





"""simple docstring"""




from sklearn.neural_network import MLPClassifier

lowerCamelCase_					:						Any      = [[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
lowerCamelCase_					:						str      = [0, 1, 0, 0]


lowerCamelCase_					:						List[str]      = MLPClassifier(
    solver='lbfgs', alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


lowerCamelCase_					:						Optional[int]      = [[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
lowerCamelCase_					:						int      = clf.predict(test)





def 						UpperCAmelCase__		(					_UpperCAmelCase ):






  """simple docstring"""





  return list(_UpperCAmelCase )


if __name__ == "__main__":
   import doctest

   doctest.testmod()






"""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 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  if not inplace:
    A_  :	Optional[int]    						=  ma + ma
  else:
    ma += ma

  return ma





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





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





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





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





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase = 1E-4 ):






  """simple docstring"""





  A_  :	Optional[Any]    						=  mask.expand(*value.shape )
  return torch.sum(mask * value			, dim=_UpperCAmelCase ) / (eps + torch.sum(_UpperCAmelCase			, dim=_UpperCAmelCase ))





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase = 2.3_125			, _UpperCAmelCase = 21.6_875			, _UpperCAmelCase = 64 ):






  """simple docstring"""





  A_  :	List[str]    						=  torch.linspace(_UpperCAmelCase			, _UpperCAmelCase			, no_bins - 1			, device=pts.device )
  A_  :	Optional[Any]    						=  torch.sqrt(torch.sum((pts.unsqueeze(-2 ) - pts.unsqueeze(-3 )) ** 2			, dim=-1 ) )
  return torch.bucketize(_UpperCAmelCase			, _UpperCAmelCase )





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  A_  :	Any    						=  dicts[0]
  A_  :	int    						=  {}
  for k, v in first.items():
    A_  :	Dict    						=  [d[k] for d in dicts]
    if isinstance(_UpperCAmelCase			, _UpperCAmelCase ):
      A_  :	List[Any]    						=  dict_multimap(_UpperCAmelCase			, _UpperCAmelCase )
    else:
      A_  :	Union[str, Any]    						=  fn(_UpperCAmelCase )

  return new_dict





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





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





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase = 0			, _UpperCAmelCase = 0 ):






  """simple docstring"""





  A_  :	List[Union[slice, torch.Tensor]]    						=  []
  for i, s in enumerate(data.shape[:no_batch_dims] ):
    A_  :	List[Any]    						=  torch.arange(_UpperCAmelCase )
    A_  :	Dict    						=  r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape ) - i - 1))) )
    ranges.append(_UpperCAmelCase )

  A_  :	List[Union[slice, torch.Tensor]]    						=  [slice(_UpperCAmelCase ) for _ in range(len(data.shape ) - no_batch_dims )]
  A_  :	Optional[Any]    						=  inds
  ranges.extend(_UpperCAmelCase )
  # 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(_UpperCAmelCase )]


lowerCamelCase_					:						Union[str, Any]      = TypeVar('T')





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  A_  :	Dict[Any, Union[dict, list, tuple, Any]]    						=  {}
  for k, v in dic.items():
    if isinstance(_UpperCAmelCase			, _UpperCAmelCase ):
      A_  :	int    						=  dict_map(_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase )
    else:
      A_  :	Any    						=  tree_map(_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase )

  return new_dict





@overload
def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  ...





@overload
def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  ...





@overload
def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  ...





@overload
def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  ...





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





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





lowerCamelCase_					:						Optional[int]      = partial(tree_map, leaf_type=torch.Tensor)