





"""simple docstring"""




class  __UpperCamelCase       :






							def __init__(     self   )			->				Any:
								a   :	Any			=       {}






							def 					__a     (     self   )			->				None:
								print(self.vertex   )
								for i in self.vertex:
									print(lowerCAmelCase__   ,      " -> "   ,      " -> ".join([str(lowerCAmelCase__   ) for j in self.vertex[i]]   )   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				None:
								# check if vertex is already present,
								if from_vertex in self.vertex:
									self.vertex[from_vertex].append(lowerCAmelCase__   )
								else:
									# else make a new vertex
									a   :	List[str]			=       [to_vertex]






							def 					__a     (     self   )			->				None:
								# visited array for storing already visited nodes
								a   :	List[str]			=       [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 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				None:
								# mark start vertex as visited
								a   :	List[str]			=       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__":
		a		:				Optional[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()
		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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
		from ..ta.tokenization_ta import TaTokenizer
else:
		from ...utils.dummy_sentencepiece_objects import TaTokenizer

a		:				List[Any]  			=    TaTokenizer

if is_tokenizers_available():
		from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
		from ...utils.dummy_tokenizers_objects import TaTokenizerFast

a		:				Union[str, Any]  			=    TaTokenizerFast

a		:				Union[str, Any]  			=    {'''configuration_mt5''': ['''MT5Config''', '''MT5OnnxConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a		:				str  			=    [
		    '''MT5EncoderModel''',
		    '''MT5ForConditionalGeneration''',
		    '''MT5ForQuestionAnswering''',
		    '''MT5Model''',
		    '''MT5PreTrainedModel''',
		    '''MT5Stack''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a		:				List[str]  			=    ['''TFMT5EncoderModel''', '''TFMT5ForConditionalGeneration''', '''TFMT5Model''']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a		:				Optional[Any]  			=    ['''FlaxMT5EncoderModel''', '''FlaxMT5ForConditionalGeneration''', '''FlaxMT5Model''']


if TYPE_CHECKING:
		from .configuration_mta import MTaConfig, MTaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mta import (
				    MTaEncoderModel,
				    MTaForConditionalGeneration,
				    MTaForQuestionAnswering,
				    MTaModel,
				    MTaPreTrainedModel,
				    MTaStack,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
		import sys

		a		:				List[Any]  			=    _LazyModule(
		    __name__,
		    globals()['''__file__'''],
		    _import_structure,
		    extra_objects={'''MT5Tokenizer''': MTaTokenizer, '''MT5TokenizerFast''': MTaTokenizerFast},
		    module_spec=__spec__,
		)


