"""simple docstring"""



import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()
class 	__UpperCAmelCase						(unittest.TestCase    ):







			'''simple docstring'''
			_UpperCamelCase     :	List[str]				    =							'hf-internal-testing/tiny-stable-diffusion-pipe'
			_UpperCamelCase     :	Any				    =							1
			_UpperCamelCase     :	List[Any]				    =							77
			_UpperCamelCase     :	str				    =							32
			_UpperCamelCase     :	str				    =							4
			_UpperCamelCase     :	Union[str, Any]				    =							64
			_UpperCamelCase     :	List[str]				    =							torch.manual_seed(0    )





			def 		lowerCamelCase				( self      ,			snake_case_=0.99_99       ):







										'''simple docstring'''


										A__					:    Tuple														= UNetaDConditionModel.from_pretrained(self.model_id      ,			subfolder="""unet"""       )
										A__					:    Optional[int]														= unet.to(__UpperCamelCase       )
										A__					:    List[str]														= EMAModel(unet.parameters()      ,			decay=__UpperCamelCase      ,			model_cls=__UpperCamelCase      ,			model_config=unet.config       )
										return unet, ema_unet





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Any														= torch.randn(
										    self.batch_size      ,			self.num_in_channels      ,			self.latent_height      ,			self.latent_width      ,			generator=self.generator       ).to(__UpperCamelCase       )
										A__					:    Union[str, Any]														= torch.randint(0      ,			1_000      ,			size=(self.batch_size,)      ,			generator=self.generator       ).to(__UpperCamelCase       )
										A__					:    List[str]														= torch.randn(
										    self.batch_size      ,			self.prompt_length      ,			self.text_encoder_hidden_dim      ,			generator=self.generator       ).to(__UpperCamelCase       )
										return noisy_latents, timesteps, encoder_hidden_states





			def 		lowerCamelCase				( self      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    List[str]														= {}
										for k, param in unet.state_dict().items():
																	A__					:    List[str]														= torch.randn_like(__UpperCamelCase       ) + (param * torch.randn_like(__UpperCamelCase       ))
																	updated_state_dict.update({k: updated_param}       )
										unet.load_state_dict(__UpperCamelCase       )
										return unet





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__							,       A__					:    Tuple														= self.get_models()
										# Take the first (hypothetical) EMA step.
										ema_unet.step(unet.parameters()       )
										assert ema_unet.optimization_step == 1

										# Take two more.
										for _ in range(2       ):
																	ema_unet.step(unet.parameters()       )
										assert ema_unet.optimization_step == 3





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__							,       A__					:    Tuple														= self.get_models()
										# Since the `unet` is not being updated (i.e., backprop'd)
										# there won't be any difference between the `params` of `unet`
										# and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
										ema_unet.step(unet.parameters()       )
										A__					:    Optional[Any]														= list(unet.parameters()       )
										for s_param, param in zip(ema_unet.shadow_params      ,			__UpperCamelCase       ):
																	assert torch.allclose(__UpperCamelCase      ,			__UpperCamelCase       )

										# The above holds true even if we call `ema.step()` multiple times since
										# `unet` params are still not being updated.
										for _ in range(4       ):
																	ema_unet.step(unet.parameters()       )
										for s_param, param in zip(ema_unet.shadow_params      ,			__UpperCamelCase       ):
																	assert torch.allclose(__UpperCamelCase      ,			__UpperCamelCase       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__							,       A__					:    Optional[Any]														= self.get_models()
										# Here we simulate the parameter updates for `unet`. Since there might
										# be some parameters which are initialized to zero we take extra care to
										# initialize their values to something non-zero before the multiplication.
										A__					:    str														= self.simulate_backprop(__UpperCamelCase       )

										# Take the EMA step.
										ema_unet.step(unet_pseudo_updated_step_one.parameters()       )

										# Now the EMA'd parameters won't be equal to the original model parameters.
										A__					:    int														= list(unet_pseudo_updated_step_one.parameters()       )
										for s_param, param in zip(ema_unet.shadow_params      ,			__UpperCamelCase       ):
																	assert ~torch.allclose(__UpperCamelCase      ,			__UpperCamelCase       )

										# Ensure this is the case when we take multiple EMA steps.
										for _ in range(4       ):
																	ema_unet.step(unet.parameters()       )
										for s_param, param in zip(ema_unet.shadow_params      ,			__UpperCamelCase       ):
																	assert ~torch.allclose(__UpperCamelCase      ,			__UpperCamelCase       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__							,       A__					:    List[Any]														= self.get_models()

										# First backprop + EMA
										A__					:    Any														= self.simulate_backprop(__UpperCamelCase       )
										ema_unet.step(unet_step_one.parameters()       )
										A__					:    Dict														= ema_unet.shadow_params

										# Second backprop + EMA
										A__					:    Dict														= self.simulate_backprop(__UpperCamelCase       )
										ema_unet.step(unet_step_two.parameters()       )
										A__					:    Tuple														= ema_unet.shadow_params

										for step_one, step_two in zip(__UpperCamelCase      ,			__UpperCamelCase       ):
																	assert ~torch.allclose(__UpperCamelCase      ,			__UpperCamelCase       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__							,       A__					:    List[str]														= self.get_models(decay=0.0       )
										A__					:    Union[str, Any]														= self.simulate_backprop(__UpperCamelCase       )
										ema_unet.step(unet_step_one.parameters()       )
										A__					:    Optional[Any]														= ema_unet.shadow_params

										A__					:    Optional[Any]														= self.simulate_backprop(__UpperCamelCase       )
										ema_unet.step(unet_step_two.parameters()       )
										A__					:    List[str]														= ema_unet.shadow_params

										for step_one, step_two in zip(__UpperCamelCase      ,			__UpperCamelCase       ):
																	assert torch.allclose(__UpperCamelCase      ,			__UpperCamelCase       )





			@skip_mps
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__							,       A__					:    Optional[int]														= self.get_models()
										A__							,       A__							,       A__					:    Union[str, Any]														= self.get_dummy_inputs()

										with tempfile.TemporaryDirectory() as tmpdir:
																	ema_unet.save_pretrained(__UpperCamelCase       )
																	A__					:    str														= UNetaDConditionModel.from_pretrained(__UpperCamelCase      ,			model_cls=__UpperCamelCase       )
																	A__					:    Union[str, Any]														= loaded_unet.to(unet.device       )

										# Since no EMA step has been performed the outputs should match.
										A__					:    str														= unet(__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase       ).sample
										A__					:    List[str]														= loaded_unet(__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase       ).sample

										assert torch.allclose(__UpperCamelCase      ,			__UpperCamelCase      ,			atol=1E-4       )






"""simple docstring"""



import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


_UpperCamelCase         =		set()
_UpperCamelCase         =		get_logger(__name__)

def    _A(		lowerCAmelCase = None       ):

							def decorator(lowerCAmelCase       ):
														global _emitted_deprecation_warnings

														if inspect.isclass(lowerCAmelCase       ):
																					A__					:    List[Any]														= deprecated_class_or_function.__init__
																					A__					:    Any														= deprecated_class_or_function.__name__
														else:
																					A__					:    Dict														= deprecated_class_or_function
																					A__					:    Optional[int]														= deprecated_function.__name__
																					# Support deprecating __init__ class method: class name instead
																					A__					:    List[str]														= name if name != """__init__""" else deprecated_function.__qualname__.split("""."""       )[-2]

														A__					:    Union[str, Any]														= (
														    F'''{name} is deprecated and will be removed in the next major version of datasets.''' + F''' {help_message}'''
														    if help_message
														    else """"""
														)

														@wraps(lowerCAmelCase       )
														def wrapper(*lowerCAmelCase ,     **lowerCAmelCase       ):
																					A__					:    Tuple														= hash(lowerCAmelCase       )
																					if func_hash not in _emitted_deprecation_warnings:
																												warnings.warn(lowerCAmelCase ,     category=lowerCAmelCase ,     stacklevel=2       )
																												_emitted_deprecation_warnings.add(lowerCAmelCase       )
																					return deprecated_function(*lowerCAmelCase ,     **lowerCAmelCase       )

														A__					:    int														= """deprecated"""

														if inspect.isclass(lowerCAmelCase       ):
																					A__					:    Union[str, Any]														= wrapper
																					return deprecated_class_or_function
														else:
																					return wrapper

							return decorator
class 	__UpperCAmelCase						(enum.EnumMeta    ):







			'''simple docstring'''





			def __getattribute__( cls      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    Union[str, Any]														= super().__getattribute__(snake_case_       )
										if isinstance(snake_case_      ,			enum.Enum       ) and obj._on_access:
																	obj._on_access()
										return obj





			def __getitem__( cls      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    Tuple														= super().__getitem__(snake_case_       )
										if member._on_access:
																	member._on_access()
										return member





			def __call__( cls      ,			snake_case_      ,			snake_case_=None      ,			*, snake_case_=None      ,			snake_case_=None      ,			snake_case_=None      ,			snake_case_=1       ):







										'''simple docstring'''


										A__					:    List[Any]														= super().__call__(snake_case_      ,			snake_case_      ,			module=snake_case_      ,			qualname=snake_case_      ,			type=snake_case_      ,			start=snake_case_       )
										if isinstance(snake_case_      ,			enum.Enum       ) and obj._on_access:
																	obj._on_access()
										return obj





class 	__UpperCAmelCase						(enum.Enum       ,      metaclass=__A    ):







			'''simple docstring'''





			def __new__( cls      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    Tuple														= object.__new__(cls       )
										A__					:    int														= value
										A__					:    Optional[int]														= member.deprecate
										return member





			@property
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										return ""





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Tuple														= F''' {self.help_message}''' if self.help_message else """"""
										warnings.warn(
										    F'''\'{self.__objclass__.__name__}\' is deprecated and will be removed in the next major version of datasets.'''
										    + help_message      ,			snake_case_      ,			stacklevel=3      ,			)





