


'''simple docstring'''




def      _a		(lowercase__ :      str	) -> str:






     """simple docstring"""
     __snake_case    			=	word.upper()
     __snake_case    			=	0
     __snake_case    			=	{}
     __snake_case    			=	[]

     for letter in word:
          if letter not in letter_nums:
               __snake_case    			=	str(lowercase__	)
               next_num += 1
          word_pattern.append(letter_nums[letter]	)
     return ".".join(lowercase__	)


if __name__ == "__main__":
       import pprint
       import time

       _a      :      Optional[int]       				=      time.time()
       with open("dictionary.txt") as in_file:
              _a      :      int       				=      in_file.read().splitlines()

       _a      :      dict       				=      {}
       for word in word_list:
              _a      :      int       				=      get_word_pattern(word)
              if pattern in all_patterns:
                     all_patterns[pattern].append(word)
              else:
                     _a      :      Union[str, Any]       				=      [word]

       with open("word_patterns.txt", "w") as out_file:
              out_file.write(pprint.pformat(all_patterns))

       _a      :      Union[str, Any]       				=      round(time.time() - start_time, 2)
       print(f'''Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.''')
       # Done!  9,581 word patterns found in 0.58 seconds.








'''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		(lowercase__ :      torch.Tensor       ,      lowercase__ :      torch.Tensor       ,      lowercase__ :      bool	) -> torch.Tensor:






     """simple docstring"""
     # 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:
          __snake_case    			=	ma + ma
     else:
          ma += ma

     return ma





def      _a		(lowercase__ :      torch.Tensor       ,      lowercase__ :      List[int]	) -> torch.Tensor:






     """simple docstring"""
     __snake_case    			=	-1 * len(lowercase__	)
     __snake_case    			=	list(range(len(tensor.shape[:zero_index]	)	)	)
     return tensor.permute(first_inds + [zero_index + i for i in inds]	)





def      _a		(lowercase__ :      torch.Tensor       ,      lowercase__ :      int	) -> torch.Tensor:






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





def      _a		(lowercase__ :      torch.Tensor       ,      lowercase__ :      torch.Tensor       ,      lowercase__ :      int       ,      lowercase__ :      float = 1e-4	) -> torch.Tensor:






     """simple docstring"""
     __snake_case    			=	mask.expand(*value.shape	)
     return torch.sum(mask * value       ,      dim=lowercase__	) / (eps + torch.sum(lowercase__       ,      dim=lowercase__	))





def      _a		(lowercase__ :      torch.Tensor       ,      lowercase__ :      torch.types.Number = 2.31_25       ,      lowercase__ :      torch.types.Number = 21.68_75       ,      lowercase__ :      int = 6_4	) -> torch.Tensor:






     """simple docstring"""
     __snake_case    			=	torch.linspace(lowercase__       ,      lowercase__       ,      no_bins - 1       ,      device=pts.device	)
     __snake_case    			=	torch.sqrt(torch.sum((pts.unsqueeze(-2	) - pts.unsqueeze(-3	)) ** 2       ,      dim=-1	)	)
     return torch.bucketize(lowercase__       ,      lowercase__	)





def      _a		(lowercase__ :      Callable[[list], Any]       ,      lowercase__ :      List[dict]	) -> dict:






     """simple docstring"""
     __snake_case    			=	dicts[0]
     __snake_case    			=	{}
     for k, v in first.items():
          __snake_case    			=	[d[k] for d in dicts]
          if isinstance(lowercase__       ,      lowercase__	):
               __snake_case    			=	dict_multimap(lowercase__       ,      lowercase__	)
          else:
               __snake_case    			=	fn(lowercase__	)

     return new_dict





def      _a		(lowercase__ :      torch.Tensor       ,      lowercase__ :      torch.Tensor	) -> torch.Tensor:






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





def      _a		(lowercase__ :      torch.Tensor       ,      lowercase__ :      torch.Tensor       ,      lowercase__ :      int = 0       ,      lowercase__ :      int = 0	) -> torch.Tensor:






     """simple docstring"""
     __snake_case    			=	[]
     for i, s in enumerate(data.shape[:no_batch_dims]	):
          __snake_case    			=	torch.arange(lowercase__	)
          __snake_case    			=	r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape	) - i - 1)))	)
          ranges.append(lowercase__	)

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


_a      :      str       				=      TypeVar("T")





def      _a		(lowercase__ :      Callable[[T], Any]       ,      lowercase__ :      Dict[Any, Union[dict, list, tuple, T]]       ,      lowercase__ :      Type[T]	) -> Dict[Any, Union[dict, list, tuple, Any]]:






     """simple docstring"""
     __snake_case    			=	{}
     for k, v in dic.items():
          if isinstance(lowercase__       ,      lowercase__	):
               __snake_case    			=	dict_map(lowercase__       ,      lowercase__       ,      lowercase__	)
          else:
               __snake_case    			=	tree_map(lowercase__       ,      lowercase__       ,      lowercase__	)

     return new_dict





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






     """simple docstring"""
     ...





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






     """simple docstring"""
     ...





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






     """simple docstring"""
     ...





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






     """simple docstring"""
     ...





def      _a		(lowercase__ :      str       ,      lowercase__ :      Any       ,      lowercase__ :      Dict	) -> Union[str, Any]:






     """simple docstring"""
     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'	)


_a      :      Union[str, Any]       				=      partial(tree_map, leaf_type=torch.Tensor)




