





import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()







def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a  )				->				Optional[Any]:
					__A     :						Any          =    ConvBertConfig.from_json_file(a  )
					__A     :						List[str]          =    ConvBertModel(a  )

					__A     :						Any          =    load_tf_weights_in_convbert(a ,						a ,						a  )
					model.save_pretrained(a  )

					__A     :						str          =    TFConvBertModel.from_pretrained(a ,						from_pt=a  )
					tf_model.save_pretrained(a  )


if __name__ == "__main__":
				UpperCAmelCase						:       str        =					argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
				)
				parser.add_argument(
				    '''--convbert_config_file''',
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        '''The config json file corresponding to the pre-trained ConvBERT model. \n'''
				        '''This specifies the model architecture.'''
				    ),
				)
				parser.add_argument(
				    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
				)
				UpperCAmelCase						:       Optional[Any]        =					parser.parse_args()
				convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)











from __future__ import annotations







def 			_SCREAMING_SNAKE_CASE    (       a  )				->				List[str]:
					global graph, reversed_graph, scc, component, visit, stack
					if visit[u]:
										return
					__A     :						List[Any]          =    True
					for v in graph[u]:
										dfs(a  )
					stack.append(a  )







def 			_SCREAMING_SNAKE_CASE    (       a  )				->				Optional[Any]:
					global graph, reversed_graph, scc, component, visit, stack
					if visit[u]:
										return
					__A     :						str          =    True
					component.append(a  )
					for v in reversed_graph[u]:
										dfsa(a  )







def 			_SCREAMING_SNAKE_CASE    (       )				->				Optional[int]:
					global graph, reversed_graph, scc, component, visit, stack
					for i in range(a  ):
										dfs(a  )
					__A     :						str          =    [False] * n
					for i in stack[::-1]:
										if visit[i]:
															continue
										__A     :						Optional[Any]          =    []
										dfsa(a  )
										scc.append(a  )
					return scc


if __name__ == "__main__":
				# n - no of nodes, m - no of edges
				UpperCAmelCase  ,			UpperCAmelCase						:       List[str]        =					list(map(int, input().strip().split()))

				UpperCAmelCase						:       list[list[int]]        =					[[] for _ in range(n)]  # graph
				UpperCAmelCase						:       list[list[int]]        =					[[] for i in range(n)]  # reversed graph
				# input graph data (edges)
				for _ in range(m):
								UpperCAmelCase  ,			UpperCAmelCase						:       int        =					list(map(int, input().strip().split()))
								graph[u].append(v)
								reversed_graph[v].append(u)

				UpperCAmelCase						:       list[int]        =					[]
				UpperCAmelCase						:       list[bool]        =					[False] * n
				UpperCAmelCase						:       list[int]        =					[]
				UpperCAmelCase						:       list[int]        =					[]
				print(kosaraju())




