

"""simple docstring"""


import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version






def        a__       (       *__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE = None   ,					__SCREAMING_SNAKE_CASE=True   ,					__SCREAMING_SNAKE_CASE=2					)      ->      List[str]:
		from .. import __version__

		__lowerCAmelCase:			Union[str, Any]        =     take_from
		__lowerCAmelCase:			List[Any]        =     ()
		if not isinstance(args[0]   ,					_SCREAMING_SNAKE_CASE					):
				__lowerCAmelCase:			Tuple        =     (args,)

		for attribute, version_name, message in args:
				if version.parse(version.parse(_SCREAMING_SNAKE_CASE					).base_version					) >= version.parse(_SCREAMING_SNAKE_CASE					):
						raise ValueError(
						    F"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\'"
						    F" version {__version__} is >= {version_name}"					)

				__lowerCAmelCase:			Dict        =     None
				if isinstance(_SCREAMING_SNAKE_CASE   ,					_SCREAMING_SNAKE_CASE					) and attribute in deprecated_kwargs:
						values += (deprecated_kwargs.pop(_SCREAMING_SNAKE_CASE					),)
						__lowerCAmelCase:			Optional[Any]        =     F"The `{attribute}` argument is deprecated and will be removed in version {version_name}."
				elif hasattr(_SCREAMING_SNAKE_CASE   ,					_SCREAMING_SNAKE_CASE					):
						values += (getattr(_SCREAMING_SNAKE_CASE   ,					_SCREAMING_SNAKE_CASE					),)
						__lowerCAmelCase:			str        =     F"The `{attribute}` attribute is deprecated and will be removed in version {version_name}."
				elif deprecated_kwargs is None:
						__lowerCAmelCase:			List[str]        =     F"`{attribute}` is deprecated and will be removed in version {version_name}."

				if warning is not None:
						__lowerCAmelCase:			Dict        =     warning + """ """ if standard_warn else """"""
						warnings.warn(warning + message   ,					_SCREAMING_SNAKE_CASE   ,					stacklevel=_SCREAMING_SNAKE_CASE					)

		if isinstance(_SCREAMING_SNAKE_CASE   ,					_SCREAMING_SNAKE_CASE					) and len(_SCREAMING_SNAKE_CASE					) > 0:
				__lowerCAmelCase:			List[str]        =     inspect.getouterframes(inspect.currentframe()					)[1]
				__lowerCAmelCase:			List[str]        =     call_frame.filename
				__lowerCAmelCase:			Optional[Any]        =     call_frame.lineno
				__lowerCAmelCase:			Any        =     call_frame.function
				__lowerCAmelCase:			Any        =     next(iter(deprecated_kwargs.items()					)					)
				raise TypeError(F"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"					)

		if len(_SCREAMING_SNAKE_CASE					) == 0:
				return
		elif len(_SCREAMING_SNAKE_CASE					) == 1:
				return values[0]
		return values



import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


SCREAMING_SNAKE_CASE		:     List[str]         =	False

class        UpperCamelCase						(				unittest.TestCase				):





						def        A_							(self			,     __UpperCamelCase=32						)				->				Optional[int]:
												set_seed(0						)
												UpperCamelCase_		:							Optional[Any]  =		UNetaDModel(sample_size=__UpperCamelCase			,     in_channels=3			,     out_channels=3						)
												UpperCamelCase_		:							Union[str, Any]  =		torch.optim.SGD(model.parameters()			,     lr=0.0_001						)
												return model, optimizer





						@slow
						def        A_							(self						)				->				str:
												UpperCamelCase_		:							str  =		"""cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
												UpperCamelCase_		:							Tuple  =		DDPMScheduler(
												    num_train_timesteps=1_000			,     beta_start=0.0_001			,     beta_end=0.02			,     beta_schedule="""linear"""			,     clip_sample=__UpperCamelCase			,     )
												UpperCamelCase_		:							List[Any]  =		DDIMScheduler(
												    num_train_timesteps=1_000			,     beta_start=0.0_001			,     beta_end=0.02			,     beta_schedule="""linear"""			,     clip_sample=__UpperCamelCase			,     )

												assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

												# shared batches for DDPM and DDIM
												set_seed(0						)
												UpperCamelCase_		:							Any  =		[torch.randn((4, 3, 32, 32)						).clip(-1			,     1						).to(__UpperCamelCase						) for _ in range(4						)]
												UpperCamelCase_		:							str  =		[torch.randn((4, 3, 32, 32)						).to(__UpperCamelCase						) for _ in range(4						)]
												UpperCamelCase_		:							Union[str, Any]  =		[torch.randint(0			,     1_000			,     (4,)						).long().to(__UpperCamelCase						) for _ in range(4						)]

												# train with a DDPM scheduler
												UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		self.get_model_optimizer(resolution=32						)
												model.train().to(__UpperCamelCase						)
												for i in range(4						):
																		optimizer.zero_grad()
																		UpperCamelCase_		:							Optional[Any]  =		ddpm_scheduler.add_noise(clean_images[i]			,     noise[i]			,     timesteps[i]						)
																		UpperCamelCase_		:							List[Any]  =		model(__UpperCamelCase			,     timesteps[i]						).sample
																		UpperCamelCase_		:							Dict  =		torch.nn.functional.mse_loss(__UpperCamelCase			,     noise[i]						)
																		loss.backward()
																		optimizer.step()
												del model, optimizer

												# recreate the model and optimizer, and retry with DDIM
												UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		self.get_model_optimizer(resolution=32						)
												model.train().to(__UpperCamelCase						)
												for i in range(4						):
																		optimizer.zero_grad()
																		UpperCamelCase_		:							str  =		ddim_scheduler.add_noise(clean_images[i]			,     noise[i]			,     timesteps[i]						)
																		UpperCamelCase_		:							Optional[int]  =		model(__UpperCamelCase			,     timesteps[i]						).sample
																		UpperCamelCase_		:							Union[str, Any]  =		torch.nn.functional.mse_loss(__UpperCamelCase			,     noise[i]						)
																		loss.backward()
																		optimizer.step()
												del model, optimizer

												self.assertTrue(torch.allclose(__UpperCamelCase			,     __UpperCamelCase			,     atol=1E-5						)						)
												self.assertTrue(torch.allclose(__UpperCamelCase			,     __UpperCamelCase			,     atol=1E-5						)						)






