

"""simple docstring"""




class 		_A       :





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__		)   ->					None:
         lowerCamelCase__						=			set_counts
         lowerCamelCase__						=			max(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			len(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			[1] * num_sets
         lowerCamelCase__						=			list(range(SCREAMING_SNAKE_CASE__		)		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					bool:
         lowerCamelCase__						=			self.get_parent(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			self.get_parent(SCREAMING_SNAKE_CASE__		)

         if src_parent == dst_parent:
            return False

         if self.ranks[dst_parent] >= self.ranks[src_parent]:
            self.set_counts[dst_parent] += self.set_counts[src_parent]
            lowerCamelCase__						=			0
            lowerCamelCase__						=			dst_parent
            if self.ranks[dst_parent] == self.ranks[src_parent]:
               self.ranks[dst_parent] += 1
            lowerCamelCase__						=			self.set_counts[dst_parent]
         else:
            self.set_counts[src_parent] += self.set_counts[dst_parent]
            lowerCamelCase__						=			0
            lowerCamelCase__						=			src_parent
            lowerCamelCase__						=			self.set_counts[src_parent]

         lowerCamelCase__						=			max(self.max_set    ,						SCREAMING_SNAKE_CASE__		)
         return True





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					int:
         if self.parents[disj_set] == disj_set:
            return disj_set
         lowerCamelCase__						=			self.get_parent(self.parents[disj_set]		)
         return self.parents[disj_set]







"""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    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=13    ,						SCREAMING_SNAKE_CASE__=7    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=False    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=99    ,						SCREAMING_SNAKE_CASE__=32    ,						SCREAMING_SNAKE_CASE__=5    ,						SCREAMING_SNAKE_CASE__=4    ,						SCREAMING_SNAKE_CASE__=37    ,						SCREAMING_SNAKE_CASE__="gelu"    ,						SCREAMING_SNAKE_CASE__=0.1    ,						SCREAMING_SNAKE_CASE__=0.1    ,						SCREAMING_SNAKE_CASE__=512    ,						SCREAMING_SNAKE_CASE__=16    ,						SCREAMING_SNAKE_CASE__=2    ,						SCREAMING_SNAKE_CASE__=0.02    ,						SCREAMING_SNAKE_CASE__=3    ,						SCREAMING_SNAKE_CASE__=4    ,						SCREAMING_SNAKE_CASE__=None    ,						)   ->					List[str]:
         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 					_lowerCamelCase  (  self		)   ->					Optional[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 					_lowerCamelCase  (  self		)   ->					List[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=SCREAMING_SNAKE_CASE__    ,						initializer_range=self.initializer_range    ,						)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					int:
         lowerCamelCase__						=			LlamaModel(config=SCREAMING_SNAKE_CASE__		)
         model.to(SCREAMING_SNAKE_CASE__		)
         model.eval()
         lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__    ,						attention_mask=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__		)
         self.parent.assertEqual(result.last_hidden_state.shape    ,						(self.batch_size, self.seq_length, self.hidden_size)		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						)   ->					Any:
         lowerCamelCase__						=			True
         lowerCamelCase__						=			LlamaModel(SCREAMING_SNAKE_CASE__		)
         model.to(SCREAMING_SNAKE_CASE__		)
         model.eval()
         lowerCamelCase__						=			model(
             SCREAMING_SNAKE_CASE__    ,						attention_mask=SCREAMING_SNAKE_CASE__    ,						encoder_hidden_states=SCREAMING_SNAKE_CASE__    ,						encoder_attention_mask=SCREAMING_SNAKE_CASE__    ,						)
         lowerCamelCase__						=			model(
             SCREAMING_SNAKE_CASE__    ,						attention_mask=SCREAMING_SNAKE_CASE__    ,						encoder_hidden_states=SCREAMING_SNAKE_CASE__    ,						)
         lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__    ,						attention_mask=SCREAMING_SNAKE_CASE__		)
         self.parent.assertEqual(result.last_hidden_state.shape    ,						(self.batch_size, self.seq_length, self.hidden_size)		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						)   ->					str:
         lowerCamelCase__						=			LlamaForCausalLM(config=SCREAMING_SNAKE_CASE__		)
         model.to(SCREAMING_SNAKE_CASE__		)
         model.eval()
         lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__    ,						attention_mask=SCREAMING_SNAKE_CASE__    ,						labels=SCREAMING_SNAKE_CASE__		)
         self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.seq_length, self.vocab_size)		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						)   ->					Union[str, Any]:
         lowerCamelCase__						=			True
         lowerCamelCase__						=			True
         lowerCamelCase__						=			LlamaForCausalLM(config=SCREAMING_SNAKE_CASE__		)
         model.to(SCREAMING_SNAKE_CASE__		)
         model.eval()

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





      def 					_lowerCamelCase  (  self		)   ->					Any:
         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 		_A       (			__a				,       __a				,       __a				,       unittest.TestCase					):
      __a								=	(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
      __a								=	(LlamaForCausalLM,) if is_torch_available() else ()
      __a								=	(
          {
              'feature-extraction': LlamaModel,
              'text-classification': LlamaForSequenceClassification,
              'text-generation': LlamaForCausalLM,
              'zero-shot': LlamaForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      __a								=	False
      __a								=	False





      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         lowerCamelCase__						=			LlamaModelTester(self		)
         lowerCamelCase__						=			ConfigTester(self    ,						config_class=SCREAMING_SNAKE_CASE__    ,						hidden_size=37		)





      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         self.config_tester.run_common_tests()





      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         lowerCamelCase__						=			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         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(*SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         lowerCamelCase__					,       lowerCamelCase__						=			self.model_tester.prepare_config_and_inputs_for_common()
         lowerCamelCase__						=			3
         lowerCamelCase__						=			input_dict["input_ids"]
         lowerCamelCase__						=			input_ids.ne(1		).to(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			ids_tensor([self.model_tester.batch_size]    ,						self.model_tester.type_sequence_label_size		)
         lowerCamelCase__						=			LlamaForSequenceClassification(SCREAMING_SNAKE_CASE__		)
         model.to(SCREAMING_SNAKE_CASE__		)
         model.eval()
         lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__    ,						attention_mask=SCREAMING_SNAKE_CASE__    ,						labels=SCREAMING_SNAKE_CASE__		)
         self.assertEqual(result.logits.shape    ,						(self.model_tester.batch_size, self.model_tester.num_labels)		)





      def 					_lowerCamelCase  (  self		)   ->					Dict:
         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(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			ids_tensor([self.model_tester.batch_size]    ,						self.model_tester.type_sequence_label_size		)
         lowerCamelCase__						=			LlamaForSequenceClassification(SCREAMING_SNAKE_CASE__		)
         model.to(SCREAMING_SNAKE_CASE__		)
         model.eval()
         lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__    ,						attention_mask=SCREAMING_SNAKE_CASE__    ,						labels=SCREAMING_SNAKE_CASE__		)
         self.assertEqual(result.logits.shape    ,						(self.model_tester.batch_size, self.model_tester.num_labels)		)





      def 					_lowerCamelCase  (  self		)   ->					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(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			ids_tensor(
             [self.model_tester.batch_size, config.num_labels]    ,						self.model_tester.type_sequence_label_size		).to(torch.float		)
         lowerCamelCase__						=			LlamaForSequenceClassification(SCREAMING_SNAKE_CASE__		)
         model.to(SCREAMING_SNAKE_CASE__		)
         model.eval()
         lowerCamelCase__						=			model(SCREAMING_SNAKE_CASE__    ,						attention_mask=SCREAMING_SNAKE_CASE__    ,						labels=SCREAMING_SNAKE_CASE__		)
         self.assertEqual(result.logits.shape    ,						(self.model_tester.batch_size, self.model_tester.num_labels)		)





      @unittest.skip("LLaMA buffers include complex numbers, which breaks this test"		)
      def 					_lowerCamelCase  (  self		)   ->					List[Any]:
         pass





      @parameterized.expand([("linear",), ("dynamic",)]		)
      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					Union[str, Any]:
         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(SCREAMING_SNAKE_CASE__		)
         original_model.to(SCREAMING_SNAKE_CASE__		)
         original_model.eval()
         lowerCamelCase__						=			original_model(SCREAMING_SNAKE_CASE__		).last_hidden_state
         lowerCamelCase__						=			original_model(SCREAMING_SNAKE_CASE__		).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(SCREAMING_SNAKE_CASE__		)
         scaled_model.to(SCREAMING_SNAKE_CASE__		)
         scaled_model.eval()
         lowerCamelCase__						=			scaled_model(SCREAMING_SNAKE_CASE__		).last_hidden_state
         lowerCamelCase__						=			scaled_model(SCREAMING_SNAKE_CASE__		).last_hidden_state

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

         # The output should be different for long inputs
         self.assertFalse(torch.allclose(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						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 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         lowerCamelCase__						=			[1, 306, 4658, 278, 6593, 310, 2834, 338]
         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		)    ,						SCREAMING_SNAKE_CASE__    ,						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]    ,						SCREAMING_SNAKE_CASE__    ,						atol=1e-5    ,						rtol=1e-5		)





      @unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!"		)
      @slow
      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         lowerCamelCase__						=			[1, 306, 4658, 278, 6593, 310, 2834, 338]
         lowerCamelCase__						=			LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf"    ,						device_map="auto"		)
         lowerCamelCase__						=			model(torch.tensor(SCREAMING_SNAKE_CASE__		)		)
         # 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		)    ,						SCREAMING_SNAKE_CASE__    ,						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]    ,						SCREAMING_SNAKE_CASE__    ,						atol=1e-5    ,						rtol=1e-5		)





      @unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!"		)
      @slow
      def 					_lowerCamelCase  (  self		)   ->					Optional[int]:
         lowerCamelCase__						=			[1, 306, 4658, 278, 6593, 310, 2834, 338]
         lowerCamelCase__						=			LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf"    ,						device_map="auto"		)
         lowerCamelCase__						=			model(torch.tensor(SCREAMING_SNAKE_CASE__		)		)
         # 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		)    ,						SCREAMING_SNAKE_CASE__    ,						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		)    ,						SCREAMING_SNAKE_CASE__    ,						atol=1e-2    ,						rtol=1e-2		)





      @unittest.skip(
          "Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test"		)
      @slow
      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         lowerCamelCase__						=			[1, 306, 4658, 278, 6593, 310, 2834, 338]
         lowerCamelCase__						=			LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-70b-hf"    ,						device_map="auto"		)
         lowerCamelCase__						=			model(torch.tensor(SCREAMING_SNAKE_CASE__		)		)

         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		)    ,						SCREAMING_SNAKE_CASE__    ,						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]    ,						SCREAMING_SNAKE_CASE__    ,						atol=1e-5    ,						rtol=1e-5		)





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

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




