




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      :
      def __init__(			self   :Union[str, Any] ,      lowerCamelCase   :Optional[int] ,      lowerCamelCase   :Tuple=13 ,      lowerCamelCase   :Any=7 ,      lowerCamelCase   :List[str]=True ,      lowerCamelCase   :Optional[Any]=True ,      lowerCamelCase   :Optional[int]=False ,      lowerCamelCase   :Optional[Any]=True ,      lowerCamelCase   :Optional[int]=99 ,      lowerCamelCase   :Tuple=32 ,      lowerCamelCase   :List[str]=5 ,      lowerCamelCase   :Dict=4 ,      lowerCamelCase   :int=37 ,      lowerCamelCase   :Union[str, Any]="gelu" ,      lowerCamelCase   :List[str]=0.1 ,      lowerCamelCase   :Dict=0.1 ,      lowerCamelCase   :List[str]=512 ,      lowerCamelCase   :List[Any]=16 ,      lowerCamelCase   :str=2 ,      lowerCamelCase   :Dict=0.02 ,      lowerCamelCase   :Optional[int]=3 ,      lowerCamelCase   :List[Any]=4 ,      lowerCamelCase   :Any=None ,      )     ->			str:
       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   :Any      )     ->			Optional[Any]:
       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   :List[Any]      )     ->			Tuple:
       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=_lowercase ,      initializer_range=self.initializer_range ,      )
      def 	UpperCAmelCase_						(			self   :str ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :Optional[int] ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :List[str] ,      lowerCamelCase   :Optional[int] ,      lowerCamelCase   :Optional[int] ,      lowerCamelCase   :List[Any]      )     ->			Dict:
       UpperCAmelCase__							 =						LlamaModel(config=_lowercase      )
       model.to(_lowercase      )
       model.eval()
       UpperCAmelCase__							 =						model(_lowercase ,      attention_mask=_lowercase      )
       UpperCAmelCase__							 =						model(_lowercase      )
       self.parent.assertEqual(result.last_hidden_state.shape ,      (self.batch_size, self.seq_length, self.hidden_size)      )
      def 	UpperCAmelCase_						(			self   :Dict ,      lowerCamelCase   :str ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :Tuple ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :List[str] ,      lowerCamelCase   :int ,      lowerCamelCase   :Union[str, Any] ,      lowerCamelCase   :List[Any] ,      )     ->			List[str]:
       UpperCAmelCase__							 =						True
       UpperCAmelCase__							 =						LlamaModel(_lowercase      )
       model.to(_lowercase      )
       model.eval()
       UpperCAmelCase__							 =						model(
           _lowercase ,      attention_mask=_lowercase ,      encoder_hidden_states=_lowercase ,      encoder_attention_mask=_lowercase ,      )
       UpperCAmelCase__							 =						model(
           _lowercase ,      attention_mask=_lowercase ,      encoder_hidden_states=_lowercase ,      )
       UpperCAmelCase__							 =						model(_lowercase ,      attention_mask=_lowercase      )
       self.parent.assertEqual(result.last_hidden_state.shape ,      (self.batch_size, self.seq_length, self.hidden_size)      )
      def 	UpperCAmelCase_						(			self   :Tuple ,      lowerCamelCase   :Any ,      lowerCamelCase   :Optional[int] ,      lowerCamelCase   :Any ,      lowerCamelCase   :Optional[Any] ,      lowerCamelCase   :int ,      lowerCamelCase   :Optional[int] ,      lowerCamelCase   :Optional[int] ,      lowerCamelCase   :Optional[Any] ,      lowerCamelCase   :Optional[Any] ,      )     ->			Tuple:
       UpperCAmelCase__							 =						LlamaForCausalLM(config=_lowercase      )
       model.to(_lowercase      )
       model.eval()
       UpperCAmelCase__							 =						model(_lowercase ,      attention_mask=_lowercase ,      labels=_lowercase      )
       self.parent.assertEqual(result.logits.shape ,      (self.batch_size, self.seq_length, self.vocab_size)      )
      def 	UpperCAmelCase_						(			self   :Union[str, Any] ,      lowerCamelCase   :Tuple ,      lowerCamelCase   :Any ,      lowerCamelCase   :Optional[Any] ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :Tuple ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :Any ,      lowerCamelCase   :Dict ,      lowerCamelCase   :List[Any] ,      )     ->			Tuple:
       UpperCAmelCase__							 =						True
       UpperCAmelCase__							 =						True
       UpperCAmelCase__							 =						LlamaForCausalLM(config=_lowercase      )
       model.to(_lowercase      )
       model.eval()

       # first forward pass
       UpperCAmelCase__							 =						model(
           _lowercase ,      attention_mask=_lowercase ,      encoder_hidden_states=_lowercase ,      encoder_attention_mask=_lowercase ,      use_cache=_lowercase ,      )
       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(
           _lowercase ,      attention_mask=_lowercase ,      encoder_hidden_states=_lowercase ,      encoder_attention_mask=_lowercase ,      output_hidden_states=_lowercase ,      )["hidden_states"][0]
       UpperCAmelCase__							 =						model(
           _lowercase ,      attention_mask=_lowercase ,      encoder_hidden_states=_lowercase ,      encoder_attention_mask=_lowercase ,      past_key_values=_lowercase ,      output_hidden_states=_lowercase ,      )["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(_lowercase ,      _lowercase ,      atol=1e-3      )      )







      def 	UpperCAmelCase_						(			self   :str      )     ->			Optional[Any]:
       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 			_UpperCamelCase      (				UpperCAmelCase_	,       UpperCAmelCase_	,       UpperCAmelCase_	,       unittest.TestCase		):
      UpperCAmelCase_        =		(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
      UpperCAmelCase_        =		(LlamaForCausalLM,) if is_torch_available() else ()
      UpperCAmelCase_        =		(
          {
              """feature-extraction""": LlamaModel,
              """text-classification""": LlamaForSequenceClassification,
              """text-generation""": LlamaForCausalLM,
              """zero-shot""": LlamaForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      UpperCAmelCase_        =		False
      UpperCAmelCase_        =		False
      def 	UpperCAmelCase_						(			self   :str      )     ->			Dict:
       UpperCAmelCase__							 =						LlamaModelTester(self      )
       UpperCAmelCase__							 =						ConfigTester(self ,      config_class=_lowercase ,      hidden_size=37      )
      def 	UpperCAmelCase_						(			self   :int      )     ->			Dict:
       self.config_tester.run_common_tests()
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowercase      )
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Optional[Any]:
       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(*_lowercase      )
      def 	UpperCAmelCase_						(			self   :str      )     ->			List[Any]:
       UpperCAmelCase__ , UpperCAmelCase__							 =						self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase__							 =						3
       UpperCAmelCase__							 =						input_dict["input_ids"]
       UpperCAmelCase__							 =						input_ids.ne(1      ).to(_lowercase      )
       UpperCAmelCase__							 =						ids_tensor([self.model_tester.batch_size] ,      self.model_tester.type_sequence_label_size      )
       UpperCAmelCase__							 =						LlamaForSequenceClassification(_lowercase      )
       model.to(_lowercase      )
       model.eval()
       UpperCAmelCase__							 =						model(_lowercase ,      attention_mask=_lowercase ,      labels=_lowercase      )
       self.assertEqual(result.logits.shape ,      (self.model_tester.batch_size, self.model_tester.num_labels)      )
      def 	UpperCAmelCase_						(			self   :List[Any]      )     ->			Optional[int]:
       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(_lowercase      )
       UpperCAmelCase__							 =						ids_tensor([self.model_tester.batch_size] ,      self.model_tester.type_sequence_label_size      )
       UpperCAmelCase__							 =						LlamaForSequenceClassification(_lowercase      )
       model.to(_lowercase      )
       model.eval()
       UpperCAmelCase__							 =						model(_lowercase ,      attention_mask=_lowercase ,      labels=_lowercase      )
       self.assertEqual(result.logits.shape ,      (self.model_tester.batch_size, self.model_tester.num_labels)      )
      def 	UpperCAmelCase_						(			self   :str      )     ->			Optional[Any]:
       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(_lowercase      )
       UpperCAmelCase__							 =						ids_tensor(
           [self.model_tester.batch_size, config.num_labels] ,      self.model_tester.type_sequence_label_size      ).to(torch.float      )
       UpperCAmelCase__							 =						LlamaForSequenceClassification(_lowercase      )
       model.to(_lowercase      )
       model.eval()
       UpperCAmelCase__							 =						model(_lowercase ,      attention_mask=_lowercase ,      labels=_lowercase      )
       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   :int      )     ->			List[Any]:
       pass







      @parameterized.expand([("linear",), ("dynamic",)]      )
      def 	UpperCAmelCase_						(			self   :Optional[int] ,      lowerCamelCase   :Any      )     ->			Optional[Any]:
       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(_lowercase      )
       original_model.to(_lowercase      )
       original_model.eval()
       UpperCAmelCase__							 =						original_model(_lowercase      ).last_hidden_state
       UpperCAmelCase__							 =						original_model(_lowercase      ).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": 10.0}
       UpperCAmelCase__							 =						LlamaModel(_lowercase      )
       scaled_model.to(_lowercase      )
       scaled_model.eval()
       UpperCAmelCase__							 =						scaled_model(_lowercase      ).last_hidden_state
       UpperCAmelCase__							 =						scaled_model(_lowercase      ).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(_lowercase ,      _lowercase ,      atol=1e-5      )      )
       else:
        self.assertFalse(torch.allclose(_lowercase ,      _lowercase ,      atol=1e-5      )      )

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



@require_torch
class 			_UpperCamelCase      (				unittest.TestCase		):
      @unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!"      )
      @slow
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			Any:
       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([[-6.65_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      ) ,      _lowercase ,      atol=1e-2 ,      rtol=1e-2      )
       # slicing logits[0, 0, 0:30]
       # fmt: off
       UpperCAmelCase__							 =						torch.tensor([-12.8281, -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, -12.8281, -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] ,      _lowercase ,      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   :List[str]      )     ->			Union[str, Any]:
       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(_lowercase      )      )
       # Expected mean on dim = -1
       UpperCAmelCase__							 =						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      ) ,      _lowercase ,      atol=1e-2 ,      rtol=1e-2      )
       # slicing logits[0, 0, 0:30]
       # fmt: off
       UpperCAmelCase__							 =						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] ,      _lowercase ,      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   :int      )     ->			List[str]:
       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(_lowercase      )      )
       # Expected mean on dim = -1
       UpperCAmelCase__							 =						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      ) ,      _lowercase ,      atol=1e-2 ,      rtol=1e-2      )
       # slicing logits[0, 0, 0:30]
       # fmt: off
       UpperCAmelCase__							 =						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      ) ,      _lowercase ,      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   :Optional[Any]      )     ->			List[Any]:
       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(_lowercase      )      )

       UpperCAmelCase__							 =						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      ) ,      _lowercase ,      atol=1e-2 ,      rtol=1e-2      )
       # fmt: off
       UpperCAmelCase__							 =						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] ,      _lowercase ,      atol=1e-5 ,      rtol=1e-5      )







      @unittest.skip("Model is curently gated"      )
      @slow
      def 	UpperCAmelCase_						(			self   :Any      )     ->			Optional[Any]:
       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(_lowercase ,      return_tensors="pt"      )
       UpperCAmelCase__							 =						LlamaForCausalLM.from_pretrained(
           "meta-llama/Llama-2-13b-chat-hf" ,      device_map="sequential" ,      use_safetensors=_lowercase      )

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











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_lowerCAmelCase						:   Optional[int]		=   {
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   List[str]		=   [
      "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
      "GPTNeoForCausalLM",
      "GPTNeoForQuestionAnswering",
      "GPTNeoForSequenceClassification",
      "GPTNeoForTokenClassification",
      "GPTNeoModel",
      "GPTNeoPreTrainedModel",
      "load_tf_weights_in_gpt_neo",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   Tuple		=   [
      "FlaxGPTNeoForCausalLM",
      "FlaxGPTNeoModel",
      "FlaxGPTNeoPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpt_neo import (
        GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTNeoForCausalLM,
        GPTNeoForQuestionAnswering,
        GPTNeoForSequenceClassification,
        GPTNeoForTokenClassification,
        GPTNeoModel,
        GPTNeoPreTrainedModel,
        load_tf_weights_in_gpt_neo,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
  import sys

  _lowerCAmelCase						:   Union[str, Any]		=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





