



from collections import deque



def  snake_case_					(     _SCREAMING_SNAKE_CASE ):
	__lowercase									=      len(snake_case__ )
	__lowercase									=      deque()
	__lowercase									=      [False for _ in range(snake_case__ )]
	__lowercase									=      [-1 for _ in range(snake_case__ )]
	__lowercase									=      index_of[:]

	def strong_connect(_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE ):
		__lowercase									=      index  # the number when this node is seen
		__lowercase									=      index  # lowest rank node reachable from here
		index += 1
		stack.append(snake_case__ )
		__lowercase									=      True

		for w in g[v]:
			if index_of[w] == -1:
				__lowercase									=      strong_connect(snake_case__	,						snake_case__	,						snake_case__ )
				__lowercase									=      (
				    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
				)
			elif on_stack[w]:
				__lowercase									=      (
				    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
				)

		if lowlink_of[v] == index_of[v]:
			__lowercase									=      []
			__lowercase									=      stack.pop()
			__lowercase									=      False
			component.append(snake_case__ )
			while w != v:
				__lowercase									=      stack.pop()
				__lowercase									=      False
				component.append(snake_case__ )
			components.append(snake_case__ )
		return index

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

	return components



def  snake_case_					(     _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE ):
	__lowercase									=      [[] for _ in range(snake_case__ )]
	for u, v in edges:
		g[u].append(snake_case__ )
	return g


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

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







"""simple docstring"""






import os
import sys

UpperCamelCase__				:Union[str, Any]			   =	os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


UpperCamelCase__				:List[Any]			   =	[
    """torch""",
    """numpy""",
    """tokenizers""",
    """filelock""",
    """requests""",
    """tqdm""",
    """regex""",
    """sentencepiece""",
    """sacremoses""",
    """importlib_metadata""",
    """huggingface_hub""",
]





@add_start_docstrings(AutoConfig.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  int:
			return AutoConfig.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoTokenizer.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  int:
			return AutoTokenizer.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModel.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModel.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForCausalLM.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModelForCausalLM.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForMaskedLM.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModelForMaskedLM.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForSequenceClassification.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModelForSequenceClassification.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Union[str, Any]:
			return AutoModelForQuestionAnswering.from_pretrained(*snake_case__       ,					**snake_case__		)

