



"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers.models.distilbert.modeling_tf_distilbert import (
      TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDistilBertForMaskedLM,
      TFDistilBertForMultipleChoice,
      TFDistilBertForQuestionAnswering,
      TFDistilBertForSequenceClassification,
      TFDistilBertForTokenClassification,
      TFDistilBertModel,
  )
class 							_UpperCAmelCase							:





     '''simple docstring'''



     def __init__(					self       ,	A       ,	)							->					Union[str, Any]:
         _UpperCAmelCase							:  str											=    parent
         _UpperCAmelCase							:  Optional[int]											=    1_3
         _UpperCAmelCase							:  List[str]											=    7
         _UpperCAmelCase							:  Union[str, Any]											=    True
         _UpperCAmelCase							:  Any											=    True
         _UpperCAmelCase							:  Dict											=    False
         _UpperCAmelCase							:  int											=    True
         _UpperCAmelCase							:  Optional[int]											=    9_9
         _UpperCAmelCase							:  Any											=    3_2
         _UpperCAmelCase							:  Dict											=    2
         _UpperCAmelCase							:  List[str]											=    4
         _UpperCAmelCase							:  Optional[int]											=    3_7
         _UpperCAmelCase							:  List[str]											=    '''gelu'''
         _UpperCAmelCase							:  int											=    0.1
         _UpperCAmelCase							:  Optional[Any]											=    0.1
         _UpperCAmelCase							:  Any											=    5_1_2
         _UpperCAmelCase							:  Union[str, Any]											=    1_6
         _UpperCAmelCase							:  Optional[int]											=    2
         _UpperCAmelCase							:  List[Any]											=    0.02
         _UpperCAmelCase							:  Dict											=    3
         _UpperCAmelCase							:  Any											=    4
         _UpperCAmelCase							:  Optional[int]											=    None





     def  __lowerCAmelCase	(					self		)							->					Optional[int]:
         _UpperCAmelCase							:  Tuple											=    ids_tensor([self.batch_size, self.seq_length]       ,	self.vocab_size		)

         _UpperCAmelCase							:  str											=    None
         if self.use_input_mask:
             _UpperCAmelCase							:  List[str]											=    random_attention_mask([self.batch_size, self.seq_length]		)

         _UpperCAmelCase							:  str											=    None
         _UpperCAmelCase							:  Dict											=    None
         _UpperCAmelCase							:  Union[str, Any]											=    None
         if self.use_labels:
             _UpperCAmelCase							:  str											=    ids_tensor([self.batch_size]       ,	self.type_sequence_label_size		)
             _UpperCAmelCase							:  int											=    ids_tensor([self.batch_size, self.seq_length]       ,	self.num_labels		)
             _UpperCAmelCase							:  Optional[int]											=    ids_tensor([self.batch_size]       ,	self.num_choices		)

         _UpperCAmelCase							:  str											=    DistilBertConfig(
             vocab_size=self.vocab_size       ,	dim=self.hidden_size       ,	n_layers=self.num_hidden_layers       ,	n_heads=self.num_attention_heads       ,	hidden_dim=self.intermediate_size       ,	hidden_act=self.hidden_act       ,	dropout=self.hidden_dropout_prob       ,	attention_dropout=self.attention_probs_dropout_prob       ,	max_position_embeddings=self.max_position_embeddings       ,	initializer_range=self.initializer_range       ,	)

         return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





     def  __lowerCAmelCase	(					self       ,	A       ,	A       ,	A       ,	A       ,	A       ,	A		)							->					List[str]:
         _UpperCAmelCase							:  Optional[Any]											=    TFDistilBertModel(config=A		)
         _UpperCAmelCase							:  str											=    {'''input_ids''': input_ids, '''attention_mask''': input_mask}

         _UpperCAmelCase							:  str											=    model(A		)

         _UpperCAmelCase							:  Optional[Any]											=    [input_ids, input_mask]

         _UpperCAmelCase							:  Optional[int]											=    model(A		)

         self.parent.assertEqual(result.last_hidden_state.shape       ,	(self.batch_size, self.seq_length, self.hidden_size)		)





     def  __lowerCAmelCase	(					self       ,	A       ,	A       ,	A       ,	A       ,	A       ,	A		)							->					Tuple:
         _UpperCAmelCase							:  Dict											=    TFDistilBertForMaskedLM(config=A		)
         _UpperCAmelCase							:  Any											=    {'''input_ids''': input_ids, '''attention_mask''': input_mask}
         _UpperCAmelCase							:  List[str]											=    model(A		)
         self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.seq_length, self.vocab_size)		)





     def  __lowerCAmelCase	(					self       ,	A       ,	A       ,	A       ,	A       ,	A       ,	A		)							->					Optional[int]:
         _UpperCAmelCase							:  str											=    TFDistilBertForQuestionAnswering(config=A		)
         _UpperCAmelCase							:  str											=    {
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
         }
         _UpperCAmelCase							:  Dict											=    model(A		)
         self.parent.assertEqual(result.start_logits.shape       ,	(self.batch_size, self.seq_length)		)
         self.parent.assertEqual(result.end_logits.shape       ,	(self.batch_size, self.seq_length)		)





     def  __lowerCAmelCase	(					self       ,	A       ,	A       ,	A       ,	A       ,	A       ,	A		)							->					Optional[int]:
         _UpperCAmelCase							:  List[Any]											=    self.num_labels
         _UpperCAmelCase							:  List[Any]											=    TFDistilBertForSequenceClassification(A		)
         _UpperCAmelCase							:  Any											=    {'''input_ids''': input_ids, '''attention_mask''': input_mask}
         _UpperCAmelCase							:  Optional[Any]											=    model(A		)
         self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.num_labels)		)





     def  __lowerCAmelCase	(					self       ,	A       ,	A       ,	A       ,	A       ,	A       ,	A		)							->					int:
         _UpperCAmelCase							:  Union[str, Any]											=    self.num_choices
         _UpperCAmelCase							:  Optional[Any]											=    TFDistilBertForMultipleChoice(A		)
         _UpperCAmelCase							:  str											=    tf.tile(tf.expand_dims(A       ,	1		)       ,	(1, self.num_choices, 1)		)
         _UpperCAmelCase							:  int											=    tf.tile(tf.expand_dims(A       ,	1		)       ,	(1, self.num_choices, 1)		)
         _UpperCAmelCase							:  Optional[Any]											=    {
             '''input_ids''': multiple_choice_inputs_ids,
             '''attention_mask''': multiple_choice_input_mask,
         }
         _UpperCAmelCase							:  Optional[int]											=    model(A		)
         self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.num_choices)		)





     def  __lowerCAmelCase	(					self       ,	A       ,	A       ,	A       ,	A       ,	A       ,	A		)							->					Optional[int]:
         _UpperCAmelCase							:  Union[str, Any]											=    self.num_labels
         _UpperCAmelCase							:  str											=    TFDistilBertForTokenClassification(A		)
         _UpperCAmelCase							:  Optional[int]											=    {'''input_ids''': input_ids, '''attention_mask''': input_mask}
         _UpperCAmelCase							:  Any											=    model(A		)
         self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.seq_length, self.num_labels)		)





     def  __lowerCAmelCase	(					self		)							->					Optional[Any]:
         _UpperCAmelCase							:  Union[str, Any]											=    self.prepare_config_and_inputs()
         ((_UpperCAmelCase)							,				(_UpperCAmelCase)							,				(_UpperCAmelCase)							,				(_UpperCAmelCase)							,				(_UpperCAmelCase)							,				(_UpperCAmelCase))							:  Any											=    config_and_inputs
         _UpperCAmelCase							:  Union[str, Any]											=    {'''input_ids''': input_ids, '''attention_mask''': input_mask}
         return config, inputs_dict



@require_tf
class 							_UpperCAmelCase							( a		,a		,unittest.TestCase      ):





     '''simple docstring'''



     a__									=(
         (
             TFDistilBertModel,
             TFDistilBertForMaskedLM,
             TFDistilBertForQuestionAnswering,
             TFDistilBertForSequenceClassification,
             TFDistilBertForTokenClassification,
             TFDistilBertForMultipleChoice,
         )
         if is_tf_available()
         else None
     )
     a__									=(
         {
             '''feature-extraction''': TFDistilBertModel,
             '''fill-mask''': TFDistilBertForMaskedLM,
             '''question-answering''': TFDistilBertForQuestionAnswering,
             '''text-classification''': TFDistilBertForSequenceClassification,
             '''token-classification''': TFDistilBertForTokenClassification,
             '''zero-shot''': TFDistilBertForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     a__									=False
     a__									=False





     def  __lowerCAmelCase	(					self		)							->					int:
         _UpperCAmelCase							:  List[str]											=    TFDistilBertModelTester(self		)
         _UpperCAmelCase							:  int											=    ConfigTester(self       ,	config_class=A       ,	dim=3_7		)





     def  __lowerCAmelCase	(					self		)							->					Any:
         self.config_tester.run_common_tests()





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         _UpperCAmelCase							:  List[str]											=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_distilbert_model(*A		)





     def  __lowerCAmelCase	(					self		)							->					str:
         _UpperCAmelCase							:  Dict											=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_distilbert_for_masked_lm(*A		)





     def  __lowerCAmelCase	(					self		)							->					str:
         _UpperCAmelCase							:  List[Any]											=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_distilbert_for_question_answering(*A		)





     def  __lowerCAmelCase	(					self		)							->					Any:
         _UpperCAmelCase							:  int											=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_distilbert_for_sequence_classification(*A		)





     def  __lowerCAmelCase	(					self		)							->					Optional[Any]:
         _UpperCAmelCase							:  Dict											=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_distilbert_for_multiple_choice(*A		)





     def  __lowerCAmelCase	(					self		)							->					List[Any]:
         _UpperCAmelCase							:  Any											=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_distilbert_for_token_classification(*A		)





     @slow
     def  __lowerCAmelCase	(					self		)							->					Optional[Any]:
         for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]		):
             _UpperCAmelCase							:  int											=    TFDistilBertModel.from_pretrained(A		)
             self.assertIsNotNone(A		)



@require_tf
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     @slow
     def  __lowerCAmelCase	(					self		)							->					int:
         _UpperCAmelCase							:  List[Any]											=    TFDistilBertModel.from_pretrained('''distilbert-base-uncased'''		)
         _UpperCAmelCase							:  List[Any]											=    tf.constant([[0, 1, 2, 3, 4, 5]]		)
         _UpperCAmelCase							:  Any											=    model(A		)[0]

         _UpperCAmelCase							:  Optional[Any]											=    [1, 6, 7_6_8]
         self.assertEqual(output.shape       ,	A		)

         _UpperCAmelCase							:  int											=    tf.constant(
             [
                 [
                     [0.19_261_885, -0.13_732_955, 0.4_119_799],
                     [0.22_150_156, -0.07_422_661, 0.39_037_204],
                     [0.22_756_018, -0.0_896_414, 0.3_701_467],
                 ]
             ]		)
         tf.debugging.assert_near(output[:, :3, :3]       ,	A       ,	atol=1E-4		)







"""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    :Tuple     =					logging.getLogger(__name__)





def  lowerCamelCase_						(UpperCamelCase__		:		List[Any]=2     ,       UpperCamelCase__		:		List[Any]=3     ,       UpperCamelCase__		:		List[Any]=16     ,       UpperCamelCase__		:		int = 10     ,       UpperCamelCase__		:		int = 2							):

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

    _UpperCAmelCase							:  Optional[Any]											=    get_dataset(UpperCamelCase__							)
    _UpperCAmelCase							:  Optional[Any]											=    get_dataset(UpperCamelCase__							)
    _UpperCAmelCase							:  List[str]											=    DataLoader(UpperCamelCase__     ,       shuffle=UpperCamelCase__     ,       batch_size=UpperCamelCase__     ,       num_workers=4							)
    _UpperCAmelCase							:  List[str]											=    DataLoader(UpperCamelCase__     ,       shuffle=UpperCamelCase__     ,       batch_size=UpperCamelCase__     ,       num_workers=4							)
    return (train_dataloader, valid_dataloader)





def  lowerCamelCase_						(UpperCamelCase__		:		Optional[int]     ,       UpperCamelCase__		:		List[Any]     ,       UpperCamelCase__		:		int     ,       UpperCamelCase__		:		Any     ,       UpperCamelCase__		:		Optional[Any]     ,       UpperCamelCase__		:		Tuple=None							):
    _UpperCAmelCase							:  Tuple											=    []
    for epoch in range(UpperCamelCase__							):
        # Train quickly
        model.train()
        for batch in dataloader:
            _UpperCAmelCase							,				_UpperCAmelCase							:  Dict											=    batch
            _UpperCAmelCase							:  int											=    model(UpperCamelCase__							)
            _UpperCAmelCase							:  Dict											=    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      ):





     '''simple docstring'''



     def __init__(					self		)							->					List[Any]:
         super().__init__()
         _UpperCAmelCase							:  List[Any]											=    nn.Parameter(torch.randn(1		)		)
         _UpperCAmelCase							:  int											=    nn.Parameter(torch.randn(1		)		)





     def  __lowerCAmelCase	(					self       ,	A		)							->					Tuple:
         return x * self.a + self.b



class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     def  __lowerCAmelCase	(					self		)							->					Any:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(4_2		)
             _UpperCAmelCase							:  int											=    DummyModel()
             _UpperCAmelCase							:  str											=    torch.optim.Adam(params=model.parameters()       ,	lr=1E-3		)
             _UpperCAmelCase							,				_UpperCAmelCase							:  List[Any]											=    dummy_dataloaders()
             _UpperCAmelCase							:  Any											=    ProjectConfiguration(total_limit=1       ,	project_dir=A       ,	automatic_checkpoint_naming=A		)
             # Train baseline
             _UpperCAmelCase							:  Union[str, Any]											=    Accelerator(project_config=A		)
             _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  int											=    accelerator.prepare(
                 A       ,	A       ,	A       ,	A		)
             # Save initial
             accelerator.save_state()

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





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(4_2		)
             _UpperCAmelCase							:  Optional[Any]											=    DummyModel()
             _UpperCAmelCase							:  int											=    torch.optim.Adam(params=model.parameters()       ,	lr=1E-3		)
             _UpperCAmelCase							,				_UpperCAmelCase							:  Dict											=    dummy_dataloaders()
             # Train baseline
             _UpperCAmelCase							:  Optional[int]											=    Accelerator()
             _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  List[str]											=    accelerator.prepare(
                 A       ,	A       ,	A       ,	A		)
             # Save initial
             _UpperCAmelCase							:  Union[str, Any]											=    os.path.join(A       ,	'''initial'''		)
             accelerator.save_state(A		)
             ((_UpperCAmelCase)							,				(_UpperCAmelCase))							:  Optional[Any]											=    model.a.item(), model.b.item()
             _UpperCAmelCase							:  str											=    optimizer.state_dict()
             _UpperCAmelCase							:  Tuple											=    train(3       ,	A       ,	A       ,	A       ,	A		)
             ((_UpperCAmelCase)							,				(_UpperCAmelCase))							:  Dict											=    model.a.item(), model.b.item()
             _UpperCAmelCase							:  List[Any]											=    optimizer.state_dict()

             # Train partially
             set_seed(4_2		)
             _UpperCAmelCase							:  Dict											=    DummyModel()
             _UpperCAmelCase							:  Optional[Any]											=    torch.optim.Adam(params=model.parameters()       ,	lr=1E-3		)
             _UpperCAmelCase							,				_UpperCAmelCase							:  Union[str, Any]											=    dummy_dataloaders()
             _UpperCAmelCase							:  Tuple											=    Accelerator()
             _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  str											=    accelerator.prepare(
                 A       ,	A       ,	A       ,	A		)
             accelerator.load_state(A		)
             ((_UpperCAmelCase)							,				(_UpperCAmelCase))							:  Union[str, Any]											=    model.a.item(), model.b.item()
             _UpperCAmelCase							:  List[str]											=    optimizer.state_dict()
             self.assertEqual(A       ,	A		)
             self.assertEqual(A       ,	A		)
             self.assertEqual(A       ,	A		)

             _UpperCAmelCase							:  Union[str, Any]											=    train(2       ,	A       ,	A       ,	A       ,	A		)
             # Save everything
             _UpperCAmelCase							:  List[str]											=    os.path.join(A       ,	'''checkpoint'''		)
             accelerator.save_state(A		)

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





     def  __lowerCAmelCase	(					self		)							->					int:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(4_2		)
             _UpperCAmelCase							:  List[Any]											=    DummyModel()
             _UpperCAmelCase							:  List[str]											=    torch.optim.Adam(params=model.parameters()       ,	lr=1E-3		)
             _UpperCAmelCase							,				_UpperCAmelCase							:  List[Any]											=    dummy_dataloaders()
             _UpperCAmelCase							:  List[str]											=    ProjectConfiguration(automatic_checkpoint_naming=A		)

             # Train baseline
             _UpperCAmelCase							:  str											=    Accelerator(project_dir=A       ,	project_config=A		)
             _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  Any											=    accelerator.prepare(
                 A       ,	A       ,	A       ,	A		)
             # Save initial
             accelerator.save_state()
             ((_UpperCAmelCase)							,				(_UpperCAmelCase))							:  Union[str, Any]											=    model.a.item(), model.b.item()
             _UpperCAmelCase							:  Dict											=    optimizer.state_dict()
             _UpperCAmelCase							:  int											=    train(3       ,	A       ,	A       ,	A       ,	A		)
             ((_UpperCAmelCase)							,				(_UpperCAmelCase))							:  Union[str, Any]											=    model.a.item(), model.b.item()
             _UpperCAmelCase							:  Union[str, Any]											=    optimizer.state_dict()

             # Train partially
             set_seed(4_2		)
             _UpperCAmelCase							:  List[Any]											=    DummyModel()
             _UpperCAmelCase							:  Union[str, Any]											=    torch.optim.Adam(params=model.parameters()       ,	lr=1E-3		)
             _UpperCAmelCase							,				_UpperCAmelCase							:  Any											=    dummy_dataloaders()
             _UpperCAmelCase							:  List[str]											=    ProjectConfiguration(iteration=1       ,	automatic_checkpoint_naming=A		)
             _UpperCAmelCase							:  Tuple											=    Accelerator(project_dir=A       ,	project_config=A		)
             _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  int											=    accelerator.prepare(
                 A       ,	A       ,	A       ,	A		)
             accelerator.load_state(os.path.join(A       ,	'''checkpoints'''       ,	'''checkpoint_0'''		)		)
             ((_UpperCAmelCase)							,				(_UpperCAmelCase))							:  Dict											=    model.a.item(), model.b.item()
             _UpperCAmelCase							:  str											=    optimizer.state_dict()
             self.assertEqual(A       ,	A		)
             self.assertEqual(A       ,	A		)
             self.assertEqual(A       ,	A		)

             _UpperCAmelCase							:  List[str]											=    train(2       ,	A       ,	A       ,	A       ,	A		)
             # Save everything
             accelerator.save_state()

             # Load everything back in and make sure all states work
             accelerator.load_state(os.path.join(A       ,	'''checkpoints'''       ,	'''checkpoint_1'''		)		)
             test_rands += train(1       ,	A       ,	A       ,	A       ,	A		)
             ((_UpperCAmelCase)							,				(_UpperCAmelCase))							:  List[str]											=    model.a.item(), model.b.item()
             _UpperCAmelCase							:  Tuple											=    optimizer.state_dict()
             self.assertEqual(A       ,	A		)
             self.assertEqual(A       ,	A		)
             self.assertEqual(A       ,	A		)
             self.assertEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					Dict:
         _UpperCAmelCase							:  List[Any]											=    torch.tensor([1, 2, 3]		)
         _UpperCAmelCase							:  List[str]											=    torch.tensor([2, 3, 4]		)
         _UpperCAmelCase							:  Optional[int]											=    DummyModel()
         _UpperCAmelCase							:  Dict											=    torch.optim.Adam(net.parameters()		)
         _UpperCAmelCase							:  Optional[int]											=    Accelerator()
         with self.assertRaises(A		) as ve:
             accelerator.register_for_checkpointing(A       ,	A       ,	A       ,	A		)
         _UpperCAmelCase							:  Dict											=    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  __lowerCAmelCase	(					self		)							->					Tuple:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(4_2		)
             _UpperCAmelCase							:  Tuple											=    DummyModel()
             _UpperCAmelCase							:  List[Any]											=    torch.optim.Adam(params=model.parameters()       ,	lr=1E-3		)
             _UpperCAmelCase							:  Optional[int]											=    torch.optim.lr_scheduler.StepLR(A       ,	step_size=1       ,	gamma=0.99		)
             _UpperCAmelCase							,				_UpperCAmelCase							:  str											=    dummy_dataloaders()
             _UpperCAmelCase							:  List[str]											=    ProjectConfiguration(automatic_checkpoint_naming=A		)
             # Train baseline
             _UpperCAmelCase							:  int											=    Accelerator(project_dir=A       ,	project_config=A		)
             _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  str											=    accelerator.prepare(
                 A       ,	A       ,	A       ,	A       ,	A		)
             # Save initial
             accelerator.save_state()
             _UpperCAmelCase							:  List[str]											=    scheduler.state_dict()
             train(3       ,	A       ,	A       ,	A       ,	A       ,	A		)
             self.assertNotEqual(A       ,	scheduler.state_dict()		)

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





     def  __lowerCAmelCase	(					self		)							->					Optional[Any]:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(4_2		)
             _UpperCAmelCase							:  int											=    DummyModel()
             _UpperCAmelCase							:  str											=    ProjectConfiguration(automatic_checkpoint_naming=A       ,	total_limit=2		)
             # Train baseline
             _UpperCAmelCase							:  Union[str, Any]											=    Accelerator(project_dir=A       ,	project_config=A		)
             _UpperCAmelCase							:  Optional[Any]											=    accelerator.prepare(A		)
             # Save 3 states:
             for _ in range(1_1		):
                 accelerator.save_state()
             self.assertTrue(not os.path.exists(os.path.join(A       ,	'''checkpoints'''       ,	'''checkpoint_0'''		)		)		)
             self.assertTrue(os.path.exists(os.path.join(A       ,	'''checkpoints'''       ,	'''checkpoint_9'''		)		)		)
             self.assertTrue(os.path.exists(os.path.join(A       ,	'''checkpoints'''       ,	'''checkpoint_10'''		)		)		)





     @require_cuda
     def  __lowerCAmelCase	(					self		)							->					Dict:
         _UpperCAmelCase							:  str											=    ['''torchrun''', f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__		)]
         execute_subprocess_async(A       ,	env=os.environ.copy()		)


if __name__ == "__main__":
  _lowerCAmelCase    :Dict     =					'/tmp/accelerate/state_checkpointing'
  _lowerCAmelCase    :Any     =					DummyModel()
  _lowerCAmelCase    :Tuple     =					torch.optim.Adam(params=model.parameters(), lr=1E-3)
  _lowerCAmelCase    :Dict     =					torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
  _lowerCAmelCase,_lowerCAmelCase    :Any     =					dummy_dataloaders()
  _lowerCAmelCase    :Tuple     =					ProjectConfiguration(automatic_checkpoint_naming=True)
  # Train baseline
  _lowerCAmelCase    :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)
  _lowerCAmelCase,_lowerCAmelCase,_lowerCAmelCase,_lowerCAmelCase,_lowerCAmelCase    :str     =					accelerator.prepare(
      model, optimizer, train_dataloader, valid_dataloader, scheduler
  )
  _lowerCAmelCase,_lowerCAmelCase    :List[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:
    _lowerCAmelCase    :int     =					group['params'][0].device
    break
  assert param_device.type == accelerator.device.type
  _lowerCAmelCase    :Dict     =					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    :List[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    :Union[str, Any]     =					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()


