


import torch

from transformers import AutoModel


class 	_a							(      torch.nn.Module      ):

							def __init__(							self:     Any   ,						UpperCamelCase_:     Optional[Any]="sayef/fsner-bert-base-uncased"							)      ->       Any:





									"""simple docstring"""







									super(UpperCamelCase_   ,						self							).__init__()

									lowercase__						=				AutoModel.from_pretrained(UpperCamelCase_   ,						return_dict=UpperCamelCase_							)
									lowercase__						=				torch.nn.CosineSimilarity(3   ,						1E-0_8							)
									lowercase__						=				torch.nn.Softmax(dim=1							)

							def 				lowerCamelCase_			(							self:     int   ,						**UpperCamelCase_:     str							)      ->       Union[str, Any]:





									"""simple docstring"""







									return self.bert(**UpperCamelCase_							).last_hidden_state

							def 				lowerCamelCase_			(							self:     Optional[Any]   ,						UpperCamelCase_:     List[str]							)      ->       List[str]:





									"""simple docstring"""







									return token_embeddings.sum(2   ,						keepdim=UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     Optional[int]   ,						UpperCamelCase_:     List[str]   ,						UpperCamelCase_:     Any   ,						UpperCamelCase_:     Dict=1							)      ->       Any:





									"""simple docstring"""







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


							def 				lowerCamelCase_			(							self:     Optional[int]   ,						UpperCamelCase_:     Any   ,						UpperCamelCase_:     Optional[Any]							)      ->       Union[str, Any]:





									"""simple docstring"""







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

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

									lowercase__						=				self.BERT(**UpperCamelCase_							)
									lowercase__						=				self.BERT(**UpperCamelCase_							)

									lowercase__						=				None
									lowercase__						=				None

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

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

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

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

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

									return p_starts, p_ends






from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass


@dataclass
class 	_a							:
							_lowercase     :      int
							_lowercase     :      TreeNode | None  				=			None
							_lowercase     :      TreeNode | None  				=			None
lowerCAmelCase	=							namedtuple('CoinsDistribResult', 'moves excess')
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		if root is None:
				return 0

		# Validation
		def count_nodes(SCREAMING_SNAKE_CASE					) -> int:
				if node is None:
						return 0

				return count_nodes(node.left					) + count_nodes(node.right					) + 1

		def count_coins(SCREAMING_SNAKE_CASE					) -> int:
				if node is None:
						return 0

				return count_coins(node.left					) + count_coins(node.right					) + node.data

		if count_nodes(SCREAMING_SNAKE_CASE					) != count_coins(SCREAMING_SNAKE_CASE					):
				raise ValueError('''The nodes number should be same as the number of coins'''					)

		# Main calculation
		def get_distrib(SCREAMING_SNAKE_CASE					) -> CoinsDistribResult:

				if node is None:
						return CoinsDistribResult(0						,    1					)

				lowercase__					,		lowercase__						=				get_distrib(node.left					)
				lowercase__					,		lowercase__						=				get_distrib(node.right					)

				lowercase__						=				1 - left_distrib_excess
				lowercase__						=				1 - right_distrib_excess

				lowercase__						=				(
				    left_distrib_moves
				    + right_distrib_moves
				    + abs(SCREAMING_SNAKE_CASE					)
				    + abs(SCREAMING_SNAKE_CASE					)
				)
				lowercase__						=				node.data - coins_to_left - coins_to_right

				return CoinsDistribResult(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)

		return get_distrib(SCREAMING_SNAKE_CASE					)[0]


if __name__ == "__main__":
			import doctest

			doctest.testmod()


