



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 						lowerCamelCase_     (			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       SCREAMING_SNAKE_CASE__		(						self   :      Any						,						__lowerCamelCase   :      List[str]=0.9999			)   ->					Union[str, Any]:
									A				:       int														=    UNetaDConditionModel.from_pretrained(self.model_id						,						subfolder="unet"			)
									A				:       Dict														=    unet.to(__lowerCamelCase			)
									A				:       List[str]														=    EMAModel(unet.parameters()						,						decay=__lowerCamelCase						,						model_cls=__lowerCamelCase						,						model_config=unet.config			)
									return unet, ema_unet







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					Union[str, Any]:
									A				:       str														=    torch.randn(
									    self.batch_size						,						self.num_in_channels						,						self.latent_height						,						self.latent_width						,						generator=self.generator			).to(__lowerCamelCase			)
									A				:       List[Any]														=    torch.randint(0						,						10_00						,						size=(self.batch_size,)						,						generator=self.generator			).to(__lowerCamelCase			)
									A				:       Tuple														=    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       SCREAMING_SNAKE_CASE__		(						self   :      str						,						__lowerCamelCase   :      Dict			)   ->					str:
									A				:       List[str]														=    {}
									for k, param in unet.state_dict().items():
												A				:       Union[str, Any]														=    torch.randn_like(__lowerCamelCase			) + (param * torch.randn_like(__lowerCamelCase			))
												updated_state_dict.update({k: updated_param}			)
									unet.load_state_dict(__lowerCamelCase			)
									return unet







						def       SCREAMING_SNAKE_CASE__		(						self   :      str			)   ->					int:
									A							,			A				:       Dict														=    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       SCREAMING_SNAKE_CASE__		(						self   :      List[str]			)   ->					Any:
									A							,			A				:       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()			)
									A				:       Dict														=    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       SCREAMING_SNAKE_CASE__		(						self   :      List[str]			)   ->					List[str]:
									A							,			A				:       Dict														=    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				:       str														=    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       SCREAMING_SNAKE_CASE__		(						self   :      Optional[Any]			)   ->					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				:       Any														=    self.get_models()

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

									# Second backprop + EMA
									A				:       Tuple														=    self.simulate_backprop(__lowerCamelCase			)
									ema_unet.step(unet_step_two.parameters()			)
									A				:       Any														=    ema_unet.shadow_params

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







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any			)   ->					Optional[int]:
									# 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				:       str														=    self.get_models(decay=0.0			)
									A				:       Union[str, Any]														=    self.simulate_backprop(__lowerCamelCase			)
									ema_unet.step(unet_step_one.parameters()			)
									A				:       Dict														=    ema_unet.shadow_params

									A				:       int														=    self.simulate_backprop(__lowerCamelCase			)
									ema_unet.step(unet_step_two.parameters()			)
									A				:       Any														=    ema_unet.shadow_params

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







						@skip_mps
						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					Union[str, Any]:
									A							,			A				:       Tuple														=    self.get_models()
									A							,			A							,			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				:       Any														=    loaded_unet.to(unet.device			)

									# Since no EMA step has been performed the outputs should match.
									A				:       Optional[Any]														=    unet(__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase			).sample
									A				:       Optional[int]														=    loaded_unet(__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase			).sample

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




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__SCREAMING_SNAKE_CASE     =	{"""configuration_yoso""": ["""YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YosoConfig"""]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE     =	[
							    """YOSO_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """YosoForMaskedLM""",
							    """YosoForMultipleChoice""",
							    """YosoForQuestionAnswering""",
							    """YosoForSequenceClassification""",
							    """YosoForTokenClassification""",
							    """YosoLayer""",
							    """YosoModel""",
							    """YosoPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_yoso import (
														    YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
														    YosoForMaskedLM,
														    YosoForMultipleChoice,
														    YosoForQuestionAnswering,
														    YosoForSequenceClassification,
														    YosoForTokenClassification,
														    YosoLayer,
														    YosoModel,
														    YosoPreTrainedModel,
														)


else:
							import sys

							__SCREAMING_SNAKE_CASE     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure)