






'''simple docstring'''



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_camembert import CamembertTokenizer
else:
 __UpperCamelCase      :       Tuple      			=      None


__UpperCamelCase      :       int      			=      logging.get_logger(__name__)

__UpperCamelCase      :       Tuple      			=      {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

__UpperCamelCase      :       int      			=      {
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/tokenizer.json""",
    },
}

__UpperCamelCase      :       str      			=      {
    """camembert-base""": 512,
}

__UpperCamelCase      :       Any      			=      """▁"""
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					=VOCAB_FILES_NAMES
      __a  					=PRETRAINED_VOCAB_FILES_MAP
      __a  					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __a  					=["input_ids", "attention_mask"]
      __a  					=CamembertTokenizer




      def __init__(  self ,   lowerCamelCase=None ,   lowerCamelCase=None ,   lowerCamelCase="<s>" ,   lowerCamelCase="</s>" ,   lowerCamelCase="</s>" ,   lowerCamelCase="<s>" ,   lowerCamelCase="<unk>" ,   lowerCamelCase="<pad>" ,   lowerCamelCase="<mask>" ,   lowerCamelCase=["<s>NOTUSED", "</s>NOTUSED"] ,   **lowerCamelCase ,   )							->int:



         '''simple docstring'''
         # Mask token behave like a normal word, i.e. include the space before it
         __a				     =    AddedToken(lowerCamelCase ,   lstrip=lowerCamelCase ,   rstrip=lowerCamelCase      ) if isinstance(lowerCamelCase ,   lowerCamelCase      ) else mask_token

         super().__init__(
             lowerCamelCase ,   tokenizer_file=lowerCamelCase ,   bos_token=lowerCamelCase ,   eos_token=lowerCamelCase ,   sep_token=lowerCamelCase ,   cls_token=lowerCamelCase ,   unk_token=lowerCamelCase ,   pad_token=lowerCamelCase ,   mask_token=lowerCamelCase ,   additional_special_tokens=lowerCamelCase ,   **lowerCamelCase ,   )

         __a				     =    vocab_file
         __a				     =    False if not self.vocab_file else True




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase = None      )							->List[int]:



         '''simple docstring'''

         if token_ids_a is None:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         __a				     =    [self.cls_token_id]
         __a				     =    [self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase = None      )							->List[int]:



         '''simple docstring'''
         __a				     =    [self.sep_token_id]
         __a				     =    [self.cls_token_id]

         if token_ids_a is None:
            return len(cls + token_ids_a + sep      ) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]






      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase = None      )							->Tuple[str]:



         '''simple docstring'''
         if not self.can_save_slow_tokenizer:
            raise ValueError(
                'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                'tokenizer.'      )

         if not os.path.isdir(lowerCamelCase      ):
            logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
            return
         __a				     =    os.path.join(
             lowerCamelCase ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )

         if os.path.abspath(self.vocab_file      ) != os.path.abspath(lowerCamelCase      ):
            copyfile(self.vocab_file ,   lowerCamelCase      )

         return (out_vocab_file,)







'''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'      )      )