






'''simple docstring'''



import unittest

from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


__UpperCamelCase      :       Any      			=      get_tests_dir("""fixtures/spiece.model""")
@require_sentencepiece
@require_tokenizers
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase      ,      unittest.TestCase     ):
      __a  					=DebertaVaTokenizer
      __a  					=DebertaVaTokenizerFast
      __a  					=True
      __a  					=True




      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         super().setUp()

         # We have a SentencePiece fixture for testing
         __a				     =    DebertaVaTokenizer(lowerCamelCase ,   unk_token='<unk>'      )
         tokenizer.save_pretrained(self.tmpdirname      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->Dict:



         '''simple docstring'''
         __a				     =    'this is a test'
         __a				     =    'this is a test'
         return input_text, output_text




      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         __a				     =    '<pad>'
         __a				     =    0

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase      ) ,   lowerCamelCase      )
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase      ) ,   lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         __a				     =    list(self.get_tokenizer().get_vocab().keys()      )
         self.assertEqual(vocab_keys[0] ,   '<pad>'      )
         self.assertEqual(vocab_keys[1] ,   '<unk>'      )
         self.assertEqual(vocab_keys[-1] ,   '[PAD]'      )
         self.assertEqual(len(lowerCamelCase      ) ,   3_0001      )




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



         '''simple docstring'''
         self.assertEqual(self.get_tokenizer().vocab_size ,   3_0000      )




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



         '''simple docstring'''
         __a				     =    ' \tHeLLo!how  \n Are yoU?  '
         __a				     =    ['▁hello', '!', 'how', '▁are', '▁you', '?']
         # fmt: on

         __a				     =    DebertaVaTokenizer(lowerCamelCase ,   do_lower_case=lowerCamelCase      )
         __a				     =    tokenizer.convert_ids_to_tokens(tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )      )

         self.assertListEqual(lowerCamelCase ,   lowerCamelCase      )

         __a				     =    DebertaVaTokenizerFast(lowerCamelCase ,   do_lower_case=lowerCamelCase      )
         __a				     =    rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )      )

         self.assertListEqual(lowerCamelCase ,   lowerCamelCase      )




      @unittest.skip('There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.'      )
      def  __UpperCamelCase							(  self      )							->Union[str, Any]:



         '''simple docstring'''
         pass




      @unittest.skip('There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.'      )
      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         pass




      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         __a				     =    'I was born in 92000, and this is falsé.'
         __a				     =    ['▁', '<unk>', '▁was', '▁born', '▁in', '▁9', '2000', '▁', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '▁', '.', ]
         # fmt: on

         __a				     =    DebertaVaTokenizer(lowerCamelCase ,   split_by_punct=lowerCamelCase      )
         __a				     =    tokenizer.convert_ids_to_tokens(tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )      )

         self.assertListEqual(lowerCamelCase ,   lowerCamelCase      )

         __a				     =    DebertaVaTokenizerFast(lowerCamelCase ,   split_by_punct=lowerCamelCase      )
         __a				     =    rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )      )

         self.assertListEqual(lowerCamelCase ,   lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         __a				     =    'I was born in 92000, and this is falsé.'
         __a				     =    ['▁i', '▁was', '▁born', '▁in', '▁9', '2000', '▁', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '▁', '.', ]
         # fmt: on

         __a				     =    DebertaVaTokenizer(lowerCamelCase ,   do_lower_case=lowerCamelCase ,   split_by_punct=lowerCamelCase      )
         __a				     =    tokenizer.convert_ids_to_tokens(tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )      )
         self.assertListEqual(lowerCamelCase ,   lowerCamelCase      )

         __a				     =    DebertaVaTokenizerFast(lowerCamelCase ,   do_lower_case=lowerCamelCase ,   split_by_punct=lowerCamelCase      )
         __a				     =    rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )      )
         self.assertListEqual(lowerCamelCase ,   lowerCamelCase      )




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



         '''simple docstring'''
         __a				     =    'I was born in 92000, and this is falsé.'
         __a				     =    ['▁i', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.', ]
         # fmt: on

         __a				     =    DebertaVaTokenizer(lowerCamelCase ,   do_lower_case=lowerCamelCase ,   split_by_punct=lowerCamelCase      )
         __a				     =    tokenizer.convert_ids_to_tokens(tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )      )

         self.assertListEqual(lowerCamelCase ,   lowerCamelCase      )

         __a				     =    DebertaVaTokenizerFast(lowerCamelCase ,   do_lower_case=lowerCamelCase ,   split_by_punct=lowerCamelCase      )
         __a				     =    rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )      )

         self.assertListEqual(lowerCamelCase ,   lowerCamelCase      )




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



         '''simple docstring'''
         __a				     =    'I was born in 92000, and this is falsé.'
         __a				     =    ['▁', '<unk>', '▁was', '▁born', '▁in', '▁9', '2000', '▁', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '▁', '.', ]
         # fmt: on

         __a				     =    DebertaVaTokenizer(lowerCamelCase ,   do_lower_case=lowerCamelCase ,   split_by_punct=lowerCamelCase      )
         __a				     =    tokenizer.convert_ids_to_tokens(tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )      )

         self.assertListEqual(lowerCamelCase ,   lowerCamelCase      )

         __a				     =    DebertaVaTokenizerFast(lowerCamelCase ,   do_lower_case=lowerCamelCase ,   split_by_punct=lowerCamelCase      )
         __a				     =    rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )      )

         self.assertListEqual(lowerCamelCase ,   lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         __a				     =    ' \tHeLLo!how  \n Are yoU?  '
         __a				     =    ['▁', '<unk>', 'e', '<unk>', 'o', '!', 'how', '▁', '<unk>', 're', '▁yo', '<unk>', '?']
         # fmt: on

         __a				     =    DebertaVaTokenizer(lowerCamelCase ,   do_lower_case=lowerCamelCase ,   split_by_punct=lowerCamelCase      )
         __a				     =    tokenizer.convert_ids_to_tokens(tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )      )

         self.assertListEqual(lowerCamelCase ,   lowerCamelCase      )

         __a				     =    DebertaVaTokenizerFast(lowerCamelCase ,   do_lower_case=lowerCamelCase ,   split_by_punct=lowerCamelCase      )
         __a				     =    rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )      )

         self.assertListEqual(lowerCamelCase ,   lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         __a				     =    self.get_tokenizer()
         __a				     =    self.get_rust_tokenizer()

         __a				     =    'I was born in 92000, and this is falsé.'

         __a				     =    tokenizer.convert_ids_to_tokens(tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )      )
         __a				     =    rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )      )
         self.assertListEqual(lowerCamelCase ,   lowerCamelCase      )

         __a				     =    tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )
         __a				     =    rust_tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )
         self.assertListEqual(lowerCamelCase ,   lowerCamelCase      )

         __a				     =    self.get_rust_tokenizer()
         __a				     =    tokenizer.encode(lowerCamelCase      )
         __a				     =    rust_tokenizer.encode(lowerCamelCase      )
         self.assertListEqual(lowerCamelCase ,   lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         __a				     =    'This is a test'
         __a				     =    [13, 1, 4398, 25, 21, 1289]
         __a				     =    ['▁', 'T', 'his', '▁is', '▁a', '▁test']
         __a				     =    ['▁', '<unk>', 'his', '▁is', '▁a', '▁test']

         __a				     =    DebertaVaTokenizer(lowerCamelCase ,   keep_accents=lowerCamelCase      )
         __a				     =    DebertaVaTokenizerFast(lowerCamelCase ,   keep_accents=lowerCamelCase      )

         __a				     =    tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )
         self.assertListEqual(lowerCamelCase ,   lowerCamelCase      )
         __a				     =    tokenizer.tokenize(lowerCamelCase      )
         self.assertListEqual(lowerCamelCase ,   lowerCamelCase      )
         __a				     =    tokenizer.convert_ids_to_tokens(lowerCamelCase      )
         self.assertListEqual(lowerCamelCase ,   lowerCamelCase      )

         __a				     =    rust_tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )
         self.assertListEqual(lowerCamelCase ,   lowerCamelCase      )
         __a				     =    rust_tokenizer.tokenize(lowerCamelCase      )
         self.assertListEqual(lowerCamelCase ,   lowerCamelCase      )
         __a				     =    rust_tokenizer.convert_ids_to_tokens(lowerCamelCase      )
         self.assertListEqual(lowerCamelCase ,   lowerCamelCase      )

         # fmt: off
         __a				     =    'I was born in 92000, and this is falsé.'
         __a				     =    [13, 1, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]
         __a				     =    ['▁', 'I', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.', ]
         __a				     =    ['▁', '<unk>', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.', ]
         # fmt: on

         __a				     =    tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )
         self.assertListEqual(lowerCamelCase ,   lowerCamelCase      )
         __a				     =    tokenizer.tokenize(lowerCamelCase      )
         self.assertListEqual(lowerCamelCase ,   lowerCamelCase      )
         __a				     =    tokenizer.convert_ids_to_tokens(lowerCamelCase      )
         self.assertListEqual(lowerCamelCase ,   lowerCamelCase      )

         __a				     =    rust_tokenizer.encode(lowerCamelCase ,   add_special_tokens=lowerCamelCase      )
         self.assertListEqual(lowerCamelCase ,   lowerCamelCase      )
         __a				     =    rust_tokenizer.tokenize(lowerCamelCase      )
         self.assertListEqual(lowerCamelCase ,   lowerCamelCase      )
         __a				     =    rust_tokenizer.convert_ids_to_tokens(lowerCamelCase      )
         self.assertListEqual(lowerCamelCase ,   lowerCamelCase      )




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



         '''simple docstring'''
         __a				     =    DebertaVaTokenizer(lowerCamelCase      )

         __a				     =    tokenizer.encode('sequence builders'      )
         __a				     =    tokenizer.encode('multi-sequence build'      )

         __a				     =    tokenizer.build_inputs_with_special_tokens(lowerCamelCase      )
         __a				     =    tokenizer.build_inputs_with_special_tokens(lowerCamelCase ,   lowerCamelCase      )

         self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] ,   lowerCamelCase      )
         self.assertEqual(
             [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id] ,   lowerCamelCase ,   )






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



         '''simple docstring'''
         __a				     =    {'input_ids': [[1, 3_9867, 36, 1_9390, 486, 27, 3_5052, 8_1436, 18, 6_0685, 1225, 7, 3_5052, 8_1436, 18, 9367, 1_6899, 18, 1_5937, 53, 594, 773, 18, 1_6287, 3_0465, 36, 1_5937, 6, 4_1139, 38, 3_6979, 6_0763, 191, 6, 3_4132, 99, 6, 5_0538, 390, 4_3230, 6, 3_4132, 2779, 2_0850, 14, 699, 1072, 1194, 36, 382, 1_0901, 53, 7, 699, 1072, 2084, 36, 2_0422, 630, 53, 19, 105, 3049, 1896, 1053, 1_6899, 1506, 11, 3_7978, 4243, 7, 1237, 3_1869, 200, 1_6566, 654, 6, 3_5052, 8_1436, 7, 5_5630, 1_3593, 4, 2], [1, 26, 1_5011, 13, 667, 8, 1053, 18, 2_3611, 1237, 7_2356, 1_2820, 34, 10_4134, 1209, 35, 1_3313, 6627, 21, 202, 347, 7, 164, 2399, 11, 46, 4485, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 1232, 2864, 1_5785, 1_4951, 105, 5, 8581, 1250, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=lowerCamelCase ,   model_name='microsoft/deberta-v2-xlarge' ,   revision='ad6e42c1532ddf3a15c39246b63f5559d558b670' ,   )














'''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'      )      )