





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

from tokenizers import processors

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


if is_sentencepiece_available():
       from .tokenization_mbart import MBartTokenizer
else:
       _A					:    Tuple   =  None


_A					:    int   =  logging.get_logger(__name__)


_A					:    Any   =  {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

_A					:    Union[str, Any]   =  {
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json",
        "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json",
    },
}

_A					:    str   =  {
    "facebook/mbart-large-en-ro": 10_24,
    "facebook/mbart-large-cc25": 10_24,
}

# fmt: off
_A					:    List[str]   =  ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]


class    __SCREAMING_SNAKE_CASE			( _a					):
       _UpperCAmelCase      :  Any     =    VOCAB_FILES_NAMES
       _UpperCAmelCase      :  str     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _UpperCAmelCase      :  Tuple     =    PRETRAINED_VOCAB_FILES_MAP
       _UpperCAmelCase      :  Tuple     =    ["input_ids", "attention_mask"]
       _UpperCAmelCase      :  Any     =    MBartTokenizer

       _UpperCAmelCase      :  int     =    []
       _UpperCAmelCase      :  Tuple     =    []







       def __init__(      self     :	Optional[int]		,					A     :	int=None		,					A     :	Union[str, Any]=None		,					A     :	int="<s>"		,					A     :	str="</s>"		,					A     :	Optional[int]="</s>"		,					A     :	Tuple="<s>"		,					A     :	Optional[Any]="<unk>"		,					A     :	Dict="<pad>"		,					A     :	Optional[Any]="<mask>"		,					A     :	List[str]=None		,					A     :	List[str]=None		,					A     :	Any=None		,					**A     :	Union[str, Any]		,					)   ->str:
          lowerCamelCase__   :						Any					   =							AddedToken(UpperCamelCase__		,					lstrip=UpperCamelCase__		,					rstrip=UpperCamelCase__			) if isinstance(UpperCamelCase__		,					UpperCamelCase__			) else mask_token

          super().__init__(
              vocab_file=UpperCamelCase__		,					tokenizer_file=UpperCamelCase__		,					bos_token=UpperCamelCase__		,					eos_token=UpperCamelCase__		,					sep_token=UpperCamelCase__		,					cls_token=UpperCamelCase__		,					unk_token=UpperCamelCase__		,					pad_token=UpperCamelCase__		,					mask_token=UpperCamelCase__		,					src_lang=UpperCamelCase__		,					tgt_lang=UpperCamelCase__		,					additional_special_tokens=UpperCamelCase__		,					**UpperCamelCase__		,					)

          lowerCamelCase__   :						Dict					   =							vocab_file
          lowerCamelCase__   :						Union[str, Any]					   =							False if not self.vocab_file else True

          lowerCamelCase__   :						str					   =							FAIRSEQ_LANGUAGE_CODES.copy()

          if additional_special_tokens is not None:
             # Only add those special tokens if they are not already there.
             _additional_special_tokens.extend(
                 [t for t in additional_special_tokens if t not in _additional_special_tokens]			)

          self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens}			)
          lowerCamelCase__   :						Any					   =							{
              lang_code: self.convert_tokens_to_ids(UpperCamelCase__			) for lang_code in FAIRSEQ_LANGUAGE_CODES
          }

          lowerCamelCase__   :						Tuple					   =							src_lang if src_lang is not None else '''en_XX'''
          lowerCamelCase__   :						List[str]					   =							self.convert_tokens_to_ids(self._src_lang			)
          lowerCamelCase__   :						List[str]					   =							tgt_lang
          self.set_src_lang_special_tokens(self._src_lang			)







       @property
       def 						__lowerCamelCase    (      self     :	str			)   ->Any:
          return self._src_lang







       @src_lang.setter
       def 						__lowerCamelCase    (      self     :	Optional[Any]		,					A     :	str			)   ->Optional[int]:
          lowerCamelCase__   :						Optional[Any]					   =							new_src_lang
          self.set_src_lang_special_tokens(self._src_lang			)







       def 						__lowerCamelCase    (      self     :	Dict		,					A     :	List[int]		,					A     :	Optional[List[int]] = None			)   ->Tuple:







          if token_ids_a is None:
             return self.prefix_tokens + token_ids_a + self.suffix_tokens
          # We don't expect to process pairs, but leave the pair logic for API consistency
          return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens







       def 						__lowerCamelCase    (      self     :	List[Any]		,					A     :	List[int]		,					A     :	Optional[List[int]] = None			)   ->Optional[int]:
          lowerCamelCase__   :						Optional[int]					   =							[self.sep_token_id]
          lowerCamelCase__   :						List[Any]					   =							[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 						__lowerCamelCase    (      self     :	List[str]		,					A     :	str		,					A     :	str		,					A     :	Optional[str]		,					A     :	Optional[str]		,					**A     :	int			)   ->Dict:







          if src_lang is None or tgt_lang is None:
             raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''			)
          lowerCamelCase__   :						Dict					   =							src_lang
          lowerCamelCase__   :						List[str]					   =							self(UpperCamelCase__		,					add_special_tokens=UpperCamelCase__		,					return_tensors=UpperCamelCase__		,					**UpperCamelCase__			)
          lowerCamelCase__   :						Optional[Any]					   =							self.convert_tokens_to_ids(UpperCamelCase__			)
          lowerCamelCase__   :						Dict					   =							tgt_lang_id
          return inputs







       def 						__lowerCamelCase    (      self     :	str		,					A     :	List[str]		,					A     :	str = "en_XX"		,					A     :	Optional[List[str]] = None		,					A     :	str = "ro_RO"		,					**A     :	int		,					)   ->List[str]:
          lowerCamelCase__   :						int					   =							src_lang
          lowerCamelCase__   :						List[str]					   =							tgt_lang
          return super().prepare_seqaseq_batch(UpperCamelCase__		,					UpperCamelCase__		,					**UpperCamelCase__			)







       def 						__lowerCamelCase    (      self     :	int			)   ->Dict:
          return self.set_src_lang_special_tokens(self.src_lang			)







       def 						__lowerCamelCase    (      self     :	Union[str, Any]			)   ->Union[str, Any]:
          return self.set_tgt_lang_special_tokens(self.tgt_lang			)







       def 						__lowerCamelCase    (      self     :	Tuple		,					A     :	Any			)   ->List[Any]:
          lowerCamelCase__   :						Union[str, Any]					   =							self.convert_tokens_to_ids(UpperCamelCase__			)
          lowerCamelCase__   :						List[str]					   =							[]
          lowerCamelCase__   :						Optional[Any]					   =							[self.eos_token_id, self.cur_lang_code]

          lowerCamelCase__   :						List[Any]					   =							self.convert_ids_to_tokens(self.prefix_tokens			)
          lowerCamelCase__   :						Union[str, Any]					   =							self.convert_ids_to_tokens(self.suffix_tokens			)

          lowerCamelCase__   :						Dict					   =							processors.TemplateProcessing(
              single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str		,					pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str		,					special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str		,					self.prefix_tokens + self.suffix_tokens			)			)		,					)







       def 						__lowerCamelCase    (      self     :	List[str]		,					A     :	str			)   ->Dict:
          lowerCamelCase__   :						Optional[int]					   =							self.convert_tokens_to_ids(UpperCamelCase__			)
          lowerCamelCase__   :						Any					   =							[]
          lowerCamelCase__   :						Optional[int]					   =							[self.eos_token_id, self.cur_lang_code]

          lowerCamelCase__   :						Any					   =							self.convert_ids_to_tokens(self.prefix_tokens			)
          lowerCamelCase__   :						Tuple					   =							self.convert_ids_to_tokens(self.suffix_tokens			)

          lowerCamelCase__   :						List[str]					   =							processors.TemplateProcessing(
              single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str		,					pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str		,					special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str		,					self.prefix_tokens + self.suffix_tokens			)			)		,					)







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







          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(UpperCamelCase__			):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory."			)
             return
          lowerCamelCase__   :						List[Any]					   =							os.path.join(
              UpperCamelCase__		,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

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

          return (out_vocab_file,)








'''simple docstring'''


import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


_lowerCamelCase	:       Optional[int]      	=	logging.getLogger(__name__)







def 				__lowerCamelCase				(   A__=2    ,						A__=3    ,						A__=16    ,						A__ = 10    ,						A__ = 2    )       ->	int:






   """simple docstring"""

   def get_dataset(A__    ):
      UpperCamelCase					=  torch.randn(batch_size * n_batches    ,						1    )
      return TensorDataset(A__    ,						a * x + b + 0.1 * torch.randn(batch_size * n_batches    ,						1    )    )

   UpperCamelCase					=  get_dataset(A__    )
   UpperCamelCase					=  get_dataset(A__    )
   UpperCamelCase					=  DataLoader(A__    ,						shuffle=A__    ,						batch_size=A__    ,						num_workers=4    )
   UpperCamelCase					=  DataLoader(A__    ,						shuffle=A__    ,						batch_size=A__    ,						num_workers=4    )
   return (train_dataloader, valid_dataloader)







def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    ,						A__    ,						A__    ,						A__=None    )       ->	int:






   """simple docstring"""
   UpperCamelCase					=  []
   for epoch in range(A__    ):
      # Train quickly
      model.train()
      for batch in dataloader:
         UpperCamelCase				,      UpperCamelCase					=  batch
         UpperCamelCase					=  model(A__    )
         UpperCamelCase					=  torch.nn.functional.mse_loss(A__    ,						A__    )
         accelerator.backward(A__    )
         optimizer.step()
         optimizer.zero_grad()
      rands.append(random.random()    )  # Introduce some randomness
      if scheduler is not None:
         scheduler.step()
   return rands

class  SCREAMING_SNAKE_CASE (  nn.Module		):






    """simple docstring"""





    def __init__(  self      :					Tuple ):


       """simple docstring"""







       super().__init__()
       UpperCamelCase					=  nn.Parameter(torch.randn(1 ) )
       UpperCamelCase					=  nn.Parameter(torch.randn(1 ) )






    def  A       (  self      :					str  ,						UpperCamelCase__      :					Dict ):


       """simple docstring"""







       return x * self.a + self.b

class  SCREAMING_SNAKE_CASE (  unittest.TestCase		):






    """simple docstring"""





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


       """simple docstring"""







       with tempfile.TemporaryDirectory() as tmpdir:
          set_seed(4_2 )
          UpperCamelCase					=  DummyModel()
          UpperCamelCase					=  torch.optim.Adam(params=model.parameters()  ,						lr=1E-3 )
          UpperCamelCase				,      UpperCamelCase					=  dummy_dataloaders()
          UpperCamelCase					=  ProjectConfiguration(total_limit=1  ,						project_dir=UpperCamelCase__  ,						automatic_checkpoint_naming=UpperCamelCase__ )
          # Train baseline
          UpperCamelCase					=  Accelerator(project_config=UpperCamelCase__ )
          UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase					=  accelerator.prepare(
              UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          # Save initial
          accelerator.save_state()

          # Save second state
          accelerator.save_state()
          self.assertEqual(len(os.listdir(accelerator.project_dir ) )  ,						1 )






    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       with tempfile.TemporaryDirectory() as tmpdir:
          set_seed(4_2 )
          UpperCamelCase					=  DummyModel()
          UpperCamelCase					=  torch.optim.Adam(params=model.parameters()  ,						lr=1E-3 )
          UpperCamelCase				,      UpperCamelCase					=  dummy_dataloaders()
          # Train baseline
          UpperCamelCase					=  Accelerator()
          UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase					=  accelerator.prepare(
              UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          # Save initial
          UpperCamelCase					=  os.path.join(UpperCamelCase__  ,						'initial' )
          accelerator.save_state(UpperCamelCase__ )
          ((UpperCamelCase)				,      (UpperCamelCase))					=  model.a.item(), model.b.item()
          UpperCamelCase					=  optimizer.state_dict()
          UpperCamelCase					=  train(3  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          ((UpperCamelCase)				,      (UpperCamelCase))					=  model.a.item(), model.b.item()
          UpperCamelCase					=  optimizer.state_dict()

          # Train partially
          set_seed(4_2 )
          UpperCamelCase					=  DummyModel()
          UpperCamelCase					=  torch.optim.Adam(params=model.parameters()  ,						lr=1E-3 )
          UpperCamelCase				,      UpperCamelCase					=  dummy_dataloaders()
          UpperCamelCase					=  Accelerator()
          UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase					=  accelerator.prepare(
              UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          accelerator.load_state(UpperCamelCase__ )
          ((UpperCamelCase)				,      (UpperCamelCase))					=  model.a.item(), model.b.item()
          UpperCamelCase					=  optimizer.state_dict()
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )

          UpperCamelCase					=  train(2  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          # Save everything
          UpperCamelCase					=  os.path.join(UpperCamelCase__  ,						'checkpoint' )
          accelerator.save_state(UpperCamelCase__ )

          # Load everything back in and make sure all states work
          accelerator.load_state(UpperCamelCase__ )
          test_rands += train(1  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          ((UpperCamelCase)				,      (UpperCamelCase))					=  model.a.item(), model.b.item()
          UpperCamelCase					=  optimizer.state_dict()
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )






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


       """simple docstring"""







       with tempfile.TemporaryDirectory() as tmpdir:
          set_seed(4_2 )
          UpperCamelCase					=  DummyModel()
          UpperCamelCase					=  torch.optim.Adam(params=model.parameters()  ,						lr=1E-3 )
          UpperCamelCase				,      UpperCamelCase					=  dummy_dataloaders()
          UpperCamelCase					=  ProjectConfiguration(automatic_checkpoint_naming=UpperCamelCase__ )

          # Train baseline
          UpperCamelCase					=  Accelerator(project_dir=UpperCamelCase__  ,						project_config=UpperCamelCase__ )
          UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase					=  accelerator.prepare(
              UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          # Save initial
          accelerator.save_state()
          ((UpperCamelCase)				,      (UpperCamelCase))					=  model.a.item(), model.b.item()
          UpperCamelCase					=  optimizer.state_dict()
          UpperCamelCase					=  train(3  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          ((UpperCamelCase)				,      (UpperCamelCase))					=  model.a.item(), model.b.item()
          UpperCamelCase					=  optimizer.state_dict()

          # Train partially
          set_seed(4_2 )
          UpperCamelCase					=  DummyModel()
          UpperCamelCase					=  torch.optim.Adam(params=model.parameters()  ,						lr=1E-3 )
          UpperCamelCase				,      UpperCamelCase					=  dummy_dataloaders()
          UpperCamelCase					=  ProjectConfiguration(iteration=1  ,						automatic_checkpoint_naming=UpperCamelCase__ )
          UpperCamelCase					=  Accelerator(project_dir=UpperCamelCase__  ,						project_config=UpperCamelCase__ )
          UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase					=  accelerator.prepare(
              UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          accelerator.load_state(os.path.join(UpperCamelCase__  ,						'checkpoints'  ,						'checkpoint_0' ) )
          ((UpperCamelCase)				,      (UpperCamelCase))					=  model.a.item(), model.b.item()
          UpperCamelCase					=  optimizer.state_dict()
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )

          UpperCamelCase					=  train(2  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          # Save everything
          accelerator.save_state()

          # Load everything back in and make sure all states work
          accelerator.load_state(os.path.join(UpperCamelCase__  ,						'checkpoints'  ,						'checkpoint_1' ) )
          test_rands += train(1  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          ((UpperCamelCase)				,      (UpperCamelCase))					=  model.a.item(), model.b.item()
          UpperCamelCase					=  optimizer.state_dict()
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )






    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       UpperCamelCase					=  torch.tensor([1, 2, 3] )
       UpperCamelCase					=  torch.tensor([2, 3, 4] )
       UpperCamelCase					=  DummyModel()
       UpperCamelCase					=  torch.optim.Adam(net.parameters() )
       UpperCamelCase					=  Accelerator()
       with self.assertRaises(UpperCamelCase__ ) as ve:
          accelerator.register_for_checkpointing(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
       UpperCamelCase					=  str(ve.exception )
       self.assertTrue('Item at index 0' in message )
       self.assertTrue('Item at index 1' in message )
       self.assertFalse('Item at index 2' in message )
       self.assertFalse('Item at index 3' in message )






    def  A       (  self      :					Dict ):


       """simple docstring"""







       with tempfile.TemporaryDirectory() as tmpdir:
          set_seed(4_2 )
          UpperCamelCase					=  DummyModel()
          UpperCamelCase					=  torch.optim.Adam(params=model.parameters()  ,						lr=1E-3 )
          UpperCamelCase					=  torch.optim.lr_scheduler.StepLR(UpperCamelCase__  ,						step_size=1  ,						gamma=0.9_9 )
          UpperCamelCase				,      UpperCamelCase					=  dummy_dataloaders()
          UpperCamelCase					=  ProjectConfiguration(automatic_checkpoint_naming=UpperCamelCase__ )
          # Train baseline
          UpperCamelCase					=  Accelerator(project_dir=UpperCamelCase__  ,						project_config=UpperCamelCase__ )
          UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase					=  accelerator.prepare(
              UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          # Save initial
          accelerator.save_state()
          UpperCamelCase					=  scheduler.state_dict()
          train(3  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          self.assertNotEqual(UpperCamelCase__  ,						scheduler.state_dict() )

          # Load everything back in and make sure all states work
          accelerator.load_state(os.path.join(UpperCamelCase__  ,						'checkpoints'  ,						'checkpoint_0' ) )
          self.assertEqual(UpperCamelCase__  ,						scheduler.state_dict() )






    def  A       (  self      :					List[str] ):


       """simple docstring"""







       with tempfile.TemporaryDirectory() as tmpdir:
          set_seed(4_2 )
          UpperCamelCase					=  DummyModel()
          UpperCamelCase					=  ProjectConfiguration(automatic_checkpoint_naming=UpperCamelCase__  ,						total_limit=2 )
          # Train baseline
          UpperCamelCase					=  Accelerator(project_dir=UpperCamelCase__  ,						project_config=UpperCamelCase__ )
          UpperCamelCase					=  accelerator.prepare(UpperCamelCase__ )
          # Save 3 states:
          for _ in range(1_1 ):
             accelerator.save_state()
          self.assertTrue(not os.path.exists(os.path.join(UpperCamelCase__  ,						'checkpoints'  ,						'checkpoint_0' ) ) )
          self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__  ,						'checkpoints'  ,						'checkpoint_9' ) ) )
          self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__  ,						'checkpoints'  ,						'checkpoint_10' ) ) )






    @require_cuda
    def  A       (  self      :					Dict ):


       """simple docstring"""







       UpperCamelCase					=  ['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__ )]
       execute_subprocess_async(UpperCamelCase__  ,						env=os.environ.copy() )


if __name__ == "__main__":
   _lowerCamelCase	:       Optional[int]      	=	"/tmp/accelerate/state_checkpointing"
   _lowerCamelCase	:       Union[str, Any]      	=	DummyModel()
   _lowerCamelCase	:       Optional[Any]      	=	torch.optim.Adam(params=model.parameters(), lr=1e-3)
   _lowerCamelCase	:       List[Any]      	=	torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
   _lowerCamelCase	,_lowerCamelCase	:       Tuple      	=	dummy_dataloaders()
   _lowerCamelCase	:       List[Any]      	=	ProjectConfiguration(automatic_checkpoint_naming=True)
   # Train baseline
   _lowerCamelCase	:       Any      	=	Accelerator(project_dir=savedir, project_config=project_config, mixed_precision="no")
   if accelerator.process_index == 0:
      if os.path.exists(savedir):
         shutil.rmtree(savedir)
      os.makedirs(savedir)
   _lowerCamelCase	,_lowerCamelCase	,_lowerCamelCase	,_lowerCamelCase	,_lowerCamelCase	:       Union[str, Any]      	=	accelerator.prepare(
       model, optimizer, train_dataloader, valid_dataloader, scheduler
   )
   _lowerCamelCase	,_lowerCamelCase	:       Tuple      	=	accelerator.prepare(model, optimizer)
   train(3, model, train_dataloader, optimizer, accelerator, scheduler)
   # Check that the intial optimizer is loaded on the GPU
   for group in optimizer.param_groups:
      _lowerCamelCase	:       Any      	=	group["params"][0].device
      break
   assert param_device.type == accelerator.device.type
   _lowerCamelCase	:       Tuple      	=	model.cpu()
   accelerator.wait_for_everyone()
   accelerator.save_state()
   accelerator.wait_for_everyone()

   # Check CPU state
   accelerator.load_state(os.path.join(savedir, "checkpoints", "checkpoint_0"), map_location="cpu")
   for group in optimizer.param_groups:
      _lowerCamelCase	:       Optional[Any]      	=	group["params"][0].device
      break
   assert (
       param_device.type == torch.device("cpu").type
   ), f"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

   # Check device state
   model.to(accelerator.device)
   accelerator.load_state(os.path.join(savedir, "checkpoints", "checkpoint_0"), map_location="on_device")
   for group in optimizer.param_groups:
      _lowerCamelCase	:       Dict      	=	group["params"][0].device
      break
   assert (
       param_device.type == accelerator.device.type
   ), f"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

   # Check error
   with pytest.raises(TypeError, match="Unsupported optimizer map location passed"):
      accelerator.load_state(os.path.join(savedir, "checkpoints", "checkpoint_0"), map_location="invalid")
   accelerator.wait_for_everyone()
   if accelerator.process_index == 0:
      shutil.rmtree(savedir)
   accelerator.wait_for_everyone()

