
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 		__magic_name__   (						unittest.TestCase    ):
		_lowerCAmelCase		=	"hf-internal-testing/tiny-stable-diffusion-pipe"
		_lowerCAmelCase		=	1
		_lowerCAmelCase		=	77
		_lowerCAmelCase		=	32
		_lowerCAmelCase		=	4
		_lowerCAmelCase		=	_lowerCAmelCase		=	64
		_lowerCAmelCase		=	torch.manual_seed(0    )





		def       _A							(							self    :	Optional[Any]						,  lowerCamelCase__    :	List[Any]=0.9_9_9_9				):
							lowerCAmelCase		:					str  	=     UNetaDConditionModel.from_pretrained(self.model_id						,  subfolder='''unet'''				)
							lowerCAmelCase		:					Union[str, Any]  	=     unet.to(lowerCamelCase__				)
							lowerCAmelCase		:					List[Any]  	=     EMAModel(unet.parameters()						,  decay=lowerCamelCase__						,  model_cls=lowerCamelCase__						,  model_config=unet.config				)
							return unet, ema_unet





		def       _A							(							self    :	Union[str, Any]				):
							lowerCAmelCase		:					Union[str, Any]  	=     torch.randn(
							    self.batch_size						,  self.num_in_channels						,  self.latent_height						,  self.latent_width						,  generator=self.generator				).to(lowerCamelCase__				)
							lowerCAmelCase		:					Optional[int]  	=     torch.randint(0						,  1_0_0_0						,  size=(self.batch_size,)						,  generator=self.generator				).to(lowerCamelCase__				)
							lowerCAmelCase		:					List[Any]  	=     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       _A							(							self    :	Tuple						,  lowerCamelCase__    :	Dict				):
							lowerCAmelCase		:					str  	=     {}
							for k, param in unet.state_dict().items():
												lowerCAmelCase		:					Tuple  	=     torch.randn_like(lowerCamelCase__				) + (param * torch.randn_like(lowerCamelCase__				))
												updated_state_dict.update({k: updated_param}				)
							unet.load_state_dict(lowerCamelCase__				)
							return unet





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

							# First backprop + EMA
							lowerCAmelCase		:					Any  	=     self.simulate_backprop(lowerCamelCase__				)
							ema_unet.step(unet_step_one.parameters()				)
							lowerCAmelCase		:					int  	=     ema_unet.shadow_params

							# Second backprop + EMA
							lowerCAmelCase		:					str  	=     self.simulate_backprop(lowerCamelCase__				)
							ema_unet.step(unet_step_two.parameters()				)
							lowerCAmelCase		:					Any  	=     ema_unet.shadow_params

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





		def       _A							(							self    :	List[str]				):
							# 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.
							lowerCAmelCase	,							lowerCAmelCase		:					Optional[int]  	=     self.get_models(decay=0.0				)
							lowerCAmelCase		:					Tuple  	=     self.simulate_backprop(lowerCamelCase__				)
							ema_unet.step(unet_step_one.parameters()				)
							lowerCAmelCase		:					List[str]  	=     ema_unet.shadow_params

							lowerCAmelCase		:					int  	=     self.simulate_backprop(lowerCamelCase__				)
							ema_unet.step(unet_step_two.parameters()				)
							lowerCAmelCase		:					List[str]  	=     ema_unet.shadow_params

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





		@skip_mps
		def       _A							(							self    :	Union[str, Any]				):
							lowerCAmelCase	,							lowerCAmelCase		:					Tuple  	=     self.get_models()
							lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase		:					Dict  	=     self.get_dummy_inputs()

							with tempfile.TemporaryDirectory() as tmpdir:
												ema_unet.save_pretrained(lowerCamelCase__				)
												lowerCAmelCase		:					Dict  	=     UNetaDConditionModel.from_pretrained(lowerCamelCase__						,  model_cls=lowerCamelCase__				)
												lowerCAmelCase		:					Tuple  	=     loaded_unet.to(unet.device				)

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

							assert torch.allclose(lowerCamelCase__						,  lowerCamelCase__						,  atol=1E-4				)


from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
		from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore


class 		__magic_name__   (						snake_case    ):
		_lowerCAmelCase		=	c_float





		@property
		def       _A							(							self    :	int				):
							return sizeof(self.value				)



def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     int							,  __magic_name__						:     int = 0  ):


					'''simple docstring'''

					if dimension <= 0:
										lowerCAmelCase		:					Tuple  	=     fixed_dimension

					dimension -= num_token_to_add
					return dimension
def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     ParameterFormat  ):


					'''simple docstring'''

					return num_parameters * dtype.size
def     UpperCAmelCase__							(       __magic_name__						:     str  ):


					'''simple docstring'''

					from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

					try:
										return AutoProcessor.from_pretrained(__magic_name__  )
					except (ValueError, OSError, KeyError):
										lowerCAmelCase	,							lowerCAmelCase		:					Any  	=     None, None
										try:
															lowerCAmelCase		:					Union[str, Any]  	=     AutoTokenizer.from_pretrained(__magic_name__  )
										except (OSError, KeyError):
															pass
										try:
															lowerCAmelCase		:					List[Any]  	=     AutoFeatureExtractor.from_pretrained(__magic_name__  )
										except (OSError, KeyError):
															pass

										if tokenizer is not None and feature_extractor is not None:
															raise ValueError(
															    f'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''  )
										elif tokenizer is None and feature_extractor is None:
															return None
										elif tokenizer is not None:
															return tokenizer
										else:
															return feature_extractor
