





from collections import deque






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Dict:



			"""simple docstring"""







			lowercase							=		len(__lowerCAmelCase       )
			lowercase							=		deque()
			lowercase							=		[False for _ in range(__lowerCAmelCase       )]
			lowercase							=		[-1 for _ in range(__lowerCAmelCase       )]
			lowercase							=		index_of[:]

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

						for w in g[v]:
									if index_of[w] == -1:
												lowercase							=		strong_connect(__lowerCAmelCase,		__lowerCAmelCase,		__lowerCAmelCase       )
												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(__lowerCAmelCase       )
									while w != v:
												lowercase							=		stack.pop()
												lowercase							=		False
												component.append(__lowerCAmelCase       )
									components.append(__lowerCAmelCase       )
						return index

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

			return components






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       Dict:



			"""simple docstring"""







			lowercase							=		[[] for _ in range(__lowerCAmelCase       )]
			for u, v in edges:
						g[u].append(__lowerCAmelCase       )
			return g


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

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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


A_      =      {
    "configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_      =      [
						    "GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "GPTBigCodeForSequenceClassification",
						    "GPTBigCodeForTokenClassification",
						    "GPTBigCodeForCausalLM",
						    "GPTBigCodeModel",
						    "GPTBigCodePreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_gpt_bigcode import (
												    GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
												    GPTBigCodeForCausalLM,
												    GPTBigCodeForSequenceClassification,
												    GPTBigCodeForTokenClassification,
												    GPTBigCodeModel,
												    GPTBigCodePreTrainedModel,
												)


else:
						import sys

						A_      =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





