




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


SCREAMING_SNAKE_CASE__     :     Optional[Any]		    =       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      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE=16      ,     SCREAMING_SNAKE_CASE=13      ,     SCREAMING_SNAKE_CASE=7      ,     SCREAMING_SNAKE_CASE=14      ,     SCREAMING_SNAKE_CASE=10      ,     SCREAMING_SNAKE_CASE=19      ,     SCREAMING_SNAKE_CASE=5      ,     SCREAMING_SNAKE_CASE=4      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=16      ,     SCREAMING_SNAKE_CASE=2      ,     SCREAMING_SNAKE_CASE=4      ,     SCREAMING_SNAKE_CASE=4      ,     SCREAMING_SNAKE_CASE="gelu"      ,     SCREAMING_SNAKE_CASE=0.1      ,     SCREAMING_SNAKE_CASE=0.1      ,     SCREAMING_SNAKE_CASE=[1, 2, 3, 4, 5]      ,     SCREAMING_SNAKE_CASE=25      ,     SCREAMING_SNAKE_CASE=5      ,     )  ->					Union[str, Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						d_model
				SCREAMING_SNAKE_CASE_								=						parent
				SCREAMING_SNAKE_CASE_								=						batch_size
				SCREAMING_SNAKE_CASE_								=						prediction_length
				SCREAMING_SNAKE_CASE_								=						context_length
				SCREAMING_SNAKE_CASE_								=						cardinality
				SCREAMING_SNAKE_CASE_								=						num_time_features
				SCREAMING_SNAKE_CASE_								=						lags_sequence
				SCREAMING_SNAKE_CASE_								=						embedding_dimension
				SCREAMING_SNAKE_CASE_								=						is_training
				SCREAMING_SNAKE_CASE_								=						hidden_size
				SCREAMING_SNAKE_CASE_								=						num_hidden_layers
				SCREAMING_SNAKE_CASE_								=						num_attention_heads
				SCREAMING_SNAKE_CASE_								=						intermediate_size
				SCREAMING_SNAKE_CASE_								=						hidden_act
				SCREAMING_SNAKE_CASE_								=						hidden_dropout_prob
				SCREAMING_SNAKE_CASE_								=						attention_probs_dropout_prob

				SCREAMING_SNAKE_CASE_								=						context_length
				SCREAMING_SNAKE_CASE_								=						prediction_length + label_length
				SCREAMING_SNAKE_CASE_								=						label_length

				SCREAMING_SNAKE_CASE_								=						moving_average
				SCREAMING_SNAKE_CASE_								=						autocorrelation_factor





	def 						A_(							self      )  ->					List[str]:




				"""simple docstring"""



				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 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Optional[int]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						config.context_length + max(config.lags_sequence      )

				SCREAMING_SNAKE_CASE_								=						ids_tensor([self.batch_size, 1]      ,     config.cardinality[0]      )
				SCREAMING_SNAKE_CASE_								=						floats_tensor([self.batch_size, _past_length, config.num_time_features]      )
				SCREAMING_SNAKE_CASE_								=						floats_tensor([self.batch_size, _past_length]      )
				SCREAMING_SNAKE_CASE_								=						floats_tensor([self.batch_size, _past_length]      ) > 0.5

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

				SCREAMING_SNAKE_CASE_								=						{
				    '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 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.get_config()
				SCREAMING_SNAKE_CASE_								=						self.prepare_autoformer_inputs_dict(SCREAMING_SNAKE_CASE      )
				return config, inputs_dict





	def 						A_(							self      )  ->					Union[str, Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						self.prepare_config_and_inputs()
				return config, inputs_dict





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						AutoformerModel(config=SCREAMING_SNAKE_CASE      ).to(SCREAMING_SNAKE_CASE      ).eval()
				SCREAMING_SNAKE_CASE_								=						model(**SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						outputs.encoder_last_hidden_state
				SCREAMING_SNAKE_CASE_								=						outputs.last_hidden_state

				with tempfile.TemporaryDirectory() as tmpdirname:
							SCREAMING_SNAKE_CASE_								=						model.get_encoder()
							encoder.save_pretrained(SCREAMING_SNAKE_CASE      )
							SCREAMING_SNAKE_CASE_								=						AutoformerEncoder.from_pretrained(SCREAMING_SNAKE_CASE      ).to(SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						model.create_network_inputs(**SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]      )

				SCREAMING_SNAKE_CASE_								=						torch.cat(
				    (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...])      ,     dim=-1      ,     )
				SCREAMING_SNAKE_CASE_								=						encoder(inputs_embeds=SCREAMING_SNAKE_CASE      )[0]
				self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3      )

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

				SCREAMING_SNAKE_CASE_								=						torch.cat(
				    (
				        torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)      ,     dim=1      ),
				        feature[:, config.context_length - config.label_length :, ...],
				    )      ,     dim=-1      ,     )
				SCREAMING_SNAKE_CASE_								=						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:
							SCREAMING_SNAKE_CASE_								=						model.get_decoder()
							decoder.save_pretrained(SCREAMING_SNAKE_CASE      )
							SCREAMING_SNAKE_CASE_								=						AutoformerDecoder.from_pretrained(SCREAMING_SNAKE_CASE      ).to(SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						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_       (     SCREAMING_SNAKE_CASE__  ,    SCREAMING_SNAKE_CASE__  ,    unittest.TestCase     ):
	A    							= (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
	A    							= (AutoformerForPrediction,) if is_torch_available() else ()
	A    							= {'''feature-extraction''': AutoformerModel} if is_torch_available() else {}
	A    							= False
	A    							= False
	A    							= False
	A    							= False
	A    							= False
	A    							= False





	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						AutoformerModelTester(self      )
				SCREAMING_SNAKE_CASE_								=						ConfigTester(self      ,     config_class=SCREAMING_SNAKE_CASE      ,     has_text_modality=SCREAMING_SNAKE_CASE      )





	def 						A_(							self      )  ->					int:




				"""simple docstring"""



				self.config_tester.run_common_tests()





	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						self.model_tester.prepare_config_and_inputs()
				for model_class in self.all_model_classes:
							SCREAMING_SNAKE_CASE_								=						model_class(SCREAMING_SNAKE_CASE      )

							with tempfile.TemporaryDirectory() as tmpdirname:
										model.save_pretrained(SCREAMING_SNAKE_CASE      )
										SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						model_class.from_pretrained(SCREAMING_SNAKE_CASE      ,     output_loading_info=SCREAMING_SNAKE_CASE      )
							self.assertEqual(info['missing_keys']      ,     []      )





	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						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 						A_(							self      )  ->					Union[str, Any]:




				"""simple docstring"""



				pass





	def 						A_(							self      )  ->					int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						inspect.signature(getattr(SCREAMING_SNAKE_CASE      ,     'forward'      )      )
				# The main input is the name of the argument after `self`
				SCREAMING_SNAKE_CASE_								=						list(model_signature.parameters.keys()      )[1]
				self.assertEqual(AutoformerModel.main_input_name      ,     SCREAMING_SNAKE_CASE      )





	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							SCREAMING_SNAKE_CASE_								=						model_class(SCREAMING_SNAKE_CASE      )
							SCREAMING_SNAKE_CASE_								=						inspect.signature(model.forward      )
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							SCREAMING_SNAKE_CASE_								=						[*signature.parameters.keys()]

							SCREAMING_SNAKE_CASE_								=						[
							    '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 						A_(							self      )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						self.model_tester.prepare_config_and_inputs_for_common()
				SCREAMING_SNAKE_CASE_								=						True

				SCREAMING_SNAKE_CASE_								=						getattr(self.model_tester      ,     'seq_length'      ,     SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						getattr(self.model_tester      ,     'decoder_seq_length'      ,     SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						getattr(self.model_tester      ,     'encoder_seq_length'      ,     SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						getattr(self.model_tester      ,     'd_model'      ,     SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						getattr(self.model_tester      ,     'num_attention_heads'      ,     SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						d_model // num_attention_heads

				for model_class in self.all_model_classes:
							SCREAMING_SNAKE_CASE_								=						True
							SCREAMING_SNAKE_CASE_								=						False
							SCREAMING_SNAKE_CASE_								=						True
							SCREAMING_SNAKE_CASE_								=						model_class(SCREAMING_SNAKE_CASE      )
							model.to(SCREAMING_SNAKE_CASE      )
							model.eval()
							with torch.no_grad():
										SCREAMING_SNAKE_CASE_								=						model(**self._prepare_for_class(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )      )
							SCREAMING_SNAKE_CASE_								=						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"]
							SCREAMING_SNAKE_CASE_								=						True
							SCREAMING_SNAKE_CASE_								=						model_class(SCREAMING_SNAKE_CASE      )
							model.to(SCREAMING_SNAKE_CASE      )
							model.eval()
							with torch.no_grad():
										SCREAMING_SNAKE_CASE_								=						model(**self._prepare_for_class(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )      )
							SCREAMING_SNAKE_CASE_								=						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]      ,     )
							SCREAMING_SNAKE_CASE_								=						len(SCREAMING_SNAKE_CASE      )

							SCREAMING_SNAKE_CASE_								=						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
							SCREAMING_SNAKE_CASE_								=						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
							SCREAMING_SNAKE_CASE_								=						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
				SCREAMING_SNAKE_CASE_								=						True
				SCREAMING_SNAKE_CASE_								=						True
				SCREAMING_SNAKE_CASE_								=						model_class(SCREAMING_SNAKE_CASE      )
				model.to(SCREAMING_SNAKE_CASE      )
				model.eval()
				with torch.no_grad():
							SCREAMING_SNAKE_CASE_								=						model(**self._prepare_for_class(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )      )

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

				SCREAMING_SNAKE_CASE_								=						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 						A_(							self      )  ->					Any:




				"""simple docstring"""



				super().test_retain_grad_hidden_states_attentions()




def lowercase    (  SCREAMING_SNAKE_CASE="train-batch.pt" )			->							str:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						hf_hub_download(repo_id='hf-internal-testing/tourism-monthly-batch'       ,    filename=SCREAMING_SNAKE_CASE       ,    repo_type='dataset' )
			SCREAMING_SNAKE_CASE_								=						torch.load(SCREAMING_SNAKE_CASE       ,    map_location=SCREAMING_SNAKE_CASE )
			return batch



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





	def 						A_(							self      )  ->					List[str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						AutoformerModel.from_pretrained('huggingface/autoformer-tourism-monthly'      ).to(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						prepare_batch()

				with torch.no_grad():
							SCREAMING_SNAKE_CASE_								=						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]

				SCREAMING_SNAKE_CASE_								=						torch.Size(
				    (64, model.config.prediction_length + model.config.label_length, model.config.feature_size)      )
				self.assertEqual(output.shape      ,     SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						torch.tensor(
				    [[0.3_5_9_3, -1.3_3_9_8, 0.6_3_3_0], [0.2_2_7_9, 1.5_3_9_6, -0.1_7_9_2], [0.0_4_5_0, 1.3_2_2_5, -0.2_3_3_5]]      ,     device=SCREAMING_SNAKE_CASE      )
				self.assertTrue(torch.allclose(output[0, :3, :3]      ,     SCREAMING_SNAKE_CASE      ,     atol=SCREAMING_SNAKE_CASE      )      )





	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly'      ).to(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						prepare_batch('val-batch.pt'      )
				with torch.no_grad():
							SCREAMING_SNAKE_CASE_								=						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
				SCREAMING_SNAKE_CASE_								=						torch.Size((64, model.config.context_length, model.config.d_model)      )
				self.assertEqual(output.shape      ,     SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						torch.tensor(
				    [[-0.0_7_3_4, -0.9_0_3_6, 0.8_3_5_8], [4.7_1_8_6, 2.4_1_1_3, 1.9_5_8_1], [1.7_9_5_3, 2.3_5_5_8, 1.2_9_7_0]]      ,     device=SCREAMING_SNAKE_CASE      )
				self.assertTrue(torch.allclose(output[0, :3, :3]      ,     SCREAMING_SNAKE_CASE      ,     atol=SCREAMING_SNAKE_CASE      )      )





	def 						A_(							self      )  ->					Any:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly'      ).to(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						prepare_batch('val-batch.pt'      )
				with torch.no_grad():
							SCREAMING_SNAKE_CASE_								=						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']      ,     )
				SCREAMING_SNAKE_CASE_								=						torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length)      )
				self.assertEqual(outputs.sequences.shape      ,     SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						torch.tensor([3_1_3_0.6_7_6_3, 4_0_5_6.5_2_9_3, 7_0_5_3.0_7_8_6]      ,     device=SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						outputs.sequences.mean(dim=1      )
				self.assertTrue(torch.allclose(mean_prediction[0, -3:]      ,     SCREAMING_SNAKE_CASE      ,     rtol=1e-1      )      )






class       a_       :





	def __init__(							self      ,     SCREAMING_SNAKE_CASE = ""      ,     SCREAMING_SNAKE_CASE = False      )  ->					None:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						{}

				# A node will be a leaf if the tree contains its word
				SCREAMING_SNAKE_CASE_								=						is_leaf

				SCREAMING_SNAKE_CASE_								=						prefix





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					tuple[str, str, str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						0
				for q, w in zip(self.prefix      ,     SCREAMING_SNAKE_CASE      ):
							if q != w:
										break

							x += 1

				return self.prefix[:x], self.prefix[x:], word[x:]





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					None:




				"""simple docstring"""



				for word in words:
							self.insert(SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					None:




				"""simple docstring"""



				if self.prefix == word:
							SCREAMING_SNAKE_CASE_								=						True

				# Case 2: The node has no edges that have a prefix to the word
				# Solution: We create an edge from the current node to a new one
				# containing the word
				elif word[0] not in self.nodes:
							SCREAMING_SNAKE_CASE_								=						RadixNode(prefix=SCREAMING_SNAKE_CASE      ,     is_leaf=SCREAMING_SNAKE_CASE      )

				else:
							SCREAMING_SNAKE_CASE_								=						self.nodes[word[0]]
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						incoming_node.match(
							    SCREAMING_SNAKE_CASE      )

							# Case 3: The node prefix is equal to the matching
							# Solution: We insert remaining word on the next node
							if remaining_prefix == "":
										self.nodes[matching_string[0]].insert(SCREAMING_SNAKE_CASE      )

							# Case 4: The word is greater equal to the matching
							# Solution: Create a node in between both nodes, change
							# prefixes and add the new node for the remaining word
							else:
										SCREAMING_SNAKE_CASE_								=						remaining_prefix

										SCREAMING_SNAKE_CASE_								=						self.nodes[matching_string[0]]
										SCREAMING_SNAKE_CASE_								=						RadixNode(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )
										SCREAMING_SNAKE_CASE_								=						aux_node

										if remaining_word == "":
													SCREAMING_SNAKE_CASE_								=						True
										else:
													self.nodes[matching_string[0]].insert(SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					bool:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.nodes.get(word[0]      ,     SCREAMING_SNAKE_CASE      )
				if not incoming_node:
							return False
				else:
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						incoming_node.match(
							    SCREAMING_SNAKE_CASE      )
							# If there is remaining prefix, the word can't be on the tree
							if remaining_prefix != "":
										return False
							# This applies when the word and the prefix are equal
							elif remaining_word == "":
										return incoming_node.is_leaf
							# We have word remaining so we check the next node
							else:
										return incoming_node.find(SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					bool:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.nodes.get(word[0]      ,     SCREAMING_SNAKE_CASE      )
				if not incoming_node:
							return False
				else:
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						incoming_node.match(
							    SCREAMING_SNAKE_CASE      )
							# If there is remaining prefix, the word can't be on the tree
							if remaining_prefix != "":
										return False
							# We have word remaining so we check the next node
							elif remaining_word != "":
										return incoming_node.delete(SCREAMING_SNAKE_CASE      )
							else:
										# If it is not a leaf, we don't have to delete
										if not incoming_node.is_leaf:
													return False
										else:
													# We delete the nodes if no edges go from it
													if len(incoming_node.nodes      ) == 0:
																del self.nodes[word[0]]
																# We merge the current node with its only child
																if len(self.nodes      ) == 1 and not self.is_leaf:
																			SCREAMING_SNAKE_CASE_								=						list(self.nodes.values()      )[0]
																			SCREAMING_SNAKE_CASE_								=						merging_node.is_leaf
																			self.prefix += merging_node.prefix
																			SCREAMING_SNAKE_CASE_								=						merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
													elif len(incoming_node.nodes      ) > 1:
																SCREAMING_SNAKE_CASE_								=						False
													# If there is 1 edge, we merge it with its child
													else:
																SCREAMING_SNAKE_CASE_								=						list(incoming_node.nodes.values()      )[0]
																SCREAMING_SNAKE_CASE_								=						merging_node.is_leaf
																incoming_node.prefix += merging_node.prefix
																SCREAMING_SNAKE_CASE_								=						merging_node.nodes

													return True





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE = 0      )  ->					None:




				"""simple docstring"""



				if self.prefix != "":
							print('-' * height      ,     self.prefix      ,     '  (leaf)' if self.is_leaf else ''      )

				for value in self.nodes.values():
							value.print_tree(height + 1      )




def lowercase    (  )			->							bool:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						'banana bananas bandana band apple all beast'.split()
			SCREAMING_SNAKE_CASE_								=						RadixNode()
			root.insert_many(SCREAMING_SNAKE_CASE )

			assert all(root.find(SCREAMING_SNAKE_CASE ) for word in words )
			assert not root.find('bandanas' )
			assert not root.find('apps' )
			root.delete('all' )
			assert not root.find('all' )
			root.delete('banana' )
			assert not root.find('banana' )
			assert root.find('bananas' )

			return True

def lowercase    (  )			->							None:


			'''simple docstring'''







			assert test_trie()

def lowercase    (  )			->							None:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						RadixNode()
			SCREAMING_SNAKE_CASE_								=						'banana bananas bandanas bandana band apple all beast'.split()
			root.insert_many(SCREAMING_SNAKE_CASE )

			print('Words:'       ,    SCREAMING_SNAKE_CASE )
			print('Tree:' )
			root.print_tree()


if __name__ == "__main__":
				main()
