
"""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__
          )							,	(
              UpperCAmelCase__
          )							,	(
              UpperCAmelCase__
          )							,	(
              UpperCAmelCase__
          )							,	(
              UpperCAmelCase__
          )							,	(
              UpperCAmelCase__
          )							,	(
              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__							,	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__							,	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__							,	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__							,	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 random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
    import tensorflow as tf
    from tensorflow.python.framework.errors_impl import ResourceExhaustedError

    from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
    import pyanvml.pyanvml as nvml

A__      :	Optional[Any]          =    logging.get_logger(__name__)



def 							a__ (				lowerCAmelCase	:       bool       ,    lowerCAmelCase	:       bool		):






     '''simple docstring'''
     def run_func(lowerCAmelCase	:       Dict		):
          @wraps(lowerCAmelCase		)
          def run_in_eager_mode(*lowerCAmelCase	:       Optional[Any]       ,    **lowerCAmelCase	:       Dict		):
               return func(*lowerCAmelCase       ,    **lowerCAmelCase		)

          @wraps(lowerCAmelCase		)
          @tf.function(experimental_compile=lowerCAmelCase		)
          def run_in_graph_mode(*lowerCAmelCase	:       Optional[Any]       ,    **lowerCAmelCase	:       Optional[Any]		):
               return func(*lowerCAmelCase       ,    **lowerCAmelCase		)

          if do_eager_mode is True:
               if use_xla is not False:
                    raise ValueError(
                        "Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`."		)
               return run_in_eager_mode
          else:
               return run_in_graph_mode

     return run_func



def 							a__ (				lowerCAmelCase	:       int       ,    lowerCAmelCase	:       int       ,    lowerCAmelCase	:       int		):






     '''simple docstring'''
     UpperCAmelCase__							: Dict 							=						random.Random()
     UpperCAmelCase__							: List[str] 							=						[rng.randint(0       ,    vocab_size - 1		) for i in range(batch_size * sequence_length		)]
     return tf.constant(lowerCAmelCase       ,    shape=(batch_size, sequence_length)       ,    dtype=tf.intaa		)







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							42
     _A								=							42
     _A								=							"TensorFlow"



     @property
     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          return tf.__version__



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					float:
          # initialize GPU on separate process
          UpperCAmelCase__							: Any 							=						self.args.strategy
          if strategy is None:
               raise ValueError("A device strategy has to be initialized before using TensorFlow."  )
          UpperCAmelCase__							: Union[str, Any] 							=						self._prepare_inference_func(__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )
          return self._measure_speed(_inference  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					float:
          UpperCAmelCase__							: List[Any] 							=						self.args.strategy
          if strategy is None:
               raise ValueError("A device strategy has to be initialized before using TensorFlow."  )
          UpperCAmelCase__							: List[Any] 							=						self._prepare_train_func(__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )
          return self._measure_speed(_train  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					[Memory, Optional[MemorySummary]]:
          # initialize GPU on separate process
          if self.args.is_gpu:
               tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] ,			__UpperCamelCase  )
          UpperCAmelCase__							: List[str] 							=						self.args.strategy
          if strategy is None:
               raise ValueError("A device strategy has to be initialized before using TensorFlow."  )
          UpperCAmelCase__							: Any 							=						self._prepare_inference_func(__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )
          return self._measure_memory(_inference  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					[Memory, Optional[MemorySummary]]:
          if self.args.is_gpu:
               tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] ,			__UpperCamelCase  )
          UpperCAmelCase__							: Any 							=						self.args.strategy
          if strategy is None:
               raise ValueError("A device strategy has to be initialized before using TensorFlow."  )

          UpperCAmelCase__							: Optional[Any] 							=						self._prepare_train_func(__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )
          return self._measure_memory(_train  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					Callable[[], None]:
          UpperCAmelCase__							: Union[str, Any] 							=						self.config_dict[model_name]

          if self.args.fpaa:
               raise NotImplementedError("Mixed precision is currently not supported."  )

          UpperCAmelCase__							: Optional[int] 							=						(
              hasattr(__UpperCamelCase ,			"architectures"  )
              and isinstance(config.architectures ,			__UpperCamelCase  )
              and len(config.architectures  ) > 0
          )
          if not self.args.only_pretrain_model and has_model_class_in_config:
               try:
                    UpperCAmelCase__							: str 							=						"TF" + config.architectures[0]  # prepend 'TF' for tensorflow model
                    UpperCAmelCase__							: Any 							=						__import__("transformers" ,			fromlist=[model_class]  )
                    UpperCAmelCase__							: List[Any] 							=						getattr(__UpperCamelCase ,			__UpperCamelCase  )
                    UpperCAmelCase__							: Dict 							=						model_cls(__UpperCamelCase  )
               except ImportError:
                    raise ImportError(
                        F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
                        " set `--only_pretrain_model` or `args.only_pretrain_model=True`."  )
          else:
               UpperCAmelCase__							: Any 							=						TF_MODEL_MAPPING[config.__class__](__UpperCamelCase  )

          # encoder-decoder has vocab size saved differently
          UpperCAmelCase__							: int 							=						config.vocab_size if hasattr(__UpperCamelCase ,			"vocab_size"  ) else config.encoder.vocab_size
          UpperCAmelCase__							: Optional[Any] 							=						random_input_ids(__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )

          @run_with_tf_optimizations(self.args.eager_mode ,			self.args.use_xla  )
          def encoder_decoder_forward():
               return model(__UpperCamelCase ,			decoder_input_ids=__UpperCamelCase ,			training=__UpperCamelCase  )

          @run_with_tf_optimizations(self.args.eager_mode ,			self.args.use_xla  )
          def encoder_forward():
               return model(__UpperCamelCase ,			training=__UpperCamelCase  )

          UpperCAmelCase__							: Dict 							=						encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

          return _inference



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					Callable[[], None]:
          UpperCAmelCase__							: List[Any] 							=						self.config_dict[model_name]

          if self.args.eager_mode is not False:
               raise ValueError("Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`."  )

          if self.args.fpaa:
               raise NotImplementedError("Mixed precision is currently not supported."  )

          UpperCAmelCase__							: Any 							=						(
              hasattr(__UpperCamelCase ,			"architectures"  )
              and isinstance(config.architectures ,			__UpperCamelCase  )
              and len(config.architectures  ) > 0
          )
          if not self.args.only_pretrain_model and has_model_class_in_config:
               try:
                    UpperCAmelCase__							: Any 							=						"TF" + config.architectures[0]  # prepend 'TF' for tensorflow model
                    UpperCAmelCase__							: int 							=						__import__("transformers" ,			fromlist=[model_class]  )
                    UpperCAmelCase__							: int 							=						getattr(__UpperCamelCase ,			__UpperCamelCase  )
                    UpperCAmelCase__							: Optional[Any] 							=						model_cls(__UpperCamelCase  )
               except ImportError:
                    raise ImportError(
                        F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
                        " set `--only_pretrain_model` or `args.only_pretrain_model=True`."  )
          else:
               UpperCAmelCase__							: List[str] 							=						TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__UpperCamelCase  )

          # encoder-decoder has vocab size saved differently
          UpperCAmelCase__							: Union[str, Any] 							=						config.vocab_size if hasattr(__UpperCamelCase ,			"vocab_size"  ) else config.encoder.vocab_size
          UpperCAmelCase__							: Dict 							=						random_input_ids(__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )

          @run_with_tf_optimizations(self.args.eager_mode ,			self.args.use_xla  )
          def encoder_decoder_train():
               UpperCAmelCase__							: Union[str, Any] 							=						model(__UpperCamelCase ,			decoder_input_ids=__UpperCamelCase ,			labels=__UpperCamelCase ,			training=__UpperCamelCase  )[0]
               UpperCAmelCase__							: Union[str, Any] 							=						tf.gradients(__UpperCamelCase ,			model.trainable_variables  )
               return gradients

          @run_with_tf_optimizations(self.args.eager_mode ,			self.args.use_xla  )
          def encoder_train():
               UpperCAmelCase__							: Union[str, Any] 							=						model(__UpperCamelCase ,			labels=__UpperCamelCase ,			training=__UpperCamelCase  )[0]
               UpperCAmelCase__							: Any 							=						tf.gradients(__UpperCamelCase ,			model.trainable_variables  )
               return gradients

          UpperCAmelCase__							: str 							=						encoder_decoder_train if config.is_encoder_decoder else encoder_train

          return _train



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					float:
          with self.args.strategy.scope():
               try:
                    if self.args.is_tpu or self.args.use_xla:
                         # run additional 10 times to stabilize compilation for tpu
                         logger.info("Do inference on TPU. Running model 5 times to stabilize compilation"  )
                         timeit.repeat(__UpperCamelCase ,			repeat=1 ,			number=5  )

                    # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
                    UpperCAmelCase__							: Optional[Any] 							=						timeit.repeat(
                        __UpperCamelCase ,			repeat=self.args.repeat ,			number=10 ,			)

                    return min(__UpperCamelCase  ) / 10.0
               except ResourceExhaustedError as e:
                    self.print_fn(F"Doesn't fit on GPU. {e}"  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					[Memory, MemorySummary]:
          logger.info(
              "Note that TensorFlow allocates more memory than "
              "it might need to speed up computation. "
              "The memory reported here corresponds to the memory "
              "reported by `nvidia-smi`, which can vary depending "
              "on total available memory on the GPU that is used."  )
          with self.args.strategy.scope():
               try:
                    if self.args.trace_memory_line_by_line:
                         if not self.args.eager_mode:
                              raise ValueError(
                                  "`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"
                                  " consumption line by line."  )
                         UpperCAmelCase__							: List[str] 							=						start_memory_tracing("transformers"  )

                    if self.args.is_tpu:
                         # tpu
                         raise NotImplementedError(
                             "Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"
                             " with `args.memory=False`"  )
                    elif self.args.is_gpu:
                         # gpu
                         if not is_pyanvml_available():
                              logger.warning(
                                  "py3nvml not installed, we won't log GPU memory usage. "
                                  "Install py3nvml (pip install py3nvml) to log information about GPU."  )
                              UpperCAmelCase__							: Optional[int] 							=						"N/A"
                         else:
                              logger.info(
                                  "Measuring total GPU usage on GPU device. Make sure to not have additional processes"
                                  " running on the same GPU."  )
                              # init nvml
                              nvml.nvmlInit()
                              func()
                              UpperCAmelCase__							: Any 							=						nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx  )
                              UpperCAmelCase__							: Optional[int] 							=						nvml.nvmlDeviceGetMemoryInfo(__UpperCamelCase  )
                              UpperCAmelCase__							: str 							=						meminfo.used
                              UpperCAmelCase__							: int 							=						Memory(__UpperCamelCase  )
                              # shutdown nvml
                              nvml.nvmlShutdown()
                    else:
                         # cpu
                         if self.args.trace_memory_line_by_line:
                              logger.info(
                                  "When enabling line by line tracing, the max peak memory for CPU is inaccurate in"
                                  " TensorFlow."  )
                              UpperCAmelCase__							: Any 							=						None
                         else:
                              UpperCAmelCase__							: List[Any] 							=						measure_peak_memory_cpu(__UpperCamelCase  )
                              UpperCAmelCase__							: Optional[Any] 							=						Memory(__UpperCamelCase  ) if isinstance(__UpperCamelCase ,			__UpperCamelCase  ) else memory_bytes
                    if self.args.trace_memory_line_by_line:
                         UpperCAmelCase__							: Optional[Any] 							=						stop_memory_tracing(__UpperCamelCase  )
                         if memory is None:
                              UpperCAmelCase__							: Tuple 							=						summary.total
                    else:
                         UpperCAmelCase__							: int 							=						None

                    return memory, summary
               except ResourceExhaustedError as e:
                    self.print_fn(F"Doesn't fit on GPU. {e}"  )
                    return "N/A", None



