
"""simple docstring"""

_A            = '\n# Diffusers installation\n! pip install diffusers transformers datasets accelerate\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/diffusers.git\n'

_A            = [{'type': 'code', 'content': INSTALL_CONTENT}]














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   SCREAMING_SNAKE_CASE (       unittest.TestCase    ):





							"""simple docstring"""

							A_       =    "hf-internal-testing/tiny-stable-diffusion-pipe"
							A_       =    1
							A_       =    77
							A_       =    32
							A_       =    4
							A_       =    A_       =    64
							A_       =    torch.manual_seed(0    )

							def 					__A       (     self:      List[Any]						,   __A:      List[Any]=0.9_999      )			-> Dict:
												_A									=       UNetaDConditionModel.from_pretrained(self.model_id						,   subfolder='''unet'''      )
												_A									=       unet.to(__A      )
												_A									=       EMAModel(unet.parameters()						,   decay=__A						,   model_cls=__A						,   model_config=unet.config      )
												return unet, ema_unet

							def 					__A       (     self:      Tuple      )			-> Union[str, Any]:
												_A									=       torch.randn(
												    self.batch_size						,   self.num_in_channels						,   self.latent_height						,   self.latent_width						,   generator=self.generator      ).to(__A      )
												_A									=       torch.randint(0						,   10_00						,   size=(self.batch_size,)						,   generator=self.generator      ).to(__A      )
												_A									=       torch.randn(
												    self.batch_size						,   self.prompt_length						,   self.text_encoder_hidden_dim						,   generator=self.generator      ).to(__A      )
												return noisy_latents, timesteps, encoder_hidden_states

							def 					__A       (     self:      Tuple						,   __A:      Optional[int]      )			-> Optional[Any]:
												_A									=       {}
												for k, param in unet.state_dict().items():
																	_A									=       torch.randn_like(__A      ) + (param * torch.randn_like(__A      ))
																	updated_state_dict.update({k: updated_param}      )
												unet.load_state_dict(__A      )
												return unet

							def 					__A       (     self:      Any      )			-> Any:
												_A							,_A									=       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:      Union[str, Any]      )			-> str:
												_A							,_A									=       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(unet.parameters()      )
												for s_param, param in zip(ema_unet.shadow_params						,   __A      ):
																	assert torch.allclose(__A						,   __A      )

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

							def 					__A       (     self:      Tuple      )			-> List[str]:
												_A							,_A									=       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									=       self.simulate_backprop(__A      )

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

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

							def 					__A       (     self:      Optional[int]      )			-> Optional[int]:
												# If we call EMA step after a backpropagation consecutively for two times,
												# the shadow params from those two steps should be different.
												_A							,_A									=       self.get_models()

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

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

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

							def 					__A       (     self:      int      )			-> Any:
												# 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.
												_A							,_A									=       self.get_models(decay=0.0      )
												_A									=       self.simulate_backprop(__A      )
												ema_unet.step(unet_step_one.parameters()      )
												_A									=       ema_unet.shadow_params

												_A									=       self.simulate_backprop(__A      )
												ema_unet.step(unet_step_two.parameters()      )
												_A									=       ema_unet.shadow_params

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



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

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

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

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



