
"""simple docstring"""




import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
						import os

						import jax.numpy as jnp
						from jax import jit

						from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
						from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

						lowerCAmelCase__							=				'0.12'  # assumed parallelism: 8


if is_torch_available():
						import torch



def        _lowerCamelCase ( __a,							__a,							__a=None    ):
		if rng is None:
				SCREAMING_SNAKE_CASE_						 = random.Random()

		SCREAMING_SNAKE_CASE_						 = 1
		for dim in shape:
				total_dims *= dim

		SCREAMING_SNAKE_CASE_						 = []
		for _ in range(__a    ):
				values.append(rng.randint(0,							vocab_size - 1    )    )

		SCREAMING_SNAKE_CASE_						 = np.array(__a,							dtype=jnp.intaa    ).reshape(__a    )

		return output



def        _lowerCamelCase ( __a,							__a=None    ):
		SCREAMING_SNAKE_CASE_						 = ids_tensor(__a,							vocab_size=2,							rng=__a    )
		# make sure that at least one token is attended to for each batch
		SCREAMING_SNAKE_CASE_						 = 1
		return attn_mask





@require_flax
class  snake_case     :
							UpperCAmelCase__						     =      None
							UpperCAmelCase__						     =      ()

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = self.model_tester.prepare_config_and_inputs_for_common()

									# cut to half length & take max batch_size 3
									SCREAMING_SNAKE_CASE_						 = 2
									SCREAMING_SNAKE_CASE_						 = inputs['''input_ids'''].shape[-1] // 2
									SCREAMING_SNAKE_CASE_						 = inputs['''input_ids'''][:max_batch_size, :sequence_length]

									SCREAMING_SNAKE_CASE_						 = jnp.ones_like(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = attention_mask[:max_batch_size, :sequence_length]

									# generate max 5 tokens
									SCREAMING_SNAKE_CASE_						 = input_ids.shape[-1] + 5
									if config.eos_token_id is not None and config.pad_token_id is None:
											# hack to allow generate for models such as GPT2 as is done in `generate()`
											SCREAMING_SNAKE_CASE_						 = config.eos_token_id
									return config, input_ids, attention_mask, max_length

							@is_pt_flax_cross_test
							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_						 = False
									SCREAMING_SNAKE_CASE_						 = max_length
									SCREAMING_SNAKE_CASE_						 = 0

									for model_class in self.all_generative_model_classes:
											SCREAMING_SNAKE_CASE_						 = model_class(SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = model_class.__name__[4:]  # Skip the "Flax" at the beginning
											SCREAMING_SNAKE_CASE_						 = getattr(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = pt_model_class(SCREAMING_SNAKE_CASE_    ).eval()
											SCREAMING_SNAKE_CASE_						 = load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE_       ,       flax_model.params    )

											SCREAMING_SNAKE_CASE_						 = flax_model.generate(SCREAMING_SNAKE_CASE_    ).sequences
											SCREAMING_SNAKE_CASE_						 = pt_model.generate(torch.tensor(SCREAMING_SNAKE_CASE_       ,       dtype=torch.long    )    )

											if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
													SCREAMING_SNAKE_CASE_						 = flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

											self.assertListEqual(pt_generation_outputs.numpy().tolist()       ,       flax_generation_outputs.tolist()    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_						 = False
									SCREAMING_SNAKE_CASE_						 = max_length

									for model_class in self.all_generative_model_classes:
											SCREAMING_SNAKE_CASE_						 = model_class(SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = model.generate(SCREAMING_SNAKE_CASE_    ).sequences
											self.assertEqual(generation_outputs.shape[-1]       ,       SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = jit(model.generate    )
											SCREAMING_SNAKE_CASE_						 = jit_generate(SCREAMING_SNAKE_CASE_    ).sequences

											self.assertListEqual(generation_outputs.tolist()       ,       jit_generation_outputs.tolist()    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_						 = True
									SCREAMING_SNAKE_CASE_						 = max_length

									for model_class in self.all_generative_model_classes:
											SCREAMING_SNAKE_CASE_						 = model_class(SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = model.generate(SCREAMING_SNAKE_CASE_    ).sequences
											self.assertEqual(generation_outputs.shape[-1]       ,       SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = jit(model.generate    )
											SCREAMING_SNAKE_CASE_						 = jit_generate(SCREAMING_SNAKE_CASE_    ).sequences

											self.assertListEqual(generation_outputs.tolist()       ,       jit_generation_outputs.tolist()    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_						 = False
									SCREAMING_SNAKE_CASE_						 = max_length
									SCREAMING_SNAKE_CASE_						 = 2

									for model_class in self.all_generative_model_classes:
											SCREAMING_SNAKE_CASE_						 = model_class(SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = model.generate(SCREAMING_SNAKE_CASE_    ).sequences
											self.assertEqual(generation_outputs.shape[-1]       ,       SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = jit(model.generate    )
											SCREAMING_SNAKE_CASE_						 = jit_generate(SCREAMING_SNAKE_CASE_    ).sequences

											self.assertListEqual(generation_outputs.tolist()       ,       jit_generation_outputs.tolist()    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_						 = False
									SCREAMING_SNAKE_CASE_						 = max_length
									SCREAMING_SNAKE_CASE_						 = 2
									SCREAMING_SNAKE_CASE_						 = 2

									for model_class in self.all_generative_model_classes:
											SCREAMING_SNAKE_CASE_						 = model_class(SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = model.generate(SCREAMING_SNAKE_CASE_    ).sequences
											self.assertEqual(generation_outputs.shape[0]       ,       input_ids.shape[0] * config.num_return_sequences    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_						 = True
									SCREAMING_SNAKE_CASE_						 = max_length
									SCREAMING_SNAKE_CASE_						 = 0.8
									SCREAMING_SNAKE_CASE_						 = 10
									SCREAMING_SNAKE_CASE_						 = 0.3
									SCREAMING_SNAKE_CASE_						 = 1
									SCREAMING_SNAKE_CASE_						 = 8
									SCREAMING_SNAKE_CASE_						 = 9

									for model_class in self.all_generative_model_classes:
											SCREAMING_SNAKE_CASE_						 = model_class(SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = model.generate(SCREAMING_SNAKE_CASE_    ).sequences
											self.assertEqual(generation_outputs.shape[-1]       ,       SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = jit(model.generate    )
											SCREAMING_SNAKE_CASE_						 = jit_generate(SCREAMING_SNAKE_CASE_    ).sequences

											self.assertListEqual(generation_outputs.tolist()       ,       jit_generation_outputs.tolist()    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_						 = max_length
									SCREAMING_SNAKE_CASE_						 = 1
									SCREAMING_SNAKE_CASE_						 = 8
									SCREAMING_SNAKE_CASE_						 = 9

									for model_class in self.all_generative_model_classes:
											SCREAMING_SNAKE_CASE_						 = model_class(SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = model.generate(SCREAMING_SNAKE_CASE_    ).sequences
											self.assertEqual(generation_outputs.shape[-1]       ,       SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = jit(model.generate    )
											SCREAMING_SNAKE_CASE_						 = jit_generate(SCREAMING_SNAKE_CASE_    ).sequences

											self.assertListEqual(generation_outputs.tolist()       ,       jit_generation_outputs.tolist()    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = self._get_input_ids_and_config()
									SCREAMING_SNAKE_CASE_						 = max_length
									SCREAMING_SNAKE_CASE_						 = 2
									SCREAMING_SNAKE_CASE_						 = 1
									SCREAMING_SNAKE_CASE_						 = 8
									SCREAMING_SNAKE_CASE_						 = 9

									for model_class in self.all_generative_model_classes:
											SCREAMING_SNAKE_CASE_						 = model_class(SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = model.generate(SCREAMING_SNAKE_CASE_    ).sequences
											self.assertEqual(generation_outputs.shape[-1]       ,       SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = jit(model.generate    )
											SCREAMING_SNAKE_CASE_						 = jit_generate(SCREAMING_SNAKE_CASE_    ).sequences

											self.assertListEqual(generation_outputs.tolist()       ,       jit_generation_outputs.tolist()    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = self._get_input_ids_and_config()

									# pad attention mask on the left
									SCREAMING_SNAKE_CASE_						 = attention_mask.at[(0, 0)].set(0    )

									SCREAMING_SNAKE_CASE_						 = False
									SCREAMING_SNAKE_CASE_						 = max_length

									for model_class in self.all_generative_model_classes:
											SCREAMING_SNAKE_CASE_						 = model_class(SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = model.generate(SCREAMING_SNAKE_CASE_       ,       attention_mask=SCREAMING_SNAKE_CASE_    ).sequences
											self.assertEqual(generation_outputs.shape[-1]       ,       SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = jit(model.generate    )
											SCREAMING_SNAKE_CASE_						 = jit_generate(SCREAMING_SNAKE_CASE_       ,       attention_mask=SCREAMING_SNAKE_CASE_    ).sequences

											self.assertListEqual(generation_outputs.tolist()       ,       jit_generation_outputs.tolist()    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = self._get_input_ids_and_config()

									# pad attention mask on the left
									SCREAMING_SNAKE_CASE_						 = attention_mask.at[(0, 0)].set(0    )

									SCREAMING_SNAKE_CASE_						 = True
									SCREAMING_SNAKE_CASE_						 = max_length

									for model_class in self.all_generative_model_classes:
											SCREAMING_SNAKE_CASE_						 = model_class(SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = model.generate(SCREAMING_SNAKE_CASE_       ,       attention_mask=SCREAMING_SNAKE_CASE_    ).sequences
											self.assertEqual(generation_outputs.shape[-1]       ,       SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = jit(model.generate    )
											SCREAMING_SNAKE_CASE_						 = jit_generate(SCREAMING_SNAKE_CASE_       ,       attention_mask=SCREAMING_SNAKE_CASE_    ).sequences

											self.assertListEqual(generation_outputs.tolist()       ,       jit_generation_outputs.tolist()    )







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = self._get_input_ids_and_config()

									# pad attention mask on the left
									SCREAMING_SNAKE_CASE_						 = attention_mask.at[(0, 0)].set(0    )

									SCREAMING_SNAKE_CASE_						 = 2
									SCREAMING_SNAKE_CASE_						 = max_length

									for model_class in self.all_generative_model_classes:
											SCREAMING_SNAKE_CASE_						 = model_class(SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = model.generate(SCREAMING_SNAKE_CASE_       ,       attention_mask=SCREAMING_SNAKE_CASE_    ).sequences
											self.assertEqual(generation_outputs.shape[-1]       ,       SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = jit(model.generate    )
											SCREAMING_SNAKE_CASE_						 = jit_generate(SCREAMING_SNAKE_CASE_       ,       attention_mask=SCREAMING_SNAKE_CASE_    ).sequences

											self.assertListEqual(generation_outputs.tolist()       ,       jit_generation_outputs.tolist()    )







@require_flax
class  snake_case     (							unittest.TestCase   ):

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert'''    )
									SCREAMING_SNAKE_CASE_						 = FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''    )

									SCREAMING_SNAKE_CASE_						 = '''Hello world'''
									SCREAMING_SNAKE_CASE_						 = tokenizer(SCREAMING_SNAKE_CASE_       ,       return_tensors='''np'''    ).input_ids

									# typos are quickly detected (the correct argument is `do_sample`)
									with self.assertRaisesRegex(SCREAMING_SNAKE_CASE_       ,       '''do_samples'''    ):
											model.generate(SCREAMING_SNAKE_CASE_       ,       do_samples=SCREAMING_SNAKE_CASE_    )

									# arbitrary arguments that will not be used anywhere are also not accepted
									with self.assertRaisesRegex(SCREAMING_SNAKE_CASE_       ,       '''foo'''    ):
											SCREAMING_SNAKE_CASE_						 = {'''foo''': '''bar'''}
											model.generate(SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    )

"""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()