



"""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      __magic_name__				(							lowercase							,			lowercase							,			lowercase							,			lowercase ):
   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      __magic_name__				(							lowercase							,			lowercase							,			lowercase							,			lowercase							,			lowercase=True ):
   model.train()
   SCREAMING_SNAKE_CASE_: Any     			=model(lowercase )
   SCREAMING_SNAKE_CASE_: int     			=F.mse_loss(lowercase							,			target.to(output.device ) )
   if not do_backward:
      loss /= accelerator.gradient_accumulation_steps
      loss.backward()
   else:
      accelerator.backward(lowercase )





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





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

      # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
      check_model_parameters(lowercase							,			lowercase							,			lowercase							,			lowercase )
      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 )
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=ddp_input[torch.randperm(len(lowercase ) )]





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

      # 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 )
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=ddp_input[torch.randperm(len(lowercase ) )]





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

      # 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(lowercase ) - 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 )
      SCREAMING_SNAKE_CASE_: int     			=ddp_input[torch.randperm(len(lowercase ) )]
   GradientState._reset_state()





def      __magic_name__				(							lowercase=False							,			lowercase=False ):
   SCREAMING_SNAKE_CASE_: Dict     			=Accelerator(
       split_batches=lowercase							,			dispatch_batches=lowercase							,			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_: str     			=get_training_setup(lowercase							,			lowercase )
   for iteration, batch in enumerate(lowercase ):
      SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Optional[Any]     			=batch.values()
      # Gather the distributed inputs and targs for the base model
      SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: str     			=accelerator.gather((ddp_input, ddp_target) )
      SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Dict     			=input.to(accelerator.device ), target.to(accelerator.device )
      # Perform our initial ground truth step in non "DDP"
      model.train()
      ddp_model.train()
      step_model(lowercase							,			lowercase							,			lowercase							,			lowercase							,			lowercase )
      opt.step()

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





def      __magic_name__				(							):
   SCREAMING_SNAKE_CASE_: List[Any]     			=Accelerator()

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

    # 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(lowercase							,			lowercase )





def      __magic_name__				(							lowercase ):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
   main()








"""simple docstring"""
from __future__ import annotations





def      __magic_name__				(							lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: List[Any]     			=sorted(numsa + numsa )
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Tuple     			=divmod(len(lowercase )							,			2 )
   if mod == 1:
      return all_numbers[div]
   else:
      return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   _UpperCAmelCase     =			[float(x) for x in input("""Enter the elements of first array: """).split()]
   _UpperCAmelCase     =			[float(x) for x in input("""Enter the elements of second array: """).split()]
   print(f"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")



