"""simple docstring"""





import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
				import onnxruntime as ort


__A  =				logging.get_logger(__name__)

__A  =				{
    '''tensor(bool)''': np.bool_,
    '''tensor(int8)''': np.inta,
    '''tensor(uint8)''': np.uinta,
    '''tensor(int16)''': np.intaa,
    '''tensor(uint16)''': np.uintaa,
    '''tensor(int32)''': np.intaa,
    '''tensor(uint32)''': np.uintaa,
    '''tensor(int64)''': np.intaa,
    '''tensor(uint64)''': np.uintaa,
    '''tensor(float16)''': np.floataa,
    '''tensor(float)''': np.floataa,
    '''tensor(double)''': np.floataa,
}
class  _snake_case     :






						def __init__(       self      :			Optional[Any]							,					UpperCAmelCase      :			List[Any]=None							,					**UpperCAmelCase      :			int    ):
													logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future."    )
													__lowerCamelCase					:		Optional[Any]		   =     model
													__lowerCamelCase					:		Any		   =     kwargs.get("model_save_dir"							,					lowerCamelCase_    )
													__lowerCamelCase					:		Optional[int]		   =     kwargs.get("latest_model_name"							,					lowerCamelCase_    )






						def __call__(       self      :			Union[str, Any]							,					**UpperCAmelCase      :			List[str]    ):
													__lowerCamelCase					:		Optional[int]		   =     {k: np.array(lowerCamelCase_    ) for k, v in kwargs.items()}
													return self.model.run(lowerCamelCase_							,					lowerCamelCase_    )






						@staticmethod
						def       lowerCamelCase__     (       UpperCAmelCase      :			Union[str, Path]							,					UpperCAmelCase      :			Union[str, Any]=None							,					UpperCAmelCase      :			int=None    ):



													if provider is None:
																				logger.info("No onnxruntime provider specified, using CPUExecutionProvider"    )
																				__lowerCamelCase					:		Optional[int]		   =     """CPUExecutionProvider"""

													return ort.InferenceSession(lowerCamelCase_							,					providers=[provider]							,					sess_options=lowerCamelCase_    )






						def       lowerCamelCase__     (       self      :			Optional[Any]							,					UpperCAmelCase      :			Union[str, Path]							,					UpperCAmelCase      :			Optional[str] = None							,					**UpperCAmelCase      :			Dict    ):
													__lowerCamelCase					:		Dict		   =     file_name if file_name is not None else ONNX_WEIGHTS_NAME

													__lowerCamelCase					:		List[Any]		   =     self.model_save_dir.joinpath(self.latest_model_name    )
													__lowerCamelCase					:		List[Any]		   =     Path(lowerCamelCase_    ).joinpath(lowerCamelCase_    )
													try:
																				shutil.copyfile(lowerCamelCase_							,					lowerCamelCase_    )
													except shutil.SameFileError:
																				pass

													# copy external weights (for models >2GB)
													__lowerCamelCase					:		Union[str, Any]		   =     self.model_save_dir.joinpath(lowerCamelCase_    )
													if src_path.exists():
																				__lowerCamelCase					:		Optional[Any]		   =     Path(lowerCamelCase_    ).joinpath(lowerCamelCase_    )
																				try:
																											shutil.copyfile(lowerCamelCase_							,					lowerCamelCase_    )
																				except shutil.SameFileError:
																											pass






						def       lowerCamelCase__     (       self      :			int							,					UpperCAmelCase      :			Union[str, os.PathLike]							,					**UpperCAmelCase      :			int							,					):



													if os.path.isfile(lowerCamelCase_    ):
																				logger.error(F"""Provided path ({save_directory}) should be a directory, not a file"""    )
																				return

													os.makedirs(lowerCamelCase_							,					exist_ok=lowerCamelCase_    )

													# saving model weights/files
													self._save_pretrained(lowerCamelCase_							,					**lowerCamelCase_    )






						@classmethod
						def       lowerCamelCase__     (       cls      :			Union[str, Any]							,					UpperCAmelCase      :			Union[str, Path]							,					UpperCAmelCase      :			Optional[Union[bool, str, None]] = None							,					UpperCAmelCase      :			Optional[Union[str, None]] = None							,					UpperCAmelCase      :			bool = False							,					UpperCAmelCase      :			Optional[str] = None							,					UpperCAmelCase      :			Optional[str] = None							,					UpperCAmelCase      :			Optional[str] = None							,					UpperCAmelCase      :			Optional["ort.SessionOptions"] = None							,					**UpperCAmelCase      :			List[Any]							,					):
													__lowerCamelCase					:		Dict		   =     file_name if file_name is not None else ONNX_WEIGHTS_NAME
													# load model from local directory
													if os.path.isdir(lowerCamelCase_    ):
																				__lowerCamelCase					:		int		   =     OnnxRuntimeModel.load_model(
																				    os.path.join(lowerCamelCase_							,					lowerCamelCase_    )							,					provider=lowerCamelCase_							,					sess_options=lowerCamelCase_    )
																				__lowerCamelCase					:		int		   =     Path(lowerCamelCase_    )
													# load model from hub
													else:
																				# download model
																				__lowerCamelCase					:		Optional[Any]		   =     hf_hub_download(
																				    repo_id=lowerCamelCase_							,					filename=lowerCamelCase_							,					use_auth_token=lowerCamelCase_							,					revision=lowerCamelCase_							,					cache_dir=lowerCamelCase_							,					force_download=lowerCamelCase_							,					)
																				__lowerCamelCase					:		str		   =     Path(lowerCamelCase_    ).parent
																				__lowerCamelCase					:		int		   =     Path(lowerCamelCase_    ).name
																				__lowerCamelCase					:		Dict		   =     OnnxRuntimeModel.load_model(lowerCamelCase_							,					provider=lowerCamelCase_							,					sess_options=lowerCamelCase_    )
													return cls(model=lowerCamelCase_							,					**lowerCamelCase_    )







						@classmethod
						def       lowerCamelCase__     (       cls      :			Any							,					UpperCAmelCase      :			Union[str, Path]							,					UpperCAmelCase      :			bool = True							,					UpperCAmelCase      :			Optional[str] = None							,					UpperCAmelCase      :			Optional[str] = None							,					**UpperCAmelCase      :			int							,					):
													__lowerCamelCase					:		List[str]		   =     None
													if len(str(lowerCamelCase_    ).split("@"    )    ) == 2:
																				__lowerCamelCase					:		str		   =     model_id.split("@"    )

													return cls._from_pretrained(
													    model_id=lowerCamelCase_							,					revision=lowerCamelCase_							,					cache_dir=lowerCamelCase_							,					force_download=lowerCamelCase_							,					use_auth_token=lowerCamelCase_							,					**lowerCamelCase_							,					)
"""simple docstring"""





import gc
import threading
import time

import psutil
import torch
class  _snake_case     :






						def __init__(       self      :			str    ):
													__lowerCamelCase					:		Optional[Any]		   =     psutil.Process()
													__lowerCamelCase					:		List[Any]		   =     False






						def       lowerCamelCase__     (       self      :			str    ):
													__lowerCamelCase					:		List[Any]		   =     -1

													while True:
																				__lowerCamelCase					:		Union[str, 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    ):
													__lowerCamelCase					:		Optional[Any]		   =     True
													__lowerCamelCase					:		Union[str, Any]		   =     threading.Thread(target=self.peak_monitor    )
													__lowerCamelCase					:		Optional[Any]		   =     True
													self.thread.start()







						def       lowerCamelCase__     (       self      :			Dict    ):
													__lowerCamelCase					:		str		   =     False
													self.thread.join()
													return self.cpu_memory_peak

__A  =				PeakCPUMemory()


def    lowercase_   (   ) ->      Union[str, Any]:



							'''simple docstring'''

							__lowerCamelCase					:		Any		   =     {"time": time.time()}

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

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

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

							return measures


def    lowercase_   (   _lowerCamelCase:	Optional[Any]      ) ->      Tuple:



							'''simple docstring'''

							__lowerCamelCase					:		Tuple		   =     {"time": time.time() - start_measures["time"]}

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

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

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

							return measures







def    lowercase_   (   _lowerCamelCase:	int						,   _lowerCamelCase:	List[Any]      ) ->      Optional[int]:



							'''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(_lowerCamelCase      )]:.2f}MiB"""      )
														__lowerCamelCase					:		List[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"""      )