




from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
       for param, grad_param in zip(model_a.parameters()  ,    model_b.parameters()     ):
              if not param.requires_grad:
                     continue
              if not did_step:
                     # Grads should not be in sync
                     assert (
                         torch.allclose(param.grad  ,    grad_param.grad     ) is False
                     ), F"Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"
              else:
                     # Grads should be in sync
                     assert (
                         torch.allclose(param.grad  ,    grad_param.grad     ) is True
                     ), F"Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase=True     ):
       model.train()
       SCREAMING_SNAKE_CASE_   	=    model(_lowerCAmelCase     )
       SCREAMING_SNAKE_CASE_   	=    F.mse_loss(_lowerCAmelCase  ,    target.to(output.device     )     )
       if not do_backward:
              loss /= accelerator.gradient_accumulation_steps
              loss.backward()
       else:
              accelerator.backward(_lowerCAmelCase     )

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase=False     ):
       set_seed(4_2     )
       SCREAMING_SNAKE_CASE_   	=    RegressionModel()
       SCREAMING_SNAKE_CASE_   	=    deepcopy(_lowerCAmelCase     )
       SCREAMING_SNAKE_CASE_   	=    RegressionDataset(length=8_0     )
       SCREAMING_SNAKE_CASE_   	=    DataLoader(_lowerCAmelCase  ,    batch_size=1_6     )
       model.to(accelerator.device     )
       if sched:
              SCREAMING_SNAKE_CASE_   	=    AdamW(params=model.parameters()  ,    lr=1e-3     )
              SCREAMING_SNAKE_CASE_   	=    AdamW(params=ddp_model.parameters()  ,    lr=1e-3     )
              SCREAMING_SNAKE_CASE_   	=    LambdaLR(_lowerCAmelCase  ,    lr_lambda=lambda _lowerCAmelCase     : epoch**0.65     )
              SCREAMING_SNAKE_CASE_   	=    LambdaLR(_lowerCAmelCase  ,    lr_lambda=lambda _lowerCAmelCase     : epoch**0.65     )
       # Make a copy of `model`
       if sched:
              SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    accelerator.prepare(_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     )
       else:
              SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    accelerator.prepare(_lowerCAmelCase  ,    _lowerCAmelCase     )
       if sched:
              return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
       return model, ddp_model, dataloader

def 			a   (_lowerCAmelCase     ):
       # Test when on a single CPU or GPU that the context manager does nothing
       SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    get_training_setup(_lowerCAmelCase     )
       # Use a single batch
       SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    next(iter(_lowerCAmelCase     )     ).values()
       for iteration in range(3     ):
              # Gather the distributed inputs and targs for the base model
              SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    accelerator.gather((ddp_input, ddp_target)     )
              SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    input.to(accelerator.device     ), target.to(accelerator.device     )
              # Perform our initial ground truth step in non "DDP"
              step_model(_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     )
              # Do "gradient accumulation" (noop)
              if iteration % 2 == 0:
                     # Accumulate grads locally
                     with accelerator.no_sync(_lowerCAmelCase     ):
                            step_model(_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     )
              else:
                     # Sync grads
                     step_model(_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     )

              # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
              check_model_parameters(_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     )
              for param, ddp_param in zip(model.parameters()  ,    ddp_model.parameters()     ):
                     if not param.requires_grad:
                            continue
                     assert torch.allclose(
                         param.grad  ,    ddp_param.grad     ), F"Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"

              # Shuffle ddp_input on each iteration
              torch.manual_seed(1_3_3_7 + iteration     )
              SCREAMING_SNAKE_CASE_   	=    ddp_input[torch.randperm(len(_lowerCAmelCase     )     )]

def 			a   (_lowerCAmelCase     ):
       # Test on distributed setup that context manager behaves properly
       SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    get_training_setup(_lowerCAmelCase     )
       # Use a single batch
       SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    next(iter(_lowerCAmelCase     )     ).values()
       for iteration in range(3     ):
              # Gather the distributed inputs and targs for the base model
              SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    accelerator.gather((ddp_input, ddp_target)     )
              SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    input.to(accelerator.device     ), target.to(accelerator.device     )
              # Perform our initial ground truth step in non "DDP"
              step_model(_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     )
              # Do "gradient accumulation" (noop)
              if iteration % 2 == 0:
                     # Accumulate grads locally
                     with accelerator.no_sync(_lowerCAmelCase     ):
                            step_model(_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     )
              else:
                     # Sync grads
                     step_model(_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     )

              # DDP model and model should only be in sync when not (iteration % 2 == 0)
              for param, ddp_param in zip(model.parameters()  ,    ddp_model.parameters()     ):
                     if not param.requires_grad:
                            continue
                     if iteration % 2 == 0:
                            # Grads should not be in sync
                            assert (
                                torch.allclose(param.grad  ,    ddp_param.grad     ) is False
                            ), F"Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"
                     else:
                            # Grads should be in sync
                            assert (
                                torch.allclose(param.grad  ,    ddp_param.grad     ) is True
                            ), F"Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"

        # Shuffle ddp_input on each iteration
              torch.manual_seed(1_3_3_7 + iteration     )
              SCREAMING_SNAKE_CASE_   	=    ddp_input[torch.randperm(len(_lowerCAmelCase     )     )]

def 			a   (_lowerCAmelCase=False  ,    _lowerCAmelCase=False     ):
       SCREAMING_SNAKE_CASE_   	=    Accelerator(
           split_batches=_lowerCAmelCase  ,    dispatch_batches=_lowerCAmelCase  ,    gradient_accumulation_steps=2     )
       # Test that context manager behaves properly
       SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    get_training_setup(_lowerCAmelCase     )
       for iteration, batch in enumerate(_lowerCAmelCase     ):
              SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    batch.values()
              # Gather the distributed inputs and targs for the base model
              SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    accelerator.gather((ddp_input, ddp_target)     )
              SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    input.to(accelerator.device     ), target.to(accelerator.device     )
              # Perform our initial ground truth step in non "DDP"
              step_model(_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     )
              # Do "gradient accumulation" (noop)
              with accelerator.accumulate(_lowerCAmelCase     ):
                     step_model(_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     )

              # DDP model and model should only be in sync when not (iteration % 2 == 0)
              for param, ddp_param in zip(model.parameters()  ,    ddp_model.parameters()     ):
                     if not param.requires_grad:
                            continue
                     if ((iteration + 1) % 2 == 0) or (iteration == len(_lowerCAmelCase     ) - 1):
                            # Grads should be in sync
                            assert (
                                torch.allclose(param.grad  ,    ddp_param.grad     ) is True
                            ), F"Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"
                     else:
                            # Grads should not be in sync
                            assert (
                                torch.allclose(param.grad  ,    ddp_param.grad     ) is False
                            ), F"Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"

        # Shuffle ddp_input on each iteration
              torch.manual_seed(1_3_3_7 + iteration     )
              SCREAMING_SNAKE_CASE_   	=    ddp_input[torch.randperm(len(_lowerCAmelCase     )     )]
       GradientState._reset_state()

def 			a   (_lowerCAmelCase=False  ,    _lowerCAmelCase=False     ):
       SCREAMING_SNAKE_CASE_   	=    Accelerator(
           split_batches=_lowerCAmelCase  ,    dispatch_batches=_lowerCAmelCase  ,    gradient_accumulation_steps=2     )
       # Test that context manager behaves properly
       SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    get_training_setup(_lowerCAmelCase  ,    _lowerCAmelCase     )
       for iteration, batch in enumerate(_lowerCAmelCase     ):
              SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    batch.values()
              # Gather the distributed inputs and targs for the base model
              SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    accelerator.gather((ddp_input, ddp_target)     )
              SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    input.to(accelerator.device     ), target.to(accelerator.device     )
              # Perform our initial ground truth step in non "DDP"
              model.train()
              ddp_model.train()
              step_model(_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     )
              opt.step()

              if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(_lowerCAmelCase     )):
                     if split_batches:
                            sched.step()
                     else:
                            for _ in range(accelerator.num_processes     ):
                                   sched.step()
              opt.zero_grad()
              # Perform gradient accumulation under wrapper
              with accelerator.accumulate(_lowerCAmelCase     ):
                     step_model(_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     )
                     ddp_opt.step()
                     ddp_sched.step()
                     ddp_opt.zero_grad()

              # Learning rates should be the same
              assert (
                  opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
              ), F"Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]['lr']}\nDDP opt: {ddp_opt.param_groups[0]['lr']}\n"
              SCREAMING_SNAKE_CASE_   	=    (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(_lowerCAmelCase     ))
              if accelerator.num_processes > 1:
                     check_model_parameters(_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     )
              # Shuffle ddp_input on each iteration
              torch.manual_seed(1_3_3_7 + iteration     )
       GradientState._reset_state()

def 			a   ():
       SCREAMING_SNAKE_CASE_   	=    Accelerator()

       SCREAMING_SNAKE_CASE_   	=    RegressionDataset(length=8_0     )
       SCREAMING_SNAKE_CASE_   	=    DataLoader(_lowerCAmelCase  ,    batch_size=1_6     )
       SCREAMING_SNAKE_CASE_   	=    RegressionDataset(length=9_6     )
       SCREAMING_SNAKE_CASE_   	=    DataLoader(_lowerCAmelCase  ,    batch_size=1_6     )
       SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    accelerator.prepare(_lowerCAmelCase  ,    _lowerCAmelCase     )
       assert accelerator.gradient_state.active_dataloader is None
       for iteration, _ in enumerate(_lowerCAmelCase     ):
              assert id(accelerator.gradient_state.active_dataloader     ) == id(_lowerCAmelCase     )
              if iteration < len(_lowerCAmelCase     ) - 1:
                     assert not accelerator.gradient_state.end_of_dataloader
                     if iteration == 1:
                            for batch_num, _ in enumerate(_lowerCAmelCase     ):
                                   assert id(accelerator.gradient_state.active_dataloader     ) == id(_lowerCAmelCase     )
                                   if batch_num < len(_lowerCAmelCase     ) - 1:
                                          assert not accelerator.gradient_state.end_of_dataloader
                                   else:
                                          assert accelerator.gradient_state.end_of_dataloader
              else:
                     assert accelerator.gradient_state.end_of_dataloader
       assert accelerator.gradient_state.active_dataloader is None

def 			a   ():
       SCREAMING_SNAKE_CASE_   	=    Accelerator()
       SCREAMING_SNAKE_CASE_   	=    accelerator.state
       if state.local_process_index == 0:
              print('''**Test `accumulate` gradient accumulation with dataloader break**'''     )
       test_dataloader_break()
       if state.distributed_type == DistributedType.NO:
              if state.local_process_index == 0:
                     print('''**Test NOOP `no_sync` context manager**'''     )
              test_noop_sync(_lowerCAmelCase     )
       if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
              if state.local_process_index == 0:
                     print('''**Test Distributed `no_sync` context manager**'''     )
              test_distributed_sync(_lowerCAmelCase     )
       if state.distributed_type == DistributedType.MULTI_GPU:
              for split_batch in [True, False]:
                     for dispatch_batches in [True, False]:
                            if state.local_process_index == 0:
                                   print(
                                       '''**Test `accumulate` gradient accumulation, '''  ,    F"`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"  ,    )
                            test_gradient_accumulation(_lowerCAmelCase  ,    _lowerCAmelCase     )

    # Currently will break on torch 2.0 +, need to investigate why
       if is_torch_version('''<'''  ,    '''2.0'''     ) or state.distributed_type == DistributedType.NO:
              if state.local_process_index == 0:
                     print(
                         '''**Test `accumulate` gradient accumulation with optimizer and scheduler, '''  ,    '''`split_batches=False`, `dispatch_batches=False`**'''  ,    )
              test_gradient_accumulation_with_opt_and_scheduler()
              if state.distributed_type == DistributedType.MULTI_GPU:
                     for split_batch in [True, False]:
                            for dispatch_batches in [True, False]:
                                   if not split_batch and not dispatch_batches:
                                          continue
                                   if state.local_process_index == 0:
                                          print(
                                              '''**Test `accumulate` gradient accumulation with optimizer and scheduler, '''  ,    F"`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"  ,    )
                                   test_gradient_accumulation_with_opt_and_scheduler(_lowerCAmelCase  ,    _lowerCAmelCase     )

def 			a   (_lowerCAmelCase     ):
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
   main()








import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
			import jax






@require_flax
class  __magic_name__						(  __UpperCAmelCase    ,  unittest.TestCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							str	      =					FlaxAutoencoderKL


				@property
				def     _A							(							self:   Any		):
											SCREAMING_SNAKE_CASE_   	=    4
											SCREAMING_SNAKE_CASE_   	=    3
											SCREAMING_SNAKE_CASE_   	=    (32, 32)

											SCREAMING_SNAKE_CASE_   	=    jax.random.PRNGKey(0		)
											SCREAMING_SNAKE_CASE_   	=    jax.random.uniform(_lowerCamelCase		,					((batch_size, num_channels) + sizes)		)

											return {"sample": image, "prng_key": prng_key}





				def     _A							(							self:   List[str]		):
											SCREAMING_SNAKE_CASE_   	=    {
											    '''block_out_channels''': [32, 64],
											    '''in_channels''': 3,
											    '''out_channels''': 3,
											    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
											    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
											    '''latent_channels''': 4,
											}
											SCREAMING_SNAKE_CASE_   	=    self.dummy_input
											return init_dict, inputs_dict


