



'''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__				:torch.Tensor   ,				UpperCamelCase__				:torch.Tensor   ,				UpperCamelCase__				:bool			)		->						torch.Tensor:
				# 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:
								snake_case__      :   Union[str, Any]      =    ma + ma
				else:
								ma += ma

				return ma


def 							__UpperCAmelCase				(				UpperCamelCase__				:torch.Tensor   ,				UpperCamelCase__				:List[int]			)		->						torch.Tensor:
				snake_case__      :   Union[str, Any]      =    -1 * len(UpperCamelCase__			)
				snake_case__      :   int      =    list(range(len(tensor.shape[:zero_index]			)			)			)
				return tensor.permute(first_inds + [zero_index + i for i in inds]			)


def 							__UpperCAmelCase				(				UpperCamelCase__				:torch.Tensor   ,				UpperCamelCase__				:int			)		->						torch.Tensor:
				return t.reshape(t.shape[:-no_dims] + (-1,)			)


def 							__UpperCAmelCase				(				UpperCamelCase__				:torch.Tensor   ,				UpperCamelCase__				:torch.Tensor   ,				UpperCamelCase__				:int   ,				UpperCamelCase__				:float = 1e-4			)		->						torch.Tensor:
				snake_case__      :   str      =    mask.expand(*value.shape			)
				return torch.sum(mask * value   ,				dim=UpperCamelCase__			) / (eps + torch.sum(UpperCamelCase__   ,				dim=UpperCamelCase__			))


def 							__UpperCAmelCase				(				UpperCamelCase__				:torch.Tensor   ,				UpperCamelCase__				:torch.types.Number = 2.3_125   ,				UpperCamelCase__				:torch.types.Number = 21.6_875   ,				UpperCamelCase__				:int = 64			)		->						torch.Tensor:
				snake_case__      :   int      =    torch.linspace(UpperCamelCase__   ,				UpperCamelCase__   ,				no_bins - 1   ,				device=pts.device			)
				snake_case__      :   Optional[int]      =    torch.sqrt(torch.sum((pts.unsqueeze(-2			) - pts.unsqueeze(-3			)) ** 2   ,				dim=-1			)			)
				return torch.bucketize(UpperCamelCase__   ,				UpperCamelCase__			)


def 							__UpperCAmelCase				(				UpperCamelCase__				:Callable[[list], Any]   ,				UpperCamelCase__				:List[dict]			)		->						dict:
				snake_case__      :   List[Any]      =    dicts[0]
				snake_case__      :   str      =    {}
				for k, v in first.items():
								snake_case__      :   Optional[Any]      =    [d[k] for d in dicts]
								if isinstance(UpperCamelCase__   ,				UpperCamelCase__			):
												snake_case__      :   Any      =    dict_multimap(UpperCamelCase__   ,				UpperCamelCase__			)
								else:
												snake_case__      :   List[str]      =    fn(UpperCamelCase__			)

				return new_dict


def 							__UpperCAmelCase				(				UpperCamelCase__				:torch.Tensor   ,				UpperCamelCase__				:torch.Tensor			)		->						torch.Tensor:
				snake_case__      :   Tuple      =    v_bins.view(((1,) * len(x.shape			)) + (len(UpperCamelCase__			),)			)
				snake_case__      :   List[Any]      =    x[..., None] - reshaped_bins
				snake_case__      :   Tuple      =    torch.argmin(torch.abs(UpperCamelCase__			)   ,				dim=-1			)
				return nn.functional.one_hot(UpperCamelCase__   ,				num_classes=len(UpperCamelCase__			)			).float()


def 							__UpperCAmelCase				(				UpperCamelCase__				:torch.Tensor   ,				UpperCamelCase__				:torch.Tensor   ,				UpperCamelCase__				:int = 0   ,				UpperCamelCase__				:int = 0			)		->						torch.Tensor:
				snake_case__      :   List[Union[slice, torch.Tensor]]      =    []
				for i, s in enumerate(data.shape[:no_batch_dims]			):
								snake_case__      :   List[Any]      =    torch.arange(UpperCamelCase__			)
								snake_case__      :   Union[str, Any]      =    r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape			) - i - 1)))			)
								ranges.append(UpperCamelCase__			)

				snake_case__      :   List[Union[slice, torch.Tensor]]      =    [slice(UpperCamelCase__			) for _ in range(len(data.shape			) - no_batch_dims			)]
				snake_case__      :   str      =    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__			)]


_lowercase       :						List[Any]						=TypeVar("T")


def 							__UpperCAmelCase				(				UpperCamelCase__				:Callable[[T], Any]   ,				UpperCamelCase__				:Dict[Any, Union[dict, list, tuple, T]]   ,				UpperCamelCase__				:Type[T]			)		->						Dict[Any, Union[dict, list, tuple, Any]]:
				snake_case__      :   Dict[Any, Union[dict, list, tuple, Any]]      =    {}
				for k, v in dic.items():
								if isinstance(UpperCamelCase__   ,				UpperCamelCase__			):
												snake_case__      :   str      =    dict_map(UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__			)
								else:
												snake_case__      :   List[Any]      =    tree_map(UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__			)

				return new_dict


@overload
def 							__UpperCAmelCase				(				UpperCamelCase__				:Callable[[T], Any]   ,				UpperCamelCase__				:T   ,				UpperCamelCase__				:Type[T]			)		->						Any:
				...


@overload
def 							__UpperCAmelCase				(				UpperCamelCase__				:Callable[[T], Any]   ,				UpperCamelCase__				:dict   ,				UpperCamelCase__				:Type[T]			)		->						dict:
				...


@overload
def 							__UpperCAmelCase				(				UpperCamelCase__				:Callable[[T], Any]   ,				UpperCamelCase__				:list   ,				UpperCamelCase__				:Type[T]			)		->						list:
				...


@overload
def 							__UpperCAmelCase				(				UpperCamelCase__				:Callable[[T], Any]   ,				UpperCamelCase__				:tuple   ,				UpperCamelCase__				:Type[T]			)		->						tuple:
				...


def 							__UpperCAmelCase				(				UpperCamelCase__				:List[Any]   ,				UpperCamelCase__				:int   ,				UpperCamelCase__				:Dict			)		->						List[str]:
				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'''			)



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










'''simple docstring'''







from __future__ import annotations





def 		snake_case_				(_a      :				list[list[int]]       ,  _a      :				list[list[int]]	):
    assert len(_a	) == len(_a	)

    UpperCAmelCase     			=						len(_a	)
    UpperCAmelCase     			=						list(range(_a	)	)
    UpperCAmelCase     			=						[-1] * n  # who the donor has donated to
    UpperCAmelCase     			=						[-1] * n  # who the recipient has received from
    UpperCAmelCase     			=						[0] * n

    while unmatched_donors:
        UpperCAmelCase     			=						unmatched_donors[0]
        UpperCAmelCase     			=						donor_pref[donor]
        UpperCAmelCase     			=						donor_preference[num_donations[donor]]
        num_donations[donor] += 1
        UpperCAmelCase     			=						recipient_pref[recipient]
        UpperCAmelCase     			=						rec_record[recipient]

        if prev_donor != -1:
            if rec_preference.index(_a	) > rec_preference.index(_a	):
                UpperCAmelCase     			=						donor
                UpperCAmelCase     			=						recipient
                unmatched_donors.append(_a	)
                unmatched_donors.remove(_a	)
        else:
            UpperCAmelCase     			=						donor
            UpperCAmelCase     			=						recipient
            unmatched_donors.remove(_a	)
    return donor_record




