code: 
  
  
  '''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)
  
  
  
  
  
style_context: 
  
  
  '''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'''				)
  
  
  
  
  