





'''simple docstring'''


from hashlib import mda, shaaaa

_lowerCamelCase					:						Optional[int]					     =							(shaaaa, mda)



class 				__UpperCAmelCase	:







		'''simple docstring'''







		def __init__(self    :   str   ,   _lowerCAmelCase    :   int = 8   ):
			A					=   0b0
			A					=   size







		def 							A  (self    :   int   ,   _lowerCAmelCase    :   str   ):
			A					=   self.hash_(UpperCamelCase_   )
			self.bitarray |= h







		def 							A  (self    :   Tuple   ,   _lowerCAmelCase    :   str   ):
			A					=   self.hash_(UpperCamelCase_   )
			return (h & self.bitarray) == h







		def __contains__(self    :   Optional[int]   ,   _lowerCAmelCase    :   str   ):
			return self.exists(UpperCamelCase_   )







		def 							A  (self    :   int   ,   _lowerCAmelCase    :   int   ):
			A					=   bin(UpperCamelCase_   )[2:]
			return res.zfill(self.size   )







		@property
		def 							A  (self    :   Union[str, Any]   ):
			return self.format_bin(self.bitarray   )







		def 							A  (self    :   Union[str, Any]   ,   _lowerCAmelCase    :   str   ):
			A					=   0b0
			for func in HASH_FUNCTIONS:
				A					=   (
				    int.from_bytes(func(value.encode()   ).digest()   ,   """little"""   ) % self.size
				)
				res |= 2**position
			return res







		def 							A  (self    :   Dict   ,   _lowerCAmelCase    :   str   ):
			return self.format_bin(self.hash_(UpperCamelCase_   )   )







		@property
		def 							A  (self    :   List[str]   ):
			A					=   bin(self.bitarray   ).count("""1"""   )
			return (n_ones / self.size) ** len(UpperCamelCase_   )







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    lowerCamelCase__			(		A__			:   torch.Tensor    ,     A__			:   torch.Tensor    ,     A__			:   bool      ):





       '''simple docstring'''



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

       return ma




def    lowerCamelCase__			(		A__			:   torch.Tensor    ,     A__			:   List[int]      ):





       '''simple docstring'''



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




def    lowerCamelCase__			(		A__			:   torch.Tensor    ,     A__			:   int      ):





       '''simple docstring'''



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




def    lowerCamelCase__			(		A__			:   torch.Tensor    ,     A__			:   torch.Tensor    ,     A__			:   int    ,     A__			:   float = 1E-4      ):





       '''simple docstring'''



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




def    lowerCamelCase__			(		A__			:   torch.Tensor    ,     A__			:   torch.types.Number = 2.3_125    ,     A__			:   torch.types.Number = 21.6_875    ,     A__			:   int = 64      ):





       '''simple docstring'''



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




def    lowerCamelCase__			(		A__			:   Callable[[list], Any]    ,     A__			:   List[dict]      ):





       '''simple docstring'''



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

       return new_dict




def    lowerCamelCase__			(		A__			:   torch.Tensor    ,     A__			:   torch.Tensor      ):





       '''simple docstring'''



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




def    lowerCamelCase__			(		A__			:   torch.Tensor    ,     A__			:   torch.Tensor    ,     A__			:   int = 0    ,     A__			:   int = 0      ):





       '''simple docstring'''



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

       __lowerCamelCase  =		[slice(A__      ) for _ in range(len(data.shape      ) - no_batch_dims      )]
       __lowerCamelCase  =		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__      )]


UpperCAmelCase_   							=      TypeVar('T')




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





       '''simple docstring'''



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

       return new_dict




@overload
def    lowerCamelCase__			(		A__			:   Callable[[T], Any]    ,     A__			:   T    ,     A__			:   Type[T]      ):





       '''simple docstring'''



       ...




@overload
def    lowerCamelCase__			(		A__			:   Callable[[T], Any]    ,     A__			:   dict    ,     A__			:   Type[T]      ):





       '''simple docstring'''



       ...




@overload
def    lowerCamelCase__			(		A__			:   Callable[[T], Any]    ,     A__			:   list    ,     A__			:   Type[T]      ):





       '''simple docstring'''



       ...




@overload
def    lowerCamelCase__			(		A__			:   Callable[[T], Any]    ,     A__			:   tuple    ,     A__			:   Type[T]      ):





       '''simple docstring'''



       ...




def    lowerCamelCase__			(		A__			:   Dict    ,     A__			:   List[str]    ,     A__			:   List[str]      ):





       '''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"""      )




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

