





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       (				_SCREAMING_SNAKE_CASE     )			->       List[Any]:


      '''simple docstring'''


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



class 		UpperCamelCase__			:



     '''simple docstring'''

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




           '''simple docstring'''


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

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

           SCREAMING_SNAKE_CASE		=						list(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						[p.clone().detach() for p in parameters]

           if kwargs.get("""device"""						,lowerCamelCase__	) is not None:
                 SCREAMING_SNAKE_CASE		=						"""The `device` argument is deprecated. Please use `to` instead."""
                 deprecate("""device"""						,"""1.0.0"""						,lowerCamelCase__						,standard_warn=lowerCamelCase__	)
                 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     SCREAMING_SNAKE_CASE__  (							cls						:   Optional[int]						,lowerCamelCase__						:   Optional[int]						,lowerCamelCase__						:   Union[str, Any]	)					->		"EMAModel":




           '''simple docstring'''


           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						model_cls.load_config(lowerCamelCase__						,return_unused_kwargs=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						model_cls.from_pretrained(lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						cls(model.parameters()						,model_cls=lowerCamelCase__						,model_config=model.config	)

           ema_model.load_state_dict(lowerCamelCase__	)
           return ema_model






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]						,lowerCamelCase__						:   str	)					->		Tuple:




           '''simple docstring'''


           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"""						,lowerCamelCase__	)

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   int	)					->		float:




           '''simple docstring'''


           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(lowerCamelCase__						,self.decay	)
           # make sure decay is not smaller than min_decay
           SCREAMING_SNAKE_CASE		=						max(lowerCamelCase__						,self.min_decay	)
           return cur_decay_value






     @torch.no_grad()
     def     SCREAMING_SNAKE_CASE__  (							self						:   str						,lowerCamelCase__						:   Iterable[torch.nn.Parameter]	)					->		Optional[Any]:




           '''simple docstring'''


           if isinstance(lowerCamelCase__						,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"""						,lowerCamelCase__						,standard_warn=lowerCamelCase__						,)
                 SCREAMING_SNAKE_CASE		=						parameters.parameters()

           SCREAMING_SNAKE_CASE		=						list(lowerCamelCase__	)

           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						,lowerCamelCase__	):
                 if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                       SCREAMING_SNAKE_CASE		=						deepspeed.zero.GatheredParameters(lowerCamelCase__						,modifier_rank=lowerCamelCase__	)

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]						,lowerCamelCase__						:   Iterable[torch.nn.Parameter]	)					->		None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						list(lowerCamelCase__	)
           for s_param, param in zip(self.shadow_params						,lowerCamelCase__	):
                 param.data.copy_(s_param.to(param.device	).data	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]						,lowerCamelCase__						:   str=None						,lowerCamelCase__						:   int=None	)					->		None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						[
               p.to(device=lowerCamelCase__						,dtype=lowerCamelCase__	) if p.is_floating_point() else p.to(device=lowerCamelCase__	)
               for p in self.shadow_params
           ]






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]	)					->		dict:




           '''simple docstring'''


           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     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]						,lowerCamelCase__						:   Iterable[torch.nn.Parameter]	)					->		None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						[param.detach().cpu().clone() for param in parameters]






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]						,lowerCamelCase__						:   Iterable[torch.nn.Parameter]	)					->		None:




           '''simple docstring'''


           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						,lowerCamelCase__	):
                 param.data.copy_(c_param.data	)

           # Better memory-wise.
           SCREAMING_SNAKE_CASE		=						None






     def     SCREAMING_SNAKE_CASE__  (							self						:   str						,lowerCamelCase__						:   dict	)					->		None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						copy.deepcopy(lowerCamelCase__	)

           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						,lowerCamelCase__	):
                 raise ValueError("""Invalid min_decay"""	)

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









import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict

@dataclass
class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     __snake_case   :     int		     =      ComputeEnvironment.AMAZON_SAGEMAKER
     __snake_case   :     List[Any]		     =      True
     __snake_case   :     Optional[int]		     =      "ml.p3.2xlarge"
     __snake_case   :     List[str]		     =      "accelerate_sagemaker_execution_role"
     __snake_case   :     Tuple		     =      "hf-sm"
     __snake_case   :     Any		     =      "us-east-1"
     __snake_case   :     Union[str, Any]		     =      1
     __snake_case   :     Dict		     =      "accelerate-sagemaker-1"
     __snake_case   :     Tuple		     =      "1.6"
     __snake_case   :     List[str]		     =      "4.4"
     __snake_case   :     str		     =      "train.py"
     __snake_case   :     List[str]		     =      [
         "--model_name_or_path",
         "bert",
         "--do_train",
         "False",
         "--epochs",
         "3",
         "--learning_rate",
         "5e-5",
         "--max_steps",
         "50.5",
     ]
     __snake_case   :     Optional[int]		     =      [
         "--model_name_or_path",
         "bert",
         "--do_train",
         "--do_test",
         "False",
         "--do_predict",
         "--epochs",
         "3",
         "--learning_rate",
         "5e-5",
         "--max_steps",
         "50.5",
     ]

class 		UpperCamelCase__			(				unittest.TestCase       ):



     '''simple docstring'''

     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args	)
           assert isinstance(converted_args["""model_name_or_path"""]						,lowerCamelCase__	)
           assert isinstance(converted_args["""do_train"""]						,lowerCamelCase__	)
           assert isinstance(converted_args["""epochs"""]						,lowerCamelCase__	)
           assert isinstance(converted_args["""learning_rate"""]						,lowerCamelCase__	)
           assert isinstance(converted_args["""max_steps"""]						,lowerCamelCase__	)

           with pytest.raises(lowerCamelCase__	):
                 _convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args	)


