





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    (       a ,						a ,						a  )				->				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:
										__A     :						Optional[Any]          =    ma + ma
					else:
										ma += ma

					return ma







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







def 			_SCREAMING_SNAKE_CASE    (       a ,						a  )				->				torch.Tensor:
					return t.reshape(t.shape[:-no_dims] + (-1,)  )







def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a ,						a = 1e-4  )				->				torch.Tensor:
					__A     :						List[Any]          =    mask.expand(*value.shape  )
					return torch.sum(mask * value ,						dim=a  ) / (eps + torch.sum(a ,						dim=a  ))







def 			_SCREAMING_SNAKE_CASE    (       a ,						a = 2.3_125 ,						a = 21.6_875 ,						a = 64  )				->				torch.Tensor:
					__A     :						List[str]          =    torch.linspace(a ,						a ,						no_bins - 1 ,						device=pts.device  )
					__A     :						Tuple          =    torch.sqrt(torch.sum((pts.unsqueeze(-2  ) - pts.unsqueeze(-3  )) ** 2 ,						dim=-1  )  )
					return torch.bucketize(a ,						a  )







def 			_SCREAMING_SNAKE_CASE    (       a ,						a  )				->				dict:
					__A     :						Optional[Any]          =    dicts[0]
					__A     :						Optional[Any]          =    {}
					for k, v in first.items():
										__A     :						Union[str, Any]          =    [d[k] for d in dicts]
										if isinstance(a ,						a  ):
															__A     :						Union[str, Any]          =    dict_multimap(a ,						a  )
										else:
															__A     :						Union[str, Any]          =    fn(a  )

					return new_dict







def 			_SCREAMING_SNAKE_CASE    (       a ,						a  )				->				torch.Tensor:
					__A     :						Tuple          =    v_bins.view(((1,) * len(x.shape  )) + (len(a  ),)  )
					__A     :						Tuple          =    x[..., None] - reshaped_bins
					__A     :						Optional[int]          =    torch.argmin(torch.abs(a  ) ,						dim=-1  )
					return nn.functional.one_hot(a ,						num_classes=len(a  )  ).float()







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

					__A     :						List[Union[slice, torch.Tensor]]          =    [slice(a  ) for _ in range(len(data.shape  ) - no_batch_dims  )]
					__A     :						Optional[int]          =    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						:       Optional[int]        =					TypeVar('''T''')







def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a  )				->				Dict[Any, Union[dict, list, tuple, Any]]:
					__A     :						Dict[Any, Union[dict, list, tuple, Any]]          =    {}
					for k, v in dic.items():
										if isinstance(a ,						a  ):
															__A     :						Optional[Any]          =    dict_map(a ,						a ,						a  )
										else:
															__A     :						Dict          =    tree_map(a ,						a ,						a  )

					return new_dict







@overload
def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a  )				->				Any:
					...







@overload
def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a  )				->				dict:
					...







@overload
def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a  )				->				list:
					...







@overload
def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a  )				->				tuple:
					...







def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a  )				->				List[str]:
					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						:       Dict        =					partial(tree_map, leaf_type=torch.Tensor)











from .hash_table import HashTable





class 						_A( snake_case__       ):


				"""simple docstring"""


				def __init__(      self					, *_A					, **_A					):
									super().__init__(*_A					, **_A					)





				def  UpperCAmelCase_		(      self					, _A					, _A=None					):
									__A     :						Optional[int]          =    1
									__A     :						Union[str, Any]          =    self.hash_function(key + i * i					)

									while self.values[new_key] is not None and self.values[new_key] != key:
														i += 1
														__A     :						Tuple          =    (
														    self.hash_function(key + i * i					)
														    if not self.balanced_factor() >= self.lim_charge
														    else None
														)

														if new_key is None:
																			break

									return new_key




