




import unittest

from parameterized import parameterized

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

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


if is_torch_available():
     import torch

     from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer

class     __a:






    """simple docstring"""





    def __init__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE=13  ,_SCREAMING_SNAKE_CASE=7  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=False  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=99  ,_SCREAMING_SNAKE_CASE=32  ,_SCREAMING_SNAKE_CASE=5  ,_SCREAMING_SNAKE_CASE=4  ,_SCREAMING_SNAKE_CASE=37  ,_SCREAMING_SNAKE_CASE="gelu"  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=512  ,_SCREAMING_SNAKE_CASE=16  ,_SCREAMING_SNAKE_CASE=2  ,_SCREAMING_SNAKE_CASE=0.02  ,_SCREAMING_SNAKE_CASE=3  ,_SCREAMING_SNAKE_CASE=4  ,_SCREAMING_SNAKE_CASE=None  ,)					->						Tuple:
         UpperCAmelCase_     :				str  =     parent
         UpperCAmelCase_     :				int  =     batch_size
         UpperCAmelCase_     :				Optional[int]  =     seq_length
         UpperCAmelCase_     :				Any  =     is_training
         UpperCAmelCase_     :				Dict  =     use_input_mask
         UpperCAmelCase_     :				Union[str, Any]  =     use_token_type_ids
         UpperCAmelCase_     :				Optional[Any]  =     use_labels
         UpperCAmelCase_     :				List[str]  =     vocab_size
         UpperCAmelCase_     :				Optional[Any]  =     hidden_size
         UpperCAmelCase_     :				Tuple  =     num_hidden_layers
         UpperCAmelCase_     :				Optional[int]  =     num_attention_heads
         UpperCAmelCase_     :				int  =     intermediate_size
         UpperCAmelCase_     :				Optional[int]  =     hidden_act
         UpperCAmelCase_     :				List[str]  =     hidden_dropout_prob
         UpperCAmelCase_     :				Dict  =     attention_probs_dropout_prob
         UpperCAmelCase_     :				Any  =     max_position_embeddings
         UpperCAmelCase_     :				Optional[Any]  =     type_vocab_size
         UpperCAmelCase_     :				Tuple  =     type_sequence_label_size
         UpperCAmelCase_     :				Tuple  =     initializer_range
         UpperCAmelCase_     :				List[str]  =     num_labels
         UpperCAmelCase_     :				Optional[Any]  =     num_choices
         UpperCAmelCase_     :				List[str]  =     scope





    def 				a__ ( self    )					->						Tuple:
         UpperCAmelCase_     :				Tuple  =     ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size    )

         UpperCAmelCase_     :				Dict  =     None
         if self.use_input_mask:
              UpperCAmelCase_     :				Tuple  =     random_attention_mask([self.batch_size, self.seq_length]    )

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

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

         UpperCAmelCase_     :				Tuple  =     self.get_config()

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





    def 				a__ ( self    )					->						Optional[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=_SCREAMING_SNAKE_CASE  ,initializer_range=self.initializer_range  ,)





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						Tuple:
         UpperCAmelCase_     :				Optional[int]  =     LlamaModel(config=_SCREAMING_SNAKE_CASE    )
         model.to(_SCREAMING_SNAKE_CASE    )
         model.eval()
         UpperCAmelCase_     :				str  =     model(_SCREAMING_SNAKE_CASE  ,attention_mask=_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				int  =     model(_SCREAMING_SNAKE_CASE    )
         self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, self.seq_length, self.hidden_size)    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,)					->						str:
         UpperCAmelCase_     :				List[Any]  =     True
         UpperCAmelCase_     :				Dict  =     LlamaModel(_SCREAMING_SNAKE_CASE    )
         model.to(_SCREAMING_SNAKE_CASE    )
         model.eval()
         UpperCAmelCase_     :				Optional[Any]  =     model(
             _SCREAMING_SNAKE_CASE  ,attention_mask=_SCREAMING_SNAKE_CASE  ,encoder_hidden_states=_SCREAMING_SNAKE_CASE  ,encoder_attention_mask=_SCREAMING_SNAKE_CASE  ,)
         UpperCAmelCase_     :				Dict  =     model(
             _SCREAMING_SNAKE_CASE  ,attention_mask=_SCREAMING_SNAKE_CASE  ,encoder_hidden_states=_SCREAMING_SNAKE_CASE  ,)
         UpperCAmelCase_     :				List[str]  =     model(_SCREAMING_SNAKE_CASE  ,attention_mask=_SCREAMING_SNAKE_CASE    )
         self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, self.seq_length, self.hidden_size)    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,)					->						Dict:
         UpperCAmelCase_     :				int  =     LlamaForCausalLM(config=_SCREAMING_SNAKE_CASE    )
         model.to(_SCREAMING_SNAKE_CASE    )
         model.eval()
         UpperCAmelCase_     :				str  =     model(_SCREAMING_SNAKE_CASE  ,attention_mask=_SCREAMING_SNAKE_CASE  ,labels=_SCREAMING_SNAKE_CASE    )
         self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.seq_length, self.vocab_size)    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,)					->						str:
         UpperCAmelCase_     :				List[Any]  =     True
         UpperCAmelCase_     :				Dict  =     True
         UpperCAmelCase_     :				str  =     LlamaForCausalLM(config=_SCREAMING_SNAKE_CASE    )
         model.to(_SCREAMING_SNAKE_CASE    )
         model.eval()

         # first forward pass
         UpperCAmelCase_     :				int  =     model(
             _SCREAMING_SNAKE_CASE  ,attention_mask=_SCREAMING_SNAKE_CASE  ,encoder_hidden_states=_SCREAMING_SNAKE_CASE  ,encoder_attention_mask=_SCREAMING_SNAKE_CASE  ,use_cache=_SCREAMING_SNAKE_CASE  ,)
         UpperCAmelCase_     :				List[Any]  =     outputs.past_key_values

         # create hypothetical multiple next token and extent to next_input_ids
         UpperCAmelCase_     :				Union[str, 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_     :				int  =     torch.cat([input_ids, next_tokens]  ,dim=-1    )
         UpperCAmelCase_     :				List[str]  =     torch.cat([input_mask, next_mask]  ,dim=-1    )

         UpperCAmelCase_     :				str  =     model(
             _SCREAMING_SNAKE_CASE  ,attention_mask=_SCREAMING_SNAKE_CASE  ,encoder_hidden_states=_SCREAMING_SNAKE_CASE  ,encoder_attention_mask=_SCREAMING_SNAKE_CASE  ,output_hidden_states=_SCREAMING_SNAKE_CASE  ,)['''hidden_states'''][0]
         UpperCAmelCase_     :				str  =     model(
             _SCREAMING_SNAKE_CASE  ,attention_mask=_SCREAMING_SNAKE_CASE  ,encoder_hidden_states=_SCREAMING_SNAKE_CASE  ,encoder_attention_mask=_SCREAMING_SNAKE_CASE  ,past_key_values=_SCREAMING_SNAKE_CASE  ,output_hidden_states=_SCREAMING_SNAKE_CASE  ,)['''hidden_states'''][0]

         # select random slice
         UpperCAmelCase_     :				str  =     ids_tensor((1,)  ,output_from_past.shape[-1]    ).item()
         UpperCAmelCase_     :				Optional[int]  =     output_from_no_past[:, -3:, random_slice_idx].detach()
         UpperCAmelCase_     :				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(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,atol=1e-3    )    )





    def 				a__ ( self    )					->						Tuple:
         UpperCAmelCase_     :				Optional[Any]  =     self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase_
         ),       (
             UpperCAmelCase_
         ),       (
             UpperCAmelCase_
         ),       (
             UpperCAmelCase_
         ),       (
             UpperCAmelCase_
         ),       (
             UpperCAmelCase_
         ),       (
             UpperCAmelCase_
         ),       
         )     :				Union[str, Any]  =     config_and_inputs
         UpperCAmelCase_     :				Any  =     {'''input_ids''': input_ids, '''attention_mask''': input_mask}
         return config, inputs_dict


@require_torch
class     __a(	_a							,		_a							,		_a							,		unittest.TestCase  ):






    """simple docstring"""





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





    def 				a__ ( self    )					->						Union[str, Any]:
         UpperCAmelCase_     :				Tuple  =     LlamaModelTester(self    )
         UpperCAmelCase_     :				Optional[Any]  =     ConfigTester(self  ,config_class=_SCREAMING_SNAKE_CASE  ,hidden_size=37    )





    def 				a__ ( self    )					->						Union[str, Any]:
         self.config_tester.run_common_tests()





    def 				a__ ( self    )					->						List[str]:
         UpperCAmelCase_     :				Optional[int]  =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Optional[Any]:
         UpperCAmelCase_     :				List[Any]  =     self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
              UpperCAmelCase_     :				List[str]  =     type
              self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE    )





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





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





    def 				a__ ( self    )					->						int:
         UpperCAmelCase_,       UpperCAmelCase_     :				Optional[int]  =     self.model_tester.prepare_config_and_inputs_for_common()
         UpperCAmelCase_     :				Optional[Any]  =     3
         UpperCAmelCase_     :				Tuple  =     '''multi_label_classification'''
         UpperCAmelCase_     :				Optional[int]  =     input_dict['''input_ids''']
         UpperCAmelCase_     :				str  =     input_ids.ne(1    ).to(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Optional[int]  =     ids_tensor(
             [self.model_tester.batch_size, config.num_labels]  ,self.model_tester.type_sequence_label_size    ).to(torch.float    )
         UpperCAmelCase_     :				str  =     LlamaForSequenceClassification(_SCREAMING_SNAKE_CASE    )
         model.to(_SCREAMING_SNAKE_CASE    )
         model.eval()
         UpperCAmelCase_     :				List[Any]  =     model(_SCREAMING_SNAKE_CASE  ,attention_mask=_SCREAMING_SNAKE_CASE  ,labels=_SCREAMING_SNAKE_CASE    )
         self.assertEqual(result.logits.shape  ,(self.model_tester.batch_size, self.model_tester.num_labels)    )





    @unittest.skip('''LLaMA buffers include complex numbers, which breaks this test'''    )
    def 				a__ ( self    )					->						Union[str, Any]:
         pass





    @parameterized.expand([('''linear''',), ('''dynamic''',)]    )
    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						Any:
         UpperCAmelCase_,       UpperCAmelCase_     :				Dict  =     self.model_tester.prepare_config_and_inputs_for_common()
         UpperCAmelCase_     :				str  =     ids_tensor([1, 10]  ,config.vocab_size    )
         UpperCAmelCase_     :				Union[str, Any]  =     ids_tensor([1, int(config.max_position_embeddings * 1.5    )]  ,config.vocab_size    )

         set_seed(42    )  # Fixed seed at init time so the two models get the same random weights
         UpperCAmelCase_     :				List[str]  =     LlamaModel(_SCREAMING_SNAKE_CASE    )
         original_model.to(_SCREAMING_SNAKE_CASE    )
         original_model.eval()
         UpperCAmelCase_     :				Any  =     original_model(_SCREAMING_SNAKE_CASE    ).last_hidden_state
         UpperCAmelCase_     :				Optional[int]  =     original_model(_SCREAMING_SNAKE_CASE    ).last_hidden_state

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

         # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
         # maximum sequence length, so the outputs for the short input should match.
         if scaling_type == "dynamic":
              self.assertTrue(torch.allclose(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,atol=1e-5    )    )
         else:
              self.assertFalse(torch.allclose(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,atol=1e-5    )    )

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


@require_torch
class     __a(	unittest.TestCase  ):






    """simple docstring"""





    @unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''    )
    @slow
    def 				a__ ( self    )					->						Union[str, Any]:
         UpperCAmelCase_     :				List[Any]  =     [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
         UpperCAmelCase_     :				Optional[Any]  =     LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-7b-hf'''  ,device_map='''auto'''    )
         UpperCAmelCase_     :				str  =     model(torch.tensor([input_ids]    )    )
         # Expected mean on dim = -1
         UpperCAmelCase_     :				Optional[int]  =     torch.tensor([[-6.65_50, -4.12_27, -4.98_59, -3.24_06, 0.82_62, -3.00_33, 1.29_64, -3.36_99]]    )
         torch.testing.assert_close(out.mean(-1    )  ,_SCREAMING_SNAKE_CASE  ,atol=1e-2  ,rtol=1e-2    )
         # slicing logits[0, 0, 0:30]
         # fmt: off
         UpperCAmelCase_     :				List[Any]  =     torch.tensor([-12.82_81, -7.44_53, -0.46_39, -8.06_25, -7.25_00, -8.00_00, -6.48_83, -7.76_95, -7.84_38, -7.03_12, -6.21_88, -7.13_28, -1.84_96, 1.99_61, -8.62_50, -6.72_27, -12.82_81, -6.94_92, -7.07_42, -7.78_52, -7.58_20, -7.90_62, -6.93_75, -7.98_05, -8.34_38, -8.15_62, -8.04_69, -7.62_50, -7.74_22, -7.33_98,]    )
         # fmt: on
         torch.testing.assert_close(out[0, 0, :30]  ,_SCREAMING_SNAKE_CASE  ,atol=1e-5  ,rtol=1e-5    )





    @unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''    )
    @slow
    def 				a__ ( self    )					->						str:
         UpperCAmelCase_     :				Any  =     [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
         UpperCAmelCase_     :				Union[str, Any]  =     LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-hf'''  ,device_map='''auto'''    )
         UpperCAmelCase_     :				Union[str, Any]  =     model(torch.tensor(_SCREAMING_SNAKE_CASE    )    )
         # Expected mean on dim = -1
         UpperCAmelCase_     :				Optional[int]  =     torch.tensor([[-2.06_22, -1.27_94, -1.16_38, -0.97_88, -1.46_03, -1.02_38, -1.78_93, -1.44_11]]    )
         torch.testing.assert_close(out.mean(-1    )  ,_SCREAMING_SNAKE_CASE  ,atol=1e-2  ,rtol=1e-2    )
         # slicing logits[0, 0, 0:30]
         # fmt: off
         UpperCAmelCase_     :				List[Any]  =     torch.tensor([-8.14_06, -8.05_47, 2.74_61, -1.23_44, -0.14_48, -1.82_62, -1.00_20, -1.81_54, -1.68_95, -1.85_16, -2.35_74, -0.92_77, 3.75_98, 6.57_42, -1.29_98, -0.11_77, -8.14_06, -2.96_88, -2.91_99, -3.16_99, -3.52_54, -2.35_55, -2.79_88, -3.41_41, -2.82_62, -4.51_95, -3.33_79, -3.31_64, -2.78_32, -3.02_73]    )
         # fmt: on
         torch.testing.assert_close(out[0, 0, :30]  ,_SCREAMING_SNAKE_CASE  ,atol=1e-5  ,rtol=1e-5    )





    @unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''    )
    @slow
    def 				a__ ( self    )					->						Optional[int]:
         UpperCAmelCase_     :				Union[str, Any]  =     [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
         UpperCAmelCase_     :				Dict  =     LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''  ,device_map='''auto'''    )
         UpperCAmelCase_     :				Any  =     model(torch.tensor(_SCREAMING_SNAKE_CASE    )    )
         # Expected mean on dim = -1
         UpperCAmelCase_     :				List[Any]  =     torch.tensor([[-0.85_62, -1.85_20, -0.75_51, -0.41_62, -1.51_61, -1.20_38, -2.48_23, -2.32_54]]    )
         torch.testing.assert_close(out.mean(-1    )  ,_SCREAMING_SNAKE_CASE  ,atol=1e-2  ,rtol=1e-2    )
         # slicing logits[0, 0, 0:30]
         # fmt: off
         UpperCAmelCase_     :				Dict  =     torch.tensor([-2.22_27, 4.88_28, 0.90_23, -0.45_78, -0.78_71, -0.10_33, -0.62_21, -0.57_86, -0.78_03, -1.06_74, -1.29_20, -0.15_70, 0.80_08, 2.07_23, -0.94_97, 0.27_71, -2.22_27, -0.76_12, -1.43_46, -1.20_61, -1.64_26, -0.30_00, -0.71_39, -1.19_34, -1.86_91, -1.69_73, -1.59_47, -1.27_05, -0.35_23, -0.55_13]    )
         # fmt: on
         torch.testing.assert_close(out.mean(-1    )  ,_SCREAMING_SNAKE_CASE  ,atol=1e-2  ,rtol=1e-2    )





    @unittest.skip(
        '''Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test'''    )
    @slow
    def 				a__ ( self    )					->						Union[str, Any]:
         UpperCAmelCase_     :				Tuple  =     [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
         UpperCAmelCase_     :				str  =     LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-70b-hf'''  ,device_map='''auto'''    )
         UpperCAmelCase_     :				Union[str, Any]  =     model(torch.tensor(_SCREAMING_SNAKE_CASE    )    )

         UpperCAmelCase_     :				int  =     torch.tensor(
             [[-4.23_27, -3.33_60, -4.66_65, -4.76_31, -1.81_80, -3.41_70, -1.42_11, -3.18_10]]  ,dtype=torch.floataa    )
         torch.testing.assert_close(out.mean(-1    )  ,_SCREAMING_SNAKE_CASE  ,atol=1e-2  ,rtol=1e-2    )
         # fmt: off
         UpperCAmelCase_     :				List[Any]  =     torch.tensor([-9.49_22, -3.95_51, 1.79_98, -5.67_58, -5.10_55, -5.89_84, -4.83_20, -6.80_86, -6.53_91, -5.61_72, -5.58_20, -5.53_52, 1.78_81, 3.62_89, -6.51_17, -3.47_85, -9.50_00, -6.03_52, -6.81_25, -6.01_95, -6.68_36, -5.47_27, -6.28_12, -6.03_91, -7.33_98, -7.42_97, -7.48_44, -6.58_20, -5.87_89, -5.53_12]    )
         # fmt: on
         torch.testing.assert_close(out[0, 0, :30]  ,_SCREAMING_SNAKE_CASE  ,atol=1e-5  ,rtol=1e-5    )





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

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





'''simple docstring'''


def 						lowerCamelCase__   (				a							):
						if number < 0:
												raise ValueError('number must not be negative'							)
						return number & (number - 1) == 0


if __name__ == "__main__":
	import doctest

	doctest.testmod()

