'''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 snake_case_      (					__snake_case  :							Optional[Any]	,   __snake_case  :							Optional[int]	,   __snake_case  :							List[str]	,   __snake_case  :							Optional[int])			->					Union[str, Any]:
   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 snake_case_      (					__snake_case  :							Union[str, Any]	,   __snake_case  :							str	,   __snake_case  :							Optional[int]	,   __snake_case  :							Union[str, Any]	,   __snake_case  :							Dict=True)			->					Union[str, Any]:
   model.train()
   lowerCAmelCase_			=      model(__snake_case)
   lowerCAmelCase_			=      F.mse_loss(__snake_case	,   target.to(output.device))
   if not do_backward:
      loss /= accelerator.gradient_accumulation_steps
      loss.backward()
   else:
      accelerator.backward(__snake_case)



def snake_case_      (					__snake_case  :							Optional[Any]	,   __snake_case  :							str=False)			->					Any:
   set_seed(42)
   lowerCAmelCase_			=      RegressionModel()
   lowerCAmelCase_			=      deepcopy(__snake_case)
   lowerCAmelCase_			=      RegressionDataset(length=80)
   lowerCAmelCase_			=      DataLoader(__snake_case	,   batch_size=16)
   model.to(accelerator.device)
   if sched:
      lowerCAmelCase_			=      AdamW(params=model.parameters()	,   lr=1E-3)
      lowerCAmelCase_			=      AdamW(params=ddp_model.parameters()	,   lr=1E-3)
      lowerCAmelCase_			=      LambdaLR(__snake_case	,   lr_lambda=lambda __snake_case: epoch**0.6_5)
      lowerCAmelCase_			=      LambdaLR(__snake_case	,   lr_lambda=lambda __snake_case: epoch**0.6_5)
   # Make a copy of `model`
   if sched:
      lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      accelerator.prepare(__snake_case	,   __snake_case	,   __snake_case	,   __snake_case)
   else:
      lowerCAmelCase_	,lowerCAmelCase_			=      accelerator.prepare(__snake_case	,   __snake_case)
   if sched:
      return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
   return model, ddp_model, dataloader



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

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



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

      # 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)
      lowerCAmelCase_			=      ddp_input[torch.randperm(len(__snake_case))]



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

      # 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(__snake_case) - 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)
      lowerCAmelCase_			=      ddp_input[torch.randperm(len(__snake_case))]
   GradientState._reset_state()



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

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



def snake_case_      (					)			->					Any:
   lowerCAmelCase_			=      Accelerator()

   lowerCAmelCase_			=      RegressionDataset(length=80)
   lowerCAmelCase_			=      DataLoader(__snake_case	,   batch_size=16)
   lowerCAmelCase_			=      RegressionDataset(length=96)
   lowerCAmelCase_			=      DataLoader(__snake_case	,   batch_size=16)
   lowerCAmelCase_	,lowerCAmelCase_			=      accelerator.prepare(__snake_case	,   __snake_case)
   assert accelerator.gradient_state.active_dataloader is None
   for iteration, _ in enumerate(__snake_case):
      assert id(accelerator.gradient_state.active_dataloader) == id(__snake_case)
      if iteration < len(__snake_case) - 1:
         assert not accelerator.gradient_state.end_of_dataloader
         if iteration == 1:
            for batch_num, _ in enumerate(__snake_case):
               assert id(accelerator.gradient_state.active_dataloader) == id(__snake_case)
               if batch_num < len(__snake_case) - 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 snake_case_      (					)			->					List[str]:
   lowerCAmelCase_			=      Accelerator()
   lowerCAmelCase_			=      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(__snake_case)
   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(__snake_case)
   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(__snake_case	,   __snake_case)

    # 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(__snake_case	,   __snake_case)



def snake_case_      (					__snake_case  :							Optional[Any])			->					Any:
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
 main()






'''simple docstring'''

from __future__ import annotations



def snake_case_      (					__snake_case  :							list[int | str])			->					None:
   create_state_space_tree(__snake_case	,   []	,   0	,   [0 for i in range(len(__snake_case))])



def snake_case_      (					__snake_case  :							list[int | str]	,   __snake_case  :							list[int | str]	,   __snake_case  :							int	,   __snake_case  :							list[int]	,   )			->					None:

   if index == len(__snake_case):
      print(__snake_case)
      return

   for i in range(len(__snake_case)):
      if not index_used[i]:
         current_sequence.append(sequence[i])
         lowerCAmelCase_			=      True
         create_state_space_tree(__snake_case	,   __snake_case	,   index + 1	,   __snake_case)
         current_sequence.pop()
         lowerCAmelCase_			=      False
A_     :						list[int | str]    							=[3, 1, 2, 4]
generate_all_permutations(sequence)

A_     :						list[int | str]    							=["A", "B", "C"]






generate_all_permutations(sequence_a)





