


import inspect
import tempfile
import unittest

from huggingface_hub import hf_hub_download

from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


lowerCamelCase						=		1E-4

if is_torch_available():
		import torch

		from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
		from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder





@require_torch
class  _a							:




	def __init__( self						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		int=16		, _SCREAMING_SNAKE_CASE						:		Dict=13		, _SCREAMING_SNAKE_CASE						:		List[str]=7		, _SCREAMING_SNAKE_CASE						:		Any=14		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=10		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=19		, _SCREAMING_SNAKE_CASE						:		str=5		, _SCREAMING_SNAKE_CASE						:		Tuple=4		, _SCREAMING_SNAKE_CASE						:		Optional[int]=True		, _SCREAMING_SNAKE_CASE						:		List[Any]=16		, _SCREAMING_SNAKE_CASE						:		List[Any]=2		, _SCREAMING_SNAKE_CASE						:		Optional[int]=4		, _SCREAMING_SNAKE_CASE						:		List[Any]=4		, _SCREAMING_SNAKE_CASE						:		int="gelu"		, _SCREAMING_SNAKE_CASE						:		Dict=0.1		, _SCREAMING_SNAKE_CASE						:		List[str]=0.1		, _SCREAMING_SNAKE_CASE						:		Optional[int]=[1, 2, 3, 4, 5]		, _SCREAMING_SNAKE_CASE						:		List[Any]=25		, _SCREAMING_SNAKE_CASE						:		List[Any]=5		, )->	Union[str, Any]:
			lowerCAmelCase__       :      List[str]   					= d_model
			lowerCAmelCase__       :      Dict   					= parent
			lowerCAmelCase__       :      Union[str, Any]   					= batch_size
			lowerCAmelCase__       :      Tuple   					= prediction_length
			lowerCAmelCase__       :      List[str]   					= context_length
			lowerCAmelCase__       :      Any   					= cardinality
			lowerCAmelCase__       :      int   					= num_time_features
			lowerCAmelCase__       :      int   					= lags_sequence
			lowerCAmelCase__       :      Any   					= embedding_dimension
			lowerCAmelCase__       :      str   					= is_training
			lowerCAmelCase__       :      List[str]   					= hidden_size
			lowerCAmelCase__       :      Optional[Any]   					= num_hidden_layers
			lowerCAmelCase__       :      List[Any]   					= num_attention_heads
			lowerCAmelCase__       :      Union[str, Any]   					= intermediate_size
			lowerCAmelCase__       :      Tuple   					= hidden_act
			lowerCAmelCase__       :      Union[str, Any]   					= hidden_dropout_prob
			lowerCAmelCase__       :      Union[str, Any]   					= attention_probs_dropout_prob

			lowerCAmelCase__       :      Dict   					= context_length
			lowerCAmelCase__       :      int   					= prediction_length + label_length
			lowerCAmelCase__       :      Optional[Any]   					= label_length

			lowerCAmelCase__       :      Optional[Any]   					= moving_average
			lowerCAmelCase__       :      Tuple   					= autocorrelation_factor




	def 							UpperCAmelCase__( self						:		Dict		)->	str:
			return AutoformerConfig(
			    d_model=self.d_model		, encoder_layers=self.num_hidden_layers		, decoder_layers=self.num_hidden_layers		, encoder_attention_heads=self.num_attention_heads		, decoder_attention_heads=self.num_attention_heads		, encoder_ffn_dim=self.intermediate_size		, decoder_ffn_dim=self.intermediate_size		, dropout=self.hidden_dropout_prob		, attention_dropout=self.attention_probs_dropout_prob		, prediction_length=self.prediction_length		, context_length=self.context_length		, label_length=self.label_length		, lags_sequence=self.lags_sequence		, num_time_features=self.num_time_features		, num_static_categorical_features=1		, cardinality=[self.cardinality]		, embedding_dimension=[self.embedding_dimension]		, moving_average=self.moving_average		, )




	def 							UpperCAmelCase__( self						:		Tuple		, _SCREAMING_SNAKE_CASE						:		int		)->	Any:
			lowerCAmelCase__       :      Dict   					= config.context_length + max(config.lags_sequence		)

			lowerCAmelCase__       :      List[str]   					= ids_tensor([self.batch_size, 1]		, config.cardinality[0]		)
			lowerCAmelCase__       :      int   					= floats_tensor([self.batch_size, _past_length, config.num_time_features]		)
			lowerCAmelCase__       :      List[Any]   					= floats_tensor([self.batch_size, _past_length]		)
			lowerCAmelCase__       :      Union[str, Any]   					= floats_tensor([self.batch_size, _past_length]		) > 0.5

			# decoder inputs
			lowerCAmelCase__       :      Dict   					= floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]		)
			lowerCAmelCase__       :      Dict   					= floats_tensor([self.batch_size, config.prediction_length]		)

			lowerCAmelCase__       :      Union[str, Any]   					= {
			    '''past_values''': past_values,
			    '''static_categorical_features''': static_categorical_features,
			    '''past_time_features''': past_time_features,
			    '''past_observed_mask''': past_observed_mask,
			    '''future_time_features''': future_time_features,
			    '''future_values''': future_values,
			}
			return inputs_dict




	def 							UpperCAmelCase__( self						:		Any		)->	List[str]:
			lowerCAmelCase__       :      List[Any]   					= self.get_config()
			lowerCAmelCase__       :      Optional[int]   					= self.prepare_autoformer_inputs_dict(_SCREAMING_SNAKE_CASE		)
			return config, inputs_dict




	def 							UpperCAmelCase__( self						:		str		)->	Union[str, Any]:
			lowerCAmelCase__					,     lowerCAmelCase__       :      Tuple   					= self.prepare_config_and_inputs()
			return config, inputs_dict




	def 							UpperCAmelCase__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Dict		)->	Tuple:
			lowerCAmelCase__       :      Optional[int]   					= AutoformerModel(config=_SCREAMING_SNAKE_CASE		).to(_SCREAMING_SNAKE_CASE		).eval()
			lowerCAmelCase__       :      int   					= model(**_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      Any   					= outputs.encoder_last_hidden_state
			lowerCAmelCase__       :      int   					= outputs.last_hidden_state

			with tempfile.TemporaryDirectory() as tmpdirname:
					lowerCAmelCase__       :      List[Any]   					= model.get_encoder()
					encoder.save_pretrained(_SCREAMING_SNAKE_CASE		)
					lowerCAmelCase__       :      Optional[int]   					= AutoformerEncoder.from_pretrained(_SCREAMING_SNAKE_CASE		).to(_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__       :      Optional[Any]   					= model.create_network_inputs(**_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__					,     lowerCAmelCase__       :      Any   					= model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]		)

			lowerCAmelCase__       :      List[str]   					= torch.cat(
			    (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...])		, dim=-1		, )
			lowerCAmelCase__       :      Union[str, Any]   					= encoder(inputs_embeds=_SCREAMING_SNAKE_CASE		)[0]
			self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3		)

			lowerCAmelCase__       :      Dict   					= (
			    torch.mean(transformer_inputs[:, : config.context_length, ...]		, dim=1		)
			    .unsqueeze(1		)
			    .repeat(1		, config.prediction_length		, 1		)
			)
			lowerCAmelCase__       :      Union[str, Any]   					= torch.zeros(
			    [transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]]		, device=enc_input.device		, )

			lowerCAmelCase__       :      str   					= torch.cat(
			    (
			        torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)		, dim=1		),
			        feature[:, config.context_length - config.label_length :, ...],
			    )		, dim=-1		, )
			lowerCAmelCase__       :      Union[str, Any]   					= torch.cat(
			    (
			        torch.cat((trend_input[:, -config.label_length :, ...], mean)		, dim=1		),
			        feature[:, config.context_length - config.label_length :, ...],
			    )		, dim=-1		, )

			with tempfile.TemporaryDirectory() as tmpdirname:
					lowerCAmelCase__       :      str   					= model.get_decoder()
					decoder.save_pretrained(_SCREAMING_SNAKE_CASE		)
					lowerCAmelCase__       :      List[str]   					= AutoformerDecoder.from_pretrained(_SCREAMING_SNAKE_CASE		).to(_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      Dict   					= decoder(
			    trend=_SCREAMING_SNAKE_CASE		, inputs_embeds=_SCREAMING_SNAKE_CASE		, encoder_hidden_states=_SCREAMING_SNAKE_CASE		, )[0]

			self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3		)





@require_torch
class  _a							(    _lowercase					,						_lowercase					,						unittest.TestCase):
	_a  :  int				   =							(AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
	_a  :  List[str]				   =							(AutoformerForPrediction,) if is_torch_available() else ()
	_a  :  Any				   =							{'''feature-extraction''': AutoformerModel} if is_torch_available() else {}
	_a  :  Tuple				   =							False
	_a  :  Dict				   =							False
	_a  :  Tuple				   =							False
	_a  :  List[str]				   =							False
	_a  :  Optional[Any]				   =							False
	_a  :  str				   =							False




	def 							UpperCAmelCase__( self						:		str		)->	str:
			lowerCAmelCase__       :      Union[str, Any]   					= AutoformerModelTester(self		)
			lowerCAmelCase__       :      Any   					= ConfigTester(self		, config_class=_SCREAMING_SNAKE_CASE		, has_text_modality=_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		List[Any]		)->	str:
			self.config_tester.run_common_tests()




	def 							UpperCAmelCase__( self						:		Any		)->	List[Any]:
			lowerCAmelCase__					,     lowerCAmelCase__       :      List[Any]   					= self.model_tester.prepare_config_and_inputs()
			for model_class in self.all_model_classes:
					lowerCAmelCase__       :      Optional[int]   					= model_class(_SCREAMING_SNAKE_CASE		)

					with tempfile.TemporaryDirectory() as tmpdirname:
							model.save_pretrained(_SCREAMING_SNAKE_CASE		)
							lowerCAmelCase__					,     lowerCAmelCase__       :      Optional[int]   					= model_class.from_pretrained(_SCREAMING_SNAKE_CASE		, output_loading_info=_SCREAMING_SNAKE_CASE		)
					self.assertEqual(info['''missing_keys''']		, []		)




	def 							UpperCAmelCase__( self						:		int		)->	Optional[int]:
			lowerCAmelCase__       :      Any   					= self.model_tester.prepare_config_and_inputs_for_common()
			self.model_tester.check_encoder_decoder_model_standalone(*_SCREAMING_SNAKE_CASE		)




	@unittest.skip(reason='''Model has no tokens embeddings'''		)
	def 							UpperCAmelCase__( self						:		Optional[Any]		)->	List[str]:
			pass




	def 							UpperCAmelCase__( self						:		List[str]		)->	Union[str, Any]:
			lowerCAmelCase__       :      Any   					= inspect.signature(getattr(_SCREAMING_SNAKE_CASE		, '''forward'''		)		)
			# The main input is the name of the argument after `self`
			lowerCAmelCase__       :      Optional[int]   					= list(model_signature.parameters.keys()		)[1]
			self.assertEqual(AutoformerModel.main_input_name		, _SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		int		)->	List[Any]:
			lowerCAmelCase__					,     lowerCAmelCase__       :      Any   					= self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					lowerCAmelCase__       :      List[Any]   					= model_class(_SCREAMING_SNAKE_CASE		)
					lowerCAmelCase__       :      Optional[int]   					= inspect.signature(model.forward		)
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					lowerCAmelCase__       :      str   					= [*signature.parameters.keys()]

					lowerCAmelCase__       :      Union[str, Any]   					= [
					    '''past_values''',
					    '''past_time_features''',
					    '''past_observed_mask''',
					    '''static_categorical_features''',
					    '''static_real_features''',
					    '''future_values''',
					    '''future_time_features''',
					]

					if model.__class__.__name__ in ["AutoformerForPrediction"]:
							expected_arg_names.append('''future_observed_mask'''		)

					expected_arg_names.extend(
					    [
					        '''decoder_attention_mask''',
					        '''head_mask''',
					        '''decoder_head_mask''',
					        '''cross_attn_head_mask''',
					        '''encoder_outputs''',
					        '''past_key_values''',
					        '''output_hidden_states''',
					        '''output_attentions''',
					        '''use_cache''',
					        '''return_dict''',
					    ]		)

					self.assertListEqual(arg_names[: len(_SCREAMING_SNAKE_CASE		)]		, _SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		List[Any]		)->	Optional[int]:
			lowerCAmelCase__					,     lowerCAmelCase__       :      Dict   					= self.model_tester.prepare_config_and_inputs_for_common()
			lowerCAmelCase__       :      Dict   					= True

			lowerCAmelCase__       :      Dict   					= getattr(self.model_tester		, '''seq_length'''		, _SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Union[str, Any]   					= getattr(self.model_tester		, '''decoder_seq_length'''		, _SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      List[Any]   					= getattr(self.model_tester		, '''encoder_seq_length'''		, _SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Optional[int]   					= getattr(self.model_tester		, '''d_model'''		, _SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      str   					= getattr(self.model_tester		, '''num_attention_heads'''		, _SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Union[str, Any]   					= d_model // num_attention_heads

			for model_class in self.all_model_classes:
					lowerCAmelCase__       :      int   					= True
					lowerCAmelCase__       :      Optional[Any]   					= False
					lowerCAmelCase__       :      Optional[int]   					= True
					lowerCAmelCase__       :      Union[str, Any]   					= model_class(_SCREAMING_SNAKE_CASE		)
					model.to(_SCREAMING_SNAKE_CASE		)
					model.eval()
					with torch.no_grad():
							lowerCAmelCase__       :      Tuple   					= model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)		)
					lowerCAmelCase__       :      Tuple   					= outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
					self.assertEqual(len(_SCREAMING_SNAKE_CASE		)		, self.model_tester.num_hidden_layers		)

					# check that output_attentions also work using config
					del inputs_dict["output_attentions"]
					lowerCAmelCase__       :      int   					= True
					lowerCAmelCase__       :      Any   					= model_class(_SCREAMING_SNAKE_CASE		)
					model.to(_SCREAMING_SNAKE_CASE		)
					model.eval()
					with torch.no_grad():
							lowerCAmelCase__       :      int   					= model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)		)
					lowerCAmelCase__       :      List[str]   					= outputs.encoder_attentions
					self.assertEqual(len(_SCREAMING_SNAKE_CASE		)		, self.model_tester.num_hidden_layers		)

					self.assertListEqual(
					    list(attentions[0].shape[-3:]		)		, [self.model_tester.num_attention_heads, encoder_seq_length, dim]		, )
					lowerCAmelCase__       :      Any   					= len(_SCREAMING_SNAKE_CASE		)

					lowerCAmelCase__       :      Any   					= 7

					if "last_hidden_state" in outputs:
							correct_outlen += 1

					if "trend" in outputs:
							correct_outlen += 1

					if "past_key_values" in outputs:
							correct_outlen += 1  # past_key_values have been returned

					if "loss" in outputs:
							correct_outlen += 1

					if "params" in outputs:
							correct_outlen += 1

					self.assertEqual(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)

					# decoder attentions
					lowerCAmelCase__       :      Union[str, Any]   					= outputs.decoder_attentions
					self.assertIsInstance(_SCREAMING_SNAKE_CASE		, (list, tuple)		)
					self.assertEqual(len(_SCREAMING_SNAKE_CASE		)		, self.model_tester.num_hidden_layers		)
					self.assertListEqual(
					    list(decoder_attentions[0].shape[-3:]		)		, [self.model_tester.num_attention_heads, decoder_seq_length, dim]		, )

					# cross attentions
					lowerCAmelCase__       :      int   					= outputs.cross_attentions
					self.assertIsInstance(_SCREAMING_SNAKE_CASE		, (list, tuple)		)
					self.assertEqual(len(_SCREAMING_SNAKE_CASE		)		, self.model_tester.num_hidden_layers		)
					self.assertListEqual(
					    list(cross_attentions[0].shape[-3:]		)		, [self.model_tester.num_attention_heads, decoder_seq_length, dim]		, )

			# Check attention is always last and order is fine
			lowerCAmelCase__       :      Tuple   					= True
			lowerCAmelCase__       :      Optional[Any]   					= True
			lowerCAmelCase__       :      Dict   					= model_class(_SCREAMING_SNAKE_CASE		)
			model.to(_SCREAMING_SNAKE_CASE		)
			model.eval()
			with torch.no_grad():
					lowerCAmelCase__       :      Union[str, Any]   					= model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)		)

			self.assertEqual(out_len + 2		, len(_SCREAMING_SNAKE_CASE		)		)

			lowerCAmelCase__       :      List[str]   					= outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

			self.assertEqual(len(_SCREAMING_SNAKE_CASE		)		, self.model_tester.num_hidden_layers		)
			self.assertListEqual(
			    list(self_attentions[0].shape[-3:]		)		, [self.model_tester.num_attention_heads, encoder_seq_length, dim]		, )




	@is_flaky()
	def 							UpperCAmelCase__( self						:		str		)->	List[str]:
			super().test_retain_grad_hidden_states_attentions()






def 					lowerCamelCase_   (	_a="train-batch.pt"	):




		"""simple docstring"""






		lowerCAmelCase__       :      str   					= hf_hub_download(repo_id='''hf-internal-testing/tourism-monthly-batch'''     ,       filename=_a     ,       repo_type='''dataset'''	)
		lowerCAmelCase__       :      List[str]   					= torch.load(_a     ,       map_location=_a	)
		return batch





@require_torch
@slow
class  _a							(    unittest.TestCase):




	def 							UpperCAmelCase__( self						:		str		)->	Dict:
			lowerCAmelCase__       :      Any   					= AutoformerModel.from_pretrained('''huggingface/autoformer-tourism-monthly'''		).to(_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Optional[int]   					= prepare_batch()

			with torch.no_grad():
					lowerCAmelCase__       :      Tuple   					= model(
					    past_values=batch['''past_values''']		, past_time_features=batch['''past_time_features''']		, past_observed_mask=batch['''past_observed_mask''']		, static_categorical_features=batch['''static_categorical_features''']		, future_values=batch['''future_values''']		, future_time_features=batch['''future_time_features''']		, )[0]

			lowerCAmelCase__       :      str   					= torch.Size(
			    (64, model.config.prediction_length + model.config.label_length, model.config.feature_size)		)
			self.assertEqual(output.shape		, _SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      int   					= torch.tensor(
			    [[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]]		, device=_SCREAMING_SNAKE_CASE		)
			self.assertTrue(torch.allclose(output[0, :3, :3]		, _SCREAMING_SNAKE_CASE		, atol=_SCREAMING_SNAKE_CASE		)		)




	def 							UpperCAmelCase__( self						:		List[Any]		)->	Optional[int]:
			lowerCAmelCase__       :      Any   					= AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly'''		).to(_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Tuple   					= prepare_batch('''val-batch.pt'''		)
			with torch.no_grad():
					lowerCAmelCase__       :      int   					= model(
					    past_values=batch['''past_values''']		, past_time_features=batch['''past_time_features''']		, past_observed_mask=batch['''past_observed_mask''']		, static_categorical_features=batch['''static_categorical_features''']		, ).encoder_last_hidden_state
			lowerCAmelCase__       :      str   					= torch.Size((64, model.config.context_length, model.config.d_model)		)
			self.assertEqual(output.shape		, _SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      str   					= torch.tensor(
			    [[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]]		, device=_SCREAMING_SNAKE_CASE		)
			self.assertTrue(torch.allclose(output[0, :3, :3]		, _SCREAMING_SNAKE_CASE		, atol=_SCREAMING_SNAKE_CASE		)		)




	def 							UpperCAmelCase__( self						:		Union[str, Any]		)->	Tuple:
			lowerCAmelCase__       :      str   					= AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly'''		).to(_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Union[str, Any]   					= prepare_batch('''val-batch.pt'''		)
			with torch.no_grad():
					lowerCAmelCase__       :      Union[str, Any]   					= model.generate(
					    static_categorical_features=batch['''static_categorical_features''']		, past_time_features=batch['''past_time_features''']		, past_values=batch['''past_values''']		, future_time_features=batch['''future_time_features''']		, past_observed_mask=batch['''past_observed_mask''']		, )
			lowerCAmelCase__       :      int   					= torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length)		)
			self.assertEqual(outputs.sequences.shape		, _SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      Union[str, Any]   					= torch.tensor([3130.6763, 4056.5293, 7053.0786]		, device=_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Optional[Any]   					= outputs.sequences.mean(dim=1		)
			self.assertTrue(torch.allclose(mean_prediction[0, -3:]		, _SCREAMING_SNAKE_CASE		, rtol=1E-1		)		)








# using dfs for finding eulerian path traversal






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a     ,       _a=None	):




		"""simple docstring"""






		lowerCAmelCase__       :      Optional[Any]   					= (path or []) + [u]
		for v in graph[u]:
				if visited_edge[u][v] is False:
						lowerCAmelCase__					,     lowerCAmelCase__       :      List[Any]   					= True, True
						lowerCAmelCase__       :      Any   					= dfs(_a     ,       _a     ,       _a     ,       _a	)
		return path






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Union[str, Any]   					= 0
		lowerCAmelCase__       :      str   					= -1
		for i in range(_a	):
				if i not in graph.keys():
						continue
				if len(graph[i]	) % 2 == 1:
						odd_degree_nodes += 1
						lowerCAmelCase__       :      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 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






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






def 					lowerCamelCase_   (	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[str]   					= {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
		lowerCAmelCase__       :      Tuple   					= {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
		lowerCAmelCase__       :      str   					= {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
		lowerCAmelCase__       :      List[str]   					= {1: [2, 3], 2: [1, 3], 3: [1, 2]}
		lowerCAmelCase__       :      List[Any]   					= {
		    1: [],
		    2: []
		    # all degree is zero
		}
		lowerCAmelCase__       :      Optional[Any]   					= 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()




