
from typing import TYPE_CHECKING

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


__snake_case					:							Dict           =							{
    'configuration_led': ['LED_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LEDConfig'],
    'tokenization_led': ['LEDTokenizer'],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__snake_case					:							Union[str, Any]           =							['LEDTokenizerFast']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__snake_case					:							Optional[Any]           =							[
						    'LED_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'LEDForConditionalGeneration',
						    'LEDForQuestionAnswering',
						    'LEDForSequenceClassification',
						    'LEDModel',
						    'LEDPreTrainedModel',
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__snake_case					:							Dict           =							['TFLEDForConditionalGeneration', 'TFLEDModel', 'TFLEDPreTrainedModel']


if TYPE_CHECKING:
						from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
						from .tokenization_led import LEDTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_led_fast import LEDTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_led import (
												    LED_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LEDForConditionalGeneration,
												    LEDForQuestionAnswering,
												    LEDForSequenceClassification,
												    LEDModel,
												    LEDPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
						import sys

						__snake_case					:							Optional[Any]           =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







from __future__ import annotations

from queue import Queue


class 	UpperCamelCase__       :

						'''simple docstring'''



						def __init__(  self	)  ->None:
									UpperCAmelCase__		:dict[int, list[int]] 		=    {}


						def    A__       (  self	)  ->None:
									for i in self.vertices:
												print(A  , ' : '  , ' -> '.join([str(A	) for j in self.vertices[i]]	)	)


						def    A__       (  self  , A  , A	)  ->None:
									if from_vertex in self.vertices:
												self.vertices[from_vertex].append(A	)
									else:
												UpperCAmelCase__		:Union[str, Any] 		=    [to_vertex]


						def    A__       (  self  , A	)  ->set[int]:
									UpperCAmelCase__		:str 		=    set()

									# create a first in first out queue to store all the vertices for BFS
									UpperCAmelCase__		:Queue 		=    Queue()

									# mark the source node as visited and enqueue it
									visited.add(A	)
									queue.put(A	)

									while not queue.empty():
												UpperCAmelCase__		:List[str] 		=    queue.get()

												# loop through all adjacent vertex and enqueue it if not yet visited
												for adjacent_vertex in self.vertices[vertex]:
															if adjacent_vertex not in visited:
																		queue.put(A	)
																		visited.add(A	)
									return visited


if __name__ == "__main__":
						from doctest import testmod

						testmod(verbose=True)

						__snake_case					:							Any           =							Graph()
						g.add_edge(0, 1)
						g.add_edge(0, 2)
						g.add_edge(1, 2)
						g.add_edge(2, 0)
						g.add_edge(2, 3)
						g.add_edge(3, 3)

						g.print_graph()
						# 0  :  1 -> 2
						# 1  :  2
						# 2  :  0 -> 3
						# 3  :  3

						assert sorted(g.bfs(2)) == [0, 1, 2, 3]





