

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


a_     :    Union[str, Any]     		=   logging.getLogger(__name__)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Union[str, Any]=2		,				_UpperCamelCase		:	List[Any]=3		,				_UpperCamelCase		:	List[Any]=16		,				_UpperCamelCase		:	int = 10		,				_UpperCamelCase		:	int = 2						)		->				int:
 '''simple docstring'''


 def get_dataset(_UpperCamelCase		:	List[Any]						):
  SCREAMING_SNAKE_CASE		    =      torch.randn(batch_size * n_batches		,				1						)
  return TensorDataset(_UpperCamelCase		,				a * x + b + 0.1 * torch.randn(batch_size * n_batches		,				1						)						)

 SCREAMING_SNAKE_CASE		    =      get_dataset(_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      get_dataset(_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      DataLoader(_UpperCamelCase		,				shuffle=_UpperCamelCase		,				batch_size=_UpperCamelCase		,				num_workers=4						)
 SCREAMING_SNAKE_CASE		    =      DataLoader(_UpperCamelCase		,				shuffle=_UpperCamelCase		,				batch_size=_UpperCamelCase		,				num_workers=4						)
 return (train_dataloader, valid_dataloader)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	int		,				_UpperCamelCase		:	Dict		,				_UpperCamelCase		:	List[Any]		,				_UpperCamelCase		:	List[Any]		,				_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	Dict=None						)		->				Union[str, Any]:
 '''simple docstring'''


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




class   UpperCamelCase			(  nn.Module ):

    def __init__(      self			:   Any     ):

     """simple docstring"""






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




    def UpperCamelCase      (      self			:   int				,     snake_case__			:   Dict     ):

     """simple docstring"""






     return x * self.a + self.b




class   UpperCamelCase			(  unittest.TestCase ):

    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






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

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

    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






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

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

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

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

    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






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

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

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

      SCREAMING_SNAKE_CASE		    =      train(2				,     snake_case__				,     snake_case__				,     snake_case__				,     snake_case__     )
      # Save everything
      accelerator.save_state()

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

    def UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      torch.tensor([1, 2, 3]     )
     SCREAMING_SNAKE_CASE		    =      torch.tensor([2, 3, 4]     )
     SCREAMING_SNAKE_CASE		    =      DummyModel()
     SCREAMING_SNAKE_CASE		    =      torch.optim.Adam(net.parameters()     )
     SCREAMING_SNAKE_CASE		    =      Accelerator()
     with self.assertRaises(snake_case__     ) as ve:
      accelerator.register_for_checkpointing(snake_case__				,     snake_case__				,     snake_case__				,     snake_case__     )
     SCREAMING_SNAKE_CASE		    =      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 UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     with tempfile.TemporaryDirectory() as tmpdir:
      set_seed(4_2     )
      SCREAMING_SNAKE_CASE		    =      DummyModel()
      SCREAMING_SNAKE_CASE		    =      torch.optim.Adam(params=model.parameters()				,     lr=1E-3     )
      SCREAMING_SNAKE_CASE		    =      torch.optim.lr_scheduler.StepLR(snake_case__				,     step_size=1				,     gamma=0.99     )
      SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      dummy_dataloaders()
      SCREAMING_SNAKE_CASE		    =      ProjectConfiguration(automatic_checkpoint_naming=snake_case__     )
      # Train baseline
      SCREAMING_SNAKE_CASE		    =      Accelerator(project_dir=snake_case__				,     project_config=snake_case__     )
      SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      accelerator.prepare(
          snake_case__				,     snake_case__				,     snake_case__				,     snake_case__				,     snake_case__     )
      # Save initial
      accelerator.save_state()
      SCREAMING_SNAKE_CASE		    =      scheduler.state_dict()
      train(3				,     snake_case__				,     snake_case__				,     snake_case__				,     snake_case__				,     snake_case__     )
      self.assertNotEqual(snake_case__				,     scheduler.state_dict()     )

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

    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






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




    @require_cuda
    def UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






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


if __name__ == "__main__":
      a_     :    Dict     		=   "/tmp/accelerate/state_checkpointing"
      a_     :    Any     		=   DummyModel()
      a_     :    Optional[Any]     		=   torch.optim.Adam(params=model.parameters(), lr=1e-3)
      a_     :    Dict     		=   torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.9_9)
      a_		,		a_     :    Optional[int]     		=   dummy_dataloaders()
      a_     :    str     		=   ProjectConfiguration(automatic_checkpoint_naming=True)
      # Train baseline
      a_     :    Optional[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)
      a_		,		a_		,		a_		,		a_		,		a_     :    Union[str, Any]     		=   accelerator.prepare(
          model, optimizer, train_dataloader, valid_dataloader, scheduler
      )
      a_		,		a_     :    Any     		=   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:
            a_     :    Optional[Any]     		=   group["params"][0].device
            break
      assert param_device.type == accelerator.device.type
      a_     :    Any     		=   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:
            a_     :    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:
            a_     :    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()




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_rembert import RemBertTokenizer
else:
      a_     :    int     		=   None

a_     :    List[Any]     		=   logging.get_logger(__name__)
a_     :    List[Any]     		=   {"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

a_     :    Optional[int]     		=   {
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
    "tokenizer_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
    },
}

a_     :    Union[str, Any]     		=   {
    "google/rembert": 256,
}

a_     :    List[Any]     		=   "▁"




class   UpperCamelCase			(  SCREAMING_SNAKE_CASE ):
    __UpperCamelCase    =VOCAB_FILES_NAMES
    __UpperCamelCase    =PRETRAINED_VOCAB_FILES_MAP
    __UpperCamelCase    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCamelCase    =RemBertTokenizer

    def __init__(      self			:   int				,     snake_case__			:   Any=None				,     snake_case__			:   Optional[Any]=None				,     snake_case__			:   Optional[int]=True				,     snake_case__			:   Any=True				,     snake_case__			:   Any=False				,     snake_case__			:   Dict="[CLS]"				,     snake_case__			:   List[str]="[SEP]"				,     snake_case__			:   List[str]="<unk>"				,     snake_case__			:   Union[str, Any]="[SEP]"				,     snake_case__			:   List[Any]="<pad>"				,     snake_case__			:   Tuple="[CLS]"				,     snake_case__			:   str="[MASK]"				,     **snake_case__			:   Union[str, Any]				,     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      AddedToken(snake_case__				,     lstrip=snake_case__				,     rstrip=snake_case__     ) if isinstance(snake_case__				,     snake_case__     ) else mask_token

     super().__init__(
         snake_case__				,     tokenizer_file=snake_case__				,     do_lower_case=snake_case__				,     remove_space=snake_case__				,     keep_accents=snake_case__				,     bos_token=snake_case__				,     eos_token=snake_case__				,     unk_token=snake_case__				,     sep_token=snake_case__				,     pad_token=snake_case__				,     cls_token=snake_case__				,     mask_token=snake_case__				,     **snake_case__				,     )

     SCREAMING_SNAKE_CASE		    =      do_lower_case
     SCREAMING_SNAKE_CASE		    =      remove_space
     SCREAMING_SNAKE_CASE		    =      keep_accents
     SCREAMING_SNAKE_CASE		    =      vocab_file
     SCREAMING_SNAKE_CASE		    =      False if not self.vocab_file else True

    def UpperCamelCase      (      self			:   Tuple				,     snake_case__			:   List[int]				,     snake_case__			:   Optional[List[int]] = None     ):

     """simple docstring"""






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

    def UpperCamelCase      (      self			:   Tuple				,     snake_case__			:   List[int]				,     snake_case__			:   Optional[List[int]] = None				,     snake_case__			:   bool = False     ):

     """simple docstring"""






     if already_has_special_tokens:
      if token_ids_a is not None:
       raise ValueError(
           'You should not supply a second sequence if the provided sequence of '
           'ids is already formatted with special tokens for the model.'     )
      return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

     if token_ids_a is not None:
      return [1] + ([0] * len(snake_case__     )) + [1] + ([0] * len(snake_case__     )) + [1]
     return [1] + ([0] * len(snake_case__     )) + [1]

    def UpperCamelCase      (      self			:   int				,     snake_case__			:   List[int]				,     snake_case__			:   Optional[List[int]] = None     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      [self.sep_token_id]
     SCREAMING_SNAKE_CASE		    =      [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     ) * [0] + len(token_ids_a + sep     ) * [1]




    def UpperCamelCase      (      self			:   Dict				,     snake_case__			:   str				,     snake_case__			:   Optional[str] = None     ):

     """simple docstring"""






     if not os.path.isdir(snake_case__     ):
      logger.error('Vocabulary path ({}) should be a directory'.format(snake_case__     )     )
      return
     SCREAMING_SNAKE_CASE		    =      os.path.join(
         snake_case__				,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )

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

     return (out_vocab_file,)

