


"""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  ):
					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 			lowerCamelCase_		:





				"""simple docstring"""







				def __init__(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = 0.9_999       ,		UpperCAmelCase__ = 0.0       ,		UpperCAmelCase__ = 0       ,		UpperCAmelCase__ = False       ,		UpperCAmelCase__ = 1.0       ,		UpperCAmelCase__ = 2 / 3       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		**UpperCAmelCase__       ,		):







									if isinstance(UpperCamelCase__       ,		torch.nn.Module     ):
														SCREAMING_SNAKE_CASE__     							=				(
														    "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__       ,		)
														SCREAMING_SNAKE_CASE__     							=				parameters.parameters()

														# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
														SCREAMING_SNAKE_CASE__     							=				True

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

									if kwargs.get("min_value"       ,		UpperCamelCase__     ) is not None:
														SCREAMING_SNAKE_CASE__     							=				"The `min_value` argument is deprecated. Please use `min_decay` instead."
														deprecate("min_value"       ,		"1.0.0"       ,		UpperCamelCase__       ,		standard_warn=UpperCamelCase__     )
														SCREAMING_SNAKE_CASE__     							=				kwargs["min_value"]

									SCREAMING_SNAKE_CASE__     							=				list(UpperCamelCase__     )
									SCREAMING_SNAKE_CASE__     							=				[p.clone().detach() for p in parameters]

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

									SCREAMING_SNAKE_CASE__     							=				None

									SCREAMING_SNAKE_CASE__     							=				decay
									SCREAMING_SNAKE_CASE__     							=				min_decay
									SCREAMING_SNAKE_CASE__     							=				update_after_step
									SCREAMING_SNAKE_CASE__     							=				use_ema_warmup
									SCREAMING_SNAKE_CASE__     							=				inv_gamma
									SCREAMING_SNAKE_CASE__     							=				power
									SCREAMING_SNAKE_CASE__     							=				0
									SCREAMING_SNAKE_CASE__     							=				None  # set in `step()`

									SCREAMING_SNAKE_CASE__     							=				model_cls
									SCREAMING_SNAKE_CASE__     							=				model_config



				@classmethod
				def   lowerCAmelCase__		(						cls       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
									SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				model_cls.load_config(UpperCamelCase__       ,		return_unused_kwargs=UpperCamelCase__     )
									SCREAMING_SNAKE_CASE__     							=				model_cls.from_pretrained(UpperCamelCase__     )

									SCREAMING_SNAKE_CASE__     							=				cls(model.parameters()       ,		model_cls=UpperCamelCase__       ,		model_config=model.config     )

									ema_model.load_state_dict(UpperCamelCase__     )
									return ema_model



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):







									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__."     )

									SCREAMING_SNAKE_CASE__     							=				self.model_cls.from_config(self.model_config     )
									SCREAMING_SNAKE_CASE__     							=				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       ,		UpperCAmelCase__     ):
									SCREAMING_SNAKE_CASE__     							=				max(0       ,		optimization_step - self.update_after_step - 1     )

									if step <= 0:
														return 0.0

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

									SCREAMING_SNAKE_CASE__     							=				min(UpperCamelCase__       ,		self.decay     )
									# make sure decay is not smaller than min_decay
									SCREAMING_SNAKE_CASE__     							=				max(UpperCamelCase__       ,		self.min_decay     )
									return cur_decay_value



				@torch.no_grad()
				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):







									if isinstance(UpperCamelCase__       ,		torch.nn.Module     ):
														SCREAMING_SNAKE_CASE__     							=				(
														    "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__       ,		)
														SCREAMING_SNAKE_CASE__     							=				parameters.parameters()

									SCREAMING_SNAKE_CASE__     							=				list(UpperCamelCase__     )

									self.optimization_step += 1

									# Compute the decay factor for the exponential moving average.
									SCREAMING_SNAKE_CASE__     							=				self.get_decay(self.optimization_step     )
									SCREAMING_SNAKE_CASE__     							=				decay
									SCREAMING_SNAKE_CASE__     							=				1 - decay

									SCREAMING_SNAKE_CASE__     							=				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():
																			SCREAMING_SNAKE_CASE__     							=				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       ,		UpperCAmelCase__     ):
									SCREAMING_SNAKE_CASE__     							=				list(UpperCamelCase__     )
									for s_param, param in zip(self.shadow_params       ,		UpperCamelCase__     ):
														param.data.copy_(s_param.to(param.device     ).data     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__=None       ,		UpperCAmelCase__=None     ):
									SCREAMING_SNAKE_CASE__     							=				[
									    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     ):
									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       ,		UpperCAmelCase__     ):
									SCREAMING_SNAKE_CASE__     							=				[param.detach().cpu().clone() for param in parameters]



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):







									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.
									SCREAMING_SNAKE_CASE__     							=				None






				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
									SCREAMING_SNAKE_CASE__     							=				copy.deepcopy(UpperCamelCase__     )

									SCREAMING_SNAKE_CASE__     							=				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"     )

									SCREAMING_SNAKE_CASE__     							=				state_dict.get("min_decay"       ,		self.min_decay     )
									if not isinstance(self.min_decay       ,		UpperCamelCase__     ):
														raise ValueError("Invalid min_decay"     )

									SCREAMING_SNAKE_CASE__     							=				state_dict.get("optimization_step"       ,		self.optimization_step     )
									if not isinstance(self.optimization_step       ,		UpperCamelCase__     ):
														raise ValueError("Invalid optimization_step"     )

									SCREAMING_SNAKE_CASE__     							=				state_dict.get("update_after_step"       ,		self.update_after_step     )
									if not isinstance(self.update_after_step       ,		UpperCamelCase__     ):
														raise ValueError("Invalid update_after_step"     )

									SCREAMING_SNAKE_CASE__     							=				state_dict.get("use_ema_warmup"       ,		self.use_ema_warmup     )
									if not isinstance(self.use_ema_warmup       ,		UpperCamelCase__     ):
														raise ValueError("Invalid use_ema_warmup"     )

									SCREAMING_SNAKE_CASE__     							=				state_dict.get("inv_gamma"       ,		self.inv_gamma     )
									if not isinstance(self.inv_gamma       ,		(float, int)     ):
														raise ValueError("Invalid inv_gamma"     )

									SCREAMING_SNAKE_CASE__     							=				state_dict.get("power"       ,		self.power     )
									if not isinstance(self.power       ,		(float, int)     ):
														raise ValueError("Invalid power"     )

									SCREAMING_SNAKE_CASE__     							=				state_dict.get("shadow_params"       ,		UpperCamelCase__     )
									if shadow_params is not None:
														SCREAMING_SNAKE_CASE__     							=				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 argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
	# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
	# setup instructions, if using on-demand hardware

	# If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
	# If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
	# Throw an error if user passes both BYO and on-demand cluster args
	# Otherwise, use default values
	_lowerCamelCase     =  argparse.ArgumentParser()
	parser.add_argument('--user', type=str, default='ubuntu')
	parser.add_argument('--host', type=str, default='localhost')
	parser.add_argument('--key_path', type=str, default=None)
	parser.add_argument('--instance', type=str, default='V100:1')
	parser.add_argument('--provider', type=str, default='cheapest')
	parser.add_argument('--use_spot', type=bool, default=False)
	parser.add_argument('--example', type=str, default='pytorch/text-generation/run_generation.py')
	_lowerCamelCase     ,     _lowerCamelCase     =  parser.parse_known_args()
	if args.host != "localhost":
		if args.instance != "V100:1" or args.provider != "cheapest":
			raise ValueError('Cannot specify both BYO and on-demand cluster args')
		_lowerCamelCase     =  rh.cluster(
		    name='rh-cluster', ips=[args.host], ssh_creds={'ssh_user': args.user, 'ssh_private_key': args.key_path}
		)
	else:
		_lowerCamelCase     =  rh.cluster(
		    name='rh-cluster', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
		)
	_lowerCamelCase     =  args.example.rsplit('/', 1)[0]

	# Set up remote environment
	cluster.install_packages(['pip:./'])  # Installs transformers from local source
	# Note transformers is copied into the home directory on the remote machine, so we can install from there
	cluster.run([f"""pip install -r transformers/examples/{example_dir}/requirements.txt"""])
	cluster.run(['pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'])

	# Run example. You can bypass the CLI wrapper and paste your own code here.
	cluster.run([f"""python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}"""])

	# Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
	# from my_script... import train
	# reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
	# launch_train_gpu = rh.function(fn=train,
	#                                system=gpu,
	#                                reqs=reqs,
	#                                name='train_bert_glue')
	#
	# We can pass in arguments just like we would to a function:
	# launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
	#                  stream_logs=True)
