


"""simple docstring"""
# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput




class 	lowercase__				(					A_				):





   def __init__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  Any:
      super().__init__()
      self.register_modules(unet=SCREAMING_SNAKE_CASE      ,   scheduler=SCREAMING_SNAKE_CASE)





   @torch.no_grad()
   def __call__(							self      ,   SCREAMING_SNAKE_CASE = 1      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = 50      ,   SCREAMING_SNAKE_CASE = "pil"      ,   SCREAMING_SNAKE_CASE = True      ,   **SCREAMING_SNAKE_CASE      ,   )  ->  Union[ImagePipelineOutput, Tuple]:
      _lowerCamelCase	:					Union[str, Any]   =		torch.randn(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)      ,   generator=SCREAMING_SNAKE_CASE      ,   )
      _lowerCamelCase	:					Any   =		image.to(self.device)

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

      for t in self.progress_bar(self.scheduler.timesteps):
         # 1. predict noise model_output
         _lowerCamelCase	:					Any   =		self.unet(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE).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
         _lowerCamelCase	:					List[Any]   =		self.scheduler.step(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE).prev_sample

      _lowerCamelCase	:					List[str]   =		(image / 2 + 0.5).clamp(0      ,   1)
      _lowerCamelCase	:					Optional[Any]   =		image.cpu().permute(0      ,   2      ,   3      ,   1).numpy()
      if output_type == "pil":
         _lowerCamelCase	:					List[str]   =		self.numpy_to_pil(SCREAMING_SNAKE_CASE)

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

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





"""simple docstring"""
from datetime import datetime

import matplotlib.pyplot as plt
import torch

def 				_snake_case      (   __snake_case							:      List[str]   ):




   """simple docstring"""







   for param in module.parameters():
      _lowerCamelCase	:					Optional[Any]   =		False

def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					Any   =		"""cuda""" if torch.cuda.is_available() else """cpu"""
   if torch.backends.mps.is_available() and torch.backends.mps.is_built():
      _lowerCamelCase	:					Any   =		"""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 				_snake_case      (   __snake_case							:      Union[str, Any]   ):




   """simple docstring"""







   _lowerCamelCase	:					int   =		plt.imshow(__snake_case   )
   fig.axes.get_xaxis().set_visible(__snake_case   )
   fig.axes.get_yaxis().set_visible(__snake_case   )
   plt.show()





def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					Tuple   =		datetime.now()
   _lowerCamelCase	:					Optional[Any]   =		current_time.strftime("""%H:%M:%S"""   )
   return timestamp

