





"""simple docstring"""






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput



class 	lowerCAmelCase					(      snake_case							):






       def __init__(   self    ,  a__    ,  a__     ):
              super().__init__()
              self.register_modules(unet=a__    ,  scheduler=a__     )






       @torch.no_grad()
       def __call__(   self    ,  a__ = 1    ,  a__ = None    ,  a__ = 50    ,  a__ = "pil"    ,  a__ = True    ,  **a__    ,  ):
              _UpperCAmelCase     =     torch.randn(
                  (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    ,  generator=a__    ,  )
              _UpperCAmelCase     =     image.to(self.device     )

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

              for t in self.progress_bar(self.scheduler.timesteps     ):
                     # 1. predict noise model_output
                     _UpperCAmelCase     =     self.unet(a__    ,  a__     ).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
                     _UpperCAmelCase     =     self.scheduler.step(a__    ,  a__    ,  a__     ).prev_sample

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

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

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













"""simple docstring"""






from datetime import datetime

import matplotlib.pyplot as plt
import torch

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       Any:






       """simple docstring"""




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

def       __lowerCamelCase    (       )		->       Dict:






       """simple docstring"""




       _UpperCAmelCase     =     'cuda' if torch.cuda.is_available() else 'cpu'
       if torch.backends.mps.is_available() and torch.backends.mps.is_built():
              _UpperCAmelCase     =     '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       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       str:






       """simple docstring"""




       _UpperCAmelCase     =     plt.imshow(SCREAMING_SNAKE_CASE							)
       fig.axes.get_xaxis().set_visible(SCREAMING_SNAKE_CASE							)
       fig.axes.get_yaxis().set_visible(SCREAMING_SNAKE_CASE							)
       plt.show()


def       __lowerCamelCase    (       )		->       Dict:






       """simple docstring"""




       _UpperCAmelCase     =     datetime.now()
       _UpperCAmelCase     =     current_time.strftime('%H:%M:%S'							)
       return timestamp






