



"""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       :







		'''simple docstring'''





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






				"""simple docstring"""



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







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






				"""simple docstring"""



				lowercase__   :    List[Any]				=			ids_tensor([self.batch_size, self.seq_length]			,self.vocab_size						)

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

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

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

				lowercase__   :    Union[str, Any]				=			self.get_config()

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







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






				"""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=_snake_case			,initializer_range=self.initializer_range			,)







		def 					UpperCAmelCase    (	self						:  Optional[int]			,_snake_case						:  List[str]			,_snake_case						:  Dict			,_snake_case						:  Optional[int]			,_snake_case						:  Dict			,_snake_case						:  Dict			,_snake_case						:  List[Any]			,_snake_case						:  str						)					->		Any:






				"""simple docstring"""



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







		def 					UpperCAmelCase    (	self						:  Dict			,_snake_case						:  Tuple			,_snake_case						:  int			,_snake_case						:  List[str]			,_snake_case						:  Optional[int]			,_snake_case						:  Optional[Any]			,_snake_case						:  Dict			,_snake_case						:  List[str]			,_snake_case						:  Optional[Any]			,_snake_case						:  Optional[int]			,)					->		List[str]:






				"""simple docstring"""



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







		def 					UpperCAmelCase    (	self						:  Dict			,_snake_case						:  str			,_snake_case						:  Any			,_snake_case						:  str			,_snake_case						:  int			,_snake_case						:  Dict			,_snake_case						:  Optional[Any]			,_snake_case						:  Optional[int]			,_snake_case						:  str			,_snake_case						:  Any			,)					->		Union[str, Any]:






				"""simple docstring"""



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







		def 					UpperCAmelCase    (	self						:  Tuple			,_snake_case						:  int			,_snake_case						:  Optional[Any]			,_snake_case						:  List[str]			,_snake_case						:  str			,_snake_case						:  Dict			,_snake_case						:  Dict			,_snake_case						:  Tuple			,_snake_case						:  str			,_snake_case						:  Any			,)					->		Dict:






				"""simple docstring"""



				lowercase__   :    Optional[Any]				=			True
				lowercase__   :    Dict				=			True
				lowercase__   :    Optional[int]				=			LlamaForCausalLM(config=_snake_case						)
				model.to(_snake_case						)
				model.eval()

				# first forward pass
				lowercase__   :    Any				=			model(
				    _snake_case			,attention_mask=_snake_case			,encoder_hidden_states=_snake_case			,encoder_attention_mask=_snake_case			,use_cache=_snake_case			,)
				lowercase__   :    Optional[int]				=			outputs.past_key_values

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

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

				lowercase__   :    Optional[int]				=			model(
				    _snake_case			,attention_mask=_snake_case			,encoder_hidden_states=_snake_case			,encoder_attention_mask=_snake_case			,output_hidden_states=_snake_case			,)['''hidden_states'''][0]
				lowercase__   :    str				=			model(
				    _snake_case			,attention_mask=_snake_case			,encoder_hidden_states=_snake_case			,encoder_attention_mask=_snake_case			,past_key_values=_snake_case			,output_hidden_states=_snake_case			,)['''hidden_states'''][0]

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







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






				"""simple docstring"""



				lowercase__   :    Optional[Any]				=			self.prepare_config_and_inputs()
				(
				    (
				    lowercase__
				)		,			(
				    lowercase__
				)		,			(
				    lowercase__
				)		,			(
				    lowercase__
				)		,			(
				    lowercase__
				)		,			(
				    lowercase__
				)		,			(
				    lowercase__
				)		,			
				)   :    Union[str, Any]				=			config_and_inputs
				lowercase__   :    Any				=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
				return config, inputs_dict





@require_torch
class        __A       (	A_     ,A_     ,A_     ,unittest.TestCase    ):







		'''simple docstring'''





		lowerCAmelCase			:					List[Any]   	=				(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
		lowerCAmelCase			:					int   	=				(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			:					List[Any]   	=				False







		def 					UpperCAmelCase    (	self						:  Tuple						)					->		str:






				"""simple docstring"""



				lowercase__   :    str				=			LlamaModelTester(self						)
				lowercase__   :    List[Any]				=			ConfigTester(self			,config_class=_snake_case			,hidden_size=37						)







		def 					UpperCAmelCase    (	self						:  Any						)					->		str:






				"""simple docstring"""



				self.config_tester.run_common_tests()







		def 					UpperCAmelCase    (	self						:  Dict						)					->		Dict:






				"""simple docstring"""



				lowercase__   :    Any				=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_snake_case						)







		def 					UpperCAmelCase    (	self						:  Any						)					->		str:






				"""simple docstring"""



				lowercase__   :    List[str]				=			self.model_tester.prepare_config_and_inputs()
				for type in ["absolute", "relative_key", "relative_key_query"]:
						lowercase__   :    List[str]				=			type
						self.model_tester.create_and_check_model(*_snake_case						)







		def 					UpperCAmelCase    (	self						:  Dict						)					->		Optional[int]:






				"""simple docstring"""



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







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






				"""simple docstring"""



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







		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		int:






				"""simple docstring"""



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






				"""simple docstring"""



				pass







		@parameterized.expand([('''linear''',), ('''dynamic''',)]						)
		def 					UpperCAmelCase    (	self						:  Optional[Any]			,_snake_case						:  str						)					->		Optional[Any]:






				"""simple docstring"""



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

				set_seed(42						)  # Fixed seed at init time so the two models get the same random weights
				lowercase__   :    List[str]				=			{'''type''': scaling_type, '''factor''': 10.0}
				lowercase__   :    Tuple				=			LlamaModel(_snake_case						)
				scaled_model.to(_snake_case						)
				scaled_model.eval()
				lowercase__   :    Union[str, Any]				=			scaled_model(_snake_case						).last_hidden_state
				lowercase__   :    Tuple				=			scaled_model(_snake_case						).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(_snake_case			,_snake_case			,atol=1e-5						)						)
				else:
						self.assertFalse(torch.allclose(_snake_case			,_snake_case			,atol=1e-5						)						)

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





@require_torch
class        __A       (	unittest.TestCase    ):







		'''simple docstring'''





		@unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''						)
		@slow
		def 					UpperCAmelCase    (	self						:  List[Any]						)					->		List[str]:






				"""simple docstring"""



				lowercase__   :    List[str]				=			[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
				lowercase__   :    Dict				=			LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-7b-hf'''			,device_map='''auto'''						)
				lowercase__   :    Optional[Any]				=			model(torch.tensor([input_ids]						)						)
				# Expected mean on dim = -1
				lowercase__   :    Optional[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						)			,_snake_case			,atol=1e-2			,rtol=1e-2						)
				# slicing logits[0, 0, 0:30]
				# fmt: off
				lowercase__   :    str				=			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]			,_snake_case			,atol=1e-5			,rtol=1e-5						)







		@unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''						)
		@slow
		def 					UpperCAmelCase    (	self						:  Union[str, Any]						)					->		Union[str, Any]:






				"""simple docstring"""



				lowercase__   :    str				=			[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
				lowercase__   :    Union[str, Any]				=			LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-hf'''			,device_map='''auto'''						)
				lowercase__   :    List[Any]				=			model(torch.tensor(_snake_case						)						)
				# Expected mean on dim = -1
				lowercase__   :    Tuple				=			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						)			,_snake_case			,atol=1e-2			,rtol=1e-2						)
				# slicing logits[0, 0, 0:30]
				# fmt: off
				lowercase__   :    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]			,_snake_case			,atol=1e-5			,rtol=1e-5						)







		@unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''						)
		@slow
		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Any:






				"""simple docstring"""



				lowercase__   :    Union[str, Any]				=			[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
				lowercase__   :    Tuple				=			LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''			,device_map='''auto'''						)
				lowercase__   :    str				=			model(torch.tensor(_snake_case						)						)
				# Expected mean on dim = -1
				lowercase__   :    List[Any]				=			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						)			,_snake_case			,atol=1e-2			,rtol=1e-2						)
				# slicing logits[0, 0, 0:30]
				# fmt: off
				lowercase__   :    Optional[Any]				=			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						)			,_snake_case			,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 					UpperCAmelCase    (	self						:  Optional[Any]						)					->		Any:






				"""simple docstring"""



				lowercase__   :    Tuple				=			[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
				lowercase__   :    Tuple				=			LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-70b-hf'''			,device_map='''auto'''						)
				lowercase__   :    Optional[Any]				=			model(torch.tensor(_snake_case						)						)

				lowercase__   :    Union[str, 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						)			,_snake_case			,atol=1e-2			,rtol=1e-2						)
				# fmt: off
				lowercase__   :    Optional[Any]				=			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]			,_snake_case			,atol=1e-5			,rtol=1e-5						)







		@unittest.skip('''Model is curently gated'''						)
		@slow
		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Optional[int]:






				"""simple docstring"""



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

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





"""simple docstring"""







from ..utils import DummyObject, requires_backends
class        __A       (	metaclass=A_    ):







		'''simple docstring'''





		lowerCAmelCase			:					List[str]   	=				["torch", "torchsde"]







		def __init__(	self						:  Tuple			,*_snake_case						:  Union[str, Any]			,**_snake_case						:  Any						)					->		Union[str, Any]:






				"""simple docstring"""



				requires_backends(self			,['''torch''', '''torchsde''']						)







		@classmethod
		def 					UpperCAmelCase    (	cls						:  List[str]			,*_snake_case						:  int			,**_snake_case						:  Union[str, Any]						)					->		str:






				"""simple docstring"""



				requires_backends(cls			,['''torch''', '''torchsde''']						)







		@classmethod
		def 					UpperCAmelCase    (	cls						:  List[Any]			,*_snake_case						:  List[Any]			,**_snake_case						:  List[str]						)					->		List[Any]:






				"""simple docstring"""



				requires_backends(cls			,['''torch''', '''torchsde''']						)
