




from __future__ import annotations




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						List[str]:

    """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        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						List[str]:

    """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        __SCREAMING_SNAKE_CASE (	)							->						Any:

    """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_		,     a_ 		=  list(map(int, input().strip().split()))

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

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














'''simple docstring'''

import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


A:   str						=					get_logger(__name__)

# Datasets
A:   Optional[Any]						=					"https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets"
A:   Any						=					"https://cdn-datasets.huggingface.co/datasets/datasets"
A:   Dict						=					"https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}"

# Metrics
A:   List[str]						=					"https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics"
A:   Any						=					"https://cdn-datasets.huggingface.co/datasets/metric"
A:   List[str]						=					"https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}"

# Hub
A:   str						=					os.environ.get("HF_ENDPOINT", "https://huggingface.co")
A:   List[Any]						=					HF_ENDPOINT + "/datasets/{repo_id}/resolve/{revision}/{path}"
A:   List[Any]						=					"main"

A:   Any						=					version.parse(platform.python_version())

if PY_VERSION < version.parse("3.8"):
						import importlib_metadata
else:
						import importlib.metadata as importlib_metadata

# General environment variables accepted values for booleans
A:   Optional[Any]						=					{"1", "ON", "YES", "TRUE"}
A:   Optional[int]						=					ENV_VARS_TRUE_VALUES.union({"AUTO"})


# Imports
A:   Any						=					version.parse(importlib_metadata.version("dill"))
A:   str						=					version.parse(importlib_metadata.version("pandas"))
A:   Tuple						=					version.parse(importlib_metadata.version("pyarrow"))

A:   str						=					os.environ.get("USE_TF", "AUTO").upper()
A:   List[Any]						=					os.environ.get("USE_TORCH", "AUTO").upper()
A:   Union[str, Any]						=					os.environ.get("USE_JAX", "AUTO").upper()

A:   Dict						=					"N/A"
A:   Optional[int]						=					False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
						A:   List[str]						=					importlib.util.find_spec("torch") is not None
						if TORCH_AVAILABLE:
												try:
																		A:   List[str]						=					version.parse(importlib_metadata.version("torch"))
																		logger.info(f"""PyTorch version {TORCH_VERSION} available.""")
												except importlib_metadata.PackageNotFoundError:
																		pass
else:
						logger.info("Disabling PyTorch because USE_TF is set")

A:   Union[str, Any]						=					"N/A"
A:   Dict						=					False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
						A:   Optional[Any]						=					importlib.util.find_spec("tensorflow") is not None
						if TF_AVAILABLE:
												# For the metadata, we have to look for both tensorflow and tensorflow-cpu
												for package in [
												    "tensorflow",
												    "tensorflow-cpu",
												    "tensorflow-gpu",
												    "tf-nightly",
												    "tf-nightly-cpu",
												    "tf-nightly-gpu",
												    "intel-tensorflow",
												    "tensorflow-rocm",
												    "tensorflow-macos",
												]:
																		try:
																								A:   Any						=					version.parse(importlib_metadata.version(package))
																		except importlib_metadata.PackageNotFoundError:
																								continue
																		else:
																								break
												else:
																		A:   Any						=					False
						if TF_AVAILABLE:
												if TF_VERSION.major < 2:
																		logger.info(f"""TensorFlow found but with version {TF_VERSION}. `datasets` requires version 2 minimum.""")
																		A:   Tuple						=					False
												else:
																		logger.info(f"""TensorFlow version {TF_VERSION} available.""")
else:
						logger.info("Disabling Tensorflow because USE_TORCH is set")


A:   Any						=					"N/A"
A:   int						=					False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
						A:   Dict						=					importlib.util.find_spec("jax") is not None and importlib.util.find_spec("jaxlib") is not None
						if JAX_AVAILABLE:
												try:
																		A:   List[str]						=					version.parse(importlib_metadata.version("jax"))
																		logger.info(f"""JAX version {JAX_VERSION} available.""")
												except importlib_metadata.PackageNotFoundError:
																		pass
else:
						logger.info("Disabling JAX because USE_JAX is set to False")


A:   Optional[Any]						=					os.environ.get("USE_BEAM", "AUTO").upper()
A:   Any						=					"N/A"
A:   List[Any]						=					False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
						try:
												A:   Tuple						=					version.parse(importlib_metadata.version("apache_beam"))
												A:   Dict						=					True
												logger.info(f"""Apache Beam version {BEAM_VERSION} available.""")
						except importlib_metadata.PackageNotFoundError:
												pass
else:
						logger.info("Disabling Apache Beam because USE_BEAM is set to False")


# Optional tools for data loading
A:   Union[str, Any]						=					importlib.util.find_spec("sqlalchemy") is not None

# Optional tools for feature decoding
A:   Optional[int]						=					importlib.util.find_spec("PIL") is not None
A:   Dict						=					importlib.util.find_spec("soundfile") is not None and version.parse(
    importlib.import_module("soundfile").__libsndfile_version__
) >= version.parse("1.0.31")
A:   Any						=					importlib.util.find_spec("soundfile") is not None and version.parse(
    importlib.import_module("soundfile").__libsndfile_version__
) >= version.parse("1.1.0")

# Optional compression tools
A:   Optional[Any]						=					importlib.util.find_spec("rarfile") is not None
A:   Any						=					importlib.util.find_spec("zstandard") is not None
A:   Union[str, Any]						=					importlib.util.find_spec("lz4") is not None
A:   str						=					importlib.util.find_spec("py7zr") is not None


# Cache location
A:   Any						=					"~/.cache"
A:   Tuple						=					os.getenv("XDG_CACHE_HOME", DEFAULT_XDG_CACHE_HOME)
A:   Dict						=					os.path.join(XDG_CACHE_HOME, "huggingface")
A:   List[str]						=					os.path.expanduser(os.getenv("HF_HOME", DEFAULT_HF_CACHE_HOME))

A:   int						=					os.path.join(HF_CACHE_HOME, "datasets")
A:   int						=					Path(os.getenv("HF_DATASETS_CACHE", DEFAULT_HF_DATASETS_CACHE))

A:   int						=					os.path.join(HF_CACHE_HOME, "metrics")
A:   int						=					Path(os.getenv("HF_METRICS_CACHE", DEFAULT_HF_METRICS_CACHE))

A:   Any						=					os.path.join(HF_CACHE_HOME, "modules")
A:   Tuple						=					Path(os.getenv("HF_MODULES_CACHE", DEFAULT_HF_MODULES_CACHE))

A:   List[str]						=					"downloads"
A:   Tuple						=					os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
A:   Union[str, Any]						=					Path(os.getenv("HF_DATASETS_DOWNLOADED_DATASETS_PATH", DEFAULT_DOWNLOADED_DATASETS_PATH))

A:   Tuple						=					"extracted"
A:   Optional[int]						=					os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
A:   Optional[int]						=					Path(os.getenv("HF_DATASETS_EXTRACTED_DATASETS_PATH", DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
A:   Union[str, Any]						=					(
    os.environ.get("HF_UPDATE_DOWNLOAD_COUNTS", "AUTO").upper() in ENV_VARS_TRUE_AND_AUTO_VALUES
)

# Batch size constants. For more info, see:
# https://github.com/apache/arrow/blob/master/docs/source/cpp/arrays.rst#size-limitations-and-recommendations)
A:   List[str]						=					1_0_0_0

# Size of the preloaded record batch in `Dataset.__iter__`
A:   Tuple						=					1_0

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
A:   List[Any]						=					4 << 3_0

# Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
A:   List[str]						=					"500MB"

# Parquet configuration
A:   Union[str, Any]						=					1_0_0
A:   Optional[int]						=					1_0_0
A:   Any						=					1_0_0

# Offline mode
A:   List[str]						=					os.environ.get("HF_DATASETS_OFFLINE", "AUTO").upper() in ENV_VARS_TRUE_VALUES

# In-memory
A:   Any						=					0  # Disabled
A:   List[str]						=					float(os.environ.get("HF_DATASETS_IN_MEMORY_MAX_SIZE", DEFAULT_IN_MEMORY_MAX_SIZE))

# File names
A:   List[str]						=					"dataset.arrow"
A:   Optional[int]						=					"indices.arrow"
A:   List[str]						=					"state.json"
A:   Tuple						=					"dataset_info.json"
A:   Tuple						=					"dataset_infos.json"
A:   Dict						=					"LICENSE"
A:   Optional[Any]						=					"metric_info.json"
A:   Any						=					"dataset_dict.json"

A:   Dict						=					"datasets_modules"

A:   List[str]						=					2_5_5

# Streaming
A:   Any						=					2_0
A:   Dict						=					5

# Datasets without script
A:   Union[str, Any]						=					2_0_0
A:   Union[str, Any]						=					1_0
A:   List[Any]						=					2_0_0

# Progress bars
A:   Tuple						=					0.05  # 20 progress updates per sec
