


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  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):







   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  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__=True   ):
   model.train()
   __a					=  model(UpperCamelCase__   )
   __a					=  F.mse_loss(UpperCamelCase__		,				target.to(output.device   )   )
   if not do_backward:
      loss /= accelerator.gradient_accumulation_steps
      loss.backward()
   else:
      accelerator.backward(UpperCamelCase__   )




def  a   (lowerCAmelCase__		,				lowerCAmelCase__=False   ):
   set_seed(42   )
   __a					=  RegressionModel()
   __a					=  deepcopy(UpperCamelCase__   )
   __a					=  RegressionDataset(length=80   )
   __a					=  DataLoader(UpperCamelCase__		,				batch_size=16   )
   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(UpperCamelCase__		,				lr_lambda=lambda lowerCAmelCase__   : epoch**0.6_5   )
      __a					=  LambdaLR(UpperCamelCase__		,				lr_lambda=lambda lowerCAmelCase__   : epoch**0.6_5   )
   # Make a copy of `model`
   if sched:
      __a	,    __a	,    __a	,    __a					=  accelerator.prepare(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__   )
   else:
      __a	,    __a					=  accelerator.prepare(UpperCamelCase__		,				UpperCamelCase__   )
   if sched:
      return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
   return model, ddp_model, dataloader




def  a   (lowerCAmelCase__   ):
   __a	,    __a	,    __a					=  get_training_setup(UpperCamelCase__   )
   # Use a single batch
   __a	,    __a					=  next(iter(UpperCamelCase__   )   ).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(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(1_337 + iteration   )
      __a					=  ddp_input[torch.randperm(len(UpperCamelCase__   )   )]




def  a   (lowerCAmelCase__   ):
   __a	,    __a	,    __a					=  get_training_setup(UpperCamelCase__   )
   # Use a single batch
   __a	,    __a					=  next(iter(UpperCamelCase__   )   ).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(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(1_337 + iteration   )
      __a					=  ddp_input[torch.randperm(len(UpperCamelCase__   )   )]




def  a   (lowerCAmelCase__=False		,				lowerCAmelCase__=False   ):
   __a					=  Accelerator(
       split_batches=UpperCamelCase__		,				dispatch_batches=UpperCamelCase__		,				gradient_accumulation_steps=2   )
   # Test that context manager behaves properly
   __a	,    __a	,    __a					=  get_training_setup(UpperCamelCase__   )
   for iteration, batch in enumerate(UpperCamelCase__   ):
      __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(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(1_337 + iteration   )
      __a					=  ddp_input[torch.randperm(len(UpperCamelCase__   )   )]
   GradientState._reset_state()




def  a   (lowerCAmelCase__=False		,				lowerCAmelCase__=False   ):
   __a					=  Accelerator(
       split_batches=UpperCamelCase__		,				dispatch_batches=UpperCamelCase__		,				gradient_accumulation_steps=2   )
   # Test that context manager behaves properly
   __a	,    __a	,    __a	,    __a	,    __a	,    __a	,    __a					=  get_training_setup(UpperCamelCase__		,				UpperCamelCase__   )
   for iteration, batch in enumerate(UpperCamelCase__   ):
      __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(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					=  (((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(1_337 + iteration   )
   GradientState._reset_state()




def  a   ():
   __a					=  Accelerator()

   __a					=  RegressionDataset(length=80   )
   __a					=  DataLoader(UpperCamelCase__		,				batch_size=16   )
   __a					=  RegressionDataset(length=96   )
   __a					=  DataLoader(UpperCamelCase__		,				batch_size=16   )
   __a	,    __a					=  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  a   ():
   __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(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  a   (lowerCAmelCase__   ):
   main()


if __name__ == "__main__":
  main()







"""simple docstring"""

def lowerCAmelCase_   (					UpperCamelCase__						:			Union[str, Any]					, UpperCamelCase__						:			Union[str, Any]					, UpperCamelCase__						:			int			):






 """simple docstring"""







 if n == 0:
  return 1

 elif n % 2 == 1:
  return (binary_exponentiation(UpperCamelCase__					, n - 1					, UpperCamelCase__			) * a) % mod

 else:
  __lowercase						=						binary_exponentiation(UpperCamelCase__					, n / 2					, UpperCamelCase__			)
  return (b * b) % mod


# a prime number
UpperCAmelCase__					=701

UpperCAmelCase__					=10_0000_0000
UpperCAmelCase__					=10

# using binary exponentiation function, O(log(p)):
print((a / b) % p == (a * binary_exponentiation(b, p - 2, p)) % p)

print((a / b) % p == (a * b ** (p - 2)) % p)
