


'''simple docstring'''
# using dfs for finding eulerian path traversal








def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__=None						)    ->	Optional[Any]:
    UpperCAmelCase__			:		str													=				(path or []) + [u]
    for v in graph[u]:
        if visited_edge[u][v] is False:
            UpperCAmelCase__			:		str													=				True, True
            UpperCAmelCase__			:		List[Any]													=				dfs(__a  ,					__a  ,					__a  ,					__a						)
    return path





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	Tuple:
    UpperCAmelCase__			:		Tuple													=				0
    UpperCAmelCase__			:		Union[str, Any]													=				-1
    for i in range(__a						):
        if i not in graph.keys():
            continue
        if len(graph[i]						) % 2 == 1:
            odd_degree_nodes += 1
            UpperCAmelCase__			:		Tuple													=				i
    if odd_degree_nodes == 0:
        return 1, odd_node
    if odd_degree_nodes == 2:
        return 2, odd_node
    return 3, odd_node





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	List[Any]:
    UpperCAmelCase__			:		Tuple													=				[[False for _ in range(max_node + 1						)] for _ in range(max_node + 1						)]
    UpperCAmelCase__			:		str													=				check_circuit_or_path(__a  ,					__a						)
    if check == 3:
        print('''graph is not Eulerian'''						)
        print('''no path'''						)
        return
    UpperCAmelCase__			:		Any													=				1
    if check == 2:
        UpperCAmelCase__			:		Union[str, Any]													=				odd_node
        print('''graph has a Euler path'''						)
    if check == 1:
        print('''graph has a Euler cycle'''						)
    UpperCAmelCase__			:		List[str]													=				dfs(__a  ,					__a  ,					__a						)
    print(__a						)





def 					a__       (					)    ->	Tuple:
    UpperCAmelCase__			:		Dict													=				{1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
    UpperCAmelCase__			:		int													=				{1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
    UpperCAmelCase__			:		Union[str, Any]													=				{1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
    UpperCAmelCase__			:		int													=				{1: [2, 3], 2: [1, 3], 3: [1, 2]}
    UpperCAmelCase__			:		Tuple													=				{
        1: [],
        2: []
        # all degree is zero
    }
    UpperCAmelCase__			:		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()



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Union[str, Any]	    =    ["image_processor", "tokenizer"]
							UpperCAmelCase__       :						Dict	    =    "ChineseCLIPImageProcessor"
							UpperCAmelCase__       :						List[str]	    =    ("BertTokenizer", "BertTokenizerFast")


							def __init__(			self       ,							_a=None       ,							_a=None       ,							**_a       )    ->    Any:
												_a	:    Optional[Any]         =		None
												if "feature_extractor" in kwargs:
																	warnings.warn(
																	    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																	    ''' instead.'''       ,							_a       ,							)
																	_a	:    Tuple         =		kwargs.pop('''feature_extractor'''       )

												_a	:    Tuple         =		image_processor if image_processor is not None else feature_extractor
												if image_processor is None:
																	raise ValueError('''You need to specify an `image_processor`.'''       )
												if tokenizer is None:
																	raise ValueError('''You need to specify a `tokenizer`.'''       )

												super().__init__(_a       ,							_a       )
												_a	:    List[str]         =		self.image_processor


							def __call__(			self       ,							_a=None       ,							_a=None       ,							_a=None       ,							**_a       )    ->    int:

												if text is None and images is None:
																	raise ValueError('''You have to specify either text or images. Both cannot be none.'''       )

												if text is not None:
																	_a	:    List[str]         =		self.tokenizer(_a       ,							return_tensors=_a       ,							**_a       )

												if images is not None:
																	_a	:    Optional[Any]         =		self.image_processor(_a       ,							return_tensors=_a       ,							**_a       )

												if text is not None and images is not None:
																	_a	:    Union[str, Any]         =		image_features.pixel_values
																	return encoding
												elif text is not None:
																	return encoding
												else:
																	return BatchEncoding(data=dict(**_a       )       ,							tensor_type=_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Union[str, Any]:
												return self.tokenizer.batch_decode(*_a       ,							**_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Any:
												return self.tokenizer.decode(*_a       ,							**_a       )


							@property
							def 				__lowercase				(			self       )    ->    Optional[Any]:
												_a	:    Any         =		self.tokenizer.model_input_names
												_a	:    Dict         =		self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )




							@property
							def 				__lowercase				(			self       )    ->    Dict:
												warnings.warn(
												    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''       ,							_a       ,							)
												return self.image_processor_class



