'''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				,     UpperCamelCase_				,     UpperCamelCase_=13				,     UpperCamelCase_=7				,     UpperCamelCase_=True				,     UpperCamelCase_=True				,     UpperCamelCase_=False				,     UpperCamelCase_=True				,     UpperCamelCase_=99				,     UpperCamelCase_=32				,     UpperCamelCase_=5				,     UpperCamelCase_=4				,     UpperCamelCase_=37				,     UpperCamelCase_="gelu"				,     UpperCamelCase_=0.1				,     UpperCamelCase_=0.1				,     UpperCamelCase_=5_12				,     UpperCamelCase_=16				,     UpperCamelCase_=2				,     UpperCamelCase_=0.0_2				,     UpperCamelCase_=3				,     UpperCamelCase_=4				,     UpperCamelCase_=None				,     ):
              __UpperCAmelCase  :				Optional[Any]       				=		parent
              __UpperCAmelCase  :				Optional[int]       				=		batch_size
              __UpperCAmelCase  :				int       				=		seq_length
              __UpperCAmelCase  :				Optional[int]       				=		is_training
              __UpperCAmelCase  :				Optional[int]       				=		use_input_mask
              __UpperCAmelCase  :				Union[str, Any]       				=		use_token_type_ids
              __UpperCAmelCase  :				Tuple       				=		use_labels
              __UpperCAmelCase  :				List[Any]       				=		vocab_size
              __UpperCAmelCase  :				int       				=		hidden_size
              __UpperCAmelCase  :				List[str]       				=		num_hidden_layers
              __UpperCAmelCase  :				str       				=		num_attention_heads
              __UpperCAmelCase  :				Dict       				=		intermediate_size
              __UpperCAmelCase  :				Tuple       				=		hidden_act
              __UpperCAmelCase  :				Union[str, Any]       				=		hidden_dropout_prob
              __UpperCAmelCase  :				Optional[int]       				=		attention_probs_dropout_prob
              __UpperCAmelCase  :				int       				=		max_position_embeddings
              __UpperCAmelCase  :				Any       				=		type_vocab_size
              __UpperCAmelCase  :				List[Any]       				=		type_sequence_label_size
              __UpperCAmelCase  :				Dict       				=		initializer_range
              __UpperCAmelCase  :				Any       				=		num_labels
              __UpperCAmelCase  :				Union[str, Any]       				=		num_choices
              __UpperCAmelCase  :				List[str]       				=		scope






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[Any]       				=		ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size      )

              __UpperCAmelCase  :				Optional[int]       				=		None
              if self.use_input_mask:
                     __UpperCAmelCase  :				Union[str, Any]       				=		random_attention_mask([self.batch_size, self.seq_length]      )

              __UpperCAmelCase  :				Optional[Any]       				=		None
              if self.use_token_type_ids:
                     __UpperCAmelCase  :				List[Any]       				=		ids_tensor([self.batch_size, self.seq_length]				,     self.type_vocab_size      )

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

              __UpperCAmelCase  :				Tuple       				=		self.get_config()

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






       def 			_snake_case						(      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=__UpperCamelCase				,     initializer_range=self.initializer_range				,     )






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Dict       				=		LlamaModel(config=__UpperCamelCase      )
              model.to(__UpperCamelCase      )
              model.eval()
              __UpperCAmelCase  :				Optional[Any]       				=		model(__UpperCamelCase				,     attention_mask=__UpperCamelCase      )
              __UpperCAmelCase  :				Any       				=		model(__UpperCamelCase      )
              self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size)      )






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     ):
              __UpperCAmelCase  :				Optional[Any]       				=		True
              __UpperCAmelCase  :				List[Any]       				=		LlamaModel(__UpperCamelCase      )
              model.to(__UpperCamelCase      )
              model.eval()
              __UpperCAmelCase  :				Optional[Any]       				=		model(
                  __UpperCamelCase				,     attention_mask=__UpperCamelCase				,     encoder_hidden_states=__UpperCamelCase				,     encoder_attention_mask=__UpperCamelCase				,     )
              __UpperCAmelCase  :				Dict       				=		model(
                  __UpperCamelCase				,     attention_mask=__UpperCamelCase				,     encoder_hidden_states=__UpperCamelCase				,     )
              __UpperCAmelCase  :				Optional[int]       				=		model(__UpperCamelCase				,     attention_mask=__UpperCamelCase      )
              self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size)      )






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     ):
              __UpperCAmelCase  :				Tuple       				=		LlamaForCausalLM(config=__UpperCamelCase      )
              model.to(__UpperCamelCase      )
              model.eval()
              __UpperCAmelCase  :				Optional[int]       				=		model(__UpperCamelCase				,     attention_mask=__UpperCamelCase				,     labels=__UpperCamelCase      )
              self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.vocab_size)      )






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     ):
              __UpperCAmelCase  :				int       				=		True
              __UpperCAmelCase  :				Dict       				=		True
              __UpperCAmelCase  :				int       				=		LlamaForCausalLM(config=__UpperCamelCase      )
              model.to(__UpperCamelCase      )
              model.eval()

              # first forward pass
              __UpperCAmelCase  :				int       				=		model(
                  __UpperCamelCase				,     attention_mask=__UpperCamelCase				,     encoder_hidden_states=__UpperCamelCase				,     encoder_attention_mask=__UpperCamelCase				,     use_cache=__UpperCamelCase				,     )
              __UpperCAmelCase  :				Dict       				=		outputs.past_key_values

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

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

              __UpperCAmelCase  :				str       				=		model(
                  __UpperCamelCase				,     attention_mask=__UpperCamelCase				,     encoder_hidden_states=__UpperCamelCase				,     encoder_attention_mask=__UpperCamelCase				,     output_hidden_states=__UpperCamelCase				,     )["hidden_states"][0]
              __UpperCAmelCase  :				List[Any]       				=		model(
                  __UpperCamelCase				,     attention_mask=__UpperCamelCase				,     encoder_hidden_states=__UpperCamelCase				,     encoder_attention_mask=__UpperCamelCase				,     past_key_values=__UpperCamelCase				,     output_hidden_states=__UpperCamelCase				,     )["hidden_states"][0]

              # select random slice
              __UpperCAmelCase  :				Dict       				=		ids_tensor((1,)				,     output_from_past.shape[-1]      ).item()
              __UpperCAmelCase  :				List[str]       				=		output_from_no_past[:, -3:, random_slice_idx].detach()
              __UpperCAmelCase  :				str       				=		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(__UpperCamelCase				,     __UpperCamelCase				,     atol=1E-3      )      )







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






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






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Dict       				=		LlamaModelTester(self      )
              __UpperCAmelCase  :				Tuple       				=		ConfigTester(self				,     config_class=__UpperCamelCase				,     hidden_size=37      )






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






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				str       				=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*__UpperCamelCase      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Tuple       				=		self.model_tester.prepare_config_and_inputs()
              for type in ["absolute", "relative_key", "relative_key_query"]:
                     __UpperCAmelCase  :				int       				=		type
                     self.model_tester.create_and_check_model(*__UpperCamelCase      )






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






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






       def 			_snake_case						(      self      ):
              __UpperCAmelCase				, __UpperCAmelCase  :				List[str]       				=		self.model_tester.prepare_config_and_inputs_for_common()
              __UpperCAmelCase  :				List[Any]       				=		3
              __UpperCAmelCase  :				str       				=		"multi_label_classification"
              __UpperCAmelCase  :				Tuple       				=		input_dict["input_ids"]
              __UpperCAmelCase  :				Tuple       				=		input_ids.ne(1      ).to(__UpperCamelCase      )
              __UpperCAmelCase  :				Optional[int]       				=		ids_tensor(
                  [self.model_tester.batch_size, config.num_labels]				,     self.model_tester.type_sequence_label_size      ).to(torch.float      )
              __UpperCAmelCase  :				Any       				=		LlamaForSequenceClassification(__UpperCamelCase      )
              model.to(__UpperCamelCase      )
              model.eval()
              __UpperCAmelCase  :				int       				=		model(__UpperCamelCase				,     attention_mask=__UpperCamelCase				,     labels=__UpperCamelCase      )
              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 			_snake_case						(      self      ):
              pass







       @parameterized.expand([("linear",), ("dynamic",)]      )
       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase				, __UpperCAmelCase  :				Optional[int]       				=		self.model_tester.prepare_config_and_inputs_for_common()
              __UpperCAmelCase  :				Union[str, Any]       				=		ids_tensor([1, 10]				,     config.vocab_size      )
              __UpperCAmelCase  :				Dict       				=		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  :				str       				=		LlamaModel(__UpperCamelCase      )
              original_model.to(__UpperCamelCase      )
              original_model.eval()
              __UpperCAmelCase  :				List[str]       				=		original_model(__UpperCamelCase      ).last_hidden_state
              __UpperCAmelCase  :				int       				=		original_model(__UpperCamelCase      ).last_hidden_state

              set_seed(42      )  # Fixed seed at init time so the two models get the same random weights
              __UpperCAmelCase  :				Tuple       				=		{"type": scaling_type, "factor": 1_0.0}
              __UpperCAmelCase  :				Any       				=		LlamaModel(__UpperCamelCase      )
              scaled_model.to(__UpperCamelCase      )
              scaled_model.eval()
              __UpperCAmelCase  :				int       				=		scaled_model(__UpperCamelCase      ).last_hidden_state
              __UpperCAmelCase  :				List[str]       				=		scaled_model(__UpperCamelCase      ).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(__UpperCamelCase				,     __UpperCamelCase				,     atol=1E-5      )      )
              else:
                     self.assertFalse(torch.allclose(__UpperCamelCase				,     __UpperCamelCase				,     atol=1E-5      )      )

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






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






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

              __UpperCAmelCase  :				Any       				=		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      )				,     __UpperCamelCase				,     atol=1E-2				,     rtol=1E-2      )
              # fmt: off
              __UpperCAmelCase  :				List[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]				,     __UpperCamelCase				,     atol=1E-5				,     rtol=1E-5      )







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

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





'''simple docstring'''




class 		__A       :






       def __init__(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Any       				=		set_counts
              __UpperCAmelCase  :				int       				=		max(UpperCamelCase_      )
              __UpperCAmelCase  :				List[str]       				=		len(UpperCamelCase_      )
              __UpperCAmelCase  :				Any       				=		[1] * num_sets
              __UpperCAmelCase  :				Any       				=		list(range(UpperCamelCase_      )      )






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Optional[int]       				=		self.get_parent(UpperCamelCase_      )
              __UpperCAmelCase  :				List[Any]       				=		self.get_parent(UpperCamelCase_      )

              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]
                     __UpperCAmelCase  :				Optional[Any]       				=		0
                     __UpperCAmelCase  :				List[Any]       				=		dst_parent
                     if self.ranks[dst_parent] == self.ranks[src_parent]:
                            self.ranks[dst_parent] += 1
                     __UpperCAmelCase  :				Union[str, Any]       				=		self.set_counts[dst_parent]
              else:
                     self.set_counts[src_parent] += self.set_counts[dst_parent]
                     __UpperCAmelCase  :				Union[str, Any]       				=		0
                     __UpperCAmelCase  :				Dict       				=		src_parent
                     __UpperCAmelCase  :				Dict       				=		self.set_counts[src_parent]

              __UpperCAmelCase  :				Dict       				=		max(self.max_set				,     UpperCamelCase_      )
              return True







       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              if self.parents[disj_set] == disj_set:
                     return disj_set
              __UpperCAmelCase  :				str       				=		self.get_parent(self.parents[disj_set]      )
              return self.parents[disj_set]






