMING_SNAKE_CASE				:							Dict						=	num_inference_steps

									SCREAMING_SNAKE_CASE				:							Optional[int]						=	scheduler.step_pred(lowerCamelCase_     ,		0     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample
									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	scheduler.step_pred(lowerCamelCase_     ,		1     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample

									self.assertEqual(output_a.shape     ,		sample.shape		)
									self.assertEqual(output_a.shape     ,		output_a.shape		)



