




'''simple docstring'''






from typing import List

from .keymap import KEYMAP, get_character





def       lowercase_      (		lowercase__						)   ->int:

						def decorator(lowercase__						):
												_snake_case:   Optional[int]  					=		getattr(lowerCAmelCase__				,  'handle_key'				,  []						)
												handle += [key]
												setattr(lowerCAmelCase__				,  'handle_key'				,  lowerCAmelCase__						)
												return func

						return decorator





def       lowercase_      (		*lowercase__						)   ->List[Any]:

						def decorator(lowercase__						):
												_snake_case:   Optional[int]  					=		getattr(lowerCAmelCase__				,  'handle_key'				,  []						)
												handle += keys
												setattr(lowerCAmelCase__				,  'handle_key'				,  lowerCAmelCase__						)
												return func

						return decorator







class 			lowerCamelCase					(						__a  ):


					def __new__(					cls	:  int       ,     __snake_case	:  Optional[int]       ,     __snake_case	:  str       ,     __snake_case	:  List[Any]       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		super().__new__(cls       ,     snake_case__       ,     snake_case__       ,     snake_case__       )
											if not hasattr(snake_case__       ,     'key_handler'       ):
																	setattr(snake_case__       ,     'key_handler'       ,     {}       )
											setattr(snake_case__       ,     'handle_input'       ,     KeyHandler.handle_input       )

											for value in attrs.values():
																	_snake_case:   Any  					=		getattr(snake_case__       ,     'handle_key'       ,     []       )
																	for key in handled_keys:
																							_snake_case:   Optional[Any]  					=		value
											return new_cls




					@staticmethod
					def    SCREAMING_SNAKE_CASE_		(					cls	:  Union[str, Any]       ):





											'''simple docstring'''





											_snake_case:   Union[str, Any]  					=		get_character()
											if char != KEYMAP["undefined"]:
																	_snake_case:   Optional[Any]  					=		ord(snake_case__       )
											_snake_case:   List[Any]  					=		cls.key_handler.get(snake_case__       )
											if handler:
																	_snake_case:   Tuple  					=		char
																	return handler(cls       )
											else:
																	return None





def       lowercase_      (		cls						)   ->Optional[int]:
						return KeyHandler(cls.__name__				,  cls.__bases__				,  cls.__dict__.copy()						)












'''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					:


					def __init__(					self	:  Optional[int]       ,     __snake_case	:  int       ,     __snake_case	:  Tuple=13       ,     __snake_case	:  Any=7       ,     __snake_case	:  Union[str, Any]=True       ,     __snake_case	:  List[str]=True       ,     __snake_case	:  Optional[int]=False       ,     __snake_case	:  List[Any]=True       ,     __snake_case	:  str=99       ,     __snake_case	:  Optional[int]=32       ,     __snake_case	:  Any=5       ,     __snake_case	:  Tuple=4       ,     __snake_case	:  List[Any]=37       ,     __snake_case	:  Union[str, Any]="gelu"       ,     __snake_case	:  List[str]=0.1       ,     __snake_case	:  int=0.1       ,     __snake_case	:  Any=5_12       ,     __snake_case	:  Dict=16       ,     __snake_case	:  int=2       ,     __snake_case	:  Any=0.02       ,     __snake_case	:  Any=3       ,     __snake_case	:  str=4       ,     __snake_case	:  int=None       ,     ):





											'''simple docstring'''





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


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											_snake_case:   str  					=		ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size       )

											_snake_case:   int  					=		None
											if self.use_input_mask:
																	_snake_case:   List[Any]  					=		random_attention_mask([self.batch_size, self.seq_length]       )

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

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

											_snake_case:   Union[str, Any]  					=		self.get_config()

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ):





											'''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=__snake_case       ,     initializer_range=self.initializer_range       ,     )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ,     __snake_case	:  List[Any]       ,     __snake_case	:  Any       ,     __snake_case	:  Any       ,     __snake_case	:  Union[str, Any]       ,     __snake_case	:  Optional[Any]       ,     __snake_case	:  Tuple       ,     __snake_case	:  int       ):





											'''simple docstring'''





											_snake_case:   Optional[Any]  					=		LlamaModel(config=__snake_case       )
											model.to(__snake_case       )
											model.eval()
											_snake_case:   Optional[Any]  					=		model(__snake_case       ,     attention_mask=__snake_case       )
											_snake_case:   Tuple  					=		model(__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]       ,     __snake_case	:  Any       ,     __snake_case	:  Tuple       ,     __snake_case	:  Tuple       ,     __snake_case	:  Dict       ,     __snake_case	:  str       ,     __snake_case	:  int       ,     __snake_case	:  List[str]       ,     __snake_case	:  Union[str, Any]       ,     __snake_case	:  Any       ,     ):





											'''simple docstring'''





											_snake_case:   str  					=		True
											_snake_case:   int  					=		LlamaModel(__snake_case       )
											model.to(__snake_case       )
											model.eval()
											_snake_case:   int  					=		model(
											    __snake_case       ,     attention_mask=__snake_case       ,     encoder_hidden_states=__snake_case       ,     encoder_attention_mask=__snake_case       ,     )
											_snake_case:   Optional[int]  					=		model(
											    __snake_case       ,     attention_mask=__snake_case       ,     encoder_hidden_states=__snake_case       ,     )
											_snake_case:   List[str]  					=		model(__snake_case       ,     attention_mask=__snake_case       )
											self.parent.assertEqual(result.last_hidden_state.shape       ,     (self.batch_size, self.seq_length, self.hidden_size)       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ,     __snake_case	:  List[str]       ,     __snake_case	:  Dict       ,     __snake_case	:  Dict       ,     __snake_case	:  List[str]       ,     __snake_case	:  Optional[int]       ,     __snake_case	:  int       ,     __snake_case	:  int       ,     __snake_case	:  List[Any]       ,     __snake_case	:  Union[str, Any]       ,     ):





											'''simple docstring'''





											_snake_case:   Union[str, Any]  					=		LlamaForCausalLM(config=__snake_case       )
											model.to(__snake_case       )
											model.eval()
											_snake_case:   str  					=		model(__snake_case       ,     attention_mask=__snake_case       ,     labels=__snake_case       )
											self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.seq_length, self.vocab_size)       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ,     __snake_case	:  Dict       ,     __snake_case	:  List[Any]       ,     __snake_case	:  Optional[Any]       ,     __snake_case	:  Optional[int]       ,     __snake_case	:  Optional[Any]       ,     __snake_case	:  Optional[Any]       ,     __snake_case	:  Union[str, Any]       ,     __snake_case	:  Optional[int]       ,     __snake_case	:  Any       ,     ):





											'''simple docstring'''





											_snake_case:   Any  					=		True
											_snake_case:   Optional[int]  					=		True
											_snake_case:   List[str]  					=		LlamaForCausalLM(config=__snake_case       )
											model.to(__snake_case       )
											model.eval()

											# first forward pass
											_snake_case:   Dict  					=		model(
											    __snake_case       ,     attention_mask=__snake_case       ,     encoder_hidden_states=__snake_case       ,     encoder_attention_mask=__snake_case       ,     use_cache=__snake_case       ,     )
											_snake_case:   Optional[int]  					=		outputs.past_key_values

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

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

											_snake_case:   Tuple  					=		model(
											    __snake_case       ,     attention_mask=__snake_case       ,     encoder_hidden_states=__snake_case       ,     encoder_attention_mask=__snake_case       ,     output_hidden_states=__snake_case       ,     )['hidden_states'][0]
											_snake_case:   Dict  					=		model(
											    __snake_case       ,     attention_mask=__snake_case       ,     encoder_hidden_states=__snake_case       ,     encoder_attention_mask=__snake_case       ,     past_key_values=__snake_case       ,     output_hidden_states=__snake_case       ,     )['hidden_states'][0]

											# select random slice
											_snake_case:   List[Any]  					=		ids_tensor((1,)       ,     output_from_past.shape[-1]       ).item()
											_snake_case:   str  					=		output_from_no_past[:, -3:, random_slice_idx].detach()
											_snake_case:   Tuple  					=		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(__snake_case       ,     __snake_case       ,     atol=1e-3       )       )




					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											_snake_case:   List[str]  					=		self.prepare_config_and_inputs()
											(
											    (
											    _snake_case
											)					,						(
											    _snake_case
											)					,						(
											    _snake_case
											)					,						(
											    _snake_case
											)					,						(
											    _snake_case
											)					,						(
											    _snake_case
											)					,						(
											    _snake_case
											)					,						
											):   Dict  					=		config_and_inputs
											_snake_case:   Any  					=		{'input_ids': input_ids, 'attention_mask': input_mask}
											return config, inputs_dict







@require_torch
class 			lowerCamelCase					(						__UpperCAmelCase     ,							__UpperCAmelCase     ,							__UpperCAmelCase     ,							unittest.TestCase  ):
					_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	:  str       ):





											'''simple docstring'''





											_snake_case:   Optional[int]  					=		LlamaModelTester(self       )
											_snake_case:   Dict  					=		ConfigTester(self       ,     config_class=__snake_case       ,     hidden_size=37       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											self.config_tester.run_common_tests()


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ):





											'''simple docstring'''





											_snake_case:   str  					=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





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


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ):





											'''simple docstring'''





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


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ):





											'''simple docstring'''





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


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





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





											'''simple docstring'''





											pass




					@parameterized.expand([('linear',), ('dynamic',)]       )
					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ,     __snake_case	:  Union[str, Any]       ):





											'''simple docstring'''





											_snake_case					,						_snake_case:   int  					=		self.model_tester.prepare_config_and_inputs_for_common()
											_snake_case:   Optional[int]  					=		ids_tensor([1, 10]       ,     config.vocab_size       )
											_snake_case:   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
											_snake_case:   Tuple  					=		LlamaModel(__snake_case       )
											original_model.to(__snake_case       )
											original_model.eval()
											_snake_case:   List[Any]  					=		original_model(__snake_case       ).last_hidden_state
											_snake_case:   List[str]  					=		original_model(__snake_case       ).last_hidden_state

											set_seed(42       )  # Fixed seed at init time so the two models get the same random weights
											_snake_case:   Tuple  					=		{'type': scaling_type, 'factor': 10.0}
											_snake_case:   List[Any]  					=		LlamaModel(__snake_case       )
											scaled_model.to(__snake_case       )
											scaled_model.eval()
											_snake_case:   Dict  					=		scaled_model(__snake_case       ).last_hidden_state
											_snake_case:   str  					=		scaled_model(__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(__snake_case       ,     __snake_case       ,     atol=1e-5       )       )
											else:
																	self.assertFalse(torch.allclose(__snake_case       ,     __snake_case       ,     atol=1e-5       )       )

											# The output should be different for long inputs
											self.assertFalse(torch.allclose(__snake_case       ,     __snake_case       ,     atol=1e-5       )       )







@require_torch
class 			lowerCamelCase					(						unittest.TestCase  ):


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





											'''simple docstring'''





											_snake_case:   List[str]  					=		[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
											_snake_case:   Optional[int]  					=		LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'       ,     device_map='auto'       )
											_snake_case:   Optional[int]  					=		model(torch.tensor([input_ids]       )       )
											# Expected mean on dim = -1
											_snake_case:   List[Any]  					=		torch.tensor([[-6.6_550, -4.1_227, -4.9_859, -3.2_406, 0.8_262, -3.0_033, 1.2_964, -3.3_699]]       )
											torch.testing.assert_close(out.mean(-1       )       ,     __snake_case       ,     atol=1e-2       ,     rtol=1e-2       )
											# slicing logits[0, 0, 0:30]
											# fmt: off
											_snake_case:   Tuple  					=		torch.tensor([-12.8_281, -7.4_453, -0.4_639, -8.0_625, -7.2_500, -8.0_000, -6.4_883, -7.7_695, -7.8_438, -7.0_312, -6.2_188, -7.1_328, -1.8_496, 1.9_961, -8.6_250, -6.7_227, -12.8_281, -6.9_492, -7.0_742, -7.7_852, -7.5_820, -7.9_062, -6.9_375, -7.9_805, -8.3_438, -8.1_562, -8.0_469, -7.6_250, -7.7_422, -7.3_398,]       )
											# fmt: on
											torch.testing.assert_close(out[0, 0, :30]       ,     __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	:  Union[str, Any]       ):





											'''simple docstring'''





											_snake_case:   Union[str, Any]  					=		[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
											_snake_case:   Any  					=		LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'       ,     device_map='auto'       )
											_snake_case:   int  					=		model(torch.tensor(__snake_case       )       )
											# Expected mean on dim = -1
											_snake_case:   List[Any]  					=		torch.tensor([[-2.0_622, -1.2_794, -1.1_638, -0.9_788, -1.4_603, -1.0_238, -1.7_893, -1.4_411]]       )
											torch.testing.assert_close(out.mean(-1       )       ,     __snake_case       ,     atol=1e-2       ,     rtol=1e-2       )
											# slicing logits[0, 0, 0:30]
											# fmt: off
											_snake_case:   Optional[Any]  					=		torch.tensor([-8.1_406, -8.0_547, 2.7_461, -1.2_344, -0.1_448, -1.8_262, -1.0_020, -1.8_154, -1.6_895, -1.8_516, -2.3_574, -0.9_277, 3.7_598, 6.5_742, -1.2_998, -0.1_177, -8.1_406, -2.9_688, -2.9_199, -3.1_699, -3.5_254, -2.3_555, -2.7_988, -3.4_141, -2.8_262, -4.5_195, -3.3_379, -3.3_164, -2.7_832, -3.0_273]       )
											# fmt: on
											torch.testing.assert_close(out[0, 0, :30]       ,     __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	:  int       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
											_snake_case:   Any  					=		LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'       ,     device_map='auto'       )
											_snake_case:   List[str]  					=		model(torch.tensor(__snake_case       )       )
											# Expected mean on dim = -1
											_snake_case:   List[Any]  					=		torch.tensor([[-0.8_562, -1.8_520, -0.7_551, -0.4_162, -1.5_161, -1.2_038, -2.4_823, -2.3_254]]       )
											torch.testing.assert_close(out.mean(-1       )       ,     __snake_case       ,     atol=1e-2       ,     rtol=1e-2       )
											# slicing logits[0, 0, 0:30]
											# fmt: off
											_snake_case:   Optional[int]  					=		torch.tensor([-2.2_227, 4.8_828, 0.9_023, -0.4_578, -0.7_871, -0.1_033, -0.6_221, -0.5_786, -0.7_803, -1.0_674, -1.2_920, -0.1_570, 0.8_008, 2.0_723, -0.9_497, 0.2_771, -2.2_227, -0.7_612, -1.4_346, -1.2_061, -1.6_426, -0.3_000, -0.7_139, -1.1_934, -1.8_691, -1.6_973, -1.5_947, -1.2_705, -0.3_523, -0.5_513]       )
											# fmt: on
											torch.testing.assert_close(out.mean(-1       )       ,     __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	:  str       ):





											'''simple docstring'''





											_snake_case:   int  					=		[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
											_snake_case:   Any  					=		LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'       ,     device_map='auto'       )
											_snake_case:   Dict  					=		model(torch.tensor(__snake_case       )       )

											_snake_case:   Optional[Any]  					=		torch.tensor(
											    [[-4.2_327, -3.3_360, -4.6_665, -4.7_631, -1.8_180, -3.4_170, -1.4_211, -3.1_810]]       ,     dtype=torch.floataa       )
											torch.testing.assert_close(out.mean(-1       )       ,     __snake_case       ,     atol=1e-2       ,     rtol=1e-2       )
											# fmt: off
											_snake_case:   str  					=		torch.tensor([-9.4_922, -3.9_551, 1.7_998, -5.6_758, -5.1_055, -5.8_984, -4.8_320, -6.8_086, -6.5_391, -5.6_172, -5.5_820, -5.5_352, 1.7_881, 3.6_289, -6.5_117, -3.4_785, -9.5_000, -6.0_352, -6.8_125, -6.0_195, -6.6_836, -5.4_727, -6.2_812, -6.0_391, -7.3_398, -7.4_297, -7.4_844, -6.5_820, -5.8_789, -5.5_312]       )
											# fmt: on
											torch.testing.assert_close(out[0, 0, :30]       ,     __snake_case       ,     atol=1e-5       ,     rtol=1e-5       )




					@unittest.skip('Model is curently gated'       )
					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											_snake_case:   Dict  					=		'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'
											_snake_case:   Dict  					=		'Simply put, the theory of relativity states that '
											_snake_case:   List[str]  					=		LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf'       )
											_snake_case:   Optional[Any]  					=		tokenizer.encode(__snake_case       ,     return_tensors='pt'       )
											_snake_case:   Optional[int]  					=		LlamaForCausalLM.from_pretrained(
											    'meta-llama/Llama-2-13b-chat-hf'       ,     device_map='sequential'       ,     use_safetensors=__snake_case       )

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





