
'''simple docstring'''





import importlib
import inspect
import json
import os
from collections import OrderedDict

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
from ...utils import FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer


a__						:				Optional[Any]           =    logging.get_logger(__name__)

a__						:				Optional[Any]           =    OrderedDict(
    [
        ("align", "AlignProcessor"),
        ("altclip", "AltCLIPProcessor"),
        ("bark", "BarkProcessor"),
        ("blip", "BlipProcessor"),
        ("blip-2", "Blip2Processor"),
        ("bridgetower", "BridgeTowerProcessor"),
        ("chinese_clip", "ChineseCLIPProcessor"),
        ("clap", "ClapProcessor"),
        ("clip", "CLIPProcessor"),
        ("clipseg", "CLIPSegProcessor"),
        ("flava", "FlavaProcessor"),
        ("git", "GitProcessor"),
        ("groupvit", "CLIPProcessor"),
        ("hubert", "Wav2Vec2Processor"),
        ("instructblip", "InstructBlipProcessor"),
        ("layoutlmv2", "LayoutLMv2Processor"),
        ("layoutlmv3", "LayoutLMv3Processor"),
        ("markuplm", "MarkupLMProcessor"),
        ("mctct", "MCTCTProcessor"),
        ("mgp-str", "MgpstrProcessor"),
        ("oneformer", "OneFormerProcessor"),
        ("owlvit", "OwlViTProcessor"),
        ("pix2struct", "Pix2StructProcessor"),
        ("sam", "SamProcessor"),
        ("sew", "Wav2Vec2Processor"),
        ("sew-d", "Wav2Vec2Processor"),
        ("speech_to_text", "Speech2TextProcessor"),
        ("speech_to_text_2", "Speech2Text2Processor"),
        ("speecht5", "SpeechT5Processor"),
        ("trocr", "TrOCRProcessor"),
        ("tvlt", "TvltProcessor"),
        ("unispeech", "Wav2Vec2Processor"),
        ("unispeech-sat", "Wav2Vec2Processor"),
        ("vilt", "ViltProcessor"),
        ("vision-text-dual-encoder", "VisionTextDualEncoderProcessor"),
        ("wav2vec2", "Wav2Vec2Processor"),
        ("wav2vec2-conformer", "Wav2Vec2Processor"),
        ("wavlm", "Wav2Vec2Processor"),
        ("whisper", "WhisperProcessor"),
        ("xclip", "XCLIPProcessor"),
    ]
)

a__						:				str           =    _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)





def     snake_case							(						UpperCAmelCase		)->     Union[str, Any]:







						"""simple docstring"""
						for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
												if class_name in processors:
																		__A    		=     model_type_to_module_name(UpperCAmelCase		)

																		__A    		=     importlib.import_module(f'.{module_name}' ,					'transformers.models'		)
																		try:
																								return getattr(UpperCAmelCase ,					UpperCAmelCase		)
																		except AttributeError:
																								continue

						for processor in PROCESSOR_MAPPING._extra_content.values():
												if getattr(UpperCAmelCase ,					'__name__' ,					UpperCAmelCase		) == class_name:
																		return processor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
						__A    		=     importlib.import_module('transformers'		)
						if hasattr(UpperCAmelCase ,					UpperCAmelCase		):
												return getattr(UpperCAmelCase ,					UpperCAmelCase		)

						return None


class 	UpperCamelCase__					:






	def __init__(     self					:Optional[int]     )			->     Union[str, Any]:







							'''simple docstring'''



							raise EnvironmentError(
							    'AutoProcessor is designed to be instantiated '
							    'using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method.'     )






	@classmethod
	@replace_list_option_in_docstrings(_A     )
	def 		lowercase_  (     cls					:List[Any]		,			_A					:List[str]		,			**_A					:Optional[int]     )			->     str:







							'''simple docstring'''



							__A    		=     kwargs.pop('config'		,			_A     )
							__A    		=     kwargs.pop('trust_remote_code'		,			_A     )
							__A    		=     True

							__A    		=     None
							__A    		=     None

							# First, let's see if we have a preprocessor config.
							# Filter the kwargs for `get_file_from_repo`.
							__A    		=     {
							    key: kwargs[key] for key in inspect.signature(_A     ).parameters.keys() if key in kwargs
							}
							# Let's start by checking whether the processor class is saved in an image processor
							__A    		=     get_file_from_repo(
							    _A		,			_A		,			**_A     )
							if preprocessor_config_file is not None:
													__A		, __A    		=     ImageProcessingMixin.get_image_processor_dict(_A		,			**_A     )
													__A    		=     config_dict.get('processor_class'		,			_A     )
													if "AutoProcessor" in config_dict.get('auto_map'		,			{}     ):
																			__A    		=     config_dict['auto_map']['AutoProcessor']

        # If not found, let's check whether the processor class is saved in a feature extractor config
							if preprocessor_config_file is not None and processor_class is None:
													__A		, __A    		=     FeatureExtractionMixin.get_feature_extractor_dict(_A		,			**_A     )
													__A    		=     config_dict.get('processor_class'		,			_A     )
													if "AutoProcessor" in config_dict.get('auto_map'		,			{}     ):
																			__A    		=     config_dict['auto_map']['AutoProcessor']

							if processor_class is None:
													# Next, let's check whether the processor class is saved in a tokenizer
													__A    		=     get_file_from_repo(
													    _A		,			_A		,			**_A     )
													if tokenizer_config_file is not None:
																			with open(_A		,			encoding='utf-8'     ) as reader:
																									__A    		=     json.load(_A     )

																			__A    		=     config_dict.get('processor_class'		,			_A     )
																			if "AutoProcessor" in config_dict.get('auto_map'		,			{}     ):
																									__A    		=     config_dict['auto_map']['AutoProcessor']

							if processor_class is None:
													# Otherwise, load config, if it can be loaded.
													if not isinstance(_A		,			_A     ):
																			__A    		=     AutoConfig.from_pretrained(
																			    _A		,			trust_remote_code=_A		,			**_A     )

													# And check if the config contains the processor class.
													__A    		=     getattr(_A		,			'processor_class'		,			_A     )
													if hasattr(_A		,			'auto_map'     ) and "AutoProcessor" in config.auto_map:
																			__A    		=     config.auto_map['AutoProcessor']

							if processor_class is not None:
													__A    		=     processor_class_from_name(_A     )

							__A    		=     processor_auto_map is not None
							__A    		=     processor_class is not None or type(_A     ) in PROCESSOR_MAPPING
							__A    		=     resolve_trust_remote_code(
							    _A		,			_A		,			_A		,			_A     )

							if has_remote_code and trust_remote_code:
													__A    		=     get_class_from_dynamic_module(
													    _A		,			_A		,			**_A     )
													__A    		=     kwargs.pop('code_revision'		,			_A     )
													if os.path.isdir(_A     ):
																			processor_class.register_for_auto_class()
													return processor_class.from_pretrained(
													    _A		,			trust_remote_code=_A		,			**_A     )
							elif processor_class is not None:
													return processor_class.from_pretrained(
													    _A		,			trust_remote_code=_A		,			**_A     )
							# Last try: we use the PROCESSOR_MAPPING.
							elif type(_A     ) in PROCESSOR_MAPPING:
													return PROCESSOR_MAPPING[type(_A     )].from_pretrained(_A		,			**_A     )

							# At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
							# tokenizer.
							try:
													return AutoTokenizer.from_pretrained(
													    _A		,			trust_remote_code=_A		,			**_A     )
							except Exception:
													try:
																			return AutoImageProcessor.from_pretrained(
																			    _A		,			trust_remote_code=_A		,			**_A     )
													except Exception:
																			pass

													try:
																			return AutoFeatureExtractor.from_pretrained(
																			    _A		,			trust_remote_code=_A		,			**_A     )
													except Exception:
																			pass

							raise ValueError(
							    F'Unrecognized processing class in {pretrained_model_name_or_path}. Can\'t instantiate a processor, a '
							    'tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains'
							    'the files of at least one of those processing classes.'     )






	@staticmethod
	def 		lowercase_  (     _A					:List[str]		,			_A					:Tuple     )			->     List[str]:







							'''simple docstring'''



							PROCESSOR_MAPPING.register(_A		,			_A     )








'''simple docstring'''





from __future__ import annotations





def     snake_case							(						UpperCAmelCase		)->     List[Any]:







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





def     snake_case							(						UpperCAmelCase		)->     List[Any]:







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





def     snake_case							(						)->     Any:







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


if __name__ == "__main__":
 # n - no of nodes, m - no of edges
 a__    ,						a__						:				Tuple           =    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__    ,						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())






