



"""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__  :




	'''simple docstring'''





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

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			parent
				__lowerCAmelCase :    Any				=			batch_size
				__lowerCAmelCase :    int				=			seq_length
				__lowerCAmelCase :    int				=			is_training
				__lowerCAmelCase :    Optional[int]				=			use_input_mask
				__lowerCAmelCase :    List[Any]				=			use_token_type_ids
				__lowerCAmelCase :    List[Any]				=			use_labels
				__lowerCAmelCase :    int				=			vocab_size
				__lowerCAmelCase :    Union[str, Any]				=			hidden_size
				__lowerCAmelCase :    str				=			num_hidden_layers
				__lowerCAmelCase :    Tuple				=			num_attention_heads
				__lowerCAmelCase :    int				=			intermediate_size
				__lowerCAmelCase :    Optional[int]				=			hidden_act
				__lowerCAmelCase :    Tuple				=			hidden_dropout_prob
				__lowerCAmelCase :    Dict				=			attention_probs_dropout_prob
				__lowerCAmelCase :    List[Any]				=			max_position_embeddings
				__lowerCAmelCase :    str				=			type_vocab_size
				__lowerCAmelCase :    Tuple				=			type_sequence_label_size
				__lowerCAmelCase :    Optional[int]				=			initializer_range
				__lowerCAmelCase :    List[Any]				=			num_labels
				__lowerCAmelCase :    int				=			num_choices
				__lowerCAmelCase :    List[str]				=			scope

	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[int])							->			Dict:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[Any]				=			ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size)

				__lowerCAmelCase :    Dict				=			None
				if self.use_input_mask:
							__lowerCAmelCase :    Tuple				=			random_attention_mask([self.batch_size, self.seq_length])

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

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

				__lowerCAmelCase :    Tuple				=			self.get_config()

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

	def 				_SCREAMING_SNAKE_CASE  (	self:						Any)							->			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=_SCREAMING_SNAKE_CASE			,     initializer_range=self.initializer_range			,     )

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						Any			,     _SCREAMING_SNAKE_CASE:						List[str]			,     _SCREAMING_SNAKE_CASE:						List[Any])							->			Any:

				"""simple docstring"""

				__lowerCAmelCase :    Union[str, Any]				=			LlamaModel(config=_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.eval()
				__lowerCAmelCase :    Tuple				=			model(_SCREAMING_SNAKE_CASE			,     attention_mask=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    str				=			model(_SCREAMING_SNAKE_CASE)
				self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.seq_length, self.hidden_size))

	def 				_SCREAMING_SNAKE_CASE  (	self:						Tuple			,     _SCREAMING_SNAKE_CASE:						List[Any]			,     _SCREAMING_SNAKE_CASE:						str			,     _SCREAMING_SNAKE_CASE:						str			,     _SCREAMING_SNAKE_CASE:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						List[Any]			,     _SCREAMING_SNAKE_CASE:						List[Any]			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						Any			,     _SCREAMING_SNAKE_CASE:						Dict			,     )							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    Tuple				=			True
				__lowerCAmelCase :    List[str]				=			LlamaModel(_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.eval()
				__lowerCAmelCase :    Optional[int]				=			model(
				    _SCREAMING_SNAKE_CASE			,     attention_mask=_SCREAMING_SNAKE_CASE			,     encoder_hidden_states=_SCREAMING_SNAKE_CASE			,     encoder_attention_mask=_SCREAMING_SNAKE_CASE			,     )
				__lowerCAmelCase :    Dict				=			model(
				    _SCREAMING_SNAKE_CASE			,     attention_mask=_SCREAMING_SNAKE_CASE			,     encoder_hidden_states=_SCREAMING_SNAKE_CASE			,     )
				__lowerCAmelCase :    Any				=			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 				_SCREAMING_SNAKE_CASE  (	self:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						Any			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						str			,     _SCREAMING_SNAKE_CASE:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						List[str]			,     )							->			Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Tuple				=			LlamaForCausalLM(config=_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.eval()
				__lowerCAmelCase :    List[str]				=			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 				_SCREAMING_SNAKE_CASE  (	self:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						List[Any]			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						Dict			,     )							->			int:

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			True
				__lowerCAmelCase :    Tuple				=			True
				__lowerCAmelCase :    str				=			LlamaForCausalLM(config=_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.eval()

				# first forward pass
				__lowerCAmelCase :    List[str]				=			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 :    List[str]				=			outputs.past_key_values

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

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

				__lowerCAmelCase :    Union[str, Any]				=			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 :    Optional[int]				=			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 :    Dict				=			ids_tensor((1,)			,     output_from_past.shape[-1]).item()
				__lowerCAmelCase :    List[str]				=			output_from_no_past[:, -3:, random_slice_idx].detach()
				__lowerCAmelCase :    Optional[int]				=			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 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			Optional[Any]:

				"""simple docstring"""

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






@require_torch
class      A__  (    __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ,						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 				_SCREAMING_SNAKE_CASE  (	self:						Union[str, Any])							->			Tuple:

				"""simple docstring"""

				__lowerCAmelCase :    int				=			LlamaModelTester(self)
				__lowerCAmelCase :    List[Any]				=			ConfigTester(self			,     config_class=_SCREAMING_SNAKE_CASE			,     hidden_size=37)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Any)							->			List[str]:

				"""simple docstring"""

				self.config_tester.run_common_tests()

	def 				_SCREAMING_SNAKE_CASE  (	self:						Tuple)							->			str:

				"""simple docstring"""

				__lowerCAmelCase :    Tuple				=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Tuple)							->			str:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[Any]				=			self.model_tester.prepare_config_and_inputs()
				for type in ["absolute", "relative_key", "relative_key_query"]:
							__lowerCAmelCase :    Union[str, Any]				=			type
							self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE)

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any])							->			Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase		,      __lowerCAmelCase :    Optional[Any]				=			self.model_tester.prepare_config_and_inputs_for_common()
				__lowerCAmelCase :    Optional[int]				=			3
				__lowerCAmelCase :    Optional[Any]				=			input_dict["input_ids"]
				__lowerCAmelCase :    List[Any]				=			input_ids.ne(1).to(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Tuple				=			ids_tensor([self.model_tester.batch_size]			,     self.model_tester.type_sequence_label_size)
				__lowerCAmelCase :    Union[str, Any]				=			LlamaForSequenceClassification(_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.eval()
				__lowerCAmelCase :    Dict				=			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 				_SCREAMING_SNAKE_CASE  (	self:						List[str])							->			Tuple:

				"""simple docstring"""

				__lowerCAmelCase		,      __lowerCAmelCase :    Optional[int]				=			self.model_tester.prepare_config_and_inputs_for_common()
				__lowerCAmelCase :    Any				=			3
				__lowerCAmelCase :    Optional[int]				=			"single_label_classification"
				__lowerCAmelCase :    Tuple				=			input_dict["input_ids"]
				__lowerCAmelCase :    Any				=			input_ids.ne(1).to(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Optional[Any]				=			ids_tensor([self.model_tester.batch_size]			,     self.model_tester.type_sequence_label_size)
				__lowerCAmelCase :    Tuple				=			LlamaForSequenceClassification(_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.eval()
				__lowerCAmelCase :    str				=			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 				_SCREAMING_SNAKE_CASE  (	self:						Optional[int])							->			str:

				"""simple docstring"""

				__lowerCAmelCase		,      __lowerCAmelCase :    List[str]				=			self.model_tester.prepare_config_and_inputs_for_common()
				__lowerCAmelCase :    str				=			3
				__lowerCAmelCase :    int				=			"multi_label_classification"
				__lowerCAmelCase :    Union[str, Any]				=			input_dict["input_ids"]
				__lowerCAmelCase :    str				=			input_ids.ne(1).to(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    int				=			ids_tensor(
				    [self.model_tester.batch_size, config.num_labels]			,     self.model_tester.type_sequence_label_size).to(torch.float)
				__lowerCAmelCase :    Dict				=			LlamaForSequenceClassification(_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.eval()
				__lowerCAmelCase :    Dict				=			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 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			List[str]:

				"""simple docstring"""

				pass







	@parameterized.expand([("linear",), ("dynamic",)])
	def 				_SCREAMING_SNAKE_CASE  (	self:						Any			,     _SCREAMING_SNAKE_CASE:						Optional[Any])							->			List[Any]:

				"""simple docstring"""

				__lowerCAmelCase		,      __lowerCAmelCase :    Optional[Any]				=			self.model_tester.prepare_config_and_inputs_for_common()
				__lowerCAmelCase :    Optional[Any]				=			ids_tensor([1, 10]			,     config.vocab_size)
				__lowerCAmelCase :    Union[str, Any]				=			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 :    List[str]				=			LlamaModel(_SCREAMING_SNAKE_CASE)
				original_model.to(_SCREAMING_SNAKE_CASE)
				original_model.eval()
				__lowerCAmelCase :    Dict				=			original_model(_SCREAMING_SNAKE_CASE).last_hidden_state
				__lowerCAmelCase :    Optional[int]				=			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 :    Dict				=			{"type": scaling_type, "factor": 10.0}
				__lowerCAmelCase :    Optional[Any]				=			LlamaModel(_SCREAMING_SNAKE_CASE)
				scaled_model.to(_SCREAMING_SNAKE_CASE)
				scaled_model.eval()
				__lowerCAmelCase :    List[Any]				=			scaled_model(_SCREAMING_SNAKE_CASE).last_hidden_state
				__lowerCAmelCase :    Union[str, Any]				=			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				):




	'''simple docstring'''





	@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!")
	@slow
	def 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			Dict:

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			[1, 306, 4658, 278, 6593, 310, 2834, 338]
				__lowerCAmelCase :    str				=			LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf"			,     device_map="auto")
				__lowerCAmelCase :    Dict				=			model(torch.tensor([input_ids]))
				# Expected mean on dim = -1
				__lowerCAmelCase :    List[Any]				=			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)			,     _SCREAMING_SNAKE_CASE			,     atol=1e-2			,     rtol=1e-2)
				# slicing logits[0, 0, 0:30]
				# fmt: off
				__lowerCAmelCase :    Dict				=			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]			,     _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 				_SCREAMING_SNAKE_CASE  (	self:						str)							->			Tuple:

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			[1, 306, 4658, 278, 6593, 310, 2834, 338]
				__lowerCAmelCase :    Union[str, Any]				=			LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf"			,     device_map="auto")
				__lowerCAmelCase :    Optional[int]				=			model(torch.tensor(_SCREAMING_SNAKE_CASE))
				# Expected mean on dim = -1
				__lowerCAmelCase :    Tuple				=			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)			,     _SCREAMING_SNAKE_CASE			,     atol=1e-2			,     rtol=1e-2)
				# slicing logits[0, 0, 0:30]
				# fmt: off
				__lowerCAmelCase :    List[str]				=			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]			,     _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 				_SCREAMING_SNAKE_CASE  (	self:						Optional[Any])							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    str				=			[1, 306, 4658, 278, 6593, 310, 2834, 338]
				__lowerCAmelCase :    Union[str, Any]				=			LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf"			,     device_map="auto")
				__lowerCAmelCase :    List[str]				=			model(torch.tensor(_SCREAMING_SNAKE_CASE))
				# Expected mean on dim = -1
				__lowerCAmelCase :    Union[str, Any]				=			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)			,     _SCREAMING_SNAKE_CASE			,     atol=1e-2			,     rtol=1e-2)
				# slicing logits[0, 0, 0:30]
				# fmt: off
				__lowerCAmelCase :    List[Any]				=			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)			,     _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 				_SCREAMING_SNAKE_CASE  (	self:						Dict)							->			str:

				"""simple docstring"""

				__lowerCAmelCase :    str				=			[1, 306, 4658, 278, 6593, 310, 2834, 338]
				__lowerCAmelCase :    Any				=			LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-70b-hf"			,     device_map="auto")
				__lowerCAmelCase :    Any				=			model(torch.tensor(_SCREAMING_SNAKE_CASE))

				__lowerCAmelCase :    int				=			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)			,     _SCREAMING_SNAKE_CASE			,     atol=1e-2			,     rtol=1e-2)
				# fmt: off
				__lowerCAmelCase :    Any				=			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]			,     _SCREAMING_SNAKE_CASE			,     atol=1e-5			,     rtol=1e-5)







	@unittest.skip("Model is curently gated")
	@slow
	def 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Tuple				=			"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 :    List[str]				=			"Simply put, the theory of relativity states that "
				__lowerCAmelCase :    Tuple				=			LlamaTokenizer.from_pretrained("meta-llama/Llama-2-13b-chat-hf")
				__lowerCAmelCase :    List[str]				=			tokenizer.encode(_SCREAMING_SNAKE_CASE			,     return_tensors="pt")
				__lowerCAmelCase :    Any				=			LlamaForCausalLM.from_pretrained(
				    "meta-llama/Llama-2-13b-chat-hf"			,     device_map="sequential"			,     use_safetensors=_SCREAMING_SNAKE_CASE)

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




"""simple docstring"""






import argparse
from collections import defaultdict


def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case   ,__snake_case   ,__snake_case      )		->  Optional[int]:
			__lowerCAmelCase :    str				=			F"""{file}_{class_name}_{test_name}"""
			done_test[_id] += 1

			with open(__snake_case   ,"r"      ) as f:
						__lowerCAmelCase :    List[str]				=			f.readlines()

			__lowerCAmelCase :    Tuple				=			F"""class {class_name}("""
			__lowerCAmelCase :    Tuple				=			F"""{4 * ' '}def {test_name}("""
			__lowerCAmelCase :    List[str]				=			F"""{8 * ' '}{correct_line.split()[0]}"""
			__lowerCAmelCase :    List[Any]				=			F"""{16 * ' '}{correct_line.split()[0]}"""
			__lowerCAmelCase :    Any				=			False
			__lowerCAmelCase :    List[Any]				=			False
			__lowerCAmelCase :    Optional[Any]				=			False
			__lowerCAmelCase :    str				=			False
			__lowerCAmelCase :    Optional[int]				=			0
			__lowerCAmelCase :    Dict				=			0

			__lowerCAmelCase :    int				=			[]
			for line in lines:
						if line.startswith(__snake_case      ):
									__lowerCAmelCase :    Any				=			True
						elif in_class and line.startswith(__snake_case      ):
									__lowerCAmelCase :    str				=			True
						elif in_class and in_func and (line.startswith(__snake_case      ) or line.startswith(__snake_case      )):
									__lowerCAmelCase :    Optional[Any]				=			len(line.split(correct_line.split()[0]      )[0]      )
									count += 1

									if count == done_test[_id]:
												__lowerCAmelCase :    Optional[int]				=			True

						if in_class and in_func and in_line:
									if ")" not in line:
												continue
									else:
												__lowerCAmelCase :    Dict				=			True

						if in_class and in_func and in_line and insert_line:
									new_lines.append(F"""{spaces * ' '}{correct_line}"""      )
									__lowerCAmelCase :    Union[str, Any]				=			False
						else:
									new_lines.append(__snake_case      )

			with open(__snake_case   ,"w"      ) as f:
						for line in new_lines:
									f.write(__snake_case      )


def      _lowercase							(    __snake_case   ,__snake_case=None      )		->  int:
			if fail is not None:
						with open(__snake_case   ,"r"      ) as f:
									__lowerCAmelCase :    Union[str, Any]				=			{l.strip() for l in f.readlines()}
			else:
						__lowerCAmelCase :    Optional[Any]				=			None

			with open(__snake_case   ,"r"      ) as f:
						__lowerCAmelCase :    Union[str, Any]				=			f.readlines()

			__lowerCAmelCase :    Optional[Any]				=			defaultdict(__snake_case      )
			for line in correct_lines:
						__lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase :    List[str]				=			line.split(";"      )
						if test_failures is None or "::".join([file, class_name, test_name]      ) in test_failures:
									overwrite_file(__snake_case   ,__snake_case   ,__snake_case   ,__snake_case   ,__snake_case      )


if __name__ == "__main__":
	__snake_case				:  Union[str, Any] =       argparse.ArgumentParser()
	parser.add_argument('--correct_filename', help='filename of tests with expected result')
	parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
	__snake_case				:  int =       parser.parse_args()

	main(args.correct_filename, args.fail_filename)