


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


__UpperCamelCase       :       str						       =				{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__UpperCamelCase       :       Any						       =				{
    "vocab_file": {
        "unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt",
    },
    "tokenizer_file": {
        "unc-nlp/lxmert-base-uncased": (
            "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"
        ),
    },
}

__UpperCamelCase       :       Any						       =				{
    "unc-nlp/lxmert-base-uncased": 512,
}

__UpperCamelCase       :       Optional[Any]						       =				{
    "unc-nlp/lxmert-base-uncased": {"do_lower_case": True},
}

class       __magic_name__	(     UpperCamelCase__):

     '''simple docstring'''





     A:		Optional[Any]			=      VOCAB_FILES_NAMES
     A:		str			=      PRETRAINED_VOCAB_FILES_MAP
     A:		Optional[int]			=      PRETRAINED_INIT_CONFIGURATION
     A:		Dict			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A:		str			=      LxmertTokenizer


     def __init__(      self							:     List[str]							,	lowerCamelCase__							:     Tuple=None							,	lowerCamelCase__							:     Dict=None							,	lowerCamelCase__							:     str=True							,	lowerCamelCase__							:     Optional[Any]="[UNK]"							,	lowerCamelCase__							:     Union[str, Any]="[SEP]"							,	lowerCamelCase__							:     str="[PAD]"							,	lowerCamelCase__							:     Optional[Any]="[CLS]"							,	lowerCamelCase__							:     Tuple="[MASK]"							,	lowerCamelCase__							:     Tuple=True							,	lowerCamelCase__							:     Dict=None							,	**lowerCamelCase__							:     Optional[Any]							,	)  ->      Optional[int]:





      '''simple docstring'''






      super().__init__(
          __A							,	tokenizer_file=__A							,	do_lower_case=__A							,	unk_token=__A							,	sep_token=__A							,	pad_token=__A							,	cls_token=__A							,	mask_token=__A							,	tokenize_chinese_chars=__A							,	strip_accents=__A							,	**__A							,	)

      UpperCamelCase__					:    List[str]          =   json.loads(self.backend_tokenizer.normalizer.__getstate__()			)
      if (
          normalizer_state.get('''lowercase'''							,	__A			) != do_lower_case
          or normalizer_state.get('''strip_accents'''							,	__A			) != strip_accents
          or normalizer_state.get('''handle_chinese_chars'''							,	__A			) != tokenize_chinese_chars
      ):
       UpperCamelCase__					:    int          =   getattr(__A							,	normalizer_state.pop('''type'''			)			)
       UpperCamelCase__					:    Dict          =   do_lower_case
       UpperCamelCase__					:    Union[str, Any]          =   strip_accents
       UpperCamelCase__					:    Optional[Any]          =   tokenize_chinese_chars
       UpperCamelCase__					:    Optional[int]          =   normalizer_class(**__A			)

      UpperCamelCase__					:    Optional[Any]          =   do_lower_case


     def      UpperCAmelCase__	(      self							:     str							,	lowerCamelCase__							:     Dict							,	lowerCamelCase__							:     str=None			)  ->      List[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   [self.cls_token_id] + token_ids_a + [self.sep_token_id]

      if token_ids_a:
       output += token_ids_a + [self.sep_token_id]

      return output


     def      UpperCAmelCase__	(      self							:     str							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     Optional[List[int]] = None			)  ->      List[int]:





      '''simple docstring'''






      UpperCamelCase__					:    str          =   [self.sep_token_id]
      UpperCamelCase__					:    Dict          =   [self.cls_token_id]
      if token_ids_a is None:
       return len(cls + token_ids_a + sep			) * [0]
      return len(cls + token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1]


     def      UpperCAmelCase__	(      self							:     List[str]							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     Optional[str] = None			)  ->      Tuple[str]:





      '''simple docstring'''






      UpperCamelCase__					:    List[Any]          =   self._tokenizer.model.save(__A							,	name=__A			)
      return tuple(__A			)






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       __magic_name__	(     nn.Module):


     def __init__(      self							:     Dict			)  ->      str:





      '''simple docstring'''






      super().__init__()
      UpperCamelCase__					:    int          =   nn.Linear(3							,	4			)
      UpperCamelCase__					:    str          =   nn.BatchNormad(4			)
      UpperCamelCase__					:    List[str]          =   nn.Linear(4							,	5			)


     def      UpperCAmelCase__	(      self							:     int							,	lowerCamelCase__							:     Dict			)  ->      Tuple:





      '''simple docstring'''






      return self.lineara(self.batchnorm(self.lineara(lowerCamelCase__			)			)			)



class       __magic_name__	(     __lowerCAmelCase):


     def      UpperCAmelCase__	(      self							:     Union[str, Any]							,	lowerCamelCase__							:     Any							,	*lowerCamelCase__							:     int							,	**lowerCamelCase__							:     List[str]			)  ->      Any:





      '''simple docstring'''






      return (args[0] + 1,) + args[1:], kwargs



class       __magic_name__	(     __lowerCAmelCase):


     def      UpperCAmelCase__	(      self							:     Any							,	lowerCamelCase__							:     Any							,	lowerCamelCase__							:     Any			)  ->      Any:





      '''simple docstring'''






      return output + 1



class       __magic_name__	(     unittest.TestCase):


     def      UpperCAmelCase__	(      self							:     Tuple			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    Tuple          =   ModelForTest()
      UpperCamelCase__					:    Union[str, Any]          =   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							:     Dict			)  ->      str:





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   ModelForTest()
      UpperCamelCase__					:    List[str]          =   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							:     List[str]			)  ->      int:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[Any]          =   ModelForTest()
      UpperCamelCase__					:    List[str]          =   torch.randn(2							,	3			)
      UpperCamelCase__					:    int          =   test_model(x + 1			)
      UpperCamelCase__					:    List[Any]          =   test_model(x + 2			)

      UpperCamelCase__					:    Optional[int]          =   PreForwardHook()
      add_hook_to_module(lowerCamelCase__							,	lowerCamelCase__			)
      UpperCamelCase__					:    List[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
      UpperCamelCase__					:    Optional[Any]          =   PreForwardHook()
      add_hook_to_module(lowerCamelCase__							,	lowerCamelCase__			)
      UpperCamelCase__					:    List[str]          =   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
      UpperCamelCase__					:    Tuple          =   SequentialHook(PreForwardHook()							,	PreForwardHook()			)
      add_hook_to_module(lowerCamelCase__							,	lowerCamelCase__			)

      UpperCamelCase__					:    Any          =   test_model(lowerCamelCase__			)
      assert torch.allclose(lowerCamelCase__							,	lowerCamelCase__							,	atol=1E-5			)


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





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   ModelForTest()
      UpperCamelCase__					:    str          =   torch.randn(2							,	3			)
      UpperCamelCase__					:    Any          =   test_model(lowerCamelCase__			)

      UpperCamelCase__					:    int          =   PostForwardHook()
      add_hook_to_module(lowerCamelCase__							,	lowerCamelCase__			)
      UpperCamelCase__					:    Dict          =   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
      UpperCamelCase__					:    Dict          =   PostForwardHook()
      add_hook_to_module(lowerCamelCase__							,	lowerCamelCase__			)
      UpperCamelCase__					:    int          =   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
      UpperCamelCase__					:    List[Any]          =   SequentialHook(PostForwardHook()							,	PostForwardHook()			)
      add_hook_to_module(lowerCamelCase__							,	lowerCamelCase__			)

      UpperCamelCase__					:    Tuple          =   test_model(lowerCamelCase__			)
      assert torch.allclose(lowerCamelCase__							,	output + 2							,	atol=1E-5			)


     def      UpperCAmelCase__	(      self							:     List[Any]			)  ->      Dict:





      '''simple docstring'''






      UpperCamelCase__					:    List[Any]          =   ModelForTest()
      UpperCamelCase__					:    Tuple          =   torch.randn(2							,	3			)
      UpperCamelCase__					:    Tuple          =   test_model(lowerCamelCase__			)

      UpperCamelCase__					:    Union[str, Any]          =   PostForwardHook()
      add_hook_to_module(lowerCamelCase__							,	lowerCamelCase__			)
      UpperCamelCase__					:    Any          =   test_model(lowerCamelCase__			)
      self.assertTrue(torch.allclose(lowerCamelCase__							,	output + 1			)			)
      self.assertTrue(outputa.requires_grad			)

      UpperCamelCase__					:    Optional[int]          =   True
      UpperCamelCase__					:    Optional[int]          =   test_model(lowerCamelCase__			)
      self.assertFalse(outputa.requires_grad			)


     @require_multi_gpu
     def      UpperCAmelCase__	(      self							:     Any			)  ->      Dict:





      '''simple docstring'''






      UpperCamelCase__					:    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
      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
      UpperCamelCase__					:    Tuple          =   torch.randn(2							,	3			)
      UpperCamelCase__					:    str          =   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__			)			)
      UpperCamelCase__					:    Tuple          =   torch.randn(2							,	3			).to(0			)
      UpperCamelCase__					:    Optional[int]          =   model(lowerCamelCase__			)
      self.assertEqual(output.device							,	torch.device(0			)			)


     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      Union[str, Any]:





      '''simple docstring'''






      UpperCamelCase__					:    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
      UpperCamelCase__					:    int          =   {'''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
      UpperCamelCase__					:    int          =   torch.device(hook_kwargs['''execution_device''']			)
      self.assertEqual(model.batchnorm.running_mean.device							,	lowerCamelCase__			)

      UpperCamelCase__					:    Dict          =   torch.randn(2							,	3			)
      UpperCamelCase__					:    List[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
      UpperCamelCase__					:    Optional[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'''			)			)

      UpperCamelCase__					:    List[Any]          =   torch.randn(2							,	3			)
      UpperCamelCase__					:    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'''			)			)


     def      UpperCAmelCase__	(      self							:     str			)  ->      Optional[int]:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[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
      UpperCamelCase__					:    Any          =   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
      UpperCamelCase__					:    str          =   torch.device(lowerCamelCase__			)
      self.assertEqual(model.batchnorm.running_mean.device							,	lowerCamelCase__			)

      UpperCamelCase__					:    int          =   torch.randn(2							,	3			)
      UpperCamelCase__					:    Tuple          =   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'''			)			)

      UpperCamelCase__					:    int          =   torch.randn(2							,	3			)
      UpperCamelCase__					:    str          =   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							:     int			)  ->      List[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    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
      UpperCamelCase__					:    List[str]          =   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
      UpperCamelCase__					:    List[str]          =   torch.device(lowerCamelCase__			)
      self.assertEqual(model.batchnorm.running_mean.device							,	lowerCamelCase__			)

      UpperCamelCase__					:    List[str]          =   torch.randn(2							,	3			)
      UpperCamelCase__					:    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'''			)			)

      UpperCamelCase__					:    Union[str, Any]          =   torch.randn(2							,	3			)
      UpperCamelCase__					:    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'''			)			)


