
"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_keras_nlp_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_			    =  {
    '''configuration_gpt2''': ['''GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPT2Config''', '''GPT2OnnxConfig'''],
    '''tokenization_gpt2''': ['''GPT2Tokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_			    =  ['''GPT2TokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_			    =  [
       '''GPT2_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''GPT2DoubleHeadsModel''',
       '''GPT2ForQuestionAnswering''',
       '''GPT2ForSequenceClassification''',
       '''GPT2ForTokenClassification''',
       '''GPT2LMHeadModel''',
       '''GPT2Model''',
       '''GPT2PreTrainedModel''',
       '''load_tf_weights_in_gpt2''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_			    =  [
       '''TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFGPT2DoubleHeadsModel''',
       '''TFGPT2ForSequenceClassification''',
       '''TFGPT2LMHeadModel''',
       '''TFGPT2MainLayer''',
       '''TFGPT2Model''',
       '''TFGPT2PreTrainedModel''',
   ]

try:
   if not is_keras_nlp_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_			    =  ['''TFGPT2Tokenizer''']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_			    =  ['''FlaxGPT2LMHeadModel''', '''FlaxGPT2Model''', '''FlaxGPT2PreTrainedModel''']

if TYPE_CHECKING:
   from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
   from .tokenization_gpta import GPTaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_gpta_fast import GPTaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_gpta import (
          GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
          GPTaDoubleHeadsModel,
          GPTaForQuestionAnswering,
          GPTaForSequenceClassification,
          GPTaForTokenClassification,
          GPTaLMHeadModel,
          GPTaModel,
          GPTaPreTrainedModel,
          load_tf_weights_in_gpta,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_gpta import (
          TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFGPTaDoubleHeadsModel,
          TFGPTaForSequenceClassification,
          TFGPTaLMHeadModel,
          TFGPTaMainLayer,
          TFGPTaModel,
          TFGPTaPreTrainedModel,
      )

   try:
      if not is_keras_nlp_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_gpta_tf import TFGPTaTokenizer

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
   import sys

   SCREAMING_SNAKE_CASE_			    =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





"""simple docstring"""

from __future__ import annotations




def 			lowercase__  (    lowerCAmelCase :    Dict       )       ->					Optional[int]:






    """simple docstring"""




    global graph, reversed_graph, scc, component, visit, stack
    if visit[u]:
        return
    UpperCAmelCase  	=    True
    for v in graph[u]:
        dfs(lowerCAmelCase       )
    stack.append(lowerCAmelCase       )




def 			lowercase__  (    lowerCAmelCase :    Optional[int]       )       ->					int:






    """simple docstring"""




    global graph, reversed_graph, scc, component, visit, stack
    if visit[u]:
        return
    UpperCAmelCase  	=    True
    component.append(lowerCAmelCase       )
    for v in reversed_graph[u]:
        dfsa(lowerCAmelCase       )




def 			lowercase__  (    )       ->					List[str]:






    """simple docstring"""




    global graph, reversed_graph, scc, component, visit, stack
    for i in range(lowerCAmelCase       ):
        dfs(lowerCAmelCase       )
    UpperCAmelCase  	=    [False] * n
    for i in stack[::-1]:
        if visit[i]:
            continue
        UpperCAmelCase  	=    []
        dfsa(lowerCAmelCase       )
        scc.append(lowerCAmelCase       )
    return scc


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

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

   SCREAMING_SNAKE_CASE_			    =  []
   SCREAMING_SNAKE_CASE_			    =  [False] * n
   SCREAMING_SNAKE_CASE_			    =  []
   SCREAMING_SNAKE_CASE_			    =  []
   print(kosaraju())



