




"""simple docstring"""






import inspect
import unittest

import torch
import torch.nn as nn

from accelerate.hooks import (
    AlignDevicesHook,
    ModelHook,
    SequentialHook,
    add_hook_to_module,
    attach_align_device_hook,
    remove_hook_from_module,
    remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu






class lowerCAmelCase__      (	nn.Module		):






			'''simple docstring'''



			def __init__(    self    ):
							super().__init__()
							_lowerCamelCase	:     int       =       nn.Linear(3    , 4    )
							_lowerCamelCase	:     Union[str, Any]       =       nn.BatchNormad(4    )
							_lowerCamelCase	:     Dict       =       nn.Linear(4    , 5    )






			def    A_       (    self    , lowercase    ):
							return self.lineara(self.batchnorm(self.lineara(lowercase    )    )    )






class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			def    A_       (    self    , lowercase    , *lowercase    , **lowercase    ):
							return (args[0] + 1,) + args[1:], kwargs






class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			def    A_       (    self    , lowercase    , lowercase    ):
							return output + 1






class lowerCAmelCase__      (	unittest.TestCase		):






			'''simple docstring'''



			def    A_       (    self    ):
							_lowerCamelCase	:     int       =       ModelForTest()
							_lowerCamelCase	:     List[str]       =       ModelHook()

							add_hook_to_module(lowercase    , lowercase    )
							self.assertEqual(test_model._hf_hook    , lowercase    )
							self.assertTrue(hasattr(lowercase    , '_old_forward'    )    )

							# Check adding the hook did not change the name or the signature
							self.assertEqual(test_model.forward.__name__    , 'forward'    )
							self.assertListEqual(list(inspect.signature(test_model.forward    ).parameters    )    , ['x']    )

							remove_hook_from_module(lowercase    )
							self.assertFalse(hasattr(lowercase    , '_hf_hook'    )    )
							self.assertFalse(hasattr(lowercase    , '_old_forward'    )    )
			def    A_       (    self    ):
							_lowerCamelCase	:     List[Any]       =       ModelForTest()
							_lowerCamelCase	:     int       =       ModelHook()

							add_hook_to_module(lowercase    , lowercase    )
							add_hook_to_module(lowercase    , lowercase    , append=lowercase    )

							self.assertEqual(isinstance(test_model._hf_hook    , lowercase    )    , lowercase    )
							self.assertEqual(len(test_model._hf_hook.hooks    )    , 2    )
							self.assertTrue(hasattr(lowercase    , '_old_forward'    )    )

							# Check adding the hook did not change the name or the signature
							self.assertEqual(test_model.forward.__name__    , 'forward'    )
							self.assertListEqual(list(inspect.signature(test_model.forward    ).parameters    )    , ['x']    )

							remove_hook_from_module(lowercase    )
							self.assertFalse(hasattr(lowercase    , '_hf_hook'    )    )
							self.assertFalse(hasattr(lowercase    , '_old_forward'    )    )
			def    A_       (    self    ):
							_lowerCamelCase	:     Dict       =       ModelForTest()
							_lowerCamelCase	:     Optional[Any]       =       torch.randn(2    , 3    )
							_lowerCamelCase	:     Union[str, Any]       =       test_model(x + 1    )
							_lowerCamelCase	:     str       =       test_model(x + 2    )

							_lowerCamelCase	:     str       =       PreForwardHook()
							add_hook_to_module(lowercase    , lowercase    )
							_lowerCamelCase	:     Dict       =       test_model(lowercase    )
							self.assertTrue(torch.allclose(lowercase    , lowercase    , atol=1E-5    )    )

							# Attaching a hook to a model when it already has one replaces, does not chain
							_lowerCamelCase	:     Union[str, Any]       =       PreForwardHook()
							add_hook_to_module(lowercase    , lowercase    )
							_lowerCamelCase	:     Optional[Any]       =       test_model(lowercase    )
							self.assertTrue(torch.allclose(lowercase    , lowercase    , atol=1E-5    )    )

							# You need to use the sequential hook to chain two or more hooks
							_lowerCamelCase	:     Union[str, Any]       =       SequentialHook(PreForwardHook()    , PreForwardHook()    )
							add_hook_to_module(lowercase    , lowercase    )

							_lowerCamelCase	:     Any       =       test_model(lowercase    )
							assert torch.allclose(lowercase    , lowercase    , atol=1E-5    )
			def    A_       (    self    ):
							_lowerCamelCase	:     Union[str, Any]       =       ModelForTest()
							_lowerCamelCase	:     Union[str, Any]       =       torch.randn(2    , 3    )
							_lowerCamelCase	:     List[str]       =       test_model(lowercase    )

							_lowerCamelCase	:     List[Any]       =       PostForwardHook()
							add_hook_to_module(lowercase    , lowercase    )
							_lowerCamelCase	:     Union[str, Any]       =       test_model(lowercase    )
							self.assertTrue(torch.allclose(lowercase    , output + 1    , atol=1E-5    )    )

							# Attaching a hook to a model when it already has one replaces, does not chain
							_lowerCamelCase	:     str       =       PostForwardHook()
							add_hook_to_module(lowercase    , lowercase    )
							_lowerCamelCase	:     List[str]       =       test_model(lowercase    )
							self.assertTrue(torch.allclose(lowercase    , output + 1    , atol=1E-5    )    )

							# You need to use the sequential hook to chain two or more hooks
							_lowerCamelCase	:     Optional[int]       =       SequentialHook(PostForwardHook()    , PostForwardHook()    )
							add_hook_to_module(lowercase    , lowercase    )

							_lowerCamelCase	:     List[Any]       =       test_model(lowercase    )
							assert torch.allclose(lowercase    , output + 2    , atol=1E-5    )
			def    A_       (    self    ):
							_lowerCamelCase	:     str       =       ModelForTest()
							_lowerCamelCase	:     Dict       =       torch.randn(2    , 3    )
							_lowerCamelCase	:     int       =       test_model(lowercase    )

							_lowerCamelCase	:     List[str]       =       PostForwardHook()
							add_hook_to_module(lowercase    , lowercase    )
							_lowerCamelCase	:     Any       =       test_model(lowercase    )
							self.assertTrue(torch.allclose(lowercase    , output + 1    )    )
							self.assertTrue(outputa.requires_grad    )

							_lowerCamelCase	:     List[Any]       =       True
							_lowerCamelCase	:     int       =       test_model(lowercase    )
							self.assertFalse(outputa.requires_grad    )
			@require_multi_gpu
			def    A_       (    self    ):
							_lowerCamelCase	:     Tuple       =       ModelForTest()
							# Everything is on CPU
							self.assertEqual(model.lineara.weight.device    , torch.device('cpu'    )    )
							self.assertEqual(model.batchnorm.weight.device    , torch.device('cpu'    )    )
							self.assertEqual(model.lineara.weight.device    , torch.device('cpu'    )    )

							# This will move each submodule on different devices
							add_hook_to_module(model.lineara    , AlignDevicesHook(execution_device=0    )    )
							add_hook_to_module(model.batchnorm    , AlignDevicesHook(execution_device=0    )    )
							add_hook_to_module(model.lineara    , AlignDevicesHook(execution_device=1    )    )

							self.assertEqual(model.lineara.weight.device    , torch.device(0    )    )
							self.assertEqual(model.batchnorm.weight.device    , torch.device(0    )    )
							self.assertEqual(model.batchnorm.running_mean.device    , torch.device(0    )    )
							self.assertEqual(model.lineara.weight.device    , torch.device(1    )    )

							# We can still make a forward pass. The input does not need to be on any particular device
							_lowerCamelCase	:     Dict       =       torch.randn(2    , 3    )
							_lowerCamelCase	:     List[str]       =       model(lowercase    )
							self.assertEqual(output.device    , torch.device(1    )    )

							# We can add a general hook to put back output on same device as input.
							add_hook_to_module(lowercase    , AlignDevicesHook(io_same_device=lowercase    )    )
							_lowerCamelCase	:     List[str]       =       torch.randn(2    , 3    ).to(0    )
							_lowerCamelCase	:     Optional[Any]       =       model(lowercase    )
							self.assertEqual(output.device    , torch.device(0    )    )
			def    A_       (    self    ):
							_lowerCamelCase	:     str       =       ModelForTest()

							# Everything is on CPU
							self.assertEqual(model.lineara.weight.device    , torch.device('cpu'    )    )
							self.assertEqual(model.batchnorm.weight.device    , torch.device('cpu'    )    )
							self.assertEqual(model.lineara.weight.device    , torch.device('cpu'    )    )

							# This will move each submodule on different devices
							_lowerCamelCase	:     Optional[Any]       =       {'execution_device': 0 if torch.cuda.is_available() else 'cpu', 'offload': True}

							add_hook_to_module(model.lineara    , AlignDevicesHook(**lowercase    )    )
							add_hook_to_module(model.batchnorm    , AlignDevicesHook(**lowercase    )    )
							add_hook_to_module(model.lineara    , AlignDevicesHook(**lowercase    )    )

							# Parameters have been offloaded, so on the meta device
							self.assertEqual(model.lineara.weight.device    , torch.device('meta'    )    )
							self.assertEqual(model.batchnorm.weight.device    , torch.device('meta'    )    )
							self.assertEqual(model.lineara.weight.device    , torch.device('meta'    )    )
							# Buffers are not included in the offload by default, so are on the execution device
							_lowerCamelCase	:     Tuple       =       torch.device(hook_kwargs['execution_device']    )
							self.assertEqual(model.batchnorm.running_mean.device    , lowercase    )

							_lowerCamelCase	:     Optional[int]       =       torch.randn(2    , 3    )
							_lowerCamelCase	:     Dict       =       model(lowercase    )
							self.assertEqual(output.device    , lowercase    )

							# Removing hooks loads back the weights in the model.
							remove_hook_from_module(model.lineara    )
							remove_hook_from_module(model.batchnorm    )
							remove_hook_from_module(model.lineara    )
							self.assertEqual(model.lineara.weight.device    , torch.device('cpu'    )    )
							self.assertEqual(model.batchnorm.weight.device    , torch.device('cpu'    )    )
							self.assertEqual(model.lineara.weight.device    , torch.device('cpu'    )    )

							# Now test with buffers included in the offload
							_lowerCamelCase	:     Any       =       {
							    'execution_device': 0 if torch.cuda.is_available() else 'cpu',
							    'offload': True,
							    'offload_buffers': True,
							}

							add_hook_to_module(model.lineara    , AlignDevicesHook(**lowercase    )    )
							add_hook_to_module(model.batchnorm    , AlignDevicesHook(**lowercase    )    )
							add_hook_to_module(model.lineara    , AlignDevicesHook(**lowercase    )    )

							# Parameters have been offloaded, so on the meta device, buffers included
							self.assertEqual(model.lineara.weight.device    , torch.device('meta'    )    )
							self.assertEqual(model.batchnorm.weight.device    , torch.device('meta'    )    )
							self.assertEqual(model.lineara.weight.device    , torch.device('meta'    )    )
							self.assertEqual(model.batchnorm.running_mean.device    , torch.device('meta'    )    )

							_lowerCamelCase	:     Tuple       =       torch.randn(2    , 3    )
							_lowerCamelCase	:     str       =       model(lowercase    )
							self.assertEqual(output.device    , lowercase    )

							# Removing hooks loads back the weights in the model.
							remove_hook_from_module(model.lineara    )
							remove_hook_from_module(model.batchnorm    )
							remove_hook_from_module(model.lineara    )
							self.assertEqual(model.lineara.weight.device    , torch.device('cpu'    )    )
							self.assertEqual(model.batchnorm.weight.device    , torch.device('cpu'    )    )
							self.assertEqual(model.lineara.weight.device    , torch.device('cpu'    )    )
			def    A_       (    self    ):
							_lowerCamelCase	:     List[Any]       =       ModelForTest()

							# Everything is on CPU
							self.assertEqual(model.lineara.weight.device    , torch.device('cpu'    )    )
							self.assertEqual(model.batchnorm.weight.device    , torch.device('cpu'    )    )
							self.assertEqual(model.lineara.weight.device    , torch.device('cpu'    )    )

							# This will move each submodule on different devices
							_lowerCamelCase	:     Union[str, Any]       =       0 if torch.cuda.is_available() else 'cpu'
							attach_align_device_hook(lowercase    , execution_device=lowercase    , offload=lowercase    )

							# Parameters have been offloaded, so on the meta device
							self.assertEqual(model.lineara.weight.device    , torch.device('meta'    )    )
							self.assertEqual(model.batchnorm.weight.device    , torch.device('meta'    )    )
							self.assertEqual(model.lineara.weight.device    , torch.device('meta'    )    )
							# Buffers are not included in the offload by default, so are on the execution device
							_lowerCamelCase	:     str       =       torch.device(lowercase    )
							self.assertEqual(model.batchnorm.running_mean.device    , lowercase    )

							_lowerCamelCase	:     str       =       torch.randn(2    , 3    )
							_lowerCamelCase	:     Any       =       model(lowercase    )
							self.assertEqual(output.device    , lowercase    )

							# Removing hooks loads back the weights in the model.
							remove_hook_from_submodules(lowercase    )
							self.assertEqual(model.lineara.weight.device    , torch.device('cpu'    )    )
							self.assertEqual(model.batchnorm.weight.device    , torch.device('cpu'    )    )
							self.assertEqual(model.lineara.weight.device    , torch.device('cpu'    )    )

							# Now test with buffers included in the offload
							attach_align_device_hook(lowercase    , execution_device=lowercase    , offload=lowercase    , offload_buffers=lowercase    )

							# Parameters have been offloaded, so on the meta device, buffers included
							self.assertEqual(model.lineara.weight.device    , torch.device('meta'    )    )
							self.assertEqual(model.batchnorm.weight.device    , torch.device('meta'    )    )
							self.assertEqual(model.lineara.weight.device    , torch.device('meta'    )    )
							self.assertEqual(model.batchnorm.running_mean.device    , torch.device('meta'    )    )

							_lowerCamelCase	:     List[str]       =       torch.randn(2    , 3    )
							_lowerCamelCase	:     str       =       model(lowercase    )
							self.assertEqual(output.device    , lowercase    )

							# Removing hooks loads back the weights in the model.
							remove_hook_from_submodules(lowercase    )
							self.assertEqual(model.lineara.weight.device    , torch.device('cpu'    )    )
							self.assertEqual(model.batchnorm.weight.device    , torch.device('cpu'    )    )
							self.assertEqual(model.lineara.weight.device    , torch.device('cpu'    )    )






			def    A_       (    self    ):
							_lowerCamelCase	:     int       =       ModelForTest()

							# Everything is on CPU
							self.assertEqual(model.lineara.weight.device    , torch.device('cpu'    )    )
							self.assertEqual(model.batchnorm.weight.device    , torch.device('cpu'    )    )
							self.assertEqual(model.lineara.weight.device    , torch.device('cpu'    )    )

							# This will move each submodule on different devices
							_lowerCamelCase	:     Any       =       0 if torch.cuda.is_available() else 'cpu'
							attach_align_device_hook(
							    lowercase    , execution_device=lowercase    , offload=lowercase    , weights_map=model.state_dict()    )

							# Parameters have been offloaded, so on the meta device
							self.assertEqual(model.lineara.weight.device    , torch.device('meta'    )    )
							self.assertEqual(model.batchnorm.weight.device    , torch.device('meta'    )    )
							self.assertEqual(model.lineara.weight.device    , torch.device('meta'    )    )
							# Buffers are not included in the offload by default, so are on the execution device
							_lowerCamelCase	:     List[Any]       =       torch.device(lowercase    )
							self.assertEqual(model.batchnorm.running_mean.device    , lowercase    )

							_lowerCamelCase	:     Dict       =       torch.randn(2    , 3    )
							_lowerCamelCase	:     Optional[Any]       =       model(lowercase    )
							self.assertEqual(output.device    , lowercase    )

							# Removing hooks loads back the weights in the model.
							remove_hook_from_submodules(lowercase    )
							self.assertEqual(model.lineara.weight.device    , torch.device('cpu'    )    )
							self.assertEqual(model.batchnorm.weight.device    , torch.device('cpu'    )    )
							self.assertEqual(model.lineara.weight.device    , torch.device('cpu'    )    )

							# Now test with buffers included in the offload
							attach_align_device_hook(
							    lowercase    , execution_device=lowercase    , offload=lowercase    , weights_map=model.state_dict()    , offload_buffers=lowercase    , )

							# Parameters have been offloaded, so on the meta device, buffers included
							self.assertEqual(model.lineara.weight.device    , torch.device('meta'    )    )
							self.assertEqual(model.batchnorm.weight.device    , torch.device('meta'    )    )
							self.assertEqual(model.lineara.weight.device    , torch.device('meta'    )    )
							self.assertEqual(model.batchnorm.running_mean.device    , torch.device('meta'    )    )

							_lowerCamelCase	:     Any       =       torch.randn(2    , 3    )
							_lowerCamelCase	:     Any       =       model(lowercase    )
							self.assertEqual(output.device    , lowercase    )

							# Removing hooks loads back the weights in the model.
							remove_hook_from_submodules(lowercase    )
							self.assertEqual(model.lineara.weight.device    , torch.device('cpu'    )    )
							self.assertEqual(model.batchnorm.weight.device    , torch.device('cpu'    )    )
							self.assertEqual(model.lineara.weight.device    , torch.device('cpu'    )    )





"""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    )