



'''simple docstring'''






import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel

class        SCREAMING_SNAKE_CASE(							pl.LightningModule    ):







	"""simple docstring"""






	def __init__(		self    :  Any	,    __snake_case    :  Any	)    ->	Any:
								super().__init__()
								UpperCAmelCase				: Optional[int]											=	model
								UpperCAmelCase				: List[str]											=	2
								UpperCAmelCase				: Tuple											=	nn.Linear(self.model.config.hidden_size	,    self.num_labels	)







	def   A			(		self    :  List[str]	)    ->	Tuple:
								pass


def 				snake_case_  (      _lowerCAmelCase						:		str     ,   _lowerCAmelCase						:		str     ,   _lowerCAmelCase						:		str						)   ->     Tuple:
							# load longformer model from model identifier
							UpperCAmelCase				: List[Any]											=	LongformerModel.from_pretrained(_lowerCAmelCase						)
							UpperCAmelCase				: Optional[int]											=	LightningModel(_lowerCAmelCase						)

							UpperCAmelCase				: Union[str, Any]											=	torch.load(_lowerCAmelCase     ,   map_location=torch.device('''cpu'''						)						)
							lightning_model.load_state_dict(ckpt['''state_dict''']						)

							# init longformer question answering model
							UpperCAmelCase				: Dict											=	LongformerForQuestionAnswering.from_pretrained(_lowerCAmelCase						)

							# transfer weights
							longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()						)
							longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()						)
							longformer_for_qa.eval()

							# save model
							longformer_for_qa.save_pretrained(_lowerCAmelCase						)

							print(f"""Conversion successful. Model saved under {pytorch_dump_folder_path}"""						)


if __name__ == "__main__":
						UpperCamelCase__:     str										=    argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--longformer_model",
						    default=None,
						    type=str,
						    required=True,
						    help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
						)
						parser.add_argument(
						    "--longformer_question_answering_ckpt_path",
						    default=None,
						    type=str,
						    required=True,
						    help="Path the official PyTorch Lightning Checkpoint.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						UpperCamelCase__:     Dict										=    parser.parse_args()
						convert_longformer_qa_checkpoint_to_pytorch(
						    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
						)










'''simple docstring'''






def 				snake_case_  (      _lowerCAmelCase						:		Tuple     ,   _lowerCAmelCase						:		Tuple     ,   _lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		Tuple						)   ->     Union[str, Any]:
							# Return True if there is node that has not iterated.
							UpperCAmelCase				: Optional[Any]											=	[False] * len(_lowerCAmelCase						)
							UpperCAmelCase				: Dict											=	[]
							queue.append(_lowerCAmelCase						)
							UpperCAmelCase				: str											=	True

							while queue:
														UpperCAmelCase				: Optional[int]											=	queue.pop(0						)
														for ind in range(len(graph[u]						)						):
																					if visited[ind] is False and graph[u][ind] > 0:
																												queue.append(_lowerCAmelCase						)
																												UpperCAmelCase				: Union[str, Any]											=	True
																												UpperCAmelCase				: Optional[int]											=	u

							return visited[t]


def 				snake_case_  (      _lowerCAmelCase						:		Union[str, Any]     ,   _lowerCAmelCase						:		List[str]     ,   _lowerCAmelCase						:		List[str]						)   ->     Any:
							# This array is filled by BFS and to store path
							UpperCAmelCase				: Dict											=	[-1] * (len(_lowerCAmelCase						))
							UpperCAmelCase				: Any											=	0
							while bfs(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase						):
														UpperCAmelCase				: List[str]											=	float('''Inf'''						)
														UpperCAmelCase				: str											=	sink

														while s != source:
																					# Find the minimum value in select path
																					UpperCAmelCase				: int											=	min(_lowerCAmelCase     ,   graph[parent[s]][s]						)
																					UpperCAmelCase				: Tuple											=	parent[s]

														max_flow += path_flow
														UpperCAmelCase				: Optional[Any]											=	sink

														while v != source:
																					UpperCAmelCase				: Dict											=	parent[v]
																					graph[u][v] -= path_flow
																					graph[v][u] += path_flow
																					UpperCAmelCase				: Dict											=	parent[v]
							return max_flow


UpperCamelCase__:     Tuple										=    [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

UpperCamelCase__			,      UpperCamelCase__:     int										=    0, 5




print(ford_fulkerson(graph, source, sink))





