



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




							def        UpperCAmelCase__      (	self					:	Union[str, Any]    ,    A_					:	Tuple=0.9999):
									lowerCAmelCase_      :					str     =			UNetaDConditionModel.from_pretrained(self.model_id    ,    subfolder='''unet''')
									lowerCAmelCase_      :					Tuple     =			unet.to(__A)
									lowerCAmelCase_      :					int     =			EMAModel(unet.parameters()    ,    decay=__A    ,    model_cls=__A    ,    model_config=unet.config)
									return unet, ema_unet




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




							def        UpperCAmelCase__      (	self					:	Optional[Any]):
									lowerCAmelCase_      :					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        UpperCAmelCase__      (	self					:	Optional[int]):
									lowerCAmelCase_      :					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_      :					int     =			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        UpperCAmelCase__      (	self					:	Optional[int]):
									lowerCAmelCase_      :					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.
									lowerCAmelCase_      :					Optional[int]     =			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.
									lowerCAmelCase_      :					Union[str, Any]     =			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        UpperCAmelCase__      (	self					:	Any):
									lowerCAmelCase_      :					List[str]     =			self.get_models()

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

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

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




							def        UpperCAmelCase__      (	self					:	str):
									lowerCAmelCase_      :					int     =			self.get_models(decay=0.0)
									lowerCAmelCase_      :					int     =			self.simulate_backprop(__A)
									ema_unet.step(unet_step_one.parameters())
									lowerCAmelCase_      :					List[Any]     =			ema_unet.shadow_params

									lowerCAmelCase_      :					int     =			self.simulate_backprop(__A)
									ema_unet.step(unet_step_two.parameters())
									lowerCAmelCase_      :					Optional[int]     =			ema_unet.shadow_params

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







							@skip_mps
							def        UpperCAmelCase__      (	self					:	Optional[Any]):
									lowerCAmelCase_      :					Optional[Any]     =			self.get_models()
									lowerCAmelCase_      :					str     =			self.get_dummy_inputs()

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

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

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










A__  :  str       = 1000


def       UpperCamelCase(  __UpperCamelCase							:		float			):
		return x * x


def       UpperCamelCase(  __UpperCamelCase							:		Optional[int]					,__UpperCamelCase							:		float					,__UpperCamelCase							:		float					,__UpperCamelCase							:		int = 4			):
		assert callable(
		    __UpperCamelCase			), f"""the function(object) passed should be callable your input : {function}"""
		assert isinstance(__UpperCamelCase					,(float, int)			), f"""a should be float or integer your input : {a}"""
		assert isinstance(function(__UpperCamelCase			)					,(float, int)			), (
		    "the function should return integer or float return type of your function, "
		    f"""{type(__UpperCamelCase			)}"""
		)
		assert isinstance(__UpperCamelCase					,(float, int)			), f"""b should be float or integer your input : {b}"""
		assert (
		    isinstance(__UpperCamelCase					,__UpperCamelCase			) and precision > 0
		), f"""precision should be positive integer your input : {precision}"""

		# just applying the formula of simpson for approximate integration written in
		# mentioned article in first comment of this file and above this function

		lowerCAmelCase_      :					List[str]     =			(b - a) / N_STEPS
		lowerCAmelCase_      :					str     =			function(__UpperCamelCase			) + function(__UpperCamelCase			)

		for i in range(1					,__UpperCamelCase			):
				lowerCAmelCase_      :					Optional[Any]     =			a + h * i
				result += function(__UpperCamelCase			) * (4 if i % 2 else 2)

		result *= h / 3
		return round(__UpperCamelCase					,__UpperCamelCase			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()





