
"""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 A_	:




  '''simple docstring'''







  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				,   ):

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







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    ids_tensor([self.batch_size, self.seq_length]				,   self.vocab_size  )

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

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

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

     UpperCAmelCase_					:					List[Any]			     =    self.get_config()

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







  def        UpperCamelCase__					(		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=a__				,   initializer_range=self.initializer_range				,   )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    LlamaModel(config=a__  )
     model.to(a__  )
     model.eval()
     UpperCAmelCase_					:					Dict			     =    model(a__				,   attention_mask=a__  )
     UpperCAmelCase_					:					str			     =    model(a__  )
     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_				,   ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    True
     UpperCAmelCase_					:					Tuple			     =    LlamaModel(a__  )
     model.to(a__  )
     model.eval()
     UpperCAmelCase_					:					List[str]			     =    model(
         a__				,   attention_mask=a__				,   encoder_hidden_states=a__				,   encoder_attention_mask=a__				,   )
     UpperCAmelCase_					:					Optional[Any]			     =    model(
         a__				,   attention_mask=a__				,   encoder_hidden_states=a__				,   )
     UpperCAmelCase_					:					Any			     =    model(a__				,   attention_mask=a__  )
     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_				,   ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    LlamaForCausalLM(config=a__  )
     model.to(a__  )
     model.eval()
     UpperCAmelCase_					:					int			     =    model(a__				,   attention_mask=a__				,   labels=a__  )
     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_				,   ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[int]			     =    True
     UpperCAmelCase_					:					Optional[int]			     =    True
     UpperCAmelCase_					:					Tuple			     =    LlamaForCausalLM(config=a__  )
     model.to(a__  )
     model.eval()

     # first forward pass
     UpperCAmelCase_					:					Union[str, Any]			     =    model(
         a__				,   attention_mask=a__				,   encoder_hidden_states=a__				,   encoder_attention_mask=a__				,   use_cache=a__				,   )
     UpperCAmelCase_					:					Dict			     =    outputs.past_key_values

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

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

     UpperCAmelCase_					:					Tuple			     =    model(
         a__				,   attention_mask=a__				,   encoder_hidden_states=a__				,   encoder_attention_mask=a__				,   output_hidden_states=a__				,   )["""hidden_states"""][0]
     UpperCAmelCase_					:					Optional[Any]			     =    model(
         a__				,   attention_mask=a__				,   encoder_hidden_states=a__				,   encoder_attention_mask=a__				,   past_key_values=a__				,   output_hidden_states=a__				,   )["""hidden_states"""][0]

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







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    self.prepare_config_and_inputs()
     (
         UpperCAmelCase_	
     )					:					Union[str, Any]			     =    config_and_inputs
     UpperCAmelCase_					:					int			     =    {"""input_ids""": input_ids, """attention_mask""": input_mask}
     return config, inputs_dict




@require_torch
class A_	(SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,unittest.TestCase      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Optional[Any]       =		(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE__			:						Dict       =		(LlamaForCausalLM,) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE__			:						Union[str, Any]       =		(
      {
          "feature-extraction": LlamaModel,
          "text-classification": LlamaForSequenceClassification,
          "text-generation": LlamaForCausalLM,
          "zero-shot": LlamaForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  SCREAMING_SNAKE_CASE__			:						Optional[Any]       =		False
  SCREAMING_SNAKE_CASE__			:						Dict       =		False







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Dict			     =    LlamaModelTester(self  )
     UpperCAmelCase_					:					List[str]			     =    ConfigTester(self				,   config_class=a__				,   hidden_size=37  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     self.config_tester.run_common_tests()







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*a__  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    self.model_tester.prepare_config_and_inputs()
     for type in ["absolute", "relative_key", "relative_key_query"]:
        UpperCAmelCase_					:					List[str]			     =    type
        self.model_tester.create_and_check_model(*a__  )







  def        UpperCamelCase__					(		self  ):

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







  def        UpperCamelCase__					(		self  ):

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







  def        UpperCamelCase__					(		self  ):

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

     """simple docstring"""
     pass







  @parameterized.expand([("linear",), ("dynamic",)]  )
  def        UpperCamelCase__					(		self				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    self.model_tester.prepare_config_and_inputs_for_common()
     UpperCAmelCase_					:					int			     =    ids_tensor([1, 10]				,   config.vocab_size  )
     UpperCAmelCase_					:					int			     =    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_					:					List[str]			     =    LlamaModel(a__  )
     original_model.to(a__  )
     original_model.eval()
     UpperCAmelCase_					:					List[str]			     =    original_model(a__  ).last_hidden_state
     UpperCAmelCase_					:					Optional[Any]			     =    original_model(a__  ).last_hidden_state

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

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




@require_torch
class A_	(unittest.TestCase      ):




  '''simple docstring'''







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

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    [1, 306, 4658, 278, 6593, 310, 2834, 338]
     UpperCAmelCase_					:					List[Any]			     =    LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf"				,   device_map="auto"  )
     UpperCAmelCase_					:					Tuple			     =    model(torch.tensor([input_ids]  )  )
     # Expected mean on dim = -1
     UpperCAmelCase_					:					Optional[int]			     =    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  )				,   a__				,   atol=1E-2				,   rtol=1E-2  )
     # slicing logits[0, 0, 0:30]
     # fmt: off
     UpperCAmelCase_					:					List[str]			     =    torch.tensor([-12.8281, -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, -12.8281, -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, :30]				,   a__				,   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  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[int]			     =    [1, 306, 4658, 278, 6593, 310, 2834, 338]
     UpperCAmelCase_					:					List[Any]			     =    LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf"				,   device_map="auto"  )
     UpperCAmelCase_					:					Tuple			     =    model(torch.tensor(a__  )  )
     # Expected mean on dim = -1
     UpperCAmelCase_					:					Union[str, Any]			     =    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  )				,   a__				,   atol=1E-2				,   rtol=1E-2  )
     # slicing logits[0, 0, 0:30]
     # fmt: off
     UpperCAmelCase_					:					List[str]			     =    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, :30]				,   a__				,   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  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    [1, 306, 4658, 278, 6593, 310, 2834, 338]
     UpperCAmelCase_					:					Dict			     =    LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf"				,   device_map="auto"  )
     UpperCAmelCase_					:					Tuple			     =    model(torch.tensor(a__  )  )
     # Expected mean on dim = -1
     UpperCAmelCase_					:					Optional[Any]			     =    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  )				,   a__				,   atol=1E-2				,   rtol=1E-2  )
     # slicing logits[0, 0, 0:30]
     # fmt: off
     UpperCAmelCase_					:					List[str]			     =    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  )				,   a__				,   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  ):

     """simple docstring"""
     UpperCAmelCase_					:					str			     =    [1, 306, 4658, 278, 6593, 310, 2834, 338]
     UpperCAmelCase_					:					Optional[Any]			     =    LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-70b-hf"				,   device_map="auto"  )
     UpperCAmelCase_					:					str			     =    model(torch.tensor(a__  )  )

     UpperCAmelCase_					:					Tuple			     =    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  )				,   a__				,   atol=1E-2				,   rtol=1E-2  )
     # fmt: off
     UpperCAmelCase_					:					Union[str, Any]			     =    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, :30]				,   a__				,   atol=1E-5				,   rtol=1E-5  )







  @unittest.skip("Model is curently gated"  )
  @slow
  def        UpperCamelCase__					(		self  ):

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

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



"""simple docstring"""




from __future__ import annotations

_a      :		List[str]           =   10





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       list[int]	)       ->      list[int]:
  _lowerCAmelCase						:       Optional[int]  			=     1
  _lowerCAmelCase						:       Union[str, Any]  			=     max(_lowerCamelCase	)
  while placement <= max_digit:
    # declare and initialize empty buckets
    _lowerCAmelCase						:       list[list]  			=     [[] for _ in range(_lowerCamelCase	)]
    # split list_of_ints between the buckets
    for i in list_of_ints:
      _lowerCAmelCase						:       Tuple  			=     int((i / placement) % RADIX	)
      buckets[tmp].append(_lowerCamelCase	)
    # put each buckets' contents into list_of_ints
    _lowerCAmelCase						:       List[str]  			=     0
    for b in range(_lowerCamelCase	):
      for i in buckets[b]:
        _lowerCAmelCase						:       Any  			=     i
        a += 1
        # move to next
    placement *= RADIX
  return list_of_ints


if __name__ == "__main__":
     import doctest

     doctest.testmod()




