
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__   (	A_,					A_,					A_ ):






  '''simple docstring'''





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

  return ma

def a__   (	A_,					A_ ):






  '''simple docstring'''





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

def a__   (	A_,					A_ ):






  '''simple docstring'''





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

def a__   (	A_,					A_,					A_,					A_ = 1e-4 ):






  '''simple docstring'''





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

def a__   (	A_,					A_ = 2.3125,					A_ = 21.6875,					A_ = 64 ):






  '''simple docstring'''





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

def a__   (	A_,					A_ ):






  '''simple docstring'''





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

  return new_dict

def a__   (	A_,					A_ ):






  '''simple docstring'''





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

def a__   (	A_,					A_,					A_ = 0,					A_ = 0 ):






  '''simple docstring'''





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

  __magic_name__    					=	[slice(A_ ) for _ in range(len(data.shape ) - no_batch_dims )]
  __magic_name__    					=	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      :					List[Any]   	=       TypeVar('T')

def a__   (	A_,					A_,					A_ ):






  '''simple docstring'''





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

  return new_dict

@overload
def a__   (	A_,					A_,					A_ ):






  '''simple docstring'''





  ...

@overload
def a__   (	A_,					A_,					A_ ):






  '''simple docstring'''





  ...

@overload
def a__   (	A_,					A_,					A_ ):






  '''simple docstring'''





  ...

@overload
def a__   (	A_,					A_,					A_ ):






  '''simple docstring'''





  ...

def a__   (	A_,					A_,					A_ ):






  '''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      :					List[str]   	=       partial(tree_map, leaf_type=torch.Tensor)



from itertools import compress, repeat
from math import ceil, sqrt

def a__   (	A_ ):






  '''simple docstring'''





  if num <= 2:
    return []
  if num == 3:
    return [2]

  # Odd sieve for numbers in range [3, num - 1]
  __magic_name__    					=	bytearray(b"""\x01""" ) * ((num >> 1) - 1)

  for i in range(3,					int(sqrt(A_ ) ) + 1,					2 ):
    if sieve[(i >> 1) - 1]:
      __magic_name__    					=	i**2
      __magic_name__    					=	repeat(
          0,					ceil((num - i_squared) / (i << 1) ) )

  return [2] + list(compress(range(3,					A_,					2 ),					A_ ) )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

