






import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class       __lowercase      (	lowercase_  ):







					'''simple docstring'''
					SCREAMING_SNAKE_CASE   						=				["image_processor", "tokenizer"]
					SCREAMING_SNAKE_CASE   						=				"AutoImageProcessor"
					SCREAMING_SNAKE_CASE   						=				"AutoTokenizer"




					def __init__(				self       :			Any ,	UpperCamelCase_       :			int=None ,	UpperCamelCase_       :			int=None ,	**UpperCamelCase_       :			Any				):


									"""simple docstring"""



									__A         =	None
									if "feature_extractor" in kwargs:
													warnings.warn(
													    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
													    """ instead.""" ,	UpperCamelCase_ ,	)
													__A         =	kwargs.pop("""feature_extractor"""				)

									__A         =	image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
													raise ValueError("""You need to specify an `image_processor`."""				)
									if tokenizer is None:
													raise ValueError("""You need to specify a `tokenizer`."""				)

									super().__init__(UpperCamelCase_ ,	UpperCamelCase_				)
									__A         =	self.image_processor
									__A         =	False




					def __call__(				self       :			Any ,	*UpperCamelCase_       :			str ,	**UpperCamelCase_       :			Optional[int]				):


									"""simple docstring"""



									if self._in_target_context_manager:
													return self.current_processor(*UpperCamelCase_ ,	**UpperCamelCase_				)

									__A         =	kwargs.pop("""images""" ,	UpperCamelCase_				)
									__A         =	kwargs.pop("""text""" ,	UpperCamelCase_				)
									if len(UpperCamelCase_				) > 0:
													__A         =	args[0]
													__A         =	args[1:]

									if images is None and text is None:
													raise ValueError("""You need to specify either an `images` or `text` input to process."""				)

									if images is not None:
													__A         =	self.image_processor(UpperCamelCase_ ,	*UpperCamelCase_ ,	**UpperCamelCase_				)
									if text is not None:
													__A         =	self.tokenizer(UpperCamelCase_ ,	**UpperCamelCase_				)

									if text is None:
													return inputs
									elif images is None:
													return encodings
									else:
													__A         =	encodings["""input_ids"""]
													return inputs




					def 		lowerCAmelCase_     (				self       :			List[Any] ,	*UpperCamelCase_       :			int ,	**UpperCamelCase_       :			int				):


									"""simple docstring"""



									return self.tokenizer.batch_decode(*UpperCamelCase_ ,	**UpperCamelCase_				)




					def 		lowerCAmelCase_     (				self       :			List[str] ,	*UpperCamelCase_       :			Optional[int] ,	**UpperCamelCase_       :			List[Any]				):


									"""simple docstring"""



									return self.tokenizer.decode(*UpperCamelCase_ ,	**UpperCamelCase_				)




					@contextmanager
					def 		lowerCAmelCase_     (				self       :			str				):


									"""simple docstring"""



									warnings.warn(
									    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
									    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
									    """your images inputs, or in a separate call."""				)
									__A         =	True
									__A         =	self.tokenizer
									yield
									__A         =	self.image_processor
									__A         =	False




					def 		lowerCAmelCase_     (				self       :			List[str] ,	UpperCamelCase_       :			List[Any] ,	UpperCamelCase_       :			Any=False ,	UpperCamelCase_       :			Tuple=None				):


									"""simple docstring"""



									if added_vocab is None:
													__A         =	self.tokenizer.get_added_vocab()

									__A         =	{}

									while tokens:
													__A         =	re.search(R"""<s_(.*?)>""" ,	UpperCamelCase_ ,	re.IGNORECASE				)
													if start_token is None:
																	break
													__A         =	start_token.group(1				)
													__A         =	re.search(RF"</s_{key}>" ,	UpperCamelCase_ ,	re.IGNORECASE				)
													__A         =	start_token.group()
													if end_token is None:
																	__A         =	tokens.replace(UpperCamelCase_ ,	""""""				)
													else:
																	__A         =	end_token.group()
																	__A         =	re.escape(UpperCamelCase_				)
																	__A         =	re.escape(UpperCamelCase_				)
																	__A         =	re.search(F"{start_token_escaped}(.*?){end_token_escaped}" ,	UpperCamelCase_ ,	re.IGNORECASE				)
																	if content is not None:
																					__A         =	content.group(1				).strip()
																					if r"<s_" in content and r"</s_" in content:  # non-leaf node
																									__A         =	self.tokenajson(UpperCamelCase_ ,	is_inner_value=UpperCamelCase_ ,	added_vocab=UpperCamelCase_				)
																									if value:
																													if len(UpperCamelCase_				) == 1:
																																	__A         =	value[0]
																													__A         =	value
																					else:  # leaf nodes
																									__A         =	[]
																									for leaf in content.split(R"""<sep/>"""				):
																													__A         =	leaf.strip()
																													if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																																	__A         =	leaf[1:-2]  # for categorical special tokens
																													output[key].append(UpperCamelCase_				)
																									if len(output[key]				) == 1:
																													__A         =	output[key][0]

																	__A         =	tokens[tokens.find(UpperCamelCase_				) + len(UpperCamelCase_				) :].strip()
																	if tokens[:6] == r"<sep/>":  # non-leaf nodes
																					return [output] + self.tokenajson(tokens[6:] ,	is_inner_value=UpperCamelCase_ ,	added_vocab=UpperCamelCase_				)

									if len(UpperCamelCase_				):
													return [output] if is_inner_value else output
									else:
													return [] if is_inner_value else {"text_sequence": tokens}




					@property
					def 		lowerCAmelCase_     (				self       :			List[Any]				):


									"""simple docstring"""



									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" ,	UpperCamelCase_ ,	)
									return self.image_processor_class





					@property
					def 		lowerCAmelCase_     (				self       :			str				):


									"""simple docstring"""



									warnings.warn(
									    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" ,	UpperCamelCase_ ,	)
									return self.image_processor












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       __lowercase      :







     '''simple docstring'''




     def __init__(				self       :			Dict ,	UpperCamelCase_       :			Optional[int] ,	UpperCamelCase_       :			Any=13 ,	UpperCamelCase_       :			int=7 ,	UpperCamelCase_       :			List[str]=True ,	UpperCamelCase_       :			Optional[int]=True ,	UpperCamelCase_       :			Optional[int]=False ,	UpperCamelCase_       :			Dict=True ,	UpperCamelCase_       :			Any=99 ,	UpperCamelCase_       :			Dict=32 ,	UpperCamelCase_       :			str=5 ,	UpperCamelCase_       :			Dict=4 ,	UpperCamelCase_       :			str=37 ,	UpperCamelCase_       :			List[Any]="gelu" ,	UpperCamelCase_       :			Dict=0.1 ,	UpperCamelCase_       :			List[str]=0.1 ,	UpperCamelCase_       :			List[Any]=512 ,	UpperCamelCase_       :			Optional[Any]=16 ,	UpperCamelCase_       :			Dict=2 ,	UpperCamelCase_       :			Any=0.02 ,	UpperCamelCase_       :			Tuple=3 ,	UpperCamelCase_       :			Optional[int]=4 ,	UpperCamelCase_       :			str=None ,	):


         """simple docstring"""



         __A         =	parent
         __A         =	batch_size
         __A         =	seq_length
         __A         =	is_training
         __A         =	use_input_mask
         __A         =	use_token_type_ids
         __A         =	use_labels
         __A         =	vocab_size
         __A         =	hidden_size
         __A         =	num_hidden_layers
         __A         =	num_attention_heads
         __A         =	intermediate_size
         __A         =	hidden_act
         __A         =	hidden_dropout_prob
         __A         =	attention_probs_dropout_prob
         __A         =	max_position_embeddings
         __A         =	type_vocab_size
         __A         =	type_sequence_label_size
         __A         =	initializer_range
         __A         =	num_labels
         __A         =	num_choices
         __A         =	scope




     def 		lowerCAmelCase_     (				self       :			str				):


         """simple docstring"""



         __A         =	ids_tensor([self.batch_size, self.seq_length] ,	self.vocab_size				)

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

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

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

         __A         =	self.get_config()

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




     def 		lowerCAmelCase_     (				self       :			Dict				):


         """simple docstring"""



         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 		lowerCAmelCase_     (				self       :			int ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			Optional[Any] ,	UpperCamelCase_       :			Optional[int] ,	UpperCamelCase_       :			Tuple ,	UpperCamelCase_       :			int ,	UpperCamelCase_       :			Union[str, Any] ,	UpperCamelCase_       :			Any				):


         """simple docstring"""



         __A         =	LlamaModel(config=UpperCamelCase_				)
         model.to(UpperCamelCase_				)
         model.eval()
         __A         =	model(UpperCamelCase_ ,	attention_mask=UpperCamelCase_				)
         __A         =	model(UpperCamelCase_				)
         self.parent.assertEqual(result.last_hidden_state.shape ,	(self.batch_size, self.seq_length, self.hidden_size)				)




     def 		lowerCAmelCase_     (				self       :			Dict ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			Union[str, Any] ,	UpperCamelCase_       :			List[Any] ,	UpperCamelCase_       :			Dict ,	UpperCamelCase_       :			List[Any] ,	UpperCamelCase_       :			Dict ,	UpperCamelCase_       :			Dict ,	UpperCamelCase_       :			Optional[Any] ,	UpperCamelCase_       :			Tuple ,	):


         """simple docstring"""



         __A         =	True
         __A         =	LlamaModel(UpperCamelCase_				)
         model.to(UpperCamelCase_				)
         model.eval()
         __A         =	model(
             UpperCamelCase_ ,	attention_mask=UpperCamelCase_ ,	encoder_hidden_states=UpperCamelCase_ ,	encoder_attention_mask=UpperCamelCase_ ,	)
         __A         =	model(
             UpperCamelCase_ ,	attention_mask=UpperCamelCase_ ,	encoder_hidden_states=UpperCamelCase_ ,	)
         __A         =	model(UpperCamelCase_ ,	attention_mask=UpperCamelCase_				)
         self.parent.assertEqual(result.last_hidden_state.shape ,	(self.batch_size, self.seq_length, self.hidden_size)				)




     def 		lowerCAmelCase_     (				self       :			Any ,	UpperCamelCase_       :			Tuple ,	UpperCamelCase_       :			Dict ,	UpperCamelCase_       :			Optional[Any] ,	UpperCamelCase_       :			List[str] ,	UpperCamelCase_       :			List[Any] ,	UpperCamelCase_       :			int ,	UpperCamelCase_       :			Optional[Any] ,	UpperCamelCase_       :			Any ,	UpperCamelCase_       :			str ,	):


         """simple docstring"""



         __A         =	LlamaForCausalLM(config=UpperCamelCase_				)
         model.to(UpperCamelCase_				)
         model.eval()
         __A         =	model(UpperCamelCase_ ,	attention_mask=UpperCamelCase_ ,	labels=UpperCamelCase_				)
         self.parent.assertEqual(result.logits.shape ,	(self.batch_size, self.seq_length, self.vocab_size)				)




     def 		lowerCAmelCase_     (				self       :			Dict ,	UpperCamelCase_       :			Dict ,	UpperCamelCase_       :			Union[str, Any] ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			Dict ,	UpperCamelCase_       :			Union[str, Any] ,	UpperCamelCase_       :			Dict ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			Union[str, Any] ,	UpperCamelCase_       :			Union[str, Any] ,	):


         """simple docstring"""



         __A         =	True
         __A         =	True
         __A         =	LlamaForCausalLM(config=UpperCamelCase_				)
         model.to(UpperCamelCase_				)
         model.eval()

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

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

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

         __A         =	model(
             UpperCamelCase_ ,	attention_mask=UpperCamelCase_ ,	encoder_hidden_states=UpperCamelCase_ ,	encoder_attention_mask=UpperCamelCase_ ,	output_hidden_states=UpperCamelCase_ ,	)["""hidden_states"""][0]
         __A         =	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
         __A         =	ids_tensor((1,) ,	output_from_past.shape[-1]				).item()
         __A         =	output_from_no_past[:, -3:, random_slice_idx].detach()
         __A         =	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 		lowerCAmelCase_     (				self       :			str				):


         """simple docstring"""



         __A         =	self.prepare_config_and_inputs()
         (
             (
             __A
         )    ,	(
             __A
         )    ,	(
             __A
         )    ,	(
             __A
         )    ,	(
             __A
         )    ,	(
             __A
         )    ,	(
             __A
         )    ,	
         )         =	config_and_inputs
         __A         =	{"""input_ids""": input_ids, """attention_mask""": input_mask}
         return config, inputs_dict


@require_torch
class       __lowercase      (	lowercase_			,      lowercase_			,      lowercase_			,      unittest.TestCase  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE   						=				(LlamaForCausalLM,) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE   						=				(
         {
             "feature-extraction": LlamaModel,
             "text-classification": LlamaForSequenceClassification,
             "text-generation": LlamaForCausalLM,
             "zero-shot": LlamaForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     SCREAMING_SNAKE_CASE   						=				False
     SCREAMING_SNAKE_CASE   						=				False




     def 		lowerCAmelCase_     (				self       :			Tuple				):


         """simple docstring"""



         __A         =	LlamaModelTester(self				)
         __A         =	ConfigTester(self ,	config_class=UpperCamelCase_ ,	hidden_size=37				)




     def 		lowerCAmelCase_     (				self       :			Optional[int]				):


         """simple docstring"""



         self.config_tester.run_common_tests()




     def 		lowerCAmelCase_     (				self       :			Dict				):


         """simple docstring"""



         __A         =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			Optional[int]				):


         """simple docstring"""



         __A         =	self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
             __A         =	type
             self.model_tester.create_and_check_model(*UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			Any				):


         """simple docstring"""



         __A    ,	__A         =	self.model_tester.prepare_config_and_inputs_for_common()
         __A         =	3
         __A         =	input_dict["""input_ids"""]
         __A         =	input_ids.ne(1				).to(UpperCamelCase_				)
         __A         =	ids_tensor([self.model_tester.batch_size] ,	self.model_tester.type_sequence_label_size				)
         __A         =	LlamaForSequenceClassification(UpperCamelCase_				)
         model.to(UpperCamelCase_				)
         model.eval()
         __A         =	model(UpperCamelCase_ ,	attention_mask=UpperCamelCase_ ,	labels=UpperCamelCase_				)
         self.assertEqual(result.logits.shape ,	(self.model_tester.batch_size, self.model_tester.num_labels)				)




     def 		lowerCAmelCase_     (				self       :			Tuple				):


         """simple docstring"""



         __A    ,	__A         =	self.model_tester.prepare_config_and_inputs_for_common()
         __A         =	3
         __A         =	"""single_label_classification"""
         __A         =	input_dict["""input_ids"""]
         __A         =	input_ids.ne(1				).to(UpperCamelCase_				)
         __A         =	ids_tensor([self.model_tester.batch_size] ,	self.model_tester.type_sequence_label_size				)
         __A         =	LlamaForSequenceClassification(UpperCamelCase_				)
         model.to(UpperCamelCase_				)
         model.eval()
         __A         =	model(UpperCamelCase_ ,	attention_mask=UpperCamelCase_ ,	labels=UpperCamelCase_				)
         self.assertEqual(result.logits.shape ,	(self.model_tester.batch_size, self.model_tester.num_labels)				)




     def 		lowerCAmelCase_     (				self       :			str				):


         """simple docstring"""



         __A    ,	__A         =	self.model_tester.prepare_config_and_inputs_for_common()
         __A         =	3
         __A         =	"""multi_label_classification"""
         __A         =	input_dict["""input_ids"""]
         __A         =	input_ids.ne(1				).to(UpperCamelCase_				)
         __A         =	ids_tensor(
             [self.model_tester.batch_size, config.num_labels] ,	self.model_tester.type_sequence_label_size				).to(torch.float				)
         __A         =	LlamaForSequenceClassification(UpperCamelCase_				)
         model.to(UpperCamelCase_				)
         model.eval()
         __A         =	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 		lowerCAmelCase_     (				self       :			Tuple				):


         """simple docstring"""



         pass





     @parameterized.expand([("""linear""",), ("""dynamic""",)]				)
     def 		lowerCAmelCase_     (				self       :			List[str] ,	UpperCamelCase_       :			Optional[Any]				):


         """simple docstring"""



         __A    ,	__A         =	self.model_tester.prepare_config_and_inputs_for_common()
         __A         =	ids_tensor([1, 10] ,	config.vocab_size				)
         __A         =	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
         __A         =	LlamaModel(UpperCamelCase_				)
         original_model.to(UpperCamelCase_				)
         original_model.eval()
         __A         =	original_model(UpperCamelCase_				).last_hidden_state
         __A         =	original_model(UpperCamelCase_				).last_hidden_state

         set_seed(42				)  # Fixed seed at init time so the two models get the same random weights
         __A         =	{"""type""": scaling_type, """factor""": 10.0}
         __A         =	LlamaModel(UpperCamelCase_				)
         scaled_model.to(UpperCamelCase_				)
         scaled_model.eval()
         __A         =	scaled_model(UpperCamelCase_				).last_hidden_state
         __A         =	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       __lowercase      (	unittest.TestCase  ):







     '''simple docstring'''




     @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""				)
     @slow
     def 		lowerCAmelCase_     (				self       :			Any				):


         """simple docstring"""



         __A         =	[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
         __A         =	LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-7b-hf""" ,	device_map="""auto"""				)
         __A         =	model(torch.tensor([input_ids]				)				)
         # Expected mean on dim = -1
         __A         =	torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]]				)
         torch.testing.assert_close(out.mean(-1				) ,	UpperCamelCase_ ,	atol=1e-2 ,	rtol=1e-2				)
         # slicing logits[0, 0, 0:30]
         # fmt: off
         __A         =	torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,]				)
         # 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 		lowerCAmelCase_     (				self       :			str				):


         """simple docstring"""



         __A         =	[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
         __A         =	LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-hf""" ,	device_map="""auto"""				)
         __A         =	model(torch.tensor(UpperCamelCase_				)				)
         # Expected mean on dim = -1
         __A         =	torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]]				)
         torch.testing.assert_close(out.mean(-1				) ,	UpperCamelCase_ ,	atol=1e-2 ,	rtol=1e-2				)
         # slicing logits[0, 0, 0:30]
         # fmt: off
         __A         =	torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273]				)
         # 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 		lowerCAmelCase_     (				self       :			Tuple				):


         """simple docstring"""



         __A         =	[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
         __A         =	LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-chat-hf""" ,	device_map="""auto"""				)
         __A         =	model(torch.tensor(UpperCamelCase_				)				)
         # Expected mean on dim = -1
         __A         =	torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]]				)
         torch.testing.assert_close(out.mean(-1				) ,	UpperCamelCase_ ,	atol=1e-2 ,	rtol=1e-2				)
         # slicing logits[0, 0, 0:30]
         # fmt: off
         __A         =	torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513]				)
         # 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 		lowerCAmelCase_     (				self       :			Tuple				):


         """simple docstring"""



         __A         =	[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
         __A         =	LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-70b-hf""" ,	device_map="""auto"""				)
         __A         =	model(torch.tensor(UpperCamelCase_				)				)

         __A         =	torch.tensor(
             [[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]] ,	dtype=torch.floataa				)
         torch.testing.assert_close(out.mean(-1				) ,	UpperCamelCase_ ,	atol=1e-2 ,	rtol=1e-2				)
         # fmt: off
         __A         =	torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312]				)
         # 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 		lowerCAmelCase_     (				self       :			List[str]				):


         """simple docstring"""



         __A         =	"""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"""
         __A         =	"""Simply put, the theory of relativity states that """
         __A         =	LlamaTokenizer.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""				)
         __A         =	tokenizer.encode(UpperCamelCase_ ,	return_tensors="""pt"""				)
         __A         =	LlamaForCausalLM.from_pretrained(
             """meta-llama/Llama-2-13b-chat-hf""" ,	device_map="""sequential""" ,	use_safetensors=UpperCamelCase_				)

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




