


import torch

from transformers import AutoModel
class 			SCREAMING_SNAKE_CASE__  (    torch.nn.Module      ):
      def __init__(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	str="sayef/fsner-bert-base-uncased"					)		->      Optional[Any]:
         super(__snake_case					,							self					).__init__()

         a_ :		List[str]							=  AutoModel.from_pretrained(__snake_case					,							return_dict=__snake_case					)
         a_ :		Optional[Any]							=  torch.nn.CosineSimilarity(3					,							1E-08					)
         a_ :		Tuple							=  torch.nn.Softmax(dim=1					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					,							**SCREAMING_SNAKE_CASE__    :	int					)		->      Tuple:
         return self.bert(**__snake_case					).last_hidden_state
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]					)		->      Optional[int]:
         return token_embeddings.sum(2					,							keepdim=__snake_case					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					,							SCREAMING_SNAKE_CASE__    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	List[Any]					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=1					)		->      Optional[int]:
         return self.softmax(T * self.cos(__snake_case					,							__snake_case					)					)

      def 	SCREAMING_SNAKE_CASE							(	self    :	int					,							SCREAMING_SNAKE_CASE__    :	int					,							SCREAMING_SNAKE_CASE__    :	Tuple					)		->      int:
         a_ :		List[Any]							=  W_supports['sizes'].tolist()
         a_ :		Any							=  W_supports['start_token_id'].item()
         a_ :		int							=  W_supports['end_token_id'].item()

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

         a_ :		Optional[Any]							=  self.BERT(**__snake_case					)
         a_ :		Tuple							=  self.BERT(**__snake_case					)

         a_ :		Dict							=  None
         a_ :		Optional[Any]							=  None

         a_ :		Union[str, Any]							=  W_supports['input_ids'] == start_token_id
         a_ :		str							=  W_supports['input_ids'] == end_token_id

         for i, size in enumerate(__snake_case					):
            if i == 0:
               a_ :		Optional[int]							=  0
            else:
               a_ :		Tuple							=  support_sizes[i - 1]

            a_ :		Tuple							=  S[s : s + size][start_token_masks[s : s + size]]
            a_ :		int							=  S[s : s + size][end_token_masks[s : s + size]]

            a_ :		Any							=  torch.matmul(q[i]					,							s_start.T					).sum(1					).softmax(0					)
            a_ :		List[str]							=  torch.matmul(q[i]					,							s_end.T					).sum(1					).softmax(0					)

            if p_starts is not None:
               a_ :		List[Any]							=  torch.vstack((p_starts, p_start)					)
               a_ :		List[Any]							=  torch.vstack((p_ends, p_end)					)
            else:
               a_ :		List[Any]							=  p_start
               a_ :		Optional[int]							=  p_end

         return p_starts, p_ends






import json
import sys







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[Any]  ,   __A			:      List[str]    )     ->		Tuple:

       """simple docstring"""

       with open(__A  ,   encoding='utf-8'    ) as f:
              a_ :		Union[str, Any]							=  json.load(__A    )

       a_ :		Any							=  ['<details>', '<summary>Show updated benchmarks!</summary>', ' ']

       for benchmark_name in sorted(__A    ):
              a_ :		List[str]							=  results[benchmark_name]

              a_ :		int							=  benchmark_name.split('/'    )[-1]
              output_md.append(F"""### Benchmark: {benchmark_file_name}"""    )

              a_ :		Any							=  '| metric |'
              a_ :		Optional[Any]							=  '|--------|'
              a_ :		int							=  '| new / old (diff) |'
              for metric_name in sorted(__A    ):
                     a_ :		List[Any]							=  benchmark_res[metric_name]
                     a_ :		int							=  metric_vals['new']
                     a_ :		Union[str, Any]							=  metric_vals.get('old'  ,   __A    )
                     a_ :		Optional[int]							=  metric_vals.get('diff'  ,   __A    )

                     a_ :		str							=  F""" {new_val:f}""" if isinstance(__A  ,   (int, float)    ) else 'None'

                     if old_val is not None:
                            val_str += F""" / {old_val:f}""" if isinstance(__A  ,   (int, float)    ) else "None"
                     if dif_val is not None:
                            val_str += F""" ({dif_val:f})""" if isinstance(__A  ,   (int, float)    ) else "None"

                     title += " " + metric_name + " |"
                     lines += "---|"
                     value += val_str + " |"

              output_md += [title, lines, value, " "]

       output_md.append('</details>'    )

       with open(__A  ,   'w'  ,   encoding='utf-8'    ) as f:
              f.writelines('\n'.join(__A    )    )


if __name__ == "__main__":
   UpperCAmelCase_				:			int   =	sys.argv[1]
   UpperCAmelCase_				:			Any   =	sys.argv[2]

   format_json_to_md(input_json_file, output_md_file)


