



'''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 lowerCAmelCase						(__A						,			__A						,			__A						,			__A):



     """simple docstring"""



     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 lowerCAmelCase						(__A						,			__A						,			__A						,			__A						,			__A=True):



     """simple docstring"""



     model.train()
     _a											=						model(__A)
     _a											=						F.mse_loss(__A						,			target.to(output.device))
     if not do_backward:
          loss /= accelerator.gradient_accumulation_steps
          loss.backward()
     else:
          accelerator.backward(__A)




def lowerCAmelCase						(__A						,			__A=False):



     """simple docstring"""



     set_seed(42)
     _a											=						RegressionModel()
     _a											=						deepcopy(__A)
     _a											=						RegressionDataset(length=80)
     _a											=						DataLoader(__A						,			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(__A						,			lr_lambda=lambda __A: epoch**0.65)
          _a											=						LambdaLR(__A						,			lr_lambda=lambda __A: epoch**0.65)
     # Make a copy of `model`
     if sched:
          _a						,					_a						,					_a						,					_a											=						accelerator.prepare(__A						,			__A						,			__A						,			__A)
     else:
          _a						,					_a											=						accelerator.prepare(__A						,			__A)
     if sched:
          return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
     return model, ddp_model, dataloader




def lowerCAmelCase						(__A):



     """simple docstring"""



     _a						,					_a						,					_a											=						get_training_setup(__A)
     # Use a single batch
     _a						,					_a											=						next(iter(__A)).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(__A						,			__A						,			__A						,			__A)
          # Do "gradient accumulation" (noop)
          if iteration % 2 == 0:
               # Accumulate grads locally
               with accelerator.no_sync(__A):
                    step_model(__A						,			__A						,			__A						,			__A)
          else:
               # Sync grads
               step_model(__A						,			__A						,			__A						,			__A)

          # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
          check_model_parameters(__A						,			__A						,			__A						,			__A)
          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(__A))]




def lowerCAmelCase						(__A):



     """simple docstring"""



     _a						,					_a						,					_a											=						get_training_setup(__A)
     # Use a single batch
     _a						,					_a											=						next(iter(__A)).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(__A						,			__A						,			__A						,			__A)
          # Do "gradient accumulation" (noop)
          if iteration % 2 == 0:
               # Accumulate grads locally
               with accelerator.no_sync(__A):
                    step_model(__A						,			__A						,			__A						,			__A)
          else:
               # Sync grads
               step_model(__A						,			__A						,			__A						,			__A)

          # 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(__A))]




def lowerCAmelCase						(__A=False						,			__A=False):



     """simple docstring"""



     _a											=						Accelerator(
         split_batches=__A						,			dispatch_batches=__A						,			gradient_accumulation_steps=2)
     # Test that context manager behaves properly
     _a						,					_a						,					_a											=						get_training_setup(__A)
     for iteration, batch in enumerate(__A):
          _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(__A						,			__A						,			__A						,			__A						,			__A)
          # Do "gradient accumulation" (noop)
          with accelerator.accumulate(__A):
               step_model(__A						,			__A						,			__A						,			__A)

          # 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(__A) - 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(__A))]
     GradientState._reset_state()




def lowerCAmelCase						(__A=False						,			__A=False):



     """simple docstring"""



     _a											=						Accelerator(
         split_batches=__A						,			dispatch_batches=__A						,			gradient_accumulation_steps=2)
     # Test that context manager behaves properly
     _a						,					_a						,					_a						,					_a						,					_a						,					_a						,					_a											=						get_training_setup(__A						,			__A)
     for iteration, batch in enumerate(__A):
          _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(__A						,			__A						,			__A						,			__A						,			__A)
          opt.step()

          if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(__A)):
               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(__A):
               step_model(__A						,			__A						,			__A						,			__A)
               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(__A))
          if accelerator.num_processes > 1:
               check_model_parameters(__A						,			__A						,			__A						,			__A)
          # Shuffle ddp_input on each iteration
          torch.manual_seed(1_337 + iteration)
     GradientState._reset_state()




def lowerCAmelCase						():



     """simple docstring"""



     _a											=						Accelerator()

     _a											=						RegressionDataset(length=80)
     _a											=						DataLoader(__A						,			batch_size=16)
     _a											=						RegressionDataset(length=96)
     _a											=						DataLoader(__A						,			batch_size=16)
     _a						,					_a											=						accelerator.prepare(__A						,			__A)
     assert accelerator.gradient_state.active_dataloader is None
     for iteration, _ in enumerate(__A):
          assert id(accelerator.gradient_state.active_dataloader) == id(__A)
          if iteration < len(__A) - 1:
               assert not accelerator.gradient_state.end_of_dataloader
               if iteration == 1:
                    for batch_num, _ in enumerate(__A):
                         assert id(accelerator.gradient_state.active_dataloader) == id(__A)
                         if batch_num < len(__A) - 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 lowerCAmelCase						():



     """simple docstring"""



     _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(__A)
     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(__A)
     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(__A						,			__A)

    # 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(__A						,			__A)




def lowerCAmelCase						(__A):



     """simple docstring"""



     main()


if __name__ == "__main__":
  main()








'''simple docstring'''





import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
  lowercase_	 =						{
      "linear": PIL.Image.Resampling.BILINEAR,
      "bilinear": PIL.Image.Resampling.BILINEAR,
      "bicubic": PIL.Image.Resampling.BICUBIC,
      "lanczos": PIL.Image.Resampling.LANCZOS,
      "nearest": PIL.Image.Resampling.NEAREST,
  }
else:
  lowercase_	 =						{
      "linear": PIL.Image.LINEAR,
      "bilinear": PIL.Image.BILINEAR,
      "bicubic": PIL.Image.BICUBIC,
      "lanczos": PIL.Image.LANCZOS,
      "nearest": PIL.Image.NEAREST,
  }




def lowerCAmelCase						(__A):



     """simple docstring"""



     _a											=						(images / 2 + 0.5).clamp(0						,			1)
     _a											=						images.cpu().permute(0						,			2						,			3						,			1).float().numpy()
     _a											=						numpy_to_pil(__A)
     return images




def lowerCAmelCase						(__A):



     """simple docstring"""



     if images.ndim == 3:
          _a											=						images[None, ...]
     _a											=						(images * 255).round().astype('''uint8''')
     if images.shape[-1] == 1:
          # special case for grayscale (single channel) images
          _a											=						[Image.fromarray(image.squeeze()						,			mode='''L''') for image in images]
     else:
          _a											=						[Image.fromarray(__A) for image in images]

     return pil_images



