





"""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__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase			):






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

							return ma






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






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






def    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase = 1E-4			):
							A						=    mask.expand(*value.shape			)
							return torch.sum(mask * value   ,    dim=lowercase__			) / (eps + torch.sum(lowercase__   ,    dim=lowercase__			))






def    A__							(     UpperCamelCase   ,    UpperCamelCase = 2.31_25   ,    UpperCamelCase = 21.6_875   ,    UpperCamelCase = 64			):
							A						=    torch.linspace(lowercase__   ,    lowercase__   ,    no_bins - 1   ,    device=pts.device			)
							A						=    torch.sqrt(torch.sum((pts.unsqueeze(-2			) - pts.unsqueeze(-3			)) ** 2   ,    dim=-1			)			)
							return torch.bucketize(lowercase__   ,    lowercase__			)






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

							return new_dict






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






def    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase = 0   ,    UpperCamelCase = 0			):
							A						=    []
							for i, s in enumerate(data.shape[:no_batch_dims]			):
														A						=    torch.arange(lowercase__			)
														A						=    r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape			) - i - 1)))			)
														ranges.append(lowercase__			)

							A						=    [slice(lowercase__			) for _ in range(len(data.shape			) - no_batch_dims			)]
							A						=    inds
							ranges.extend(lowercase__			)
							# 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(lowercase__			)]


_snake_case	:						str					=					TypeVar('T')






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

							return new_dict






@overload
def    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase			):
							...






@overload
def    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase			):
							...






@overload
def    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase			):
							...






@overload
def    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase			):
							...






def    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase			):






							if isinstance(lowercase__   ,    lowercase__			):
														return dict_map(lowercase__   ,    lowercase__   ,    lowercase__			)
							elif isinstance(lowercase__   ,    lowercase__			):
														return [tree_map(lowercase__   ,    lowercase__   ,    lowercase__			) for x in tree]
							elif isinstance(lowercase__   ,    lowercase__			):
														return tuple(tree_map(lowercase__   ,    lowercase__   ,    lowercase__			) for x in tree			)
							elif isinstance(lowercase__   ,    lowercase__			):
														return fn(lowercase__			)
							else:
														print(type(lowercase__			)			)
														raise ValueError("Not supported"			)
_snake_case	:						Any					=					partial(tree_map, leaf_type=torch.Tensor)






def 		snake_case_   (				lowercase__     :    str     ):






				'''simple docstring'''






				_lowerCAmelCase  						=53_81
				for x in s:
								_lowerCAmelCase  						=((hash_value << 5) + hash_value) + ord(lowercase__     )
				return hash_value & 0Xffff_ffff

