"""simple docstring"""







import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer

class 	UpperCamelCase    :



				def __init__(self       :   int			,						_A       :   Optional[Any]			,						_A       :   Optional[int]=13			,						_A       :   str=7			,						_A       :   Tuple=True			,						_A       :   int=True			,						_A       :   Union[str, Any]=False			,						_A       :   Any=True			,						_A       :   Dict=99			,						_A       :   Optional[Any]=32			,						_A       :   int=5			,						_A       :   List[Any]=4			,						_A       :   str=37			,						_A       :   Optional[int]="gelu"			,						_A       :   Union[str, Any]=0.1			,						_A       :   Dict=0.1			,						_A       :   List[str]=5_12			,						_A       :   Tuple=16			,						_A       :   int=2			,						_A       :   List[str]=0.02			,						_A       :   List[Any]=3			,						_A       :   Any=4			,						_A       :   Dict=None			,						)					->			Union[str, Any]:
					__snake_case    :							Dict   	=				parent
					__snake_case    :							Dict   	=				batch_size
					__snake_case    :							Optional[int]   	=				seq_length
					__snake_case    :							Tuple   	=				is_training
					__snake_case    :							List[Any]   	=				use_input_mask
					__snake_case    :							Optional[int]   	=				use_token_type_ids
					__snake_case    :							int   	=				use_labels
					__snake_case    :							Dict   	=				vocab_size
					__snake_case    :							Union[str, Any]   	=				hidden_size
					__snake_case    :							List[Any]   	=				num_hidden_layers
					__snake_case    :							str   	=				num_attention_heads
					__snake_case    :							str   	=				intermediate_size
					__snake_case    :							Tuple   	=				hidden_act
					__snake_case    :							Any   	=				hidden_dropout_prob
					__snake_case    :							Optional[int]   	=				attention_probs_dropout_prob
					__snake_case    :							Union[str, Any]   	=				max_position_embeddings
					__snake_case    :							str   	=				type_vocab_size
					__snake_case    :							Optional[int]   	=				type_sequence_label_size
					__snake_case    :							List[Any]   	=				initializer_range
					__snake_case    :							Optional[Any]   	=				num_labels
					__snake_case    :							List[str]   	=				num_choices
					__snake_case    :							Optional[int]   	=				scope



				def       _lowercase  (self       :   int)					->			Optional[Any]:
					__snake_case    :							Optional[Any]   	=				ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size)

					__snake_case    :							Tuple   	=				None
					if self.use_input_mask:
						__snake_case    :							Union[str, Any]   	=				random_attention_mask([self.batch_size, self.seq_length])

					__snake_case    :							Optional[Any]   	=				None
					if self.use_token_type_ids:
						__snake_case    :							str   	=				ids_tensor([self.batch_size, self.seq_length]			,						self.type_vocab_size)

					__snake_case    :							Dict   	=				None
					__snake_case    :							List[Any]   	=				None
					__snake_case    :							Dict   	=				None
					if self.use_labels:
						__snake_case    :							List[str]   	=				ids_tensor([self.batch_size]			,						self.type_sequence_label_size)
						__snake_case    :							Union[str, Any]   	=				ids_tensor([self.batch_size, self.seq_length]			,						self.num_labels)
						__snake_case    :							List[str]   	=				ids_tensor([self.batch_size]			,						self.num_choices)

					__snake_case    :							Optional[Any]   	=				self.get_config()

					return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



				def       _lowercase  (self       :   List[str])					->			Any:
					return LlamaConfig(
					    vocab_size=self.vocab_size			,						hidden_size=self.hidden_size			,						num_hidden_layers=self.num_hidden_layers			,						num_attention_heads=self.num_attention_heads			,						intermediate_size=self.intermediate_size			,						hidden_act=self.hidden_act			,						hidden_dropout_prob=self.hidden_dropout_prob			,						attention_probs_dropout_prob=self.attention_probs_dropout_prob			,						max_position_embeddings=self.max_position_embeddings			,						type_vocab_size=self.type_vocab_size			,						is_decoder=_A			,						initializer_range=self.initializer_range			,						)



				def       _lowercase  (self       :   str			,						_A       :   Union[str, Any]			,						_A       :   str			,						_A       :   Optional[Any]			,						_A       :   Dict			,						_A       :   Dict			,						_A       :   Union[str, Any]			,						_A       :   Dict)					->			Tuple:
					__snake_case    :							Optional[int]   	=				LlamaModel(config=_A)
					model.to(_A)
					model.eval()
					__snake_case    :							str   	=				model(_A			,						attention_mask=_A)
					__snake_case    :							Any   	=				model(_A)
					self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size))



				def       _lowercase  (self       :   List[str]			,						_A       :   Dict			,						_A       :   Dict			,						_A       :   Optional[int]			,						_A       :   Dict			,						_A       :   List[Any]			,						_A       :   Union[str, Any]			,						_A       :   Optional[int]			,						_A       :   Dict			,						_A       :   int			,						)					->			Dict:
					__snake_case    :							Tuple   	=				True
					__snake_case    :							Dict   	=				LlamaModel(_A)
					model.to(_A)
					model.eval()
					__snake_case    :							List[str]   	=				model(
					    _A			,						attention_mask=_A			,						encoder_hidden_states=_A			,						encoder_attention_mask=_A			,						)
					__snake_case    :							int   	=				model(
					    _A			,						attention_mask=_A			,						encoder_hidden_states=_A			,						)
					__snake_case    :							Tuple   	=				model(_A			,						attention_mask=_A)
					self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size))



				def       _lowercase  (self       :   str			,						_A       :   Tuple			,						_A       :   Union[str, Any]			,						_A       :   Tuple			,						_A       :   str			,						_A       :   Dict			,						_A       :   List[Any]			,						_A       :   Optional[Any]			,						_A       :   List[Any]			,						_A       :   Any			,						)					->			Optional[Any]:
					__snake_case    :							List[str]   	=				LlamaForCausalLM(config=_A)
					model.to(_A)
					model.eval()
					__snake_case    :							Tuple   	=				model(_A			,						attention_mask=_A			,						labels=_A)
					self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.vocab_size))



				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   Optional[Any]			,						_A       :   Union[str, Any]			,						_A       :   Union[str, Any]			,						_A       :   List[str]			,						_A       :   List[Any]			,						_A       :   Union[str, Any]			,						_A       :   Dict			,						_A       :   Tuple			,						_A       :   Any			,						)					->			List[str]:
					__snake_case    :							Union[str, Any]   	=				True
					__snake_case    :							List[str]   	=				True
					__snake_case    :							Any   	=				LlamaForCausalLM(config=_A)
					model.to(_A)
					model.eval()

					# first forward pass
					__snake_case    :							int   	=				model(
					    _A			,						attention_mask=_A			,						encoder_hidden_states=_A			,						encoder_attention_mask=_A			,						use_cache=_A			,						)
					__snake_case    :							List[str]   	=				outputs.past_key_values

					# create hypothetical multiple next token and extent to next_input_ids
					__snake_case    :							Union[str, Any]   	=				ids_tensor((self.batch_size, 3)			,						config.vocab_size)
					__snake_case    :							str   	=				ids_tensor((self.batch_size, 3)			,						vocab_size=2)

					# append to next input_ids and
					__snake_case    :							List[Any]   	=				torch.cat([input_ids, next_tokens]			,						dim=-1)
					__snake_case    :							Dict   	=				torch.cat([input_mask, next_mask]			,						dim=-1)

					__snake_case    :							Optional[int]   	=				model(
					    _A			,						attention_mask=_A			,						encoder_hidden_states=_A			,						encoder_attention_mask=_A			,						output_hidden_states=_A			,						)['hidden_states'][0]
					__snake_case    :							Optional[int]   	=				model(
					    _A			,						attention_mask=_A			,						encoder_hidden_states=_A			,						encoder_attention_mask=_A			,						past_key_values=_A			,						output_hidden_states=_A			,						)['hidden_states'][0]

					# select random slice
					__snake_case    :							List[Any]   	=				ids_tensor((1,)			,						output_from_past.shape[-1]).item()
					__snake_case    :							Union[str, Any]   	=				output_from_no_past[:, -3:, random_slice_idx].detach()
					__snake_case    :							Tuple   	=				output_from_past[:, :, random_slice_idx].detach()

					self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

					# test that outputs are equal for slice
					self.parent.assertTrue(torch.allclose(_A			,						_A			,						atol=1E-3))






				def       _lowercase  (self       :   Any)					->			Union[str, Any]:
					__snake_case    :							Tuple   	=				self.prepare_config_and_inputs()
					(
					    (
					    __snake_case
					)	,       (
					    __snake_case
					)	,       (
					    __snake_case
					)	,       (
					    __snake_case
					)	,       (
					    __snake_case
					)	,       (
					    __snake_case
					)	,       (
					    __snake_case
					)	,       
					)    :							Any   	=				config_and_inputs
					__snake_case    :							Dict   	=				{'input_ids': input_ids, 'attention_mask': input_mask}
					return config, inputs_dict







@require_torch
class 	UpperCamelCase    (					lowercase   ,			lowercase   ,			lowercase   ,			unittest.TestCase		):
				UpperCAmelCase       :   Tuple					=	(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
				UpperCAmelCase       :   Dict					=	(LlamaForCausalLM,) if is_torch_available() else ()
				UpperCAmelCase       :   Optional[int]					=	(
				    {
				        """feature-extraction""": LlamaModel,
				        """text-classification""": LlamaForSequenceClassification,
				        """text-generation""": LlamaForCausalLM,
				        """zero-shot""": LlamaForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				UpperCAmelCase       :   Optional[int]					=	False
				UpperCAmelCase       :   Optional[int]					=	False



				def       _lowercase  (self       :   Dict)					->			Union[str, Any]:
					__snake_case    :							Optional[int]   	=				LlamaModelTester(self)
					__snake_case    :							str   	=				ConfigTester(self			,						config_class=_A			,						hidden_size=37)



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



				def       _lowercase  (self       :   Tuple)					->			Union[str, Any]:
					__snake_case    :							Union[str, Any]   	=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*_A)



				def       _lowercase  (self       :   Tuple)					->			List[str]:
					__snake_case    :							Optional[Any]   	=				self.model_tester.prepare_config_and_inputs()
					for type in ["absolute", "relative_key", "relative_key_query"]:
						__snake_case    :							Tuple   	=				type
						self.model_tester.create_and_check_model(*_A)



				def       _lowercase  (self       :   Dict)					->			List[str]:
					__snake_case	,       __snake_case    :							List[str]   	=				self.model_tester.prepare_config_and_inputs_for_common()
					__snake_case    :							Any   	=				3
					__snake_case    :							Optional[Any]   	=				input_dict['input_ids']
					__snake_case    :							Any   	=				input_ids.ne(1).to(_A)
					__snake_case    :							int   	=				ids_tensor([self.model_tester.batch_size]			,						self.model_tester.type_sequence_label_size)
					__snake_case    :							Tuple   	=				LlamaForSequenceClassification(_A)
					model.to(_A)
					model.eval()
					__snake_case    :							List[Any]   	=				model(_A			,						attention_mask=_A			,						labels=_A)
					self.assertEqual(result.logits.shape			,						(self.model_tester.batch_size, self.model_tester.num_labels))



				def       _lowercase  (self       :   Tuple)					->			List[Any]:
					__snake_case	,       __snake_case    :							List[str]   	=				self.model_tester.prepare_config_and_inputs_for_common()
					__snake_case    :							str   	=				3
					__snake_case    :							int   	=				'single_label_classification'
					__snake_case    :							Tuple   	=				input_dict['input_ids']
					__snake_case    :							Union[str, Any]   	=				input_ids.ne(1).to(_A)
					__snake_case    :							List[Any]   	=				ids_tensor([self.model_tester.batch_size]			,						self.model_tester.type_sequence_label_size)
					__snake_case    :							str   	=				LlamaForSequenceClassification(_A)
					model.to(_A)
					model.eval()
					__snake_case    :							int   	=				model(_A			,						attention_mask=_A			,						labels=_A)
					self.assertEqual(result.logits.shape			,						(self.model_tester.batch_size, self.model_tester.num_labels))



				def       _lowercase  (self       :   List[str])					->			Union[str, Any]:
					__snake_case	,       __snake_case    :							List[str]   	=				self.model_tester.prepare_config_and_inputs_for_common()
					__snake_case    :							List[str]   	=				3
					__snake_case    :							List[str]   	=				'multi_label_classification'
					__snake_case    :							str   	=				input_dict['input_ids']
					__snake_case    :							List[str]   	=				input_ids.ne(1).to(_A)
					__snake_case    :							Tuple   	=				ids_tensor(
					    [self.model_tester.batch_size, config.num_labels]			,						self.model_tester.type_sequence_label_size).to(torch.float)
					__snake_case    :							Optional[int]   	=				LlamaForSequenceClassification(_A)
					model.to(_A)
					model.eval()
					__snake_case    :							Optional[int]   	=				model(_A			,						attention_mask=_A			,						labels=_A)
					self.assertEqual(result.logits.shape			,						(self.model_tester.batch_size, self.model_tester.num_labels))



				@unittest.skip('LLaMA buffers include complex numbers, which breaks this test')
				def       _lowercase  (self       :   List[Any])					->			Any:
					pass






				@parameterized.expand([('linear',), ('dynamic',)])
				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   List[Any])					->			Any:
					__snake_case	,       __snake_case    :							List[str]   	=				self.model_tester.prepare_config_and_inputs_for_common()
					__snake_case    :							List[Any]   	=				ids_tensor([1, 10]			,						config.vocab_size)
					__snake_case    :							Optional[int]   	=				ids_tensor([1, int(config.max_position_embeddings * 1.5)]			,						config.vocab_size)

					set_seed(42)  # Fixed seed at init time so the two models get the same random weights
					__snake_case    :							int   	=				LlamaModel(_A)
					original_model.to(_A)
					original_model.eval()
					__snake_case    :							Dict   	=				original_model(_A).last_hidden_state
					__snake_case    :							int   	=				original_model(_A).last_hidden_state

					set_seed(42)  # Fixed seed at init time so the two models get the same random weights
					__snake_case    :							Tuple   	=				{'type': scaling_type, 'factor': 10.0}
					__snake_case    :							Optional[Any]   	=				LlamaModel(_A)
					scaled_model.to(_A)
					scaled_model.eval()
					__snake_case    :							Union[str, Any]   	=				scaled_model(_A).last_hidden_state
					__snake_case    :							Optional[int]   	=				scaled_model(_A).last_hidden_state

					# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
					# maximum sequence length, so the outputs for the short input should match.
					if scaling_type == "dynamic":
						self.assertTrue(torch.allclose(_A			,						_A			,						atol=1E-5))
					else:
						self.assertFalse(torch.allclose(_A			,						_A			,						atol=1E-5))

					# The output should be different for long inputs
					self.assertFalse(torch.allclose(_A			,						_A			,						atol=1E-5))







@require_torch
class 	UpperCamelCase    (					unittest.TestCase		):



				@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!')
				@slow
				def       _lowercase  (self       :   Any)					->			Any:
					__snake_case    :							List[str]   	=				[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
					__snake_case    :							Union[str, Any]   	=				LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'			,						device_map='auto')
					__snake_case    :							Any   	=				model(torch.tensor([input_ids]))
					# Expected mean on dim = -1
					__snake_case    :							List[str]   	=				torch.tensor([[-6.6_550, -4.1_227, -4.9_859, -3.2_406, 0.8_262, -3.0_033, 1.2_964, -3.3_699]])
					torch.testing.assert_close(out.mean(-1)			,						_A			,						atol=1E-2			,						rtol=1E-2)
					# slicing logits[0, 0, 0:30]
					# fmt: off
					__snake_case    :							Dict   	=				torch.tensor([-12.8_281, -7.4_453, -0.4_639, -8.0_625, -7.2_500, -8.0_000, -6.4_883, -7.7_695, -7.8_438, -7.0_312, -6.2_188, -7.1_328, -1.8_496, 1.9_961, -8.6_250, -6.7_227, -12.8_281, -6.9_492, -7.0_742, -7.7_852, -7.5_820, -7.9_062, -6.9_375, -7.9_805, -8.3_438, -8.1_562, -8.0_469, -7.6_250, -7.7_422, -7.3_398,])
					# fmt: on
					torch.testing.assert_close(out[0, 0, :30]			,						_A			,						atol=1E-5			,						rtol=1E-5)



				@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!')
				@slow
				def       _lowercase  (self       :   Dict)					->			Dict:
					__snake_case    :							Optional[Any]   	=				[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
					__snake_case    :							str   	=				LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'			,						device_map='auto')
					__snake_case    :							List[str]   	=				model(torch.tensor(_A))
					# Expected mean on dim = -1
					__snake_case    :							int   	=				torch.tensor([[-2.0_622, -1.2_794, -1.1_638, -0.9_788, -1.4_603, -1.0_238, -1.7_893, -1.4_411]])
					torch.testing.assert_close(out.mean(-1)			,						_A			,						atol=1E-2			,						rtol=1E-2)
					# slicing logits[0, 0, 0:30]
					# fmt: off
					__snake_case    :							Dict   	=				torch.tensor([-8.1_406, -8.0_547, 2.7_461, -1.2_344, -0.1_448, -1.8_262, -1.0_020, -1.8_154, -1.6_895, -1.8_516, -2.3_574, -0.9_277, 3.7_598, 6.5_742, -1.2_998, -0.1_177, -8.1_406, -2.9_688, -2.9_199, -3.1_699, -3.5_254, -2.3_555, -2.7_988, -3.4_141, -2.8_262, -4.5_195, -3.3_379, -3.3_164, -2.7_832, -3.0_273])
					# fmt: on
					torch.testing.assert_close(out[0, 0, :30]			,						_A			,						atol=1E-5			,						rtol=1E-5)



				@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!')
				@slow
				def       _lowercase  (self       :   int)					->			Optional[int]:
					__snake_case    :							Dict   	=				[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
					__snake_case    :							Tuple   	=				LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'			,						device_map='auto')
					__snake_case    :							Optional[int]   	=				model(torch.tensor(_A))
					# Expected mean on dim = -1
					__snake_case    :							str   	=				torch.tensor([[-0.8_562, -1.8_520, -0.7_551, -0.4_162, -1.5_161, -1.2_038, -2.4_823, -2.3_254]])
					torch.testing.assert_close(out.mean(-1)			,						_A			,						atol=1E-2			,						rtol=1E-2)
					# slicing logits[0, 0, 0:30]
					# fmt: off
					__snake_case    :							Optional[Any]   	=				torch.tensor([-2.2_227, 4.8_828, 0.9_023, -0.4_578, -0.7_871, -0.1_033, -0.6_221, -0.5_786, -0.7_803, -1.0_674, -1.2_920, -0.1_570, 0.8_008, 2.0_723, -0.9_497, 0.2_771, -2.2_227, -0.7_612, -1.4_346, -1.2_061, -1.6_426, -0.3_000, -0.7_139, -1.1_934, -1.8_691, -1.6_973, -1.5_947, -1.2_705, -0.3_523, -0.5_513])
					# fmt: on
					torch.testing.assert_close(out.mean(-1)			,						_A			,						atol=1E-2			,						rtol=1E-2)



				@unittest.skip(
				    'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test')
				@slow
				def       _lowercase  (self       :   Optional[int])					->			str:
					__snake_case    :							Optional[int]   	=				[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
					__snake_case    :							List[Any]   	=				LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'			,						device_map='auto')
					__snake_case    :							Tuple   	=				model(torch.tensor(_A))

					__snake_case    :							str   	=				torch.tensor(
					    [[-4.2_327, -3.3_360, -4.6_665, -4.7_631, -1.8_180, -3.4_170, -1.4_211, -3.1_810]]			,						dtype=torch.floataa)
					torch.testing.assert_close(out.mean(-1)			,						_A			,						atol=1E-2			,						rtol=1E-2)
					# fmt: off
					__snake_case    :							List[str]   	=				torch.tensor([-9.4_922, -3.9_551, 1.7_998, -5.6_758, -5.1_055, -5.8_984, -4.8_320, -6.8_086, -6.5_391, -5.6_172, -5.5_820, -5.5_352, 1.7_881, 3.6_289, -6.5_117, -3.4_785, -9.5_000, -6.0_352, -6.8_125, -6.0_195, -6.6_836, -5.4_727, -6.2_812, -6.0_391, -7.3_398, -7.4_297, -7.4_844, -6.5_820, -5.8_789, -5.5_312])
					# fmt: on
					torch.testing.assert_close(out[0, 0, :30]			,						_A			,						atol=1E-5			,						rtol=1E-5)






				@unittest.skip('Model is curently gated')
				@slow
				def       _lowercase  (self       :   Union[str, Any])					->			Optional[int]:
					__snake_case    :							Optional[Any]   	=				'Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'
					__snake_case    :							int   	=				'Simply put, the theory of relativity states that '
					__snake_case    :							Optional[Any]   	=				LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf')
					__snake_case    :							Optional[int]   	=				tokenizer.encode(_A			,						return_tensors='pt')
					__snake_case    :							Union[str, Any]   	=				LlamaForCausalLM.from_pretrained(
					    'meta-llama/Llama-2-13b-chat-hf'			,						device_map='sequential'			,						use_safetensors=_A)

					# greedy generation outputs
					__snake_case    :							Tuple   	=				model.generate(_A			,						max_new_tokens=64			,						top_p=_A			,						temperature=1			,						do_sample=_A)
					__snake_case    :							str   	=				tokenizer.decode(generated_ids[0]			,						skip_special_tokens=_A)
					self.assertEqual(_A			,						_A)






"""simple docstring"""







from __future__ import annotations

_a  :  str=				"#"

class 	UpperCamelCase    :



				def __init__(self       :   str)					->			None:
					__snake_case    :							dict   	=				{}



				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   str)					->			None:
					__snake_case    :							Optional[Any]   	=				self._trie
					for char in text:
						if char not in trie:
							__snake_case    :							Optional[int]   	=				{}
						__snake_case    :							Optional[int]   	=				trie[char]
					__snake_case    :							Optional[int]   	=				True



				def       _lowercase  (self       :   List[str]			,						_A       :   str)					->			tuple | list:
					__snake_case    :							str   	=				self._trie
					for char in prefix:
						if char in trie:
							__snake_case    :							Optional[int]   	=				trie[char]
						else:
							return []
					return self._elements(_A)






				def       _lowercase  (self       :   List[Any]			,						_A       :   dict)					->			tuple:
					__snake_case    :							str   	=				[]
					for c, v in d.items():
						__snake_case    :							Optional[Any]   	=				[' '] if c == END else [(c + s) for s in self._elements(_A)]
						result.extend(_A)
					return tuple(_A)







_a  :  str=				Trie()
_a  :  Any=				("depart", "detergent", "daring", "dog", "deer", "deal")
for word in words:
	trie.insert_word(word)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       str						)							->    tuple:

	'''simple docstring'''





	__snake_case    :							Optional[int]   	=				trie.find_word(UpperCAmelCase_						)
	return tuple(string + word for word in suffixes						)
def 						__UpperCAmelCase       (					)							->    None:

	'''simple docstring'''





	print(autocomplete_using_trie('de'						)						)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()





