
"""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 __lowerCAmelCase	:





     def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase=13			,  __UpperCAmelCase=7			,  __UpperCAmelCase=True			,  __UpperCAmelCase=True			,  __UpperCAmelCase=False			,  __UpperCAmelCase=True			,  __UpperCAmelCase=99			,  __UpperCAmelCase=32			,  __UpperCAmelCase=5			,  __UpperCAmelCase=4			,  __UpperCAmelCase=37			,  __UpperCAmelCase="gelu"			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase=512			,  __UpperCAmelCase=16			,  __UpperCAmelCase=2			,  __UpperCAmelCase=0.0_2			,  __UpperCAmelCase=3			,  __UpperCAmelCase=4			,  __UpperCAmelCase=None			,  ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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

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

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

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

      __UpperCamelCase       						= 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=__UpperCAmelCase			,  initializer_range=self.initializer_range			,  )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= True
      __UpperCamelCase       						= LlamaModel(__UpperCAmelCase   )
      model.to(__UpperCAmelCase   )
      model.eval()
      __UpperCamelCase       						= model(
          __UpperCAmelCase			,  attention_mask=__UpperCAmelCase			,  encoder_hidden_states=__UpperCAmelCase			,  encoder_attention_mask=__UpperCAmelCase			,  )
      __UpperCamelCase       						= model(
          __UpperCAmelCase			,  attention_mask=__UpperCAmelCase			,  encoder_hidden_states=__UpperCAmelCase			,  )
      __UpperCamelCase       						= model(__UpperCAmelCase			,  attention_mask=__UpperCAmelCase   )
      self.parent.assertEqual(result.last_hidden_state.shape			,  (self.batch_size, self.seq_length, self.hidden_size)   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= LlamaForCausalLM(config=__UpperCAmelCase   )
      model.to(__UpperCAmelCase   )
      model.eval()
      __UpperCamelCase       						= model(__UpperCAmelCase			,  attention_mask=__UpperCAmelCase			,  labels=__UpperCAmelCase   )
      self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.seq_length, self.vocab_size)   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= True
      __UpperCamelCase       						= True
      __UpperCamelCase       						= LlamaForCausalLM(config=__UpperCAmelCase   )
      model.to(__UpperCAmelCase   )
      model.eval()

      # first forward pass
      __UpperCamelCase       						= model(
          __UpperCAmelCase			,  attention_mask=__UpperCAmelCase			,  encoder_hidden_states=__UpperCAmelCase			,  encoder_attention_mask=__UpperCAmelCase			,  use_cache=__UpperCAmelCase			,  )
      __UpperCamelCase       						= outputs.past_key_values

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

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

      __UpperCamelCase       						= model(
          __UpperCAmelCase			,  attention_mask=__UpperCAmelCase			,  encoder_hidden_states=__UpperCAmelCase			,  encoder_attention_mask=__UpperCAmelCase			,  output_hidden_states=__UpperCAmelCase			,  )['hidden_states'][0]
      __UpperCamelCase       						= 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
      __UpperCamelCase       						= ids_tensor((1,)			,  output_from_past.shape[-1]   ).item()
      __UpperCamelCase       						= output_from_no_past[:, -3:, random_slice_idx].detach()
      __UpperCamelCase       						= 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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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


@require_torch
class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ,						unittest.TestCase    ):
     lowercase		     =		(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
     lowercase		     =		(LlamaForCausalLM,) if is_torch_available() else ()
     lowercase		     =		(
         {
             "feature-extraction": LlamaModel,
             "text-classification": LlamaForSequenceClassification,
             "text-generation": LlamaForCausalLM,
             "zero-shot": LlamaForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     lowercase		     =		False
     lowercase		     =		False





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= LlamaModelTester(self   )
      __UpperCamelCase       						= ConfigTester(self			,  config_class=__UpperCAmelCase			,  hidden_size=37   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      self.config_tester.run_common_tests()





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase						,			__UpperCamelCase       						= self.model_tester.prepare_config_and_inputs_for_common()
      __UpperCamelCase       						= 3
      __UpperCamelCase       						= input_dict['input_ids']
      __UpperCamelCase       						= input_ids.ne(1   ).to(__UpperCAmelCase   )
      __UpperCamelCase       						= ids_tensor([self.model_tester.batch_size]			,  self.model_tester.type_sequence_label_size   )
      __UpperCamelCase       						= LlamaForSequenceClassification(__UpperCAmelCase   )
      model.to(__UpperCAmelCase   )
      model.eval()
      __UpperCamelCase       						= model(__UpperCAmelCase			,  attention_mask=__UpperCAmelCase			,  labels=__UpperCAmelCase   )
      self.assertEqual(result.logits.shape			,  (self.model_tester.batch_size, self.model_tester.num_labels)   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase						,			__UpperCamelCase       						= self.model_tester.prepare_config_and_inputs_for_common()
      __UpperCamelCase       						= 3
      __UpperCamelCase       						= 'single_label_classification'
      __UpperCamelCase       						= input_dict['input_ids']
      __UpperCamelCase       						= input_ids.ne(1   ).to(__UpperCAmelCase   )
      __UpperCamelCase       						= ids_tensor([self.model_tester.batch_size]			,  self.model_tester.type_sequence_label_size   )
      __UpperCamelCase       						= LlamaForSequenceClassification(__UpperCAmelCase   )
      model.to(__UpperCAmelCase   )
      model.eval()
      __UpperCamelCase       						= model(__UpperCAmelCase			,  attention_mask=__UpperCAmelCase			,  labels=__UpperCAmelCase   )
      self.assertEqual(result.logits.shape			,  (self.model_tester.batch_size, self.model_tester.num_labels)   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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

      '''simple docstring'''




      pass







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

      '''simple docstring'''




      __UpperCamelCase						,			__UpperCamelCase       						= self.model_tester.prepare_config_and_inputs_for_common()
      __UpperCamelCase       						= ids_tensor([1, 10]			,  config.vocab_size   )
      __UpperCamelCase       						= 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       						= LlamaModel(__UpperCAmelCase   )
      original_model.to(__UpperCAmelCase   )
      original_model.eval()
      __UpperCamelCase       						= original_model(__UpperCAmelCase   ).last_hidden_state
      __UpperCamelCase       						= original_model(__UpperCAmelCase   ).last_hidden_state

      set_seed(42   )  # Fixed seed at init time so the two models get the same random weights
      __UpperCamelCase       						= {'type': scaling_type, 'factor': 1_0.0}
      __UpperCamelCase       						= LlamaModel(__UpperCAmelCase   )
      scaled_model.to(__UpperCAmelCase   )
      scaled_model.eval()
      __UpperCamelCase       						= scaled_model(__UpperCAmelCase   ).last_hidden_state
      __UpperCamelCase       						= 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 __lowerCAmelCase	(   unittest.TestCase    ):





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

      '''simple docstring'''




      __UpperCamelCase       						= [1, 306, 4658, 278, 6593, 310, 2834, 338]
      __UpperCamelCase       						= LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'			,  device_map='auto'   )
      __UpperCamelCase       						= model(torch.tensor([input_ids]   )   )
      # Expected mean on dim = -1
      __UpperCamelCase       						= torch.tensor([[-6.6_5_5_0, -4.1_2_2_7, -4.9_8_5_9, -3.2_4_0_6, 0.8_2_6_2, -3.0_0_3_3, 1.2_9_6_4, -3.3_6_9_9]]   )
      torch.testing.assert_close(out.mean(-1   )			,  __UpperCAmelCase			,  atol=1E-2			,  rtol=1E-2   )
      # slicing logits[0, 0, 0:30]
      # fmt: off
      __UpperCamelCase       						= torch.tensor([-1_2.8_2_8_1, -7.4_4_5_3, -0.4_6_3_9, -8.0_6_2_5, -7.2_5_0_0, -8.0_0_0_0, -6.4_8_8_3, -7.7_6_9_5, -7.8_4_3_8, -7.0_3_1_2, -6.2_1_8_8, -7.1_3_2_8, -1.8_4_9_6, 1.9_9_6_1, -8.6_2_5_0, -6.7_2_2_7, -1_2.8_2_8_1, -6.9_4_9_2, -7.0_7_4_2, -7.7_8_5_2, -7.5_8_2_0, -7.9_0_6_2, -6.9_3_7_5, -7.9_8_0_5, -8.3_4_3_8, -8.1_5_6_2, -8.0_4_6_9, -7.6_2_5_0, -7.7_4_2_2, -7.3_3_9_8,]   )
      # fmt: on
      torch.testing.assert_close(out[0, 0, :30]			,  __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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= [1, 306, 4658, 278, 6593, 310, 2834, 338]
      __UpperCamelCase       						= LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'			,  device_map='auto'   )
      __UpperCamelCase       						= model(torch.tensor(__UpperCAmelCase   )   )
      # Expected mean on dim = -1
      __UpperCamelCase       						= torch.tensor([[-2.0_6_2_2, -1.2_7_9_4, -1.1_6_3_8, -0.9_7_8_8, -1.4_6_0_3, -1.0_2_3_8, -1.7_8_9_3, -1.4_4_1_1]]   )
      torch.testing.assert_close(out.mean(-1   )			,  __UpperCAmelCase			,  atol=1E-2			,  rtol=1E-2   )
      # slicing logits[0, 0, 0:30]
      # fmt: off
      __UpperCamelCase       						= torch.tensor([-8.1_4_0_6, -8.0_5_4_7, 2.7_4_6_1, -1.2_3_4_4, -0.1_4_4_8, -1.8_2_6_2, -1.0_0_2_0, -1.8_1_5_4, -1.6_8_9_5, -1.8_5_1_6, -2.3_5_7_4, -0.9_2_7_7, 3.7_5_9_8, 6.5_7_4_2, -1.2_9_9_8, -0.1_1_7_7, -8.1_4_0_6, -2.9_6_8_8, -2.9_1_9_9, -3.1_6_9_9, -3.5_2_5_4, -2.3_5_5_5, -2.7_9_8_8, -3.4_1_4_1, -2.8_2_6_2, -4.5_1_9_5, -3.3_3_7_9, -3.3_1_6_4, -2.7_8_3_2, -3.0_2_7_3]   )
      # fmt: on
      torch.testing.assert_close(out[0, 0, :30]			,  __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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= [1, 306, 4658, 278, 6593, 310, 2834, 338]
      __UpperCamelCase       						= LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'			,  device_map='auto'   )
      __UpperCamelCase       						= model(torch.tensor(__UpperCAmelCase   )   )
      # Expected mean on dim = -1
      __UpperCamelCase       						= torch.tensor([[-0.8_5_6_2, -1.8_5_2_0, -0.7_5_5_1, -0.4_1_6_2, -1.5_1_6_1, -1.2_0_3_8, -2.4_8_2_3, -2.3_2_5_4]]   )
      torch.testing.assert_close(out.mean(-1   )			,  __UpperCAmelCase			,  atol=1E-2			,  rtol=1E-2   )
      # slicing logits[0, 0, 0:30]
      # fmt: off
      __UpperCamelCase       						= torch.tensor([-2.2_2_2_7, 4.8_8_2_8, 0.9_0_2_3, -0.4_5_7_8, -0.7_8_7_1, -0.1_0_3_3, -0.6_2_2_1, -0.5_7_8_6, -0.7_8_0_3, -1.0_6_7_4, -1.2_9_2_0, -0.1_5_7_0, 0.8_0_0_8, 2.0_7_2_3, -0.9_4_9_7, 0.2_7_7_1, -2.2_2_2_7, -0.7_6_1_2, -1.4_3_4_6, -1.2_0_6_1, -1.6_4_2_6, -0.3_0_0_0, -0.7_1_3_9, -1.1_9_3_4, -1.8_6_9_1, -1.6_9_7_3, -1.5_9_4_7, -1.2_7_0_5, -0.3_5_2_3, -0.5_5_1_3]   )
      # 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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= [1, 306, 4658, 278, 6593, 310, 2834, 338]
      __UpperCamelCase       						= LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'			,  device_map='auto'   )
      __UpperCamelCase       						= model(torch.tensor(__UpperCAmelCase   )   )

      __UpperCamelCase       						= torch.tensor(
          [[-4.2_3_2_7, -3.3_3_6_0, -4.6_6_6_5, -4.7_6_3_1, -1.8_1_8_0, -3.4_1_7_0, -1.4_2_1_1, -3.1_8_1_0]]			,  dtype=torch.floataa   )
      torch.testing.assert_close(out.mean(-1   )			,  __UpperCAmelCase			,  atol=1E-2			,  rtol=1E-2   )
      # fmt: off
      __UpperCamelCase       						= torch.tensor([-9.4_9_2_2, -3.9_5_5_1, 1.7_9_9_8, -5.6_7_5_8, -5.1_0_5_5, -5.8_9_8_4, -4.8_3_2_0, -6.8_0_8_6, -6.5_3_9_1, -5.6_1_7_2, -5.5_8_2_0, -5.5_3_5_2, 1.7_8_8_1, 3.6_2_8_9, -6.5_1_1_7, -3.4_7_8_5, -9.5_0_0_0, -6.0_3_5_2, -6.8_1_2_5, -6.0_1_9_5, -6.6_8_3_6, -5.4_7_2_7, -6.2_8_1_2, -6.0_3_9_1, -7.3_3_9_8, -7.4_2_9_7, -7.4_8_4_4, -6.5_8_2_0, -5.8_7_8_9, -5.5_3_1_2]   )
      # fmt: on
      torch.testing.assert_close(out[0, 0, :30]			,  __UpperCAmelCase			,  atol=1E-5			,  rtol=1E-5   )







     @unittest.skip('Model is curently gated'   )
     @slow
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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

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





"""simple docstring"""

def 							A		(       snake_case   :int	)   ->			bool:
 return str(snake_case	) == str(snake_case	)[::-1]






def 							A		(       snake_case   :int	)   ->			int:
 return int(snake_case	) + int(str(snake_case	)[::-1]	)






def 							A		(       snake_case   :int = 1_0_0_0_0	)   ->			int:
 __UpperCamelCase       						= []
 for num in range(1       ,      snake_case	):
  __UpperCamelCase       						= 0
  __UpperCamelCase       						= num
  while iterations < 5_0:
   __UpperCamelCase       						= sum_reverse(snake_case	)
   iterations += 1
   if is_palindrome(snake_case	):
    break
  else:
   lychrel_nums.append(snake_case	)
 return len(snake_case	)


if __name__ == "__main__":
      print(f'''{solution() = }''')



