

import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


_lowerCamelCase      :					List[str]					    =      logging.get_logger(__name__)





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       def __init__(							self ,   lowercase__     ):







             '''simple docstring'''



             super().__init__()
             __A    =nn.ModuleList(lowercase__     )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = False ,   lowercase__ = True ,   ):







             '''simple docstring'''



             for i, (image, scale, controlnet) in enumerate(zip(lowercase__ ,   lowercase__ ,   self.nets     )     ):
                   __A    ,		__A    =controlnet(
                       lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   )

                   # merge samples
                   if i == 0:
                         __A    ,		__A    =down_samples, mid_sample
                   else:
                         __A    =[
                             samples_prev + samples_curr
                             for samples_prev, samples_curr in zip(lowercase__ ,   lowercase__     )
                         ]
                         mid_block_res_sample += mid_sample

             return down_block_res_samples, mid_block_res_sample

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = True ,   lowercase__ = None ,   lowercase__ = False ,   lowercase__ = None ,   ):







             '''simple docstring'''



             __A    =0
             __A    =save_directory
             for controlnet in self.nets:
                   controlnet.save_pretrained(
                       lowercase__ ,   is_main_process=lowercase__ ,   save_function=lowercase__ ,   safe_serialization=lowercase__ ,   variant=lowercase__ ,   )

                   idx += 1
                   __A    =model_path_to_save + f'''_{idx}'''


       @classmethod
       def     __UpperCamelCase     (							cls ,   lowercase__ ,   **lowercase__     ):







             '''simple docstring'''



             __A    =0
             __A    =[]

             # load controlnet and append to list until no controlnet directory exists anymore
             # first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
             # second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
             __A    =pretrained_model_path
             while os.path.isdir(lowercase__     ):
                   __A    =ControlNetModel.from_pretrained(lowercase__ ,   **lowercase__     )
                   controlnets.append(lowercase__     )

                   idx += 1
                   __A    =pretrained_model_path + f'''_{idx}'''

             logger.info(f'''{len(lowercase__     )} controlnets loaded from {pretrained_model_path}.'''     )

             if len(lowercase__     ) == 0:
                   raise ValueError(
                       f'''No ControlNets found under {os.path.dirname(lowercase__     )}. Expected at least {pretrained_model_path + '_0'}.'''     )

             return cls(lowercase__     )





import gc
import threading
import time

import psutil
import torch





class        lowerCAmelCase__  :



       '''simple docstring'''







       def __init__(							self     ):







             '''simple docstring'''



             __A    =psutil.Process()
             __A    =False

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =-1

             while True:
                   __A    =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     ):







             '''simple docstring'''



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


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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

_lowerCamelCase      :					Union[str, Any]					    =      PeakCPUMemory()





def      A__					(			)					->Dict:
      # Time
      __A    ={'''time''': time.time()}

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

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

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

      return measures





def      A__					(			__A  :						Any			)					->Any:
      # Time
      __A    ={'''time''': time.time() - start_measures['''time''']}

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

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

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

      return measures





def      A__					(			__A  :						Union[str, Any]     ,				__A  :						Any			)					->Union[str, Any]:
      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(__A			)]:.2f}MiB'''			)
            __A    =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'''			)


