
"""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 		UpperCAmelCase_						:
 """simple docstring"""




 def __init__(			self							:			Dict  , a_							:			Union[str, Any]  , a_							:			int=13  , a_							:			Optional[Any]=7  , a_							:			Optional[int]=True  , a_							:			Any=True  , a_							:			List[str]=False  , a_							:			int=True  , a_							:			Dict=99  , a_							:			List[str]=32  , a_							:			Tuple=5  , a_							:			Union[str, Any]=4  , a_							:			Tuple=37  , a_							:			Any="gelu"  , a_							:			Optional[int]=0.1  , a_							:			Any=0.1  , a_							:			Dict=5_12  , a_							:			List[str]=16  , a_							:			Tuple=2  , a_							:			Dict=0.02  , a_							:			str=3  , a_							:			Dict=4  , a_							:			Any=None  , )->       Any:



      """simple docstring"""

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




 def 	a   (			self							:			Optional[int]			)->       Optional[Any]:



      """simple docstring"""

      UpperCAmelCase_    :     Optional[Any]            =	ids_tensor([self.batch_size, self.seq_length]  , self.vocab_size			)

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

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

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

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

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




 def 	a   (			self							:			Optional[int]			)->       str:



      """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 	a   (			self							:			List[str]  , a_							:			Union[str, Any]  , a_							:			Tuple  , a_							:			List[str]  , a_							:			Optional[int]  , a_							:			Optional[Any]  , a_							:			List[Any]  , a_							:			Tuple			)->       List[Any]:



      """simple docstring"""

      UpperCAmelCase_    :     List[str]            =	LlamaModel(config=a_			)
      model.to(a_			)
      model.eval()
      UpperCAmelCase_    :     List[Any]            =	model(a_  , attention_mask=a_			)
      UpperCAmelCase_    :     Tuple            =	model(a_			)
      self.parent.assertEqual(result.last_hidden_state.shape  , (self.batch_size, self.seq_length, self.hidden_size)			)




 def 	a   (			self							:			Any  , a_							:			List[str]  , a_							:			Dict  , a_							:			List[Any]  , a_							:			Tuple  , a_							:			Any  , a_							:			int  , a_							:			Any  , a_							:			int  , a_							:			Optional[Any]  , )->       Optional[int]:



      """simple docstring"""

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




 def 	a   (			self							:			Any  , a_							:			List[Any]  , a_							:			int  , a_							:			str  , a_							:			Optional[int]  , a_							:			int  , a_							:			Optional[Any]  , a_							:			List[Any]  , a_							:			Optional[Any]  , a_							:			int  , )->       List[str]:



      """simple docstring"""

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




 def 	a   (			self							:			Dict  , a_							:			str  , a_							:			Any  , a_							:			str  , a_							:			List[str]  , a_							:			Any  , a_							:			Union[str, Any]  , a_							:			str  , a_							:			Any  , a_							:			Any  , )->       Optional[Any]:



      """simple docstring"""

      UpperCAmelCase_    :     List[Any]            =	True
      UpperCAmelCase_    :     int            =	True
      UpperCAmelCase_    :     int            =	LlamaForCausalLM(config=a_			)
      model.to(a_			)
      model.eval()

      # first forward pass
      UpperCAmelCase_    :     Optional[int]            =	model(
          a_  , attention_mask=a_  , encoder_hidden_states=a_  , encoder_attention_mask=a_  , use_cache=a_  , )
      UpperCAmelCase_    :     List[Any]            =	outputs.past_key_values

      # create hypothetical multiple next token and extent to next_input_ids
      UpperCAmelCase_    :     List[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_    :     Dict            =	torch.cat([input_mask, next_mask]  , dim=-1			)

      UpperCAmelCase_    :     int            =	model(
          a_  , attention_mask=a_  , encoder_hidden_states=a_  , encoder_attention_mask=a_  , output_hidden_states=a_  , )["""hidden_states"""][0]
      UpperCAmelCase_    :     int            =	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_    :     int            =	ids_tensor((1,)  , output_from_past.shape[-1]			).item()
      UpperCAmelCase_    :     int            =	output_from_no_past[:, -3:, random_slice_idx].detach()
      UpperCAmelCase_    :     Tuple            =	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 	a   (			self							:			Optional[int]			)->       Union[str, Any]:



      """simple docstring"""

      UpperCAmelCase_    :     List[Any]            =	self.prepare_config_and_inputs()
      (
          (
          UpperCAmelCase_
      )       ,(
          UpperCAmelCase_
      )       ,(
          UpperCAmelCase_
      )       ,(
          UpperCAmelCase_
      )       ,(
          UpperCAmelCase_
      )       ,(
          UpperCAmelCase_
      )       ,(
          UpperCAmelCase_
      )       ,
      )    :     Any            =	config_and_inputs
      UpperCAmelCase_    :     Tuple            =	{"""input_ids""": input_ids, """attention_mask""": input_mask}
      return config, inputs_dict



@require_torch
class 		UpperCAmelCase_						(lowerCamelCase_	,			lowerCamelCase_	,			lowerCamelCase_	,			unittest.TestCase    ):
 """simple docstring"""




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




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



      """simple docstring"""

      UpperCAmelCase_    :     int            =	LlamaModelTester(self			)
      UpperCAmelCase_    :     List[Any]            =	ConfigTester(self  , config_class=a_  , hidden_size=37			)




 def 	a   (			self							:			Tuple			)->       List[Any]:



      """simple docstring"""

      self.config_tester.run_common_tests()




 def 	a   (			self							:			Union[str, Any]			)->       Union[str, Any]:



      """simple docstring"""

      UpperCAmelCase_    :     Union[str, Any]            =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*a_			)




 def 	a   (			self							:			int			)->       str:



      """simple docstring"""

      UpperCAmelCase_    :     str            =	self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
           UpperCAmelCase_    :     Optional[Any]            =	type
           self.model_tester.create_and_check_model(*a_			)




 def 	a   (			self							:			Tuple			)->       List[Any]:



      """simple docstring"""

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




 def 	a   (			self							:			Optional[int]			)->       int:



      """simple docstring"""

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




 def 	a   (			self							:			int			)->       Optional[int]:



      """simple docstring"""

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



      """simple docstring"""

      pass






 @parameterized.expand([("""linear""",), ("""dynamic""",)]			)
 def 	a   (			self							:			Tuple  , a_							:			List[str]			)->       Tuple:



      """simple docstring"""

      UpperCAmelCase_       ,UpperCAmelCase_    :     str            =	self.model_tester.prepare_config_and_inputs_for_common()
      UpperCAmelCase_    :     str            =	ids_tensor([1, 10]  , config.vocab_size			)
      UpperCAmelCase_    :     str            =	ids_tensor([1, int(config.max_position_embeddings * 1.5			)]  , config.vocab_size			)

      set_seed(42			)  # Fixed seed at init time so the two models get the same random weights
      UpperCAmelCase_    :     Dict            =	LlamaModel(a_			)
      original_model.to(a_			)
      original_model.eval()
      UpperCAmelCase_    :     List[str]            =	original_model(a_			).last_hidden_state
      UpperCAmelCase_    :     str            =	original_model(a_			).last_hidden_state

      set_seed(42			)  # Fixed seed at init time so the two models get the same random weights
      UpperCAmelCase_    :     Any            =	{"""type""": scaling_type, """factor""": 10.0}
      UpperCAmelCase_    :     Union[str, Any]            =	LlamaModel(a_			)
      scaled_model.to(a_			)
      scaled_model.eval()
      UpperCAmelCase_    :     Optional[int]            =	scaled_model(a_			).last_hidden_state
      UpperCAmelCase_    :     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 		UpperCAmelCase_						(unittest.TestCase    ):
 """simple docstring"""




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



      """simple docstring"""

      UpperCAmelCase_    :     List[str]            =	[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
      UpperCAmelCase_    :     Tuple            =	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([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]]			)
      torch.testing.assert_close(out.mean(-1			)  , a_  , atol=1E-2  , rtol=1E-2			)
      # slicing logits[0, 0, 0:30]
      # fmt: off
      UpperCAmelCase_    :     Tuple            =	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, :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 	a   (			self							:			List[str]			)->       int:



      """simple docstring"""

      UpperCAmelCase_    :     List[Any]            =	[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
      UpperCAmelCase_    :     List[Any]            =	LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-hf"""  , device_map="""auto"""			)
      UpperCAmelCase_    :     str            =	model(torch.tensor(a_			)			)
      # Expected mean on dim = -1
      UpperCAmelCase_    :     Tuple            =	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			)  , a_  , atol=1E-2  , rtol=1E-2			)
      # slicing logits[0, 0, 0:30]
      # fmt: off
      UpperCAmelCase_    :     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, :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 	a   (			self							:			Tuple			)->       int:



      """simple docstring"""

      UpperCAmelCase_    :     Dict            =	[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
      UpperCAmelCase_    :     str            =	LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""  , device_map="""auto"""			)
      UpperCAmelCase_    :     int            =	model(torch.tensor(a_			)			)
      # Expected mean on dim = -1
      UpperCAmelCase_    :     str            =	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			)  , a_  , atol=1E-2  , rtol=1E-2			)
      # slicing logits[0, 0, 0:30]
      # fmt: off
      UpperCAmelCase_    :     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			)  , 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 	a   (			self							:			Any			)->       Optional[int]:



      """simple docstring"""

      UpperCAmelCase_    :     Optional[Any]            =	[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
      UpperCAmelCase_    :     Optional[Any]            =	LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-70b-hf"""  , device_map="""auto"""			)
      UpperCAmelCase_    :     List[Any]            =	model(torch.tensor(a_			)			)

      UpperCAmelCase_    :     Tuple            =	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			)  , a_  , atol=1E-2  , rtol=1E-2			)
      # fmt: off
      UpperCAmelCase_    :     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, :30]  , a_  , atol=1E-5  , rtol=1E-5			)






 @unittest.skip("""Model is curently gated"""			)
 @slow
 def 	a   (			self							:			Union[str, Any]			)->       Tuple:



      """simple docstring"""

      UpperCAmelCase_    :     List[Any]            =	"""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_    :     Optional[Any]            =	LlamaTokenizer.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""			)
      UpperCAmelCase_    :     List[Any]            =	tokenizer.encode(a_  , return_tensors="""pt"""			)
      UpperCAmelCase_    :     str            =	LlamaForCausalLM.from_pretrained(
          """meta-llama/Llama-2-13b-chat-hf"""  , device_map="""sequential"""  , use_safetensors=a_			)

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



"""simple docstring"""







import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate



@dataclass(frozen=lowerCamelCase_    )
class 		UpperCAmelCase_						(lowerCamelCase_    ):
 """simple docstring"""




 UpperCamelCase_    :  str					 =     field(default="""audio-classification"""	,			metadata={"""include_in_asdict_even_if_is_default""": True}    )
 UpperCamelCase_    :  ClassVar[Features]					 =     Features({"""audio""": Audio()}    )
 UpperCamelCase_    :  ClassVar[Features]					 =     Features({"""labels""": ClassLabel}    )
 UpperCamelCase_    :  str					 =     "audio"
 UpperCamelCase_    :  str					 =     "labels"




 def 	a   (			self							:			List[str]  , a_							:			Union[str, Any]			)->       Optional[int]:



      """simple docstring"""

      if self.label_column not in features:
           raise ValueError(f'''Column {self.label_column} is not present in features.'''			)
      if not isinstance(features[self.label_column]  , a_			):
           raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''			)
      UpperCAmelCase_    :     Any            =	copy.deepcopy(self			)
      UpperCAmelCase_    :     List[str]            =	self.label_schema.copy()
      UpperCAmelCase_    :     List[str]            =	features[self.label_column]
      UpperCAmelCase_    :     Optional[Any]            =	label_schema
      return task_template






 @property
 def 	a   (			self							:			Optional[Any]			)->       Dict[str, str]:



      """simple docstring"""

      return {
          self.audio_column: "audio",
          self.label_column: "labels",
      }

