




import torch

from transformers import AutoModel




class 					snake_case__							(torch.nn.Module     ):
    """simple docstring"""



    def __init__(   self       :  int      ,   __lowerCamelCase       :  Tuple="sayef/fsner-bert-base-uncased"							)      ->  Dict:
       super(UpperCAmelCase__      ,   self							).__init__()

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





    def 							__UpperCAmelCase							(   self       :  Union[str, Any]      ,   **__lowerCamelCase       :  Dict							)      ->  Any:
       return self.bert(**UpperCAmelCase__							).last_hidden_state





    def 							__UpperCAmelCase							(   self       :  Optional[int]      ,   __lowerCamelCase       :  List[str]							)      ->  Optional[Any]:
       return token_embeddings.sum(2      ,   keepdim=UpperCAmelCase__							)





    def 							__UpperCAmelCase							(   self       :  Optional[Any]      ,   __lowerCamelCase       :  List[Any]      ,   __lowerCamelCase       :  str      ,   __lowerCamelCase       :  Optional[Any]=1							)      ->  List[Any]:
       return self.softmax(T * self.cos(UpperCAmelCase__      ,   UpperCAmelCase__							)							)





    def 							__UpperCAmelCase							(   self       :  List[str]      ,   __lowerCamelCase       :  int      ,   __lowerCamelCase       :  Dict							)      ->  Optional[Any]:
       a					=    W_supports['''sizes'''].tolist()
       a					=    W_supports['''start_token_id'''].item()
       a					=    W_supports['''end_token_id'''].item()

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

       a					=    self.BERT(**UpperCAmelCase__							)
       a					=    self.BERT(**UpperCAmelCase__							)

       a					=    None
       a					=    None

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

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

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

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

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

       return p_starts, p_ends












# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


__lowerCAmelCase	:       Tuple     =	subprocess.check_output('git merge-base main HEAD'.split()).decode('utf-8')
__lowerCAmelCase	:       Tuple     =	subprocess.check_output(F'''git diff --name-only {fork_point_sha}'''.split()).decode('utf-8').split()

__lowerCAmelCase	:       Dict     =	'|'.join(sys.argv[1:])
__lowerCAmelCase	:       List[Any]     =	re.compile(rF'''^({joined_dirs}).*?\.py$''')

__lowerCAmelCase	:       List[Any]     =	[x for x in modified_files if regex.match(x)]
print(' '.join(relevant_modified_files), end='')






