





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        snake_case_		:







		def __init__(		self			,     __lowercase			,     __lowercase=1_3			,     __lowercase=7			,     __lowercase=True			,     __lowercase=True			,     __lowercase=False			,     __lowercase=True			,     __lowercase=9_9			,     __lowercase=3_2			,     __lowercase=5			,     __lowercase=4			,     __lowercase=3_7			,     __lowercase="gelu"			,     __lowercase=0.1			,     __lowercase=0.1			,     __lowercase=5_1_2			,     __lowercase=1_6			,     __lowercase=2			,     __lowercase=0.0_2			,     __lowercase=3			,     __lowercase=4			,     __lowercase=None			,     )				->   Any:
				_SCREAMING_SNAKE_CASE   : str								=parent
				_SCREAMING_SNAKE_CASE   : str								=batch_size
				_SCREAMING_SNAKE_CASE   : int								=seq_length
				_SCREAMING_SNAKE_CASE   : Optional[int]								=is_training
				_SCREAMING_SNAKE_CASE   : int								=use_input_mask
				_SCREAMING_SNAKE_CASE   : str								=use_token_type_ids
				_SCREAMING_SNAKE_CASE   : Optional[int]								=use_labels
				_SCREAMING_SNAKE_CASE   : Dict								=vocab_size
				_SCREAMING_SNAKE_CASE   : str								=hidden_size
				_SCREAMING_SNAKE_CASE   : Union[str, Any]								=num_hidden_layers
				_SCREAMING_SNAKE_CASE   : Tuple								=num_attention_heads
				_SCREAMING_SNAKE_CASE   : Dict								=intermediate_size
				_SCREAMING_SNAKE_CASE   : Dict								=hidden_act
				_SCREAMING_SNAKE_CASE   : int								=hidden_dropout_prob
				_SCREAMING_SNAKE_CASE   : Tuple								=attention_probs_dropout_prob
				_SCREAMING_SNAKE_CASE   : Optional[Any]								=max_position_embeddings
				_SCREAMING_SNAKE_CASE   : Optional[int]								=type_vocab_size
				_SCREAMING_SNAKE_CASE   : Any								=type_sequence_label_size
				_SCREAMING_SNAKE_CASE   : Tuple								=initializer_range
				_SCREAMING_SNAKE_CASE   : Dict								=num_labels
				_SCREAMING_SNAKE_CASE   : Dict								=num_choices
				_SCREAMING_SNAKE_CASE   : List[Any]								=scope







		def 			__lowercase   (		self   )				->   Optional[Any]:
				_SCREAMING_SNAKE_CASE   : str								=ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size   )

				_SCREAMING_SNAKE_CASE   : List[Any]								=None
				if self.use_input_mask:
						_SCREAMING_SNAKE_CASE   : Any								=random_attention_mask([self.batch_size, self.seq_length]   )

				_SCREAMING_SNAKE_CASE   : Dict								=None
				if self.use_token_type_ids:
						_SCREAMING_SNAKE_CASE   : Union[str, Any]								=ids_tensor([self.batch_size, self.seq_length]			,     self.type_vocab_size   )

				_SCREAMING_SNAKE_CASE   : Optional[int]								=None
				_SCREAMING_SNAKE_CASE   : int								=None
				_SCREAMING_SNAKE_CASE   : int								=None
				if self.use_labels:
						_SCREAMING_SNAKE_CASE   : Dict								=ids_tensor([self.batch_size]			,     self.type_sequence_label_size   )
						_SCREAMING_SNAKE_CASE   : Optional[Any]								=ids_tensor([self.batch_size, self.seq_length]			,     self.num_labels   )
						_SCREAMING_SNAKE_CASE   : str								=ids_tensor([self.batch_size]			,     self.num_choices   )

				_SCREAMING_SNAKE_CASE   : Optional[int]								=self.get_config()

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







		def 			__lowercase   (		self   )				->   List[str]:
				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=__lowercase			,     initializer_range=self.initializer_range			,     )







		def 			__lowercase   (		self			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase   )				->   Any:
				_SCREAMING_SNAKE_CASE   : int								=LlamaModel(config=__lowercase   )
				model.to(__lowercase   )
				model.eval()
				_SCREAMING_SNAKE_CASE   : str								=model(__lowercase			,     attention_mask=__lowercase   )
				_SCREAMING_SNAKE_CASE   : Optional[int]								=model(__lowercase   )
				self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.seq_length, self.hidden_size)   )







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







		def 			__lowercase   (		self			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     )				->   List[str]:
				_SCREAMING_SNAKE_CASE   : Tuple								=LlamaForCausalLM(config=__lowercase   )
				model.to(__lowercase   )
				model.eval()
				_SCREAMING_SNAKE_CASE   : Optional[int]								=model(__lowercase			,     attention_mask=__lowercase			,     labels=__lowercase   )
				self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.seq_length, self.vocab_size)   )







		def 			__lowercase   (		self			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     )				->   int:
				_SCREAMING_SNAKE_CASE   : Any								=True
				_SCREAMING_SNAKE_CASE   : Union[str, Any]								=True
				_SCREAMING_SNAKE_CASE   : Dict								=LlamaForCausalLM(config=__lowercase   )
				model.to(__lowercase   )
				model.eval()

				# first forward pass
				_SCREAMING_SNAKE_CASE   : Union[str, Any]								=model(
				    __lowercase			,     attention_mask=__lowercase			,     encoder_hidden_states=__lowercase			,     encoder_attention_mask=__lowercase			,     use_cache=__lowercase			,     )
				_SCREAMING_SNAKE_CASE   : int								=outputs.past_key_values

				# create hypothetical multiple next token and extent to next_input_ids
				_SCREAMING_SNAKE_CASE   : Any								=ids_tensor((self.batch_size, 3)			,     config.vocab_size   )
				_SCREAMING_SNAKE_CASE   : int								=ids_tensor((self.batch_size, 3)			,     vocab_size=2   )

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

				_SCREAMING_SNAKE_CASE   : Optional[Any]								=model(
				    __lowercase			,     attention_mask=__lowercase			,     encoder_hidden_states=__lowercase			,     encoder_attention_mask=__lowercase			,     output_hidden_states=__lowercase			,     )['''hidden_states'''][0]
				_SCREAMING_SNAKE_CASE   : int								=model(
				    __lowercase			,     attention_mask=__lowercase			,     encoder_hidden_states=__lowercase			,     encoder_attention_mask=__lowercase			,     past_key_values=__lowercase			,     output_hidden_states=__lowercase			,     )['''hidden_states'''][0]

				# select random slice
				_SCREAMING_SNAKE_CASE   : Union[str, Any]								=ids_tensor((1,)			,     output_from_past.shape[-1]   ).item()
				_SCREAMING_SNAKE_CASE   : Union[str, Any]								=output_from_no_past[:, -3:, random_slice_idx].detach()
				_SCREAMING_SNAKE_CASE   : Optional[int]								=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(__lowercase			,     __lowercase			,     atol=1e-3   )   )







		def 			__lowercase   (		self   )				->   Optional[int]:
				_SCREAMING_SNAKE_CASE   : Optional[Any]								=self.prepare_config_and_inputs()
				(
				    _SCREAMING_SNAKE_CASE   
				)   : Dict								=config_and_inputs
				_SCREAMING_SNAKE_CASE   : str								={'''input_ids''': input_ids, '''attention_mask''': input_mask}
				return config, inputs_dict

@require_torch
class        snake_case_		(			_A						,					_A						,					_A						,					unittest.TestCase):
		lowerCamelCase    :Any						=     (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
		lowerCamelCase    :Optional[Any]						=     (LlamaForCausalLM,) if is_torch_available() else ()
		lowerCamelCase    :int						=     (
		    {
		        "feature-extraction": LlamaModel,
		        "text-classification": LlamaForSequenceClassification,
		        "text-generation": LlamaForCausalLM,
		        "zero-shot": LlamaForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		lowerCamelCase    :List[str]						=     False
		lowerCamelCase    :Tuple						=     False







		def 			__lowercase   (		self   )				->   int:
				_SCREAMING_SNAKE_CASE   : Optional[int]								=LlamaModelTester(self   )
				_SCREAMING_SNAKE_CASE   : Optional[Any]								=ConfigTester(self			,     config_class=__lowercase			,     hidden_size=3_7   )







		def 			__lowercase   (		self   )				->   Optional[Any]:
				self.config_tester.run_common_tests()







		def 			__lowercase   (		self   )				->   str:
				_SCREAMING_SNAKE_CASE   : Dict								=self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__lowercase   )







		def 			__lowercase   (		self   )				->   List[Any]:
				_SCREAMING_SNAKE_CASE   : int								=self.model_tester.prepare_config_and_inputs()
				for type in ["absolute", "relative_key", "relative_key_query"]:
						_SCREAMING_SNAKE_CASE   : Any								=type
						self.model_tester.create_and_check_model(*__lowercase   )







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







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







		def 			__lowercase   (		self   )				->   Tuple:
				_SCREAMING_SNAKE_CASE   : str								=self.model_tester.prepare_config_and_inputs_for_common()
				_SCREAMING_SNAKE_CASE   : Union[str, Any]								=3
				_SCREAMING_SNAKE_CASE   : Dict								='''multi_label_classification'''
				_SCREAMING_SNAKE_CASE   : int								=input_dict['''input_ids''']
				_SCREAMING_SNAKE_CASE   : Dict								=input_ids.ne(1   ).to(__lowercase   )
				_SCREAMING_SNAKE_CASE   : Optional[int]								=ids_tensor(
				    [self.model_tester.batch_size, config.num_labels]			,     self.model_tester.type_sequence_label_size   ).to(torch.float   )
				_SCREAMING_SNAKE_CASE   : Optional[int]								=LlamaForSequenceClassification(__lowercase   )
				model.to(__lowercase   )
				model.eval()
				_SCREAMING_SNAKE_CASE   : str								=model(__lowercase			,     attention_mask=__lowercase			,     labels=__lowercase   )
				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]:
				pass







		@parameterized.expand([('''linear''',), ('''dynamic''',)]   )
		def 			__lowercase   (		self			,     __lowercase   )				->   Any:
				_SCREAMING_SNAKE_CASE   : int								=self.model_tester.prepare_config_and_inputs_for_common()
				_SCREAMING_SNAKE_CASE   : Dict								=ids_tensor([1, 1_0]			,     config.vocab_size   )
				_SCREAMING_SNAKE_CASE   : List[Any]								=ids_tensor([1, int(config.max_position_embeddings * 1.5   )]			,     config.vocab_size   )

				set_seed(4_2   )  # Fixed seed at init time so the two models get the same random weights
				_SCREAMING_SNAKE_CASE   : Union[str, Any]								=LlamaModel(__lowercase   )
				original_model.to(__lowercase   )
				original_model.eval()
				_SCREAMING_SNAKE_CASE   : Union[str, Any]								=original_model(__lowercase   ).last_hidden_state
				_SCREAMING_SNAKE_CASE   : Optional[Any]								=original_model(__lowercase   ).last_hidden_state

				set_seed(4_2   )  # Fixed seed at init time so the two models get the same random weights
				_SCREAMING_SNAKE_CASE   : str								={'''type''': scaling_type, '''factor''': 1_0.0}
				_SCREAMING_SNAKE_CASE   : Optional[int]								=LlamaModel(__lowercase   )
				scaled_model.to(__lowercase   )
				scaled_model.eval()
				_SCREAMING_SNAKE_CASE   : Tuple								=scaled_model(__lowercase   ).last_hidden_state
				_SCREAMING_SNAKE_CASE   : List[Any]								=scaled_model(__lowercase   ).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(__lowercase			,     __lowercase			,     atol=1e-5   )   )
				else:
						self.assertFalse(torch.allclose(__lowercase			,     __lowercase			,     atol=1e-5   )   )

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

@require_torch
class        snake_case_		(			unittest.TestCase):







		@unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''   )
		@slow
		def 			__lowercase   (		self   )				->   int:
				_SCREAMING_SNAKE_CASE   : Dict								=[1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
				_SCREAMING_SNAKE_CASE   : Optional[int]								=LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-7b-hf'''			,     device_map='''auto'''   )
				_SCREAMING_SNAKE_CASE   : Optional[Any]								=model(torch.tensor([input_ids]   )   )
				# Expected mean on dim = -1
				_SCREAMING_SNAKE_CASE   : Union[str, Any]								=torch.tensor([[-6.6_5_5_0, -4.1_2_2_7, -4.9_8_5_9, -3.2_4_0_6, 0.8_2_6_2, -3.0_0_3_3, 1.2_9_6_4, -3.3_6_9_9]]   )
				torch.testing.assert_close(out.mean(-1   )			,     __lowercase			,     atol=1e-2			,     rtol=1e-2   )
				# slicing logits[0, 0, 0:30]
				# fmt: off
				_SCREAMING_SNAKE_CASE   : List[str]								=torch.tensor([-1_2.8_2_8_1, -7.4_4_5_3, -0.4_6_3_9, -8.0_6_2_5, -7.2_5_0_0, -8.0_0_0_0, -6.4_8_8_3, -7.7_6_9_5, -7.8_4_3_8, -7.0_3_1_2, -6.2_1_8_8, -7.1_3_2_8, -1.8_4_9_6, 1.9_9_6_1, -8.6_2_5_0, -6.7_2_2_7, -1_2.8_2_8_1, -6.9_4_9_2, -7.0_7_4_2, -7.7_8_5_2, -7.5_8_2_0, -7.9_0_6_2, -6.9_3_7_5, -7.9_8_0_5, -8.3_4_3_8, -8.1_5_6_2, -8.0_4_6_9, -7.6_2_5_0, -7.7_4_2_2, -7.3_3_9_8,]   )
				# fmt: on
				torch.testing.assert_close(out[0, 0, :3_0]			,     __lowercase			,     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:
				_SCREAMING_SNAKE_CASE   : List[Any]								=[1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
				_SCREAMING_SNAKE_CASE   : Dict								=LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-hf'''			,     device_map='''auto'''   )
				_SCREAMING_SNAKE_CASE   : Optional[int]								=model(torch.tensor(__lowercase   )   )
				# Expected mean on dim = -1
				_SCREAMING_SNAKE_CASE   : List[str]								=torch.tensor([[-2.0_6_2_2, -1.2_7_9_4, -1.1_6_3_8, -0.9_7_8_8, -1.4_6_0_3, -1.0_2_3_8, -1.7_8_9_3, -1.4_4_1_1]]   )
				torch.testing.assert_close(out.mean(-1   )			,     __lowercase			,     atol=1e-2			,     rtol=1e-2   )
				# slicing logits[0, 0, 0:30]
				# fmt: off
				_SCREAMING_SNAKE_CASE   : Any								=torch.tensor([-8.1_4_0_6, -8.0_5_4_7, 2.7_4_6_1, -1.2_3_4_4, -0.1_4_4_8, -1.8_2_6_2, -1.0_0_2_0, -1.8_1_5_4, -1.6_8_9_5, -1.8_5_1_6, -2.3_5_7_4, -0.9_2_7_7, 3.7_5_9_8, 6.5_7_4_2, -1.2_9_9_8, -0.1_1_7_7, -8.1_4_0_6, -2.9_6_8_8, -2.9_1_9_9, -3.1_6_9_9, -3.5_2_5_4, -2.3_5_5_5, -2.7_9_8_8, -3.4_1_4_1, -2.8_2_6_2, -4.5_1_9_5, -3.3_3_7_9, -3.3_1_6_4, -2.7_8_3_2, -3.0_2_7_3]   )
				# fmt: on
				torch.testing.assert_close(out[0, 0, :3_0]			,     __lowercase			,     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   )				->   List[str]:
				_SCREAMING_SNAKE_CASE   : Optional[int]								=[1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
				_SCREAMING_SNAKE_CASE   : Optional[int]								=LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''			,     device_map='''auto'''   )
				_SCREAMING_SNAKE_CASE   : int								=model(torch.tensor(__lowercase   )   )
				# Expected mean on dim = -1
				_SCREAMING_SNAKE_CASE   : Union[str, Any]								=torch.tensor([[-0.8_5_6_2, -1.8_5_2_0, -0.7_5_5_1, -0.4_1_6_2, -1.5_1_6_1, -1.2_0_3_8, -2.4_8_2_3, -2.3_2_5_4]]   )
				torch.testing.assert_close(out.mean(-1   )			,     __lowercase			,     atol=1e-2			,     rtol=1e-2   )
				# slicing logits[0, 0, 0:30]
				# fmt: off
				_SCREAMING_SNAKE_CASE   : Any								=torch.tensor([-2.2_2_2_7, 4.8_8_2_8, 0.9_0_2_3, -0.4_5_7_8, -0.7_8_7_1, -0.1_0_3_3, -0.6_2_2_1, -0.5_7_8_6, -0.7_8_0_3, -1.0_6_7_4, -1.2_9_2_0, -0.1_5_7_0, 0.8_0_0_8, 2.0_7_2_3, -0.9_4_9_7, 0.2_7_7_1, -2.2_2_2_7, -0.7_6_1_2, -1.4_3_4_6, -1.2_0_6_1, -1.6_4_2_6, -0.3_0_0_0, -0.7_1_3_9, -1.1_9_3_4, -1.8_6_9_1, -1.6_9_7_3, -1.5_9_4_7, -1.2_7_0_5, -0.3_5_2_3, -0.5_5_1_3]   )
				# fmt: on
				torch.testing.assert_close(out.mean(-1   )			,     __lowercase			,     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   )				->   List[Any]:
				_SCREAMING_SNAKE_CASE   : int								=[1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
				_SCREAMING_SNAKE_CASE   : Optional[Any]								=LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-70b-hf'''			,     device_map='''auto'''   )
				_SCREAMING_SNAKE_CASE   : Union[str, Any]								=model(torch.tensor(__lowercase   )   )

				_SCREAMING_SNAKE_CASE   : int								=torch.tensor(
				    [[-4.2_3_2_7, -3.3_3_6_0, -4.6_6_6_5, -4.7_6_3_1, -1.8_1_8_0, -3.4_1_7_0, -1.4_2_1_1, -3.1_8_1_0]]			,     dtype=torch.floataa   )
				torch.testing.assert_close(out.mean(-1   )			,     __lowercase			,     atol=1e-2			,     rtol=1e-2   )
				# fmt: off
				_SCREAMING_SNAKE_CASE   : List[Any]								=torch.tensor([-9.4_9_2_2, -3.9_5_5_1, 1.7_9_9_8, -5.6_7_5_8, -5.1_0_5_5, -5.8_9_8_4, -4.8_3_2_0, -6.8_0_8_6, -6.5_3_9_1, -5.6_1_7_2, -5.5_8_2_0, -5.5_3_5_2, 1.7_8_8_1, 3.6_2_8_9, -6.5_1_1_7, -3.4_7_8_5, -9.5_0_0_0, -6.0_3_5_2, -6.8_1_2_5, -6.0_1_9_5, -6.6_8_3_6, -5.4_7_2_7, -6.2_8_1_2, -6.0_3_9_1, -7.3_3_9_8, -7.4_2_9_7, -7.4_8_4_4, -6.5_8_2_0, -5.8_7_8_9, -5.5_3_1_2]   )
				# fmt: on
				torch.testing.assert_close(out[0, 0, :3_0]			,     __lowercase			,     atol=1e-5			,     rtol=1e-5   )







		@unittest.skip('''Model is curently gated'''   )
		@slow
		def 			__lowercase   (		self   )				->   int:
				_SCREAMING_SNAKE_CASE   : str								='''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'''
				_SCREAMING_SNAKE_CASE   : str								='''Simply put, the theory of relativity states that '''
				_SCREAMING_SNAKE_CASE   : List[Any]								=LlamaTokenizer.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''   )
				_SCREAMING_SNAKE_CASE   : Optional[Any]								=tokenizer.encode(__lowercase			,     return_tensors='''pt'''   )
				_SCREAMING_SNAKE_CASE   : Any								=LlamaForCausalLM.from_pretrained(
				    '''meta-llama/Llama-2-13b-chat-hf'''			,     device_map='''sequential'''			,     use_safetensors=__lowercase   )

				# greedy generation outputs
				_SCREAMING_SNAKE_CASE   : int								=model.generate(__lowercase			,     max_new_tokens=6_4			,     top_p=__lowercase			,     temperature=1			,     do_sample=__lowercase   )
				_SCREAMING_SNAKE_CASE   : Any								=tokenizer.decode(generated_ids[0]			,     skip_special_tokens=__lowercase   )
				self.assertEqual(__lowercase			,     __lowercase   )








from __future__ import annotations

import queue
class        snake_case_		:







		def __init__(		self			,     __lowercase   )				->   int:
				lowerCamelCase   : int								=data
				lowerCamelCase   : str								=None
				lowerCamelCase   : Dict								=None


def 					A__	(       )   ->     TreeNode:
		print('''\n********Press N to stop entering at any point of time********\n'''		)
		lowerCamelCase   : Tuple								=input('''Enter the value of the root node: '''		).strip().lower()
		lowerCamelCase   : queue.Queue								=queue.Queue()
		lowerCamelCase   : Dict								=TreeNode(int(SCREAMING_SNAKE_CASE_		)		)
		q.put(SCREAMING_SNAKE_CASE_		)
		while not q.empty():
				lowerCamelCase   : Dict								=q.get()
				lowerCamelCase   : Optional[int]								=F"Enter the left node of {node_found.data}: "
				lowerCamelCase   : Tuple								=input(SCREAMING_SNAKE_CASE_		).strip().lower() or '''n'''
				if check == "n":
						return tree_node
				lowerCamelCase   : Union[str, Any]								=TreeNode(int(SCREAMING_SNAKE_CASE_		)		)
				lowerCamelCase   : Optional[Any]								=left_node
				q.put(SCREAMING_SNAKE_CASE_		)
				lowerCamelCase   : List[Any]								=F"Enter the right node of {node_found.data}: "
				lowerCamelCase   : int								=input(SCREAMING_SNAKE_CASE_		).strip().lower() or '''n'''
				if check == "n":
						return tree_node
				lowerCamelCase   : str								=TreeNode(int(SCREAMING_SNAKE_CASE_		)		)
				lowerCamelCase   : Optional[Any]								=right_node
				q.put(SCREAMING_SNAKE_CASE_		)
		raise


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     None:
		if not isinstance(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		) or not node:
				return
		print(node.data							,       end=''','''		)
		pre_order(node.left		)
		pre_order(node.right		)


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     None:
		if not isinstance(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		) or not node:
				return
		in_order(node.left		)
		print(node.data							,       end=''','''		)
		in_order(node.right		)


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     None:
		if not isinstance(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		) or not node:
				return
		post_order(node.left		)
		post_order(node.right		)
		print(node.data							,       end=''','''		)


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     None:
		if not isinstance(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		) or not node:
				return
		lowerCamelCase   : queue.Queue								=queue.Queue()
		q.put(SCREAMING_SNAKE_CASE_		)
		while not q.empty():
				lowerCamelCase   : Optional[int]								=q.get()
				print(node_dequeued.data							,       end=''','''		)
				if node_dequeued.left:
						q.put(node_dequeued.left		)
				if node_dequeued.right:
						q.put(node_dequeued.right		)


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     None:
		if not isinstance(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		) or not node:
				return
		lowerCamelCase   : queue.Queue								=queue.Queue()
		q.put(SCREAMING_SNAKE_CASE_		)
		while not q.empty():
				lowerCamelCase   : Tuple								=[]
				while not q.empty():
						lowerCamelCase   : List[str]								=q.get()
						print(node_dequeued.data							,       end=''','''		)
						if node_dequeued.left:
								list_.append(node_dequeued.left		)
						if node_dequeued.right:
								list_.append(node_dequeued.right		)
				print()
				for node in list_:
						q.put(SCREAMING_SNAKE_CASE_		)


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     None:
		if not isinstance(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		) or not node:
				return
		lowerCamelCase   : list[TreeNode]								=[]
		lowerCamelCase   : Tuple								=node
		while n or stack:
				while n:  # start from root node, find its left child
						print(n.data							,       end=''','''		)
						stack.append(SCREAMING_SNAKE_CASE_		)
						lowerCamelCase   : Any								=n.left
				# end of while means current node doesn't have left child
				lowerCamelCase   : Tuple								=stack.pop()
				# start to traverse its right child
				lowerCamelCase   : Union[str, Any]								=n.right


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     None:
		if not isinstance(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		) or not node:
				return
		lowerCamelCase   : list[TreeNode]								=[]
		lowerCamelCase   : List[Any]								=node
		while n or stack:
				while n:
						stack.append(SCREAMING_SNAKE_CASE_		)
						lowerCamelCase   : List[Any]								=n.left
				lowerCamelCase   : Dict								=stack.pop()
				print(n.data							,       end=''','''		)
				lowerCamelCase   : Union[str, Any]								=n.right


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     None:
		if not isinstance(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		) or not node:
				return
		lowerCamelCase		,   lowerCamelCase   : Optional[int]								=[], []
		lowerCamelCase   : Dict								=node
		stacka.append(SCREAMING_SNAKE_CASE_		)
		while stacka:  # to find the reversed order of post order, store it in stack2
				lowerCamelCase   : int								=stacka.pop()
				if n.left:
						stacka.append(n.left		)
				if n.right:
						stacka.append(n.right		)
				stacka.append(SCREAMING_SNAKE_CASE_		)
		while stacka:  # pop up from stack2 will be the post order
				print(stacka.pop().data							,       end=''','''		)


def 					A__	(       SCREAMING_SNAKE_CASE_ = ""							,       SCREAMING_SNAKE_CASE_=5_0							,       SCREAMING_SNAKE_CASE_="*"		)   ->     str:
		if not s:
				return "\n" + width * char
		lowerCamelCase		,   lowerCamelCase   : Dict								=divmod(width - len(SCREAMING_SNAKE_CASE_		) - 2							,       2		)
		return F"{left * char} {s} {(left + extra) * char}"


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				print(prompt('''Binary Tree Traversals'''))

				snake_case_							=			build_tree()
				print(prompt('''Pre Order Traversal'''))
				pre_order(node)
				print(prompt() + '''\n''')

				print(prompt('''In Order Traversal'''))
				in_order(node)
				print(prompt() + '''\n''')

				print(prompt('''Post Order Traversal'''))
				post_order(node)
				print(prompt() + '''\n''')

				print(prompt('''Level Order Traversal'''))
				level_order(node)
				print(prompt() + '''\n''')

				print(prompt('''Actual Level Order Traversal'''))
				level_order_actual(node)
				print('''*''' * 5_0 + '''\n''')

				print(prompt('''Pre Order Traversal - Iteration Version'''))
				pre_order_iter(node)
				print(prompt() + '''\n''')

				print(prompt('''In Order Traversal - Iteration Version'''))
				in_order_iter(node)
				print(prompt() + '''\n''')

				print(prompt('''Post Order Traversal - Iteration Version'''))
				post_order_iter(node)
				print(prompt())

