




'''simple docstring'''





import torch

from transformers import AutoModel

class        _a    (						torch.nn.Module							):






     def __init__(			self ,_SCREAMING_SNAKE_CASE="sayef/fsner-bert-base-uncased"   )							->      List[str]:
          super(_SCREAMING_SNAKE_CASE ,self   ).__init__()

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






     def     _lowercase     (			self ,**_SCREAMING_SNAKE_CASE   )							->      Optional[int]:
          return self.bert(**_SCREAMING_SNAKE_CASE   ).last_hidden_state






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      str:
          return token_embeddings.sum(2 ,keepdim=_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=1   )							->      Dict:
          return self.softmax(T * self.cos(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      Union[str, Any]:
          _snake_case							=				W_supports["sizes"].tolist()
          _snake_case							=				W_supports["start_token_id"].item()
          _snake_case							=				W_supports["end_token_id"].item()

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

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

          _snake_case							=				None
          _snake_case							=				None

          _snake_case							=				W_supports["input_ids"] == start_token_id
          _snake_case							=				W_supports["input_ids"] == end_token_id

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

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

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

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

          return p_starts, p_ends








'''simple docstring'''





def      __a     (       _UpperCamelCase:   int   )						->			None:

     """simple docstring"""




     _snake_case							=				generate_pascal_triangle(_UpperCamelCase   )
     for row_idx in range(_UpperCamelCase   ):
          # Print left spaces
          for _ in range(num_rows - row_idx - 1   ):
               print(end=" "   )
          # Print row values
          for col_idx in range(row_idx + 1   ):
               if col_idx != row_idx:
                    print(triangle[row_idx][col_idx] ,						end=" "   )
               else:
                    print(triangle[row_idx][col_idx] ,						end=""   )
          print()





def      __a     (       _UpperCamelCase:   int   )						->			list[list[int]]:

     """simple docstring"""




     if not isinstance(_UpperCamelCase ,						_UpperCamelCase   ):
          raise TypeError("The input value of 'num_rows' should be 'int'"   )

     if num_rows == 0:
          return []
     elif num_rows < 0:
          raise ValueError(
              "The input value of 'num_rows' should be greater than or equal to 0"   )

     _snake_case							=				[]
     for current_row_idx in range(_UpperCamelCase   ):
          _snake_case							=				populate_current_row(_UpperCamelCase ,						_UpperCamelCase   )
          triangle.append(_UpperCamelCase   )
     return triangle





def      __a     (       _UpperCamelCase:   list[list[int]] ,						_UpperCamelCase:   int   )						->			list[int]:

     """simple docstring"""




     _snake_case							=				[-1] * (current_row_idx + 1)
     # first and last elements of current row are equal to 1
     _snake_case							, _snake_case							=				1, 1
     for current_col_idx in range(1 ,						_UpperCamelCase   ):
          calculate_current_element(
              _UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase   )
     return current_row





def      __a     (       _UpperCamelCase:   list[list[int]] ,						_UpperCamelCase:   list[int] ,						_UpperCamelCase:   int ,						_UpperCamelCase:   int ,						)						->			None:

     """simple docstring"""




     _snake_case							=				triangle[current_row_idx - 1][current_col_idx - 1]
     _snake_case							=				triangle[current_row_idx - 1][current_col_idx]
     _snake_case							=				above_to_left_elt + above_to_right_elt





def      __a     (       _UpperCamelCase:   int   )						->			list[list[int]]:

     """simple docstring"""




     if not isinstance(_UpperCamelCase ,						_UpperCamelCase   ):
          raise TypeError("The input value of 'num_rows' should be 'int'"   )

     if num_rows == 0:
          return []
     elif num_rows < 0:
          raise ValueError(
              "The input value of 'num_rows' should be greater than or equal to 0"   )

     _snake_case							=				[[1]]

     for row_index in range(1 ,						_UpperCamelCase   ):
          _snake_case							=				[0] + result[-1] + [0]
          _snake_case							=				row_index + 1
          # Calculate the number of distinct elements in a row
          _snake_case							=				sum(divmod(_UpperCamelCase ,						2   )   )
          _snake_case							=				[
              temp_row[i - 1] + temp_row[i] for i in range(1 ,						distinct_elements + 1   )
          ]
          _snake_case							=				row_first_half[: (row_index + 1) // 2]
          row_second_half.reverse()
          _snake_case							=				row_first_half + row_second_half
          result.append(_UpperCamelCase   )

     return result





def      __a     (       )						->			None:

     """simple docstring"""




     from collections.abc import Callable
     from timeit import timeit

     def benchmark_a_function(_UpperCamelCase:   Callable ,						_UpperCamelCase:   int   ) -> None:
          _snake_case							=				F"""{func.__name__}({value})"""
          _snake_case							=				timeit(F"""__main__.{call}""" ,						setup="import __main__"   )
          # print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
          print(F"""{call:38} -- {timing:.4f} seconds"""   )

     for value in range(15   ):  # (1, 7, 14):
          for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
               benchmark_a_function(_UpperCamelCase ,						_UpperCamelCase   )
          print()


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   benchmark()


