
"""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   _lowercase						:


     '''simple docstring'''





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



     def  lowerCAmelCase__       (      self  )->					Optional[Any]:
          UpperCAmelCase__							: List[str] 							=						ids_tensor([self.batch_size, self.seq_length] ,			self.vocab_size  )

          UpperCAmelCase__							: List[str] 							=						None
          if self.use_input_mask:
               UpperCAmelCase__							: Tuple 							=						random_attention_mask([self.batch_size, self.seq_length]  )

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

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

          UpperCAmelCase__							: List[Any] 							=						self.get_config()

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



     def  lowerCAmelCase__       (      self  )->					List[Any]:
          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  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					List[str]:
          UpperCAmelCase__							: Union[str, Any] 							=						LlamaModel(config=__UpperCamelCase  )
          model.to(__UpperCamelCase  )
          model.eval()
          UpperCAmelCase__							: List[Any] 							=						model(__UpperCamelCase ,			attention_mask=__UpperCamelCase  )
          UpperCAmelCase__							: Optional[Any] 							=						model(__UpperCamelCase  )
          self.parent.assertEqual(result.last_hidden_state.shape ,			(self.batch_size, self.seq_length, self.hidden_size)  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			)->					Tuple:
          UpperCAmelCase__							: Optional[int] 							=						True
          UpperCAmelCase__							: Dict 							=						LlamaModel(__UpperCamelCase  )
          model.to(__UpperCamelCase  )
          model.eval()
          UpperCAmelCase__							: int 							=						model(
              __UpperCamelCase ,			attention_mask=__UpperCamelCase ,			encoder_hidden_states=__UpperCamelCase ,			encoder_attention_mask=__UpperCamelCase ,			)
          UpperCAmelCase__							: Any 							=						model(
              __UpperCamelCase ,			attention_mask=__UpperCamelCase ,			encoder_hidden_states=__UpperCamelCase ,			)
          UpperCAmelCase__							: Any 							=						model(__UpperCamelCase ,			attention_mask=__UpperCamelCase  )
          self.parent.assertEqual(result.last_hidden_state.shape ,			(self.batch_size, self.seq_length, self.hidden_size)  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			)->					Optional[Any]:
          UpperCAmelCase__							: List[str] 							=						LlamaForCausalLM(config=__UpperCamelCase  )
          model.to(__UpperCamelCase  )
          model.eval()
          UpperCAmelCase__							: Dict 							=						model(__UpperCamelCase ,			attention_mask=__UpperCamelCase ,			labels=__UpperCamelCase  )
          self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.seq_length, self.vocab_size)  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			)->					int:
          UpperCAmelCase__							: Optional[int] 							=						True
          UpperCAmelCase__							: Optional[Any] 							=						True
          UpperCAmelCase__							: List[Any] 							=						LlamaForCausalLM(config=__UpperCamelCase  )
          model.to(__UpperCamelCase  )
          model.eval()

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

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

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

          UpperCAmelCase__							: Tuple 							=						model(
              __UpperCamelCase ,			attention_mask=__UpperCamelCase ,			encoder_hidden_states=__UpperCamelCase ,			encoder_attention_mask=__UpperCamelCase ,			output_hidden_states=__UpperCamelCase ,			)["hidden_states"][0]
          UpperCAmelCase__							: int 							=						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
          UpperCAmelCase__							: Any 							=						ids_tensor((1,) ,			output_from_past.shape[-1]  ).item()
          UpperCAmelCase__							: Any 							=						output_from_no_past[:, -3:, random_slice_idx].detach()
          UpperCAmelCase__							: 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  lowerCAmelCase__       (      self  )->					List[Any]:
          UpperCAmelCase__							: Dict 							=						self.prepare_config_and_inputs()
          (
              UpperCAmelCase__	
          )							: Any 							=						config_and_inputs
          UpperCAmelCase__							: List[Any] 							=						{"input_ids": input_ids, "attention_mask": input_mask}
          return config, inputs_dict







@require_torch
class   _lowercase						(       lowerCAmelCase_	,   lowerCAmelCase_	,   lowerCAmelCase_	,   unittest.TestCase				):


     '''simple docstring'''





     _A								=							(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
     _A								=							(LlamaForCausalLM,) if is_torch_available() else ()
     _A								=							(
         {
             'feature-extraction': LlamaModel,
             'text-classification': LlamaForSequenceClassification,
             'text-generation': LlamaForCausalLM,
             'zero-shot': LlamaForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     _A								=							False
     _A								=							False



     def  lowerCAmelCase__       (      self  )->					List[Any]:
          UpperCAmelCase__							: Optional[int] 							=						LlamaModelTester(self  )
          UpperCAmelCase__							: Any 							=						ConfigTester(self ,			config_class=__UpperCamelCase ,			hidden_size=37  )



     def  lowerCAmelCase__       (      self  )->					int:
          self.config_tester.run_common_tests()



     def  lowerCAmelCase__       (      self  )->					Any:
          UpperCAmelCase__							: Union[str, Any] 							=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          UpperCAmelCase__							: Any 							=						self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
               UpperCAmelCase__							: int 							=						type
               self.model_tester.create_and_check_model(*__UpperCamelCase  )



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



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



     def  lowerCAmelCase__       (      self  )->					int:
          UpperCAmelCase__							: str 							=						self.model_tester.prepare_config_and_inputs_for_common()
          UpperCAmelCase__							: Any 							=						3
          UpperCAmelCase__							: Union[str, Any] 							=						"multi_label_classification"
          UpperCAmelCase__							: Optional[Any] 							=						input_dict["input_ids"]
          UpperCAmelCase__							: str 							=						input_ids.ne(1  ).to(__UpperCamelCase  )
          UpperCAmelCase__							: Optional[Any] 							=						ids_tensor(
              [self.model_tester.batch_size, config.num_labels] ,			self.model_tester.type_sequence_label_size  ).to(torch.float  )
          UpperCAmelCase__							: int 							=						LlamaForSequenceClassification(__UpperCamelCase  )
          model.to(__UpperCamelCase  )
          model.eval()
          UpperCAmelCase__							: Union[str, Any] 							=						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  lowerCAmelCase__       (      self  )->					int:
          pass



     @parameterized.expand([("linear",), ("dynamic",)]  )
     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					List[str]:
          UpperCAmelCase__							: Tuple 							=						self.model_tester.prepare_config_and_inputs_for_common()
          UpperCAmelCase__							: Optional[int] 							=						ids_tensor([1, 10] ,			config.vocab_size  )
          UpperCAmelCase__							: List[str] 							=						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__							: Optional[Any] 							=						LlamaModel(__UpperCamelCase  )
          original_model.to(__UpperCamelCase  )
          original_model.eval()
          UpperCAmelCase__							: Optional[Any] 							=						original_model(__UpperCamelCase  ).last_hidden_state
          UpperCAmelCase__							: Dict 							=						original_model(__UpperCamelCase  ).last_hidden_state

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


     '''simple docstring'''





     @unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!"  )
     @slow
     def  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          UpperCAmelCase__							: Dict 							=						[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
          UpperCAmelCase__							: List[str] 							=						LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf" ,			device_map="auto"  )
          UpperCAmelCase__							: List[Any] 							=						model(torch.tensor([input_ids]  )  )
          # Expected mean on dim = -1
          UpperCAmelCase__							: 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
          UpperCAmelCase__							: 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  lowerCAmelCase__       (      self  )->					int:
          UpperCAmelCase__							: str 							=						[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
          UpperCAmelCase__							: Optional[Any] 							=						LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf" ,			device_map="auto"  )
          UpperCAmelCase__							: List[str] 							=						model(torch.tensor(__UpperCamelCase  )  )
          # Expected mean on dim = -1
          UpperCAmelCase__							: str 							=						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
          UpperCAmelCase__							: str 							=						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  lowerCAmelCase__       (      self  )->					str:
          UpperCAmelCase__							: int 							=						[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
          UpperCAmelCase__							: int 							=						LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf" ,			device_map="auto"  )
          UpperCAmelCase__							: Union[str, Any] 							=						model(torch.tensor(__UpperCamelCase  )  )
          # 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  ) ,			__UpperCamelCase ,			atol=1E-2 ,			rtol=1E-2  )
          # slicing logits[0, 0, 0:30]
          # fmt: off
          UpperCAmelCase__							: Tuple 							=						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  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          UpperCAmelCase__							: Optional[int] 							=						[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
          UpperCAmelCase__							: Union[str, Any] 							=						LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-70b-hf" ,			device_map="auto"  )
          UpperCAmelCase__							: Optional[int] 							=						model(torch.tensor(__UpperCamelCase  )  )

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

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





"""simple docstring"""




import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD



def 							a__ (				lowerCAmelCase	:       List[str]		):






     '''simple docstring'''
     def wrapper(*lowerCAmelCase	:       Any       ,    **lowerCAmelCase	:       Tuple		):
          UpperCAmelCase__							: Optional[int] 							=						timeit.default_timer()
          UpperCAmelCase__							: int 							=						func(*lowerCAmelCase       ,    **lowerCAmelCase		)
          UpperCAmelCase__							: List[Any] 							=						timeit.default_timer() - starttime
          return delta

     UpperCAmelCase__							: int 							=						func.__name__

     return wrapper



def 							a__ (				lowerCAmelCase	:       dict       ,    lowerCAmelCase	:       Optional[int]=100       ,    lowerCAmelCase	:       List[str]=None		):






     '''simple docstring'''
     UpperCAmelCase__							: str 							=						[]
     UpperCAmelCase__							: Optional[Any] 							=						seq_shapes or {}
     for i in range(lowerCAmelCase		):
          UpperCAmelCase__							: int 							=						{}
          for col_id, (k, v) in enumerate(features.items()		):
               if isinstance(lowerCAmelCase       ,    _ArrayXD		):
                    UpperCAmelCase__							: List[str] 							=						np.random.rand(*v.shape		).astype(v.dtype		)
               elif isinstance(lowerCAmelCase       ,    datasets.Value		):
                    if v.dtype == "string":
                         UpperCAmelCase__							: Dict 							=						"The small grey turtle was surprisingly fast when challenged."
                    else:
                         UpperCAmelCase__							: str 							=						np.random.randint(10       ,    size=1		).astype(v.dtype		).item()
               elif isinstance(lowerCAmelCase       ,    datasets.Sequence		):
                    while isinstance(lowerCAmelCase       ,    datasets.Sequence		):
                         UpperCAmelCase__							: List[str] 							=						v.feature
                    UpperCAmelCase__							: Optional[int] 							=						seq_shapes[k]
                    UpperCAmelCase__							: Optional[int] 							=						np.random.rand(*lowerCAmelCase		).astype(v.dtype		)
               UpperCAmelCase__							: Union[str, Any] 							=						data

          dummy_data.append((i, example)		)

     return dummy_data



def 							a__ (				lowerCAmelCase	:       List[str]       ,    lowerCAmelCase	:       Tuple       ,    lowerCAmelCase	:       List[str]=100       ,    lowerCAmelCase	:       Optional[int]=None		):






     '''simple docstring'''
     UpperCAmelCase__							: int 							=						generate_examples(lowerCAmelCase       ,    num_examples=lowerCAmelCase       ,    seq_shapes=lowerCAmelCase		)

     with ArrowWriter(features=lowerCAmelCase       ,    path=lowerCAmelCase		) as writer:
          for key, record in dummy_data:
               UpperCAmelCase__							: List[Any] 							=						features.encode_example(lowerCAmelCase		)
               writer.write(lowerCAmelCase		)

          UpperCAmelCase__							,	UpperCAmelCase__							: Optional[Any] 							=						writer.finalize()

     if not num_final_examples == num_examples:
          raise ValueError(
              F"Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."		)

     UpperCAmelCase__							: Optional[int] 							=						datasets.Dataset.from_file(filename=lowerCAmelCase       ,    info=datasets.DatasetInfo(features=lowerCAmelCase		)		)

     return dataset



