






"""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


_UpperCamelCase	     =    logging.getLogger(__name__)

def _a					(						_snake_case=2 ,			_snake_case=3 ,			_snake_case=16 ,			_snake_case = 10 ,			_snake_case = 2				):







      """simple docstring"""






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

      UpperCAmelCase							     =			get_dataset(lowercase__				)
      UpperCAmelCase							     =			get_dataset(lowercase__				)
      UpperCAmelCase							     =			DataLoader(lowercase__ ,			shuffle=lowercase__ ,			batch_size=lowercase__ ,			num_workers=4				)
      UpperCAmelCase							     =			DataLoader(lowercase__ ,			shuffle=lowercase__ ,			batch_size=lowercase__ ,			num_workers=4				)
      return (train_dataloader, valid_dataloader)

def _a					(						_snake_case ,			_snake_case ,			_snake_case ,			_snake_case ,			_snake_case ,			_snake_case=None				):







      """simple docstring"""






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







class    lowerCamelCase__   (			nn.Module					):






    def __init__(					self   ):
          super().__init__()
          UpperCAmelCase							     =			nn.Parameter(torch.randn(1   )   )
          UpperCAmelCase							     =			nn.Parameter(torch.randn(1   )   )






    def 							_UpperCamelCase				(					self      ,A   ):
          return x * self.a + self.b







class    lowerCamelCase__   (			unittest.TestCase					):






    def 							_UpperCamelCase				(					self   ):


          with tempfile.TemporaryDirectory() as tmpdir:
                set_seed(42   )
                UpperCAmelCase							     =			DummyModel()
                UpperCAmelCase							     =			torch.optim.Adam(params=model.parameters()      ,lr=1e-3   )
                UpperCAmelCase     ,   UpperCAmelCase							     =			dummy_dataloaders()
                UpperCAmelCase							     =			ProjectConfiguration(total_limit=1      ,project_dir=__UpperCamelCase      ,automatic_checkpoint_naming=__UpperCamelCase   )
                # Train baseline
                UpperCAmelCase							     =			Accelerator(project_config=__UpperCamelCase   )
                UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase							     =			accelerator.prepare(
                    __UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase   )
                # Save initial
                accelerator.save_state()

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






    def 							_UpperCamelCase				(					self   ):


          with tempfile.TemporaryDirectory() as tmpdir:
                set_seed(42   )
                UpperCAmelCase							     =			DummyModel()
                UpperCAmelCase							     =			torch.optim.Adam(params=model.parameters()      ,lr=1e-3   )
                UpperCAmelCase     ,   UpperCAmelCase							     =			dummy_dataloaders()
                # Train baseline
                UpperCAmelCase							     =			Accelerator()
                UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase							     =			accelerator.prepare(
                    __UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase   )
                # Save initial
                UpperCAmelCase							     =			os.path.join(__UpperCamelCase      ,"""initial"""   )
                accelerator.save_state(__UpperCamelCase   )
                ((UpperCAmelCase)     ,   (UpperCAmelCase))							     =			model.a.item(), model.b.item()
                UpperCAmelCase							     =			optimizer.state_dict()
                UpperCAmelCase							     =			train(3      ,__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase   )
                ((UpperCAmelCase)     ,   (UpperCAmelCase))							     =			model.a.item(), model.b.item()
                UpperCAmelCase							     =			optimizer.state_dict()

                # Train partially
                set_seed(42   )
                UpperCAmelCase							     =			DummyModel()
                UpperCAmelCase							     =			torch.optim.Adam(params=model.parameters()      ,lr=1e-3   )
                UpperCAmelCase     ,   UpperCAmelCase							     =			dummy_dataloaders()
                UpperCAmelCase							     =			Accelerator()
                UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase							     =			accelerator.prepare(
                    __UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase   )
                accelerator.load_state(__UpperCamelCase   )
                ((UpperCAmelCase)     ,   (UpperCAmelCase))							     =			model.a.item(), model.b.item()
                UpperCAmelCase							     =			optimizer.state_dict()
                self.assertEqual(__UpperCamelCase      ,__UpperCamelCase   )
                self.assertEqual(__UpperCamelCase      ,__UpperCamelCase   )
                self.assertEqual(__UpperCamelCase      ,__UpperCamelCase   )

                UpperCAmelCase							     =			train(2      ,__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase   )
                # Save everything
                UpperCAmelCase							     =			os.path.join(__UpperCamelCase      ,"""checkpoint"""   )
                accelerator.save_state(__UpperCamelCase   )

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






    def 							_UpperCamelCase				(					self   ):


          with tempfile.TemporaryDirectory() as tmpdir:
                set_seed(42   )
                UpperCAmelCase							     =			DummyModel()
                UpperCAmelCase							     =			torch.optim.Adam(params=model.parameters()      ,lr=1e-3   )
                UpperCAmelCase     ,   UpperCAmelCase							     =			dummy_dataloaders()
                UpperCAmelCase							     =			ProjectConfiguration(automatic_checkpoint_naming=__UpperCamelCase   )

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

                # Train partially
                set_seed(42   )
                UpperCAmelCase							     =			DummyModel()
                UpperCAmelCase							     =			torch.optim.Adam(params=model.parameters()      ,lr=1e-3   )
                UpperCAmelCase     ,   UpperCAmelCase							     =			dummy_dataloaders()
                UpperCAmelCase							     =			ProjectConfiguration(iteration=1      ,automatic_checkpoint_naming=__UpperCamelCase   )
                UpperCAmelCase							     =			Accelerator(project_dir=__UpperCamelCase      ,project_config=__UpperCamelCase   )
                UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase							     =			accelerator.prepare(
                    __UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase   )
                accelerator.load_state(os.path.join(__UpperCamelCase      ,"""checkpoints"""      ,"""checkpoint_0"""   )   )
                ((UpperCAmelCase)     ,   (UpperCAmelCase))							     =			model.a.item(), model.b.item()
                UpperCAmelCase							     =			optimizer.state_dict()
                self.assertEqual(__UpperCamelCase      ,__UpperCamelCase   )
                self.assertEqual(__UpperCamelCase      ,__UpperCamelCase   )
                self.assertEqual(__UpperCamelCase      ,__UpperCamelCase   )

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

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






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






    def 							_UpperCamelCase				(					self   ):


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

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






    def 							_UpperCamelCase				(					self   ):


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






    @require_cuda
    def 							_UpperCamelCase				(					self   ):
          UpperCAmelCase							     =			["""torchrun""", F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__   )]
          execute_subprocess_async(__UpperCamelCase      ,env=os.environ.copy()   )


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






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A									=  logging.get_logger(__name__)

A									=  {
    'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/main/config.json',
}


class SCREAMING_SNAKE_CASE	(		__snake_case				,    __snake_case				):






			"""simple docstring"""




			__A    	=	"""bit"""
			__A    	=	["""preactivation""", """bottleneck"""]
			__A    	=	["""SAME""", """VALID"""]


			def __init__(     self			,					__UpperCamelCase=3			,					__UpperCamelCase=64			,					__UpperCamelCase=[2_56, 5_12, 10_24, 20_48]			,					__UpperCamelCase=[3, 4, 6, 3]			,					__UpperCamelCase="preactivation"			,					__UpperCamelCase="relu"			,					__UpperCamelCase=None			,					__UpperCamelCase=32			,					__UpperCamelCase=0.0			,					__UpperCamelCase=False			,					__UpperCamelCase=32			,					__UpperCamelCase=1			,					__UpperCamelCase=None			,					__UpperCamelCase=None			,					**__UpperCamelCase			,					):

				"""simple docstring"""


				super().__init__(**__UpperCamelCase				)
				if layer_type not in self.layer_types:
					raise ValueError(f"""layer_type={layer_type} is not one of {','.join(self.layer_types				)}"""				)
				if global_padding is not None:
					if global_padding.upper() in self.supported_padding:
						snake_case_   		=			global_padding.upper()
					else:
						raise ValueError(f"""Padding strategy {global_padding} not supported"""				)
				snake_case_   		=			num_channels
				snake_case_   		=			embedding_size
				snake_case_   		=			hidden_sizes
				snake_case_   		=			depths
				snake_case_   		=			layer_type
				snake_case_   		=			hidden_act
				snake_case_   		=			global_padding
				snake_case_   		=			num_groups
				snake_case_   		=			drop_path_rate
				snake_case_   		=			embedding_dynamic_padding
				snake_case_   		=			output_stride
				snake_case_   		=			width_factor

				snake_case_   		=			['stem'] + [f"""stage{idx}""" for idx in range(1			,					len(__UpperCamelCase				) + 1				)]
				snake_case_		,							snake_case_   		=			get_aligned_output_features_output_indices(
				    out_features=__UpperCamelCase			,					out_indices=__UpperCamelCase			,					stage_names=self.stage_names				)
