




'''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_      :





     def __init__(     self     :					List[str]     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					List[Any]=13     ,							__lowerCamelCase     :					Optional[Any]=7     ,							__lowerCamelCase     :					Union[str, Any]=True     ,							__lowerCamelCase     :					Any=True     ,							__lowerCamelCase     :					List[str]=False     ,							__lowerCamelCase     :					Any=True     ,							__lowerCamelCase     :					int=99     ,							__lowerCamelCase     :					Optional[Any]=32     ,							__lowerCamelCase     :					Dict=5     ,							__lowerCamelCase     :					int=4     ,							__lowerCamelCase     :					List[str]=37     ,							__lowerCamelCase     :					Any="gelu"     ,							__lowerCamelCase     :					List[Any]=0.1     ,							__lowerCamelCase     :					int=0.1     ,							__lowerCamelCase     :					Dict=512     ,							__lowerCamelCase     :					Dict=16     ,							__lowerCamelCase     :					Tuple=2     ,							__lowerCamelCase     :					List[str]=0.0_2     ,							__lowerCamelCase     :					int=3     ,							__lowerCamelCase     :					int=4     ,							__lowerCamelCase     :					Union[str, Any]=None     ,							):
           snake_case__       :   Any	      =    parent
           snake_case__       :   List[Any]	      =    batch_size
           snake_case__       :   Dict	      =    seq_length
           snake_case__       :   Dict	      =    is_training
           snake_case__       :   str	      =    use_input_mask
           snake_case__       :   Optional[Any]	      =    use_token_type_ids
           snake_case__       :   int	      =    use_labels
           snake_case__       :   Dict	      =    vocab_size
           snake_case__       :   Union[str, Any]	      =    hidden_size
           snake_case__       :   int	      =    num_hidden_layers
           snake_case__       :   List[Any]	      =    num_attention_heads
           snake_case__       :   List[Any]	      =    intermediate_size
           snake_case__       :   Dict	      =    hidden_act
           snake_case__       :   List[Any]	      =    hidden_dropout_prob
           snake_case__       :   Any	      =    attention_probs_dropout_prob
           snake_case__       :   Optional[int]	      =    max_position_embeddings
           snake_case__       :   Dict	      =    type_vocab_size
           snake_case__       :   List[str]	      =    type_sequence_label_size
           snake_case__       :   Optional[int]	      =    initializer_range
           snake_case__       :   Tuple	      =    num_labels
           snake_case__       :   List[Any]	      =    num_choices
           snake_case__       :   List[Any]	      =    scope





     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           snake_case__       :   Any	      =    ids_tensor([self.batch_size, self.seq_length]     ,							self.vocab_size			)

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

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

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

           snake_case__       :   Union[str, Any]	      =    self.get_config()

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





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           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=__lowerCamelCase     ,							initializer_range=self.initializer_range     ,							)





     def 			_lowerCAmelCase      (     self     :					Optional[int]     ,							__lowerCamelCase     :					int     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					Union[str, Any]     ,							__lowerCamelCase     :					List[Any]     ,							__lowerCamelCase     :					int     ,							__lowerCamelCase     :					List[Any]     ,							__lowerCamelCase     :					Optional[Any]			):
           snake_case__       :   Any	      =    LlamaModel(config=__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()
           snake_case__       :   Optional[int]	      =    model(__lowerCamelCase     ,							attention_mask=__lowerCamelCase			)
           snake_case__       :   Union[str, Any]	      =    model(__lowerCamelCase			)
           self.parent.assertEqual(result.last_hidden_state.shape     ,							(self.batch_size, self.seq_length, self.hidden_size)			)





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





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





     def 			_lowerCAmelCase      (     self     :					str     ,							__lowerCamelCase     :					Any     ,							__lowerCamelCase     :					Optional[Any]     ,							__lowerCamelCase     :					Optional[int]     ,							__lowerCamelCase     :					Any     ,							__lowerCamelCase     :					List[str]     ,							__lowerCamelCase     :					Dict     ,							__lowerCamelCase     :					int     ,							__lowerCamelCase     :					List[Any]     ,							__lowerCamelCase     :					Union[str, Any]     ,							):
           snake_case__       :   List[str]	      =    True
           snake_case__       :   List[Any]	      =    True
           snake_case__       :   Dict	      =    LlamaForCausalLM(config=__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()

           # first forward pass
           snake_case__       :   Any	      =    model(
               __lowerCamelCase     ,							attention_mask=__lowerCamelCase     ,							encoder_hidden_states=__lowerCamelCase     ,							encoder_attention_mask=__lowerCamelCase     ,							use_cache=__lowerCamelCase     ,							)
           snake_case__       :   str	      =    outputs.past_key_values

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

           # append to next input_ids and
           snake_case__       :   Tuple	      =    torch.cat([input_ids, next_tokens]     ,							dim=-1			)
           snake_case__       :   Dict	      =    torch.cat([input_mask, next_mask]     ,							dim=-1			)

           snake_case__       :   Optional[Any]	      =    model(
               __lowerCamelCase     ,							attention_mask=__lowerCamelCase     ,							encoder_hidden_states=__lowerCamelCase     ,							encoder_attention_mask=__lowerCamelCase     ,							output_hidden_states=__lowerCamelCase     ,							)['hidden_states'][0]
           snake_case__       :   Any	      =    model(
               __lowerCamelCase     ,							attention_mask=__lowerCamelCase     ,							encoder_hidden_states=__lowerCamelCase     ,							encoder_attention_mask=__lowerCamelCase     ,							past_key_values=__lowerCamelCase     ,							output_hidden_states=__lowerCamelCase     ,							)['hidden_states'][0]

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

           self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]			)

           # test that outputs are equal for slice
           self.parent.assertTrue(torch.allclose(__lowerCamelCase     ,							__lowerCamelCase     ,							atol=1E-3			)			)





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           snake_case__       :   Union[str, Any]	      =    self.prepare_config_and_inputs()
           (
               (
               snake_case__
           )      ,							(
               snake_case__
           )      ,							(
               snake_case__
           )      ,							(
               snake_case__
           )      ,							(
               snake_case__
           )      ,							(
               snake_case__
           )      ,							(
               snake_case__
           )      ,							
           )       :   Tuple	      =    config_and_inputs
           snake_case__       :   Union[str, Any]	      =    {'input_ids': input_ids, 'attention_mask': input_mask}
           return config, inputs_dict






@require_torch
class        lowercase_      (						lowerCAmelCase_						,       lowerCAmelCase_						,       lowerCAmelCase_						,       unittest.TestCase							):
     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     :					Union[str, Any]			):
           snake_case__       :   List[str]	      =    LlamaModelTester(self			)
           snake_case__       :   Tuple	      =    ConfigTester(self     ,							config_class=__lowerCamelCase     ,							hidden_size=37			)





     def 			_lowerCAmelCase      (     self     :					List[Any]			):
           self.config_tester.run_common_tests()





     def 			_lowerCAmelCase      (     self     :					int			):
           snake_case__       :   Optional[int]	      =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           snake_case__       :   Optional[int]	      =    self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
                 snake_case__       :   str	      =    type
                 self.model_tester.create_and_check_model(*__lowerCamelCase			)





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





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





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





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

           set_seed(42			)  # Fixed seed at init time so the two models get the same random weights
           snake_case__       :   Optional[Any]	      =    {'type': scaling_type, 'factor': 1_0.0}
           snake_case__       :   Tuple	      =    LlamaModel(__lowerCamelCase			)
           scaled_model.to(__lowerCamelCase			)
           scaled_model.eval()
           snake_case__       :   int	      =    scaled_model(__lowerCamelCase			).last_hidden_state
           snake_case__       :   Any	      =    scaled_model(__lowerCamelCase			).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(__lowerCamelCase     ,							__lowerCamelCase     ,							atol=1E-5			)			)
           else:
                 self.assertFalse(torch.allclose(__lowerCamelCase     ,							__lowerCamelCase     ,							atol=1E-5			)			)

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






@require_torch
class        lowercase_      (						unittest.TestCase							):





     @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'			)
     @slow
     def 			_lowerCAmelCase      (     self     :					Tuple			):
           snake_case__       :   Optional[Any]	      =    [1, 306, 4658, 278, 6593, 310, 2834, 338]
           snake_case__       :   str	      =    LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'     ,							device_map='auto'			)
           snake_case__       :   Any	      =    model(torch.tensor([input_ids]			)			)
           # Expected mean on dim = -1
           snake_case__       :   List[str]	      =    torch.tensor([[-6.6_5_5_0, -4.1_2_2_7, -4.9_8_5_9, -3.2_4_0_6, 0.8_2_6_2, -3.0_0_3_3, 1.2_9_6_4, -3.3_6_9_9]]			)
           torch.testing.assert_close(out.mean(-1			)     ,							__lowerCamelCase     ,							atol=1E-2     ,							rtol=1E-2			)
           # slicing logits[0, 0, 0:30]
           # fmt: off
           snake_case__       :   Union[str, Any]	      =    torch.tensor([-1_2.8_2_8_1, -7.4_4_5_3, -0.4_6_3_9, -8.0_6_2_5, -7.2_5_0_0, -8.0_0_0_0, -6.4_8_8_3, -7.7_6_9_5, -7.8_4_3_8, -7.0_3_1_2, -6.2_1_8_8, -7.1_3_2_8, -1.8_4_9_6, 1.9_9_6_1, -8.6_2_5_0, -6.7_2_2_7, -1_2.8_2_8_1, -6.9_4_9_2, -7.0_7_4_2, -7.7_8_5_2, -7.5_8_2_0, -7.9_0_6_2, -6.9_3_7_5, -7.9_8_0_5, -8.3_4_3_8, -8.1_5_6_2, -8.0_4_6_9, -7.6_2_5_0, -7.7_4_2_2, -7.3_3_9_8,]			)
           # fmt: on
           torch.testing.assert_close(out[0, 0, :30]     ,							__lowerCamelCase     ,							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     :					Any			):
           snake_case__       :   str	      =    [1, 306, 4658, 278, 6593, 310, 2834, 338]
           snake_case__       :   Tuple	      =    LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'     ,							device_map='auto'			)
           snake_case__       :   Optional[int]	      =    model(torch.tensor(__lowerCamelCase			)			)
           # Expected mean on dim = -1
           snake_case__       :   Tuple	      =    torch.tensor([[-2.0_6_2_2, -1.2_7_9_4, -1.1_6_3_8, -0.9_7_8_8, -1.4_6_0_3, -1.0_2_3_8, -1.7_8_9_3, -1.4_4_1_1]]			)
           torch.testing.assert_close(out.mean(-1			)     ,							__lowerCamelCase     ,							atol=1E-2     ,							rtol=1E-2			)
           # slicing logits[0, 0, 0:30]
           # fmt: off
           snake_case__       :   Optional[Any]	      =    torch.tensor([-8.1_4_0_6, -8.0_5_4_7, 2.7_4_6_1, -1.2_3_4_4, -0.1_4_4_8, -1.8_2_6_2, -1.0_0_2_0, -1.8_1_5_4, -1.6_8_9_5, -1.8_5_1_6, -2.3_5_7_4, -0.9_2_7_7, 3.7_5_9_8, 6.5_7_4_2, -1.2_9_9_8, -0.1_1_7_7, -8.1_4_0_6, -2.9_6_8_8, -2.9_1_9_9, -3.1_6_9_9, -3.5_2_5_4, -2.3_5_5_5, -2.7_9_8_8, -3.4_1_4_1, -2.8_2_6_2, -4.5_1_9_5, -3.3_3_7_9, -3.3_1_6_4, -2.7_8_3_2, -3.0_2_7_3]			)
           # fmt: on
           torch.testing.assert_close(out[0, 0, :30]     ,							__lowerCamelCase     ,							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     :					Tuple			):
           snake_case__       :   Tuple	      =    [1, 306, 4658, 278, 6593, 310, 2834, 338]
           snake_case__       :   List[str]	      =    LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'     ,							device_map='auto'			)
           snake_case__       :   int	      =    model(torch.tensor(__lowerCamelCase			)			)
           # Expected mean on dim = -1
           snake_case__       :   Tuple	      =    torch.tensor([[-0.8_5_6_2, -1.8_5_2_0, -0.7_5_5_1, -0.4_1_6_2, -1.5_1_6_1, -1.2_0_3_8, -2.4_8_2_3, -2.3_2_5_4]]			)
           torch.testing.assert_close(out.mean(-1			)     ,							__lowerCamelCase     ,							atol=1E-2     ,							rtol=1E-2			)
           # slicing logits[0, 0, 0:30]
           # fmt: off
           snake_case__       :   Dict	      =    torch.tensor([-2.2_2_2_7, 4.8_8_2_8, 0.9_0_2_3, -0.4_5_7_8, -0.7_8_7_1, -0.1_0_3_3, -0.6_2_2_1, -0.5_7_8_6, -0.7_8_0_3, -1.0_6_7_4, -1.2_9_2_0, -0.1_5_7_0, 0.8_0_0_8, 2.0_7_2_3, -0.9_4_9_7, 0.2_7_7_1, -2.2_2_2_7, -0.7_6_1_2, -1.4_3_4_6, -1.2_0_6_1, -1.6_4_2_6, -0.3_0_0_0, -0.7_1_3_9, -1.1_9_3_4, -1.8_6_9_1, -1.6_9_7_3, -1.5_9_4_7, -1.2_7_0_5, -0.3_5_2_3, -0.5_5_1_3]			)
           # fmt: on
           torch.testing.assert_close(out.mean(-1			)     ,							__lowerCamelCase     ,							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     :					List[Any]			):
           snake_case__       :   Tuple	      =    [1, 306, 4658, 278, 6593, 310, 2834, 338]
           snake_case__       :   List[Any]	      =    LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'     ,							device_map='auto'			)
           snake_case__       :   List[Any]	      =    model(torch.tensor(__lowerCamelCase			)			)

           snake_case__       :   Any	      =    torch.tensor(
               [[-4.2_3_2_7, -3.3_3_6_0, -4.6_6_6_5, -4.7_6_3_1, -1.8_1_8_0, -3.4_1_7_0, -1.4_2_1_1, -3.1_8_1_0]]     ,							dtype=torch.floataa			)
           torch.testing.assert_close(out.mean(-1			)     ,							__lowerCamelCase     ,							atol=1E-2     ,							rtol=1E-2			)
           # fmt: off
           snake_case__       :   Union[str, Any]	      =    torch.tensor([-9.4_9_2_2, -3.9_5_5_1, 1.7_9_9_8, -5.6_7_5_8, -5.1_0_5_5, -5.8_9_8_4, -4.8_3_2_0, -6.8_0_8_6, -6.5_3_9_1, -5.6_1_7_2, -5.5_8_2_0, -5.5_3_5_2, 1.7_8_8_1, 3.6_2_8_9, -6.5_1_1_7, -3.4_7_8_5, -9.5_0_0_0, -6.0_3_5_2, -6.8_1_2_5, -6.0_1_9_5, -6.6_8_3_6, -5.4_7_2_7, -6.2_8_1_2, -6.0_3_9_1, -7.3_3_9_8, -7.4_2_9_7, -7.4_8_4_4, -6.5_8_2_0, -5.8_7_8_9, -5.5_3_1_2]			)
           # fmt: on
           torch.testing.assert_close(out[0, 0, :30]     ,							__lowerCamelCase     ,							atol=1E-5     ,							rtol=1E-5			)





     @unittest.skip('Model is curently gated'			)
     @slow
     def 			_lowerCAmelCase      (     self     :					List[Any]			):
           snake_case__       :   Tuple	      =    'Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'
           snake_case__       :   Dict	      =    'Simply put, the theory of relativity states that '
           snake_case__       :   Optional[Any]	      =    LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf'			)
           snake_case__       :   Optional[int]	      =    tokenizer.encode(__lowerCamelCase     ,							return_tensors='pt'			)
           snake_case__       :   Dict	      =    LlamaForCausalLM.from_pretrained(
               'meta-llama/Llama-2-13b-chat-hf'     ,							device_map='sequential'     ,							use_safetensors=__lowerCamelCase			)

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






'''simple docstring'''






from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *
