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




				def 	__snake_case							(							self	:				Optional[int]      ):
										snake_case       :     List[str] =    ids_tensor([self.batch_size, self.seq_length],    self.vocab_size      )

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

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

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

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

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




				def 	__snake_case							(							self	:				Tuple      ):
										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=SCREAMING_SNAKE_CASE_,    initializer_range=self.initializer_range,    )




				def 	__snake_case							(							self	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				List[Any],    SCREAMING_SNAKE_CASE_	:				List[str],    SCREAMING_SNAKE_CASE_	:				Tuple      ):
										snake_case       :     Optional[int] =    LlamaModel(config=SCREAMING_SNAKE_CASE_      )
										model.to(SCREAMING_SNAKE_CASE_      )
										model.eval()
										snake_case       :     int =    model(SCREAMING_SNAKE_CASE_,    attention_mask=SCREAMING_SNAKE_CASE_      )
										snake_case       :     List[Any] =    model(SCREAMING_SNAKE_CASE_      )
										self.parent.assertEqual(result.last_hidden_state.shape,    (self.batch_size, self.seq_length, self.hidden_size)      )




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




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




				def 	__snake_case							(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				List[Any],    SCREAMING_SNAKE_CASE_	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				List[Any],    SCREAMING_SNAKE_CASE_	:				Any,    SCREAMING_SNAKE_CASE_	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				List[Any],    SCREAMING_SNAKE_CASE_	:				List[Any],    SCREAMING_SNAKE_CASE_	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				Dict,    ):
										snake_case       :     List[Any] =    True
										snake_case       :     Optional[Any] =    True
										snake_case       :     int =    LlamaForCausalLM(config=SCREAMING_SNAKE_CASE_      )
										model.to(SCREAMING_SNAKE_CASE_      )
										model.eval()

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

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

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

										snake_case       :     List[str] =    model(
										    SCREAMING_SNAKE_CASE_,    attention_mask=SCREAMING_SNAKE_CASE_,    encoder_hidden_states=SCREAMING_SNAKE_CASE_,    encoder_attention_mask=SCREAMING_SNAKE_CASE_,    output_hidden_states=SCREAMING_SNAKE_CASE_,    )['''hidden_states'''][0]
										snake_case       :     List[str] =    model(
										    SCREAMING_SNAKE_CASE_,    attention_mask=SCREAMING_SNAKE_CASE_,    encoder_hidden_states=SCREAMING_SNAKE_CASE_,    encoder_attention_mask=SCREAMING_SNAKE_CASE_,    past_key_values=SCREAMING_SNAKE_CASE_,    output_hidden_states=SCREAMING_SNAKE_CASE_,    )['''hidden_states'''][0]

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




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




@require_torch
class 						a   (				__magic_name__				,__magic_name__				,__magic_name__				,unittest.TestCase    ):
				_snake_case		    = (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
				_snake_case		    = (LlamaForCausalLM,) if is_torch_available() else ()
				_snake_case		    = (
				    {
				        '''feature-extraction''': LlamaModel,
				        '''text-classification''': LlamaForSequenceClassification,
				        '''text-generation''': LlamaForCausalLM,
				        '''zero-shot''': LlamaForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				_snake_case		    = False
				_snake_case		    = False




				def 	__snake_case							(							self	:				Any      ):
										snake_case       :     Optional[Any] =    LlamaModelTester(self      )
										snake_case       :     Optional[int] =    ConfigTester(self,    config_class=SCREAMING_SNAKE_CASE_,    hidden_size=37      )




				def 	__snake_case							(							self	:				Optional[int]      ):
										self.config_tester.run_common_tests()




				def 	__snake_case							(							self	:				int      ):
										snake_case       :     Union[str, Any] =    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				List[Any]      ):
										snake_case       :     List[Any] =    self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
																snake_case       :     Optional[int] =    type
																self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_      )




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




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




				def 	__snake_case							(							self	:				str      ):
										snake_case,					snake_case       :     int =    self.model_tester.prepare_config_and_inputs_for_common()
										snake_case       :     List[str] =    3
										snake_case       :     Dict =    '''multi_label_classification'''
										snake_case       :     Optional[int] =    input_dict['''input_ids''']
										snake_case       :     Union[str, Any] =    input_ids.ne(1      ).to(SCREAMING_SNAKE_CASE_      )
										snake_case       :     str =    ids_tensor(
										    [self.model_tester.batch_size, config.num_labels],    self.model_tester.type_sequence_label_size      ).to(torch.float      )
										snake_case       :     Dict =    LlamaForSequenceClassification(SCREAMING_SNAKE_CASE_      )
										model.to(SCREAMING_SNAKE_CASE_      )
										model.eval()
										snake_case       :     int =    model(SCREAMING_SNAKE_CASE_,    attention_mask=SCREAMING_SNAKE_CASE_,    labels=SCREAMING_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 	__snake_case							(							self	:				Union[str, Any]      ):
										pass




				@parameterized.expand([('''linear''',), ('''dynamic''',)]      )
				def 	__snake_case							(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				Union[str, Any]      ):
										snake_case,					snake_case       :     Optional[Any] =    self.model_tester.prepare_config_and_inputs_for_common()
										snake_case       :     Union[str, Any] =    ids_tensor([1, 10],    config.vocab_size      )
										snake_case       :     List[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
										snake_case       :     List[Any] =    LlamaModel(SCREAMING_SNAKE_CASE_      )
										original_model.to(SCREAMING_SNAKE_CASE_      )
										original_model.eval()
										snake_case       :     Optional[int] =    original_model(SCREAMING_SNAKE_CASE_      ).last_hidden_state
										snake_case       :     Dict =    original_model(SCREAMING_SNAKE_CASE_      ).last_hidden_state

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

										# The output should be different for long inputs
										self.assertFalse(torch.allclose(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    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 	__snake_case							(							self	:				Any      ):
										snake_case       :     List[str] =    [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
										snake_case       :     str =    LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-7b-hf''',    device_map='''auto'''      )
										snake_case       :     List[Any] =    model(torch.tensor([input_ids]      )      )
										# Expected mean on dim = -1
										snake_case       :     Optional[Any] =    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      ),    SCREAMING_SNAKE_CASE_,    atol=1e-2,    rtol=1e-2      )
										# slicing logits[0, 0, 0:30]
										# fmt: off
										snake_case       :     Optional[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],    SCREAMING_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 	__snake_case							(							self	:				List[str]      ):
										snake_case       :     Optional[Any] =    [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
										snake_case       :     Dict =    LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-hf''',    device_map='''auto'''      )
										snake_case       :     int =    model(torch.tensor(SCREAMING_SNAKE_CASE_      )      )
										# Expected mean on dim = -1
										snake_case       :     Dict =    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      ),    SCREAMING_SNAKE_CASE_,    atol=1e-2,    rtol=1e-2      )
										# slicing logits[0, 0, 0:30]
										# fmt: off
										snake_case       :     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],    SCREAMING_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 	__snake_case							(							self	:				Any      ):
										snake_case       :     List[str] =    [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
										snake_case       :     Optional[Any] =    LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-chat-hf''',    device_map='''auto'''      )
										snake_case       :     List[Any] =    model(torch.tensor(SCREAMING_SNAKE_CASE_      )      )
										# Expected mean on dim = -1
										snake_case       :     Optional[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      ),    SCREAMING_SNAKE_CASE_,    atol=1e-2,    rtol=1e-2      )
										# slicing logits[0, 0, 0:30]
										# fmt: off
										snake_case       :     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      ),    SCREAMING_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 	__snake_case							(							self	:				Optional[Any]      ):
										snake_case       :     Optional[Any] =    [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       :     Dict =    model(torch.tensor(SCREAMING_SNAKE_CASE_      )      )

										snake_case       :     int =    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      ),    SCREAMING_SNAKE_CASE_,    atol=1e-2,    rtol=1e-2      )
										# fmt: off
										snake_case       :     List[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],    SCREAMING_SNAKE_CASE_,    atol=1e-5,    rtol=1e-5      )




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

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



'''simple docstring'''





from __future__ import annotations

def      A (       A_							:					list[int]    ,							A_							:					int       ):
						snake_case       :     list[list[int]] =    []
						snake_case       :     list[int] =    []
						snake_case       :     int =    0
						snake_case       :     int =    sum(A_       )
						create_state_space_tree(A_    ,							A_    ,							A_    ,							A_    ,							A_    ,							A_       )
						return result

def      A (       A_							:					list[int]    ,							A_							:					int    ,							A_							:					int    ,							A_							:					list[int]    ,							A_							:					list[list[int]]    ,							A_							:					int    ,							):
						if sum(A_       ) > max_sum or (remaining_nums_sum + sum(A_       )) < max_sum:
												return
						if sum(A_       ) == max_sum:
												result.append(A_       )
												return
						for index in range(A_    ,							len(A_       )       ):
												create_state_space_tree(
												    A_    ,							A_    ,							index + 1    ,							[*path, nums[index]]    ,							A_    ,							remaining_nums_sum - nums[index]    ,							)
UpperCAmelCase    					=   [3, 34, 4, 12, 5, 2]
UpperCAmelCase    					=   9
UpperCAmelCase    					=   generate_sum_of_subsets_soln(nums, max_sum)





print(*result)

