


from __future__ import annotations

class 						snake_case			:
			'''simple docstring'''

			def __init__(	self     :     Tuple							,							lowerCAmelCase_     :     int					)					->					None:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       vertices
								SCREAMING_SNAKE_CASE_	      =       [[0] * vertices for _ in range(lowerCAmelCase_					)]

			def _lowercase (	self     :     Optional[Any]							,							lowerCAmelCase_     :     list[int]					)					->					None:




								"""simple docstring"""






								print('''Vertex \t Distance from Source'''					)
								for vertex in range(self.vertices					):
													print(lowerCAmelCase_							,							'''\t\t'''							,							distances_from_source[vertex]					)

			def _lowercase (	self     :     List[str]							,							lowerCAmelCase_     :     list[int]							,							lowerCAmelCase_     :     list[bool]					)					->					int:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       1e7
								SCREAMING_SNAKE_CASE_	      =       0

								# Search not nearest vertex not in the shortest path tree
								for vertex in range(self.vertices					):
													if distances_from_source[vertex] < minimum and visited[vertex] is False:
																		SCREAMING_SNAKE_CASE_	      =       distances_from_source[vertex]
																		SCREAMING_SNAKE_CASE_	      =       vertex
								return min_index




			def _lowercase (	self     :     int							,							lowerCAmelCase_     :     int					)					->					None:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       [int(1e7					)] * self.vertices  # distances from the source
								SCREAMING_SNAKE_CASE_	      =       0
								SCREAMING_SNAKE_CASE_	      =       [False] * self.vertices

								for _ in range(self.vertices					):
													SCREAMING_SNAKE_CASE_	      =       self.minimum_distance(lowerCAmelCase_							,							lowerCAmelCase_					)
													SCREAMING_SNAKE_CASE_	      =       True

													# Update dist value of the adjacent vertices
													# of the picked vertex only if the current
													# distance is greater than new distance and
													# the vertex in not in the shortest path tree
													for v in range(self.vertices					):
																		if (
																		    self.graph[u][v] > 0
																		    and visited[v] is False
																		    and distances[v] > distances[u] + self.graph[u][v]
																		):
																							SCREAMING_SNAKE_CASE_	      =       distances[u] + self.graph[u][v]

								self.print_solution(lowerCAmelCase_					)


if __name__ == "__main__":
		A_														=      Graph(9)
		A_														=      [
		    [0, 4, 0, 0, 0, 0, 0, 8, 0],
		    [4, 0, 8, 0, 0, 0, 0, 1_1, 0],
		    [0, 8, 0, 7, 0, 4, 0, 0, 2],
		    [0, 0, 7, 0, 9, 1_4, 0, 0, 0],
		    [0, 0, 0, 9, 0, 1_0, 0, 0, 0],
		    [0, 0, 4, 1_4, 1_0, 0, 2, 0, 0],
		    [0, 0, 0, 0, 0, 2, 0, 1, 6],
		    [8, 1_1, 0, 0, 0, 0, 1, 0, 7],
		    [0, 0, 2, 0, 0, 0, 6, 7, 0],
		]
		graph.dijkstra(0)






from typing import TYPE_CHECKING

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


A_														=      {
    "configuration_marian": ["MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarianConfig", "MarianOnnxConfig"],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A_														=      ["MarianTokenizer"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A_														=      [
		    "MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "MarianForCausalLM",
		    "MarianModel",
		    "MarianMTModel",
		    "MarianPreTrainedModel",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A_														=      ["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A_														=      ["FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel"]

if TYPE_CHECKING:
		from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_marian import MarianTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_marian import (
				    MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MarianForCausalLM,
				    MarianModel,
				    MarianMTModel,
				    MarianPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
		import sys

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


