






'''simple docstring'''



import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin
class 							__SCREAMING_SNAKE_CASE       (							UpperCamelCase__      ,      unittest.TestCase     ):
      __a  					=CanineTokenizer
      __a  					=False




      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         super().setUp()
         __a				     =    CanineTokenizer()
         tokenizer.save_pretrained(self.tmpdirname      )




      @cached_property
      def  __UpperCamelCase							(  self      )							->List[str]:



         '''simple docstring'''
         return CanineTokenizer.from_pretrained('google/canine-s'      )




      def  __UpperCamelCase							(  self ,   **lowerCamelCase      )							->CanineTokenizer:



         '''simple docstring'''
         __a				     =    self.tokenizer_class.from_pretrained(self.tmpdirname ,   **_a      )
         __a				     =    1024
         return tokenizer




      @require_torch
      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         __a				     =    self.canine_tokenizer
         __a				     =    ["""Life is like a box of chocolates.""", """You never know what you're gonna get."""]
         # fmt: off
         __a				     =    [5_7344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 5_7345, 0, 0, 0, 0]
         # fmt: on
         __a				     =    tokenizer(_a ,   padding=_a ,   return_tensors='pt'      )
         self.assertIsInstance(_a ,   _a      )

         __a				     =    list(batch.input_ids.numpy()[0]      )

         self.assertListEqual(_a ,   _a      )

         self.assertEqual((2, 39) ,   batch.input_ids.shape      )
         self.assertEqual((2, 39) ,   batch.attention_mask.shape      )




      @require_torch
      def  __UpperCamelCase							(  self      )							->Dict:



         '''simple docstring'''
         __a				     =    self.canine_tokenizer
         __a				     =    ["""Once there was a man.""", """He wrote a test in HuggingFace Tranformers."""]
         __a				     =    tokenizer(_a ,   padding=_a ,   return_tensors='pt'      )
         # check if input_ids, attention_mask and token_type_ids are returned
         self.assertIn('input_ids' ,   _a      )
         self.assertIn('attention_mask' ,   _a      )
         self.assertIn('token_type_ids' ,   _a      )




      @require_torch
      def  __UpperCamelCase							(  self      )							->List[Any]:



         '''simple docstring'''
         __a				     =    self.canine_tokenizer
         __a				     =    [
             """What's the weater?""",
             """It's about 25 degrees.""",
         ]
         __a				     =    tokenizer(
             text_target=_a ,   max_length=32 ,   padding='max_length' ,   truncation=_a ,   return_tensors='pt'      )
         self.assertEqual(32 ,   targets['input_ids'].shape[1]      )




      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         # safety check on max_len default value so we are sure the test works
         __a				     =    self.get_tokenizers()
         for tokenizer in tokenizers:
            with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
               self.assertNotEqual(tokenizer.model_max_length ,   42      )

        # Now let's start the test
         __a				     =    self.get_tokenizers()
         for tokenizer in tokenizers:
            with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
               # Isolate this from the other tests because we save additional tokens/etc
               __a				     =    tempfile.mkdtemp()

               __a				     =    """ He is very happy, UNwant\u00E9d,running"""
               __a				     =    tokenizer.encode(_a ,   add_special_tokens=_a      )
               tokenizer.save_pretrained(_a      )

               __a				     =    tokenizer.__class__.from_pretrained(_a      )
               __a				     =    after_tokenizer.encode(_a ,   add_special_tokens=_a      )
               self.assertListEqual(_a ,   _a      )

               shutil.rmtree(_a      )

         __a				     =    self.get_tokenizers(model_max_length=42      )
         for tokenizer in tokenizers:
            with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
               # Isolate this from the other tests because we save additional tokens/etc
               __a				     =    tempfile.mkdtemp()

               __a				     =    """ He is very happy, UNwant\u00E9d,running"""

               __a				     =    tokenizer.additional_special_tokens

               # We can add a new special token for Canine as follows:
               __a				     =    chr(0xe007      )
               additional_special_tokens.append(_a      )
               tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}      )
               __a				     =    tokenizer.encode(_a ,   add_special_tokens=_a      )
               tokenizer.save_pretrained(_a      )

               __a				     =    tokenizer.__class__.from_pretrained(_a      )
               __a				     =    after_tokenizer.encode(_a ,   add_special_tokens=_a      )
               self.assertListEqual(_a ,   _a      )
               self.assertIn(_a ,   after_tokenizer.additional_special_tokens      )
               self.assertEqual(after_tokenizer.model_max_length ,   42      )

               __a				     =    tokenizer.__class__.from_pretrained(_a ,   model_max_length=43      )
               self.assertEqual(tokenizer.model_max_length ,   43      )

               shutil.rmtree(_a      )




      def  __UpperCamelCase							(  self      )							->List[str]:



         '''simple docstring'''
         __a				     =    self.get_tokenizers(do_lower_case=_a      )
         for tokenizer in tokenizers:
            with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
               __a				     =    self.get_clean_sequence(_a      )

               # a special token for Canine can be defined as follows:
               __a				     =    0xe005
               __a				     =    chr(_a      )

               tokenizer.add_special_tokens({'cls_token': special_token}      )
               __a				     =    tokenizer.encode(_a ,   add_special_tokens=_a      )
               self.assertEqual(len(_a      ) ,   1      )

               __a				     =    tokenizer.decode(ids + encoded_special_token ,   clean_up_tokenization_spaces=_a      )
               __a				     =    tokenizer.encode(_a ,   add_special_tokens=_a      )

               __a				     =    tokenizer.encode(_a ,   add_special_tokens=_a      )
               __a				     =    tokenizer.encode(_a ,   add_special_tokens=_a      )
               self.assertEqual(_a ,   input_encoded + special_token_id      )

               __a				     =    tokenizer.decode(_a ,   skip_special_tokens=_a      )
               self.assertTrue(special_token not in decoded      )




      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         __a				     =    self.get_tokenizers(do_lower_case=_a      )
         for tokenizer in tokenizers:
            with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
               __a				     =    chr(0xe005      )
               __a				     =    chr(0xe006      )

               # `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
               tokenizer.add_tokens([SPECIAL_TOKEN_1] ,   special_tokens=_a      )
               # `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
               # which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
               tokenizer.add_special_tokens({'additional_special_tokens': [SPECIAL_TOKEN_2]}      )

               __a				     =    tokenizer.tokenize(_a      )
               __a				     =    tokenizer.tokenize(_a      )

               self.assertEqual(len(_a      ) ,   1      )
               self.assertEqual(len(_a      ) ,   1      )
               self.assertEqual(token_a[0] ,   _a      )
               self.assertEqual(token_a[0] ,   _a      )




      @require_tokenizers
      def  __UpperCamelCase							(  self      )							->Union[str, Any]:



         '''simple docstring'''
         __a				     =    self.get_tokenizers(do_lower_case=_a      )
         for tokenizer in tokenizers:
            with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
               # a special token for Canine can be defined as follows:
               __a				     =    0xe006
               __a				     =    chr(_a      )

               __a				     =    AddedToken(_a ,   lstrip=_a      )
               tokenizer.add_special_tokens({'additional_special_tokens': [new_token]}      )

               with tempfile.TemporaryDirectory() as tmp_dir_name:
                  tokenizer.save_pretrained(_a      )
                  tokenizer.from_pretrained(_a      )




      def  __UpperCamelCase							(  self      )							->List[str]:



         '''simple docstring'''
         __a				     =    []
         if self.test_slow_tokenizer:
            tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())      )

         if self.test_rust_tokenizer:
            tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())      )

         for tokenizer_class, tokenizer_utils in tokenizer_list:
            with tempfile.TemporaryDirectory() as tmp_dir:
               tokenizer_utils.save_pretrained(_a      )

               with open(os.path.join(_a ,   'special_tokens_map.json'      ) ,   encoding='utf-8'      ) as json_file:
                  __a				     =    json.load(_a      )

               with open(os.path.join(_a ,   'tokenizer_config.json'      ) ,   encoding='utf-8'      ) as json_file:
                  __a				     =    json.load(_a      )

               # a special token for Canine can be defined as follows:
               __a				     =    0xe006
               __a				     =    chr(_a      )

               __a				     =    [new_token_a]
               __a				     =    [new_token_a]

               with open(os.path.join(_a ,   'special_tokens_map.json'      ) ,   'w' ,   encoding='utf-8'      ) as outfile:
                  json.dump(_a ,   _a      )
               with open(os.path.join(_a ,   'tokenizer_config.json'      ) ,   'w' ,   encoding='utf-8'      ) as outfile:
                  json.dump(_a ,   _a      )

               # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
               # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
               # "special_tokens_map.json" files
               __a				     =    tokenizer_class.from_pretrained(_a ,   extra_ids=0      )
               self.assertIn(_a ,   tokenizer_without_change_in_init.additional_special_tokens      )
               # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
               self.assertEqual(
                   [new_token_a] ,   tokenizer_without_change_in_init.convert_ids_to_tokens(
                       tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]      )      ) ,   )

               __a				     =    0xe007
               __a				     =    chr(_a      )
               # Now we test that we can change the value of additional_special_tokens in the from_pretrained
               __a				     =    [AddedToken(_a ,   lstrip=_a      )]
               __a				     =    tokenizer_class.from_pretrained(
                   _a ,   additional_special_tokens=_a ,   extra_ids=0      )

               self.assertIn(_a ,   tokenizer.additional_special_tokens      )
               # self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
               self.assertEqual(
                   [new_token_a] ,   tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]      )      )      )




      @require_tokenizers
      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         __a				     =    self.get_tokenizers(do_lower_case=_a      )
         for tokenizer in tokenizers:
            with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
               __a				     =    """hello world"""
               if self.space_between_special_tokens:
                  __a				     =    """[CLS] hello world [SEP]"""
               else:
                  __a				     =    input
               __a				     =    tokenizer.encode(_a ,   add_special_tokens=_a      )
               __a				     =    tokenizer.decode(_a ,   spaces_between_special_tokens=self.space_between_special_tokens      )
               self.assertIn(_a ,   [output, output.lower()]      )




      def  __UpperCamelCase							(  self      )							->Optional[int]:



         '''simple docstring'''
         __a				     =    self.get_tokenizers()
         for tokenizer in tokenizers:
            with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
               __a				     =    [
                   """bos_token""",
                   """eos_token""",
                   """unk_token""",
                   """sep_token""",
                   """pad_token""",
                   """cls_token""",
                   """mask_token""",
               ]

         __a				     =    """a"""
         __a				     =    ord(_a      )

         for attr in attributes_list:
            setattr(_a ,   attr + '_id' ,   _a      )
            self.assertEqual(getattr(_a ,   _a      ) ,   _a      )
            self.assertEqual(getattr(_a ,   attr + '_id'      ) ,   _a      )

            setattr(_a ,   attr + '_id' ,   _a      )
            self.assertEqual(getattr(_a ,   _a      ) ,   _a      )
            self.assertEqual(getattr(_a ,   attr + '_id'      ) ,   _a      )

         setattr(_a ,   'additional_special_tokens_ids' ,   []      )
         self.assertListEqual(getattr(_a ,   'additional_special_tokens'      ) ,   []      )
         self.assertListEqual(getattr(_a ,   'additional_special_tokens_ids'      ) ,   []      )

         __a				     =    0xe006
         __a				     =    chr(_a      )
         setattr(_a ,   'additional_special_tokens_ids' ,   [additional_special_token_id]      )
         self.assertListEqual(getattr(_a ,   'additional_special_tokens'      ) ,   [additional_special_token]      )
         self.assertListEqual(getattr(_a ,   'additional_special_tokens_ids'      ) ,   [additional_special_token_id]      )




      def  __UpperCamelCase							(  self      )							->Optional[int]:



         '''simple docstring'''
         pass




      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         pass




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



         '''simple docstring'''
         pass




      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         pass




      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         pass




      def  __UpperCamelCase							(  self      )							->List[Any]:



         '''simple docstring'''
         pass




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



         '''simple docstring'''
         pass






      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         pass














'''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 							__SCREAMING_SNAKE_CASE       (							nn.Module     ):




      def __init__(  self      )							->Dict:



         '''simple docstring'''
         super().__init__()
         __a				     =    nn.Linear(3 ,   4      )
         __a				     =    nn.BatchNormad(4      )
         __a				     =    nn.Linear(4 ,   5      )






      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->str:



         '''simple docstring'''
         return self.lineara(self.batchnorm(self.lineara(lowerCamelCase      )      )      )







class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   *lowerCamelCase ,   **lowerCamelCase      )							->str:



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







class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase      )							->int:



         '''simple docstring'''
         return output + 1







class 							__SCREAMING_SNAKE_CASE       (							unittest.TestCase     ):




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



         '''simple docstring'''
         __a				     =    ModelForTest()
         __a				     =    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      )							->Tuple:



         '''simple docstring'''
         __a				     =    ModelForTest()
         __a				     =    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      )							->Tuple:



         '''simple docstring'''
         __a				     =    ModelForTest()
         __a				     =    torch.randn(2 ,   3      )
         __a				     =    test_model(x + 1      )
         __a				     =    test_model(x + 2      )

         __a				     =    PreForwardHook()
         add_hook_to_module(lowerCamelCase ,   lowerCamelCase      )
         __a				     =    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
         __a				     =    PreForwardHook()
         add_hook_to_module(lowerCamelCase ,   lowerCamelCase      )
         __a				     =    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
         __a				     =    SequentialHook(PreForwardHook() ,   PreForwardHook()      )
         add_hook_to_module(lowerCamelCase ,   lowerCamelCase      )

         __a				     =    test_model(lowerCamelCase      )
         assert torch.allclose(lowerCamelCase ,   lowerCamelCase ,   atol=1e-5      )




      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         __a				     =    ModelForTest()
         __a				     =    torch.randn(2 ,   3      )
         __a				     =    test_model(lowerCamelCase      )

         __a				     =    PostForwardHook()
         add_hook_to_module(lowerCamelCase ,   lowerCamelCase      )
         __a				     =    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
         __a				     =    PostForwardHook()
         add_hook_to_module(lowerCamelCase ,   lowerCamelCase      )
         __a				     =    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
         __a				     =    SequentialHook(PostForwardHook() ,   PostForwardHook()      )
         add_hook_to_module(lowerCamelCase ,   lowerCamelCase      )

         __a				     =    test_model(lowerCamelCase      )
         assert torch.allclose(lowerCamelCase ,   output + 2 ,   atol=1e-5      )




      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         __a				     =    ModelForTest()
         __a				     =    torch.randn(2 ,   3      )
         __a				     =    test_model(lowerCamelCase      )

         __a				     =    PostForwardHook()
         add_hook_to_module(lowerCamelCase ,   lowerCamelCase      )
         __a				     =    test_model(lowerCamelCase      )
         self.assertTrue(torch.allclose(lowerCamelCase ,   output + 1      )      )
         self.assertTrue(outputa.requires_grad      )

         __a				     =    True
         __a				     =    test_model(lowerCamelCase      )
         self.assertFalse(outputa.requires_grad      )




      @require_multi_gpu
      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         __a				     =    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
         __a				     =    torch.randn(2 ,   3      )
         __a				     =    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      )      )
         __a				     =    torch.randn(2 ,   3      ).to(0      )
         __a				     =    model(lowerCamelCase      )
         self.assertEqual(output.device ,   torch.device(0      )      )




      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         __a				     =    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
         __a				     =    {'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
         __a				     =    torch.device(hook_kwargs['execution_device']      )
         self.assertEqual(model.batchnorm.running_mean.device ,   lowerCamelCase      )

         __a				     =    torch.randn(2 ,   3      )
         __a				     =    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
         __a				     =    {
             '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'      )      )

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



         '''simple docstring'''
         __a				     =    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
         __a				     =    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
         __a				     =    torch.device(lowerCamelCase      )
         self.assertEqual(model.batchnorm.running_mean.device ,   lowerCamelCase      )

         __a				     =    torch.randn(2 ,   3      )
         __a				     =    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'      )      )

         __a				     =    torch.randn(2 ,   3      )
         __a				     =    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:



         '''simple docstring'''
         __a				     =    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
         __a				     =    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
         __a				     =    torch.device(lowerCamelCase      )
         self.assertEqual(model.batchnorm.running_mean.device ,   lowerCamelCase      )

         __a				     =    torch.randn(2 ,   3      )
         __a				     =    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'      )      )

         __a				     =    torch.randn(2 ,   3      )
         __a				     =    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'      )      )