
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	:



      """simple docstring"""







      def __init__(      self		:       Union[str, Any]				,       _snake_case		:       Any				,       _snake_case		:       Dict=13				,       _snake_case		:       int=7				,       _snake_case		:       Union[str, Any]=True				,       _snake_case		:       Optional[Any]=True				,       _snake_case		:       int=False				,       _snake_case		:       Tuple=True				,       _snake_case		:       List[Any]=99				,       _snake_case		:       int=32				,       _snake_case		:       List[str]=5				,       _snake_case		:       Union[str, Any]=4				,       _snake_case		:       List[str]=37				,       _snake_case		:       Tuple="gelu"				,       _snake_case		:       Any=0.1				,       _snake_case		:       Union[str, Any]=0.1				,       _snake_case		:       Tuple=512				,       _snake_case		:       Any=16				,       _snake_case		:       str=2				,       _snake_case		:       List[str]=0.02				,       _snake_case		:       str=3				,       _snake_case		:       List[Any]=4				,       _snake_case		:       Union[str, Any]=None				,       ):
          __lowercase		:			Any	    =       parent
          __lowercase		:			List[Any]	    =       batch_size
          __lowercase		:			int	    =       seq_length
          __lowercase		:			Dict	    =       is_training
          __lowercase		:			Optional[Any]	    =       use_input_mask
          __lowercase		:			Optional[int]	    =       use_token_type_ids
          __lowercase		:			List[Any]	    =       use_labels
          __lowercase		:			int	    =       vocab_size
          __lowercase		:			Optional[Any]	    =       hidden_size
          __lowercase		:			Union[str, Any]	    =       num_hidden_layers
          __lowercase		:			Dict	    =       num_attention_heads
          __lowercase		:			int	    =       intermediate_size
          __lowercase		:			Union[str, Any]	    =       hidden_act
          __lowercase		:			Dict	    =       hidden_dropout_prob
          __lowercase		:			List[str]	    =       attention_probs_dropout_prob
          __lowercase		:			Optional[Any]	    =       max_position_embeddings
          __lowercase		:			List[Any]	    =       type_vocab_size
          __lowercase		:			Union[str, Any]	    =       type_sequence_label_size
          __lowercase		:			int	    =       initializer_range
          __lowercase		:			Union[str, Any]	    =       num_labels
          __lowercase		:			Optional[int]	    =       num_choices
          __lowercase		:			int	    =       scope




      def 		snake_case_			(      self		:       Optional[int]				):
          __lowercase		:			List[str]	    =       ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size				)

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

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

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

          __lowercase		:			List[str]	    =       self.get_config()

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




      def 		snake_case_			(      self		:       Optional[int]				):
          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=_snake_case				,       initializer_range=self.initializer_range				,       )




      def 		snake_case_			(      self		:       Union[str, Any]				,       _snake_case		:       int				,       _snake_case		:       Union[str, Any]				,       _snake_case		:       Dict				,       _snake_case		:       Optional[Any]				,       _snake_case		:       Dict				,       _snake_case		:       Dict				,       _snake_case		:       Union[str, Any]				):
          __lowercase		:			Optional[Any]	    =       LlamaModel(config=_snake_case				)
          model.to(_snake_case				)
          model.eval()
          __lowercase		:			Tuple	    =       model(_snake_case				,       attention_mask=_snake_case				)
          __lowercase		:			Optional[Any]	    =       model(_snake_case				)
          self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.hidden_size)				)




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




      def 		snake_case_			(      self		:       Any				,       _snake_case		:       int				,       _snake_case		:       Dict				,       _snake_case		:       Dict				,       _snake_case		:       str				,       _snake_case		:       Dict				,       _snake_case		:       List[Any]				,       _snake_case		:       Optional[int]				,       _snake_case		:       Optional[int]				,       _snake_case		:       Any				,       ):
          __lowercase		:			Dict	    =       LlamaForCausalLM(config=_snake_case				)
          model.to(_snake_case				)
          model.eval()
          __lowercase		:			Any	    =       model(_snake_case				,       attention_mask=_snake_case				,       labels=_snake_case				)
          self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.vocab_size)				)




      def 		snake_case_			(      self		:       List[Any]				,       _snake_case		:       List[str]				,       _snake_case		:       Union[str, Any]				,       _snake_case		:       List[Any]				,       _snake_case		:       Tuple				,       _snake_case		:       Optional[int]				,       _snake_case		:       Union[str, Any]				,       _snake_case		:       Any				,       _snake_case		:       Tuple				,       _snake_case		:       Dict				,       ):
          __lowercase		:			Any	    =       True
          __lowercase		:			Tuple	    =       True
          __lowercase		:			Union[str, Any]	    =       LlamaForCausalLM(config=_snake_case				)
          model.to(_snake_case				)
          model.eval()

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

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

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

          __lowercase		:			Optional[Any]	    =       model(
              _snake_case				,       attention_mask=_snake_case				,       encoder_hidden_states=_snake_case				,       encoder_attention_mask=_snake_case				,       output_hidden_states=_snake_case				,       )['''hidden_states'''][0]
          __lowercase		:			Optional[Any]	    =       model(
              _snake_case				,       attention_mask=_snake_case				,       encoder_hidden_states=_snake_case				,       encoder_attention_mask=_snake_case				,       past_key_values=_snake_case				,       output_hidden_states=_snake_case				,       )['''hidden_states'''][0]

          # select random slice
          __lowercase		:			Dict	    =       ids_tensor((1,)				,       output_from_past.shape[-1]				).item()
          __lowercase		:			Any	    =       output_from_no_past[:, -3:, random_slice_idx].detach()
          __lowercase		:			Optional[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(_snake_case				,       _snake_case				,       atol=1E-3				)				)




      def 		snake_case_			(      self		:       List[Any]				):
          __lowercase		:			Dict	    =       self.prepare_config_and_inputs()
          (
              __lowercase      
          )		:			List[str]	    =       config_and_inputs
          __lowercase		:			Dict	    =       {'''input_ids''': input_ids, '''attention_mask''': input_mask}
          return config, inputs_dict






@require_torch
class 						__lowerCAmelCase	(	lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase      ):



      """simple docstring"""







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




      def 		snake_case_			(      self		:       Dict				):
          __lowercase		:			Any	    =       LlamaModelTester(self				)
          __lowercase		:			Optional[Any]	    =       ConfigTester(self				,       config_class=_snake_case				,       hidden_size=37				)




      def 		snake_case_			(      self		:       Tuple				):
          self.config_tester.run_common_tests()




      def 		snake_case_			(      self		:       List[str]				):
          __lowercase		:			Optional[Any]	    =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_snake_case				)




      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			str	    =       self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
              __lowercase		:			Tuple	    =       type
              self.model_tester.create_and_check_model(*_snake_case				)




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




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




      def 		snake_case_			(      self		:       List[Any]				):
          __lowercase		:			Tuple	    =       self.model_tester.prepare_config_and_inputs_for_common()
          __lowercase		:			Any	    =       3
          __lowercase		:			str	    =       '''multi_label_classification'''
          __lowercase		:			int	    =       input_dict['''input_ids''']
          __lowercase		:			int	    =       input_ids.ne(1				).to(_snake_case				)
          __lowercase		:			int	    =       ids_tensor(
              [self.model_tester.batch_size, config.num_labels]				,       self.model_tester.type_sequence_label_size				).to(torch.float				)
          __lowercase		:			Dict	    =       LlamaForSequenceClassification(_snake_case				)
          model.to(_snake_case				)
          model.eval()
          __lowercase		:			Tuple	    =       model(_snake_case				,       attention_mask=_snake_case				,       labels=_snake_case				)
          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 		snake_case_			(      self		:       int				):
          pass




      @parameterized.expand([('''linear''',), ('''dynamic''',)]				)
      def 		snake_case_			(      self		:       Optional[int]				,       _snake_case		:       Optional[Any]				):
          __lowercase		:			Optional[Any]	    =       self.model_tester.prepare_config_and_inputs_for_common()
          __lowercase		:			Optional[Any]	    =       ids_tensor([1, 10]				,       config.vocab_size				)
          __lowercase		:			Optional[Any]	    =       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
          __lowercase		:			Any	    =       LlamaModel(_snake_case				)
          original_model.to(_snake_case				)
          original_model.eval()
          __lowercase		:			List[str]	    =       original_model(_snake_case				).last_hidden_state
          __lowercase		:			str	    =       original_model(_snake_case				).last_hidden_state

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

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






@require_torch
class 						__lowerCAmelCase	(	unittest.TestCase      ):



      """simple docstring"""







      @unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''				)
      @slow
      def 		snake_case_			(      self		:       Tuple				):
          __lowercase		:			List[Any]	    =       [1, 306, 4658, 278, 6593, 310, 2834, 338]
          __lowercase		:			int	    =       LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-7b-hf'''				,       device_map='''auto'''				)
          __lowercase		:			Tuple	    =       model(torch.tensor([input_ids]				)				)
          # Expected mean on dim = -1
          __lowercase		:			Dict	    =       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				)				,       _snake_case				,       atol=1E-2				,       rtol=1E-2				)
          # slicing logits[0, 0, 0:30]
          # fmt: off
          __lowercase		:			Union[str, Any]	    =       torch.tensor([-12.82_81, -7.44_53, -0.46_39, -8.06_25, -7.25_00, -8.00_00, -6.48_83, -7.76_95, -7.84_38, -7.03_12, -6.21_88, -7.13_28, -1.84_96, 1.99_61, -8.62_50, -6.72_27, -12.82_81, -6.94_92, -7.07_42, -7.78_52, -7.58_20, -7.90_62, -6.93_75, -7.98_05, -8.34_38, -8.15_62, -8.04_69, -7.62_50, -7.74_22, -7.33_98,]				)
          # fmt: on
          torch.testing.assert_close(out[0, 0, :30]				,       _snake_case				,       atol=1E-5				,       rtol=1E-5				)




      @unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''				)
      @slow
      def 		snake_case_			(      self		:       List[Any]				):
          __lowercase		:			Any	    =       [1, 306, 4658, 278, 6593, 310, 2834, 338]
          __lowercase		:			List[Any]	    =       LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-hf'''				,       device_map='''auto'''				)
          __lowercase		:			List[str]	    =       model(torch.tensor(_snake_case				)				)
          # Expected mean on dim = -1
          __lowercase		:			Dict	    =       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				)				,       _snake_case				,       atol=1E-2				,       rtol=1E-2				)
          # slicing logits[0, 0, 0:30]
          # fmt: off
          __lowercase		:			Tuple	    =       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]				,       _snake_case				,       atol=1E-5				,       rtol=1E-5				)




      @unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''				)
      @slow
      def 		snake_case_			(      self		:       Any				):
          __lowercase		:			str	    =       [1, 306, 4658, 278, 6593, 310, 2834, 338]
          __lowercase		:			Optional[int]	    =       LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''				,       device_map='''auto'''				)
          __lowercase		:			Dict	    =       model(torch.tensor(_snake_case				)				)
          # Expected mean on dim = -1
          __lowercase		:			List[str]	    =       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				)				,       _snake_case				,       atol=1E-2				,       rtol=1E-2				)
          # slicing logits[0, 0, 0:30]
          # fmt: off
          __lowercase		:			Optional[int]	    =       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				)				,       _snake_case				,       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 		snake_case_			(      self		:       Any				):
          __lowercase		:			Optional[Any]	    =       [1, 306, 4658, 278, 6593, 310, 2834, 338]
          __lowercase		:			Optional[int]	    =       LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-70b-hf'''				,       device_map='''auto'''				)
          __lowercase		:			List[Any]	    =       model(torch.tensor(_snake_case				)				)

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




      @unittest.skip('''Model is curently gated'''				)
      @slow
      def 		snake_case_			(      self		:       Tuple				):
          __lowercase		:			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'''
          __lowercase		:			Any	    =       '''Simply put, the theory of relativity states that '''
          __lowercase		:			Any	    =       LlamaTokenizer.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''				)
          __lowercase		:			Optional[Any]	    =       tokenizer.encode(_snake_case				,       return_tensors='''pt'''				)
          __lowercase		:			List[Any]	    =       LlamaForCausalLM.from_pretrained(
              '''meta-llama/Llama-2-13b-chat-hf'''				,       device_map='''sequential'''				,       use_safetensors=_snake_case				)

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




def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						int:

    if not isinstance(__lowerCAmelCase	,					__lowerCAmelCase	):
        raise ValueError('''multiplicative_persistence() only accepts integral values'''	)
    if num < 0:
        raise ValueError('''multiplicative_persistence() does not accept negative values'''	)

    __lowercase		:			Dict	    =       0
    __lowercase		:			Optional[int]	    =       str(__lowerCAmelCase	)

    while len(__lowerCAmelCase	) != 1:
        __lowercase		:			str	    =       [int(__lowerCAmelCase	) for i in num_string]

        __lowercase		:			Union[str, Any]	    =       1
        for i in range(0	,					len(__lowerCAmelCase	)	):
            total *= numbers[i]

        __lowercase		:			Any	    =       str(__lowerCAmelCase	)

        steps += 1
    return steps


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						int:

    if not isinstance(__lowerCAmelCase	,					__lowerCAmelCase	):
        raise ValueError('''additive_persistence() only accepts integral values'''	)
    if num < 0:
        raise ValueError('''additive_persistence() does not accept negative values'''	)

    __lowercase		:			Any	    =       0
    __lowercase		:			Union[str, Any]	    =       str(__lowerCAmelCase	)

    while len(__lowerCAmelCase	) != 1:
        __lowercase		:			List[str]	    =       [int(__lowerCAmelCase	) for i in num_string]

        __lowercase		:			Tuple	    =       0
        for i in range(0	,					len(__lowerCAmelCase	)	):
            total += numbers[i]

        __lowercase		:			List[str]	    =       str(__lowerCAmelCase	)

        steps += 1
    return steps


if __name__ == "__main__":
 import doctest

 doctest.testmod()


