code: 
  
  
  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 		_A		(		_lowercase				,     _lowercase				,     _lowercase   ):
  
  
       """simple docstring"""
  
  
       if not inplace:
            __UpperCamelCase   = ma + ma
       else:
            ma += ma
  
       return ma
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase   ):
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = -1 * len(_lowercase   )
       __UpperCamelCase   = list(range(len(tensor.shape[:zero_index]   )   )   )
       return tensor.permute(first_inds + [zero_index + i for i in inds]   )
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase   ):
  
  
       """simple docstring"""
  
  
       return t.reshape(t.shape[:-no_dims] + (-1,)   )
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase				,     _lowercase				,     _lowercase = 1e-4   ):
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = mask.expand(*value.shape   )
       return torch.sum(mask * value				,     dim=_lowercase   ) / (eps + torch.sum(_lowercase				,     dim=_lowercase   ))
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase = 2.31_25				,     _lowercase = 21.68_75				,     _lowercase = 64   ):
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = torch.linspace(_lowercase				,     _lowercase				,     no_bins - 1				,     device=pts.device   )
       __UpperCamelCase   = torch.sqrt(torch.sum((pts.unsqueeze(-2   ) - pts.unsqueeze(-3   )) ** 2				,     dim=-1   )   )
       return torch.bucketize(_lowercase				,     _lowercase   )
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase   ):
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = dicts[0]
       __UpperCamelCase   = {}
       for k, v in first.items():
            __UpperCamelCase   = [d[k] for d in dicts]
            if isinstance(_lowercase				,     _lowercase   ):
                 __UpperCamelCase   = dict_multimap(_lowercase				,     _lowercase   )
            else:
                 __UpperCamelCase   = fn(_lowercase   )
  
       return new_dict
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase   ):
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = v_bins.view(((1,) * len(x.shape   )) + (len(_lowercase   ),)   )
       __UpperCamelCase   = x[..., None] - reshaped_bins
       __UpperCamelCase   = torch.argmin(torch.abs(_lowercase   )				,     dim=-1   )
       return nn.functional.one_hot(_lowercase				,     num_classes=len(_lowercase   )   ).float()
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase				,     _lowercase = 0				,     _lowercase = 0   ):
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = []
       for i, s in enumerate(data.shape[:no_batch_dims]   ):
            __UpperCamelCase   = torch.arange(_lowercase   )
            __UpperCamelCase   = r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape   ) - i - 1)))   )
            ranges.append(_lowercase   )
  
       __UpperCamelCase   = [slice(_lowercase   ) for _ in range(len(data.shape   ) - no_batch_dims   )]
       __UpperCamelCase   = inds
       ranges.extend(_lowercase   )
       # 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(_lowercase   )]
  
  
  __snake_case             =	TypeVar('''T''')
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase				,     _lowercase   ):
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = {}
       for k, v in dic.items():
            if isinstance(_lowercase				,     _lowercase   ):
                 __UpperCamelCase   = dict_map(_lowercase				,     _lowercase				,     _lowercase   )
            else:
                 __UpperCamelCase   = tree_map(_lowercase				,     _lowercase				,     _lowercase   )
  
       return new_dict
  
  
  
  
  
  
  @overload
  def 		_A		(		_lowercase				,     _lowercase				,     _lowercase   ):
  
  
       """simple docstring"""
  
  
       ...
  
  
  
  
  
  
  @overload
  def 		_A		(		_lowercase				,     _lowercase				,     _lowercase   ):
  
  
       """simple docstring"""
  
  
       ...
  
  
  
  
  
  
  @overload
  def 		_A		(		_lowercase				,     _lowercase				,     _lowercase   ):
  
  
       """simple docstring"""
  
  
       ...
  
  
  
  
  
  
  @overload
  def 		_A		(		_lowercase				,     _lowercase				,     _lowercase   ):
  
  
       """simple docstring"""
  
  
       ...
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase				,     _lowercase   ):
  
  
       """simple docstring"""
  
  
       if isinstance(_lowercase				,     _lowercase   ):
            return dict_map(_lowercase				,     _lowercase				,     _lowercase   )
       elif isinstance(_lowercase				,     _lowercase   ):
            return [tree_map(_lowercase				,     _lowercase				,     _lowercase   ) for x in tree]
       elif isinstance(_lowercase				,     _lowercase   ):
            return tuple(tree_map(_lowercase				,     _lowercase				,     _lowercase   ) for x in tree   )
       elif isinstance(_lowercase				,     _lowercase   ):
            return fn(_lowercase   )
       else:
            print(type(_lowercase   )   )
            raise ValueError('Not supported'   )
  
  
  __snake_case             =	partial(tree_map, leaf_type=torch.Tensor)
  
  
  
style_context: 
  
  
  from ..utils import DummyObject, requires_backends
  
  
  
  class   __lowerCamelCase (metaclass=_a	):
         _lowercase       							=    ["""torch""", """transformers""", """k_diffusion"""]
  
  
  
  
  
  
  
         def __init__( self:   Tuple,*A_:   Union[str, Any],**A_:   Tuple				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              requires_backends(self,['torch', 'transformers', 'k_diffusion']				)
  
  
  
  
  
  
  
         @classmethod
         def 		snake_case_  ( cls:   int,*A_:   Optional[Any],**A_:   Tuple				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              requires_backends(cls,['torch', 'transformers', 'k_diffusion']				)
  
  
  
  
  
  
  
         @classmethod
         def 		snake_case_  ( cls:   int,*A_:   Tuple,**A_:   Tuple				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              requires_backends(cls,['torch', 'transformers', 'k_diffusion']				)
  
  
  