import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints

import yaml


_lowerCamelCase   :				List[str]     			=						NewType('''DataClass''', Any)
_lowerCamelCase   :				Tuple     			=						NewType('''DataClassType''', Any)





def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[Any]					):
					if isinstance(UpperCAmelCase__ ,	UpperCAmelCase__					):
										return v
					if v.lower() in ("yes", "true", "t", "y", "1"):
										return True
					elif v.lower() in ("no", "false", "f", "n", "0"):
										return False
					else:
										raise ArgumentTypeError(
										    F"Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive)."					)





def 		__lowerCamelCase							(UpperCAmelCase__       :							list					):
					SCREAMING_SNAKE_CASE           =    {str(UpperCAmelCase__					): choice for choice in choices}
					return lambda UpperCAmelCase__					: str_to_choice.get(UpperCAmelCase__ ,	UpperCAmelCase__					)





def 		__lowerCamelCase							(*,
    UpperCAmelCase__       :							Union[str, List[str]] = None ,	UpperCAmelCase__       :							str = None ,	UpperCAmelCase__       :							Any = dataclasses.MISSING ,	UpperCAmelCase__       :							Callable[[], Any] = dataclasses.MISSING ,	UpperCAmelCase__       :							dict = None ,	**UpperCAmelCase__       :							Optional[Any] ,	):
					if metadata is None:
										# Important, don't use as default param in function signature because dict is mutable and shared across function calls
										SCREAMING_SNAKE_CASE           =    {}
					if aliases is not None:
										SCREAMING_SNAKE_CASE           =    aliases
					if help is not None:
										SCREAMING_SNAKE_CASE           =    help

					return dataclasses.field(metadata=UpperCAmelCase__ ,	default=UpperCAmelCase__ ,	default_factory=UpperCAmelCase__ ,	**UpperCAmelCase__					)


class   lowercase      (       a       ):
							lowercase__  :    Iterable[DataClassType]




							def __init__(  self				:   str		,							_UpperCamelCase				:   Union[DataClassType, Iterable[DataClassType]]		,							**_UpperCamelCase				:   Union[str, Any] )   ->			Tuple:



												'''simple docstring'''

												if "formatter_class" not in kwargs:
																	SCREAMING_SNAKE_CASE           =    ArgumentDefaultsHelpFormatter
												super().__init__(**_UpperCamelCase )
												if dataclasses.is_dataclass(_UpperCamelCase ):
																	SCREAMING_SNAKE_CASE           =    [dataclass_types]
												SCREAMING_SNAKE_CASE           =    list(_UpperCamelCase )
												for dtype in self.dataclass_types:
																	self._add_dataclass_arguments(_UpperCamelCase )




							@staticmethod
							def 		__snake_case(  _UpperCamelCase				:   ArgumentParser		,							_UpperCamelCase				:   dataclasses.Field )   ->			Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    F"--{field.name}"
												SCREAMING_SNAKE_CASE           =    field.metadata.copy()
												# field.metadata is not used at all by Data Classes,
												# it is provided as a third-party extension mechanism.
												if isinstance(field.type		,							_UpperCamelCase ):
																	raise RuntimeError(
																	    "Unresolved type detected, which should have been done with the help of "
																	    "`typing.get_type_hints` method by default" )

												SCREAMING_SNAKE_CASE           =    kwargs.pop("aliases"		,							[] )
												if isinstance(_UpperCamelCase		,							_UpperCamelCase ):
																	SCREAMING_SNAKE_CASE           =    [aliases]

												SCREAMING_SNAKE_CASE           =    getattr(field.type		,							"__origin__"		,							field.type )
												if origin_type is Union or (hasattr(_UpperCamelCase		,							"UnionType" ) and isinstance(_UpperCamelCase		,							types.UnionType )):
																	if str not in field.type.__args__ and (
																	    len(field.type.__args__ ) != 2 or type(_UpperCamelCase ) not in field.type.__args__
																	):
																						raise ValueError(
																						    "Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because"
																						    " the argument parser only supports one type per argument."
																						    F" Problem encountered in field '{field.name}'." )
																	if type(_UpperCamelCase ) not in field.type.__args__:
																						# filter `str` in Union
																						SCREAMING_SNAKE_CASE           =    field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
																						SCREAMING_SNAKE_CASE           =    getattr(field.type		,							"__origin__"		,							field.type )
																	elif bool not in field.type.__args__:
																						# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
																						SCREAMING_SNAKE_CASE           =    (
																						    field.type.__args__[0] if isinstance(_UpperCamelCase		,							field.type.__args__[1] ) else field.type.__args__[1]
																						)
																						SCREAMING_SNAKE_CASE           =    getattr(field.type		,							"__origin__"		,							field.type )

        # A variable to store kwargs for a boolean field, if needed
        # so that we can init a `no_*` complement argument (see below)
												SCREAMING_SNAKE_CASE           =    {}
												if origin_type is Literal or (isinstance(field.type		,							_UpperCamelCase ) and issubclass(field.type		,							_UpperCamelCase )):
																	if origin_type is Literal:
																						SCREAMING_SNAKE_CASE           =    field.type.__args__
																	else:
																						SCREAMING_SNAKE_CASE           =    [x.value for x in field.type]

																	SCREAMING_SNAKE_CASE           =    make_choice_type_function(kwargs["choices"] )

																	if field.default is not dataclasses.MISSING:
																						SCREAMING_SNAKE_CASE           =    field.default
																	else:
																						SCREAMING_SNAKE_CASE           =    True
												elif field.type is bool or field.type == Optional[bool]:
																	# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
																	# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
																	SCREAMING_SNAKE_CASE           =    copy(_UpperCamelCase )

																	# Hack because type=bool in argparse does not behave as we want.
																	SCREAMING_SNAKE_CASE           =    string_to_bool
																	if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
																						# Default value is False if we have no default when of type bool.
																						SCREAMING_SNAKE_CASE           =    False if field.default is dataclasses.MISSING else field.default
																						# This is the value that will get picked if we don't include --field_name in any way
																						SCREAMING_SNAKE_CASE           =    default
																						# This tells argparse we accept 0 or 1 value after --field_name
																						SCREAMING_SNAKE_CASE           =    "?"
																						# This is the value that will get picked if we do --field_name (without value)
																						SCREAMING_SNAKE_CASE           =    True
												elif isclass(_UpperCamelCase ) and issubclass(_UpperCamelCase		,							_UpperCamelCase ):
																	SCREAMING_SNAKE_CASE           =    field.type.__args__[0]
																	SCREAMING_SNAKE_CASE           =    "+"
																	if field.default_factory is not dataclasses.MISSING:
																						SCREAMING_SNAKE_CASE           =    field.default_factory()
																	elif field.default is dataclasses.MISSING:
																						SCREAMING_SNAKE_CASE           =    True
												else:
																	SCREAMING_SNAKE_CASE           =    field.type
																	if field.default is not dataclasses.MISSING:
																						SCREAMING_SNAKE_CASE           =    field.default
																	elif field.default_factory is not dataclasses.MISSING:
																						SCREAMING_SNAKE_CASE           =    field.default_factory()
																	else:
																						SCREAMING_SNAKE_CASE           =    True
												parser.add_argument(_UpperCamelCase		,							*_UpperCamelCase		,							**_UpperCamelCase )

												# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
												# Order is important for arguments with the same destination!
												# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
												# here and we do not need those changes/additional keys.
												if field.default is True and (field.type is bool or field.type == Optional[bool]):
																	SCREAMING_SNAKE_CASE           =    False
																	parser.add_argument(F"--no_{field.name}"		,							action="store_false"		,							dest=field.name		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   DataClassType )   ->			List[Any]:



												'''simple docstring'''

												if hasattr(_UpperCamelCase		,							"_argument_group_name" ):
																	SCREAMING_SNAKE_CASE           =    self.add_argument_group(dtype._argument_group_name )
												else:
																	SCREAMING_SNAKE_CASE           =    self

												try:
																	SCREAMING_SNAKE_CASE           =    get_type_hints(_UpperCamelCase )
												except NameError:
																	raise RuntimeError(
																	    F"Type resolution failed for {dtype}. Try declaring the class in global scope or "
																	    "removing line of `from __future__ import annotations` which opts in Postponed "
																	    "Evaluation of Annotations (PEP 563)" )
												except TypeError as ex:
																	# Remove this block when we drop Python 3.9 support
																	if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(_UpperCamelCase ):
																						SCREAMING_SNAKE_CASE           =    ".".join(map(_UpperCamelCase		,							sys.version_info[:3] ) )
																						raise RuntimeError(
																						    F"Type resolution failed for {dtype} on Python {python_version}. Try removing "
																						    "line of `from __future__ import annotations` which opts in union types as "
																						    "`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To "
																						    "support Python versions that lower than 3.10, you need to use "
																						    "`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of "
																						    "`X | None`." ) from ex
																	raise

												for field in dataclasses.fields(_UpperCamelCase ):
																	if not field.init:
																						continue
																	SCREAMING_SNAKE_CASE           =    type_hints[field.name]
																	self._parse_dataclass_field(_UpperCamelCase		,							_UpperCamelCase )




							def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   List[str]=None		,							_UpperCamelCase				:   str=False		,							_UpperCamelCase				:   Dict=True		,							_UpperCamelCase				:   Tuple=None		,							_UpperCamelCase				:   Dict=None		,							)   ->			Tuple[DataClass, ...]:



												'''simple docstring'''

												if args_file_flag or args_filename or (look_for_args_file and len(sys.argv )):
																	SCREAMING_SNAKE_CASE           =    []

																	if args_filename:
																						args_files.append(Path(_UpperCamelCase ) )
																	elif look_for_args_file and len(sys.argv ):
																						args_files.append(Path(sys.argv[0] ).with_suffix(".args" ) )

																	# args files specified via command line flag should overwrite default args files so we add them last
																	if args_file_flag:
																						# Create special parser just to extract the args_file_flag values
																						SCREAMING_SNAKE_CASE           =    ArgumentParser()
																						args_file_parser.add_argument(_UpperCamelCase		,							type=_UpperCamelCase		,							action="append" )

																						# Use only remaining args for further parsing (remove the args_file_flag)
																						SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    args_file_parser.parse_known_args(args=_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    vars(_UpperCamelCase ).get(args_file_flag.lstrip("-" )		,							_UpperCamelCase )

																						if cmd_args_file_paths:
																											args_files.extend([Path(_UpperCamelCase ) for p in cmd_args_file_paths] )

																	SCREAMING_SNAKE_CASE           =    []
																	for args_file in args_files:
																						if args_file.exists():
																											file_args += args_file.read_text().split()

            # in case of duplicate arguments the last one has precedence
            # args specified via the command line should overwrite args from files, so we add them last
																	SCREAMING_SNAKE_CASE           =    file_args + args if args is not None else file_args + sys.argv[1:]
												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    self.parse_known_args(args=_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    []
												for dtype in self.dataclass_types:
																	SCREAMING_SNAKE_CASE           =    {f.name for f in dataclasses.fields(_UpperCamelCase ) if f.init}
																	SCREAMING_SNAKE_CASE           =    {k: v for k, v in vars(_UpperCamelCase ).items() if k in keys}
																	for k in keys:
																						delattr(_UpperCamelCase		,							_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    dtype(**_UpperCamelCase )
																	outputs.append(_UpperCamelCase )
												if len(namespace.__dict__ ) > 0:
																	# additional namespace.
																	outputs.append(_UpperCamelCase )
												if return_remaining_strings:
																	return (*outputs, remaining_args)
												else:
																	if remaining_args:
																						raise ValueError(F"Some specified arguments are not used by the HfArgumentParser: {remaining_args}" )

																	return (*outputs,)




							def 		__snake_case(  self				:   Tuple		,							_UpperCamelCase				:   Dict[str, Any]		,							_UpperCamelCase				:   bool = False )   ->			Tuple[DataClass, ...]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    set(args.keys() )
												SCREAMING_SNAKE_CASE           =    []
												for dtype in self.dataclass_types:
																	SCREAMING_SNAKE_CASE           =    {f.name for f in dataclasses.fields(_UpperCamelCase ) if f.init}
																	SCREAMING_SNAKE_CASE           =    {k: v for k, v in args.items() if k in keys}
																	unused_keys.difference_update(inputs.keys() )
																	SCREAMING_SNAKE_CASE           =    dtype(**_UpperCamelCase )
																	outputs.append(_UpperCamelCase )
												if not allow_extra_keys and unused_keys:
																	raise ValueError(F"Some keys are not used by the HfArgumentParser: {sorted(_UpperCamelCase )}" )
												return tuple(_UpperCamelCase )




							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   bool = False )   ->			Tuple[DataClass, ...]:



												'''simple docstring'''

												with open(Path(_UpperCamelCase )		,							encoding="utf-8" ) as open_json_file:
																	SCREAMING_SNAKE_CASE           =    json.loads(open_json_file.read() )
												SCREAMING_SNAKE_CASE           =    self.parse_dict(_UpperCamelCase		,							allow_extra_keys=_UpperCamelCase )
												return tuple(_UpperCamelCase )





							def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   bool = False )   ->			Tuple[DataClass, ...]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.parse_dict(yaml.safe_load(Path(_UpperCamelCase ).read_text() )		,							allow_extra_keys=_UpperCamelCase )
												return tuple(_UpperCamelCase )





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest


class   lowercase      (       a       ):
							lowercase__  :    Dict									=							(UniPCMultistepScheduler,)
							lowercase__  :    Optional[int]									=							(("""num_inference_steps""", 25),)




							def 		__snake_case(  self				:   List[str]		,							**_UpperCamelCase				:   Tuple )   ->			Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    {
												    "num_train_timesteps": 1_000,
												    "beta_start": 0.0_0_0_1,
												    "beta_end": 0.0_2,
												    "beta_schedule": "linear",
												    "solver_order": 2,
												    "solver_type": "bh2",
												}

												config.update(**_UpperCamelCase )
												return config




							def 		__snake_case(  self				:   List[str]		,							_UpperCamelCase				:   Dict=0		,							**_UpperCamelCase				:   List[str] )   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    dict(self.forward_default_kwargs )
												SCREAMING_SNAKE_CASE           =    kwargs.pop("num_inference_steps"		,							_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    self.dummy_sample
												SCREAMING_SNAKE_CASE           =    0.1 * sample
												SCREAMING_SNAKE_CASE           =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

												for scheduler_class in self.scheduler_classes:
																	SCREAMING_SNAKE_CASE           =    self.get_scheduler_config(**_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    scheduler_class(**_UpperCamelCase )
																	scheduler.set_timesteps(_UpperCamelCase )
																	# copy over dummy past residuals
																	SCREAMING_SNAKE_CASE           =    dummy_past_residuals[: scheduler.config.solver_order]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    scheduler_class.from_pretrained(_UpperCamelCase )
																						new_scheduler.set_timesteps(_UpperCamelCase )
																						# copy over dummy past residuals
																						SCREAMING_SNAKE_CASE           =    dummy_past_residuals[: new_scheduler.config.solver_order]

																	SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    sample, sample
																	for t in range(_UpperCamelCase		,							time_step + scheduler.config.solver_order + 1 ):
																						SCREAMING_SNAKE_CASE           =    scheduler.step(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample
																						SCREAMING_SNAKE_CASE           =    new_scheduler.step(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample

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




							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   Union[str, Any]=0		,							**_UpperCamelCase				:   Dict )   ->			Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    dict(self.forward_default_kwargs )
												SCREAMING_SNAKE_CASE           =    kwargs.pop("num_inference_steps"		,							_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    self.dummy_sample
												SCREAMING_SNAKE_CASE           =    0.1 * sample
												SCREAMING_SNAKE_CASE           =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

												for scheduler_class in self.scheduler_classes:
																	SCREAMING_SNAKE_CASE           =    self.get_scheduler_config()
																	SCREAMING_SNAKE_CASE           =    scheduler_class(**_UpperCamelCase )
																	scheduler.set_timesteps(_UpperCamelCase )

																	# copy over dummy past residuals (must be after setting timesteps)
																	SCREAMING_SNAKE_CASE           =    dummy_past_residuals[: scheduler.config.solver_order]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    scheduler_class.from_pretrained(_UpperCamelCase )
																						# copy over dummy past residuals
																						new_scheduler.set_timesteps(_UpperCamelCase )

																						# copy over dummy past residual (must be after setting timesteps)
																						SCREAMING_SNAKE_CASE           =    dummy_past_residuals[: new_scheduler.config.solver_order]

																	SCREAMING_SNAKE_CASE           =    scheduler.step(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample
																	SCREAMING_SNAKE_CASE           =    new_scheduler.step(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample

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




							def 		__snake_case(  self				:   List[str]		,							_UpperCamelCase				:   Tuple=None		,							**_UpperCamelCase				:   List[Any] )   ->			str:



												'''simple docstring'''

												if scheduler is None:
																	SCREAMING_SNAKE_CASE           =    self.scheduler_classes[0]
																	SCREAMING_SNAKE_CASE           =    self.get_scheduler_config(**_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    scheduler_class(**_UpperCamelCase )

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

												SCREAMING_SNAKE_CASE           =    10
												SCREAMING_SNAKE_CASE           =    self.dummy_model()
												SCREAMING_SNAKE_CASE           =    self.dummy_sample_deter
												scheduler.set_timesteps(_UpperCamelCase )

												for i, t in enumerate(scheduler.timesteps ):
																	SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    scheduler.step(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase ).prev_sample

												return sample




							def 		__snake_case(  self				:   int )   ->			Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    dict(self.forward_default_kwargs )

												SCREAMING_SNAKE_CASE           =    kwargs.pop("num_inference_steps"		,							_UpperCamelCase )

												for scheduler_class in self.scheduler_classes:
																	SCREAMING_SNAKE_CASE           =    self.get_scheduler_config()
																	SCREAMING_SNAKE_CASE           =    scheduler_class(**_UpperCamelCase )

																	SCREAMING_SNAKE_CASE           =    self.dummy_sample
																	SCREAMING_SNAKE_CASE           =    0.1 * sample

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

																	# copy over dummy past residuals (must be done after set_timesteps)
																	SCREAMING_SNAKE_CASE           =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
																	SCREAMING_SNAKE_CASE           =    dummy_past_residuals[: scheduler.config.solver_order]

																	SCREAMING_SNAKE_CASE           =    scheduler.timesteps[5]
																	SCREAMING_SNAKE_CASE           =    scheduler.timesteps[6]

																	SCREAMING_SNAKE_CASE           =    scheduler.step(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample
																	SCREAMING_SNAKE_CASE           =    scheduler.step(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample

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




							def 		__snake_case(  self				:   str )   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    UniPCMultistepScheduler(**self.get_scheduler_config() )
												SCREAMING_SNAKE_CASE           =    self.full_loop(scheduler=_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    torch.mean(torch.abs(_UpperCamelCase ) )

												assert abs(result_mean.item() - 0.2_4_6_4 ) < 1e-3

												SCREAMING_SNAKE_CASE           =    DPMSolverSinglestepScheduler.from_config(scheduler.config )
												SCREAMING_SNAKE_CASE           =    DEISMultistepScheduler.from_config(scheduler.config )
												SCREAMING_SNAKE_CASE           =    DPMSolverMultistepScheduler.from_config(scheduler.config )
												SCREAMING_SNAKE_CASE           =    UniPCMultistepScheduler.from_config(scheduler.config )

												SCREAMING_SNAKE_CASE           =    self.full_loop(scheduler=_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    torch.mean(torch.abs(_UpperCamelCase ) )

												assert abs(result_mean.item() - 0.2_4_6_4 ) < 1e-3




							def 		__snake_case(  self				:   Optional[int] )   ->			List[Any]:



												'''simple docstring'''

												for timesteps in [25, 50, 100, 999, 1_000]:
																	self.check_over_configs(num_train_timesteps=_UpperCamelCase )




							def 		__snake_case(  self				:   Tuple )   ->			Union[str, Any]:



												'''simple docstring'''

												self.check_over_configs(thresholding=_UpperCamelCase )
												for order in [1, 2, 3]:
																	for solver_type in ["bh1", "bh2"]:
																						for threshold in [0.5, 1.0, 2.0]:
																											for prediction_type in ["epsilon", "sample"]:
																																self.check_over_configs(
																																    thresholding=_UpperCamelCase		,							prediction_type=_UpperCamelCase		,							sample_max_value=_UpperCamelCase		,							solver_order=_UpperCamelCase		,							solver_type=_UpperCamelCase		,							)




							def 		__snake_case(  self				:   Tuple )   ->			int:



												'''simple docstring'''

												for prediction_type in ["epsilon", "v_prediction"]:
																	self.check_over_configs(prediction_type=_UpperCamelCase )




							def 		__snake_case(  self				:   Dict )   ->			int:



												'''simple docstring'''

												for solver_type in ["bh1", "bh2"]:
																	for order in [1, 2, 3]:
																						for prediction_type in ["epsilon", "sample"]:
																											self.check_over_configs(
																											    solver_order=_UpperCamelCase		,							solver_type=_UpperCamelCase		,							prediction_type=_UpperCamelCase		,							)
																											SCREAMING_SNAKE_CASE           =    self.full_loop(
																											    solver_order=_UpperCamelCase		,							solver_type=_UpperCamelCase		,							prediction_type=_UpperCamelCase		,							)
																											assert not torch.isnan(_UpperCamelCase ).any(), "Samples have nan numbers"




							def 		__snake_case(  self				:   List[str] )   ->			Tuple:



												'''simple docstring'''

												self.check_over_configs(lower_order_final=_UpperCamelCase )
												self.check_over_configs(lower_order_final=_UpperCamelCase )




							def 		__snake_case(  self				:   Optional[int] )   ->			List[Any]:



												'''simple docstring'''

												for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
																	self.check_over_forward(num_inference_steps=_UpperCamelCase		,							time_step=0 )




							def 		__snake_case(  self				:   Tuple )   ->			Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.full_loop()
												SCREAMING_SNAKE_CASE           =    torch.mean(torch.abs(_UpperCamelCase ) )

												assert abs(result_mean.item() - 0.2_4_6_4 ) < 1e-3




							def 		__snake_case(  self				:   List[str] )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.full_loop(prediction_type="v_prediction" )
												SCREAMING_SNAKE_CASE           =    torch.mean(torch.abs(_UpperCamelCase ) )

												assert abs(result_mean.item() - 0.1_0_1_4 ) < 1e-3




							def 		__snake_case(  self				:   int )   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE           =    self.get_scheduler_config(thresholding=_UpperCamelCase		,							dynamic_thresholding_ratio=0 )
												SCREAMING_SNAKE_CASE           =    scheduler_class(**_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    10
												SCREAMING_SNAKE_CASE           =    self.dummy_model()
												SCREAMING_SNAKE_CASE           =    self.dummy_sample_deter.half()
												scheduler.set_timesteps(_UpperCamelCase )

												for i, t in enumerate(scheduler.timesteps ):
																	SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    scheduler.step(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase ).prev_sample

												assert sample.dtype == torch.floataa





							def 		__snake_case(  self				:   List[str]		,							**_UpperCamelCase				:   Dict )   ->			Union[str, Any]:



												'''simple docstring'''

												for scheduler_class in self.scheduler_classes:
																	SCREAMING_SNAKE_CASE           =    self.get_scheduler_config(**_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    scheduler_class(**_UpperCamelCase )

																	scheduler.set_timesteps(scheduler.config.num_train_timesteps )
																	assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps




