






'''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 		__a(SCREAMING_SNAKE_CASE_					:   torch.Tensor						,    SCREAMING_SNAKE_CASE_					:   torch.Tensor						,    SCREAMING_SNAKE_CASE_					:   bool      ):



    '''simple docstring'''





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

    return ma


def 		__a(SCREAMING_SNAKE_CASE_					:   torch.Tensor						,    SCREAMING_SNAKE_CASE_					:   List[int]      ):



    '''simple docstring'''





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


def 		__a(SCREAMING_SNAKE_CASE_					:   torch.Tensor						,    SCREAMING_SNAKE_CASE_					:   int      ):



    '''simple docstring'''





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


def 		__a(SCREAMING_SNAKE_CASE_					:   torch.Tensor						,    SCREAMING_SNAKE_CASE_					:   torch.Tensor						,    SCREAMING_SNAKE_CASE_					:   int						,    SCREAMING_SNAKE_CASE_					:   float = 1e-4      ):



    '''simple docstring'''





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


def 		__a(SCREAMING_SNAKE_CASE_					:   torch.Tensor						,    SCREAMING_SNAKE_CASE_					:   torch.types.Number = 2.3125						,    SCREAMING_SNAKE_CASE_					:   torch.types.Number = 21.6875						,    SCREAMING_SNAKE_CASE_					:   int = 64      ):



    '''simple docstring'''





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


def 		__a(SCREAMING_SNAKE_CASE_					:   Callable[[list], Any]						,    SCREAMING_SNAKE_CASE_					:   List[dict]      ):



    '''simple docstring'''





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

    return new_dict


def 		__a(SCREAMING_SNAKE_CASE_					:   torch.Tensor						,    SCREAMING_SNAKE_CASE_					:   torch.Tensor      ):



    '''simple docstring'''





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


def 		__a(SCREAMING_SNAKE_CASE_					:   torch.Tensor						,    SCREAMING_SNAKE_CASE_					:   torch.Tensor						,    SCREAMING_SNAKE_CASE_					:   int = 0						,    SCREAMING_SNAKE_CASE_					:   int = 0      ):



    '''simple docstring'''





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

    _lowerCAmelCase		     = [slice(SCREAMING_SNAKE_CASE_      ) for _ in range(len(data.shape      ) - no_batch_dims      )]
    _lowerCAmelCase		     = inds
    ranges.extend(SCREAMING_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(SCREAMING_SNAKE_CASE_      )]


_SCREAMING_SNAKE_CASE			=							TypeVar("T")


def 		__a(SCREAMING_SNAKE_CASE_					:   Callable[[T], Any]						,    SCREAMING_SNAKE_CASE_					:   Dict[Any, Union[dict, list, tuple, T]]						,    SCREAMING_SNAKE_CASE_					:   Type[T]      ):



    '''simple docstring'''





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

    return new_dict


@overload
def 		__a(SCREAMING_SNAKE_CASE_					:   Callable[[T], Any]						,    SCREAMING_SNAKE_CASE_					:   T						,    SCREAMING_SNAKE_CASE_					:   Type[T]      ):



    '''simple docstring'''





    ...


@overload
def 		__a(SCREAMING_SNAKE_CASE_					:   Callable[[T], Any]						,    SCREAMING_SNAKE_CASE_					:   dict						,    SCREAMING_SNAKE_CASE_					:   Type[T]      ):



    '''simple docstring'''





    ...


@overload
def 		__a(SCREAMING_SNAKE_CASE_					:   Callable[[T], Any]						,    SCREAMING_SNAKE_CASE_					:   list						,    SCREAMING_SNAKE_CASE_					:   Type[T]      ):



    '''simple docstring'''





    ...


@overload
def 		__a(SCREAMING_SNAKE_CASE_					:   Callable[[T], Any]						,    SCREAMING_SNAKE_CASE_					:   tuple						,    SCREAMING_SNAKE_CASE_					:   Type[T]      ):



    '''simple docstring'''





    ...


def 		__a(SCREAMING_SNAKE_CASE_					:   int						,    SCREAMING_SNAKE_CASE_					:   Any						,    SCREAMING_SNAKE_CASE_					:   Optional[Any]      ):



    '''simple docstring'''





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





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









'''simple docstring'''


def 		__a(SCREAMING_SNAKE_CASE_					:   List[Any]						,    SCREAMING_SNAKE_CASE_					:   str      ):



    '''simple docstring'''





    if n < 0:
        return 0
    # table[i] represents the number of ways to get to amount i
    _lowerCAmelCase		     = [0] * (n + 1)

    # There is exactly 1 way to get to zero(You pick no coins).
    _lowerCAmelCase		     = 1

    # Pick all coins one by one and update table[] values
    # after the index greater than or equal to the value of the
    # picked coin
    for coin_val in s:
        for j in range(SCREAMING_SNAKE_CASE_						,    n + 1      ):
            table[j] += table[j - coin_val]

    return table[n]


if __name__ == "__main__":
       import doctest

       doctest.testmod()


