
'''simple docstring'''





import unittest

from parameterized import parameterized

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

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


if is_torch_available():
      import torch

      from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer






class 							UpperCAmelCase_							:

 '''simple docstring'''







 def __init__(					self		:  Dict			,				snake_case__		:  Optional[int]			,				snake_case__		:  Optional[int]=13			,				snake_case__		:  Tuple=7			,				snake_case__		:  Tuple=True			,				snake_case__		:  str=True			,				snake_case__		:  int=False			,				snake_case__		:  Any=True			,				snake_case__		:  List[Any]=99			,				snake_case__		:  Tuple=32			,				snake_case__		:  Tuple=5			,				snake_case__		:  Any=4			,				snake_case__		:  int=37			,				snake_case__		:  int="gelu"			,				snake_case__		:  Any=0.1			,				snake_case__		:  Optional[int]=0.1			,				snake_case__		:  List[Any]=5_12			,				snake_case__		:  Union[str, Any]=16			,				snake_case__		:  Any=2			,				snake_case__		:  List[str]=0.02			,				snake_case__		:  Optional[int]=3			,				snake_case__		:  Tuple=4			,				snake_case__		:  int=None			,				):



   '''simple docstring'''




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







 def 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




   UpperCAmelCase__      :					str				       =							ids_tensor([self.batch_size, self.seq_length]			,				self.vocab_size )

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

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

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

   UpperCAmelCase__      :					Any				       =							self.get_config()

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







 def 							UpperCamelCase  (					self		:  Dict ):



   '''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		:  Union[str, Any]			,				snake_case__		:  Dict			,				snake_case__		:  Any			,				snake_case__		:  Dict			,				snake_case__		:  str			,				snake_case__		:  Tuple			,				snake_case__		:  Dict			,				snake_case__		:  str ):



   '''simple docstring'''




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







 def 							UpperCamelCase  (					self		:  List[str]			,				snake_case__		:  Any			,				snake_case__		:  Tuple			,				snake_case__		:  Optional[Any]			,				snake_case__		:  Dict			,				snake_case__		:  Union[str, Any]			,				snake_case__		:  Optional[int]			,				snake_case__		:  Optional[int]			,				snake_case__		:  Any			,				snake_case__		:  Dict			,				):



   '''simple docstring'''




   UpperCAmelCase__      :					Union[str, Any]				       =							True
   UpperCAmelCase__      :					Any				       =							LlamaModel(snake_case__ )
   model.to(snake_case__ )
   model.eval()
   UpperCAmelCase__      :					Optional[Any]				       =							model(
       snake_case__			,				attention_mask=snake_case__			,				encoder_hidden_states=snake_case__			,				encoder_attention_mask=snake_case__			,				)
   UpperCAmelCase__      :					Dict				       =							model(
       snake_case__			,				attention_mask=snake_case__			,				encoder_hidden_states=snake_case__			,				)
   UpperCAmelCase__      :					str				       =							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		:  int			,				snake_case__		:  Dict			,				snake_case__		:  Dict			,				snake_case__		:  Tuple			,				snake_case__		:  Optional[int]			,				snake_case__		:  Optional[Any]			,				snake_case__		:  str			,				snake_case__		:  Optional[int]			,				snake_case__		:  Any			,				snake_case__		:  Optional[Any]			,				):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							LlamaForCausalLM(config=snake_case__ )
   model.to(snake_case__ )
   model.eval()
   UpperCAmelCase__      :					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		:  List[Any]			,				snake_case__		:  Tuple			,				snake_case__		:  Optional[Any]			,				snake_case__		:  Optional[Any]			,				snake_case__		:  List[str]			,				snake_case__		:  int			,				snake_case__		:  int			,				snake_case__		:  str			,				snake_case__		:  Optional[int]			,				snake_case__		:  List[str]			,				):



   '''simple docstring'''




   UpperCAmelCase__      :					List[str]				       =							True
   UpperCAmelCase__      :					Tuple				       =							True
   UpperCAmelCase__      :					str				       =							LlamaForCausalLM(config=snake_case__ )
   model.to(snake_case__ )
   model.eval()

   # first forward pass
   UpperCAmelCase__      :					Optional[Any]				       =							model(
       snake_case__			,				attention_mask=snake_case__			,				encoder_hidden_states=snake_case__			,				encoder_attention_mask=snake_case__			,				use_cache=snake_case__			,				)
   UpperCAmelCase__      :					List[Any]				       =							outputs.past_key_values

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

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

   UpperCAmelCase__      :					Dict				       =							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]
   UpperCAmelCase__      :					Any				       =							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
   UpperCAmelCase__      :					Any				       =							ids_tensor((1,)			,				output_from_past.shape[-1] ).item()
   UpperCAmelCase__      :					Union[str, Any]				       =							output_from_no_past[:, -3:, random_slice_idx].detach()
   UpperCAmelCase__      :					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(snake_case__			,				snake_case__			,				atol=1e-3 ) )







 def 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[Any]				       =							self.prepare_config_and_inputs()
   (
       (
       UpperCAmelCase__
   )       ,			(
       UpperCAmelCase__
   )       ,			(
       UpperCAmelCase__
   )       ,			(
       UpperCAmelCase__
   )       ,			(
       UpperCAmelCase__
   )       ,			(
       UpperCAmelCase__
   )       ,			(
       UpperCAmelCase__
   )       ,			
   )      :					Optional[Any]				       =							config_and_inputs
   UpperCAmelCase__      :					Optional[Any]				       =							{"input_ids": input_ids, "attention_mask": input_mask}
   return config, inputs_dict






@require_torch
class 							UpperCAmelCase_							(    A					,				A					,				A					,				unittest.TestCase				):

 '''simple docstring'''
 lowercase_					:    Optional[int]				=							(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
 lowercase_					:    Optional[Any]				=							(LlamaForCausalLM,) if is_torch_available() else ()
 lowercase_					:    Optional[Any]				=							(
     {
         "feature-extraction": LlamaModel,
         "text-classification": LlamaForSequenceClassification,
         "text-generation": LlamaForCausalLM,
         "zero-shot": LlamaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 lowercase_					:    List[str]				=							False
 lowercase_					:    Tuple				=							False







 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					str				       =							LlamaModelTester(self )
   UpperCAmelCase__      :					Tuple				       =							ConfigTester(self			,				config_class=snake_case__			,				hidden_size=37 )







 def 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




   self.config_tester.run_common_tests()







 def 							UpperCamelCase  (					self		:  Dict ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[str]				       =							self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*snake_case__ )







 def 							UpperCamelCase  (					self		:  Optional[Any] ):



   '''simple docstring'''




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







 def 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__      :					List[Any]				       =							self.model_tester.prepare_config_and_inputs_for_common()
   UpperCAmelCase__      :					List[str]				       =							3
   UpperCAmelCase__      :					List[str]				       =							input_dict["input_ids"]
   UpperCAmelCase__      :					Tuple				       =							input_ids.ne(1 ).to(snake_case__ )
   UpperCAmelCase__      :					List[Any]				       =							ids_tensor([self.model_tester.batch_size]			,				self.model_tester.type_sequence_label_size )
   UpperCAmelCase__      :					Tuple				       =							LlamaForSequenceClassification(snake_case__ )
   model.to(snake_case__ )
   model.eval()
   UpperCAmelCase__      :					str				       =							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		:  str ):



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__      :					str				       =							self.model_tester.prepare_config_and_inputs_for_common()
   UpperCAmelCase__      :					Dict				       =							3
   UpperCAmelCase__      :					Optional[Any]				       =							"single_label_classification"
   UpperCAmelCase__      :					Tuple				       =							input_dict["input_ids"]
   UpperCAmelCase__      :					Union[str, Any]				       =							input_ids.ne(1 ).to(snake_case__ )
   UpperCAmelCase__      :					Optional[Any]				       =							ids_tensor([self.model_tester.batch_size]			,				self.model_tester.type_sequence_label_size )
   UpperCAmelCase__      :					Optional[Any]				       =							LlamaForSequenceClassification(snake_case__ )
   model.to(snake_case__ )
   model.eval()
   UpperCAmelCase__      :					List[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[str] ):



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__      :					List[Any]				       =							self.model_tester.prepare_config_and_inputs_for_common()
   UpperCAmelCase__      :					List[Any]				       =							3
   UpperCAmelCase__      :					List[Any]				       =							"multi_label_classification"
   UpperCAmelCase__      :					Tuple				       =							input_dict["input_ids"]
   UpperCAmelCase__      :					List[Any]				       =							input_ids.ne(1 ).to(snake_case__ )
   UpperCAmelCase__      :					Dict				       =							ids_tensor(
       [self.model_tester.batch_size, config.num_labels]			,				self.model_tester.type_sequence_label_size ).to(torch.float )
   UpperCAmelCase__      :					Tuple				       =							LlamaForSequenceClassification(snake_case__ )
   model.to(snake_case__ )
   model.eval()
   UpperCAmelCase__      :					List[str]				       =							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		:  Optional[int] ):



   '''simple docstring'''




   pass







 @parameterized.expand([("linear",), ("dynamic",)] )
 def 							UpperCamelCase  (					self		:  List[str]			,				snake_case__		:  Tuple ):



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__      :					List[Any]				       =							self.model_tester.prepare_config_and_inputs_for_common()
   UpperCAmelCase__      :					Dict				       =							ids_tensor([1, 10]			,				config.vocab_size )
   UpperCAmelCase__      :					Tuple				       =							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
   UpperCAmelCase__      :					Tuple				       =							LlamaModel(snake_case__ )
   original_model.to(snake_case__ )
   original_model.eval()
   UpperCAmelCase__      :					Optional[Any]				       =							original_model(snake_case__ ).last_hidden_state
   UpperCAmelCase__      :					Tuple				       =							original_model(snake_case__ ).last_hidden_state

   set_seed(42 )  # Fixed seed at init time so the two models get the same random weights
   UpperCAmelCase__      :					Optional[Any]				       =							{"type": scaling_type, "factor": 10.0}
   UpperCAmelCase__      :					List[str]				       =							LlamaModel(snake_case__ )
   scaled_model.to(snake_case__ )
   scaled_model.eval()
   UpperCAmelCase__      :					Union[str, Any]				       =							scaled_model(snake_case__ ).last_hidden_state
   UpperCAmelCase__      :					List[str]				       =							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 							UpperCAmelCase_							(    unittest.TestCase				):

 '''simple docstring'''







 @unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!" )
 @slow
 def 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[Any]				       =							[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
   UpperCAmelCase__      :					List[Any]				       =							LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf"			,				device_map="auto" )
   UpperCAmelCase__      :					Tuple				       =							model(torch.tensor([input_ids] ) )
   # Expected mean on dim = -1
   UpperCAmelCase__      :					str				       =							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
   UpperCAmelCase__      :					List[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		:  List[str] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Any				       =							[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
   UpperCAmelCase__      :					List[Any]				       =							LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf"			,				device_map="auto" )
   UpperCAmelCase__      :					Any				       =							model(torch.tensor(snake_case__ ) )
   # Expected mean on dim = -1
   UpperCAmelCase__      :					Optional[int]				       =							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
   UpperCAmelCase__      :					Dict				       =							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		:  int ):



   '''simple docstring'''




   UpperCAmelCase__      :					Any				       =							[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
   UpperCAmelCase__      :					Tuple				       =							LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf"			,				device_map="auto" )
   UpperCAmelCase__      :					List[Any]				       =							model(torch.tensor(snake_case__ ) )
   # Expected mean on dim = -1
   UpperCAmelCase__      :					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 )			,				snake_case__			,				atol=1e-2			,				rtol=1e-2 )
   # slicing logits[0, 0, 0:30]
   # fmt: off
   UpperCAmelCase__      :					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		:  Dict ):



   '''simple docstring'''




   UpperCAmelCase__      :					Tuple				       =							[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
   UpperCAmelCase__      :					Optional[Any]				       =							LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-70b-hf"			,				device_map="auto" )
   UpperCAmelCase__      :					int				       =							model(torch.tensor(snake_case__ ) )

   UpperCAmelCase__      :					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 )			,				snake_case__			,				atol=1e-2			,				rtol=1e-2 )
   # fmt: off
   UpperCAmelCase__      :					Tuple				       =							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		:  Union[str, Any] ):



   '''simple docstring'''




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

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





'''simple docstring'''





import math







def snake_case_     (					lowercase__		):

  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5   ,       int(math.sqrt(lowercase__		) + 1		)   ,       6		):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True







def snake_case_     (					lowercase__ = 0.1		):
  UpperCAmelCase__      :					Dict				       =							3
  UpperCAmelCase__      :					Tuple				       =							3

  while primes / (2 * j - 1) >= ratio:
    for i in range(j * j + j + 1   ,       (j + 2) * (j + 2)   ,       j + 1		):
      primes += is_prime(lowercase__		)
    j += 2
  return j


if __name__ == "__main__":
      import doctest

      doctest.testmod()



