


"""simple docstring"""
from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 	lowercase__				(					A_				):





   def __init__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  Tuple:
      super().__init__()

      # make sure scheduler can always be converted to DDIM
      _lowerCamelCase	:					List[Any]   =		DDIMScheduler.from_config(scheduler.config)

      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 = 0.0      ,   SCREAMING_SNAKE_CASE = 50      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = "pil"      ,   SCREAMING_SNAKE_CASE = True      ,   )  ->  Union[ImagePipelineOutput, Tuple]:

      # Sample gaussian noise to begin loop
      if isinstance(self.unet.config.sample_size      ,   SCREAMING_SNAKE_CASE):
         _lowerCamelCase	:					int   =		(
             batch_size,
             self.unet.config.in_channels,
             self.unet.config.sample_size,
             self.unet.config.sample_size,
         )
      else:
         _lowerCamelCase	:					List[str]   =		(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

      if isinstance(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE) and len(SCREAMING_SNAKE_CASE) != batch_size:
         raise ValueError(
             F'You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE)}, but requested an effective batch'
             F' size of {batch_size}. Make sure the batch size matches the length of the generators.')

      _lowerCamelCase	:					int   =		randn_tensor(SCREAMING_SNAKE_CASE      ,   generator=SCREAMING_SNAKE_CASE      ,   device=self.device      ,   dtype=self.unet.dtype)

      # 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	:					int   =		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	:					Optional[int]   =		self.scheduler.step(
             SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   eta=SCREAMING_SNAKE_CASE      ,   use_clipped_model_output=SCREAMING_SNAKE_CASE      ,   generator=SCREAMING_SNAKE_CASE).prev_sample

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

      if not return_dict:
         return (image,)

      return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE)





"""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

