

"""simple docstring"""





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"""

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






			def    _lowerCAmelCase			(							self     :					List[str]						,  lowercase_     :					int=0.9_9_9_9						):
								UpperCamelCase__		:    Optional[Any]         =UNetaDConditionModel.from_pretrained(self.model_id						,  subfolder='''unet'''						)
								UpperCamelCase__		:    List[Any]         =unet.to(lowercase_						)
								UpperCamelCase__		:    Dict         =EMAModel(unet.parameters()						,  decay=lowercase_						,  model_cls=lowercase_						,  model_config=unet.config						)
								return unet, ema_unet






			def    _lowerCAmelCase			(							self     :					Any						):
								UpperCamelCase__		:    Optional[Any]         =torch.randn(
								    self.batch_size						,  self.num_in_channels						,  self.latent_height						,  self.latent_width						,  generator=self.generator						).to(lowercase_						)
								UpperCamelCase__		:    Optional[Any]         =torch.randint(0						,  1000						,  size=(self.batch_size,)						,  generator=self.generator						).to(lowercase_						)
								UpperCamelCase__		:    Optional[int]         =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    _lowerCAmelCase			(							self     :					Union[str, Any]						,  lowercase_     :					Optional[int]						):
								UpperCamelCase__		:    int         ={}
								for k, param in unet.state_dict().items():
													UpperCamelCase__		:    Optional[Any]         =torch.randn_like(lowercase_						) + (param * torch.randn_like(lowercase_						))
													updated_state_dict.update({k: updated_param}						)
								unet.load_state_dict(lowercase_						)
								return unet






			def    _lowerCAmelCase			(							self     :					Dict						):
								UpperCamelCase__  ,			UpperCamelCase__		:    Union[str, Any]         =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    _lowerCAmelCase			(							self     :					List[Any]						):
								UpperCamelCase__  ,			UpperCamelCase__		:    Dict         =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    _lowerCAmelCase			(							self     :					int						):
								UpperCamelCase__  ,			UpperCamelCase__		:    Union[str, 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.
								UpperCamelCase__		:    List[Any]         =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    _lowerCAmelCase			(							self     :					Optional[Any]						):
								# If we call EMA step after a backpropagation consecutively for two times,
								# the shadow params from those two steps should be different.
								UpperCamelCase__  ,			UpperCamelCase__		:    Dict         =self.get_models()

								# First backprop + EMA
								UpperCamelCase__		:    List[Any]         =self.simulate_backprop(lowercase_						)
								ema_unet.step(unet_step_one.parameters()						)
								UpperCamelCase__		:    int         =ema_unet.shadow_params

								# Second backprop + EMA
								UpperCamelCase__		:    Optional[int]         =self.simulate_backprop(lowercase_						)
								ema_unet.step(unet_step_two.parameters()						)
								UpperCamelCase__		:    Union[str, Any]         =ema_unet.shadow_params

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






			def    _lowerCAmelCase			(							self     :					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.
								UpperCamelCase__  ,			UpperCamelCase__		:    List[Any]         =self.get_models(decay=0.0						)
								UpperCamelCase__		:    Any         =self.simulate_backprop(lowercase_						)
								ema_unet.step(unet_step_one.parameters()						)
								UpperCamelCase__		:    Any         =ema_unet.shadow_params

								UpperCamelCase__		:    Union[str, Any]         =self.simulate_backprop(lowercase_						)
								ema_unet.step(unet_step_two.parameters()						)
								UpperCamelCase__		:    int         =ema_unet.shadow_params

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






			@skip_mps
			def    _lowerCAmelCase			(							self     :					Optional[int]						):
								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__		:    Dict         =UNetaDConditionModel.from_pretrained(lowercase_						,  model_cls=lowercase_						)
													UpperCamelCase__		:    Any         =loaded_unet.to(unet.device						)

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

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





"""simple docstring"""





from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE       :  List[Any]        = {
    """configuration_deta""": ["""DETA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DetaConfig"""],
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  int        = ["""DetaImageProcessor"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Dict        = [
				    """DETA_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """DetaForObjectDetection""",
				    """DetaModel""",
				    """DetaPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_deta import DetaImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_deta import (
								    DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DetaForObjectDetection,
								    DetaModel,
								    DetaPreTrainedModel,
								)

else:
				import sys

				_SCREAMING_SNAKE_CASE       :  Optional[int]        = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


