






'''simple docstring'''

import torch

from transformers import AutoModel






class     a_						(      torch.nn.Module    ):

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE="sayef/fsner-bert-base-uncased"				)  ->				Any:



        """simple docstring"""






        super(_SCREAMING_SNAKE_CASE      ,		self				).__init__()

        UpperCamelCase      = AutoModel.from_pretrained(_SCREAMING_SNAKE_CASE      ,		return_dict=_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = torch.nn.CosineSimilarity(3      ,		1e-08				)
        UpperCamelCase      = torch.nn.Softmax(dim=1				)

  def   A__						(				self      ,		**_SCREAMING_SNAKE_CASE				)  ->				int:



        """simple docstring"""






        return self.bert(**_SCREAMING_SNAKE_CASE				).last_hidden_state

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				List[Any]:



        """simple docstring"""






        return token_embeddings.sum(2      ,		keepdim=_SCREAMING_SNAKE_CASE				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=1				)  ->				Optional[Any]:



        """simple docstring"""






        return self.softmax(T * self.cos(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)				)


  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				int:



        """simple docstring"""






        UpperCamelCase      = W_supports['sizes'].tolist()
        UpperCamelCase      = W_supports['start_token_id'].item()
        UpperCamelCase      = W_supports['end_token_id'].item()

        del W_supports["sizes"]
        del W_supports["start_token_id"]
        del W_supports["end_token_id"]

        UpperCamelCase      = self.BERT(**_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = self.BERT(**_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = None
        UpperCamelCase      = None

        UpperCamelCase      = W_supports['input_ids'] == start_token_id
        UpperCamelCase      = W_supports['input_ids'] == end_token_id

        for i, size in enumerate(_SCREAMING_SNAKE_CASE				):
              if i == 0:
                    UpperCamelCase      = 0
              else:
                    UpperCamelCase      = support_sizes[i - 1]

              UpperCamelCase      = S[s : s + size][start_token_masks[s : s + size]]
              UpperCamelCase      = S[s : s + size][end_token_masks[s : s + size]]

              UpperCamelCase      = torch.matmul(q[i]      ,		s_start.T				).sum(1				).softmax(0				)
              UpperCamelCase      = torch.matmul(q[i]      ,		s_end.T				).sum(1				).softmax(0				)

              if p_starts is not None:
                    UpperCamelCase      = torch.vstack((p_starts, p_start)				)
                    UpperCamelCase      = torch.vstack((p_ends, p_end)				)
              else:
                    UpperCamelCase      = p_start
                    UpperCamelCase      = p_end

        return p_starts, p_ends












'''simple docstring'''

from __future__ import annotations

import collections
import pprint
from pathlib import Path







def    lowercase__			(				__UpperCamelCase   )->			str:
      return "".join(sorted(__UpperCamelCase   )   )







def    lowercase__			(				__UpperCamelCase   )->			list[str]:
      return word_by_signature[signature(__UpperCamelCase   )]


SCREAMING_SNAKE_CASE__     =       Path(__file__).parent.joinpath('words.txt').read_text(encoding='utf-8')
SCREAMING_SNAKE_CASE__     =       sorted({word.strip().lower() for word in data.splitlines()})

SCREAMING_SNAKE_CASE__     =       collections.defaultdict(list)
for word in word_list:
     word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__     =       {word: anagram(word) for word in word_list if len(anagram(word)) > 1}

     with open('anagrams.txt', 'w') as file:
          file.write('all_anagrams = \n ')
          file.write(pprint.pformat(all_anagrams))
