



'''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 	a__     :


	def __init__(self : Dict,					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : Dict=13,					__UpperCAmelCase : Tuple=7,					__UpperCAmelCase : Tuple=True,					__UpperCAmelCase : Optional[int]=True,					__UpperCAmelCase : int=False,					__UpperCAmelCase : Union[str, Any]=True,					__UpperCAmelCase : Optional[int]=99,					__UpperCAmelCase : Tuple=32,					__UpperCAmelCase : int=5,					__UpperCAmelCase : List[Any]=4,					__UpperCAmelCase : List[Any]=37,					__UpperCAmelCase : Optional[Any]="gelu",					__UpperCAmelCase : Tuple=0.1,					__UpperCAmelCase : Optional[int]=0.1,					__UpperCAmelCase : Optional[Any]=512,					__UpperCAmelCase : Dict=16,					__UpperCAmelCase : Dict=2,					__UpperCAmelCase : Optional[int]=0.02,					__UpperCAmelCase : Optional[int]=3,					__UpperCAmelCase : Tuple=4,					__UpperCAmelCase : str=None,					)     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= parent
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= batch_size
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= seq_length
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= is_training
					SCREAMING_SNAKE_CASE  :						int      					= use_input_mask
					SCREAMING_SNAKE_CASE  :						Dict      					= use_token_type_ids
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= use_labels
					SCREAMING_SNAKE_CASE  :						str      					= vocab_size
					SCREAMING_SNAKE_CASE  :						Dict      					= hidden_size
					SCREAMING_SNAKE_CASE  :						Tuple      					= num_hidden_layers
					SCREAMING_SNAKE_CASE  :						List[Any]      					= num_attention_heads
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= intermediate_size
					SCREAMING_SNAKE_CASE  :						List[Any]      					= hidden_act
					SCREAMING_SNAKE_CASE  :						Any      					= hidden_dropout_prob
					SCREAMING_SNAKE_CASE  :						int      					= attention_probs_dropout_prob
					SCREAMING_SNAKE_CASE  :						Any      					= max_position_embeddings
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= type_vocab_size
					SCREAMING_SNAKE_CASE  :						int      					= type_sequence_label_size
					SCREAMING_SNAKE_CASE  :						Tuple      					= initializer_range
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= num_labels
					SCREAMING_SNAKE_CASE  :						List[Any]      					= num_choices
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= scope


	def 				lowercase__  (self : List[str]      )     ->			Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= ids_tensor([self.batch_size, self.seq_length],					self.vocab_size      )

					SCREAMING_SNAKE_CASE  :						Dict      					= None
					if self.use_input_mask:
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= random_attention_mask([self.batch_size, self.seq_length]      )

					SCREAMING_SNAKE_CASE  :						int      					= None
					if self.use_token_type_ids:
									SCREAMING_SNAKE_CASE  :						str      					= ids_tensor([self.batch_size, self.seq_length],					self.type_vocab_size      )

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

					SCREAMING_SNAKE_CASE  :						Tuple      					= self.get_config()

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


	def 				lowercase__  (self : List[Any]      )     ->			List[str]:

					"""simple docstring"""

					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=__UpperCAmelCase,					initializer_range=self.initializer_range,					)


	def 				lowercase__  (self : Union[str, Any],					__UpperCAmelCase : str,					__UpperCAmelCase : int,					__UpperCAmelCase : Any,					__UpperCAmelCase : Dict,					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : int,					__UpperCAmelCase : List[Any]      )     ->			Tuple:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= LlamaModel(config=__UpperCAmelCase      )
					model.to(__UpperCAmelCase      )
					model.eval()
					SCREAMING_SNAKE_CASE  :						List[Any]      					= model(__UpperCAmelCase,					attention_mask=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= model(__UpperCAmelCase      )
					self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, self.seq_length, self.hidden_size)      )


	def 				lowercase__  (self : Any,					__UpperCAmelCase : Dict,					__UpperCAmelCase : str,					__UpperCAmelCase : str,					__UpperCAmelCase : Tuple,					__UpperCAmelCase : Any,					__UpperCAmelCase : int,					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : Any,					__UpperCAmelCase : Union[str, Any],					)     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= True
					SCREAMING_SNAKE_CASE  :						List[str]      					= LlamaModel(__UpperCAmelCase      )
					model.to(__UpperCAmelCase      )
					model.eval()
					SCREAMING_SNAKE_CASE  :						List[Any]      					= model(
					    __UpperCAmelCase,					attention_mask=__UpperCAmelCase,					encoder_hidden_states=__UpperCAmelCase,					encoder_attention_mask=__UpperCAmelCase,					)
					SCREAMING_SNAKE_CASE  :						List[Any]      					= model(
					    __UpperCAmelCase,					attention_mask=__UpperCAmelCase,					encoder_hidden_states=__UpperCAmelCase,					)
					SCREAMING_SNAKE_CASE  :						List[str]      					= model(__UpperCAmelCase,					attention_mask=__UpperCAmelCase      )
					self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, self.seq_length, self.hidden_size)      )


	def 				lowercase__  (self : Tuple,					__UpperCAmelCase : str,					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : Any,					__UpperCAmelCase : List[str],					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : Dict,					__UpperCAmelCase : Tuple,					)     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= LlamaForCausalLM(config=__UpperCAmelCase      )
					model.to(__UpperCAmelCase      )
					model.eval()
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= model(__UpperCAmelCase,					attention_mask=__UpperCAmelCase,					labels=__UpperCAmelCase      )
					self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length, self.vocab_size)      )


	def 				lowercase__  (self : Optional[Any],					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : int,					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : List[str],					__UpperCAmelCase : Dict,					__UpperCAmelCase : Dict,					__UpperCAmelCase : Tuple,					)     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= True
					SCREAMING_SNAKE_CASE  :						str      					= True
					SCREAMING_SNAKE_CASE  :						Any      					= LlamaForCausalLM(config=__UpperCAmelCase      )
					model.to(__UpperCAmelCase      )
					model.eval()

					# first forward pass
					SCREAMING_SNAKE_CASE  :						int      					= model(
					    __UpperCAmelCase,					attention_mask=__UpperCAmelCase,					encoder_hidden_states=__UpperCAmelCase,					encoder_attention_mask=__UpperCAmelCase,					use_cache=__UpperCAmelCase,					)
					SCREAMING_SNAKE_CASE  :						str      					= outputs.past_key_values

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

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

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= model(
					    __UpperCAmelCase,					attention_mask=__UpperCAmelCase,					encoder_hidden_states=__UpperCAmelCase,					encoder_attention_mask=__UpperCAmelCase,					output_hidden_states=__UpperCAmelCase,					)['''hidden_states'''][0]
					SCREAMING_SNAKE_CASE  :						str      					= model(
					    __UpperCAmelCase,					attention_mask=__UpperCAmelCase,					encoder_hidden_states=__UpperCAmelCase,					encoder_attention_mask=__UpperCAmelCase,					past_key_values=__UpperCAmelCase,					output_hidden_states=__UpperCAmelCase,					)['''hidden_states'''][0]

					# select random slice
					SCREAMING_SNAKE_CASE  :						List[str]      					= ids_tensor((1,),					output_from_past.shape[-1]      ).item()
					SCREAMING_SNAKE_CASE  :						str      					= output_from_no_past[:, -3:, random_slice_idx].detach()
					SCREAMING_SNAKE_CASE  :						str      					= 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(__UpperCAmelCase,					__UpperCAmelCase,					atol=1e-3      )      )






	def 				lowercase__  (self : Optional[int]      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.prepare_config_and_inputs()
					(
					    (
					    SCREAMING_SNAKE_CASE
					)					,					(
					    SCREAMING_SNAKE_CASE
					)					,					(
					    SCREAMING_SNAKE_CASE
					)					,					(
					    SCREAMING_SNAKE_CASE
					)					,					(
					    SCREAMING_SNAKE_CASE
					)					,					(
					    SCREAMING_SNAKE_CASE
					)					,					(
					    SCREAMING_SNAKE_CASE
					)					,					
					)  :						List[str]      					= config_and_inputs
					SCREAMING_SNAKE_CASE  :						int      					= {'''input_ids''': input_ids, '''attention_mask''': input_mask}
					return config, inputs_dict



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


	def 				lowercase__  (self : Tuple      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Tuple      					= LlamaModelTester(self      )
					SCREAMING_SNAKE_CASE  :						str      					= ConfigTester(self,					config_class=__UpperCAmelCase,					hidden_size=37      )


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

					"""simple docstring"""

					self.config_tester.run_common_tests()


	def 				lowercase__  (self : Any      )     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*__UpperCAmelCase      )


	def 				lowercase__  (self : Optional[int]      )     ->			Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						str      					= self.model_tester.prepare_config_and_inputs()
					for type in ["absolute", "relative_key", "relative_key_query"]:
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= type
									self.model_tester.create_and_check_model(*__UpperCAmelCase      )


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.model_tester.prepare_config_and_inputs_for_common()
					SCREAMING_SNAKE_CASE  :						Any      					= 3
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= input_dict['''input_ids''']
					SCREAMING_SNAKE_CASE  :						Any      					= input_ids.ne(1      ).to(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= ids_tensor([self.model_tester.batch_size],					self.model_tester.type_sequence_label_size      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= LlamaForSequenceClassification(__UpperCAmelCase      )
					model.to(__UpperCAmelCase      )
					model.eval()
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= model(__UpperCAmelCase,					attention_mask=__UpperCAmelCase,					labels=__UpperCAmelCase      )
					self.assertEqual(result.logits.shape,					(self.model_tester.batch_size, self.model_tester.num_labels)      )


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Any      					= self.model_tester.prepare_config_and_inputs_for_common()
					SCREAMING_SNAKE_CASE  :						Any      					= 3
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= '''single_label_classification'''
					SCREAMING_SNAKE_CASE  :						Dict      					= input_dict['''input_ids''']
					SCREAMING_SNAKE_CASE  :						int      					= input_ids.ne(1      ).to(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= ids_tensor([self.model_tester.batch_size],					self.model_tester.type_sequence_label_size      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= LlamaForSequenceClassification(__UpperCAmelCase      )
					model.to(__UpperCAmelCase      )
					model.eval()
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= model(__UpperCAmelCase,					attention_mask=__UpperCAmelCase,					labels=__UpperCAmelCase      )
					self.assertEqual(result.logits.shape,					(self.model_tester.batch_size, self.model_tester.num_labels)      )


	def 				lowercase__  (self : Any      )     ->			Optional[int]:

					"""simple docstring"""

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

					"""simple docstring"""

					pass






	@parameterized.expand([('''linear''',), ('''dynamic''',)]      )
	def 				lowercase__  (self : Tuple,					__UpperCAmelCase : str      )     ->			Tuple:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						int      					= self.model_tester.prepare_config_and_inputs_for_common()
					SCREAMING_SNAKE_CASE  :						Any      					= ids_tensor([1, 10],					config.vocab_size      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= 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
					SCREAMING_SNAKE_CASE  :						int      					= LlamaModel(__UpperCAmelCase      )
					original_model.to(__UpperCAmelCase      )
					original_model.eval()
					SCREAMING_SNAKE_CASE  :						Dict      					= original_model(__UpperCAmelCase      ).last_hidden_state
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= original_model(__UpperCAmelCase      ).last_hidden_state

					set_seed(42      )  # Fixed seed at init time so the two models get the same random weights
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= {'''type''': scaling_type, '''factor''': 10.0}
					SCREAMING_SNAKE_CASE  :						Dict      					= LlamaModel(__UpperCAmelCase      )
					scaled_model.to(__UpperCAmelCase      )
					scaled_model.eval()
					SCREAMING_SNAKE_CASE  :						int      					= scaled_model(__UpperCAmelCase      ).last_hidden_state
					SCREAMING_SNAKE_CASE  :						Dict      					= scaled_model(__UpperCAmelCase      ).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(__UpperCAmelCase,					__UpperCAmelCase,					atol=1e-5      )      )
					else:
									self.assertFalse(torch.allclose(__UpperCAmelCase,					__UpperCAmelCase,					atol=1e-5      )      )

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



@require_torch
class 	a__     (				unittest.TestCase	):


	@unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''      )
	@slow
	def 				lowercase__  (self : Optional[int]      )     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= [1, 306, 4658, 278, 6593, 310, 2834, 338]
					SCREAMING_SNAKE_CASE  :						Dict      					= LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-7b-hf''',					device_map='''auto'''      )
					SCREAMING_SNAKE_CASE  :						Any      					= model(torch.tensor([input_ids]      )      )
					# Expected mean on dim = -1
					SCREAMING_SNAKE_CASE  :						int      					= torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]]      )
					torch.testing.assert_close(out.mean(-1      ),					__UpperCAmelCase,					atol=1e-2,					rtol=1e-2      )
					# slicing logits[0, 0, 0:30]
					# fmt: off
					SCREAMING_SNAKE_CASE  :						List[Any]      					= torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,]      )
					# fmt: on
					torch.testing.assert_close(out[0, 0, :30],					__UpperCAmelCase,					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[Any]      )     ->			Optional[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= [1, 306, 4658, 278, 6593, 310, 2834, 338]
					SCREAMING_SNAKE_CASE  :						str      					= LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-hf''',					device_map='''auto'''      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= model(torch.tensor(__UpperCAmelCase      )      )
					# Expected mean on dim = -1
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]]      )
					torch.testing.assert_close(out.mean(-1      ),					__UpperCAmelCase,					atol=1e-2,					rtol=1e-2      )
					# slicing logits[0, 0, 0:30]
					# fmt: off
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273]      )
					# fmt: on
					torch.testing.assert_close(out[0, 0, :30],					__UpperCAmelCase,					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 : Union[str, Any]      )     ->			Optional[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= [1, 306, 4658, 278, 6593, 310, 2834, 338]
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-chat-hf''',					device_map='''auto'''      )
					SCREAMING_SNAKE_CASE  :						str      					= model(torch.tensor(__UpperCAmelCase      )      )
					# Expected mean on dim = -1
					SCREAMING_SNAKE_CASE  :						str      					= torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]]      )
					torch.testing.assert_close(out.mean(-1      ),					__UpperCAmelCase,					atol=1e-2,					rtol=1e-2      )
					# slicing logits[0, 0, 0:30]
					# fmt: off
					SCREAMING_SNAKE_CASE  :						Dict      					= torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513]      )
					# fmt: on
					torch.testing.assert_close(out.mean(-1      ),					__UpperCAmelCase,					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[str]      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= [1, 306, 4658, 278, 6593, 310, 2834, 338]
					SCREAMING_SNAKE_CASE  :						Any      					= LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-70b-hf''',					device_map='''auto'''      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= model(torch.tensor(__UpperCAmelCase      )      )

					SCREAMING_SNAKE_CASE  :						Any      					= torch.tensor(
					    [[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]],					dtype=torch.floataa      )
					torch.testing.assert_close(out.mean(-1      ),					__UpperCAmelCase,					atol=1e-2,					rtol=1e-2      )
					# fmt: off
					SCREAMING_SNAKE_CASE  :						str      					= torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312]      )
					# fmt: on
					torch.testing.assert_close(out[0, 0, :30],					__UpperCAmelCase,					atol=1e-5,					rtol=1e-5      )






	@unittest.skip('''Model is curently gated'''      )
	@slow
	def 				lowercase__  (self : Tuple      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Tuple      					= '''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  :						List[Any]      					= '''Simply put, the theory of relativity states that '''
					SCREAMING_SNAKE_CASE  :						int      					= LlamaTokenizer.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''      )
					SCREAMING_SNAKE_CASE  :						Dict      					= tokenizer.encode(__UpperCAmelCase,					return_tensors='''pt'''      )
					SCREAMING_SNAKE_CASE  :						Any      					= LlamaForCausalLM.from_pretrained(
					    '''meta-llama/Llama-2-13b-chat-hf''',					device_map='''sequential''',					use_safetensors=__UpperCAmelCase      )

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









'''simple docstring'''

from __future__ import annotations






def       __lowercase  (_SCREAMING_SNAKE_CASE      :int | str       ):
				SCREAMING_SNAKE_CASE  :						int      					= str(_SCREAMING_SNAKE_CASE       )
				return n == n[::-1]






def       __lowercase  (_SCREAMING_SNAKE_CASE      :int = 1_00_00_00       ):
				SCREAMING_SNAKE_CASE  :						int      					= 0

				for i in range(1    ,	_SCREAMING_SNAKE_CASE       ):
								if is_palindrome(_SCREAMING_SNAKE_CASE       ) and is_palindrome(bin(_SCREAMING_SNAKE_CASE       ).split('''b'''       )[1]       ):
												total += i
				return total


if __name__ == "__main__":
	print(solution(int(str(input().strip()))))




