




"""simple docstring"""






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


lowercase__			=		R"""
    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.

    Args:
        title_sep (`str`, *optional*, defaults to  `\" / \"`):
            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].
        doc_sep (`str`, *optional*, defaults to  `\" // \"`):
            Separator inserted between the text of the retrieved document and the original input when calling
            [`RagRetriever`].
        n_docs (`int`, *optional*, defaults to 5):
            Number of documents to retrieve.
        max_combined_length (`int`, *optional*, defaults to 300):
            Max length of contextualized input returned by [`~RagRetriever.__call__`].
        retrieval_vector_size (`int`, *optional*, defaults to 768):
            Dimensionality of the document embeddings indexed by [`RagRetriever`].
        retrieval_batch_size (`int`, *optional*, defaults to 8):
            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated
            [`RagRetriever`].
        dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):
            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids
            using `datasets.list_datasets()`).
        dataset_split (`str`, *optional*, defaults to `\"train\"`)
            Which split of the `dataset` to load.
        index_name (`str`, *optional*, defaults to `\"compressed\"`)
            The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and
            `\"compressed\"`.
        index_path (`str`, *optional*)
            The path to the serialized faiss index on disk.
        passages_path (`str`, *optional*):
            A path to text passages compatible with the faiss index. Required if using
            [`~models.rag.retrieval_rag.LegacyIndex`]
        use_dummy_dataset (`bool`, *optional*, defaults to `False`)
            Whether to load a \"dummy\" variant of the dataset specified by `dataset`.
        label_smoothing (`float`, *optional*, defaults to 0.0):
            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing
            in the loss calculation. If set to 0, no label smoothing is performed.
        do_marginalize (`bool`, *optional*, defaults to `False`):
            If `True`, the logits are marginalized over all documents by making use of
            `torch.nn.functional.log_softmax`.
        reduce_loss (`bool`, *optional*, defaults to `False`):
            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.
        do_deduplication (`bool`, *optional*, defaults to `True`):
            Whether or not to deduplicate the generations from different context documents for a given input. Has to be
            set to `False` if used while training with distributed backend.
        exclude_bos_score (`bool`, *optional*, defaults to `False`):
            Whether or not to disregard the BOS token when computing the loss.
        output_retrieved(`bool`, *optional*, defaults to `False`):
            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and
            `context_attention_mask` are returned. See returned tensors for more detail.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
        forced_eos_token_id (`int`, *optional*):
            The id of the token to force as the last generated token when `max_length` is reached. Usually set to
            `eos_token_id`.
"""






@add_start_docstrings(lowercase		)
class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     """rag"""
			lowerCamelCase__ =     True
			def __init__(    self    , lowercase=None    , lowercase=True    , lowercase=None    , lowercase=None    , lowercase=None    , lowercase=None    , lowercase=None    , lowercase=" / "    , lowercase=" // "    , lowercase=5    , lowercase=300    , lowercase=768    , lowercase=8    , lowercase="wiki_dpr"    , lowercase="train"    , lowercase="compressed"    , lowercase=None    , lowercase=None    , lowercase=False    , lowercase=False    , lowercase=0.0    , lowercase=True    , lowercase=False    , lowercase=False    , lowercase=False    , lowercase=True    , lowercase=None    , **lowercase    , ):
							super().__init__(
							    bos_token_id=lowercase    , pad_token_id=lowercase    , eos_token_id=lowercase    , decoder_start_token_id=lowercase    , forced_eos_token_id=lowercase    , is_encoder_decoder=lowercase    , prefix=lowercase    , vocab_size=lowercase    , **lowercase    , )
							assert (
							    "question_encoder" in kwargs and "generator" in kwargs
							), "Config has to be initialized with question_encoder and generator config"
							_lowerCamelCase	:     Union[str, Any]       =       kwargs.pop('question_encoder'    )
							_lowerCamelCase	:     Optional[Any]       =       question_encoder_config.pop('model_type'    )
							_lowerCamelCase	:     Any       =       kwargs.pop('generator'    )
							_lowerCamelCase	:     Any       =       decoder_config.pop('model_type'    )

							from ..auto.configuration_auto import AutoConfig

							_lowerCamelCase	:     List[Any]       =       AutoConfig.for_model(lowercase    , **lowercase    )
							_lowerCamelCase	:     str       =       AutoConfig.for_model(lowercase    , **lowercase    )

							_lowerCamelCase	:     str       =       reduce_loss
							_lowerCamelCase	:     Tuple       =       label_smoothing
							_lowerCamelCase	:     int       =       exclude_bos_score
							_lowerCamelCase	:     Any       =       do_marginalize

							_lowerCamelCase	:     Any       =       title_sep
							_lowerCamelCase	:     Optional[int]       =       doc_sep
							_lowerCamelCase	:     List[str]       =       n_docs
							_lowerCamelCase	:     int       =       max_combined_length

							_lowerCamelCase	:     Optional[Any]       =       dataset
							_lowerCamelCase	:     Dict       =       dataset_split
							_lowerCamelCase	:     Tuple       =       index_name

							_lowerCamelCase	:     int       =       retrieval_vector_size
							_lowerCamelCase	:     str       =       retrieval_batch_size
							_lowerCamelCase	:     Optional[int]       =       passages_path
							_lowerCamelCase	:     Optional[int]       =       index_path
							_lowerCamelCase	:     Dict       =       use_dummy_dataset

							_lowerCamelCase	:     Dict       =       output_retrieved

							_lowerCamelCase	:     str       =       do_deduplication

							_lowerCamelCase	:     List[str]       =       use_cache

							if self.forced_eos_token_id is None:
											_lowerCamelCase	:     List[Any]       =       getattr(self.generator    , 'forced_eos_token_id'    , lowercase    )
			@classmethod
			def    A_       (    cls    , lowercase    , lowercase    , **lowercase    ):
							return cls(question_encoder=question_encoder_config.to_dict()    , generator=generator_config.to_dict()    , **lowercase    )






			def    A_       (    self    ):
							_lowerCamelCase	:     Optional[int]       =       copy.deepcopy(self.__dict__    )
							_lowerCamelCase	:     str       =       self.question_encoder.to_dict()
							_lowerCamelCase	:     Optional[int]       =       self.generator.to_dict()
							_lowerCamelCase	:     Dict       =       self.__class__.model_type
							return output





"""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 lowerCAmelCase__      :






			'''simple docstring'''



			def __init__(    self    , lowercase    , lowercase=13    , lowercase=7    , lowercase=True    , lowercase=True    , lowercase=False    , lowercase=True    , lowercase=99    , lowercase=32    , lowercase=5    , lowercase=4    , lowercase=37    , lowercase="gelu"    , lowercase=0.1    , lowercase=0.1    , lowercase=512    , lowercase=16    , lowercase=2    , lowercase=0.02    , lowercase=3    , lowercase=4    , lowercase=None    , ):
							_lowerCamelCase	:     Tuple       =       parent
							_lowerCamelCase	:     Optional[int]       =       batch_size
							_lowerCamelCase	:     str       =       seq_length
							_lowerCamelCase	:     Dict       =       is_training
							_lowerCamelCase	:     List[Any]       =       use_input_mask
							_lowerCamelCase	:     Optional[int]       =       use_token_type_ids
							_lowerCamelCase	:     str       =       use_labels
							_lowerCamelCase	:     int       =       vocab_size
							_lowerCamelCase	:     Any       =       hidden_size
							_lowerCamelCase	:     Tuple       =       num_hidden_layers
							_lowerCamelCase	:     Optional[int]       =       num_attention_heads
							_lowerCamelCase	:     Union[str, Any]       =       intermediate_size
							_lowerCamelCase	:     Dict       =       hidden_act
							_lowerCamelCase	:     Any       =       hidden_dropout_prob
							_lowerCamelCase	:     str       =       attention_probs_dropout_prob
							_lowerCamelCase	:     List[Any]       =       max_position_embeddings
							_lowerCamelCase	:     Any       =       type_vocab_size
							_lowerCamelCase	:     Dict       =       type_sequence_label_size
							_lowerCamelCase	:     List[Any]       =       initializer_range
							_lowerCamelCase	:     Any       =       num_labels
							_lowerCamelCase	:     List[str]       =       num_choices
							_lowerCamelCase	:     Tuple       =       scope
			def    A_       (    self    ):
							_lowerCamelCase	:     List[str]       =       ids_tensor([self.batch_size, self.seq_length]    , self.vocab_size    )

							_lowerCamelCase	:     int       =       None
							if self.use_input_mask:
											_lowerCamelCase	:     str       =       random_attention_mask([self.batch_size, self.seq_length]    )

							_lowerCamelCase	:     Tuple       =       None
							if self.use_token_type_ids:
											_lowerCamelCase	:     Optional[int]       =       ids_tensor([self.batch_size, self.seq_length]    , self.type_vocab_size    )

							_lowerCamelCase	:     List[Any]       =       None
							_lowerCamelCase	:     Optional[Any]       =       None
							_lowerCamelCase	:     Optional[Any]       =       None
							if self.use_labels:
											_lowerCamelCase	:     Any       =       ids_tensor([self.batch_size]    , self.type_sequence_label_size    )
											_lowerCamelCase	:     Tuple       =       ids_tensor([self.batch_size, self.seq_length]    , self.num_labels    )
											_lowerCamelCase	:     Optional[Any]       =       ids_tensor([self.batch_size]    , self.num_choices    )

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

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
			def    A_       (    self    ):
							return LlamaConfig(
							    vocab_size=self.vocab_size    , hidden_size=self.hidden_size    , num_hidden_layers=self.num_hidden_layers    , num_attention_heads=self.num_attention_heads    , intermediate_size=self.intermediate_size    , hidden_act=self.hidden_act    , hidden_dropout_prob=self.hidden_dropout_prob    , attention_probs_dropout_prob=self.attention_probs_dropout_prob    , max_position_embeddings=self.max_position_embeddings    , type_vocab_size=self.type_vocab_size    , is_decoder=lowercase    , initializer_range=self.initializer_range    , )
			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    ):
							_lowerCamelCase	:     str       =       LlamaModel(config=lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     List[Any]       =       model(lowercase    , attention_mask=lowercase    )
							_lowerCamelCase	:     Any       =       model(lowercase    )
							self.parent.assertEqual(result.last_hidden_state.shape    , (self.batch_size, self.seq_length, self.hidden_size)    )
			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , ):
							_lowerCamelCase	:     str       =       True
							_lowerCamelCase	:     Dict       =       LlamaModel(lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     Union[str, Any]       =       model(
							    lowercase    , attention_mask=lowercase    , encoder_hidden_states=lowercase    , encoder_attention_mask=lowercase    , )
							_lowerCamelCase	:     Tuple       =       model(
							    lowercase    , attention_mask=lowercase    , encoder_hidden_states=lowercase    , )
							_lowerCamelCase	:     Union[str, Any]       =       model(lowercase    , attention_mask=lowercase    )
							self.parent.assertEqual(result.last_hidden_state.shape    , (self.batch_size, self.seq_length, self.hidden_size)    )
			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , ):
							_lowerCamelCase	:     Optional[Any]       =       LlamaForCausalLM(config=lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     List[Any]       =       model(lowercase    , attention_mask=lowercase    , labels=lowercase    )
							self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.seq_length, self.vocab_size)    )
			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , ):
							_lowerCamelCase	:     int       =       True
							_lowerCamelCase	:     Optional[Any]       =       True
							_lowerCamelCase	:     str       =       LlamaForCausalLM(config=lowercase    )
							model.to(lowercase    )
							model.eval()

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

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

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

							_lowerCamelCase	:     Any       =       model(
							    lowercase    , attention_mask=lowercase    , encoder_hidden_states=lowercase    , encoder_attention_mask=lowercase    , output_hidden_states=lowercase    , )['hidden_states'][0]
							_lowerCamelCase	:     Dict       =       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
							_lowerCamelCase	:     Optional[Any]       =       ids_tensor((1,)    , output_from_past.shape[-1]    ).item()
							_lowerCamelCase	:     Any       =       output_from_no_past[:, -3:, random_slice_idx].detach()
							_lowerCamelCase	:     Any       =       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    A_       (    self    ):
							_lowerCamelCase	:     Tuple       =       self.prepare_config_and_inputs()
							(
							    (
							    _lowerCamelCase
							),					(
							    _lowerCamelCase
							),					(
							    _lowerCamelCase
							),					(
							    _lowerCamelCase
							),					(
							    _lowerCamelCase
							),					(
							    _lowerCamelCase
							),					(
							    _lowerCamelCase
							),					
							)	:     List[str]       =       config_and_inputs
							_lowerCamelCase	:     int       =       {'input_ids': input_ids, 'attention_mask': input_mask}
							return config, inputs_dict






@require_torch
class lowerCAmelCase__      (	lowercase,      lowercase,      lowercase,      unittest.TestCase		):






			'''simple docstring'''



			lowerCamelCase__ =     (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
			lowerCamelCase__ =     (LlamaForCausalLM,) if is_torch_available() else ()
			lowerCamelCase__ =     (
			    {
			        """feature-extraction""": LlamaModel,
			        """text-classification""": LlamaForSequenceClassification,
			        """text-generation""": LlamaForCausalLM,
			        """zero-shot""": LlamaForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			lowerCamelCase__ =     False
			lowerCamelCase__ =     False
			def    A_       (    self    ):
							_lowerCamelCase	:     Optional[Any]       =       LlamaModelTester(self    )
							_lowerCamelCase	:     List[Any]       =       ConfigTester(self    , config_class=lowercase    , hidden_size=37    )
			def    A_       (    self    ):
							self.config_tester.run_common_tests()
			def    A_       (    self    ):
							_lowerCamelCase	:     Optional[int]       =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowercase    )
			def    A_       (    self    ):
							_lowerCamelCase	:     Any       =       self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
											_lowerCamelCase	:     Any       =       type
											self.model_tester.create_and_check_model(*lowercase    )
			def    A_       (    self    ):
							_lowerCamelCase,					_lowerCamelCase	:     Tuple       =       self.model_tester.prepare_config_and_inputs_for_common()
							_lowerCamelCase	:     List[str]       =       3
							_lowerCamelCase	:     Dict       =       input_dict['input_ids']
							_lowerCamelCase	:     List[str]       =       input_ids.ne(1    ).to(lowercase    )
							_lowerCamelCase	:     Tuple       =       ids_tensor([self.model_tester.batch_size]    , self.model_tester.type_sequence_label_size    )
							_lowerCamelCase	:     Union[str, Any]       =       LlamaForSequenceClassification(lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     List[str]       =       model(lowercase    , attention_mask=lowercase    , labels=lowercase    )
							self.assertEqual(result.logits.shape    , (self.model_tester.batch_size, self.model_tester.num_labels)    )
			def    A_       (    self    ):
							_lowerCamelCase,					_lowerCamelCase	:     Optional[Any]       =       self.model_tester.prepare_config_and_inputs_for_common()
							_lowerCamelCase	:     Dict       =       3
							_lowerCamelCase	:     List[str]       =       'single_label_classification'
							_lowerCamelCase	:     Union[str, Any]       =       input_dict['input_ids']
							_lowerCamelCase	:     List[Any]       =       input_ids.ne(1    ).to(lowercase    )
							_lowerCamelCase	:     Optional[Any]       =       ids_tensor([self.model_tester.batch_size]    , self.model_tester.type_sequence_label_size    )
							_lowerCamelCase	:     List[Any]       =       LlamaForSequenceClassification(lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     int       =       model(lowercase    , attention_mask=lowercase    , labels=lowercase    )
							self.assertEqual(result.logits.shape    , (self.model_tester.batch_size, self.model_tester.num_labels)    )
			def    A_       (    self    ):
							_lowerCamelCase,					_lowerCamelCase	:     List[str]       =       self.model_tester.prepare_config_and_inputs_for_common()
							_lowerCamelCase	:     List[Any]       =       3
							_lowerCamelCase	:     Union[str, Any]       =       'multi_label_classification'
							_lowerCamelCase	:     Dict       =       input_dict['input_ids']
							_lowerCamelCase	:     Optional[Any]       =       input_ids.ne(1    ).to(lowercase    )
							_lowerCamelCase	:     List[Any]       =       ids_tensor(
							    [self.model_tester.batch_size, config.num_labels]    , self.model_tester.type_sequence_label_size    ).to(torch.float    )
							_lowerCamelCase	:     Optional[Any]       =       LlamaForSequenceClassification(lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     int       =       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    A_       (    self    ):
							pass






			@parameterized.expand([('linear',), ('dynamic',)]    )
			def    A_       (    self    , lowercase    ):
							_lowerCamelCase,					_lowerCamelCase	:     Any       =       self.model_tester.prepare_config_and_inputs_for_common()
							_lowerCamelCase	:     List[Any]       =       ids_tensor([1, 10]    , config.vocab_size    )
							_lowerCamelCase	:     int       =       ids_tensor([1, int(config.max_position_embeddings * 1.5    )]    , config.vocab_size    )

							set_seed(42    )  # Fixed seed at init time so the two models get the same random weights
							_lowerCamelCase	:     Union[str, Any]       =       LlamaModel(lowercase    )
							original_model.to(lowercase    )
							original_model.eval()
							_lowerCamelCase	:     str       =       original_model(lowercase    ).last_hidden_state
							_lowerCamelCase	:     int       =       original_model(lowercase    ).last_hidden_state

							set_seed(42    )  # Fixed seed at init time so the two models get the same random weights
							_lowerCamelCase	:     List[str]       =       {'type': scaling_type, 'factor': 10.0}
							_lowerCamelCase	:     Optional[int]       =       LlamaModel(lowercase    )
							scaled_model.to(lowercase    )
							scaled_model.eval()
							_lowerCamelCase	:     int       =       scaled_model(lowercase    ).last_hidden_state
							_lowerCamelCase	:     Union[str, Any]       =       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 lowerCAmelCase__      (	unittest.TestCase		):






			'''simple docstring'''



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

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

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