'''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 lowerCamelCase							:




  '''simple docstring'''



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






  def  lowerCAmelCase__ (      self			:       List[Any]  )				->int:
        UpperCAmelCase_            =  ids_tensor([self.batch_size, self.seq_length]  ,							self.vocab_size  )

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

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

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

        UpperCAmelCase_            =  self.get_config()

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






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






  def  lowerCAmelCase__ (      self			:       str  ,							UpperCAmelCase__			:       Any  ,							UpperCAmelCase__			:       Union[str, Any]  ,							UpperCAmelCase__			:       Union[str, Any]  ,							UpperCAmelCase__			:       int  ,							UpperCAmelCase__			:       List[Any]  ,							UpperCAmelCase__			:       str  ,							UpperCAmelCase__			:       Dict  ,							UpperCAmelCase__			:       int  ,							UpperCAmelCase__			:       List[Any]  ,							)				->List[Any]:
        UpperCAmelCase_            =  True
        UpperCAmelCase_            =  LlamaModel(UpperCAmelCase__  )
        model.to(UpperCAmelCase__  )
        model.eval()
        UpperCAmelCase_            =  model(
            UpperCAmelCase__  ,							attention_mask=UpperCAmelCase__  ,							encoder_hidden_states=UpperCAmelCase__  ,							encoder_attention_mask=UpperCAmelCase__  ,							)
        UpperCAmelCase_            =  model(
            UpperCAmelCase__  ,							attention_mask=UpperCAmelCase__  ,							encoder_hidden_states=UpperCAmelCase__  ,							)
        UpperCAmelCase_            =  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			:       str  ,							UpperCAmelCase__			:       Optional[Any]  ,							UpperCAmelCase__			:       Dict  ,							UpperCAmelCase__			:       Optional[Any]  ,							UpperCAmelCase__			:       str  ,							UpperCAmelCase__			:       List[Any]  ,							UpperCAmelCase__			:       Tuple  ,							UpperCAmelCase__			:       Optional[Any]  ,							UpperCAmelCase__			:       List[str]  ,							UpperCAmelCase__			:       str  ,							)				->Optional[int]:
        UpperCAmelCase_            =  LlamaForCausalLM(config=UpperCAmelCase__  )
        model.to(UpperCAmelCase__  )
        model.eval()
        UpperCAmelCase_            =  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			:       str  ,							UpperCAmelCase__			:       Optional[int]  ,							UpperCAmelCase__			:       Tuple  ,							UpperCAmelCase__			:       Any  ,							UpperCAmelCase__			:       Tuple  ,							UpperCAmelCase__			:       int  ,							UpperCAmelCase__			:       Union[str, Any]  ,							UpperCAmelCase__			:       Dict  ,							UpperCAmelCase__			:       Optional[int]  ,							UpperCAmelCase__			:       Dict  ,							)				->Tuple:
        UpperCAmelCase_            =  True
        UpperCAmelCase_            =  True
        UpperCAmelCase_            =  LlamaForCausalLM(config=UpperCAmelCase__  )
        model.to(UpperCAmelCase__  )
        model.eval()

        # first forward pass
        UpperCAmelCase_            =  model(
            UpperCAmelCase__  ,							attention_mask=UpperCAmelCase__  ,							encoder_hidden_states=UpperCAmelCase__  ,							encoder_attention_mask=UpperCAmelCase__  ,							use_cache=UpperCAmelCase__  ,							)
        UpperCAmelCase_            =  outputs.past_key_values

        # create hypothetical multiple next token and extent to next_input_ids
        UpperCAmelCase_            =  ids_tensor((self.batch_size, 3)  ,							config.vocab_size  )
        UpperCAmelCase_            =  ids_tensor((self.batch_size, 3)  ,							vocab_size=2  )

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

        UpperCAmelCase_            =  model(
            UpperCAmelCase__  ,							attention_mask=UpperCAmelCase__  ,							encoder_hidden_states=UpperCAmelCase__  ,							encoder_attention_mask=UpperCAmelCase__  ,							output_hidden_states=UpperCAmelCase__  ,							)['''hidden_states'''][0]
        UpperCAmelCase_            =  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_            =  ids_tensor((1,)  ,							output_from_past.shape[-1]  ).item()
        UpperCAmelCase_            =  output_from_no_past[:, -3:, random_slice_idx].detach()
        UpperCAmelCase_            =  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]  )				->int:
        UpperCAmelCase_            =  self.prepare_config_and_inputs()
        (
            (
            UpperCAmelCase_
        )      ,		(
            UpperCAmelCase_
        )      ,		(
            UpperCAmelCase_
        )      ,		(
            UpperCAmelCase_
        )      ,		(
            UpperCAmelCase_
        )      ,		(
            UpperCAmelCase_
        )      ,		(
            UpperCAmelCase_
        )      ,		
        )            =  config_and_inputs
        UpperCAmelCase_            =  {'''input_ids''': input_ids, '''attention_mask''': input_mask}
        return config, inputs_dict







@require_torch
class lowerCamelCase							(	lowerCamelCase     ,		lowerCamelCase     ,		lowerCamelCase     ,		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  lowerCAmelCase__ (      self			:       int  )				->Optional[int]:
        UpperCAmelCase_            =  LlamaModelTester(self  )
        UpperCAmelCase_            =  ConfigTester(self  ,							config_class=UpperCAmelCase__  ,							hidden_size=37  )






  def  lowerCAmelCase__ (      self			:       Any  )				->Tuple:
        self.config_tester.run_common_tests()






  def  lowerCAmelCase__ (      self			:       str  )				->Optional[int]:
        UpperCAmelCase_            =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCAmelCase__  )






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






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






  @parameterized.expand([('''linear''',), ('''dynamic''',)]  )
  def  lowerCAmelCase__ (      self			:       Tuple  ,							UpperCAmelCase__			:       str  )				->Tuple:
        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.model_tester.prepare_config_and_inputs_for_common()
        UpperCAmelCase_            =  ids_tensor([1, 10]  ,							config.vocab_size  )
        UpperCAmelCase_            =  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_            =  LlamaModel(UpperCAmelCase__  )
        original_model.to(UpperCAmelCase__  )
        original_model.eval()
        UpperCAmelCase_            =  original_model(UpperCAmelCase__  ).last_hidden_state
        UpperCAmelCase_            =  original_model(UpperCAmelCase__  ).last_hidden_state

        set_seed(42  )  # Fixed seed at init time so the two models get the same random weights
        UpperCAmelCase_            =  {'''type''': scaling_type, '''factor''': 10.0}
        UpperCAmelCase_            =  LlamaModel(UpperCAmelCase__  )
        scaled_model.to(UpperCAmelCase__  )
        scaled_model.eval()
        UpperCAmelCase_            =  scaled_model(UpperCAmelCase__  ).last_hidden_state
        UpperCAmelCase_            =  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 lowerCamelCase							(	unittest.TestCase					):




  '''simple docstring'''



  @unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''  )
  @slow
  def  lowerCAmelCase__ (      self			:       Any  )				->Optional[int]:
        UpperCAmelCase_            =  [1, 306, 4658, 278, 6593, 310, 2834, 338]
        UpperCAmelCase_            =  LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-7b-hf'''  ,							device_map='''auto'''  )
        UpperCAmelCase_            =  model(torch.tensor([input_ids]  )  )
        # Expected mean on dim = -1
        UpperCAmelCase_            =  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_            =  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  )				->List[str]:
        UpperCAmelCase_            =  [1, 306, 4658, 278, 6593, 310, 2834, 338]
        UpperCAmelCase_            =  LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-hf'''  ,							device_map='''auto'''  )
        UpperCAmelCase_            =  model(torch.tensor(UpperCAmelCase__  )  )
        # Expected mean on dim = -1
        UpperCAmelCase_            =  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_            =  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			:       int  )				->int:
        UpperCAmelCase_            =  [1, 306, 4658, 278, 6593, 310, 2834, 338]
        UpperCAmelCase_            =  LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''  ,							device_map='''auto'''  )
        UpperCAmelCase_            =  model(torch.tensor(UpperCAmelCase__  )  )
        # Expected mean on dim = -1
        UpperCAmelCase_            =  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_            =  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			:       Tuple  )				->Dict:
        UpperCAmelCase_            =  [1, 306, 4658, 278, 6593, 310, 2834, 338]
        UpperCAmelCase_            =  LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-70b-hf'''  ,							device_map='''auto'''  )
        UpperCAmelCase_            =  model(torch.tensor(UpperCAmelCase__  )  )

        UpperCAmelCase_            =  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_            =  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			:       Union[str, Any]  )				->Any:
        UpperCAmelCase_            =  '''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_            =  '''Simply put, the theory of relativity states that '''
        UpperCAmelCase_            =  LlamaTokenizer.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''  )
        UpperCAmelCase_            =  tokenizer.encode(UpperCAmelCase__  ,							return_tensors='''pt'''  )
        UpperCAmelCase_            =  LlamaForCausalLM.from_pretrained(
            '''meta-llama/Llama-2-13b-chat-hf'''  ,							device_map='''sequential'''  ,							use_safetensors=UpperCAmelCase__  )

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






'''simple docstring'''
lowercase__  :      Union[str, Any]        =					"\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

lowercase__  :      str        =					[{"type": "code", "content": INSTALL_CONTENT}]
lowercase__  :      Any        =					{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}





