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__									=    'hf-internal-testing/tiny-stable-diffusion-pipe'
				lowerCamelCase__									=    1
				lowerCamelCase__									=    7_7
				lowerCamelCase__									=    3_2
				lowerCamelCase__									=    4
				lowerCamelCase__									=    lowerCamelCase__									=    6_4
				lowerCamelCase__									=    torch.manual_seed(0					)

				def     A	(       self    :      Union[str, Any]    ,      _lowercase    :      Union[str, Any]=0.9999       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						UNetaDConditionModel.from_pretrained(self.model_id    ,      subfolder="""unet"""       )
										UpperCAmelCase__   		=						unet.to(_lowercase       )
										UpperCAmelCase__   		=						EMAModel(unet.parameters()    ,      decay=_lowercase    ,      model_cls=_lowercase    ,      model_config=unet.config       )
										return unet, ema_unet

				def     A	(       self    :      Optional[int]       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						torch.randn(
										    self.batch_size    ,      self.num_in_channels    ,      self.latent_height    ,      self.latent_width    ,      generator=self.generator       ).to(_lowercase       )
										UpperCAmelCase__   		=						torch.randint(0    ,      1_0_0_0    ,      size=(self.batch_size,)    ,      generator=self.generator       ).to(_lowercase       )
										UpperCAmelCase__   		=						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     A	(       self    :      Tuple    ,      _lowercase    :      Tuple       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						{}
										for k, param in unet.state_dict().items():
																UpperCAmelCase__   		=						torch.randn_like(_lowercase       ) + (param * torch.randn_like(_lowercase       ))
																updated_state_dict.update({k: updated_param}       )
										unet.load_state_dict(_lowercase       )
										return unet

				def     A	(       self    :      Any       ):


										'''simple docstring'''
										UpperCAmelCase__		, UpperCAmelCase__   		=						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[Any]       ):


										'''simple docstring'''
										UpperCAmelCase__		, UpperCAmelCase__   		=						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__   		=						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     A	(       self    :      List[Any]       ):


										'''simple docstring'''
										UpperCAmelCase__		, UpperCAmelCase__   		=						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__   		=						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__   		=						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     A	(       self    :      Union[str, Any]       ):


										'''simple docstring'''
										UpperCAmelCase__		, UpperCAmelCase__   		=						self.get_models()

										# First backprop + EMA
										UpperCAmelCase__   		=						self.simulate_backprop(_lowercase       )
										ema_unet.step(unet_step_one.parameters()       )
										UpperCAmelCase__   		=						ema_unet.shadow_params

										# Second backprop + EMA
										UpperCAmelCase__   		=						self.simulate_backprop(_lowercase       )
										ema_unet.step(unet_step_two.parameters()       )
										UpperCAmelCase__   		=						ema_unet.shadow_params

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

				def     A	(       self    :      Dict       ):


										'''simple docstring'''
										UpperCAmelCase__		, UpperCAmelCase__   		=						self.get_models(decay=0.0       )
										UpperCAmelCase__   		=						self.simulate_backprop(_lowercase       )
										ema_unet.step(unet_step_one.parameters()       )
										UpperCAmelCase__   		=						ema_unet.shadow_params

										UpperCAmelCase__   		=						self.simulate_backprop(_lowercase       )
										ema_unet.step(unet_step_two.parameters()       )
										UpperCAmelCase__   		=						ema_unet.shadow_params

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

				@skip_mps
				def     A	(       self    :      Optional[Any]       ):


										'''simple docstring'''
										UpperCAmelCase__		, UpperCAmelCase__   		=						self.get_models()
										UpperCAmelCase__		, UpperCAmelCase__		, UpperCAmelCase__   		=						self.get_dummy_inputs()

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

										# Since no EMA step has been performed the outputs should match.
										UpperCAmelCase__   		=						unet(_lowercase    ,      _lowercase    ,      _lowercase       ).sample
										UpperCAmelCase__   		=						loaded_unet(_lowercase    ,      _lowercase    ,      _lowercase       ).sample

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


"""simple docstring"""







import safs


class      _lowercase						(				safs.SaFileSystem					):
				pass

