





'''simple docstring'''



class 							__lowercase    :







	'''simple docstring'''

	def __init__(self	)							->							Optional[int]:







				'''simple docstring'''





				__lowercase			  =							{}


	def   _UpperCAmelCase       (self	)							->							None:







				'''simple docstring'''





				print(self.vertex	)
				for i in self.vertex:
							print(_lowerCamelCase       ,''' -> '''       ,''' -> '''.join([str(_lowerCamelCase	) for j in self.vertex[i]]	)	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase	)							->							None:







				'''simple docstring'''





				if from_vertex in self.vertex:
							self.vertex[from_vertex].append(_lowerCamelCase	)
				else:
							# else make a new vertex
							__lowercase			  =							[to_vertex]


	def   _UpperCAmelCase       (self	)							->							None:







				'''simple docstring'''





				__lowercase			  =							[False] * len(self.vertex	)

				# call the recursive helper function
				for i in range(len(self.vertex	)	):
							if not visited[i]:
										self.dfs_recursive(_lowerCamelCase       ,_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase	)							->							None:







				'''simple docstring'''





				__lowercase			  =							True

				print(_lowerCamelCase       ,end=''' '''	)

				# Recur for all the vertices that are adjacent to this node
				for i in self.vertex:
							if not visited[i]:
										self.dfs_recursive(_lowerCamelCase       ,_lowerCamelCase	)


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE   =      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()
	print('''DFS:''')
	g.dfs()

	# OUTPUT:
	# 0  ->  1 -> 2
	# 1  ->  2
	# 2  ->  0 -> 3
	# 3  ->  3
	# DFS:
	#  0 1 2 3












'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_SCREAMING_SNAKE_CASE   =      {
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      ['''MvpTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''MvpForCausalLM''',
	    '''MvpForConditionalGeneration''',
	    '''MvpForQuestionAnswering''',
	    '''MvpForSequenceClassification''',
	    '''MvpModel''',
	    '''MvpPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
	from .tokenization_mvp import MvpTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mvp_fast import MvpTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mvp import (
		    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MvpForCausalLM,
		    MvpForConditionalGeneration,
		    MvpForQuestionAnswering,
		    MvpForSequenceClassification,
		    MvpModel,
		    MvpPreTrainedModel,
		)

else:
	import sys

	_SCREAMING_SNAKE_CASE   =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





