




'''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 			SCREAMING_SNAKE_CASE(							unittest.TestCase	):
       snake_case_					:	str       =   """hf-internal-testing/tiny-stable-diffusion-pipe"""
       snake_case_					:	Union[str, Any]       =   1
       snake_case_					:	Tuple       =   77
       snake_case_					:	int       =   32
       snake_case_					:	Union[str, Any]       =   4
       snake_case_					:	Dict       =   64
       snake_case_					:	str       =   torch.manual_seed(0	)

       def        snake_case__     (       self   ,						lowerCamelCase__=0.99_99	)       ->   List[str]:

           """simple docstring"""



           __lowercase     							=		UNetaDConditionModel.from_pretrained(self.model_id   ,						subfolder="""unet"""	)
           __lowercase     							=		unet.to(lowerCamelCase__	)
           __lowercase     							=		EMAModel(unet.parameters()   ,						decay=lowerCamelCase__   ,						model_cls=lowerCamelCase__   ,						model_config=unet.config	)
           return unet, ema_unet

       def        snake_case__     (       self	)       ->   List[str]:

           """simple docstring"""



           __lowercase     							=		torch.randn(
               self.batch_size   ,						self.num_in_channels   ,						self.latent_height   ,						self.latent_width   ,						generator=self.generator	).to(lowerCamelCase__	)
           __lowercase     							=		torch.randint(0   ,						1000   ,						size=(self.batch_size,)   ,						generator=self.generator	).to(lowerCamelCase__	)
           __lowercase     							=		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        snake_case__     (       self   ,						lowerCamelCase__	)       ->   Any:

           """simple docstring"""



           __lowercase     							=		{}
           for k, param in unet.state_dict().items():
               __lowercase     							=		torch.randn_like(lowerCamelCase__	) + (param * torch.randn_like(lowerCamelCase__	))
               updated_state_dict.update({k: updated_param}	)
           unet.load_state_dict(lowerCamelCase__	)
           return unet

       def        snake_case__     (       self	)       ->   List[Any]:

           """simple docstring"""



           __lowercase							,__lowercase     							=		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        snake_case__     (       self	)       ->   str:

           """simple docstring"""



           __lowercase							,__lowercase     							=		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()	)
           __lowercase     							=		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        snake_case__     (       self	)       ->   int:

           """simple docstring"""



           __lowercase							,__lowercase     							=		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.
           __lowercase     							=		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.
           __lowercase     							=		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        snake_case__     (       self	)       ->   Optional[int]:

           """simple docstring"""



           __lowercase							,__lowercase     							=		self.get_models()

           # First backprop + EMA
           __lowercase     							=		self.simulate_backprop(lowerCamelCase__	)
           ema_unet.step(unet_step_one.parameters()	)
           __lowercase     							=		ema_unet.shadow_params

           # Second backprop + EMA
           __lowercase     							=		self.simulate_backprop(lowerCamelCase__	)
           ema_unet.step(unet_step_two.parameters()	)
           __lowercase     							=		ema_unet.shadow_params

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

       def        snake_case__     (       self	)       ->   Union[str, Any]:

           """simple docstring"""



           __lowercase							,__lowercase     							=		self.get_models(decay=0.0	)
           __lowercase     							=		self.simulate_backprop(lowerCamelCase__	)
           ema_unet.step(unet_step_one.parameters()	)
           __lowercase     							=		ema_unet.shadow_params

           __lowercase     							=		self.simulate_backprop(lowerCamelCase__	)
           ema_unet.step(unet_step_two.parameters()	)
           __lowercase     							=		ema_unet.shadow_params

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




       @skip_mps
       def        snake_case__     (       self	)       ->   List[str]:

           """simple docstring"""



           __lowercase							,__lowercase     							=		self.get_models()
           __lowercase							,__lowercase							,__lowercase     							=		self.get_dummy_inputs()

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

           # Since no EMA step has been performed the outputs should match.
           __lowercase     							=		unet(lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__	).sample
           __lowercase     							=		loaded_unet(lowerCamelCase__   ,						lowerCamelCase__   ,						lowerCamelCase__	).sample

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










'''simple docstring'''
def 							snake_case_    (  a__						:      List[str]       ,a__						:      Optional[int]     ):



    """simple docstring"""




    __lowercase     							=		(boundary[1] - boundary[0]) / steps
    __lowercase     							=		boundary[0]
    __lowercase     							=		boundary[1]
    __lowercase     							=		make_points(a__       ,a__       ,a__     )
    __lowercase     							=		0.0
    y += (h / 3.0) * f(a__     )
    __lowercase     							=		2
    for i in x_i:
        y += (h / 3) * (4 - 2 * (cnt % 2)) * f(a__     )
        cnt += 1
    y += (h / 3.0) * f(a__     )
    return y
def 							snake_case_    (  a__						:      Optional[Any]       ,a__						:      List[str]       ,a__						:      Optional[Any]     ):



    """simple docstring"""




    __lowercase     							=		a + h
    while x < (b - h):
        yield x
        __lowercase     							=		x + h
def 							snake_case_    (  a__						:      List[Any]     ):  # enter your function here



    """simple docstring"""




    __lowercase     							=		(x - 0) * (x - 0)
    return y
def 							snake_case_    (  ):



    """simple docstring"""




    __lowercase     							=		0.0  # Lower bound of integration
    __lowercase     							=		1.0  # Upper bound of integration
    __lowercase     							=		1_0.0  # define number of steps or resolution
    __lowercase     							=		[a, b]  # define boundary of integration
    __lowercase     							=		method_a(a__       ,a__     )
    print(f'y = {y}'     )


if __name__ == "__main__":
  main()




