





"""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 					_SCREAMING_SNAKE_CASE(   A__  ):




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





             """simple docstring"""






             super().__init__()

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

             self.register_modules(unet=_a		,scheduler=_a							)




       @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]:





             """simple docstring"""






             if isinstance(self.unet.config.sample_size		,_a							):
                   __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  (
                       batch_size,
                       self.unet.config.in_channels,
                       self.unet.config.sample_size,
                       self.unet.config.sample_size,
                   )
             else:
                   __SCREAMING_SNAKE_CASE   :Tuple						=  (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  randn_tensor(_a		,generator=_a		,device=self.device		,dtype=self.unet.dtype							)

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

             for t in self.progress_bar(self.scheduler.timesteps							):
                   # 1. predict noise model_output
                   __SCREAMING_SNAKE_CASE   :Optional[int]						=  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
                   __SCREAMING_SNAKE_CASE   :str						=  self.scheduler.step(
                       _a		,_a		,_a		,eta=_a		,use_clipped_model_output=_a		,generator=_a							).prev_sample

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

             if not return_dict:
                   return (image,)

             return ImagePipelineOutput(images=_a							)


'''simple docstring'''
import numpy as np
from PIL import Image


def        _lowerCAmelCase					(						_UpperCamelCase						:     np.ndarray							, _UpperCamelCase						:     int							, _UpperCamelCase						:     int				)      ->			np.ndarray:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =np.array(_UpperCamelCase				)
     if arr.shape[0] != arr.shape[1]:
          raise ValueError('The input array is not a square matrix'				)
     _SCREAMING_SNAKE_CASE							    =0
     _SCREAMING_SNAKE_CASE							    =0
     _SCREAMING_SNAKE_CASE							    =0
     _SCREAMING_SNAKE_CASE							    =0

     # compute the shape of the output matrix
     _SCREAMING_SNAKE_CASE							    =(arr.shape[0] - size) // stride + 1
     # initialize the output matrix with zeros of shape maxpool_shape
     _SCREAMING_SNAKE_CASE							    =np.zeros((maxpool_shape, maxpool_shape)				)

     while i < arr.shape[0]:
          if i + size > arr.shape[0]:
               # if the end of the matrix is reached, break
               break
          while j < arr.shape[1]:
               # if the end of the matrix is reached, break
               if j + size > arr.shape[1]:
                    break
               # compute the maximum of the pooling matrix
               _SCREAMING_SNAKE_CASE							    =np.max(arr[i : i + size, j : j + size]				)
               # shift the pooling matrix by stride of column pixels
               j += stride
               mat_j += 1

          # shift the pooling matrix by stride of row pixels
          i += stride
          mat_i += 1

          # reset the column index to 0
          _SCREAMING_SNAKE_CASE							    =0
          _SCREAMING_SNAKE_CASE							    =0

     return updated_arr


def        _lowerCAmelCase					(						_UpperCamelCase						:     np.ndarray							, _UpperCamelCase						:     int							, _UpperCamelCase						:     int				)      ->			np.ndarray:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =np.array(_UpperCamelCase				)
     if arr.shape[0] != arr.shape[1]:
          raise ValueError('The input array is not a square matrix'				)
     _SCREAMING_SNAKE_CASE							    =0
     _SCREAMING_SNAKE_CASE							    =0
     _SCREAMING_SNAKE_CASE							    =0
     _SCREAMING_SNAKE_CASE							    =0

     # compute the shape of the output matrix
     _SCREAMING_SNAKE_CASE							    =(arr.shape[0] - size) // stride + 1
     # initialize the output matrix with zeros of shape avgpool_shape
     _SCREAMING_SNAKE_CASE							    =np.zeros((avgpool_shape, avgpool_shape)				)

     while i < arr.shape[0]:
          # if the end of the matrix is reached, break
          if i + size > arr.shape[0]:
               break
          while j < arr.shape[1]:
               # if the end of the matrix is reached, break
               if j + size > arr.shape[1]:
                    break
               # compute the average of the pooling matrix
               _SCREAMING_SNAKE_CASE							    =int(np.average(arr[i : i + size, j : j + size]				)				)
               # shift the pooling matrix by stride of column pixels
               j += stride
               mat_j += 1

          # shift the pooling matrix by stride of row pixels
          i += stride
          mat_i += 1
          # reset the column index to 0
          _SCREAMING_SNAKE_CASE							    =0
          _SCREAMING_SNAKE_CASE							    =0

     return updated_arr


# Main Function
if __name__ == "__main__":
     from doctest import testmod

     testmod(name="avgpooling", verbose=True)

     # Loading the image
     lowerCamelCase :      Optional[Any]					 =     Image.open("path_to_image")

     # Converting the image to numpy array and maxpooling, displaying the result
     # Ensure that the image is a square matrix

     Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()

     # Converting the image to numpy array and averagepooling, displaying the result
     # Ensure that the image is a square matrix

     Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()
