code: 
  
  
  
  
  
  
  from typing import Optional, Tuple
  
  import flax.linen as nn
  import jax
  import jax.numpy as jnp
  from configuration_hybrid_clip import HybridCLIPConfig
  from flax.core.frozen_dict import FrozenDict
  
  from transformers import FLAX_MODEL_MAPPING, FlaxCLIPVisionModel
  from transformers.modeling_flax_utils import FlaxPreTrainedModel
  from transformers.models.clip.modeling_flax_clip import FlaxCLIPOutput
  from transformers.utils import logging
  
  
  lowerCamelCase										=  logging.get_logger(__name__)
  
  class 					snake_case_							(       nn.Module      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     __lowerCAmelCase      =						42
     __lowerCAmelCase      =						jnp.floataa
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]	)  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.config.text_config
         _lowercase     =	self.config.vision_config
  
         _lowercase     =	self.config.projection_dim
         _lowercase     =	text_config.hidden_size
         _lowercase     =	vision_config.hidden_size
  
         _lowercase     =	FLAX_MODEL_MAPPING[self.config.text_config.__class__].module_class
         _lowercase     =	FLAX_MODEL_MAPPING.get(self.config.vision_config.__class__   ,       snake_case__	).module_class
  
         _lowercase     =	text_module(snake_case__   ,       dtype=self.dtype	)
         _lowercase     =	vision_module(snake_case__   ,       dtype=self.dtype	)
  
         _lowercase     =	nn.Dense(
             self.projection_dim   ,       dtype=self.dtype   ,       kernel_init=jax.nn.initializers.normal(0.02	)   ,       use_bias=snake_case__   ,       )
         _lowercase     =	nn.Dense(
             self.projection_dim   ,       dtype=self.dtype   ,       kernel_init=jax.nn.initializers.normal(0.02	)   ,       use_bias=snake_case__   ,       )
         _lowercase     =	self.param('logit_scale'   ,       jax.nn.initializers.ones   ,       []	)
  
     def __call__(    self:	Any   ,       snake_case__:	Any=None   ,       snake_case__:	List[Any]=None   ,       snake_case__:	Dict=None   ,       snake_case__:	Dict=None   ,       snake_case__:	List[Any]=None   ,       snake_case__:	bool = True   ,       snake_case__:	Optional[Any]=None   ,       snake_case__:	int=None   ,       snake_case__:	int=None   ,       )  ->		Any:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	return_dict if return_dict is not None else self.config.return_dict
  
         _lowercase     =	self.vision_model(
             pixel_values=snake_case__   ,       deterministic=snake_case__   ,       output_attentions=snake_case__   ,       output_hidden_states=snake_case__   ,       return_dict=snake_case__   ,       )
  
         _lowercase     =	self.text_model(
             input_ids=snake_case__   ,       attention_mask=snake_case__   ,       token_type_ids=snake_case__   ,       position_ids=snake_case__   ,       deterministic=snake_case__   ,       output_attentions=snake_case__   ,       output_hidden_states=snake_case__   ,       return_dict=snake_case__   ,       )
  
         _lowercase     =	vision_outputs[1]
         _lowercase     =	self.visual_projection(snake_case__	)
  
         _lowercase     =	text_outputs[1]
         _lowercase     =	self.text_projection(snake_case__	)
  
         # normalized features
         _lowercase     =	image_embeds / jnp.linalg.norm(snake_case__   ,       axis=-1   ,       keepdims=snake_case__	)
         _lowercase     =	text_embeds / jnp.linalg.norm(snake_case__   ,       axis=-1   ,       keepdims=snake_case__	)
  
         # cosine similarity as logits
         _lowercase     =	jnp.exp(self.logit_scale	)
         _lowercase     =	jnp.matmul(snake_case__   ,       image_embeds.T	) * logit_scale
         _lowercase     =	logits_per_text.T
  
         if not return_dict:
             return (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)
  
         return FlaxCLIPOutput(
             logits_per_image=snake_case__   ,       logits_per_text=snake_case__   ,       text_embeds=snake_case__   ,       image_embeds=snake_case__   ,       text_model_output=snake_case__   ,       vision_model_output=snake_case__   ,       )
  
  
  
  
  
  
  class 					snake_case_							(       lowercase_      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     __lowerCAmelCase      =						HybridCLIPConfig
     __lowerCAmelCase      =						FlaxHybridCLIPModule
  
     def __init__(    self:	List[Any]   ,       snake_case__:	HybridCLIPConfig   ,       snake_case__:	Optional[Tuple] = None   ,       snake_case__:	int = 0   ,       snake_case__:	jnp.dtype = jnp.floataa   ,       **snake_case__:	Tuple   ,       )  ->		Tuple:
  
  
         """simple docstring"""
  
  
  
         if input_shape is None:
             _lowercase     =	((1, 1), (1, config.vision_config.image_size, config.vision_config.image_size, 3))
  
         _lowercase     =	self.module_class(config=snake_case__   ,       dtype=snake_case__   ,       **snake_case__	)
         super().__init__(snake_case__   ,       snake_case__   ,       input_shape=snake_case__   ,       seed=snake_case__   ,       dtype=snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Tuple   ,       snake_case__:	jax.random.PRNGKey   ,       snake_case__:	Tuple   ,       snake_case__:	FrozenDict = None	)  ->		FrozenDict:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	jnp.zeros(input_shape[0]   ,       dtype='i4'	)
         _lowercase     =	jnp.broadcast_to(jnp.arange(jnp.atleast_ad(snake_case__	).shape[-1]	)   ,       input_shape[0]	)
         _lowercase     =	jnp.ones_like(snake_case__	)
         _lowercase     =	jnp.ones_like(snake_case__	)
  
         _lowercase     =	jax.random.normal(snake_case__   ,       input_shape[1]	)
  
         _lowercase ,    _lowercase     =	jax.random.split(snake_case__	)
         _lowercase     =	{'params': params_rng, 'dropout': dropout_rng}
  
         return self.module.init(snake_case__   ,       snake_case__   ,       snake_case__   ,       snake_case__   ,       snake_case__   ,       snake_case__	)["params"]
  
     def __call__(    self:	Tuple   ,       snake_case__:	Optional[int]   ,       snake_case__:	Tuple   ,       snake_case__:	List[str]=None   ,       snake_case__:	Optional[int]=None   ,       snake_case__:	int=None   ,       snake_case__:	dict = None   ,       snake_case__:	jax.random.PRNGKey = None   ,       snake_case__:	bool = False   ,       snake_case__:	Optional[bool] = None   ,       snake_case__:	Optional[bool] = None   ,       snake_case__:	Optional[bool] = None   ,       )  ->		List[str]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	output_attentions if output_attentions is not None else self.config.output_attentions
         _lowercase     =	(
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         _lowercase     =	return_dict if return_dict is not None else self.config.return_dict
  
         if position_ids is None:
             _lowercase     =	jnp.broadcast_to(jnp.arange(jnp.atleast_ad(snake_case__	).shape[-1]	)   ,       input_ids.shape	)
  
         if token_type_ids is None:
             _lowercase     =	jnp.zeros_like(snake_case__	)
  
         if attention_mask is None:
             _lowercase     =	jnp.ones_like(snake_case__	)
  
         # Handle any PRNG if needed
         _lowercase     =	{}
         if dropout_rng is not None:
             _lowercase     =	dropout_rng
  
         return self.module.apply(
             {'params': params or self.params}   ,       jnp.array(snake_case__   ,       dtype='i4'	)   ,       jnp.array(snake_case__   ,       dtype=jnp.floataa	)   ,       jnp.array(snake_case__   ,       dtype='i4'	)   ,       jnp.array(snake_case__   ,       dtype='i4'	)   ,       jnp.array(snake_case__   ,       dtype='i4'	)   ,       not train   ,       snake_case__   ,       snake_case__   ,       snake_case__   ,       rngs=snake_case__   ,       )
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[Any]   ,       snake_case__:	Optional[Any]   ,       snake_case__:	Optional[int]=None   ,       snake_case__:	int=None   ,       snake_case__:	List[str]=None   ,       snake_case__:	dict = None   ,       snake_case__:	jax.random.PRNGKey = None   ,       snake_case__:	Optional[Any]=False   ,       )  ->		Union[str, Any]:
  
  
         """simple docstring"""
  
  
  
         if position_ids is None:
             _lowercase     =	jnp.broadcast_to(jnp.arange(jnp.atleast_ad(snake_case__	).shape[-1]	)   ,       input_ids.shape	)
  
         if token_type_ids is None:
             _lowercase     =	jnp.zeros_like(snake_case__	)
  
         if attention_mask is None:
             _lowercase     =	jnp.ones_like(snake_case__	)
  
         # Handle any PRNG if needed
         _lowercase     =	{}
         if dropout_rng is not None:
             _lowercase     =	dropout_rng
  
         def _get_features(snake_case__:	Tuple   ,       snake_case__:	List[Any]   ,       snake_case__:	Optional[Any]   ,       snake_case__:	Tuple   ,       snake_case__:	Union[str, Any]   ,       snake_case__:	Union[str, Any]	):
             _lowercase     =	module.text_model(
                 input_ids=snake_case__   ,       attention_mask=snake_case__   ,       position_ids=snake_case__   ,       token_type_ids=snake_case__   ,       deterministic=snake_case__   ,       )
             _lowercase     =	text_outputs[1]
             _lowercase     =	module.text_projection(snake_case__	)
             return text_features
  
         return self.module.apply(
             {'params': params or self.params}   ,       jnp.array(snake_case__   ,       dtype='i4'	)   ,       jnp.array(snake_case__   ,       dtype='i4'	)   ,       jnp.array(snake_case__   ,       dtype='i4'	)   ,       jnp.array(snake_case__   ,       dtype='i4'	)   ,       not train   ,       method=_get_features   ,       rngs=snake_case__   ,       )
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Dict   ,       snake_case__:	str   ,       snake_case__:	dict = None   ,       snake_case__:	jax.random.PRNGKey = None   ,       snake_case__:	Optional[int]=False	)  ->		Union[str, Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	{}
         if dropout_rng is not None:
             _lowercase     =	dropout_rng
  
         def _get_features(snake_case__:	Any   ,       snake_case__:	Dict   ,       snake_case__:	Any	):
             _lowercase     =	module.vision_model(pixel_values=snake_case__   ,       deterministic=snake_case__	)
             _lowercase     =	vision_outputs[1]  # pooled_output
             _lowercase     =	module.visual_projection(snake_case__	)
             return image_features
  
         return self.module.apply(
             {'params': params or self.params}   ,       jnp.array(snake_case__   ,       dtype=jnp.floataa	)   ,       not train   ,       method=_get_features   ,       rngs=snake_case__   ,       )
  
     @classmethod
     def 			SCREAMING_SNAKE_CASE__				(    cls:	Optional[Any]   ,       snake_case__:	str = None   ,       snake_case__:	str = None   ,       *snake_case__:	List[str]   ,       **snake_case__:	Optional[Any]   ,       )  ->		FlaxPreTrainedModel:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	{
             argument[len('text_'	) :]: value for argument, value in kwargs.items() if argument.startswith('text_'	)
         }
  
         _lowercase     =	{
             argument[len('vision_'	) :]: value for argument, value in kwargs.items() if argument.startswith('vision_'	)
         }
  
         # remove text, vision kwargs from kwargs
         for key in kwargs_text.keys():
             del kwargs["text_" + key]
         for key in kwargs_vision.keys():
             del kwargs["vision_" + key]
  
         # Load and initialize the text and vision model
         _lowercase     =	kwargs_text.pop('model'   ,       snake_case__	)
         if text_model is None:
             assert (
                 text_model_name_or_path is not None
             ), "If `model` is not defined as an argument, a `text_model_name_or_path` has to be defined"
             from transformers import FlaxAutoModel
  
             if "config" not in kwargs_text:
                 from transformers import AutoConfig
  
                 _lowercase     =	AutoConfig.from_pretrained(snake_case__	)
                 _lowercase     =	text_config
  
             _lowercase     =	FlaxAutoModel.from_pretrained(snake_case__   ,       *snake_case__   ,       **snake_case__	)
  
         _lowercase     =	kwargs_vision.pop('model'   ,       snake_case__	)
         if vision_model is None:
             assert (
                 vision_model_name_or_path is not None
             ), "If `model` is not defined as an argument, a `vision_model_name_or_path` has to be defined"
             from transformers import FlaxAutoModel
  
             if "config" not in kwargs_vision:
                 from transformers import AutoConfig
  
                 _lowercase     =	AutoConfig.from_pretrained(snake_case__	)
                 _lowercase     =	vision_config
  
             _lowercase     =	FlaxAutoModel.from_pretrained(snake_case__   ,       *snake_case__   ,       **snake_case__	)
  
         # instantiate config with corresponding kwargs
         _lowercase     =	kwargs.pop('dtype'   ,       jnp.floataa	)
         _lowercase     =	HybridCLIPConfig.from_text_vision_configs(text_model.config   ,       vision_model.config   ,       **snake_case__	)
  
         # init model
         _lowercase     =	cls(snake_case__   ,       *snake_case__   ,       dtype=snake_case__   ,       **snake_case__	)
  
         if vision_config.model_type == "clip":
             _lowercase     =	vision_model.params['vision_model']
             _lowercase     =	vision_model.params['visual_projection']['kernel']
         else:
             _lowercase     =	vision_model.params
  
         _lowercase     =	text_model.params
  
         return model
  
style_context: 
  
  
  
  
  
  
  from __future__ import annotations
  def        UpperCAmelCase      (   _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      global graph, reversed_graph, scc, component, visit, stack
      if visit[u]:
          return
      _lowercase     =	True
      for v in graph[u]:
          dfs(_lowercase      )
      stack.append(_lowercase      )
  def        UpperCAmelCase      (   _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      global graph, reversed_graph, scc, component, visit, stack
      if visit[u]:
          return
      _lowercase     =	True
      component.append(_lowercase      )
      for v in reversed_graph[u]:
          dfsa(_lowercase      )
  def        UpperCAmelCase      (   ):
  
  
      '''simple docstring'''
  
  
  
      global graph, reversed_graph, scc, component, visit, stack
      for i in range(_lowercase      ):
          dfs(_lowercase      )
      _lowercase     =	[False] * n
      for i in stack[::-1]:
          if visit[i]:
              continue
          _lowercase     =	[]
          dfsa(_lowercase      )
          scc.append(_lowercase      )
      return scc
  
  
  if __name__ == "__main__":
     # n - no of nodes, m - no of edges
     lowerCamelCase						,		lowerCamelCase										=  list(map(int, input().strip().split()))
  
     lowerCamelCase										=  [[] for _ in range(n)]  # graph
     lowerCamelCase										=  [[] for i in range(n)]  # reversed graph
     # input graph data (edges)
     for _ in range(m):
        lowerCamelCase						,		lowerCamelCase										=  list(map(int, input().strip().split()))
        graph[u].append(v)
        reversed_graph[v].append(u)
  
     lowerCamelCase										=  []
     lowerCamelCase										=  [False] * n
     lowerCamelCase										=  []
     lowerCamelCase										=  []
     print(kosaraju())
  