





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						:







  def __init__(		self     ,     lowercase__     ,     lowercase__=13     ,     lowercase__=7     ,     lowercase__=True     ,     lowercase__=True     ,     lowercase__=False     ,     lowercase__=True     ,     lowercase__=99     ,     lowercase__=32     ,     lowercase__=5     ,     lowercase__=4     ,     lowercase__=37     ,     lowercase__="gelu"     ,     lowercase__=0.1     ,     lowercase__=0.1     ,     lowercase__=512     ,     lowercase__=16     ,     lowercase__=2     ,     lowercase__=0.02     ,     lowercase__=3     ,     lowercase__=4     ,     lowercase__=None     ,     )		->						Dict:






        """simple docstring"""





        _snake_case		:	List[str]					     =     parent
        _snake_case		:	Optional[Any]					     =     batch_size
        _snake_case		:	Optional[Any]					     =     seq_length
        _snake_case		:	Optional[Any]					     =     is_training
        _snake_case		:	Optional[Any]					     =     use_input_mask
        _snake_case		:	Optional[int]					     =     use_token_type_ids
        _snake_case		:	Union[str, Any]					     =     use_labels
        _snake_case		:	Tuple					     =     vocab_size
        _snake_case		:	Any					     =     hidden_size
        _snake_case		:	Optional[int]					     =     num_hidden_layers
        _snake_case		:	Any					     =     num_attention_heads
        _snake_case		:	Tuple					     =     intermediate_size
        _snake_case		:	Union[str, Any]					     =     hidden_act
        _snake_case		:	Tuple					     =     hidden_dropout_prob
        _snake_case		:	List[Any]					     =     attention_probs_dropout_prob
        _snake_case		:	List[Any]					     =     max_position_embeddings
        _snake_case		:	str					     =     type_vocab_size
        _snake_case		:	str					     =     type_sequence_label_size
        _snake_case		:	str					     =     initializer_range
        _snake_case		:	Dict					     =     num_labels
        _snake_case		:	Tuple					     =     num_choices
        _snake_case		:	List[Any]					     =     scope







  def 				UpperCAmelCase_				(		self		)		->						Any:






        """simple docstring"""





        _snake_case		:	List[Any]					     =     ids_tensor([self.batch_size, self.seq_length]     ,     self.vocab_size		)

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

        _snake_case		:	Optional[Any]					     =     None
        if self.use_token_type_ids:
              _snake_case		:	List[str]					     =     ids_tensor([self.batch_size, self.seq_length]     ,     self.type_vocab_size		)

        _snake_case		:	Optional[int]					     =     None
        _snake_case		:	Tuple					     =     None
        _snake_case		:	str					     =     None
        if self.use_labels:
              _snake_case		:	Any					     =     ids_tensor([self.batch_size]     ,     self.type_sequence_label_size		)
              _snake_case		:	int					     =     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		:	List[str]					     =     self.get_config()

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







  def 				UpperCAmelCase_				(		self		)		->						Any:






        """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=lowercase__     ,     initializer_range=self.initializer_range     ,     )







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						Optional[int]:






        """simple docstring"""





        _snake_case		:	List[Any]					     =     LlamaModel(config=lowercase__		)
        model.to(lowercase__		)
        model.eval()
        _snake_case		:	Dict					     =     model(lowercase__     ,     attention_mask=lowercase__		)
        _snake_case		:	Union[str, Any]					     =     model(lowercase__		)
        self.parent.assertEqual(result.last_hidden_state.shape     ,     (self.batch_size, self.seq_length, self.hidden_size)		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     )		->						List[str]:






        """simple docstring"""





        _snake_case		:	Any					     =     True
        _snake_case		:	Any					     =     LlamaModel(lowercase__		)
        model.to(lowercase__		)
        model.eval()
        _snake_case		:	Tuple					     =     model(
            lowercase__     ,     attention_mask=lowercase__     ,     encoder_hidden_states=lowercase__     ,     encoder_attention_mask=lowercase__     ,     )
        _snake_case		:	int					     =     model(
            lowercase__     ,     attention_mask=lowercase__     ,     encoder_hidden_states=lowercase__     ,     )
        _snake_case		:	int					     =     model(lowercase__     ,     attention_mask=lowercase__		)
        self.parent.assertEqual(result.last_hidden_state.shape     ,     (self.batch_size, self.seq_length, self.hidden_size)		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     )		->						Optional[Any]:






        """simple docstring"""





        _snake_case		:	int					     =     LlamaForCausalLM(config=lowercase__		)
        model.to(lowercase__		)
        model.eval()
        _snake_case		:	str					     =     model(lowercase__     ,     attention_mask=lowercase__     ,     labels=lowercase__		)
        self.parent.assertEqual(result.logits.shape     ,     (self.batch_size, self.seq_length, self.vocab_size)		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     )		->						List[Any]:






        """simple docstring"""





        _snake_case		:	List[Any]					     =     True
        _snake_case		:	List[Any]					     =     True
        _snake_case		:	List[str]					     =     LlamaForCausalLM(config=lowercase__		)
        model.to(lowercase__		)
        model.eval()

        # first forward pass
        _snake_case		:	int					     =     model(
            lowercase__     ,     attention_mask=lowercase__     ,     encoder_hidden_states=lowercase__     ,     encoder_attention_mask=lowercase__     ,     use_cache=lowercase__     ,     )
        _snake_case		:	List[str]					     =     outputs.past_key_values

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

        # append to next input_ids and
        _snake_case		:	Union[str, Any]					     =     torch.cat([input_ids, next_tokens]     ,     dim=-1		)
        _snake_case		:	Optional[Any]					     =     torch.cat([input_mask, next_mask]     ,     dim=-1		)

        _snake_case		:	Any					     =     model(
            lowercase__     ,     attention_mask=lowercase__     ,     encoder_hidden_states=lowercase__     ,     encoder_attention_mask=lowercase__     ,     output_hidden_states=lowercase__     ,     )["hidden_states"][0]
        _snake_case		:	Optional[int]					     =     model(
            lowercase__     ,     attention_mask=lowercase__     ,     encoder_hidden_states=lowercase__     ,     encoder_attention_mask=lowercase__     ,     past_key_values=lowercase__     ,     output_hidden_states=lowercase__     ,     )["hidden_states"][0]

        # select random slice
        _snake_case		:	List[Any]					     =     ids_tensor((1,)     ,     output_from_past.shape[-1]		).item()
        _snake_case		:	Optional[Any]					     =     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(lowercase__     ,     lowercase__     ,     atol=1E-3		)		)







  def 				UpperCAmelCase_				(		self		)		->						Tuple:






        """simple docstring"""





        _snake_case		:	int					     =     self.prepare_config_and_inputs()
        (
            _snake_case							
        )		:	Tuple					     =     config_and_inputs
        _snake_case		:	Tuple					     =     {"input_ids": input_ids, "attention_mask": input_mask}
        return config, inputs_dict





@require_torch
class lowerCamelCase						(lowercase__						,      lowercase__						,      lowercase__						,      unittest.TestCase					):
  _lowercase  :	int       					=				(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
  _lowercase  :	Optional[Any]       					=				(LlamaForCausalLM,) if is_torch_available() else ()
  _lowercase  :	Any       					=				(
      {
          """feature-extraction""": LlamaModel,
          """text-classification""": LlamaForSequenceClassification,
          """text-generation""": LlamaForCausalLM,
          """zero-shot""": LlamaForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  _lowercase  :	int       					=				False
  _lowercase  :	Union[str, Any]       					=				False







  def 				UpperCAmelCase_				(		self		)		->						Tuple:






        """simple docstring"""





        _snake_case		:	int					     =     LlamaModelTester(self		)
        _snake_case		:	str					     =     ConfigTester(self     ,     config_class=lowercase__     ,     hidden_size=37		)







  def 				UpperCAmelCase_				(		self		)		->						List[str]:






        """simple docstring"""





        self.config_tester.run_common_tests()







  def 				UpperCAmelCase_				(		self		)		->						Optional[Any]:






        """simple docstring"""





        _snake_case		:	str					     =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						List[Any]:






        """simple docstring"""





        _snake_case		:	Union[str, Any]					     =     self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
              _snake_case		:	Optional[int]					     =     type
              self.model_tester.create_and_check_model(*lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						Tuple:






        """simple docstring"""





        _snake_case		:	str					     =     self.model_tester.prepare_config_and_inputs_for_common()
        _snake_case		:	Tuple					     =     3
        _snake_case		:	List[str]					     =     input_dict["input_ids"]
        _snake_case		:	int					     =     input_ids.ne(1		).to(lowercase__		)
        _snake_case		:	Dict					     =     ids_tensor([self.model_tester.batch_size]     ,     self.model_tester.type_sequence_label_size		)
        _snake_case		:	List[Any]					     =     LlamaForSequenceClassification(lowercase__		)
        model.to(lowercase__		)
        model.eval()
        _snake_case		:	Tuple					     =     model(lowercase__     ,     attention_mask=lowercase__     ,     labels=lowercase__		)
        self.assertEqual(result.logits.shape     ,     (self.model_tester.batch_size, self.model_tester.num_labels)		)







  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





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







  def 				UpperCAmelCase_				(		self		)		->						Optional[Any]:






        """simple docstring"""





        _snake_case		:	str					     =     self.model_tester.prepare_config_and_inputs_for_common()
        _snake_case		:	Union[str, Any]					     =     3
        _snake_case		:	Dict					     =     "multi_label_classification"
        _snake_case		:	List[str]					     =     input_dict["input_ids"]
        _snake_case		:	Optional[Any]					     =     input_ids.ne(1		).to(lowercase__		)
        _snake_case		:	Optional[int]					     =     ids_tensor(
            [self.model_tester.batch_size, config.num_labels]     ,     self.model_tester.type_sequence_label_size		).to(torch.float		)
        _snake_case		:	Tuple					     =     LlamaForSequenceClassification(lowercase__		)
        model.to(lowercase__		)
        model.eval()
        _snake_case		:	List[str]					     =     model(lowercase__     ,     attention_mask=lowercase__     ,     labels=lowercase__		)
        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 				UpperCAmelCase_				(		self		)		->						Dict:






        """simple docstring"""





        pass







  @parameterized.expand([('''linear''',), ('''dynamic''',)]		)
  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						Optional[int]:






        """simple docstring"""





        _snake_case		:	Any					     =     self.model_tester.prepare_config_and_inputs_for_common()
        _snake_case		:	Optional[int]					     =     ids_tensor([1, 10]     ,     config.vocab_size		)
        _snake_case		:	List[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		:	Optional[Any]					     =     LlamaModel(lowercase__		)
        original_model.to(lowercase__		)
        original_model.eval()
        _snake_case		:	Optional[Any]					     =     original_model(lowercase__		).last_hidden_state
        _snake_case		:	Union[str, Any]					     =     original_model(lowercase__		).last_hidden_state

        set_seed(42		)  # Fixed seed at init time so the two models get the same random weights
        _snake_case		:	str					     =     {"type": scaling_type, "factor": 10.0}
        _snake_case		:	int					     =     LlamaModel(lowercase__		)
        scaled_model.to(lowercase__		)
        scaled_model.eval()
        _snake_case		:	Tuple					     =     scaled_model(lowercase__		).last_hidden_state
        _snake_case		:	Any					     =     scaled_model(lowercase__		).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(lowercase__     ,     lowercase__     ,     atol=1E-5		)		)
        else:
              self.assertFalse(torch.allclose(lowercase__     ,     lowercase__     ,     atol=1E-5		)		)

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





@require_torch
class lowerCamelCase						(unittest.TestCase					):







  @unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''		)
  @slow
  def 				UpperCAmelCase_				(		self		)		->						Union[str, Any]:






        """simple docstring"""





        _snake_case		:	Optional[Any]					     =     [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
        _snake_case		:	List[Any]					     =     LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-7b-hf'''     ,     device_map='''auto'''		)
        _snake_case		:	Optional[int]					     =     model(torch.tensor([input_ids]		)		)
        # Expected mean on dim = -1
        _snake_case		:	str					     =     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		)     ,     lowercase__     ,     atol=1E-2     ,     rtol=1E-2		)
        # slicing logits[0, 0, 0:30]
        # fmt: off
        _snake_case		:	Optional[Any]					     =     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]     ,     lowercase__     ,     atol=1E-5     ,     rtol=1E-5		)







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






        """simple docstring"""





        _snake_case		:	Union[str, Any]					     =     [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
        _snake_case		:	int					     =     LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-hf'''     ,     device_map='''auto'''		)
        _snake_case		:	List[Any]					     =     model(torch.tensor(lowercase__		)		)
        # Expected mean on dim = -1
        _snake_case		:	Any					     =     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		)     ,     lowercase__     ,     atol=1E-2     ,     rtol=1E-2		)
        # slicing logits[0, 0, 0:30]
        # fmt: off
        _snake_case		:	List[Any]					     =     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]     ,     lowercase__     ,     atol=1E-5     ,     rtol=1E-5		)







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






        """simple docstring"""





        _snake_case		:	Union[str, Any]					     =     [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
        _snake_case		:	List[str]					     =     LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''     ,     device_map='''auto'''		)
        _snake_case		:	List[str]					     =     model(torch.tensor(lowercase__		)		)
        # Expected mean on dim = -1
        _snake_case		:	Optional[int]					     =     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		)     ,     lowercase__     ,     atol=1E-2     ,     rtol=1E-2		)
        # slicing logits[0, 0, 0:30]
        # fmt: off
        _snake_case		:	Any					     =     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		)     ,     lowercase__     ,     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 				UpperCAmelCase_				(		self		)		->						Any:






        """simple docstring"""





        _snake_case		:	Any					     =     [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
        _snake_case		:	str					     =     LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-70b-hf'''     ,     device_map='''auto'''		)
        _snake_case		:	Tuple					     =     model(torch.tensor(lowercase__		)		)

        _snake_case		:	List[Any]					     =     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		)     ,     lowercase__     ,     atol=1E-2     ,     rtol=1E-2		)
        # fmt: off
        _snake_case		:	Dict					     =     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]     ,     lowercase__     ,     atol=1E-5     ,     rtol=1E-5		)







  @unittest.skip('''Model is curently gated'''		)
  @slow
  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





        _snake_case		:	int					     =     "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		:	Tuple					     =     "Simply put, the theory of relativity states that "
        _snake_case		:	Optional[int]					     =     LlamaTokenizer.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''		)
        _snake_case		:	List[Any]					     =     tokenizer.encode(lowercase__     ,     return_tensors='''pt'''		)
        _snake_case		:	List[Any]					     =     LlamaForCausalLM.from_pretrained(
            '''meta-llama/Llama-2-13b-chat-hf'''     ,     device_map='''sequential'''     ,     use_safetensors=lowercase__		)

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









'''simple docstring'''

from collections.abc import Generator




def    _a    (							):





      """simple docstring"""



      _snake_case     ,							_snake_case		:	Union[str, Any]					     =     0, 1
      while True:
            _snake_case     ,							_snake_case		:	List[str]					     =     b, a + b
            yield b




def    _a    (							lowerCAmelCase_ = 1_000	):





      """simple docstring"""



      _snake_case		:	List[str]					     =     1
      _snake_case		:	Dict					     =     fibonacci_generator()
      while len(str(next(lowerCAmelCase_	)	)	) < n:
            answer += 1
      return answer + 1


if __name__ == "__main__":
   print(solution(int(str(input()).strip())))


