"""simple docstring"""




import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer
class 						__A							:






     def __init__(		self				,     a__				,     a__=13				,     a__=7				,     a__=True				,     a__=True				,     a__=False				,     a__=True				,     a__=99				,     a__=32				,     a__=5				,     a__=4				,     a__=37				,     a__="gelu"				,     a__=0.1				,     a__=0.1				,     a__=512				,     a__=16				,     a__=2				,     a__=0.0_2				,     a__=3				,     a__=4				,     a__=None				,     ):
       _lowerCAmelCase						:       List[str]  			=     parent
       _lowerCAmelCase						:       Optional[int]  			=     batch_size
       _lowerCAmelCase						:       str  			=     seq_length
       _lowerCAmelCase						:       List[Any]  			=     is_training
       _lowerCAmelCase						:       Union[str, Any]  			=     use_input_mask
       _lowerCAmelCase						:       List[str]  			=     use_token_type_ids
       _lowerCAmelCase						:       Tuple  			=     use_labels
       _lowerCAmelCase						:       List[Any]  			=     vocab_size
       _lowerCAmelCase						:       Union[str, Any]  			=     hidden_size
       _lowerCAmelCase						:       Union[str, Any]  			=     num_hidden_layers
       _lowerCAmelCase						:       Any  			=     num_attention_heads
       _lowerCAmelCase						:       str  			=     intermediate_size
       _lowerCAmelCase						:       Dict  			=     hidden_act
       _lowerCAmelCase						:       List[Any]  			=     hidden_dropout_prob
       _lowerCAmelCase						:       int  			=     attention_probs_dropout_prob
       _lowerCAmelCase						:       Dict  			=     max_position_embeddings
       _lowerCAmelCase						:       List[Any]  			=     type_vocab_size
       _lowerCAmelCase						:       int  			=     type_sequence_label_size
       _lowerCAmelCase						:       Union[str, Any]  			=     initializer_range
       _lowerCAmelCase						:       List[str]  			=     num_labels
       _lowerCAmelCase						:       Optional[int]  			=     num_choices
       _lowerCAmelCase						:       Optional[Any]  			=     scope






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

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

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

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

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

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






     def     __A  (		self  ):
       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				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       Any  			=     LlamaModel(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       Dict  			=     model(a__				,     attention_mask=a__  )
       _lowerCAmelCase						:       str  			=     model(a__  )
       self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size)  )






     def     __A  (		self				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     ):
       _lowerCAmelCase						:       Tuple  			=     True
       _lowerCAmelCase						:       Tuple  			=     LlamaModel(a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       List[str]  			=     model(
           a__				,     attention_mask=a__				,     encoder_hidden_states=a__				,     encoder_attention_mask=a__				,     )
       _lowerCAmelCase						:       Optional[Any]  			=     model(
           a__				,     attention_mask=a__				,     encoder_hidden_states=a__				,     )
       _lowerCAmelCase						:       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				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     ):
       _lowerCAmelCase						:       Tuple  			=     LlamaForCausalLM(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       int  			=     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				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     ):
       _lowerCAmelCase						:       Optional[int]  			=     True
       _lowerCAmelCase						:       Optional[int]  			=     True
       _lowerCAmelCase						:       Tuple  			=     LlamaForCausalLM(config=a__  )
       model.to(a__  )
       model.eval()

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

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

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

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

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

       self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]  )

       # test that outputs are equal for slice
       self.parent.assertTrue(torch.allclose(a__				,     a__				,     atol=1e-3  )  )






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







@require_torch
class 						__A							( SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			Optional[Any]							=		(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
     _UpperCamelCase							:			Dict							=		(LlamaForCausalLM,) if is_torch_available() else ()
     _UpperCamelCase							:			Union[str, Any]							=		(
         {
             "feature-extraction": LlamaModel,
             "text-classification": LlamaForSequenceClassification,
             "text-generation": LlamaForCausalLM,
             "zero-shot": LlamaForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     _UpperCamelCase							:			Optional[Any]							=		False
     _UpperCamelCase							:			Dict							=		False






     def     __A  (		self  ):
       _lowerCAmelCase						:       Dict  			=     LlamaModelTester(self  )
       _lowerCAmelCase						:       List[str]  			=     ConfigTester(self				,     config_class=a__				,     hidden_size=37  )






     def     __A  (		self  ):
       self.config_tester.run_common_tests()






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a__  )






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






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






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






     @parameterized.expand([("""linear""",), ("""dynamic""",)]  )
     def     __A  (		self				,     a__  ):
       _lowerCAmelCase			,	_lowerCAmelCase						:       int  			=     self.model_tester.prepare_config_and_inputs_for_common()
       _lowerCAmelCase						:       int  			=     ids_tensor([1, 10]				,     config.vocab_size  )
       _lowerCAmelCase						:       int  			=     ids_tensor([1, int(config.max_position_embeddings * 1.5  )]				,     config.vocab_size  )

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

       set_seed(42  )  # Fixed seed at init time so the two models get the same random weights
       _lowerCAmelCase						:       Tuple  			=     {"""type""": scaling_type, """factor""": 1_0.0}
       _lowerCAmelCase						:       Optional[Any]  			=     LlamaModel(a__  )
       scaled_model.to(a__  )
       scaled_model.eval()
       _lowerCAmelCase						:       Any  			=     scaled_model(a__  ).last_hidden_state
       _lowerCAmelCase						:       Optional[Any]  			=     scaled_model(a__  ).last_hidden_state

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

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







@require_torch
class 						__A							( unittest.TestCase		):






     @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""  )
     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       Tuple  			=     [1, 306, 4658, 278, 6593, 310, 2834, 338]
       _lowerCAmelCase						:       List[Any]  			=     LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-7b-hf"""				,     device_map="""auto"""  )
       _lowerCAmelCase						:       Tuple  			=     model(torch.tensor([input_ids]  )  )
       # Expected mean on dim = -1
       _lowerCAmelCase						:       Optional[int]  			=     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  )				,     a__				,     atol=1e-2				,     rtol=1e-2  )
       # slicing logits[0, 0, 0:30]
       # fmt: off
       _lowerCAmelCase						:       List[str]  			=     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]				,     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  ):
       _lowerCAmelCase						:       Optional[int]  			=     [1, 306, 4658, 278, 6593, 310, 2834, 338]
       _lowerCAmelCase						:       List[Any]  			=     LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-hf"""				,     device_map="""auto"""  )
       _lowerCAmelCase						:       Tuple  			=     model(torch.tensor(a__  )  )
       # Expected mean on dim = -1
       _lowerCAmelCase						:       Union[str, Any]  			=     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  )				,     a__				,     atol=1e-2				,     rtol=1e-2  )
       # slicing logits[0, 0, 0:30]
       # fmt: off
       _lowerCAmelCase						:       List[str]  			=     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]				,     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  ):
       _lowerCAmelCase						:       int  			=     [1, 306, 4658, 278, 6593, 310, 2834, 338]
       _lowerCAmelCase						:       Dict  			=     LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""				,     device_map="""auto"""  )
       _lowerCAmelCase						:       Tuple  			=     model(torch.tensor(a__  )  )
       # Expected mean on dim = -1
       _lowerCAmelCase						:       Optional[Any]  			=     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  )				,     a__				,     atol=1e-2				,     rtol=1e-2  )
       # slicing logits[0, 0, 0:30]
       # fmt: off
       _lowerCAmelCase						:       List[str]  			=     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  )				,     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  ):
       _lowerCAmelCase						:       str  			=     [1, 306, 4658, 278, 6593, 310, 2834, 338]
       _lowerCAmelCase						:       Optional[Any]  			=     LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-70b-hf"""				,     device_map="""auto"""  )
       _lowerCAmelCase						:       str  			=     model(torch.tensor(a__  )  )

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






     @unittest.skip("""Model is curently gated"""  )
     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     """Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the \"princi"""
       _lowerCAmelCase						:       str  			=     """Simply put, the theory of relativity states that """
       _lowerCAmelCase						:       List[str]  			=     LlamaTokenizer.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""  )
       _lowerCAmelCase						:       Optional[Any]  			=     tokenizer.encode(a__				,     return_tensors="""pt"""  )
       _lowerCAmelCase						:       Optional[Any]  			=     LlamaForCausalLM.from_pretrained(
           """meta-llama/Llama-2-13b-chat-hf"""				,     device_map="""sequential"""				,     use_safetensors=a__  )

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





"""simple docstring"""




_a      :		List[str]           =   {
    'Pillow': 'Pillow',
    'accelerate': 'accelerate>=0.11.0',
    'compel': 'compel==0.1.8',
    'black': 'black~=23.1',
    'datasets': 'datasets',
    'filelock': 'filelock',
    'flax': 'flax>=0.4.1',
    'hf-doc-builder': 'hf-doc-builder>=0.3.0',
    'huggingface-hub': 'huggingface-hub>=0.13.2',
    'requests-mock': 'requests-mock==1.10.0',
    'importlib_metadata': 'importlib_metadata',
    'invisible-watermark': 'invisible-watermark',
    'isort': 'isort>=5.5.4',
    'jax': 'jax>=0.2.8,!=0.3.2',
    'jaxlib': 'jaxlib>=0.1.65',
    'Jinja2': 'Jinja2',
    'k-diffusion': 'k-diffusion>=0.0.12',
    'torchsde': 'torchsde',
    'note_seq': 'note_seq',
    'librosa': 'librosa',
    'numpy': 'numpy',
    'omegaconf': 'omegaconf',
    'parameterized': 'parameterized',
    'protobuf': 'protobuf>=3.20.3,<4',
    'pytest': 'pytest',
    'pytest-timeout': 'pytest-timeout',
    'pytest-xdist': 'pytest-xdist',
    'ruff': 'ruff>=0.0.241',
    'safetensors': 'safetensors',
    'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
    'scipy': 'scipy',
    'onnx': 'onnx',
    'regex': 'regex!=2019.12.17',
    'requests': 'requests',
    'tensorboard': 'tensorboard',
    'torch': 'torch>=1.4',
    'torchvision': 'torchvision',
    'transformers': 'transformers>=4.25.1',
    'urllib3': 'urllib3<=2.0.0',
}




