





# using dfs for finding eulerian path traversal


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a=None   ):
	snake_case_		:			Union[str, Any]											=	(path or []) + [u]
	for v in graph[u]:
		if visited_edge[u][v] is False:
			snake_case_						,snake_case_		:			Optional[int]											=	True, True
			snake_case_		:			Optional[int]											=	dfs(__a   , __a   , __a   , __a   )
	return path


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	snake_case_		:			Optional[int]											=	0
	snake_case_		:			Any											=	-1
	for i in range(__a   ):
		if i not in graph.keys():
			continue
		if len(graph[i]   ) % 2 == 1:
			odd_degree_nodes += 1
			snake_case_		:			Optional[Any]											=	i
	if odd_degree_nodes == 0:
		return 1, odd_node
	if odd_degree_nodes == 2:
		return 2, odd_node
	return 3, odd_node


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	snake_case_		:			Dict											=	[[False for _ in range(max_node + 1   )] for _ in range(max_node + 1   )]
	snake_case_						,snake_case_		:			Any											=	check_circuit_or_path(__a   , __a   )
	if check == 3:
		print('graph is not Eulerian'   )
		print('no path'   )
		return
	snake_case_		:			Union[str, Any]											=	1
	if check == 2:
		snake_case_		:			str											=	odd_node
		print('graph has a Euler path'   )
	if check == 1:
		print('graph has a Euler cycle'   )
	snake_case_		:			Optional[int]											=	dfs(__a   , __a   , __a   )
	print(__a   )


def 							SCREAMING_SNAKE_CASE__						(    ):
	snake_case_		:			Optional[int]											=	{1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
	snake_case_		:			List[Any]											=	{1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
	snake_case_		:			Optional[int]											=	{1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
	snake_case_		:			List[str]											=	{1: [2, 3], 2: [1, 3], 3: [1, 2]}
	snake_case_		:			Dict											=	{
	    1: [],
	    2: []
	    # all degree is zero
	}
	snake_case_		:			str											=	10
	check_euler(__a   , __a   )
	check_euler(__a   , __a   )
	check_euler(__a   , __a   )
	check_euler(__a   , __a   )
	check_euler(__a   , __a   )


if __name__ == "__main__":
						main()








from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE      	=		{
    """configuration_lxmert""": ["""LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LxmertConfig"""],
    """tokenization_lxmert""": ["""LxmertTokenizer"""],
}

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE      	=		[
						    """LxmertEncoder""",
						    """LxmertForPreTraining""",
						    """LxmertForQuestionAnswering""",
						    """LxmertModel""",
						    """LxmertPreTrainedModel""",
						    """LxmertVisualFeatureEncoder""",
						    """LxmertXLayer""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE      	=		[
						    """TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFLxmertForPreTraining""",
						    """TFLxmertMainLayer""",
						    """TFLxmertModel""",
						    """TFLxmertPreTrainedModel""",
						    """TFLxmertVisualFeatureEncoder""",
						]


if TYPE_CHECKING:
						from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
						from .tokenization_lxmert import LxmertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_lxmert_fast import LxmertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_lxmert import (
												    LxmertEncoder,
												    LxmertForPreTraining,
												    LxmertForQuestionAnswering,
												    LxmertModel,
												    LxmertPreTrainedModel,
												    LxmertVisualFeatureEncoder,
												    LxmertXLayer,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_lxmert import (
												    TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFLxmertForPreTraining,
												    TFLxmertMainLayer,
												    TFLxmertModel,
												    TFLxmertPreTrainedModel,
												    TFLxmertVisualFeatureEncoder,
												)

else:
						import sys

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

