


"""simple docstring"""

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__	: Dict				,   UpperCamelCase__	: Any				,   UpperCamelCase__	: str				,   UpperCamelCase__	: Optional[int] ):






 """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__	: Optional[int]				,   UpperCamelCase__	: str				,   UpperCamelCase__	: Dict				,   UpperCamelCase__	: Union[str, Any]				,   UpperCamelCase__	: Dict=True ):






 """simple docstring"""







 model.train()
 __lowercase									=	model(_lowerCamelCase )
 __lowercase									=	F.mse_loss(_lowerCamelCase				,   target.to(output.device ) )
 if not do_backward:
  loss /= accelerator.gradient_accumulation_steps
  loss.backward()
 else:
  accelerator.backward(_lowerCamelCase )


def lowerCAmelCase_      (				UpperCamelCase__	: List[str]				,   UpperCamelCase__	: Any=False ):






 """simple docstring"""







 set_seed(42 )
 __lowercase									=	RegressionModel()
 __lowercase									=	deepcopy(_lowerCamelCase )
 __lowercase									=	RegressionDataset(length=80 )
 __lowercase									=	DataLoader(_lowerCamelCase				,   batch_size=16 )
 model.to(accelerator.device )
 if sched:
  __lowercase									=	AdamW(params=model.parameters()				,   lr=1E-3 )
  __lowercase									=	AdamW(params=ddp_model.parameters()				,   lr=1E-3 )
  __lowercase									=	LambdaLR(_lowerCamelCase				,   lr_lambda=lambda UpperCamelCase__ : epoch**0.65 )
  __lowercase									=	LambdaLR(_lowerCamelCase				,   lr_lambda=lambda UpperCamelCase__ : epoch**0.65 )
 # Make a copy of `model`
 if sched:
  __lowercase									=	accelerator.prepare(_lowerCamelCase				,   _lowerCamelCase				,   _lowerCamelCase				,   _lowerCamelCase )
 else:
  __lowercase									=	accelerator.prepare(_lowerCamelCase				,   _lowerCamelCase )
 if sched:
  return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
 return model, ddp_model, dataloader


def lowerCAmelCase_      (				UpperCamelCase__	: List[Any] ):






 """simple docstring"""







 __lowercase									=	get_training_setup(_lowerCamelCase )
 # Use a single batch
 __lowercase									=	next(iter(_lowerCamelCase ) ).values()
 for iteration in range(3 ):
  # Gather the distributed inputs and targs for the base model
  __lowercase									=	accelerator.gather((ddp_input, ddp_target) )
  __lowercase									=	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(1337 + iteration )
  __lowercase									=	ddp_input[torch.randperm(len(_lowerCamelCase ) )]


def lowerCAmelCase_      (				UpperCamelCase__	: Dict ):






 """simple docstring"""







 __lowercase									=	get_training_setup(_lowerCamelCase )
 # Use a single batch
 __lowercase									=	next(iter(_lowerCamelCase ) ).values()
 for iteration in range(3 ):
  # Gather the distributed inputs and targs for the base model
  __lowercase									=	accelerator.gather((ddp_input, ddp_target) )
  __lowercase									=	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(1337 + iteration )
  __lowercase									=	ddp_input[torch.randperm(len(_lowerCamelCase ) )]


def lowerCAmelCase_      (				UpperCamelCase__	: Optional[int]=False				,   UpperCamelCase__	: List[str]=False ):






 """simple docstring"""







 __lowercase									=	Accelerator(
     split_batches=_lowerCamelCase				,   dispatch_batches=_lowerCamelCase				,   gradient_accumulation_steps=2 )
 # Test that context manager behaves properly
 __lowercase									=	get_training_setup(_lowerCamelCase )
 for iteration, batch in enumerate(_lowerCamelCase ):
  __lowercase									=	batch.values()
  # Gather the distributed inputs and targs for the base model
  __lowercase									=	accelerator.gather((ddp_input, ddp_target) )
  __lowercase									=	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(1337 + iteration )
  __lowercase									=	ddp_input[torch.randperm(len(_lowerCamelCase ) )]
 GradientState._reset_state()


def lowerCAmelCase_      (				UpperCamelCase__	: Optional[Any]=False				,   UpperCamelCase__	: int=False ):






 """simple docstring"""







 __lowercase									=	Accelerator(
     split_batches=_lowerCamelCase				,   dispatch_batches=_lowerCamelCase				,   gradient_accumulation_steps=2 )
 # Test that context manager behaves properly
 __lowercase									=	get_training_setup(_lowerCamelCase				,   _lowerCamelCase )
 for iteration, batch in enumerate(_lowerCamelCase ):
  __lowercase									=	batch.values()
  # Gather the distributed inputs and targs for the base model
  __lowercase									=	accelerator.gather((ddp_input, ddp_target) )
  __lowercase									=	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'''
  __lowercase									=	(((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(1337 + iteration )
 GradientState._reset_state()


def lowerCAmelCase_      (				):






 """simple docstring"""







 __lowercase									=	Accelerator()

 __lowercase									=	RegressionDataset(length=80 )
 __lowercase									=	DataLoader(_lowerCamelCase				,   batch_size=16 )
 __lowercase									=	RegressionDataset(length=96 )
 __lowercase									=	DataLoader(_lowerCamelCase				,   batch_size=16 )
 __lowercase									=	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 lowerCAmelCase_      (				):






 """simple docstring"""







 __lowercase									=	Accelerator()
 __lowercase									=	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 lowerCAmelCase_      (				UpperCamelCase__	: Union[str, Any] ):






 """simple docstring"""







 main()


if __name__ == "__main__":
    main()




"""simple docstring"""

from typing import List

from .keymap import KEYMAP, get_character


def lowerCAmelCase_      (				UpperCamelCase__	: str ):






 """simple docstring"""







 def decorator(UpperCamelCase__	: Tuple ):
  __lowercase									=	getattr(UpperCamelCase__				,   """handle_key"""				,   [] )
  handle += [key]
  setattr(UpperCamelCase__				,   """handle_key"""				,   UpperCamelCase__ )
  return func

 return decorator


def lowerCAmelCase_      (				*UpperCamelCase__	: List[str] ):






 """simple docstring"""







 def decorator(UpperCamelCase__	: Tuple ):
  __lowercase									=	getattr(UpperCamelCase__				,   """handle_key"""				,   [] )
  handle += keys
  setattr(UpperCamelCase__				,   """handle_key"""				,   UpperCamelCase__ )
  return func

 return decorator





class    lowerCamelCase__						(		_a    ):
      def __new__(		cls	:	str      ,       A_	:	Optional[Any]      ,       A_	:	Union[str, Any]      ,       A_	:	int							):






       '''simple docstring'''


       __lowercase									=	super().__new__(cls      ,       A_      ,       A_      ,       A_							)
       if not hasattr(A_      ,       """key_handler"""							):
        setattr(A_      ,       """key_handler"""      ,       {}							)
       setattr(A_      ,       """handle_input"""      ,       KeyHandler.handle_input							)

       for value in attrs.values():
        __lowercase									=	getattr(A_      ,       """handle_key"""      ,       []							)
        for key in handled_keys:
         __lowercase									=	value
       return new_cls






      @staticmethod
      def SCREAMING_SNAKE_CASE_	(		cls	:	Dict							):






       '''simple docstring'''


       __lowercase									=	get_character()
       if char != KEYMAP["undefined"]:
        __lowercase									=	ord(A_							)
       __lowercase									=	cls.key_handler.get(A_							)
       if handler:
        __lowercase									=	char
        return handler(cls							)
       else:
        return None






def lowerCAmelCase_      (				cls	: int ):






 """simple docstring"""







 return KeyHandler(cls.__name__				,   cls.__bases__				,   cls.__dict__.copy() )
