






import gc
import threading
import time

import psutil
import torch






class 					SCREAMING_SNAKE_CASE_  :
       """simple docstring"""


       def __init__(				self	:Dict):



          """simple docstring"""

          _lowercase              =psutil.Process()
          _lowercase              =False





       def 				UpperCamelCase__						(				self	:List[str]):



          """simple docstring"""

          _lowercase              =-1

          while True:
             _lowercase              =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	:List[Any]):



          """simple docstring"""

          _lowercase              =True
          _lowercase              =threading.Thread(target=self.peak_monitor)
          _lowercase              =True
          self.thread.start()





       def 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

          _lowercase              =False
          self.thread.join()
          return self.cpu_memory_peak

_SCREAMING_SNAKE_CASE   =  PeakCPUMemory()





def        _snake_case   ()					->	str:
   # Time
   _lowercase              ={'time': time.time()}

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

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

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

   return measures





def        _snake_case   (_snake_case				:       Tuple)					->	List[Any]:
   # Time
   _lowercase              ={'time': time.time() - start_measures['time']}

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

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

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

   return measures





def        _snake_case   (_snake_case				:       Dict      ,    _snake_case				:       List[Any])					->	Optional[int]:
   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''')
      _lowercase              =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''')










import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       @property
       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

          torch.manual_seed(0)
          _lowercase              =UNetaDModel(
              block_out_channels=(32, 64),      layers_per_block=2,      sample_size=32,      in_channels=3,      out_channels=3,      down_block_types=('DownBlock2D', 'AttnDownBlock2D'),      up_block_types=('AttnUpBlock2D', 'UpBlock2D'),      )
          return model





       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          _lowercase              =self.dummy_uncond_unet
          _lowercase              =ScoreSdeVeScheduler()

          _lowercase              =ScoreSdeVePipeline(unet=snake_case,      scheduler=snake_case)
          sde_ve.to(snake_case)
          sde_ve.set_progress_bar_config(disable=snake_case)

          _lowercase              =torch.manual_seed(0)
          _lowercase              =sde_ve(num_inference_steps=2,      output_type='numpy',      generator=snake_case).images

          _lowercase              =torch.manual_seed(0)
          _lowercase              =sde_ve(num_inference_steps=2,      output_type='numpy',      generator=snake_case,      return_dict=snake_case)[
              0
          ]

          _lowercase              =image[0, -3:, -3:, -1]
          _lowercase              =image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          _lowercase              =np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2






@slow
@require_torch
class 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

          _lowercase              ='google/ncsnpp-church-256'
          _lowercase              =UNetaDModel.from_pretrained(snake_case)

          _lowercase              =ScoreSdeVeScheduler.from_pretrained(snake_case)

          _lowercase              =ScoreSdeVePipeline(unet=snake_case,      scheduler=snake_case)
          sde_ve.to(snake_case)
          sde_ve.set_progress_bar_config(disable=snake_case)

          _lowercase              =torch.manual_seed(0)
          _lowercase              =sde_ve(num_inference_steps=10,      output_type='numpy',      generator=snake_case).images

          _lowercase              =image[0, -3:, -3:, -1]

          assert image.shape == (1, 256, 256, 3)

          _lowercase              =np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2


