



'''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(					snake_case_    :     torch.Tensor ,       snake_case_    :     torch.Tensor ,       snake_case_    :     bool  )		->torch.Tensor:






							'''simple docstring'''
							# The first operation in a checkpoint can't be in-place, but it's
							# nice to have in-place addition during inference. Thus...
							if not inplace:
														_lowercase							:		Any				=   ma + ma
							else:
														ma += ma

							return ma





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     torch.Tensor ,       snake_case_    :     List[int]  )		->torch.Tensor:






							'''simple docstring'''
							_lowercase							:		Any				=   -1 * len(snake_case_  )
							_lowercase							:		List[Any]				=   list(range(len(tensor.shape[:zero_index]  )  )  )
							return tensor.permute(first_inds + [zero_index + i for i in inds]  )





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     torch.Tensor ,       snake_case_    :     int  )		->torch.Tensor:






							'''simple docstring'''
							return t.reshape(t.shape[:-no_dims] + (-1,)  )





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     torch.Tensor ,       snake_case_    :     torch.Tensor ,       snake_case_    :     int ,       snake_case_    :     float = 1E-4  )		->torch.Tensor:






							'''simple docstring'''
							_lowercase							:		Union[str, Any]				=   mask.expand(*value.shape  )
							return torch.sum(mask * value ,       dim=snake_case_  ) / (eps + torch.sum(snake_case_ ,       dim=snake_case_  ))





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     torch.Tensor ,       snake_case_    :     torch.types.Number = 2.3_1_2_5 ,       snake_case_    :     torch.types.Number = 2_1.6_8_7_5 ,       snake_case_    :     int = 64  )		->torch.Tensor:






							'''simple docstring'''
							_lowercase							:		List[Any]				=   torch.linspace(snake_case_ ,       snake_case_ ,       no_bins - 1 ,       device=pts.device  )
							_lowercase							:		Union[str, Any]				=   torch.sqrt(torch.sum((pts.unsqueeze(-2  ) - pts.unsqueeze(-3  )) ** 2 ,       dim=-1  )  )
							return torch.bucketize(snake_case_ ,       snake_case_  )





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Callable[[list], Any] ,       snake_case_    :     List[dict]  )		->dict:






							'''simple docstring'''
							_lowercase							:		Any				=   dicts[0]
							_lowercase							:		Tuple				=   {}
							for k, v in first.items():
														_lowercase							:		List[str]				=   [d[k] for d in dicts]
														if isinstance(snake_case_ ,       snake_case_  ):
																					_lowercase							:		Dict				=   dict_multimap(snake_case_ ,       snake_case_  )
														else:
																					_lowercase							:		List[str]				=   fn(snake_case_  )

							return new_dict





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






							'''simple docstring'''
							_lowercase							:		Optional[Any]				=   v_bins.view(((1,) * len(x.shape  )) + (len(snake_case_  ),)  )
							_lowercase							:		Optional[int]				=   x[..., None] - reshaped_bins
							_lowercase							:		Optional[int]				=   torch.argmin(torch.abs(snake_case_  ) ,       dim=-1  )
							return nn.functional.one_hot(snake_case_ ,       num_classes=len(snake_case_  )  ).float()





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






							'''simple docstring'''
							_lowercase							:		List[Union[slice, torch.Tensor]]				=   []
							for i, s in enumerate(data.shape[:no_batch_dims]  ):
														_lowercase							:		Tuple				=   torch.arange(snake_case_  )
														_lowercase							:		Optional[int]				=   r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape  ) - i - 1)))  )
														ranges.append(snake_case_  )

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


lowerCamelCase__	  =		TypeVar('T')





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Callable[[T], Any] ,       snake_case_    :     Dict[Any, Union[dict, list, tuple, T]] ,       snake_case_    :     Type[T]  )		->Dict[Any, Union[dict, list, tuple, Any]]:






							'''simple docstring'''
							_lowercase							:		Dict[Any, Union[dict, list, tuple, Any]]				=   {}
							for k, v in dic.items():
														if isinstance(snake_case_ ,       snake_case_  ):
																					_lowercase							:		Any				=   dict_map(snake_case_ ,       snake_case_ ,       snake_case_  )
														else:
																					_lowercase							:		List[str]				=   tree_map(snake_case_ ,       snake_case_ ,       snake_case_  )

							return new_dict





@overload
def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Callable[[T], Any] ,       snake_case_    :     T ,       snake_case_    :     Type[T]  )		->Any:






							'''simple docstring'''
							...





@overload
def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Callable[[T], Any] ,       snake_case_    :     dict ,       snake_case_    :     Type[T]  )		->dict:






							'''simple docstring'''
							...





@overload
def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Callable[[T], Any] ,       snake_case_    :     list ,       snake_case_    :     Type[T]  )		->list:






							'''simple docstring'''
							...





@overload
def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Callable[[T], Any] ,       snake_case_    :     tuple ,       snake_case_    :     Type[T]  )		->tuple:






							'''simple docstring'''
							...





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     int ,       snake_case_    :     Dict ,       snake_case_    :     Optional[int]  )		->Optional[int]:






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




lowerCamelCase__	  =		partial(tree_map, leaf_type=torch.Tensor)








'''simple docstring'''






class 	_lowerCAmelCase     :






		'''simple docstring'''
		def __init__(						self							:       int      ,		UpperCamelCase_							:       int		)      ->			None:







									'''simple docstring'''





									_lowercase							:		Optional[Any]				=   data
									_lowercase							:		int
									_lowercase							:		Node





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Node  )		->None:






							'''simple docstring'''
							_lowercase							:		str				=   0
							_lowercase							:		List[Any]				=   x





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Node ,       snake_case_    :     Node  )		->None:






							'''simple docstring'''
							_lowercase			, _lowercase							:		Union[str, Any]				=   find_set(snake_case_  ), find_set(snake_case_  )
							if x == y:
														return

							elif x.rank > y.rank:
														_lowercase							:		Optional[Any]				=   x
							else:
														_lowercase							:		Optional[int]				=   y
														if x.rank == y.rank:
																					y.rank += 1





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Node  )		->Node:






							'''simple docstring'''
							if x != x.parent:
														_lowercase							:		str				=   find_set(x.parent  )
							return x.parent





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Node  )		->set:






							'''simple docstring'''
							_lowercase							:		str				=   ({0, 1, 2}, {3, 4, 5})
							for s in sets:
														if node.data in s:
																					return s
							_lowercase							:		Optional[Any]				=   F"{node.data} is not in {sets}"
							raise ValueError(snake_case_  )





def 				_SCREAMING_SNAKE_CASE(					)		->None:






							'''simple docstring'''
							_lowercase							:		Any				=   [Node(snake_case_  ) for i in range(6  )]
							for v in vertex:
														make_set(snake_case_  )

							union_set(vertex[0] ,       vertex[1]  )
							union_set(vertex[1] ,       vertex[2]  )
							union_set(vertex[3] ,       vertex[4]  )
							union_set(vertex[3] ,       vertex[5]  )

							for nodea in vertex:
														for nodea in vertex:
																					if find_python_set(snake_case_  ).isdisjoint(find_python_set(snake_case_  )  ):
																												assert find_set(snake_case_  ) != find_set(snake_case_  )
																					else:
																												assert find_set(snake_case_  ) == find_set(snake_case_  )


if __name__ == "__main__":
						test_disjoint_set()



