





"""simple docstring"""





import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin




class 							snake_case							(			__UpperCAmelCase      ,  unittest.TestCase ):

      '''simple docstring'''



      _A     :       Optional[int]         =       BlenderbotSmallTokenizer
      _A     :       List[Any]         =       False

      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        super().setUp()

        __UpperCAmelCase    :						List[str]       =						['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
        __UpperCAmelCase    :						Dict       =						dict(zip(__lowercase						,				range(len(__lowercase ) ) ) )

        __UpperCAmelCase    :						str       =						['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
        __UpperCAmelCase    :						List[str]       =						{'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

        __UpperCAmelCase    :						Any       =						os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES['''vocab_file'''] )
        __UpperCAmelCase    :						Tuple       =						os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES['''merges_file'''] )
        with open(self.vocab_file						,				'''w'''						,				encoding='''utf-8''' ) as fp:
          fp.write(json.dumps(__lowercase ) + '''\n''' )
        with open(self.merges_file						,				'''w'''						,				encoding='''utf-8''' ) as fp:
          fp.write('''\n'''.join(__lowercase ) )

      def       A_					(							self       :      int						,				**__lowercase       :      Optional[Any] ):


        '''simple docstring'''




        kwargs.update(self.special_tokens_map )
        return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname						,				**__lowercase )

      def       A_					(							self       :      str						,				__lowercase       :      Any ):


        '''simple docstring'''




        __UpperCAmelCase    :						Any       =						'''adapt act apte'''
        __UpperCAmelCase    :						Optional[Any]       =						'''adapt act apte'''
        return input_text, output_text

      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[Any]       =						BlenderbotSmallTokenizer(self.vocab_file						,				self.merges_file						,				**self.special_tokens_map )
        __UpperCAmelCase    :						Dict       =						'''adapt act apte'''
        __UpperCAmelCase    :						Optional[Any]       =						['''adapt''', '''act''', '''ap@@''', '''te''']
        __UpperCAmelCase    :						List[str]       =						tokenizer.tokenize(__lowercase )
        self.assertListEqual(__lowercase						,				__lowercase )

        __UpperCAmelCase    :						Tuple       =						[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

        __UpperCAmelCase    :						List[Any]       =						[0, 1, 2, 3, 4, 5]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase )						,				__lowercase )

      def       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						int       =						BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
        assert tok('''sam''' ).input_ids == [1_384]
        __UpperCAmelCase    :						List[str]       =						'''I am a small frog.'''
        __UpperCAmelCase    :						Union[str, Any]       =						tok([src_text]						,				padding=__lowercase						,				truncation=__lowercase )['''input_ids''']
        __UpperCAmelCase    :						Any       =						tok.batch_decode(__lowercase						,				skip_special_tokens=__lowercase						,				clean_up_tokenization_spaces=__lowercase )[0]
        assert src_text != decoded  # I wish it did!
        assert decoded == "i am a small frog ."







      def       A_					(							self       :      Dict ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[str]       =						BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
        __UpperCAmelCase    :						Tuple       =						'''I am a small frog .'''
        __UpperCAmelCase    :						str       =						'''.'''
        __UpperCAmelCase    :						List[str]       =						tok(__lowercase )['''input_ids''']
        __UpperCAmelCase    :						Optional[Any]       =						tok(__lowercase )['''input_ids''']

        assert encoded[-1] == encoded_dot[0]






"""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 							snake_case							(			nn.Module ):

      '''simple docstring'''



      def __init__(							self       :      List[Any] ):


        '''simple docstring'''




        super().__init__()
        __UpperCAmelCase    :						Dict       =						nn.Linear(3						,				4 )
        __UpperCAmelCase    :						Union[str, Any]       =						nn.BatchNormad(4 )
        __UpperCAmelCase    :						List[str]       =						nn.Linear(4						,				5 )







      def       A_					(							self       :      Any						,				__lowercase       :      Any ):


        '''simple docstring'''




        return self.lineara(self.batchnorm(self.lineara(__lowercase ) ) )




class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      def       A_					(							self       :      Union[str, Any]						,				__lowercase       :      Optional[int]						,				*__lowercase       :      str						,				**__lowercase       :      Optional[int] ):


        '''simple docstring'''




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




class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      def       A_					(							self       :      Any						,				__lowercase       :      Tuple						,				__lowercase       :      Any ):


        '''simple docstring'''




        return output + 1




class 							snake_case							(			unittest.TestCase ):

      '''simple docstring'''



      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Dict       =						ModelForTest()
        __UpperCAmelCase    :						Optional[int]       =						ModelHook()

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

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

        remove_hook_from_module(__lowercase )
        self.assertFalse(hasattr(__lowercase						,				'''_hf_hook''' ) )
        self.assertFalse(hasattr(__lowercase						,				'''_old_forward''' ) )

      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						str       =						ModelForTest()
        __UpperCAmelCase    :						Tuple       =						ModelHook()

        add_hook_to_module(__lowercase						,				__lowercase )
        add_hook_to_module(__lowercase						,				__lowercase						,				append=__lowercase )

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

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

        remove_hook_from_module(__lowercase )
        self.assertFalse(hasattr(__lowercase						,				'''_hf_hook''' ) )
        self.assertFalse(hasattr(__lowercase						,				'''_old_forward''' ) )

      def       A_					(							self       :      Any ):


        '''simple docstring'''




        __UpperCAmelCase    :						Dict       =						ModelForTest()
        __UpperCAmelCase    :						Tuple       =						torch.randn(2						,				3 )
        __UpperCAmelCase    :						Optional[int]       =						test_model(x + 1 )
        __UpperCAmelCase    :						Optional[Any]       =						test_model(x + 2 )

        __UpperCAmelCase    :						Optional[int]       =						PreForwardHook()
        add_hook_to_module(__lowercase						,				__lowercase )
        __UpperCAmelCase    :						Union[str, Any]       =						test_model(__lowercase )
        self.assertTrue(torch.allclose(__lowercase						,				__lowercase						,				atol=1e-5 ) )

        # Attaching a hook to a model when it already has one replaces, does not chain
        __UpperCAmelCase    :						int       =						PreForwardHook()
        add_hook_to_module(__lowercase						,				__lowercase )
        __UpperCAmelCase    :						Any       =						test_model(__lowercase )
        self.assertTrue(torch.allclose(__lowercase						,				__lowercase						,				atol=1e-5 ) )

        # You need to use the sequential hook to chain two or more hooks
        __UpperCAmelCase    :						Any       =						SequentialHook(PreForwardHook()						,				PreForwardHook() )
        add_hook_to_module(__lowercase						,				__lowercase )

        __UpperCAmelCase    :						Tuple       =						test_model(__lowercase )
        assert torch.allclose(__lowercase						,				__lowercase						,				atol=1e-5 )

      def       A_					(							self       :      Any ):


        '''simple docstring'''




        __UpperCAmelCase    :						int       =						ModelForTest()
        __UpperCAmelCase    :						List[Any]       =						torch.randn(2						,				3 )
        __UpperCAmelCase    :						Tuple       =						test_model(__lowercase )

        __UpperCAmelCase    :						int       =						PostForwardHook()
        add_hook_to_module(__lowercase						,				__lowercase )
        __UpperCAmelCase    :						Optional[int]       =						test_model(__lowercase )
        self.assertTrue(torch.allclose(__lowercase						,				output + 1						,				atol=1e-5 ) )

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

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

        __UpperCAmelCase    :						Dict       =						test_model(__lowercase )
        assert torch.allclose(__lowercase						,				output + 2						,				atol=1e-5 )

      def       A_					(							self       :      Dict ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[str]       =						ModelForTest()
        __UpperCAmelCase    :						Union[str, Any]       =						torch.randn(2						,				3 )
        __UpperCAmelCase    :						str       =						test_model(__lowercase )

        __UpperCAmelCase    :						Union[str, Any]       =						PostForwardHook()
        add_hook_to_module(__lowercase						,				__lowercase )
        __UpperCAmelCase    :						Optional[int]       =						test_model(__lowercase )
        self.assertTrue(torch.allclose(__lowercase						,				output + 1 ) )
        self.assertTrue(outputa.requires_grad )

        __UpperCAmelCase    :						Optional[Any]       =						True
        __UpperCAmelCase    :						int       =						test_model(__lowercase )
        self.assertFalse(outputa.requires_grad )

      @require_multi_gpu
      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[Any]       =						ModelForTest()
        # Everything is on CPU
        self.assertEqual(model.lineara.weight.device						,				torch.device('''cpu''' ) )
        self.assertEqual(model.batchnorm.weight.device						,				torch.device('''cpu''' ) )
        self.assertEqual(model.lineara.weight.device						,				torch.device('''cpu''' ) )

        # This will move each submodule on different devices
        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    :						Dict       =						torch.randn(2						,				3 )
        __UpperCAmelCase    :						Any       =						model(__lowercase )
        self.assertEqual(output.device						,				torch.device(1 ) )

        # We can add a general hook to put back output on same device as input.
        add_hook_to_module(__lowercase						,				AlignDevicesHook(io_same_device=__lowercase ) )
        __UpperCAmelCase    :						List[Any]       =						torch.randn(2						,				3 ).to(0 )
        __UpperCAmelCase    :						int       =						model(__lowercase )
        self.assertEqual(output.device						,				torch.device(0 ) )

      def       A_					(							self       :      Tuple ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[Any]       =						ModelForTest()

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

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

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

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

        __UpperCAmelCase    :						int       =						torch.randn(2						,				3 )
        __UpperCAmelCase    :						Optional[int]       =						model(__lowercase )
        self.assertEqual(output.device						,				__lowercase )

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

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

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

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

        __UpperCAmelCase    :						Optional[Any]       =						torch.randn(2						,				3 )
        __UpperCAmelCase    :						List[Any]       =						model(__lowercase )
        self.assertEqual(output.device						,				__lowercase )

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

      def       A_					(							self       :      List[str] ):


        '''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    :						Optional[Any]       =						0 if torch.cuda.is_available() else '''cpu'''
        attach_align_device_hook(__lowercase						,				execution_device=__lowercase						,				offload=__lowercase )

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

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

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

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

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

        __UpperCAmelCase    :						Dict       =						torch.randn(2						,				3 )
        __UpperCAmelCase    :						str       =						model(__lowercase )
        self.assertEqual(output.device						,				__lowercase )

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







      def       A_					(							self       :      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    :						str       =						0 if torch.cuda.is_available() else '''cpu'''
        attach_align_device_hook(
            __lowercase						,				execution_device=__lowercase						,				offload=__lowercase						,				weights_map=model.state_dict() )

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

        __UpperCAmelCase    :						Any       =						torch.randn(2						,				3 )
        __UpperCAmelCase    :						Dict       =						model(__lowercase )
        self.assertEqual(output.device						,				__lowercase )

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

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

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

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

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