


'''simple docstring'''

from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types

def _SCREAMING_SNAKE_CASE   (A						,				A						,				A ) -> torch.Tensor:






       """simple docstring"""







       if not inplace:
              lowercase__       =				ma + ma
       else:
              ma += ma

       return ma

def _SCREAMING_SNAKE_CASE   (A						,				A ) -> torch.Tensor:






       """simple docstring"""







       lowercase__       =				-1 * len(A )
       lowercase__       =				list(range(len(tensor.shape[:zero_index] ) ) )
       return tensor.permute(first_inds + [zero_index + i for i in inds] )

def _SCREAMING_SNAKE_CASE   (A						,				A ) -> torch.Tensor:






       """simple docstring"""







       return t.reshape(t.shape[:-no_dims] + (-1,) )

def _SCREAMING_SNAKE_CASE   (A						,				A						,				A						,				A = 1E-4 ) -> torch.Tensor:






       """simple docstring"""







       lowercase__       =				mask.expand(*value.shape )
       return torch.sum(mask * value						,				dim=A ) / (eps + torch.sum(A						,				dim=A ))

def _SCREAMING_SNAKE_CASE   (A						,				A = 2.3_125						,				A = 21.6_875						,				A = 64 ) -> torch.Tensor:






       """simple docstring"""







       lowercase__       =				torch.linspace(A						,				A						,				no_bins - 1						,				device=pts.device )
       lowercase__       =				torch.sqrt(torch.sum((pts.unsqueeze(-2 ) - pts.unsqueeze(-3 )) ** 2						,				dim=-1 ) )
       return torch.bucketize(A						,				A )

def _SCREAMING_SNAKE_CASE   (A						,				A ) -> dict:






       """simple docstring"""







       lowercase__       =				dicts[0]
       lowercase__       =				{}
       for k, v in first.items():
              lowercase__       =				[d[k] for d in dicts]
              if isinstance(A						,				A ):
                     lowercase__       =				dict_multimap(A						,				A )
              else:
                     lowercase__       =				fn(A )

       return new_dict

def _SCREAMING_SNAKE_CASE   (A						,				A ) -> torch.Tensor:






       """simple docstring"""







       lowercase__       =				v_bins.view(((1,) * len(x.shape )) + (len(A ),) )
       lowercase__       =				x[..., None] - reshaped_bins
       lowercase__       =				torch.argmin(torch.abs(A )						,				dim=-1 )
       return nn.functional.one_hot(A						,				num_classes=len(A ) ).float()

def _SCREAMING_SNAKE_CASE   (A						,				A						,				A = 0						,				A = 0 ) -> torch.Tensor:






       """simple docstring"""







       lowercase__       =				[]
       for i, s in enumerate(data.shape[:no_batch_dims] ):
              lowercase__       =				torch.arange(A )
              lowercase__       =				r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape ) - i - 1))) )
              ranges.append(A )

       lowercase__       =				[slice(A ) for _ in range(len(data.shape ) - no_batch_dims )]
       lowercase__       =				inds
       ranges.extend(A )
       # Matt note: Editing this to get around the behaviour of using a list as an array index changing
       # in recent Numpy versions
       return data[tuple(A )]


lowerCamelCase							:     Dict								=			TypeVar('T')

def _SCREAMING_SNAKE_CASE   (A						,				A						,				A ) -> Dict[Any, Union[dict, list, tuple, Any]]:






       """simple docstring"""







       lowercase__       =				{}
       for k, v in dic.items():
              if isinstance(A						,				A ):
                     lowercase__       =				dict_map(A						,				A						,				A )
              else:
                     lowercase__       =				tree_map(A						,				A						,				A )

       return new_dict

@overload
def _SCREAMING_SNAKE_CASE   (A						,				A						,				A ) -> Any:






       """simple docstring"""







       ...

@overload
def _SCREAMING_SNAKE_CASE   (A						,				A						,				A ) -> dict:






       """simple docstring"""







       ...

@overload
def _SCREAMING_SNAKE_CASE   (A						,				A						,				A ) -> list:






       """simple docstring"""







       ...

@overload
def _SCREAMING_SNAKE_CASE   (A						,				A						,				A ) -> tuple:






       """simple docstring"""







       ...

def _SCREAMING_SNAKE_CASE   (A						,				A						,				A ) -> List[str]:






       """simple docstring"""







       if isinstance(A						,				A ):
              return dict_map(A						,				A						,				A )
       elif isinstance(A						,				A ):
              return [tree_map(A						,				A						,				A ) for x in tree]
       elif isinstance(A						,				A ):
              return tuple(tree_map(A						,				A						,				A ) for x in tree )
       elif isinstance(A						,				A ):
              return fn(A )
       else:
              print(type(A ) )
              raise ValueError('''Not supported''' )

lowerCamelCase							:     Any								=			partial(tree_map, leaf_type=torch.Tensor)








'''simple docstring'''

import torch

from accelerate import Accelerator, DistributedType
class 						__lowerCAmelCase		:





      '''simple docstring'''






      def __enter__(self					:					List[Any]				):

             '''simple docstring'''







             if self.enabled:
                    lowercase__       =				self.model.no_sync()
                    self.model_sync_obj.__enter__()

             return self



      def __exit__(self					:					List[str]     ,		UpperCamelCase					:					List[Any]     ,		UpperCamelCase					:					Optional[int]     ,		UpperCamelCase					:					Optional[int]				):

             '''simple docstring'''







             if self.enabled:
                    # Average all models on exit
                    self._sync_and_avg_model_params()
                    self.model_sync_obj.__exit__(UpperCamelCase     ,		UpperCamelCase     ,		UpperCamelCase				)



      def __init__(self					:					Dict     ,		UpperCamelCase					:					Accelerator     ,		UpperCamelCase					:					torch.nn.Module     ,		UpperCamelCase					:					int     ,		UpperCamelCase					:					bool = True				):

             '''simple docstring'''







             if accelerator.distributed_type not in [
                 DistributedType.NO,
                 DistributedType.MULTI_CPU,
                 DistributedType.MULTI_GPU,
             ]:
                    raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''				)
             lowercase__       =				enabled and accelerator.distributed_type != DistributedType.NO
             lowercase__       =				0
             if self.enabled:
                    lowercase__       =				accelerator
                    lowercase__       =				model
                    lowercase__       =				local_sgd_steps



      def    UpperCamelCase__	(self					:					List[str]				):

             '''simple docstring'''







             self.num_steps += 1
             if not self.enabled:
                    return

             if self.num_steps % self.local_sgd_steps == 0:
                    self._sync_and_avg_model_params()






      def    UpperCamelCase__	(self					:					Any				):

             '''simple docstring'''







             self.accelerator.wait_for_everyone()
             with self.accelerator.autocast():
                    for param in self.model.parameters():
                           lowercase__       =				self.accelerator.reduce(param.data     ,		reduction='''mean'''				)




