


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 							__lowercase       (			__lowerCAmelCase							:    Union[str, Any] ,   __lowerCAmelCase							:    Any ,   __lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    str       ):
       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 							__lowercase       (			__lowerCAmelCase							:    Optional[Any] ,   __lowerCAmelCase							:    Any ,   __lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    Tuple=True       ):
       model.train()
       a__					=					model(__lowerCAmelCase       )
       a__					=					F.mse_loss(__lowerCAmelCase ,   target.to(output.device       )       )
       if not do_backward:
              loss /= accelerator.gradient_accumulation_steps
              loss.backward()
       else:
              accelerator.backward(__lowerCAmelCase       )






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






def 							__lowercase       (			__lowerCAmelCase							:    List[Any]       ):
       # Test when on a single CPU or GPU that the context manager does nothing
       a__				, a__				, a__					=					get_training_setup(__lowerCAmelCase       )
       # Use a single batch
       a__				, a__					=					next(iter(__lowerCAmelCase       )       ).values()
       for iteration in range(3       ):
              # Gather the distributed inputs and targs for the base model
              a__				, a__					=					accelerator.gather((ddp_input, ddp_target)       )
              a__				, a__					=					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       )
              a__					=					ddp_input[torch.randperm(len(__lowerCAmelCase       )       )]






def 							__lowercase       (			__lowerCAmelCase							:    Optional[int]       ):
       # Test on distributed setup that context manager behaves properly
       a__				, a__				, a__					=					get_training_setup(__lowerCAmelCase       )
       # Use a single batch
       a__				, a__					=					next(iter(__lowerCAmelCase       )       ).values()
       for iteration in range(3       ):
              # Gather the distributed inputs and targs for the base model
              a__				, a__					=					accelerator.gather((ddp_input, ddp_target)       )
              a__				, a__					=					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       )
              a__					=					ddp_input[torch.randperm(len(__lowerCAmelCase       )       )]






def 							__lowercase       (			__lowerCAmelCase							:    Any=False ,   __lowerCAmelCase							:    Optional[Any]=False       ):
       a__					=					Accelerator(
           split_batches=__lowerCAmelCase ,   dispatch_batches=__lowerCAmelCase ,   gradient_accumulation_steps=2       )
       # Test that context manager behaves properly
       a__				, a__				, a__					=					get_training_setup(__lowerCAmelCase       )
       for iteration, batch in enumerate(__lowerCAmelCase       ):
              a__				, a__					=					batch.values()
              # Gather the distributed inputs and targs for the base model
              a__				, a__					=					accelerator.gather((ddp_input, ddp_target)       )
              a__				, a__					=					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       )
              a__					=					ddp_input[torch.randperm(len(__lowerCAmelCase       )       )]
       GradientState._reset_state()






def 							__lowercase       (			__lowerCAmelCase							:    Union[str, Any]=False ,   __lowerCAmelCase							:    List[Any]=False       ):
       a__					=					Accelerator(
           split_batches=__lowerCAmelCase ,   dispatch_batches=__lowerCAmelCase ,   gradient_accumulation_steps=2       )
       # Test that context manager behaves properly
       a__				, a__				, a__				, a__				, a__				, a__				, a__					=					get_training_setup(__lowerCAmelCase ,   __lowerCAmelCase       )
       for iteration, batch in enumerate(__lowerCAmelCase       ):
              a__				, a__					=					batch.values()
              # Gather the distributed inputs and targs for the base model
              a__				, a__					=					accelerator.gather((ddp_input, ddp_target)       )
              a__				, a__					=					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'
              a__					=					(((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 							__lowercase       (			):
       a__					=					Accelerator()

       a__					=					RegressionDataset(length=8_0       )
       a__					=					DataLoader(__lowerCAmelCase ,   batch_size=1_6       )
       a__					=					RegressionDataset(length=9_6       )
       a__					=					DataLoader(__lowerCAmelCase ,   batch_size=1_6       )
       a__				, a__					=					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 							__lowercase       (			):
       a__					=					Accelerator()
       a__					=					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 							__lowercase       (			__lowerCAmelCase							:    Any       ):
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
      main()








from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case							:	Tuple					      =				logging.get_logger(__name__)

snake_case							:	List[Any]					      =				{
    '''snap-research/efficientformer-l1-300''': (
        '''https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'''
    ),
}







class 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   int   				=      '''efficientformer'''
					def __init__(				self       :List[str]      ,__snake_case       :List[int] = [3, 2, 6, 4]      ,__snake_case       :List[int] = [48, 96, 2_24, 4_48]      ,__snake_case       :List[bool] = [True, True, True, True]      ,__snake_case       :int = 4_48      ,__snake_case       :int = 32      ,__snake_case       :int = 4      ,__snake_case       :int = 7      ,__snake_case       :int = 5      ,__snake_case       :int = 8      ,__snake_case       :int = 4      ,__snake_case       :float = 0.0      ,__snake_case       :int = 16      ,__snake_case       :int = 3      ,__snake_case       :int = 3      ,__snake_case       :int = 3      ,__snake_case       :int = 2      ,__snake_case       :int = 1      ,__snake_case       :float = 0.0      ,__snake_case       :int = 1      ,__snake_case       :bool = True      ,__snake_case       :bool = True      ,__snake_case       :float = 1E-5      ,__snake_case       :str = "gelu"      ,__snake_case       :float = 0.02      ,__snake_case       :float = 1E-12      ,__snake_case       :int = 2_24      ,__snake_case       :float = 1E-05      ,**__snake_case       :Dict      ,) ->   None:
												super().__init__(**__snake_case				)

												a__					=					hidden_act
												a__					=					hidden_dropout_prob
												a__					=					hidden_sizes
												a__					=					num_hidden_layers
												a__					=					num_attention_heads
												a__					=					initializer_range
												a__					=					layer_norm_eps
												a__					=					patch_size
												a__					=					num_channels
												a__					=					depths
												a__					=					mlp_expansion_ratio
												a__					=					downsamples
												a__					=					dim
												a__					=					key_dim
												a__					=					attention_ratio
												a__					=					resolution
												a__					=					pool_size
												a__					=					downsample_patch_size
												a__					=					downsample_stride
												a__					=					downsample_pad
												a__					=					drop_path_rate
												a__					=					num_metaad_blocks
												a__					=					distillation
												a__					=					use_layer_scale
												a__					=					layer_scale_init_value
												a__					=					image_size
												a__					=					batch_norm_eps




