
"""simple docstring"""




from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
      import tensorflow as tf

      from transformers import (
          TFDeiTForImageClassification,
          TFDeiTForImageClassificationWithTeacher,
          TFDeiTForMaskedImageModeling,
          TFDeiTModel,
      )
      from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import DeiTImageProcessor





class  snake_case     :

       def __init__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=13       ,       SCREAMING_SNAKE_CASE_=30       ,       SCREAMING_SNAKE_CASE_=2       ,       SCREAMING_SNAKE_CASE_=3       ,       SCREAMING_SNAKE_CASE_=True       ,       SCREAMING_SNAKE_CASE_=True       ,       SCREAMING_SNAKE_CASE_=32       ,       SCREAMING_SNAKE_CASE_=2       ,       SCREAMING_SNAKE_CASE_=4       ,       SCREAMING_SNAKE_CASE_=37       ,       SCREAMING_SNAKE_CASE_="gelu"       ,       SCREAMING_SNAKE_CASE_=0.1       ,       SCREAMING_SNAKE_CASE_=0.1       ,       SCREAMING_SNAKE_CASE_=10       ,       SCREAMING_SNAKE_CASE_=0.02       ,       SCREAMING_SNAKE_CASE_=3       ,       SCREAMING_SNAKE_CASE_=None       ,       SCREAMING_SNAKE_CASE_=2       ,       ):

         """simple docstring"""







         SCREAMING_SNAKE_CASE_						 = parent
         SCREAMING_SNAKE_CASE_						 = batch_size
         SCREAMING_SNAKE_CASE_						 = image_size
         SCREAMING_SNAKE_CASE_						 = patch_size
         SCREAMING_SNAKE_CASE_						 = num_channels
         SCREAMING_SNAKE_CASE_						 = is_training
         SCREAMING_SNAKE_CASE_						 = use_labels
         SCREAMING_SNAKE_CASE_						 = hidden_size
         SCREAMING_SNAKE_CASE_						 = num_hidden_layers
         SCREAMING_SNAKE_CASE_						 = num_attention_heads
         SCREAMING_SNAKE_CASE_						 = intermediate_size
         SCREAMING_SNAKE_CASE_						 = hidden_act
         SCREAMING_SNAKE_CASE_						 = hidden_dropout_prob
         SCREAMING_SNAKE_CASE_						 = attention_probs_dropout_prob
         SCREAMING_SNAKE_CASE_						 = type_sequence_label_size
         SCREAMING_SNAKE_CASE_						 = initializer_range
         SCREAMING_SNAKE_CASE_						 = scope
         SCREAMING_SNAKE_CASE_						 = encoder_stride

         # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
         SCREAMING_SNAKE_CASE_						 = (image_size // patch_size) ** 2
         SCREAMING_SNAKE_CASE_						 = num_patches + 2

       def _lowercase					(self    ):

         """simple docstring"""







         SCREAMING_SNAKE_CASE_						 = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

         SCREAMING_SNAKE_CASE_						 = None
         if self.use_labels:
           SCREAMING_SNAKE_CASE_						 = ids_tensor([self.batch_size]       ,       self.type_sequence_label_size    )

         SCREAMING_SNAKE_CASE_						 = self.get_config()

         return config, pixel_values, labels

       def _lowercase					(self    ):

         """simple docstring"""







         return DeiTConfig(
             image_size=self.image_size       ,       patch_size=self.patch_size       ,       num_channels=self.num_channels       ,       hidden_size=self.hidden_size       ,       num_hidden_layers=self.num_hidden_layers       ,       num_attention_heads=self.num_attention_heads       ,       intermediate_size=self.intermediate_size       ,       hidden_act=self.hidden_act       ,       hidden_dropout_prob=self.hidden_dropout_prob       ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,       is_decoder=_SCREAMING_SNAKE_CASE       ,       initializer_range=self.initializer_range       ,       encoder_stride=self.encoder_stride       ,       )

       def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

         """simple docstring"""







         SCREAMING_SNAKE_CASE_						 = TFDeiTModel(config=_SCREAMING_SNAKE_CASE    )
         SCREAMING_SNAKE_CASE_						 = model(_SCREAMING_SNAKE_CASE    )
         self.parent.assertEqual(result.last_hidden_state.shape       ,       (self.batch_size, self.seq_length, self.hidden_size)    )

       def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

         """simple docstring"""







         SCREAMING_SNAKE_CASE_						 = TFDeiTForMaskedImageModeling(config=_SCREAMING_SNAKE_CASE    )
         SCREAMING_SNAKE_CASE_						 = model(_SCREAMING_SNAKE_CASE    )
         self.parent.assertEqual(
             result.reconstruction.shape       ,       (self.batch_size, self.num_channels, self.image_size, self.image_size)    )

         # test greyscale images
         SCREAMING_SNAKE_CASE_						 = 1
         SCREAMING_SNAKE_CASE_						 = TFDeiTForMaskedImageModeling(_SCREAMING_SNAKE_CASE    )

         SCREAMING_SNAKE_CASE_						 = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
         SCREAMING_SNAKE_CASE_						 = model(_SCREAMING_SNAKE_CASE    )
         self.parent.assertEqual(result.reconstruction.shape       ,       (self.batch_size, 1, self.image_size, self.image_size)    )

       def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

         """simple docstring"""







         SCREAMING_SNAKE_CASE_						 = self.type_sequence_label_size
         SCREAMING_SNAKE_CASE_						 = TFDeiTForImageClassification(_SCREAMING_SNAKE_CASE    )
         SCREAMING_SNAKE_CASE_						 = model(_SCREAMING_SNAKE_CASE       ,       labels=_SCREAMING_SNAKE_CASE    )
         self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.type_sequence_label_size)    )

         # test greyscale images
         SCREAMING_SNAKE_CASE_						 = 1
         SCREAMING_SNAKE_CASE_						 = TFDeiTForImageClassification(_SCREAMING_SNAKE_CASE    )

         SCREAMING_SNAKE_CASE_						 = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
         SCREAMING_SNAKE_CASE_						 = model(_SCREAMING_SNAKE_CASE       ,       labels=_SCREAMING_SNAKE_CASE    )
         self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.type_sequence_label_size)    )







       def _lowercase					(self    ):

         """simple docstring"""







         SCREAMING_SNAKE_CASE_						 = self.prepare_config_and_inputs()
         SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = config_and_inputs
         SCREAMING_SNAKE_CASE_						 = {'''pixel_values''': pixel_values}
         return config, inputs_dict







@require_tf
class  snake_case     (							__lowercase       ,			__lowercase       ,			unittest.TestCase   ):
       UpperCAmelCase__						     =      (
           (
               TFDeiTModel,
               TFDeiTForImageClassification,
               TFDeiTForImageClassificationWithTeacher,
               TFDeiTForMaskedImageModeling,
           )
           if is_tf_available()
           else ()
       )
       UpperCAmelCase__						     =      (
           {
               '''feature-extraction''': TFDeiTModel,
               '''image-classification''': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
           }
           if is_tf_available()
           else {}
       )

       UpperCAmelCase__						     =      False
       UpperCAmelCase__						     =      False
       UpperCAmelCase__						     =      False
       UpperCAmelCase__						     =      False

       def _lowercase					(self    ):

         """simple docstring"""







         SCREAMING_SNAKE_CASE_						 = TFDeiTModelTester(self    )
         SCREAMING_SNAKE_CASE_						 = ConfigTester(self       ,       config_class=_SCREAMING_SNAKE_CASE       ,       has_text_modality=_SCREAMING_SNAKE_CASE       ,       hidden_size=37    )

       def _lowercase					(self    ):

         """simple docstring"""







         self.config_tester.run_common_tests()

       @unittest.skip(reason='''DeiT does not use inputs_embeds'''    )
       def _lowercase					(self    ):

         """simple docstring"""







         pass

       def _lowercase					(self    ):

         """simple docstring"""







         SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           SCREAMING_SNAKE_CASE_						 = model_class(_SCREAMING_SNAKE_CASE    )
           self.assertIsInstance(model.get_input_embeddings()       ,       (tf.keras.layers.Layer)    )
           SCREAMING_SNAKE_CASE_						 = model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE       ,       tf.keras.layers.Dense    )    )

       def _lowercase					(self    ):

         """simple docstring"""







         SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           SCREAMING_SNAKE_CASE_						 = model_class(_SCREAMING_SNAKE_CASE    )
           SCREAMING_SNAKE_CASE_						 = inspect.signature(model.call    )
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           SCREAMING_SNAKE_CASE_						 = [*signature.parameters.keys()]

           SCREAMING_SNAKE_CASE_						 = ['''pixel_values''']
           self.assertListEqual(arg_names[:1]       ,       _SCREAMING_SNAKE_CASE    )

       def _lowercase					(self    ):

         """simple docstring"""







         SCREAMING_SNAKE_CASE_						 = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE    )

       def _lowercase					(self    ):

         """simple docstring"""







         SCREAMING_SNAKE_CASE_						 = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_image_modeling(*_SCREAMING_SNAKE_CASE    )

       def _lowercase					(self    ):

         """simple docstring"""







         SCREAMING_SNAKE_CASE_						 = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE    )

       def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=False    ):

         """simple docstring"""







         SCREAMING_SNAKE_CASE_						 = super()._prepare_for_class(_SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE       ,       return_labels=_SCREAMING_SNAKE_CASE    )

         if return_labels:
           if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call    ).parameters:
             del inputs_dict["labels"]

         return inputs_dict







       @slow
       def _lowercase					(self    ):

         """simple docstring"""







         for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           SCREAMING_SNAKE_CASE_						 = TFDeiTModel.from_pretrained(_SCREAMING_SNAKE_CASE    )
           self.assertIsNotNone(_SCREAMING_SNAKE_CASE    )







def        _lowerCamelCase ( ):
  SCREAMING_SNAKE_CASE_						 = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
  return image





@require_tf
@require_vision
class  snake_case     (							unittest.TestCase   ):

       @cached_property
       def _lowercase					(self    ):

         """simple docstring"""







         return (
             DeiTImageProcessor.from_pretrained('''facebook/deit-base-distilled-patch16-224'''    )
             if is_vision_available()
             else None
         )







       @slow
       def _lowercase					(self    ):

         """simple docstring"""







         SCREAMING_SNAKE_CASE_						 = TFDeiTForImageClassificationWithTeacher.from_pretrained('''facebook/deit-base-distilled-patch16-224'''    )

         SCREAMING_SNAKE_CASE_						 = self.default_image_processor
         SCREAMING_SNAKE_CASE_						 = prepare_img()
         SCREAMING_SNAKE_CASE_						 = image_processor(images=_SCREAMING_SNAKE_CASE       ,       return_tensors='''tf'''    )

         # forward pass
         SCREAMING_SNAKE_CASE_						 = model(**_SCREAMING_SNAKE_CASE    )

         # verify the logits
         SCREAMING_SNAKE_CASE_						 = tf.TensorShape((1, 10_00)    )
         self.assertEqual(outputs.logits.shape       ,       _SCREAMING_SNAKE_CASE    )

         SCREAMING_SNAKE_CASE_						 = tf.constant([-1.02_66, 0.19_12, -1.28_61]    )

         self.assertTrue(np.allclose(outputs.logits[0, :3]       ,       _SCREAMING_SNAKE_CASE       ,       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__							=				logging.getLogger(__name__)



def        _lowerCamelCase ( __a=2,							__a=3,							__a=16,							__a = 10,							__a = 2    ):

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

		SCREAMING_SNAKE_CASE_						 = get_dataset(__a    )
		SCREAMING_SNAKE_CASE_						 = get_dataset(__a    )
		SCREAMING_SNAKE_CASE_						 = DataLoader(__a,							shuffle=__a,							batch_size=__a,							num_workers=4    )
		SCREAMING_SNAKE_CASE_						 = DataLoader(__a,							shuffle=__a,							batch_size=__a,							num_workers=4    )
		return (train_dataloader, valid_dataloader)



def        _lowerCamelCase ( __a,							__a,							__a,							__a,							__a,							__a=None    ):
		SCREAMING_SNAKE_CASE_						 = []
		for epoch in range(__a    ):
				# Train quickly
				model.train()
				for batch in dataloader:
						SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = batch
						SCREAMING_SNAKE_CASE_						 = model(__a    )
						SCREAMING_SNAKE_CASE_						 = 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  snake_case     (							nn.Module   ):

							def __init__(self    ):

									"""simple docstring"""







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







							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									return x * self.a + self.b







class  snake_case     (							unittest.TestCase   ):

							def _lowercase					(self    ):

									"""simple docstring"""







									with tempfile.TemporaryDirectory() as tmpdir:
											set_seed(42    )
											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=SCREAMING_SNAKE_CASE_       ,       automatic_checkpoint_naming=SCREAMING_SNAKE_CASE_    )
											# Train baseline
											SCREAMING_SNAKE_CASE_						 = Accelerator(project_config=SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = accelerator.prepare(
											    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											# Save initial
											accelerator.save_state()

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

							def _lowercase					(self    ):

									"""simple docstring"""







									with tempfile.TemporaryDirectory() as tmpdir:
											set_seed(42    )
											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(
											    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											# Save initial
											SCREAMING_SNAKE_CASE_						 = os.path.join(SCREAMING_SNAKE_CASE_       ,       '''initial'''    )
											accelerator.save_state(SCREAMING_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       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											((SCREAMING_SNAKE_CASE_) ,(SCREAMING_SNAKE_CASE_))						 = model.a.item(), model.b.item()
											SCREAMING_SNAKE_CASE_						 = optimizer.state_dict()

											# Train partially
											set_seed(42    )
											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(
											    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											accelerator.load_state(SCREAMING_SNAKE_CASE_    )
											((SCREAMING_SNAKE_CASE_) ,(SCREAMING_SNAKE_CASE_))						 = model.a.item(), model.b.item()
											SCREAMING_SNAKE_CASE_						 = optimizer.state_dict()
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

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

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

							def _lowercase					(self    ):

									"""simple docstring"""







									with tempfile.TemporaryDirectory() as tmpdir:
											set_seed(42    )
											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=SCREAMING_SNAKE_CASE_    )

											# Train baseline
											SCREAMING_SNAKE_CASE_						 = Accelerator(project_dir=SCREAMING_SNAKE_CASE_       ,       project_config=SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = accelerator.prepare(
											    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_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       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											((SCREAMING_SNAKE_CASE_) ,(SCREAMING_SNAKE_CASE_))						 = model.a.item(), model.b.item()
											SCREAMING_SNAKE_CASE_						 = optimizer.state_dict()

											# Train partially
											set_seed(42    )
											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=SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = Accelerator(project_dir=SCREAMING_SNAKE_CASE_       ,       project_config=SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = accelerator.prepare(
											    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											accelerator.load_state(os.path.join(SCREAMING_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(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

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

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

							def _lowercase					(self    ):

									"""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(SCREAMING_SNAKE_CASE_    ) as ve:
											accelerator.register_for_checkpointing(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_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 _lowercase					(self    ):

									"""simple docstring"""







									with tempfile.TemporaryDirectory() as tmpdir:
											set_seed(42    )
											SCREAMING_SNAKE_CASE_						 = DummyModel()
											SCREAMING_SNAKE_CASE_						 = torch.optim.Adam(params=model.parameters()       ,       lr=1e-3    )
											SCREAMING_SNAKE_CASE_						 = torch.optim.lr_scheduler.StepLR(SCREAMING_SNAKE_CASE_       ,       step_size=1       ,       gamma=0.99    )
											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = dummy_dataloaders()
											SCREAMING_SNAKE_CASE_						 = ProjectConfiguration(automatic_checkpoint_naming=SCREAMING_SNAKE_CASE_    )
											# Train baseline
											SCREAMING_SNAKE_CASE_						 = Accelerator(project_dir=SCREAMING_SNAKE_CASE_       ,       project_config=SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = accelerator.prepare(
											    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											# Save initial
											accelerator.save_state()
											SCREAMING_SNAKE_CASE_						 = scheduler.state_dict()
											train(3       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											self.assertNotEqual(SCREAMING_SNAKE_CASE_       ,       scheduler.state_dict()    )

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

							def _lowercase					(self    ):

									"""simple docstring"""







									with tempfile.TemporaryDirectory() as tmpdir:
											set_seed(42    )
											SCREAMING_SNAKE_CASE_						 = DummyModel()
											SCREAMING_SNAKE_CASE_						 = ProjectConfiguration(automatic_checkpoint_naming=SCREAMING_SNAKE_CASE_       ,       total_limit=2    )
											# Train baseline
											SCREAMING_SNAKE_CASE_						 = Accelerator(project_dir=SCREAMING_SNAKE_CASE_       ,       project_config=SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = accelerator.prepare(SCREAMING_SNAKE_CASE_    )
											# Save 3 states:
											for _ in range(11    ):
													accelerator.save_state()
											self.assertTrue(not os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_       ,       '''checkpoints'''       ,       '''checkpoint_0'''    )    )    )
											self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_       ,       '''checkpoints'''       ,       '''checkpoint_9'''    )    )    )
											self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_       ,       '''checkpoints'''       ,       '''checkpoint_10'''    )    )    )







							@require_cuda
							def _lowercase					(self    ):

									"""simple docstring"""







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


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