





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      )						->      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:
          lowerCAmelCase_       :							Tuple			 =		ma + ma
     else:
          ma += ma

     return ma






def 				_lowerCAmelCase			(	_a :       torch.Tensor       , _a :       List[int]      )						->      torch.Tensor:
     lowerCAmelCase_       :							Optional[int]			 =		-1 * len(_a      )
     lowerCAmelCase_       :							Any			 =		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      )						->      torch.Tensor:
     return t.reshape(t.shape[:-no_dims] + (-1,)      )






def 				_lowerCAmelCase			(	_a :       torch.Tensor       , _a :       torch.Tensor       , _a :       int       , _a :       float = 1E-4      )						->      torch.Tensor:
     lowerCAmelCase_       :							Optional[Any]			 =		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      )						->      torch.Tensor:
     lowerCAmelCase_       :							Dict			 =		torch.linspace(_a       , _a       , no_bins - 1       , device=pts.device      )
     lowerCAmelCase_       :							Optional[int]			 =		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]      )						->      dict:
     lowerCAmelCase_       :							Union[str, Any]			 =		dicts[0]
     lowerCAmelCase_       :							int			 =		{}
     for k, v in first.items():
          lowerCAmelCase_       :							int			 =		[d[k] for d in dicts]
          if isinstance(_a       , _a      ):
               lowerCAmelCase_       :							Optional[int]			 =		dict_multimap(_a       , _a      )
          else:
               lowerCAmelCase_       :							List[str]			 =		fn(_a      )

     return new_dict






def 				_lowerCAmelCase			(	_a :       torch.Tensor       , _a :       torch.Tensor      )						->      torch.Tensor:
     lowerCAmelCase_       :							str			 =		v_bins.view(((1,) * len(x.shape      )) + (len(_a      ),)      )
     lowerCAmelCase_       :							Union[str, Any]			 =		x[..., None] - reshaped_bins
     lowerCAmelCase_       :							Optional[Any]			 =		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      )						->      torch.Tensor:
     lowerCAmelCase_       :							List[Union[slice, torch.Tensor]]			 =		[]
     for i, s in enumerate(data.shape[:no_batch_dims]      ):
          lowerCAmelCase_       :							Union[str, Any]			 =		torch.arange(_a      )
          lowerCAmelCase_       :							Tuple			 =		r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape      ) - i - 1)))      )
          ranges.append(_a      )

     lowerCAmelCase_       :							List[Union[slice, torch.Tensor]]			 =		[slice(_a      ) for _ in range(len(data.shape      ) - no_batch_dims      )]
     lowerCAmelCase_       :							Dict			 =		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_       : List[Any]							=    TypeVar("""T""")






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

     return new_dict






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






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






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






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






def 				_lowerCAmelCase			(	_a :       str       , _a :       str       , _a :       Optional[Any]      )						->      Dict:
     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_       : List[Any]							=    partial(tree_map, leaf_type=torch.Tensor)







def 				_lowerCAmelCase			(	_a :       str      )						->      int:
     lowerCAmelCase_       :							Dict			 =		0
     for plain_chr in plain_text:
          lowerCAmelCase_       :							List[Any]			 =		(
              ord(_a      ) + (hash_value << 6) + (hash_value << 16) - hash_value
          )
     return hash_value
