






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 					a					(				unittest.TestCase     ):



      """simple docstring"""


      a						:				int							 =			'hf-internal-testing/tiny-stable-diffusion-pipe'
      a						:				Union[str, Any]							 =			1
      a						:				Optional[int]							 =			77
      a						:				str							 =			32
      a						:				int							 =			4
      a						:				List[Any]							 =			64
      a						:				Optional[int]							 =			torch.manual_seed(0     )
      def 			UpperCAmelCase   (   self      :    int	,   __lowercase      :    Any=0.9_999      )							->   Dict:
        __UpperCAmelCase							:			int     =						UNetaDConditionModel.from_pretrained(self.model_id	,   subfolder="""unet"""      )
        __UpperCAmelCase							:			Optional[Any]     =						unet.to(__lowercase      )
        __UpperCAmelCase							:			Dict     =						EMAModel(unet.parameters()	,   decay=__lowercase	,   model_cls=__lowercase	,   model_config=unet.config      )
        return unet, ema_unet
      def 			UpperCAmelCase   (   self      :    Union[str, Any]      )							->   List[str]:
        __UpperCAmelCase							:			List[str]     =						torch.randn(
            self.batch_size	,   self.num_in_channels	,   self.latent_height	,   self.latent_width	,   generator=self.generator      ).to(__lowercase      )
        __UpperCAmelCase							:			Dict     =						torch.randint(0	,   1000	,   size=(self.batch_size,)	,   generator=self.generator      ).to(__lowercase      )
        __UpperCAmelCase							:			List[Any]     =						torch.randn(
            self.batch_size	,   self.prompt_length	,   self.text_encoder_hidden_dim	,   generator=self.generator      ).to(__lowercase      )
        return noisy_latents, timesteps, encoder_hidden_states
      def 			UpperCAmelCase   (   self      :    Dict	,   __lowercase      :    Optional[int]      )							->   Union[str, Any]:
        __UpperCAmelCase							:			str     =						{}
        for k, param in unet.state_dict().items():
          __UpperCAmelCase							:			int     =						torch.randn_like(__lowercase      ) + (param * torch.randn_like(__lowercase      ))
          updated_state_dict.update({k: updated_param}      )
        unet.load_state_dict(__lowercase      )
        return unet
      def 			UpperCAmelCase   (   self      :    int      )							->   List[Any]:
        __UpperCAmelCase   ,				__UpperCAmelCase							:			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 			UpperCAmelCase   (   self      :    int      )							->   List[Any]:
        __UpperCAmelCase   ,				__UpperCAmelCase							:			List[Any]     =						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()      )
        __UpperCAmelCase							:			Dict     =						list(unet.parameters()      )
        for s_param, param in zip(ema_unet.shadow_params	,   __lowercase      ):
          assert torch.allclose(__lowercase	,   __lowercase      )

        # 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	,   __lowercase      ):
          assert torch.allclose(__lowercase	,   __lowercase      )
      def 			UpperCAmelCase   (   self      :    str      )							->   int:
        __UpperCAmelCase   ,				__UpperCAmelCase							:			str     =						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.
        __UpperCAmelCase							:			Tuple     =						self.simulate_backprop(__lowercase      )

        # 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.
        __UpperCAmelCase							:			Dict     =						list(unet_pseudo_updated_step_one.parameters()      )
        for s_param, param in zip(ema_unet.shadow_params	,   __lowercase      ):
          assert ~torch.allclose(__lowercase	,   __lowercase      )

        # 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	,   __lowercase      ):
          assert ~torch.allclose(__lowercase	,   __lowercase      )
      def 			UpperCAmelCase   (   self      :    Any      )							->   str:
        # If we call EMA step after a backpropagation consecutively for two times,
        # the shadow params from those two steps should be different.
        __UpperCAmelCase   ,				__UpperCAmelCase							:			int     =						self.get_models()

        # First backprop + EMA
        __UpperCAmelCase							:			Optional[Any]     =						self.simulate_backprop(__lowercase      )
        ema_unet.step(unet_step_one.parameters()      )
        __UpperCAmelCase							:			Union[str, Any]     =						ema_unet.shadow_params

        # Second backprop + EMA
        __UpperCAmelCase							:			Any     =						self.simulate_backprop(__lowercase      )
        ema_unet.step(unet_step_two.parameters()      )
        __UpperCAmelCase							:			List[str]     =						ema_unet.shadow_params

        for step_one, step_two in zip(__lowercase	,   __lowercase      ):
          assert ~torch.allclose(__lowercase	,   __lowercase      )
      def 			UpperCAmelCase   (   self      :    str      )							->   Tuple:
        # If there's no decay even if there are backprops, EMA steps
        # won't take any effect i.e., the shadow params would remain the
        # same.
        __UpperCAmelCase   ,				__UpperCAmelCase							:			str     =						self.get_models(decay=0.0      )
        __UpperCAmelCase							:			Union[str, Any]     =						self.simulate_backprop(__lowercase      )
        ema_unet.step(unet_step_one.parameters()      )
        __UpperCAmelCase							:			int     =						ema_unet.shadow_params

        __UpperCAmelCase							:			Union[str, Any]     =						self.simulate_backprop(__lowercase      )
        ema_unet.step(unet_step_two.parameters()      )
        __UpperCAmelCase							:			List[Any]     =						ema_unet.shadow_params

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




      @skip_mps
      def 			UpperCAmelCase   (   self      :    List[str]      )							->   Any:
        __UpperCAmelCase   ,				__UpperCAmelCase							:			Optional[int]     =						self.get_models()
        __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase							:			int     =						self.get_dummy_inputs()

        with tempfile.TemporaryDirectory() as tmpdir:
          ema_unet.save_pretrained(__lowercase      )
          __UpperCAmelCase							:			str     =						UNetaDConditionModel.from_pretrained(__lowercase	,   model_cls=__lowercase      )
          __UpperCAmelCase							:			str     =						loaded_unet.to(unet.device      )

        # Since no EMA step has been performed the outputs should match.
        __UpperCAmelCase							:			Union[str, Any]     =						unet(__lowercase	,   __lowercase	,   __lowercase      ).sample
        __UpperCAmelCase							:			Dict     =						loaded_unet(__lowercase	,   __lowercase	,   __lowercase      ).sample

        assert torch.allclose(__lowercase	,   __lowercase	,   atol=1e-4      )










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

from .logging import get_logger


a	: Any      =				set()
a	: List[Any]      =				get_logger(__name__)






def        lowerCamelCase__   (					__lowerCamelCase    :  Optional[str] = None	):

  def decorator(__lowerCamelCase    :  Callable	):
    global _emitted_deprecation_warnings

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

    __UpperCAmelCase							:			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    :  List[str]						,	**__lowerCamelCase    :  Tuple	):
      __UpperCAmelCase							:			Optional[Any]     =						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	)

    __UpperCAmelCase							:			List[str]     =						"""deprecated"""

    if inspect.isclass(__lowerCamelCase	):
      __UpperCAmelCase							:			int     =						wrapper
      return deprecated_class_or_function
    else:
      return wrapper

  return decorator
class 					a					(				enum.EnumMeta     ):



      """simple docstring"""


      def __getattribute__(   cls      :    Optional[int]	,   __lowercase      :    List[Any]      )							->   str:
        __UpperCAmelCase							:			List[Any]     =						super().__getattribute__(__lowercase      )
        if isinstance(__lowercase	,   enum.Enum      ) and obj._on_access:
          obj._on_access()
        return obj
      def __getitem__(   cls      :    Union[str, Any]	,   __lowercase      :    Optional[int]      )							->   Optional[int]:
        __UpperCAmelCase							:			Union[str, Any]     =						super().__getitem__(__lowercase      )
        if member._on_access:
          member._on_access()
        return member




      def __call__(   cls      :    List[str]	,   __lowercase      :    Dict	,   __lowercase      :    List[Any]=None	,   *, __lowercase      :    Dict=None	,   __lowercase      :    Any=None	,   __lowercase      :    Optional[Any]=None	,   __lowercase      :    Dict=1      )							->   List[Any]:
        __UpperCAmelCase							:			Union[str, Any]     =						super().__call__(__lowercase	,   __lowercase	,   module=__lowercase	,   qualname=__lowercase	,   type=__lowercase	,   start=__lowercase      )
        if isinstance(__lowercase	,   enum.Enum      ) and obj._on_access:
          obj._on_access()
        return obj


class 					a					(				enum.Enum							, metaclass=lowercase__     ):



      """simple docstring"""


      def __new__(   cls      :    Dict	,   __lowercase      :    Tuple      )							->   Optional[Any]:
        __UpperCAmelCase							:			List[Any]     =						object.__new__(cls      )
        __UpperCAmelCase							:			Any     =						value
        __UpperCAmelCase							:			int     =						member.deprecate
        return member
      @property
      def 			UpperCAmelCase   (   self      :    Optional[Any]      )							->   Dict:
        return ""




      def 			UpperCAmelCase   (   self      :    str      )							->   Dict:
        __UpperCAmelCase							:			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	,   __lowercase	,   stacklevel=3	,   )


