

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 :	Optional[Any]     ,						A_ :	Optional[int]     ,						A_ :	Union[str, Any]=1_3     ,						A_ :	Optional[int]=7     ,						A_ :	List[Any]=True     ,						A_ :	int=True     ,						A_ :	List[Any]=False     ,						A_ :	List[str]=True     ,						A_ :	Optional[int]=9_9     ,						A_ :	List[Any]=3_2     ,						A_ :	Tuple=5     ,						A_ :	Optional[int]=4     ,						A_ :	Dict=3_7     ,						A_ :	Any="gelu"     ,						A_ :	Tuple=0.1     ,						A_ :	List[str]=0.1     ,						A_ :	Union[str, Any]=5_1_2     ,						A_ :	Tuple=1_6     ,						A_ :	Tuple=2     ,						A_ :	Dict=0.02     ,						A_ :	Optional[int]=3     ,						A_ :	Tuple=4     ,						A_ :	Tuple=None     ,						):






      '''simple docstring'''




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

 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    ids_tensor([self.batch_size, self.seq_length]     ,						self.vocab_size       )

      _lowerCAmelCase   :					List[str]        =    None
      if self.use_input_mask:
           _lowerCAmelCase   :					List[str]        =    random_attention_mask([self.batch_size, self.seq_length]       )

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

      _lowerCAmelCase   :					List[Any]        =    None
      _lowerCAmelCase   :					Optional[Any]        =    None
      _lowerCAmelCase   :					Any        =    None
      if self.use_labels:
           _lowerCAmelCase   :					List[Any]        =    ids_tensor([self.batch_size]     ,						self.type_sequence_label_size       )
           _lowerCAmelCase   :					Tuple        =    ids_tensor([self.batch_size, self.seq_length]     ,						self.num_labels       )
           _lowerCAmelCase   :					int        =    ids_tensor([self.batch_size]     ,						self.num_choices       )

      _lowerCAmelCase   :					Any        =    self.get_config()

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

 def    __magic_name__       (		self :	Union[str, 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=UpperCAmelCase__     ,						initializer_range=self.initializer_range     ,						)

 def    __magic_name__       (		self :	List[Any]     ,						A_ :	Optional[Any]     ,						A_ :	Union[str, Any]     ,						A_ :	str     ,						A_ :	List[Any]     ,						A_ :	Union[str, Any]     ,						A_ :	List[str]     ,						A_ :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    LlamaModel(config=UpperCAmelCase__       )
      model.to(UpperCAmelCase__       )
      model.eval()
      _lowerCAmelCase   :					Optional[int]        =    model(UpperCAmelCase__     ,						attention_mask=UpperCAmelCase__       )
      _lowerCAmelCase   :					Optional[int]        =    model(UpperCAmelCase__       )
      self.parent.assertEqual(result.last_hidden_state.shape     ,						(self.batch_size, self.seq_length, self.hidden_size)       )

 def    __magic_name__       (		self :	Tuple     ,						A_ :	Dict     ,						A_ :	Any     ,						A_ :	Any     ,						A_ :	List[Any]     ,						A_ :	Union[str, Any]     ,						A_ :	str     ,						A_ :	Dict     ,						A_ :	List[str]     ,						A_ :	int     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    True
      _lowerCAmelCase   :					List[Any]        =    LlamaModel(UpperCAmelCase__       )
      model.to(UpperCAmelCase__       )
      model.eval()
      _lowerCAmelCase   :					Optional[Any]        =    model(
          UpperCAmelCase__     ,						attention_mask=UpperCAmelCase__     ,						encoder_hidden_states=UpperCAmelCase__     ,						encoder_attention_mask=UpperCAmelCase__     ,						)
      _lowerCAmelCase   :					Optional[int]        =    model(
          UpperCAmelCase__     ,						attention_mask=UpperCAmelCase__     ,						encoder_hidden_states=UpperCAmelCase__     ,						)
      _lowerCAmelCase   :					List[str]        =    model(UpperCAmelCase__     ,						attention_mask=UpperCAmelCase__       )
      self.parent.assertEqual(result.last_hidden_state.shape     ,						(self.batch_size, self.seq_length, self.hidden_size)       )

 def    __magic_name__       (		self :	str     ,						A_ :	List[str]     ,						A_ :	Optional[int]     ,						A_ :	int     ,						A_ :	List[str]     ,						A_ :	Dict     ,						A_ :	Optional[Any]     ,						A_ :	List[str]     ,						A_ :	str     ,						A_ :	Optional[int]     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    LlamaForCausalLM(config=UpperCAmelCase__       )
      model.to(UpperCAmelCase__       )
      model.eval()
      _lowerCAmelCase   :					Any        =    model(UpperCAmelCase__     ,						attention_mask=UpperCAmelCase__     ,						labels=UpperCAmelCase__       )
      self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.seq_length, self.vocab_size)       )

 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	List[Any]     ,						A_ :	str     ,						A_ :	List[str]     ,						A_ :	Tuple     ,						A_ :	str     ,						A_ :	str     ,						A_ :	Tuple     ,						A_ :	Union[str, Any]     ,						A_ :	Tuple     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    True
      _lowerCAmelCase   :					Optional[int]        =    True
      _lowerCAmelCase   :					Optional[int]        =    LlamaForCausalLM(config=UpperCAmelCase__       )
      model.to(UpperCAmelCase__       )
      model.eval()

      # first forward pass
      _lowerCAmelCase   :					str        =    model(
          UpperCAmelCase__     ,						attention_mask=UpperCAmelCase__     ,						encoder_hidden_states=UpperCAmelCase__     ,						encoder_attention_mask=UpperCAmelCase__     ,						use_cache=UpperCAmelCase__     ,						)
      _lowerCAmelCase   :					int        =    outputs.past_key_values

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

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

      _lowerCAmelCase   :					List[Any]        =    model(
          UpperCAmelCase__     ,						attention_mask=UpperCAmelCase__     ,						encoder_hidden_states=UpperCAmelCase__     ,						encoder_attention_mask=UpperCAmelCase__     ,						output_hidden_states=UpperCAmelCase__     ,						)['''hidden_states'''][0]
      _lowerCAmelCase   :					int        =    model(
          UpperCAmelCase__     ,						attention_mask=UpperCAmelCase__     ,						encoder_hidden_states=UpperCAmelCase__     ,						encoder_attention_mask=UpperCAmelCase__     ,						past_key_values=UpperCAmelCase__     ,						output_hidden_states=UpperCAmelCase__     ,						)['''hidden_states'''][0]

      # select random slice
      _lowerCAmelCase   :					List[str]        =    ids_tensor((1,)     ,						output_from_past.shape[-1]       ).item()
      _lowerCAmelCase   :					Tuple        =    output_from_no_past[:, -3:, random_slice_idx].detach()
      _lowerCAmelCase   :					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(UpperCAmelCase__     ,						UpperCAmelCase__     ,						atol=1E-3       )       )


 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    self.prepare_config_and_inputs()
      (
          _lowerCAmelCase	
      )   :					Tuple        =    config_and_inputs
      _lowerCAmelCase   :					Dict        =    {'''input_ids''': input_ids, '''attention_mask''': input_mask}
      return config, inputs_dict





@require_torch
class UpperCAmelCase_    ( A  ,		A  ,		A  ,		unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					Optional[Any]      =						(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
 _lowercase     :					Any      =						(LlamaForCausalLM,) if is_torch_available() else ()
 _lowercase     :					Union[str, Any]      =						(
     {
         'feature-extraction': LlamaModel,
         'text-classification': LlamaForSequenceClassification,
         'text-generation': LlamaForCausalLM,
         'zero-shot': LlamaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 _lowercase     :					List[str]      =						False
 _lowercase     :					Tuple      =						False

 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    LlamaModelTester(self       )
      _lowerCAmelCase   :					Optional[int]        =    ConfigTester(self     ,						config_class=UpperCAmelCase__     ,						hidden_size=3_7       )

 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      self.config_tester.run_common_tests()

 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCAmelCase__       )

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




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

 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




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

 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




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

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    self.model_tester.prepare_config_and_inputs_for_common()
      _lowerCAmelCase   :					Tuple        =    3
      _lowerCAmelCase   :					Dict        =    '''multi_label_classification'''
      _lowerCAmelCase   :					Optional[int]        =    input_dict['''input_ids''']
      _lowerCAmelCase   :					Optional[Any]        =    input_ids.ne(1       ).to(UpperCAmelCase__       )
      _lowerCAmelCase   :					Tuple        =    ids_tensor(
          [self.model_tester.batch_size, config.num_labels]     ,						self.model_tester.type_sequence_label_size       ).to(torch.float       )
      _lowerCAmelCase   :					Dict        =    LlamaForSequenceClassification(UpperCAmelCase__       )
      model.to(UpperCAmelCase__       )
      model.eval()
      _lowerCAmelCase   :					Union[str, Any]        =    model(UpperCAmelCase__     ,						attention_mask=UpperCAmelCase__     ,						labels=UpperCAmelCase__       )
      self.assertEqual(result.logits.shape     ,						(self.model_tester.batch_size, self.model_tester.num_labels)       )

 @unittest.skip("LLaMA buffers include complex numbers, which breaks this test"       )
 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      pass


 @parameterized.expand([("linear",), ("dynamic",)]       )
 def    __magic_name__       (		self :	Any     ,						A_ :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    self.model_tester.prepare_config_and_inputs_for_common()
      _lowerCAmelCase   :					List[str]        =    ids_tensor([1, 1_0]     ,						config.vocab_size       )
      _lowerCAmelCase   :					Tuple        =    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   :					Dict        =    LlamaModel(UpperCAmelCase__       )
      original_model.to(UpperCAmelCase__       )
      original_model.eval()
      _lowerCAmelCase   :					Dict        =    original_model(UpperCAmelCase__       ).last_hidden_state
      _lowerCAmelCase   :					Union[str, Any]        =    original_model(UpperCAmelCase__       ).last_hidden_state

      set_seed(4_2       )  # Fixed seed at init time so the two models get the same random weights
      _lowerCAmelCase   :					List[Any]        =    {'''type''': scaling_type, '''factor''': 10.0}
      _lowerCAmelCase   :					int        =    LlamaModel(UpperCAmelCase__       )
      scaled_model.to(UpperCAmelCase__       )
      scaled_model.eval()
      _lowerCAmelCase   :					Optional[Any]        =    scaled_model(UpperCAmelCase__       ).last_hidden_state
      _lowerCAmelCase   :					List[Any]        =    scaled_model(UpperCAmelCase__       ).last_hidden_state

      # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
      # maximum sequence length, so the outputs for the short input should match.
      if scaling_type == "dynamic":
           self.assertTrue(torch.allclose(UpperCAmelCase__     ,						UpperCAmelCase__     ,						atol=1E-5       )       )
      else:
           self.assertFalse(torch.allclose(UpperCAmelCase__     ,						UpperCAmelCase__     ,						atol=1E-5       )       )

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





@require_torch
class UpperCAmelCase_    ( unittest.TestCase ):



 """simple docstring"""







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






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    [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   :					int        =    LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf"     ,						device_map="auto"       )
      _lowerCAmelCase   :					List[str]        =    model(torch.tensor([input_ids]       )       )
      # Expected mean on dim = -1
      _lowerCAmelCase   :					Optional[Any]        =    torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]]       )
      torch.testing.assert_close(out.mean(-1       )     ,						UpperCAmelCase__     ,						atol=1E-2     ,						rtol=1E-2       )
      # slicing logits[0, 0, 0:30]
      # fmt: off
      _lowerCAmelCase   :					List[Any]        =    torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,]       )
      # fmt: on
      torch.testing.assert_close(out[0, 0, :3_0]     ,						UpperCAmelCase__     ,						atol=1E-5     ,						rtol=1E-5       )

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






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    [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   :					List[str]        =    LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf"     ,						device_map="auto"       )
      _lowerCAmelCase   :					Any        =    model(torch.tensor(UpperCAmelCase__       )       )
      # Expected mean on dim = -1
      _lowerCAmelCase   :					Dict        =    torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]]       )
      torch.testing.assert_close(out.mean(-1       )     ,						UpperCAmelCase__     ,						atol=1E-2     ,						rtol=1E-2       )
      # slicing logits[0, 0, 0:30]
      # fmt: off
      _lowerCAmelCase   :					List[Any]        =    torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273]       )
      # fmt: on
      torch.testing.assert_close(out[0, 0, :3_0]     ,						UpperCAmelCase__     ,						atol=1E-5     ,						rtol=1E-5       )

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






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    [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   :					List[str]        =    LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf"     ,						device_map="auto"       )
      _lowerCAmelCase   :					str        =    model(torch.tensor(UpperCAmelCase__       )       )
      # Expected mean on dim = -1
      _lowerCAmelCase   :					Optional[int]        =    torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]]       )
      torch.testing.assert_close(out.mean(-1       )     ,						UpperCAmelCase__     ,						atol=1E-2     ,						rtol=1E-2       )
      # slicing logits[0, 0, 0:30]
      # fmt: off
      _lowerCAmelCase   :					Optional[Any]        =    torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513]       )
      # fmt: on
      torch.testing.assert_close(out.mean(-1       )     ,						UpperCAmelCase__     ,						atol=1E-2     ,						rtol=1E-2       )

 @unittest.skip(
     "Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test"       )
 @slow
 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    [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   :					List[Any]        =    LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-70b-hf"     ,						device_map="auto"       )
      _lowerCAmelCase   :					Tuple        =    model(torch.tensor(UpperCAmelCase__       )       )

      _lowerCAmelCase   :					Union[str, Any]        =    torch.tensor(
          [[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]]     ,						dtype=torch.floataa       )
      torch.testing.assert_close(out.mean(-1       )     ,						UpperCAmelCase__     ,						atol=1E-2     ,						rtol=1E-2       )
      # fmt: off
      _lowerCAmelCase   :					Optional[int]        =    torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312]       )
      # fmt: on
      torch.testing.assert_close(out[0, 0, :3_0]     ,						UpperCAmelCase__     ,						atol=1E-5     ,						rtol=1E-5       )


 @unittest.skip("Model is curently gated"       )
 @slow
 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




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

      # greedy generation outputs
      _lowerCAmelCase   :					str        =    model.generate(UpperCAmelCase__     ,						max_new_tokens=6_4     ,						top_p=UpperCAmelCase__     ,						temperature=1     ,						do_sample=UpperCAmelCase__       )
      _lowerCAmelCase   :					List[str]        =    tokenizer.decode(generated_ids[0]     ,						skip_special_tokens=UpperCAmelCase__       )
      self.assertEqual(UpperCAmelCase__     ,						UpperCAmelCase__       )









def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      "list[int]":

     """simple docstring"""







     if upper_limit < 0:
          raise ValueError("Limit for the Catalan sequence must be ≥ 0"				)

     _lowerCAmelCase   :					Optional[Any]        =    [0] * (upper_limit + 1)

     # Base case: C(0) = C(1) = 1
     _lowerCAmelCase   :					Optional[int]        =    1
     if upper_limit > 0:
          _lowerCAmelCase   :					Any        =    1

     # Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
     for i in range(2		,							upper_limit + 1				):
          for j in range(SCREAMING_SNAKE_CASE				):
               catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

     return catalan_list


if __name__ == "__main__":
  print('\n********* Catalan Numbers Using Dynamic Programming ************\n')
  print('\n*** Enter -1 at any time to quit ***')
  print('\nEnter the upper limit (≥ 0) for the Catalan number sequence: ', end='')
  try:
    while True:
      __UpperCAmelCase    				=     int(input().strip())
      if N < 0:
        print('\n********* Goodbye!! ************')
        break
      else:
        print(f'''The Catalan numbers from 0 through {N} are:''')
        print(catalan_numbers(N))
        print('Try another upper limit for the sequence: ', end='')
  except (NameError, ValueError):
    print('\n********* Invalid input, goodbye! ************\n')

  import doctest

  doctest.testmod()






