




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     lowercase (      unittest.TestCase):
				__lowerCAmelCase      :      Optional[int]					    =				"""hf-internal-testing/tiny-stable-diffusion-pipe"""
				__lowerCAmelCase      :      List[str]					    =				1
				__lowerCAmelCase      :      Any					    =				77
				__lowerCAmelCase      :      Tuple					    =				32
				__lowerCAmelCase      :      int					    =				4
				__lowerCAmelCase      :      Any					    =				64
				__lowerCAmelCase      :      Optional[int]					    =				torch.manual_seed(0)







				def    a_					(							self				:   Tuple ,       _lowerCamelCase				:   Union[str, Any]=0.9999    ):



					"""simple docstring"""

					A_					:			Optional[int]						=						UNetaDConditionModel.from_pretrained(self.model_id ,       subfolder='''unet'''    )
					A_					:			int						=						unet.to(_lowerCamelCase    )
					A_					:			Optional[int]						=						EMAModel(unet.parameters() ,       decay=_lowerCamelCase ,       model_cls=_lowerCamelCase ,       model_config=unet.config    )
					return unet, ema_unet







				def    a_					(							self				:   Optional[Any]    ):



					"""simple docstring"""

					A_					:			Union[str, Any]						=						torch.randn(
					    self.batch_size ,       self.num_in_channels ,       self.latent_height ,       self.latent_width ,       generator=self.generator    ).to(_lowerCamelCase    )
					A_					:			Union[str, Any]						=						torch.randint(0 ,       10_00 ,       size=(self.batch_size,) ,       generator=self.generator    ).to(_lowerCamelCase    )
					A_					:			Optional[int]						=						torch.randn(
					    self.batch_size ,       self.prompt_length ,       self.text_encoder_hidden_dim ,       generator=self.generator    ).to(_lowerCamelCase    )
					return noisy_latents, timesteps, encoder_hidden_states







				def    a_					(							self				:   Optional[Any] ,       _lowerCamelCase				:   List[str]    ):



					"""simple docstring"""

					A_					:			Any						=						{}
					for k, param in unet.state_dict().items():
						A_					:			Dict						=						torch.randn_like(_lowerCamelCase    ) + (param * torch.randn_like(_lowerCamelCase    ))
						updated_state_dict.update({k: updated_param}    )
					unet.load_state_dict(_lowerCamelCase    )
					return unet







				def    a_					(							self				:   Tuple    ):



					"""simple docstring"""

					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    a_					(							self				:   Optional[int]    ):



					"""simple docstring"""

					A_					:			str						=						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_					:			List[Any]						=						list(unet.parameters()    )
					for s_param, param in zip(ema_unet.shadow_params ,       _lowerCamelCase    ):
						assert torch.allclose(_lowerCamelCase ,       _lowerCamelCase    )

					# 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 ,       _lowerCamelCase    ):
						assert torch.allclose(_lowerCamelCase ,       _lowerCamelCase    )







				def    a_					(							self				:   int    ):



					"""simple docstring"""

					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_					:			List[Any]						=						self.simulate_backprop(_lowerCamelCase    )

					# 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 ,       _lowerCamelCase    ):
						assert ~torch.allclose(_lowerCamelCase ,       _lowerCamelCase    )

					# 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 ,       _lowerCamelCase    ):
						assert ~torch.allclose(_lowerCamelCase ,       _lowerCamelCase    )







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



					"""simple docstring"""

					A_					:			Union[str, Any]						=						self.get_models()

					# First backprop + EMA
					A_					:			Any						=						self.simulate_backprop(_lowerCamelCase    )
					ema_unet.step(unet_step_one.parameters()    )
					A_					:			Any						=						ema_unet.shadow_params

					# Second backprop + EMA
					A_					:			Tuple						=						self.simulate_backprop(_lowerCamelCase    )
					ema_unet.step(unet_step_two.parameters()    )
					A_					:			Tuple						=						ema_unet.shadow_params

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







				def    a_					(							self				:   int    ):



					"""simple docstring"""

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

					A_					:			Dict						=						self.simulate_backprop(_lowerCamelCase    )
					ema_unet.step(unet_step_two.parameters()    )
					A_					:			Optional[Any]						=						ema_unet.shadow_params

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







				@skip_mps
				def    a_					(							self				:   Dict    ):



					"""simple docstring"""

					A_					:			List[Any]						=						self.get_models()
					A_					:			Optional[int]						=						self.get_dummy_inputs()

					with tempfile.TemporaryDirectory() as tmpdir:
						ema_unet.save_pretrained(_lowerCamelCase    )
						A_					:			int						=						UNetaDConditionModel.from_pretrained(_lowerCamelCase ,       model_cls=_lowerCamelCase    )
						A_					:			Tuple						=						loaded_unet.to(unet.device    )

					# Since no EMA step has been performed the outputs should match.
					A_					:			Tuple						=						unet(_lowerCamelCase ,       _lowerCamelCase ,       _lowerCamelCase    ).sample
					A_					:			Tuple						=						loaded_unet(_lowerCamelCase ,       _lowerCamelCase ,       _lowerCamelCase    ).sample

					assert torch.allclose(_lowerCamelCase ,       _lowerCamelCase ,       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


_lowerCamelCase					:     Optional[int]				       =    set()
_lowerCamelCase					:     Union[str, Any]				       =    get_logger(__name__)

def 						lowercase_ (						_UpperCAmelCase = None		):




	"""simple docstring"""






	def decorator(_UpperCAmelCase		):
		global _emitted_deprecation_warnings

		if inspect.isclass(_UpperCAmelCase		):
			A_					:			Union[str, Any]						=						deprecated_class_or_function.__init__
			A_					:			int						=						deprecated_class_or_function.__name__
		else:
			A_					:			Union[str, Any]						=						deprecated_class_or_function
			A_					:			List[Any]						=						deprecated_function.__name__
			# Support deprecating __init__ class method: class name instead
			A_					:			List[Any]						=						name if name != '''__init__''' else deprecated_function.__qualname__.split('''.'''		)[-2]

		A_					:			str						=						(
		    f"""{name} is deprecated and will be removed in the next major version of datasets.""" + f""" {help_message}"""
		    if help_message
		    else ''''''
		)

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

		A_					:			Any						=						'''deprecated'''

		if inspect.isclass(_UpperCAmelCase		):
			A_					:			List[Any]						=						wrapper
			return deprecated_class_or_function
		else:
			return wrapper

	return decorator

class     lowercase (      enum.EnumMeta):







				def __getattribute__(							cls				:   Any ,       _lowerCamelCase				:   List[Any]    ):



					"""simple docstring"""

					A_					:			str						=						super().__getattribute__(_lowerCamelCase    )
					if isinstance(_lowerCamelCase ,       enum.Enum    ) and obj._on_access:
						obj._on_access()
					return obj







				def __getitem__(							cls				:   Any ,       _lowerCamelCase				:   Union[str, Any]    ):



					"""simple docstring"""

					A_					:			Optional[Any]						=						super().__getitem__(_lowerCamelCase    )
					if member._on_access:
						member._on_access()
					return member







				def __call__(							cls				:   str ,       _lowerCamelCase				:   str ,       _lowerCamelCase				:   List[Any]=None ,       *, _lowerCamelCase				:   int=None ,       _lowerCamelCase				:   str=None ,       _lowerCamelCase				:   Dict=None ,       _lowerCamelCase				:   Tuple=1    ):



					"""simple docstring"""

					A_					:			Dict						=						super().__call__(_lowerCamelCase ,       _lowerCamelCase ,       module=_lowerCamelCase ,       qualname=_lowerCamelCase ,       type=_lowerCamelCase ,       start=_lowerCamelCase    )
					if isinstance(_lowerCamelCase ,       enum.Enum    ) and obj._on_access:
						obj._on_access()
					return obj

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







				def __new__(							cls				:   Optional[Any] ,       _lowerCamelCase				:   int    ):



					"""simple docstring"""

					A_					:			Tuple						=						object.__new__(cls    )
					A_					:			Union[str, Any]						=						value
					A_					:			Union[str, Any]						=						member.deprecate
					return member







				@property
				def    a_					(							self				:   Tuple    ):



					"""simple docstring"""

					return ""







				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					A_					:			Optional[int]						=						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 ,       _lowerCamelCase ,       stacklevel=3 ,       )





