

'''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					:




      def __init__(      self      :			Dict      ,      __snake_case      :			Dict      ,      __snake_case      :			List[str]=13      ,      __snake_case      :			Optional[Any]=7      ,      __snake_case      :			Tuple=True      ,      __snake_case      :			Any=True      ,      __snake_case      :			List[str]=False      ,      __snake_case      :			Union[str, Any]=True      ,      __snake_case      :			Union[str, Any]=99      ,      __snake_case      :			Union[str, Any]=32      ,      __snake_case      :			Tuple=5      ,      __snake_case      :			int=4      ,      __snake_case      :			Optional[int]=37      ,      __snake_case      :			Tuple="gelu"      ,      __snake_case      :			Dict=0.1      ,      __snake_case      :			str=0.1      ,      __snake_case      :			str=5_12      ,      __snake_case      :			Dict=16      ,      __snake_case      :			Any=2      ,      __snake_case      :			Dict=0.02      ,      __snake_case      :			List[Any]=3      ,      __snake_case      :			Optional[int]=4      ,      __snake_case      :			Dict=None      ,      )    ->				Optional[Any]:
            _lowerCAmelCase												=							parent
            _lowerCAmelCase												=							batch_size
            _lowerCAmelCase												=							seq_length
            _lowerCAmelCase												=							is_training
            _lowerCAmelCase												=							use_input_mask
            _lowerCAmelCase												=							use_token_type_ids
            _lowerCAmelCase												=							use_labels
            _lowerCAmelCase												=							vocab_size
            _lowerCAmelCase												=							hidden_size
            _lowerCAmelCase												=							num_hidden_layers
            _lowerCAmelCase												=							num_attention_heads
            _lowerCAmelCase												=							intermediate_size
            _lowerCAmelCase												=							hidden_act
            _lowerCAmelCase												=							hidden_dropout_prob
            _lowerCAmelCase												=							attention_probs_dropout_prob
            _lowerCAmelCase												=							max_position_embeddings
            _lowerCAmelCase												=							type_vocab_size
            _lowerCAmelCase												=							type_sequence_label_size
            _lowerCAmelCase												=							initializer_range
            _lowerCAmelCase												=							num_labels
            _lowerCAmelCase												=							num_choices
            _lowerCAmelCase												=							scope




      def     lowercase__   (      self      :			Tuple  )    ->				List[Any]:
            _lowerCAmelCase												=							ids_tensor([self.batch_size, self.seq_length]      ,      self.vocab_size  )

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

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

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

            _lowerCAmelCase												=							self.get_config()

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




      def     lowercase__   (      self      :			Tuple  )    ->				Optional[Any]:
            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=lowerCAmelCase__      ,      initializer_range=self.initializer_range      ,      )




      def     lowercase__   (      self      :			Tuple      ,      __snake_case      :			Optional[int]      ,      __snake_case      :			Dict      ,      __snake_case      :			List[Any]      ,      __snake_case      :			str      ,      __snake_case      :			Any      ,      __snake_case      :			Dict      ,      __snake_case      :			Optional[Any]  )    ->				List[str]:
            _lowerCAmelCase												=							LlamaModel(config=lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )
            model.eval()
            _lowerCAmelCase												=							model(lowerCAmelCase__      ,      attention_mask=lowerCAmelCase__  )
            _lowerCAmelCase												=							model(lowerCAmelCase__  )
            self.parent.assertEqual(result.last_hidden_state.shape      ,      (self.batch_size, self.seq_length, self.hidden_size)  )




      def     lowercase__   (      self      :			Any      ,      __snake_case      :			List[Any]      ,      __snake_case      :			List[Any]      ,      __snake_case      :			Tuple      ,      __snake_case      :			Tuple      ,      __snake_case      :			Tuple      ,      __snake_case      :			List[Any]      ,      __snake_case      :			Any      ,      __snake_case      :			Optional[int]      ,      __snake_case      :			List[Any]      ,      )    ->				Dict:
            _lowerCAmelCase												=							True
            _lowerCAmelCase												=							LlamaModel(lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )
            model.eval()
            _lowerCAmelCase												=							model(
                lowerCAmelCase__      ,      attention_mask=lowerCAmelCase__      ,      encoder_hidden_states=lowerCAmelCase__      ,      encoder_attention_mask=lowerCAmelCase__      ,      )
            _lowerCAmelCase												=							model(
                lowerCAmelCase__      ,      attention_mask=lowerCAmelCase__      ,      encoder_hidden_states=lowerCAmelCase__      ,      )
            _lowerCAmelCase												=							model(lowerCAmelCase__      ,      attention_mask=lowerCAmelCase__  )
            self.parent.assertEqual(result.last_hidden_state.shape      ,      (self.batch_size, self.seq_length, self.hidden_size)  )




      def     lowercase__   (      self      :			Any      ,      __snake_case      :			Optional[Any]      ,      __snake_case      :			List[Any]      ,      __snake_case      :			Dict      ,      __snake_case      :			Optional[int]      ,      __snake_case      :			str      ,      __snake_case      :			Dict      ,      __snake_case      :			Union[str, Any]      ,      __snake_case      :			Tuple      ,      __snake_case      :			Any      ,      )    ->				Dict:
            _lowerCAmelCase												=							LlamaForCausalLM(config=lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )
            model.eval()
            _lowerCAmelCase												=							model(lowerCAmelCase__      ,      attention_mask=lowerCAmelCase__      ,      labels=lowerCAmelCase__  )
            self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.seq_length, self.vocab_size)  )




      def     lowercase__   (      self      :			Union[str, Any]      ,      __snake_case      :			List[str]      ,      __snake_case      :			int      ,      __snake_case      :			Any      ,      __snake_case      :			int      ,      __snake_case      :			Any      ,      __snake_case      :			Any      ,      __snake_case      :			Optional[int]      ,      __snake_case      :			Dict      ,      __snake_case      :			Optional[Any]      ,      )    ->				Any:
            _lowerCAmelCase												=							True
            _lowerCAmelCase												=							True
            _lowerCAmelCase												=							LlamaForCausalLM(config=lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )
            model.eval()

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

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

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

            _lowerCAmelCase												=							model(
                lowerCAmelCase__      ,      attention_mask=lowerCAmelCase__      ,      encoder_hidden_states=lowerCAmelCase__      ,      encoder_attention_mask=lowerCAmelCase__      ,      output_hidden_states=lowerCAmelCase__      ,      )["""hidden_states"""][0]
            _lowerCAmelCase												=							model(
                lowerCAmelCase__      ,      attention_mask=lowerCAmelCase__      ,      encoder_hidden_states=lowerCAmelCase__      ,      encoder_attention_mask=lowerCAmelCase__      ,      past_key_values=lowerCAmelCase__      ,      output_hidden_states=lowerCAmelCase__      ,      )["""hidden_states"""][0]

            # select random slice
            _lowerCAmelCase												=							ids_tensor((1,)      ,      output_from_past.shape[-1]  ).item()
            _lowerCAmelCase												=							output_from_no_past[:, -3:, random_slice_idx].detach()
            _lowerCAmelCase												=							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(lowerCAmelCase__      ,      lowerCAmelCase__      ,      atol=1E-3  )  )




      def     lowercase__   (      self      :			Any  )    ->				int:
            _lowerCAmelCase												=							self.prepare_config_and_inputs()
            (
                (
                _lowerCAmelCase    
            )					,							(
                _lowerCAmelCase    
            )					,							(
                _lowerCAmelCase    
            )					,							(
                _lowerCAmelCase    
            )					,							(
                _lowerCAmelCase    
            )					,							(
                _lowerCAmelCase    
            )					,							(
                _lowerCAmelCase    
            )					,							    
            )												=							config_and_inputs
            _lowerCAmelCase												=							{"""input_ids""": input_ids, """attention_mask""": input_mask}
            return config, inputs_dict




@require_torch
class 			UpperCAmelCase					(      lowerCamelCase_							,		lowerCamelCase_							,		lowerCamelCase_							,		unittest.TestCase       ):
      _lowercase:      int											=       (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
      _lowercase:      Dict											=       (LlamaForCausalLM,) if is_torch_available() else ()
      _lowercase:      List[Any]											=       (
          {
              """feature-extraction""": LlamaModel,
              """text-classification""": LlamaForSequenceClassification,
              """text-generation""": LlamaForCausalLM,
              """zero-shot""": LlamaForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      _lowercase:      List[str]											=       False
      _lowercase:      int											=       False




      def     lowercase__   (      self      :			Any  )    ->				Union[str, Any]:
            _lowerCAmelCase												=							LlamaModelTester(self  )
            _lowerCAmelCase												=							ConfigTester(self      ,      config_class=lowerCAmelCase__      ,      hidden_size=37  )




      def     lowercase__   (      self      :			str  )    ->				Any:
            self.config_tester.run_common_tests()




      def     lowercase__   (      self      :			int  )    ->				str:
            _lowerCAmelCase												=							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCAmelCase__  )




      def     lowercase__   (      self      :			int  )    ->				Tuple:
            _lowerCAmelCase												=							self.model_tester.prepare_config_and_inputs()
            for type in ["absolute", "relative_key", "relative_key_query"]:
                  _lowerCAmelCase												=							type
                  self.model_tester.create_and_check_model(*lowerCAmelCase__  )




      def     lowercase__   (      self      :			int  )    ->				int:
            _lowerCAmelCase					,							_lowerCAmelCase												=							self.model_tester.prepare_config_and_inputs_for_common()
            _lowerCAmelCase												=							3
            _lowerCAmelCase												=							input_dict["""input_ids"""]
            _lowerCAmelCase												=							input_ids.ne(1  ).to(lowerCAmelCase__  )
            _lowerCAmelCase												=							ids_tensor([self.model_tester.batch_size]      ,      self.model_tester.type_sequence_label_size  )
            _lowerCAmelCase												=							LlamaForSequenceClassification(lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )
            model.eval()
            _lowerCAmelCase												=							model(lowerCAmelCase__      ,      attention_mask=lowerCAmelCase__      ,      labels=lowerCAmelCase__  )
            self.assertEqual(result.logits.shape      ,      (self.model_tester.batch_size, self.model_tester.num_labels)  )




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




      def     lowercase__   (      self      :			Any  )    ->				Optional[Any]:
            _lowerCAmelCase					,							_lowerCAmelCase												=							self.model_tester.prepare_config_and_inputs_for_common()
            _lowerCAmelCase												=							3
            _lowerCAmelCase												=							"""multi_label_classification"""
            _lowerCAmelCase												=							input_dict["""input_ids"""]
            _lowerCAmelCase												=							input_ids.ne(1  ).to(lowerCAmelCase__  )
            _lowerCAmelCase												=							ids_tensor(
                [self.model_tester.batch_size, config.num_labels]      ,      self.model_tester.type_sequence_label_size  ).to(torch.float  )
            _lowerCAmelCase												=							LlamaForSequenceClassification(lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )
            model.eval()
            _lowerCAmelCase												=							model(lowerCAmelCase__      ,      attention_mask=lowerCAmelCase__      ,      labels=lowerCAmelCase__  )
            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     lowercase__   (      self      :			str  )    ->				List[str]:
            pass




      @parameterized.expand([("""linear""",), ("""dynamic""",)]  )
      def     lowercase__   (      self      :			Union[str, Any]      ,      __snake_case      :			List[Any]  )    ->				Dict:
            _lowerCAmelCase					,							_lowerCAmelCase												=							self.model_tester.prepare_config_and_inputs_for_common()
            _lowerCAmelCase												=							ids_tensor([1, 10]      ,      config.vocab_size  )
            _lowerCAmelCase												=							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												=							LlamaModel(lowerCAmelCase__  )
            original_model.to(lowerCAmelCase__  )
            original_model.eval()
            _lowerCAmelCase												=							original_model(lowerCAmelCase__  ).last_hidden_state
            _lowerCAmelCase												=							original_model(lowerCAmelCase__  ).last_hidden_state

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

            # The output should be different for long inputs
            self.assertFalse(torch.allclose(lowerCAmelCase__      ,      lowerCAmelCase__      ,      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     lowercase__   (      self      :			Optional[Any]  )    ->				List[str]:
            _lowerCAmelCase												=							[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
            _lowerCAmelCase												=							LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-7b-hf"""      ,      device_map="""auto"""  )
            _lowerCAmelCase												=							model(torch.tensor([input_ids]  )  )
            # Expected mean on dim = -1
            _lowerCAmelCase												=							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  )      ,      lowerCAmelCase__      ,      atol=1E-2      ,      rtol=1E-2  )
            # slicing logits[0, 0, 0:30]
            # fmt: off
            _lowerCAmelCase												=							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]      ,      lowerCAmelCase__      ,      atol=1E-5      ,      rtol=1E-5  )




      @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""  )
      @slow
      def     lowercase__   (      self      :			Optional[Any]  )    ->				Union[str, Any]:
            _lowerCAmelCase												=							[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
            _lowerCAmelCase												=							LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-hf"""      ,      device_map="""auto"""  )
            _lowerCAmelCase												=							model(torch.tensor(lowerCAmelCase__  )  )
            # Expected mean on dim = -1
            _lowerCAmelCase												=							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  )      ,      lowerCAmelCase__      ,      atol=1E-2      ,      rtol=1E-2  )
            # slicing logits[0, 0, 0:30]
            # fmt: off
            _lowerCAmelCase												=							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]      ,      lowerCAmelCase__      ,      atol=1E-5      ,      rtol=1E-5  )




      @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""  )
      @slow
      def     lowercase__   (      self      :			Optional[int]  )    ->				str:
            _lowerCAmelCase												=							[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
            _lowerCAmelCase												=							LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""      ,      device_map="""auto"""  )
            _lowerCAmelCase												=							model(torch.tensor(lowerCAmelCase__  )  )
            # Expected mean on dim = -1
            _lowerCAmelCase												=							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  )      ,      lowerCAmelCase__      ,      atol=1E-2      ,      rtol=1E-2  )
            # slicing logits[0, 0, 0:30]
            # fmt: off
            _lowerCAmelCase												=							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  )      ,      lowerCAmelCase__      ,      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     lowercase__   (      self      :			List[Any]  )    ->				int:
            _lowerCAmelCase												=							[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
            _lowerCAmelCase												=							LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-70b-hf"""      ,      device_map="""auto"""  )
            _lowerCAmelCase												=							model(torch.tensor(lowerCAmelCase__  )  )

            _lowerCAmelCase												=							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  )      ,      lowerCAmelCase__      ,      atol=1E-2      ,      rtol=1E-2  )
            # fmt: off
            _lowerCAmelCase												=							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]      ,      lowerCAmelCase__      ,      atol=1E-5      ,      rtol=1E-5  )




      @unittest.skip("""Model is curently gated"""  )
      @slow
      def     lowercase__   (      self      :			List[str]  )    ->				str:
            _lowerCAmelCase												=							"""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												=							"""Simply put, the theory of relativity states that """
            _lowerCAmelCase												=							LlamaTokenizer.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""  )
            _lowerCAmelCase												=							tokenizer.encode(lowerCAmelCase__      ,      return_tensors="""pt"""  )
            _lowerCAmelCase												=							LlamaForCausalLM.from_pretrained(
                """meta-llama/Llama-2-13b-chat-hf"""      ,      device_map="""sequential"""      ,      use_safetensors=lowerCAmelCase__  )

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













'''simple docstring'''






# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

A__    :       List[str]              =TypeVar('''T''')




class 			UpperCAmelCase					(      Generic[T]       ):




      def __init__(      self      :			Tuple      ,      __snake_case      :			bool = True  )    ->				None:
            _lowerCAmelCase												=							{}  # dictionary of lists
            _lowerCAmelCase												=							directed




      def     lowercase__   (      self      :			Union[str, Any]      ,      __snake_case      :			T      ,      __snake_case      :			T  )    ->				GraphAdjacencyList[T]:

            if not self.directed:  # For undirected graphs
                  # if both source vertex and destination vertex are both present in the
                  # adjacency list, add destination vertex to source vertex list of adjacent
                  # vertices and add source vertex to destination vertex list of adjacent
                  # vertices.
                  if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                        self.adj_list[source_vertex].append(__snake_case  )
                        self.adj_list[destination_vertex].append(__snake_case  )
                  # if only source vertex is present in adjacency list, add destination vertex
                  # to source vertex list of adjacent vertices, then create a new vertex with
                  # destination vertex as key and assign a list containing the source vertex
                  # as it's first adjacent vertex.
                  elif source_vertex in self.adj_list:
                        self.adj_list[source_vertex].append(__snake_case  )
                        _lowerCAmelCase												=							[source_vertex]
                  # if only destination vertex is present in adjacency list, add source vertex
                  # to destination vertex list of adjacent vertices, then create a new vertex
                  # with source vertex as key and assign a list containing the source vertex
                  # as it's first adjacent vertex.
                  elif destination_vertex in self.adj_list:
                        self.adj_list[destination_vertex].append(__snake_case  )
                        _lowerCAmelCase												=							[destination_vertex]
                  # if both source vertex and destination vertex are not present in adjacency
                  # list, create a new vertex with source vertex as key and assign a list
                  # containing the destination vertex as it's first adjacent vertex also
                  # create a new vertex with destination vertex as key and assign a list
                  # containing the source vertex as it's first adjacent vertex.
                  else:
                        _lowerCAmelCase												=							[destination_vertex]
                        _lowerCAmelCase												=							[source_vertex]
            else:  # For directed graphs
                  # if both source vertex and destination vertex are present in adjacency
                  # list, add destination vertex to source vertex list of adjacent vertices.
                  if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                        self.adj_list[source_vertex].append(__snake_case  )
                  # if only source vertex is present in adjacency list, add destination
                  # vertex to source vertex list of adjacent vertices and create a new vertex
                  # with destination vertex as key, which has no adjacent vertex
                  elif source_vertex in self.adj_list:
                        self.adj_list[source_vertex].append(__snake_case  )
                        _lowerCAmelCase												=							[]
                  # if only destination vertex is present in adjacency list, create a new
                  # vertex with source vertex as key and assign a list containing destination
                  # vertex as first adjacent vertex
                  elif destination_vertex in self.adj_list:
                        _lowerCAmelCase												=							[destination_vertex]
                  # if both source vertex and destination vertex are not present in adjacency
                  # list, create a new vertex with source vertex as key and a list containing
                  # destination vertex as it's first adjacent vertex. Then create a new vertex
                  # with destination vertex as key, which has no adjacent vertex
                  else:
                        _lowerCAmelCase												=							[destination_vertex]
                        _lowerCAmelCase												=							[]

            return self




      def __repr__(      self      :			int  )    ->				str:
            return pformat(self.adj_list  )






