
"""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 			_lowercase  (							unittest.TestCase						):
	lowercase_								=      'hf-internal-testing/tiny-stable-diffusion-pipe'
	lowercase_								=      1
	lowercase_								=      7_7
	lowercase_								=      3_2
	lowercase_								=      4
	lowercase_								=      lowercase_								=      6_4
	lowercase_								=      torch.manual_seed(0						)




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_=0.9999   )						->				str:
			lowerCamelCase	:     str            =       UNetaDConditionModel.from_pretrained(self.model_id						,					subfolder='unet'   )
			lowerCamelCase	:     Tuple            =       unet.to(__lowerCamelCase   )
			lowerCamelCase	:     Any            =       EMAModel(unet.parameters()						,					decay=__lowerCamelCase						,					model_cls=__lowerCamelCase						,					model_config=unet.config   )
			return unet, ema_unet




	def      _UpperCamelCase       (					self   )						->				str:
			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	:     List[Any]            =       torch.randint(0						,					1000						,					size=(self.batch_size,)						,					generator=self.generator   ).to(__lowerCamelCase   )
			lowerCamelCase	:     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      _UpperCamelCase       (					self						,					UpperCAmelCase_   )						->				Optional[Any]:
			lowerCamelCase	:     Tuple            =       {}
			for k, param in unet.state_dict().items():
					lowerCamelCase	:     Optional[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      _UpperCamelCase       (					self   )						->				Optional[int]:
			lowerCamelCase      ,       lowerCamelCase	:     Optional[int]            =       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      _UpperCamelCase       (					self   )						->				List[Any]:
			lowerCamelCase      ,       lowerCamelCase	:     Optional[int]            =       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	:     Any            =       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      _UpperCamelCase       (					self   )						->				List[str]:
			lowerCamelCase      ,       lowerCamelCase	:     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.
			lowerCamelCase	:     Union[str, 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.
			lowerCamelCase	:     Optional[int]            =       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      _UpperCamelCase       (					self   )						->				Tuple:
			# If we call EMA step after a backpropagation consecutively for two times,
			# the shadow params from those two steps should be different.
			lowerCamelCase      ,       lowerCamelCase	:     List[str]            =       self.get_models()

			# First backprop + EMA
			lowerCamelCase	:     Tuple            =       self.simulate_backprop(__lowerCamelCase   )
			ema_unet.step(unet_step_one.parameters()   )
			lowerCamelCase	:     Dict            =       ema_unet.shadow_params

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

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




	def      _UpperCamelCase       (					self   )						->				List[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.
			lowerCamelCase      ,       lowerCamelCase	:     int            =       self.get_models(decay=0.0   )
			lowerCamelCase	:     Any            =       self.simulate_backprop(__lowerCamelCase   )
			ema_unet.step(unet_step_one.parameters()   )
			lowerCamelCase	:     Optional[Any]            =       ema_unet.shadow_params

			lowerCamelCase	:     List[str]            =       self.simulate_backprop(__lowerCamelCase   )
			ema_unet.step(unet_step_two.parameters()   )
			lowerCamelCase	:     Optional[int]            =       ema_unet.shadow_params

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




	@skip_mps
	def      _UpperCamelCase       (					self   )						->				str:
			lowerCamelCase      ,       lowerCamelCase	:     Union[str, Any]            =       self.get_models()
			lowerCamelCase      ,       lowerCamelCase      ,       lowerCamelCase	:     Optional[int]            =       self.get_dummy_inputs()

			with tempfile.TemporaryDirectory() as tmpdir:
					ema_unet.save_pretrained(__lowerCamelCase   )
					lowerCamelCase	:     int            =       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	:     str            =       loaded_unet(__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase   ).sample

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














"""simple docstring"""

from cva import destroyAllWindows, imread, imshow, waitKey






def _lowerCAmelCase						(	__lowerCamelCase:Tuple			,    __lowerCamelCase:int ):

						'''simple docstring'''






						__magic_name__   ,     __magic_name__      =  img.shape[0], img.shape[1]

						def to_grayscale(__lowerCamelCase:List[str]			,    __lowerCamelCase:Optional[Any]			,    __lowerCamelCase:int ):
												return 0.2_1_2_6 * red + 0.5_8_7 * green + 0.1_1_4 * blue

						def normalize(__lowerCamelCase:Union[str, Any] ):
												return min(__lowerCamelCase			,    2_5_5 )

						for i in range(__lowerCamelCase ):
												for j in range(__lowerCamelCase ):
																		__magic_name__      =  int(to_grayscale(*img[i][j] ) )
																		__magic_name__      =  [
																		    normalize(__lowerCamelCase ),
																		    normalize(greyscale + factor ),
																		    normalize(greyscale + 2 * factor ),
																		]

						return img


if __name__ == "__main__":
			# read original image
			lowercase           =							{
			    percentage: imread('''image_data/lena.jpg''', 1) for percentage in (10, 20, 30, 40)
			}

			for percentage, img in images.items():
						make_sepia(img, percentage)

			for percentage, img in images.items():
						imshow(f'''Original image with sepia (factor: {percentage})''', img)

			waitKey(0)
			destroyAllWindows()



