
'''simple docstring'''


from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   _snake_case		   =							42
   _snake_case		   =							42







   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__ = 2_0_0_0 ,   lowerCamelCase__ = None ,   lowerCamelCase__ = "pil" ,   lowerCamelCase__ = True ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      UpperCamelCase       =  self.unet.config.sample_size
      UpperCamelCase       =  (batch_size, 3, img_size, img_size)

      UpperCamelCase       =  self.unet

      UpperCamelCase       =  randn_tensor(lowerCamelCase__ ,   generator=lowerCamelCase__   ) * self.scheduler.init_noise_sigma
      UpperCamelCase       =  sample.to(self.device   )

      self.scheduler.set_timesteps(lowerCamelCase__   )
      self.scheduler.set_sigmas(lowerCamelCase__   )

      for i, t in enumerate(self.progress_bar(self.scheduler.timesteps   )   ):
         UpperCamelCase       =  self.scheduler.sigmas[i] * torch.ones(shape[0] ,   device=self.device   )

         # correction step
         for _ in range(self.scheduler.config.correct_steps   ):
            UpperCamelCase       =  self.unet(lowerCamelCase__ ,   lowerCamelCase__   ).sample
            UpperCamelCase       =  self.scheduler.step_correct(lowerCamelCase__ ,   lowerCamelCase__ ,   generator=lowerCamelCase__   ).prev_sample

         # prediction step
         UpperCamelCase       =  model(lowerCamelCase__ ,   lowerCamelCase__   ).sample
         UpperCamelCase       =  self.scheduler.step_pred(lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   generator=lowerCamelCase__   )

         UpperCamelCase     , UpperCamelCase       =  output.prev_sample, output.prev_sample_mean

      UpperCamelCase       =  sample_mean.clamp(0 ,   1   )
      UpperCamelCase       =  sample.cpu().permute(0 ,   2 ,   3 ,   1   ).numpy()
      if output_type == "pil":
         UpperCamelCase       =  self.numpy_to_pil(lowerCamelCase__   )

      if not return_dict:
         return (sample,)

      return ImagePipelineOutput(images=lowerCamelCase__   )







'''simple docstring'''


from random import randint
from tempfile import TemporaryFile

import numpy as np



def  __snake_case     (    _UpperCAmelCase					:       Optional[int],      _UpperCAmelCase					:       Tuple,      _UpperCAmelCase					:       Any):
   UpperCamelCase       =  0
   if start < end:
      UpperCamelCase       =  randint(_UpperCAmelCase,      _UpperCAmelCase)
      UpperCamelCase       =  a[end]
      UpperCamelCase       =  a[pivot]
      UpperCamelCase       =  temp

      UpperCamelCase     , UpperCamelCase       =  _in_place_partition(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase)
      count += _in_place_quick_sort(_UpperCAmelCase,      _UpperCAmelCase,      p - 1)
      count += _in_place_quick_sort(_UpperCAmelCase,      p + 1,      _UpperCAmelCase)
   return count



def  __snake_case     (    _UpperCAmelCase					:       Dict,      _UpperCAmelCase					:       Tuple,      _UpperCAmelCase					:       Any):
   UpperCamelCase       =  0
   UpperCamelCase       =  randint(_UpperCAmelCase,      _UpperCAmelCase)
   UpperCamelCase       =  a[end]
   UpperCamelCase       =  a[pivot]
   UpperCamelCase       =  temp
   UpperCamelCase       =  start - 1
   for index in range(_UpperCAmelCase,      _UpperCAmelCase):
      count += 1
      if a[index] < a[end]:  # check if current val is less than pivot value
         UpperCamelCase       =  new_pivot_index + 1
         UpperCamelCase       =  a[new_pivot_index]
         UpperCamelCase       =  a[index]
         UpperCamelCase       =  temp

   UpperCamelCase       =  a[new_pivot_index + 1]
   UpperCamelCase       =  a[end]
   UpperCamelCase       =  temp
   return new_pivot_index + 1, count


snake_case_ :  List[Any]     =					TemporaryFile()
snake_case_ :  Optional[Any]     =					100  # 1000 elements are to be sorted


snake_case_  , snake_case_ :  List[str]     =					0, 1  # mean and standard deviation
snake_case_ :  Any     =					np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)


outfile.seek(0)  # using the same array
snake_case_ :  Optional[int]     =					np.load(outfile)
snake_case_ :  Optional[int]     =					len(M) - 1
snake_case_ :  List[str]     =					_in_place_quick_sort(M, 0, r)

print(
    'No of Comparisons for 100 elements selected from a standard normal distribution'
    'is :'
)

print(z)





