"""simple docstring"""





import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
				import transformers


def    lowercase_   (   _lowerCamelCase:	int      ) ->      Any:



							'''simple docstring'''

							random.seed(_lowerCamelCase      )
							np.random.seed(_lowerCamelCase      )
							torch.manual_seed(_lowerCamelCase      )
							torch.cuda.manual_seed_all(_lowerCamelCase      )
							# ^^ safe to call this function even if cuda is not available







class  _snake_case     :






						def __init__(       self      :			List[Any]							,					UpperCAmelCase      :			Iterable[torch.nn.Parameter]							,					UpperCAmelCase      :			float = 0.9_9_9_9							,					UpperCAmelCase      :			float = 0.0							,					UpperCAmelCase      :			int = 0							,					UpperCAmelCase      :			bool = False							,					UpperCAmelCase      :			Union[float, int] = 1.0							,					UpperCAmelCase      :			Union[float, int] = 2 / 3							,					UpperCAmelCase      :			Optional[Any] = None							,					UpperCAmelCase      :			Dict[str, Any] = None							,					**UpperCAmelCase      :			List[str]							,					):

													if isinstance(UpperCAmelCase							,					torch.nn.Module    ):
																				__lowerCamelCase					:		Union[str, Any]		   =     (
																				    "Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. "
																				    "Please pass the parameters of the module instead."
																				)
																				deprecate(
																				    "passing a `torch.nn.Module` to `ExponentialMovingAverage`"							,					"1.0.0"							,					UpperCAmelCase							,					standard_warn=UpperCAmelCase							,					)
																				__lowerCamelCase					:		Union[str, Any]		   =     parameters.parameters()

																				# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
																				__lowerCamelCase					:		List[Any]		   =     True

													if kwargs.get("max_value"							,					UpperCAmelCase    ) is not None:
																				__lowerCamelCase					:		Optional[int]		   =     "The `max_value` argument is deprecated. Please use `decay` instead."
																				deprecate("max_value"							,					"1.0.0"							,					UpperCAmelCase							,					standard_warn=UpperCAmelCase    )
																				__lowerCamelCase					:		Optional[int]		   =     kwargs["max_value"]

													if kwargs.get("min_value"							,					UpperCAmelCase    ) is not None:
																				__lowerCamelCase					:		str		   =     "The `min_value` argument is deprecated. Please use `min_decay` instead."
																				deprecate("min_value"							,					"1.0.0"							,					UpperCAmelCase							,					standard_warn=UpperCAmelCase    )
																				__lowerCamelCase					:		List[Any]		   =     kwargs["min_value"]

													__lowerCamelCase					:		List[str]		   =     list(UpperCAmelCase    )
													__lowerCamelCase					:		Dict		   =     [p.clone().detach() for p in parameters]

													if kwargs.get("device"							,					UpperCAmelCase    ) is not None:
																				__lowerCamelCase					:		Optional[int]		   =     "The `device` argument is deprecated. Please use `to` instead."
																				deprecate("device"							,					"1.0.0"							,					UpperCAmelCase							,					standard_warn=UpperCAmelCase    )
																				self.to(device=kwargs["device"]    )

													__lowerCamelCase					:		str		   =     None

													__lowerCamelCase					:		Optional[int]		   =     decay
													__lowerCamelCase					:		Optional[Any]		   =     min_decay
													__lowerCamelCase					:		int		   =     update_after_step
													__lowerCamelCase					:		List[str]		   =     use_ema_warmup
													__lowerCamelCase					:		List[Any]		   =     inv_gamma
													__lowerCamelCase					:		Optional[Any]		   =     power
													__lowerCamelCase					:		Tuple		   =     0
													__lowerCamelCase					:		str		   =     None  # set in `step()`

													__lowerCamelCase					:		Tuple		   =     model_cls
													__lowerCamelCase					:		int		   =     model_config






						@classmethod
						def       lowerCamelCase__     (       cls      :			List[str]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Optional[int]    ):
													__lowerCamelCase   ,  __lowerCamelCase					:		List[Any]		   =     model_cls.load_config(UpperCAmelCase							,					return_unused_kwargs=UpperCAmelCase    )
													__lowerCamelCase					:		Dict		   =     model_cls.from_pretrained(UpperCAmelCase    )

													__lowerCamelCase					:		List[str]		   =     cls(model.parameters()							,					model_cls=UpperCAmelCase							,					model_config=model.config    )

													ema_model.load_state_dict(UpperCAmelCase    )
													return ema_model






						def       lowerCamelCase__     (       self      :			Optional[Any]							,					UpperCAmelCase      :			List[Any]    ):
													if self.model_cls is None:
																				raise ValueError("`save_pretrained` can only be used if `model_cls` was defined at __init__."    )

													if self.model_config is None:
																				raise ValueError("`save_pretrained` can only be used if `model_config` was defined at __init__."    )

													__lowerCamelCase					:		Dict		   =     self.model_cls.from_config(self.model_config    )
													__lowerCamelCase					:		Union[str, Any]		   =     self.state_dict()
													state_dict.pop("shadow_params"							,					UpperCAmelCase    )

													model.register_to_config(**UpperCAmelCase    )
													self.copy_to(model.parameters()    )
													model.save_pretrained(UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Optional[Any]							,					UpperCAmelCase      :			int    ):
													__lowerCamelCase					:		Tuple		   =     max(0							,					optimization_step - self.update_after_step - 1    )

													if step <= 0:
																				return 0.0

													if self.use_ema_warmup:
																				__lowerCamelCase					:		int		   =     1 - (1 + step / self.inv_gamma) ** -self.power
													else:
																				__lowerCamelCase					:		int		   =     (1 + step) / (10 + step)

													__lowerCamelCase					:		Dict		   =     min(UpperCAmelCase							,					self.decay    )
													# make sure decay is not smaller than min_decay
													__lowerCamelCase					:		str		   =     max(UpperCAmelCase							,					self.min_decay    )
													return cur_decay_value






						@torch.no_grad()
						def       lowerCamelCase__     (       self      :			Union[str, Any]							,					UpperCAmelCase      :			Iterable[torch.nn.Parameter]    ):
													if isinstance(UpperCAmelCase							,					torch.nn.Module    ):
																				__lowerCamelCase					:		Dict		   =     (
																				    "Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. "
																				    "Please pass the parameters of the module instead."
																				)
																				deprecate(
																				    "passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"							,					"1.0.0"							,					UpperCAmelCase							,					standard_warn=UpperCAmelCase							,					)
																				__lowerCamelCase					:		Tuple		   =     parameters.parameters()

													__lowerCamelCase					:		Optional[Any]		   =     list(UpperCAmelCase    )

													self.optimization_step += 1

													# Compute the decay factor for the exponential moving average.
													__lowerCamelCase					:		List[str]		   =     self.get_decay(self.optimization_step    )
													__lowerCamelCase					:		Optional[Any]		   =     decay
													__lowerCamelCase					:		Union[str, Any]		   =     1 - decay

													__lowerCamelCase					:		Tuple		   =     contextlib.nullcontext
													if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																				import deepspeed

													for s_param, param in zip(self.shadow_params							,					UpperCAmelCase    ):
																				if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																											__lowerCamelCase					:		Union[str, Any]		   =     deepspeed.zero.GatheredParameters(UpperCAmelCase							,					modifier_rank=UpperCAmelCase    )

																				with context_manager():
																											if param.requires_grad:
																																		s_param.sub_(one_minus_decay * (s_param - param)    )
																											else:
																																		s_param.copy_(UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Tuple							,					UpperCAmelCase      :			Iterable[torch.nn.Parameter]    ):
													__lowerCamelCase					:		Optional[Any]		   =     list(UpperCAmelCase    )
													for s_param, param in zip(self.shadow_params							,					UpperCAmelCase    ):
																				param.data.copy_(s_param.to(param.device    ).data    )






						def       lowerCamelCase__     (       self      :			str							,					UpperCAmelCase      :			str=None							,					UpperCAmelCase      :			List[str]=None    ):
													__lowerCamelCase					:		Any		   =     [
													    p.to(device=UpperCAmelCase							,					dtype=UpperCAmelCase    ) if p.is_floating_point() else p.to(device=UpperCAmelCase    )
													    for p in self.shadow_params
													]






						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													return {
													    "decay": self.decay,
													    "min_decay": self.min_decay,
													    "optimization_step": self.optimization_step,
													    "update_after_step": self.update_after_step,
													    "use_ema_warmup": self.use_ema_warmup,
													    "inv_gamma": self.inv_gamma,
													    "power": self.power,
													    "shadow_params": self.shadow_params,
													}






						def       lowerCamelCase__     (       self      :			Union[str, Any]							,					UpperCAmelCase      :			Iterable[torch.nn.Parameter]    ):
													__lowerCamelCase					:		int		   =     [param.detach().cpu().clone() for param in parameters]






						def       lowerCamelCase__     (       self      :			Dict							,					UpperCAmelCase      :			Iterable[torch.nn.Parameter]    ):
													if self.temp_stored_params is None:
																				raise RuntimeError("This ExponentialMovingAverage has no `store()`ed weights " "to `restore()`"    )
													for c_param, param in zip(self.temp_stored_params							,					UpperCAmelCase    ):
																				param.data.copy_(c_param.data    )

													# Better memory-wise.
													__lowerCamelCase					:		List[Any]		   =     None







						def       lowerCamelCase__     (       self      :			Optional[Any]							,					UpperCAmelCase      :			dict    ):
													__lowerCamelCase					:		Any		   =     copy.deepcopy(UpperCAmelCase    )

													__lowerCamelCase					:		List[str]		   =     state_dict.get("decay"							,					self.decay    )
													if self.decay < 0.0 or self.decay > 1.0:
																				raise ValueError("Decay must be between 0 and 1"    )

													__lowerCamelCase					:		str		   =     state_dict.get("min_decay"							,					self.min_decay    )
													if not isinstance(self.min_decay							,					UpperCAmelCase    ):
																				raise ValueError("Invalid min_decay"    )

													__lowerCamelCase					:		Union[str, Any]		   =     state_dict.get("optimization_step"							,					self.optimization_step    )
													if not isinstance(self.optimization_step							,					UpperCAmelCase    ):
																				raise ValueError("Invalid optimization_step"    )

													__lowerCamelCase					:		Tuple		   =     state_dict.get("update_after_step"							,					self.update_after_step    )
													if not isinstance(self.update_after_step							,					UpperCAmelCase    ):
																				raise ValueError("Invalid update_after_step"    )

													__lowerCamelCase					:		List[Any]		   =     state_dict.get("use_ema_warmup"							,					self.use_ema_warmup    )
													if not isinstance(self.use_ema_warmup							,					UpperCAmelCase    ):
																				raise ValueError("Invalid use_ema_warmup"    )

													__lowerCamelCase					:		List[Any]		   =     state_dict.get("inv_gamma"							,					self.inv_gamma    )
													if not isinstance(self.inv_gamma							,					(float, int)    ):
																				raise ValueError("Invalid inv_gamma"    )

													__lowerCamelCase					:		List[str]		   =     state_dict.get("power"							,					self.power    )
													if not isinstance(self.power							,					(float, int)    ):
																				raise ValueError("Invalid power"    )

													__lowerCamelCase					:		Tuple		   =     state_dict.get("shadow_params"							,					UpperCAmelCase    )
													if shadow_params is not None:
																				__lowerCamelCase					:		int		   =     shadow_params
																				if not isinstance(self.shadow_params							,					UpperCAmelCase    ):
																											raise ValueError("shadow_params must be a list"    )
																				if not all(isinstance(UpperCAmelCase							,					torch.Tensor    ) for p in self.shadow_params    ):
																											raise ValueError("shadow_params must all be Tensors"    )
"""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"""      )