





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 				SCREAMING_SNAKE_CASE__							(	snake_case__	:int  )   ->    Tuple:
  random.seed(snake_case__  )
  np.random.seed(snake_case__  )
  torch.manual_seed(snake_case__  )
  torch.cuda.manual_seed_all(snake_case__  )
  # ^^ safe to call this function even if cuda is not available







class 					A_					:







       """simple docstring"""





       def __init__(     self						:						Dict    ,__A						:						Iterable[torch.nn.Parameter]    ,__A						:						float = 0.9999    ,__A						:						float = 0.0    ,__A						:						int = 0    ,__A						:						bool = False    ,__A						:						Union[float, int] = 1.0    ,__A						:						Union[float, int] = 2 / 3    ,__A						:						Optional[Any] = None    ,__A						:						Dict[str, Any] = None    ,**__A						:						int    ,)    ->       Tuple:

         if isinstance(__A    ,torch.nn.Module	):
           _lowercase						    =			(
               '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'    ,__A    ,standard_warn=__A    ,)
           _lowercase						    =			parameters.parameters()

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

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

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

         _lowercase						    =			list(__A	)
         _lowercase						    =			[p.clone().detach() for p in parameters]

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

         _lowercase						    =			None

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

         _lowercase						    =			model_cls
         _lowercase						    =			model_config







       @classmethod
       def  __UpperCAmelCase			(     cls						:						Union[str, Any]    ,__A						:						int    ,__A						:						List[Any]	)    ->       "EMAModel":
         _lowercase    ,		_lowercase						    =			model_cls.load_config(__A    ,return_unused_kwargs=__A	)
         _lowercase						    =			model_cls.from_pretrained(__A	)

         _lowercase						    =			cls(model.parameters()    ,model_cls=__A    ,model_config=model.config	)

         ema_model.load_state_dict(__A	)
         return ema_model







       def  __UpperCAmelCase			(     self						:						Optional[Any]    ,__A						:						Optional[Any]	)    ->       Union[str, 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__.'	)

         _lowercase						    =			self.model_cls.from_config(self.model_config	)
         _lowercase						    =			self.state_dict()
         state_dict.pop('shadow_params'    ,__A	)

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







       def  __UpperCAmelCase			(     self						:						Optional[Any]    ,__A						:						int	)    ->       float:
         _lowercase						    =			max(0    ,optimization_step - self.update_after_step - 1	)

         if step <= 0:
           return 0.0

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

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







       @torch.no_grad()
       def  __UpperCAmelCase			(     self						:						Optional[int]    ,__A						:						Iterable[torch.nn.Parameter]	)    ->       Optional[Any]:
         if isinstance(__A    ,torch.nn.Module	):
           _lowercase						    =			(
               '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'    ,__A    ,standard_warn=__A    ,)
           _lowercase						    =			parameters.parameters()

         _lowercase						    =			list(__A	)

         self.optimization_step += 1

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

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

         for s_param, param in zip(self.shadow_params    ,__A	):
           if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
             _lowercase						    =			deepspeed.zero.GatheredParameters(__A    ,modifier_rank=__A	)

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







       def  __UpperCAmelCase			(     self						:						Any    ,__A						:						Iterable[torch.nn.Parameter]	)    ->       None:
         _lowercase						    =			list(__A	)
         for s_param, param in zip(self.shadow_params    ,__A	):
           param.data.copy_(s_param.to(param.device	).data	)







       def  __UpperCAmelCase			(     self						:						Any    ,__A						:						Optional[int]=None    ,__A						:						Union[str, Any]=None	)    ->       None:
         _lowercase						    =			[
             p.to(device=__A    ,dtype=__A	) if p.is_floating_point() else p.to(device=__A	)
             for p in self.shadow_params
         ]







       def  __UpperCAmelCase			(     self						:						Optional[Any]	)    ->       dict:
         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  __UpperCAmelCase			(     self						:						Dict    ,__A						:						Iterable[torch.nn.Parameter]	)    ->       None:
         _lowercase						    =			[param.detach().cpu().clone() for param in parameters]







       def  __UpperCAmelCase			(     self						:						int    ,__A						:						Iterable[torch.nn.Parameter]	)    ->       None:
         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    ,__A	):
           param.data.copy_(c_param.data	)

         # Better memory-wise.
         _lowercase						    =			None







       def  __UpperCAmelCase			(     self						:						List[str]    ,__A						:						dict	)    ->       None:
         _lowercase						    =			copy.deepcopy(__A	)

         _lowercase						    =			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'	)

         _lowercase						    =			state_dict.get('min_decay'    ,self.min_decay	)
         if not isinstance(self.min_decay    ,__A	):
           raise ValueError('Invalid min_decay'	)

         _lowercase						    =			state_dict.get('optimization_step'    ,self.optimization_step	)
         if not isinstance(self.optimization_step    ,__A	):
           raise ValueError('Invalid optimization_step'	)

         _lowercase						    =			state_dict.get('update_after_step'    ,self.update_after_step	)
         if not isinstance(self.update_after_step    ,__A	):
           raise ValueError('Invalid update_after_step'	)

         _lowercase						    =			state_dict.get('use_ema_warmup'    ,self.use_ema_warmup	)
         if not isinstance(self.use_ema_warmup    ,__A	):
           raise ValueError('Invalid use_ema_warmup'	)

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

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

         _lowercase						    =			state_dict.get('shadow_params'    ,__A	)
         if shadow_params is not None:
           _lowercase						    =			shadow_params
           if not isinstance(self.shadow_params    ,__A	):
             raise ValueError('shadow_params must be a list'	)
           if not all(isinstance(__A    ,torch.Tensor	) for p in self.shadow_params	):
             raise ValueError('shadow_params must all be Tensors'	)

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
 _snake_case      = 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''')
 _snake_case  ,      _snake_case      = 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''')
  _snake_case      = rh.cluster(
      name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
  )
 else:
  _snake_case      = rh.cluster(
      name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
  )
 _snake_case      = 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)


