





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






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






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:List[Any]   )      ->   Tuple:
 # Test when on a single CPU or GPU that the context manager does nothing
 __lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					:	int      =       get_training_setup(__lowerCAmelCase   )
 # Use a single batch
 __lowerCAmelCase ,					__lowerCAmelCase					:	List[Any]      =       next(iter(__lowerCAmelCase   )   ).values()
 for iteration in range(3   ):
  # Gather the distributed inputs and targs for the base model
  __lowerCAmelCase ,					__lowerCAmelCase					:	str      =       accelerator.gather((ddp_input, ddp_target)   )
  __lowerCAmelCase ,					__lowerCAmelCase					:	Tuple      =       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_337 + iteration   )
  __lowerCAmelCase					:	Union[str, Any]      =       ddp_input[torch.randperm(len(__lowerCAmelCase   )   )]






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Optional[int]   )      ->   Any:
 # Test on distributed setup that context manager behaves properly
 __lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					:	List[str]      =       get_training_setup(__lowerCAmelCase   )
 # Use a single batch
 __lowerCAmelCase ,					__lowerCAmelCase					:	Union[str, Any]      =       next(iter(__lowerCAmelCase   )   ).values()
 for iteration in range(3   ):
  # Gather the distributed inputs and targs for the base model
  __lowerCAmelCase ,					__lowerCAmelCase					:	str      =       accelerator.gather((ddp_input, ddp_target)   )
  __lowerCAmelCase ,					__lowerCAmelCase					:	int      =       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_337 + iteration   )
  __lowerCAmelCase					:	Any      =       ddp_input[torch.randperm(len(__lowerCAmelCase   )   )]






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Any=False   ,   SCREAMING_SNAKE_CASE		:Optional[Any]=False   )      ->   List[Any]:
 __lowerCAmelCase					:	str      =       Accelerator(
     split_batches=__lowerCAmelCase   ,   dispatch_batches=__lowerCAmelCase   ,   gradient_accumulation_steps=2   )
 # Test that context manager behaves properly
 __lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					:	Union[str, Any]      =       get_training_setup(__lowerCAmelCase   )
 for iteration, batch in enumerate(__lowerCAmelCase   ):
  __lowerCAmelCase ,					__lowerCAmelCase					:	str      =       batch.values()
  # Gather the distributed inputs and targs for the base model
  __lowerCAmelCase ,					__lowerCAmelCase					:	Any      =       accelerator.gather((ddp_input, ddp_target)   )
  __lowerCAmelCase ,					__lowerCAmelCase					:	Union[str, Any]      =       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_337 + iteration   )
  __lowerCAmelCase					:	Tuple      =       ddp_input[torch.randperm(len(__lowerCAmelCase   )   )]
 GradientState._reset_state()






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Union[str, Any]=False   ,   SCREAMING_SNAKE_CASE		:List[Any]=False   )      ->   Optional[int]:
 __lowerCAmelCase					:	Tuple      =       Accelerator(
     split_batches=__lowerCAmelCase   ,   dispatch_batches=__lowerCAmelCase   ,   gradient_accumulation_steps=2   )
 # Test that context manager behaves properly
 __lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					:	int      =       get_training_setup(__lowerCAmelCase   ,   __lowerCAmelCase   )
 for iteration, batch in enumerate(__lowerCAmelCase   ):
  __lowerCAmelCase ,					__lowerCAmelCase					:	Dict      =       batch.values()
  # Gather the distributed inputs and targs for the base model
  __lowerCAmelCase ,					__lowerCAmelCase					:	List[str]      =       accelerator.gather((ddp_input, ddp_target)   )
  __lowerCAmelCase ,					__lowerCAmelCase					:	Optional[Any]      =       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'''
  __lowerCAmelCase					:	Tuple      =       (((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_337 + iteration   )
 GradientState._reset_state()






def 	_SCREAMING_SNAKE_CASE		(      )      ->   Any:
 __lowerCAmelCase					:	Optional[Any]      =       Accelerator()

 __lowerCAmelCase					:	Optional[Any]      =       RegressionDataset(length=80   )
 __lowerCAmelCase					:	Optional[Any]      =       DataLoader(__lowerCAmelCase   ,   batch_size=16   )
 __lowerCAmelCase					:	Dict      =       RegressionDataset(length=96   )
 __lowerCAmelCase					:	Dict      =       DataLoader(__lowerCAmelCase   ,   batch_size=16   )
 __lowerCAmelCase ,					__lowerCAmelCase					:	List[str]      =       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 	_SCREAMING_SNAKE_CASE		(      )      ->   Optional[Any]:
 __lowerCAmelCase					:	List[Any]      =       Accelerator()
 __lowerCAmelCase					:	Dict      =       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 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Any   )      ->   str:
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
      main()






def 	_SCREAMING_SNAKE_CASE		(      )      ->   list[list[int]]:
 return [list(range(1_000 - i   ,   -1_000 - i   ,   -1   )   ) for i in range(1_000   )]


_UpperCAmelCase             =							generate_large_matrix()
_UpperCAmelCase             =							(
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list[list[int]]   )      ->   None:
 assert all(row == sorted(SCREAMING_SNAKE_CASE   ,   reverse=SCREAMING_SNAKE_CASE   ) for row in grid   )
 assert all(list(SCREAMING_SNAKE_CASE   ) == sorted(SCREAMING_SNAKE_CASE   ,   reverse=SCREAMING_SNAKE_CASE   ) for col in zip(*SCREAMING_SNAKE_CASE   )   )






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list[int]   )      ->   int:
 __lowerCAmelCase					:	Optional[int]      =       0
 __lowerCAmelCase					:	Tuple      =       len(SCREAMING_SNAKE_CASE   ) - 1

 # Edge cases such as no values or all numbers are negative.
 if not array or array[0] < 0:
  return 0

 while right + 1 > left:
  __lowerCAmelCase					:	Any      =       (left + right) // 2
  __lowerCAmelCase					:	Optional[int]      =       array[mid]

  # Num must be negative and the index must be greater than or equal to 0.
  if num < 0 and array[mid - 1] >= 0:
   return mid

  if num >= 0:
   __lowerCAmelCase					:	Dict      =       mid + 1
  else:
   __lowerCAmelCase					:	str      =       mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
 return len(SCREAMING_SNAKE_CASE   )






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list[list[int]]   )      ->   int:
 __lowerCAmelCase					:	Any      =       0
 __lowerCAmelCase					:	Union[str, Any]      =       len(grid[0]   )

 for i in range(len(SCREAMING_SNAKE_CASE   )   ):
  __lowerCAmelCase					:	str      =       find_negative_index(grid[i][:bound]   )
  total += bound
 return (len(SCREAMING_SNAKE_CASE   ) * len(grid[0]   )) - total






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list[list[int]]   )      ->   int:
 return len([number for row in grid for number in row if number < 0]   )






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list[list[int]]   )      ->   int:
 __lowerCAmelCase					:	List[str]      =       0
 for row in grid:
  for i, number in enumerate(SCREAMING_SNAKE_CASE   ):
   if number < 0:
    total += len(SCREAMING_SNAKE_CASE   ) - i
    break
 return total






def 	_SCREAMING_SNAKE_CASE		(      )      ->   None:
 from timeit import timeit

 print("""Running benchmarks"""   )
 __lowerCAmelCase					:	Tuple      =       (
     """from __main__ import count_negatives_binary_search, """
     """count_negatives_brute_force, count_negatives_brute_force_with_break, grid"""
 )
 for func in (
     "count_negatives_binary_search",  # took 0.7727 seconds
     "count_negatives_brute_force_with_break",  # took 4.6505 seconds
     "count_negatives_brute_force",  # took 12.8160 seconds
 ):
  __lowerCAmelCase					:	Optional[int]      =       timeit(F'''{func}(grid=grid)'''   ,   setup=SCREAMING_SNAKE_CASE   ,   number=500   )
  print(F'''{func}() took {time:0.4f} seconds'''   )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      benchmark()