



'''simple docstring'''
import gc
import threading
import time

import psutil
import torch


class        _UpperCAmelCase						:




    def __init__(			self  :       Dict			):






       '''simple docstring'''






       lowercase_	:    Tuple           =     psutil.Process()
       lowercase_	:    Union[str, Any]           =     False




    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       lowercase_	:    Tuple           =     -1

       while True:
          lowercase_	:    List[Any]           =     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 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       lowercase_	:    Optional[int]           =     True
       lowercase_	:    Any           =     threading.Thread(target=self.peak_monitor			)
       lowercase_	:    Optional[int]           =     True
       self.thread.start()





    def 				lowerCAmelCase__				(			self  :       List[Any]			):






       '''simple docstring'''






       lowercase_	:    Any           =     False
       self.thread.join()
       return self.cpu_memory_peak


UpperCamelCase__ =			PeakCPUMemory()




def 		__SCREAMING_SNAKE_CASE					(  ):


   """simple docstring"""




   lowercase_	:    Tuple           =     {"time": time.time()}

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

   # CPU mem
   lowercase_	:    Dict           =     psutil.Process().memory_info().rss
   cpu_peak_tracker.start()

   # GPU mem
   for i in range(torch.cuda.device_count()							):
      lowercase_	:    Union[str, Any]           =     torch.cuda.memory_allocated(_UpperCamelCase							)
   torch.cuda.reset_peak_memory_stats()

   return measures




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    List[Any]           =     {"time": time.time() - start_measures["time"]}

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

   # CPU mem
   lowercase_	:    Any           =     (psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**20
   lowercase_	:    int           =     (cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**20

   # GPU mem
   for i in range(torch.cuda.device_count()							):
      lowercase_	:    List[str]           =     (torch.cuda.memory_allocated(_UpperCamelCase							) - start_measures[str(_UpperCamelCase							)]) / 2**20
      lowercase_	:    Optional[Any]           =     (torch.cuda.max_memory_allocated(_UpperCamelCase							) - start_measures[str(_UpperCamelCase							)]) / 2**20

   return measures





def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   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(_UpperCamelCase							)]:.2f}MiB"""							)
      lowercase_	:    List[str]           =     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"""							)












'''simple docstring'''
# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    'pipelines_utils',
    '0.22.0',
    'Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.',
    standard_warn=False,
    stacklevel=3,
)




