
from collections import deque





def     _a					(						lowerCamelCase							):
 lowerCamelCase		:	Dict							=      len(lowerCamelCase							)
 lowerCamelCase		:	Optional[Any]							=      deque()
 lowerCamelCase		:	Optional[Any]							=      [False for _ in range(lowerCamelCase							)]
 lowerCamelCase		:	Optional[int]							=      [-1 for _ in range(lowerCamelCase							)]
 lowerCamelCase		:	Tuple							=      index_of[:]

 def strong_connect(lowerCamelCase,	lowerCamelCase,	lowerCamelCase							):
  lowerCamelCase		:	List[Any]							=      index  # the number when this node is seen
  lowerCamelCase		:	List[str]							=      index  # lowest rank node reachable from here
  index += 1
  stack.append(lowerCamelCase							)
  lowerCamelCase		:	Tuple							=      True

  for w in g[v]:
   if index_of[w] == -1:
    lowerCamelCase		:	int							=      strong_connect(lowerCamelCase,	lowerCamelCase,	lowerCamelCase							)
    lowerCamelCase		:	List[str]							=      (
        lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
    )
   elif on_stack[w]:
    lowerCamelCase		:	str							=      (
        lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
    )

  if lowlink_of[v] == index_of[v]:
   lowerCamelCase		:	Optional[int]							=      []
   lowerCamelCase		:	str							=      stack.pop()
   lowerCamelCase		:	int							=      False
   component.append(lowerCamelCase							)
   while w != v:
    lowerCamelCase		:	Tuple							=      stack.pop()
    lowerCamelCase		:	int							=      False
    component.append(lowerCamelCase							)
   components.append(lowerCamelCase							)
  return index

 lowerCamelCase		:	Any							=      []
 for v in range(lowerCamelCase							):
  if index_of[v] == -1:
   strong_connect(lowerCamelCase,	0,	lowerCamelCase							)

 return components





def     _a					(						lowerCamelCase,	lowerCamelCase							):
 lowerCamelCase		:	Union[str, Any]							=      [[] for _ in range(lowerCamelCase							)]
 for u, v in edges:
  g[u].append(lowerCamelCase							)
 return g


if __name__ == "__main__":
      # Test
      _lowerCamelCase					   =7
      _lowerCamelCase					   =[0, 0, 1, 2, 3, 3, 4, 4, 6]
      _lowerCamelCase					   =[1, 3, 2, 0, 1, 4, 5, 6, 5]
      _lowerCamelCase					   =[(u, v) for u, v in zip(source, target)]
      _lowerCamelCase					   =create_graph(n_vertices, edges)

      assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)






from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)




