



from datetime import datetime

import matplotlib.pyplot as plt
import torch






def     a(      A       :    Union[str, Any]       )		->       List[str]:
					"""simple docstring"""





					for param in module.parameters():
										a					     =   False






def     a(      )		->       int:
					"""simple docstring"""





					a					     =   "cuda" if torch.cuda.is_available() else "cpu"
					if torch.backends.mps.is_available() and torch.backends.mps.is_built():
										a					     =   "mps"
					if device == "mps":
										print(
										    "WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"
										    " errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"
										    " with generations."       )
					return device






def     a(      A       :    Tuple       )		->       str:
					"""simple docstring"""





					a					     =   plt.imshow(A       )
					fig.axes.get_xaxis().set_visible(A       )
					fig.axes.get_yaxis().set_visible(A       )
					plt.show()







def     a(      )		->       List[Any]:
					"""simple docstring"""





					a					     =   datetime.now()
					a					     =   current_time.strftime("%H:%M:%S"       )
					return timestamp





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput



class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						super().__init__()
						self.register_modules(unet=lowerCamelCase_		,       scheduler=lowerCamelCase_		)




	@torch.no_grad()
	def __call__(self		,       lowerCamelCase_ = 1		,       lowerCamelCase_ = None		,       lowerCamelCase_ = 50		,       lowerCamelCase_ = "pil"		,       lowerCamelCase_ = True		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						a					     =   torch.randn(
						    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,       generator=lowerCamelCase_		,       )
						a					     =   image.to(self.device		)

						# set step values
						self.scheduler.set_timesteps(lowerCamelCase_		)

						for t in self.progress_bar(self.scheduler.timesteps		):
											# 1. predict noise model_output
											a					     =   self.unet(lowerCamelCase_		,       lowerCamelCase_		).sample

											# 2. predict previous mean of image x_t-1 and add variance depending on eta
											# eta corresponds to η in paper and should be between [0, 1]
											# do x_t -> x_t-1
											a					     =   self.scheduler.step(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		).prev_sample

						a					     =   (image / 2 + 0.5).clamp(0		,       1		)
						a					     =   image.cpu().permute(0		,       2		,       3		,       1		).numpy()
						if output_type == "pil":
											a					     =   self.numpy_to_pil(lowerCamelCase_		)

						if not return_dict:
											return (image,), "This is a local test"

						return ImagePipelineOutput(images=lowerCamelCase_		), "This is a local test"
