


'''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     (  A				,					A				,					A				,					A )		->					str:







       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     (  A				,					A				,					A				,					A				,					A=True )		->					List[Any]:
       model.train()
       lowerCAmelCase__      =			model(lowerCAmelCase__ )
       lowerCAmelCase__      =			F.mse_loss(lowerCAmelCase__				,					target.to(output.device ) )
       if not do_backward:
              loss /= accelerator.gradient_accumulation_steps
              loss.backward()
       else:
              accelerator.backward(lowerCAmelCase__ )


def       _snake_case     (  A				,					A=False )		->					Dict:
       set_seed(42 )
       lowerCAmelCase__      =			RegressionModel()
       lowerCAmelCase__      =			deepcopy(lowerCAmelCase__ )
       lowerCAmelCase__      =			RegressionDataset(length=80 )
       lowerCAmelCase__      =			DataLoader(lowerCAmelCase__				,					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(lowerCAmelCase__				,					lr_lambda=lambda A : epoch**0.65 )
              lowerCAmelCase__      =			LambdaLR(lowerCAmelCase__				,					lr_lambda=lambda A : epoch**0.65 )
       # Make a copy of `model`
       if sched:
              lowerCAmelCase__      =			accelerator.prepare(lowerCAmelCase__				,					lowerCAmelCase__				,					lowerCAmelCase__				,					lowerCAmelCase__ )
       else:
              lowerCAmelCase__      =			accelerator.prepare(lowerCAmelCase__				,					lowerCAmelCase__ )
       if sched:
              return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
       return model, ddp_model, dataloader


def       _snake_case     (  A )		->					int:
       lowerCAmelCase__      =			get_training_setup(lowerCAmelCase__ )
       # Use a single batch
       lowerCAmelCase__      =			next(iter(lowerCAmelCase__ ) ).values()
       for iteration in range(3 ):
              # Gather the distributed inputs and targs for the base model
              lowerCAmelCase__      =			accelerator.gather((ddp_input, ddp_target) )
              lowerCAmelCase__      =			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 )
              lowerCAmelCase__      =			ddp_input[torch.randperm(len(lowerCAmelCase__ ) )]


def       _snake_case     (  A )		->					Optional[Any]:
       lowerCAmelCase__      =			get_training_setup(lowerCAmelCase__ )
       # Use a single batch
       lowerCAmelCase__      =			next(iter(lowerCAmelCase__ ) ).values()
       for iteration in range(3 ):
              # Gather the distributed inputs and targs for the base model
              lowerCAmelCase__      =			accelerator.gather((ddp_input, ddp_target) )
              lowerCAmelCase__      =			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 )
              lowerCAmelCase__      =			ddp_input[torch.randperm(len(lowerCAmelCase__ ) )]


def       _snake_case     (  A=False				,					A=False )		->					int:
       lowerCAmelCase__      =			Accelerator(
           split_batches=lowerCAmelCase__				,					dispatch_batches=lowerCAmelCase__				,					gradient_accumulation_steps=2 )
       # Test that context manager behaves properly
       lowerCAmelCase__      =			get_training_setup(lowerCAmelCase__ )
       for iteration, batch in enumerate(lowerCAmelCase__ ):
              lowerCAmelCase__      =			batch.values()
              # Gather the distributed inputs and targs for the base model
              lowerCAmelCase__      =			accelerator.gather((ddp_input, ddp_target) )
              lowerCAmelCase__      =			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 )
              lowerCAmelCase__      =			ddp_input[torch.randperm(len(lowerCAmelCase__ ) )]
       GradientState._reset_state()


def       _snake_case     (  A=False				,					A=False )		->					List[Any]:
       lowerCAmelCase__      =			Accelerator(
           split_batches=lowerCAmelCase__				,					dispatch_batches=lowerCAmelCase__				,					gradient_accumulation_steps=2 )
       # Test that context manager behaves properly
       lowerCAmelCase__      =			get_training_setup(lowerCAmelCase__				,					lowerCAmelCase__ )
       for iteration, batch in enumerate(lowerCAmelCase__ ):
              lowerCAmelCase__      =			batch.values()
              # Gather the distributed inputs and targs for the base model
              lowerCAmelCase__      =			accelerator.gather((ddp_input, ddp_target) )
              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(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__      =			(((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       _snake_case     (  )		->					int:
       lowerCAmelCase__      =			Accelerator()

       lowerCAmelCase__      =			RegressionDataset(length=80 )
       lowerCAmelCase__      =			DataLoader(lowerCAmelCase__				,					batch_size=16 )
       lowerCAmelCase__      =			RegressionDataset(length=96 )
       lowerCAmelCase__      =			DataLoader(lowerCAmelCase__				,					batch_size=16 )
       lowerCAmelCase__      =			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       _snake_case     (  )		->					Dict:
       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(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       _snake_case     (  A )		->					Tuple:
       main()


if __name__ == "__main__":
  main()



'''simple docstring'''






import doctest
from collections import deque

import numpy as np




class 	a__       :





							'''simple docstring'''






							def __init__(	self     ) ->							None:
														lowerCAmelCase__      =			[2, 1, 2, -1]
														lowerCAmelCase__      =			[1, 2, 3, 4]







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							list[float]:
														lowerCAmelCase__      =			len(self.first_signal     )
														lowerCAmelCase__      =			len(self.second_signal     )

														lowerCAmelCase__      =			max(lowerCamelCase_      ,					lowerCamelCase_     )

														# create a zero matrix of max_length x max_length
														lowerCAmelCase__      =			[[0] * max_length for i in range(lowerCamelCase_     )]

														# fills the smaller signal with zeros to make both signals of same length
														if length_first_signal < length_second_signal:
																					self.first_signal += [0] * (max_length - length_first_signal)
														elif length_first_signal > length_second_signal:
																					self.second_signal += [0] * (max_length - length_second_signal)
														for i in range(lowerCamelCase_     ):
																					lowerCAmelCase__      =			deque(self.second_signal     )
																					rotated_signal.rotate(lowerCamelCase_     )
																					for j, item in enumerate(lowerCamelCase_     ):
																												matrix[i][j] += item

        # multiply the matrix with the first signal
														lowerCAmelCase__      =			np.matmul(np.transpose(lowerCamelCase_     )      ,					np.transpose(self.first_signal     )     )

														# rounding-off to two decimal places
														return [round(lowerCamelCase_      ,					2     ) for i in final_signal]


if __name__ == "__main__":
		doctest.testmod()