
import gc
import threading
import time

import psutil
import torch






class     UpperCAmelCase						:



   def __init__(self						:   List[str]					)						->      str:


          '''simple docstring'''


          snake_case			:  List[str]     							= psutil.Process()
          snake_case			:  str     							= False



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]					)						->      Tuple:


          '''simple docstring'''


          snake_case			:  List[Any]     							= -1

          while True:
                 snake_case			:  int     							= 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 							_SCREAMING_SNAKE_CASE  (self						:   int					)						->      Dict:


          '''simple docstring'''


          snake_case			:  Optional[Any]     							= True
          snake_case			:  Tuple     							= threading.Thread(target=self.peak_monitor					)
          snake_case			:  List[str]     							= True
          self.thread.start()



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[Any]					)						->      int:


          '''simple docstring'''


          snake_case			:  Union[str, Any]     							= False
          self.thread.join()
          return self.cpu_memory_peak



__lowerCamelCase				=			PeakCPUMemory()


def      UpperCamelCase   (       ):
       # Time
       snake_case			:  List[str]     							= {"time": time.time()}

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

       # CPU mem
       snake_case			:  Optional[int]     							= psutil.Process().memory_info().rss
       cpu_peak_tracker.start()

       # GPU mem
       for i in range(torch.cuda.device_count()       ):
              snake_case			:  Optional[int]     							= torch.cuda.memory_allocated(__lowerCamelCase       )
       torch.cuda.reset_peak_memory_stats()

       return measures


def      UpperCamelCase   (       __lowerCamelCase	:					Optional[Any]       ):
       # Time
       snake_case			:  Dict     							= {"time": time.time() - start_measures["time"]}

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

       # CPU mem
       snake_case			:  Dict     							= (psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**20
       snake_case			:  List[str]     							= (cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**20

       # GPU mem
       for i in range(torch.cuda.device_count()       ):
              snake_case			:  Optional[int]     							= (torch.cuda.memory_allocated(__lowerCamelCase       ) - start_measures[str(__lowerCamelCase       )]) / 2**20
              snake_case			:  Union[str, Any]     							= (torch.cuda.max_memory_allocated(__lowerCamelCase       ) - start_measures[str(__lowerCamelCase       )]) / 2**20

       return measures




def      UpperCamelCase   (       __lowerCamelCase	:					List[str]							,   __lowerCamelCase	:					List[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(__lowerCamelCase       )]:.2f}MiB"""       )
              snake_case			:  Union[str, Any]     							= 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 argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


__lowerCamelCase				=			version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")


def      UpperCamelCase   (       __lowerCamelCase	:					List[Any]							,   __lowerCamelCase	:					tuple							,   __lowerCamelCase	:					Path							,   __lowerCamelCase	:					Tuple							,   __lowerCamelCase	:					Any							,   __lowerCamelCase	:					Dict							,   __lowerCamelCase	:					str							,   __lowerCamelCase	:					Optional[Any]=False							,   ):
       output_path.parent.mkdir(parents=__lowerCamelCase							,   exist_ok=__lowerCamelCase       )
       # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
       # so we check the torch version for backwards compatibility
       if is_torch_less_than_1_11:
              export(
                  __lowerCamelCase							,   __lowerCamelCase							,   f=output_path.as_posix()							,   input_names=__lowerCamelCase							,   output_names=__lowerCamelCase							,   dynamic_axes=__lowerCamelCase							,   do_constant_folding=__lowerCamelCase							,   use_external_data_format=__lowerCamelCase							,   enable_onnx_checker=__lowerCamelCase							,   opset_version=__lowerCamelCase							,   )
       else:
              export(
                  __lowerCamelCase							,   __lowerCamelCase							,   f=output_path.as_posix()							,   input_names=__lowerCamelCase							,   output_names=__lowerCamelCase							,   dynamic_axes=__lowerCamelCase							,   do_constant_folding=__lowerCamelCase							,   opset_version=__lowerCamelCase							,   )


@torch.no_grad()
def      UpperCamelCase   (       __lowerCamelCase	:					str							,   __lowerCamelCase	:					str							,   __lowerCamelCase	:					int							,   __lowerCamelCase	:					bool = False       ):
       snake_case			:  Optional[int]     							= torch.floataa if fpaa else torch.floataa
       if fpaa and torch.cuda.is_available():
              snake_case			:  str     							= "cuda"
       elif fpaa and not torch.cuda.is_available():
              raise ValueError("`float16` model export is only supported on GPUs with CUDA"       )
       else:
              snake_case			:  Tuple     							= "cpu"
       snake_case			:  Union[str, Any]     							= Path(__lowerCamelCase       )

       # VAE DECODER
       snake_case			:  Any     							= AutoencoderKL.from_pretrained(model_path + "/vae"       )
       snake_case			:  Tuple     							= vae_decoder.config.latent_channels
       # forward only through the decoder part
       snake_case			:  Tuple     							= vae_decoder.decode
       onnx_export(
           __lowerCamelCase							,   model_args=(
               torch.randn(1							,   __lowerCamelCase							,   25							,   25       ).to(device=__lowerCamelCase							,   dtype=__lowerCamelCase       ),
               False,
           )							,   output_path=output_path / "vae_decoder" / "model.onnx"							,   ordered_input_names=["latent_sample", "return_dict"]							,   output_names=["sample"]							,   dynamic_axes={
               "latent_sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
           }							,   opset=__lowerCamelCase							,   )
       del vae_decoder


if __name__ == "__main__":
   __lowerCamelCase				=			argparse.ArgumentParser()

   parser.add_argument(
       """--model_path""",
       type=str,
       required=True,
       help="""Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).""",
   )

   parser.add_argument("""--output_path""", type=str, required=True, help="""Path to the output model.""")
   parser.add_argument(
       """--opset""",
       default=14,
       type=int,
       help="""The version of the ONNX operator set to use.""",
   )
   parser.add_argument("""--fp16""", action="""store_true""", default=False, help="""Export the models in `float16` mode""")

   __lowerCamelCase				=			parser.parse_args()
   print(args.output_path)
   convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
   print("""SD: Done: ONNX""")





