


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 				__lowerCamelCase						( UpperCAmelCase_						:		Any ,  UpperCAmelCase_						:		List[Any] ,  UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		Union[str, Any]						):




      """simple docstring"""





      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 				__lowerCamelCase						( UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		Optional[Any] ,  UpperCAmelCase_						:		Optional[Any]=True						):




      """simple docstring"""





      model.train()
      a						:str 							=       model(UpperCAmelCase_						)
      a						:List[str] 							=       F.mse_loss(UpperCAmelCase_ ,  target.to(output.device						)						)
      if not do_backward:
            loss /= accelerator.gradient_accumulation_steps
            loss.backward()
      else:
            accelerator.backward(UpperCAmelCase_						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		int=False						):




      """simple docstring"""





      set_seed(42						)
      a						:List[Any] 							=       RegressionModel()
      a						:Any 							=       deepcopy(UpperCAmelCase_						)
      a						:Tuple 							=       RegressionDataset(length=80						)
      a						:Tuple 							=       DataLoader(UpperCAmelCase_ ,  batch_size=16						)
      model.to(accelerator.device						)
      if sched:
            a						:str 							=       AdamW(params=model.parameters() ,  lr=1E-3						)
            a						:str 							=       AdamW(params=ddp_model.parameters() ,  lr=1E-3						)
            a						:List[str] 							=       LambdaLR(UpperCAmelCase_ ,  lr_lambda=lambda UpperCAmelCase_						: epoch**0.65						)
            a						:List[str] 							=       LambdaLR(UpperCAmelCase_ ,  lr_lambda=lambda UpperCAmelCase_						: epoch**0.65						)
      # Make a copy of `model`
      if sched:
            a							,				a							,				a							,				a						:List[Any] 							=       accelerator.prepare(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
      else:
            a							,				a						:str 							=       accelerator.prepare(UpperCAmelCase_ ,  UpperCAmelCase_						)
      if sched:
            return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
      return model, ddp_model, dataloader
def 				__lowerCamelCase						( UpperCAmelCase_						:		Union[str, Any]						):




      """simple docstring"""





      a							,				a							,				a						:str 							=       get_training_setup(UpperCAmelCase_						)
      # Use a single batch
      a							,				a						:Dict 							=       next(iter(UpperCAmelCase_						)						).values()
      for iteration in range(3						):
            # Gather the distributed inputs and targs for the base model
            a							,				a						:int 							=       accelerator.gather((ddp_input, ddp_target)						)
            a							,				a						:Union[str, Any] 							=       input.to(accelerator.device						), target.to(accelerator.device						)
            # Perform our initial ground truth step in non "DDP"
            step_model(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
            # Do "gradient accumulation" (noop)
            if iteration % 2 == 0:
                  # Accumulate grads locally
                  with accelerator.no_sync(UpperCAmelCase_						):
                        step_model(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
            else:
                  # Sync grads
                  step_model(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)

            # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
            check_model_parameters(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
            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(1337 + iteration						)
            a						:Union[str, Any] 							=       ddp_input[torch.randperm(len(UpperCAmelCase_						)						)]
def 				__lowerCamelCase						( UpperCAmelCase_						:		Union[str, Any]						):




      """simple docstring"""





      a							,				a							,				a						:List[str] 							=       get_training_setup(UpperCAmelCase_						)
      # Use a single batch
      a							,				a						:List[str] 							=       next(iter(UpperCAmelCase_						)						).values()
      for iteration in range(3						):
            # Gather the distributed inputs and targs for the base model
            a							,				a						:List[Any] 							=       accelerator.gather((ddp_input, ddp_target)						)
            a							,				a						:Union[str, Any] 							=       input.to(accelerator.device						), target.to(accelerator.device						)
            # Perform our initial ground truth step in non "DDP"
            step_model(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
            # Do "gradient accumulation" (noop)
            if iteration % 2 == 0:
                  # Accumulate grads locally
                  with accelerator.no_sync(UpperCAmelCase_						):
                        step_model(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
            else:
                  # Sync grads
                  step_model(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)

            # 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(1337 + iteration						)
            a						:Any 							=       ddp_input[torch.randperm(len(UpperCAmelCase_						)						)]
def 				__lowerCamelCase						( UpperCAmelCase_						:		Union[str, Any]=False ,  UpperCAmelCase_						:		int=False						):




      """simple docstring"""





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

            # 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(UpperCAmelCase_						) - 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(1337 + iteration						)
            a						:List[str] 							=       ddp_input[torch.randperm(len(UpperCAmelCase_						)						)]
      GradientState._reset_state()
def 				__lowerCamelCase						( UpperCAmelCase_						:		Any=False ,  UpperCAmelCase_						:		Optional[int]=False						):




      """simple docstring"""





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

            if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(UpperCAmelCase_						)):
                  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(UpperCAmelCase_						):
                  step_model(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
                  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						:Tuple 							=       (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(UpperCAmelCase_						))
            if accelerator.num_processes > 1:
                  check_model_parameters(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
            # Shuffle ddp_input on each iteration
            torch.manual_seed(1337 + iteration						)
      GradientState._reset_state()
def 				__lowerCamelCase						( ):




      """simple docstring"""





      a						:Optional[Any] 							=       Accelerator()

      a						:int 							=       RegressionDataset(length=80						)
      a						:List[str] 							=       DataLoader(UpperCAmelCase_ ,  batch_size=16						)
      a						:List[Any] 							=       RegressionDataset(length=96						)
      a						:Any 							=       DataLoader(UpperCAmelCase_ ,  batch_size=16						)
      a							,				a						:Optional[int] 							=       accelerator.prepare(UpperCAmelCase_ ,  UpperCAmelCase_						)
      assert accelerator.gradient_state.active_dataloader is None
      for iteration, _ in enumerate(UpperCAmelCase_						):
            assert id(accelerator.gradient_state.active_dataloader						) == id(UpperCAmelCase_						)
            if iteration < len(UpperCAmelCase_						) - 1:
                  assert not accelerator.gradient_state.end_of_dataloader
                  if iteration == 1:
                        for batch_num, _ in enumerate(UpperCAmelCase_						):
                              assert id(accelerator.gradient_state.active_dataloader						) == id(UpperCAmelCase_						)
                              if batch_num < len(UpperCAmelCase_						) - 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 				__lowerCamelCase						( ):




      """simple docstring"""





      a						:Optional[int] 							=       Accelerator()
      a						:Optional[int] 							=       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(UpperCAmelCase_						)
      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(UpperCAmelCase_						)
      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(UpperCAmelCase_ ,  UpperCAmelCase_						)

    # 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(UpperCAmelCase_ ,  UpperCAmelCase_						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		Tuple						):




      """simple docstring"""





      main()


if __name__ == "__main__":
     main()









'''simple docstring'''



import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[Any]   ):
					__a			:       Any					    =  test_file.split(os.path.sep   )
					if components[0:2] != ["tests", "models"]:
										raise ValueError(
										    '`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '
										    F"""{test_file} instead."""   )
					__a			:       Tuple					    =  components[-1]
					if not test_fn.endswith('py'   ):
										raise ValueError(F"""`test_file` should be a python file. Got {test_fn} instead."""   )
					if not test_fn.startswith('test_modeling_'   ):
										raise ValueError(
										    F"""`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead."""   )

					__a			:       List[str]					    =  components[:-1] + [test_fn.replace('.py'						,      ''   )]
					__a			:       Optional[Any]					    =  '.'.join(_SCREAMING_SNAKE_CASE   )

					return test_module_path






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple   ):
					__a			:       List[str]					    =  get_module_path(_SCREAMING_SNAKE_CASE   )
					__a			:       Dict					    =  importlib.import_module(_SCREAMING_SNAKE_CASE   )

					return test_module






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple   ):
					__a			:       List[str]					    =  []
					__a			:       List[str]					    =  get_test_module(_SCREAMING_SNAKE_CASE   )
					for attr in dir(_SCREAMING_SNAKE_CASE   ):
										if attr.endswith('ModelTester'   ):
															tester_classes.append(getattr(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )   )

    # sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple   ):
					__a			:       Any					    =  []
					__a			:       str					    =  get_test_module(_SCREAMING_SNAKE_CASE   )
					for attr in dir(_SCREAMING_SNAKE_CASE   ):
										__a			:       int					    =  getattr(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
										# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
										# `all_model_classes` is not empty (which also excludes other special classes).
										__a			:       Optional[Any]					    =  getattr(_SCREAMING_SNAKE_CASE						,      'all_model_classes'						,      []   )
										if len(_SCREAMING_SNAKE_CASE   ) > 0:
															test_classes.append(_SCREAMING_SNAKE_CASE   )

    # sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int   ):
					__a			:       str					    =  get_test_classes(_SCREAMING_SNAKE_CASE   )
					__a			:       Any					    =  set()
					for test_class in test_classes:
										model_classes.update(test_class.all_model_classes   )

					# sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
					__a			:       Tuple					    =  test_class()
					if hasattr(_SCREAMING_SNAKE_CASE						,      'setUp'   ):
										test.setUp()

					__a			:       List[Any]					    =  None
					if hasattr(_SCREAMING_SNAKE_CASE						,      'model_tester'   ):
										# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
										if test.model_tester is not None:
															__a			:       List[str]					    =  test.model_tester.__class__

					return model_tester






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
					__a			:       str					    =  get_test_classes(_SCREAMING_SNAKE_CASE   )

					__a			:       int					    =  []
					for test_class in test_classes:
										if model_class in test_class.all_model_classes:
															target_test_classes.append(_SCREAMING_SNAKE_CASE   )

    # sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       List[Any]					    =  get_test_classes_for_model(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

					__a			:       Any					    =  []
					for test_class in test_classes:
										__a			:       Any					    =  get_model_tester_from_test_class(_SCREAMING_SNAKE_CASE   )
										if tester_class is not None:
															tester_classes.append(_SCREAMING_SNAKE_CASE   )

    # sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[int]   ):
					__a			:       str					    =  get_test_classes(_SCREAMING_SNAKE_CASE   )
					__a			:       int					    =  {test_class: get_model_tester_from_test_class(_SCREAMING_SNAKE_CASE   ) for test_class in test_classes}
					return test_tester_mapping






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       Optional[Any]					    =  get_model_classes(_SCREAMING_SNAKE_CASE   )
					__a			:       Optional[int]					    =  {
					    model_class: get_test_classes_for_model(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ) for model_class in model_classes
					}
					return model_test_mapping






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       Optional[Any]					    =  get_model_classes(_SCREAMING_SNAKE_CASE   )
					__a			:       str					    =  {
					    model_class: get_tester_classes_for_model(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ) for model_class in model_classes
					}
					return model_to_tester_mapping






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str   ):
					if isinstance(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ):
										return o
					elif isinstance(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ):
										return o.__name__
					elif isinstance(_SCREAMING_SNAKE_CASE						,      (list, tuple)   ):
										return [to_json(_SCREAMING_SNAKE_CASE   ) for x in o]
					elif isinstance(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ):
										return {to_json(_SCREAMING_SNAKE_CASE   ): to_json(_SCREAMING_SNAKE_CASE   ) for k, v in o.items()}
					else:
										return o



