





'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

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







class __a			( _snake_case      ):
   __UpperCamelCase :			UNetaDModel
   __UpperCamelCase :			KarrasVeScheduler





   def __init__(       self					:      Tuple					,lowerCamelCase					:      UNetaDModel					,lowerCamelCase					:      KarrasVeScheduler     ):

      '''simple docstring'''

      super().__init__()
      self.register_modules(unet=lowerCamelCase					,scheduler=lowerCamelCase     )





   @torch.no_grad()
   def __call__(       self					:      Union[str, Any]					,lowerCamelCase					:      int = 1					,lowerCamelCase					:      int = 50					,lowerCamelCase					:      Optional[Union[torch.Generator, List[torch.Generator]]] = None					,lowerCamelCase					:      Optional[str] = "pil"					,lowerCamelCase					:      bool = True					,**lowerCamelCase					:      Tuple					,):

      '''simple docstring'''

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

      __SCREAMING_SNAKE_CASE   				=							self.unet

      # sample x_0 ~ N(0, sigma_0^2 * I)
      __SCREAMING_SNAKE_CASE   				=							randn_tensor(lowerCamelCase					,generator=lowerCamelCase					,device=self.device     ) * self.scheduler.init_noise_sigma

      self.scheduler.set_timesteps(lowerCamelCase     )

      for t in self.progress_bar(self.scheduler.timesteps     ):
         # here sigma_t == t_i from the paper
         __SCREAMING_SNAKE_CASE   				=							self.scheduler.schedule[t]
         __SCREAMING_SNAKE_CASE   				=							self.scheduler.schedule[t - 1] if t > 0 else 0

         # 1. Select temporarily increased noise level sigma_hat
         # 2. Add new noise to move from sample_i to sample_hat
         __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.scheduler.add_noise_to_input(lowerCamelCase					,lowerCamelCase					,generator=lowerCamelCase     )

         # 3. Predict the noise residual given the noise magnitude `sigma_hat`
         # The model inputs and output are adjusted by following eq. (213) in [1].
         __SCREAMING_SNAKE_CASE   				=							(sigma_hat / 2) * model((sample_hat + 1) / 2					,sigma_hat / 2     ).sample

         # 4. Evaluate dx/dt at sigma_hat
         # 5. Take Euler step from sigma to sigma_prev
         __SCREAMING_SNAKE_CASE   				=							self.scheduler.step(lowerCamelCase					,lowerCamelCase					,lowerCamelCase					,lowerCamelCase     )

         if sigma_prev != 0:
            # 6. Apply 2nd order correction
            # The model inputs and output are adjusted by following eq. (213) in [1].
            __SCREAMING_SNAKE_CASE   				=							(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2					,sigma_prev / 2     ).sample
            __SCREAMING_SNAKE_CASE   				=							self.scheduler.step_correct(
                lowerCamelCase					,lowerCamelCase					,lowerCamelCase					,lowerCamelCase					,step_output.prev_sample					,step_output["""derivative"""]					,)
         __SCREAMING_SNAKE_CASE   				=							step_output.prev_sample

      __SCREAMING_SNAKE_CASE   				=							(sample / 2 + 0.5).clamp(0					,1     )
      __SCREAMING_SNAKE_CASE   				=							sample.cpu().permute(0					,2					,3					,1     ).numpy()
      if output_type == "pil":
         __SCREAMING_SNAKE_CASE   				=							self.numpy_to_pil(lowerCamelCase     )

      if not return_dict:
         return (image,)

      return ImagePipelineOutput(images=lowerCamelCase     )






'''simple docstring'''

import gc
import threading
import time

import psutil
import torch


class     __UpperCAmelCase       :





  def __init__(	self ):
     lowerCAmelCase_			=      psutil.Process()
     lowerCAmelCase_			=      False





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      -1

     while True:
        lowerCAmelCase_			=      max(self.process.memory_info().rss    ,     self.cpu_memory_peak )

        # can't sleep or will not catch the peak right (this comment is here on purpose)
        if not self.peak_monitoring:
           break





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      True
     lowerCAmelCase_			=      threading.Thread(target=self.peak_monitor )
     lowerCAmelCase_			=      True
     self.thread.start()







  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      False
     self.thread.join()
     return self.cpu_memory_peak






A_     :						List[str]    							=PeakCPUMemory()



def snake_case_      (					)			->					Tuple:
   # Time
   lowerCAmelCase_			=      {'''time''': time.time()}

   gc.collect()
   torch.cuda.empty_cache()

   # CPU mem
   lowerCAmelCase_			=      psutil.Process().memory_info().rss
   cpu_peak_tracker.start()

   # GPU mem
   for i in range(torch.cuda.device_count()):
      lowerCAmelCase_			=      torch.cuda.memory_allocated(__snake_case)
   torch.cuda.reset_peak_memory_stats()

   return measures



def snake_case_      (					__snake_case  :							Any)			->					List[str]:
   # Time
   lowerCAmelCase_			=      {'''time''': time.time() - start_measures['''time''']}

   gc.collect()
   torch.cuda.empty_cache()

   # CPU mem
   lowerCAmelCase_			=      (psutil.Process().memory_info().rss - start_measures['''cpu''']) / 2**20
   lowerCAmelCase_			=      (cpu_peak_tracker.stop() - start_measures['''cpu''']) / 2**20

   # GPU mem
   for i in range(torch.cuda.device_count()):
      lowerCAmelCase_			=      (torch.cuda.memory_allocated(__snake_case) - start_measures[str(__snake_case)]) / 2**20
      lowerCAmelCase_			=      (torch.cuda.max_memory_allocated(__snake_case) - start_measures[str(__snake_case)]) / 2**20

   return measures






def snake_case_      (					__snake_case  :							Dict	,   __snake_case  :							Optional[int])			->					Dict:
   print(F'''{description}:''')
   print(F'''- Time: {measures['time']:.2f}s''')
   for i in range(torch.cuda.device_count()):
      print(F'''- GPU {i} allocated: {measures[str(__snake_case)]:.2f}MiB''')
      lowerCAmelCase_			=      measures[F'''{i}-peak''']
      print(F'''- GPU {i} peak: {peak:.2f}MiB''')
   print(F'''- CPU RAM allocated: {measures['cpu']:.2f}MiB''')
   print(F'''- CPU RAM peak: {measures['cpu-peak']:.2f}MiB''')





