




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			:   Tuple  )						->   str:
								super().__init__()
								__magic_name__							:	Dict    							=							nn.Linear(3       ,						4  )
								__magic_name__							:	Optional[int]    							=							nn.BatchNormad(4  )
								__magic_name__							:	Optional[Any]    							=							nn.Linear(4       ,						5  )


			def 							UpperCAmelCase__       ( self			:   List[str]       ,						lowerCamelCase_			:   Dict  )						->   Dict:
								return self.lineara(self.batchnorm(self.lineara(lowerCamelCase_  )  )  )






class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			def 							UpperCAmelCase__       ( self			:   int       ,						lowerCamelCase_			:   Optional[Any]       ,						*lowerCamelCase_			:   Optional[Any]       ,						**lowerCamelCase_			:   Optional[Any]  )						->   List[str]:
								return (args[0] + 1,) + args[1:], kwargs






class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			def 							UpperCAmelCase__       ( self			:   List[Any]       ,						lowerCamelCase_			:   Any       ,						lowerCamelCase_			:   List[Any]  )						->   Optional[Any]:
								return output + 1






class    lowerCamelCase    (		unittest.TestCase						):






			'''simple docstring'''





			def 							UpperCAmelCase__       ( self			:   List[str]  )						->   List[str]:
								__magic_name__							:	Any    							=							ModelForTest()
								__magic_name__							:	Dict    							=							ModelHook()

								add_hook_to_module(lowerCamelCase_       ,						lowerCamelCase_  )
								self.assertEqual(test_model._hf_hook       ,						lowerCamelCase_  )
								self.assertTrue(hasattr(lowerCamelCase_       ,						'''_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(lowerCamelCase_  )
								self.assertFalse(hasattr(lowerCamelCase_       ,						'''_hf_hook'''  )  )
								self.assertFalse(hasattr(lowerCamelCase_       ,						'''_old_forward'''  )  )

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   List[Any]:
								__magic_name__							:	Tuple    							=							ModelForTest()
								__magic_name__							:	Optional[int]    							=							ModelHook()

								add_hook_to_module(lowerCamelCase_       ,						lowerCamelCase_  )
								add_hook_to_module(lowerCamelCase_       ,						lowerCamelCase_       ,						append=lowerCamelCase_  )

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

			def 							UpperCAmelCase__       ( self			:   int  )						->   Dict:
								__magic_name__							:	int    							=							ModelForTest()
								__magic_name__							:	List[Any]    							=							torch.randn(2       ,						3  )
								__magic_name__							:	Union[str, Any]    							=							test_model(x + 1  )
								__magic_name__							:	Any    							=							test_model(x + 2  )

								__magic_name__							:	Dict    							=							PreForwardHook()
								add_hook_to_module(lowerCamelCase_       ,						lowerCamelCase_  )
								__magic_name__							:	Union[str, Any]    							=							test_model(lowerCamelCase_  )
								self.assertTrue(torch.allclose(lowerCamelCase_       ,						lowerCamelCase_       ,						atol=1E-5  )  )

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

								# You need to use the sequential hook to chain two or more hooks
								__magic_name__							:	Dict    							=							SequentialHook(PreForwardHook()       ,						PreForwardHook()  )
								add_hook_to_module(lowerCamelCase_       ,						lowerCamelCase_  )

								__magic_name__							:	List[Any]    							=							test_model(lowerCamelCase_  )
								assert torch.allclose(lowerCamelCase_       ,						lowerCamelCase_       ,						atol=1E-5  )

			def 							UpperCAmelCase__       ( self			:   Optional[Any]  )						->   Optional[Any]:
								__magic_name__							:	Any    							=							ModelForTest()
								__magic_name__							:	int    							=							torch.randn(2       ,						3  )
								__magic_name__							:	int    							=							test_model(lowerCamelCase_  )

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

								# Attaching a hook to a model when it already has one replaces, does not chain
								__magic_name__							:	Any    							=							PostForwardHook()
								add_hook_to_module(lowerCamelCase_       ,						lowerCamelCase_  )
								__magic_name__							:	Dict    							=							test_model(lowerCamelCase_  )
								self.assertTrue(torch.allclose(lowerCamelCase_       ,						output + 1       ,						atol=1E-5  )  )

								# You need to use the sequential hook to chain two or more hooks
								__magic_name__							:	int    							=							SequentialHook(PostForwardHook()       ,						PostForwardHook()  )
								add_hook_to_module(lowerCamelCase_       ,						lowerCamelCase_  )

								__magic_name__							:	Any    							=							test_model(lowerCamelCase_  )
								assert torch.allclose(lowerCamelCase_       ,						output + 2       ,						atol=1E-5  )

			def 							UpperCAmelCase__       ( self			:   Optional[Any]  )						->   Dict:
								__magic_name__							:	Dict    							=							ModelForTest()
								__magic_name__							:	Optional[Any]    							=							torch.randn(2       ,						3  )
								__magic_name__							:	str    							=							test_model(lowerCamelCase_  )

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

								__magic_name__							:	List[Any]    							=							True
								__magic_name__							:	Any    							=							test_model(lowerCamelCase_  )
								self.assertFalse(outputa.requires_grad  )

			@require_multi_gpu
			def 							UpperCAmelCase__       ( self			:   List[Any]  )						->   Dict:
								__magic_name__							:	Dict    							=							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
								__magic_name__							:	int    							=							torch.randn(2       ,						3  )
								__magic_name__							:	Tuple    							=							model(lowerCamelCase_  )
								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(lowerCamelCase_       ,						AlignDevicesHook(io_same_device=lowerCamelCase_  )  )
								__magic_name__							:	List[str]    							=							torch.randn(2       ,						3  ).to(0  )
								__magic_name__							:	Union[str, Any]    							=							model(lowerCamelCase_  )
								self.assertEqual(output.device       ,						torch.device(0  )  )

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   Union[str, Any]:
								__magic_name__							:	List[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
								__magic_name__							:	List[Any]    							=							{'''execution_device''': 0 if torch.cuda.is_available() else '''cpu''', '''offload''': True}

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

								# 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
								__magic_name__							:	Union[str, Any]    							=							torch.device(hook_kwargs['''execution_device''']  )
								self.assertEqual(model.batchnorm.running_mean.device       ,						lowerCamelCase_  )

								__magic_name__							:	int    							=							torch.randn(2       ,						3  )
								__magic_name__							:	str    							=							model(lowerCamelCase_  )
								self.assertEqual(output.device       ,						lowerCamelCase_  )

								# 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
								__magic_name__							:	Union[str, Any]    							=							{
								    '''execution_device''': 0 if torch.cuda.is_available() else '''cpu''',
								    '''offload''': True,
								    '''offload_buffers''': True,
								}

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

								# 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'''  )  )

								__magic_name__							:	List[str]    							=							torch.randn(2       ,						3  )
								__magic_name__							:	Optional[int]    							=							model(lowerCamelCase_  )
								self.assertEqual(output.device       ,						lowerCamelCase_  )

								# 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 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   str:
								__magic_name__							:	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
								__magic_name__							:	Dict    							=							0 if torch.cuda.is_available() else '''cpu'''
								attach_align_device_hook(lowerCamelCase_       ,						execution_device=lowerCamelCase_       ,						offload=lowerCamelCase_  )

								# 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
								__magic_name__							:	Union[str, Any]    							=							torch.device(lowerCamelCase_  )
								self.assertEqual(model.batchnorm.running_mean.device       ,						lowerCamelCase_  )

								__magic_name__							:	Tuple    							=							torch.randn(2       ,						3  )
								__magic_name__							:	Optional[int]    							=							model(lowerCamelCase_  )
								self.assertEqual(output.device       ,						lowerCamelCase_  )

								# Removing hooks loads back the weights in the model.
								remove_hook_from_submodules(lowerCamelCase_  )
								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(lowerCamelCase_       ,						execution_device=lowerCamelCase_       ,						offload=lowerCamelCase_       ,						offload_buffers=lowerCamelCase_  )

								# 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'''  )  )

								__magic_name__							:	Tuple    							=							torch.randn(2       ,						3  )
								__magic_name__							:	int    							=							model(lowerCamelCase_  )
								self.assertEqual(output.device       ,						lowerCamelCase_  )

								# Removing hooks loads back the weights in the model.
								remove_hook_from_submodules(lowerCamelCase_  )
								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 							UpperCAmelCase__       ( self			:   List[Any]  )						->   Optional[int]:
								__magic_name__							:	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
								__magic_name__							:	Tuple    							=							0 if torch.cuda.is_available() else '''cpu'''
								attach_align_device_hook(
								    lowerCamelCase_       ,						execution_device=lowerCamelCase_       ,						offload=lowerCamelCase_       ,						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
								__magic_name__							:	Dict    							=							torch.device(lowerCamelCase_  )
								self.assertEqual(model.batchnorm.running_mean.device       ,						lowerCamelCase_  )

								__magic_name__							:	Union[str, Any]    							=							torch.randn(2       ,						3  )
								__magic_name__							:	List[Any]    							=							model(lowerCamelCase_  )
								self.assertEqual(output.device       ,						lowerCamelCase_  )

								# Removing hooks loads back the weights in the model.
								remove_hook_from_submodules(lowerCamelCase_  )
								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(
								    lowerCamelCase_       ,						execution_device=lowerCamelCase_       ,						offload=lowerCamelCase_       ,						weights_map=model.state_dict()       ,						offload_buffers=lowerCamelCase_       ,						)

								# 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'''  )  )

								__magic_name__							:	List[str]    							=							torch.randn(2       ,						3  )
								__magic_name__							:	Optional[int]    							=							model(lowerCamelCase_  )
								self.assertEqual(output.device       ,						lowerCamelCase_  )

								# Removing hooks loads back the weights in the model.
								remove_hook_from_submodules(lowerCamelCase_  )
								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'''  )  )









from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput


@dataclass
class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=None
			UpperCamelCase__								=None
			UpperCamelCase__								=None
			UpperCamelCase__								=None






class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			def __init__( self			:   List[Any]       ,						lowerCamelCase_			:   Optional[Any]=1       ,						lowerCamelCase_			:   Any=0       ,						lowerCamelCase_			:   Dict=2       ,						lowerCamelCase_			:   Dict=512       ,						lowerCamelCase_			:   int="cls"       ,						lowerCamelCase_			:   Dict=False       ,						lowerCamelCase_			:   List[Any]=True       ,						**lowerCamelCase_			:   Optional[Any]       ,						)						->   Optional[int]:
								super().__init__(pad_token_id=lowerCamelCase_       ,						bos_token_id=lowerCamelCase_       ,						eos_token_id=lowerCamelCase_       ,						**lowerCamelCase_  )
								__magic_name__							:	Dict    							=							project_dim
								__magic_name__							:	int    							=							pooler_fn
								__magic_name__							:	str    							=							learn_encoder
								__magic_name__							:	Optional[int]    							=							use_attention_mask






class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=[R'''pooler''', R'''logit_scale''']
			UpperCamelCase__								=[R'''position_ids''', R'''predictions.decoder.bias''']
			UpperCamelCase__								='''roberta'''
			UpperCamelCase__								=RobertaSeriesConfig

			def __init__( self			:   str       ,						lowerCamelCase_			:   Optional[Any]  )						->   Optional[Any]:
								super().__init__(lowerCamelCase_  )
								__magic_name__							:	Tuple    							=							XLMRobertaModel(lowerCamelCase_  )
								__magic_name__							:	Any    							=							nn.Linear(config.hidden_size       ,						config.project_dim  )
								__magic_name__							:	Optional[int]    							=							getattr(lowerCamelCase_       ,						'''has_pre_transformation'''       ,						lowerCamelCase_  )
								if self.has_pre_transformation:
													__magic_name__							:	List[Any]    							=							nn.Linear(config.hidden_size       ,						config.project_dim  )
													__magic_name__							:	Union[str, Any]    							=							nn.LayerNorm(config.hidden_size       ,						eps=config.layer_norm_eps  )
								self.post_init()


			def 							UpperCAmelCase__       ( self			:   Optional[Any]       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[bool] = None       ,						lowerCamelCase_			:   Optional[bool] = None       ,						lowerCamelCase_			:   Optional[bool] = None       ,						)						->   List[Any]:
								__magic_name__							:	Optional[int]    							=							return_dict if return_dict is not None else self.config.use_return_dict

								__magic_name__							:	Any    							=							self.base_model(
								    input_ids=lowerCamelCase_       ,						attention_mask=lowerCamelCase_       ,						token_type_ids=lowerCamelCase_       ,						position_ids=lowerCamelCase_       ,						head_mask=lowerCamelCase_       ,						inputs_embeds=lowerCamelCase_       ,						encoder_hidden_states=lowerCamelCase_       ,						encoder_attention_mask=lowerCamelCase_       ,						output_attentions=lowerCamelCase_       ,						output_hidden_states=True if self.has_pre_transformation else output_hidden_states       ,						return_dict=lowerCamelCase_       ,						)

								if self.has_pre_transformation:
													__magic_name__							:	Any    							=							outputs['''hidden_states'''][-2]
													__magic_name__							:	Dict    							=							self.pre_LN(lowerCamelCase_  )
													__magic_name__							:	int    							=							self.transformation_pre(lowerCamelCase_  )

													return TransformationModelOutput(
													    projection_state=lowerCamelCase_       ,						last_hidden_state=outputs.last_hidden_state       ,						hidden_states=outputs.hidden_states       ,						attentions=outputs.attentions       ,						)
								else:
													__magic_name__							:	Union[str, Any]    							=							self.transformation(outputs.last_hidden_state  )
													return TransformationModelOutput(
													    projection_state=lowerCamelCase_       ,						last_hidden_state=outputs.last_hidden_state       ,						hidden_states=outputs.hidden_states       ,						attentions=outputs.attentions       ,						)



