code: 
  
  
  
  '''simple docstring'''
  
  
  
  from __future__ import annotations
  
  
  
  
  
  
  
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   global graph, reversed_graph, scc, component, visit, stack
   if visit[u]:
    return
   __UpperCamelCase				      =  True
   for v in graph[u]:
    dfs(lowercase__       )
   stack.append(lowercase__       )
  
  
  
  
  
  
  
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   global graph, reversed_graph, scc, component, visit, stack
   if visit[u]:
    return
   __UpperCamelCase				      =  True
   component.append(lowercase__       )
   for v in reversed_graph[u]:
    dfsa(lowercase__       )
  
  
  
  
  
  
  
  def _lowercase   (  ):
  
  
  
   '''simple docstring'''
  
  
  
   global graph, reversed_graph, scc, component, visit, stack
   for i in range(lowercase__       ):
    dfs(lowercase__       )
   __UpperCamelCase				      =  [False] * n
   for i in stack[::-1]:
    if visit[i]:
     continue
    __UpperCamelCase				      =  []
    dfsa(lowercase__       )
    scc.append(lowercase__       )
   return scc
  
  
  if __name__ == "__main__":
   # n - no of nodes, m - no of edges
   a__						:			List[str]										=			list(map(int, input().strip().split()))
  
   a__						:			list[list[int]]										=			[[] for _ in range(n)]  # graph
   a__						:			list[list[int]]										=			[[] for i in range(n)]  # reversed graph
   # input graph data (edges)
   for _ in range(m):
    a__						:			Tuple										=			list(map(int, input().strip().split()))
    graph[u].append(v)
    reversed_graph[v].append(u)
  
   a__						:			list[int]										=			[]
   a__						:			list[bool]										=			[False] * n
   a__						:			list[int]										=			[]
   a__						:			list[int]										=			[]
   print(kosaraju())
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  import argparse
  
  from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  TaConfig.from_json_file(__A       )
   print(f"Building PyTorch model from configuration: {config}"       )
   __UpperCamelCase				      =  TaForConditionalGeneration(__A       )
  
   # Load weights from tf checkpoint
   load_tf_weights_in_ta(__A							,__A							,__A       )
  
   # Save pytorch-model
   print(f"Save PyTorch model to {pytorch_dump_path}"       )
   model.save_pretrained(__A       )
  
  
  if __name__ == "__main__":
   a__						:			int										=			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(
       '--config_file',
       default=None,
       type=str,
       required=True,
       help=(
           'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
       ),
   )
   parser.add_argument(
       '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   a__						:			Optional[Any]										=			parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  
  
  
  
  