

'''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 				_snake_case					:

  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=5_12      , _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      , ):





      '''simple docstring'''

      lowerCAmelCase 						=							parent
      lowerCAmelCase 						=							batch_size
      lowerCAmelCase 						=							seq_length
      lowerCAmelCase 						=							is_training
      lowerCAmelCase 						=							use_input_mask
      lowerCAmelCase 						=							use_token_type_ids
      lowerCAmelCase 						=							use_labels
      lowerCAmelCase 						=							vocab_size
      lowerCAmelCase 						=							hidden_size
      lowerCAmelCase 						=							num_hidden_layers
      lowerCAmelCase 						=							num_attention_heads
      lowerCAmelCase 						=							intermediate_size
      lowerCAmelCase 						=							hidden_act
      lowerCAmelCase 						=							hidden_dropout_prob
      lowerCAmelCase 						=							attention_probs_dropout_prob
      lowerCAmelCase 						=							max_position_embeddings
      lowerCAmelCase 						=							type_vocab_size
      lowerCAmelCase 						=							type_sequence_label_size
      lowerCAmelCase 						=							initializer_range
      lowerCAmelCase 						=							num_labels
      lowerCAmelCase 						=							num_choices
      lowerCAmelCase 						=							scope

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							ids_tensor([self.batch_size, self.seq_length]      , self.vocab_size	)

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

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

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

      lowerCAmelCase 						=							self.get_config()

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      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   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							LlamaModel(config=_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , attention_mask=_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.last_hidden_state.shape      , (self.batch_size, self.seq_length, self.hidden_size)	)

  def   _SCREAMING_SNAKE_CASE				(				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      , ):





      '''simple docstring'''

      lowerCAmelCase 						=							True
      lowerCAmelCase 						=							LlamaModel(_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      lowerCAmelCase 						=							model(
          _SCREAMING_SNAKE_CASE      , attention_mask=_SCREAMING_SNAKE_CASE      , encoder_hidden_states=_SCREAMING_SNAKE_CASE      , encoder_attention_mask=_SCREAMING_SNAKE_CASE      , )
      lowerCAmelCase 						=							model(
          _SCREAMING_SNAKE_CASE      , attention_mask=_SCREAMING_SNAKE_CASE      , encoder_hidden_states=_SCREAMING_SNAKE_CASE      , )
      lowerCAmelCase 						=							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   _SCREAMING_SNAKE_CASE				(				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      , ):





      '''simple docstring'''

      lowerCAmelCase 						=							LlamaForCausalLM(config=_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      lowerCAmelCase 						=							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   _SCREAMING_SNAKE_CASE				(				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      , ):





      '''simple docstring'''

      lowerCAmelCase 						=							True
      lowerCAmelCase 						=							True
      lowerCAmelCase 						=							LlamaForCausalLM(config=_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()

      # first forward pass
      lowerCAmelCase 						=							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      , )
      lowerCAmelCase 						=							outputs.past_key_values

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

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

      lowerCAmelCase 						=							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]
      lowerCAmelCase 						=							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
      lowerCAmelCase 						=							ids_tensor((1,)      , output_from_past.shape[-1]	).item()
      lowerCAmelCase 						=							output_from_no_past[:, -3:, random_slice_idx].detach()
      lowerCAmelCase 						=							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   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.prepare_config_and_inputs()
      (
          (
          lowerCAmelCase
      )						,   (
          lowerCAmelCase
      )						,   (
          lowerCAmelCase
      )						,   (
          lowerCAmelCase
      )						,   (
          lowerCAmelCase
      )						,   (
          lowerCAmelCase
      )						,   (
          lowerCAmelCase
      )						,   
      ) 						=							config_and_inputs
      lowerCAmelCase 						=							{'input_ids': input_ids, 'attention_mask': input_mask}
      return config, inputs_dict



@require_torch
class 				_snake_case					(   a_     , a_     , a_     , unittest.TestCase			):
  SCREAMING_SNAKE_CASE			:       List[str]				=     (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE			:       Optional[Any]				=     (LlamaForCausalLM,) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE			:       str				=     (
      {
          '''feature-extraction''': LlamaModel,
          '''text-classification''': LlamaForSequenceClassification,
          '''text-generation''': LlamaForCausalLM,
          '''zero-shot''': LlamaForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  SCREAMING_SNAKE_CASE			:       List[str]				=     False
  SCREAMING_SNAKE_CASE			:       Optional[int]				=     False

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							LlamaModelTester(self	)
      lowerCAmelCase 						=							ConfigTester(self      , config_class=_SCREAMING_SNAKE_CASE      , hidden_size=37	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      self.config_tester.run_common_tests()

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
          lowerCAmelCase 						=							type
          self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase						,   lowerCAmelCase 						=							self.model_tester.prepare_config_and_inputs_for_common()
      lowerCAmelCase 						=							3
      lowerCAmelCase 						=							input_dict['input_ids']
      lowerCAmelCase 						=							input_ids.ne(1	).to(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							ids_tensor([self.model_tester.batch_size]      , self.model_tester.type_sequence_label_size	)
      lowerCAmelCase 						=							LlamaForSequenceClassification(_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      lowerCAmelCase 						=							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   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase						,   lowerCAmelCase 						=							self.model_tester.prepare_config_and_inputs_for_common()
      lowerCAmelCase 						=							3
      lowerCAmelCase 						=							'single_label_classification'
      lowerCAmelCase 						=							input_dict['input_ids']
      lowerCAmelCase 						=							input_ids.ne(1	).to(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							ids_tensor([self.model_tester.batch_size]      , self.model_tester.type_sequence_label_size	)
      lowerCAmelCase 						=							LlamaForSequenceClassification(_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      lowerCAmelCase 						=							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   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase						,   lowerCAmelCase 						=							self.model_tester.prepare_config_and_inputs_for_common()
      lowerCAmelCase 						=							3
      lowerCAmelCase 						=							'multi_label_classification'
      lowerCAmelCase 						=							input_dict['input_ids']
      lowerCAmelCase 						=							input_ids.ne(1	).to(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							ids_tensor(
          [self.model_tester.batch_size, config.num_labels]      , self.model_tester.type_sequence_label_size	).to(torch.float	)
      lowerCAmelCase 						=							LlamaForSequenceClassification(_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      lowerCAmelCase 						=							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   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      pass


  @parameterized.expand([('linear',), ('dynamic',)]	)
  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase						,   lowerCAmelCase 						=							self.model_tester.prepare_config_and_inputs_for_common()
      lowerCAmelCase 						=							ids_tensor([1, 10]      , config.vocab_size	)
      lowerCAmelCase 						=							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
      lowerCAmelCase 						=							LlamaModel(_SCREAMING_SNAKE_CASE	)
      original_model.to(_SCREAMING_SNAKE_CASE	)
      original_model.eval()
      lowerCAmelCase 						=							original_model(_SCREAMING_SNAKE_CASE	).last_hidden_state
      lowerCAmelCase 						=							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
      lowerCAmelCase 						=							{'type': scaling_type, 'factor': 10.0}
      lowerCAmelCase 						=							LlamaModel(_SCREAMING_SNAKE_CASE	)
      scaled_model.to(_SCREAMING_SNAKE_CASE	)
      scaled_model.eval()
      lowerCAmelCase 						=							scaled_model(_SCREAMING_SNAKE_CASE	).last_hidden_state
      lowerCAmelCase 						=							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 				_snake_case					(   unittest.TestCase			):

  @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'	)
  @slow
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
      lowerCAmelCase 						=							LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'      , device_map='auto'	)
      lowerCAmelCase 						=							model(torch.tensor([input_ids]	)	)
      # Expected mean on dim = -1
      lowerCAmelCase 						=							torch.tensor([[-6.6_550, -4.1_227, -4.9_859, -3.2_406, 0.8_262, -3.0_033, 1.2_964, -3.3_699]]	)
      torch.testing.assert_close(out.mean(-1	)      , _SCREAMING_SNAKE_CASE      , atol=1e-2      , rtol=1e-2	)
      # slicing logits[0, 0, 0:30]
      # fmt: off
      lowerCAmelCase 						=							torch.tensor([-12.8_281, -7.4_453, -0.4_639, -8.0_625, -7.2_500, -8.0_000, -6.4_883, -7.7_695, -7.8_438, -7.0_312, -6.2_188, -7.1_328, -1.8_496, 1.9_961, -8.6_250, -6.7_227, -12.8_281, -6.9_492, -7.0_742, -7.7_852, -7.5_820, -7.9_062, -6.9_375, -7.9_805, -8.3_438, -8.1_562, -8.0_469, -7.6_250, -7.7_422, -7.3_398,]	)
      # 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   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
      lowerCAmelCase 						=							LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'      , device_map='auto'	)
      lowerCAmelCase 						=							model(torch.tensor(_SCREAMING_SNAKE_CASE	)	)
      # Expected mean on dim = -1
      lowerCAmelCase 						=							torch.tensor([[-2.0_622, -1.2_794, -1.1_638, -0.9_788, -1.4_603, -1.0_238, -1.7_893, -1.4_411]]	)
      torch.testing.assert_close(out.mean(-1	)      , _SCREAMING_SNAKE_CASE      , atol=1e-2      , rtol=1e-2	)
      # slicing logits[0, 0, 0:30]
      # fmt: off
      lowerCAmelCase 						=							torch.tensor([-8.1_406, -8.0_547, 2.7_461, -1.2_344, -0.1_448, -1.8_262, -1.0_020, -1.8_154, -1.6_895, -1.8_516, -2.3_574, -0.9_277, 3.7_598, 6.5_742, -1.2_998, -0.1_177, -8.1_406, -2.9_688, -2.9_199, -3.1_699, -3.5_254, -2.3_555, -2.7_988, -3.4_141, -2.8_262, -4.5_195, -3.3_379, -3.3_164, -2.7_832, -3.0_273]	)
      # 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   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
      lowerCAmelCase 						=							LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'      , device_map='auto'	)
      lowerCAmelCase 						=							model(torch.tensor(_SCREAMING_SNAKE_CASE	)	)
      # Expected mean on dim = -1
      lowerCAmelCase 						=							torch.tensor([[-0.8_562, -1.8_520, -0.7_551, -0.4_162, -1.5_161, -1.2_038, -2.4_823, -2.3_254]]	)
      torch.testing.assert_close(out.mean(-1	)      , _SCREAMING_SNAKE_CASE      , atol=1e-2      , rtol=1e-2	)
      # slicing logits[0, 0, 0:30]
      # fmt: off
      lowerCAmelCase 						=							torch.tensor([-2.2_227, 4.8_828, 0.9_023, -0.4_578, -0.7_871, -0.1_033, -0.6_221, -0.5_786, -0.7_803, -1.0_674, -1.2_920, -0.1_570, 0.8_008, 2.0_723, -0.9_497, 0.2_771, -2.2_227, -0.7_612, -1.4_346, -1.2_061, -1.6_426, -0.3_000, -0.7_139, -1.1_934, -1.8_691, -1.6_973, -1.5_947, -1.2_705, -0.3_523, -0.5_513]	)
      # 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   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
      lowerCAmelCase 						=							LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'      , device_map='auto'	)
      lowerCAmelCase 						=							model(torch.tensor(_SCREAMING_SNAKE_CASE	)	)

      lowerCAmelCase 						=							torch.tensor(
          [[-4.2_327, -3.3_360, -4.6_665, -4.7_631, -1.8_180, -3.4_170, -1.4_211, -3.1_810]]      , dtype=torch.floataa	)
      torch.testing.assert_close(out.mean(-1	)      , _SCREAMING_SNAKE_CASE      , atol=1e-2      , rtol=1e-2	)
      # fmt: off
      lowerCAmelCase 						=							torch.tensor([-9.4_922, -3.9_551, 1.7_998, -5.6_758, -5.1_055, -5.8_984, -4.8_320, -6.8_086, -6.5_391, -5.6_172, -5.5_820, -5.5_352, 1.7_881, 3.6_289, -6.5_117, -3.4_785, -9.5_000, -6.0_352, -6.8_125, -6.0_195, -6.6_836, -5.4_727, -6.2_812, -6.0_391, -7.3_398, -7.4_297, -7.4_844, -6.5_820, -5.8_789, -5.5_312]	)
      # 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   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							'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'
      lowerCAmelCase 						=							'Simply put, the theory of relativity states that '
      lowerCAmelCase 						=							LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf'	)
      lowerCAmelCase 						=							tokenizer.encode(_SCREAMING_SNAKE_CASE      , return_tensors='pt'	)
      lowerCAmelCase 						=							LlamaForCausalLM.from_pretrained(
          'meta-llama/Llama-2-13b-chat-hf'      , device_map='sequential'      , use_safetensors=_SCREAMING_SNAKE_CASE	)

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








'''simple docstring'''



from ..utils import DummyObject, requires_backends



class 				_snake_case					(   metaclass=a_			):
  SCREAMING_SNAKE_CASE			:       int				=     ['''torch''', '''transformers''', '''onnx''']

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(self      , ['torch', 'transformers', 'onnx']	)

  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)


  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)



class 				_snake_case					(   metaclass=a_			):
  SCREAMING_SNAKE_CASE			:       List[Any]				=     ['''torch''', '''transformers''', '''onnx''']

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(self      , ['torch', 'transformers', 'onnx']	)

  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)


  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)



class 				_snake_case					(   metaclass=a_			):
  SCREAMING_SNAKE_CASE			:       int				=     ['''torch''', '''transformers''', '''onnx''']

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(self      , ['torch', 'transformers', 'onnx']	)

  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)


  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)



class 				_snake_case					(   metaclass=a_			):
  SCREAMING_SNAKE_CASE			:       Dict				=     ['''torch''', '''transformers''', '''onnx''']

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(self      , ['torch', 'transformers', 'onnx']	)

  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)


  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)



class 				_snake_case					(   metaclass=a_			):
  SCREAMING_SNAKE_CASE			:       List[Any]				=     ['''torch''', '''transformers''', '''onnx''']

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(self      , ['torch', 'transformers', 'onnx']	)

  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)


  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)



class 				_snake_case					(   metaclass=a_			):
  SCREAMING_SNAKE_CASE			:       Optional[Any]				=     ['''torch''', '''transformers''', '''onnx''']

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(self      , ['torch', 'transformers', 'onnx']	)

  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)


  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)





