





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 							_UpperCAmelCase       :

  """simple docstring"""


  def __init__(							self					:						Any   ,    lowerCAmelCase_					:						Optional[int]   ,    lowerCAmelCase_					:						Any=1_3   ,    lowerCAmelCase_					:						List[Any]=7   ,    lowerCAmelCase_					:						List[str]=True   ,    lowerCAmelCase_					:						Union[str, Any]=True   ,    lowerCAmelCase_					:						List[Any]=False   ,    lowerCAmelCase_					:						List[Any]=True   ,    lowerCAmelCase_					:						Any=9_9   ,    lowerCAmelCase_					:						List[str]=3_2   ,    lowerCAmelCase_					:						List[Any]=5   ,    lowerCAmelCase_					:						List[Any]=4   ,    lowerCAmelCase_					:						List[Any]=3_7   ,    lowerCAmelCase_					:						Tuple="gelu"   ,    lowerCAmelCase_					:						Dict=0.1   ,    lowerCAmelCase_					:						List[Any]=0.1   ,    lowerCAmelCase_					:						Any=5_1_2   ,    lowerCAmelCase_					:						List[Any]=1_6   ,    lowerCAmelCase_					:						Tuple=2   ,    lowerCAmelCase_					:						int=0.02   ,    lowerCAmelCase_					:						List[str]=3   ,    lowerCAmelCase_					:						List[Any]=4   ,    lowerCAmelCase_					:						Dict=None   ,    )				->		List[Any]:
     __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 							lowercase						(							self					:						List[Any]	)				->		Dict:
     __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 							lowercase						(							self					:						Union[str, Any]	)				->		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=lowerCAmelCase_   ,    initializer_range=self.initializer_range   ,    )






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






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






  def 							lowercase						(							self					:						int   ,    lowerCAmelCase_					:						List[str]   ,    lowerCAmelCase_					:						Optional[int]   ,    lowerCAmelCase_					:						Optional[Any]   ,    lowerCAmelCase_					:						Any   ,    lowerCAmelCase_					:						Union[str, Any]   ,    lowerCAmelCase_					:						List[Any]   ,    lowerCAmelCase_					:						Union[str, Any]   ,    lowerCAmelCase_					:						List[Any]   ,    lowerCAmelCase_					:						Optional[int]   ,    )				->		Optional[Any]:
     __lowerCAmelCase				      =      LlamaForCausalLM(config=lowerCAmelCase_	)
     model.to(lowerCAmelCase_	)
     model.eval()
     __lowerCAmelCase				      =      model(lowerCAmelCase_   ,    attention_mask=lowerCAmelCase_   ,    labels=lowerCAmelCase_	)
     self.parent.assertEqual(result.logits.shape   ,    (self.batch_size, self.seq_length, self.vocab_size)	)






  def 							lowercase						(							self					:						Tuple   ,    lowerCAmelCase_					:						Union[str, Any]   ,    lowerCAmelCase_					:						List[Any]   ,    lowerCAmelCase_					:						List[str]   ,    lowerCAmelCase_					:						Any   ,    lowerCAmelCase_					:						Optional[Any]   ,    lowerCAmelCase_					:						Any   ,    lowerCAmelCase_					:						Any   ,    lowerCAmelCase_					:						Optional[Any]   ,    lowerCAmelCase_					:						List[str]   ,    )				->		Optional[int]:
     __lowerCAmelCase				      =      True
     __lowerCAmelCase				      =      True
     __lowerCAmelCase				      =      LlamaForCausalLM(config=lowerCAmelCase_	)
     model.to(lowerCAmelCase_	)
     model.eval()

     # first forward pass
     __lowerCAmelCase				      =      model(
         lowerCAmelCase_   ,    attention_mask=lowerCAmelCase_   ,    encoder_hidden_states=lowerCAmelCase_   ,    encoder_attention_mask=lowerCAmelCase_   ,    use_cache=lowerCAmelCase_   ,    )
     __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(
         lowerCAmelCase_   ,    attention_mask=lowerCAmelCase_   ,    encoder_hidden_states=lowerCAmelCase_   ,    encoder_attention_mask=lowerCAmelCase_   ,    output_hidden_states=lowerCAmelCase_   ,    )['hidden_states'][0]
     __lowerCAmelCase				      =      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
     __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(lowerCAmelCase_   ,    lowerCAmelCase_   ,    atol=1e-3	)	)






  def 							lowercase						(							self					:						Optional[int]	)				->		Union[str, Any]:
     __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 							_UpperCAmelCase       (       _UpperCamelCase						,       _UpperCamelCase						,       _UpperCamelCase						,       unittest.TestCase       ):

  """simple docstring"""


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






  def 							lowercase						(							self					:						Union[str, Any]	)				->		Optional[int]:
     __lowerCAmelCase				      =      LlamaModelTester(self	)
     __lowerCAmelCase				      =      ConfigTester(self   ,    config_class=lowerCAmelCase_   ,    hidden_size=3_7	)






  def 							lowercase						(							self					:						Any	)				->		Optional[Any]:
     self.config_tester.run_common_tests()






  def 							lowercase						(							self					:						Any	)				->		List[Any]:
     __lowerCAmelCase				      =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowerCAmelCase_	)






  def 							lowercase						(							self					:						int	)				->		Dict:
     __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(*lowerCAmelCase_	)






  def 							lowercase						(							self					:						Optional[Any]	)				->		Any:
     __lowerCAmelCase      ,      __lowerCAmelCase				      =      self.model_tester.prepare_config_and_inputs_for_common()
     __lowerCAmelCase				      =      3
     __lowerCAmelCase				      =      input_dict['input_ids']
     __lowerCAmelCase				      =      input_ids.ne(1	).to(lowerCAmelCase_	)
     __lowerCAmelCase				      =      ids_tensor([self.model_tester.batch_size]   ,    self.model_tester.type_sequence_label_size	)
     __lowerCAmelCase				      =      LlamaForSequenceClassification(lowerCAmelCase_	)
     model.to(lowerCAmelCase_	)
     model.eval()
     __lowerCAmelCase				      =      model(lowerCAmelCase_   ,    attention_mask=lowerCAmelCase_   ,    labels=lowerCAmelCase_	)
     self.assertEqual(result.logits.shape   ,    (self.model_tester.batch_size, self.model_tester.num_labels)	)






  def 							lowercase						(							self					:						Optional[Any]	)				->		Optional[Any]:
     __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(lowerCAmelCase_	)
     __lowerCAmelCase				      =      ids_tensor([self.model_tester.batch_size]   ,    self.model_tester.type_sequence_label_size	)
     __lowerCAmelCase				      =      LlamaForSequenceClassification(lowerCAmelCase_	)
     model.to(lowerCAmelCase_	)
     model.eval()
     __lowerCAmelCase				      =      model(lowerCAmelCase_   ,    attention_mask=lowerCAmelCase_   ,    labels=lowerCAmelCase_	)
     self.assertEqual(result.logits.shape   ,    (self.model_tester.batch_size, self.model_tester.num_labels)	)






  def 							lowercase						(							self					:						Tuple	)				->		Optional[Any]:
     __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(lowerCAmelCase_	)
     __lowerCAmelCase				      =      ids_tensor(
         [self.model_tester.batch_size, config.num_labels]   ,    self.model_tester.type_sequence_label_size	).to(torch.float	)
     __lowerCAmelCase				      =      LlamaForSequenceClassification(lowerCAmelCase_	)
     model.to(lowerCAmelCase_	)
     model.eval()
     __lowerCAmelCase				      =      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 							lowercase						(							self					:						List[str]	)				->		Optional[int]:
     pass






  @parameterized.expand([('linear',), ('dynamic',)]	)
  def 							lowercase						(							self					:						Optional[int]   ,    lowerCAmelCase_					:						List[str]	)				->		Union[str, Any]:
     __lowerCAmelCase      ,      __lowerCAmelCase				      =      self.model_tester.prepare_config_and_inputs_for_common()
     __lowerCAmelCase				      =      ids_tensor([1, 1_0]   ,    config.vocab_size	)
     __lowerCAmelCase				      =      ids_tensor([1, int(config.max_position_embeddings * 1.5	)]   ,    config.vocab_size	)

     set_seed(4_2	)  # Fixed seed at init time so the two models get the same random weights
     __lowerCAmelCase				      =      LlamaModel(lowerCAmelCase_	)
     original_model.to(lowerCAmelCase_	)
     original_model.eval()
     __lowerCAmelCase				      =      original_model(lowerCAmelCase_	).last_hidden_state
     __lowerCAmelCase				      =      original_model(lowerCAmelCase_	).last_hidden_state

     set_seed(4_2	)  # Fixed seed at init time so the two models get the same random weights
     __lowerCAmelCase				      =      {'type': scaling_type, 'factor': 10.0}
     __lowerCAmelCase				      =      LlamaModel(lowerCAmelCase_	)
     scaled_model.to(lowerCAmelCase_	)
     scaled_model.eval()
     __lowerCAmelCase				      =      scaled_model(lowerCAmelCase_	).last_hidden_state
     __lowerCAmelCase				      =      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 							_UpperCAmelCase       (       unittest.TestCase       ):

  """simple docstring"""


  @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'	)
  @slow
  def 							lowercase						(							self					:						Any	)				->		Optional[int]:
     __lowerCAmelCase				      =      [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
     __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([[-6.65_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	)   ,    lowerCAmelCase_   ,    atol=1e-2   ,    rtol=1e-2	)
     # slicing logits[0, 0, 0:30]
     # fmt: off
     __lowerCAmelCase				      =      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, :3_0]   ,    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 							lowercase						(							self					:						List[str]	)				->		List[str]:
     __lowerCAmelCase				      =      [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
     __lowerCAmelCase				      =      LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'   ,    device_map='auto'	)
     __lowerCAmelCase				      =      model(torch.tensor(lowerCAmelCase_	)	)
     # Expected mean on dim = -1
     __lowerCAmelCase				      =      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	)   ,    lowerCAmelCase_   ,    atol=1e-2   ,    rtol=1e-2	)
     # slicing logits[0, 0, 0:30]
     # fmt: off
     __lowerCAmelCase				      =      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, :3_0]   ,    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 							lowercase						(							self					:						int	)				->		Tuple:
     __lowerCAmelCase				      =      [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
     __lowerCAmelCase				      =      LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'   ,    device_map='auto'	)
     __lowerCAmelCase				      =      model(torch.tensor(lowerCAmelCase_	)	)
     # Expected mean on dim = -1
     __lowerCAmelCase				      =      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	)   ,    lowerCAmelCase_   ,    atol=1e-2   ,    rtol=1e-2	)
     # slicing logits[0, 0, 0:30]
     # fmt: off
     __lowerCAmelCase				      =      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	)   ,    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 							lowercase						(							self					:						Optional[Any]	)				->		Any:
     __lowerCAmelCase				      =      [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
     __lowerCAmelCase				      =      LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'   ,    device_map='auto'	)
     __lowerCAmelCase				      =      model(torch.tensor(lowerCAmelCase_	)	)

     __lowerCAmelCase				      =      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	)   ,    lowerCAmelCase_   ,    atol=1e-2   ,    rtol=1e-2	)
     # fmt: off
     __lowerCAmelCase				      =      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, :3_0]   ,    lowerCAmelCase_   ,    atol=1e-5   ,    rtol=1e-5	)






  @unittest.skip('Model is curently gated'	)
  @slow
  def 							lowercase						(							self					:						Union[str, Any]	)				->		Optional[Any]:
     __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(lowerCAmelCase_   ,    return_tensors='pt'	)
     __lowerCAmelCase				      =      LlamaForCausalLM.from_pretrained(
         'meta-llama/Llama-2-13b-chat-hf'   ,    device_map='sequential'   ,    use_safetensors=lowerCAmelCase_	)

     # greedy generation outputs
     __lowerCAmelCase				      =      model.generate(lowerCAmelCase_   ,    max_new_tokens=6_4   ,    top_p=lowerCAmelCase_   ,    temperature=1   ,    do_sample=lowerCAmelCase_	)
     __lowerCAmelCase				      =      tokenizer.decode(generated_ids[0]   ,    skip_special_tokens=lowerCAmelCase_	)
     self.assertEqual(lowerCAmelCase_   ,    lowerCAmelCase_	)



import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

UpperCamelCase_						:     List[Any]	       =							3



def 		UpperCamelCase     (       _UpperCAmelCase  :   int  )   ->			int:






						'''simple docstring'''

						print("Generating primitive root of p"  )
						while True:
												_lowercase       :	Any           =   random.randrange(3 ,			_UpperCAmelCase  )
												if pow(_UpperCAmelCase ,			2 ,			_UpperCAmelCase  ) == 1:
																		continue
												if pow(_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase  ) == 1:
																		continue
												return g



def 		UpperCamelCase     (       _UpperCAmelCase  :   int  )   ->			tuple[tuple[int, int, int, int], tuple[int, int]]:






						'''simple docstring'''

						print("Generating prime p..."  )
						_lowercase       :	Optional[int]           =   rabin_miller.generate_large_prime(_UpperCAmelCase  )  # select large prime number.
						_lowercase       :	Dict           =   primitive_root(_UpperCAmelCase  )  # one primitive root on modulo p.
						_lowercase       :	Any           =   random.randrange(3 ,			_UpperCAmelCase  )  # private_key -> have to be greater than 2 for safety.
						_lowercase       :	Tuple           =   cryptomath.find_mod_inverse(pow(_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase  ) ,			_UpperCAmelCase  )

						_lowercase       :	List[Any]           =   (key_size, e_a, e_a, p)
						_lowercase       :	Union[str, Any]           =   (key_size, d)

						return public_key, private_key



def 		UpperCamelCase     (       _UpperCAmelCase  :   str ,			_UpperCAmelCase  :   int  )   ->			None:






						'''simple docstring'''

						if os.path.exists(f"""{name}_pubkey.txt"""  ) or os.path.exists(f"""{name}_privkey.txt"""  ):
												print("\nWARNING:"  )
												print(
												    f"""\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"""
												    "Use a different name or delete these files and re-run this program."  )
												sys.exit()

						_lowercase      ,    _lowercase       :	Optional[Any]           =   generate_key(_UpperCAmelCase  )
						print(f"""\nWriting public key to file {name}_pubkey.txt..."""  )
						with open(f"""{name}_pubkey.txt""" ,			"w"  ) as fo:
												fo.write(f"""{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}"""  )

						print(f"""Writing private key to file {name}_privkey.txt..."""  )
						with open(f"""{name}_privkey.txt""" ,			"w"  ) as fo:
												fo.write(f"""{private_key[0]},{private_key[1]}"""  )



def 		UpperCamelCase     (       )   ->			None:






						'''simple docstring'''

						print("Making key files..."  )
						make_key_files("elgamal" ,			2048  )
						print("Key files generation successful"  )


if __name__ == "__main__":
							main()




